From efda55e769d09e8980e55baf4a5477c5625a96e1 Mon Sep 17 00:00:00 2001 From: zackAemmer Date: Wed, 30 Nov 2022 10:17:41 -0800 Subject: [PATCH 01/16] Testing for TRB update and the replaced mode implementation --- .../replacement_model_data_processing.ipynb | 580 ++++++++++++++++++ viz_scripts/replacement_models.py | 122 ++++ viz_scripts/run_replacement_models.ipynb | 454 ++++++++++++++ 3 files changed, 1156 insertions(+) create mode 100644 viz_scripts/replacement_model_data_processing.ipynb create mode 100644 viz_scripts/replacement_models.py create mode 100644 viz_scripts/run_replacement_models.ipynb diff --git a/viz_scripts/replacement_model_data_processing.ipynb b/viz_scripts/replacement_model_data_processing.ipynb new file mode 100644 index 0000000..30810e1 --- /dev/null +++ b/viz_scripts/replacement_model_data_processing.ipynb @@ -0,0 +1,580 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "year = None\n", + "month = None\n", + "program = \"prepilot\"\n", + "study_type = \"program\"\n", + "mode_of_interest = \"pilot_ebike\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "SAVE_DIR = '/plots/'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import defaultdict\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from sklearn import linear_model\n", + "\n", + "from plots import *\n", + "import scaffolding\n", + "\n", + "sns.set_style(\"whitegrid\")\n", + "sns.set()\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Settings and imports specific to this notebook\n", + "\n", + "include_replaced_modes_as_valid = True # Flip this when we want to get results versus generate the replaced_mode correction graphs\n", + "model_with_sensed = False\n", + "input_dataset = \"ONLY_LABELED\" # \"ONLY_LABELED\", \"ONLY_SENSED\" or \"BEST_AVAILABLE\" for sensitivity analysis\n", + "LABEL_ASSIST_THRESHOLD = 0.3\n", + "\n", + "# For reloading modules from Jupyter\n", + "# import importlib\n", + "# importlib.reload(replacement_models)\n", + "\n", + "import datetime\n", + "import pickle\n", + "import sklearn.metrics\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import train_test_split\n", + "import emission.core.get_database as edb\n", + "import emission.core.wrapper.entry as ecwe\n", + "import emission.storage.decorations.analysis_timeseries_queries as esda\n", + "import emission.storage.decorations.trip_queries as esdt\n", + "import emission.storage.decorations.timeline as esdl\n", + "import emission.storage.timeseries.abstract_timeseries as esta\n", + "import emission.storage.timeseries.timequery as estt\n", + "from uuid import UUID" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Do not run this notebook at all unless it is for a program; nbclient will run up through this cell\n", + "if study_type != \"program\":\n", + " raise Exception(\"The plots in this notebook are only relevant to programs\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Loading mapping dictionaries from mapping_dictionaries notebook\n", + "%store -r dic_re\n", + "%store -r dic_pur\n", + "\n", + "# convert a dictionary to a defaultdict\n", + "dic_re = defaultdict(lambda: 'Other',dic_re)\n", + "dic_pur = defaultdict(lambda: 'Other',dic_pur)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get UUIDs by Program" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Split UUIDs by program\n", + "program_uuid_map = {}\n", + "for ue in edb.get_uuid_db().find():\n", + " program = ue['user_email'].split(\"_\")[0]\n", + " if program in program_uuid_map.keys():\n", + " program_uuid_map[program].append(str(ue['uuid']))\n", + " else:\n", + " print(f\"Found new program {program}, creating new list\")\n", + " program_uuid_map[program] = []\n", + " program_uuid_map[program].append(str(ue['uuid']))\n", + "\n", + "uuid_program_list = []\n", + "for ue in edb.get_uuid_db().find():\n", + " program = ue['user_email'].split(\"_\")[0]\n", + " uuid_program_list.append({\"program\": program, \"opcode\": ue[\"user_email\"], \"user_id_str\": str(ue['uuid'])})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "uuid_program_df = pd.DataFrame.from_dict(uuid_program_list)\n", + "uuid_program_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Collect Data From Database" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "expanded_ct, file_suffix, quality_text = scaffolding.load_viz_notebook_data(year,\n", + " month,\n", + " program,\n", + " study_type,\n", + " dic_re,\n", + " dic_pur=dic_pur)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Join to the program df to get each user's program\n", + "expanded_ct['user_id_str'] = expanded_ct['user_id'].astype(str)\n", + "expanded_ct = expanded_ct.merge(uuid_program_df, on='user_id_str')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Add non-label category\n", + "expanded_ct['replaced_mode'] = expanded_ct['replaced_mode'].fillna('Unlabeled')\n", + "expanded_ct.loc[expanded_ct['replaced_mode'] == 'Unlabeled', 'Replaced_mode'] = \"Unlabeled\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Join the expanded database data to socioeconomic data\n", + "socio_data = pd.read_csv('./Can Do Colorado eBike Program - en.csv')\n", + "socio_data.rename(columns={'Unique User ID (auto-filled, do not edit)':'user_id',\n", + " 'Please identify which category represents your total household income, before taxes, for last year.':'HHINC',\n", + " 'How many motor vehicles are owned, leased, or available for regular use by the people who currently live in your household?':'VEH',\n", + " 'In which year were you born?':'AGE',\n", + " 'Including yourself, how many people live in your home?':'HHSIZE',\n", + " 'How many children under age 18 live in your home?':'CHILDREN',\n", + " 'What is your gender?':'GENDER',\n", + " 'If you were unable to use your household vehicle(s), which of the following options would be available to you to get you from place to place?':'available_modes',\n", + " 'Are you a student?':'STUDENT',\n", + " \"Including yourself, how many people have a driver's license in your household?\":'DRIVERS'}, inplace=True)\n", + "socio_data = socio_data[~socio_data.user_id.isnull()]\n", + "\n", + "# Deal with people who have multiple responses by using most recent\n", + "socio_data = socio_data.sort_values(by=['user_id', 'Timestamp'])\n", + "socio_data.drop_duplicates(subset=['user_id'], keep='last', inplace=True)\n", + "socio_data['user_id_socio'] = socio_data.user_id\n", + "socio_data = socio_data.drop(labels='user_id', axis=1)\n", + "\n", + "# Lose some trips due to people with no survey responses\n", + "expanded_ct['user_id_socio'] = expanded_ct.user_id.astype(str)\n", + "expanded_ct.user_id_socio = [i.replace('-','') for i in expanded_ct.user_id_socio] # remove all dashes from strings\n", + "expanded_ct = expanded_ct.merge(socio_data, on='user_id_socio')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "## Filter database to variables of modeling interest\n", + "data = expanded_ct[['Mode_confirm','Replaced_mode','replaced_mode','Trip_purpose','duration','distance_miles','start_local_dt_weekday','available_modes','AGE','HHINC','VEH','HHSIZE','CHILDREN','GENDER','STUDENT','DRIVERS','user_id','_id','start_local_dt_year','start_local_dt_month','start_local_dt_day','cleaned_trip','start_fmt_time','start_loc','end_loc']].copy()\n", + "\n", + "\n", + "## Pre-filter round of variable creation\n", + "# Make copy of user_id to be categorized since both versions are needed\n", + "data['user_id_int'] = data['user_id']\n", + "\n", + "# Get timestamp from known year/month/day aggregated to days\n", + "data.rename(columns={'start_local_dt_year':'year','start_local_dt_month':'month','start_local_dt_day':'day'}, inplace=True)\n", + "data['date_time'] = pd.to_datetime(data[['year','month','day']])\n", + "data = data.drop(columns=['year','day'])\n", + "\n", + "# Get time of day\n", + "data['hour'] = [int(x[1][:2]) for x in expanded_ct.start_fmt_time.str.split('T')]\n", + "\n", + "# Fix age\n", + "data['AGE'] = 2022 - data['AGE']\n", + "\n", + "# Number of workers\n", + "data['WORKERS'] = (data['HHSIZE'] - data['CHILDREN']).astype(int)\n", + "\n", + "# Vehicles per driver\n", + "data['VEH'] = data['VEH'].replace('4+', '4')\n", + "\n", + "# Recoded Cyclical Time of Day\n", + "hours_in_day = 24\n", + "months_in_year = 12\n", + "data['sin_time'] = np.sin(2*np.pi*data.hour/hours_in_day)\n", + "data['cos_time'] = np.cos(2*np.pi*data.hour/hours_in_day)\n", + "data['sin_month'] = np.sin(2*np.pi*data.month/months_in_year)\n", + "data['cos_month'] = np.cos(2*np.pi*data.month/months_in_year)\n", + "\n", + "# Duration in minutes\n", + "data['duration'] = data['duration'] / 60\n", + "\n", + "# Add coordinates to the data\n", + "z = pd.json_normalize(data.start_loc)['coordinates']\n", + "olon = [str(x[0]) for x in z]\n", + "olat = [str(x[1]) for x in z]\n", + "data['olat'] = olat\n", + "data['olon'] = olon\n", + "z = pd.json_normalize(data.end_loc)['coordinates']\n", + "dlon = [str(x[0]) for x in z]\n", + "dlat = [str(x[1]) for x in z]\n", + "data['dlat'] = dlat\n", + "data['dlon'] = dlon\n", + "\n", + "# Recode variables\n", + "data.Mode_confirm = data.Mode_confirm.replace(\n", + " ['Gas Car, drove alone',\n", + " 'Gas Car, with others',\n", + " 'Bikeshare',\n", + " 'Scooter share',\n", + " 'Regular Bike',\n", + " 'Skate board',\n", + " 'Train',\n", + " 'Free Shuttle',\n", + " 'Bus',\n", + " 'Walk',\n", + " 'Taxi/Uber/Lyft',\n", + " 'E-bike'],\n", + " ['car',\n", + " 's_car',\n", + " 's_micro',\n", + " 's_micro',\n", + " 'p_micro',\n", + " 'p_micro',\n", + " 'transit',\n", + " 'transit',\n", + " 'transit',\n", + " 'walk',\n", + " 'ridehail',\n", + " 'ebike']\n", + ")\n", + "data.Replaced_mode = data.Replaced_mode.replace(\n", + " ['Gas Car, drove alone',\n", + " 'Gas Car, with others',\n", + " 'Bikeshare',\n", + " 'Scooter share',\n", + " 'Regular Bike',\n", + " 'Skate board',\n", + " 'Train',\n", + " 'Free Shuttle',\n", + " 'Bus',\n", + " 'Walk',\n", + " 'Taxi/Uber/Lyft',\n", + " 'E-bike',\n", + " 'No Travel'],\n", + " ['car',\n", + " 's_car',\n", + " 's_micro',\n", + " 's_micro',\n", + " 'p_micro',\n", + " 'p_micro',\n", + " 'transit',\n", + " 'transit',\n", + " 'transit',\n", + " 'walk',\n", + " 'ridehail',\n", + " 'ebike',\n", + " 'no_travel']\n", + ")\n", + "data.Trip_purpose = data.Trip_purpose.replace(\n", + " ['Work',\n", + " 'School',\n", + " 'Recreation/Exercise',\n", + " 'Transit transfer',\n", + " 'Meal',\n", + " 'Entertainment/Social',\n", + " 'Shopping',\n", + " 'Personal/Medical',\n", + " 'Religious',\n", + " 'Pick-up/Drop off'],\n", + " ['commute',\n", + " 'commute',\n", + " 'recreation',\n", + " 'transit_transfer',\n", + " 'discretionary',\n", + " 'discretionary',\n", + " 'discretionary',\n", + " 'discretionary',\n", + " 'discretionary',\n", + " 'pudo']\n", + ")\n", + "data['is_weekend'] = 0\n", + "data.loc[data['start_local_dt_weekday'].isin(['0','6']), 'is_weekend'] = 1\n", + "data['is_male'] = 0\n", + "data.loc[data['GENDER'].isin(['Man']), 'is_male'] = 1\n", + "\n", + "## Filter data\n", + "# Filter out responses to data that are not workable\n", + "data = data[~data['Mode_confirm'].isin(['Not a Trip','Other'])]\n", + "data = data[~data['Replaced_mode'].isin(['Not a Trip','Other','Unlabeled'])]\n", + "data = data[~data['available_modes'].isin(['None', 'Prefer not to say'])]\n", + "data = data[~data['Trip_purpose'].isin(['not_a_trip','Other'])]\n", + "data = data[~data['HHINC'].isin(['Prefer not to say','$100,000 -$149,999','$150,000','$150,000-$199,999','$200,000 or more'])] # Side note why is 150k (n=7) its own bin?\n", + "data = data[~data['VEH'].isin(['Prefer not to say / Prefiero no decir.'])]\n", + "data = data[data['distance_miles']<50]\n", + "data = data[data['AGE']<100]\n", + "data = data[data['HHSIZE']<10]\n", + "data = data[data['HHSIZE']>data['CHILDREN']]\n", + "\n", + "\n", + "## Post-filter round of variable creation\n", + "# OHE any categorical, non-ordinal variables\n", + "ohe_vars = ['Trip_purpose','HHINC','STUDENT']\n", + "ohe_prefixes = ['purp','hhinc','student']\n", + "data = pd.get_dummies(data, columns=ohe_vars, prefix=ohe_prefixes)\n", + "\n", + "# Calculate travel times for each trip, across every mode\n", + "def add_all_mode_tt(data, mode_col, duration_col, dist_col):\n", + " wait_times_init = {'car':0.00,\n", + " 's_car':0.00,\n", + " 'ridehail':5.00,\n", + " 's_micro':5.00,\n", + " 'p_micro':0.00,\n", + " 'transit':7.00,\n", + " 'walk':5.00,\n", + " 'ebike':0.00}\n", + " mode_travel_times = {}\n", + " for mode in pd.unique(data[mode_col]):\n", + "\n", + " # Linear model for duration based on distance for trips belonging to each mode\n", + " mode_data = data[data[mode_col]==mode]\n", + " regr = linear_model.LinearRegression()\n", + " regr.fit(np.log(mode_data[dist_col].values.reshape(-1,1)), np.log(mode_data[duration_col].values.reshape(-1,1)))\n", + " \n", + " # Make prediction for ALL trips, reverse the log transform to get mins\n", + " mode_duration_pred = regr.predict(np.log(data[dist_col].values.reshape(-1,1)))\n", + " mode_travel_times['tt_'+mode] = np.exp(mode_duration_pred) + wait_times_init[mode]\n", + "\n", + " # Apply for each mode existing in the dataframe\n", + " for mode in mode_travel_times:\n", + " data[mode] = mode_travel_times[mode]\n", + "\n", + " return regr, data\n", + "\n", + "# Calculate all mode travel times and add to dataframe\n", + "regr, data = add_all_mode_tt(data,'Mode_confirm','duration','distance_miles')\n", + "\n", + "# Calculate vehicle costs\n", + "cost_factors_init = {'car':0.00,\n", + " 's_car':0.00,\n", + " 'ridehail':5.00,\n", + " 's_micro':1.00,\n", + " 'p_micro':0.00,\n", + " 'transit':3.50,\n", + " 'ebike':0.00,\n", + " 'walk':0.00}\n", + "cost_factors = {'car':0.62,\n", + " 's_car':0.31,\n", + " 'ridehail':0.80,\n", + " 's_micro':0.90,\n", + " 'p_micro':0.00,\n", + " 'transit':0.00,\n", + " 'ebike':0.11,\n", + " 'walk':0.00}\n", + "\n", + "def add_all_mode_cost(data, cost_factors, dist_col):\n", + " for factor in cost_factors:\n", + " data['cost_'+factor] = cost_factors_init[factor] + (cost_factors[factor] * data[dist_col])\n", + " return data\n", + "\n", + "# Calculate all mode travel costs and add to dataframe\n", + "add_all_mode_cost(data, cost_factors, 'distance_miles')\n", + "\n", + "# Labels for modes in the availability survey\n", + "availability_codes = {'Public transportation (bus, subway, light rail, etc.)':'transit',\n", + " 'Get a ride from a friend or family member':'s_car',\n", + " 'Rental car (including Zipcar/ Car2Go)':'car',\n", + " 'Taxi (regular taxi, Uber, Lyft, etc)':'ridehail',\n", + " 'Bicycle':'p_micro',\n", + " 'Shared bicycle or scooter':'s_micro',\n", + " 'Walk/roll':'walk',\n", + " 'Skateboard':'p_micro',\n", + " 'ebike':'ebike',\n", + " 'None':'none'}\n", + "\n", + "def add_mode_availability(data, availability_codes, availability_col, choice_col, replaced_col, is_sp):\n", + " mode_list = np.unique(list(availability_codes.values())[:-1])\n", + " choice_list = data[choice_col].values\n", + " replaced_list = data[replaced_col].values\n", + " for mode in mode_list:\n", + " mode_avail = []\n", + " for i, available in enumerate(data[availability_col].values):\n", + " available_modes = [availability_codes[x] for x in available.split(';')]\n", + " # For SP: Replacement/stated available should be 1, chosen should be 0\n", + " if is_sp:\n", + " if mode==choice_list[i]:\n", + " mode_check = False\n", + " else:\n", + " mode_check = mode==replaced_list[i] or mode in available_modes\n", + " # For RP: Chosen/replacement/stated available should be 1\n", + " else:\n", + " mode_check = mode==choice_list[i] or mode==replaced_list[i] or mode in available_modes\n", + " # Keep binary list of which trips the mode was available for\n", + " if mode_check:\n", + " mode_avail.append(1)\n", + " else:\n", + " mode_avail.append(0)\n", + " # For each mode add a column with binary availability\n", + " data['av_'+mode] = mode_avail\n", + " return data\n", + "\n", + "# Add mode availability according to survey responses\n", + "data = add_mode_availability(data, availability_codes, 'available_modes', 'Mode_confirm', 'Replaced_mode', is_sp=False)\n", + "\n", + "# # Add mode availability as all available\n", + "mode_list = ['car','s_car','ridehail','transit','p_micro','s_micro','walk','ebike','no_travel']\n", + "# for mode in mode_list:\n", + "# data[f\"av_{mode}\"] = 1\n", + "\n", + "# Handle all variables that are ordinal; otherwise they may not end up in correct order\n", + "# Make sure that all mode variables align after being converted to numeric variables\n", + "data.Mode_confirm = pd.Categorical(data.Mode_confirm, ordered=True, categories=mode_list)\n", + "data.Replaced_mode = pd.Categorical(data.Replaced_mode, ordered=True, categories=mode_list)\n", + "data['Mode_confirm_num'] = data.Mode_confirm.cat.codes\n", + "data['Replaced_mode_num'] = data.Replaced_mode.cat.codes\n", + "data = data[data['Mode_confirm_num']!=data['Replaced_mode_num']]\n", + "\n", + "## Save cleaned data to be used in modeling\n", + "data.to_csv(\"processed_replacement_modeling_data.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Checks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Data stats before cleaning\n", + "print(f\"Trips: {len(expanded_ct)}\")\n", + "print(f\"Users: {len(np.unique(expanded_ct.user_id))}\")\n", + "print(f\"Trips per user: {len(expanded_ct) / len(pd.unique(expanded_ct.user_id))}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Data stats after cleaning + columns available\n", + "print(f\"Trips: {len(data)}\")\n", + "print(f\"Users: {len(np.unique(data.user_id))}\")\n", + "print(f\"Trips per user: {len(data) / len(pd.unique(data.user_id))}\\n\")\n", + "print(f\"Columns: \\n{data.columns.values}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Check for NAs\n", + "data.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check # obs for each class\n", + "print(f\"Mode_confirm:\\n{pd.value_counts(data.Mode_confirm)}\\n\")\n", + "print(f\"Replaced_mode:\\n{pd.value_counts(data.Replaced_mode)}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/viz_scripts/replacement_models.py b/viz_scripts/replacement_models.py new file mode 100644 index 0000000..5e303dc --- /dev/null +++ b/viz_scripts/replacement_models.py @@ -0,0 +1,122 @@ +import numpy as np +import pandas as pd +import sklearn.metrics +from sklearn.preprocessing import MinMaxScaler +from sklearn.neighbors import KNeighborsClassifier +from sklearn.ensemble import RandomForestClassifier +from sklearn.ensemble import GradientBoostingClassifier +from sklearn.svm import SVC + +def gbdt(data, choice_col, feature_list, kf): + # Save metrics from each run + accuracy = [] + f1 = [] + confusion = [] +# # Scale numeric variables +# scaler = MinMaxScaler() +# data_scaled = scaler.fit_transform(data[feature_list]) + data_scaled = data[feature_list].values + + for train_indices, test_indices in kf.split(data.values): + X_train, X_test = data_scaled[train_indices], data_scaled[test_indices] + y_train, y_test = data[choice_col].values[train_indices], data[choice_col].values[test_indices] + + # Train random forest on training set + model = GradientBoostingClassifier(n_estimators=50) + model.fit(X_train, y_train) + + # Predict for test set + y_pred = model.predict(X_test) + + # Track metrics + accuracy.append(sklearn.metrics.accuracy_score(y_test, y_pred)) + f1.append(sklearn.metrics.f1_score(y_test, y_pred, average='weighted')) + confusion.append(sklearn.metrics.confusion_matrix(y_test, y_pred, labels=[0,1,2,3,4,5,6,7], normalize='pred')) + + # Collect all model scores for comparison at the end + return model, accuracy, f1, confusion + +def rf(data, choice_col, feature_list, kf): + # Save metrics from each run + accuracy = [] + f1 = [] + confusion = [] +# # Scale numeric variables +# scaler = MinMaxScaler() +# data_scaled = scaler.fit_transform(data[feature_list]) + data_scaled = data[feature_list].values + + for train_indices, test_indices in kf.split(data.values): + X_train, X_test = data_scaled[train_indices], data_scaled[test_indices] + y_train, y_test = data[choice_col].values[train_indices], data[choice_col].values[test_indices] + + # Train random forest on training set + model = RandomForestClassifier(n_estimators=50) + model.fit(X_train, y_train) + + # Predict for test set + y_pred = model.predict(X_test) + + # Track metrics + accuracy.append(sklearn.metrics.accuracy_score(y_test, y_pred)) + f1.append(sklearn.metrics.f1_score(y_test, y_pred, average='weighted')) + confusion.append(sklearn.metrics.confusion_matrix(y_test, y_pred, labels=[0,1,2,3,4,5,6,7], normalize='pred')) + + # Collect all model scores for comparison at the end + return model, accuracy, f1, confusion + +def svm(data, choice_col, feature_list, kf): + # Save metrics from each run + accuracy = [] + f1 = [] + confusion = [] + # Scale numeric variables + scaler = MinMaxScaler() + data_scaled = scaler.fit_transform(data[feature_list]) + + for train_indices, test_indices in kf.split(data.values): + X_train, X_test = data_scaled[train_indices], data_scaled[test_indices] + y_train, y_test = data[choice_col].values[train_indices], data[choice_col].values[test_indices] + + # Train random forest on training set + model = SVC() + model.fit(X_train, y_train) + + # Predict for test set + y_pred = model.predict(X_test) + + # Track metrics + accuracy.append(sklearn.metrics.accuracy_score(y_test, y_pred)) + f1.append(sklearn.metrics.f1_score(y_test, y_pred, average='weighted')) + confusion.append(sklearn.metrics.confusion_matrix(y_test, y_pred, labels=[0,1,2,3,4,5,6,7], normalize='pred')) + + # Collect all model scores for comparison at the end + return model, accuracy, f1, confusion + +def knn(data, choice_col, feature_list, kf): + # Save metrics from each run + accuracy = [] + f1 = [] + confusion = [] + # Scale numeric variables + scaler = MinMaxScaler() + data_scaled = scaler.fit_transform(data[feature_list]) + + for train_indices, test_indices in kf.split(data.values): + X_train, X_test = data_scaled[train_indices], data_scaled[test_indices] + y_train, y_test = data[choice_col].values[train_indices], data[choice_col].values[test_indices] + + # Train random forest on training set + model = KNeighborsClassifier(n_neighbors=len(pd.unique(y_train))) + model.fit(X_train, y_train) + + # Predict for test set + y_pred = model.predict(X_test) + + # Track metrics + accuracy.append(sklearn.metrics.accuracy_score(y_test, y_pred)) + f1.append(sklearn.metrics.f1_score(y_test, y_pred, average='weighted')) + confusion.append(sklearn.metrics.confusion_matrix(y_test, y_pred, labels=[0,1,2,3,4,5,6,7], normalize='pred')) + + # Collect all model scores for comparison at the end + return model, accuracy, f1, confusion \ No newline at end of file diff --git a/viz_scripts/run_replacement_models.ipynb b/viz_scripts/run_replacement_models.ipynb new file mode 100644 index 0000000..364040c --- /dev/null +++ b/viz_scripts/run_replacement_models.ipynb @@ -0,0 +1,454 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import sklearn\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "from plots import *\n", + "import replacement_models as rm\n", + "import scaffolding\n", + "\n", + "# For reloading modules from Jupyter\n", + "import importlib\n", + "importlib.reload(rm)\n", + "\n", + "sns.set_style(\"whitegrid\")\n", + "sns.set()\n", + "%matplotlib inline\n", + "\n", + "SAVE_DIR = '/plots/'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv('./processed_replacement_modeling_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "choice_col = 'Replaced_mode_num'\n", + "feature_list = ['Mode_confirm_num',\n", + " 'hhinc_$25,000-$49,999',\n", + " 'hhinc_$50,000-$99,999',\n", + " 'hhinc_Less than $24,999',\n", + " 'purp_Home',\n", + " 'purp_commute',\n", + " 'purp_discretionary',\n", + " 'purp_pudo',\n", + " 'purp_recreation',\n", + " 'purp_transit_transfer',\n", + " 'AGE',\n", + " 'VEH',\n", + " 'HHSIZE',\n", + " 'is_male',\n", + " 'sin_time',\n", + " 'cos_time',\n", + " 'sin_month',\n", + " 'cos_month',\n", + " 'is_weekend',\n", + " 'duration',\n", + " 'distance_miles',\n", + " 'av_car',\n", + " 'av_s_car',\n", + " 'av_ebike',\n", + " 'av_p_micro',\n", + " 'av_s_micro',\n", + " 'av_ridehail',\n", + " 'av_transit',\n", + " 'av_walk']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up K-fold cross validation\n", + "kf = KFold(n_splits=3)\n", + "\n", + "# Collect all scores to show at end of modeling\n", + "score_results = {}\n", + "score_results_holdout = {}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up holdout users\n", + "all_users = pd.unique(data['user_id'])\n", + "holdout_users = np.random.choice(all_users, 10)\n", + "holdout_data = data[data['user_id'].isin(holdout_users)]\n", + "non_holdout_data = data[~data['user_id'].isin(holdout_users)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Random Forest" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test on All Users" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Train and test model\n", + "rf, accuracy, f1, confusion = rm.rf(data, choice_col, feature_list, kf)\n", + "\n", + "# Save scores for model comparison\n", + "score_results['rf'] = (np.mean(accuracy), np.mean(f1))\n", + "print(f\"Accuracy: {np.mean(accuracy)}\")\n", + "print(f\"F1: {np.mean(f1)}\")\n", + "\n", + "# Average and plot the confusion matrices\n", + "confusion_mean = np.mean(np.array(confusion), axis=0)\n", + "fig, ax = plt.subplots(figsize=(6,6))\n", + "sns.heatmap(confusion_mean, annot=True, fmt='.1%', cmap='YlGnBu', linewidths=.5, cbar=False).set(title='Random Forest Confusion Matrix', xlabel='Predicted', ylabel='Actual')\n", + "plt.subplots_adjust(bottom=0.25)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test on Holdout Users" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Train and test model\n", + "rf, accuracy, f1, confusion = rm.rf(non_holdout_data, choice_col, feature_list, kf)\n", + "\n", + "holdout_true = holdout_data[choice_col].values\n", + "holdout_pred = rf.predict(holdout_data[feature_list].values)\n", + "\n", + "accuracy = sklearn.metrics.accuracy_score(holdout_true, holdout_pred)\n", + "f1 = sklearn.metrics.f1_score(holdout_true, holdout_pred, average='weighted')\n", + "score_results_holdout['rf'] = (np.mean(accuracy), np.mean(f1))\n", + "\n", + "print(f\"Holdout Accuracy: {accuracy}\")\n", + "print(f\"Holdout F1: {f1}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GBDT" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test on All Users" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Train and test model\n", + "rf, accuracy, f1, confusion = rm.gbdt(data, choice_col, feature_list, kf)\n", + "\n", + "# Save scores for model comparison\n", + "score_results['gbdt'] = (np.mean(accuracy), np.mean(f1))\n", + "print(f\"Accuracy: {np.mean(accuracy)}\")\n", + "print(f\"F1: {np.mean(f1)}\")\n", + "\n", + "# Average and plot the confusion matrices\n", + "confusion_mean = np.mean(np.array(confusion), axis=0)\n", + "fig, ax = plt.subplots(figsize=(6,6))\n", + "sns.heatmap(confusion_mean, annot=True, fmt='.1%', cmap='YlGnBu', linewidths=.5, cbar=False).set(title='GBDT Confusion Matrix', xlabel='Predicted', ylabel='Actual')\n", + "plt.subplots_adjust(bottom=0.25)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test on Holdout Users" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Train and test model\n", + "gbdt, accuracy, f1, confusion = rm.gbdt(non_holdout_data, choice_col, feature_list, kf)\n", + "\n", + "holdout_true = holdout_data[choice_col].values\n", + "holdout_pred = gbdt.predict(holdout_data[feature_list].values)\n", + "\n", + "accuracy = sklearn.metrics.accuracy_score(holdout_true, holdout_pred)\n", + "f1 = sklearn.metrics.f1_score(holdout_true, holdout_pred, average='weighted')\n", + "score_results_holdout['gbdt'] = (np.mean(accuracy), np.mean(f1))\n", + "\n", + "print(f\"Holdout Accuracy: {accuracy}\")\n", + "print(f\"Holdout F1: {f1}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SVM" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test on All Users" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Train and test model\n", + "rf, accuracy, f1, confusion = rm.svm(data, choice_col, feature_list, kf)\n", + "\n", + "# Save scores for model comparison\n", + "score_results['svm'] = (np.mean(accuracy), np.mean(f1))\n", + "print(f\"Accuracy: {np.mean(accuracy)}\")\n", + "print(f\"F1: {np.mean(f1)}\")\n", + "\n", + "# Average and plot the confusion matrices\n", + "confusion_mean = np.mean(np.array(confusion), axis=0)\n", + "fig, ax = plt.subplots(figsize=(6,6))\n", + "sns.heatmap(confusion_mean, annot=True, fmt='.1%', cmap='YlGnBu', linewidths=.5, cbar=False).set(title='SVM Confusion Matrix', xlabel='Predicted', ylabel='Actual')\n", + "plt.subplots_adjust(bottom=0.25)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test on Holdout Users" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Train and test model\n", + "svm, accuracy, f1, confusion = rm.svm(non_holdout_data, choice_col, feature_list, kf)\n", + "\n", + "holdout_true = holdout_data[choice_col].values\n", + "holdout_pred = svm.predict(holdout_data[feature_list].values)\n", + "\n", + "accuracy = sklearn.metrics.accuracy_score(holdout_true, holdout_pred)\n", + "f1 = sklearn.metrics.f1_score(holdout_true, holdout_pred, average='weighted')\n", + "score_results_holdout['svm'] = (np.mean(accuracy), np.mean(f1))\n", + "\n", + "print(f\"Holdout Accuracy: {accuracy}\")\n", + "print(f\"Holdout F1: {f1}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# K Nearest Neighbors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test on All Users" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Train and test model\n", + "knn, accuracy, f1, confusion = rm.knn(data, choice_col, feature_list, kf)\n", + "\n", + "# Save scores for model comparison\n", + "score_results['knn'] = (np.mean(accuracy), np.mean(f1))\n", + "print(f\"Accuracy: {np.mean(accuracy)}\")\n", + "print(f\"F1: {np.mean(f1)}\")\n", + "\n", + "# Average and plot the confusion matrices\n", + "confusion_mean = np.mean(np.array(confusion), axis=0)\n", + "fig, ax = plt.subplots(figsize=(6,6))\n", + "sns.heatmap(confusion_mean, annot=True, fmt='.1%', cmap='YlGnBu', linewidths=.5, cbar=False).set(title='KNN Confusion Matrix', xlabel='Predicted', ylabel='Actual')\n", + "plt.subplots_adjust(bottom=0.25)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test on Holdout Users" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Train and test model\n", + "knn, accuracy, f1, confusion = rm.knn(non_holdout_data, choice_col, feature_list, kf)\n", + "\n", + "holdout_true = holdout_data[choice_col].values\n", + "holdout_pred = knn.predict(holdout_data[feature_list].values)\n", + "\n", + "accuracy = sklearn.metrics.accuracy_score(holdout_true, holdout_pred)\n", + "f1 = sklearn.metrics.f1_score(holdout_true, holdout_pred, average='weighted')\n", + "score_results_holdout['knn'] = (np.mean(accuracy), np.mean(f1))\n", + "\n", + "print(f\"Holdout Accuracy: {accuracy}\")\n", + "print(f\"Holdout F1: {f1}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Comparison" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Overall results\n", + "accuracy_all = pd.DataFrame(pd.DataFrame(score_results).iloc[0,:]).reset_index()\n", + "accuracy_all.columns = ['Model','Score']\n", + "accuracy_all['Type'] = 'All'\n", + "f1_all = pd.DataFrame(pd.DataFrame(score_results).iloc[1,:]).reset_index()\n", + "f1_all.columns = ['Model','Score']\n", + "f1_all['Type'] = 'All'\n", + "score_results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Overall results\n", + "accuracy_holdout = pd.DataFrame(pd.DataFrame(score_results_holdout).iloc[0,:]).reset_index()\n", + "accuracy_holdout.columns = ['Model','Score']\n", + "accuracy_holdout['Type'] = 'Holdout'\n", + "f1_holdout = pd.DataFrame(pd.DataFrame(score_results_holdout).iloc[1,:]).reset_index()\n", + "f1_holdout.columns = ['Model','Score']\n", + "f1_holdout['Type'] = 'Holdout'\n", + "score_results_holdout" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "accuracy = pd.concat([accuracy_all, accuracy_holdout])\n", + "f1 = pd.concat([f1_all, f1_holdout])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "sns.barplot(data=accuracy, x='Model', y='Score', hue='Type').set(title='Accuracy')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sns.barplot(data=f1, x='Model', y='Score', hue='Type').set(title='F1')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 5e5014a8447aa255d4d8b49250ad07e6d672097d Mon Sep 17 00:00:00 2001 From: zackAemmer Date: Wed, 30 Nov 2022 10:43:12 -0800 Subject: [PATCH 02/16] Small change to accommodate scaffolding changes --- viz_scripts/replacement_model_data_processing.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/viz_scripts/replacement_model_data_processing.ipynb b/viz_scripts/replacement_model_data_processing.ipynb index 30810e1..19684a3 100644 --- a/viz_scripts/replacement_model_data_processing.ipynb +++ b/viz_scripts/replacement_model_data_processing.ipynb @@ -158,7 +158,7 @@ }, "outputs": [], "source": [ - "expanded_ct, file_suffix, quality_text = scaffolding.load_viz_notebook_data(year,\n", + "expanded_ct, file_suffix, quality_text, debug_df = scaffolding.load_viz_notebook_data(year,\n", " month,\n", " program,\n", " study_type,\n", From f574d1c5b86b617ef33e77fe31c0b88918d3b4a1 Mon Sep 17 00:00:00 2001 From: Rahul Kulhalli Date: Fri, 17 Nov 2023 13:43:22 -0500 Subject: [PATCH 03/16] Added exploratory, preprocessing, and intitial modeling notebooks --- viz_scripts/mapping_dictionaries.ipynb | 44 +- .../rm_src/biogeme_modeling train_test.ipynb | 530 ++++++++++++++++ viz_scripts/rm_src/biogeme_modeling.ipynb | 383 +++++++++++ .../rm_src/cost_time_avl_preprocessing.ipynb | 594 ++++++++++++++++++ .../rm_src/time_distance_estimation.ipynb | 235 +++++++ .../rm_src/user_data_preprocessing.ipynb | 586 +++++++++++++++++ viz_scripts/scaffolding.py | 86 ++- 7 files changed, 2442 insertions(+), 16 deletions(-) create mode 100644 viz_scripts/rm_src/biogeme_modeling train_test.ipynb create mode 100644 viz_scripts/rm_src/biogeme_modeling.ipynb create mode 100644 viz_scripts/rm_src/cost_time_avl_preprocessing.ipynb create mode 100644 viz_scripts/rm_src/time_distance_estimation.ipynb create mode 100644 viz_scripts/rm_src/user_data_preprocessing.ipynb diff --git a/viz_scripts/mapping_dictionaries.ipynb b/viz_scripts/mapping_dictionaries.ipynb index aa6046d..1252544 100644 --- a/viz_scripts/mapping_dictionaries.ipynb +++ b/viz_scripts/mapping_dictionaries.ipynb @@ -2,17 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "available-fusion", "metadata": {}, "outputs": [], "source": [ - "import pandas as pd" + "import pandas as pd\n", + "import pickle" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "obvious-chapter", "metadata": {}, "outputs": [], @@ -29,21 +30,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "younger-indication", "metadata": {}, "outputs": [], "source": [ - "%store df_ei\n", - "%store dic_re\n", - "%store dic_pur\n", - "%store dic_fuel" + "# %store df_ei\n", + "# %store dic_re\n", + "# %store dic_pur\n", + "# %store dic_fuel\n", + "\n", + "with open('auxiliary_files/df_ei.pkl', 'wb') as f:\n", + " f.write(pickle.dumps(df_ei))\n", + "\n", + "with open('auxiliary_files/dic_re.pkl', 'wb') as f:\n", + " f.write(pickle.dumps(dic_re))\n", + "\n", + "with open('auxiliary_files/dic_pur.pkl', 'wb') as f:\n", + " f.write(pickle.dumps(dic_pur))\n", + "\n", + "with open('auxiliary_files/dic_fuel.pkl', 'wb') as f:\n", + " f.write(pickle.dumps(dic_fuel))" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8e389b3d", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { + "interpreter": { + "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" + }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.9.16 ('emission')", "language": "python", "name": "python3" }, @@ -57,7 +81,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.12" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/viz_scripts/rm_src/biogeme_modeling train_test.ipynb b/viz_scripts/rm_src/biogeme_modeling train_test.ipynb new file mode 100644 index 0000000..ba15c71 --- /dev/null +++ b/viz_scripts/rm_src/biogeme_modeling train_test.ipynb @@ -0,0 +1,530 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import biogeme.biogeme as bio\n", + "import biogeme.database as db\n", + "from biogeme import models\n", + "from biogeme.expressions import Beta\n", + "from biogeme.expressions import Variable\n", + "import numpy as np\n", + "from enum import Enum" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv('../data/preprocessed_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data.user_id.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class SPLIT_TYPE(Enum):\n", + " INTRA_USER = 0\n", + " INTER_USER = 1\n", + "\n", + "\n", + "def get_splits(count_df: pd.DataFrame, n:int, test_size=0.2):\n", + " maxsize = int(n * test_size)\n", + "\n", + " max_threshold = int(maxsize * 1.05)\n", + " min_threshold = int(maxsize * 0.95)\n", + "\n", + " print(f\"{min_threshold=}, {max_threshold=}\")\n", + " \n", + " # Allow a 10% tolerance\n", + " def _dp(ix, curr_size, ids, cache):\n", + " \n", + " if ix >= count_df.shape[0]:\n", + " return []\n", + "\n", + " key = ix\n", + "\n", + " if key in cache:\n", + " return cache[key]\n", + "\n", + " if curr_size > max_threshold:\n", + " return []\n", + "\n", + " if min_threshold <= curr_size <= max_threshold:\n", + " return ids\n", + "\n", + " # two options - either pick the current id or skip it.\n", + " branch_a = _dp(ix, curr_size+count_df.loc[ix, 'count'], ids+[count_df.loc[ix, 'index']], cache)\n", + " branch_b = _dp(ix+1, curr_size, ids, cache)\n", + " \n", + " curr_max = []\n", + " if branch_a and len(branch_a) > 0:\n", + " curr_max = branch_a\n", + " \n", + " if branch_b and len(branch_b) > len(branch_a):\n", + " curr_max = branch_b\n", + " \n", + " cache[key] = curr_max\n", + " return cache[key]\n", + " \n", + " return _dp(0, 0, ids=list(), cache=dict())\n", + "\n", + "\n", + "def get_train_test_splits(data: pd.DataFrame, how=SPLIT_TYPE, test_ratio=0.2, shuffle=True):\n", + "\n", + " n_users = list(data.user_id.unique())\n", + " n = data.shape[0]\n", + " \n", + " if shuffle:\n", + " data = data.sample(data.shape[0]).reset_index(drop=True, inplace=False)\n", + "\n", + " if how == SPLIT_TYPE.INTER_USER:\n", + " # Make the split, ensuring that a user in one fold is not leaked into the other fold.\n", + " # Basic idea: we want to start with the users with the highest instances and place alternating users in each set.\n", + " counts = data.user_id.value_counts().reset_index(drop=False, inplace=False, name='count')\n", + "\n", + " # Now, start with the user_id at the top, and keep adding to either split.\n", + " # This can be achieved using a simple DP program to fill a knapsack with at most (test_ratio * size) entries.\n", + " test_ids = get_splits(counts, data.shape[0])\n", + "\n", + " print(\"Final test IDs: \", test_ids)\n", + " \n", + " return test_ids" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ids = get_train_test_splits(data=data, how=SPLIT_TYPE.INTER_USER, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ids" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data.user_id.value_counts()[ids]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a.loc[0, 'count']\n", + "a.loc[0, 'index']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "USER_MAP = {\n", + " u: i+1 for (i, u) in enumerate(data.user_id.unique())\n", + "}\n", + "\n", + "# Drop section_mode_argmax and available_modes.\n", + "data.drop(columns=[\n", + " 'section_mode_argmax', 'available_modes', 'section_distance_argmax', 'section_duration_argmax'\n", + " ], inplace=True)\n", + "\n", + "data['user_id'] = data['user_id'].apply(lambda x: USER_MAP[x])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert from min -> hrs\n", + "data[[c for c in data.columns if 'tt_' in c]] /= 60." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Scale currency by 1/10.\n", + "data[[c for c in data.columns if 'cost_' in c]] /= 10." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display(data[[c for c in data.columns if 'tt_' in c]].describe())\n", + "display(data[[c for c in data.columns if 'cost_' in c]].describe())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "for c in data.columns:\n", + " if 'av_' in c:\n", + " print(f\"Feature {c} availability: {data.loc[:, c].sum()}/{data.shape[0]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# data.drop(columns=['tt_unknown','av_unknown'], inplace=True)\n", + "# data.drop(index=data.loc[(data.chosen == 9)].index, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "database = db.Database('emission', data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Variable definitions.\n", + "\n", + "# ID.\n", + "USER_ID = Variable('user_id')\n", + "\n", + "# Availability.\n", + "AV_P_MICRO = Variable('av_p_micro')\n", + "AV_NO_TRIP = Variable('av_no_trip')\n", + "AV_S_CAR = Variable('av_s_car')\n", + "AV_TRANSIT = Variable('av_transit')\n", + "AV_CAR = Variable('av_car')\n", + "AV_S_MICRO = Variable('av_s_micro')\n", + "AV_RIDEHAIL = Variable('av_ridehail')\n", + "AV_WALK = Variable('av_walk')\n", + "AV_UNKNOWN = Variable('av_unknown')\n", + "\n", + "# Time.\n", + "TT_P_MICRO = Variable('tt_p_micro')\n", + "TT_NO_TRIP = Variable('tt_no_trip')\n", + "TT_S_CAR = Variable('tt_s_car')\n", + "TT_TRANSIT = Variable('tt_transit')\n", + "TT_CAR = Variable('tt_car')\n", + "TT_S_MICRO = Variable('tt_s_micro')\n", + "TT_RIDEHAIL = Variable('tt_ridehail')\n", + "TT_WALK = Variable('tt_walk')\n", + "TT_UNKNOWN = Variable('tt_unknown')\n", + "\n", + "# Cost.\n", + "CO_P_MICRO = Variable('cost_p_micro')\n", + "CO_NO_TRIP = Variable('cost_no_trip')\n", + "CO_S_CAR = Variable('cost_s_car')\n", + "CO_TRANSIT = Variable('cost_transit')\n", + "CO_CAR = Variable('cost_car')\n", + "CO_S_MICRO = Variable('cost_s_micro')\n", + "CO_RIDEHAIL = Variable('cost_ridehail')\n", + "CO_WALK = Variable('cost_walk')\n", + "CO_UNKNOWN = Variable('cost_unknown')\n", + "\n", + "# Choice.\n", + "CHOICE = Variable('chosen')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Parameters\n", + "\n", + "# Create a coefficient from time and set the initial value to 0. There is no lower bound for this coefficient,\n", + "# but we would like to constrain the ceil to 0 since we want these coefficients to denote the utiltiy function.\n", + "# Finally, we want this parameter to be updated by the optimization algorithm.\n", + "B_TIME = Beta('B_TIME', 0, None, 0, 0)\n", + "B_COST = Beta('B_COST', 0, None, 0, 0)\n", + "\n", + "# Alternative-Specific Constants.\n", + "ASC_P_MICRO = Beta('ASC_P_MICRO', 0, None, None, 0)\n", + "ASC_NO_TRIP = Beta('ASC_NO_TRIP', 0, None, None, 1)\n", + "ASC_S_CAR = Beta('ASC_S_CAR', 0, None, None, 0)\n", + "ASC_TRANSIT = Beta('ASC_TRANSIT', 0, None, None, 0)\n", + "ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)\n", + "ASC_S_MICRO = Beta('ASC_S_MICRO', 0, None, None, 0)\n", + "ASC_RIDEHAIL = Beta('ASC_RIDEHAIL', 0, None, None, 0)\n", + "ASC_WALK = Beta('ASC_WALK', 0, None, None, 0)\n", + "ASC_UNKNOWN = Beta('ASC_UNKNOWN', 0, None, None, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the utility functions.\n", + "# number of alternatives = number of UFs.\n", + "\n", + "V_P_MICRO = (\n", + " ASC_P_MICRO +\n", + " B_TIME * TT_P_MICRO +\n", + " B_COST * CO_P_MICRO\n", + ")\n", + "\n", + "V_NO_TRIP = (\n", + " ASC_NO_TRIP +\n", + " B_TIME * TT_NO_TRIP +\n", + " B_COST * CO_NO_TRIP\n", + ")\n", + "\n", + "V_S_CAR = (\n", + " ASC_CAR +\n", + " B_TIME * TT_CAR +\n", + " B_COST * CO_CAR\n", + ")\n", + "\n", + "V_TRANSIT = (\n", + " ASC_TRANSIT +\n", + " B_TIME * TT_TRANSIT +\n", + " B_COST * CO_TRANSIT\n", + ")\n", + "\n", + "V_CAR = (\n", + " ASC_CAR +\n", + " B_TIME * TT_CAR +\n", + " B_COST * CO_CAR\n", + ")\n", + "\n", + "V_S_MICRO = (\n", + " ASC_S_MICRO +\n", + " B_TIME * TT_S_MICRO +\n", + " B_COST * CO_S_MICRO\n", + ")\n", + "\n", + "V_RIDEHAIL = (\n", + " ASC_RIDEHAIL +\n", + " B_TIME * TT_RIDEHAIL +\n", + " B_COST * CO_RIDEHAIL\n", + ")\n", + "\n", + "V_WALK = (\n", + " ASC_WALK +\n", + " B_TIME * TT_WALK +\n", + " B_COST * CO_WALK\n", + ")\n", + "\n", + "V_UNKNOWN = (\n", + " ASC_UNKNOWN +\n", + " B_TIME * TT_UNKNOWN +\n", + " B_COST * CO_UNKNOWN\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Map alterative to utility functions.\n", + "V = {\n", + " 1: V_P_MICRO, \n", + " 2: V_NO_TRIP,\n", + " 3: V_S_CAR, \n", + " 4: V_TRANSIT,\n", + " 5: V_CAR, \n", + " 6: V_S_MICRO,\n", + " 7: V_RIDEHAIL, \n", + " 8: V_WALK, \n", + " 9: V_UNKNOWN\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Availability.\n", + "# We have already masked-off costs and times for the instances where a particular mode is not available to the user.\n", + "# So here, we can specify the availability as 1 for every mode, saying that every other mode should be included when computing\n", + "# the logprob for a certain mode.\n", + "\n", + "av = {\n", + " 1: AV_P_MICRO,\n", + " 2: AV_NO_TRIP,\n", + " 3: AV_S_CAR,\n", + " 4: AV_TRANSIT,\n", + " 5: AV_CAR,\n", + " 6: AV_S_MICRO,\n", + " 7: AV_RIDEHAIL,\n", + " 8: AV_WALK,\n", + " 9: AV_UNKNOWN\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "logprob = models.loglogit(V, av, CHOICE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = bio.BIOGEME(database, logprob)\n", + "model.modelName = 'noMaskNoTrip'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results = model.estimate()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(results.short_summary())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.getEstimatedParameters()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.getCorrelationResults()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_metrics(model: bio.BIOGEME, data: pd.DataFrame):\n", + " \"\"\"\n", + " Given a test dataframe and the trained model, generate precision, recall, F1.\n", + " \"\"\"\n", + "\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vals = model.validate(results)\n", + "\n", + "for val in vals:\n", + " print(val.shape, val.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" + }, + "kernelspec": { + "display_name": "emission", + "language": "python", + "name": "emission" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/viz_scripts/rm_src/biogeme_modeling.ipynb b/viz_scripts/rm_src/biogeme_modeling.ipynb new file mode 100644 index 0000000..54ccbd6 --- /dev/null +++ b/viz_scripts/rm_src/biogeme_modeling.ipynb @@ -0,0 +1,383 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import biogeme.biogeme as bio\n", + "import biogeme.database as db\n", + "from biogeme import models\n", + "from biogeme.expressions import Beta\n", + "from biogeme.expressions import Variable\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv('../data/preprocessed_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "USER_MAP = {\n", + " u: i+1 for (i, u) in enumerate(data.user_id.unique())\n", + "}\n", + "\n", + "# Drop section_mode_argmax and available_modes.\n", + "data.drop(columns=[\n", + " 'section_mode_argmax', 'available_modes', 'section_distance_argmax', 'section_duration_argmax'\n", + " ], inplace=True)\n", + "\n", + "data['user_id'] = data['user_id'].apply(lambda x: USER_MAP[x])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert from min -> hrs\n", + "data[[c for c in data.columns if 'tt_' in c]] /= 60." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Scale currency by 1/10.\n", + "data[[c for c in data.columns if 'cost_' in c]] /= 10." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display(data[[c for c in data.columns if 'tt_' in c]].describe())\n", + "display(data[[c for c in data.columns if 'cost_' in c]].describe())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "for c in data.columns:\n", + " if 'av_' in c:\n", + " print(f\"Feature {c} availability: {data.loc[:, c].sum()}/{data.shape[0]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# data.drop(columns=['tt_unknown','av_unknown'], inplace=True)\n", + "# data.drop(index=data.loc[(data.chosen == 9)].index, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "database = db.Database('emission', data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Variable definitions.\n", + "\n", + "# ID.\n", + "USER_ID = Variable('user_id')\n", + "\n", + "# Availability.\n", + "AV_P_MICRO = Variable('av_p_micro')\n", + "AV_NO_TRIP = Variable('av_no_trip')\n", + "AV_S_CAR = Variable('av_s_car')\n", + "AV_TRANSIT = Variable('av_transit')\n", + "AV_CAR = Variable('av_car')\n", + "AV_S_MICRO = Variable('av_s_micro')\n", + "AV_RIDEHAIL = Variable('av_ridehail')\n", + "AV_WALK = Variable('av_walk')\n", + "AV_UNKNOWN = Variable('av_unknown')\n", + "\n", + "# Time.\n", + "TT_P_MICRO = Variable('tt_p_micro')\n", + "TT_NO_TRIP = Variable('tt_no_trip')\n", + "TT_S_CAR = Variable('tt_s_car')\n", + "TT_TRANSIT = Variable('tt_transit')\n", + "TT_CAR = Variable('tt_car')\n", + "TT_S_MICRO = Variable('tt_s_micro')\n", + "TT_RIDEHAIL = Variable('tt_ridehail')\n", + "TT_WALK = Variable('tt_walk')\n", + "TT_UNKNOWN = Variable('tt_unknown')\n", + "\n", + "# Cost.\n", + "CO_P_MICRO = Variable('cost_p_micro')\n", + "CO_NO_TRIP = Variable('cost_no_trip')\n", + "CO_S_CAR = Variable('cost_s_car')\n", + "CO_TRANSIT = Variable('cost_transit')\n", + "CO_CAR = Variable('cost_car')\n", + "CO_S_MICRO = Variable('cost_s_micro')\n", + "CO_RIDEHAIL = Variable('cost_ridehail')\n", + "CO_WALK = Variable('cost_walk')\n", + "CO_UNKNOWN = Variable('cost_unknown')\n", + "\n", + "# Choice.\n", + "CHOICE = Variable('chosen')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Parameters\n", + "\n", + "# Create a coefficient from time and set the initial value to 0. There is no lower bound for this coefficient,\n", + "# but we would like to constrain the ceil to 0 since we want these coefficients to denote the utiltiy function.\n", + "# Finally, we want this parameter to be updated by the optimization algorithm.\n", + "B_TIME = Beta('B_TIME', 0, None, 0, 0)\n", + "B_COST = Beta('B_COST', 0, None, 0, 0)\n", + "\n", + "# Alternative-Specific Constants.\n", + "ASC_P_MICRO = Beta('ASC_P_MICRO', 0, None, None, 0)\n", + "ASC_NO_TRIP = Beta('ASC_NO_TRIP', 0, None, None, 1)\n", + "ASC_S_CAR = Beta('ASC_S_CAR', 0, None, None, 0)\n", + "ASC_TRANSIT = Beta('ASC_TRANSIT', 0, None, None, 0)\n", + "ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)\n", + "ASC_S_MICRO = Beta('ASC_S_MICRO', 0, None, None, 0)\n", + "ASC_RIDEHAIL = Beta('ASC_RIDEHAIL', 0, None, None, 0)\n", + "ASC_WALK = Beta('ASC_WALK', 0, None, None, 0)\n", + "ASC_UNKNOWN = Beta('ASC_UNKNOWN', 0, None, None, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the utility functions.\n", + "# number of alternatives = number of UFs.\n", + "\n", + "V_P_MICRO = (\n", + " ASC_P_MICRO +\n", + " B_TIME * TT_P_MICRO +\n", + " B_COST * CO_P_MICRO\n", + ")\n", + "\n", + "V_NO_TRIP = (\n", + " ASC_NO_TRIP +\n", + " B_TIME * TT_NO_TRIP +\n", + " B_COST * CO_NO_TRIP\n", + ")\n", + "\n", + "V_S_CAR = (\n", + " ASC_CAR +\n", + " B_TIME * TT_CAR +\n", + " B_COST * CO_CAR\n", + ")\n", + "\n", + "V_TRANSIT = (\n", + " ASC_TRANSIT +\n", + " B_TIME * TT_TRANSIT +\n", + " B_COST * CO_TRANSIT\n", + ")\n", + "\n", + "V_CAR = (\n", + " ASC_CAR +\n", + " B_TIME * TT_CAR +\n", + " B_COST * CO_CAR\n", + ")\n", + "\n", + "V_S_MICRO = (\n", + " ASC_S_MICRO +\n", + " B_TIME * TT_S_MICRO +\n", + " B_COST * CO_S_MICRO\n", + ")\n", + "\n", + "V_RIDEHAIL = (\n", + " ASC_RIDEHAIL +\n", + " B_TIME * TT_RIDEHAIL +\n", + " B_COST * CO_RIDEHAIL\n", + ")\n", + "\n", + "V_WALK = (\n", + " ASC_WALK +\n", + " B_TIME * TT_WALK +\n", + " B_COST * CO_WALK\n", + ")\n", + "\n", + "V_UNKNOWN = (\n", + " ASC_UNKNOWN +\n", + " B_TIME * TT_UNKNOWN +\n", + " B_COST * CO_UNKNOWN\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Map alterative to utility functions.\n", + "V = {\n", + " 1: V_P_MICRO, \n", + " 2: V_NO_TRIP,\n", + " 3: V_S_CAR, \n", + " 4: V_TRANSIT,\n", + " 5: V_CAR, \n", + " 6: V_S_MICRO,\n", + " 7: V_RIDEHAIL, \n", + " 8: V_WALK, \n", + " 9: V_UNKNOWN\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Availability.\n", + "# We have already masked-off costs and times for the instances where a particular mode is not available to the user.\n", + "# So here, we can specify the availability as 1 for every mode, saying that every other mode should be included when computing\n", + "# the logprob for a certain mode.\n", + "\n", + "av = {\n", + " 1: AV_P_MICRO,\n", + " 2: AV_NO_TRIP,\n", + " 3: AV_S_CAR,\n", + " 4: AV_TRANSIT,\n", + " 5: AV_CAR,\n", + " 6: AV_S_MICRO,\n", + " 7: AV_RIDEHAIL,\n", + " 8: AV_WALK,\n", + " 9: AV_UNKNOWN\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "logprob = models.loglogit(V, av, CHOICE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = bio.BIOGEME(database, logprob)\n", + "model.modelName = 'noMaskNoTrip'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results = model.estimate()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(results.short_summary())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.getEstimatedParameters()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.getCorrelationResults()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_metrics(model: bio.BIOGEME, data: pd.DataFrame):\n", + " \"\"\"\n", + " Given a test dataframe and the trained model, generate precision, recall, F1.\n", + " \"\"\"\n", + "\n", + " pass" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" + }, + "kernelspec": { + "display_name": "Python 3.9.16 ('emission')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/viz_scripts/rm_src/cost_time_avl_preprocessing.ipynb b/viz_scripts/rm_src/cost_time_avl_preprocessing.ipynb new file mode 100644 index 0000000..e535e2f --- /dev/null +++ b/viz_scripts/rm_src/cost_time_avl_preprocessing.ipynb @@ -0,0 +1,594 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import ast\n", + "import geopandas as gpd\n", + "from traceback import print_exception\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sections\n", + "\n", + "### 0: Refer to the mnl_exploration.ipynb notebook for specifics about user-level preprocessing\n", + "### 1. Creating the dummy variables for the available modes\n", + "### 2. Creating trip-specific variables for each available mode (cost and time)\n", + "### 3. ??" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Data loading. Refer to the mnl_exploration.ipynb file for details on how user-level features are generated.\n", + "\n", + "df = pd.read_csv('../data/modeling_w_duration.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.drop_duplicates(inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating the dummy variables for the available modes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_argmax(df: pd.DataFrame):\n", + " # Create the two columns.\n", + " df[['section_distance_argmax', 'section_duration_argmax', 'section_mode_argmax', 'section_coordinates_argmax']] = None\n", + " rows = list()\n", + "\n", + " for ix, row in df.iterrows():\n", + " row_dict = row.to_dict()\n", + " \n", + " try:\n", + " parsed_distances = ast.literal_eval(row_dict['section_distances'])\n", + " parsed_durations = ast.literal_eval(row_dict['section_durations'])\n", + " parsed_modes = ast.literal_eval(row_dict['section_modes'])\n", + "\n", + " argmax_ix = np.argmax(parsed_distances)\n", + "\n", + " row_dict['section_distance_argmax'] = parsed_distances[argmax_ix] * 0.0006213712 # Pick the argmax and scale to miles.\n", + " row_dict['section_duration_argmax'] = parsed_durations[argmax_ix] / 60 # Pick the argmax and scale to minutes.\n", + " row_dict['section_mode_argmax'] = parsed_modes[argmax_ix]\n", + " \n", + " row_dict['mark'] = False\n", + "\n", + " except Exception as e:\n", + " row_dict['mark'] = True\n", + "\n", + " rows.append(row_dict)\n", + "\n", + " return pd.DataFrame(rows)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's plot the mode-wise durations as a function of distance.\n", + "df_modded = compute_argmax(df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_modded = df_modded.loc[~df_modded.mark, :].reset_index(drop=True, inplace=False).drop(columns=['mark'], inplace=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Intercept and coefficient values for determining the duration from distance (Obtained from time_distance_estimation.ipynb):\n", + "\n", + "```\n", + "Format:\n", + "-> mode train_r2 test_r2\n", + "-> intercept: x, coefficient: y\n", + "\n", + "\n", + "walking 0.24312136039586707 0.5572752052765471\n", + "intercept: 14.09698152519682 coeff: 19.153802211061134\n", + "bicycling 0.5986205637411164 0.646267142326254\n", + "intercept: 4.753590860550791 coeff: 4.2723965916811935\n", + "car 0.45228118668156203 0.6040302860034135\n", + "intercept: 10.515751350707365 coeff: 1.112111276629702\n", + "no_sensed 0.5188696686147352 0.5028191556675814\n", + "intercept: 9.423026094931313 coeff: 2.640429444466706\n", + "public_transport 0.47377939742617625 0.5916737429017003\n", + "intercept: 7.5000351741492395 coeff: 1.839562017986485\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_modded['no_trip'] = df_modded.apply(\n", + " lambda x: 'Do not have vehicle' in x.available_modes or 'None' in x.available_modes, axis=1\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "no_sensed = df_modded.apply(\n", + " lambda x: 'no_sensed' == x.section_mode_argmax, axis=1\n", + ")\n", + "\n", + "n, d = no_sensed[no_sensed == True].shape[0], no_sensed.shape[0]\n", + "print(n, d)\n", + "print((n/d)*100.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n = df_modded.loc[df_modded.no_trip, :].shape[0]\n", + "d = df_modded.shape[0]\n", + "print(n, d)\n", + "print((n/d) * 100.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_available_features(df: pd.DataFrame, available_mode_map: dict, section_mode_map: dict, ordinal_mapper: dict):\n", + " \"\"\"\n", + " This method generates the wide-form dummy features for available modes.\n", + " The current implementation uses (section_mode_argmax OR available_modes) to indicate mode availability.\n", + " \"\"\"\n", + "\n", + " # Reset indices.\n", + " df = df.reset_index(drop=True, inplace=False)\n", + "\n", + " columns = list(available_mode_map.values()) + ['unknown']\n", + "\n", + " # Create the columns along with the 'chosen' column.\n", + " df[columns + ['chosen']] = 0\n", + "\n", + " row_dicts = list()\n", + "\n", + " for _, row in df.iterrows():\n", + " row_dict = row.to_dict()\n", + "\n", + " # Access the available modes. Split on ; and strip all the elements.\n", + " available_modes = [x.strip() for x in str(row_dict['available_modes']).split(';')]\n", + "\n", + " # Update the available modes.\n", + " for available_mode in available_modes:\n", + " row_dict[available_mode_map[available_mode]] = 1\n", + " \n", + " # Now, look at the section_mode_argmax as a proxy for the confirmed mode.\n", + " chosen_modes = section_mode_map[row_dict['section_mode_argmax']]\n", + "\n", + " # Determine the length of the mapping.\n", + " n = len(chosen_modes)\n", + "\n", + " # Go ahead and modify the current dict with the first value.\n", + " row_dict['chosen'] = ordinal_mapper[chosen_modes[0]]\n", + " row_dict['primary_mode'] = 1\n", + "\n", + " # Also update the available modes.\n", + " for chosen_mode in chosen_modes:\n", + " row_dict[chosen_mode] = 1\n", + " \n", + " # Append thee first row to the list.\n", + " row_dicts.append(row_dict)\n", + " \n", + " constructed = pd.DataFrame(row_dicts)\n", + " constructed.rename(columns=dict([(c, 'av_'+c) for c in ordinal_mapper.keys()]), inplace=True)\n", + "\n", + " return constructed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Define the mapping strategy\n", + "\n", + "available = {\n", + " 'Bicycle': 'p_micro',\n", + " 'Do not have vehicle': 'no_trip',\n", + " 'Get a ride from a friend or family member': 's_car',\n", + " 'None': 'no_trip',\n", + " 'Public transportation (bus, subway, light rail, etc.)': 'transit',\n", + " 'Rental car (including Zipcar/ Car2Go)': 'car',\n", + " 'Shared bicycle or scooter': 's_micro',\n", + " 'Skateboard': 'p_micro',\n", + " 'Taxi (regular taxi, Uber, Lyft, etc)': 'ridehail',\n", + " 'Walk/roll': 'walk'\n", + "}\n", + "\n", + "# Map each sensed mode to the binary indicators.\n", + "section_mode_mapping = {\n", + " 'bicycling': ['p_micro', 's_micro'],\n", + " 'bus': ['transit'],\n", + " 'car': ['s_car', 'car', 'ridehail'],\n", + " 'no_sensed': ['unknown'],\n", + " 'train': ['transit'],\n", + " 'walking': ['walk']\n", + "}\n", + "\n", + "# These values were obtained using a simple linear regression model. Specifics can be found in time_distance_estimation.ipynb\n", + "duration_dict = {\n", + " 'walk': {\n", + " 'intercept': 14.09698152519682,\n", + " 'coef': 19.153802211061134\n", + " },\n", + " 'p_micro': {\n", + " 'intercept': 4.753590860550791,\n", + " 'coef': 4.2723965916811935\n", + " },\n", + " 's_micro': {\n", + " 'intercept': 4.753590860550791,\n", + " 'coef': 4.2723965916811935\n", + " },\n", + " 's_car': {\n", + " 'intercept': 10.515751350707365, \n", + " 'coef': 1.112111276629702\n", + " },\n", + " 'car': {\n", + " 'intercept': 10.515751350707365, \n", + " 'coef': 1.112111276629702\n", + " },\n", + " 'ridehail': {\n", + " 'intercept': 10.515751350707365, \n", + " 'coef': 1.112111276629702\n", + " },\n", + " 'transit': {\n", + " 'intercept': 7.5000351741492395,\n", + " 'coef': 1.839562017986485\n", + " },\n", + " 'unknown': {\n", + " 'intercept': 9.423026094931313,\n", + " 'coef': 2.640429444466706\n", + " },\n", + " 'no_trip': {\n", + " 'intercept': 0.,\n", + " 'coef': 0.\n", + " }\n", + "}\n", + "\n", + "ordinal_mapping = {'p_micro': 1, 'no_trip': 2, 's_car': 3, 'transit': 4, 'car': 5, 's_micro': 6, 'ridehail': 7, 'walk': 8, 'unknown': 9}\n", + "reverse_ordinal_mapping = {v:k for k,v in ordinal_mapping.items()}\n", + "\n", + "print(ordinal_mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's only select data that we require and copy it into a separate df.\n", + "av_df = df_modded[['user_id', 'section_distance_argmax', 'section_duration_argmax', 'section_mode_argmax', 'available_modes']].copy()\n", + "\n", + "# Update the same df.\n", + "av_df = generate_available_features(av_df, available, section_mode_mapping, ordinal_mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "av_df.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_alt_durations(df: pd.DataFrame, reverse_mapper: dict, duration_mapper: dict):\n", + " '''\n", + " We re-iterate over the generated available mode df and populate the modes.\n", + " We are only concerned with the rows that have primary_mode = 0, which indicates that they are alternate mode rows.\n", + " This method uses the intercept and coefficients obtained from the regression models.\n", + " '''\n", + "\n", + " column_names = list(reverse_mapper.values())\n", + "\n", + " # Start with a 0 cost for everyone.\n", + " df[column_names] = 0\n", + "\n", + " rows = list()\n", + " for ix, row in df.iterrows():\n", + " \n", + " # Convert to a dict\n", + " row_dict = row.to_dict()\n", + "\n", + " for mode in column_names:\n", + " if mode == reverse_mapper[row['chosen']]:\n", + " row_dict[mode] = row_dict['section_distance_argmax']\n", + " continue\n", + " \n", + " mode_params = duration_mapper[mode]\n", + " # use availability as a mask.\n", + " row_dict[mode] = row_dict['av_' + mode] * (mode_params['intercept'] + (mode_params['coef'] * row_dict['section_distance_argmax']))\n", + " \n", + " rows.append(row_dict)\n", + " \n", + " return pd.DataFrame(rows).rename(columns=dict([(c, 'tt_'+c) for c in column_names]), inplace=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_durations_using_data(df: pd.DataFrame, section_mapper: dict, reverse_ordinal_mapping: dict):\n", + " \"\"\"\n", + " Function to perform imputation using historical averages. Instead of estimating mode duration from mode distance, we simply\n", + " impute it using the mean of the particular mode's historical duration.\n", + " \"\"\"\n", + "\n", + " # First, using the history, generate the average durations calculated per mode.\n", + " duration_dict = df.groupby('section_mode_argmax')['section_duration_argmax'].mean().to_dict()\n", + "\n", + " extended_dict = dict()\n", + " for k, v in duration_dict.items():\n", + " mapped_sections = section_mapper[k]\n", + " for _k in mapped_sections:\n", + " extended_dict[_k] = v\n", + "\n", + " # Manually add one more.\n", + " extended_dict['no_trip'] = 0.\n", + "\n", + " print(extended_dict)\n", + "\n", + " # NOW, we iterate over the dataframe.\n", + " new_columns = list(reverse_ordinal_mapping.values())\n", + "\n", + " # Create and init to 0.\n", + " df[new_columns] = 0.\n", + "\n", + " rows = []\n", + "\n", + " for _, row in df.iterrows():\n", + " row_dict = row.to_dict()\n", + "\n", + " # First, look at the section_mode_argmax\n", + " mapped_modes = section_mapper[row_dict['section_mode_argmax']]\n", + "\n", + " for mode in mapped_modes:\n", + " section_duration = row_dict['section_duration_argmax']\n", + " if section_duration > 0:\n", + " row_dict[mode] = row_dict['section_duration_argmax']\n", + " else:\n", + " row_dict[mode] = extended_dict[mode]\n", + "\n", + " # for the remaining modes, estimate their duration through the dict.\n", + " remaining = [mode for mode in new_columns if mode not in mapped_modes]\n", + "\n", + " for mode in remaining:\n", + " # If you'd like to use a mask here, use the av_ dummy values to mask-out irrelevant data.\n", + " row_dict[mode] = extended_dict[mode]\n", + " \n", + " rows.append(row_dict)\n", + " \n", + " return_df = pd.DataFrame(rows)\n", + " return_df.rename(columns=dict([(c, 'tt_'+c) for c in new_columns]), inplace=True)\n", + " return return_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "av_time_df = compute_durations_using_data(av_df, section_mode_mapping, reverse_ordinal_mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "av_time_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cost estimation\n", + "\n", + "```\n", + "{'p_micro': 1, 'no_trip': 2, 's_car': 3, 'transit': 4, 'car': 5, 's_micro': 6, 'ridehail': 7, 'walk': 8, 'unknown': 9}\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# All values are taken from VTPI.\n", + "\n", + "mode_cost_per_mile = {\n", + " # bicycle/skateboard\n", + " 'p_micro': 0.,\n", + " 'no_trip': 0.,\n", + " # Shared car would be half the cost of regular car, which is $0.6/mile.\n", + " 's_car': 0.3,\n", + " # Rental car.\n", + " 'car': 0.6,\n", + " # Average of bus and train taken.\n", + " 'transit': 0.6,\n", + " # Shared bicyle or scooter - values taken from https://nacto.org/shared-micromobility-2020-2021/ and \n", + " # https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/how-sharing-the-road-is-likely-to-transform-american-mobility\n", + " 's_micro': 0.3,\n", + " # uber/taxi/lyft\n", + " 'ridehail': 2.,\n", + " 'walk': 0.,\n", + " 'unknown': 0.\n", + "}\n", + "\n", + "# All 0 for now.\n", + "mode_init_cost = {\n", + " k: 0 for k in mode_cost_per_mile.keys()\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "av_time_df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_cost_estimates(df: pd.DataFrame, cost_mapper: dict, init_cost_mapper: dict, ordinal_mapper: dict):\n", + " \n", + " # Create some extra colums.\n", + " columns = list(ordinal_mapper.keys())\n", + "\n", + " print(columns)\n", + "\n", + " # Initialize the columns to 0.\n", + " df[columns] = 0.\n", + "\n", + " rows = list()\n", + "\n", + " # Iterate over every row.\n", + " for _, row in df.iterrows():\n", + " # Check which flags are active.\n", + " row_dict = row.to_dict()\n", + "\n", + " # ---- Not using the av_ features as a mask. ----\n", + " # # Iterate over the columns and retain the columns with an available mode of 1. Returns a list of [av_* keys]\n", + " # columns = [c for c in row_dict.keys() if c.startswith('av_') and row_dict[c] == 1]\n", + "\n", + " # Access the section_distance_argmax attribute for the distance. Note that this is now in miles.\n", + " distance = row_dict['section_distance_argmax']\n", + "\n", + " for lookup in columns:\n", + " row_dict[lookup] = init_cost_mapper[lookup] + (cost_mapper[lookup] * distance)\n", + "\n", + " rows.append(row_dict)\n", + "\n", + " new_df = pd.DataFrame(rows)\n", + " new_df.rename(columns=dict([(c, 'cost_'+c) for c in ordinal_mapper.keys()]), inplace=True)\n", + "\n", + " return new_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cost_df = compute_cost_estimates(av_time_df, cost_mapper=mode_cost_per_mile, init_cost_mapper=mode_init_cost, ordinal_mapper=ordinal_mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cost_df[['user_id'] + [c for c in cost_df.columns if 'av_' in c or 'cost_' in c or 'tt_' in c]].head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cost_df.to_csv('../data/preprocessed_data.csv', index=False)" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" + }, + "kernelspec": { + "display_name": "Python 3.9.16 ('emission')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/viz_scripts/rm_src/time_distance_estimation.ipynb b/viz_scripts/rm_src/time_distance_estimation.ipynb new file mode 100644 index 0000000..7591030 --- /dev/null +++ b/viz_scripts/rm_src/time_distance_estimation.ipynb @@ -0,0 +1,235 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Data loading. Refer to the user_data_preprocessing.ipynb file for details on how user-level features are generated.\n", + "\n", + "df = pd.read_csv('../data/modeling_w_duration.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Drop duplicate rows (if any)\n", + "df.drop_duplicates(inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_argmax(df: pd.DataFrame):\n", + " # Create the two columns.\n", + " df[['section_distance_argmax', 'section_duration_argmax', 'section_mode_argmax']] = None\n", + " rows = list()\n", + "\n", + " for ix, row in df.iterrows():\n", + " parsed_distances = eval(row['section_distances'])\n", + " parsed_durations = eval(row['section_durations'])\n", + " parsed_modes = eval(row['section_modes'])\n", + "\n", + " argmax_ix = np.argmax(parsed_distances)\n", + "\n", + " row['section_distance_argmax'] = parsed_distances[argmax_ix]\n", + " row['section_duration_argmax'] = parsed_durations[argmax_ix]\n", + " row['section_mode_argmax'] = parsed_modes[argmax_ix]\n", + "\n", + " rows.append(row.to_dict())\n", + " \n", + " return pd.DataFrame(rows)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's plot the mode-wise durations as a function of distance.\n", + "df_modded = compute_argmax(df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_modded.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "analysis = df_modded[['section_mode_argmax', 'section_duration_argmax', 'section_distance_argmax']].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "analysis.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import pearsonr\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import r2_score\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "analysis.loc[(analysis.section_mode_argmax == 'bus') | (analysis.section_mode_argmax == 'train'), ['section_mode_argmax']] = 'public_transport'\n", + "\n", + "for mode in analysis.section_mode_argmax.unique():\n", + " mode_df = analysis.loc[(analysis.section_mode_argmax == mode) \n", + " & (analysis.section_duration_argmax >=0) \n", + " & ((analysis.section_distance_argmax >=0)), ['section_duration_argmax', 'section_distance_argmax']]\n", + "\n", + " X = mode_df[['section_distance_argmax']].values * 0.00062\n", + " Y = mode_df[['section_duration_argmax']].values / 60\n", + "\n", + " X_tr, X_te, Y_tr, Y_te = train_test_split(X, Y, test_size=0.2, random_state=42, shuffle=True)\n", + "\n", + " # scaler = StandardScaler()\n", + "\n", + " # X_tr = scaler.fit_transform(X_tr)\n", + " # X_te = scaler.transform(X_te)\n", + "\n", + " # regr = LinearRegression()\n", + " regr = LinearRegression(fit_intercept=True)\n", + " regr.fit(X_tr.reshape(-1,1), Y_tr.reshape(-1,1))\n", + "\n", + " y_tr_pred = regr.predict(X_tr)\n", + " y_te_pred = regr.predict(X_te)\n", + "\n", + " train_r2 = r2_score(y_true=Y_tr.flatten(), y_pred=y_tr_pred.flatten())\n", + " test_r2 = r2_score(y_true=Y_te.flatten(), y_pred=y_te_pred.flatten())\n", + "\n", + " print(mode, train_r2, test_r2)\n", + " print('intercept: ', regr.intercept_[0], 'coeff: ', regr.coef_[0][0])\n", + "\n", + "\n", + "'''\n", + "Previous results:\n", + "\n", + "walking 0.3843977052858275 0.3749466865077252\n", + "bicycling 0.7396768569714562 0.735986721086616\n", + "car 0.5839819702140339 0.5918942114399524\n", + "no_sensed 0.8045590529878717 0.8059202285373765\n", + "public_transport 0.44880904441364 0.6020723455289356\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import pearsonr\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics import r2_score\n", + "\n", + "\n", + "# Combine bus and train into 'public transport'\n", + "analysis.loc[(analysis.section_mode_argmax == 'bus') | (analysis.section_mode_argmax == 'train'), ['section_mode_argmax']] = 'public_transport'\n", + "\n", + "for mode in analysis.section_mode_argmax.unique():\n", + "\n", + " mode_df = analysis.loc[\n", + " (analysis.section_mode_argmax == mode) & (analysis.section_duration_argmax >= 0) & (analysis.section_distance_argmax >= 0), \n", + " ['section_duration_argmax', 'section_distance_argmax']\n", + " ]\n", + " \n", + "\n", + " X = mode_df[['section_distance_argmax']].values\n", + " Y = mode_df[['section_duration_argmax']].values\n", + "\n", + " scaler = StandardScaler()\n", + "\n", + " X_tr, X_te, Y_tr, Y_te = train_test_split(X, Y, test_size=0.2, random_state=42, shuffle=True)\n", + "\n", + " X_tr = X_tr.flatten()\n", + " X_te = X_te.flatten()\n", + " Y_tr, Y_te = Y_tr.flatten(), Y_te.flatten()\n", + "\n", + " fit = np.polyfit(X_tr, Y_tr.flatten(), 1)\n", + " # Using the fit, predict tr and te.\n", + " y_tr_pred = np.poly1d(fit)(X_tr)\n", + " y_te_pred = np.poly1d(fit)(X_te)\n", + "\n", + " train_r2 = r2_score(y_true=Y_tr, y_pred=y_tr_pred)\n", + " test_r2 = r2_score(y_true=Y_te, y_pred=y_te_pred)\n", + "\n", + " fig, ax = plt.subplots()\n", + " # Plot the testing samples.\n", + " sns.scatterplot(x=X_te, y=Y_te, ax=ax)\n", + " # Plot the line.\n", + " # line = mode_model.intercept_ + (mode_model.coef_[0] * X_te.flatten())\n", + " sns.lineplot(x=X_te, y=y_te_pred, ax=ax)\n", + " plt.show()\n", + "\n", + " print(mode, train_r2, test_r2)" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" + }, + "kernelspec": { + "display_name": "Python 3.9.16 ('emission')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/viz_scripts/rm_src/user_data_preprocessing.ipynb b/viz_scripts/rm_src/user_data_preprocessing.ipynb new file mode 100644 index 0000000..b8a756c --- /dev/null +++ b/viz_scripts/rm_src/user_data_preprocessing.ipynb @@ -0,0 +1,586 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import pickle\n", + "import importlib\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "\n", + "from pathlib import Path\n", + "from uuid import UUID\n", + "from collections import defaultdict\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "emission_path = Path(os.getcwd()).parent.parent / 'my_emission_server' / 'e-mission-server'\n", + "sys.path.append(str(emission_path))\n", + "\n", + "# Also add the home (viz_scripts) to the path\n", + "sys.path.append('../viz_scripts')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import scaffolding\n", + "import emission.core.get_database as edb\n", + "import emission.core.wrapper.entry as ecwe\n", + "import emission.storage.decorations.analysis_timeseries_queries as esda\n", + "import emission.storage.decorations.trip_queries as esdt\n", + "import emission.storage.decorations.timeline as esdl\n", + "import emission.storage.timeseries.abstract_timeseries as esta\n", + "import emission.storage.timeseries.timequery as estt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "importlib.reload(scaffolding)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def r(df: pd.DataFrame) -> pd.DataFrame:\n", + " return df.reset_index(drop=True, inplace=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# (Please run the mapping_dictionaries.ipynb notebook before the next cell block is run)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with open('../viz_scripts/auxiliary_files/dic_re.pkl', 'rb') as f:\n", + " dic_re = pickle.loads(f.read())\n", + "\n", + "with open('../viz_scripts/auxiliary_files/dic_pur.pkl', 'rb') as f:\n", + " dic_pur = pickle.loads(f.read())\n", + "\n", + "# convert a dictionary to a defaultdict\n", + "dic_re = defaultdict(lambda: 'Other', dic_re)\n", + "dic_pur = defaultdict(lambda: 'Other', dic_pur)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Split UUIDs by program\n", + "program_uuid_map = {}\n", + "for ue in edb.get_uuid_db().find():\n", + " uuid = str(ue['uuid'])\n", + " # uuid = str(ue['uuid'])\n", + " program = ue['user_email'].split(\"_\")[0]\n", + " if program in program_uuid_map.keys():\n", + " program_uuid_map[program].append(uuid)\n", + " else:\n", + " print(f\"Found new program {program}, creating new list\")\n", + " program_uuid_map[program] = []\n", + " program_uuid_map[program].append(uuid)\n", + "\n", + "uuid_program_list = []\n", + "for ue in edb.get_uuid_db().find():\n", + " # uuid = str(ue['uuid'].as_uuid(3))\n", + " uuid = str(ue['uuid'])\n", + " program = ue['user_email'].split(\"_\")[0]\n", + " uuid_program_list.append({\"program\": program, \"opcode\": ue[\"user_email\"], \"user_id\": uuid})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "uuid_program_df = pd.DataFrame.from_dict(uuid_program_list)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# %%capture\n", + "\n", + "# for program in uuid_program_df.program.unique():\n", + "expanded_ct, file_suffix, quality_text, debug_df = scaffolding.load_viz_notebook_data(None,\n", + " None,\n", + " 'prepilot',\n", + " 'program',\n", + " dic_re,\n", + " dic_pur=dic_pur)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Join to the program df to get each user's program\n", + "# Keep a copy of the user_id column for querying section durations.\n", + "expanded_ct['original_user_id'] = expanded_ct['user_id'].copy()\n", + "expanded_ct['user_id'] = expanded_ct['user_id'].apply(lambda x: str(x))\n", + "expanded_ct = expanded_ct.merge(uuid_program_df, on='user_id')\n", + "expanded_ct['user_id'] = expanded_ct['user_id'].apply(lambda x: str(x).replace(\"-\", \"\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Rename the target column.\n", + "expanded_ct.drop(columns=['replaced_mode'], axis='rows', inplace=True)\n", + "expanded_ct['Replaced_mode'] = expanded_ct['Replaced_mode'].fillna('Unlabeled')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We only wish to focus on Denver data for now.\n", + "\n", + "denver_data = r(expanded_ct.loc[\n", + " (expanded_ct.start_local_dt_timezone == \"America/Denver\") & (expanded_ct.end_local_dt_timezone == \"America/Denver\"), \n", + " :])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Parse the datetime to Denver time.\n", + "denver_data['start_fmt_time'] = pd.to_datetime(denver_data['start_fmt_time'], utc=True).dt.tz_convert('America/Denver')\n", + "\n", + "# Re-compute all the start variables.\n", + "denver_data['start_local_dt_year'] = denver_data['start_fmt_time'].dt.year\n", + "denver_data['start_local_dt_month'] = denver_data['start_fmt_time'].dt.month\n", + "denver_data['start_local_dt_day'] = denver_data['start_fmt_time'].dt.day\n", + "denver_data['start_local_dt_hour'] = denver_data['start_fmt_time'].dt.hour\n", + "denver_data['start_local_dt_weekday'] = denver_data['start_fmt_time'].dt.weekday\n", + "\n", + "## Do the same with the end time.\n", + "denver_data['end_fmt_time'] = pd.to_datetime(denver_data['end_fmt_time'], utc=True).dt.tz_convert('America/Denver')\n", + "\n", + "# Re-compute all the end variables.\n", + "denver_data['end_local_dt_year'] = denver_data['end_fmt_time'].dt.year\n", + "denver_data['end_local_dt_month'] = denver_data['end_fmt_time'].dt.month\n", + "denver_data['end_local_dt_day'] = denver_data['end_fmt_time'].dt.day\n", + "denver_data['end_local_dt_hour'] = denver_data['end_fmt_time'].dt.hour\n", + "denver_data['end_local_dt_weekday'] = denver_data['end_fmt_time'].dt.weekday" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Read the Demographic data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read the Denver dedmographic info\n", + "survey_data = pd.read_csv('../viz_scripts/Can Do Colorado eBike Program - en.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# column renaming here!\n", + "\n", + "survey_data.rename(\n", + " {\n", + " \"Unique User ID (auto-filled, do not edit)\": \"user_id\",\n", + " \"In which year were you born?\": \"birth_year\",\n", + " \"What is your gender?\": \"gender\",\n", + " \"Do you have a valid driver's license?\": \"has_drivers_license\",\n", + " \"Are you a student?\": \"is_student\",\n", + " \"What is the highest grade or degree that you have completed?\": \"highest_education\",\n", + " \"Do you work for either pay or profit?\": \"is_paid\",\n", + " \"Do you have more than one job?\": \"has_multiple_jobs\",\n", + " \"Do you work full-time or part-time at your primary job?\": \"primary_job_type\",\n", + " \"Which best describes your primary job?\": \"primary_job_description\",\n", + " \"How did you usually get to your primary job last week? \": \"primary_job_commute_mode\",\n", + " \"Thinking about your daily commute to work last week, how many minutes did it usually take to get from home to the primary job/work place?\": \"primary_job_commute_time\",\n", + " \"At your primary job, do you have the ability to set or change your own start time?\": \"is_primary_job_flexible\",\n", + " \"Do you have the option of working from home or an alternate location instead of going into your primary work place?\": \"primary_job_can_wfh\",\n", + " \"How many days per week do you usually work from home or an alternate location?\": \"wfh_days\",\n", + " \"Do you own or rent your place of residence?\": \"residence_ownership_type\",\n", + " \"What is your home type?\": \"residence_type\",\n", + " \"Please identify which category represents your total household income, before taxes, for last year.\": \"income_category\",\n", + " \"Including yourself, how many people live in your home?\": \"n_residence_members\",\n", + " \"How many children under age 18 live in your home?\": \"n_residents_u18\",\n", + " \"Including yourself, how many people have a driver's license in your household?\": \"n_residents_with_license\",\n", + " \"How many motor vehicles are owned, leased, or available for regular use by the people who currently live in your household?\": \"n_motor_vehicles\",\n", + " \"If you were unable to use your household vehicle(s), which of the following options would be available to you to get you from place to place?\": \"available_modes\",\n", + " \"Do you have a medical condition that makes it difficult to travel outside of the home?\": \"has_medical_condition\",\n", + " \"How long have you had this condition?\": \"medical_condition_duration\"\n", + " },\n", + " axis='columns',\n", + " inplace=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Now, if we have duplicate users, we'd like to retain the last chronological entry.\n", + "survey_data = survey_data.loc[~((survey_data.user_id.isna())|(survey_data.user_id == \"\")), :]\n", + "\n", + "# timezonoe-aware parsing:\n", + "survey_data['Timestamp'] = survey_data['Timestamp'].str.replace('PDT|PST', '', regex=True)\n", + "survey_data['Timestamp'] = pd.to_datetime(survey_data['Timestamp']).dt.tz_localize('America/Denver')\n", + "\n", + "# Sort by user_id and time, then drop everything but the last entry.\n", + "survey_data.sort_values(by=['user_id', 'Timestamp'], ascending=True, inplace=True, axis='rows')\n", + "survey_data.drop_duplicates(['user_id'], keep='last', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Merge the trip data with the survey data.\n", + "\n", + "merged_data = denver_data.merge(\n", + " survey_data, left_on='user_id', right_on='user_id'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's start choosing features for modeling.\n", + "\n", + "base_time_features = ['fmt_time', 'local_dt_year', 'local_dt_month', 'local_dt_day', 'local_dt_hour', 'local_dt_weekday']\n", + "time_features = ['start_' + x for x in base_time_features] + ['end_' + x for x in base_time_features]\n", + "\n", + "demographic_features = ['available_modes',\n", + " 'birth_year', 'income_category', 'n_motor_vehicles', 'n_residence_members', 'n_residents_u18', 'gender', \n", + " 'is_student', 'n_residents_with_license']\n", + "\n", + "sensed_features = ['duration', 'distance_miles', 'cleaned_trip', 'start_loc', 'end_loc', 'section_modes', 'section_distances']\n", + "\n", + "modeling_data = merged_data[['user_id', '_id', 'original_user_id', 'cleaned_trip', 'Replaced_mode', 'Mode_confirm'] + time_features + demographic_features + sensed_features].copy()\n", + "\n", + "# Rename columns in-place.\n", + "modeling_data.rename(columns={\n", + " 'start_local_dt_year': 'start:year', 'start_local_dt_month': 'start:month', 'start_local_dt_day': 'start:day', 'start_local_dt_hour': 'start:hour',\n", + " 'end_local_dt_year': 'end:year', 'end_local_dt_month': 'end:month', 'end_local_dt_day': 'end:day', 'end_local_dt_hour': 'end:hour'\n", + " }, inplace=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from calendar import monthrange\n", + "\n", + "# Find day of month: use monthrange with (mm, yyyy) args and find how many days that month had (leap years are supported).\n", + "def get_num_days_in_month(yyyy, mm):\n", + " return monthrange(yyyy, mm)[1]\n", + "\n", + "def is_overnight_trip(start_date, end_date):\n", + " return int((end_date - start_date).days > 0)\n", + "\n", + "# get the number of days for the start and end times.\n", + "modeling_data['start:n_days_in_month'] = modeling_data.apply(lambda x: get_num_days_in_month(x['start:year'], x['start:month']), axis=1)\n", + "modeling_data['end:n_days_in_month'] = modeling_data.apply(lambda x: get_num_days_in_month(x['end:year'], x['end:month']), axis=1)\n", + "\n", + "# age = current year - year of birth\n", + "modeling_data['age'] = 2023 - modeling_data['birth_year']\n", + "\n", + "# overnight trips may be more likely taken by car.\n", + "modeling_data['is_overnight_trip'] = modeling_data.apply(lambda x: is_overnight_trip(x.start_fmt_time, x.end_fmt_time), axis=1)\n", + "\n", + "# Number of working individuals in the household = number of individuals in the house - number of children.\n", + "modeling_data['n_working_residents'] = (modeling_data['n_residence_members'] - modeling_data['n_residents_u18']).astype(int)\n", + "\n", + "# Create a binary indicator.\n", + "modeling_data['is_male'] = modeling_data.gender.apply(lambda x: 1 if x==\"Male\" else 0)\n", + "\n", + "# Bin the number of vehicles owned.\n", + "# Drop the observations with (Prefer not to say)\n", + "modeling_data = modeling_data.loc[~modeling_data['n_motor_vehicles'].isin(['Prefer not to say / Prefiero no decir.']), :]\n", + "modeling_data.loc[modeling_data['n_motor_vehicles'].isin(['4+']), 'n_motor_vehicles'] = 4\n", + "modeling_data['n_motor_vehicles'] = modeling_data['n_motor_vehicles'].astype(int)\n", + "\n", + "# Convert the total duration of the trip into minutes.\n", + "modeling_data[['duration']] = modeling_data[['duration']]/60\n", + "\n", + "# Extract start and end latitudes and longitudes.\n", + "modeling_data['start_lat'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][1])\n", + "modeling_data['start_lng'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][0])\n", + "\n", + "modeling_data['end_lat'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][1])\n", + "modeling_data['end_lng'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from calendar import monthrange\n", + "\n", + "# Find day of month: use monthrange with (mm, yyyy) args and find how many days that month had (leap years are supported).\n", + "def get_num_days_in_month(yyyy, mm):\n", + " return monthrange(yyyy, mm)[1]\n", + "\n", + "def is_overnight_trip(start_date, end_date):\n", + " return int((end_date - start_date).days > 0)\n", + "\n", + "# get the number of days for the start and end times.\n", + "modeling_data['start:n_days_in_month'] = modeling_data.apply(lambda x: get_num_days_in_month(x['start:year'], x['start:month']), axis=1)\n", + "modeling_data['end:n_days_in_month'] = modeling_data.apply(lambda x: get_num_days_in_month(x['end:year'], x['end:month']), axis=1)\n", + "\n", + "# age = current year - year of birth\n", + "modeling_data['age'] = 2023 - modeling_data['birth_year']\n", + "\n", + "# overnight trips may be more likely taken by car.\n", + "modeling_data['is_overnight_trip'] = modeling_data.apply(lambda x: is_overnight_trip(x.start_fmt_time, x.end_fmt_time), axis=1)\n", + "\n", + "# Number of working individuals in the household = number of individuals in the house - number of children.\n", + "modeling_data['n_working_residents'] = (modeling_data['n_residence_members'] - modeling_data['n_residents_u18']).astype(int)\n", + "\n", + "# Create a binary indicator.\n", + "modeling_data['is_male'] = modeling_data.gender.apply(lambda x: 1 if x==\"Male\" else 0)\n", + "\n", + "# Bin the number of vehicles owned.\n", + "# Drop the observations with (Prefer not to say)\n", + "modeling_data = modeling_data.loc[~modeling_data['n_motor_vehicles'].isin(['Prefer not to say / Prefiero no decir.']), :]\n", + "modeling_data.loc[modeling_data['n_motor_vehicles'].isin(['4+']), 'n_motor_vehicles'] = 4\n", + "modeling_data['n_motor_vehicles'] = modeling_data['n_motor_vehicles'].astype(int)\n", + "\n", + "# Convert the total duration of the trip into minutes.\n", + "modeling_data[['duration']] = modeling_data[['duration']]/60\n", + "\n", + "# Extract start and end latitudes and longitudes.\n", + "modeling_data['start_lat'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][1])\n", + "modeling_data['start_lng'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][0])\n", + "\n", + "modeling_data['end_lat'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][1])\n", + "modeling_data['end_lng'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Time-related feature engineeering:\n", + "'''\n", + "HOD: hour of day\n", + "DOM: day of month\n", + "MOY: month of year\n", + "'''\n", + "\n", + "def get_HOD(hour, how='sin'):\n", + " if how == 'sin':\n", + " return np.sin(2 * np.pi * (hour/24))\n", + " return np.cos(2 * np.pi * (hour/24))\n", + "\n", + "def get_DOM(day, n_days, how='sin'):\n", + " if how == 'sin':\n", + " return np.sin(2 * np.pi * (day/n_days))\n", + " return np.cos(2 * np.pi * (day/n_days))\n", + "\n", + "def get_MOY(month, how='sin'):\n", + " if how == 'sin':\n", + " return np.sin(2 * np.pi * (month/12))\n", + " return np.cos(2 * np.pi * (month/12))\n", + "\n", + "# Start - sin\n", + "modeling_data['start:sin_HOD'] = modeling_data.apply(lambda x: get_HOD(x['start:hour']), axis=1)\n", + "modeling_data['start:sin_DOM'] = modeling_data.apply(lambda x: get_DOM(x['start:day'], x['start:n_days_in_month']), axis=1)\n", + "modeling_data['start:sin_MOY'] = modeling_data.apply(lambda x: get_MOY(x['start:year']), axis=1)\n", + "\n", + "# Start - cos\n", + "modeling_data['start:cos_HOD'] = modeling_data.apply(lambda x: get_HOD(x['start:hour'], how='cos'), axis=1)\n", + "modeling_data['start:cos_DOM'] = modeling_data.apply(lambda x: get_DOM(x['start:day'], x['start:n_days_in_month'], how='cos'), axis=1)\n", + "modeling_data['start:cos_MOY'] = modeling_data.apply(lambda x: get_MOY(x['start:year'], how='cos'), axis=1)\n", + "\n", + "# End - sin\n", + "modeling_data['end:sin_HOD'] = modeling_data.apply(lambda x: get_HOD(x['end:hour']), axis=1)\n", + "modeling_data['end:sin_DOM'] = modeling_data.apply(lambda x: get_DOM(x['end:day'], x['end:n_days_in_month']), axis=1)\n", + "modeling_data['end:sin_MOY'] = modeling_data.apply(lambda x: get_MOY(x['end:year']), axis=1)\n", + "\n", + "# End - cos\n", + "modeling_data['end:cos_HOD'] = modeling_data.apply(lambda x: get_HOD(x['end:hour'], how='cos'), axis=1)\n", + "modeling_data['end:cos_DOM'] = modeling_data.apply(lambda x: get_DOM(x['end:day'], x['end:n_days_in_month'], how='cos'), axis=1)\n", + "modeling_data['end:cos_MOY'] = modeling_data.apply(lambda x: get_MOY(x['end:year'], how='cos'), axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, for every trip, we have the corresponding section mode that covered the longest distance for the trip.\n", + "\n", + "Using this as well as the `available_modes` column:\n", + "\n", + "```language=python\n", + "\n", + " # unique available modes:\n", + " {'Bicycle',\n", + " 'Do not have vehicle ',\n", + " 'Get a ride from a friend or family member',\n", + " 'None',\n", + " 'Public transportation (bus, subway, light rail, etc.)',\n", + " 'Rental car (including Zipcar/ Car2Go)',\n", + " 'Shared bicycle or scooter',\n", + " 'Skateboard',\n", + " 'Taxi (regular taxi, Uber, Lyft, etc)',\n", + " 'Walk/roll'}\n", + "\n", + " # unique section modes:\n", + " {'bicycling', 'bus', 'car', 'no_sensed', 'train', 'walking'}\n", + "\n", + " \n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "Remove air trips.\n", + "\"\"\"\n", + "def remove_air_or_hsr(df):\n", + "\n", + " df['mark'] = 0\n", + "\n", + " for ix, row in df.iterrows():\n", + " sections = row['section_modes']\n", + " if 'air_or_hsr' in sections:\n", + " df.loc[ix, 'mark'] = 1\n", + " \n", + " df = r(df.loc[df.mark == 0, :])\n", + " df.drop(columns=['mark'], inplace=True)\n", + "\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "modeling_data = remove_air_or_hsr(modeling_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get section durations for trips.\n", + "modeling_data = scaffolding.get_section_durations(modeling_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Save the dataframe.\n", + "# final_df.to_csv('../data/modeling_w_duration.csv', index=False)" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "88f8afe72b095c7f825e3358bd97ba362c33037cbe77fe6effd118f9a11a2d38" + }, + "kernelspec": { + "display_name": "Python 3.8.18 ('mnl')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.18" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index 01b4158..5918672 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -1,10 +1,16 @@ import pandas as pd import numpy as np import sys +from tqdm import tqdm +from shapely import Point import emission.storage.timeseries.abstract_timeseries as esta +import emission.storage.timeseries.builtin_timeseries as bts import emission.storage.timeseries.tcquery as esttc import emission.core.wrapper.localdate as ecwl +import emission.storage.decorations.trip_queries as esdt +from pandarallel import pandarallel + # Module for pretty-printing outputs (e.g. head) to help users # understand what is going on @@ -45,14 +51,14 @@ def get_participant_uuids(program, load_test_users): else: participant_list = all_users[np.logical_not(all_users.user_email.str.contains("_test_"))] participant_uuid_str = participant_list.uuid - disp.display(participant_list.user_email) + # disp.display(participant_list.user_email) return participant_uuid_str def load_all_confirmed_trips(tq): agg = esta.TimeSeries.get_aggregate_time_series() all_ct = agg.get_data_df("analysis/confirmed_trip", tq) print("Loaded all confirmed trips of length %s" % len(all_ct)) - disp.display(all_ct.head()) + # disp.display(all_ct.head()) return all_ct def load_all_participant_trips(program, tq, load_test_users): @@ -63,7 +69,7 @@ def load_all_participant_trips(program, tq, load_test_users): return all_ct participant_ct_df = all_ct[all_ct.user_id.isin(participant_list)] print("After filtering, found %s participant trips " % len(participant_ct_df)) - disp.display(participant_ct_df.head()) + # disp.display(participant_ct_df.head()) return participant_ct_df def filter_labeled_trips(mixed_trip_df): @@ -72,7 +78,7 @@ def filter_labeled_trips(mixed_trip_df): return mixed_trip_df labeled_ct = mixed_trip_df[mixed_trip_df.user_input != {}] print("After filtering, found %s labeled trips" % len(labeled_ct)) - disp.display(labeled_ct.head()) + # disp.display(labeled_ct.head()) return labeled_ct def expand_userinputs(labeled_ct): @@ -87,7 +93,7 @@ def expand_userinputs(labeled_ct): if len(labeled_ct) == 0: return labeled_ct label_only = pd.DataFrame(labeled_ct.user_input.to_list(), index=labeled_ct.index) - disp.display(label_only.head()) + # disp.display(label_only.head()) labels_per_trip = len(label_only.columns) print("Found %s columns of length %d" % (label_only.columns, labels_per_trip)) expanded_ct = pd.concat([labeled_ct, label_only], axis=1) @@ -99,9 +105,76 @@ def expand_userinputs(labeled_ct): assert len(expanded_ct.columns) == len(labeled_ct.columns) + labels_per_trip, \ ("Mismatch after expanding labels, expanded_ct.columns = %s != labeled_ct.columns %s" % (len(expanded_ct.columns), len(labeled_ct.columns))) - disp.display(expanded_ct.head()) + # disp.display(expanded_ct.head()) return expanded_ct +def get_section_durations(confirmed_trips: pd.DataFrame): + + # Initialize the parallel processing. + pandarallel.initialize(progress_bar=True) + + """ + Extract section-wise durations from trips for every trips. + + TOOO: There is a massive scope to improve performance here. + Since we apply row-wise and there are no inter-row dependencies, + Speeding this up is very possible. Exploring packages like parallel-pandas + is definitely something we could do. + """ + + # No worries, the inner function has access to these variables. + fallback_key = 'analysis/inferred_section' + primary_key = 'analysis/cleaned_section' + + def get_durations(user_id, trip_id): + + inferred_sections = esdt.get_sections_for_trip(key = fallback_key, + user_id = user_id, trip_id = trip_id) + + if inferred_sections and len(inferred_sections) > 0: + return [x.data.duration for x in inferred_sections] + + print("Falling back to confirmed trips...") + + cleaned_sections = esdt.get_sections_for_trip(key = primary_key, + user_id = user_id, trip_id = trip_id) + + if cleaned_sections and len(cleaned_sections) > 0: + return [x.data.duration for x in cleaned_sections] + + return [] + + confirmed_trips['section_durations'] = confirmed_trips.parallel_apply( + lambda x: get_durations(x.original_user_id, x.cleaned_trip), axis=1 + ) + + return confirmed_trips + + +def get_section_coordinates(confirmed_trips: pd.DataFrame): + # Initialize pandarallel + pandarallel.initialize(progress_bar=True) + + key = 'analysis/inferred_section' + + def get_coordinates(user_id, trip_id, distances): + sections = esdt.get_sections_for_trip(key = key, + user_id = user_id, trip_id = trip_id) + + if sections and len(sections) > 0 and len(distances) == len(sections): + argmax = np.argmax(distances) + section = sections[argmax] + return section.data.start_loc['coordinates'], section.data.end_loc['coordinates'] + + return [] + + confirmed_trips['section_locations_argmax'] = confirmed_trips.parallel_apply( + lambda x: get_coordinates(x.original_user_id, x.cleaned_trip, x.section_distances), axis=1 + ) + + return confirmed_trips + + # CASE 2 of https://github.com/e-mission/em-public-dashboard/issues/69#issuecomment-1256835867 unique_users = lambda df: len(df.user_id.unique()) if "user_id" in df.columns else 0 trip_label_count = lambda s, df: len(df[s].dropna()) if s in df.columns else 0 @@ -117,6 +190,7 @@ def load_viz_notebook_data(year, month, program, study_type, dic_re, dic_pur=Non tq = get_time_query(year, month) participant_ct_df = load_all_participant_trips(program, tq, include_test_users) labeled_ct = filter_labeled_trips(participant_ct_df) + # labeled_ct = get_section_durations(labeled_ct) expanded_ct = expand_userinputs(labeled_ct) expanded_ct = data_quality_check(expanded_ct) From e1c74ecf46e38f2144c97bb813b3f42fb954b65e Mon Sep 17 00:00:00 2001 From: Rahul Kulhalli Date: Mon, 4 Dec 2023 12:21:08 -0500 Subject: [PATCH 04/16] Replaced biogeme modeling code --- viz_scripts/rm_src/biogeme_modeling.ipynb | 213 ++++++++++++++++++---- 1 file changed, 178 insertions(+), 35 deletions(-) diff --git a/viz_scripts/rm_src/biogeme_modeling.ipynb b/viz_scripts/rm_src/biogeme_modeling.ipynb index 54ccbd6..035d597 100644 --- a/viz_scripts/rm_src/biogeme_modeling.ipynb +++ b/viz_scripts/rm_src/biogeme_modeling.ipynb @@ -10,9 +10,11 @@ "import biogeme.biogeme as bio\n", "import biogeme.database as db\n", "from biogeme import models\n", - "from biogeme.expressions import Beta\n", + "from biogeme.expressions import Beta, DefineVariable\n", "from biogeme.expressions import Variable\n", - "import numpy as np" + "import numpy as np\n", + "\n", + "from sklearn.preprocessing import MinMaxScaler" ] }, { @@ -36,7 +38,7 @@ "\n", "# Drop section_mode_argmax and available_modes.\n", "data.drop(columns=[\n", - " 'section_mode_argmax', 'available_modes', 'section_distance_argmax', 'section_duration_argmax'\n", + " 'section_mode_argmax', 'available_modes', 'section_duration_argmax', 'section_distance_argmax'\n", " ], inplace=True)\n", "\n", "data['user_id'] = data['user_id'].apply(lambda x: USER_MAP[x])" @@ -49,7 +51,14 @@ "outputs": [], "source": [ "# Convert from min -> hrs\n", - "data[[c for c in data.columns if 'tt_' in c]] /= 60." + "data[[c for c in data.columns if 'tt_' in c]] /= 60.\n", + "\n", + "# Scale costs using StandardScaler.\n", + "costs = data[[c for c in data.columns if 'cost_' in c]].copy()\n", + "\n", + "scaler = MinMaxScaler()\n", + "cost_scaled = pd.DataFrame(scaler.fit_transform(costs), columns=['scaled_' + c for c in costs.columns], index=costs.index)\n", + "data = data.merge(right=cost_scaled, left_index=True, right_index=True)" ] }, { @@ -58,8 +67,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Scale currency by 1/10.\n", - "data[[c for c in data.columns if 'cost_' in c]] /= 10." + "data.head()" ] }, { @@ -68,8 +76,8 @@ "metadata": {}, "outputs": [], "source": [ - "display(data[[c for c in data.columns if 'tt_' in c]].describe())\n", - "display(data[[c for c in data.columns if 'cost_' in c]].describe())" + "# display(data[[c for c in data.columns if 'tt_' in c]].describe())\n", + "# display(data[[c for c in data.columns if 'cost_' in c]].describe())" ] }, { @@ -137,18 +145,23 @@ "TT_UNKNOWN = Variable('tt_unknown')\n", "\n", "# Cost.\n", - "CO_P_MICRO = Variable('cost_p_micro')\n", - "CO_NO_TRIP = Variable('cost_no_trip')\n", - "CO_S_CAR = Variable('cost_s_car')\n", - "CO_TRANSIT = Variable('cost_transit')\n", - "CO_CAR = Variable('cost_car')\n", - "CO_S_MICRO = Variable('cost_s_micro')\n", - "CO_RIDEHAIL = Variable('cost_ridehail')\n", - "CO_WALK = Variable('cost_walk')\n", - "CO_UNKNOWN = Variable('cost_unknown')\n", + "CO_P_MICRO = Variable('scaled_cost_p_micro')\n", + "CO_NO_TRIP = Variable('scaled_cost_no_trip')\n", + "CO_S_CAR = Variable('scaled_cost_s_car')\n", + "CO_TRANSIT = Variable('scaled_cost_transit')\n", + "CO_CAR = Variable('scaled_cost_car')\n", + "CO_S_MICRO = Variable('scaled_cost_s_micro')\n", + "CO_RIDEHAIL = Variable('scaled_cost_ridehail')\n", + "CO_WALK = Variable('scaled_cost_walk')\n", + "CO_UNKNOWN = Variable('scaled_cost_unknown')\n", "\n", "# Choice.\n", - "CHOICE = Variable('chosen')" + "CHOICE = Variable('chosen')\n", + "\n", + "# Exclusion. Choice must not be equal to or either 2 or 9.\n", + "EXCLUDE = (CHOICE == 2) + (CHOICE == 9) > 0\n", + "\n", + "database.remove(EXCLUDE)" ] }, { @@ -163,18 +176,18 @@ "# but we would like to constrain the ceil to 0 since we want these coefficients to denote the utiltiy function.\n", "# Finally, we want this parameter to be updated by the optimization algorithm.\n", "B_TIME = Beta('B_TIME', 0, None, 0, 0)\n", - "B_COST = Beta('B_COST', 0, None, 0, 0)\n", + "B_COST = Beta('B_COST', 0, None, None, 0)\n", "\n", "# Alternative-Specific Constants.\n", "ASC_P_MICRO = Beta('ASC_P_MICRO', 0, None, None, 0)\n", - "ASC_NO_TRIP = Beta('ASC_NO_TRIP', 0, None, None, 1)\n", + "ASC_NO_TRIP = Beta('ASC_NO_TRIP', 0, None, None, 0)\n", "ASC_S_CAR = Beta('ASC_S_CAR', 0, None, None, 0)\n", "ASC_TRANSIT = Beta('ASC_TRANSIT', 0, None, None, 0)\n", "ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)\n", "ASC_S_MICRO = Beta('ASC_S_MICRO', 0, None, None, 0)\n", "ASC_RIDEHAIL = Beta('ASC_RIDEHAIL', 0, None, None, 0)\n", "ASC_WALK = Beta('ASC_WALK', 0, None, None, 0)\n", - "ASC_UNKNOWN = Beta('ASC_UNKNOWN', 0, None, None, 1)" + "ASC_UNKNOWN = Beta('ASC_UNKNOWN', 0, None, None, 0)" ] }, { @@ -186,10 +199,11 @@ "# Define the utility functions.\n", "# number of alternatives = number of UFs.\n", "\n", + "# Not multiplying by cost.\n", "V_P_MICRO = (\n", " ASC_P_MICRO +\n", - " B_TIME * TT_P_MICRO +\n", - " B_COST * CO_P_MICRO\n", + " B_TIME * TT_P_MICRO\n", + " + B_COST * CO_P_MICRO\n", ")\n", "\n", "V_NO_TRIP = (\n", @@ -199,9 +213,9 @@ ")\n", "\n", "V_S_CAR = (\n", - " ASC_CAR +\n", - " B_TIME * TT_CAR +\n", - " B_COST * CO_CAR\n", + " ASC_S_CAR +\n", + " B_TIME * TT_S_CAR +\n", + " B_COST * CO_S_CAR\n", ")\n", "\n", "V_TRANSIT = (\n", @@ -228,10 +242,11 @@ " B_COST * CO_RIDEHAIL\n", ")\n", "\n", + "# Cost is 0, so no point multiplying.\n", "V_WALK = (\n", " ASC_WALK +\n", - " B_TIME * TT_WALK +\n", - " B_COST * CO_WALK\n", + " B_TIME * TT_WALK\n", + " + B_COST * CO_WALK\n", ")\n", "\n", "V_UNKNOWN = (\n", @@ -301,7 +316,7 @@ "outputs": [], "source": [ "model = bio.BIOGEME(database, logprob)\n", - "model.modelName = 'noMaskNoTrip'" + "model.modelName = 'linearModelCostsModel'" ] }, { @@ -337,7 +352,29 @@ "metadata": {}, "outputs": [], "source": [ - "results.getCorrelationResults()" + "def generate_metrics(model: bio.BIOGEME, database: db.Database, V: dict):\n", + " \"\"\"\n", + " Given a test dataframe and the trained model, generate precision, recall, F1.\n", + " \"\"\"\n", + "\n", + " predictions = list()\n", + "\n", + " for row_ix in range(data.shape[0]):\n", + " preds = dict()\n", + " for alt_id, utility in V.items():\n", + " preds[alt_id] = utility.getValue_c(database)\n", + " \n", + " predictions.append(preds)\n", + " return pd.DataFrame(predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.getBetaValues()" ] }, { @@ -346,13 +383,119 @@ "metadata": {}, "outputs": [], "source": [ - "def generate_metrics(model: bio.BIOGEME, data: pd.DataFrame):\n", - " \"\"\"\n", - " Given a test dataframe and the trained model, generate precision, recall, F1.\n", - " \"\"\"\n", + "def get_utility_df(results, data):\n", + "\n", + " ''' \n", + " 1: V_P_MICRO, \n", + " 2: V_NO_TRIP,\n", + " 3: V_S_CAR, \n", + " 4: V_TRANSIT,\n", + " 5: V_CAR, \n", + " 6: V_S_MICRO,\n", + " 7: V_RIDEHAIL, \n", + " 8: V_WALK, \n", + " # 9: V_UNKNOWN\n", + " # '''\n", + "\n", + " def compute_utilities(betas, row: pd.Series):\n", + " data = row.to_dict()\n", + "\n", + " utility_p_micro = betas['ASC_P_MICRO'] + (betas['B_TIME'] * data['tt_p_micro'])\n", + " utility_no_trip = betas['ASC_NO_TRIP'] + (betas['B_TIME'] * data['tt_no_trip']) + (betas['B_COST'] * data['scaled_cost_no_trip'])\n", + " utility_s_car = betas['ASC_S_CAR'] + (betas['B_COST'] * data['scaled_cost_s_car']) + (betas['B_TIME'] * data['tt_s_car'])\n", + " utility_transit = betas['ASC_TRANSIT'] + (betas['B_COST'] * data['scaled_cost_transit']) + (betas['B_TIME'] * data['tt_transit'])\n", + " utility_car = betas['ASC_CAR'] + (betas['B_COST'] * data['scaled_cost_car'] + (betas['B_TIME'] * data['tt_car']))\n", + " utility_s_micro = betas['ASC_S_MICRO'] + (betas['B_COST'] * data['scaled_cost_s_micro']) + (betas['B_TIME'] * data['tt_s_micro'])\n", + " utility_ridehail = betas['ASC_RIDEHAIL'] + (betas['B_COST'] * data['scaled_cost_ridehail']) + (betas['B_TIME'] * data['tt_ridehail'])\n", + " utility_walk = betas['ASC_WALK'] + (betas['B_TIME'] * data['tt_walk'])\n", + " utility_unknown = betas['ASC_UNKNOWN'] + (betas['B_TIME'] * data['tt_unknown']) + (betas['B_COST'] * data['scaled_cost_unknown'])\n", + "\n", + " return {\n", + " 'utility_p_micro': utility_p_micro, 'utility_no_trip': utility_no_trip,\n", + " 'utility_s_car': utility_s_car, 'utility_transit': utility_transit,\n", + " 'utility_car': utility_car, 'utility_s_micro': utility_s_micro,\n", + " 'utility_ridehail': utility_ridehail, 'utility_walk': utility_walk, \n", + " 'utility_unknown': utility_unknown, \n", + " }\n", + " \n", + " betas = results.getBetaValues()\n", + "\n", + " u_data = data.apply(lambda x: compute_utilities(betas, x), axis=1).tolist()\n", + " return pd.DataFrame(u_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "u_df = get_utility_df(results, database.fullData)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "u_np = u_df.values\n", + "choice_df = np.exp(u_np)/np.sum(np.exp(u_np), axis=1, keepdims=True)\n", + "\n", + "choice_df = pd.DataFrame(choice_df, columns=u_df.columns)\n", + "display(choice_df.head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import f1_score\n", "\n", - " pass" + "y_pred = np.argmax(choice_df.values, axis=1) + 1\n", + "\n", + "print(np.unique(y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.Series(y_pred).hist()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# labels are 1-9\n", + "y_true = data.chosen\n", + "\n", + "pd.Series(y_true).hist()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "score = f1_score(y_true, y_pred, average='weighted')\n", + "print(score)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 9a1db4aec24b26a9fb9936cf929de8131607d541 Mon Sep 17 00:00:00 2001 From: Rahul Kulhalli Date: Mon, 4 Dec 2023 12:46:52 -0500 Subject: [PATCH 05/16] Modified biogeme_train_test.py by adding helper functions --- .../rm_src/biogeme_modeling train_test.ipynb | 530 -------- .../rm_src/biogeme_modeling_train_test.ipynb | 1099 +++++++++++++++++ 2 files changed, 1099 insertions(+), 530 deletions(-) delete mode 100644 viz_scripts/rm_src/biogeme_modeling train_test.ipynb create mode 100644 viz_scripts/rm_src/biogeme_modeling_train_test.ipynb diff --git a/viz_scripts/rm_src/biogeme_modeling train_test.ipynb b/viz_scripts/rm_src/biogeme_modeling train_test.ipynb deleted file mode 100644 index ba15c71..0000000 --- a/viz_scripts/rm_src/biogeme_modeling train_test.ipynb +++ /dev/null @@ -1,530 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import biogeme.biogeme as bio\n", - "import biogeme.database as db\n", - "from biogeme import models\n", - "from biogeme.expressions import Beta\n", - "from biogeme.expressions import Variable\n", - "import numpy as np\n", - "from enum import Enum" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = pd.read_csv('../data/preprocessed_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data.user_id.value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class SPLIT_TYPE(Enum):\n", - " INTRA_USER = 0\n", - " INTER_USER = 1\n", - "\n", - "\n", - "def get_splits(count_df: pd.DataFrame, n:int, test_size=0.2):\n", - " maxsize = int(n * test_size)\n", - "\n", - " max_threshold = int(maxsize * 1.05)\n", - " min_threshold = int(maxsize * 0.95)\n", - "\n", - " print(f\"{min_threshold=}, {max_threshold=}\")\n", - " \n", - " # Allow a 10% tolerance\n", - " def _dp(ix, curr_size, ids, cache):\n", - " \n", - " if ix >= count_df.shape[0]:\n", - " return []\n", - "\n", - " key = ix\n", - "\n", - " if key in cache:\n", - " return cache[key]\n", - "\n", - " if curr_size > max_threshold:\n", - " return []\n", - "\n", - " if min_threshold <= curr_size <= max_threshold:\n", - " return ids\n", - "\n", - " # two options - either pick the current id or skip it.\n", - " branch_a = _dp(ix, curr_size+count_df.loc[ix, 'count'], ids+[count_df.loc[ix, 'index']], cache)\n", - " branch_b = _dp(ix+1, curr_size, ids, cache)\n", - " \n", - " curr_max = []\n", - " if branch_a and len(branch_a) > 0:\n", - " curr_max = branch_a\n", - " \n", - " if branch_b and len(branch_b) > len(branch_a):\n", - " curr_max = branch_b\n", - " \n", - " cache[key] = curr_max\n", - " return cache[key]\n", - " \n", - " return _dp(0, 0, ids=list(), cache=dict())\n", - "\n", - "\n", - "def get_train_test_splits(data: pd.DataFrame, how=SPLIT_TYPE, test_ratio=0.2, shuffle=True):\n", - "\n", - " n_users = list(data.user_id.unique())\n", - " n = data.shape[0]\n", - " \n", - " if shuffle:\n", - " data = data.sample(data.shape[0]).reset_index(drop=True, inplace=False)\n", - "\n", - " if how == SPLIT_TYPE.INTER_USER:\n", - " # Make the split, ensuring that a user in one fold is not leaked into the other fold.\n", - " # Basic idea: we want to start with the users with the highest instances and place alternating users in each set.\n", - " counts = data.user_id.value_counts().reset_index(drop=False, inplace=False, name='count')\n", - "\n", - " # Now, start with the user_id at the top, and keep adding to either split.\n", - " # This can be achieved using a simple DP program to fill a knapsack with at most (test_ratio * size) entries.\n", - " test_ids = get_splits(counts, data.shape[0])\n", - "\n", - " print(\"Final test IDs: \", test_ids)\n", - " \n", - " return test_ids" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ids = get_train_test_splits(data=data, how=SPLIT_TYPE.INTER_USER, shuffle=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ids" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data.user_id.value_counts()[ids]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a.loc[0, 'count']\n", - "a.loc[0, 'index']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "USER_MAP = {\n", - " u: i+1 for (i, u) in enumerate(data.user_id.unique())\n", - "}\n", - "\n", - "# Drop section_mode_argmax and available_modes.\n", - "data.drop(columns=[\n", - " 'section_mode_argmax', 'available_modes', 'section_distance_argmax', 'section_duration_argmax'\n", - " ], inplace=True)\n", - "\n", - "data['user_id'] = data['user_id'].apply(lambda x: USER_MAP[x])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Convert from min -> hrs\n", - "data[[c for c in data.columns if 'tt_' in c]] /= 60." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Scale currency by 1/10.\n", - "data[[c for c in data.columns if 'cost_' in c]] /= 10." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "display(data[[c for c in data.columns if 'tt_' in c]].describe())\n", - "display(data[[c for c in data.columns if 'cost_' in c]].describe())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "for c in data.columns:\n", - " if 'av_' in c:\n", - " print(f\"Feature {c} availability: {data.loc[:, c].sum()}/{data.shape[0]}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# data.drop(columns=['tt_unknown','av_unknown'], inplace=True)\n", - "# data.drop(index=data.loc[(data.chosen == 9)].index, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "database = db.Database('emission', data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Variable definitions.\n", - "\n", - "# ID.\n", - "USER_ID = Variable('user_id')\n", - "\n", - "# Availability.\n", - "AV_P_MICRO = Variable('av_p_micro')\n", - "AV_NO_TRIP = Variable('av_no_trip')\n", - "AV_S_CAR = Variable('av_s_car')\n", - "AV_TRANSIT = Variable('av_transit')\n", - "AV_CAR = Variable('av_car')\n", - "AV_S_MICRO = Variable('av_s_micro')\n", - "AV_RIDEHAIL = Variable('av_ridehail')\n", - "AV_WALK = Variable('av_walk')\n", - "AV_UNKNOWN = Variable('av_unknown')\n", - "\n", - "# Time.\n", - "TT_P_MICRO = Variable('tt_p_micro')\n", - "TT_NO_TRIP = Variable('tt_no_trip')\n", - "TT_S_CAR = Variable('tt_s_car')\n", - "TT_TRANSIT = Variable('tt_transit')\n", - "TT_CAR = Variable('tt_car')\n", - "TT_S_MICRO = Variable('tt_s_micro')\n", - "TT_RIDEHAIL = Variable('tt_ridehail')\n", - "TT_WALK = Variable('tt_walk')\n", - "TT_UNKNOWN = Variable('tt_unknown')\n", - "\n", - "# Cost.\n", - "CO_P_MICRO = Variable('cost_p_micro')\n", - "CO_NO_TRIP = Variable('cost_no_trip')\n", - "CO_S_CAR = Variable('cost_s_car')\n", - "CO_TRANSIT = Variable('cost_transit')\n", - "CO_CAR = Variable('cost_car')\n", - "CO_S_MICRO = Variable('cost_s_micro')\n", - "CO_RIDEHAIL = Variable('cost_ridehail')\n", - "CO_WALK = Variable('cost_walk')\n", - "CO_UNKNOWN = Variable('cost_unknown')\n", - "\n", - "# Choice.\n", - "CHOICE = Variable('chosen')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Parameters\n", - "\n", - "# Create a coefficient from time and set the initial value to 0. There is no lower bound for this coefficient,\n", - "# but we would like to constrain the ceil to 0 since we want these coefficients to denote the utiltiy function.\n", - "# Finally, we want this parameter to be updated by the optimization algorithm.\n", - "B_TIME = Beta('B_TIME', 0, None, 0, 0)\n", - "B_COST = Beta('B_COST', 0, None, 0, 0)\n", - "\n", - "# Alternative-Specific Constants.\n", - "ASC_P_MICRO = Beta('ASC_P_MICRO', 0, None, None, 0)\n", - "ASC_NO_TRIP = Beta('ASC_NO_TRIP', 0, None, None, 1)\n", - "ASC_S_CAR = Beta('ASC_S_CAR', 0, None, None, 0)\n", - "ASC_TRANSIT = Beta('ASC_TRANSIT', 0, None, None, 0)\n", - "ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)\n", - "ASC_S_MICRO = Beta('ASC_S_MICRO', 0, None, None, 0)\n", - "ASC_RIDEHAIL = Beta('ASC_RIDEHAIL', 0, None, None, 0)\n", - "ASC_WALK = Beta('ASC_WALK', 0, None, None, 0)\n", - "ASC_UNKNOWN = Beta('ASC_UNKNOWN', 0, None, None, 1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the utility functions.\n", - "# number of alternatives = number of UFs.\n", - "\n", - "V_P_MICRO = (\n", - " ASC_P_MICRO +\n", - " B_TIME * TT_P_MICRO +\n", - " B_COST * CO_P_MICRO\n", - ")\n", - "\n", - "V_NO_TRIP = (\n", - " ASC_NO_TRIP +\n", - " B_TIME * TT_NO_TRIP +\n", - " B_COST * CO_NO_TRIP\n", - ")\n", - "\n", - "V_S_CAR = (\n", - " ASC_CAR +\n", - " B_TIME * TT_CAR +\n", - " B_COST * CO_CAR\n", - ")\n", - "\n", - "V_TRANSIT = (\n", - " ASC_TRANSIT +\n", - " B_TIME * TT_TRANSIT +\n", - " B_COST * CO_TRANSIT\n", - ")\n", - "\n", - "V_CAR = (\n", - " ASC_CAR +\n", - " B_TIME * TT_CAR +\n", - " B_COST * CO_CAR\n", - ")\n", - "\n", - "V_S_MICRO = (\n", - " ASC_S_MICRO +\n", - " B_TIME * TT_S_MICRO +\n", - " B_COST * CO_S_MICRO\n", - ")\n", - "\n", - "V_RIDEHAIL = (\n", - " ASC_RIDEHAIL +\n", - " B_TIME * TT_RIDEHAIL +\n", - " B_COST * CO_RIDEHAIL\n", - ")\n", - "\n", - "V_WALK = (\n", - " ASC_WALK +\n", - " B_TIME * TT_WALK +\n", - " B_COST * CO_WALK\n", - ")\n", - "\n", - "V_UNKNOWN = (\n", - " ASC_UNKNOWN +\n", - " B_TIME * TT_UNKNOWN +\n", - " B_COST * CO_UNKNOWN\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Map alterative to utility functions.\n", - "V = {\n", - " 1: V_P_MICRO, \n", - " 2: V_NO_TRIP,\n", - " 3: V_S_CAR, \n", - " 4: V_TRANSIT,\n", - " 5: V_CAR, \n", - " 6: V_S_MICRO,\n", - " 7: V_RIDEHAIL, \n", - " 8: V_WALK, \n", - " 9: V_UNKNOWN\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Availability.\n", - "# We have already masked-off costs and times for the instances where a particular mode is not available to the user.\n", - "# So here, we can specify the availability as 1 for every mode, saying that every other mode should be included when computing\n", - "# the logprob for a certain mode.\n", - "\n", - "av = {\n", - " 1: AV_P_MICRO,\n", - " 2: AV_NO_TRIP,\n", - " 3: AV_S_CAR,\n", - " 4: AV_TRANSIT,\n", - " 5: AV_CAR,\n", - " 6: AV_S_MICRO,\n", - " 7: AV_RIDEHAIL,\n", - " 8: AV_WALK,\n", - " 9: AV_UNKNOWN\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "logprob = models.loglogit(V, av, CHOICE)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = bio.BIOGEME(database, logprob)\n", - "model.modelName = 'noMaskNoTrip'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results = model.estimate()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(results.short_summary())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results.getEstimatedParameters()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results.getCorrelationResults()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_metrics(model: bio.BIOGEME, data: pd.DataFrame):\n", - " \"\"\"\n", - " Given a test dataframe and the trained model, generate precision, recall, F1.\n", - " \"\"\"\n", - "\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vals = model.validate(results)\n", - "\n", - "for val in vals:\n", - " print(val.shape, val.columns)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "interpreter": { - "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" - }, - "kernelspec": { - "display_name": "emission", - "language": "python", - "name": "emission" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/viz_scripts/rm_src/biogeme_modeling_train_test.ipynb b/viz_scripts/rm_src/biogeme_modeling_train_test.ipynb new file mode 100644 index 0000000..00ed265 --- /dev/null +++ b/viz_scripts/rm_src/biogeme_modeling_train_test.ipynb @@ -0,0 +1,1099 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from enum import Enum\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class SPLIT_TYPE(Enum):\n", + " INTRA_USER = 0\n", + " INTER_USER = 1\n", + " \n", + "\n", + "class SPLIT(Enum):\n", + " TRAIN = 0\n", + " TEST = 1\n", + "\n", + "\n", + "def get_splits(count_df: pd.DataFrame, n:int, test_size=0.2):\n", + " maxsize = int(n * test_size)\n", + "\n", + " max_threshold = int(maxsize * 1.05)\n", + " min_threshold = int(maxsize * 0.95)\n", + "\n", + " print(f\"{min_threshold=}, {max_threshold=}\")\n", + " \n", + " # Allow a 10% tolerance\n", + " def _dp(ix, curr_size, ids, cache):\n", + " \n", + " if ix >= count_df.shape[0]:\n", + " return []\n", + "\n", + " key = ix\n", + "\n", + " if key in cache:\n", + " return cache[key]\n", + "\n", + " if curr_size > max_threshold:\n", + " return []\n", + "\n", + " if min_threshold <= curr_size <= max_threshold:\n", + " return ids\n", + "\n", + " # two options - either pick the current id or skip it.\n", + " branch_a = _dp(ix, curr_size+count_df.loc[ix, 'count'], ids+[count_df.loc[ix, 'index']], cache)\n", + " branch_b = _dp(ix+1, curr_size, ids, cache)\n", + " \n", + " curr_max = []\n", + " if branch_a and len(branch_a) > 0:\n", + " curr_max = branch_a\n", + " \n", + " if branch_b and len(branch_b) > len(branch_a):\n", + " curr_max = branch_b\n", + " \n", + " cache[key] = curr_max\n", + " return cache[key]\n", + " \n", + " return _dp(0, 0, ids=list(), cache=dict())\n", + "\n", + "\n", + "def get_train_test_splits(data: pd.DataFrame, how=SPLIT_TYPE, test_ratio=0.2, shuffle=True):\n", + "\n", + " n_users = list(data.user_id.unique())\n", + " n = data.shape[0]\n", + " \n", + " if shuffle:\n", + " data = data.sample(data.shape[0]).reset_index(drop=True, inplace=False)\n", + "\n", + " if how == SPLIT_TYPE.INTER_USER:\n", + " # Make the split, ensuring that a user in one fold is not leaked into the other fold.\n", + " # Basic idea: we want to start with the users with the highest instances and place alternating users in each set.\n", + " counts = data.user_id.value_counts().reset_index(drop=False, inplace=False, name='count')\n", + "\n", + " # Now, start with the user_id at the top, and keep adding to either split.\n", + " # This can be achieved using a simple DP program.\n", + " test_ids = get_splits(counts, data.shape[0])\n", + " test_data = data.loc[data.user_id.isin(test_ids), :]\n", + " train_index = data.index.difference(test_data.index)\n", + " train_data = data.loc[data.user_id.isin(train_index), :]\n", + " \n", + " return train_data, test_data\n", + " \n", + " elif how == SPLIT_TYPE.INTRA_USER:\n", + " \n", + " # There are certain users with only one observation. What do we do with those?\n", + " # As per the mobilitynet modeling pipeline, we randomly assign them to either the\n", + " # training or test set.\n", + " \n", + " value_counts = data.user_id.value_counts()\n", + " single_count_ids = value_counts[value_counts == 1].index\n", + " \n", + " data_filtered = data.loc[~data.user_id.isin(single_count_ids), :].reset_index(drop=True)\n", + " data_single_counts = data.loc[data.user_id.isin(single_count_ids), :].reset_index(drop=True)\n", + " \n", + " X_tr, X_te = train_test_split(\n", + " data_filtered, test_size=test_ratio, shuffle=shuffle, stratify=data_filtered.user_id\n", + " )\n", + " \n", + " data_single_counts['assigned'] = np.random.choice(['train', 'test'], len(data_single_counts))\n", + " X_tr_merged = pd.concat(\n", + " [X_tr, data_single_counts.loc[data_single_counts.assigned == 'train', :].drop(\n", + " columns=['assigned'], inplace=False\n", + " )],\n", + " ignore_index=True, axis=0\n", + " )\n", + " \n", + " X_te_merged = pd.concat(\n", + " [X_te, data_single_counts.loc[data_single_counts.assigned == 'test', :].drop(\n", + " columns=['assigned'], inplace=False\n", + " )],\n", + " ignore_index=True, axis=0\n", + " )\n", + " \n", + " return X_tr_merged, X_te_merged\n", + " \n", + " raise NotImplementedError(\"Unknown split type\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modeling" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import biogeme.biogeme as bio\n", + "import biogeme.database as db\n", + "from biogeme import models\n", + "from biogeme.expressions import Beta, DefineVariable\n", + "from biogeme.expressions import Variable\n", + "import numpy as np\n", + "\n", + "from sklearn.preprocessing import MinMaxScaler" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Read the data.\n", + "data = pd.read_csv('../data/preprocessed_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# First, we map the user IDs to ints.\n", + "\n", + "USER_MAP = {\n", + " u: i+1 for (i, u) in enumerate(data.user_id.unique())\n", + "}\n", + "\n", + "data['user_id'] = data['user_id'].apply(lambda x: USER_MAP[x])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Now, we split the data (either inter-user or intra-user split)\n", + "\n", + "# 0.98 with this approach.????\n", + "# train_data, test_data = get_train_test_splits(data=data, how=SPLIT_TYPE.INTER_USER, shuffle=True)\n", + "\n", + "# 0.965 with this approach.???\n", + "train_data, test_data = get_train_test_splits(data=data, how=SPLIT_TYPE.INTRA_USER, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Some helper functions that will help ease redundancy in the code.\n", + "\n", + "def drop_columns(df: pd.DataFrame):\n", + " # Drop section_mode_argmax and available_modes.\n", + " return df.drop(columns=[\n", + " 'section_mode_argmax', 'available_modes', 'section_duration_argmax', 'section_distance_argmax'\n", + " ], inplace=False)\n", + "\n", + "\n", + "def scale_time(df: pd.DataFrame):\n", + " # Convert from min -> hrs\n", + " df[[c for c in df.columns if 'tt_' in c]] /= 60.\n", + " return df\n", + "\n", + "\n", + "def scale_cost(df: pd.DataFrame, split: SPLIT, scaler=None):\n", + " # Scale costs using MinMaxScaler.\n", + " costs = df[[c for c in df.columns if 'cost_' in c]].copy()\n", + " \n", + " if split == SPLIT.TRAIN and scaler is None:\n", + " scaler = MinMaxScaler()\n", + " cost_scaled = pd.DataFrame(\n", + " scaler.fit_transform(costs), \n", + " columns=['scaled_' + c for c in costs.columns], \n", + " index=costs.index\n", + " )\n", + " \n", + " elif split == SPLIT.TEST and scaler is not None:\n", + " cost_scaled = pd.DataFrame(\n", + " scaler.transform(costs), \n", + " columns=['scaled_' + c for c in costs.columns], \n", + " index=costs.index\n", + " )\n", + " \n", + " else:\n", + " raise NotImplementedError(\"Unknown split\")\n", + " \n", + " df = df.merge(right=cost_scaled, left_index=True, right_index=True)\n", + " \n", + " return df, scaler\n", + "\n", + "\n", + "def get_database(df: pd.DataFrame, split: SPLIT):\n", + " return db.Database(split.name + '_db', df)\n", + "\n", + "\n", + "def get_variables():\n", + " USER_ID = Variable('user_id')\n", + "\n", + " # Availability.\n", + " AV_P_MICRO = Variable('av_p_micro')\n", + " AV_NO_TRIP = Variable('av_no_trip')\n", + " AV_S_CAR = Variable('av_s_car')\n", + " AV_TRANSIT = Variable('av_transit')\n", + " AV_CAR = Variable('av_car')\n", + " AV_S_MICRO = Variable('av_s_micro')\n", + " AV_RIDEHAIL = Variable('av_ridehail')\n", + " AV_WALK = Variable('av_walk')\n", + " AV_UNKNOWN = Variable('av_unknown')\n", + "\n", + " # Time.\n", + " TT_P_MICRO = Variable('tt_p_micro')\n", + " TT_NO_TRIP = Variable('tt_no_trip')\n", + " TT_S_CAR = Variable('tt_s_car')\n", + " TT_TRANSIT = Variable('tt_transit')\n", + " TT_CAR = Variable('tt_car')\n", + " TT_S_MICRO = Variable('tt_s_micro')\n", + " TT_RIDEHAIL = Variable('tt_ridehail')\n", + " TT_WALK = Variable('tt_walk')\n", + " TT_UNKNOWN = Variable('tt_unknown')\n", + "\n", + " # Cost.\n", + " CO_P_MICRO = Variable('scaled_cost_p_micro')\n", + " CO_NO_TRIP = Variable('scaled_cost_no_trip')\n", + " CO_S_CAR = Variable('scaled_cost_s_car')\n", + " CO_TRANSIT = Variable('scaled_cost_transit')\n", + " CO_CAR = Variable('scaled_cost_car')\n", + " CO_S_MICRO = Variable('scaled_cost_s_micro')\n", + " CO_RIDEHAIL = Variable('scaled_cost_ridehail')\n", + " CO_WALK = Variable('scaled_cost_walk')\n", + " CO_UNKNOWN = Variable('scaled_cost_unknown')\n", + "\n", + " # Choice.\n", + " CHOICE = Variable('chosen')\n", + " \n", + " # return the filtered locals() dictionary.\n", + " return {k:v for k,v in locals().items() if not k.startswith('_')}\n", + "\n", + "\n", + "def exclude_from_db(v_dict: dict, db: db.Database):\n", + " EXCLUDE = (v_dict['CHOICE'] == 2) + (v_dict['CHOICE'] == 9) > 0\n", + " db.remove(EXCLUDE)\n", + "\n", + "\n", + "def get_params():\n", + " B_TIME = Beta('B_TIME', 0, None, 0, 0)\n", + " B_COST = Beta('B_COST', 0, None, None, 0)\n", + "\n", + " # Alternative-Specific Constants.\n", + " ASC_P_MICRO = Beta('ASC_P_MICRO', 0, None, None, 0)\n", + " ASC_NO_TRIP = Beta('ASC_NO_TRIP', 0, None, None, 0)\n", + " ASC_S_CAR = Beta('ASC_S_CAR', 0, None, None, 0)\n", + " ASC_TRANSIT = Beta('ASC_TRANSIT', 0, None, None, 0)\n", + " ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)\n", + " ASC_S_MICRO = Beta('ASC_S_MICRO', 0, None, None, 0)\n", + " ASC_RIDEHAIL = Beta('ASC_RIDEHAIL', 0, None, None, 0)\n", + " ASC_WALK = Beta('ASC_WALK', 0, None, None, 0)\n", + " ASC_UNKNOWN = Beta('ASC_UNKNOWN', 0, None, None, 0)\n", + " \n", + " # Return filtered locals dict.\n", + " return {k:v for k,v in locals().items() if not k.startswith('_')}\n", + "\n", + "\n", + "def get_utility_functions(v: dict):\n", + " V_P_MICRO = (\n", + " v['ASC_P_MICRO'] +\n", + " v['B_TIME'] * v['TT_P_MICRO']\n", + " + v['B_COST'] * v['CO_P_MICRO']\n", + " )\n", + "\n", + " V_NO_TRIP = (\n", + " v['ASC_NO_TRIP'] +\n", + " v['B_TIME'] * v['TT_NO_TRIP'] +\n", + " v['B_COST'] * v['CO_NO_TRIP']\n", + " )\n", + "\n", + " V_S_CAR = (\n", + " v['ASC_S_CAR'] +\n", + " v['B_TIME'] * v['TT_S_CAR'] +\n", + " v['B_COST'] * v['CO_S_CAR']\n", + " )\n", + "\n", + " V_TRANSIT = (\n", + " v['ASC_TRANSIT'] +\n", + " v['B_TIME'] * v['TT_TRANSIT'] +\n", + " v['B_COST'] * v['CO_TRANSIT']\n", + " )\n", + "\n", + " V_CAR = (\n", + " v['ASC_CAR'] +\n", + " v['B_TIME'] * v['TT_CAR'] +\n", + " v['B_COST'] * v['CO_CAR']\n", + " )\n", + "\n", + " V_S_MICRO = (\n", + " v['ASC_S_MICRO'] +\n", + " v['B_TIME'] * v['TT_S_MICRO'] +\n", + " v['B_COST'] * v['CO_S_MICRO']\n", + " )\n", + "\n", + " V_RIDEHAIL = (\n", + " v['ASC_RIDEHAIL'] +\n", + " v['B_TIME'] * v['TT_RIDEHAIL'] +\n", + " v['B_COST'] * v['CO_RIDEHAIL']\n", + " )\n", + "\n", + " V_WALK = (\n", + " v['ASC_WALK'] +\n", + " v['B_TIME'] * v['TT_WALK']\n", + " + v['B_COST'] * v['CO_WALK']\n", + " )\n", + "\n", + " V_UNKNOWN = (\n", + " v['ASC_UNKNOWN'] +\n", + " v['B_TIME'] * v['TT_UNKNOWN'] +\n", + " v['B_COST'] * v['CO_UNKNOWN']\n", + " )\n", + " \n", + " # Remember to exclude the input argument.\n", + " return {k:v for k,v in locals().items() if not k.startswith('_') and k != 'v'}\n", + "\n", + "\n", + "def get_utility_mapping(var: dict):\n", + " # Map alterative to utility functions.\n", + " return {\n", + " 1: var['V_P_MICRO'], \n", + " 2: var['V_NO_TRIP'],\n", + " 3: var['V_S_CAR'], \n", + " 4: var['V_TRANSIT'],\n", + " 5: var['V_CAR'], \n", + " 6: var['V_S_MICRO'],\n", + " 7: var['V_RIDEHAIL'], \n", + " 8: var['V_WALK'], \n", + " 9: var['V_UNKNOWN']\n", + " }\n", + "\n", + "\n", + "def get_availability_mapping(var: dict):\n", + " return {\n", + " 1: var['AV_P_MICRO'],\n", + " 2: var['AV_NO_TRIP'],\n", + " 3: var['AV_S_CAR'],\n", + " 4: var['AV_TRANSIT'],\n", + " 5: var['AV_CAR'],\n", + " 6: var['AV_S_MICRO'],\n", + " 7: var['AV_RIDEHAIL'],\n", + " 8: var['AV_WALK'],\n", + " 9: var['AV_UNKNOWN']\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# First, drop columns.\n", + "\n", + "train_data = drop_columns(train_data)\n", + "\n", + "# Next, scale time.\n", + "train_data = scale_time(train_data)\n", + "\n", + "# Scale cost.\n", + "train_data, scaler = scale_cost(train_data, SPLIT.TRAIN, None)\n", + "\n", + "# get dbs.\n", + "train_db = get_database(train_data, SPLIT.TRAIN)\n", + "\n", + "# get vars.\n", + "train_vars = get_variables()\n", + "\n", + "# exclude wrong points.\n", + "exclude_from_db(train_vars, train_db)\n", + "\n", + "train_params = get_params()\n", + "train_vars.update(train_params)\n", + "\n", + "train_V = get_utility_functions(train_vars)\n", + "train_vars.update(train_V)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "V = get_utility_mapping(train_vars)\n", + "av = get_availability_mapping(train_vars)\n", + "train_logprob = models.loglogit(V, av, train_vars['CHOICE'])\n", + "\n", + "model = bio.BIOGEME(train_db, train_logprob)\n", + "model.modelName = 'linearModelCostsModel-intraUser'" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rkulhall/miniconda3/envs/emission/lib/python3.9/site-packages/biogeme_optimization/simple_bounds.py:213: RuntimeWarning: invalid value encountered in scalar divide\n", + " rho = num / denom\n" + ] + } + ], + "source": [ + "train_results = model.estimate()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for model linearModelCostsModel-intraUser\n", + "Nbr of parameters:\t\t11\n", + "Sample size:\t\t\t57579\n", + "Excluded data:\t\t\t2118\n", + "Final log likelihood:\t\t-3.102707e-05\n", + "Akaike Information Criterion:\t22.00006\n", + "Bayesian Information Criterion:\t120.5701\n", + "\n" + ] + } + ], + "source": [ + "print(train_results.short_summary())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ValueRob. Std errRob. t-testRob. p-value
ASC_CAR42.6844941.797693e+3082.374404e-3071.0
ASC_NO_TRIP-260.4133749.457320e+00-2.753564e+010.0
ASC_P_MICRO33.0861801.797693e+3081.840480e-3071.0
ASC_RIDEHAIL34.0023201.797693e+3081.891442e-3071.0
ASC_S_CAR40.6975321.797693e+3082.263875e-3071.0
ASC_S_MICRO18.9711891.797693e+3081.055307e-3071.0
ASC_TRANSIT35.8428131.797693e+3081.993823e-3071.0
ASC_UNKNOWN0.0000001.845808e-130.000000e+001.0
ASC_WALK55.1288451.797693e+3083.066644e-3071.0
B_COST-2272.7710031.214323e+02-1.871636e+010.0
B_TIME-145.5888655.280683e-01-2.757008e+020.0
\n", + "
" + ], + "text/plain": [ + " Value Rob. Std err Rob. t-test Rob. p-value\n", + "ASC_CAR 42.684494 1.797693e+308 2.374404e-307 1.0\n", + "ASC_NO_TRIP -260.413374 9.457320e+00 -2.753564e+01 0.0\n", + "ASC_P_MICRO 33.086180 1.797693e+308 1.840480e-307 1.0\n", + "ASC_RIDEHAIL 34.002320 1.797693e+308 1.891442e-307 1.0\n", + "ASC_S_CAR 40.697532 1.797693e+308 2.263875e-307 1.0\n", + "ASC_S_MICRO 18.971189 1.797693e+308 1.055307e-307 1.0\n", + "ASC_TRANSIT 35.842813 1.797693e+308 1.993823e-307 1.0\n", + "ASC_UNKNOWN 0.000000 1.845808e-13 0.000000e+00 1.0\n", + "ASC_WALK 55.128845 1.797693e+308 3.066644e-307 1.0\n", + "B_COST -2272.771003 1.214323e+02 -1.871636e+01 0.0\n", + "B_TIME -145.588865 5.280683e-01 -2.757008e+02 0.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(train_results.getEstimatedParameters())" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def get_utility_df(results, data):\n", + "\n", + " def compute_utilities(betas, row: pd.Series):\n", + " data = row.to_dict()\n", + "\n", + " utility_p_micro = betas['ASC_P_MICRO'] + (betas['B_TIME'] * data['tt_p_micro'])\n", + " utility_no_trip = betas['ASC_NO_TRIP'] + (betas['B_TIME'] * data['tt_no_trip']) + (betas['B_COST'] * data['scaled_cost_no_trip'])\n", + " utility_s_car = betas['ASC_S_CAR'] + (betas['B_COST'] * data['scaled_cost_s_car']) + (betas['B_TIME'] * data['tt_s_car'])\n", + " utility_transit = betas['ASC_TRANSIT'] + (betas['B_COST'] * data['scaled_cost_transit']) + (betas['B_TIME'] * data['tt_transit'])\n", + " utility_car = betas['ASC_CAR'] + (betas['B_COST'] * data['scaled_cost_car'] + (betas['B_TIME'] * data['tt_car']))\n", + " utility_s_micro = betas['ASC_S_MICRO'] + (betas['B_COST'] * data['scaled_cost_s_micro']) + (betas['B_TIME'] * data['tt_s_micro'])\n", + " utility_ridehail = betas['ASC_RIDEHAIL'] + (betas['B_COST'] * data['scaled_cost_ridehail']) + (betas['B_TIME'] * data['tt_ridehail'])\n", + " utility_walk = betas['ASC_WALK'] + (betas['B_TIME'] * data['tt_walk'])\n", + " utility_unknown = betas['ASC_UNKNOWN'] + (betas['B_TIME'] * data['tt_unknown']) + (betas['B_COST'] * data['scaled_cost_unknown'])\n", + "\n", + " return {\n", + " 'utility_p_micro': utility_p_micro, 'utility_no_trip': utility_no_trip,\n", + " 'utility_s_car': utility_s_car, 'utility_transit': utility_transit,\n", + " 'utility_car': utility_car, 'utility_s_micro': utility_s_micro,\n", + " 'utility_ridehail': utility_ridehail, 'utility_walk': utility_walk, \n", + " 'utility_unknown': utility_unknown, \n", + " }\n", + " \n", + " betas = results.getBetaValues()\n", + "\n", + " u_data = data.apply(lambda x: compute_utilities(betas, x), axis=1).tolist()\n", + " return pd.DataFrame(u_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "test_data = drop_columns(test_data)\n", + "\n", + "# Next, scale time.\n", + "test_data = scale_time(test_data)\n", + "\n", + "# Scale cost.\n", + "test_data, _ = scale_cost(test_data, SPLIT.TEST, scaler)\n", + "\n", + "# get dbs.\n", + "test_db = get_database(test_data, SPLIT.TEST)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "test_utilities = get_utility_df(train_results, test_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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utility_p_microutility_no_triputility_s_carutility_transitutility_carutility_s_microutility_ridehailutility_walkutility_unknown
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" + ], + "text/plain": [ + " utility_p_micro utility_no_trip utility_s_car utility_transit \\\n", + "0 -18.241189 -260.413374 17.827530 -13.045518 \n", + "1 15.548732 -260.413374 11.626470 13.067221 \n", + "2 14.974088 -260.413374 -31.381507 -42.094303 \n", + "3 -21.417490 -260.413374 15.997066 -15.500152 \n", + "4 -54.925702 -260.413374 -3.313322 -41.395187 \n", + "\n", + " utility_car utility_s_micro utility_ridehail utility_walk \\\n", + "0 -6.745978 -45.912237 -15.428152 -157.474693 \n", + "1 13.613432 -0.558474 4.931258 53.723791 \n", + "2 -29.394545 -96.365456 -38.076719 -325.992645 \n", + "3 -8.659791 -50.175555 -17.341965 -171.714529 \n", + "4 -28.849463 -95.151201 -37.531637 -321.936932 \n", + "\n", + " utility_unknown \n", + "0 -47.457612 \n", + "1 -26.574742 \n", + "2 -70.688496 \n", + "3 -49.420631 \n", + "4 -70.129400 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(test_utilities.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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utility_p_microutility_no_triputility_s_carutility_transitutility_carutility_s_microutility_ridehailutility_walkutility_unknown
02.165480e-161.450476e-1211.000000e+003.908458e-142.127458e-112.080515e-283.607785e-157.365664e-774.436322e-29
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21.000000e+002.516197e-1207.379397e-211.642527e-255.382046e-204.424358e-499.126982e-248.318201e-1496.269204e-38
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43.846168e-232.202248e-1121.000000e+002.892386e-178.124469e-121.304116e-401.377764e-154.202665e-1399.597242e-30
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" + ], + "text/plain": [ + " utility_p_micro utility_no_trip utility_s_car utility_transit \\\n", + "0 2.165480e-16 1.450476e-121 1.000000e+00 3.908458e-14 \n", + "1 2.635013e-17 3.732181e-137 5.216339e-19 2.203312e-18 \n", + "2 1.000000e+00 2.516197e-120 7.379397e-21 1.642527e-25 \n", + "3 5.637221e-17 9.046302e-121 1.000000e+00 2.093784e-14 \n", + "4 3.846168e-23 2.202248e-112 1.000000e+00 2.892386e-17 \n", + "\n", + " utility_car utility_s_micro utility_ridehail utility_walk \\\n", + "0 2.127458e-11 2.080515e-28 3.607785e-15 7.365664e-77 \n", + "1 3.804454e-18 2.663865e-24 6.451670e-22 1.000000e+00 \n", + "2 5.382046e-20 4.424358e-49 9.126982e-24 8.318201e-149 \n", + "3 1.957325e-11 1.826401e-29 3.319272e-15 3.005315e-82 \n", + "4 8.124469e-12 1.304116e-40 1.377764e-15 4.202665e-139 \n", + "\n", + " utility_unknown \n", + "0 4.436322e-29 \n", + "1 1.339030e-35 \n", + "2 6.269204e-38 \n", + "3 3.885574e-29 \n", + "4 9.597242e-30 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "u_np = test_utilities.values\n", + "choice_df = np.exp(u_np)/np.sum(np.exp(u_np), axis=1, keepdims=True)\n", + "\n", + "choice_df = pd.DataFrame(choice_df, columns=test_utilities.columns)\n", + "display(choice_df.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 2 3 4 5 8 9]\n" + ] + } + ], + "source": [ + "from sklearn.metrics import f1_score\n", + "\n", + "y_pred = np.argmax(choice_df.values, axis=1) + 1\n", + "\n", + "print(np.unique(y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9692918926841162\n" + ] + } + ], + "source": [ + "y_true = test_data.chosen\n", + "score = f1_score(y_true, y_pred, average='weighted')\n", + "\n", + "print(score)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 5))\n", + "\n", + "sns.histplot(y_pred, ax=ax[0])\n", + "sns.histplot(y_true, ax=ax[1])\n", + "\n", + "labels = [\n", + " 'p_micro', \n", + " 'no_trip',\n", + " 's_car', \n", + " 'transit',\n", + " 'car', \n", + " 's_micro',\n", + " 'ridehail', \n", + " 'walk', \n", + " 'unknown'\n", + "]\n", + "\n", + "ax[0].set(\n", + " title='predicted label distribution',\n", + " xlabel='Labels',\n", + " xticks=range(1, 10),\n", + " xticklabels=labels\n", + ")\n", + "\n", + "ax[1].set(\n", + " title='true label distribution',\n", + " xlabel='Labels',\n", + " xticks=range(1, 10),\n", + " xticklabels=labels\n", + ")\n", + "\n", + "ax[0].set_xticklabels(ax[0].get_xticklabels(), rotation=45)\n", + "ax[1].set_xticklabels(ax[0].get_xticklabels(), rotation=45)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" + }, + "kernelspec": { + "display_name": "emission", + "language": "python", + "name": "emission" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 34d81757f9de7ef10a8a1ba2f492909407b1c5e0 Mon Sep 17 00:00:00 2001 From: Rahul Kulhalli Date: Fri, 9 Feb 2024 10:40:22 -0500 Subject: [PATCH 06/16] Added latest code --- viz_scripts/rm_src/LSTM.ipynb | 2774 +++++++++++++++++ viz_scripts/rm_src/baseline_modeling0.ipynb | 1136 +++++++ .../rm_src/cost_time_avl_preprocessing.ipynb | 1725 ++++++++-- viz_scripts/rm_src/mnl_exploration.ipynb | 935 ++++++ 4 files changed, 6305 insertions(+), 265 deletions(-) create mode 100644 viz_scripts/rm_src/LSTM.ipynb create mode 100644 viz_scripts/rm_src/baseline_modeling0.ipynb create mode 100644 viz_scripts/rm_src/mnl_exploration.ipynb diff --git a/viz_scripts/rm_src/LSTM.ipynb b/viz_scripts/rm_src/LSTM.ipynb new file mode 100644 index 0000000..fcaca73 --- /dev/null +++ b/viz_scripts/rm_src/LSTM.ipynb @@ -0,0 +1,2774 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "5f2cdb77", + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "from abc import ABC, abstractmethod\n", + "from typing import List\n", + "import ast" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0ebc3879", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import random\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import torch.optim as optim\n", + "import numpy as np\n", + "import pandas as pd\n", + "from torch.utils.data import Dataset, DataLoader\n", + "from enum import Enum\n", + "import matplotlib.pyplot as plt\n", + "from torch.nn.utils.rnn import pad_sequence\n", + "from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import r2_score\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a2ace37f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Global experiment flags and variables.\n", + "SEED = 19348\n", + "TARGETS = ['p_micro', 'no_trip', 's_car', 'transit', 'car', 's_micro', 'ridehail', 'walk', 'unknown']\n", + "\n", + "# Set the Numpy seed too.\n", + "random.seed(SEED)\n", + "np.random.seed(SEED)\n", + "torch.manual_seed(SEED)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9addd580", + "metadata": {}, + "outputs": [], + "source": [ + "# lstm = nn.LSTM(12, 64, batch_first=True, bidirectional=True, bias=False, num_layers=3)\n", + "# inp = torch.FloatTensor(8, 3, 12).fill_(0.)\n", + "# out, _ = lstm(inp)\n", + "\n", + "# print(out.size())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "889bd770", + "metadata": {}, + "outputs": [], + "source": [ + "class SPLIT_TYPE(Enum):\n", + " INTRA_USER = 0\n", + " INTER_USER = 1\n", + " TARGET = 2\n", + " MIXED = 3\n", + " \n", + "\n", + "class SPLIT(Enum):\n", + " TRAIN = 0\n", + " TEST = 1\n", + "\n", + "\n", + "def get_train_test_splits(data: pd.DataFrame, how=SPLIT_TYPE, test_ratio=0.2, shuffle=True):\n", + "\n", + " n_users = list(data.user_id.unique())\n", + " n = data.shape[0]\n", + " \n", + " if shuffle:\n", + " data = data.sample(data.shape[0], random_state=SEED).reset_index(drop=True, inplace=False)\n", + " \n", + " # There are certain users with only one observation. What do we do with those?\n", + " # As per the mobilitynet modeling pipeline, we randomly assign them to either the\n", + " # training or test set.\n", + "\n", + " value_counts = data.user_id.value_counts()\n", + " single_count_ids = value_counts[value_counts == 1].index\n", + "\n", + " data_filtered = data.loc[~data.user_id.isin(single_count_ids), :].reset_index(drop=True)\n", + " data_single_counts = data.loc[data.user_id.isin(single_count_ids), :].reset_index(drop=True)\n", + "\n", + " X_tr, X_te = train_test_split(\n", + " data_filtered, test_size=test_ratio, shuffle=shuffle, stratify=data_filtered.user_id,\n", + " random_state=SEED\n", + " )\n", + "\n", + " data_single_counts['assigned'] = np.random.choice(['train', 'test'], len(data_single_counts))\n", + " X_tr_merged = pd.concat(\n", + " [X_tr, data_single_counts.loc[data_single_counts.assigned == 'train', :].drop(\n", + " columns=['assigned'], inplace=False\n", + " )],\n", + " ignore_index=True, axis=0\n", + " )\n", + "\n", + " X_te_merged = pd.concat(\n", + " [X_te, data_single_counts.loc[data_single_counts.assigned == 'test', :].drop(\n", + " columns=['assigned'], inplace=False\n", + " )],\n", + " ignore_index=True, axis=0\n", + " )\n", + "\n", + " return X_tr_merged, X_te_merged" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6cfa847a", + "metadata": {}, + "outputs": [], + "source": [ + "# RM_weather contains more samples because data is not processed.\n", + "# _Fix contains lesser samples, but is cleaned using heuristics.\n", + "\n", + "data = pd.read_csv('../data/FULL_preprocessed_data_RM_weather.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8fa73ebc", + "metadata": {}, + "outputs": [], + "source": [ + "class DataPipeline(ABC):\n", + " \n", + " def __init__(self, df: pd.DataFrame):\n", + " self.df = df\n", + " \n", + " # Establish hooks.\n", + " @abstractmethod\n", + " def remove_negative_sections(self):\n", + " raise NotImplementedError()\n", + " \n", + " @abstractmethod\n", + " def get_mode_outliers(self):\n", + " raise NotImplementedError()\n", + " \n", + " @abstractmethod\n", + " def drop_outliers(self, outliers: List):\n", + " raise NotImplementedError()\n", + " \n", + " @abstractmethod\n", + " def compute_mph_feature(self):\n", + " raise NotImplementedError()\n", + " \n", + " def run_pipeline(self):\n", + " self.remove_negative_sections()\n", + " outlier_ix = self.get_mode_outliers()\n", + " self.drop_outliers(list(outlier_ix))\n", + " return self.compute_mph_feature()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d8e129c8", + "metadata": {}, + "outputs": [], + "source": [ + "class SectionDataPipeline(DataPipeline):\n", + " def __init__(self, df: pd.DataFrame):\n", + " super().__init__(df)\n", + " \n", + " def remove_negative_sections(self):\n", + " f_rows = list()\n", + " for _, row in self.df[['section_durations', 'section_distances', 'section_modes']].iterrows():\n", + "\n", + " dist = np.array(ast.literal_eval(row['section_distances']))\n", + " dur = np.array(ast.literal_eval(row['section_durations']))\n", + " modes = np.array(ast.literal_eval(row['section_modes']))\n", + "\n", + " assert len(dist) == len(dur) == len(modes)\n", + "\n", + " mask = np.logical_and(dist > 0, dur > 0)\n", + "\n", + " f_dist, f_dur, f_modes = dist[mask], dur[mask], modes[mask]\n", + "\n", + " assert len(f_dist) == len(f_dur) == len(f_modes)\n", + "\n", + " f_rows.append({\n", + " # scale to miles.\n", + " 'distances': f_dist * 0.00062,\n", + " # Scale to hours.\n", + " 'durations': f_dur / 3600.,\n", + " 'modes': f_modes\n", + " })\n", + "\n", + " parsed = pd.DataFrame(f_rows, index=data.index)\n", + "\n", + " self.df.drop(columns=['section_durations', 'section_distances', 'section_modes'], inplace=True)\n", + " self.df = pd.concat([self.df, parsed], axis=1)\n", + " \n", + " def get_mode_outliers(self):\n", + " \n", + " def compute_outliers(mode: str):\n", + " x = self.df[self.df.modes.str.contains(mode, regex=False)]\n", + " outlier_ix = []\n", + " dist, dur = np.array([]), np.array([])\n", + "\n", + " # First, iterate to compute the 99th percentile for the mode.\n", + " for row_ix, row in x.iterrows():\n", + " ix = np.where(row['modes'] == mode)[0]\n", + " dist = np.append(dist, row['distances'][ix])\n", + " dur = np.append(dur, row['durations'][ix])\n", + "\n", + " dist_99p = np.percentile(dist, 99)\n", + " dur_99p = np.percentile(dur, 99)\n", + "\n", + " print(f\"99th Percentile for {mode}:\\n\\tdistance = {dist_99p}\\n\\tduration = {dur_99p}\")\n", + "\n", + " # Iterate again.\n", + " for row_ix, row in x.iterrows():\n", + " ix = np.where(row['modes'] == mode)[0]\n", + " row_distances = row['distances'][ix]\n", + " row_durations = row['durations'][ix]\n", + "\n", + " if np.any(np.logical_or(row_distances > dist_99p, row_durations > dur_99p)):\n", + " outlier_ix.append(row_ix)\n", + "\n", + " print(\"Number of candidate rows for deletion: \", len(outlier_ix))\n", + "\n", + " return outlier_ix\n", + " \n", + " all_bad_ix = set()\n", + " for mode in ['walking', 'bicycling', 'train', 'bus', 'car']:\n", + " bad_mode_ix = compute_outliers(mode)\n", + " all_bad_ix = all_bad_ix.union(set(bad_mode_ix))\n", + " print(50*'=')\n", + " \n", + " print(f\"After analysis, {len(all_bad_ix)} total unique rows will be dropped.\")\n", + " \n", + " return all_bad_ix\n", + " \n", + " def drop_outliers(self, outliers: List):\n", + " self.df.drop(index=outliers, inplace=True)\n", + " \n", + " def compute_mph_feature(self):\n", + " self.df['mph'] = self.df['distances']/self.df['durations']\n", + " return self.df" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "38af3aad", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "99th Percentile for walking:\n", + "\tdistance = 4.305297486707881\n", + "\tduration = 2.9548623706234824\n", + "Number of candidate rows for deletion: 765\n", + "==================================================\n", + "99th Percentile for bicycling:\n", + "\tdistance = 8.82048257219703\n", + "\tduration = 0.9153121545202207\n", + "Number of candidate rows for deletion: 228\n", + "==================================================\n", + "99th Percentile for train:\n", + "\tdistance = 16.629460194644828\n", + "\tduration = 0.7250753228180938\n", + "Number of candidate rows for deletion: 2\n", + "==================================================\n", + "99th Percentile for bus:\n", + "\tdistance = 7.551944729816364\n", + "\tduration = 0.7316025587360053\n", + "Number of candidate rows for deletion: 12\n", + "==================================================\n", + "99th Percentile for car:\n", + "\tdistance = 40.548478579524726\n", + "\tduration = 1.2145738347887987\n", + "Number of candidate rows for deletion: 721\n", + "==================================================\n", + "After analysis, 1718 total unique rows will be dropped.\n" + ] + } + ], + "source": [ + "pipeline = SectionDataPipeline(data)\n", + "cleaned = pipeline.run_pipeline()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "81151c12", + "metadata": {}, + "outputs": [], + "source": [ + "def preprocess(df: pd.DataFrame):\n", + " \n", + " # Offset by -1 for torch.\n", + " df.chosen -= 1\n", + " \n", + "# df['start_fmt_time'] = pd.to_datetime(df['start_fmt_time'], utc=True)\n", + "# df['start_fmt_time'] = df['start_fmt_time'].dt.tz_convert('America/Denver')\n", + " \n", + " df.rename(\n", + " columns={'start_local_dt_weekday': 'start:DOW', 'end_local_dt_weekday': 'end:DOW'},\n", + " inplace=True\n", + " )\n", + "\n", + " df.n_working_residents = df.n_working_residents.apply(lambda x: 0 if x < 0 else x)\n", + "\n", + " # Fix some age preprocessing issues.\n", + " df.age = df.age.apply(lambda x: x if x < 100 else 2024-x)\n", + " \n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0d4d6b76", + "metadata": {}, + "outputs": [], + "source": [ + "def get_duration_estimate(df: pd.DataFrame, dset: SPLIT, model_dict: dict):\n", + " \n", + " X_features = ['section_distance_argmax', 'age']\n", + " \n", + " if 'mph' in df.columns:\n", + " X_features += ['mph']\n", + " \n", + " if dset == SPLIT.TRAIN and model_dict is None:\n", + " model_dict = dict()\n", + " \n", + " if dset == SPLIT.TEST and model_dict is None:\n", + " raise AttributeError(\"Expected model dict for testing.\")\n", + " \n", + " if dset == SPLIT.TRAIN:\n", + " for section_mode in df.section_mode_argmax.unique():\n", + " section_data = df.loc[df.section_mode_argmax == section_mode, :]\n", + " if section_mode not in model_dict:\n", + " model_dict[section_mode] = dict()\n", + "\n", + " model = LinearRegression(fit_intercept=True)\n", + "\n", + " X = section_data[\n", + " X_features\n", + " ]\n", + " Y = section_data[['section_duration_argmax']]\n", + "\n", + " model.fit(X, Y.values.ravel())\n", + "\n", + " r2 = r2_score(y_pred=model.predict(X), y_true=Y.values.ravel())\n", + " print(f\"Train R2 for {section_mode}: {r2}\")\n", + "\n", + " model_dict[section_mode]['model'] = model\n", + " \n", + " elif dset == SPLIT.TEST:\n", + " for section_mode in df.section_mode_argmax.unique():\n", + " section_data = df.loc[df.section_mode_argmax == section_mode, :]\n", + " X = section_data[\n", + " X_features\n", + " ]\n", + " \n", + " Y = section_data[['section_duration_argmax']]\n", + " \n", + " y_pred = model_dict[section_mode]['model'].predict(X)\n", + " r2 = r2_score(y_pred=y_pred, y_true=Y.values.ravel())\n", + " print(f\"Test R2 for {section_mode}: {r2}\")\n", + " \n", + " # Create the new columns for the duration.\n", + " df[TARGETS] = 0\n", + " df['temp'] = 0\n", + " \n", + " for section in df.section_mode_argmax.unique():\n", + " X_section = df.loc[df.section_mode_argmax == section, X_features]\n", + " \n", + " # broadcast to all columns.\n", + " df.loc[df.section_mode_argmax == section, 'temp'] = model_dict[section]['model'].predict(X_section)\n", + " \n", + " for c in TARGETS:\n", + " df[c] = df['av_' + c] * df['temp']\n", + " \n", + " df.drop(columns=['temp'], inplace=True)\n", + " \n", + " df.rename(columns=dict([(x, 'tt_'+x) for x in TARGETS]), inplace=True)\n", + " \n", + " # return model_dict, result_df\n", + " return model_dict, df" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7b34ced0", + "metadata": {}, + "outputs": [], + "source": [ + "def drop_columns(df: pd.DataFrame):\n", + " u_time_features = [\n", + " 'start:year', 'start:month', 'start:day',\n", + " 'start:hour', 'end_fmt_time', 'end:year',\n", + " 'end:month', 'end:day', 'end:hour', 'end:n_days_in_month', \n", + " 'start:sin_DOM', 'start:sin_MOY', 'start:cos_MOY', 'start:cos_DOM',\n", + " 'end:sin_DOM', 'end:sin_MOY', 'end:cos_DOM', 'end:cos_MOY', 'start:n_days_in_month',\n", + " 'start_local_dt_weekday', 'end_local_dt_weekday', 'start_fmt_time'\n", + " ]\n", + "\n", + " u_user_features = [\n", + " '_id', 'original_user_id', 'gender', 'birth_year', 'user_id'\n", + " ]\n", + "\n", + " u_trip_features = [\n", + " 'cleaned_trip', 'Mode_confirm', 'available_modes', 'duration', 'start_loc',\n", + " 'end_loc', 'section_locations_argmax', 'section_coordinates_argmax',\n", + " 'start_lat', 'start_lng', 'end_lat', 'end_lng', 'section_duration_argmax',\n", + " 'section_distance_argmax', 'section_mode_argmax'\n", + " ]\n", + "\n", + " # Drop section_mode_argmax and available_modes.\n", + " return df.drop(\n", + " columns=u_time_features + u_user_features + u_trip_features, \n", + " inplace=False\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "904fa4dc", + "metadata": {}, + "outputs": [], + "source": [ + "train_df, test_df = get_train_test_splits(data=cleaned, how=SPLIT_TYPE.INTRA_USER, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "da30bcdc", + "metadata": {}, + "outputs": [], + "source": [ + "train_df, test_df = preprocess(drop_columns(train_df)), preprocess(drop_columns(test_df))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "78683828", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(train_df.columns == test_df.columns).all()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "cbb81799", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "def normalize_data(\n", + " df: pd.DataFrame, split: SPLIT, section_scalers: dict = None, scaler: StandardScaler = None\n", + "):\n", + " # We start by normalizing the temporal features first. This has to be done carefully.\n", + " \n", + " if split == SPLIT.TRAIN:\n", + " \n", + " mode_dict = dict()\n", + "\n", + " for _, row in df[['modes', 'durations', 'distances', 'mph']].iterrows():\n", + "\n", + " for (mode, duration, distance, mph) in zip(\n", + " row['modes'], row['durations'], row['distances'], row['mph']\n", + " ):\n", + " if mode not in mode_dict:\n", + " mode_dict[mode] = {\n", + " 'duration': np.array([duration]), \n", + " 'distance': np.array([distance]),\n", + " 'mph': np.array([mph])\n", + " }\n", + " else:\n", + " mode_dict[mode] = {\n", + " 'duration': np.append(mode_dict[mode]['duration'], duration),\n", + " 'distance': np.append(mode_dict[mode]['distance'], distance),\n", + " 'mph': np.append(mode_dict[mode]['mph'], mph)\n", + " }\n", + "\n", + " section_scalers = dict()\n", + "\n", + " for mode in mode_dict.keys():\n", + " # Fit, but don't transform. Instead, get the mean and scale.\n", + " section_scalers[mode] = {'duration': dict(), 'distance': dict(), 'mph': dict()}\n", + " \n", + " section_scalers[mode]['duration']['mean'] = mode_dict[mode]['duration'].mean()\n", + " section_scalers[mode]['duration']['std'] = mode_dict[mode]['duration'].std()\n", + " \n", + " section_scalers[mode]['distance']['mean'] = mode_dict[mode]['distance'].mean()\n", + " section_scalers[mode]['distance']['std'] = mode_dict[mode]['distance'].std()\n", + " \n", + " section_scalers[mode]['mph']['mean'] = mode_dict[mode]['mph'].mean()\n", + " section_scalers[mode]['mph']['std'] = mode_dict[mode]['mph'].std()\n", + " \n", + " # Normalize the temporal features.\n", + " rows = []\n", + " for _, row in df.iterrows():\n", + " row_dict = row.to_dict()\n", + " for ix, mode in enumerate(row_dict['modes']):\n", + " row_dict['durations'][ix] = (\n", + " row_dict['durations'][ix] - section_scalers[mode]['duration']['mean']\n", + " )/section_scalers[mode]['duration']['std']\n", + " \n", + " row_dict['distances'][ix] = (\n", + " row_dict['distances'][ix] - section_scalers[mode]['distance']['mean']\n", + " )/section_scalers[mode]['distance']['std']\n", + " \n", + " row_dict['mph'][ix] = (\n", + " row_dict['mph'][ix] - section_scalers[mode]['mph']['mean']\n", + " )/section_scalers[mode]['mph']['std']\n", + " \n", + " rows.append(row_dict)\n", + " \n", + " df = pd.DataFrame(rows)\n", + " \n", + " # -----------------------------------------\n", + " # Now, we start with the regular features.\n", + " # -----------------------------------------\n", + " \n", + " required_features = [\n", + " 'income_category', 'n_motor_vehicles', 'n_residence_members', 'n_residents_u18',\n", + " 'n_residents_with_license', 'distance_miles', 'age', 'n_working_residents', \n", + " 'start:sin_HOD', 'start:cos_HOD', 'end:sin_HOD', 'end:cos_HOD', \n", + " 'temperature_2m (°F)', 'relative_humidity_2m (%)', 'dew_point_2m (°F)', \n", + " 'rain (inch)', 'snowfall (inch)', 'cloud_cover (%)', 'wind_speed_10m (mp/h)',\n", + " 'wind_gusts_10m (mp/h)', 'cost_p_micro', 'cost_no_trip', 'cost_s_car', \n", + " 'cost_transit', 'cost_car', 'cost_s_micro', 'cost_ridehail', 'cost_walk', 'cost_unknown',\n", + " ]\n", + " \n", + " if split == SPLIT.TRAIN:\n", + " scaler = StandardScaler()\n", + " \n", + " t_df = pd.DataFrame(\n", + " scaler.fit_transform(df[required_features]),\n", + " columns=required_features,\n", + " index=df.index\n", + " )\n", + " else:\n", + " t_df = pd.DataFrame(\n", + " scaler.transform(df[required_features]),\n", + " columns=required_features,\n", + " index=df.index\n", + " )\n", + " \n", + " df.drop(columns=required_features, inplace=True)\n", + " \n", + " return pd.concat([df, t_df], axis=1), section_scalers, scaler" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "9b2ab162", + "metadata": {}, + "outputs": [], + "source": [ + "train_df, section_scaler, scaler = normalize_data(train_df, SPLIT.TRAIN)\n", + "test_df, _, _ = normalize_data(test_df, SPLIT.TEST, section_scaler, scaler)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "df774dd9", + "metadata": {}, + "outputs": [], + "source": [ + "train_df.reset_index(drop=True, inplace=True)\n", + "test_df.reset_index(drop=True, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "5ce6bafe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final features for training:\n", + "['is_student', 'is_overnight_trip', 'is_male', 'mph', 'chosen', 'av_no_trip', 'av_s_car', 'av_p_micro', 'av_walk', 'av_s_micro', 'av_transit', 'av_ridehail', 'av_car', 'av_unknown', 'distances', 'durations', 'modes', 'income_category', 'n_motor_vehicles', 'n_residence_members', 'n_residents_u18', 'n_residents_with_license', 'distance_miles', 'age', 'n_working_residents', 'start:sin_HOD', 'start:cos_HOD', 'end:sin_HOD', 'end:cos_HOD', 'temperature_2m (°F)', 'relative_humidity_2m (%)', 'dew_point_2m (°F)', 'rain (inch)', 'snowfall (inch)', 'cloud_cover (%)', 'wind_speed_10m (mp/h)', 'wind_gusts_10m (mp/h)', 'cost_p_micro', 'cost_no_trip', 'cost_s_car', 'cost_transit', 'cost_car', 'cost_s_micro', 'cost_ridehail', 'cost_walk', 'cost_unknown']\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " is_student is_overnight_trip is_male \\\n", + "0 0 0.0 1 \n", + "1 0 0.0 1 \n", + "2 0 0.0 1 \n", + "3 0 0.0 1 \n", + "4 1 0.0 0 \n", + "\n", + " mph chosen av_no_trip av_s_car \\\n", + "0 [-0.6226942218711282] 8 0 0 \n", + "1 [-0.32572174462565845] 8 0 1 \n", + "2 [1.2722796444295434] 1 0 1 \n", + "3 [1.785796573896156] 2 0 0 \n", + "4 [-0.12709085963426053, -0.8967034453081592] 1 0 1 \n", + "\n", + " av_p_micro av_walk av_s_micro ... wind_gusts_10m (mp/h) cost_p_micro \\\n", + "0 0 1 0 ... -1.118155 0.0 \n", + "1 1 0 0 ... -0.598757 0.0 \n", + "2 1 1 0 ... 0.046555 0.0 \n", + "3 1 0 0 ... 1.305702 0.0 \n", + "4 1 1 0 ... -0.457104 0.0 \n", + "\n", + " cost_no_trip cost_s_car cost_transit cost_car cost_s_micro \\\n", + "0 0.0 -0.614415 -0.286713 -0.550431 -0.431234 \n", + "1 0.0 0.102862 -0.529027 0.167956 -0.431234 \n", + "2 0.0 -0.409306 -0.304397 -0.550431 -0.431234 \n", + "3 0.0 -0.614415 -0.529027 -0.550431 -0.431234 \n", + "4 0.0 -0.177887 -0.529027 -0.113227 -0.431234 \n", + "\n", + " cost_ridehail cost_walk cost_unknown \n", + "0 -0.592465 0.0 0.0 \n", + "1 0.128516 0.0 0.0 \n", + "2 -0.386296 0.0 0.0 \n", + "3 -0.592465 0.0 0.0 \n", + "4 -0.153682 0.0 0.0 \n", + "\n", + "[5 rows x 46 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(f\"Final features for training:\\n{list(train_df.columns)}\")\n", + "display(train_df.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "97a8d6b2", + "metadata": {}, + "outputs": [], + "source": [ + "class CustomDataset(Dataset):\n", + " def __init__(self, df: pd.DataFrame):\n", + " self.seq_df = df[['modes', 'durations', 'distances', 'mph']]\n", + " self.chosen = df['chosen']\n", + " self.features = df.drop(columns=[\n", + " 'modes', 'durations', 'distances', 'mph', 'chosen', 'distance_miles'\n", + " ])\n", + " \n", + " # Start from 1 so that 0 becomes padding.\n", + " self.modes_dict = {\n", + " m: ix+1 for (ix, m) in enumerate(['walking', 'car', 'train', 'bus', 'bicycling', 'no_sensed'])\n", + " }\n", + " \n", + " def __len__(self):\n", + " return self.seq_df.shape[0]\n", + " \n", + " def __getitem__(self, ix):\n", + " \n", + " t_modes = list()\n", + " t_metrics = list()\n", + " \n", + " chosen = self.chosen[ix]\n", + " seq = self.seq_df.loc[ix, :]\n", + " \n", + " # OHE the target.\n", + " y = F.one_hot(torch.tensor(chosen), num_classes=9)\n", + " \n", + " # Parse the sequence.\n", + " for seq_ix in range(len(seq['modes'])):\n", + " mode_ix = self.modes_dict[seq['modes'][seq_ix]]\n", + " \n", + " t_modes.append(torch.tensor(mode_ix))\n", + " \n", + " # distances, durations.\n", + " seq_vector = torch.cat(\n", + " [\n", + " torch.tensor([seq['durations'][seq_ix]]), \n", + " torch.tensor([seq['distances'][seq_ix]]), \n", + " torch.tensor([seq['mph'][seq_ix]])\n", + " ], dim=-1\n", + " )\n", + " \n", + " t_metrics.append(seq_vector)\n", + " \n", + " # Flat feature vector.\n", + " features = torch.tensor(self.features.loc[ix, :].values)\n", + " \n", + " return [torch.stack(t_modes).long(), torch.stack(t_metrics), features], y" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "02b78758", + "metadata": {}, + "outputs": [], + "source": [ + "train_dset = CustomDataset(train_df)\n", + "test_dset = CustomDataset(test_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "627b6fa4", + "metadata": {}, + "outputs": [], + "source": [ + "def collate(batch):\n", + " X, y = zip(*batch)\n", + " \n", + " seq_modes = [x[0] for x in X]\n", + " seq_metrics = [x[1] for x in X]\n", + " features = [x[-1] for x in X]\n", + "\n", + " padded_seq = pad_sequence([s for s in seq_modes], batch_first=True)\n", + " padded_metrics = pad_sequence([m for m in seq_metrics], batch_first=True)\n", + " lengths = [len(seq) for seq in seq_modes]\n", + " stacked_features = torch.stack(features)\n", + "\n", + " return (padded_seq, padded_metrics, stacked_features), torch.stack(y), lengths" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "5ca34681", + "metadata": {}, + "outputs": [], + "source": [ + "train_loader = DataLoader(train_dset, batch_size=16, collate_fn=collate, shuffle=True, drop_last=False)\n", + "test_loader = DataLoader(test_dset, batch_size=8, collate_fn=collate, shuffle=False, drop_last=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "31ca5ab1", + "metadata": {}, + "outputs": [], + "source": [ + "(modes, metrics, features), sY1, lX = next(iter(train_loader))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "9eb5a93a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(torch.Size([16, 6, 3]), torch.Size([16, 6]))" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metrics.size(), modes.size()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "a0abf380", + "metadata": {}, + "outputs": [], + "source": [ + "# Set to 0 for no dropout.\n", + "DROPOUT = 0." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "48871ea4", + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "\n", + "def new_gelu(x):\n", + " \"\"\"\n", + " Taken from OpenAI GPT-2 implementation.\n", + " \"\"\"\n", + " return 0.5 * x * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))\n", + "\n", + "\n", + "class DilatedBlock(nn.Module):\n", + " def __init__(self, n_c):\n", + " super(DilatedBlock, self).__init__()\n", + " \n", + " self.block = nn.Sequential(\n", + " nn.Linear(n_c, 4*n_c, bias=False),\n", + " nn.ELU(),\n", + " nn.Linear(4*n_c, n_c, bias=False),\n", + " nn.Dropout(DROPOUT)\n", + " )\n", + " \n", + " def forward(self, x):\n", + " return self.block(x)\n", + "\n", + " \n", + "class SelfAttention(nn.Module):\n", + " def __init__(self, n_features, head_size):\n", + " super(SelfAttention, self).__init__()\n", + " # in: (B, F, 64)\n", + " self.k = nn.Linear(n_features, head_size, bias=False)\n", + " self.q = nn.Linear(n_features, head_size, bias=False)\n", + " self.v = nn.Linear(n_features, head_size, bias=False)\n", + " self.dpt = nn.Dropout(DROPOUT)\n", + " self.sqrt_d = torch.sqrt(torch.tensor(head_size))\n", + " \n", + " def forward(self, x):\n", + " k = self.k(x)\n", + " q = self.q(x)\n", + " v = self.v(x)\n", + " \n", + " # Q.K.t\n", + " dot = torch.bmm(q, k.permute(0, 2, 1))\n", + " \n", + " # normalize dot product.\n", + " dot /= self.sqrt_d\n", + " \n", + " # softmax over -1 dim.\n", + " softmax = self.dpt(torch.softmax(dot, dim=-1))\n", + " \n", + " # dot with values. (B, F, F) * (B, F, x) = (B, F, x)\n", + " return torch.bmm(softmax, v)\n", + " \n", + "\n", + "class MultiHeadAttention(nn.Module):\n", + " def __init__(self, n_heads, n_dim):\n", + " super(MultiHeadAttention, self).__init__()\n", + " \n", + " # 64 dims, 4 heads => 16 dims per head.\n", + " head_size = n_dim//n_heads\n", + " self.heads = nn.ModuleList([SelfAttention(n_dim, head_size) for _ in range(n_heads)])\n", + " self.proj = nn.Linear(n_dim, n_dim, bias=False)\n", + " \n", + " def forward(self, x):\n", + " # x is (B, seq, n_dim)\n", + " cat = torch.cat([head(x) for head in self.heads], dim=-1)\n", + " return self.proj(cat)\n", + "\n", + "\n", + "class Block(nn.Module):\n", + " def __init__(self, n_c):\n", + " super(Block, self).__init__()\n", + " \n", + " self.sa = MultiHeadAttention(n_heads=4, n_dim=n_c)\n", + " self.dilated = DilatedBlock(n_c)\n", + " self.ln1 = nn.LayerNorm(n_c)\n", + " self.ln2 = nn.LayerNorm(n_c)\n", + " \n", + " \n", + " def forward(self, x):\n", + " x = x + self.sa(self.ln1(x))\n", + " x = x + self.dilated(self.ln2(x))\n", + " return x\n", + " \n", + "\n", + "class LSTMLayer(nn.Module):\n", + " def __init__(\n", + " self, input_size: int, hidden_size: int, \n", + " output_size: int, n_lstm_layers: int = 1\n", + " ):\n", + " super(LSTMLayer, self).__init__()\n", + " \n", + " self.hidden_size = hidden_size\n", + " self.embedding = nn.Embedding(7, 4, padding_idx=0)\n", + " self.dpt = nn.Dropout(DROPOUT)\n", + " \n", + " self.lstm = nn.LSTM(\n", + " input_size=input_size + 4,\n", + " hidden_size=hidden_size,\n", + " bias=False,\n", + " bidirectional=True,\n", + " batch_first=True,\n", + " num_layers=n_lstm_layers\n", + " )\n", + " \n", + " def forward(self, modes, x, lengths):\n", + " mode_emb = self.embedding(modes)\n", + " x = torch.cat([x, mode_emb], dim=-1)\n", + " \n", + " packed = pack_padded_sequence(x, lengths, batch_first=True, enforce_sorted=False)\n", + " out, _ = self.lstm(packed)\n", + " unpacked, _ = pad_packed_sequence(out, batch_first=True)\n", + " \n", + " return self.dpt(unpacked)\n", + "\n", + "\n", + "class Model(nn.Module):\n", + " def __init__(\n", + " self, input_size: int, hidden_size: int, output_size: int, \n", + " n_features: int, n_lstm_layers: int = 1, **kwargs\n", + " ):\n", + " super(Model, self).__init__()\n", + " \n", + " block1_ip_dim = hidden_size*2\n", + " block2_ip_dim = (hidden_size*2) + n_features\n", + " \n", + " self.lstm = LSTMLayer(\n", + " input_size, hidden_size, \n", + " output_size, n_lstm_layers\n", + " )\n", + " \n", + " self.block_l1 = nn.ModuleList([Block(block1_ip_dim) for _ in range(kwargs['l1_blocks'])])\n", + " self.block_l2 = nn.ModuleList([Block(block2_ip_dim) for _ in range(kwargs['l2_blocks'])])\n", + " self.final_proj = nn.Linear(block2_ip_dim, output_size, bias=True)\n", + " \n", + " def forward(self, modes, x, features, lengths):\n", + " \n", + " b = x.size(0)\n", + " \n", + " # Out = (B, seq, hidden*2)\n", + " lstm_out = self.lstm(modes, x, lengths)\n", + " \n", + " # Pass the raw output through the blocks.\n", + " for module in self.block_l1:\n", + " lstm_out = module(lstm_out)\n", + " \n", + " features_rshp = features.unsqueeze(1).expand(b, lstm_out.size(1), -1)\n", + " \n", + " # Out = (B, seq, n+40)\n", + " cat = torch.cat([lstm_out, features_rshp], dim=-1)\n", + " \n", + " for module in self.block_l2:\n", + " cat = module(cat)\n", + " \n", + " # (8, 3, 104) -> (B, 104)\n", + " # flattened = cat.view(b, -1)\n", + " \n", + " # proj = self.runtime_ffw(flattened.size(-1), 64)(flattened)\n", + " proj = cat.mean(dim=1)\n", + " \n", + " return self.final_proj(proj)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "70b4d4ea", + "metadata": {}, + "outputs": [], + "source": [ + "import torch.nn.init as init\n", + "\n", + "def init_weights(module):\n", + " if isinstance(module, nn.Embedding):\n", + " module.weight.data.normal_(mean=0.0, std=1.0)\n", + " if module.padding_idx is not None:\n", + " module.weight.data[module.padding_idx].zero_()\n", + " elif isinstance(module, nn.LayerNorm):\n", + " module.bias.data.zero_()\n", + " module.weight.data.fill_(1.0)\n", + " elif isinstance(module, nn.BatchNorm1d):\n", + " init.normal_(m.weight.data, mean=1, std=0.02)\n", + " init.constant_(m.bias.data, 0)\n", + " elif isinstance(module, nn.Linear):\n", + " init.xavier_normal_(module.weight.data)\n", + " if module.bias is not None:\n", + " init.normal_(module.bias.data)\n", + " elif isinstance(module, nn.LSTM):\n", + " for param in module.parameters():\n", + " if len(param.shape) >= 2:\n", + " init.orthogonal_(param.data)\n", + " else:\n", + " init.normal_(param.data)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "282ecd1a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model(\n", + " (lstm): LSTMLayer(\n", + " (embedding): Embedding(7, 4, padding_idx=0)\n", + " (dpt): Dropout(p=0.0, inplace=False)\n", + " (lstm): LSTM(7, 16, bias=False, batch_first=True, bidirectional=True)\n", + " )\n", + " (block_l1): ModuleList(\n", + " (0): Block(\n", + " (sa): MultiHeadAttention(\n", + " (heads): ModuleList(\n", + " (0): SelfAttention(\n", + " (k): Linear(in_features=32, out_features=8, bias=False)\n", + " (q): Linear(in_features=32, out_features=8, bias=False)\n", + " (v): Linear(in_features=32, out_features=8, bias=False)\n", + " (dpt): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (1): SelfAttention(\n", + " (k): Linear(in_features=32, out_features=8, bias=False)\n", + " (q): Linear(in_features=32, out_features=8, bias=False)\n", + " (v): Linear(in_features=32, out_features=8, bias=False)\n", + " (dpt): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (2): SelfAttention(\n", + " (k): Linear(in_features=32, out_features=8, bias=False)\n", + " (q): Linear(in_features=32, out_features=8, bias=False)\n", + " (v): Linear(in_features=32, out_features=8, bias=False)\n", + " (dpt): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (3): SelfAttention(\n", + " (k): Linear(in_features=32, out_features=8, bias=False)\n", + " (q): Linear(in_features=32, out_features=8, bias=False)\n", + " (v): Linear(in_features=32, out_features=8, bias=False)\n", + " (dpt): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " (proj): Linear(in_features=32, out_features=32, bias=False)\n", + " )\n", + " (dilated): DilatedBlock(\n", + " (block): Sequential(\n", + " (0): Linear(in_features=32, out_features=128, bias=False)\n", + " (1): ELU(alpha=1.0)\n", + " (2): Linear(in_features=128, out_features=32, bias=False)\n", + " (3): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " (ln1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)\n", + " (ln2): LayerNorm((32,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (1): Block(\n", + " (sa): MultiHeadAttention(\n", + " (heads): ModuleList(\n", + " (0): SelfAttention(\n", + " (k): Linear(in_features=32, out_features=8, bias=False)\n", + " (q): Linear(in_features=32, out_features=8, bias=False)\n", + " (v): Linear(in_features=32, out_features=8, bias=False)\n", + " (dpt): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (1): SelfAttention(\n", + " (k): Linear(in_features=32, out_features=8, bias=False)\n", + " (q): Linear(in_features=32, out_features=8, bias=False)\n", + " (v): Linear(in_features=32, out_features=8, bias=False)\n", + " (dpt): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (2): SelfAttention(\n", + " (k): Linear(in_features=32, out_features=8, bias=False)\n", + " (q): Linear(in_features=32, out_features=8, bias=False)\n", + " (v): Linear(in_features=32, out_features=8, bias=False)\n", + " (dpt): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (3): SelfAttention(\n", + " (k): Linear(in_features=32, out_features=8, bias=False)\n", + " (q): Linear(in_features=32, out_features=8, bias=False)\n", + " (v): Linear(in_features=32, out_features=8, bias=False)\n", + " (dpt): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " (proj): Linear(in_features=32, out_features=32, bias=False)\n", + " )\n", + " (dilated): DilatedBlock(\n", + " (block): Sequential(\n", + " (0): Linear(in_features=32, out_features=128, bias=False)\n", + " (1): ELU(alpha=1.0)\n", + " (2): Linear(in_features=128, out_features=32, bias=False)\n", + " (3): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " (ln1): LayerNorm((32,), eps=1e-05, elementwise_affine=True)\n", + " (ln2): LayerNorm((32,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (2): Block(\n", + " (sa): MultiHeadAttention(\n", + " (heads): ModuleList(\n", + " (0): SelfAttention(\n", + " (k): Linear(in_features=32, out_features=8, bias=False)\n", + " (q): Linear(in_features=32, out_features=8, bias=False)\n", + " (v): Linear(in_features=32, out_features=8, bias=False)\n", + " (dpt): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (1): SelfAttention(\n", + " (k): Linear(in_features=32, out_features=8, bias=False)\n", + " (q): 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LayerNorm((72,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (5): Block(\n", + " (sa): MultiHeadAttention(\n", + " (heads): ModuleList(\n", + " (0): SelfAttention(\n", + " (k): Linear(in_features=72, out_features=18, bias=False)\n", + " (q): Linear(in_features=72, out_features=18, bias=False)\n", + " (v): Linear(in_features=72, out_features=18, bias=False)\n", + " (dpt): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (1): SelfAttention(\n", + " (k): Linear(in_features=72, out_features=18, bias=False)\n", + " (q): Linear(in_features=72, out_features=18, bias=False)\n", + " (v): Linear(in_features=72, out_features=18, bias=False)\n", + " (dpt): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (2): SelfAttention(\n", + " (k): Linear(in_features=72, out_features=18, bias=False)\n", + " (q): Linear(in_features=72, out_features=18, bias=False)\n", + " (v): Linear(in_features=72, out_features=18, bias=False)\n", + " (dpt): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (3): SelfAttention(\n", + " (k): Linear(in_features=72, out_features=18, bias=False)\n", + " (q): Linear(in_features=72, out_features=18, bias=False)\n", + " (v): Linear(in_features=72, out_features=18, bias=False)\n", + " (dpt): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " (proj): Linear(in_features=72, out_features=72, bias=False)\n", + " )\n", + " (dilated): DilatedBlock(\n", + " (block): Sequential(\n", + " (0): Linear(in_features=72, out_features=288, bias=False)\n", + " (1): ELU(alpha=1.0)\n", + " (2): Linear(in_features=288, out_features=72, bias=False)\n", + " (3): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " (ln1): LayerNorm((72,), eps=1e-05, elementwise_affine=True)\n", + " (ln2): LayerNorm((72,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " )\n", + " (final_proj): Linear(in_features=72, out_features=9, bias=True)\n", + ")\n" + ] + } + ], + "source": [ + "model = Model(\n", + " n_lstm_layers=1,\n", + " input_size=3,\n", + " hidden_size=16, \n", + " output_size=9,\n", + " n_features=40,\n", + " l1_blocks=6,\n", + " l2_blocks=6\n", + ")\n", + "\n", + "model = model.apply(init_weights)\n", + "\n", + "print(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "20fec22b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "453101\n" + ] + } + ], + "source": [ + "print(sum(p.numel() for p in model.parameters()))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "1ca4b65a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1.31637209 0.45785206 0.97595891 2.59617829 0.35062587 72.2209596\n", + " 4.71122642 1.08826104 1.53996715]\n" + ] + } + ], + "source": [ + "weights = train_df.shape[0]/(np.bincount(train_df.chosen.values) * len(np.unique(train_df.chosen)))\n", + "\n", + "print(weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "e7a2017b", + "metadata": {}, + "outputs": [], + "source": [ + "INIT_LR = 7e-4\n", + "optimizer = optim.Adam(model.parameters(), lr=INIT_LR)\n", + "criterion = nn.CrossEntropyLoss(weight=torch.Tensor(weights))" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "c6f1f235", + "metadata": {}, + "outputs": [], + "source": [ + "# sX, sY, sL = next(iter(train_loader))\n", + "\n", + "# print(sX.size())" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "e53e4fd1", + "metadata": {}, + "outputs": [], + "source": [ + "def train(epoch, model, loader, opt, criterion):\n", + " \n", + " print(\"\\tBeginning training.\")\n", + " \n", + " model.train()\n", + " \n", + " print_every = len(loader)//5\n", + " \n", + " losses = []\n", + " \n", + " for ix, (X, y, lengths) in enumerate(loader):\n", + " \n", + " modes, metrics, features = X\n", + " y = y.float()\n", + "\n", + " opt.zero_grad()\n", + "\n", + " y_pred = model(modes, metrics.float(), features.float(), lengths)\n", + "\n", + " loss = criterion(y_pred.view(-1, 9), y.view(-1, 9))\n", + "\n", + " losses.append(loss.item())\n", + " \n", + " if ix and ix % print_every == 0:\n", + " print(f\"\\t-> Average loss: {np.nanmean(losses)}\")\n", + "\n", + " loss.backward()\n", + "\n", + " optimizer.step()\n", + "\n", + " print(50*'-')" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "3a33fefa", + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate(epoch, model, loader, criterion):\n", + " \n", + " print(\"\\tBeginning evaluation.\")\n", + " \n", + " model.eval()\n", + " \n", + " print_every = len(loader)//5\n", + " \n", + " losses = []\n", + " \n", + " for ix, (X, y, lengths) in enumerate(loader):\n", + " \n", + " modes, metrics, features = X\n", + "\n", + " y_pred = model(modes, metrics.float(), features.float(), lengths)\n", + " y = y.float()\n", + " \n", + " loss = criterion(y_pred.view(-1, 9), y.view(-1, 9))\n", + "\n", + " losses.append(loss.item())\n", + " \n", + " if ix and ix % print_every == 0:\n", + " print(f\"\\t -> Average loss: {np.nanmean(losses)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "650a5240", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import f1_score\n", + "\n", + "\n", + "def evaluate_f1(model, tr_loader, te_loader):\n", + " tr_preds, te_preds = np.array([]), np.array([])\n", + " tr_gt, te_gt = np.array([]), np.array([])\n", + " \n", + " model.eval()\n", + " print(\"\\tEvaluating F1...\")\n", + " \n", + " with torch.no_grad():\n", + " for ix, (X, y, lengths) in enumerate(tr_loader):\n", + " \n", + " modes, metrics, features = X\n", + "\n", + " y_pred = model(modes, metrics.float(), features.float(), lengths).view(-1, 9)\n", + " y = y.float().view(-1, 9)\n", + "\n", + " preds = torch.argmax(F.softmax(y_pred, dim=-1), dim=-1).numpy().ravel()\n", + " true = torch.argmax(y.long(), dim=-1).numpy().ravel()\n", + "\n", + " tr_preds = np.append(tr_preds, preds)\n", + " tr_gt = np.append(tr_gt, true)\n", + " \n", + " tr_f1 = f1_score(y_true=tr_gt, y_pred=tr_preds, average='weighted')\n", + " print(f\"\\t -> Train F1: {tr_f1}\")\n", + " \n", + " for ix, (X, y, lengths) in enumerate(te_loader):\n", + " \n", + " modes, metrics, features = X\n", + "\n", + " y_pred = model(modes, metrics.float(), features.float(), lengths).view(-1, 9)\n", + " y = y.float().view(-1, 9)\n", + " \n", + " preds = torch.argmax(F.softmax(y_pred, dim=-1), dim=-1).numpy().ravel()\n", + " true = torch.argmax(y.long(), dim=-1).numpy().ravel()\n", + "\n", + " te_preds = np.append(te_preds, preds)\n", + " te_gt = np.append(te_gt, true)\n", + " \n", + " te_f1 = f1_score(y_true=te_gt, y_pred=te_preds, average='weighted')\n", + " print(f\"\\t -> Test F1: {te_f1}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "7191e78b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 - 5 :: 0.00070\n", + "5 - 10 :: 0.00067\n", + "10 - 15 :: 0.00063\n", + "15 - 20 :: 0.00060\n", + "20 - 25 :: 0.00057\n", + "25 - 30 :: 0.00054\n" + ] + } + ], + "source": [ + "# Other training hyperparameters.\n", + "num_epochs = 30\n", + "num_decays = 6\n", + "decay_at = num_epochs // num_decays\n", + "decay = 0.95\n", + "\n", + "# Just checking what LRs should be after decaying.\n", + "for power in range(num_decays):\n", + " print(f\"{decay_at * power} - {decay_at * (power + 1)} :: {INIT_LR * decay**power:.5f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc4b72de", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1:\n", + "\tBeginning training.\n", + "\t-> Average loss: 3.4292205003553264\n", + "\t-> Average loss: 3.0203611136815667\n", + "\t-> Average loss: 2.9269992429190226\n", + "\t-> Average loss: 2.7440315224998524\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.9845308899546468\n", + "\t -> Average loss: 2.0912061837169675\n", + "\t -> Average loss: 2.0278757847845554\n", + "\t -> Average loss: 2.079901454872481\n", + "\t -> Average loss: 2.119112247215273\n", + "\tEvaluating F1...\n", + "\t -> Train F1: 0.17357571210719355\n", + "\t -> Test F1: 0.16929205554584628\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 2:\n", + "\tBeginning training.\n", + "\t-> Average loss: 2.2447872489024805\n", + "\t-> Average loss: 2.179984362536923\n", + "\t-> Average loss: 2.184303961117914\n", + "\t-> Average loss: 2.1579395363231675\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.8604771541483576\n", + "\t -> Average loss: 1.96936972224629\n", + "\t -> Average loss: 1.9072258116594\n", + "\t -> Average loss: 1.9617513231750632\n", + "\t -> Average loss: 1.9915817378616547\n", + "\tEvaluating F1...\n", + "\t -> Train F1: 0.2375672788266048\n", + "\t -> Test F1: 0.23161577435832603\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 3:\n", + "\tBeginning training.\n", + "\t-> Average loss: 2.009245126523785\n", + "\t-> Average loss: 1.9820919889074202\n", + "\t-> Average loss: 2.020936541432881\n", + "\t-> Average loss: 1.9902592548037359\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.948894374363915\n", + "\t -> Average loss: 2.0106319545865894\n", + "\t -> Average loss: 1.9851899344418475\n", + "\t -> Average loss: 2.007645890627648\n", + "\t -> Average loss: 1.9973548486545132\n", + "\tEvaluating F1...\n", + "\t -> Train F1: 0.178078853483658\n", + "\t -> Test F1: 0.17889559699341365\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 4:\n", + "\tBeginning training.\n", + "\t-> Average loss: 1.9483501227707836\n", + "\t-> Average loss: 1.954028883528493\n", + "\t-> Average loss: 1.9755866212591542\n", + "\t-> Average loss: 1.923766012835111\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.8338173299861353\n", + "\t -> Average loss: 1.917397732834716\n", + "\t -> Average loss: 1.8882776866875477\n", + "\t -> Average loss: 1.9411674299967547\n", + "\t -> Average loss: 1.9390272717393124\n", + "\tEvaluating F1...\n", + "\t -> Train F1: 0.2937676034584698\n", + "\t -> Test F1: 0.2797533840531726\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 5:\n", + "\tBeginning training.\n", + "\t-> Average loss: 1.912619205160514\n", + "\t-> Average loss: 1.9341210979918848\n", + "\t-> Average loss: 1.8943603618724927\n", + "\t-> Average loss: 1.8774088714056938\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.690006344844509\n", + "\t -> Average loss: 1.7453476386470395\n", + "\t -> Average loss: 1.7205723014229268\n", + "\t -> Average loss: 1.749604014185778\n", + "\t -> Average loss: 1.742094624483038\n", + "\tEvaluating F1...\n", + "\t -> Train F1: 0.19402127381450823\n", + "\t -> Test F1: 0.1897796133960193\n", + "Learning rate is now: 0.00067\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 6:\n", + "\tBeginning training.\n", + "\t-> Average loss: 1.6016220477040253\n", + "\t-> Average loss: 1.7033424217912552\n", + "\t-> Average loss: 1.6674059770536112\n", + "\t-> Average loss: 1.6761879756166485\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.5424185771515915\n", + "\t -> Average loss: 1.579921578193878\n", + "\t -> Average loss: 1.5411476174842065\n", + "\t -> Average loss: 1.5840133090508075\n", + "\t -> Average loss: 1.5650895305837256\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 7:\n", + "\tBeginning training.\n", + "\t-> Average loss: 1.6066383687810526\n", + "\t-> Average loss: 1.5486349471376795\n", + "\t-> Average loss: 1.5466778286405511\n", + "\t-> Average loss: 1.5999376366539295\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.6492426906551063\n", + "\t -> Average loss: 1.664988368999708\n", + "\t -> Average loss: 1.6178990316980366\n", + "\t -> Average loss: 1.6296465851319595\n", + "\t -> Average loss: 1.640547810166173\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 8:\n", + "\tBeginning training.\n", + "\t-> Average loss: 1.4707723498011434\n", + "\t-> Average loss: 1.5709665634680428\n", + "\t-> Average loss: 1.554789892461182\n", + "\t-> Average loss: 1.5148731287814903\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.3899180381324705\n", + "\t -> Average loss: 1.4627432638531799\n", + "\t -> Average loss: 1.4123834414101804\n", + "\t -> Average loss: 1.4201137627063722\n", + "\t -> Average loss: 1.4303626401260212\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 9:\n", + "\tBeginning training.\n", + "\t-> Average loss: 1.5543432552471508\n", + "\t-> Average loss: 1.4533692163074874\n", + "\t-> Average loss: 1.3998552637881245\n", + "\t-> Average loss: 1.4387366909199102\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.3969969056005584\n", + "\t -> Average loss: 1.4605997060562348\n", + "\t -> Average loss: 1.4105439760458114\n", + "\t -> Average loss: 1.4442899707441483\n", + "\t -> Average loss: 1.4501932569907734\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 10:\n", + "\tBeginning training.\n", + "\t-> Average loss: 1.4883403652789873\n", + "\t-> Average loss: 1.4671129221103143\n", + "\t-> Average loss: 1.4545180360540984\n", + "\t-> Average loss: 1.4436850206850294\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.508042848642978\n", + "\t -> Average loss: 1.502184015554148\n", + "\t -> Average loss: 1.44931043248012\n", + "\t -> Average loss: 1.4784588873428093\n", + "\t -> Average loss: 1.4841084074967386\n", + "\tEvaluating F1...\n", + "\t -> Train F1: 0.48333374111246125\n", + "\t -> Test F1: 0.4722792652308931\n", + "Learning rate is now: 0.00063\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 11:\n", + "\tBeginning training.\n", + "\t-> Average loss: 1.4074798150185766\n", + "\t-> Average loss: 1.3590504143056463\n", + "\t-> Average loss: 1.3730208680991913\n", + "\t-> Average loss: 1.4093443684042937\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.65324043395133\n", + "\t -> Average loss: 1.6848366409748585\n", + "\t -> Average loss: 1.639395641301995\n", + "\t -> Average loss: 1.626926458699458\n", + "\t -> Average loss: 1.6171495253501786\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 12:\n", + "\tBeginning training.\n", + "\t-> Average loss: 1.31716444697127\n", + "\t-> Average loss: 1.2871220497340277\n", + "\t-> Average loss: 1.2746168654707137\n", + "\t-> Average loss: 1.2706415657187125\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.2732290915270756\n", + "\t -> Average loss: 1.3285720576564748\n", + "\t -> Average loss: 1.2918836618982144\n", + "\t -> Average loss: 1.2952009379613474\n", + "\t -> Average loss: 1.2965014228495078\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 13:\n", + "\tBeginning training.\n", + "\t-> Average loss: 1.2921466177235768\n", + "\t-> Average loss: 1.2243878973021864\n", + "\t-> Average loss: 1.2450620088472837\n", + "\t-> Average loss: 1.251400762683901\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.206730307313983\n", + "\t -> Average loss: 1.2312866071721056\n", + "\t -> Average loss: 1.208183620277959\n", + "\t -> Average loss: 1.2216688226973618\n", + "\t -> Average loss: 1.2299620342197903\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 14:\n", + "\tBeginning training.\n", + "\t-> Average loss: 1.1780336717736788\n", + "\t-> Average loss: 1.2387724560844906\n", + "\t-> Average loss: 1.2171782795114152\n", + "\t-> Average loss: 1.2286130927401546\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.1736671675683399\n", + "\t -> Average loss: 1.214920432480065\n", + "\t -> Average loss: 1.1988129002083816\n", + "\t -> Average loss: 1.1989111086031705\n", + "\t -> Average loss: 1.2194756852068667\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 15:\n", + "\tBeginning training.\n", + "\t-> Average loss: 1.2168475217576133\n", + "\t-> Average loss: 1.1433030896716647\n", + "\t-> Average loss: 1.1906079676311259\n", + "\t-> Average loss: 1.2125332475604398\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.4089278240776595\n", + "\t -> Average loss: 1.445234133480312\n", + "\t -> Average loss: 1.4113176648240926\n", + "\t -> Average loss: 1.4362174157624148\n", + "\t -> Average loss: 1.435766333294876\n", + "\tEvaluating F1...\n", + "\t -> Train F1: 0.5093168030147552\n", + "\t -> Test F1: 0.5026682286547539\n", + "Learning rate is now: 0.00060\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 16:\n", + "\tBeginning training.\n", + "\t-> Average loss: 1.1724162185824782\n", + "\t-> Average loss: 1.1224480470021565\n", + "\t-> Average loss: 1.1285216524044377\n", + "\t-> Average loss: 1.1660368838476076\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.210316137894572\n", + "\t -> Average loss: 1.246378187157891\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t -> Average loss: 1.2420701925267479\n", + "\t -> Average loss: 1.2695545183091919\n", + "\t -> Average loss: 1.273952293582052\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 17:\n", + "\tBeginning training.\n", + "\t-> Average loss: 1.0339172488734043\n", + "\t-> Average loss: 1.0751683378473684\n", + "\t-> Average loss: 1.1208586298338965\n", + "\t-> Average loss: 1.1269230102115642\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.248624034475681\n", + "\t -> Average loss: 1.2845101706423125\n", + "\t -> Average loss: 1.2307171229161877\n", + "\t -> Average loss: 1.2223140466163174\n", + "\t -> Average loss: 1.2198367260838676\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 18:\n", + "\tBeginning training.\n", + "\t-> Average loss: 1.0498548433933843\n", + "\t-> Average loss: 1.0665208559345316\n", + "\t-> Average loss: 1.0762758616007386\n", + "\t-> Average loss: 1.099983853102772\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.3901189185221101\n", + "\t -> Average loss: 1.4441073030024976\n", + "\t -> Average loss: 1.373450560356254\n", + "\t -> Average loss: 1.3496260701087575\n", + "\t -> Average loss: 1.3835973867276747\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 19:\n", + "\tBeginning training.\n", + "\t-> Average loss: 1.146373841807496\n", + "\t-> Average loss: 1.085291807392025\n", + "\t-> Average loss: 1.079380213429688\n", + "\t-> Average loss: 1.1145779568127758\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.263674235169115\n", + "\t -> Average loss: 1.2977842691269788\n", + "\t -> Average loss: 1.243837918641407\n", + "\t -> Average loss: 1.2629515259071111\n", + "\t -> Average loss: 1.2418897094670163\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 20:\n", + "\tBeginning training.\n", + "\t-> Average loss: 0.9654322663665483\n", + "\t-> Average loss: 1.0184031338499278\n", + "\t-> Average loss: 1.0715286856706412\n", + "\t-> Average loss: 1.0910986138052643\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.1675123429165206\n", + "\t -> Average loss: 1.2705587090520591\n", + "\t -> Average loss: 1.200293446571302\n", + "\t -> Average loss: 1.2205115221362584\n", + "\t -> Average loss: 1.2177007432530602\n", + "\tEvaluating F1...\n", + "\t -> Train F1: 0.6190054946992625\n", + "\t -> Test F1: 0.6097765604266691\n", + "Learning rate is now: 0.00057\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 21:\n", + "\tBeginning training.\n", + "\t-> Average loss: 1.0445893098855152\n", + "\t-> Average loss: 1.1112236296230893\n", + "\t-> Average loss: 1.0962648112726123\n", + "\t-> Average loss: 1.089011266353621\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.0674441020165741\n", + "\t -> Average loss: 1.1406326175361245\n", + "\t -> Average loss: 1.1207181137305364\n", + "\t -> Average loss: 1.1347275835346557\n", + "\t -> Average loss: 1.1342128750775322\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 22:\n", + "\tBeginning training.\n", + "\t-> Average loss: 1.0676555513877775\n", + "\t-> Average loss: 1.0773798781600055\n", + "\t-> Average loss: 1.0710756811421827\n", + "\t-> Average loss: 1.0416493509739522\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.1352787101068975\n", + "\t -> Average loss: 1.2286776423037469\n", + "\t -> Average loss: 1.1571767105262225\n", + "\t -> Average loss: 1.1650525711899125\n", + "\t -> Average loss: 1.1702847076456864\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 23:\n", + "\tBeginning training.\n", + "\t-> Average loss: 0.9855309208053783\n", + "\t-> Average loss: 1.0090662437060927\n", + "\t-> Average loss: 1.0291784378419606\n", + "\t-> Average loss: 1.0479850026693265\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.190445274031362\n", + "\t -> Average loss: 1.2569891696834898\n", + "\t -> Average loss: 1.1954538017761573\n", + "\t -> Average loss: 1.2319692372545819\n", + "\t -> Average loss: 1.2384895061925971\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 24:\n", + "\tBeginning training.\n", + "\t-> Average loss: 1.035038524541442\n", + "\t-> Average loss: 1.007654058233497\n", + "\t-> Average loss: 1.0265389064957022\n", + "\t-> Average loss: 1.0250597899920955\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.27851615721287\n", + "\t -> Average loss: 1.3390878153847647\n", + "\t -> Average loss: 1.315815781799159\n", + "\t -> Average loss: 1.2937761211286816\n", + "\t -> Average loss: 1.2826113693858314\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 25:\n", + "\tBeginning training.\n", + "\t-> Average loss: 0.993596878173298\n", + "\t-> Average loss: 1.0079565033431157\n", + "\t-> Average loss: 1.0364318410870301\n", + "\t-> Average loss: 1.032162597538999\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.2048218791378278\n", + "\t -> Average loss: 1.2166278918723126\n", + "\t -> Average loss: 1.1783969860607342\n", + "\t -> Average loss: 1.1786176204535408\n", + "\t -> Average loss: 1.1798632480913287\n", + "\tEvaluating F1...\n", + "\t -> Train F1: 0.6030226790226174\n", + "\t -> Test F1: 0.5861836286317499\n", + "Learning rate is now: 0.00054\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 26:\n", + "\tBeginning training.\n", + "\t-> Average loss: 0.9192066853189601\n", + "\t-> Average loss: 1.0098440380341351\n", + "\t-> Average loss: 1.006183010403619\n", + "\t-> Average loss: 1.0023870221366835\n", + "--------------------------------------------------\n", + "\tBeginning evaluation.\n", + "\t -> Average loss: 1.1226611448316601\n", + "\t -> Average loss: 1.1899717054792218\n", + "\t -> Average loss: 1.124479945067928\n", + "\t -> Average loss: 1.1427228787120112\n", + "\t -> Average loss: 1.1324994321057338\n", + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "Epoch 27:\n", + "\tBeginning training.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR:root:Internal Python error in the inspect module.\n", + "Below is the traceback from this internal error.\n", + "\n", + "ERROR:root:Internal Python error in the inspect module.\n", + "Below is the traceback from this internal error.\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/654011310.py\", line 5, in \n", + " train(epoch_ix, model, train_loader, optimizer, criterion)\n", + " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/2806942638.py\", line 18, in train\n", + " y_pred = model(modes, metrics.float(), features.float(), lengths)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", + " return forward_call(*input, **kwargs)\n", + " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 143, in forward\n", + " lstm_out = module(lstm_out)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", + " return forward_call(*input, **kwargs)\n", + " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 79, in forward\n", + " x = x + self.sa(self.ln1(x))\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", + " return forward_call(*input, **kwargs)\n", + " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 64, in forward\n", + " cat = torch.cat([head(x) for head in self.heads], dim=-1)\n", + " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 64, in \n", + " cat = torch.cat([head(x) for head in self.heads], dim=-1)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", + " return forward_call(*input, **kwargs)\n", + " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 37, in forward\n", + " q = self.q(x)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", + " return forward_call(*input, **kwargs)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/linear.py\", line 114, in forward\n", + " return F.linear(input, self.weight, self.bias)\n", + "KeyboardInterrupt\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n", + " stb = value._render_traceback_()\n", + "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1101, in get_records\n", + " return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 248, in wrapped\n", + " return f(*args, **kwargs)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 281, in _fixed_getinnerframes\n", + " records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 1502, in getinnerframes\n", + " frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 1460, in getframeinfo\n", + " filename = getsourcefile(frame) or getfile(frame)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 696, in getsourcefile\n", + " if getattr(getmodule(object, filename), '__loader__', None) is not None:\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 742, in getmodule\n", + " os.path.realpath(f)] = module.__name__\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 395, in realpath\n", + " path, ok = _joinrealpath(filename[:0], filename, {})\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 428, in _joinrealpath\n", + " newpath = join(path, name)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 81, in join\n", + " sep = _get_sep(a)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 42, in _get_sep\n", + " if isinstance(path, bytes):\n", + "KeyboardInterrupt\n", + "Traceback (most recent call last):\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/654011310.py\", line 5, in \n", + " train(epoch_ix, model, train_loader, optimizer, criterion)\n", + " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/2806942638.py\", line 18, in train\n", + " y_pred = model(modes, metrics.float(), features.float(), lengths)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", + " return forward_call(*input, **kwargs)\n", + " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 143, in forward\n", + " lstm_out = module(lstm_out)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", + " return forward_call(*input, **kwargs)\n", + " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 79, in forward\n", + " x = x + self.sa(self.ln1(x))\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", + " return forward_call(*input, **kwargs)\n", + " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 64, in forward\n", + " cat = torch.cat([head(x) for head in self.heads], dim=-1)\n", + " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 64, in \n", + " cat = torch.cat([head(x) for head in self.heads], dim=-1)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", + " return forward_call(*input, **kwargs)\n", + " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 37, in forward\n", + " q = self.q(x)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", + " return forward_call(*input, **kwargs)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/linear.py\", line 114, in forward\n", + " return F.linear(input, self.weight, self.bias)\n", + "KeyboardInterrupt\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n", + " stb = value._render_traceback_()\n", + "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3473, in run_ast_nodes\n", + " if (await self.run_code(code, result, async_=asy)):\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3575, in run_code\n", + " self.showtraceback(running_compiled_code=True)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2102, in showtraceback\n", + " value, tb, tb_offset=tb_offset)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1368, in structured_traceback\n", + " self, etype, value, tb, tb_offset, number_of_lines_of_context)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1268, in structured_traceback\n", + " self, etype, value, tb, tb_offset, number_of_lines_of_context\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1125, in structured_traceback\n", + " tb_offset)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1082, in format_exception_as_a_whole\n", + " last_unique, recursion_repeat = find_recursion(orig_etype, evalue, records)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 382, in find_recursion\n", + " return len(records), 0\n", + "TypeError: object of type 'NoneType' has no len()\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n", + " stb = value._render_traceback_()\n", + "AttributeError: 'TypeError' object has no attribute '_render_traceback_'\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1101, in get_records\n", + " return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 248, in wrapped\n", + " return f(*args, **kwargs)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 281, in _fixed_getinnerframes\n", + " records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 1502, in getinnerframes\n", + " frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 1460, in getframeinfo\n", + " filename = getsourcefile(frame) or getfile(frame)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 696, in getsourcefile\n", + " if getattr(getmodule(object, filename), '__loader__', None) is not None:\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 742, in getmodule\n", + " os.path.realpath(f)] = module.__name__\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 395, in realpath\n", + " path, ok = _joinrealpath(filename[:0], filename, {})\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 429, in _joinrealpath\n", + " if not islink(newpath):\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 171, in islink\n", + " st = os.lstat(path)\n", + "KeyboardInterrupt\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR:root:Internal Python error in the inspect module.\n", + "Below is the traceback from this internal error.\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/654011310.py\", line 5, in \n", + " train(epoch_ix, model, train_loader, optimizer, criterion)\n", + " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/2806942638.py\", line 18, in train\n", + " y_pred = model(modes, metrics.float(), features.float(), lengths)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", + " return forward_call(*input, **kwargs)\n", + " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 143, in forward\n", + " lstm_out = module(lstm_out)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", + " return forward_call(*input, **kwargs)\n", + " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 79, in forward\n", + " x = x + self.sa(self.ln1(x))\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", + " return forward_call(*input, **kwargs)\n", + " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 64, in forward\n", + " cat = torch.cat([head(x) for head in self.heads], dim=-1)\n", + " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 64, in \n", + " cat = torch.cat([head(x) for head in self.heads], dim=-1)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", + " return forward_call(*input, **kwargs)\n", + " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 37, in forward\n", + " q = self.q(x)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", + " return forward_call(*input, **kwargs)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/linear.py\", line 114, in forward\n", + " return F.linear(input, self.weight, self.bias)\n", + "KeyboardInterrupt\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n", + " stb = value._render_traceback_()\n", + "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3473, in run_ast_nodes\n", + " if (await self.run_code(code, result, async_=asy)):\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3575, in run_code\n", + " self.showtraceback(running_compiled_code=True)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2102, in showtraceback\n", + " value, tb, tb_offset=tb_offset)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1368, in structured_traceback\n", + " self, etype, value, tb, tb_offset, number_of_lines_of_context)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1268, in structured_traceback\n", + " self, etype, value, tb, tb_offset, number_of_lines_of_context\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1125, in structured_traceback\n", + " tb_offset)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1082, in format_exception_as_a_whole\n", + " last_unique, recursion_repeat = find_recursion(orig_etype, evalue, records)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 382, in find_recursion\n", + " return len(records), 0\n", + "TypeError: object of type 'NoneType' has no len()\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n", + " stb = value._render_traceback_()\n", + "AttributeError: 'TypeError' object has no attribute '_render_traceback_'\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3030, in _run_cell\n", + " return runner(coro)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/async_helpers.py\", line 78, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3258, in run_cell_async\n", + " interactivity=interactivity, compiler=compiler, result=result)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3492, in run_ast_nodes\n", + " self.showtraceback()\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2102, in showtraceback\n", + " value, tb, tb_offset=tb_offset)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1368, in structured_traceback\n", + " self, etype, value, tb, tb_offset, number_of_lines_of_context)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1268, in structured_traceback\n", + " self, etype, value, tb, tb_offset, number_of_lines_of_context\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1143, in structured_traceback\n", + " chained_exceptions_tb_offset)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1082, in format_exception_as_a_whole\n", + " last_unique, recursion_repeat = find_recursion(orig_etype, evalue, records)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 382, in find_recursion\n", + " return len(records), 0\n", + "TypeError: object of type 'NoneType' has no len()\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n", + " stb = value._render_traceback_()\n", + "AttributeError: 'TypeError' object has no attribute '_render_traceback_'\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1101, in get_records\n", + " return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 248, in wrapped\n", + " return f(*args, **kwargs)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 281, in _fixed_getinnerframes\n", + " records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 1502, in getinnerframes\n", + " frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 1460, in getframeinfo\n", + " filename = getsourcefile(frame) or getfile(frame)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 696, in getsourcefile\n", + " if getattr(getmodule(object, filename), '__loader__', None) is not None:\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 742, in getmodule\n", + " os.path.realpath(f)] = module.__name__\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 395, in realpath\n", + " path, ok = _joinrealpath(filename[:0], filename, {})\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 428, in _joinrealpath\n", + " newpath = join(path, name)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 81, in join\n", + " sep = _get_sep(a)\n", + " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 42, in _get_sep\n", + " if isinstance(path, bytes):\n", + "KeyboardInterrupt\n" + ] + } + ], + "source": [ + "# We'd like to start at a loss of at most -ln(1/9) ~ 2.19\n", + "\n", + "for epoch_ix in range(1, num_epochs+1):\n", + " print(f\"Epoch {epoch_ix}:\")\n", + " train(epoch_ix, model, train_loader, optimizer, criterion)\n", + " evaluate(epoch_ix, model, test_loader, criterion)\n", + " \n", + " if epoch_ix < 5 or epoch_ix % 5 == 0:\n", + " # Evaluate every 5 epochs.\n", + " evaluate_f1(model, train_loader, test_loader)\n", + " \n", + " if epoch_ix % decay_at == 0:\n", + " optimizer.param_groups[0]['lr'] *= decay\n", + " print(f\"Learning rate is now: {optimizer.param_groups[0]['lr']:.5f}\")\n", + " \n", + " print(40*'~')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8bcc396c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "a7d53498", + "metadata": {}, + "source": [ + "## Benchmarking\n", + "\n", + "```\n", + "\n", + "model = Model(\n", + " n_lstm_layers=1,\n", + " input_size=3,\n", + " hidden_size=16, \n", + " output_size=9,\n", + " n_features=40,\n", + " l1_blocks=3,\n", + " l2_blocks=3\n", + ")\n", + "\n", + "emb_dim = 16\n", + "\n", + "Best stats:\n", + "-> Train F1: 0.6327794050521978\n", + "-> Test F1: 0.6208816430930885\n", + "```\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46a8dc7d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pytorch", + "language": "python", + "name": "pytorch" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/viz_scripts/rm_src/baseline_modeling0.ipynb b/viz_scripts/rm_src/baseline_modeling0.ipynb new file mode 100644 index 0000000..043572e --- /dev/null +++ b/viz_scripts/rm_src/baseline_modeling0.ipynb @@ -0,0 +1,1136 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### All experiments are logged in Notion [here](https://www.notion.so/Replacement-mode-modeling-257c2f460377498d921e6b167f465945)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from enum import Enum\n", + "import random\n", + "\n", + "# Math and graphing.\n", + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# sklearn imports.\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import f1_score, r2_score, ConfusionMatrixDisplay\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Global experiment flags and variables.\n", + "SEED = 19348\n", + "TARGETS = ['p_micro', 'no_trip', 's_car', 'transit', 'car', 's_micro', 'ridehail', 'walk', 'unknown']\n", + "\n", + "# Set the Numpy seed too.\n", + "random.seed(SEED)\n", + "np.random.seed(SEED)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "class SPLIT_TYPE(Enum):\n", + " # 5 trips for user1, 4 trips in train, 1 in test\n", + " INTRA_USER = 0\n", + " # 5 users, 4 users in train, 1 user in test\n", + " INTER_USER = 1\n", + " # stratifying using target (chosen)\n", + " # [5000 samples (c=1), 8000 samples (c=2)]\n", + " # [4000 samples c=1, 3200 samples c=2 in train]\n", + " TARGET = 2\n", + " \n", + " MIXED = 3\n", + " \n", + "\n", + "class SPLIT(Enum):\n", + " TRAIN = 0\n", + " TEST = 1\n", + "\n", + "def get_splits(count_df: pd.DataFrame, n:int, test_size=0.2):\n", + " maxsize = int(n * test_size)\n", + "\n", + " max_threshold = int(maxsize * 1.05)\n", + " min_threshold = int(maxsize * 0.95)\n", + "\n", + " print(f\"{min_threshold}, {max_threshold}\")\n", + " \n", + " # Allow a 10% tolerance\n", + " def _dp(ix, curr_size, ids, cache):\n", + " \n", + " if ix >= count_df.shape[0]:\n", + " return []\n", + "\n", + " key = ix\n", + "\n", + " if key in cache:\n", + " return cache[key]\n", + "\n", + " if curr_size > max_threshold:\n", + " return []\n", + "\n", + " if min_threshold <= curr_size <= max_threshold:\n", + " return ids\n", + "\n", + " # two options - either pick the current id or skip it.\n", + " branch_a = _dp(ix, curr_size+count_df.loc[ix, 'count'], ids+[count_df.loc[ix, 'index']], cache)\n", + " branch_b = _dp(ix+1, curr_size, ids, cache)\n", + " \n", + " curr_max = []\n", + " if branch_a and len(branch_a) > 0:\n", + " curr_max = branch_a\n", + " \n", + " if branch_b and len(branch_b) > len(branch_a):\n", + " curr_max = branch_b\n", + " \n", + " cache[key] = curr_max\n", + " return cache[key]\n", + " \n", + " return _dp(0, 0, ids=list(), cache=dict())\n", + "\n", + "\n", + "def get_train_test_splits(data: pd.DataFrame, how=SPLIT_TYPE, test_ratio=0.2, shuffle=True):\n", + "\n", + " n_users = list(data.user_id.unique())\n", + " n = data.shape[0]\n", + " \n", + " if shuffle:\n", + " data = data.sample(data.shape[0], random_state=SEED).reset_index(drop=True, inplace=False)\n", + "\n", + " if how == SPLIT_TYPE.INTER_USER:\n", + " # Make the split, ensuring that a user in one fold is not leaked into the other fold.\n", + " # Basic idea: we want to start with the users with the highest instances and place \n", + " # alternating users in each set.\n", + " counts = data.user_id.value_counts().reset_index(drop=False, inplace=False, name='count')\n", + "\n", + " # Now, start with the user_id at the top, and keep adding to either split.\n", + " # This can be achieved using a simple DP program.\n", + " test_ids = get_splits(counts, data.shape[0])\n", + " test_data = data.loc[data.user_id.isin(test_ids), :]\n", + " train_index = data.index.difference(test_data.index)\n", + " train_data = data.loc[data.user_id.isin(train_index), :]\n", + " \n", + " return train_data, test_data\n", + " \n", + " elif how == SPLIT_TYPE.INTRA_USER:\n", + " \n", + " # There are certain users with only one observation. What do we do with those?\n", + " # As per the mobilitynet modeling pipeline, we randomly assign them to either the\n", + " # training or test set.\n", + " \n", + " value_counts = data.user_id.value_counts()\n", + " single_count_ids = value_counts[value_counts == 1].index\n", + " \n", + " data_filtered = data.loc[~data.user_id.isin(single_count_ids), :].reset_index(drop=True)\n", + " data_single_counts = data.loc[data.user_id.isin(single_count_ids), :].reset_index(drop=True)\n", + " \n", + " X_tr, X_te = train_test_split(\n", + " data_filtered, test_size=test_ratio, shuffle=shuffle, stratify=data_filtered.user_id,\n", + " random_state=SEED\n", + " )\n", + " \n", + " data_single_counts['assigned'] = np.random.choice(['train', 'test'], len(data_single_counts))\n", + " X_tr_merged = pd.concat(\n", + " [X_tr, data_single_counts.loc[data_single_counts.assigned == 'train', :].drop(\n", + " columns=['assigned'], inplace=False\n", + " )],\n", + " ignore_index=True, axis=0\n", + " )\n", + " \n", + " X_te_merged = pd.concat(\n", + " [X_te, data_single_counts.loc[data_single_counts.assigned == 'test', :].drop(\n", + " columns=['assigned'], inplace=False\n", + " )],\n", + " ignore_index=True, axis=0\n", + " )\n", + " \n", + " return X_tr_merged, X_te_merged\n", + " \n", + " elif how == SPLIT_TYPE.TARGET:\n", + " \n", + " X_tr, X_te = train_test_split(\n", + " data, test_size=test_ratio, shuffle=shuffle, stratify=data.chosen,\n", + " random_state=SEED\n", + " )\n", + " \n", + " return X_tr, X_te\n", + " \n", + " raise NotImplementedError(\"Unknown split type\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modeling" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Read the data.\n", + "# data = pd.read_csv('../data/FULL_preprocessed_data_RM_weather.csv')\n", + "# data = pd.read_csv('../data/ReplacedMode_Fix.csv')\n", + "data = pd.read_csv('../data/ReplacedMode_Fix_02072024.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data.drop_duplicates(inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_hist(df, features=None):\n", + " if not features:\n", + " # All features.\n", + " features = df.columns.tolist()\n", + " \n", + " n_features = len(features)\n", + " \n", + " ncols = 6\n", + " nrows = n_features//ncols if n_features%ncols == 0 else (n_features//ncols) + 1\n", + " \n", + " fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(10, 10))\n", + " for ix, ax in enumerate(axes.flatten()):\n", + " \n", + " if ix > n_features:\n", + " break\n", + " \n", + " df[features[ix]].hist(ax=ax)\n", + " ax.set(title=features[ix])\n", + " \n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# First, we map the user IDs to ints.\n", + "\n", + "USERS = list(data.user_id.unique())\n", + "\n", + "USER_MAP = {\n", + " u: i+1 for (i, u) in enumerate(USERS)\n", + "}\n", + "\n", + "data['user_id'] = data['user_id'].apply(lambda x: USER_MAP[x])\n", + "\n", + "# data.rename(\n", + "# columns={'start_local_dt_weekday': 'start:DOW', 'end_local_dt_weekday': 'end:DOW'},\n", + "# inplace=True\n", + "# )\n", + "\n", + "# Drop the samples with chosen == no trip or chosen == unknown\n", + "# data.drop(index=data.loc[data.chosen.isin([2, 9])].index, inplace=True)\n", + "\n", + "# data.n_working_residents = data.n_working_residents.apply(lambda x: 0 if x < 0 else x)\n", + "\n", + "# Fix some age preprocessing issues.\n", + "# data.age = data.age.apply(lambda x: x if x < 100 else 2024-x)\n", + "\n", + "# Collapse 'train' and 'bus' into 'transit'\n", + "# data.loc[\n", + "# data.section_mode_argmax.isin(['train', 'bus']), 'section_mode_argmax'\n", + "# ] = 'transit'" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# display(data.section_mode_argmax.value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# transit = data.loc[data.section_mode_argmax == 'transit', :].copy()\n", + "# transit['section_duration_argmax'] /= 60.\n", + "\n", + "# transit['mph'] = transit['section_distance_argmax']/transit['section_duration_argmax']\n", + "\n", + "# display(transit[['section_duration_argmax', 'section_distance_argmax', 'mph']].describe())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# import plotly.express as px\n", + "\n", + "# sp = data.loc[data.section_mode_argmax.isin(['car', 'transit', 'walking']), :]\n", + "# fig = px.line(sp, y='section_distance_argmax', color='section_mode_argmax')\n", + "# fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Close the figure above.\n", + "# plt.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def get_duration_estimate(df: pd.DataFrame, dset: SPLIT, model_dict: dict):\n", + " \n", + " X_features = ['section_distance_argmax', 'age']\n", + " \n", + " if 'mph' in df.columns:\n", + " X_features += ['mph']\n", + " \n", + " if dset == SPLIT.TRAIN and model_dict is None:\n", + " model_dict = dict()\n", + " \n", + " if dset == SPLIT.TEST and model_dict is None:\n", + " raise AttributeError(\"Expected model dict for testing.\")\n", + " \n", + " if dset == SPLIT.TRAIN:\n", + " for section_mode in df.section_mode_argmax.unique():\n", + " section_data = df.loc[df.section_mode_argmax == section_mode, :]\n", + " if section_mode not in model_dict:\n", + " model_dict[section_mode] = dict()\n", + "\n", + " model = LinearRegression(fit_intercept=True)\n", + "\n", + " X = section_data[\n", + " X_features\n", + " ]\n", + " Y = section_data[['section_duration_argmax']]\n", + "\n", + " model.fit(X, Y.values.ravel())\n", + "\n", + " r2 = r2_score(y_pred=model.predict(X), y_true=Y.values.ravel())\n", + " print(f\"Train R2 for {section_mode}: {r2}\")\n", + "\n", + " model_dict[section_mode]['model'] = model\n", + " \n", + " elif dset == SPLIT.TEST:\n", + " for section_mode in df.section_mode_argmax.unique():\n", + " section_data = df.loc[df.section_mode_argmax == section_mode, :]\n", + " X = section_data[\n", + " X_features\n", + " ]\n", + " Y = section_data[['section_duration_argmax']]\n", + " \n", + " y_pred = model_dict[section_mode]['model'].predict(X)\n", + " r2 = r2_score(y_pred=y_pred, y_true=Y.values.ravel())\n", + " print(f\"Test R2 for {section_mode}: {r2}\")\n", + " \n", + " # Create the new columns for the duration.\n", + " new_columns = ['p_micro','no_trip','s_car','transit','car','s_micro','ridehail','walk','unknown']\n", + " df[new_columns] = 0\n", + " df['temp'] = 0\n", + " \n", + " for section in df.section_mode_argmax.unique():\n", + " X_section = df.loc[df.section_mode_argmax == section, X_features]\n", + " \n", + " # broadcast to all columns.\n", + " df.loc[df.section_mode_argmax == section, 'temp'] = model_dict[section]['model'].predict(X_section)\n", + " \n", + " for c in new_columns:\n", + " df[c] = df['av_' + c] * df['temp']\n", + " \n", + " df.drop(columns=['temp'], inplace=True)\n", + " \n", + " df.rename(columns=dict([(x, 'tt_'+x) for x in new_columns]), inplace=True)\n", + " \n", + " # return model_dict, result_df\n", + " return model_dict, df" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Now, we split the data (either inter-user or intra-user split)\n", + "\n", + "# train_data, test_data = get_train_test_splits(data=data, how=SPLIT_TYPE.INTER_USER, shuffle=True)\n", + "\n", + "train_data, test_data = get_train_test_splits(data=data, how=SPLIT_TYPE.INTRA_USER, shuffle=True)\n", + "\n", + "# train_data, test_data = get_train_test_splits(data=data, how=SPLIT_TYPE.TARGET, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['user_id', '_id', 'original_user_id', 'cleaned_trip', 'Mode_confirm',\n", + " 'start_fmt_time', 'start:year', 'start:month', 'start:day',\n", + " 'start:hour', 'start:DOW', 'end_fmt_time', 'end:year', 'end:month',\n", + " 'end:day', 'end:hour', 'end:DOW', 'available_modes', 'birth_year',\n", + " 'income_category', 'n_motor_vehicles', 'n_residence_members',\n", + " 'n_residents_u18', 'gender', 'is_student', 'n_residents_with_license',\n", + " 'duration', 'distance_miles', 'start_loc', 'end_loc', 'section_modes',\n", + " 'section_distances', 'start:n_days_in_month', 'end:n_days_in_month',\n", + " 'age', 'is_overnight_trip', 'n_working_residents', 'is_male',\n", + " 'start_lat', 'start_lng', 'end_lat', 'end_lng', 'start:sin_HOD',\n", + " 'start:sin_DOM', 'start:sin_MOY', 'start:cos_HOD', 'start:cos_DOM',\n", + " 'start:cos_MOY', 'end:sin_HOD', 'end:sin_DOM', 'end:sin_MOY',\n", + " 'end:cos_HOD', 'end:cos_DOM', 'end:cos_MOY', 'section_durations',\n", + " 'section_locations_argmax', 'temperature_2m (°F)',\n", + " 'relative_humidity_2m (%)', 'dew_point_2m (°F)', 'rain (inch)',\n", + " 'snowfall (inch)', 'cloud_cover (%)', 'wind_speed_10m (mp/h)',\n", + " 'wind_gusts_10m (mp/h)', 'section_distance_argmax',\n", + " 'section_duration_argmax', 'section_mode_argmax',\n", + " 'section_coordinates_argmax', 'mph', 'chosen', 'av_car', 'av_s_car',\n", + " 'av_no_trip', 'av_walk', 'av_transit', 'av_s_micro', 'av_p_micro',\n", + " 'av_ridehail', 'av_unknown', 'cost_p_micro', 'cost_no_trip',\n", + " 'cost_s_car', 'cost_transit', 'cost_car', 'cost_s_micro',\n", + " 'cost_ridehail', 'cost_walk', 'cost_unknown'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "print(train_data.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train R2 for car: 0.9107819633844028\n", + "Train R2 for bicycling: 0.9381499933467025\n", + "Train R2 for walking: 0.7840120837242898\n", + "Train R2 for no_sensed: 0.838164213315293\n", + "Train R2 for transit: 0.9167190695089265\n", + "----------\n", + "Test R2 for car: 0.9115083509175145\n", + "Test R2 for walking: 0.7836715824022498\n", + "Test R2 for no_sensed: 0.8457638150514823\n", + "Test R2 for bicycling: 0.94215202813422\n", + "Test R2 for transit: 0.9130004787209818\n" + ] + } + ], + "source": [ + "params, train_data = get_duration_estimate(train_data, SPLIT.TRAIN, None)\n", + "print(10 * \"-\")\n", + "_, test_data = get_duration_estimate(test_data, SPLIT.TEST, params)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((34064, 97), (8517, 97))" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data.shape, test_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Some helper functions that will help ease redundancy in the code.\n", + "\n", + "def drop_columns(df: pd.DataFrame):\n", + " u_time_features = [\n", + " 'start_fmt_time', 'start:year', 'start:month', 'start:day',\n", + " 'start:hour', 'end_fmt_time', 'end:year',\n", + " 'end:month', 'end:day', 'end:hour', 'end:n_days_in_month', \n", + " 'start:sin_DOM', 'start:sin_MOY', 'start:cos_MOY', 'start:cos_DOM',\n", + " 'end:sin_DOM', 'end:sin_MOY', 'end:cos_DOM', 'end:cos_MOY', 'start:n_days_in_month'\n", + " ]\n", + "\n", + " u_user_features = [\n", + " '_id', 'original_user_id', 'gender', 'birth_year',\n", + "# 'user_id', \n", + " ]\n", + " \n", + " u_trip_features = [\n", + " 'cleaned_trip', 'Mode_confirm', 'available_modes', 'duration', 'start_loc',\n", + " 'end_loc', 'section_modes', 'section_distances', 'section_durations',\n", + " 'section_locations_argmax', 'section_mode_argmax', 'section_coordinates_argmax',\n", + "# 'start_lat', 'start_lng', 'end_lat', 'end_lng'\n", + " ]\n", + " \n", + " # Drop section_mode_argmax and available_modes.\n", + " return df.drop(\n", + " columns=u_time_features + u_user_features + u_trip_features, \n", + " inplace=False\n", + " )\n", + "\n", + "\n", + "def scale_values(df: pd.DataFrame, split: SPLIT, scalers=None):\n", + " # Scale costs using StandardScaler.\n", + " costs = df[[c for c in df.columns if 'cost_' in c]].copy()\n", + " times = df[[c for c in df.columns if 'tt_' in c or 'duration' in c]].copy()\n", + " distances = df[[c for c in df.columns if 'distance' in c]]\n", + " \n", + " print(\n", + " \"Cost columns to be scaled: \", costs.columns,\"\\nTime columns to be scaled: \", times.columns, \\\n", + " \"\\nDistance columns to be scaled: \", distances.columns\n", + " )\n", + " \n", + " if split == SPLIT.TRAIN and scalers is None:\n", + " cost_scaler = StandardScaler()\n", + " tt_scaler = StandardScaler()\n", + " dist_scaler = StandardScaler()\n", + " \n", + " cost_scaled = pd.DataFrame(\n", + " cost_scaler.fit_transform(costs), \n", + " columns=costs.columns, \n", + " index=costs.index\n", + " )\n", + " \n", + " tt_scaled = pd.DataFrame(\n", + " tt_scaler.fit_transform(times),\n", + " columns=times.columns,\n", + " index=times.index\n", + " )\n", + " \n", + " dist_scaled = pd.DataFrame(\n", + " dist_scaler.fit_transform(distances),\n", + " columns=distances.columns,\n", + " index=distances.index\n", + " )\n", + " \n", + " elif split == SPLIT.TEST and scalers is not None:\n", + " \n", + " cost_scaler, tt_scaler, dist_scaler = scalers\n", + " \n", + " cost_scaled = pd.DataFrame(\n", + " cost_scaler.transform(costs), \n", + " columns=costs.columns, \n", + " index=costs.index\n", + " )\n", + " \n", + " tt_scaled = pd.DataFrame(\n", + " tt_scaler.transform(times), \n", + " columns=times.columns, \n", + " index=times.index\n", + " )\n", + " \n", + " dist_scaled = pd.DataFrame(\n", + " dist_scaler.transform(distances),\n", + " columns=distances.columns,\n", + " index=distances.index\n", + " )\n", + " \n", + " else:\n", + " raise NotImplementedError(\"Unknown split\")\n", + " \n", + " # Drop the original columns.\n", + " df.drop(\n", + " columns=costs.columns.tolist() + times.columns.tolist() + distances.columns.tolist(), \n", + " inplace=True\n", + " )\n", + " \n", + " df = df.merge(right=cost_scaled, left_index=True, right_index=True)\n", + " df = df.merge(right=tt_scaled, left_index=True, right_index=True)\n", + " df = df.merge(right=dist_scaled, left_index=True, right_index=True)\n", + " \n", + " return df, (cost_scaler, tt_scaler, dist_scaler)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# First, drop columns.\n", + "\n", + "train_data = drop_columns(train_data)\n", + "\n", + "# Scale cost.\n", + "# train_data, scalers = scale_values(train_data, SPLIT.TRAIN, None)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "test_data = drop_columns(test_data)\n", + "\n", + "# Scale cost.\n", + "# test_data, _ = scale_values(test_data, SPLIT.TEST, scalers)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['user_id', 'start:DOW', 'end:DOW', 'income_category',\n", + " 'n_motor_vehicles', 'n_residence_members', 'n_residents_u18',\n", + " 'is_student', 'n_residents_with_license', 'distance_miles', 'age',\n", + " 'is_overnight_trip', 'n_working_residents', 'is_male', 'start_lat',\n", + " 'start_lng', 'end_lat', 'end_lng', 'start:sin_HOD', 'start:cos_HOD',\n", + " 'end:sin_HOD', 'end:cos_HOD', 'temperature_2m (°F)',\n", + " 'relative_humidity_2m (%)', 'dew_point_2m (°F)', 'rain (inch)',\n", + " 'snowfall (inch)', 'cloud_cover (%)', 'wind_speed_10m (mp/h)',\n", + " 'wind_gusts_10m (mp/h)', 'section_distance_argmax',\n", + " 'section_duration_argmax', 'mph', 'chosen', 'av_car', 'av_s_car',\n", + " 'av_no_trip', 'av_walk', 'av_transit', 'av_s_micro', 'av_p_micro',\n", + " 'av_ridehail', 'av_unknown', 'cost_p_micro', 'cost_no_trip',\n", + " 'cost_s_car', 'cost_transit', 'cost_car', 'cost_s_micro',\n", + " 'cost_ridehail', 'cost_walk', 'cost_unknown', 'tt_p_micro',\n", + " 'tt_no_trip', 'tt_s_car', 'tt_transit', 'tt_car', 'tt_s_micro',\n", + " 'tt_ridehail', 'tt_walk', 'tt_unknown'],\n", + " dtype='object')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(train_data.chosen.unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import classification_report\n", + "from sklearn.model_selection import GridSearchCV, StratifiedKFold\n", + "from pprint import pprint\n", + "from sklearn.inspection import permutation_importance\n", + "from time import perf_counter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Random Forest classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "CV = True" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 3 folds for each of 12 candidates, totalling 36 fits\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "# exp question - compute sample weights using user_id.\n", + "\n", + "rf_train = train_data.drop(columns=['chosen', 'start_lat', 'start_lng', 'end_lat', 'end_lng', 'user_id'])\n", + "rf_test = test_data.drop(columns=['chosen', 'start_lat', 'start_lng', 'end_lat', 'end_lng', 'user_id'])\n", + "\n", + "if CV:\n", + "\n", + " model = RandomForestClassifier(random_state=SEED)\n", + "\n", + " # We want to build bootstrapped trees that would not always use all the features.\n", + "\n", + " param_set2 = {\n", + " 'n_estimators': [150, 200, 250],\n", + " 'min_samples_split': [2, 3],\n", + " 'class_weight': ['balanced_subsample'],\n", + " 'max_features': [None, 'sqrt'],\n", + " 'bootstrap': [True]\n", + " }\n", + "\n", + " cv_set2 = StratifiedKFold(n_splits=3, shuffle=True, random_state=SEED)\n", + "\n", + " clf_set2 = GridSearchCV(model, param_set2, cv=cv_set2, n_jobs=-1, scoring='f1_weighted', verbose=1)\n", + "\n", + " start = perf_counter()\n", + "\n", + " clf_set2.fit(\n", + " rf_train,\n", + " train_data.chosen.values.ravel()\n", + " )\n", + "\n", + " time_req = (perf_counter() - start)/60.\n", + "\n", + " best_model = clf_set2.best_estimator_\n", + "else:\n", + " best_model = RandomForestClassifier(\n", + " n_estimators=200,\n", + " max_depth=None,\n", + " min_samples_split=3,\n", + " bootstrap=True,\n", + " class_weight='balanced_subsample',\n", + " random_state=SEED,\n", + " n_jobs=-1\n", + " ).fit(rf_train, train_data.chosen.values.ravel())" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# tr_f1_set1 = f1_score(\n", + "# y_true=train_data.chosen.values,\n", + "# y_pred=model_set1.predict(rf_train),\n", + "# average='weighted'\n", + "# )\n", + "\n", + "tr_f1_set2 = f1_score(\n", + " y_true=train_data.chosen.values,\n", + " y_pred=best_model.predict(rf_train),\n", + " average='weighted'\n", + ")\n", + "\n", + "# print(tr_f1)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# te_f1_set1 = f1_score(\n", + "# y_true=test_data.chosen.values,s\n", + "# y_pred=model_set1.predict(rf_test),\n", + "# average='weighted'\n", + "# )\n", + "\n", + "te_f1_set2 = f1_score(\n", + " y_true=test_data.chosen.values,\n", + " y_pred=best_model.predict(rf_test),\n", + " average='weighted'\n", + ")\n", + "\n", + "# print(te_f1)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[BOOTSTRAPPED] | Train F1: 1.0, Test F1: 0.7344136324607913\n" + ] + } + ], + "source": [ + "# print(f\"[NON BOOTSTRAPPED] | Train F1: {tr_f1_set1}, Test F1: {te_f1_set1}\")\n", + "print(f\"[BOOTSTRAPPED] | Train F1: {tr_f1_set2}, Test F1: {te_f1_set2}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[('age', 0.13052971325233453),\n", + " ('income_category', 0.05563529282438796),\n", + " ('n_motor_vehicles', 0.05117069898186447),\n", + " ('mph', 0.04901096852140811),\n", + " ('dew_point_2m (°F)', 0.046445523590839706),\n", + " ('temperature_2m (°F)', 0.04332851959366878),\n", + " ('n_residents_u18', 0.04079428459138862),\n", + " ('cost_transit', 0.03599739479849181),\n", + " ('distance_miles', 0.03410054518532979),\n", + " ('wind_gusts_10m (mp/h)', 0.02664916084517161),\n", + " ('relative_humidity_2m (%)', 0.026557460924608728),\n", + " ('wind_speed_10m (mp/h)', 0.02621237413168378),\n", + " ('n_residence_members', 0.024503799663918274),\n", + " ('section_duration_argmax', 0.024244750804804545),\n", + " ('n_working_residents', 0.023773443113737733),\n", + " ('n_residents_with_license', 0.023628029234229537),\n", + " ('cloud_cover (%)', 0.02246590194480327),\n", + " ('cost_s_micro', 0.022458268111427003),\n", + " ('tt_p_micro', 0.02038772534963909),\n", + " ('start:cos_HOD', 0.019229222189042564),\n", + " ('is_male', 0.01922412856640586),\n", + " ('end:cos_HOD', 0.019203897735717918),\n", + " ('section_distance_argmax', 0.015253896310661041),\n", + " ('end:sin_HOD', 0.015124047093651035),\n", + " ('tt_walk', 0.015027814935913562),\n", + " ('start:DOW', 0.014649650753683777),\n", + " ('start:sin_HOD', 0.014574215098017478),\n", + " ('cost_s_car', 0.013824194676480045),\n", + " ('end:DOW', 0.013763191399800697),\n", + " ('cost_car', 0.013558725784127607),\n", + " ('cost_ridehail', 0.013029878623553506),\n", + " ('tt_transit', 0.012674148135131848),\n", + " ('is_student', 0.010245832739145545),\n", + " ('tt_s_car', 0.009950842169405066),\n", + " ('tt_car', 0.00979054895155533),\n", + " ('tt_s_micro', 0.008712803885105263),\n", + " ('tt_ridehail', 0.008579952310351583),\n", + " ('av_p_micro', 0.005419336568222103),\n", + " ('av_walk', 0.003300488646146801),\n", + " ('av_transit', 0.0028638611907256797),\n", + " ('rain (inch)', 0.0026761074332931224),\n", + " ('av_car', 0.002642247634705974),\n", + " ('av_s_car', 0.0018072513622939949),\n", + " ('snowfall (inch)', 0.0016959335576669646),\n", + " ('tt_unknown', 0.001566922069109617),\n", + " ('av_ridehail', 0.0014388235245583704),\n", + " ('av_s_micro', 0.0009841196788956074),\n", + " ('tt_no_trip', 0.0007220255139425819),\n", + " ('av_unknown', 0.0004000980804160885),\n", + " ('av_no_trip', 0.00017193791853587658),\n", + " ('is_overnight_trip', 0.0),\n", + " ('cost_p_micro', 0.0),\n", + " ('cost_no_trip', 0.0),\n", + " ('cost_walk', 0.0),\n", + " ('cost_unknown', 0.0)]\n" + ] + } + ], + "source": [ + "# Feature importances - gini entropy\n", + "\n", + "pprint(\n", + " sorted(\n", + " zip(\n", + " best_model.feature_names_in_, \n", + " best_model.feature_importances_\n", + " ), \n", + " key=lambda x: x[-1], reverse=True\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'permutation_importance' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m importance \u001b[38;5;241m=\u001b[39m \u001b[43mpermutation_importance\u001b[49m(\n\u001b[1;32m 2\u001b[0m best_model,\n\u001b[1;32m 3\u001b[0m rf_test,\n\u001b[1;32m 4\u001b[0m test_data\u001b[38;5;241m.\u001b[39mchosen\u001b[38;5;241m.\u001b[39mvalues,\n\u001b[1;32m 5\u001b[0m n_repeats\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m,\n\u001b[1;32m 6\u001b[0m random_state\u001b[38;5;241m=\u001b[39mSEED,\n\u001b[1;32m 7\u001b[0m n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 8\u001b[0m scoring\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mf1_weighted\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 9\u001b[0m )\n", + "\u001b[0;31mNameError\u001b[0m: name 'permutation_importance' is not defined" + ] + } + ], + "source": [ + "importance = permutation_importance(\n", + " best_model,\n", + " rf_test,\n", + " test_data.chosen.values,\n", + " n_repeats=5,\n", + " random_state=SEED,\n", + " n_jobs=-1,\n", + " scoring='f1_weighted'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.DataFrame(\n", + " {\n", + " 'feature names': test_data.columns.delete(\n", + " test_data.columns.isin(['chosen'])\n", + " ),\n", + " 'imp_mean': importance.importances_mean, \n", + " 'imp_std': importance.importances_std\n", + " }\n", + ").sort_values(by=['imp_mean'], axis='rows', ascending=False).head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fig, ax = plt.subplots(nrows=1, ncols=2)\n", + "y_pred = best_model.predict(rf_test)\n", + "pred_df = pd.DataFrame(\n", + " {\n", + " 'y_pred': y_pred.ravel(),\n", + " 'y_true': test_data.chosen.values.ravel()\n", + " }\n", + ")\n", + "\n", + "# pred_df.y_pred.hist(ax=ax[0])\n", + "# pred_df.y_true.hist(ax=ax[1])\n", + "\n", + "# ax[0].set(\n", + "# xlabel=\"Label\",\n", + "# ylabel=\"Count\",\n", + "# title=\"Prediction\"\n", + "# )\n", + "\n", + "# ax[1].set(\n", + "# xlabel=\"Label\",\n", + "# ylabel=\"Count\",\n", + "# title=\"GT\"\n", + "# )\n", + "\n", + "# plt.tight_layout()\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 7))\n", + "cm = ConfusionMatrixDisplay.from_estimator(\n", + " best_model,\n", + " X=rf_test,\n", + " y=test_data[['chosen']],\n", + " ax=ax\n", + ")\n", + "# ax.set_xticklabels(TARGETS, rotation=45)\n", + "# ax.set_yticklabels(TARGETS)\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(classification_report(y_true=pred_df.y_true, y_pred=pred_df.y_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## XGBoost" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# from sklearn.utils.class_weight import compute_sample_weight\n", + "\n", + "# sample_weights = compute_sample_weight(class_weight='balanced', y=train_data.user_id.values.ravel())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from xgboost import XGBClassifier\n", + "\n", + "y_train = train_data.chosen.values.ravel() - 1\n", + "y_test = test_data.chosen.values.ravel()\n", + "\n", + "# weights = compute_class_weight(class_weight='balanced', classes=np.unique(y_pred), y_pred)\n", + "\n", + "xgm = XGBClassifier(\n", + " n_estimators=250,\n", + " max_depth=None,\n", + " tree_method='hist',\n", + " objective='multi:softmax',\n", + " num_class=9\n", + ").fit(rf_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "preds = xgm.predict(rf_test) + 1\n", + "\n", + "print(classification_report(y_true=y_test, y_pred=preds))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import pickle\n", + "\n", + "# # RF_RM.pkl = 0.8625 on test.\n", + "# # RF_RM_1.pkl = 0.77 on test.\n", + "# with open('../models/RF_RM_1.pkl', 'wb') as f:\n", + "# f.write(pickle.dumps(model))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TODO:\n", + "\n", + "\n", + "- Explain why location might not be a good feature to add (plot start and end on map and explain how model might just overfit to the raw coordinates)\n", + "- Merge `unknown` and `no_trip` into one category and validate against models trained on (a) separate labels (b) dropped labels\n", + "- Explore more of the abnormal `walking` trips" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" + }, + "kernelspec": { + "display_name": "emission", + "language": "python", + "name": "emission" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/viz_scripts/rm_src/cost_time_avl_preprocessing.ipynb b/viz_scripts/rm_src/cost_time_avl_preprocessing.ipynb index e535e2f..9f2bb05 100644 --- a/viz_scripts/rm_src/cost_time_avl_preprocessing.ipynb +++ b/viz_scripts/rm_src/cost_time_avl_preprocessing.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -11,42 +11,304 @@ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import ast\n", - "import geopandas as gpd\n", "from traceback import print_exception\n", "\n", "%matplotlib inline" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 2, "metadata": {}, + "outputs": [], "source": [ - "# Sections\n", + "# Data loading. Refer to the mnl_exploration.ipynb file for details on how user-level features are generated.\n", "\n", - "### 0: Refer to the mnl_exploration.ipynb notebook for specifics about user-level preprocessing\n", - "### 1. Creating the dummy variables for the available modes\n", - "### 2. Creating trip-specific variables for each available mode (cost and time)\n", - "### 3. ??" + "df = pd.read_csv('../data/final_modeling_data.csv')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape: (74631, 57)\n" + ] + } + ], + "source": [ + "print(\"Shape: \", df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "# Data loading. Refer to the mnl_exploration.ipynb file for details on how user-level features are generated.\n", + "df.drop_duplicates(inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add weather info" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Downloaded using OpenMeteo API.\n", + "weather_df = pd.read_csv('../data/denver_weather_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "weather_df.time = pd.to_datetime(weather_df.time, utc=True).dt.tz_convert('America/Denver')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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temperature_2m (°F)relative_humidity_2m (%)dew_point_2m (°F)rain (inch)snowfall (inch)cloud_cover (%)wind_speed_10m (mp/h)wind_gusts_10m (mp/h)
count64728.00000064728.00000064728.00000064728.00000064728.00000064728.00000064728.00000064728.000000
mean49.36332552.40535829.1052540.0011580.00495829.3645415.53808411.855783
std21.50211223.37017915.5786720.0086320.03559033.1812153.1671995.973471
min-22.4000005.000000-27.8000000.0000000.0000000.0000000.0000001.100000
25%32.50000033.00000018.0000000.0000000.0000000.0000003.5000007.600000
50%49.20000053.00000028.9000000.0000000.00000018.0000004.90000010.500000
75%65.60000071.00000041.5000000.0000000.00000046.0000006.80000015.000000
max100.900000100.00000068.8000000.4490001.378000100.00000045.90000077.600000
\n", + "
" + ], + "text/plain": [ + " temperature_2m (°F) relative_humidity_2m (%) dew_point_2m (°F) \\\n", + "count 64728.000000 64728.000000 64728.000000 \n", + "mean 49.363325 52.405358 29.105254 \n", + "std 21.502112 23.370179 15.578672 \n", + "min -22.400000 5.000000 -27.800000 \n", + "25% 32.500000 33.000000 18.000000 \n", + "50% 49.200000 53.000000 28.900000 \n", + "75% 65.600000 71.000000 41.500000 \n", + "max 100.900000 100.000000 68.800000 \n", + "\n", + " rain (inch) snowfall (inch) cloud_cover (%) wind_speed_10m (mp/h) \\\n", + "count 64728.000000 64728.000000 64728.000000 64728.000000 \n", + "mean 0.001158 0.004958 29.364541 5.538084 \n", + "std 0.008632 0.035590 33.181215 3.167199 \n", + "min 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 3.500000 \n", + "50% 0.000000 0.000000 18.000000 4.900000 \n", + "75% 0.000000 0.000000 46.000000 6.800000 \n", + "max 0.449000 1.378000 100.000000 45.900000 \n", + "\n", + " wind_gusts_10m (mp/h) \n", + "count 64728.000000 \n", + "mean 11.855783 \n", + "std 5.973471 \n", + "min 1.100000 \n", + "25% 7.600000 \n", + "50% 10.500000 \n", + "75% 15.000000 \n", + "max 77.600000 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weather_df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# First, convert to UTC. Then, parse to America/Denver.\n", + "df['start_fmt_time'] = pd.to_datetime(\n", + " df['start_fmt_time'], utc=True\n", + ").dt.tz_convert('America/Denver')\n", "\n", - "df = pd.read_csv('../data/modeling_w_duration.csv')" + "original_shape = df.shape[0]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "df.drop_duplicates(inplace=True)" + "# Round start time to nearest hour.\n", + "df['start_time_rounded'] = df.start_fmt_time.dt.round(\n", + " 'H', ambiguous='infer', nonexistent='shift_backward'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# True intersection\n", + "df = df.merge(right=weather_df, left_on='start_time_rounded', right_on='time', how='inner')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Old shape: 74631. new shape: 74622\n" + ] + } + ], + "source": [ + "print(f\"Old shape: {original_shape}. new shape: {df.shape[0]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "df.drop(columns=['start_time_rounded', 'time'], inplace=True)" ] }, { @@ -58,10 +320,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ + "# We want this to be ordinal because 2 > 1 implies that it has higher associated value.\n", + "\n", + "income_ordinal_mapping = {\n", + " np.nan: 0,\n", + " 'Prefer not to say': 0,\n", + " 'Less than $24,999': 1,\n", + " '$25,000-$49,999': 2,\n", + " '$50,000-$99,999': 3,\n", + " '$100,000 -$149,999': 4,\n", + " '$150,000-$199,999': 5\n", + "}\n", + "\n", + "df.income_category = df.income_category.apply(lambda x: income_ordinal_mapping[x])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# | [d1, d2, d3] | [t1, t2, t3] | [m1, m2, m3] |\n", + "\n", + "\n", "def compute_argmax(df: pd.DataFrame):\n", " # Create the two columns.\n", " df[['section_distance_argmax', 'section_duration_argmax', 'section_mode_argmax', 'section_coordinates_argmax']] = None\n", @@ -76,9 +362,14 @@ " parsed_modes = ast.literal_eval(row_dict['section_modes'])\n", "\n", " argmax_ix = np.argmax(parsed_distances)\n", - "\n", - " row_dict['section_distance_argmax'] = parsed_distances[argmax_ix] * 0.0006213712 # Pick the argmax and scale to miles.\n", - " row_dict['section_duration_argmax'] = parsed_durations[argmax_ix] / 60 # Pick the argmax and scale to minutes.\n", + " \n", + " # Pick the argmax and scale to miles. (meters -> miles)\n", + " row_dict['section_distance_argmax'] = parsed_distances[argmax_ix] * 0.0006213712\n", + " \n", + " # Pick the argmax and scale to minutes.\n", + " row_dict['section_duration_argmax'] = parsed_durations[argmax_ix] / 60\n", + " \n", + " # Pick the argmax mode.\n", " row_dict['section_mode_argmax'] = parsed_modes[argmax_ix]\n", " \n", " row_dict['mark'] = False\n", @@ -93,93 +384,676 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# Let's plot the mode-wise durations as a function of distance.\n", - "df_modded = compute_argmax(df)" + "df = compute_argmax(df)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ - "df_modded = df_modded.loc[~df_modded.mark, :].reset_index(drop=True, inplace=False).drop(columns=['mark'], inplace=False)" + "df = df.loc[~df.mark, :].reset_index(drop=True, inplace=False).drop(columns=['mark'], inplace=False)" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 17, "metadata": {}, + "outputs": [], "source": [ - "Intercept and coefficient values for determining the duration from distance (Obtained from time_distance_estimation.ipynb):\n", - "\n", - "```\n", - "Format:\n", - "-> mode train_r2 test_r2\n", - "-> intercept: x, coefficient: y\n", - "\n", - "\n", - "walking 0.24312136039586707 0.5572752052765471\n", - "intercept: 14.09698152519682 coeff: 19.153802211061134\n", - "bicycling 0.5986205637411164 0.646267142326254\n", - "intercept: 4.753590860550791 coeff: 4.2723965916811935\n", - "car 0.45228118668156203 0.6040302860034135\n", - "intercept: 10.515751350707365 coeff: 1.112111276629702\n", - "no_sensed 0.5188696686147352 0.5028191556675814\n", - "intercept: 9.423026094931313 coeff: 2.640429444466706\n", - "public_transport 0.47377939742617625 0.5916737429017003\n", - "intercept: 7.5000351741492395 coeff: 1.839562017986485\n", - "```" + "# Drop instances where duration/distance is unusable.\n", + "df.drop(\n", + " index=df.loc[(df.section_distance_argmax <= 0) | (df.section_duration_argmax <= 0), :].index,\n", + " inplace=False\n", + ").reset_index(drop=True, inplace=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# bus, train, bicycling, walking, car\n", + "# split-apply-combine\n", + "def drop_outliers(df: pd.DataFrame) -> pd.DataFrame:\n", + " def filter_by_percentiles(group):\n", + " distance_low = group['section_distance_argmax'].quantile(0.1)\n", + " distance_high = group['section_distance_argmax'].quantile(0.9)\n", + " duration_low = group['section_duration_argmax'].quantile(0.1)\n", + " duration_high = group['section_duration_argmax'].quantile(0.9)\n", + " \n", + " l1_filter = group[\n", + " (group['section_distance_argmax'] >= distance_low) &\n", + " (group['section_distance_argmax'] <= distance_high)\n", + " ].reset_index(drop=True)\n", + " \n", + " l2_filter = l1_filter[\n", + " (l1_filter['section_duration_argmax'] >= duration_low) &\n", + " (l1_filter['section_duration_argmax'] <= duration_high)\n", + " ].reset_index(drop=True)\n", + " \n", + " return l2_filter\n", + " \n", + " return df.groupby('section_mode_argmax').apply(filter_by_percentiles).reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ - "df_modded['no_trip'] = df_modded.apply(\n", - " lambda x: 'Do not have vehicle' in x.available_modes or 'None' in x.available_modes, axis=1\n", + "filtered_df = drop_outliers(df)\n", + "\n", + "# Ideal speed. distance/time.\n", + "filtered_df['mph'] = (\n", + " (filtered_df['section_distance_argmax'] * 60.)/filtered_df['section_duration_argmax']\n", ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ - "no_sensed = df_modded.apply(\n", - " lambda x: 'no_sensed' == x.section_mode_argmax, axis=1\n", - ")\n", + "def filter_mph(df: pd.DataFrame) -> pd.DataFrame:\n", + " \n", + " MPH_THRESHOLDS = {\n", + " # https://www.sciencedirect.com/science/article/pii/S2210670718304682\n", + " 'bicycling': 15.,\n", + " # https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806575/\n", + " 'walking': 2.93\n", + " }\n", + " \n", + " def custom_filter(group):\n", + " # Drop data specified in the dict manually.\n", + " if group.name in MPH_THRESHOLDS.keys():\n", + " f_df = group[group['mph'] <= MPH_THRESHOLDS[group.name]]\n", + " else:\n", + " mph_low = group['mph'].quantile(0.1)\n", + " mph_high = group['mph'].quantile(0.9)\n", "\n", - "n, d = no_sensed[no_sensed == True].shape[0], no_sensed.shape[0]\n", - "print(n, d)\n", - "print((n/d)*100.)" + " f_df = group[(group['mph'] >= mph_low) & (group['mph'] <= mph_high)]\n", + " \n", + " return f_df\n", + " \n", + " return df.groupby('section_mode_argmax').apply(custom_filter).reset_index(drop=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ - "n = df_modded.loc[df_modded.no_trip, :].shape[0]\n", - "d = df_modded.shape[0]\n", - "print(n, d)\n", - "print((n/d) * 100.)" + "filtered_df = filter_mph(filtered_df)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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section_distance_argmaxsection_duration_argmax
countmeanstdmin25%50%75%maxcountmeanstdmin25%50%75%max
section_mode_argmax
bicycling6894.02.1394110.8631500.8163011.5082451.9541512.5704084.8630866894.013.6059434.9737915.8833339.91527912.60526816.44149428.536488
bus262.01.4152400.5975870.3728750.9224741.4553521.9141972.790900262.08.8709383.4957832.9631776.5598038.24097410.53729019.882077
car22816.03.9718982.4654580.9025742.1413053.3318985.10122914.18317622816.013.6251996.4578265.6133498.62463511.84566416.99331134.438154
no_sensed1537.02.5943222.2860700.1759341.0195841.9249043.36957913.1542171537.015.28034811.2996531.0450787.12964912.03516720.78817652.847837
train40.011.0875402.2841916.35232510.18673010.38422312.41472215.74303740.026.4695555.94026318.18095321.97713524.72284429.04570040.568931
walking11032.00.4786250.3742180.1195070.2202220.3473970.5986982.25137111032.020.02442913.3724015.2842339.43475615.74349526.81028561.655195
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" + ], + "text/plain": [ + " section_distance_argmax \\\n", + " count mean std min \n", + "section_mode_argmax \n", + "bicycling 6894.0 2.139411 0.863150 0.816301 \n", + "bus 262.0 1.415240 0.597587 0.372875 \n", + "car 22816.0 3.971898 2.465458 0.902574 \n", + "no_sensed 1537.0 2.594322 2.286070 0.175934 \n", + "train 40.0 11.087540 2.284191 6.352325 \n", + "walking 11032.0 0.478625 0.374218 0.119507 \n", + "\n", + " \\\n", + " 25% 50% 75% max \n", + "section_mode_argmax \n", + "bicycling 1.508245 1.954151 2.570408 4.863086 \n", + "bus 0.922474 1.455352 1.914197 2.790900 \n", + "car 2.141305 3.331898 5.101229 14.183176 \n", + "no_sensed 1.019584 1.924904 3.369579 13.154217 \n", + "train 10.186730 10.384223 12.414722 15.743037 \n", + "walking 0.220222 0.347397 0.598698 2.251371 \n", + "\n", + " section_duration_argmax \\\n", + " count mean std min \n", + "section_mode_argmax \n", + "bicycling 6894.0 13.605943 4.973791 5.883333 \n", + "bus 262.0 8.870938 3.495783 2.963177 \n", + "car 22816.0 13.625199 6.457826 5.613349 \n", + "no_sensed 1537.0 15.280348 11.299653 1.045078 \n", + "train 40.0 26.469555 5.940263 18.180953 \n", + "walking 11032.0 20.024429 13.372401 5.284233 \n", + "\n", + " \n", + " 25% 50% 75% max \n", + "section_mode_argmax \n", + "bicycling 9.915279 12.605268 16.441494 28.536488 \n", + "bus 6.559803 8.240974 10.537290 19.882077 \n", + "car 8.624635 11.845664 16.993311 34.438154 \n", + "no_sensed 7.129649 12.035167 20.788176 52.847837 \n", + "train 21.977135 24.722844 29.045700 40.568931 \n", + "walking 9.434756 15.743495 26.810285 61.655195 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filtered_df.groupby('section_mode_argmax')[['section_distance_argmax', 'section_duration_argmax']].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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mph
countmeanstdmin25%50%75%max
section_mode_argmax
bicycling6894.09.6205812.3537762.8175807.9284989.65686611.28416714.998472
bus262.09.8418723.1534654.2331957.2158559.81802412.48795315.579753
car22816.017.1542125.4561968.51005512.61861016.42148521.20360829.536059
no_sensed1537.010.3879264.5759564.6081776.5969609.28998013.20603122.796647
train40.025.4152283.46861117.51393823.23404825.79593427.93265730.865389
walking11032.01.6053260.7474900.1315920.9830491.5913852.2474522.929953
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" + ], + "text/plain": [ + " mph \\\n", + " count mean std min 25% \n", + "section_mode_argmax \n", + "bicycling 6894.0 9.620581 2.353776 2.817580 7.928498 \n", + "bus 262.0 9.841872 3.153465 4.233195 7.215855 \n", + "car 22816.0 17.154212 5.456196 8.510055 12.618610 \n", + "no_sensed 1537.0 10.387926 4.575956 4.608177 6.596960 \n", + "train 40.0 25.415228 3.468611 17.513938 23.234048 \n", + "walking 11032.0 1.605326 0.747490 0.131592 0.983049 \n", + "\n", + " \n", + " 50% 75% max \n", + "section_mode_argmax \n", + "bicycling 9.656866 11.284167 14.998472 \n", + "bus 9.818024 12.487953 15.579753 \n", + "car 16.421485 21.203608 29.536059 \n", + "no_sensed 9.289980 13.206031 22.796647 \n", + "train 25.795934 27.932657 30.865389 \n", + "walking 1.591385 2.247452 2.929953 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filtered_df.groupby('section_mode_argmax')[['mph']].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dropped 32041 rows.\n" + ] + } + ], + "source": [ + "print(f\"Dropped {df.shape[0] - filtered_df.shape[0]} rows.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'p_micro': 1, 'no_trip': 2, 's_car': 3, 'transit': 4, 'car': 5, 's_micro': 6, 'ridehail': 7, 'walk': 8, 'unknown': 9}\n" + ] + } + ], + "source": [ + "## Define the mapping strategy\n", + "\n", + "\"\"\"\n", + "p_micro: Personal micromobility\n", + "s_micro: Shared micromobility\n", + "s_car: Shared car\n", + "car: Car/rental car\n", + "transit: train + bus\n", + "no_trip: No vehicle / None\n", + "ridehail: Uber/Lyft\n", + "\"\"\"\n", + "\n", + "available = {\n", + " 'Bicycle': 'p_micro',\n", + " 'Do not have vehicle': 'no_trip',\n", + " 'Get a ride from a friend or family member': 's_car',\n", + " 'None': 'no_trip',\n", + " 'Public transportation (bus, subway, light rail, etc.)': 'transit',\n", + " 'Rental car (including Zipcar/ Car2Go)': 'car',\n", + " 'Shared bicycle or scooter': 's_micro',\n", + " 'Skateboard': 'p_micro',\n", + " 'Taxi (regular taxi, Uber, Lyft, etc)': 'ridehail',\n", + " 'Walk/roll': 'walk'\n", + "}\n", + "\n", + "# Map each sensed mode to the binary indicators.\n", + "section_mode_mapping = {\n", + " 'bicycling': ['p_micro', 's_micro'],\n", + " 'bus': ['transit'],\n", + " 'car': ['s_car', 'car', 'ridehail'],\n", + " 'no_sensed': ['unknown'],\n", + " 'train': ['transit'],\n", + " 'walking': ['walk']\n", + "}\n", + "\n", + "# For target - mode_confirm\n", + "# target_mapping = {\n", + "# 'Regular Bike': 'p_micro',\n", + "# 'Walk': 'walk',\n", + "# 'Gas Car, with others': 's_car',\n", + "# 'Gas Car, drove alone': 'car',\n", + "# 'Bikeshare': 's_micro',\n", + "# 'Other': 'unknown',\n", + "# 'Bus': 'transit',\n", + "# 'Not a Trip': 'no_trip',\n", + "# 'E-bike': 'p_micro',\n", + "# 'Train': 'transit',\n", + "# 'Taxi/Uber/Lyft': 'ridehail',\n", + "# 'Free Shuttle': 'transit',\n", + "# 'Scooter share': 's_micro',\n", + "# 'Skate board': 'p_micro'\n", + "# }\n", + "\n", + "# For target - replaced_mode\n", + "target_mapping = {\n", + " 'Regular Bike': 'p_micro', \n", + " 'Walk': 'walk', \n", + " 'No Travel': 'no_trip', \n", + " 'Other': 'unknown',\n", + " 'Gas Car, with others': 's_car',\n", + " 'Gas Car, drove alone': 'car',\n", + " 'Train': 'transit',\n", + " 'Scooter share': 's_micro', \n", + " 'Taxi/Uber/Lyft': 'ridehail', \n", + " 'Free Shuttle': 'transit', \n", + " 'Skate board': 'p_micro',\n", + " 'Bikeshare': 's_micro',\n", + " 'Bus': 'transit',\n", + " 'Not a Trip': 'no_trip',\n", + " 'E-bike': 'p_micro'\n", + "}\n", + "\n", + "# # These values were obtained using a simple linear regression model. Specifics can be found in time_distance_estimation.ipynb\n", + "# duration_dict = {\n", + "# 'walk': {\n", + "# 'intercept': 14.09698152519682,\n", + "# 'coef': 19.153802211061134\n", + "# },\n", + "# 'p_micro': {\n", + "# 'intercept': 4.753590860550791,\n", + "# 'coef': 4.2723965916811935\n", + "# },\n", + "# 's_micro': {\n", + "# 'intercept': 4.753590860550791,\n", + "# 'coef': 4.2723965916811935\n", + "# },\n", + "# 's_car': {\n", + "# 'intercept': 10.515751350707365, \n", + "# 'coef': 1.112111276629702\n", + "# },\n", + "# 'car': {\n", + "# 'intercept': 10.515751350707365, \n", + "# 'coef': 1.112111276629702\n", + "# },\n", + "# 'ridehail': {\n", + "# 'intercept': 10.515751350707365, \n", + "# 'coef': 1.112111276629702\n", + "# },\n", + "# 'transit': {\n", + "# 'intercept': 7.5000351741492395,\n", + "# 'coef': 1.839562017986485\n", + "# },\n", + "# 'unknown': {\n", + "# 'intercept': 9.423026094931313,\n", + "# 'coef': 2.640429444466706\n", + "# },\n", + "# 'no_trip': {\n", + "# 'intercept': 0.,\n", + "# 'coef': 0.\n", + "# }\n", + "# }\n", + "\n", + "ordinal_mapping = {'p_micro': 1, 'no_trip': 2, 's_car': 3, 'transit': 4, 'car': 5, 's_micro': 6, 'ridehail': 7, 'walk': 8, 'unknown': 9}\n", + "reverse_ordinal_mapping = {v:k for k,v in ordinal_mapping.items()}\n", + "\n", + "print(ordinal_mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ - "def generate_available_features(df: pd.DataFrame, available_mode_map: dict, section_mode_map: dict, ordinal_mapper: dict):\n", + "def generate_available_features(\n", + " df: pd.DataFrame, av: dict, sm: dict, target_mapper: dict\n", + "):\n", " \"\"\"\n", " This method generates the wide-form dummy features for available modes.\n", " The current implementation uses (section_mode_argmax OR available_modes) to indicate mode availability.\n", @@ -188,134 +1062,86 @@ " # Reset indices.\n", " df = df.reset_index(drop=True, inplace=False)\n", "\n", - " columns = list(available_mode_map.values()) + ['unknown']\n", + " columns = list(set(av.values())) + ['unknown']\n", "\n", " # Create the columns along with the 'chosen' column.\n", - " df[columns + ['chosen']] = 0\n", + " df[columns] = 0\n", "\n", " row_dicts = list()\n", "\n", - " for _, row in df.iterrows():\n", + " for i, row in df.iterrows():\n", " row_dict = row.to_dict()\n", "\n", " # Access the available modes. Split on ; and strip all the elements.\n", " available_modes = [x.strip() for x in str(row_dict['available_modes']).split(';')]\n", - "\n", + " \n", + " # Set all the available modes associated with the chosen modes too.\n", + " # Toggle this flag off if you don't want the target to be considered as an available mode.\n", + " # row_dict[row_dict['chosen']] = 1\n", + " \n", + " row_dict['chosen'] = target_mapper[row_dict['chosen']]\n", + " \n", " # Update the available modes.\n", " for available_mode in available_modes:\n", - " row_dict[available_mode_map[available_mode]] = 1\n", + " row_dict[av[available_mode]] = 1\n", " \n", " # Now, look at the section_mode_argmax as a proxy for the confirmed mode.\n", - " chosen_modes = section_mode_map[row_dict['section_mode_argmax']]\n", + " chosen_modes = sm[row_dict['section_mode_argmax']]\n", "\n", " # Determine the length of the mapping.\n", " n = len(chosen_modes)\n", "\n", - " # Go ahead and modify the current dict with the first value.\n", - " row_dict['chosen'] = ordinal_mapper[chosen_modes[0]]\n", - " row_dict['primary_mode'] = 1\n", + " # # Go ahead and modify the current dict with the first value.\n", + " # row_dict['chosen'] = ordinal_mapper[chosen_modes[0]]\n", + " # row_dict['primary_mode'] = 1\n", "\n", " # Also update the available modes.\n", " for chosen_mode in chosen_modes:\n", " row_dict[chosen_mode] = 1\n", - " \n", - " # Append thee first row to the list.\n", + "\n", + " # # Append the first row to the list.\n", " row_dicts.append(row_dict)\n", " \n", " constructed = pd.DataFrame(row_dicts)\n", - " constructed.rename(columns=dict([(c, 'av_'+c) for c in ordinal_mapper.keys()]), inplace=True)\n", + " constructed.rename(columns=dict([(c, 'av_'+c) for c in target_mapper.keys()]), inplace=True)\n", "\n", " return constructed" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```language=python\n", + "{'p_micro': 1, 'no_trip': 2, 's_car': 3, 'transit': 4, 'car': 5, 's_micro': 6, 'ridehail': 7, 'walk': 8, 'unknown': 9}\n", + "```" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ - "## Define the mapping strategy\n", + "def generate_target_variable(df: pd.DataFrame, mapper: dict):\n", + " df['chosen'] = df['Replaced_mode'].apply(lambda x: mapper[x])\n", + " df.drop(columns=['Replaced_mode'], inplace=True)\n", + " return df\n", "\n", - "available = {\n", - " 'Bicycle': 'p_micro',\n", - " 'Do not have vehicle': 'no_trip',\n", - " 'Get a ride from a friend or family member': 's_car',\n", - " 'None': 'no_trip',\n", - " 'Public transportation (bus, subway, light rail, etc.)': 'transit',\n", - " 'Rental car (including Zipcar/ Car2Go)': 'car',\n", - " 'Shared bicycle or scooter': 's_micro',\n", - " 'Skateboard': 'p_micro',\n", - " 'Taxi (regular taxi, Uber, Lyft, etc)': 'ridehail',\n", - " 'Walk/roll': 'walk'\n", - "}\n", - "\n", - "# Map each sensed mode to the binary indicators.\n", - "section_mode_mapping = {\n", - " 'bicycling': ['p_micro', 's_micro'],\n", - " 'bus': ['transit'],\n", - " 'car': ['s_car', 'car', 'ridehail'],\n", - " 'no_sensed': ['unknown'],\n", - " 'train': ['transit'],\n", - " 'walking': ['walk']\n", - "}\n", - "\n", - "# These values were obtained using a simple linear regression model. Specifics can be found in time_distance_estimation.ipynb\n", - "duration_dict = {\n", - " 'walk': {\n", - " 'intercept': 14.09698152519682,\n", - " 'coef': 19.153802211061134\n", - " },\n", - " 'p_micro': {\n", - " 'intercept': 4.753590860550791,\n", - " 'coef': 4.2723965916811935\n", - " },\n", - " 's_micro': {\n", - " 'intercept': 4.753590860550791,\n", - " 'coef': 4.2723965916811935\n", - " },\n", - " 's_car': {\n", - " 'intercept': 10.515751350707365, \n", - " 'coef': 1.112111276629702\n", - " },\n", - " 'car': {\n", - " 'intercept': 10.515751350707365, \n", - " 'coef': 1.112111276629702\n", - " },\n", - " 'ridehail': {\n", - " 'intercept': 10.515751350707365, \n", - " 'coef': 1.112111276629702\n", - " },\n", - " 'transit': {\n", - " 'intercept': 7.5000351741492395,\n", - " 'coef': 1.839562017986485\n", - " },\n", - " 'unknown': {\n", - " 'intercept': 9.423026094931313,\n", - " 'coef': 2.640429444466706\n", - " },\n", - " 'no_trip': {\n", - " 'intercept': 0.,\n", - " 'coef': 0.\n", - " }\n", - "}\n", - "\n", - "ordinal_mapping = {'p_micro': 1, 'no_trip': 2, 's_car': 3, 'transit': 4, 'car': 5, 's_micro': 6, 'ridehail': 7, 'walk': 8, 'unknown': 9}\n", - "reverse_ordinal_mapping = {v:k for k,v in ordinal_mapping.items()}\n", - "\n", - "print(ordinal_mapping)" + "filtered_df = generate_target_variable(filtered_df, target_mapping)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ - "# Let's only select data that we require and copy it into a separate df.\n", - "av_df = df_modded[['user_id', 'section_distance_argmax', 'section_duration_argmax', 'section_mode_argmax', 'available_modes']].copy()\n", + "# Generate wide-format availability indicators..\n", + "av_df = generate_available_features(filtered_df, available, section_mode_mapping, ordinal_mapping)\n", "\n", - "# Update the same df.\n", - "av_df = generate_available_features(av_df, available, section_mode_mapping, ordinal_mapping)" + "# Collapse train and bus into 'transit'.\n", + "av_df.loc[av_df.section_mode_argmax.isin(['bus', 'train']), 'section_mode_argmax'] = 'transit'" ] }, { @@ -324,124 +1150,275 @@ "metadata": {}, "outputs": [], "source": [ - "av_df.head(10)" + "av_df.section_mode_argmax.unique()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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5 rows × 79 columns

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" + ], + "text/plain": [ + " user_id _id \\\n", + "0 6373dfb8cb9b47e88e8f76adcfadde20 611fdd7cbc8ce4a9e3911adb \n", + "1 6373dfb8cb9b47e88e8f76adcfadde20 611fdd7cbc8ce4a9e3911add \n", + "2 6373dfb8cb9b47e88e8f76adcfadde20 611fdd7ebc8ce4a9e3911af4 \n", + "3 6373dfb8cb9b47e88e8f76adcfadde20 611fdd7fbc8ce4a9e3911af9 \n", + "4 6373dfb8cb9b47e88e8f76adcfadde20 611fdd7fbc8ce4a9e3911afb \n", + "\n", + " original_user_id cleaned_trip \\\n", + "0 6373dfb8-cb9b-47e8-8e8f-76adcfadde20 6082e48127dcf1f393b70ea8 \n", + "1 6373dfb8-cb9b-47e8-8e8f-76adcfadde20 60830ead2b9762e085f774f2 \n", + "2 6373dfb8-cb9b-47e8-8e8f-76adcfadde20 608b21db654c4a5ab2fa4364 \n", + "3 6373dfb8-cb9b-47e8-8e8f-76adcfadde20 608c1f002f548f5db71d16f9 \n", + "4 6373dfb8-cb9b-47e8-8e8f-76adcfadde20 608c8f7fb2d3b88f6947766b \n", + "\n", + " Mode_confirm start_fmt_time start:year start:month \\\n", + "0 Regular Bike 2021-04-22 18:48:48.365291-06:00 2021.0 4.0 \n", + "1 Regular Bike 2021-04-23 10:45:48.219950-06:00 2021.0 4.0 \n", + "2 Other 2021-04-29 14:05:50.979267-06:00 2021.0 4.0 \n", + "3 Other 2021-04-30 08:34:49.162054-06:00 2021.0 4.0 \n", + "4 Regular Bike 2021-04-30 11:58:07.194775-06:00 2021.0 4.0 \n", + "\n", + " start:day start:hour ... chosen av_s_car av_walk av_car av_ridehail \\\n", + "0 22.0 18.0 ... 1 0 0 0 0 \n", + "1 23.0 10.0 ... 1 0 0 0 0 \n", + "2 29.0 14.0 ... 9 0 0 0 0 \n", + "3 30.0 8.0 ... 9 0 0 0 0 \n", + "4 30.0 11.0 ... 1 0 0 0 0 \n", + "\n", + " av_p_micro av_no_trip av_s_micro av_transit av_unknown \n", + "0 1 0 1 0 0 \n", + "1 1 0 1 0 0 \n", + "2 1 0 1 0 0 \n", + "3 1 0 1 0 0 \n", + "4 1 0 1 0 0 \n", + "\n", + "[5 rows x 79 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "def compute_alt_durations(df: pd.DataFrame, reverse_mapper: dict, duration_mapper: dict):\n", - " '''\n", - " We re-iterate over the generated available mode df and populate the modes.\n", - " We are only concerned with the rows that have primary_mode = 0, which indicates that they are alternate mode rows.\n", - " This method uses the intercept and coefficients obtained from the regression models.\n", - " '''\n", - "\n", - " column_names = list(reverse_mapper.values())\n", - "\n", - " # Start with a 0 cost for everyone.\n", - " df[column_names] = 0\n", - "\n", - " rows = list()\n", - " for ix, row in df.iterrows():\n", - " \n", - " # Convert to a dict\n", - " row_dict = row.to_dict()\n", - "\n", - " for mode in column_names:\n", - " if mode == reverse_mapper[row['chosen']]:\n", - " row_dict[mode] = row_dict['section_distance_argmax']\n", - " continue\n", - " \n", - " mode_params = duration_mapper[mode]\n", - " # use availability as a mask.\n", - " row_dict[mode] = row_dict['av_' + mode] * (mode_params['intercept'] + (mode_params['coef'] * row_dict['section_distance_argmax']))\n", - " \n", - " rows.append(row_dict)\n", - " \n", - " return pd.DataFrame(rows).rename(columns=dict([(c, 'tt_'+c) for c in column_names]), inplace=False)" + "display(av_df.head())" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ - "def compute_durations_using_data(df: pd.DataFrame, section_mapper: dict, reverse_ordinal_mapping: dict):\n", - " \"\"\"\n", - " Function to perform imputation using historical averages. Instead of estimating mode duration from mode distance, we simply\n", - " impute it using the mean of the particular mode's historical duration.\n", - " \"\"\"\n", - "\n", - " # First, using the history, generate the average durations calculated per mode.\n", - " duration_dict = df.groupby('section_mode_argmax')['section_duration_argmax'].mean().to_dict()\n", - "\n", - " extended_dict = dict()\n", - " for k, v in duration_dict.items():\n", - " mapped_sections = section_mapper[k]\n", - " for _k in mapped_sections:\n", - " extended_dict[_k] = v\n", - "\n", - " # Manually add one more.\n", - " extended_dict['no_trip'] = 0.\n", - "\n", - " print(extended_dict)\n", - "\n", - " # NOW, we iterate over the dataframe.\n", - " new_columns = list(reverse_ordinal_mapping.values())\n", - "\n", - " # Create and init to 0.\n", - " df[new_columns] = 0.\n", - "\n", - " rows = []\n", - "\n", - " for _, row in df.iterrows():\n", - " row_dict = row.to_dict()\n", + "# def compute_alt_durations(df: pd.DataFrame, reverse_mapper: dict, duration_mapper: dict):\n", + "# '''\n", + "# We re-iterate over the generated available mode df and populate the modes.\n", + "# We are only concerned with the rows that have primary_mode = 0, which indicates that they are alternate mode rows.\n", + "# '''\n", "\n", - " # First, look at the section_mode_argmax\n", - " mapped_modes = section_mapper[row_dict['section_mode_argmax']]\n", + "# column_names = list(reverse_mapper.values())\n", "\n", - " for mode in mapped_modes:\n", - " section_duration = row_dict['section_duration_argmax']\n", - " if section_duration > 0:\n", - " row_dict[mode] = row_dict['section_duration_argmax']\n", - " else:\n", - " row_dict[mode] = extended_dict[mode]\n", + "# # Start with a 0 cost for everyone.\n", + "# df[column_names] = 0\n", "\n", - " # for the remaining modes, estimate their duration through the dict.\n", - " remaining = [mode for mode in new_columns if mode not in mapped_modes]\n", + "# rows = list()\n", + "# for ix, row in df.iterrows():\n", + " \n", + "# # Convert to a dict\n", + "# row_dict = row.to_dict()\n", "\n", - " for mode in remaining:\n", - " # If you'd like to use a mask here, use the av_ dummy values to mask-out irrelevant data.\n", - " row_dict[mode] = extended_dict[mode]\n", + "# for mode in column_names:\n", + "# if mode == reverse_mapper[row['chosen']]:\n", + "# row_dict[mode] = row_dict['section_distance_argmax']\n", + "# continue\n", + " \n", + "# mode_params = duration_mapper[mode]\n", + "# # use availability as a mask.\n", + "# row_dict[mode] = mode_params['intercept'] + (mode_params['coef'] * row_dict['section_distance_argmax'])\n", " \n", - " rows.append(row_dict)\n", + "# rows.append(row_dict)\n", " \n", - " return_df = pd.DataFrame(rows)\n", - " return_df.rename(columns=dict([(c, 'tt_'+c) for c in new_columns]), inplace=True)\n", - " return return_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "av_time_df = compute_durations_using_data(av_df, section_mode_mapping, reverse_ordinal_mapping)" + "# return pd.DataFrame(rows).rename(columns=dict([(c, 'tt_'+c) for c in column_names]), inplace=False)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ - "av_time_df.head()" + "# av_time_df = compute_alt_durations(av_df, reverse_ordinal_mapping, duration_dict)" ] }, { @@ -457,17 +1434,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "# All values are taken from VTPI.\n", - "\n", + "# https://www.vtpi.org/tca/tca0501.pdf\n", "mode_cost_per_mile = {\n", " # bicycle/skateboard\n", " 'p_micro': 0.,\n", " 'no_trip': 0.,\n", - " # Shared car would be half the cost of regular car, which is $0.6/mile.\n", + " # Shared car is half the cost of regular car, which is $0.6/mile.\n", " 's_car': 0.3,\n", " # Rental car.\n", " 'car': 0.6,\n", @@ -484,22 +1461,26 @@ "\n", "# All 0 for now.\n", "mode_init_cost = {\n", - " k: 0 for k in mode_cost_per_mile.keys()\n", + " 'p_micro': 0.,\n", + " 'no_trip': 0.,\n", + " # Shared car is half the cost of regular car, which is $0.6/mile.\n", + " 's_car': 0.,\n", + " # Rental car.\n", + " 'car': 0.,\n", + " # Average of bus and train taken.\n", + " 'transit': 0.,\n", + " # $1 unlocking cost.\n", + " 's_micro': 1.,\n", + " # uber/taxi/lyft\n", + " 'ridehail': 0.,\n", + " 'walk': 0.,\n", + " 'unknown': 0.\n", "}" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "av_time_df.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -508,8 +1489,6 @@ " # Create some extra colums.\n", " columns = list(ordinal_mapper.keys())\n", "\n", - " print(columns)\n", - "\n", " # Initialize the columns to 0.\n", " df[columns] = 0.\n", "\n", @@ -520,15 +1499,12 @@ " # Check which flags are active.\n", " row_dict = row.to_dict()\n", "\n", - " # ---- Not using the av_ features as a mask. ----\n", - " # # Iterate over the columns and retain the columns with an available mode of 1. Returns a list of [av_* keys]\n", - " # columns = [c for c in row_dict.keys() if c.startswith('av_') and row_dict[c] == 1]\n", - "\n", " # Access the section_distance_argmax attribute for the distance. Note that this is now in miles.\n", " distance = row_dict['section_distance_argmax']\n", - "\n", + " \n", + " # Mask using availability.\n", " for lookup in columns:\n", - " row_dict[lookup] = init_cost_mapper[lookup] + (cost_mapper[lookup] * distance)\n", + " row_dict[lookup] = row_dict['av_' + lookup] * (init_cost_mapper[lookup] + (cost_mapper[lookup] * distance))\n", "\n", " rows.append(row_dict)\n", "\n", @@ -538,13 +1514,195 @@ " return new_df" ] }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "cost_df = compute_cost_estimates(\n", + " av_df, cost_mapper=mode_cost_per_mile, \n", + " init_cost_mapper=mode_init_cost, \n", + " ordinal_mapper=ordinal_mapping\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cost_p_microcost_no_tripcost_s_carcost_transitcost_carcost_s_microcost_ridehailcost_walkcost_unknown
count42581.042581.042581.00000042581.00000042581.00000042581.00000042581.00000042581.042581.0
mean0.00.00.7306771.0990121.2922300.4135214.6961480.00.0
std0.00.00.7667831.4103881.6028410.7745875.1796420.00.0
min0.00.00.0000000.0000000.0000000.0000000.0000000.00.0
25%0.00.00.0640500.0000000.0000000.0000000.0000000.00.0
50%0.00.00.5569000.6609250.8623170.0000003.5109220.00.0
75%0.00.01.0790301.6329992.1067100.0000007.1414600.00.0
max0.00.04.2549539.4458228.5099055.25376428.3663510.00.0
\n", + "
" + ], + "text/plain": [ + " cost_p_micro cost_no_trip cost_s_car cost_transit cost_car \\\n", + "count 42581.0 42581.0 42581.000000 42581.000000 42581.000000 \n", + "mean 0.0 0.0 0.730677 1.099012 1.292230 \n", + "std 0.0 0.0 0.766783 1.410388 1.602841 \n", + "min 0.0 0.0 0.000000 0.000000 0.000000 \n", + "25% 0.0 0.0 0.064050 0.000000 0.000000 \n", + "50% 0.0 0.0 0.556900 0.660925 0.862317 \n", + "75% 0.0 0.0 1.079030 1.632999 2.106710 \n", + "max 0.0 0.0 4.254953 9.445822 8.509905 \n", + "\n", + " cost_s_micro cost_ridehail cost_walk cost_unknown \n", + "count 42581.000000 42581.000000 42581.0 42581.0 \n", + "mean 0.413521 4.696148 0.0 0.0 \n", + "std 0.774587 5.179642 0.0 0.0 \n", + "min 0.000000 0.000000 0.0 0.0 \n", + "25% 0.000000 0.000000 0.0 0.0 \n", + "50% 0.000000 3.510922 0.0 0.0 \n", + "75% 0.000000 7.141460 0.0 0.0 \n", + "max 5.253764 28.366351 0.0 0.0 " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cost_df[[c for c in cost_df.columns if 'cost_' in c]].describe()" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "cost_df = compute_cost_estimates(av_time_df, cost_mapper=mode_cost_per_mile, init_cost_mapper=mode_init_cost, ordinal_mapper=ordinal_mapping)" + "cost_df.is_male = cost_df.apply(lambda x: 1 if x.gender == 'Man' else 0, axis=1)" ] }, { @@ -553,7 +1711,23 @@ "metadata": {}, "outputs": [], "source": [ - "cost_df[['user_id'] + [c for c in cost_df.columns if 'av_' in c or 'cost_' in c or 'tt_' in c]].head()" + "student_status = {\n", + " 'Not a student': 0,\n", + " 'Yes - Full Time College/University': 1,\n", + " 'Yes - Vocation/Technical/Trade School': 1,\n", + " 'Yes - Part-Time College/University': 1,\n", + " 'Fire Fighter 2 Training': 0,\n", + " 'Taking prerequisites missing for grad program ': 1,\n", + " 'Work': 0,\n", + " 'Graduate': 1,\n", + " 'Work at csu': 0,\n", + " 'Custodian': 0, \n", + " 'taking classes toward early childhood licensure': 1,\n", + " 'Prefer not to say': 0\n", + "}\n", + "\n", + "\n", + "cost_df['is_student'] = cost_df['is_student'].apply(lambda x: student_status[x])" ] }, { @@ -562,8 +1736,30 @@ "metadata": {}, "outputs": [], "source": [ - "cost_df.to_csv('../data/preprocessed_data.csv', index=False)" + "cost_df['age'] = cost_df['age'].apply(lambda x: x if x < 100 else 2024 - x)\n", + "cost_df['n_working_residents'] = cost_df['n_working_residents'].apply(lambda x: 0 if x < 0 else x)\n", + "cost_df.rename(\n", + " columns={'start_local_dt_weekday': 'start:DOW', 'end_local_dt_weekday': 'end:DOW'},\n", + " inplace=True\n", + ")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# cost_df.to_csv('../data/FULL_preprocessed_data_RM_weather.csv', index=False)\n", + "cost_df.to_csv('../data/ReplacedMode_Fix_02072024.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -571,9 +1767,9 @@ "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" }, "kernelspec": { - "display_name": "Python 3.9.16 ('emission')", + "display_name": "emission", "language": "python", - "name": "python3" + "name": "emission" }, "language_info": { "codemirror_mode": { @@ -586,8 +1782,7 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.16" - }, - "orig_nbformat": 4 + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/viz_scripts/rm_src/mnl_exploration.ipynb b/viz_scripts/rm_src/mnl_exploration.ipynb new file mode 100644 index 0000000..9ca40bb --- /dev/null +++ b/viz_scripts/rm_src/mnl_exploration.ipynb @@ -0,0 +1,935 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import pickle\n", + "import importlib\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "\n", + "from pathlib import Path\n", + "from uuid import UUID\n", + "from collections import defaultdict\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "emission_path = Path(os.getcwd()).parent.parent / 'my_emission_server' / 'e-mission-server'\n", + "sys.path.append(str(emission_path))\n", + "\n", + "# Also add the home (viz_scripts) to the path\n", + "sys.path.append('../viz_scripts')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import scaffolding\n", + "import emission.core.get_database as edb" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "importlib.reload(scaffolding)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def r(df: pd.DataFrame) -> pd.DataFrame:\n", + " return df.reset_index(drop=True, inplace=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "with open('../viz_scripts/auxiliary_files/dic_re.pkl', 'rb') as f:\n", + " dic_re = pickle.loads(f.read())\n", + "\n", + "with open('../viz_scripts/auxiliary_files/dic_pur.pkl', 'rb') as f:\n", + " dic_pur = pickle.loads(f.read())\n", + "\n", + "# convert a dictionary to a defaultdict\n", + "dic_re = defaultdict(lambda: 'Other', dic_re)\n", + "dic_pur = defaultdict(lambda: 'Other', dic_pur)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found new program stage, creating new list\n", + "Found new program 4c, creating new list\n", + "Found new program cc, creating new list\n", + "Found new program fc, creating new list\n", + "Found new program pc, creating new list\n", + "Found new program sc, creating new list\n", + "Found new program vail, creating new list\n", + "Found new program prepilot, creating new list\n" + ] + } + ], + "source": [ + "# Split UUIDs by program\n", + "program_uuid_map = {}\n", + "for ue in edb.get_uuid_db().find():\n", + " uuid = str(ue['uuid'])\n", + " # uuid = str(ue['uuid'])\n", + " program = ue['user_email'].split(\"_\")[0]\n", + " if program in program_uuid_map.keys():\n", + " program_uuid_map[program].append(uuid)\n", + " else:\n", + " print(f\"Found new program {program}, creating new list\")\n", + " program_uuid_map[program] = []\n", + " program_uuid_map[program].append(uuid)\n", + "\n", + "uuid_program_list = []\n", + "for ue in edb.get_uuid_db().find():\n", + " # uuid = str(ue['uuid'].as_uuid(3))\n", + " uuid = str(ue['uuid'])\n", + " program = ue['user_email'].split(\"_\")[0]\n", + " uuid_program_list.append({\"program\": program, \"opcode\": ue[\"user_email\"], \"user_id\": uuid})" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "uuid_program_df = pd.DataFrame(uuid_program_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded all confirmed trips of length 241123\n", + "After filtering, found 241123 participant trips \n", + "After filtering, found 92446 labeled trips\n", + "Found Index(['mode_confirm', 'purpose_confirm', 'replaced_mode'], dtype='object') columns of length 3\n", + "After expanding, columns went from 41 -> 44\n", + "_prepilot\n", + "Based on 92395 confirmed trips from 235 users\n", + "of 241123 total trips from 261 users (38.32%)\n" + ] + } + ], + "source": [ + "# %%capture\n", + "\n", + "# for program in uuid_program_df.program.unique():\n", + "expanded_ct, file_suffix, quality_text, debug_df = scaffolding.load_viz_notebook_data(None,\n", + " None,\n", + " 'prepilot',\n", + " 'program',\n", + " dic_re,\n", + " dic_pur=dic_pur)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Join to the program df to get each user's program\n", + "expanded_ct['original_user_id'] = expanded_ct['user_id'].copy()\n", + "expanded_ct['user_id'] = expanded_ct['user_id'].apply(lambda x: str(x))\n", + "expanded_ct = expanded_ct.merge(uuid_program_df, on='user_id')\n", + "expanded_ct['user_id'] = expanded_ct['user_id'].apply(lambda x: str(x).replace(\"-\", \"\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['source', 'end_ts', 'end_fmt_time', 'end_loc', 'raw_trip', 'start_ts',\n", + " 'start_fmt_time', 'start_loc', 'duration', 'distance', 'start_place',\n", + " 'end_place', 'cleaned_trip', 'inferred_labels', 'inferred_trip',\n", + " 'expectation', 'confidence_threshold', 'expected_trip', 'user_input',\n", + " 'section_modes', 'section_distances', 'start_local_dt_year',\n", + " 'start_local_dt_month', 'start_local_dt_day', 'start_local_dt_hour',\n", + " 'start_local_dt_minute', 'start_local_dt_second',\n", + " 'start_local_dt_weekday', 'start_local_dt_timezone',\n", + " 'end_local_dt_year', 'end_local_dt_month', 'end_local_dt_day',\n", + " 'end_local_dt_hour', 'end_local_dt_minute', 'end_local_dt_second',\n", + " 'end_local_dt_weekday', 'end_local_dt_timezone', '_id', 'user_id',\n", + " 'metadata_write_ts', 'additions', 'mode_confirm', 'purpose_confirm',\n", + " 'replaced_mode', 'distance_miles', 'Mode_confirm', 'Replaced_mode',\n", + " 'Trip_purpose', 'original_user_id', 'program', 'opcode'],\n", + " dtype='object')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expanded_ct.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Rename the target column.\n", + "expanded_ct.drop(columns=['replaced_mode'], axis='rows', inplace=True)\n", + "expanded_ct['Replaced_mode'] = expanded_ct['Replaced_mode'].fillna('Unlabeled')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# We only wish to focus on Denver data for now.\n", + "\n", + "denver_data = r(expanded_ct.loc[\n", + " (expanded_ct.start_local_dt_timezone == \"America/Denver\") & (expanded_ct.end_local_dt_timezone == \"America/Denver\"), \n", + " :])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "denver_data['start_fmt_time'] = pd.to_datetime(\n", + " denver_data['start_fmt_time'], utc=True\n", + ").dt.tz_convert('America/Denver')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2020-09-22 17:13:55.883513-06:00 2022-12-30 23:33:27.147785-07:00\n" + ] + } + ], + "source": [ + "print(denver_data.start_fmt_time.min(), denver_data.start_fmt_time.max())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Parse the datetime to Denver time.\n", + "# denver_data['start_fmt_time'] = pd.to_datetime(denver_data['start_fmt_time'], utc=True).dt.tz_convert('America/Denver')\n", + "\n", + "# Re-compute all the start variables.\n", + "denver_data['start_local_dt_year'] = denver_data['start_fmt_time'].dt.year\n", + "denver_data['start_local_dt_month'] = denver_data['start_fmt_time'].dt.month\n", + "denver_data['start_local_dt_day'] = denver_data['start_fmt_time'].dt.day\n", + "denver_data['start_local_dt_hour'] = denver_data['start_fmt_time'].dt.hour\n", + "denver_data['start_local_dt_weekday'] = denver_data['start_fmt_time'].dt.weekday\n", + "\n", + "## Do the same with the end time.\n", + "denver_data['end_fmt_time'] = pd.to_datetime(denver_data['end_fmt_time'], utc=True).dt.tz_convert('America/Denver')\n", + "\n", + "# Re-compute all the end variables.\n", + "denver_data['end_local_dt_year'] = denver_data['end_fmt_time'].dt.year\n", + "denver_data['end_local_dt_month'] = denver_data['end_fmt_time'].dt.month\n", + "denver_data['end_local_dt_day'] = denver_data['end_fmt_time'].dt.day\n", + "denver_data['end_local_dt_hour'] = denver_data['end_fmt_time'].dt.hour\n", + "denver_data['end_local_dt_weekday'] = denver_data['end_fmt_time'].dt.weekday" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Read the Demographic data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read the Denver dedmographic info\n", + "survey_data = pd.read_csv('../viz_scripts/Can Do Colorado eBike Program - en.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# column renaming here!\n", + "\n", + "survey_data.rename(\n", + " {\n", + " \"Unique User ID (auto-filled, do not edit)\": \"user_id\",\n", + " \"In which year were you born?\": \"birth_year\",\n", + " \"What is your gender?\": \"gender\",\n", + " \"Do you have a valid driver's license?\": \"has_drivers_license\",\n", + " \"Are you a student?\": \"is_student\",\n", + " \"What is the highest grade or degree that you have completed?\": \"highest_education\",\n", + " \"Do you work for either pay or profit?\": \"is_paid\",\n", + " \"Do you have more than one job?\": \"has_multiple_jobs\",\n", + " \"Do you work full-time or part-time at your primary job?\": \"primary_job_type\",\n", + " \"Which best describes your primary job?\": \"primary_job_description\",\n", + " \"How did you usually get to your primary job last week? \": \"primary_job_commute_mode\",\n", + " \"Thinking about your daily commute to work last week, how many minutes did it usually take to get from home to the primary job/work place?\": \"primary_job_commute_time\",\n", + " \"At your primary job, do you have the ability to set or change your own start time?\": \"is_primary_job_flexible\",\n", + " \"Do you have the option of working from home or an alternate location instead of going into your primary work place?\": \"primary_job_can_wfh\",\n", + " \"How many days per week do you usually work from home or an alternate location?\": \"wfh_days\",\n", + " \"Do you own or rent your place of residence?\": \"residence_ownership_type\",\n", + " \"What is your home type?\": \"residence_type\",\n", + " \"Please identify which category represents your total household income, before taxes, for last year.\": \"income_category\",\n", + " \"Including yourself, how many people live in your home?\": \"n_residence_members\",\n", + " \"How many children under age 18 live in your home?\": \"n_residents_u18\",\n", + " \"Including yourself, how many people have a driver's license in your household?\": \"n_residents_with_license\",\n", + " \"How many motor vehicles are owned, leased, or available for regular use by the people who currently live in your household?\": \"n_motor_vehicles\",\n", + " \"If you were unable to use your household vehicle(s), which of the following options would be available to you to get you from place to place?\": \"available_modes\",\n", + " \"Do you have a medical condition that makes it difficult to travel outside of the home?\": \"has_medical_condition\",\n", + " \"How long have you had this condition?\": \"medical_condition_duration\"\n", + " },\n", + " axis='columns',\n", + " inplace=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Now, if we have duplicate users, we'd like to retain the last chronological entry.\n", + "survey_data = survey_data.loc[~((survey_data.user_id.isna())|(survey_data.user_id == \"\")), :]\n", + "\n", + "# timezonoe-aware parsing:\n", + "survey_data['Timestamp'] = survey_data['Timestamp'].str.replace('PDT|PST', '', regex=True)\n", + "survey_data['Timestamp'] = pd.to_datetime(survey_data['Timestamp']).dt.tz_localize('America/Denver')\n", + "\n", + "# Sort by user_id and time, then drop everything but the last entry.\n", + "survey_data.sort_values(by=['user_id', 'Timestamp'], ascending=True, inplace=True, axis='rows')\n", + "survey_data.drop_duplicates(['user_id'], keep='last', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Merge the trip data with the survey data.\n", + "\n", + "merged_data = denver_data.merge(\n", + " survey_data, left_on='user_id', right_on='user_id'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's start choosing features for modeling.\n", + "\n", + "base_time_features = ['fmt_time', 'local_dt_year', 'local_dt_month', 'local_dt_day', 'local_dt_hour', 'local_dt_weekday']\n", + "time_features = ['start_' + x for x in base_time_features] + ['end_' + x for x in base_time_features]\n", + "\n", + "demographic_features = ['available_modes',\n", + " 'birth_year', 'income_category', 'n_motor_vehicles', 'n_residence_members', 'n_residents_u18', 'gender', \n", + " 'is_student', 'n_residents_with_license']\n", + "\n", + "sensed_features = ['duration', 'distance_miles', 'cleaned_trip', 'start_loc', 'end_loc', 'section_modes', 'section_distances']\n", + "\n", + "modeling_data = merged_data[['user_id', '_id', 'original_user_id', 'cleaned_trip', 'Replaced_mode', 'Mode_confirm'] + time_features + demographic_features + sensed_features].copy()\n", + "\n", + "# Rename columns in-place.\n", + "modeling_data.rename(columns={\n", + " 'start_local_dt_year': 'start:year', 'start_local_dt_month': 'start:month', 'start_local_dt_day': 'start:day', 'start_local_dt_hour': 'start:hour',\n", + " 'end_local_dt_year': 'end:year', 'end_local_dt_month': 'end:month', 'end_local_dt_day': 'end:day', 'end_local_dt_hour': 'end:hour'\n", + " }, inplace=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "modeling_data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from calendar import monthrange\n", + "\n", + "# Find day of month: use monthrange with (mm, yyyy) args and find how many days that month had (leap years are supported).\n", + "def get_num_days_in_month(yyyy, mm):\n", + " return monthrange(yyyy, mm)[1]\n", + "\n", + "def is_overnight_trip(start_date, end_date):\n", + " return int((end_date - start_date).days > 0)\n", + "\n", + "# get the number of days for the start and end times.\n", + "modeling_data['start:n_days_in_month'] = modeling_data.apply(lambda x: get_num_days_in_month(x['start:year'], x['start:month']), axis=1)\n", + "modeling_data['end:n_days_in_month'] = modeling_data.apply(lambda x: get_num_days_in_month(x['end:year'], x['end:month']), axis=1)\n", + "\n", + "# age = current year - year of birth\n", + "modeling_data['age'] = 2023 - modeling_data['birth_year']\n", + "\n", + "# overnight trips may be more likely taken by car.\n", + "modeling_data['is_overnight_trip'] = modeling_data.apply(lambda x: is_overnight_trip(x.start_fmt_time, x.end_fmt_time), axis=1)\n", + "\n", + "# Number of working individuals in the household = number of individuals in the house - number of children.\n", + "modeling_data['n_working_residents'] = (modeling_data['n_residence_members'] - modeling_data['n_residents_u18']).astype(int)\n", + "\n", + "# Create a binary indicator.\n", + "modeling_data['is_male'] = modeling_data.gender.apply(lambda x: 1 if x==\"Male\" else 0)\n", + "\n", + "# Bin the number of vehicles owned.\n", + "# Drop the observations with (Prefer not to say)\n", + "modeling_data = modeling_data.loc[~modeling_data['n_motor_vehicles'].isin(['Prefer not to say / Prefiero no decir.']), :]\n", + "modeling_data.loc[modeling_data['n_motor_vehicles'].isin(['4+']), 'n_motor_vehicles'] = 4\n", + "modeling_data['n_motor_vehicles'] = modeling_data['n_motor_vehicles'].astype(int)\n", + "\n", + "# Convert the total duration of the trip into minutes.\n", + "modeling_data[['duration']] = modeling_data[['duration']]/60\n", + "\n", + "# Extract start and end latitudes and longitudes.\n", + "modeling_data['start_lat'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][1])\n", + "modeling_data['start_lng'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][0])\n", + "\n", + "modeling_data['end_lat'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][1])\n", + "modeling_data['end_lng'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from calendar import monthrange\n", + "\n", + "# Find day of month: use monthrange with (mm, yyyy) args and find how many days that month had (leap years are supported).\n", + "def get_num_days_in_month(yyyy, mm):\n", + " return monthrange(yyyy, mm)[1]\n", + "\n", + "def is_overnight_trip(start_date, end_date):\n", + " return int((end_date - start_date).days > 0)\n", + "\n", + "# get the number of days for the start and end times.\n", + "modeling_data['start:n_days_in_month'] = modeling_data.apply(lambda x: get_num_days_in_month(x['start:year'], x['start:month']), axis=1)\n", + "modeling_data['end:n_days_in_month'] = modeling_data.apply(lambda x: get_num_days_in_month(x['end:year'], x['end:month']), axis=1)\n", + "\n", + "# age = current year - year of birth\n", + "modeling_data['age'] = 2023 - modeling_data['birth_year']\n", + "\n", + "# overnight trips may be more likely taken by car.\n", + "modeling_data['is_overnight_trip'] = modeling_data.apply(lambda x: is_overnight_trip(x.start_fmt_time, x.end_fmt_time), axis=1)\n", + "\n", + "# Number of working individuals in the household = number of individuals in the house - number of children.\n", + "modeling_data['n_working_residents'] = (modeling_data['n_residence_members'] - modeling_data['n_residents_u18']).astype(int)\n", + "\n", + "# Create a binary indicator.\n", + "modeling_data['is_male'] = modeling_data.gender.apply(lambda x: 1 if x==\"Male\" else 0)\n", + "\n", + "# Bin the number of vehicles owned.\n", + "# Drop the observations with (Prefer not to say)\n", + "modeling_data = modeling_data.loc[~modeling_data['n_motor_vehicles'].isin(['Prefer not to say / Prefiero no decir.']), :]\n", + "modeling_data.loc[modeling_data['n_motor_vehicles'].isin(['4+']), 'n_motor_vehicles'] = 4\n", + "modeling_data['n_motor_vehicles'] = modeling_data['n_motor_vehicles'].astype(int)\n", + "\n", + "# Convert the total duration of the trip into minutes.\n", + "modeling_data[['duration']] = modeling_data[['duration']]/60\n", + "\n", + "# Extract start and end latitudes and longitudes.\n", + "modeling_data['start_lat'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][1])\n", + "modeling_data['start_lng'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][0])\n", + "\n", + "modeling_data['end_lat'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][1])\n", + "modeling_data['end_lng'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Time-related feature engineeering:\n", + "'''\n", + "HOD: hour of day\n", + "DOM: day of month\n", + "MOY: month of year\n", + "'''\n", + "\n", + "def get_HOD(hour, how='sin'):\n", + " if how == 'sin':\n", + " return np.sin(2 * np.pi * (hour/24))\n", + " return np.cos(2 * np.pi * (hour/24))\n", + "\n", + "def get_DOM(day, n_days, how='sin'):\n", + " if how == 'sin':\n", + " return np.sin(2 * np.pi * (day/n_days))\n", + " return np.cos(2 * np.pi * (day/n_days))\n", + "\n", + "def get_MOY(month, how='sin'):\n", + " if how == 'sin':\n", + " return np.sin(2 * np.pi * (month/12))\n", + " return np.cos(2 * np.pi * (month/12))\n", + "\n", + "# Start - sin\n", + "modeling_data['start:sin_HOD'] = modeling_data.apply(lambda x: get_HOD(x['start:hour']), axis=1)\n", + "modeling_data['start:sin_DOM'] = modeling_data.apply(lambda x: get_DOM(x['start:day'], x['start:n_days_in_month']), axis=1)\n", + "modeling_data['start:sin_MOY'] = modeling_data.apply(lambda x: get_MOY(x['start:year']), axis=1)\n", + "\n", + "# Start - cos\n", + "modeling_data['start:cos_HOD'] = modeling_data.apply(lambda x: get_HOD(x['start:hour'], how='cos'), axis=1)\n", + "modeling_data['start:cos_DOM'] = modeling_data.apply(lambda x: get_DOM(x['start:day'], x['start:n_days_in_month'], how='cos'), axis=1)\n", + "modeling_data['start:cos_MOY'] = modeling_data.apply(lambda x: get_MOY(x['start:year'], how='cos'), axis=1)\n", + "\n", + "# End - sin\n", + "modeling_data['end:sin_HOD'] = modeling_data.apply(lambda x: get_HOD(x['end:hour']), axis=1)\n", + "modeling_data['end:sin_DOM'] = modeling_data.apply(lambda x: get_DOM(x['end:day'], x['end:n_days_in_month']), axis=1)\n", + "modeling_data['end:sin_MOY'] = modeling_data.apply(lambda x: get_MOY(x['end:year']), axis=1)\n", + "\n", + "# End - cos\n", + "modeling_data['end:cos_HOD'] = modeling_data.apply(lambda x: get_HOD(x['end:hour'], how='cos'), axis=1)\n", + "modeling_data['end:cos_DOM'] = modeling_data.apply(lambda x: get_DOM(x['end:day'], x['end:n_days_in_month'], how='cos'), axis=1)\n", + "modeling_data['end:cos_MOY'] = modeling_data.apply(lambda x: get_MOY(x['end:year'], how='cos'), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "modeling_data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, for every trip, we have the corresponding section mode that covered the longest distance for the trip.\n", + "\n", + "Using this as well as the `available_modes` column:\n", + "\n", + "```language=python\n", + "\n", + " # unique available modes:\n", + " {'Bicycle',\n", + " 'Do not have vehicle ',\n", + " 'Get a ride from a friend or family member',\n", + " 'None',\n", + " 'Public transportation (bus, subway, light rail, etc.)',\n", + " 'Rental car (including Zipcar/ Car2Go)',\n", + " 'Shared bicycle or scooter',\n", + " 'Skateboard',\n", + " 'Taxi (regular taxi, Uber, Lyft, etc)',\n", + " 'Walk/roll'}\n", + "\n", + " # unique section modes:\n", + " {'bicycling', 'bus', 'car', 'no_sensed', 'train', 'walking'}\n", + "\n", + " \n", + "```\n", + "\n", + "What mapping can we establish here? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def remove_air_or_hsr(df):\n", + "\n", + " df['mark'] = 0\n", + "\n", + " for ix, row in df.iterrows():\n", + " sections = row['section_modes']\n", + " if 'air_or_hsr' in sections:\n", + " df.loc[ix, 'mark'] = 1\n", + " \n", + " df = r(df.loc[df.mark == 0, :])\n", + " df.drop(columns=['mark'], inplace=True)\n", + "\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "modeling_data = remove_air_or_hsr(modeling_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "modeling_data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "importlib.reload(scaffolding)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results = list()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "modeling_data = modeling_data.loc[:,~modeling_data.columns.duplicated()].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# | [a, b, c] | start_time | end_time |\n", + "# -> | [a, b, c] | [s1, s2, s3] |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print(modeling_data.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# I manually split the modeling data into chunks of 10000 points at one time.\n", + "# This significantly expedited the processing time. Each chunk takes ~26 minutes to finish.\n", + "# split = modeling_data.loc[70000:, :]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# display(split[['original_user_id', 'cleaned_trip']].head())\n", + "# print(split.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# from time import perf_counter\n", + "\n", + "# now = perf_counter()\n", + "# result = scaffolding.get_section_durations(split)\n", + "# end = perf_counter() - now\n", + "\n", + "# print(f\"Took {end/60} minutes to complete\")\n", + "# results.append(result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print(len(results))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# final_df = pd.concat(results, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print(modeling_data.shape[0], final_df.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# modeling_data.to_csv('../data/modeling_data.csv', index=False)\n", + "# final_df.to_csv('../data/modeling_w_duration.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# from time import perf_counter\n", + "# importlib.reload(scaffolding)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('../data/modeling_w_duration.csv')\n", + "df.drop_duplicates(inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# df.shape, modeling_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dummy = modeling_data.iloc[50000:, :]\n", + "\n", + "print(dummy.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# now = perf_counter()\n", + "df_modded = scaffolding.get_section_coordinates(dummy)\n", + "# end = perf_counter() - now\n", + "\n", + "# print(f\"Fetched sections in {end/60} minutes\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def verify(locations):\n", + " return locations is not None and len(locations) > 0\n", + "\n", + "\n", + "bools = df_modded.apply(lambda x: verify(x.section_locations_argmax), axis=1)\n", + "print(bools.all())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.append(df_modded)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(len(results))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_results = pd.concat(results, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_df = pd.concat([df, final_results['section_locations_argmax']], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_df[['section_modes', 'section_distances', 'section_durations', 'section_locations_argmax']].head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_df.to_csv('../data/final_modeling_data.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "final_df.section_locations_argmax[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" + }, + "kernelspec": { + "display_name": "emission", + "language": "python", + "name": "emission" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 9bc5760b51e4af97fd1ee639b19350c6049bdae8 Mon Sep 17 00:00:00 2001 From: Rahul Kulhalli Date: Wed, 14 Feb 2024 10:50:35 -0500 Subject: [PATCH 07/16] Fixed broken commit --- rm_src/benchmark_analysis.ipynb | 381 + rm_src/cost_time_avl_preprocessing.ipynb | 1847 ++ rm_src/mnl_exploration.ipynb | 2115 ++ viz_scripts/denver_weather_data.csv | 26329 +++++++++++++++++++++ 4 files changed, 30672 insertions(+) create mode 100644 rm_src/benchmark_analysis.ipynb create mode 100644 rm_src/cost_time_avl_preprocessing.ipynb create mode 100644 rm_src/mnl_exploration.ipynb create mode 100644 viz_scripts/denver_weather_data.csv diff --git a/rm_src/benchmark_analysis.ipynb b/rm_src/benchmark_analysis.ipynb new file mode 100644 index 0000000..104dd51 --- /dev/null +++ b/rm_src/benchmark_analysis.ipynb @@ -0,0 +1,381 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "269547d8", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "28343f2b", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('../data/FULL_preprocessed_data_RM_weather.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "fa60582f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "E-bike 20029\n", + "Gas Car, drove alone 18412\n", + "Gas Car, with others 16684\n", + "Walk 10857\n", + "Not a Trip 2420\n", + "Regular Bike 1703\n", + "Bus 1363\n", + "Other 1145\n", + "Taxi/Uber/Lyft 310\n", + "Bikeshare 84\n", + "Train 67\n", + "Scooter share 52\n", + "Free Shuttle 50\n", + "Skate board 41\n", + "Name: Mode_confirm, dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.Mode_confirm.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "7e380c56", + "metadata": {}, + "outputs": [], + "source": [ + "# hyp = df[df.Mode_confirm == 'E-bike']\n", + "hyp = df.copy()\n", + "hyp = hyp[['income_category', 'n_motor_vehicles',\n", + " 'n_residence_members', 'n_residents_u18', 'is_student',\n", + " 'n_residents_with_license', 'duration', 'distance_miles',\n", + " 'age', 'is_overnight_trip', 'n_working_residents', 'is_male',\n", + " 'start:sin_HOD', \n", + " 'start:cos_HOD',\n", + " 'end:sin_HOD', 'end:cos_HOD', 'temperature_2m (°F)',\n", + " 'relative_humidity_2m (%)', 'dew_point_2m (°F)', 'rain (inch)',\n", + " 'snowfall (inch)', 'cloud_cover (%)', 'wind_speed_10m (mp/h)',\n", + " 'wind_gusts_10m (mp/h)', 'section_distance_argmax',\n", + " 'section_duration_argmax', 'mph', 'chosen', 'av_no_trip', 'av_s_car',\n", + " 'av_p_micro', 'av_walk', 'av_s_micro', 'av_transit', 'av_ridehail',\n", + " 'av_car', 'av_unknown', 'cost_p_micro', 'cost_no_trip', 'cost_s_car',\n", + " 'cost_transit', 'cost_car', 'cost_s_micro', 'cost_ridehail',\n", + " 'cost_walk', 'cost_unknown']]" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "77e331a5", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import f1_score\n", + "from sklearn.ensemble import RandomForestClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "1ccfe253", + "metadata": {}, + "outputs": [], + "source": [ + "X_tr, X_te, Y_tr, Y_te = train_test_split(\n", + " hyp.drop(columns=['chosen']), hyp[['chosen']], test_size=0.2, shuffle=True, stratify=hyp[['chosen']]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "2ea90f37", + "metadata": {}, + "outputs": [], + "source": [ + "model = RandomForestClassifier().fit(X_tr, Y_tr.values.ravel())" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "46c50e0d", + "metadata": {}, + "outputs": [], + "source": [ + "y_tr_pred = model.predict(X_tr)\n", + "y_te_pred = model.predict(X_te)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "77f9f005", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f1_score(y_true=Y_tr.values.ravel(), y_pred=y_tr_pred, average='weighted')" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "c08ccd5a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7247243014745804" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f1_score(y_true=Y_te.values.ravel(), y_pred=y_te_pred, average='weighted')" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "bb5be29d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[('age', 0.07034490454052576), ('mph', 0.046077458861916464), ('distance_miles', 0.04227922185622416), ('income_category', 0.042045300984451986), ('dew_point_2m (°F)', 0.040326971736931075), ('temperature_2m (°F)', 0.04026013502695752), ('n_residence_members', 0.04011768641381229), ('duration', 0.038452743580322295), ('section_distance_argmax', 0.038170155240570436), ('cost_transit', 0.03781163632826536), ('section_duration_argmax', 0.035462758618913716), ('cost_s_car', 0.034390094889762884), ('n_motor_vehicles', 0.03379247604608051), ('n_residents_with_license', 0.03070869700490467), ('cost_ridehail', 0.03048846642958717), ('relative_humidity_2m (%)', 0.030090075033090597), ('wind_gusts_10m (mp/h)', 0.029081664383436266), ('n_working_residents', 0.028816144221162413), ('wind_speed_10m (mp/h)', 0.02858556819484081), ('cloud_cover (%)', 0.02495871571321774), ('cost_car', 0.023892323875139673), ('end:cos_HOD', 0.023765393583672044), ('start:cos_HOD', 0.023437391395931484), ('start:sin_HOD', 0.022230720826376518), ('end:sin_HOD', 0.02207708217592035), ('n_residents_u18', 0.021771464179907447), ('is_male', 0.02009990945010731), ('cost_s_micro', 0.01639305932751749), ('av_p_micro', 0.015927295338181373), ('av_walk', 0.012539972617736221), ('av_transit', 0.011339815089503233), ('is_student', 0.010405977662256977), ('av_s_micro', 0.007322014585269799), ('av_car', 0.006833582392128755), ('av_ridehail', 0.006552208955349027), ('av_s_car', 0.006497932334664742), ('rain (inch)', 0.0026762209888453866), ('av_unknown', 0.0016465804016971863), ('snowfall (inch)', 0.0016132591409545494), ('av_no_trip', 0.0007168051104359397), ('is_overnight_trip', 1.1546343053889719e-07), ('cost_p_micro', 0.0), ('cost_no_trip', 0.0), ('cost_walk', 0.0), ('cost_unknown', 0.0)]\n" + ] + } + ], + "source": [ + "print(\n", + " sorted(list(zip(model.feature_names_in_, model.feature_importances_)), key=lambda x: x[-1], reverse=True)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "7c886ef1", + "metadata": {}, + "outputs": [], + "source": [ + "survey_data = pd.read_csv('../viz_scripts/Can Do Colorado eBike Program - en.csv')\n", + "df = pd.read_csv('../data/FULL_preprocessed_data_RM_weather.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "88434a04", + "metadata": {}, + "outputs": [], + "source": [ + "# column renaming here!\n", + "\n", + "survey_data.rename(\n", + " {\n", + " \"Unique User ID (auto-filled, do not edit)\": \"user_id\",\n", + " \"In which year were you born?\": \"birth_year\",\n", + " \"What is your gender?\": \"gender\",\n", + " \"Do you have a valid driver's license?\": \"has_drivers_license\",\n", + " \"Are you a student?\": \"is_student\",\n", + " \"What is the highest grade or degree that you have completed?\": \"highest_education\",\n", + " \"Do you work for either pay or profit?\": \"is_paid\",\n", + " \"Do you have more than one job?\": \"has_multiple_jobs\",\n", + " \"Do you work full-time or part-time at your primary job?\": \"primary_job_type\",\n", + " \"Which best describes your primary job?\": \"primary_job_description\",\n", + " \"How did you usually get to your primary job last week? \": \"primary_job_commute_mode\",\n", + " \"Thinking about your daily commute to work last week, how many minutes did it usually take to get from home to the primary job/work place?\": \"primary_job_commute_time\",\n", + " \"At your primary job, do you have the ability to set or change your own start time?\": \"is_primary_job_flexible\",\n", + " \"Do you have the option of working from home or an alternate location instead of going into your primary work place?\": \"primary_job_can_wfh\",\n", + " \"How many days per week do you usually work from home or an alternate location?\": \"wfh_days\",\n", + " \"Do you own or rent your place of residence?\": \"residence_ownership_type\",\n", + " \"What is your home type?\": \"residence_type\",\n", + " \"Please identify which category represents your total household income, before taxes, for last year.\": \"income_category\",\n", + " \"Including yourself, how many people live in your home?\": \"n_residence_members\",\n", + " \"How many children under age 18 live in your home?\": \"n_residents_u18\",\n", + " \"Including yourself, how many people have a driver's license in your household?\": \"n_residents_with_license\",\n", + " \"How many motor vehicles are owned, leased, or available for regular use by the people who currently live in your household?\": \"n_motor_vehicles\",\n", + " \"If you were unable to use your household vehicle(s), which of the following options would be available to you to get you from place to place?\": \"available_modes\",\n", + " \"Do you have a medical condition that makes it difficult to travel outside of the home?\": \"has_medical_condition\",\n", + " \"How long have you had this condition?\": \"medical_condition_duration\"\n", + " },\n", + " axis='columns',\n", + " inplace=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "48a2ece6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 a2d48b05d5454d428c0841432c7467b6\n", + "1 f2799dc202bc4249b42a4fda8770d1b6\n", + "2 b2bbe715b6a14fd19f751cae8adf6b4e\n", + "3 6373dfb8cb9b47e88e8f76adcfadde20\n", + "4 93c6e0f156a44e07b920ded664419dc6\n", + "Name: user_id, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(survey_data.user_id.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "f88441fe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 6373dfb8cb9b47e88e8f76adcfadde20\n", + "1 6373dfb8cb9b47e88e8f76adcfadde20\n", + "2 6373dfb8cb9b47e88e8f76adcfadde20\n", + "3 6373dfb8cb9b47e88e8f76adcfadde20\n", + "4 6373dfb8cb9b47e88e8f76adcfadde20\n", + "Name: user_id, dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(df.user_id.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "2af9b36f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "203" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(survey_data.user_id.unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "de46b68c", + "metadata": {}, + "outputs": [], + "source": [ + "f_survey = survey_data.loc[survey_data.user_id.isin(df.user_id), :]" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "9b77fd2f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "156" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(f_survey.user_id.unique())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e23fa31b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "emission", + "language": "python", + "name": "emission" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/rm_src/cost_time_avl_preprocessing.ipynb b/rm_src/cost_time_avl_preprocessing.ipynb new file mode 100644 index 0000000..1fee0a1 --- /dev/null +++ b/rm_src/cost_time_avl_preprocessing.ipynb @@ -0,0 +1,1847 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import ast\n", + "from traceback import print_exception\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_84693/3166671539.py:3: DtypeWarning: Columns (40) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df = pd.read_csv('../data/final_modeling_data_02142024.csv')\n" + ] + } + ], + "source": [ + "# Data loading. Refer to the mnl_exploration.ipynb file for details on how user-level features are generated.\n", + "\n", + "df = pd.read_csv('../data/final_modeling_data_02142024.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape: (80690, 105)\n" + ] + } + ], + "source": [ + "print(\"Shape: \", df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "df.drop_duplicates(inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add weather info" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "df.start_fmt_time = pd.to_datetime(df.start_fmt_time, utc=True).dt.tz_convert('America/Denver')" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(Timestamp('2020-11-02 18:48:13.450000-0700', tz='America/Denver'),\n", + " Timestamp('2022-12-30 23:33:27.147785-0700', tz='America/Denver'))" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We now need weather data. To get that, we download data from OpenMeteo for the given dates.\n", + "df.start_fmt_time.min(), df.start_fmt_time.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "# Downloaded using OpenMeteo API.\n", + "weather_df = pd.read_csv('../data/denver_weather_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "weather_df.time = pd.to_datetime(weather_df.time, utc=True).dt.tz_convert('America/Denver')" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "original_shape = df.shape[0]\n", + "\n", + "# Round start time to nearest hour.\n", + "df['start_time_rounded'] = df.start_fmt_time.dt.round(\n", + " 'H', ambiguous='infer', nonexistent='shift_backward'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "# True intersection\n", + "df = df.merge(right=weather_df, left_on='start_time_rounded', right_on='time', how='left')" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "# Drop rows where weather data is NA.\n", + "df.dropna(subset=['temperature_2m (°F)','relative_humidity_2m (%)', 'dew_point_2m (°F)', 'rain (inch)',\n", + " 'snowfall (inch)', 'wind_speed_10m (mp/h)', 'wind_gusts_10m (mp/h)'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Old shape: 80690. new shape: 80688\n" + ] + } + ], + "source": [ + "print(f\"Old shape: {original_shape}. new shape: {df.shape[0]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "df.drop(columns=['start_time_rounded', 'time'], inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating the dummy variables for the available modes" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'source': 0,\n", + " 'end_ts': 0,\n", + " 'end_fmt_time': 0,\n", + " 'end_loc': 0,\n", + " 'raw_trip': 0,\n", + " 'start_ts': 0,\n", + " 'start_fmt_time': 0,\n", + " 'start_loc': 0,\n", + " 'duration': 0,\n", + " 'distance': 0,\n", + " 'start_place': 0,\n", + " 'end_place': 0,\n", + " 'cleaned_trip': 0,\n", + " 'inferred_labels': 216,\n", + " 'inferred_trip': 216,\n", + " 'expectation': 216,\n", + " 'confidence_threshold': 216,\n", + " 'expected_trip': 216,\n", + " 'user_input': 0,\n", + " 'section_modes': 0,\n", + " 'section_distances': 0,\n", + " 'start:year': 0,\n", + " 'start:month': 0,\n", + " 'start:day': 0,\n", + " 'start:hour': 0,\n", + " 'start_local_dt_minute': 0,\n", + " 'start_local_dt_second': 0,\n", + " 'start_local_dt_weekday': 0,\n", + " 'start_local_dt_timezone': 0,\n", + " 'end:year': 0,\n", + " 'end:month': 0,\n", + " 'end:day': 0,\n", + " 'end:hour': 0,\n", + " 'end_local_dt_minute': 0,\n", + " 'end_local_dt_second': 0,\n", + " 'end_local_dt_weekday': 0,\n", + " 'end_local_dt_timezone': 0,\n", + " '_id': 0,\n", + " 'user_id': 0,\n", + " 'metadata_write_ts': 0,\n", + " 'additions': 80686,\n", + " 'mode_confirm': 337,\n", + " 'purpose_confirm': 527,\n", + " 'distance_miles': 0,\n", + " 'Mode_confirm': 0,\n", + " 'Replaced_mode': 0,\n", + " 'Trip_purpose': 0,\n", + " 'original_user_id': 0,\n", + " 'program': 0,\n", + " 'opcode': 0,\n", + " 'Timestamp': 0,\n", + " 'birth_year': 0,\n", + " 'primary_job_commute_time': 0,\n", + " 'income_category': 0,\n", + " 'n_residence_members': 0,\n", + " 'n_residents_u18': 0,\n", + " 'n_residents_with_license': 0,\n", + " 'n_motor_vehicles': 0,\n", + " 'available_modes': 0,\n", + " 'age': 0,\n", + " 'gender_Man': 0,\n", + " 'gender_Man;Nonbinary/genderqueer/genderfluid': 0,\n", + " 'gender_Nonbinary/genderqueer/genderfluid': 0,\n", + " 'gender_Prefer not to say': 0,\n", + " 'gender_Woman': 0,\n", + " 'gender_Woman;Nonbinary/genderqueer/genderfluid': 0,\n", + " 'has_drivers_license_No': 0,\n", + " 'has_drivers_license_Prefer not to say': 0,\n", + " 'has_drivers_license_Yes': 0,\n", + " 'has_multiple_jobs_No': 0,\n", + " 'has_multiple_jobs_Prefer not to say': 0,\n", + " 'has_multiple_jobs_Yes': 0,\n", + " \"highest_education_Bachelor's degree\": 0,\n", + " 'highest_education_Graduate degree or professional degree': 0,\n", + " 'highest_education_High school graduate or GED': 0,\n", + " 'highest_education_Less than a high school graduate': 0,\n", + " 'highest_education_Prefer not to say': 0,\n", + " 'highest_education_Some college or associates degree': 0,\n", + " 'primary_job_type_Full-time': 0,\n", + " 'primary_job_type_Part-time': 0,\n", + " 'primary_job_type_Prefer not to say': 0,\n", + " 'primary_job_description_Clerical': 0,\n", + " 'primary_job_description_Clerical or administrative support': 0,\n", + " 'primary_job_description_Custodial': 0,\n", + " 'primary_job_description_Education': 0,\n", + " 'primary_job_description_Food service': 0,\n", + " 'primary_job_description_Maintenance': 0,\n", + " 'primary_job_description_Manufacturing, construction, maintenance, or farming': 0,\n", + " 'primary_job_description_Medical/healthcare': 0,\n", + " 'primary_job_description_Other': 0,\n", + " 'primary_job_description_Professional, managerial, or technical': 0,\n", + " 'primary_job_description_Sales or service': 0,\n", + " 'primary_job_commute_mode_Active transport': 0,\n", + " 'primary_job_commute_mode_Car transport': 0,\n", + " 'primary_job_commute_mode_Hybrid': 0,\n", + " 'primary_job_commute_mode_Public transport': 0,\n", + " 'primary_job_commute_mode_Unknown': 0,\n", + " 'primary_job_commute_mode_WFH': 0,\n", + " 'is_overnight_trip': 0,\n", + " 'n_working_residents': 0,\n", + " 'start_lat': 0,\n", + " 'start_lng': 0,\n", + " 'end_lat': 0,\n", + " 'end_lng': 0,\n", + " 'section_durations': 0,\n", + " 'temperature_2m (°F)': 0,\n", + " 'relative_humidity_2m (%)': 0,\n", + " 'dew_point_2m (°F)': 0,\n", + " 'rain (inch)': 0,\n", + " 'snowfall (inch)': 0,\n", + " 'wind_speed_10m (mp/h)': 0,\n", + " 'wind_gusts_10m (mp/h)': 0}" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isna().sum().to_dict()" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "# | [d1, d2, d3] | [t1, t2, t3] | [m1, m2, m3] |\n", + "\n", + "\n", + "def compute_argmax(df: pd.DataFrame):\n", + " # Create the two columns.\n", + " df[['section_distance_argmax', 'section_duration_argmax', 'section_mode_argmax', 'section_coordinates_argmax']] = None\n", + " rows = list()\n", + "\n", + " for ix, row in df.iterrows():\n", + " row_dict = row.to_dict()\n", + " \n", + " try:\n", + " parsed_distances = ast.literal_eval(row_dict['section_distances'])\n", + " parsed_durations = ast.literal_eval(row_dict['section_durations'])\n", + " parsed_modes = ast.literal_eval(row_dict['section_modes'])\n", + "\n", + " argmax_ix = np.argmax(parsed_distances)\n", + " \n", + " # Pick the argmax and scale to miles. (meters -> miles)\n", + " row_dict['section_distance_argmax'] = parsed_distances[argmax_ix] * 0.0006213712\n", + " \n", + " # Pick the argmax and scale to minutes.\n", + " row_dict['section_duration_argmax'] = parsed_durations[argmax_ix] / 60\n", + " \n", + " # Pick the argmax mode.\n", + " row_dict['section_mode_argmax'] = parsed_modes[argmax_ix]\n", + " \n", + " row_dict['mark'] = False\n", + "\n", + " except Exception as e:\n", + " row_dict['mark'] = True\n", + "\n", + " rows.append(row_dict)\n", + "\n", + " return pd.DataFrame(rows)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's plot the mode-wise durations as a function of distance.\n", + "df = compute_argmax(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "df.drop(columns=['section_distances', 'section_durations', 'section_modes'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "df = df.loc[~df.mark, :].reset_index(drop=True, inplace=False).drop(columns=['mark'], inplace=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "# Drop instances where duration/distance is unusable.\n", + "df.drop(\n", + " index=df.loc[(df.section_distance_argmax <= 0) | (df.section_duration_argmax <= 0), :].index,\n", + " inplace=False\n", + ").reset_index(drop=True, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "# bus, train, bicycling, walking, car\n", + "# split-apply-combine\n", + "def drop_outliers(df: pd.DataFrame) -> pd.DataFrame:\n", + " def filter_by_percentiles(group):\n", + " distance_low = group['section_distance_argmax'].quantile(0.1)\n", + " distance_high = group['section_distance_argmax'].quantile(0.9)\n", + " duration_low = group['section_duration_argmax'].quantile(0.1)\n", + " duration_high = group['section_duration_argmax'].quantile(0.9)\n", + " \n", + " l1_filter = group[\n", + " (group['section_distance_argmax'] >= distance_low) &\n", + " (group['section_distance_argmax'] <= distance_high)\n", + " ].reset_index(drop=True)\n", + " \n", + " l2_filter = l1_filter[\n", + " (l1_filter['section_duration_argmax'] >= duration_low) &\n", + " (l1_filter['section_duration_argmax'] <= duration_high)\n", + " ].reset_index(drop=True)\n", + " \n", + " return l2_filter\n", + " \n", + " return df.groupby('section_mode_argmax').apply(filter_by_percentiles).reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "filtered_df = drop_outliers(df)\n", + "\n", + "# Ideal speed. distance/time.\n", + "filtered_df['mph'] = (\n", + " (filtered_df['section_distance_argmax'] * 60.)/filtered_df['section_duration_argmax']\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "def filter_mph(df: pd.DataFrame) -> pd.DataFrame:\n", + " \n", + " MPH_THRESHOLDS = {\n", + " # https://www.sciencedirect.com/science/article/pii/S2210670718304682\n", + " 'bicycling': 15.,\n", + " # https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806575/\n", + " 'walking': 2.93\n", + " }\n", + " \n", + " def custom_filter(group):\n", + " # Drop data specified in the dict manually.\n", + " if group.name in MPH_THRESHOLDS.keys():\n", + " f_df = group[group['mph'] <= MPH_THRESHOLDS[group.name]]\n", + " else:\n", + " mph_low = group['mph'].quantile(0.1)\n", + " mph_high = group['mph'].quantile(0.9)\n", + "\n", + " f_df = group[(group['mph'] >= mph_low) & (group['mph'] <= mph_high)]\n", + " \n", + " return f_df\n", + " \n", + " return df.groupby('section_mode_argmax').apply(custom_filter).reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [], + "source": [ + "filtered_df = filter_mph(filtered_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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section_distance_argmaxsection_duration_argmax
countmeanstdmin25%50%75%maxcountmeanstdmin25%50%75%max
section_mode_argmax
bicycling7196.02.1472810.8880860.8022211.5001711.9554792.5857634.8921377196.013.7543135.1485645.8866399.92784412.65573016.65578029.141947
bus290.01.4324930.6188670.3728750.9402111.4867441.9175823.016720290.08.9601153.6159532.9631776.4969728.24097410.66893520.585433
car24744.04.1324102.6019940.9114462.1926613.4290225.35017014.68807724744.013.9729716.6663585.6728308.77901612.12382017.50581135.306784
no_sensed1681.02.6773262.3654480.1780711.0261751.9546733.54760013.5540131681.015.96726412.1124981.0798687.16666712.30740021.62607755.445744
subway7.023.44147910.54068713.11312814.80424123.58765527.44459042.8919057.041.87396817.61057223.75777229.86640734.40000051.80143371.624326
train63.012.2623004.0212154.32211010.03039210.69183015.44695222.82090863.029.72245110.54837212.51929621.97667226.40000038.51193755.713396
walking12072.00.4759990.3681410.1200820.2219630.3482520.5945412.20840812072.019.66887913.0490735.2842339.39120515.45729126.25143360.423767
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" + ], + "text/plain": [ + " section_distance_argmax \\\n", + " count mean std min \n", + "section_mode_argmax \n", + "bicycling 7196.0 2.147281 0.888086 0.802221 \n", + "bus 290.0 1.432493 0.618867 0.372875 \n", + "car 24744.0 4.132410 2.601994 0.911446 \n", + "no_sensed 1681.0 2.677326 2.365448 0.178071 \n", + "subway 7.0 23.441479 10.540687 13.113128 \n", + "train 63.0 12.262300 4.021215 4.322110 \n", + "walking 12072.0 0.475999 0.368141 0.120082 \n", + "\n", + " \\\n", + " 25% 50% 75% max \n", + "section_mode_argmax \n", + "bicycling 1.500171 1.955479 2.585763 4.892137 \n", + "bus 0.940211 1.486744 1.917582 3.016720 \n", + "car 2.192661 3.429022 5.350170 14.688077 \n", + "no_sensed 1.026175 1.954673 3.547600 13.554013 \n", + "subway 14.804241 23.587655 27.444590 42.891905 \n", + "train 10.030392 10.691830 15.446952 22.820908 \n", + "walking 0.221963 0.348252 0.594541 2.208408 \n", + "\n", + " section_duration_argmax \\\n", + " count mean std min \n", + "section_mode_argmax \n", + "bicycling 7196.0 13.754313 5.148564 5.886639 \n", + "bus 290.0 8.960115 3.615953 2.963177 \n", + "car 24744.0 13.972971 6.666358 5.672830 \n", + "no_sensed 1681.0 15.967264 12.112498 1.079868 \n", + "subway 7.0 41.873968 17.610572 23.757772 \n", + "train 63.0 29.722451 10.548372 12.519296 \n", + "walking 12072.0 19.668879 13.049073 5.284233 \n", + "\n", + " \n", + " 25% 50% 75% max \n", + "section_mode_argmax \n", + "bicycling 9.927844 12.655730 16.655780 29.141947 \n", + "bus 6.496972 8.240974 10.668935 20.585433 \n", + "car 8.779016 12.123820 17.505811 35.306784 \n", + "no_sensed 7.166667 12.307400 21.626077 55.445744 \n", + "subway 29.866407 34.400000 51.801433 71.624326 \n", + "train 21.976672 26.400000 38.511937 55.713396 \n", + "walking 9.391205 15.457291 26.251433 60.423767 " + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filtered_df.groupby('section_mode_argmax')[['section_distance_argmax', 'section_duration_argmax']].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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mph
countmeanstdmin25%50%75%max
section_mode_argmax
bicycling7196.09.5503662.3712042.8175807.8401119.58799411.23963914.998472
bus290.09.8481503.1127114.2643247.2754539.85353212.42111815.596840
car24744.017.3543815.5879488.56685312.71655816.57989421.47049530.036223
no_sensed1681.010.2995874.5143424.6083166.5719069.21338213.05042722.598194
subway7.034.70867612.04792424.45714426.51763932.86680736.51556659.570373
train63.025.0956163.08480819.88346822.69968724.74644127.29282431.597822
walking12072.01.6215290.7453580.1315920.9989441.6168222.2614442.929953
\n", + "
" + ], + "text/plain": [ + " mph \\\n", + " count mean std min 25% \n", + "section_mode_argmax \n", + "bicycling 7196.0 9.550366 2.371204 2.817580 7.840111 \n", + "bus 290.0 9.848150 3.112711 4.264324 7.275453 \n", + "car 24744.0 17.354381 5.587948 8.566853 12.716558 \n", + "no_sensed 1681.0 10.299587 4.514342 4.608316 6.571906 \n", + "subway 7.0 34.708676 12.047924 24.457144 26.517639 \n", + "train 63.0 25.095616 3.084808 19.883468 22.699687 \n", + "walking 12072.0 1.621529 0.745358 0.131592 0.998944 \n", + "\n", + " \n", + " 50% 75% max \n", + "section_mode_argmax \n", + "bicycling 9.587994 11.239639 14.998472 \n", + "bus 9.853532 12.421118 15.596840 \n", + "car 16.579894 21.470495 30.036223 \n", + "no_sensed 9.213382 13.050427 22.598194 \n", + "subway 32.866807 36.515566 59.570373 \n", + "train 24.746441 27.292824 31.597822 \n", + "walking 1.616822 2.261444 2.929953 " + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filtered_df.groupby('section_mode_argmax')[['mph']].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dropped 34542 rows.\n" + ] + } + ], + "source": [ + "print(f\"Dropped {df.shape[0] - filtered_df.shape[0]} rows.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Regular Bike', 'Other', 'No Travel', 'Gas Car, drove alone',\n", + " 'Gas Car, with others', 'Walk', 'Taxi/Uber/Lyft', 'Train',\n", + " 'Bikeshare', 'Bus', 'Skate board', 'Free Shuttle', 'Scooter share',\n", + " 'E-bike', 'Not a Trip'], dtype=object)" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filtered_df.Replaced_mode.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'p_micro': 1, 'no_trip': 2, 's_car': 3, 'transit': 4, 'car': 5, 's_micro': 6, 'ridehail': 7, 'walk': 8, 'unknown': 9}\n" + ] + } + ], + "source": [ + "## Define the mapping strategy\n", + "\n", + "\"\"\"\n", + "p_micro: Personal micromobility\n", + "s_micro: Shared micromobility\n", + "s_car: Shared car\n", + "car: Car/rental car\n", + "transit: train + bus\n", + "no_trip: No vehicle / None\n", + "ridehail: Uber/Lyft\n", + "\"\"\"\n", + "\n", + "available = {\n", + " 'Bicycle': 'p_micro',\n", + " 'Do not have vehicle': 'unknown',\n", + " 'Get a ride from a friend or family member': 's_car',\n", + " 'None': 'no_trip',\n", + " 'Public transportation (bus, subway, light rail, etc.)': 'transit',\n", + " 'Rental car (including Zipcar/ Car2Go)': 'car',\n", + " 'Shared bicycle or scooter': 's_micro',\n", + " 'Skateboard': 'p_micro',\n", + " 'Taxi (regular taxi, Uber, Lyft, etc)': 'ridehail',\n", + " 'Walk/roll': 'walk',\n", + " 'Prefer not to say': 'unknown'\n", + "}\n", + "\n", + "# Map each sensed mode to the binary indicators.\n", + "section_mode_mapping = {\n", + " 'bicycling': ['p_micro', 's_micro'],\n", + " 'bus': ['transit'],\n", + " 'car': ['s_car', 'car', 'ridehail'],\n", + " 'no_sensed': ['unknown'],\n", + " 'train': ['transit'],\n", + " 'walking': ['walk'],\n", + " 'subway': ['transit']\n", + "}\n", + "\n", + "# For target - mode_confirm\n", + "# target_mapping = {\n", + "# 'Regular Bike': 'p_micro',\n", + "# 'Walk': 'walk',\n", + "# 'Gas Car, with others': 's_car',\n", + "# 'Gas Car, drove alone': 'car',\n", + "# 'Bikeshare': 's_micro',\n", + "# 'Other': 'unknown',\n", + "# 'Bus': 'transit',\n", + "# 'Not a Trip': 'no_trip',\n", + "# 'E-bike': 'p_micro',\n", + "# 'Train': 'transit',\n", + "# 'Taxi/Uber/Lyft': 'ridehail',\n", + "# 'Free Shuttle': 'transit',\n", + "# 'Scooter share': 's_micro',\n", + "# 'Skate board': 'p_micro'\n", + "# }\n", + "\n", + "# For target - replaced_mode\n", + "target_mapping = {\n", + " 'Regular Bike': 'p_micro', \n", + " 'Walk': 'walk', \n", + " 'No Travel': 'no_trip', \n", + " 'Other': 'unknown',\n", + " 'Gas Car, with others': 's_car',\n", + " 'Gas Car, drove alone': 'car',\n", + " 'Train': 'transit',\n", + " 'Scooter share': 's_micro', \n", + " 'Taxi/Uber/Lyft': 'ridehail', \n", + " 'Free Shuttle': 'transit', \n", + " 'Skate board': 'p_micro',\n", + " 'Bikeshare': 's_micro',\n", + " 'Bus': 'transit',\n", + " 'Not a Trip': 'no_trip',\n", + " 'E-bike': 'p_micro'\n", + "}\n", + "\n", + "# # These values were obtained using a simple linear regression model. Specifics can be found in time_distance_estimation.ipynb\n", + "# duration_dict = {\n", + "# 'walk': {\n", + "# 'intercept': 14.09698152519682,\n", + "# 'coef': 19.153802211061134\n", + "# },\n", + "# 'p_micro': {\n", + "# 'intercept': 4.753590860550791,\n", + "# 'coef': 4.2723965916811935\n", + "# },\n", + "# 's_micro': {\n", + "# 'intercept': 4.753590860550791,\n", + "# 'coef': 4.2723965916811935\n", + "# },\n", + "# 's_car': {\n", + "# 'intercept': 10.515751350707365, \n", + "# 'coef': 1.112111276629702\n", + "# },\n", + "# 'car': {\n", + "# 'intercept': 10.515751350707365, \n", + "# 'coef': 1.112111276629702\n", + "# },\n", + "# 'ridehail': {\n", + "# 'intercept': 10.515751350707365, \n", + "# 'coef': 1.112111276629702\n", + "# },\n", + "# 'transit': {\n", + "# 'intercept': 7.5000351741492395,\n", + "# 'coef': 1.839562017986485\n", + "# },\n", + "# 'unknown': {\n", + "# 'intercept': 9.423026094931313,\n", + "# 'coef': 2.640429444466706\n", + "# },\n", + "# 'no_trip': {\n", + "# 'intercept': 0.,\n", + "# 'coef': 0.\n", + "# }\n", + "# }\n", + "\n", + "ordinal_mapping = {'p_micro': 1, 'no_trip': 2, 's_car': 3, 'transit': 4, 'car': 5, 's_micro': 6, 'ridehail': 7, 'walk': 8, 'unknown': 9}\n", + "reverse_ordinal_mapping = {v:k for k,v in ordinal_mapping.items()}\n", + "\n", + "print(ordinal_mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_available_features(\n", + " df: pd.DataFrame, av: dict, sm: dict, target_mapper: dict\n", + "):\n", + " \"\"\"\n", + " This method generates the wide-form dummy features for available modes.\n", + " The current implementation uses (section_mode_argmax OR available_modes) to indicate mode availability.\n", + " \"\"\"\n", + "\n", + " # Reset indices.\n", + " df = df.reset_index(drop=True, inplace=False)\n", + "\n", + " columns = list(set(av.values())) + ['unknown']\n", + "\n", + " # Create the columns along with the 'chosen' column.\n", + " df[columns] = 0\n", + "\n", + " row_dicts = list()\n", + "\n", + " for i, row in df.iterrows():\n", + " row_dict = row.to_dict()\n", + "\n", + " # Access the available modes. Split on ; and strip all the elements.\n", + " available_modes = [x.strip() for x in str(row_dict['available_modes']).split(';')]\n", + " \n", + " # Set all the available modes associated with the chosen modes too.\n", + " # Toggle this flag off if you don't want the target to be considered as an available mode.\n", + " # row_dict[row_dict['chosen']] = 1\n", + " \n", + " row_dict['target'] = target_mapper[row_dict['target']]\n", + " \n", + " # Update the available modes.\n", + " for available_mode in available_modes:\n", + " row_dict[av[available_mode]] = 1\n", + " \n", + " # Now, look at the section_mode_argmax as a proxy for the confirmed mode.\n", + " chosen_modes = sm[row_dict['section_mode_argmax']]\n", + "\n", + " # Determine the length of the mapping.\n", + " n = len(chosen_modes)\n", + "\n", + " # # Go ahead and modify the current dict with the first value.\n", + " # row_dict['chosen'] = ordinal_mapper[chosen_modes[0]]\n", + " # row_dict['primary_mode'] = 1\n", + "\n", + " # Also update the available modes.\n", + " for chosen_mode in chosen_modes:\n", + " row_dict[chosen_mode] = 1\n", + "\n", + " # # Append the first row to the list.\n", + " row_dicts.append(row_dict)\n", + " \n", + " constructed = pd.DataFrame(row_dicts)\n", + " constructed.rename(columns=dict([(c, 'av_'+c) for c in target_mapper.keys()]), inplace=True)\n", + "\n", + " return constructed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```language=python\n", + "{'p_micro': 1, 'no_trip': 2, 's_car': 3, 'transit': 4, 'car': 5, 's_micro': 6, 'ridehail': 7, 'walk': 8, 'unknown': 9}\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_target_variable(df: pd.DataFrame, mapper: dict):\n", + " df['target'] = df['Replaced_mode'].apply(lambda x: mapper[x])\n", + " df.drop(columns=['Replaced_mode'], inplace=True)\n", + " return df\n", + "\n", + "filtered_df = generate_target_variable(filtered_df, target_mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate wide-format availability indicators..\n", + "av_df = generate_available_features(filtered_df, available, section_mode_mapping, ordinal_mapping)\n", + "\n", + "# Collapse train and bus into 'transit'.\n", + "av_df.loc[av_df.section_mode_argmax.isin(['bus', 'train', 'subway']), 'section_mode_argmax'] = 'transit'" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['bicycling', 'transit', 'car', 'no_sensed', 'walking'],\n", + " dtype=object)" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "av_df.section_mode_argmax.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sourceend_tsend_fmt_timeend_locraw_tripstart_tsstart_fmt_timestart_locdurationdistance...targetav_s_microav_walkav_no_tripav_unknownav_p_microav_carav_transitav_s_carav_ridehail
0DwellSegmentationTimeFilter1.619140e+092021-04-22 18:59:39-06:00{'type': 'Point', 'coordinates': [-105.1002447...6082e47f27dcf1f393b70dd81.619139e+092021-04-22 18:48:48.365291-06:00{'type': 'Point', 'coordinates': [-105.0772567...10.8439121995.541857...1100010000
1DwellSegmentationTimeFilter1.619197e+092021-04-23 11:04:53-06:00{'type': 'Point', 'coordinates': [-105.0776493...60830eac2b9762e085f774e81.619196e+092021-04-23 10:45:48.219950-06:00{'type': 'Point', 'coordinates': [-105.1228294...19.0796674764.133777...1100010000
2DwellSegmentationTimeFilter1.619728e+092021-04-29 14:23:25-06:00{'type': 'Point', 'coordinates': [-105.0776309...608b21db654c4a5ab2fa435a1.619727e+092021-04-29 14:05:50.979267-06:00{'type': 'Point', 'coordinates': [-105.1228283...17.5670124731.604069...9100010000
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5 rows × 123 columns

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" + ], + "text/plain": [ + " source end_ts end_fmt_time \\\n", + "0 DwellSegmentationTimeFilter 1.619140e+09 2021-04-22 18:59:39-06:00 \n", + "1 DwellSegmentationTimeFilter 1.619197e+09 2021-04-23 11:04:53-06:00 \n", + "2 DwellSegmentationTimeFilter 1.619728e+09 2021-04-29 14:23:25-06:00 \n", + "3 DwellSegmentationTimeFilter 1.619794e+09 2021-04-30 08:54:03-06:00 \n", + "4 DwellSegmentationTimeFilter 1.619807e+09 2021-04-30 12:15:42-06:00 \n", + "\n", + " end_loc \\\n", + "0 {'type': 'Point', 'coordinates': [-105.1002447... \n", + "1 {'type': 'Point', 'coordinates': [-105.0776493... \n", + "2 {'type': 'Point', 'coordinates': [-105.0776309... \n", + "3 {'type': 'Point', 'coordinates': [-105.0775022... \n", + "4 {'type': 'Point', 'coordinates': [-105.1228054... \n", + "\n", + " raw_trip start_ts start_fmt_time \\\n", + "0 6082e47f27dcf1f393b70dd8 1.619139e+09 2021-04-22 18:48:48.365291-06:00 \n", + "1 60830eac2b9762e085f774e8 1.619196e+09 2021-04-23 10:45:48.219950-06:00 \n", + "2 608b21db654c4a5ab2fa435a 1.619727e+09 2021-04-29 14:05:50.979267-06:00 \n", + "3 608c1f002f548f5db71d16ef 1.619793e+09 2021-04-30 08:34:49.162054-06:00 \n", + "4 608c8f7fb2d3b88f6947765e 1.619805e+09 2021-04-30 11:58:07.194775-06:00 \n", + "\n", + " start_loc duration distance \\\n", + "0 {'type': 'Point', 'coordinates': [-105.0772567... 10.843912 1995.541857 \n", + "1 {'type': 'Point', 'coordinates': [-105.1228294... 19.079667 4764.133777 \n", + "2 {'type': 'Point', 'coordinates': [-105.1228283... 17.567012 4731.604069 \n", + "3 {'type': 'Point', 'coordinates': [-105.1226652... 19.230632 4675.329725 \n", + "4 {'type': 'Point', 'coordinates': [-105.0960769... 17.580087 2855.830523 \n", + "\n", + " ... target av_s_micro av_walk av_no_trip av_unknown av_p_micro av_car \\\n", + "0 ... 1 1 0 0 0 1 0 \n", + "1 ... 1 1 0 0 0 1 0 \n", + "2 ... 9 1 0 0 0 1 0 \n", + "3 ... 9 1 0 0 0 1 0 \n", + "4 ... 1 1 0 0 0 1 0 \n", + "\n", + " av_transit av_s_car av_ridehail \n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "2 0 0 0 \n", + "3 0 0 0 \n", + "4 0 0 0 \n", + "\n", + "[5 rows x 123 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(av_df.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [], + "source": [ + "# def compute_alt_durations(df: pd.DataFrame, reverse_mapper: dict, duration_mapper: dict):\n", + "# '''\n", + "# We re-iterate over the generated available mode df and populate the modes.\n", + "# We are only concerned with the rows that have primary_mode = 0, which indicates that they are alternate mode rows.\n", + "# '''\n", + "\n", + "# column_names = list(reverse_mapper.values())\n", + "\n", + "# # Start with a 0 cost for everyone.\n", + "# df[column_names] = 0\n", + "\n", + "# rows = list()\n", + "# for ix, row in df.iterrows():\n", + " \n", + "# # Convert to a dict\n", + "# row_dict = row.to_dict()\n", + "\n", + "# for mode in column_names:\n", + "# if mode == reverse_mapper[row['chosen']]:\n", + "# row_dict[mode] = row_dict['section_distance_argmax']\n", + "# continue\n", + " \n", + "# mode_params = duration_mapper[mode]\n", + "# # use availability as a mask.\n", + "# row_dict[mode] = mode_params['intercept'] + (mode_params['coef'] * row_dict['section_distance_argmax'])\n", + " \n", + "# rows.append(row_dict)\n", + " \n", + "# return pd.DataFrame(rows).rename(columns=dict([(c, 'tt_'+c) for c in column_names]), inplace=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [], + "source": [ + "# av_time_df = compute_alt_durations(av_df, reverse_ordinal_mapping, duration_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cost estimation\n", + "\n", + "```\n", + "{'p_micro': 1, 'no_trip': 2, 's_car': 3, 'transit': 4, 'car': 5, 's_micro': 6, 'ridehail': 7, 'walk': 8, 'unknown': 9}\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [], + "source": [ + "# All values are taken from VTPI.\n", + "# https://www.vtpi.org/tca/tca0501.pdf\n", + "mode_cost_per_mile = {\n", + " # bicycle/skateboard\n", + " 'p_micro': 0.,\n", + " 'no_trip': 0.,\n", + " # Shared car is half the cost of regular car, which is $0.6/mile.\n", + " 's_car': 0.3,\n", + " # Rental car.\n", + " 'car': 0.6,\n", + " # Average of bus and train taken.\n", + " 'transit': 0.5,\n", + " # Shared bicyle or scooter - values taken from https://nacto.org/shared-micromobility-2020-2021/ and \n", + " # https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/how-sharing-the-road-is-likely-to-transform-american-mobility\n", + " 's_micro': 0.3,\n", + " # uber/taxi/lyft\n", + " 'ridehail': 2.,\n", + " 'walk': 0.,\n", + " 'unknown': 0.\n", + "}\n", + "\n", + "# Assumptions.\n", + "mode_init_cost = {\n", + " 'p_micro': 0.,\n", + " 'no_trip': 0.,\n", + " # Shared car is half the cost of regular car, which is $0.6/mile.\n", + " 's_car': 0.,\n", + " # Rental car.\n", + " 'car': 0.,\n", + " # Average of bus and train taken.\n", + " 'transit': 0.,\n", + " # $1 unlocking cost.\n", + " 's_micro': 1.,\n", + " # uber/taxi/lyft\n", + " 'ridehail': 1.,\n", + " 'walk': 0.,\n", + " 'unknown': 0.\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_cost_estimates(df: pd.DataFrame, cost_mapper: dict, init_cost_mapper: dict, ordinal_mapper: dict):\n", + " \n", + " # Create some extra colums.\n", + " columns = list(ordinal_mapper.keys())\n", + "\n", + " # Initialize the columns to 0.\n", + " df[columns] = 0.\n", + "\n", + " rows = list()\n", + "\n", + " # Iterate over every row.\n", + " for _, row in df.iterrows():\n", + " # Check which flags are active.\n", + " row_dict = row.to_dict()\n", + "\n", + " # Access the section_distance_argmax attribute for the distance. Note that this is now in miles.\n", + " distance = row_dict['section_distance_argmax']\n", + " \n", + " # Mask using availability.\n", + " for lookup in columns:\n", + " row_dict[lookup] = row_dict['av_' + lookup] * (init_cost_mapper[lookup] + (cost_mapper[lookup] * distance))\n", + "\n", + " rows.append(row_dict)\n", + "\n", + " new_df = pd.DataFrame(rows)\n", + " new_df.rename(columns=dict([(c, 'cost_'+c) for c in ordinal_mapper.keys()]), inplace=True)\n", + "\n", + " return new_df" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [], + "source": [ + "cost_df = compute_cost_estimates(\n", + " av_df, cost_mapper=mode_cost_per_mile, \n", + " init_cost_mapper=mode_init_cost, \n", + " ordinal_mapper=ordinal_mapping\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cost_p_microcost_no_tripcost_s_carcost_transitcost_carcost_s_microcost_ridehailcost_walkcost_unknown
count46053.046053.046053.00000046053.00000046053.00000046053.00000046053.00000046053.046053.0
mean0.00.00.7596020.9246031.3549170.3996585.6044210.00.0
std0.00.00.8115861.2280511.6888350.7707155.7398800.00.0
min0.00.00.0000000.0000000.0000000.0000000.0000000.00.0
25%0.00.00.0685020.0000000.0000000.0000000.0000000.00.0
50%0.00.00.5656420.5084740.8908920.0000004.5696880.00.0
75%0.00.01.1122211.3611752.1780410.0000008.3663280.00.0
max0.00.012.86757221.44595325.7351435.35157486.7838110.00.0
\n", + "
" + ], + "text/plain": [ + " cost_p_micro cost_no_trip cost_s_car cost_transit cost_car \\\n", + "count 46053.0 46053.0 46053.000000 46053.000000 46053.000000 \n", + "mean 0.0 0.0 0.759602 0.924603 1.354917 \n", + "std 0.0 0.0 0.811586 1.228051 1.688835 \n", + "min 0.0 0.0 0.000000 0.000000 0.000000 \n", + "25% 0.0 0.0 0.068502 0.000000 0.000000 \n", + "50% 0.0 0.0 0.565642 0.508474 0.890892 \n", + "75% 0.0 0.0 1.112221 1.361175 2.178041 \n", + "max 0.0 0.0 12.867572 21.445953 25.735143 \n", + "\n", + " cost_s_micro cost_ridehail cost_walk cost_unknown \n", + "count 46053.000000 46053.000000 46053.0 46053.0 \n", + "mean 0.399658 5.604421 0.0 0.0 \n", + "std 0.770715 5.739880 0.0 0.0 \n", + "min 0.000000 0.000000 0.0 0.0 \n", + "25% 0.000000 0.000000 0.0 0.0 \n", + "50% 0.000000 4.569688 0.0 0.0 \n", + "75% 0.000000 8.366328 0.0 0.0 \n", + "max 5.351574 86.783811 0.0 0.0 " + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cost_df[[c for c in cost_df.columns if 'cost_' in c]].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [], + "source": [ + "# student_status = {\n", + "# 'Not a student': 0,\n", + "# 'Yes - Full Time College/University': 1,\n", + "# 'Yes - Vocation/Technical/Trade School': 1,\n", + "# 'Yes - Part-Time College/University': 1,\n", + "# 'Fire Fighter 2 Training': 0,\n", + "# 'Taking prerequisites missing for grad program ': 1,\n", + "# 'Work': 0,\n", + "# 'Graduate': 1,\n", + "# 'Work at csu': 0,\n", + "# 'Custodian': 0, \n", + "# 'taking classes toward early childhood licensure': 1,\n", + "# 'Prefer not to say': 0\n", + "# }\n", + "\n", + "\n", + "# cost_df['is_student'] = cost_df['is_student'].apply(lambda x: student_status[x])" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [], + "source": [ + "# cost_df['age'] = cost_df['age'].apply(lambda x: x if x < 100 else 2024 - x)\n", + "# cost_df['n_working_residents'] = cost_df['n_working_residents'].apply(lambda x: 0 if x < 0 else x)\n", + "# cost_df.rename(\n", + "# columns={'start_local_dt_weekday': 'start:DOW', 'end_local_dt_weekday': 'end:DOW'},\n", + "# inplace=True\n", + "# )" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [], + "source": [ + "cost_df.to_csv('../data/ReplacedMode_Fix_02142024.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" + }, + "kernelspec": { + "display_name": "emission", + "language": "python", + "name": "emission" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/rm_src/mnl_exploration.ipynb b/rm_src/mnl_exploration.ipynb new file mode 100644 index 0000000..dacce32 --- /dev/null +++ b/rm_src/mnl_exploration.ipynb @@ -0,0 +1,2115 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import pickle\n", + "import importlib\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "\n", + "from pathlib import Path\n", + "from uuid import UUID\n", + "from collections import defaultdict\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "emission_path = Path(os.getcwd()).parent.parent / 'my_emission_server' / 'e-mission-server'\n", + "sys.path.append(str(emission_path))\n", + "\n", + "# Also add the home (viz_scripts) to the path\n", + "sys.path.append('../viz_scripts')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "storage not configured, falling back to sample, default configuration\n", + "URL not formatted, defaulting to \"Stage_database\"\n", + "Connecting to database URL localhost\n" + ] + } + ], + "source": [ + "import scaffolding\n", + "import emission.core.get_database as edb" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "importlib.reload(scaffolding)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def r(df: pd.DataFrame) -> pd.DataFrame:\n", + " return df.reset_index(drop=True, inplace=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "with open('../viz_scripts/auxiliary_files/dic_re.pkl', 'rb') as f:\n", + " dic_re = pickle.loads(f.read())\n", + "\n", + "with open('../viz_scripts/auxiliary_files/dic_pur.pkl', 'rb') as f:\n", + " dic_pur = pickle.loads(f.read())\n", + "\n", + "# convert a dictionary to a defaultdict\n", + "dic_re = defaultdict(lambda: 'Other', dic_re)\n", + "dic_pur = defaultdict(lambda: 'Other', dic_pur)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found new program stage, creating new list\n", + "Found new program 4c, creating new list\n", + "Found new program cc, creating new list\n", + "Found new program fc, creating new list\n", + "Found new program pc, creating new list\n", + "Found new program sc, creating new list\n", + "Found new program vail, creating new list\n", + "Found new program prepilot, creating new list\n" + ] + } + ], + "source": [ + "# Split UUIDs by program\n", + "program_uuid_map = {}\n", + "for ue in edb.get_uuid_db().find():\n", + " uuid = str(ue['uuid'])\n", + " # uuid = str(ue['uuid'])\n", + " program = ue['user_email'].split(\"_\")[0]\n", + " if program in program_uuid_map.keys():\n", + " program_uuid_map[program].append(uuid)\n", + " else:\n", + " print(f\"Found new program {program}, creating new list\")\n", + " program_uuid_map[program] = []\n", + " program_uuid_map[program].append(uuid)\n", + "\n", + "uuid_program_list = []\n", + "for ue in edb.get_uuid_db().find():\n", + " # uuid = str(ue['uuid'].as_uuid(3))\n", + " uuid = str(ue['uuid'])\n", + " program = ue['user_email'].split(\"_\")[0]\n", + " uuid_program_list.append({\"program\": program, \"opcode\": ue[\"user_email\"], \"user_id\": uuid})" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "uuid_program_df = pd.DataFrame(uuid_program_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded all confirmed trips of length 241123\n", + "After filtering, found 241123 participant trips \n", + "After filtering, found 92446 labeled trips\n", + "Found Index(['mode_confirm', 'purpose_confirm', 'replaced_mode'], dtype='object') columns of length 3\n", + "After expanding, columns went from 41 -> 44\n", + "_prepilot\n", + "Based on 92395 confirmed trips from 235 users\n", + "of 241123 total trips from 261 users (38.32%)\n" + ] + } + ], + "source": [ + "# %%capture\n", + "\n", + "# for program in uuid_program_df.program.unique():\n", + "expanded_ct, file_suffix, quality_text, debug_df = scaffolding.load_viz_notebook_data(None,\n", + " None,\n", + " 'prepilot',\n", + " 'program',\n", + " dic_re,\n", + " dic_pur=dic_pur)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Join to the program df to get each user's program\n", + "expanded_ct['original_user_id'] = expanded_ct['user_id'].copy()\n", + "expanded_ct['user_id'] = expanded_ct['user_id'].apply(lambda x: str(x))\n", + "expanded_ct = expanded_ct.merge(uuid_program_df, on='user_id')\n", + "expanded_ct['user_id'] = expanded_ct['user_id'].apply(lambda x: str(x).replace(\"-\", \"\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['source', 'end_ts', 'end_fmt_time', 'end_loc', 'raw_trip', 'start_ts',\n", + " 'start_fmt_time', 'start_loc', 'duration', 'distance', 'start_place',\n", + " 'end_place', 'cleaned_trip', 'inferred_labels', 'inferred_trip',\n", + " 'expectation', 'confidence_threshold', 'expected_trip', 'user_input',\n", + " 'section_modes', 'section_distances', 'start_local_dt_year',\n", + " 'start_local_dt_month', 'start_local_dt_day', 'start_local_dt_hour',\n", + " 'start_local_dt_minute', 'start_local_dt_second',\n", + " 'start_local_dt_weekday', 'start_local_dt_timezone',\n", + " 'end_local_dt_year', 'end_local_dt_month', 'end_local_dt_day',\n", + " 'end_local_dt_hour', 'end_local_dt_minute', 'end_local_dt_second',\n", + " 'end_local_dt_weekday', 'end_local_dt_timezone', '_id', 'user_id',\n", + " 'metadata_write_ts', 'additions', 'mode_confirm', 'purpose_confirm',\n", + " 'replaced_mode', 'distance_miles', 'Mode_confirm', 'Replaced_mode',\n", + " 'Trip_purpose', 'original_user_id', 'program', 'opcode'],\n", + " dtype='object')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expanded_ct.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Rename the target column.\n", + "expanded_ct.drop(columns=['replaced_mode'], axis='rows', inplace=True)\n", + "expanded_ct['Replaced_mode'] = expanded_ct['Replaced_mode'].fillna('Unlabeled')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "235" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(expanded_ct.user_id.unique())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Survey Data preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Read the Denver dedmographic info\n", + "survey_data = pd.read_csv('../viz_scripts/Can Do Colorado eBike Program - en.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "survey_data.rename(\n", + " {\n", + " \"Unique User ID (auto-filled, do not edit)\": \"user_id\",\n", + " \"In which year were you born?\": \"birth_year\",\n", + " \"What is your gender?\": \"gender\",\n", + " \"Do you have a valid driver's license?\": \"has_drivers_license\",\n", + " \"Are you a student?\": \"is_student\",\n", + " \"What is the highest grade or degree that you have completed?\": \"highest_education\",\n", + " \"Do you work for either pay or profit?\": \"is_paid\",\n", + " \"Do you have more than one job?\": \"has_multiple_jobs\",\n", + " \"Do you work full-time or part-time at your primary job?\": \"primary_job_type\",\n", + " \"Which best describes your primary job?\": \"primary_job_description\",\n", + " \"How did you usually get to your primary job last week? \": \"primary_job_commute_mode\",\n", + " \"Thinking about your daily commute to work last week, how many minutes did it usually take to get from home to the primary job/work place?\": \"primary_job_commute_time\",\n", + " \"At your primary job, do you have the ability to set or change your own start time?\": \"is_primary_job_flexible\",\n", + " \"Do you have the option of working from home or an alternate location instead of going into your primary work place?\": \"primary_job_can_wfh\",\n", + " \"How many days per week do you usually work from home or an alternate location?\": \"wfh_days\",\n", + " \"Do you own or rent your place of residence?\": \"residence_ownership_type\",\n", + " \"What is your home type?\": \"residence_type\",\n", + " \"Please identify which category represents your total household income, before taxes, for last year.\": \"income_category\",\n", + " \"Including yourself, how many people live in your home?\": \"n_residence_members\",\n", + " \"How many children under age 18 live in your home?\": \"n_residents_u18\",\n", + " \"Including yourself, how many people have a driver's license in your household?\": \"n_residents_with_license\",\n", + " \"How many motor vehicles are owned, leased, or available for regular use by the people who currently live in your household?\": \"n_motor_vehicles\",\n", + " \"If you were unable to use your household vehicle(s), which of the following options would be available to you to get you from place to place?\": \"available_modes\",\n", + " \"Do you have a medical condition that makes it difficult to travel outside of the home?\": \"has_medical_condition\",\n", + " \"How long have you had this condition?\": \"medical_condition_duration\"\n", + " },\n", + " axis='columns',\n", + " inplace=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rkulhall/miniconda3/envs/emission/lib/python3.9/site-packages/dateutil/parser/_parser.py:1207: UnknownTimezoneWarning: tzname PDT identified but not understood. Pass `tzinfos` argument in order to correctly return a timezone-aware datetime. In a future version, this will raise an exception.\n", + " warnings.warn(\"tzname {tzname} identified but not understood. \"\n", + "/Users/rkulhall/miniconda3/envs/emission/lib/python3.9/site-packages/dateutil/parser/_parser.py:1207: UnknownTimezoneWarning: tzname PST identified but not understood. Pass `tzinfos` argument in order to correctly return a timezone-aware datetime. In a future version, this will raise an exception.\n", + " warnings.warn(\"tzname {tzname} identified but not understood. \"\n" + ] + } + ], + "source": [ + "survey_data['Timestamp'] = pd.to_datetime(survey_data['Timestamp'])\n", + "survey_data['Timestamp'] = survey_data['Timestamp'].dt.tz_localize('America/Los_Angeles', ambiguous='infer')\n", + "survey_data['Timestamp'] = survey_data['Timestamp'].dt.tz_convert('America/Denver')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(Timestamp('2021-05-23 16:21:52-0600', tz='America/Denver'),\n", + " Timestamp('2022-12-21 06:33:59-0700', tz='America/Denver'))" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_data.Timestamp.min(), survey_data.Timestamp.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "f_survey = survey_data.loc[survey_data.user_id.isin(expanded_ct.user_id), :]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(170, 203)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(f_survey.user_id.unique()), len(survey_data.user_id.unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "f_survey = f_survey.sort_values(\n", + " by=['user_id', 'Timestamp'], ascending=True\n", + ").drop_duplicates(\n", + " subset=['user_id'], keep='last', ignore_index=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "assert f_survey.user_id.value_counts().max() == 1" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# Remove outliers.\n", + "\n", + "bad_ix = list()\n", + "\n", + "# obvious. WFH is not a good feature to include because of its high sparsity.\n", + "# wfh_mask = f_survey.wfh_days > 7\n", + "\n", + "# Computed by calculating 99th percentile.\n", + "commute_time_mask = f_survey.primary_job_commute_time > 75\n", + "\n", + "# Computed by calculating 99th percentile.\n", + "residence_members_mask = f_survey.n_residence_members > 8\n", + "\n", + "for mask in [commute_time_mask, residence_members_mask]:\n", + " bad_ix += f_survey[mask].index.tolist()\n", + "\n", + "f_survey.drop(index=set(bad_ix), inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# Find age at the time of the timestamp. subtract birth year from timestamp year.\n", + "def compute_age(row):\n", + " if row['birth_year'] < 100:\n", + " return row['birth_year']\n", + " return row['Timestamp'].year - row['birth_year']\n", + "\n", + "f_survey['age'] = f_survey.apply(lambda x: compute_age(x), axis=1)\n", + "# f_survey.drop(columns=['birth_year'], inplace=True)\n", + "\n", + "\n", + "# For those who leave it NA, categorize them as 'prefer not to say'.\n", + "f_survey.loc[f_survey['has_multiple_jobs'].isna(), 'has_multiple_jobs'] = 'Prefer not to say'\n", + "\n", + "\n", + "# Fix primary commute mode.\n", + "f_survey.loc[\n", + " f_survey.primary_job_commute_mode.isna(), 'primary_job_commute_mode'\n", + "] = 'Unknown'\n", + "\n", + "f_survey.loc[\n", + " f_survey.primary_job_commute_mode.isin([\n", + " 'Car', 'SUV', 'Pickup truck', 'Taxi/Limo (including services like Uber or Lyft)', 'Van',\n", + " 'Shared vehicle'\n", + " ]), 'primary_job_commute_mode'\n", + "] = 'Car transport'\n", + "\n", + "f_survey.loc[\n", + " f_survey.primary_job_commute_mode.isin([\n", + " 'Public or commuter bus', 'Buss'\n", + " ]), 'primary_job_commute_mode'\n", + "] = 'Public transport'\n", + "\n", + "f_survey.loc[\n", + " f_survey.primary_job_commute_mode.isin([\n", + " 'Walk', 'Skateboard', 'Bicycle'\n", + " ]), 'primary_job_commute_mode'\n", + "] = 'Active transport'\n", + "\n", + "f_survey.loc[\n", + " f_survey.primary_job_commute_mode.isin([\n", + " 'Telecommute', 'Work from home', 'I work from home'\n", + " ]), 'primary_job_commute_mode'\n", + "] = 'WFH'\n", + "\n", + "f_survey.loc[\n", + " f_survey.primary_job_commute_mode.isin([\n", + " 'Walk, bike, bus, uber or lyft.', 'Car and E-Bike'\n", + " ]), 'primary_job_commute_mode'\n", + "] = 'Hybrid'\n", + "\n", + "# Fix primary job type\n", + "f_survey.loc[f_survey.primary_job_type.isna(), 'primary_job_type'] = 'Prefer not to say'\n", + "\n", + "# Normalize the job description. Inspired from the 'e-bike trips by occupation' graph.\n", + "f_survey.loc[\n", + " f_survey.primary_job_description.isin([\n", + " 'Paraprofessional ', 'Education ', 'education/early childhood', 'Teacher',\n", + " 'Education non-profit manager', 'Scientific research'\n", + " ]), 'primary_job_description', \n", + "] = 'Education'\n", + "\n", + "f_survey.loc[\n", + " f_survey.primary_job_description.isin([\n", + " 'Custodian', 'Custodial ', 'Csu custodian ', 'Janitorial '\n", + " ]), 'primary_job_description'\n", + "] = 'Custodial'\n", + "\n", + "f_survey.loc[\n", + " f_survey.primary_job_description.isin([\n", + " 'Inbound cs', 'Accounting Technician ', \n", + " ]), 'primary_job_description'\n", + "] = 'Clerical'\n", + "\n", + "f_survey.loc[\n", + " f_survey.primary_job_description.isin([\n", + " 'Restaurant manager', 'Transportaion Services',\n", + " ]), 'primary_job_description'\n", + "] = 'Sales or service'\n", + "\n", + "f_survey.loc[\n", + " f_survey.primary_job_description.isin([\n", + " 'Pastry chef and line cook', 'Cook', 'Chef', 'Dining Services',\n", + " 'Food Service', 'Cooking ', 'Residential Dining Services'\n", + " ]), 'primary_job_description'\n", + "] = 'Food service'\n", + "\n", + "f_survey.loc[\n", + " f_survey.primary_job_description.isin([\n", + " 'CNA', 'Caregiver/ Qmap', 'Health care', 'Nurse',\n", + " 'Healthcare', 'Medical', 'Medical field',\n", + " 'Family support '\n", + " ]), 'primary_job_description'\n", + "] = 'Medical/healthcare'\n", + "\n", + "f_survey.loc[\n", + " f_survey.primary_job_description.isin([\n", + " 'Amazon ', 'Hockey rink ', 'Caregiver', 'Security', 'Nonprofit social work '\n", + " ]), 'primary_job_description'\n", + "] = 'Other'\n", + "\n", + "f_survey.loc[\n", + " f_survey.primary_job_description.isin([\n", + " 'Hospital laundry', 'Maintenance ', 'Matreal handler',\n", + " 'Co op laundry'\n", + " ]), 'primary_job_description'\n", + "] = 'Manufacturing, construction, maintenance, or farming'\n", + "\n", + "f_survey.loc[f_survey.primary_job_description.isna(), 'primary_job_description'] = 'Other'" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Impute the commute time by grouping by occupation type and using median.\n", + "job_commutes = f_survey.groupby('primary_job_description')['primary_job_commute_time'].median()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "for job_type in job_commutes.index:\n", + " f_survey.loc[\n", + " (f_survey.primary_job_description == job_type)&(f_survey.primary_job_commute_time.isna()), \n", + " 'primary_job_commute_time'\n", + " ] = job_commutes[job_type]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix motor vehicles. Keep this ordinal.\n", + "f_survey.loc[f_survey.n_motor_vehicles == '4+', 'n_motor_vehicles'] = 4\n", + "f_survey.loc[f_survey.n_motor_vehicles == 'Prefer not to say / Prefiero no decir.', 'n_motor_vehicles'] = 0\n", + "f_survey.n_motor_vehicles = f_survey.n_motor_vehicles.astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "to_drop = [\n", + " 'Which one below describe you best?', 'wfh_days', 'primary_job_can_wfh', 'is_primary_job_flexible',\n", + " 'medical_condition_duration', 'has_medical_condition', 'residence_type', 'residence_ownership_type',\n", + " 'is_paid', 'is_student'\n", + "]\n", + "\n", + "f_survey.drop(columns=to_drop, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Timestamp 0\n", + "user_id 0\n", + "birth_year 0\n", + "gender 0\n", + "has_drivers_license 0\n", + "highest_education 0\n", + "has_multiple_jobs 0\n", + "primary_job_type 0\n", + "primary_job_description 0\n", + "primary_job_commute_mode 0\n", + "primary_job_commute_time 0\n", + "income_category 0\n", + "n_residence_members 0\n", + "n_residents_u18 0\n", + "n_residents_with_license 0\n", + "n_motor_vehicles 0\n", + "available_modes 0\n", + "age 0\n", + "dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(f_survey.isna().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "'''\n", + "Let us think about this. Given that the income category is not defined, it does not make sense\n", + "to encode the factor as a non-zero ordinal number. 9.6% of the people in the dataset choose to\n", + "not disclose their income. We could just drop them, but then we risk losing quite a lot of information.\n", + "'''\n", + "\n", + "f_survey.loc[f_survey.income_category == 'Prefer not to say', 'income_category'] = 0\n", + "f_survey.loc[f_survey.income_category == 'Less than $24,999', 'income_category'] = 1\n", + "f_survey.loc[f_survey.income_category == '$25,000-$49,999', 'income_category'] = 2\n", + "f_survey.loc[f_survey.income_category == '$50,000-$99,999', 'income_category'] = 3\n", + "f_survey.loc[f_survey.income_category == '$100,000 -$149,999', 'income_category'] = 4\n", + "f_survey.loc[f_survey.income_category == '$150,000-$199,999', 'income_category'] = 5\n", + "f_survey.loc[f_survey.income_category == '$150,000', 'income_category'] = 5\n", + "f_survey.loc[f_survey.income_category == '$150,000-$199,999', 'income_category'] = 6\n", + "f_survey.loc[f_survey.income_category == '$200,000 or more', 'income_category'] = 7\n", + "f_survey.income_category = f_survey.income_category.astype(int)\n", + "\n", + "# TODO: Think of a strategy here." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Sales or service 47\n", + "Professional, managerial, or technical 37\n", + "Manufacturing, construction, maintenance, or farming 17\n", + "Clerical or administrative support 17\n", + "Other 13\n", + "Education 9\n", + "Food service 8\n", + "Medical/healthcare 8\n", + "Custodial 7\n", + "Clerical 2\n", + "Maintenance 1\n", + "Name: primary_job_description, dtype: int64" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f_survey.primary_job_description.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 166 entries, 0 to 169\n", + "Data columns (total 18 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Timestamp 166 non-null datetime64[ns, America/Denver]\n", + " 1 user_id 166 non-null object \n", + " 2 birth_year 166 non-null int64 \n", + " 3 gender 166 non-null object \n", + " 4 has_drivers_license 166 non-null object \n", + " 5 highest_education 166 non-null object \n", + " 6 has_multiple_jobs 166 non-null object \n", + " 7 primary_job_type 166 non-null object \n", + " 8 primary_job_description 166 non-null object \n", + " 9 primary_job_commute_mode 166 non-null object \n", + " 10 primary_job_commute_time 166 non-null float64 \n", + " 11 income_category 166 non-null int64 \n", + " 12 n_residence_members 166 non-null int64 \n", + " 13 n_residents_u18 166 non-null int64 \n", + " 14 n_residents_with_license 166 non-null int64 \n", + " 15 n_motor_vehicles 166 non-null int64 \n", + " 16 available_modes 166 non-null object \n", + " 17 age 166 non-null int64 \n", + "dtypes: datetime64[ns, America/Denver](1), float64(1), int64(7), object(9)\n", + "memory usage: 24.6+ KB\n" + ] + }, + { + "data": { + "text/plain": [ + "None" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(f_survey.info())" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import OneHotEncoder\n", + "\n", + "def generate_ohe_features(df, feature_name):\n", + " ohe = OneHotEncoder()\n", + " ohe.fit(df[[feature_name]])\n", + " return pd.DataFrame(\n", + " ohe.transform(df[[feature_name]]).todense(), \n", + " columns=ohe.get_feature_names_out(),\n", + " index=df.index\n", + " ), ohe" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "ohe_features = [\n", + " 'gender', 'has_drivers_license', 'has_multiple_jobs', 'highest_education', \n", + " 'primary_job_type', 'primary_job_description', 'primary_job_commute_mode'\n", + "]\n", + "\n", + "for feature in ohe_features:\n", + " df, _ = generate_ohe_features(f_survey, feature)\n", + " f_survey = f_survey.merge(right=df, left_index=True, right_index=True)\n", + "\n", + "f_survey.drop(columns=ohe_features, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 166 entries, 0 to 169\n", + "Data columns (total 49 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Timestamp 166 non-null datetime64[ns, America/Denver]\n", + " 1 user_id 166 non-null object \n", + " 2 birth_year 166 non-null int64 \n", + " 3 primary_job_commute_time 166 non-null float64 \n", + " 4 income_category 166 non-null int64 \n", + " 5 n_residence_members 166 non-null int64 \n", + " 6 n_residents_u18 166 non-null int64 \n", + " 7 n_residents_with_license 166 non-null int64 \n", + " 8 n_motor_vehicles 166 non-null int64 \n", + " 9 available_modes 166 non-null object \n", + " 10 age 166 non-null int64 \n", + " 11 gender_Man 166 non-null float64 \n", + " 12 gender_Man;Nonbinary/genderqueer/genderfluid 166 non-null float64 \n", + " 13 gender_Nonbinary/genderqueer/genderfluid 166 non-null float64 \n", + " 14 gender_Prefer not to say 166 non-null float64 \n", + " 15 gender_Woman 166 non-null float64 \n", + " 16 gender_Woman;Nonbinary/genderqueer/genderfluid 166 non-null float64 \n", + " 17 has_drivers_license_No 166 non-null float64 \n", + " 18 has_drivers_license_Prefer not to say 166 non-null float64 \n", + " 19 has_drivers_license_Yes 166 non-null float64 \n", + " 20 has_multiple_jobs_No 166 non-null float64 \n", + " 21 has_multiple_jobs_Prefer not to say 166 non-null float64 \n", + " 22 has_multiple_jobs_Yes 166 non-null float64 \n", + " 23 highest_education_Bachelor's degree 166 non-null float64 \n", + " 24 highest_education_Graduate degree or professional degree 166 non-null float64 \n", + " 25 highest_education_High school graduate or GED 166 non-null float64 \n", + " 26 highest_education_Less than a high school graduate 166 non-null float64 \n", + " 27 highest_education_Prefer not to say 166 non-null float64 \n", + " 28 highest_education_Some college or associates degree 166 non-null float64 \n", + " 29 primary_job_type_Full-time 166 non-null float64 \n", + " 30 primary_job_type_Part-time 166 non-null float64 \n", + " 31 primary_job_type_Prefer not to say 166 non-null float64 \n", + " 32 primary_job_description_Clerical 166 non-null float64 \n", + " 33 primary_job_description_Clerical or administrative support 166 non-null float64 \n", + " 34 primary_job_description_Custodial 166 non-null float64 \n", + " 35 primary_job_description_Education 166 non-null float64 \n", + " 36 primary_job_description_Food service 166 non-null float64 \n", + " 37 primary_job_description_Maintenance 166 non-null float64 \n", + " 38 primary_job_description_Manufacturing, construction, maintenance, or farming 166 non-null float64 \n", + " 39 primary_job_description_Medical/healthcare 166 non-null float64 \n", + " 40 primary_job_description_Other 166 non-null float64 \n", + " 41 primary_job_description_Professional, managerial, or technical 166 non-null float64 \n", + " 42 primary_job_description_Sales or service 166 non-null float64 \n", + " 43 primary_job_commute_mode_Active transport 166 non-null float64 \n", + " 44 primary_job_commute_mode_Car transport 166 non-null float64 \n", + " 45 primary_job_commute_mode_Hybrid 166 non-null float64 \n", + " 46 primary_job_commute_mode_Public transport 166 non-null float64 \n", + " 47 primary_job_commute_mode_Unknown 166 non-null float64 \n", + " 48 primary_job_commute_mode_WFH 166 non-null float64 \n", + "dtypes: datetime64[ns, America/Denver](1), float64(39), int64(7), object(2)\n", + "memory usage: 68.9+ KB\n" + ] + }, + { + "data": { + "text/plain": [ + "None" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(f_survey.info())" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import List\n", + "\n", + "def plot_survey_distribution(df: pd.DataFrame, feature_names: List[str]):\n", + " \n", + " n = len(feature_names)\n", + " \n", + " # No more than 4 plots in on subplot.\n", + " assert n <= 4, \"Only 4 features can be visualized at a time.\"\n", + " \n", + " ncols = 2\n", + " nrows = n//ncols if n%ncols == 0 else (n//ncols) + 1\n", + " \n", + " h_scale = 4*nrows\n", + " \n", + " fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(8, h_scale))\n", + " for ix, ax in enumerate(axes.flatten()):\n", + " if ix < n:\n", + " sns.histplot(data=df, y=feature_names[ix], ax=ax)\n", + " else:\n", + " ax.set_axis_off()\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Could not interpret value `primary_job_commute_mode` for parameter `y`", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[36], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplot_survey_distribution\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mf_survey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mincome_category\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mn_motor_vehicles\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mprimary_job_commute_mode\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[35], line 18\u001b[0m, in \u001b[0;36mplot_survey_distribution\u001b[0;34m(df, feature_names)\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ix, ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(axes\u001b[38;5;241m.\u001b[39mflatten()):\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ix \u001b[38;5;241m<\u001b[39m n:\n\u001b[0;32m---> 18\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhistplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeature_names\u001b[49m\u001b[43m[\u001b[49m\u001b[43mix\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 20\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_axis_off()\n", + "File \u001b[0;32m~/miniconda3/envs/emission/lib/python3.9/site-packages/seaborn/distributions.py:1395\u001b[0m, in \u001b[0;36mhistplot\u001b[0;34m(data, x, y, hue, weights, stat, bins, binwidth, binrange, discrete, cumulative, common_bins, common_norm, multiple, element, fill, shrink, kde, kde_kws, line_kws, thresh, pthresh, pmax, cbar, cbar_ax, cbar_kws, palette, hue_order, hue_norm, color, log_scale, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m 1374\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhistplot\u001b[39m(\n\u001b[1;32m 1375\u001b[0m data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m,\n\u001b[1;32m 1376\u001b[0m \u001b[38;5;66;03m# Vector variables\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1392\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 1393\u001b[0m ):\n\u001b[0;32m-> 1395\u001b[0m p \u001b[38;5;241m=\u001b[39m \u001b[43m_DistributionPlotter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1396\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1397\u001b[0m \u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_DistributionPlotter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_semantics\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlocals\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1398\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1400\u001b[0m p\u001b[38;5;241m.\u001b[39mmap_hue(palette\u001b[38;5;241m=\u001b[39mpalette, order\u001b[38;5;241m=\u001b[39mhue_order, norm\u001b[38;5;241m=\u001b[39mhue_norm)\n\u001b[1;32m 1402\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/miniconda3/envs/emission/lib/python3.9/site-packages/seaborn/distributions.py:113\u001b[0m, in \u001b[0;36m_DistributionPlotter.__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\n\u001b[1;32m 108\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 109\u001b[0m data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 110\u001b[0m variables\u001b[38;5;241m=\u001b[39m{},\n\u001b[1;32m 111\u001b[0m ):\n\u001b[0;32m--> 113\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/emission/lib/python3.9/site-packages/seaborn/_oldcore.py:640\u001b[0m, in \u001b[0;36mVectorPlotter.__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m 635\u001b[0m \u001b[38;5;66;03m# var_ordered is relevant only for categorical axis variables, and may\u001b[39;00m\n\u001b[1;32m 636\u001b[0m \u001b[38;5;66;03m# be better handled by an internal axis information object that tracks\u001b[39;00m\n\u001b[1;32m 637\u001b[0m \u001b[38;5;66;03m# such information and is set up by the scale_* methods. The analogous\u001b[39;00m\n\u001b[1;32m 638\u001b[0m \u001b[38;5;66;03m# information for numeric axes would be information about log scales.\u001b[39;00m\n\u001b[1;32m 639\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_var_ordered \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m} \u001b[38;5;66;03m# alt., used DefaultDict\u001b[39;00m\n\u001b[0;32m--> 640\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massign_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 642\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m var, \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_semantic_mappings\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 643\u001b[0m \n\u001b[1;32m 644\u001b[0m \u001b[38;5;66;03m# Create the mapping function\u001b[39;00m\n\u001b[1;32m 645\u001b[0m map_func \u001b[38;5;241m=\u001b[39m partial(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mmap, plotter\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n", + "File \u001b[0;32m~/miniconda3/envs/emission/lib/python3.9/site-packages/seaborn/_oldcore.py:701\u001b[0m, in \u001b[0;36mVectorPlotter.assign_variables\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m 699\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 700\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_format \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlong\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 701\u001b[0m plot_data, variables \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_assign_variables_longform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 702\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mvariables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 703\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 705\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mplot_data \u001b[38;5;241m=\u001b[39m plot_data\n\u001b[1;32m 706\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvariables \u001b[38;5;241m=\u001b[39m variables\n", + "File \u001b[0;32m~/miniconda3/envs/emission/lib/python3.9/site-packages/seaborn/_oldcore.py:938\u001b[0m, in \u001b[0;36mVectorPlotter._assign_variables_longform\u001b[0;34m(self, data, **kwargs)\u001b[0m\n\u001b[1;32m 933\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(val, (\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbytes\u001b[39m)):\n\u001b[1;32m 934\u001b[0m \n\u001b[1;32m 935\u001b[0m \u001b[38;5;66;03m# This looks like a column name but we don't know what it means!\u001b[39;00m\n\u001b[1;32m 937\u001b[0m err \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not interpret value `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mval\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` for parameter `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 938\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(err)\n\u001b[1;32m 940\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 941\u001b[0m \n\u001b[1;32m 942\u001b[0m \u001b[38;5;66;03m# Otherwise, assume the value is itself data\u001b[39;00m\n\u001b[1;32m 943\u001b[0m \n\u001b[1;32m 944\u001b[0m \u001b[38;5;66;03m# Raise when data object is present and a vector can't matched\u001b[39;00m\n\u001b[1;32m 945\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, pd\u001b[38;5;241m.\u001b[39mDataFrame) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(val, pd\u001b[38;5;241m.\u001b[39mSeries):\n", + "\u001b[0;31mValueError\u001b[0m: Could not interpret value `primary_job_commute_mode` for parameter `y`" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_survey_distribution(\n", + " f_survey, ['income_category', 'n_motor_vehicles', 'primary_job_commute_mode']\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(len(f_survey.user_id.unique()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## GPS Data Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# We only wish to focus on Denver data for now.\n", + "# denver_data = r(expanded_ct.loc[\n", + "# (expanded_ct.start_local_dt_timezone == \"America/Denver\") & (expanded_ct.end_local_dt_timezone == \"America/Denver\"), \n", + "# :])\n", + "\n", + "denver_data = expanded_ct.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "denver_data['start_fmt_time'] = pd.to_datetime(\n", + " denver_data['start_fmt_time'], utc=True\n", + ").dt.tz_convert('America/Denver')" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2016-08-15 07:59:32.418000-06:00 2022-12-30 23:33:27.147785-07:00\n" + ] + } + ], + "source": [ + "print(denver_data.start_fmt_time.min(), denver_data.start_fmt_time.max())" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "# Parse the datetime to Denver time.\n", + "denver_data['start_fmt_time'] = pd.to_datetime(\n", + " denver_data['start_fmt_time'], utc=True\n", + ").dt.tz_convert('America/Denver')\n", + "\n", + "# Re-compute all the start variables.\n", + "# denver_data['start_local_dt_year'] = denver_data['start_fmt_time'].dt.year\n", + "# denver_data['start_local_dt_month'] = denver_data['start_fmt_time'].dt.month\n", + "# denver_data['start_local_dt_day'] = denver_data['start_fmt_time'].dt.day\n", + "# denver_data['start_local_dt_hour'] = denver_data['start_fmt_time'].dt.hour\n", + "# denver_data['start_local_dt_weekday'] = denver_data['start_fmt_time'].dt.weekday\n", + "\n", + "# ## Do the same with the end time.\n", + "denver_data['end_fmt_time'] = pd.to_datetime(\n", + " denver_data['end_fmt_time'], utc=True\n", + ").dt.tz_convert('America/Denver')\n", + "\n", + "# # Re-compute all the end variables.\n", + "# denver_data['end_local_dt_year'] = denver_data['end_fmt_time'].dt.year\n", + "# denver_data['end_local_dt_month'] = denver_data['end_fmt_time'].dt.month\n", + "# denver_data['end_local_dt_day'] = denver_data['end_fmt_time'].dt.day\n", + "# denver_data['end_local_dt_hour'] = denver_data['end_fmt_time'].dt.hour\n", + "# denver_data['end_local_dt_weekday'] = denver_data['end_fmt_time'].dt.weekday" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# Merge the trip data with the survey data.\n", + "\n", + "# inner join.\n", + "merged_data = denver_data.merge(\n", + " f_survey, left_on='user_id', right_on='user_id'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "166\n" + ] + } + ], + "source": [ + "print(len(merged_data.user_id.unique()))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's start choosing features for modeling.\n", + "\n", + "# base_time_features = ['fmt_time', 'local_dt_year', 'local_dt_month', 'local_dt_day', 'local_dt_hour', 'local_dt_weekday']\n", + "# time_features = ['start_' + x for x in base_time_features] + ['end_' + x for x in base_time_features]\n", + "\n", + "# demographic_features = ['available_modes',\n", + "# 'birth_year', 'income_category', 'n_motor_vehicles', 'n_residence_members', 'n_residents_u18', 'gender', \n", + "# 'is_student', 'n_residents_with_license']\n", + "\n", + "# sensed_features = ['duration', 'distance_miles', 'cleaned_trip', 'start_loc', 'end_loc', 'section_modes', 'section_distances']\n", + "\n", + "# modeling_data = merged_data[['user_id', '_id', 'original_user_id', 'cleaned_trip', 'Replaced_mode', 'Mode_confirm'] + time_features + demographic_features + sensed_features].copy()\n", + "modeling_data = merged_data.copy()\n", + "\n", + "# Rename columns in-place.\n", + "modeling_data.rename(columns={\n", + " 'start_local_dt_year': 'start:year', 'start_local_dt_month': 'start:month', 'start_local_dt_day': 'start:day', 'start_local_dt_hour': 'start:hour',\n", + " 'end_local_dt_year': 'end:year', 'end_local_dt_month': 'end:month', 'end_local_dt_day': 'end:day', 'end_local_dt_hour': 'end:hour'\n", + " }, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "def is_overnight_trip(start_date, end_date):\n", + " return int((end_date - start_date).days > 0)\n", + "\n", + "# overnight trips may be more likely taken by car.\n", + "modeling_data['is_overnight_trip'] = modeling_data.apply(\n", + " lambda x: is_overnight_trip(x.start_fmt_time, x.end_fmt_time), axis=1\n", + ")\n", + "\n", + "# Number of working individuals in the household = number of individuals in the house - number of children.\n", + "modeling_data['n_working_residents'] = (modeling_data['n_residence_members'] - modeling_data['n_residents_u18']).astype(int)\n", + "\n", + "# Convert the total duration of the trip into minutes.\n", + "modeling_data[['duration']] = modeling_data[['duration']]/60\n", + "\n", + "# Extract start and end latitudes and longitudes.\n", + "modeling_data['start_lat'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][1])\n", + "modeling_data['start_lng'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][0])\n", + "\n", + "modeling_data['end_lat'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][1])\n", + "modeling_data['end_lng'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][0])" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "# from calendar import monthrange\n", + "\n", + "# # Find day of month: use monthrange with (mm, yyyy) args and find how many days that month had (leap years are supported).\n", + "# def get_num_days_in_month(yyyy, mm):\n", + "# return monthrange(yyyy, mm)[1]\n", + "\n", + "# def is_overnight_trip(start_date, end_date):\n", + "# return int((end_date - start_date).days > 0)\n", + "\n", + "# # get the number of days for the start and end times.\n", + "# modeling_data['start:n_days_in_month'] = modeling_data.apply(lambda x: get_num_days_in_month(x['start:year'], x['start:month']), axis=1)\n", + "# modeling_data['end:n_days_in_month'] = modeling_data.apply(lambda x: get_num_days_in_month(x['end:year'], x['end:month']), axis=1)\n", + "\n", + "# # age = current year - year of birth\n", + "# modeling_data['age'] = 2023 - modeling_data['birth_year']\n", + "\n", + "# # overnight trips may be more likely taken by car.\n", + "# modeling_data['is_overnight_trip'] = modeling_data.apply(lambda x: is_overnight_trip(x.start_fmt_time, x.end_fmt_time), axis=1)\n", + "\n", + "# # Number of working individuals in the household = number of individuals in the house - number of children.\n", + "# modeling_data['n_working_residents'] = (modeling_data['n_residence_members'] - modeling_data['n_residents_u18']).astype(int)\n", + "\n", + "# # Create a binary indicator.\n", + "# modeling_data['is_male'] = modeling_data.gender.apply(lambda x: 1 if x==\"Male\" else 0)\n", + "\n", + "# # Bin the number of vehicles owned.\n", + "# # Drop the observations with (Prefer not to say)\n", + "# modeling_data = modeling_data.loc[~modeling_data['n_motor_vehicles'].isin(['Prefer not to say / Prefiero no decir.']), :]\n", + "# modeling_data.loc[modeling_data['n_motor_vehicles'].isin(['4+']), 'n_motor_vehicles'] = 4\n", + "# modeling_data['n_motor_vehicles'] = modeling_data['n_motor_vehicles'].astype(int)\n", + "\n", + "# # Convert the total duration of the trip into minutes.\n", + "# modeling_data[['duration']] = modeling_data[['duration']]/60\n", + "\n", + "# # Extract start and end latitudes and longitudes.\n", + "# modeling_data['start_lat'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][1])\n", + "# modeling_data['start_lng'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][0])\n", + "\n", + "# modeling_data['end_lat'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][1])\n", + "# modeling_data['end_lng'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][0])" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "# # Time-related feature engineeering:\n", + "# '''\n", + "# HOD: hour of day\n", + "# DOM: day of month\n", + "# MOY: month of year\n", + "# '''\n", + "\n", + "# def get_HOD(hour, how='sin'):\n", + "# if how == 'sin':\n", + "# return np.sin(2 * np.pi * (hour/24))\n", + "# return np.cos(2 * np.pi * (hour/24))\n", + "\n", + "# def get_DOM(day, n_days, how='sin'):\n", + "# if how == 'sin':\n", + "# return np.sin(2 * np.pi * (day/n_days))\n", + "# return np.cos(2 * np.pi * (day/n_days))\n", + "\n", + "# def get_MOY(month, how='sin'):\n", + "# if how == 'sin':\n", + "# return np.sin(2 * np.pi * (month/12))\n", + "# return np.cos(2 * np.pi * (month/12))\n", + "\n", + "# # Start - sin\n", + "# modeling_data['start:sin_HOD'] = modeling_data.apply(lambda x: get_HOD(x['start:hour']), axis=1)\n", + "# modeling_data['start:sin_DOM'] = modeling_data.apply(lambda x: get_DOM(x['start:day'], x['start:n_days_in_month']), axis=1)\n", + "# modeling_data['start:sin_MOY'] = modeling_data.apply(lambda x: get_MOY(x['start:year']), axis=1)\n", + "\n", + "# # Start - cos\n", + "# modeling_data['start:cos_HOD'] = modeling_data.apply(lambda x: get_HOD(x['start:hour'], how='cos'), axis=1)\n", + "# modeling_data['start:cos_DOM'] = modeling_data.apply(lambda x: get_DOM(x['start:day'], x['start:n_days_in_month'], how='cos'), axis=1)\n", + "# modeling_data['start:cos_MOY'] = modeling_data.apply(lambda x: get_MOY(x['start:year'], how='cos'), axis=1)\n", + "\n", + "# # End - sin\n", + "# modeling_data['end:sin_HOD'] = modeling_data.apply(lambda x: get_HOD(x['end:hour']), axis=1)\n", + "# modeling_data['end:sin_DOM'] = modeling_data.apply(lambda x: get_DOM(x['end:day'], x['end:n_days_in_month']), axis=1)\n", + "# modeling_data['end:sin_MOY'] = modeling_data.apply(lambda x: get_MOY(x['end:year']), axis=1)\n", + "\n", + "# # End - cos\n", + "# modeling_data['end:cos_HOD'] = modeling_data.apply(lambda x: get_HOD(x['end:hour'], how='cos'), axis=1)\n", + "# modeling_data['end:cos_DOM'] = modeling_data.apply(lambda x: get_DOM(x['end:day'], x['end:n_days_in_month'], how='cos'), axis=1)\n", + "# modeling_data['end:cos_MOY'] = modeling_data.apply(lambda x: get_MOY(x['end:year'], how='cos'), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sourceend_tsend_fmt_timeend_locraw_tripstart_tsstart_fmt_timestart_locdurationdistance...primary_job_commute_mode_Hybridprimary_job_commute_mode_Public transportprimary_job_commute_mode_Unknownprimary_job_commute_mode_WFHis_overnight_tripn_working_residentsstart_latstart_lngend_latend_lng
0DwellSegmentationDistFilter1.629238e+092021-08-17 16:02:26.493017-06:00{'type': 'Point', 'coordinates': [-71.08788681...611c350df3bfdd5663c34e521.629237e+092021-08-17 15:51:32.998732-06:00{'type': 'Point', 'coordinates': [-71.11831245...10.8915712700.231495...0.00.01.00.00342.351336-71.11831242.347875-71.087887
1DwellSegmentationDistFilter1.645386e+092022-02-20 12:32:43.451677-07:00{'type': 'Point', 'coordinates': [-111.9450490...6212ce379106a670c421b8881.645383e+092022-02-20 11:58:02.051102-07:00{'type': 'Point', 'coordinates': [-111.9167906...34.69001018093.352854...0.00.01.00.00333.595163-111.91679133.461827-111.945049
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5 rows × 104 columns

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" + ], + "text/plain": [ + " source end_ts end_fmt_time \\\n", + "0 DwellSegmentationDistFilter 1.629238e+09 2021-08-17 16:02:26.493017-06:00 \n", + "1 DwellSegmentationDistFilter 1.645386e+09 2022-02-20 12:32:43.451677-07:00 \n", + "2 DwellSegmentationDistFilter 1.645391e+09 2022-02-20 14:07:17.998778-07:00 \n", + "3 DwellSegmentationDistFilter 1.645397e+09 2022-02-20 15:35:51.018136-07:00 \n", + "4 DwellSegmentationDistFilter 1.645405e+09 2022-02-20 17:52:13.000176-07:00 \n", + "\n", + " end_loc \\\n", + "0 {'type': 'Point', 'coordinates': [-71.08788681... \n", + "1 {'type': 'Point', 'coordinates': [-111.9450490... \n", + "2 {'type': 'Point', 'coordinates': [-111.9440840... \n", + "3 {'type': 'Point', 'coordinates': [-111.9448744... \n", + "4 {'type': 'Point', 'coordinates': [-110.8175686... \n", + "\n", + " raw_trip start_ts start_fmt_time \\\n", + "0 611c350df3bfdd5663c34e52 1.629237e+09 2021-08-17 15:51:32.998732-06:00 \n", + "1 6212ce379106a670c421b888 1.645383e+09 2022-02-20 11:58:02.051102-07:00 \n", + "2 6212ce459106a670c421b88c 1.645388e+09 2022-02-20 13:20:30.462982-07:00 \n", + "3 6212ce459106a670c421b88e 1.645397e+09 2022-02-20 15:35:16.506669-07:00 \n", + "4 6214032d39cfe183d0343a07 1.645397e+09 2022-02-20 15:43:38.468902-07:00 \n", + "\n", + " start_loc duration \\\n", + "0 {'type': 'Point', 'coordinates': [-71.11831245... 10.891571 \n", + "1 {'type': 'Point', 'coordinates': [-111.9167906... 34.690010 \n", + "2 {'type': 'Point', 'coordinates': [-111.9437005... 46.792263 \n", + "3 {'type': 'Point', 'coordinates': [-111.9440840... 0.575191 \n", + "4 {'type': 'Point', 'coordinates': [-111.9448744... 128.575521 \n", + "\n", + " distance ... primary_job_commute_mode_Hybrid \\\n", + "0 2700.231495 ... 0.0 \n", + "1 18093.352854 ... 0.0 \n", + "2 984.681194 ... 0.0 \n", + "3 183.932256 ... 0.0 \n", + "4 192258.374492 ... 0.0 \n", + "\n", + " primary_job_commute_mode_Public transport primary_job_commute_mode_Unknown \\\n", + "0 0.0 1.0 \n", + "1 0.0 1.0 \n", + "2 0.0 1.0 \n", + "3 0.0 1.0 \n", + "4 0.0 1.0 \n", + "\n", + " primary_job_commute_mode_WFH is_overnight_trip n_working_residents \\\n", + "0 0.0 0 3 \n", + "1 0.0 0 3 \n", + "2 0.0 0 3 \n", + "3 0.0 0 3 \n", + "4 0.0 0 3 \n", + "\n", + " start_lat start_lng end_lat end_lng \n", + "0 42.351336 -71.118312 42.347875 -71.087887 \n", + "1 33.595163 -111.916791 33.461827 -111.945049 \n", + "2 33.461366 -111.943701 33.460592 -111.944084 \n", + "3 33.460592 -111.944084 33.462015 -111.944874 \n", + "4 33.462015 -111.944874 32.276548 -110.817569 \n", + "\n", + "[5 rows x 104 columns]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "modeling_data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, for every trip, we have the corresponding section mode that covered the longest distance for the trip.\n", + "\n", + "Using this as well as the `available_modes` column:\n", + "\n", + "```language=python\n", + "\n", + " # unique available modes:\n", + " {'Bicycle',\n", + " 'Do not have vehicle ',\n", + " 'Get a ride from a friend or family member',\n", + " 'None',\n", + " 'Public transportation (bus, subway, light rail, etc.)',\n", + " 'Rental car (including Zipcar/ Car2Go)',\n", + " 'Shared bicycle or scooter',\n", + " 'Skateboard',\n", + " 'Taxi (regular taxi, Uber, Lyft, etc)',\n", + " 'Walk/roll'}\n", + "\n", + " # unique section modes:\n", + " {'bicycling', 'bus', 'car', 'no_sensed', 'train', 'walking'}\n", + "\n", + " \n", + "```\n", + "\n", + "What mapping can we establish here? " + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "def remove_air_or_hsr(df):\n", + "\n", + " df['mark'] = 0\n", + "\n", + " for ix, row in df.iterrows():\n", + " sections = row['section_modes']\n", + " if 'air_or_hsr' in sections:\n", + " df.loc[ix, 'mark'] = 1\n", + " \n", + " df = r(df.loc[df.mark == 0, :])\n", + " df.drop(columns=['mark'], inplace=True)\n", + "\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "modeling_data = remove_air_or_hsr(modeling_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "importlib.reload(scaffolding)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "results = list()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "modeling_data = r(modeling_data.loc[:,~modeling_data.columns.duplicated()].copy())\n", + "subset = modeling_data[['original_user_id', 'cleaned_trip']]" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "num_splits = 10\n", + "samples_per_split = modeling_data.shape[0]//num_splits" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Split 0 size: 8069\n", + "INFO: Pandarallel will run on 4 workers.\n", + "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Falling back to confirmed trips...\n", + "Took 13.07597050956667 minutes to complete\n", + "Split 1 size: 8069\n", + "INFO: Pandarallel will run on 4 workers.\n", + "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rkulhall/em-public-dashboard/rm_src/../viz_scripts/scaffolding.py:147: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " confirmed_trips['section_durations'] = confirmed_trips.parallel_apply(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Took 20.352822523033332 minutes to complete\n", + "Split 2 size: 8069\n", + "INFO: Pandarallel will run on 4 workers.\n", + "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rkulhall/em-public-dashboard/rm_src/../viz_scripts/scaffolding.py:147: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " confirmed_trips['section_durations'] = confirmed_trips.parallel_apply(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Took 19.824597845733333 minutes to complete\n", + "Split 3 size: 8069\n", + "INFO: Pandarallel will run on 4 workers.\n", + "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rkulhall/em-public-dashboard/rm_src/../viz_scripts/scaffolding.py:147: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " confirmed_trips['section_durations'] = confirmed_trips.parallel_apply(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Took 20.249677114833336 minutes to complete\n", + "Split 4 size: 8069\n", + "INFO: Pandarallel will run on 4 workers.\n", + "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rkulhall/em-public-dashboard/rm_src/../viz_scripts/scaffolding.py:147: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " confirmed_trips['section_durations'] = confirmed_trips.parallel_apply(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Took 18.196551645316656 minutes to complete\n", + "Split 5 size: 8069\n", + "INFO: Pandarallel will run on 4 workers.\n", + "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rkulhall/em-public-dashboard/rm_src/../viz_scripts/scaffolding.py:147: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " confirmed_trips['section_durations'] = confirmed_trips.parallel_apply(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Took 14.953972044866669 minutes to complete\n", + "Split 6 size: 8069\n", + "INFO: Pandarallel will run on 4 workers.\n", + "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rkulhall/em-public-dashboard/rm_src/../viz_scripts/scaffolding.py:147: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " confirmed_trips['section_durations'] = confirmed_trips.parallel_apply(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Took 13.908668193450012 minutes to complete\n", + "Split 7 size: 8069\n", + "INFO: Pandarallel will run on 4 workers.\n", + "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rkulhall/em-public-dashboard/rm_src/../viz_scripts/scaffolding.py:147: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " confirmed_trips['section_durations'] = confirmed_trips.parallel_apply(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Took 15.91554533118333 minutes to complete\n", + "Split 8 size: 8069\n", + "INFO: Pandarallel will run on 4 workers.\n", + "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rkulhall/em-public-dashboard/rm_src/../viz_scripts/scaffolding.py:147: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " confirmed_trips['section_durations'] = confirmed_trips.parallel_apply(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Took 14.596019812649986 minutes to complete\n", + "Split 9 size: 8069\n", + "INFO: Pandarallel will run on 4 workers.\n", + "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rkulhall/em-public-dashboard/rm_src/../viz_scripts/scaffolding.py:147: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " confirmed_trips['section_durations'] = confirmed_trips.parallel_apply(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Took 12.495607870950002 minutes to complete\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rkulhall/em-public-dashboard/rm_src/../viz_scripts/scaffolding.py:147: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " confirmed_trips['section_durations'] = confirmed_trips.parallel_apply(\n" + ] + } + ], + "source": [ + "from time import perf_counter\n", + "\n", + "for split_ix in range(num_splits):\n", + " low = samples_per_split * split_ix\n", + " high = samples_per_split * (split_ix + 1) - 1\n", + " split = subset.loc[low:high, :]\n", + " \n", + " print(f\"Split {split_ix} size: {split.shape[0]}\")\n", + " \n", + " now = perf_counter()\n", + " result = scaffolding.get_section_durations(split)\n", + " end = perf_counter() - now\n", + "\n", + " print(f\"Took {end/60} minutes to complete\")\n", + " results.append(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10\n" + ] + } + ], + "source": [ + "print(len(results))" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "cat = pd.concat(results, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "80691 80690\n" + ] + } + ], + "source": [ + "print(modeling_data.shape[0], cat.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "80690\n" + ] + } + ], + "source": [ + "modeling_data = modeling_data.loc[:cat.shape[0]-1, :]\n", + "print(modeling_data.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(modeling_data.original_user_id == cat.original_user_id).all()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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original_user_idcleaned_tripsection_durations
08a0473ca-e53d-4720-a99c-0696cc1fb407611c350ef3bfdd5663c34e5b[393.00192403793335, 258.49230194091797]
18a0473ca-e53d-4720-a99c-0696cc1fb4076212ce4c9106a670c421b89b[2081.4005744457245]
28a0473ca-e53d-4720-a99c-0696cc1fb4076212ce4e9106a670c421b903[2807.5357959270477]
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" + ], + "text/plain": [ + " original_user_id cleaned_trip \\\n", + "0 8a0473ca-e53d-4720-a99c-0696cc1fb407 611c350ef3bfdd5663c34e5b \n", + "1 8a0473ca-e53d-4720-a99c-0696cc1fb407 6212ce4c9106a670c421b89b \n", + "2 8a0473ca-e53d-4720-a99c-0696cc1fb407 6212ce4e9106a670c421b903 \n", + "3 8a0473ca-e53d-4720-a99c-0696cc1fb407 6212ce4f9106a670c421b965 \n", + "4 8a0473ca-e53d-4720-a99c-0696cc1fb407 6214033c39cfe183d0343a2f \n", + "\n", + " section_durations \n", + "0 [393.00192403793335, 258.49230194091797] \n", + "1 [2081.4005744457245] \n", + "2 [2807.5357959270477] \n", + "3 [34.51146650314331] \n", + "4 [7714.531273841858] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(cat.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "modeling_data = pd.concat([modeling_data, cat[['section_durations']]], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "modeling_data.to_csv('../data/final_modeling_data_02142024.csv', index=False)" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" + }, + "kernelspec": { + "display_name": "emission", + "language": "python", + "name": "emission" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/viz_scripts/denver_weather_data.csv b/viz_scripts/denver_weather_data.csv new file mode 100644 index 0000000..77def6f --- /dev/null +++ b/viz_scripts/denver_weather_data.csv @@ -0,0 +1,26329 @@ +time,temperature_2m (°F),relative_humidity_2m (%),dew_point_2m (°F),rain (inch),snowfall (inch),wind_speed_10m (mp/h),wind_gusts_10m 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From: Rahul Kulhalli Date: Fri, 16 Feb 2024 11:01:50 -0500 Subject: [PATCH 08/16] Added baseline sweep script --- rm_src/run_baseline_experiments.py | 499 +++++++++++++++++++++++++++++ 1 file changed, 499 insertions(+) create mode 100644 rm_src/run_baseline_experiments.py diff --git a/rm_src/run_baseline_experiments.py b/rm_src/run_baseline_experiments.py new file mode 100644 index 0000000..2b0b7df --- /dev/null +++ b/rm_src/run_baseline_experiments.py @@ -0,0 +1,499 @@ +from enum import Enum +import random +from pathlib import Path + +# Math and graphing. +import pandas as pd +import numpy as np +import seaborn as sns +import matplotlib.pyplot as plt + +# sklearn imports. +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.linear_model import LinearRegression +from sklearn.metrics import f1_score, r2_score, ConfusionMatrixDisplay + +from sklearn.metrics import classification_report +from sklearn.model_selection import GridSearchCV, StratifiedKFold +from pprint import pprint +from sklearn.inspection import permutation_importance +from time import perf_counter +from sklearn.ensemble import RandomForestClassifier + + +# Global experiment flags and variables. +SEED = 19348 +TARGETS = ['p_micro', 'no_trip', 's_car', 'transit', 'car', 's_micro', 'ridehail', 'walk', 'unknown'] +CV = False + +# Set the Numpy seed too. +random.seed(SEED) +np.random.seed(SEED) + +class SPLIT_TYPE(Enum): + INTRA_USER = 0 + INTER_USER = 1 + TARGET = 2 + MODE = 3 + + +class SPLIT(Enum): + TRAIN = 0 + TEST = 1 + +def get_splits(count_df: pd.DataFrame, n:int, test_size=0.2): + maxsize = int(n * test_size) + + max_threshold = int(maxsize * 1.05) + min_threshold = int(maxsize * 0.95) + + print(f"{min_threshold}, {max_threshold}") + + # Allow a 10% tolerance + def _dp(ix, curr_size, ids, cache): + + if ix >= count_df.shape[0]: + return [] + + key = ix + + if key in cache: + return cache[key] + + if curr_size > max_threshold: + return [] + + if min_threshold <= curr_size <= max_threshold: + return ids + + # two options - either pick the current id or skip it. + branch_a = _dp(ix, curr_size+count_df.loc[ix, 'count'], ids+[count_df.loc[ix, 'index']], cache) + branch_b = _dp(ix+1, curr_size, ids, cache) + + curr_max = [] + if branch_a and len(branch_a) > 0: + curr_max = branch_a + + if branch_b and len(branch_b) > len(branch_a): + curr_max = branch_b + + cache[key] = curr_max + return cache[key] + + return _dp(0, 0, ids=list(), cache=dict()) + + +def get_train_test_splits(data: pd.DataFrame, how=SPLIT_TYPE, test_ratio=0.2, shuffle=True): + + n_users = list(data.user_id.unique()) + n = data.shape[0] + + if shuffle: + data = data.sample(data.shape[0], random_state=SEED).reset_index(drop=True, inplace=False) + + if how == SPLIT_TYPE.INTER_USER: + # Make the split, ensuring that a user in one fold is not leaked into the other fold. + # Basic idea: we want to start with the users with the highest instances and place + # alternating users in each set. + counts = data.user_id.value_counts().reset_index(drop=False, inplace=False, name='count') + + # Now, start with the user_id at the top, and keep adding to either split. + # This can be achieved using a simple DP program. + test_ids = get_splits(counts, data.shape[0]) + test_data = data.loc[data.user_id.isin(test_ids), :] + train_index = data.index.difference(test_data.index) + train_data = data.loc[data.user_id.isin(train_index), :] + + return train_data, test_data + + elif how == SPLIT_TYPE.INTRA_USER: + + # There are certain users with only one observation. What do we do with those? + # As per the mobilitynet modeling pipeline, we randomly assign them to either the + # training or test set. + + value_counts = data.user_id.value_counts() + single_count_ids = value_counts[value_counts == 1].index + + data_filtered = data.loc[~data.user_id.isin(single_count_ids), :].reset_index(drop=True) + data_single_counts = data.loc[data.user_id.isin(single_count_ids), :].reset_index(drop=True) + + X_tr, X_te = train_test_split( + data_filtered, test_size=test_ratio, shuffle=shuffle, stratify=data_filtered.user_id, + random_state=SEED + ) + + data_single_counts['assigned'] = np.random.choice(['train', 'test'], len(data_single_counts)) + X_tr_merged = pd.concat( + [X_tr, data_single_counts.loc[data_single_counts.assigned == 'train', :].drop( + columns=['assigned'], inplace=False + )], + ignore_index=True, axis=0 + ) + + X_te_merged = pd.concat( + [X_te, data_single_counts.loc[data_single_counts.assigned == 'test', :].drop( + columns=['assigned'], inplace=False + )], + ignore_index=True, axis=0 + ) + + return X_tr_merged, X_te_merged + + elif how == SPLIT_TYPE.TARGET: + + X_tr, X_te = train_test_split( + data, test_size=test_ratio, shuffle=shuffle, stratify=data.target, + random_state=SEED + ) + + return X_tr, X_te + + elif how == SPLIT_TYPE.MODE: + X_tr, X_te = train_test_split( + data, test_size=test_ratio, shuffle=shuffle, stratify=data.section_mode_argmax, + random_state=SEED + ) + + return X_tr, X_te + + raise NotImplementedError("Unknown split type") + + +def get_duration_estimate(df: pd.DataFrame, dset: SPLIT, model_dict: dict): + + X_features = ['section_distance_argmax', 'age'] + + if 'mph' in df.columns: + X_features += ['mph'] + + if dset == SPLIT.TRAIN and model_dict is None: + model_dict = dict() + + if dset == SPLIT.TEST and model_dict is None: + raise AttributeError("Expected model dict for testing.") + + if dset == SPLIT.TRAIN: + for section_mode in df.section_mode_argmax.unique(): + section_data = df.loc[df.section_mode_argmax == section_mode, :] + if section_mode not in model_dict: + model_dict[section_mode] = dict() + + model = LinearRegression(fit_intercept=True) + + X = section_data[ + X_features + ] + Y = section_data[['section_duration_argmax']] + + model.fit(X, Y.values.ravel()) + + r2 = r2_score(y_pred=model.predict(X), y_true=Y.values.ravel()) + print(f"\t-> Train R2 for {section_mode}: {r2}") + + model_dict[section_mode]['model'] = model + + elif dset == SPLIT.TEST: + for section_mode in df.section_mode_argmax.unique(): + section_data = df.loc[df.section_mode_argmax == section_mode, :] + X = section_data[ + X_features + ] + Y = section_data[['section_duration_argmax']] + + y_pred = model_dict[section_mode]['model'].predict(X) + r2 = r2_score(y_pred=y_pred, y_true=Y.values.ravel()) + print(f"\t-> Test R2 for {section_mode}: {r2}") + + # Create the new columns for the duration. + new_columns = ['p_micro','no_trip','s_car','transit','car','s_micro','ridehail','walk','unknown'] + df[new_columns] = 0 + df['temp'] = 0 + + for section in df.section_mode_argmax.unique(): + X_section = df.loc[df.section_mode_argmax == section, X_features] + + # broadcast to all columns. + df.loc[df.section_mode_argmax == section, 'temp'] = model_dict[section]['model'].predict(X_section) + + for c in new_columns: + df[c] = df['av_' + c] * df['temp'] + + df.drop(columns=['temp'], inplace=True) + + df.rename(columns=dict([(x, 'tt_'+x) for x in new_columns]), inplace=True) + + # return model_dict, result_df + return model_dict, df + +# Some helper functions that will help ease redundancy in the code. + +def drop_columns(df: pd.DataFrame): + to_drop = [ + 'source', 'end_ts', 'end_fmt_time', 'end_loc', 'raw_trip', 'start_ts', + 'start_fmt_time', 'start_loc', 'duration', 'distance', 'start_place', + 'end_place', 'cleaned_trip', 'inferred_labels', 'inferred_trip', 'expectation', + 'confidence_threshold', 'expected_trip', 'user_input', 'start:year', 'start:month', + 'start:day', 'start_local_dt_minute', 'start_local_dt_second', + 'start_local_dt_weekday', 'start_local_dt_timezone', 'end:year', 'end:month', 'end:day', + 'end_local_dt_minute', 'end_local_dt_second', 'end_local_dt_weekday', + 'end_local_dt_timezone', '_id', 'user_id', 'metadata_write_ts', 'additions', + 'mode_confirm', 'purpose_confirm', 'Mode_confirm', 'Trip_purpose', + 'original_user_id', 'program', 'opcode', 'Timestamp', 'birth_year', + 'available_modes', 'section_coordinates_argmax', 'section_mode_argmax' + ] + + # Drop section_mode_argmax and available_modes. + return df.drop( + columns=to_drop, + inplace=False + ) + + +def scale_values(df: pd.DataFrame, split: SPLIT, scalers=None): + # Scale costs using StandardScaler. + costs = df[[c for c in df.columns if 'cost_' in c]].copy() + times = df[[c for c in df.columns if 'tt_' in c or 'duration' in c]].copy() + distances = df[[c for c in df.columns if 'distance' in c]] + + print( + "Cost columns to be scaled: ", costs.columns,"\nTime columns to be scaled: ", times.columns, \ + "\nDistance columns to be scaled: ", distances.columns + ) + + if split == SPLIT.TRAIN and scalers is None: + cost_scaler = StandardScaler() + tt_scaler = StandardScaler() + dist_scaler = StandardScaler() + + cost_scaled = pd.DataFrame( + cost_scaler.fit_transform(costs), + columns=costs.columns, + index=costs.index + ) + + tt_scaled = pd.DataFrame( + tt_scaler.fit_transform(times), + columns=times.columns, + index=times.index + ) + + dist_scaled = pd.DataFrame( + dist_scaler.fit_transform(distances), + columns=distances.columns, + index=distances.index + ) + + elif split == SPLIT.TEST and scalers is not None: + + cost_scaler, tt_scaler, dist_scaler = scalers + + cost_scaled = pd.DataFrame( + cost_scaler.transform(costs), + columns=costs.columns, + index=costs.index + ) + + tt_scaled = pd.DataFrame( + tt_scaler.transform(times), + columns=times.columns, + index=times.index + ) + + dist_scaled = pd.DataFrame( + dist_scaler.transform(distances), + columns=distances.columns, + index=distances.index + ) + + else: + raise NotImplementedError("Unknown split") + + # Drop the original columns. + df.drop( + columns=costs.columns.tolist() + times.columns.tolist() + distances.columns.tolist(), + inplace=True + ) + + df = df.merge(right=cost_scaled, left_index=True, right_index=True) + df = df.merge(right=tt_scaled, left_index=True, right_index=True) + df = df.merge(right=dist_scaled, left_index=True, right_index=True) + + return df, (cost_scaler, tt_scaler, dist_scaler) + + +def train(X_tr, Y_tr): + if CV: + + model = RandomForestClassifier(random_state=SEED) + + # We want to build bootstrapped trees that would not always use all the features. + param_set2 = { + 'n_estimators': [150, 200, 250], + 'min_samples_split': [2, 3, 4], + 'min_samples_leaf': [1, 2, 3], + 'class_weight': ['balanced_subsample'], + 'max_features': [None, 'sqrt'], + 'bootstrap': [True] + } + + cv_set2 = StratifiedKFold(n_splits=3, shuffle=True, random_state=SEED) + + clf_set2 = GridSearchCV(model, param_set2, cv=cv_set2, n_jobs=-1, scoring='f1_weighted', verbose=1) + + start = perf_counter() + + clf_set2.fit( + X_tr, + Y_tr + ) + + time_req = (perf_counter() - start)/60. + + best_model = clf_set2.best_estimator_ + else: + best_model = RandomForestClassifier( + n_estimators=150, + max_depth=None, + min_samples_leaf=2, + bootstrap=True, + class_weight='balanced_subsample', + random_state=SEED, + n_jobs=-1 + ).fit(X_tr, Y_tr) + + return best_model + + +def predict(model, X_tr, Y_tr, X_te, Y_te): + + y_test_pred = model.predict(X_te) + y_train_pred = model.predict(X_tr) + + train_f1 = f1_score( + y_true=Y_tr, + y_pred=y_train_pred, + average='weighted' + ) + + test_f1 = f1_score( + y_true=Y_te, + y_pred=y_test_pred, + average='weighted' + ) + + return y_train_pred, train_f1, y_test_pred, test_f1 + + +def run_sampled_sweep(df: pd.DataFrame, dir_name: Path, **kwargs): + + targets = TARGETS.copy() + + drop_s_micro = kwargs.pop('drop_s_micro', None) + + if drop_s_micro: + df.drop( + index=df.loc[data.target == 6, :].index, + inplace=True + ) + + # Shift all values after 6 by -1 + df.loc[data.target > 5, 'target'] -= 1 + + # Update targets. + targets.pop(targets.index('s_micro')) + + split = kwargs.pop('split', None) + + train_data, test_data = get_train_test_splits(data=df, how=split, shuffle=True) + + params, train_data = get_duration_estimate(train_data, SPLIT.TRAIN, None) + _, test_data = get_duration_estimate(test_data, SPLIT.TEST, params) + + train_data = drop_columns(train_data) + test_data = drop_columns(test_data) + + drop_location = kwargs.pop('drop_location', None) + + if drop_location: + train_data.drop(columns=['start_lat', 'start_lng', 'end_lat', 'end_lng'], inplace=True) + test_data.drop(columns=['start_lat', 'start_lng', 'end_lat', 'end_lng'], inplace=True) + + X_tr, Y_tr = train_data.drop(columns=['target'], inplace=False), train_data.target.values.ravel() + X_te, Y_te = test_data.drop(columns=['target'], inplace=False), test_data.target.values.ravel() + + model = train(X_tr, Y_tr) + tr_preds, tr_f1, te_preds, te_f1 = predict(model, X_tr, Y_tr, X_te, Y_te) + + print(f"\t-> Train F1: {tr_f1}, Test F1: {te_f1}") + + importance = sorted( + zip( + model.feature_names_in_, + model.feature_importances_ + ), + key=lambda x: x[-1], reverse=True + ) + + with open(dir_name / 'f1_scores.txt', 'w') as f: + f.write(f"Train F1: {tr_f1}\nTest F1: {te_f1}") + + importance_df = pd.DataFrame(importance, columns=['feature_name', 'importance']) + importance_df.to_csv(dir_name / 'feature_importance.csv', index=False) + + with open(dir_name / 'classification_report.txt', 'w') as f: + f.write(classification_report(y_true=Y_te, y_pred=te_preds, target_names=targets)) + + fig, ax = plt.subplots(figsize=(7, 7)) + cm = ConfusionMatrixDisplay.from_estimator( + model, + X=X_te, + y=Y_te, + ax=ax + ) + ax.set_xticklabels(targets, rotation=45) + ax.set_yticklabels(targets) + fig.tight_layout() + plt.savefig(dir_name / 'test_confusion_matrix.png') + plt.close('all') + + +def save_metadata(dir_name: Path, **kwargs): + with open(dir_name / 'metadata.txt', 'w') as f: + for k, v in kwargs.items(): + f.write(f"{k}: {v}\n") + + +if __name__ == "__main__": + data = pd.read_csv('../data/ReplacedMode_Fix_02142024.csv') + data.drop_duplicates(inplace=True) + + print("Beginning sweeps.") + + start = perf_counter() + sweep_number = 1 + + for split in [SPLIT_TYPE.INTRA_USER, SPLIT_TYPE.TARGET, SPLIT_TYPE.MODE]: + for drop in [True, False]: + for location_drop in [True, False]: + kwargs = { + 'drop_s_micro': drop, + 'split': split, + 'drop_location': location_drop + } + dir_name = Path(f'../benchmark_results/benchmark_{sweep_number}') + + if not dir_name.exists(): + dir_name.mkdir() + + print(f"\t-> Running sweep #{sweep_number}...") + save_metadata(dir_name, **kwargs) + run_sampled_sweep(data.copy(), dir_name, **kwargs) + print(f"Completed benchmarking for {sweep_number} experiment.") + print(50*'-') + sweep_number += 1 + + elapsed = perf_counter() - start + + print(f"Completed sweeps in {elapsed/60.} minutes") \ No newline at end of file From c7bb087dfc6dfa7fa8893d65eaeb6b2683a14987 Mon Sep 17 00:00:00 2001 From: Rahul Kulhalli Date: Fri, 16 Feb 2024 13:38:58 -0500 Subject: [PATCH 09/16] Added modeling notebook --- rm_src/baseline_modeling0.ipynb | 1233 +++++++++++++++++++++++++++++++ 1 file changed, 1233 insertions(+) create mode 100644 rm_src/baseline_modeling0.ipynb diff --git a/rm_src/baseline_modeling0.ipynb b/rm_src/baseline_modeling0.ipynb new file mode 100644 index 0000000..549b468 --- /dev/null +++ b/rm_src/baseline_modeling0.ipynb @@ -0,0 +1,1233 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### All experiments are logged in Notion [here](https://www.notion.so/Replacement-mode-modeling-257c2f460377498d921e6b167f465945)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from enum import Enum\n", + "import random\n", + "\n", + "# Math and graphing.\n", + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# sklearn imports.\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import f1_score, r2_score, ConfusionMatrixDisplay\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Global experiment flags and variables.\n", + "SEED = 19348\n", + "TARGETS = ['p_micro', 'no_trip', 's_car', 'transit', 'car', 's_micro', 'ridehail', 'walk', 'unknown']\n", + "\n", + "DROP_S_MICRO = False\n", + "\n", + "# Set the Numpy seed too.\n", + "random.seed(SEED)\n", + "np.random.seed(SEED)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "class SPLIT_TYPE(Enum):\n", + " INTRA_USER = 0\n", + " INTER_USER = 1\n", + " TARGET = 2\n", + " MODE = 3\n", + " \n", + "\n", + "class SPLIT(Enum):\n", + " TRAIN = 0\n", + " TEST = 1\n", + "\n", + "def get_splits(count_df: pd.DataFrame, n:int, test_size=0.2):\n", + " maxsize = int(n * test_size)\n", + "\n", + " max_threshold = int(maxsize * 1.05)\n", + " min_threshold = int(maxsize * 0.95)\n", + "\n", + " print(f\"{min_threshold}, {max_threshold}\")\n", + " \n", + " # Allow a 10% tolerance\n", + " def _dp(ix, curr_size, ids, cache):\n", + " \n", + " if ix >= count_df.shape[0]:\n", + " return []\n", + "\n", + " key = ix\n", + "\n", + " if key in cache:\n", + " return cache[key]\n", + "\n", + " if curr_size > max_threshold:\n", + " return []\n", + "\n", + " if min_threshold <= curr_size <= max_threshold:\n", + " return ids\n", + "\n", + " # two options - either pick the current id or skip it.\n", + " branch_a = _dp(ix, curr_size+count_df.loc[ix, 'count'], ids+[count_df.loc[ix, 'index']], cache)\n", + " branch_b = _dp(ix+1, curr_size, ids, cache)\n", + " \n", + " curr_max = []\n", + " if branch_a and len(branch_a) > 0:\n", + " curr_max = branch_a\n", + " \n", + " if branch_b and len(branch_b) > len(branch_a):\n", + " curr_max = branch_b\n", + " \n", + " cache[key] = curr_max\n", + " return cache[key]\n", + " \n", + " return _dp(0, 0, ids=list(), cache=dict())\n", + "\n", + "\n", + "def get_train_test_splits(data: pd.DataFrame, how=SPLIT_TYPE, test_ratio=0.2, shuffle=True):\n", + "\n", + " n_users = list(data.user_id.unique())\n", + " n = data.shape[0]\n", + " \n", + " if shuffle:\n", + " data = data.sample(data.shape[0], random_state=SEED).reset_index(drop=True, inplace=False)\n", + "\n", + " if how == SPLIT_TYPE.INTER_USER:\n", + " # Make the split, ensuring that a user in one fold is not leaked into the other fold.\n", + " # Basic idea: we want to start with the users with the highest instances and place \n", + " # alternating users in each set.\n", + " counts = data.user_id.value_counts().reset_index(drop=False, inplace=False, name='count')\n", + "\n", + " # Now, start with the user_id at the top, and keep adding to either split.\n", + " # This can be achieved using a simple DP program.\n", + " test_ids = get_splits(counts, data.shape[0])\n", + " test_data = data.loc[data.user_id.isin(test_ids), :]\n", + " train_index = data.index.difference(test_data.index)\n", + " train_data = data.loc[data.user_id.isin(train_index), :]\n", + " \n", + " return train_data, test_data\n", + " \n", + " elif how == SPLIT_TYPE.INTRA_USER:\n", + " \n", + " # There are certain users with only one observation. What do we do with those?\n", + " # As per the mobilitynet modeling pipeline, we randomly assign them to either the\n", + " # training or test set.\n", + " \n", + " value_counts = data.user_id.value_counts()\n", + " single_count_ids = value_counts[value_counts == 1].index\n", + " \n", + " data_filtered = data.loc[~data.user_id.isin(single_count_ids), :].reset_index(drop=True)\n", + " data_single_counts = data.loc[data.user_id.isin(single_count_ids), :].reset_index(drop=True)\n", + " \n", + " X_tr, X_te = train_test_split(\n", + " data_filtered, test_size=test_ratio, shuffle=shuffle, stratify=data_filtered.user_id,\n", + " random_state=SEED\n", + " )\n", + " \n", + " data_single_counts['assigned'] = np.random.choice(['train', 'test'], len(data_single_counts))\n", + " X_tr_merged = pd.concat(\n", + " [X_tr, data_single_counts.loc[data_single_counts.assigned == 'train', :].drop(\n", + " columns=['assigned'], inplace=False\n", + " )],\n", + " ignore_index=True, axis=0\n", + " )\n", + " \n", + " X_te_merged = pd.concat(\n", + " [X_te, data_single_counts.loc[data_single_counts.assigned == 'test', :].drop(\n", + " columns=['assigned'], inplace=False\n", + " )],\n", + " ignore_index=True, axis=0\n", + " )\n", + " \n", + " return X_tr_merged, X_te_merged\n", + " \n", + " elif how == SPLIT_TYPE.TARGET:\n", + " \n", + " X_tr, X_te = train_test_split(\n", + " data, test_size=test_ratio, shuffle=shuffle, stratify=data.target,\n", + " random_state=SEED\n", + " )\n", + " \n", + " return X_tr, X_te\n", + " \n", + " elif how == SPLIT_TYPE.MODE:\n", + " X_tr, X_te = train_test_split(\n", + " data, test_size=test_ratio, shuffle=shuffle, stratify=data.section_mode_argmax,\n", + " random_state=SEED\n", + " )\n", + " \n", + " return X_tr, X_te\n", + " \n", + " raise NotImplementedError(\"Unknown split type\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modeling" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_75614/2274972291.py:4: DtypeWarning: Columns (38) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " data = pd.read_csv('../data/ReplacedMode_Fix_02142024.csv')\n" + ] + } + ], + "source": [ + "# Read the data.\n", + "# data = pd.read_csv('../data/FULL_preprocessed_data_RM_weather.csv')\n", + "# data = pd.read_csv('../data/ReplacedMode_Fix.csv')\n", + "data = pd.read_csv('../data/ReplacedMode_Fix_02142024.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "if DROP_S_MICRO:\n", + " data.drop(\n", + " index=data.loc[data.target == 6, :].index,\n", + " inplace=True\n", + " )\n", + " \n", + " # Shift all values after 6 by -1\n", + " data.loc[data.target > 5, 'target'] -= 1" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "data.drop_duplicates(inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_hist(df, features=None):\n", + " if not features:\n", + " # All features.\n", + " features = df.columns.tolist()\n", + " \n", + " n_features = len(features)\n", + " \n", + " ncols = 6\n", + " nrows = n_features//ncols if n_features%ncols == 0 else (n_features//ncols) + 1\n", + " \n", + " fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(10, 10))\n", + " for ix, ax in enumerate(axes.flatten()):\n", + " \n", + " if ix > n_features:\n", + " break\n", + " \n", + " df[features[ix]].hist(ax=ax)\n", + " ax.set(title=features[ix])\n", + " \n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# First, we map the user IDs to ints.\n", + "\n", + "USERS = list(data.user_id.unique())\n", + "\n", + "USER_MAP = {\n", + " u: i+1 for (i, u) in enumerate(USERS)\n", + "}\n", + "\n", + "data['user_id'] = data['user_id'].apply(lambda x: USER_MAP[x])\n", + "\n", + "# data.rename(\n", + "# columns={'start_local_dt_weekday': 'start:DOW', 'end_local_dt_weekday': 'end:DOW'},\n", + "# inplace=True\n", + "# )\n", + "\n", + "# Drop the samples with chosen == no trip or chosen == unknown\n", + "# data.drop(index=data.loc[data.chosen.isin([2, 9])].index, inplace=True)\n", + "\n", + "# data.n_working_residents = data.n_working_residents.apply(lambda x: 0 if x < 0 else x)\n", + "\n", + "# Fix some age preprocessing issues.\n", + "# data.age = data.age.apply(lambda x: x if x < 100 else 2024-x)\n", + "\n", + "# Collapse 'train' and 'bus' into 'transit'\n", + "# data.loc[\n", + "# data.section_mode_argmax.isin(['train', 'bus']), 'section_mode_argmax'\n", + "# ] = 'transit'" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# display(data.section_mode_argmax.value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# transit = data.loc[data.section_mode_argmax == 'transit', :].copy()\n", + "# transit['section_duration_argmax'] /= 60.\n", + "\n", + "# transit['mph'] = transit['section_distance_argmax']/transit['section_duration_argmax']\n", + "\n", + "# display(transit[['section_duration_argmax', 'section_distance_argmax', 'mph']].describe())" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# import plotly.express as px\n", + "\n", + "# sp = data.loc[data.section_mode_argmax.isin(['car', 'transit', 'walking']), :]\n", + "# fig = px.line(sp, y='section_distance_argmax', color='section_mode_argmax')\n", + "# fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Close the figure above.\n", + "# plt.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def get_duration_estimate(df: pd.DataFrame, dset: SPLIT, model_dict: dict):\n", + " \n", + " X_features = ['section_distance_argmax', 'age']\n", + " \n", + " if 'mph' in df.columns:\n", + " X_features += ['mph']\n", + " \n", + " if dset == SPLIT.TRAIN and model_dict is None:\n", + " model_dict = dict()\n", + " \n", + " if dset == SPLIT.TEST and model_dict is None:\n", + " raise AttributeError(\"Expected model dict for testing.\")\n", + " \n", + " if dset == SPLIT.TRAIN:\n", + " for section_mode in df.section_mode_argmax.unique():\n", + " section_data = df.loc[df.section_mode_argmax == section_mode, :]\n", + " if section_mode not in model_dict:\n", + " model_dict[section_mode] = dict()\n", + "\n", + " model = LinearRegression(fit_intercept=True)\n", + "\n", + " X = section_data[\n", + " X_features\n", + " ]\n", + " Y = section_data[['section_duration_argmax']]\n", + "\n", + " model.fit(X, Y.values.ravel())\n", + "\n", + " r2 = r2_score(y_pred=model.predict(X), y_true=Y.values.ravel())\n", + " print(f\"Train R2 for {section_mode}: {r2}\")\n", + "\n", + " model_dict[section_mode]['model'] = model\n", + " \n", + " elif dset == SPLIT.TEST:\n", + " for section_mode in df.section_mode_argmax.unique():\n", + " section_data = df.loc[df.section_mode_argmax == section_mode, :]\n", + " X = section_data[\n", + " X_features\n", + " ]\n", + " Y = section_data[['section_duration_argmax']]\n", + " \n", + " y_pred = model_dict[section_mode]['model'].predict(X)\n", + " r2 = r2_score(y_pred=y_pred, y_true=Y.values.ravel())\n", + " print(f\"Test R2 for {section_mode}: {r2}\")\n", + " \n", + " # Create the new columns for the duration.\n", + " new_columns = ['p_micro','no_trip','s_car','transit','car','s_micro','ridehail','walk','unknown']\n", + " df[new_columns] = 0\n", + " df['temp'] = 0\n", + " \n", + " for section in df.section_mode_argmax.unique():\n", + " X_section = df.loc[df.section_mode_argmax == section, X_features]\n", + " \n", + " # broadcast to all columns.\n", + " df.loc[df.section_mode_argmax == section, 'temp'] = model_dict[section]['model'].predict(X_section)\n", + " \n", + " for c in new_columns:\n", + " df[c] = df['av_' + c] * df['temp']\n", + " \n", + " df.drop(columns=['temp'], inplace=True)\n", + " \n", + " df.rename(columns=dict([(x, 'tt_'+x) for x in new_columns]), inplace=True)\n", + " \n", + " # return model_dict, result_df\n", + " return model_dict, df" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Now, we split the data.\n", + "train_data, test_data = get_train_test_splits(data=data, how=SPLIT_TYPE.TARGET, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['source', 'end_ts', 'end_fmt_time', 'end_loc', 'raw_trip', 'start_ts', 'start_fmt_time', 'start_loc', 'duration', 'distance', 'start_place', 'end_place', 'cleaned_trip', 'inferred_labels', 'inferred_trip', 'expectation', 'confidence_threshold', 'expected_trip', 'user_input', 'start:year', 'start:month', 'start:day', 'start:hour', 'start_local_dt_minute', 'start_local_dt_second', 'start_local_dt_weekday', 'start_local_dt_timezone', 'end:year', 'end:month', 'end:day', 'end:hour', 'end_local_dt_minute', 'end_local_dt_second', 'end_local_dt_weekday', 'end_local_dt_timezone', '_id', 'user_id', 'metadata_write_ts', 'additions', 'mode_confirm', 'purpose_confirm', 'distance_miles', 'Mode_confirm', 'Trip_purpose', 'original_user_id', 'program', 'opcode', 'Timestamp', 'birth_year', 'primary_job_commute_time', 'income_category', 'n_residence_members', 'n_residents_u18', 'n_residents_with_license', 'n_motor_vehicles', 'available_modes', 'age', 'gender_Man', 'gender_Man;Nonbinary/genderqueer/genderfluid', 'gender_Nonbinary/genderqueer/genderfluid', 'gender_Prefer not to say', 'gender_Woman', 'gender_Woman;Nonbinary/genderqueer/genderfluid', 'has_drivers_license_No', 'has_drivers_license_Prefer not to say', 'has_drivers_license_Yes', 'has_multiple_jobs_No', 'has_multiple_jobs_Prefer not to say', 'has_multiple_jobs_Yes', \"highest_education_Bachelor's degree\", 'highest_education_Graduate degree or professional degree', 'highest_education_High school graduate or GED', 'highest_education_Less than a high school graduate', 'highest_education_Prefer not to say', 'highest_education_Some college or associates degree', 'primary_job_type_Full-time', 'primary_job_type_Part-time', 'primary_job_type_Prefer not to say', 'primary_job_description_Clerical or administrative support', 'primary_job_description_Custodial', 'primary_job_description_Education', 'primary_job_description_Food service', 'primary_job_description_Manufacturing, construction, maintenance, or farming', 'primary_job_description_Medical/healthcare', 'primary_job_description_Other', 'primary_job_description_Professional, managerial, or technical', 'primary_job_description_Sales or service', 'primary_job_commute_mode_Active transport', 'primary_job_commute_mode_Car transport', 'primary_job_commute_mode_Hybrid', 'primary_job_commute_mode_Public transport', 'primary_job_commute_mode_Unknown', 'primary_job_commute_mode_WFH', 'is_overnight_trip', 'n_working_residents', 'start_lat', 'start_lng', 'end_lat', 'end_lng', 'temperature_2m (°F)', 'relative_humidity_2m (%)', 'dew_point_2m (°F)', 'rain (inch)', 'snowfall (inch)', 'wind_speed_10m (mp/h)', 'wind_gusts_10m (mp/h)', 'section_distance_argmax', 'section_duration_argmax', 'section_mode_argmax', 'section_coordinates_argmax', 'mph', 'target', 'av_s_micro', 'av_ridehail', 'av_unknown', 'av_car', 'av_transit', 'av_walk', 'av_s_car', 'av_no_trip', 'av_p_micro', 'cost_p_micro', 'cost_no_trip', 'cost_s_car', 'cost_transit', 'cost_car', 'cost_s_micro', 'cost_ridehail', 'cost_walk', 'cost_unknown']\n" + ] + } + ], + "source": [ + "print(train_data.columns.tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train R2 for car: 0.7578875871366312\n", + "Train R2 for bicycling: 0.9103687323238318\n", + "Train R2 for walking: 0.6383980197916885\n", + "Train R2 for no_sensed: 0.6360068166249293\n", + "Train R2 for transit: 0.751057562851356\n", + "----------\n", + "Test R2 for car: 0.7754447909826347\n", + "Test R2 for walking: 0.670974597223546\n", + "Test R2 for bicycling: 0.9172082504523482\n", + "Test R2 for no_sensed: 0.6829614441643039\n", + "Test R2 for transit: 0.6870857316497596\n" + ] + } + ], + "source": [ + "params, train_data = get_duration_estimate(train_data, SPLIT.TRAIN, None)\n", + "print(10 * \"-\")\n", + "_, test_data = get_duration_estimate(test_data, SPLIT.TEST, params)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((56108, 139), (14028, 139))" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data.shape, test_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Some helper functions that will help ease redundancy in the code.\n", + "\n", + "def drop_columns(df: pd.DataFrame):\n", + " to_drop = [\n", + " 'source', 'end_ts', 'end_fmt_time', 'end_loc', 'raw_trip', 'start_ts', \n", + " 'start_fmt_time', 'start_loc', 'duration', 'distance', 'start_place', \n", + " 'end_place', 'cleaned_trip', 'inferred_labels', 'inferred_trip', 'expectation',\n", + " 'confidence_threshold', 'expected_trip', 'user_input', 'start:year', 'start:month', \n", + " 'start:day', 'start_local_dt_minute', 'start_local_dt_second', \n", + " 'start_local_dt_weekday', 'start_local_dt_timezone', 'end:year', 'end:month', 'end:day', \n", + " 'end_local_dt_minute', 'end_local_dt_second', 'end_local_dt_weekday', \n", + " 'end_local_dt_timezone', '_id', 'user_id', 'metadata_write_ts', 'additions', \n", + " 'mode_confirm', 'purpose_confirm', 'Mode_confirm', 'Trip_purpose', \n", + " 'original_user_id', 'program', 'opcode', 'Timestamp', 'birth_year', \n", + " 'available_modes', 'section_coordinates_argmax', 'section_mode_argmax'\n", + " ]\n", + " \n", + " # Drop section_mode_argmax and available_modes.\n", + " return df.drop(\n", + " columns=to_drop, \n", + " inplace=False\n", + " )\n", + "\n", + "\n", + "def scale_values(df: pd.DataFrame, split: SPLIT, scalers=None):\n", + " # Scale costs using StandardScaler.\n", + " costs = df[[c for c in df.columns if 'cost_' in c]].copy()\n", + " times = df[[c for c in df.columns if 'tt_' in c or 'duration' in c]].copy()\n", + " distances = df[[c for c in df.columns if 'distance' in c]]\n", + " \n", + " print(\n", + " \"Cost columns to be scaled: \", costs.columns,\"\\nTime columns to be scaled: \", times.columns, \\\n", + " \"\\nDistance columns to be scaled: \", distances.columns\n", + " )\n", + " \n", + " if split == SPLIT.TRAIN and scalers is None:\n", + " cost_scaler = StandardScaler()\n", + " tt_scaler = StandardScaler()\n", + " dist_scaler = StandardScaler()\n", + " \n", + " cost_scaled = pd.DataFrame(\n", + " cost_scaler.fit_transform(costs), \n", + " columns=costs.columns, \n", + " index=costs.index\n", + " )\n", + " \n", + " tt_scaled = pd.DataFrame(\n", + " tt_scaler.fit_transform(times),\n", + " columns=times.columns,\n", + " index=times.index\n", + " )\n", + " \n", + " dist_scaled = pd.DataFrame(\n", + " dist_scaler.fit_transform(distances),\n", + " columns=distances.columns,\n", + " index=distances.index\n", + " )\n", + " \n", + " elif split == SPLIT.TEST and scalers is not None:\n", + " \n", + " cost_scaler, tt_scaler, dist_scaler = scalers\n", + " \n", + " cost_scaled = pd.DataFrame(\n", + " cost_scaler.transform(costs), \n", + " columns=costs.columns, \n", + " index=costs.index\n", + " )\n", + " \n", + " tt_scaled = pd.DataFrame(\n", + " tt_scaler.transform(times), \n", + " columns=times.columns, \n", + " index=times.index\n", + " )\n", + " \n", + " dist_scaled = pd.DataFrame(\n", + " dist_scaler.transform(distances),\n", + " columns=distances.columns,\n", + " index=distances.index\n", + " )\n", + " \n", + " else:\n", + " raise NotImplementedError(\"Unknown split\")\n", + " \n", + " # Drop the original columns.\n", + " df.drop(\n", + " columns=costs.columns.tolist() + times.columns.tolist() + distances.columns.tolist(), \n", + " inplace=True\n", + " )\n", + " \n", + " df = df.merge(right=cost_scaled, left_index=True, right_index=True)\n", + " df = df.merge(right=tt_scaled, left_index=True, right_index=True)\n", + " df = df.merge(right=dist_scaled, left_index=True, right_index=True)\n", + " \n", + " return df, (cost_scaler, tt_scaler, dist_scaler)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# First, drop columns.\n", + "\n", + "train_data = drop_columns(train_data)\n", + "\n", + "# Scale cost.\n", + "# train_data, scalers = scale_values(train_data, SPLIT.TRAIN, None)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "test_data = drop_columns(test_data)\n", + "\n", + "# Scale cost.\n", + "# test_data, _ = scale_values(test_data, SPLIT.TEST, scalers)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['start:hour', 'end:hour', 'distance_miles', 'primary_job_commute_time',\n", + " 'income_category', 'n_residence_members', 'n_residents_u18',\n", + " 'n_residents_with_license', 'n_motor_vehicles', 'age', 'gender_Man',\n", + " 'gender_Man;Nonbinary/genderqueer/genderfluid',\n", + " 'gender_Nonbinary/genderqueer/genderfluid', 'gender_Prefer not to say',\n", + " 'gender_Woman', 'gender_Woman;Nonbinary/genderqueer/genderfluid',\n", + " 'has_drivers_license_No', 'has_drivers_license_Prefer not to say',\n", + " 'has_drivers_license_Yes', 'has_multiple_jobs_No',\n", + " 'has_multiple_jobs_Prefer not to say', 'has_multiple_jobs_Yes',\n", + " 'highest_education_Bachelor's degree',\n", + " 'highest_education_Graduate degree or professional degree',\n", + " 'highest_education_High school graduate or GED',\n", + " 'highest_education_Less than a high school graduate',\n", + " 'highest_education_Prefer not to say',\n", + " 'highest_education_Some college or associates degree',\n", + " 'primary_job_type_Full-time', 'primary_job_type_Part-time',\n", + " 'primary_job_type_Prefer not to say',\n", + " 'primary_job_description_Clerical or administrative support',\n", + " 'primary_job_description_Custodial',\n", + " 'primary_job_description_Education',\n", + " 'primary_job_description_Food service',\n", + " 'primary_job_description_Manufacturing, construction, maintenance, or farming',\n", + " 'primary_job_description_Medical/healthcare',\n", + " 'primary_job_description_Other',\n", + " 'primary_job_description_Professional, managerial, or technical',\n", + " 'primary_job_description_Sales or service',\n", + " 'primary_job_commute_mode_Active transport',\n", + " 'primary_job_commute_mode_Car transport',\n", + " 'primary_job_commute_mode_Hybrid',\n", + " 'primary_job_commute_mode_Public transport',\n", + " 'primary_job_commute_mode_Unknown', 'primary_job_commute_mode_WFH',\n", + " 'is_overnight_trip', 'n_working_residents', 'start_lat', 'start_lng',\n", + " 'end_lat', 'end_lng', 'temperature_2m (°F)', 'relative_humidity_2m (%)',\n", + " 'dew_point_2m (°F)', 'rain (inch)', 'snowfall (inch)',\n", + " 'wind_speed_10m (mp/h)', 'wind_gusts_10m (mp/h)',\n", + " 'section_distance_argmax', 'section_duration_argmax', 'mph', 'target',\n", + " 'av_s_micro', 'av_ridehail', 'av_unknown', 'av_car', 'av_transit',\n", + " 'av_walk', 'av_s_car', 'av_no_trip', 'av_p_micro', 'cost_p_micro',\n", + " 'cost_no_trip', 'cost_s_car', 'cost_transit', 'cost_car',\n", + " 'cost_s_micro', 'cost_ridehail', 'cost_walk', 'cost_unknown',\n", + " 'tt_p_micro', 'tt_no_trip', 'tt_s_car', 'tt_transit', 'tt_car',\n", + " 'tt_s_micro', 'tt_ridehail', 'tt_walk', 'tt_unknown'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "print(train_data.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(train_data.target.unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# train_data.to_csv('../data/train.csv', index=False)\n", + "# test_data.to_csv('../data/test.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import classification_report\n", + "from sklearn.model_selection import GridSearchCV, StratifiedKFold\n", + "from pprint import pprint\n", + "from sklearn.inspection import permutation_importance\n", + "from time import perf_counter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Random Forest classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "CV = False\n", + "SAVE_MODEL = True" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "# exp question - compute sample weights using user_id.\n", + "\n", + "rf_train = train_data.drop(columns=['target', \n", + " 'start_lat', 'start_lng', 'end_lat', 'end_lng'\n", + " ])\n", + "rf_test = test_data.drop(columns=['target', \n", + " 'start_lat', 'start_lng', 'end_lat', 'end_lng'\n", + " ])\n", + "\n", + "if CV:\n", + "\n", + " model = RandomForestClassifier(random_state=SEED)\n", + "\n", + " # We want to build bootstrapped trees that would not always use all the features.\n", + "\n", + " param_set2 = {\n", + " 'n_estimators': [150, 200, 250],\n", + " 'min_samples_split': [2, 3, 4],\n", + " 'min_samples_leaf': [1, 2, 3],\n", + " 'class_weight': ['balanced_subsample'],\n", + " 'max_features': [None, 'sqrt'],\n", + " 'bootstrap': [True]\n", + " }\n", + "\n", + " cv_set2 = StratifiedKFold(n_splits=3, shuffle=True, random_state=SEED)\n", + "\n", + " clf_set2 = GridSearchCV(model, param_set2, cv=cv_set2, n_jobs=-1, scoring='f1_weighted', verbose=1)\n", + "\n", + " start = perf_counter()\n", + "\n", + " clf_set2.fit(\n", + " rf_train,\n", + " train_data.target.values.ravel()\n", + " )\n", + "\n", + " time_req = (perf_counter() - start)/60.\n", + "\n", + " best_model = clf_set2.best_estimator_\n", + "else:\n", + " best_model = RandomForestClassifier(\n", + " n_estimators=150,\n", + " max_depth=None,\n", + " min_samples_leaf=2,\n", + " bootstrap=True,\n", + " class_weight='balanced_subsample',\n", + " random_state=SEED,\n", + " n_jobs=-1\n", + " ).fit(rf_train, train_data.target.values.ravel())" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
RandomForestClassifier(class_weight='balanced_subsample', min_samples_leaf=2,\n",
+       "                       n_estimators=150, n_jobs=-1, random_state=19348)
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" + ], + "text/plain": [ + "RandomForestClassifier(class_weight='balanced_subsample', min_samples_leaf=2,\n", + " n_estimators=150, n_jobs=-1, random_state=19348)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "best_model" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "tr_f1_set2 = f1_score(\n", + " y_true=train_data.target.values,\n", + " y_pred=best_model.predict(rf_train),\n", + " average='weighted'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "te_f1_set2 = f1_score(\n", + " y_true=test_data.target.values,\n", + " y_pred=best_model.predict(rf_test),\n", + " average='weighted'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[BOOTSTRAPPED] | Train F1: 0.9437613701101397, Test F1: 0.7241888194981588\n" + ] + } + ], + "source": [ + "# Without location:\n", + "#. intra-user split:\n", + "# [BOOTSTRAPPED] | Train F1: 0.9983454261487021, Test F1: 0.7192048995905516\n", + "# if stratified by section_mode_argmax:\n", + "# [BOOTSTRAPPED] | Train F1: 0.9987250576328509, Test F1: 0.7242573620109232\n", + "\n", + "# With location:\n", + "# [BOOTSTRAPPED] | Train F1: 0.9992402006853468, Test F1: 0.7654135199070202\n", + "\n", + "print(f\"[BOOTSTRAPPED] | Train F1: {tr_f1_set2}, Test F1: {te_f1_set2}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "if SAVE_MODEL:\n", + "\n", + " import pickle\n", + "\n", + " with open('../models/tuned_rf_model.pkl', 'wb') as f:\n", + " f.write(pickle.dumps(best_model))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[('primary_job_commute_time', 0.043128713683595575),\n", + " ('age', 0.037455585582861765),\n", + " ('income_category', 0.03397706582439294),\n", + " ('distance_miles', 0.033258410055039066),\n", + " ('mph', 0.03262583547311039),\n", + " ('cost_transit', 0.031198791353082865),\n", + " ('section_distance_argmax', 0.030785647240196036),\n", + " ('dew_point_2m (°F)', 0.030211627865642073),\n", + " ('temperature_2m (°F)', 0.03007553220598789),\n", + " ('cost_s_car', 0.026055459738986344),\n", + " ('n_motor_vehicles', 0.02573541697937522),\n", + " ('cost_ridehail', 0.02456504397826428),\n", + " ('section_duration_argmax', 0.0228622986411679),\n", + " ('tt_p_micro', 0.022129815815942814),\n", + " ('relative_humidity_2m (%)', 0.02210319245106569),\n", + " ('end:hour', 0.021229903100266623),\n", + " ('tt_transit', 0.021206105210141206),\n", + " ('tt_walk', 0.02114515141258671),\n", + " ('start:hour', 0.020865360139350614),\n", + " ('primary_job_commute_mode_Car transport', 0.02081332151659349),\n", + " ('wind_gusts_10m (mp/h)', 0.020018561778756445),\n", + " ('n_residence_members', 0.019478670935887614),\n", + " ('wind_speed_10m (mp/h)', 0.019328357449251703),\n", + " ('cost_car', 0.01919696517190616),\n", + " ('n_residents_with_license', 0.0185836822165526),\n", + " ('tt_s_car', 0.017942959071607922),\n", + " ('n_working_residents', 0.017846139269221274),\n", + " ('tt_ridehail', 0.017430384620268677),\n", + " ('tt_car', 0.01558778045627846),\n", + " ('primary_job_commute_mode_Active transport', 0.014666396181188963),\n", + " ('cost_s_micro', 0.014091401054182044),\n", + " ('n_residents_u18', 0.012847507015434825),\n", + " ('highest_education_High school graduate or GED', 0.012395830421911084),\n", + " ('primary_job_description_Sales or service', 0.01213476693202911),\n", + " ('has_drivers_license_Yes', 0.011937170972796346),\n", + " ('tt_s_micro', 0.010932737957648923),\n", + " ('has_drivers_license_No', 0.010728975175435857),\n", + " ('av_transit', 0.01072203571287725),\n", + " ('highest_education_Some college or associates degree', 0.010449467308141266),\n", + " ('primary_job_commute_mode_Public transport', 0.010366529446513166),\n", + " ('gender_Woman', 0.009888767288248466),\n", + " ('primary_job_type_Full-time', 0.008728756077694473),\n", + " ('gender_Man', 0.00812404588127069),\n", + " ('primary_job_description_Professional, managerial, or technical',\n", + " 0.007993902388034105),\n", + " ('primary_job_description_Clerical or administrative support',\n", + " 0.007853483068559344),\n", + " ('primary_job_type_Part-time', 0.00773301630642922),\n", + " ('primary_job_description_Manufacturing, construction, maintenance, or '\n", + " 'farming',\n", + " 0.007073264089663813),\n", + " ('av_p_micro', 0.006819985655591969),\n", + " (\"highest_education_Bachelor's degree\", 0.0067744027509219346),\n", + " ('has_multiple_jobs_No', 0.005940630272941997),\n", + " ('av_walk', 0.005833534850107814),\n", + " ('primary_job_description_Education', 0.005601947324621126),\n", + " ('has_multiple_jobs_Yes', 0.0054762812973311375),\n", + " ('av_car', 0.005327083620534075),\n", + " ('av_s_micro', 0.004999887743199054),\n", + " ('primary_job_description_Custodial', 0.004967736001212933),\n", + " ('highest_education_Graduate degree or professional degree',\n", + " 0.004846691708506593),\n", + " ('av_ridehail', 0.0036346537681732137),\n", + " ('highest_education_Prefer not to say', 0.003564532937658746),\n", + " ('av_s_car', 0.003451427187397134),\n", + " ('primary_job_description_Food service', 0.0032926467536171333),\n", + " ('gender_Nonbinary/genderqueer/genderfluid', 0.0028942303106160934),\n", + " ('gender_Woman;Nonbinary/genderqueer/genderfluid', 0.00260835513020193),\n", + " ('primary_job_description_Medical/healthcare', 0.002369006457211943),\n", + " ('primary_job_type_Prefer not to say', 0.0022687588158498907),\n", + " ('primary_job_description_Other', 0.0022630321931658144),\n", + " ('tt_unknown', 0.0017387153721121465),\n", + " ('rain (inch)', 0.0014057957565409885),\n", + " ('primary_job_commute_mode_WFH', 0.0013711655929834956),\n", + " ('highest_education_Less than a high school graduate', 0.001295731028717082),\n", + " ('has_drivers_license_Prefer not to say', 0.0011005915907342048),\n", + " ('has_multiple_jobs_Prefer not to say', 0.0010434582607183032),\n", + " ('av_unknown', 0.0010228613615574766),\n", + " ('snowfall (inch)', 0.0008481943054743177),\n", + " ('primary_job_commute_mode_Unknown', 0.0007922654044467312),\n", + " ('primary_job_commute_mode_Hybrid', 0.000557116214113174),\n", + " ('gender_Man;Nonbinary/genderqueer/genderfluid', 0.00032688458412609075),\n", + " ('gender_Prefer not to say', 3.048996616614434e-05),\n", + " ('tt_no_trip', 1.2634619172866944e-05),\n", + " ('av_no_trip', 9.438648837193385e-06),\n", + " ('is_overnight_trip', 0.0),\n", + " ('cost_p_micro', 0.0),\n", + " ('cost_no_trip', 0.0),\n", + " ('cost_walk', 0.0),\n", + " ('cost_unknown', 0.0)]\n" + ] + } + ], + "source": [ + "# Feature importances - gini entropy\n", + "\n", + "pprint(\n", + " sorted(\n", + " zip(\n", + " best_model.feature_names_in_, \n", + " best_model.feature_importances_\n", + " ), \n", + " key=lambda x: x[-1], reverse=True\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# importance = permutation_importance(\n", + "# best_model,\n", + "# rf_test,\n", + "# test_data.target.values,\n", + "# n_repeats=5,\n", + "# random_state=SEED,\n", + "# n_jobs=-1,\n", + "# scoring='f1_weighted'\n", + "# )" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# pd.DataFrame(\n", + "# {\n", + "# 'feature names': test_data.columns.delete(\n", + "# test_data.columns.isin(['target'])\n", + "# ),\n", + "# 'imp_mean': importance.importances_mean, \n", + "# 'imp_std': importance.importances_std\n", + "# }\n", + "# ).sort_values(by=['imp_mean'], axis='rows', ascending=False).head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# fig, ax = plt.subplots(nrows=1, ncols=2)\n", + "y_pred = best_model.predict(rf_test)\n", + "pred_df = pd.DataFrame(\n", + " {\n", + " 'y_pred': y_pred.ravel(),\n", + " 'y_true': test_data.target.values.ravel()\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 7))\n", + "cm = ConfusionMatrixDisplay.from_estimator(\n", + " best_model,\n", + " X=rf_test,\n", + " y=test_data[['target']],\n", + " ax=ax\n", + ")\n", + "# ax.set_xticklabels(TARGETS, rotation=45)\n", + "# ax.set_yticklabels(TARGETS)\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 1 0.71 0.73 0.72 1087\n", + " 2 0.78 0.74 0.76 3358\n", + " 3 0.61 0.59 0.60 1690\n", + " 4 0.53 0.61 0.57 565\n", + " 5 0.79 0.76 0.77 4288\n", + " 6 0.57 0.65 0.61 348\n", + " 7 0.65 0.73 0.69 1351\n", + " 8 0.77 0.82 0.79 1310\n", + "\n", + " accuracy 0.73 13997\n", + " macro avg 0.68 0.70 0.69 13997\n", + "weighted avg 0.73 0.73 0.73 13997\n", + "\n" + ] + } + ], + "source": [ + "# print(classification_report(y_true=pred_df.y_true, y_pred=pred_df.y_pred, target_names=TARGETS))\n", + "print(classification_report(y_true=pred_df.y_true, y_pred=pred_df.y_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## XGBoost" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# from sklearn.utils.class_weight import compute_sample_weight\n", + "\n", + "# sample_weights = compute_sample_weight(class_weight='balanced', y=train_data.user_id.values.ravel())" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# from xgboost import XGBClassifier\n", + "\n", + "# y_train = train_data.target.values.ravel() - 1\n", + "# y_test = test_data.target.values.ravel() - 1\n", + "\n", + "# # weights = compute_class_weight(class_weight='balanced', classes=np.unique(y_pred), y_pred)\n", + "\n", + "# xgm = XGBClassifier(\n", + "# n_estimators=300,\n", + "# max_depth=None,\n", + "# tree_method='hist',\n", + "# objective='multi:softmax',\n", + "# num_class=9\n", + "# ).fit(rf_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "# preds = xgm.predict(rf_test)\n", + "\n", + "# print(classification_report(y_true=y_test, y_pred=preds, target_names=TARGETS))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "# import pickle\n", + "\n", + "# # RF_RM.pkl = 0.8625 on test.\n", + "# # RF_RM_1.pkl = 0.77 on test.\n", + "# with open('../models/RF_RM_1.pkl', 'wb') as f:\n", + "# f.write(pickle.dumps(model))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TODO:\n", + "\n", + "\n", + "- Explain why location might not be a good feature to add (plot start and end on map and explain how model might just overfit to the raw coordinates)\n", + "- Merge `unknown` and `no_trip` into one category and validate against models trained on (a) separate labels (b) dropped labels\n", + "- Explore more of the abnormal `walking` trips" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" + }, + "kernelspec": { + "display_name": "emission", + "language": "python", + "name": "emission" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From fe79c40af7c7cf44ba75f39a372c664b9188c926 Mon Sep 17 00:00:00 2001 From: Rahul Kulhalli Date: Thu, 14 Mar 2024 17:53:16 -0400 Subject: [PATCH 10/16] Updated inter-user splitting method; removed pandas shuffling --- rm_src/env.txt | 214 +++++++++++++++++++++++++++++ rm_src/run_baseline_experiments.py | 166 +++++++++++++--------- 2 files changed, 318 insertions(+), 62 deletions(-) create mode 100644 rm_src/env.txt diff --git a/rm_src/env.txt b/rm_src/env.txt new file mode 100644 index 0000000..b2d31ea --- /dev/null +++ b/rm_src/env.txt @@ -0,0 +1,214 @@ +# This file may be used to create an environment using: +# $ conda create --name --file +# platform: osx-64 +@EXPLICIT +https://conda.anaconda.org/conda-forge/osx-64/_py-xgboost-mutex-2.0-cpu_0.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/bzip2-1.0.8-h0d85af4_4.tar.bz2 +https://conda.anaconda.org/conda-forge/osx-64/ca-certificates-2024.2.2-h8857fd0_0.conda +https://conda.anaconda.org/conda-forge/osx-64/libbrotlicommon-1.0.9-hb7f2c08_9.conda +https://conda.anaconda.org/conda-forge/osx-64/libcxx-16.0.6-hd57cbcb_0.conda +https://conda.anaconda.org/conda-forge/osx-64/libdeflate-1.18-hac1461d_0.conda 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a/rm_src/run_baseline_experiments.py +++ b/rm_src/run_baseline_experiments.py @@ -1,6 +1,9 @@ from enum import Enum import random +import warnings +import argparse from pathlib import Path +from collections import Counter # Math and graphing. import pandas as pd @@ -13,14 +16,15 @@ from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LinearRegression from sklearn.metrics import f1_score, r2_score, ConfusionMatrixDisplay - +from scipy.special import kl_div from sklearn.metrics import classification_report -from sklearn.model_selection import GridSearchCV, StratifiedKFold +from sklearn.model_selection import GridSearchCV, StratifiedGroupKFold from pprint import pprint from sklearn.inspection import permutation_importance from time import perf_counter from sklearn.ensemble import RandomForestClassifier +warnings.simplefilter(action='ignore', category=Warning) # Global experiment flags and variables. SEED = 19348 @@ -42,70 +46,40 @@ class SPLIT(Enum): TRAIN = 0 TEST = 1 -def get_splits(count_df: pd.DataFrame, n:int, test_size=0.2): - maxsize = int(n * test_size) - - max_threshold = int(maxsize * 1.05) - min_threshold = int(maxsize * 0.95) - - print(f"{min_threshold}, {max_threshold}") - # Allow a 10% tolerance - def _dp(ix, curr_size, ids, cache): - - if ix >= count_df.shape[0]: - return [] +class SPLIT_TYPE(Enum): + INTRA_USER = 0 + INTER_USER = 1 + TARGET = 2 + MODE = 3 + INTER_USER_STATIC = 4 + - key = ix +class SPLIT(Enum): + TRAIN = 0 + TEST = 1 - if key in cache: - return cache[key] - if curr_size > max_threshold: - return [] +def get_train_test_splits(data: pd.DataFrame, how=SPLIT_TYPE, test_ratio=0.2, shuffle=True): + + if how == SPLIT_TYPE.INTER_USER: - if min_threshold <= curr_size <= max_threshold: - return ids + X = data.drop(columns=['target']) + y = data['target'].values + groups = data.user_id.values - # two options - either pick the current id or skip it. - branch_a = _dp(ix, curr_size+count_df.loc[ix, 'count'], ids+[count_df.loc[ix, 'index']], cache) - branch_b = _dp(ix+1, curr_size, ids, cache) + # n_splits determines split size. So n=5, is 20% for each split, which is what we want. + splitter = StratifiedGroupKFold(n_splits=5, shuffle=shuffle, random_state=SEED) + # splitter = GroupKFold(n_splits=5) - curr_max = [] - if branch_a and len(branch_a) > 0: - curr_max = branch_a - - if branch_b and len(branch_b) > len(branch_a): - curr_max = branch_b + for train_index, test_index in splitter.split(X, y, groups): + X_tr = data.iloc[train_index, :] + X_te = data.iloc[test_index, :] - cache[key] = curr_max - return cache[key] - - return _dp(0, 0, ids=list(), cache=dict()) + # Iterate only once and break. + break - -def get_train_test_splits(data: pd.DataFrame, how=SPLIT_TYPE, test_ratio=0.2, shuffle=True): - - n_users = list(data.user_id.unique()) - n = data.shape[0] - - if shuffle: - data = data.sample(data.shape[0], random_state=SEED).reset_index(drop=True, inplace=False) - - if how == SPLIT_TYPE.INTER_USER: - # Make the split, ensuring that a user in one fold is not leaked into the other fold. - # Basic idea: we want to start with the users with the highest instances and place - # alternating users in each set. - counts = data.user_id.value_counts().reset_index(drop=False, inplace=False, name='count') - - # Now, start with the user_id at the top, and keep adding to either split. - # This can be achieved using a simple DP program. - test_ids = get_splits(counts, data.shape[0]) - test_data = data.loc[data.user_id.isin(test_ids), :] - train_index = data.index.difference(test_data.index) - train_data = data.loc[data.user_id.isin(train_index), :] - - return train_data, test_data + return X_tr, X_te elif how == SPLIT_TYPE.INTRA_USER: @@ -158,6 +132,59 @@ def get_train_test_splits(data: pd.DataFrame, how=SPLIT_TYPE, test_ratio=0.2, sh return X_tr, X_te + elif how == SPLIT_TYPE.INTER_USER_STATIC: + + train_ids = ['810be63d084746e3b7da9d943dd88e8c', 'bf774cbe6c3040b0a022278d36a23f19', '8a8332a53a1b4cdd9f3680434e91a6ef', + '5ad862e79a6341f69f28c0096fe884da', '7f89656bd4a94d12ad8e5ad9f0afecaf', 'fbaa338d7cd7457c8cad4d0e60a44d18', + '3b25446778824941a4c70ae5774f4c68', '28cb1dde85514bbabfd42145bdaf7e0a', '3aeb5494088542fdaf798532951aebb0', + '531732fee3c24366a286d76eb534aebc', '950f4287bab5444aa0527cc23fb082b2', '737ef8494f26407b8b2a6b1b1dc631a4', + 'e06cf95717f448ecb81c440b1b2fe1ab', '7347df5e0ac94a109790b31ba2e8a02a', 'bd9cffc8dbf1402da479f9f148ec9e60', + '2f3b66a5f98546d4b7691fba57fa640f', 'f289f7001bd94db0b33a7d2e1cd28b19', '19a043d1f2414dbcafcca44ea2bd1f19', + '68788082836e4762b26ad0877643fdcf', '4e8b1b7f026c4384827f157225da13fa', '703a9cee8315441faff7eb63f2bfa93f', + 'add706b73839413da13344c355dde0bb', '47b5d57bd4354276bb6d2dcd1438901d', 'e4cfb2a8f600426897569985e234636e', + '0154d71439284c34b865e5a417cd48af', '234f4f2366244fe682dccded2fa7cc4e', '0d0ae3a556414d138c52a6040a203d24', + '44c10f66dec244d6b8644231d4a8fecb', '30e9b141d7894fbfaacecd2fa18929f9', '0eb313ab00e6469da78cc2d2e94660fb', + 'fc51d1258e4649ecbfb0e6ecdaeca454', 'a1954793b1454b2f8cf95917d7547169', '6656c04c6cba4c189fed805eaa529741', + '6a0f3653b80a4c949e127d6504debb55', 'dfe5ca1bb0854b67a6ffccad9565d669', '8b1f3ba43de945bea79de6a81716ad04', + 'cde34edb8e3a4278a18e0adb062999e5', '6d96909e5ca442ccb5679d9cdf3c8f5b', 'a60a64d82d1c439a901b683b73a74d73', + '60e6a6f6ed2e4e838f2bbed6a427028d', '88041eddad7542ea8c92b30e5c64e198', '1635c003b1f94a399ebebe21640ffced', + '1581993b404a4b9c9ca6b0e0b8212316', 'b1aed24c863949bfbfa3a844ecf60593', '4b89612d7f1f4b368635c2bc48bd7993', + 'eb2e2a5211564a9290fcb06032f9b4af', '26767f9f3da54e93b692f8be6acdac43', '8a98e383a2d143e798fc23869694934a', + 'b346b83b9f7c4536b809d5f92074fdae', 'd929e7f8b7624d76bdb0ec9ada6cc650', '863e9c6c8ec048c4b7653f73d839c85b', + 'f50537eb104e4213908f1862c8160a3e', '4a9db5a9bac046a59403b44b883cc0ba', 'cded005d5fd14c64a5bba3f5c4fe8385', + 'c7ce889c796f4e2a8859fa2d7d5068fe', '405b221abe9e43bc86a57ca7fccf2227', '0b3e78fa91d84aa6a3203440143c8c16', + 'fbff5e08b7f24a94ab4b2d7371999ef7', 'e35e65107a34496db49fa5a0b41a1e9e', 'd5137ebd4f034dc193d216128bb7fc9a', + '3f7f2e536ba9481e92f8379b796ad1d0', 'dc75e0b776214e1b9888f6abd042fd95', 'b41dd7d7c6d94fe6afe2fd26fa4ac0bd', + 'eec6936e1ac347ef9365881845ec74df', '8c7d261fe8284a42a777ffa6f380ba3b', '4baf8c8af7b7445e9067854065e3e612', + 'c6e4db31c18b4355b02a7dd97deca70b', 'f0db3b1999c2410ba5933103eca9212f', '487e20ab774742378198f94f5b5b0b43', + 'dc1ed4d71e3645d0993885398d5628ca', '8c3c63abb3ec4fc3a61e7bf316ee4efd', '15eb78dd6e104966ba6112589c29dc41', + 'c23768ccb817416eaf08be487b2e3643', 'ecd2ae17d5184807abd87a287115c299', '71f21d53b655463784f3a3c63c56707b', + '2931e0a34319495bbb5898201a54feb5', '92bde0d0662f45ac864629f486cffe77', '42b3ee0bc02a481ab1a94644a8cd7a0d', + '15aa4ba144a34b8b8079ed7e049d84df', '509b909390934e988eb120b58ed9bd8c', '14103cda12c94642974129989d39e50d', + '8b0876430c2641bcaea954ea00520e64', 'baa4ff1573ae411183e10aeb17c71c53', '14fe8002bbdc4f97acbd1a00de241bf6', + '1b7d6dfea8464bcab9321018b10ec9c9', '487ad897ba93404a8cbe5de7d1922691', '5182d93d69754d7ba06200cd1ac5980a', + '91f3ca1c278247f79a806e49e9cc236f', 'e66e63b206784a559d977d4cb5f1ec34', '840297ae39484e26bfebe83ee30c5b3e', + 'c6807997194c4c528a8fa8c1f6ee1595', '802667b6371f45b29c7abb051244836a', 'b2bbe715b6a14fd19f751cae8adf6b4e', + 'feb1d940cd3647d1a101580c2a3b3f8c', '1b9883393ab344a69bc1a0fab192a94c', 'ac604b44fdca482fb753034cb55d1351', + 'f446bf3102ff4bd99ea1c98f7d2f7af0', 'c2c5d4b9a607487ea405a99c721079d4', '85ddd3c34c58407392953c47a32f5428', + 'd51de709f95045f8bacf473574b96ba5', '6373dfb8cb9b47e88e8f76adcfadde20', '313d003df34b4bd9823b3474fc93f9f9', + '53e78583db87421f8decb529ba859ca4', '8fdc9b926a674a9ea07d91df2c5e06f2', '90480ac60a3d475a88fbdab0a003dd5d', + '7559c3f880f341e898a402eba96a855d', '19a4c2cf718d40588eb96ac25a566353', 'f4427cccaa9442b48b42bedab5ab648e', + 'e192b8a00b6c422296851c93785deaf7', '355e25bdfc244c5e85d358e39432bd44', 'a0c3a7b410b24e18995f63369a31d123', + '03a395b4d8614757bb8432b4984559b0', 'a2d48b05d5454d428c0841432c7467b6', '3d981e617b304afab0f21ce8aa6c9786', + '2cd5668ac9054e2eb2c88bb4ed94bc6d', 'd7a732f4a8644bcbb8dedfc8be242fb2', '367eb90b929d4f6e9470d15c700d2e3f', + 'e049a7b2a6cb44259f907abbb44c5abc', 'a231added8674bef95092b32bc254ac8', 'e88a8f520dde445484c0a9395e1a0599', + 'cba570ae38f341faa6257342727377b7', '97953af1b97d4e268c52e1e54dcf421a', 'd200a61757d84b1dab8fbac35ff52c28', + 'fc68a5bb0a7b4b6386b3f08a69ead36f', '4a8210aec25e443391efb924cc0e5f23', '903742c353ce42c3ad9ab039fc418816', + '2114e2a75304475fad06ad201948fbad', 'ac917eae407c4deb96625dd0dc2f2ba9', '3dddfb70e7cd40f18a63478654182e9a', + 'd3735ba212dd4c768e1675dca7bdcb6f', '7abe572148864412a33979592fa985fb', 'd3dff742d07942ca805c2f72e49e12c5' + ] + + X_tr = data.loc[data.user_id.isin(train_ids), :] + X_te = data.loc[~data.user_id.isin(train_ids), :] + + return X_tr, X_te + raise NotImplementedError("Unknown split type") @@ -374,13 +401,15 @@ def predict(model, X_tr, Y_tr, X_te, Y_te): train_f1 = f1_score( y_true=Y_tr, y_pred=y_train_pred, - average='weighted' + average='weighted', + zero_division=0. ) test_f1 = f1_score( y_true=Y_te, y_pred=y_test_pred, - average='weighted' + average='weighted', + zero_division=0. ) return y_train_pred, train_f1, y_test_pred, test_f1 @@ -465,16 +494,28 @@ def save_metadata(dir_name: Path, **kwargs): f.write(f"{k}: {v}\n") +# def parse_args(): +# parser = argparse.ArgumentParser() +# parser.add_argument('--max-iters', default=10000, type=int) +# return parser.parse_args() + + if __name__ == "__main__": data = pd.read_csv('../data/ReplacedMode_Fix_02142024.csv') data.drop_duplicates(inplace=True) print("Beginning sweeps.") + # args = parse_args() + start = perf_counter() sweep_number = 1 - for split in [SPLIT_TYPE.INTRA_USER, SPLIT_TYPE.TARGET, SPLIT_TYPE.MODE]: + root = Path('../benchmark_results') + if not root.exists(): + root.mkdir() + + for split in [SPLIT_TYPE.INTER_USER, SPLIT_TYPE.INTRA_USER, SPLIT_TYPE.TARGET, SPLIT_TYPE.MODE]: for drop in [True, False]: for location_drop in [True, False]: kwargs = { @@ -482,12 +523,13 @@ def save_metadata(dir_name: Path, **kwargs): 'split': split, 'drop_location': location_drop } - dir_name = Path(f'../benchmark_results/benchmark_{sweep_number}') + + dir_name = root / f'benchmark_{sweep_number}' if not dir_name.exists(): dir_name.mkdir() - print(f"\t-> Running sweep #{sweep_number}...") + print(f"\t-> Running sweep #{sweep_number} with metadata={str(kwargs)}") save_metadata(dir_name, **kwargs) run_sampled_sweep(data.copy(), dir_name, **kwargs) print(f"Completed benchmarking for {sweep_number} experiment.") From 6d4afcf5bab3101d2b78f64f6c7bcbc029ceb1c8 Mon Sep 17 00:00:00 2001 From: Rahul Kulhalli Date: Thu, 14 Mar 2024 17:56:25 -0400 Subject: [PATCH 11/16] Removed redundant enum declarations --- rm_src/run_baseline_experiments.py | 13 +------------ 1 file changed, 1 insertion(+), 12 deletions(-) diff --git a/rm_src/run_baseline_experiments.py b/rm_src/run_baseline_experiments.py index 63a84d7..f876598 100644 --- a/rm_src/run_baseline_experiments.py +++ b/rm_src/run_baseline_experiments.py @@ -35,17 +35,6 @@ random.seed(SEED) np.random.seed(SEED) -class SPLIT_TYPE(Enum): - INTRA_USER = 0 - INTER_USER = 1 - TARGET = 2 - MODE = 3 - - -class SPLIT(Enum): - TRAIN = 0 - TEST = 1 - class SPLIT_TYPE(Enum): INTRA_USER = 0 @@ -538,4 +527,4 @@ def save_metadata(dir_name: Path, **kwargs): elapsed = perf_counter() - start - print(f"Completed sweeps in {elapsed/60.} minutes") \ No newline at end of file + print(f"Completed sweeps in {elapsed/60.} minutes") From e47ab9a52ff3a7ea922bc9d494d9029a8e274c7c Mon Sep 17 00:00:00 2001 From: Rahul Kulhalli Date: Fri, 15 Mar 2024 09:42:09 -0400 Subject: [PATCH 12/16] Re-uploading all the preprocessing files for consistency --- rm_src/baseline_modeling0.ipynb | 560 +++++++--- rm_src/cost_time_avl_preprocessing.ipynb | 790 ++++++------- rm_src/mnl_exploration.ipynb | 1278 ++++------------------ rm_src/rf_bayesian_optim.py | 280 +++++ viz_scripts/scaffolding.py | 4 +- 5 files changed, 1253 insertions(+), 1659 deletions(-) create mode 100644 rm_src/rf_bayesian_optim.py diff --git a/rm_src/baseline_modeling0.ipynb b/rm_src/baseline_modeling0.ipynb index 549b468..8e623b4 100644 --- a/rm_src/baseline_modeling0.ipynb +++ b/rm_src/baseline_modeling0.ipynb @@ -23,7 +23,7 @@ "import matplotlib.pyplot as plt\n", "\n", "# sklearn imports.\n", - "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import train_test_split, StratifiedGroupKFold, GroupKFold\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.metrics import f1_score, r2_score, ConfusionMatrixDisplay\n", @@ -41,7 +41,7 @@ "SEED = 19348\n", "TARGETS = ['p_micro', 'no_trip', 's_car', 'transit', 'car', 's_micro', 'ridehail', 'walk', 'unknown']\n", "\n", - "DROP_S_MICRO = False\n", + "DROP_S_MICRO = True\n", "\n", "# Set the Numpy seed too.\n", "random.seed(SEED)\n", @@ -59,76 +59,33 @@ " INTER_USER = 1\n", " TARGET = 2\n", " MODE = 3\n", + " INTER_USER_STATIC = 4\n", " \n", "\n", "class SPLIT(Enum):\n", " TRAIN = 0\n", " TEST = 1\n", "\n", - "def get_splits(count_df: pd.DataFrame, n:int, test_size=0.2):\n", - " maxsize = int(n * test_size)\n", "\n", - " max_threshold = int(maxsize * 1.05)\n", - " min_threshold = int(maxsize * 0.95)\n", - "\n", - " print(f\"{min_threshold}, {max_threshold}\")\n", + "def get_train_test_splits(data: pd.DataFrame, how=SPLIT_TYPE, test_ratio=0.2, shuffle=True):\n", " \n", - " # Allow a 10% tolerance\n", - " def _dp(ix, curr_size, ids, cache):\n", - " \n", - " if ix >= count_df.shape[0]:\n", - " return []\n", - "\n", - " key = ix\n", - "\n", - " if key in cache:\n", - " return cache[key]\n", - "\n", - " if curr_size > max_threshold:\n", - " return []\n", - "\n", - " if min_threshold <= curr_size <= max_threshold:\n", - " return ids\n", + " if how == SPLIT_TYPE.INTER_USER:\n", "\n", - " # two options - either pick the current id or skip it.\n", - " branch_a = _dp(ix, curr_size+count_df.loc[ix, 'count'], ids+[count_df.loc[ix, 'index']], cache)\n", - " branch_b = _dp(ix+1, curr_size, ids, cache)\n", + " X = data.drop(columns=['target'])\n", + " y = data['target'].values\n", + " groups = data.user_id.values\n", " \n", - " curr_max = []\n", - " if branch_a and len(branch_a) > 0:\n", - " curr_max = branch_a\n", + " splitter = StratifiedGroupKFold(n_splits=5, shuffle=shuffle, random_state=SEED)\n", + " # splitter = GroupKFold(n_splits=5)\n", " \n", - " if branch_b and len(branch_b) > len(branch_a):\n", - " curr_max = branch_b\n", + " for train_index, test_index in splitter.split(X, y, groups):\n", + " X_tr = data.iloc[train_index, :]\n", + " X_te = data.iloc[test_index, :]\n", " \n", - " cache[key] = curr_max\n", - " return cache[key]\n", - " \n", - " return _dp(0, 0, ids=list(), cache=dict())\n", - "\n", - "\n", - "def get_train_test_splits(data: pd.DataFrame, how=SPLIT_TYPE, test_ratio=0.2, shuffle=True):\n", - "\n", - " n_users = list(data.user_id.unique())\n", - " n = data.shape[0]\n", - " \n", - " if shuffle:\n", - " data = data.sample(data.shape[0], random_state=SEED).reset_index(drop=True, inplace=False)\n", - "\n", - " if how == SPLIT_TYPE.INTER_USER:\n", - " # Make the split, ensuring that a user in one fold is not leaked into the other fold.\n", - " # Basic idea: we want to start with the users with the highest instances and place \n", - " # alternating users in each set.\n", - " counts = data.user_id.value_counts().reset_index(drop=False, inplace=False, name='count')\n", + " # Iterate only once and break.\n", + " break\n", "\n", - " # Now, start with the user_id at the top, and keep adding to either split.\n", - " # This can be achieved using a simple DP program.\n", - " test_ids = get_splits(counts, data.shape[0])\n", - " test_data = data.loc[data.user_id.isin(test_ids), :]\n", - " train_index = data.index.difference(test_data.index)\n", - " train_data = data.loc[data.user_id.isin(train_index), :]\n", - " \n", - " return train_data, test_data\n", + " return X_tr, X_te\n", " \n", " elif how == SPLIT_TYPE.INTRA_USER:\n", " \n", @@ -181,6 +138,59 @@ " \n", " return X_tr, X_te\n", " \n", + " elif how == SPLIT_TYPE.INTER_USER_STATIC:\n", + " \n", + " train_ids = ['810be63d084746e3b7da9d943dd88e8c', 'bf774cbe6c3040b0a022278d36a23f19', '8a8332a53a1b4cdd9f3680434e91a6ef', \n", + " '5ad862e79a6341f69f28c0096fe884da', '7f89656bd4a94d12ad8e5ad9f0afecaf', 'fbaa338d7cd7457c8cad4d0e60a44d18', \n", + " '3b25446778824941a4c70ae5774f4c68', '28cb1dde85514bbabfd42145bdaf7e0a', '3aeb5494088542fdaf798532951aebb0', \n", + " '531732fee3c24366a286d76eb534aebc', '950f4287bab5444aa0527cc23fb082b2', '737ef8494f26407b8b2a6b1b1dc631a4', \n", + " 'e06cf95717f448ecb81c440b1b2fe1ab', '7347df5e0ac94a109790b31ba2e8a02a', 'bd9cffc8dbf1402da479f9f148ec9e60', \n", + " '2f3b66a5f98546d4b7691fba57fa640f', 'f289f7001bd94db0b33a7d2e1cd28b19', '19a043d1f2414dbcafcca44ea2bd1f19', \n", + " '68788082836e4762b26ad0877643fdcf', '4e8b1b7f026c4384827f157225da13fa', '703a9cee8315441faff7eb63f2bfa93f', \n", + " 'add706b73839413da13344c355dde0bb', '47b5d57bd4354276bb6d2dcd1438901d', 'e4cfb2a8f600426897569985e234636e', \n", + " '0154d71439284c34b865e5a417cd48af', '234f4f2366244fe682dccded2fa7cc4e', '0d0ae3a556414d138c52a6040a203d24', \n", + " '44c10f66dec244d6b8644231d4a8fecb', '30e9b141d7894fbfaacecd2fa18929f9', '0eb313ab00e6469da78cc2d2e94660fb', \n", + " 'fc51d1258e4649ecbfb0e6ecdaeca454', 'a1954793b1454b2f8cf95917d7547169', '6656c04c6cba4c189fed805eaa529741', \n", + " '6a0f3653b80a4c949e127d6504debb55', 'dfe5ca1bb0854b67a6ffccad9565d669', '8b1f3ba43de945bea79de6a81716ad04', \n", + " 'cde34edb8e3a4278a18e0adb062999e5', '6d96909e5ca442ccb5679d9cdf3c8f5b', 'a60a64d82d1c439a901b683b73a74d73', \n", + " '60e6a6f6ed2e4e838f2bbed6a427028d', '88041eddad7542ea8c92b30e5c64e198', '1635c003b1f94a399ebebe21640ffced', \n", + " '1581993b404a4b9c9ca6b0e0b8212316', 'b1aed24c863949bfbfa3a844ecf60593', '4b89612d7f1f4b368635c2bc48bd7993', \n", + " 'eb2e2a5211564a9290fcb06032f9b4af', '26767f9f3da54e93b692f8be6acdac43', '8a98e383a2d143e798fc23869694934a', \n", + " 'b346b83b9f7c4536b809d5f92074fdae', 'd929e7f8b7624d76bdb0ec9ada6cc650', '863e9c6c8ec048c4b7653f73d839c85b', \n", + " 'f50537eb104e4213908f1862c8160a3e', '4a9db5a9bac046a59403b44b883cc0ba', 'cded005d5fd14c64a5bba3f5c4fe8385', \n", + " 'c7ce889c796f4e2a8859fa2d7d5068fe', '405b221abe9e43bc86a57ca7fccf2227', '0b3e78fa91d84aa6a3203440143c8c16', \n", + " 'fbff5e08b7f24a94ab4b2d7371999ef7', 'e35e65107a34496db49fa5a0b41a1e9e', 'd5137ebd4f034dc193d216128bb7fc9a', \n", + " '3f7f2e536ba9481e92f8379b796ad1d0', 'dc75e0b776214e1b9888f6abd042fd95', 'b41dd7d7c6d94fe6afe2fd26fa4ac0bd', \n", + " 'eec6936e1ac347ef9365881845ec74df', '8c7d261fe8284a42a777ffa6f380ba3b', '4baf8c8af7b7445e9067854065e3e612', \n", + " 'c6e4db31c18b4355b02a7dd97deca70b', 'f0db3b1999c2410ba5933103eca9212f', '487e20ab774742378198f94f5b5b0b43', \n", + " 'dc1ed4d71e3645d0993885398d5628ca', '8c3c63abb3ec4fc3a61e7bf316ee4efd', '15eb78dd6e104966ba6112589c29dc41', \n", + " 'c23768ccb817416eaf08be487b2e3643', 'ecd2ae17d5184807abd87a287115c299', '71f21d53b655463784f3a3c63c56707b', \n", + " '2931e0a34319495bbb5898201a54feb5', '92bde0d0662f45ac864629f486cffe77', '42b3ee0bc02a481ab1a94644a8cd7a0d', \n", + " '15aa4ba144a34b8b8079ed7e049d84df', '509b909390934e988eb120b58ed9bd8c', '14103cda12c94642974129989d39e50d', \n", + " '8b0876430c2641bcaea954ea00520e64', 'baa4ff1573ae411183e10aeb17c71c53', '14fe8002bbdc4f97acbd1a00de241bf6', \n", + " '1b7d6dfea8464bcab9321018b10ec9c9', '487ad897ba93404a8cbe5de7d1922691', '5182d93d69754d7ba06200cd1ac5980a', \n", + " '91f3ca1c278247f79a806e49e9cc236f', 'e66e63b206784a559d977d4cb5f1ec34', '840297ae39484e26bfebe83ee30c5b3e', \n", + " 'c6807997194c4c528a8fa8c1f6ee1595', '802667b6371f45b29c7abb051244836a', 'b2bbe715b6a14fd19f751cae8adf6b4e', \n", + " 'feb1d940cd3647d1a101580c2a3b3f8c', '1b9883393ab344a69bc1a0fab192a94c', 'ac604b44fdca482fb753034cb55d1351', \n", + " 'f446bf3102ff4bd99ea1c98f7d2f7af0', 'c2c5d4b9a607487ea405a99c721079d4', '85ddd3c34c58407392953c47a32f5428', \n", + " 'd51de709f95045f8bacf473574b96ba5', '6373dfb8cb9b47e88e8f76adcfadde20', '313d003df34b4bd9823b3474fc93f9f9', \n", + " '53e78583db87421f8decb529ba859ca4', '8fdc9b926a674a9ea07d91df2c5e06f2', '90480ac60a3d475a88fbdab0a003dd5d', \n", + " '7559c3f880f341e898a402eba96a855d', '19a4c2cf718d40588eb96ac25a566353', 'f4427cccaa9442b48b42bedab5ab648e', \n", + " 'e192b8a00b6c422296851c93785deaf7', '355e25bdfc244c5e85d358e39432bd44', 'a0c3a7b410b24e18995f63369a31d123', \n", + " '03a395b4d8614757bb8432b4984559b0', 'a2d48b05d5454d428c0841432c7467b6', '3d981e617b304afab0f21ce8aa6c9786', \n", + " '2cd5668ac9054e2eb2c88bb4ed94bc6d', 'd7a732f4a8644bcbb8dedfc8be242fb2', '367eb90b929d4f6e9470d15c700d2e3f', \n", + " 'e049a7b2a6cb44259f907abbb44c5abc', 'a231added8674bef95092b32bc254ac8', 'e88a8f520dde445484c0a9395e1a0599',\n", + " 'cba570ae38f341faa6257342727377b7', '97953af1b97d4e268c52e1e54dcf421a', 'd200a61757d84b1dab8fbac35ff52c28', \n", + " 'fc68a5bb0a7b4b6386b3f08a69ead36f', '4a8210aec25e443391efb924cc0e5f23', '903742c353ce42c3ad9ab039fc418816', \n", + " '2114e2a75304475fad06ad201948fbad', 'ac917eae407c4deb96625dd0dc2f2ba9', '3dddfb70e7cd40f18a63478654182e9a', \n", + " 'd3735ba212dd4c768e1675dca7bdcb6f', '7abe572148864412a33979592fa985fb', 'd3dff742d07942ca805c2f72e49e12c5' \n", + " ]\n", + " \n", + " X_tr = data.loc[data.user_id.isin(train_ids), :]\n", + " X_te = data.loc[~data.user_id.isin(train_ids), :]\n", + " \n", + " return X_tr, X_te\n", + " \n", " raise NotImplementedError(\"Unknown split type\")" ] }, @@ -200,7 +210,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_75614/2274972291.py:4: DtypeWarning: Columns (38) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/2274972291.py:4: DtypeWarning: Columns (38) have mixed types. Specify dtype option on import or set low_memory=False.\n", " data = pd.read_csv('../data/ReplacedMode_Fix_02142024.csv')\n" ] } @@ -274,13 +284,13 @@ "source": [ "# First, we map the user IDs to ints.\n", "\n", - "USERS = list(data.user_id.unique())\n", + "# USERS = list(data.user_id.unique())\n", "\n", - "USER_MAP = {\n", - " u: i+1 for (i, u) in enumerate(USERS)\n", - "}\n", + "# USER_MAP = {\n", + "# u: i+1 for (i, u) in enumerate(USERS)\n", + "# }\n", "\n", - "data['user_id'] = data['user_id'].apply(lambda x: USER_MAP[x])\n", + "# data['user_id'] = data['user_id'].apply(lambda x: USER_MAP[x])\n", "\n", "# data.rename(\n", "# columns={'start_local_dt_weekday': 'start:DOW', 'end_local_dt_weekday': 'end:DOW'},\n", @@ -427,13 +437,37 @@ "outputs": [], "source": [ "# Now, we split the data.\n", - "train_data, test_data = get_train_test_splits(data=data, how=SPLIT_TYPE.TARGET, shuffle=True)" + "train_data, test_data = get_train_test_splits(data=data, how=SPLIT_TYPE.INTER_USER)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7757575757575758\n", + "0.8380486968449932\n" + ] + } + ], + "source": [ + "# If split is inter-user, we should verify test size.\n", + "\n", + "n_tr, n_te = len(train_data.user_id.unique()), len(test_data.user_id.unique())\n", + "n_ex_tr, n_ex_te = train_data.shape[0], test_data.shape[0]\n", + "\n", + "print(n_tr/(n_tr+n_te))\n", + "print(n_ex_tr/(n_ex_tr+n_ex_te))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -449,24 +483,188 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Train R2 for car: 0.7578875871366312\n", - "Train R2 for bicycling: 0.9103687323238318\n", - "Train R2 for walking: 0.6383980197916885\n", - "Train R2 for no_sensed: 0.6360068166249293\n", - "Train R2 for transit: 0.751057562851356\n", + "Train R2 for bicycling: 0.9131217438269534\n", + "Train R2 for transit: 0.7868655990732462\n", + "Train R2 for car: 0.7673262331496208\n", + "Train R2 for no_sensed: 0.6274006368569693\n", + "Train R2 for walking: 0.6356687086293809\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[new_columns] = 0\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[new_columns] = 0\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[new_columns] = 0\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[new_columns] = 0\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[new_columns] = 0\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[new_columns] = 0\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[new_columns] = 0\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[new_columns] = 0\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[new_columns] = 0\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:49: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['temp'] = 0\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:58: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[c] = df['av_' + c] * df['temp']\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:60: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df.drop(columns=['temp'], inplace=True)\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:62: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df.rename(columns=dict([(x, 'tt_'+x) for x in new_columns]), inplace=True)\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[new_columns] = 0\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[new_columns] = 0\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[new_columns] = 0\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[new_columns] = 0\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[new_columns] = 0\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[new_columns] = 0\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[new_columns] = 0\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[new_columns] = 0\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[new_columns] = 0\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:49: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['temp'] = 0\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:58: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df[c] = df['av_' + c] * df['temp']\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:60: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df.drop(columns=['temp'], inplace=True)\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:62: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df.rename(columns=dict([(x, 'tt_'+x) for x in new_columns]), inplace=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "----------\n", - "Test R2 for car: 0.7754447909826347\n", - "Test R2 for walking: 0.670974597223546\n", - "Test R2 for bicycling: 0.9172082504523482\n", - "Test R2 for no_sensed: 0.6829614441643039\n", - "Test R2 for transit: 0.6870857316497596\n" + "Test R2 for bicycling: 0.9035643287707561\n", + "Test R2 for transit: 0.41808445188600185\n", + "Test R2 for car: 0.729524725154663\n", + "Test R2 for no_sensed: 0.708585489321987\n", + "Test R2 for walking: 0.6801138679986298\n" ] } ], @@ -478,16 +676,16 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "((56108, 139), (14028, 139))" + "((58650, 139), (11334, 139))" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -498,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -600,7 +798,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -614,7 +812,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -626,7 +824,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -685,16 +883,16 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "9" + "8" ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -705,7 +903,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -715,7 +913,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -735,7 +933,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -745,7 +943,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -767,7 +965,7 @@ " # We want to build bootstrapped trees that would not always use all the features.\n", "\n", " param_set2 = {\n", - " 'n_estimators': [150, 200, 250],\n", + " 'n_estimators': [150, 200, 250, 300],\n", " 'min_samples_split': [2, 3, 4],\n", " 'min_samples_leaf': [1, 2, 3],\n", " 'class_weight': ['balanced_subsample'],\n", @@ -803,7 +1001,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -818,7 +1016,7 @@ " n_estimators=150, n_jobs=-1, random_state=19348)" ] }, - "execution_count": 27, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -829,7 +1027,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -842,7 +1040,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -855,14 +1053,14 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[BOOTSTRAPPED] | Train F1: 0.9437613701101397, Test F1: 0.7241888194981588\n" + "[BOOTSTRAPPED] | Train F1: 0.9429078702826239, Test F1: 0.3260387751184679\n" ] } ], @@ -895,98 +1093,98 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[('primary_job_commute_time', 0.043128713683595575),\n", - " ('age', 0.037455585582861765),\n", - " ('income_category', 0.03397706582439294),\n", - " ('distance_miles', 0.033258410055039066),\n", - " ('mph', 0.03262583547311039),\n", - " ('cost_transit', 0.031198791353082865),\n", - " ('section_distance_argmax', 0.030785647240196036),\n", - " ('dew_point_2m (°F)', 0.030211627865642073),\n", - " ('temperature_2m (°F)', 0.03007553220598789),\n", - " ('cost_s_car', 0.026055459738986344),\n", - " ('n_motor_vehicles', 0.02573541697937522),\n", - " ('cost_ridehail', 0.02456504397826428),\n", - " ('section_duration_argmax', 0.0228622986411679),\n", - " ('tt_p_micro', 0.022129815815942814),\n", - " ('relative_humidity_2m (%)', 0.02210319245106569),\n", - " ('end:hour', 0.021229903100266623),\n", - " ('tt_transit', 0.021206105210141206),\n", - " ('tt_walk', 0.02114515141258671),\n", - " ('start:hour', 0.020865360139350614),\n", - " ('primary_job_commute_mode_Car transport', 0.02081332151659349),\n", - " ('wind_gusts_10m (mp/h)', 0.020018561778756445),\n", - " ('n_residence_members', 0.019478670935887614),\n", - " ('wind_speed_10m (mp/h)', 0.019328357449251703),\n", - " ('cost_car', 0.01919696517190616),\n", - " ('n_residents_with_license', 0.0185836822165526),\n", - " ('tt_s_car', 0.017942959071607922),\n", - " ('n_working_residents', 0.017846139269221274),\n", - " ('tt_ridehail', 0.017430384620268677),\n", - " ('tt_car', 0.01558778045627846),\n", - " ('primary_job_commute_mode_Active transport', 0.014666396181188963),\n", - " ('cost_s_micro', 0.014091401054182044),\n", - " ('n_residents_u18', 0.012847507015434825),\n", - " ('highest_education_High school graduate or GED', 0.012395830421911084),\n", - " ('primary_job_description_Sales or service', 0.01213476693202911),\n", - " ('has_drivers_license_Yes', 0.011937170972796346),\n", - " ('tt_s_micro', 0.010932737957648923),\n", - " ('has_drivers_license_No', 0.010728975175435857),\n", - " ('av_transit', 0.01072203571287725),\n", - " ('highest_education_Some college or associates degree', 0.010449467308141266),\n", - " ('primary_job_commute_mode_Public transport', 0.010366529446513166),\n", - " ('gender_Woman', 0.009888767288248466),\n", - " ('primary_job_type_Full-time', 0.008728756077694473),\n", - " ('gender_Man', 0.00812404588127069),\n", - " ('primary_job_description_Professional, managerial, or technical',\n", - " 0.007993902388034105),\n", + "[('primary_job_commute_time', 0.03656116744063983),\n", + " ('distance_miles', 0.03384896885706385),\n", + " ('mph', 0.03365824428294463),\n", + " ('cost_transit', 0.03351839132356644),\n", + " ('income_category', 0.03327663593086925),\n", + " ('age', 0.03320140844541059),\n", + " ('dew_point_2m (°F)', 0.03174850829935579),\n", + " ('section_distance_argmax', 0.0316844184072864),\n", + " ('temperature_2m (°F)', 0.031062635841221802),\n", + " ('n_motor_vehicles', 0.02809262054525041),\n", + " ('cost_s_car', 0.02590076039294508),\n", + " ('cost_ridehail', 0.02390316297823517),\n", + " ('section_duration_argmax', 0.023124663088762328),\n", + " ('n_residents_with_license', 0.022957697145547393),\n", + " ('tt_p_micro', 0.022799905074206473),\n", + " ('tt_transit', 0.02231532316999558),\n", + " ('relative_humidity_2m (%)', 0.02223392911205542),\n", + " ('tt_walk', 0.02156031560995313),\n", + " ('end:hour', 0.02127261764701738),\n", + " ('start:hour', 0.02113307125915479),\n", + " ('cost_car', 0.02069943868032831),\n", + " ('primary_job_commute_mode_Car transport', 0.020695999999399473),\n", + " ('n_residence_members', 0.02039769915552014),\n", + " ('wind_gusts_10m (mp/h)', 0.020371151744672206),\n", + " ('wind_speed_10m (mp/h)', 0.0202171294903448),\n", + " ('n_working_residents', 0.019856756085641247),\n", + " ('tt_s_car', 0.018478958407386708),\n", + " ('tt_car', 0.017174957078456614),\n", + " ('tt_ridehail', 0.01676576006316021),\n", + " ('cost_s_micro', 0.014284462066383688),\n", + " ('primary_job_description_Sales or service', 0.012984251205446487),\n", + " ('n_residents_u18', 0.011854045241279415),\n", + " ('tt_s_micro', 0.011691299016354224),\n", + " ('primary_job_commute_mode_Public transport', 0.011604917446563265),\n", + " ('primary_job_commute_mode_Active transport', 0.011563480793645827),\n", + " ('gender_Woman', 0.011263214031603247),\n", + " ('highest_education_High school graduate or GED', 0.010576049151755485),\n", + " ('highest_education_Some college or associates degree', 0.01020534553535622),\n", + " ('av_transit', 0.009289915096852685),\n", + " ('primary_job_type_Full-time', 0.009228727317286755),\n", " ('primary_job_description_Clerical or administrative support',\n", - " 0.007853483068559344),\n", - " ('primary_job_type_Part-time', 0.00773301630642922),\n", + " 0.009038264768764997),\n", + " ('primary_job_type_Part-time', 0.008895929760766197),\n", + " ('av_p_micro', 0.00863641893397233),\n", + " ('gender_Man', 0.008452778636530149),\n", + " ('primary_job_description_Professional, managerial, or technical',\n", + " 0.008401553611939554),\n", + " ('has_drivers_license_Yes', 0.007324804786898662),\n", " ('primary_job_description_Manufacturing, construction, maintenance, or '\n", " 'farming',\n", - " 0.007073264089663813),\n", - " ('av_p_micro', 0.006819985655591969),\n", - " (\"highest_education_Bachelor's degree\", 0.0067744027509219346),\n", - " ('has_multiple_jobs_No', 0.005940630272941997),\n", - " ('av_walk', 0.005833534850107814),\n", - " ('primary_job_description_Education', 0.005601947324621126),\n", - " ('has_multiple_jobs_Yes', 0.0054762812973311375),\n", - " ('av_car', 0.005327083620534075),\n", - " ('av_s_micro', 0.004999887743199054),\n", - " ('primary_job_description_Custodial', 0.004967736001212933),\n", + " 0.007086900844546432),\n", + " ('av_walk', 0.0065497221649774935),\n", + " ('primary_job_description_Education', 0.0062592730468462535),\n", + " ('has_drivers_license_No', 0.0061996065304084184),\n", + " (\"highest_education_Bachelor's degree\", 0.006032319692096047),\n", + " ('has_multiple_jobs_Yes', 0.005882443681020677),\n", + " ('has_multiple_jobs_No', 0.005335658287831361),\n", + " ('av_s_micro', 0.005050550371666917),\n", + " ('av_car', 0.004728243532015504),\n", " ('highest_education_Graduate degree or professional degree',\n", - " 0.004846691708506593),\n", - " ('av_ridehail', 0.0036346537681732137),\n", - " ('highest_education_Prefer not to say', 0.003564532937658746),\n", - " ('av_s_car', 0.003451427187397134),\n", - " ('primary_job_description_Food service', 0.0032926467536171333),\n", - " ('gender_Nonbinary/genderqueer/genderfluid', 0.0028942303106160934),\n", - " ('gender_Woman;Nonbinary/genderqueer/genderfluid', 0.00260835513020193),\n", - " ('primary_job_description_Medical/healthcare', 0.002369006457211943),\n", - " ('primary_job_type_Prefer not to say', 0.0022687588158498907),\n", - " ('primary_job_description_Other', 0.0022630321931658144),\n", - " ('tt_unknown', 0.0017387153721121465),\n", - " ('rain (inch)', 0.0014057957565409885),\n", - " ('primary_job_commute_mode_WFH', 0.0013711655929834956),\n", - " ('highest_education_Less than a high school graduate', 0.001295731028717082),\n", - " ('has_drivers_license_Prefer not to say', 0.0011005915907342048),\n", - " ('has_multiple_jobs_Prefer not to say', 0.0010434582607183032),\n", - " ('av_unknown', 0.0010228613615574766),\n", - " ('snowfall (inch)', 0.0008481943054743177),\n", - " ('primary_job_commute_mode_Unknown', 0.0007922654044467312),\n", - " ('primary_job_commute_mode_Hybrid', 0.000557116214113174),\n", - " ('gender_Man;Nonbinary/genderqueer/genderfluid', 0.00032688458412609075),\n", - " ('gender_Prefer not to say', 3.048996616614434e-05),\n", - " ('tt_no_trip', 1.2634619172866944e-05),\n", - " ('av_no_trip', 9.438648837193385e-06),\n", + " 0.004642958375833981),\n", + " ('gender_Woman;Nonbinary/genderqueer/genderfluid', 0.00421330092341653),\n", + " ('primary_job_description_Custodial', 0.003419042865152697),\n", + " ('av_s_car', 0.0033783136347606295),\n", + " ('av_ridehail', 0.0027666404305310257),\n", + " ('gender_Nonbinary/genderqueer/genderfluid', 0.0026293269089904046),\n", + " ('highest_education_Prefer not to say', 0.002504632146302557),\n", + " ('primary_job_description_Food service', 0.002417226793032642),\n", + " ('primary_job_description_Medical/healthcare', 0.002318202110078709),\n", + " ('primary_job_type_Prefer not to say', 0.0018874661300219261),\n", + " ('tt_unknown', 0.0017189591933169184),\n", + " ('primary_job_description_Other', 0.0016913655476757901),\n", + " ('rain (inch)', 0.0014573980261950155),\n", + " ('has_drivers_license_Prefer not to say', 0.0012507320988533646),\n", + " ('primary_job_commute_mode_WFH', 0.0010339629984452642),\n", + " ('av_unknown', 0.0009490470975242473),\n", + " ('highest_education_Less than a high school graduate', 0.0009240966799280463),\n", + " ('has_multiple_jobs_Prefer not to say', 0.0009222062920680996),\n", + " ('snowfall (inch)', 0.0009015378230446266),\n", + " ('primary_job_commute_mode_Hybrid', 0.0008805253765039911),\n", + " ('gender_Man;Nonbinary/genderqueer/genderfluid', 0.0005723629022594494),\n", + " ('primary_job_commute_mode_Unknown', 0.0005385851614091888),\n", + " ('gender_Prefer not to say', 3.0396144453361146e-05),\n", + " ('tt_no_trip', 1.0172431126169753e-05),\n", + " ('av_no_trip', 9.070799875677394e-06),\n", " ('is_overnight_trip', 0.0),\n", " ('cost_p_micro', 0.0),\n", " ('cost_no_trip', 0.0),\n", @@ -1229,5 +1427,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/rm_src/cost_time_avl_preprocessing.ipynb b/rm_src/cost_time_avl_preprocessing.ipynb index 1fee0a1..9ed8ed4 100644 --- a/rm_src/cost_time_avl_preprocessing.ipynb +++ b/rm_src/cost_time_avl_preprocessing.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 74, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -18,14 +18,14 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_84693/3166671539.py:3: DtypeWarning: Columns (40) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_62467/3166671539.py:3: DtypeWarning: Columns (40) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df = pd.read_csv('../data/final_modeling_data_02142024.csv')\n" ] } @@ -38,14 +38,14 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Shape: (80690, 105)\n" + "Shape: (80691, 103)\n" ] } ], @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -80,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -90,7 +90,7 @@ " Timestamp('2022-12-30 23:33:27.147785-0700', tz='America/Denver'))" ] }, - "execution_count": 79, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -135,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -145,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -156,14 +156,14 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Old shape: 80690. new shape: 80688\n" + "Old shape: 80691. new shape: 80689\n" ] } ], @@ -173,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -189,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -208,11 +208,11 @@ " 'start_place': 0,\n", " 'end_place': 0,\n", " 'cleaned_trip': 0,\n", - " 'inferred_labels': 216,\n", - " 'inferred_trip': 216,\n", - " 'expectation': 216,\n", - " 'confidence_threshold': 216,\n", - " 'expected_trip': 216,\n", + " 'inferred_labels': 217,\n", + " 'inferred_trip': 217,\n", + " 'expectation': 217,\n", + " 'confidence_threshold': 217,\n", + " 'expected_trip': 217,\n", " 'user_input': 0,\n", " 'section_modes': 0,\n", " 'section_distances': 0,\n", @@ -235,7 +235,7 @@ " '_id': 0,\n", " 'user_id': 0,\n", " 'metadata_write_ts': 0,\n", - " 'additions': 80686,\n", + " 'additions': 80687,\n", " 'mode_confirm': 337,\n", " 'purpose_confirm': 527,\n", " 'distance_miles': 0,\n", @@ -276,12 +276,10 @@ " 'primary_job_type_Full-time': 0,\n", " 'primary_job_type_Part-time': 0,\n", " 'primary_job_type_Prefer not to say': 0,\n", - " 'primary_job_description_Clerical': 0,\n", " 'primary_job_description_Clerical or administrative support': 0,\n", " 'primary_job_description_Custodial': 0,\n", " 'primary_job_description_Education': 0,\n", " 'primary_job_description_Food service': 0,\n", - " 'primary_job_description_Maintenance': 0,\n", " 'primary_job_description_Manufacturing, construction, maintenance, or farming': 0,\n", " 'primary_job_description_Medical/healthcare': 0,\n", " 'primary_job_description_Other': 0,\n", @@ -309,7 +307,7 @@ " 'wind_gusts_10m (mp/h)': 0}" ] }, - "execution_count": 87, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -320,7 +318,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -363,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -373,7 +371,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -382,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -391,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -404,18 +402,18 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# bus, train, bicycling, walking, car\n", "# split-apply-combine\n", - "def drop_outliers(df: pd.DataFrame) -> pd.DataFrame:\n", + "def drop_outliers(df: pd.DataFrame, low=0.1, high=0.9) -> pd.DataFrame:\n", " def filter_by_percentiles(group):\n", - " distance_low = group['section_distance_argmax'].quantile(0.1)\n", - " distance_high = group['section_distance_argmax'].quantile(0.9)\n", - " duration_low = group['section_duration_argmax'].quantile(0.1)\n", - " duration_high = group['section_duration_argmax'].quantile(0.9)\n", + " distance_low = group['section_distance_argmax'].quantile(low)\n", + " distance_high = group['section_distance_argmax'].quantile(high)\n", + " duration_low = group['section_duration_argmax'].quantile(low)\n", + " duration_high = group['section_duration_argmax'].quantile(high)\n", " \n", " l1_filter = group[\n", " (group['section_distance_argmax'] >= distance_low) &\n", @@ -434,11 +432,11 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ - "filtered_df = drop_outliers(df)\n", + "filtered_df = drop_outliers(df, low=0.01, high=0.99)\n", "\n", "# Ideal speed. distance/time.\n", "filtered_df['mph'] = (\n", @@ -448,11 +446,11 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ - "def filter_mph(df: pd.DataFrame) -> pd.DataFrame:\n", + "def filter_mph(df: pd.DataFrame, low=0.1, high=0.9) -> pd.DataFrame:\n", " \n", " MPH_THRESHOLDS = {\n", " # https://www.sciencedirect.com/science/article/pii/S2210670718304682\n", @@ -466,8 +464,8 @@ " if group.name in MPH_THRESHOLDS.keys():\n", " f_df = group[group['mph'] <= MPH_THRESHOLDS[group.name]]\n", " else:\n", - " mph_low = group['mph'].quantile(0.1)\n", - " mph_high = group['mph'].quantile(0.9)\n", + " mph_low = group['mph'].quantile(low)\n", + " mph_high = group['mph'].quantile(high)\n", "\n", " f_df = group[(group['mph'] >= mph_low) & (group['mph'] <= mph_high)]\n", " \n", @@ -478,16 +476,16 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ - "filtered_df = filter_mph(filtered_df)" + "filtered_df = filter_mph(filtered_df, low=0.01, high=0.99)" ] }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -560,188 +558,188 @@ " \n", " \n", " bicycling\n", - " 7196.0\n", - " 2.147281\n", - " 0.888086\n", - " 0.802221\n", - " 1.500171\n", - " 1.955479\n", - " 2.585763\n", - " 4.892137\n", - " 7196.0\n", - " 13.754313\n", - " 5.148564\n", - " 5.886639\n", - " 9.927844\n", - " 12.655730\n", - " 16.655780\n", - " 29.141947\n", + " 9330.0\n", + " 2.344544\n", + " 1.591889\n", + " 0.237542\n", + " 1.356889\n", + " 1.944232\n", + " 2.849511\n", + " 11.063928\n", + " 9330.0\n", + " 15.473286\n", + " 10.290371\n", + " 1.944501\n", + " 9.035766\n", + " 12.605268\n", + " 18.452003\n", + " 73.866648\n", " \n", " \n", " bus\n", - " 290.0\n", - " 1.432493\n", - " 0.618867\n", - " 0.372875\n", - " 0.940211\n", - " 1.486744\n", - " 1.917582\n", - " 3.016720\n", - " 290.0\n", - " 8.960115\n", - " 3.615953\n", - " 2.963177\n", - " 6.496972\n", - " 8.240974\n", - " 10.668935\n", - " 20.585433\n", + " 478.0\n", + " 1.552230\n", + " 1.413280\n", + " 0.090773\n", + " 0.646264\n", + " 1.294477\n", + " 1.960493\n", + " 11.592445\n", + " 478.0\n", + " 10.017439\n", + " 7.302110\n", + " 0.518257\n", + " 5.801983\n", + " 8.164530\n", + " 12.269039\n", + " 59.147400\n", " \n", " \n", " car\n", - " 24744.0\n", - " 4.132410\n", - " 2.601994\n", - " 0.911446\n", - " 2.192661\n", - " 3.429022\n", - " 5.350170\n", - " 14.688077\n", - " 24744.0\n", - " 13.972971\n", - " 6.666358\n", - " 5.672830\n", - " 8.779016\n", - " 12.123820\n", - " 17.505811\n", - " 35.306784\n", + " 40885.0\n", + " 5.940542\n", + " 7.984688\n", + " 0.196141\n", + " 1.713557\n", + " 3.346060\n", + " 6.762013\n", + " 74.464352\n", + " 40885.0\n", + " 16.717427\n", + " 13.274385\n", + " 2.089567\n", + " 8.291839\n", + " 12.638901\n", + " 20.472570\n", + " 110.945333\n", " \n", " \n", " no_sensed\n", - " 1681.0\n", - " 2.677326\n", - " 2.365448\n", - " 0.178071\n", - " 1.026175\n", - " 1.954673\n", - " 3.547600\n", - " 13.554013\n", - " 1681.0\n", - " 15.967264\n", - " 12.112498\n", - " 1.079868\n", - " 7.166667\n", - " 12.307400\n", - " 21.626077\n", - " 55.445744\n", + " 2730.0\n", + " 4.635380\n", + " 7.477224\n", + " 0.103854\n", + " 0.751998\n", + " 1.992592\n", + " 4.917120\n", + " 71.856971\n", + " 2730.0\n", + " 21.321379\n", + " 26.638677\n", + " 0.262941\n", + " 5.851082\n", + " 12.440902\n", + " 26.501255\n", + " 215.273083\n", " \n", " \n", " subway\n", - " 7.0\n", - " 23.441479\n", - " 10.540687\n", - " 13.113128\n", - " 14.804241\n", + " 9.0\n", + " 22.422637\n", + " 12.175141\n", + " 3.259611\n", + " 14.510151\n", " 23.587655\n", - " 27.444590\n", + " 27.756583\n", " 42.891905\n", - " 7.0\n", - " 41.873968\n", - " 17.610572\n", - " 23.757772\n", - " 29.866407\n", + " 9.0\n", + " 43.065269\n", + " 23.009173\n", + " 13.103616\n", + " 27.562759\n", " 34.400000\n", - " 51.801433\n", - " 71.624326\n", + " 58.714000\n", + " 81.366026\n", " \n", " \n", " train\n", - " 63.0\n", - " 12.262300\n", - " 4.021215\n", - " 4.322110\n", - " 10.030392\n", - " 10.691830\n", - " 15.446952\n", - " 22.820908\n", - " 63.0\n", - " 29.722451\n", - " 10.548372\n", - " 12.519296\n", - " 21.976672\n", - " 26.400000\n", - " 38.511937\n", - " 55.713396\n", + " 96.0\n", + " 14.303936\n", + " 13.079218\n", + " 0.886342\n", + " 8.647429\n", + " 10.572576\n", + " 15.516670\n", + " 67.987009\n", + " 96.0\n", + " 30.687195\n", + " 17.134854\n", + " 6.332442\n", + " 20.763549\n", + " 25.695106\n", + " 40.190158\n", + " 88.009998\n", " \n", " \n", " walking\n", - " 12072.0\n", - " 0.475999\n", - " 0.368141\n", - " 0.120082\n", - " 0.221963\n", - " 0.348252\n", - " 0.594541\n", - " 2.208408\n", - " 12072.0\n", - " 19.668879\n", - " 13.049073\n", - " 5.284233\n", - " 9.391205\n", - " 15.457291\n", - " 26.251433\n", - " 60.423767\n", + " 16608.0\n", + " 0.545524\n", + " 0.719691\n", + " 0.071237\n", + " 0.173010\n", + " 0.309777\n", + " 0.604884\n", + " 8.706443\n", + " 16608.0\n", + " 27.730449\n", + " 35.489695\n", + " 2.283617\n", + " 8.551447\n", + " 15.590376\n", + " 31.206353\n", + " 295.377052\n", " \n", " \n", "\n", "" ], "text/plain": [ - " section_distance_argmax \\\n", - " count mean std min \n", - "section_mode_argmax \n", - "bicycling 7196.0 2.147281 0.888086 0.802221 \n", - "bus 290.0 1.432493 0.618867 0.372875 \n", - "car 24744.0 4.132410 2.601994 0.911446 \n", - "no_sensed 1681.0 2.677326 2.365448 0.178071 \n", - "subway 7.0 23.441479 10.540687 13.113128 \n", - "train 63.0 12.262300 4.021215 4.322110 \n", - "walking 12072.0 0.475999 0.368141 0.120082 \n", + " section_distance_argmax \\\n", + " count mean std min \n", + "section_mode_argmax \n", + "bicycling 9330.0 2.344544 1.591889 0.237542 \n", + "bus 478.0 1.552230 1.413280 0.090773 \n", + "car 40885.0 5.940542 7.984688 0.196141 \n", + "no_sensed 2730.0 4.635380 7.477224 0.103854 \n", + "subway 9.0 22.422637 12.175141 3.259611 \n", + "train 96.0 14.303936 13.079218 0.886342 \n", + "walking 16608.0 0.545524 0.719691 0.071237 \n", "\n", " \\\n", " 25% 50% 75% max \n", "section_mode_argmax \n", - "bicycling 1.500171 1.955479 2.585763 4.892137 \n", - "bus 0.940211 1.486744 1.917582 3.016720 \n", - "car 2.192661 3.429022 5.350170 14.688077 \n", - "no_sensed 1.026175 1.954673 3.547600 13.554013 \n", - "subway 14.804241 23.587655 27.444590 42.891905 \n", - "train 10.030392 10.691830 15.446952 22.820908 \n", - "walking 0.221963 0.348252 0.594541 2.208408 \n", + "bicycling 1.356889 1.944232 2.849511 11.063928 \n", + "bus 0.646264 1.294477 1.960493 11.592445 \n", + "car 1.713557 3.346060 6.762013 74.464352 \n", + "no_sensed 0.751998 1.992592 4.917120 71.856971 \n", + "subway 14.510151 23.587655 27.756583 42.891905 \n", + "train 8.647429 10.572576 15.516670 67.987009 \n", + "walking 0.173010 0.309777 0.604884 8.706443 \n", "\n", " section_duration_argmax \\\n", " count mean std min \n", "section_mode_argmax \n", - "bicycling 7196.0 13.754313 5.148564 5.886639 \n", - "bus 290.0 8.960115 3.615953 2.963177 \n", - "car 24744.0 13.972971 6.666358 5.672830 \n", - "no_sensed 1681.0 15.967264 12.112498 1.079868 \n", - "subway 7.0 41.873968 17.610572 23.757772 \n", - "train 63.0 29.722451 10.548372 12.519296 \n", - "walking 12072.0 19.668879 13.049073 5.284233 \n", + "bicycling 9330.0 15.473286 10.290371 1.944501 \n", + "bus 478.0 10.017439 7.302110 0.518257 \n", + "car 40885.0 16.717427 13.274385 2.089567 \n", + "no_sensed 2730.0 21.321379 26.638677 0.262941 \n", + "subway 9.0 43.065269 23.009173 13.103616 \n", + "train 96.0 30.687195 17.134854 6.332442 \n", + "walking 16608.0 27.730449 35.489695 2.283617 \n", "\n", - " \n", - " 25% 50% 75% max \n", - "section_mode_argmax \n", - "bicycling 9.927844 12.655730 16.655780 29.141947 \n", - "bus 6.496972 8.240974 10.668935 20.585433 \n", - "car 8.779016 12.123820 17.505811 35.306784 \n", - "no_sensed 7.166667 12.307400 21.626077 55.445744 \n", - "subway 29.866407 34.400000 51.801433 71.624326 \n", - "train 21.976672 26.400000 38.511937 55.713396 \n", - "walking 9.391205 15.457291 26.251433 60.423767 " + " \n", + " 25% 50% 75% max \n", + "section_mode_argmax \n", + "bicycling 9.035766 12.605268 18.452003 73.866648 \n", + "bus 5.801983 8.164530 12.269039 59.147400 \n", + "car 8.291839 12.638901 20.472570 110.945333 \n", + "no_sensed 5.851082 12.440902 26.501255 215.273083 \n", + "subway 27.562759 34.400000 58.714000 81.366026 \n", + "train 20.763549 25.695106 40.190158 88.009998 \n", + "walking 8.551447 15.590376 31.206353 295.377052 " ] }, - "execution_count": 97, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -752,7 +750,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -808,79 +806,79 @@ " \n", " \n", " bicycling\n", - " 7196.0\n", - " 9.550366\n", - " 2.371204\n", - " 2.817580\n", - " 7.840111\n", - " 9.587994\n", - " 11.239639\n", + " 9330.0\n", + " 9.247391\n", + " 2.596876\n", + " 2.579201\n", + " 7.363136\n", + " 9.299940\n", + " 11.149174\n", " 14.998472\n", " \n", " \n", " bus\n", - " 290.0\n", - " 9.848150\n", - " 3.112711\n", - " 4.264324\n", - " 7.275453\n", - " 9.853532\n", - " 12.421118\n", - " 15.596840\n", + " 478.0\n", + " 9.802104\n", + " 4.985744\n", + " 0.541139\n", + " 5.871760\n", + " 9.732367\n", + " 13.059672\n", + " 28.627234\n", " \n", " \n", " car\n", - " 24744.0\n", - " 17.354381\n", - " 5.587948\n", - " 8.566853\n", - " 12.716558\n", - " 16.579894\n", - " 21.470495\n", - " 30.036223\n", + " 40885.0\n", + " 18.588257\n", + " 10.342023\n", + " 1.644719\n", + " 11.099615\n", + " 16.538740\n", + " 24.238533\n", + " 55.344377\n", " \n", " \n", " no_sensed\n", - " 1681.0\n", - " 10.299587\n", - " 4.514342\n", - " 4.608316\n", - " 6.571906\n", - " 9.213382\n", - " 13.050427\n", - " 22.598194\n", + " 2730.0\n", + " 13.042674\n", + " 9.209485\n", + " 3.891109\n", + " 6.289566\n", + " 10.042121\n", + " 16.658858\n", + " 53.570005\n", " \n", " \n", " subway\n", - " 7.0\n", - " 34.708676\n", - " 12.047924\n", - " 24.457144\n", - " 26.517639\n", - " 32.866807\n", - " 36.515566\n", + " 9.0\n", + " 31.476960\n", + " 12.524113\n", + " 14.925398\n", + " 25.308404\n", + " 27.726875\n", + " 35.930730\n", " 59.570373\n", " \n", " \n", " train\n", - " 63.0\n", - " 25.095616\n", - " 3.084808\n", - " 19.883468\n", - " 22.699687\n", - " 24.746441\n", - " 27.292824\n", - " 31.597822\n", + " 96.0\n", + " 25.335050\n", + " 10.249756\n", + " 4.682300\n", + " 21.237739\n", + " 24.781257\n", + " 28.723128\n", + " 62.081000\n", " \n", " \n", " walking\n", - " 12072.0\n", - " 1.621529\n", - " 0.745358\n", - " 0.131592\n", - " 0.998944\n", - " 1.616822\n", - " 2.261444\n", + " 16608.0\n", + " 1.494737\n", + " 0.807007\n", + " 0.022550\n", + " 0.804196\n", + " 1.475082\n", + " 2.192542\n", " 2.929953\n", " \n", " \n", @@ -891,27 +889,27 @@ " mph \\\n", " count mean std min 25% \n", "section_mode_argmax \n", - "bicycling 7196.0 9.550366 2.371204 2.817580 7.840111 \n", - "bus 290.0 9.848150 3.112711 4.264324 7.275453 \n", - "car 24744.0 17.354381 5.587948 8.566853 12.716558 \n", - "no_sensed 1681.0 10.299587 4.514342 4.608316 6.571906 \n", - "subway 7.0 34.708676 12.047924 24.457144 26.517639 \n", - "train 63.0 25.095616 3.084808 19.883468 22.699687 \n", - "walking 12072.0 1.621529 0.745358 0.131592 0.998944 \n", + "bicycling 9330.0 9.247391 2.596876 2.579201 7.363136 \n", + "bus 478.0 9.802104 4.985744 0.541139 5.871760 \n", + "car 40885.0 18.588257 10.342023 1.644719 11.099615 \n", + "no_sensed 2730.0 13.042674 9.209485 3.891109 6.289566 \n", + "subway 9.0 31.476960 12.524113 14.925398 25.308404 \n", + "train 96.0 25.335050 10.249756 4.682300 21.237739 \n", + "walking 16608.0 1.494737 0.807007 0.022550 0.804196 \n", "\n", " \n", " 50% 75% max \n", "section_mode_argmax \n", - "bicycling 9.587994 11.239639 14.998472 \n", - "bus 9.853532 12.421118 15.596840 \n", - "car 16.579894 21.470495 30.036223 \n", - "no_sensed 9.213382 13.050427 22.598194 \n", - "subway 32.866807 36.515566 59.570373 \n", - "train 24.746441 27.292824 31.597822 \n", - "walking 1.616822 2.261444 2.929953 " + "bicycling 9.299940 11.149174 14.998472 \n", + "bus 9.732367 13.059672 28.627234 \n", + "car 16.538740 24.238533 55.344377 \n", + "no_sensed 10.042121 16.658858 53.570005 \n", + "subway 27.726875 35.930730 59.570373 \n", + "train 24.781257 28.723128 62.081000 \n", + "walking 1.475082 2.192542 2.929953 " ] }, - "execution_count": 98, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -922,14 +920,14 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Dropped 34542 rows.\n" + "Dropped 10460 rows.\n" ] } ], @@ -939,7 +937,27 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.12963353121243293" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(80689 - 70229)/80689" + ] + }, + { + "cell_type": "code", + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -951,7 +969,7 @@ " 'E-bike', 'Not a Trip'], dtype=object)" ] }, - "execution_count": 100, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -962,7 +980,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1096,7 +1114,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1168,7 +1186,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -1182,7 +1200,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -1195,7 +1213,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1205,7 +1223,7 @@ " dtype=object)" ] }, - "execution_count": 105, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1216,7 +1234,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1253,14 +1271,14 @@ " ...\n", " target\n", " av_s_micro\n", - " av_walk\n", - " av_no_trip\n", + " av_ridehail\n", " av_unknown\n", - " av_p_micro\n", " av_car\n", " av_transit\n", + " av_walk\n", " av_s_car\n", - " av_ridehail\n", + " av_no_trip\n", + " av_p_micro\n", " \n", " \n", " \n", @@ -1282,11 +1300,11 @@ " 0\n", " 0\n", " 0\n", - " 1\n", " 0\n", " 0\n", " 0\n", " 0\n", + " 1\n", " \n", " \n", " 1\n", @@ -1306,15 +1324,39 @@ " 0\n", " 0\n", " 0\n", - " 1\n", " 0\n", " 0\n", " 0\n", " 0\n", + " 1\n", " \n", " \n", " 2\n", " DwellSegmentationTimeFilter\n", + " 1.619662e+09\n", + " 2021-04-28 19:58:51-06:00\n", + " {'type': 'Point', 'coordinates': [-105.0854639...\n", + " 608adb94b706ddbc669dabb5\n", + " 1.619658e+09\n", + " 2021-04-28 19:07:47.510844-06:00\n", + " {'type': 'Point', 'coordinates': [-105.1227172...\n", + " 51.058153\n", + " 9049.744908\n", + " ...\n", + " 1\n", + " 1\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 1\n", + " \n", + " \n", + " 3\n", + " DwellSegmentationTimeFilter\n", " 1.619728e+09\n", " 2021-04-29 14:23:25-06:00\n", " {'type': 'Point', 'coordinates': [-105.0776309...\n", @@ -1330,14 +1372,14 @@ " 0\n", " 0\n", " 0\n", - " 1\n", " 0\n", " 0\n", " 0\n", " 0\n", + " 1\n", " \n", " \n", - " 3\n", + " 4\n", " DwellSegmentationTimeFilter\n", " 1.619794e+09\n", " 2021-04-30 08:54:03-06:00\n", @@ -1354,85 +1396,61 @@ " 0\n", " 0\n", " 0\n", - " 1\n", - " 0\n", - " 0\n", - " 0\n", " 0\n", - " \n", - " \n", - " 4\n", - " DwellSegmentationTimeFilter\n", - " 1.619807e+09\n", - " 2021-04-30 12:15:42-06:00\n", - " {'type': 'Point', 'coordinates': [-105.1228054...\n", - " 608c8f7fb2d3b88f6947765e\n", - " 1.619805e+09\n", - " 2021-04-30 11:58:07.194775-06:00\n", - " {'type': 'Point', 'coordinates': [-105.0960769...\n", - " 17.580087\n", - " 2855.830523\n", - " ...\n", - " 1\n", - " 1\n", " 0\n", " 0\n", " 0\n", " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", " \n", " \n", "\n", - "

5 rows × 123 columns

\n", + "

5 rows × 121 columns

\n", "" ], "text/plain": [ " source end_ts end_fmt_time \\\n", "0 DwellSegmentationTimeFilter 1.619140e+09 2021-04-22 18:59:39-06:00 \n", "1 DwellSegmentationTimeFilter 1.619197e+09 2021-04-23 11:04:53-06:00 \n", - "2 DwellSegmentationTimeFilter 1.619728e+09 2021-04-29 14:23:25-06:00 \n", - "3 DwellSegmentationTimeFilter 1.619794e+09 2021-04-30 08:54:03-06:00 \n", - "4 DwellSegmentationTimeFilter 1.619807e+09 2021-04-30 12:15:42-06:00 \n", + "2 DwellSegmentationTimeFilter 1.619662e+09 2021-04-28 19:58:51-06:00 \n", + "3 DwellSegmentationTimeFilter 1.619728e+09 2021-04-29 14:23:25-06:00 \n", + "4 DwellSegmentationTimeFilter 1.619794e+09 2021-04-30 08:54:03-06:00 \n", "\n", " end_loc \\\n", "0 {'type': 'Point', 'coordinates': [-105.1002447... \n", "1 {'type': 'Point', 'coordinates': [-105.0776493... \n", - "2 {'type': 'Point', 'coordinates': [-105.0776309... \n", - "3 {'type': 'Point', 'coordinates': [-105.0775022... \n", - "4 {'type': 'Point', 'coordinates': [-105.1228054... \n", + "2 {'type': 'Point', 'coordinates': [-105.0854639... \n", + "3 {'type': 'Point', 'coordinates': [-105.0776309... \n", + "4 {'type': 'Point', 'coordinates': [-105.0775022... \n", "\n", " raw_trip start_ts start_fmt_time \\\n", "0 6082e47f27dcf1f393b70dd8 1.619139e+09 2021-04-22 18:48:48.365291-06:00 \n", "1 60830eac2b9762e085f774e8 1.619196e+09 2021-04-23 10:45:48.219950-06:00 \n", - "2 608b21db654c4a5ab2fa435a 1.619727e+09 2021-04-29 14:05:50.979267-06:00 \n", - "3 608c1f002f548f5db71d16ef 1.619793e+09 2021-04-30 08:34:49.162054-06:00 \n", - "4 608c8f7fb2d3b88f6947765e 1.619805e+09 2021-04-30 11:58:07.194775-06:00 \n", + "2 608adb94b706ddbc669dabb5 1.619658e+09 2021-04-28 19:07:47.510844-06:00 \n", + "3 608b21db654c4a5ab2fa435a 1.619727e+09 2021-04-29 14:05:50.979267-06:00 \n", + "4 608c1f002f548f5db71d16ef 1.619793e+09 2021-04-30 08:34:49.162054-06:00 \n", "\n", " start_loc duration distance \\\n", "0 {'type': 'Point', 'coordinates': [-105.0772567... 10.843912 1995.541857 \n", "1 {'type': 'Point', 'coordinates': [-105.1228294... 19.079667 4764.133777 \n", - "2 {'type': 'Point', 'coordinates': [-105.1228283... 17.567012 4731.604069 \n", - "3 {'type': 'Point', 'coordinates': [-105.1226652... 19.230632 4675.329725 \n", - "4 {'type': 'Point', 'coordinates': [-105.0960769... 17.580087 2855.830523 \n", + "2 {'type': 'Point', 'coordinates': [-105.1227172... 51.058153 9049.744908 \n", + "3 {'type': 'Point', 'coordinates': [-105.1228283... 17.567012 4731.604069 \n", + "4 {'type': 'Point', 'coordinates': [-105.1226652... 19.230632 4675.329725 \n", "\n", - " ... target av_s_micro av_walk av_no_trip av_unknown av_p_micro av_car \\\n", - "0 ... 1 1 0 0 0 1 0 \n", - "1 ... 1 1 0 0 0 1 0 \n", - "2 ... 9 1 0 0 0 1 0 \n", - "3 ... 9 1 0 0 0 1 0 \n", - "4 ... 1 1 0 0 0 1 0 \n", + " ... target av_s_micro av_ridehail av_unknown av_car av_transit av_walk \\\n", + "0 ... 1 1 0 0 0 0 0 \n", + "1 ... 1 1 0 0 0 0 0 \n", + "2 ... 1 1 0 0 0 0 0 \n", + "3 ... 9 1 0 0 0 0 0 \n", + "4 ... 9 1 0 0 0 0 0 \n", "\n", - " av_transit av_s_car av_ridehail \n", - "0 0 0 0 \n", - "1 0 0 0 \n", - "2 0 0 0 \n", - "3 0 0 0 \n", - "4 0 0 0 \n", + " av_s_car av_no_trip av_p_micro \n", + "0 0 0 1 \n", + "1 0 0 1 \n", + "2 0 0 1 \n", + "3 0 0 1 \n", + "4 0 0 1 \n", "\n", - "[5 rows x 123 columns]" + "[5 rows x 121 columns]" ] }, "metadata": {}, @@ -1445,7 +1463,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -1482,7 +1500,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1502,7 +1520,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1540,7 +1558,7 @@ " # $1 unlocking cost.\n", " 's_micro': 1.,\n", " # uber/taxi/lyft\n", - " 'ridehail': 1.,\n", + " 'ridehail': 1.5,\n", " 'walk': 0.,\n", " 'unknown': 0.\n", "}" @@ -1548,7 +1566,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -1584,7 +1602,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1597,7 +1615,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1635,25 +1653,25 @@ " \n", " \n", " count\n", - " 46053.0\n", - " 46053.0\n", - " 46053.000000\n", - " 46053.000000\n", - " 46053.000000\n", - " 46053.000000\n", - " 46053.000000\n", - " 46053.0\n", - " 46053.0\n", + " 70136.0\n", + " 70136.0\n", + " 70136.000000\n", + " 70136.000000\n", + " 70136.000000\n", + " 70136.000000\n", + " 70136.000000\n", + " 70136.0\n", + " 70136.0\n", " \n", " \n", " mean\n", " 0.0\n", " 0.0\n", - " 0.759602\n", - " 0.924603\n", - " 1.354917\n", - " 0.399658\n", - " 5.604421\n", + " 1.141106\n", + " 1.377155\n", + " 2.105648\n", + " 0.424135\n", + " 8.499317\n", " 0.0\n", " 0.0\n", " \n", @@ -1661,11 +1679,11 @@ " std\n", " 0.0\n", " 0.0\n", - " 0.811586\n", - " 1.228051\n", - " 1.688835\n", - " 0.770715\n", - " 5.739880\n", + " 2.036475\n", + " 2.882227\n", + " 4.072574\n", + " 1.092019\n", + " 13.763483\n", " 0.0\n", " 0.0\n", " \n", @@ -1685,7 +1703,7 @@ " 25%\n", " 0.0\n", " 0.0\n", - " 0.068502\n", + " 0.072439\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -1697,11 +1715,11 @@ " 50%\n", " 0.0\n", " 0.0\n", - " 0.565642\n", - " 0.508474\n", - " 0.890892\n", + " 0.507384\n", + " 0.414311\n", + " 0.784733\n", " 0.000000\n", - " 4.569688\n", + " 4.668714\n", " 0.0\n", " 0.0\n", " \n", @@ -1709,11 +1727,11 @@ " 75%\n", " 0.0\n", " 0.0\n", - " 1.112221\n", - " 1.361175\n", - " 2.178041\n", + " 1.254198\n", + " 1.419553\n", + " 2.388235\n", " 0.000000\n", - " 8.366328\n", + " 9.727618\n", " 0.0\n", " 0.0\n", " \n", @@ -1721,11 +1739,11 @@ " max\n", " 0.0\n", " 0.0\n", - " 12.867572\n", - " 21.445953\n", - " 25.735143\n", - " 5.351574\n", - " 86.783811\n", + " 22.339306\n", + " 37.232176\n", + " 44.678611\n", + " 23.262930\n", + " 150.428703\n", " 0.0\n", " 0.0\n", " \n", @@ -1735,27 +1753,27 @@ ], "text/plain": [ " cost_p_micro cost_no_trip cost_s_car cost_transit cost_car \\\n", - "count 46053.0 46053.0 46053.000000 46053.000000 46053.000000 \n", - "mean 0.0 0.0 0.759602 0.924603 1.354917 \n", - "std 0.0 0.0 0.811586 1.228051 1.688835 \n", + "count 70136.0 70136.0 70136.000000 70136.000000 70136.000000 \n", + "mean 0.0 0.0 1.141106 1.377155 2.105648 \n", + "std 0.0 0.0 2.036475 2.882227 4.072574 \n", "min 0.0 0.0 0.000000 0.000000 0.000000 \n", - "25% 0.0 0.0 0.068502 0.000000 0.000000 \n", - "50% 0.0 0.0 0.565642 0.508474 0.890892 \n", - "75% 0.0 0.0 1.112221 1.361175 2.178041 \n", - "max 0.0 0.0 12.867572 21.445953 25.735143 \n", + "25% 0.0 0.0 0.072439 0.000000 0.000000 \n", + "50% 0.0 0.0 0.507384 0.414311 0.784733 \n", + "75% 0.0 0.0 1.254198 1.419553 2.388235 \n", + "max 0.0 0.0 22.339306 37.232176 44.678611 \n", "\n", " cost_s_micro cost_ridehail cost_walk cost_unknown \n", - "count 46053.000000 46053.000000 46053.0 46053.0 \n", - "mean 0.399658 5.604421 0.0 0.0 \n", - "std 0.770715 5.739880 0.0 0.0 \n", + "count 70136.000000 70136.000000 70136.0 70136.0 \n", + "mean 0.424135 8.499317 0.0 0.0 \n", + "std 1.092019 13.763483 0.0 0.0 \n", "min 0.000000 0.000000 0.0 0.0 \n", "25% 0.000000 0.000000 0.0 0.0 \n", - "50% 0.000000 4.569688 0.0 0.0 \n", - "75% 0.000000 8.366328 0.0 0.0 \n", - "max 5.351574 86.783811 0.0 0.0 " + "50% 0.000000 4.668714 0.0 0.0 \n", + "75% 0.000000 9.727618 0.0 0.0 \n", + "max 23.262930 150.428703 0.0 0.0 " ] }, - "execution_count": 113, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -1766,7 +1784,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -1791,7 +1809,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -1805,7 +1823,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ diff --git a/rm_src/mnl_exploration.ipynb b/rm_src/mnl_exploration.ipynb index dacce32..6518977 100644 --- a/rm_src/mnl_exploration.ipynb +++ b/rm_src/mnl_exploration.ipynb @@ -246,22 +246,57 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "235" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "92383 trips across 235 users\n" + ] } ], "source": [ - "len(expanded_ct.user_id.unique())" + "print(\n", + " \"{n_trips} trips across {n_users} users\".format(\n", + " n_trips=len(expanded_ct.cleaned_trip.unique()),\n", + " n_users=len(expanded_ct.user_id.unique())\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "dates = pd.to_datetime(\n", + " expanded_ct.start_fmt_time, utc=True\n", + ").dt.tz_convert('America/Denver')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Date range from 2016-08-15 07:59:32.418000-06:00 to 2022-12-30 23:33:27.147785-07:00\n" + ] + } + ], + "source": [ + "print(\n", + " \"Date range from {min_dt} to {max_dt}\".format(\n", + " min_dt=dates.min(),\n", + " max_dt=dates.max()\n", + " )\n", + ")" ] }, { @@ -273,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -283,7 +318,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -322,20 +357,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rkulhall/miniconda3/envs/emission/lib/python3.9/site-packages/dateutil/parser/_parser.py:1207: UnknownTimezoneWarning: tzname PDT identified but not understood. Pass `tzinfos` argument in order to correctly return a timezone-aware datetime. In a future version, this will raise an exception.\n", - " warnings.warn(\"tzname {tzname} identified but not understood. \"\n", - "/Users/rkulhall/miniconda3/envs/emission/lib/python3.9/site-packages/dateutil/parser/_parser.py:1207: UnknownTimezoneWarning: tzname PST identified but not understood. Pass `tzinfos` argument in order to correctly return a timezone-aware datetime. In a future version, this will raise an exception.\n", - " warnings.warn(\"tzname {tzname} identified but not understood. \"\n" - ] - } - ], + "outputs": [], "source": [ "survey_data['Timestamp'] = pd.to_datetime(survey_data['Timestamp'])\n", "survey_data['Timestamp'] = survey_data['Timestamp'].dt.tz_localize('America/Los_Angeles', ambiguous='infer')\n", @@ -344,28 +368,16 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(Timestamp('2021-05-23 16:21:52-0600', tz='America/Denver'),\n", - " Timestamp('2022-12-21 06:33:59-0700', tz='America/Denver'))" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "survey_data.Timestamp.min(), survey_data.Timestamp.max()" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -374,27 +386,16 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(170, 203)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "len(f_survey.user_id.unique()), len(survey_data.user_id.unique())" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -407,7 +408,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -416,7 +417,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -424,7 +425,7 @@ "\n", "bad_ix = list()\n", "\n", - "# obvious. WFH is not a good feature to include because of its high sparsity.\n", + "# WFH is not a good feature to include because of its high sparsity.\n", "# wfh_mask = f_survey.wfh_days > 7\n", "\n", "# Computed by calculating 99th percentile.\n", @@ -441,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -454,16 +455,18 @@ "f_survey['age'] = f_survey.apply(lambda x: compute_age(x), axis=1)\n", "# f_survey.drop(columns=['birth_year'], inplace=True)\n", "\n", - "\n", "# For those who leave it NA, categorize them as 'prefer not to say'.\n", "f_survey.loc[f_survey['has_multiple_jobs'].isna(), 'has_multiple_jobs'] = 'Prefer not to say'\n", "\n", - "\n", "# Fix primary commute mode.\n", + "\n", "f_survey.loc[\n", " f_survey.primary_job_commute_mode.isna(), 'primary_job_commute_mode'\n", "] = 'Unknown'\n", "\n", + "# Remove all leading and trailing whitespace.\n", + "f_survey.primary_job_commute_mode = f_survey.primary_job_commute_mode.str.strip()\n", + "\n", "f_survey.loc[\n", " f_survey.primary_job_commute_mode.isin([\n", " 'Car', 'SUV', 'Pickup truck', 'Taxi/Limo (including services like Uber or Lyft)', 'Van',\n", @@ -498,25 +501,32 @@ "# Fix primary job type\n", "f_survey.loc[f_survey.primary_job_type.isna(), 'primary_job_type'] = 'Prefer not to say'\n", "\n", - "# Normalize the job description. Inspired from the 'e-bike trips by occupation' graph.\n", + "# Remove whitespace.\n", + "f_survey.primary_job_description = f_survey.primary_job_description.str.strip()\n", + "\n", + "# Normalize the job description. Inspired from the 'e-bike trips by occupation' \n", + "# plot in the CanBikeCo full pilot paper.\n", "f_survey.loc[\n", " f_survey.primary_job_description.isin([\n", - " 'Paraprofessional ', 'Education ', 'education/early childhood', 'Teacher',\n", - " 'Education non-profit manager', 'Scientific research'\n", - " ]), 'primary_job_description', \n", + " 'Paraprofessional', 'Education', 'education/early childhood', 'Teacher',\n", + " 'Education non-profit manager', 'Scientific research', 'Research',\n", + " 'Preschool Tracher'\n", + " ]), 'primary_job_description'\n", "] = 'Education'\n", "\n", "f_survey.loc[\n", " f_survey.primary_job_description.isin([\n", - " 'Custodian', 'Custodial ', 'Csu custodian ', 'Janitorial '\n", + " 'Custodian', 'Custodial', 'Csu custodian', 'Janitorial',\n", + " 'Custodial Maintanace'\n", " ]), 'primary_job_description'\n", "] = 'Custodial'\n", "\n", "f_survey.loc[\n", " f_survey.primary_job_description.isin([\n", - " 'Inbound cs', 'Accounting Technician ', \n", + " 'Inbound cs', 'Accounting Technician', \n", + " 'Clerical'\n", " ]), 'primary_job_description'\n", - "] = 'Clerical'\n", + "] = 'Clerical or administrative support'\n", "\n", "f_survey.loc[\n", " f_survey.primary_job_description.isin([\n", @@ -527,7 +537,7 @@ "f_survey.loc[\n", " f_survey.primary_job_description.isin([\n", " 'Pastry chef and line cook', 'Cook', 'Chef', 'Dining Services',\n", - " 'Food Service', 'Cooking ', 'Residential Dining Services'\n", + " 'Food Service', 'Cooking', 'Residential Dining Services', 'Line Cook'\n", " ]), 'primary_job_description'\n", "] = 'Food service'\n", "\n", @@ -535,19 +545,20 @@ " f_survey.primary_job_description.isin([\n", " 'CNA', 'Caregiver/ Qmap', 'Health care', 'Nurse',\n", " 'Healthcare', 'Medical', 'Medical field',\n", - " 'Family support '\n", + " 'Family support'\n", " ]), 'primary_job_description'\n", "] = 'Medical/healthcare'\n", "\n", "f_survey.loc[\n", " f_survey.primary_job_description.isin([\n", - " 'Amazon ', 'Hockey rink ', 'Caregiver', 'Security', 'Nonprofit social work '\n", + " 'Amazon', 'Hockey rink', 'Caregiver', 'Security', 'Nonprofit social work',\n", + " 'Therapeutic', 'Driver'\n", " ]), 'primary_job_description'\n", "] = 'Other'\n", "\n", "f_survey.loc[\n", " f_survey.primary_job_description.isin([\n", - " 'Hospital laundry', 'Maintenance ', 'Matreal handler',\n", + " 'Hospital laundry', 'Matreal handler', 'Maintenance',\n", " 'Co op laundry'\n", " ]), 'primary_job_description'\n", "] = 'Manufacturing, construction, maintenance, or farming'\n", @@ -557,7 +568,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "f_survey.primary_job_description.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -567,7 +587,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -580,7 +600,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -592,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -607,44 +627,16 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Timestamp 0\n", - "user_id 0\n", - "birth_year 0\n", - "gender 0\n", - "has_drivers_license 0\n", - "highest_education 0\n", - "has_multiple_jobs 0\n", - "primary_job_type 0\n", - "primary_job_description 0\n", - "primary_job_commute_mode 0\n", - "primary_job_commute_time 0\n", - "income_category 0\n", - "n_residence_members 0\n", - "n_residents_u18 0\n", - "n_residents_with_license 0\n", - "n_motor_vehicles 0\n", - "available_modes 0\n", - "age 0\n", - "dtype: int64" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "display(f_survey.isna().sum())" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -670,88 +662,25 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Sales or service 47\n", - "Professional, managerial, or technical 37\n", - "Manufacturing, construction, maintenance, or farming 17\n", - "Clerical or administrative support 17\n", - "Other 13\n", - "Education 9\n", - "Food service 8\n", - "Medical/healthcare 8\n", - "Custodial 7\n", - "Clerical 2\n", - "Maintenance 1\n", - "Name: primary_job_description, dtype: int64" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "f_survey.primary_job_description.value_counts()" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 166 entries, 0 to 169\n", - "Data columns (total 18 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Timestamp 166 non-null datetime64[ns, America/Denver]\n", - " 1 user_id 166 non-null object \n", - " 2 birth_year 166 non-null int64 \n", - " 3 gender 166 non-null object \n", - " 4 has_drivers_license 166 non-null object \n", - " 5 highest_education 166 non-null object \n", - " 6 has_multiple_jobs 166 non-null object \n", - " 7 primary_job_type 166 non-null object \n", - " 8 primary_job_description 166 non-null object \n", - " 9 primary_job_commute_mode 166 non-null object \n", - " 10 primary_job_commute_time 166 non-null float64 \n", - " 11 income_category 166 non-null int64 \n", - " 12 n_residence_members 166 non-null int64 \n", - " 13 n_residents_u18 166 non-null int64 \n", - " 14 n_residents_with_license 166 non-null int64 \n", - " 15 n_motor_vehicles 166 non-null int64 \n", - " 16 available_modes 166 non-null object \n", - " 17 age 166 non-null int64 \n", - "dtypes: datetime64[ns, America/Denver](1), float64(1), int64(7), object(9)\n", - "memory usage: 24.6+ KB\n" - ] - }, - { - "data": { - "text/plain": [ - "None" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "display(f_survey.info())" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -769,7 +698,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -787,88 +716,16 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 166 entries, 0 to 169\n", - "Data columns (total 49 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Timestamp 166 non-null datetime64[ns, America/Denver]\n", - " 1 user_id 166 non-null object \n", - " 2 birth_year 166 non-null int64 \n", - " 3 primary_job_commute_time 166 non-null float64 \n", - " 4 income_category 166 non-null int64 \n", - " 5 n_residence_members 166 non-null int64 \n", - " 6 n_residents_u18 166 non-null int64 \n", - " 7 n_residents_with_license 166 non-null int64 \n", - " 8 n_motor_vehicles 166 non-null int64 \n", - " 9 available_modes 166 non-null object \n", - " 10 age 166 non-null int64 \n", - " 11 gender_Man 166 non-null float64 \n", - " 12 gender_Man;Nonbinary/genderqueer/genderfluid 166 non-null float64 \n", - " 13 gender_Nonbinary/genderqueer/genderfluid 166 non-null float64 \n", - " 14 gender_Prefer not to say 166 non-null float64 \n", - " 15 gender_Woman 166 non-null float64 \n", - " 16 gender_Woman;Nonbinary/genderqueer/genderfluid 166 non-null float64 \n", - " 17 has_drivers_license_No 166 non-null float64 \n", - " 18 has_drivers_license_Prefer not to say 166 non-null float64 \n", - " 19 has_drivers_license_Yes 166 non-null float64 \n", - " 20 has_multiple_jobs_No 166 non-null float64 \n", - " 21 has_multiple_jobs_Prefer not to say 166 non-null float64 \n", - " 22 has_multiple_jobs_Yes 166 non-null float64 \n", - " 23 highest_education_Bachelor's degree 166 non-null float64 \n", - " 24 highest_education_Graduate degree or professional degree 166 non-null float64 \n", - " 25 highest_education_High school graduate or GED 166 non-null float64 \n", - " 26 highest_education_Less than a high school graduate 166 non-null float64 \n", - " 27 highest_education_Prefer not to say 166 non-null float64 \n", - " 28 highest_education_Some college or associates degree 166 non-null float64 \n", - " 29 primary_job_type_Full-time 166 non-null float64 \n", - " 30 primary_job_type_Part-time 166 non-null float64 \n", - " 31 primary_job_type_Prefer not to say 166 non-null float64 \n", - " 32 primary_job_description_Clerical 166 non-null float64 \n", - " 33 primary_job_description_Clerical or administrative support 166 non-null float64 \n", - " 34 primary_job_description_Custodial 166 non-null float64 \n", - " 35 primary_job_description_Education 166 non-null float64 \n", - " 36 primary_job_description_Food service 166 non-null float64 \n", - " 37 primary_job_description_Maintenance 166 non-null float64 \n", - " 38 primary_job_description_Manufacturing, construction, maintenance, or farming 166 non-null float64 \n", - " 39 primary_job_description_Medical/healthcare 166 non-null float64 \n", - " 40 primary_job_description_Other 166 non-null float64 \n", - " 41 primary_job_description_Professional, managerial, or technical 166 non-null float64 \n", - " 42 primary_job_description_Sales or service 166 non-null float64 \n", - " 43 primary_job_commute_mode_Active transport 166 non-null float64 \n", - " 44 primary_job_commute_mode_Car transport 166 non-null float64 \n", - " 45 primary_job_commute_mode_Hybrid 166 non-null float64 \n", - " 46 primary_job_commute_mode_Public transport 166 non-null float64 \n", - " 47 primary_job_commute_mode_Unknown 166 non-null float64 \n", - " 48 primary_job_commute_mode_WFH 166 non-null float64 \n", - "dtypes: datetime64[ns, America/Denver](1), float64(39), int64(7), object(2)\n", - "memory usage: 68.9+ KB\n" - ] - }, - { - "data": { - "text/plain": [ - "None" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "display(f_survey.info())" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -898,41 +755,13 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Could not interpret value `primary_job_commute_mode` for parameter `y`", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[36], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplot_survey_distribution\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mf_survey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mincome_category\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mn_motor_vehicles\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mprimary_job_commute_mode\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[35], line 18\u001b[0m, in \u001b[0;36mplot_survey_distribution\u001b[0;34m(df, feature_names)\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ix, ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(axes\u001b[38;5;241m.\u001b[39mflatten()):\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ix \u001b[38;5;241m<\u001b[39m n:\n\u001b[0;32m---> 18\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhistplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeature_names\u001b[49m\u001b[43m[\u001b[49m\u001b[43mix\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 20\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_axis_off()\n", - "File \u001b[0;32m~/miniconda3/envs/emission/lib/python3.9/site-packages/seaborn/distributions.py:1395\u001b[0m, in \u001b[0;36mhistplot\u001b[0;34m(data, x, y, hue, weights, stat, bins, binwidth, binrange, discrete, cumulative, common_bins, common_norm, multiple, element, fill, shrink, kde, kde_kws, line_kws, thresh, pthresh, pmax, cbar, cbar_ax, cbar_kws, palette, hue_order, hue_norm, color, log_scale, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m 1374\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhistplot\u001b[39m(\n\u001b[1;32m 1375\u001b[0m data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m,\n\u001b[1;32m 1376\u001b[0m \u001b[38;5;66;03m# Vector variables\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1392\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 1393\u001b[0m ):\n\u001b[0;32m-> 1395\u001b[0m p \u001b[38;5;241m=\u001b[39m \u001b[43m_DistributionPlotter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1396\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1397\u001b[0m \u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_DistributionPlotter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_semantics\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlocals\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1398\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1400\u001b[0m p\u001b[38;5;241m.\u001b[39mmap_hue(palette\u001b[38;5;241m=\u001b[39mpalette, order\u001b[38;5;241m=\u001b[39mhue_order, norm\u001b[38;5;241m=\u001b[39mhue_norm)\n\u001b[1;32m 1402\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/miniconda3/envs/emission/lib/python3.9/site-packages/seaborn/distributions.py:113\u001b[0m, in \u001b[0;36m_DistributionPlotter.__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\n\u001b[1;32m 108\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 109\u001b[0m data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 110\u001b[0m variables\u001b[38;5;241m=\u001b[39m{},\n\u001b[1;32m 111\u001b[0m ):\n\u001b[0;32m--> 113\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/emission/lib/python3.9/site-packages/seaborn/_oldcore.py:640\u001b[0m, in \u001b[0;36mVectorPlotter.__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m 635\u001b[0m \u001b[38;5;66;03m# var_ordered is relevant only for categorical axis variables, and may\u001b[39;00m\n\u001b[1;32m 636\u001b[0m \u001b[38;5;66;03m# be better handled by an internal axis information object that tracks\u001b[39;00m\n\u001b[1;32m 637\u001b[0m \u001b[38;5;66;03m# such information and is set up by the scale_* methods. The analogous\u001b[39;00m\n\u001b[1;32m 638\u001b[0m \u001b[38;5;66;03m# information for numeric axes would be information about log scales.\u001b[39;00m\n\u001b[1;32m 639\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_var_ordered \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m} \u001b[38;5;66;03m# alt., used DefaultDict\u001b[39;00m\n\u001b[0;32m--> 640\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massign_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 642\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m var, \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_semantic_mappings\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 643\u001b[0m \n\u001b[1;32m 644\u001b[0m \u001b[38;5;66;03m# Create the mapping function\u001b[39;00m\n\u001b[1;32m 645\u001b[0m map_func \u001b[38;5;241m=\u001b[39m partial(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mmap, plotter\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n", - "File \u001b[0;32m~/miniconda3/envs/emission/lib/python3.9/site-packages/seaborn/_oldcore.py:701\u001b[0m, in \u001b[0;36mVectorPlotter.assign_variables\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m 699\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 700\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_format \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlong\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 701\u001b[0m plot_data, variables \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_assign_variables_longform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 702\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mvariables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 703\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 705\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mplot_data \u001b[38;5;241m=\u001b[39m plot_data\n\u001b[1;32m 706\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvariables \u001b[38;5;241m=\u001b[39m variables\n", - "File \u001b[0;32m~/miniconda3/envs/emission/lib/python3.9/site-packages/seaborn/_oldcore.py:938\u001b[0m, in \u001b[0;36mVectorPlotter._assign_variables_longform\u001b[0;34m(self, data, **kwargs)\u001b[0m\n\u001b[1;32m 933\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(val, (\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbytes\u001b[39m)):\n\u001b[1;32m 934\u001b[0m \n\u001b[1;32m 935\u001b[0m \u001b[38;5;66;03m# This looks like a column name but we don't know what it means!\u001b[39;00m\n\u001b[1;32m 937\u001b[0m err \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not interpret value `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mval\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` for parameter `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 938\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(err)\n\u001b[1;32m 940\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 941\u001b[0m \n\u001b[1;32m 942\u001b[0m \u001b[38;5;66;03m# Otherwise, assume the value is itself data\u001b[39;00m\n\u001b[1;32m 943\u001b[0m \n\u001b[1;32m 944\u001b[0m \u001b[38;5;66;03m# Raise when data object is present and a vector can't matched\u001b[39;00m\n\u001b[1;32m 945\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, pd\u001b[38;5;241m.\u001b[39mDataFrame) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(val, pd\u001b[38;5;241m.\u001b[39mSeries):\n", - "\u001b[0;31mValueError\u001b[0m: Could not interpret value `primary_job_commute_mode` for parameter `y`" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "plot_survey_distribution(\n", - " f_survey, ['income_category', 'n_motor_vehicles', 'primary_job_commute_mode']\n", - ")" + "# plot_survey_distribution(\n", + "# f_survey, ['income_category', 'n_motor_vehicles', 'primary_job_commute_mode']\n", + "# )" ] }, { @@ -953,7 +782,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -967,7 +796,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -978,24 +807,16 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2016-08-15 07:59:32.418000-06:00 2022-12-30 23:33:27.147785-07:00\n" - ] - } - ], + "outputs": [], "source": [ "print(denver_data.start_fmt_time.min(), denver_data.start_fmt_time.max())" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1026,7 +847,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1040,24 +861,16 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "166\n" - ] - } - ], + "outputs": [], "source": [ - "print(len(merged_data.user_id.unique()))" + "print(len(denver_data.user_id.unique()), len(merged_data.user_id.unique()))" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1084,7 +897,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1112,7 +925,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1160,7 +973,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1209,282 +1022,16 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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5 rows × 104 columns

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" - ], - "text/plain": [ - " source end_ts end_fmt_time \\\n", - "0 DwellSegmentationDistFilter 1.629238e+09 2021-08-17 16:02:26.493017-06:00 \n", - "1 DwellSegmentationDistFilter 1.645386e+09 2022-02-20 12:32:43.451677-07:00 \n", - "2 DwellSegmentationDistFilter 1.645391e+09 2022-02-20 14:07:17.998778-07:00 \n", - "3 DwellSegmentationDistFilter 1.645397e+09 2022-02-20 15:35:51.018136-07:00 \n", - "4 DwellSegmentationDistFilter 1.645405e+09 2022-02-20 17:52:13.000176-07:00 \n", - "\n", - " end_loc \\\n", - "0 {'type': 'Point', 'coordinates': [-71.08788681... \n", - "1 {'type': 'Point', 'coordinates': [-111.9450490... \n", - "2 {'type': 'Point', 'coordinates': [-111.9440840... \n", - "3 {'type': 'Point', 'coordinates': [-111.9448744... \n", - "4 {'type': 'Point', 'coordinates': [-110.8175686... \n", - "\n", - " raw_trip start_ts start_fmt_time \\\n", - "0 611c350df3bfdd5663c34e52 1.629237e+09 2021-08-17 15:51:32.998732-06:00 \n", - "1 6212ce379106a670c421b888 1.645383e+09 2022-02-20 11:58:02.051102-07:00 \n", - "2 6212ce459106a670c421b88c 1.645388e+09 2022-02-20 13:20:30.462982-07:00 \n", - "3 6212ce459106a670c421b88e 1.645397e+09 2022-02-20 15:35:16.506669-07:00 \n", - "4 6214032d39cfe183d0343a07 1.645397e+09 2022-02-20 15:43:38.468902-07:00 \n", - "\n", - " start_loc duration \\\n", - "0 {'type': 'Point', 'coordinates': [-71.11831245... 10.891571 \n", - "1 {'type': 'Point', 'coordinates': [-111.9167906... 34.690010 \n", - "2 {'type': 'Point', 'coordinates': [-111.9437005... 46.792263 \n", - "3 {'type': 'Point', 'coordinates': [-111.9440840... 0.575191 \n", - "4 {'type': 'Point', 'coordinates': [-111.9448744... 128.575521 \n", - "\n", - " distance ... primary_job_commute_mode_Hybrid \\\n", - "0 2700.231495 ... 0.0 \n", - "1 18093.352854 ... 0.0 \n", - "2 984.681194 ... 0.0 \n", - "3 183.932256 ... 0.0 \n", - "4 192258.374492 ... 0.0 \n", - "\n", - " primary_job_commute_mode_Public transport primary_job_commute_mode_Unknown \\\n", - "0 0.0 1.0 \n", - "1 0.0 1.0 \n", - "2 0.0 1.0 \n", - "3 0.0 1.0 \n", - "4 0.0 1.0 \n", - "\n", - " primary_job_commute_mode_WFH is_overnight_trip n_working_residents \\\n", - "0 0.0 0 3 \n", - "1 0.0 0 3 \n", - "2 0.0 0 3 \n", - "3 0.0 0 3 \n", - "4 0.0 0 3 \n", - "\n", - " start_lat start_lng end_lat end_lng \n", - "0 42.351336 -71.118312 42.347875 -71.087887 \n", - "1 33.595163 -111.916791 33.461827 -111.945049 \n", - "2 33.461366 -111.943701 33.460592 -111.944084 \n", - "3 33.460592 -111.944084 33.462015 -111.944874 \n", - "4 33.462015 -111.944874 32.276548 -110.817569 \n", - "\n", - "[5 rows x 104 columns]" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "modeling_data.head()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, for every trip, we have the corresponding section mode that covered the longest distance for the trip.\n", - "\n", - "Using this as well as the `available_modes` column:\n", - "\n", - "```language=python\n", - "\n", - " # unique available modes:\n", - " {'Bicycle',\n", - " 'Do not have vehicle ',\n", - " 'Get a ride from a friend or family member',\n", - " 'None',\n", - " 'Public transportation (bus, subway, light rail, etc.)',\n", - " 'Rental car (including Zipcar/ Car2Go)',\n", - " 'Shared bicycle or scooter',\n", - " 'Skateboard',\n", - " 'Taxi (regular taxi, Uber, Lyft, etc)',\n", - " 'Walk/roll'}\n", - "\n", - " # unique section modes:\n", - " {'bicycling', 'bus', 'car', 'no_sensed', 'train', 'walking'}\n", - "\n", - " \n", - "```\n", - "\n", - "What mapping can we establish here? " - ] - }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1505,7 +1052,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1514,27 +1061,16 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "importlib.reload(scaffolding)" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1543,7 +1079,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1553,539 +1089,101 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "num_splits = 10\n", - "samples_per_split = modeling_data.shape[0]//num_splits" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Split 0 size: 8069\n", - "INFO: Pandarallel will run on 4 workers.\n", - "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Falling back to confirmed trips...\n", - "Took 13.07597050956667 minutes to complete\n", - "Split 1 size: 8069\n", - "INFO: Pandarallel will run on 4 workers.\n", - "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rkulhall/em-public-dashboard/rm_src/../viz_scripts/scaffolding.py:147: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " confirmed_trips['section_durations'] = confirmed_trips.parallel_apply(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Took 20.352822523033332 minutes to complete\n", - "Split 2 size: 8069\n", - "INFO: Pandarallel will run on 4 workers.\n", - "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rkulhall/em-public-dashboard/rm_src/../viz_scripts/scaffolding.py:147: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " confirmed_trips['section_durations'] = confirmed_trips.parallel_apply(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Took 19.824597845733333 minutes to complete\n", - "Split 3 size: 8069\n", - "INFO: Pandarallel will run on 4 workers.\n", - "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rkulhall/em-public-dashboard/rm_src/../viz_scripts/scaffolding.py:147: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " confirmed_trips['section_durations'] = confirmed_trips.parallel_apply(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Took 20.249677114833336 minutes to complete\n", - "Split 4 size: 8069\n", - "INFO: Pandarallel will run on 4 workers.\n", - "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rkulhall/em-public-dashboard/rm_src/../viz_scripts/scaffolding.py:147: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " confirmed_trips['section_durations'] = confirmed_trips.parallel_apply(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Took 18.196551645316656 minutes to complete\n", - "Split 5 size: 8069\n", - "INFO: Pandarallel will run on 4 workers.\n", - "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rkulhall/em-public-dashboard/rm_src/../viz_scripts/scaffolding.py:147: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " confirmed_trips['section_durations'] = confirmed_trips.parallel_apply(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Took 14.953972044866669 minutes to complete\n", - "Split 6 size: 8069\n", - "INFO: Pandarallel will run on 4 workers.\n", - "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rkulhall/em-public-dashboard/rm_src/../viz_scripts/scaffolding.py:147: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " confirmed_trips['section_durations'] = confirmed_trips.parallel_apply(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Took 13.908668193450012 minutes to complete\n", - "Split 7 size: 8069\n", - "INFO: Pandarallel will run on 4 workers.\n", - "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rkulhall/em-public-dashboard/rm_src/../viz_scripts/scaffolding.py:147: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " confirmed_trips['section_durations'] = confirmed_trips.parallel_apply(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Took 15.91554533118333 minutes to complete\n", - "Split 8 size: 8069\n", - "INFO: Pandarallel will run on 4 workers.\n", - "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rkulhall/em-public-dashboard/rm_src/../viz_scripts/scaffolding.py:147: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " confirmed_trips['section_durations'] = confirmed_trips.parallel_apply(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Took 14.596019812649986 minutes to complete\n", - "Split 9 size: 8069\n", - "INFO: Pandarallel will run on 4 workers.\n", - "INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rkulhall/em-public-dashboard/rm_src/../viz_scripts/scaffolding.py:147: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " confirmed_trips['section_durations'] = confirmed_trips.parallel_apply(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Took 12.495607870950002 minutes to complete\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rkulhall/em-public-dashboard/rm_src/../viz_scripts/scaffolding.py:147: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " confirmed_trips['section_durations'] = confirmed_trips.parallel_apply(\n" - ] - } - ], - "source": [ - "from time import perf_counter\n", - "\n", - "for split_ix in range(num_splits):\n", - " low = samples_per_split * split_ix\n", - " high = samples_per_split * (split_ix + 1) - 1\n", - " split = subset.loc[low:high, :]\n", - " \n", - " print(f\"Split {split_ix} size: {split.shape[0]}\")\n", - " \n", - " now = perf_counter()\n", - " result = scaffolding.get_section_durations(split)\n", - " end = perf_counter() - now\n", - "\n", - " print(f\"Took {end/60} minutes to complete\")\n", - " results.append(result)" + "EXTRACT_DURATIONS = False" ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10\n" - ] - } - ], + "outputs": [], "source": [ - "print(len(results))" + "# Takes ~150 minutes if n=5.\n", + "num_splits = 5\n", + "samples_per_split = modeling_data.shape[0]//num_splits" ] }, { "cell_type": "code", - "execution_count": 56, - "metadata": {}, + "execution_count": null, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ - "cat = pd.concat(results, axis=0)" + "from time import perf_counter\n", + "\n", + "if EXTRACT_DURATIONS:\n", + "\n", + " for split_ix in range(num_splits):\n", + " low = samples_per_split * split_ix\n", + "\n", + " # -1 since .loc is index-inclusive.\n", + " high = samples_per_split * (split_ix + 1) - 1\n", + "\n", + " # For last split, include last index too.\n", + " if split_ix < num_splits - 1:\n", + " split = subset.loc[low:high, :]\n", + " else:\n", + " split = subset.loc[low:, :]\n", + "\n", + " print(f\"Split {split_ix} size: {split.shape[0]}\")\n", + "\n", + " now = perf_counter()\n", + " result = scaffolding.get_section_durations(split)\n", + " end = perf_counter() - now\n", + "\n", + " print(f\"Took {end/60} minutes to complete\")\n", + "\n", + " results.append(result)\n", + "\n", + " print(50*'-')\n", + "\n", + " cat = pd.concat(results, axis=0)\n", + " \n", + " # This will save a LOT of time.\n", + " # cat.to_csv('../data/section_durations.csv', index=False)\n", + "else:\n", + " cat = pd.read_csv('../data/section_durations.csv')" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "80691 80690\n" - ] - } - ], + "outputs": [], "source": [ "print(modeling_data.shape[0], cat.shape[0])" ] }, { "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "80690\n" - ] - } - ], - "source": [ - "modeling_data = modeling_data.loc[:cat.shape[0]-1, :]\n", - "print(modeling_data.shape[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(modeling_data.original_user_id == cat.original_user_id).all()" - ] - }, - { - "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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original_user_idcleaned_tripsection_durations
08a0473ca-e53d-4720-a99c-0696cc1fb407611c350ef3bfdd5663c34e5b[393.00192403793335, 258.49230194091797]
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" - ], - "text/plain": [ - " original_user_id cleaned_trip \\\n", - "0 8a0473ca-e53d-4720-a99c-0696cc1fb407 611c350ef3bfdd5663c34e5b \n", - "1 8a0473ca-e53d-4720-a99c-0696cc1fb407 6212ce4c9106a670c421b89b \n", - "2 8a0473ca-e53d-4720-a99c-0696cc1fb407 6212ce4e9106a670c421b903 \n", - "3 8a0473ca-e53d-4720-a99c-0696cc1fb407 6212ce4f9106a670c421b965 \n", - "4 8a0473ca-e53d-4720-a99c-0696cc1fb407 6214033c39cfe183d0343a2f \n", - "\n", - " section_durations \n", - "0 [393.00192403793335, 258.49230194091797] \n", - "1 [2081.4005744457245] \n", - "2 [2807.5357959270477] \n", - "3 [34.51146650314331] \n", - "4 [7714.531273841858] " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "display(cat.head())" + "modeling_data = pd.concat([modeling_data, cat[['section_durations']]], axis=1)" ] }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "modeling_data = pd.concat([modeling_data, cat[['section_durations']]], axis=1)" + "modeling_data.to_csv('../data/final_modeling_data_02142024.csv', index=False)" ] }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "modeling_data.to_csv('../data/final_modeling_data_02142024.csv', index=False)" - ] + "source": [] } ], "metadata": { @@ -2111,5 +1209,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/rm_src/rf_bayesian_optim.py b/rm_src/rf_bayesian_optim.py new file mode 100644 index 0000000..6c911bd --- /dev/null +++ b/rm_src/rf_bayesian_optim.py @@ -0,0 +1,280 @@ +import warnings +warnings.simplefilter(action='ignore', category=Warning) + +import os +import numpy as np +import pandas as pd +import pickle +from bayes_opt import BayesianOptimization +from sklearn.linear_model import LinearRegression +from sklearn.ensemble import RandomForestClassifier +from sklearn.model_selection import StratifiedGroupKFold +from sklearn.metrics import f1_score, log_loss, r2_score + +SEED = 13210 + +class BayesianCV: + def __init__(self, data): + + init_splitter = StratifiedGroupKFold(n_splits=5, shuffle=True, random_state=SEED) + X = data.drop(columns=['target']) + groups = data.user_id.values + y = data.target.values + + for train_ix, test_ix in init_splitter.split(X, y, groups): + train = data.iloc[train_ix, :] + test = data.iloc[test_ix, :] + + break + + # Can't have split, so let it happen for two times. + # train, test = train_test_split(data, test_size=0.2, shuffle=True, stratify=data.target) + + print("Train-test split done.") + + # Estimate the test durations using the train data. + params, train = self._get_duration_estimate(train, 'train', None) + _, test = self._get_duration_estimate(test, 'test', params) + + # We drop the training duration estimates since we will be re-computing them during CV. + train.drop(columns=[c for c in train.columns if 'tt_' in c], inplace=True) + + # This is out final train and test data. + self.data = train.reset_index(drop=True) + self.test = test.reset_index(drop=True) + + self._optimizer = self._setup_optimizer() + + + def _drop_columns(self, df: pd.DataFrame): + to_drop = [ + 'source', 'end_ts', 'end_fmt_time', 'end_loc', 'raw_trip', 'start_ts', + 'start_fmt_time', 'start_loc', 'duration', 'distance', 'start_place', + 'end_place', 'cleaned_trip', 'inferred_labels', 'inferred_trip', 'expectation', + 'confidence_threshold', 'expected_trip', 'user_input', 'start:year', 'start:month', + 'start:day', 'start_local_dt_minute', 'start_local_dt_second', + 'start_local_dt_weekday', 'start_local_dt_timezone', 'end:year', 'end:month', 'end:day', + 'end_local_dt_minute', 'end_local_dt_second', 'end_local_dt_weekday', + 'end_local_dt_timezone', '_id', 'user_id', 'metadata_write_ts', 'additions', + 'mode_confirm', 'purpose_confirm', 'Mode_confirm', 'Trip_purpose', + 'original_user_id', 'program', 'opcode', 'Timestamp', 'birth_year', + 'available_modes', 'section_coordinates_argmax', 'section_mode_argmax', + 'start_lat', 'start_lng', 'end_lat', 'end_lng' + ] + + # Drop section_mode_argmax and available_modes. + return df.drop( + columns=to_drop, + inplace=False + ) + + + def _get_duration_estimate(self, df: pd.DataFrame, dset: str, model_dict: dict): + + X_features = ['section_distance_argmax', 'age'] + + if 'mph' in df.columns: + X_features += ['mph'] + + if dset == 'train' and model_dict is None: + model_dict = dict() + + if dset == 'test' and model_dict is None: + raise AttributeError("Expected model dict for testing.") + + if dset == 'train': + for section_mode in df.section_mode_argmax.unique(): + section_data = df.loc[df.section_mode_argmax == section_mode, :] + if section_mode not in model_dict: + model_dict[section_mode] = dict() + + model = LinearRegression(fit_intercept=True) + + X = section_data[ + X_features + ] + Y = section_data[['section_duration_argmax']] + + model.fit(X, Y.values.ravel()) + + r2 = r2_score(y_pred=model.predict(X), y_true=Y.values.ravel()) + # print(f"Train R2 for {section_mode}: {r2}") + + model_dict[section_mode]['model'] = model + + elif dset == 'test': + for section_mode in df.section_mode_argmax.unique(): + section_data = df.loc[df.section_mode_argmax == section_mode, :] + X = section_data[ + X_features + ] + Y = section_data[['section_duration_argmax']] + + y_pred = model_dict[section_mode]['model'].predict(X) + r2 = r2_score(y_pred=y_pred, y_true=Y.values.ravel()) + # print(f"Test R2 for {section_mode}: {r2}") + + # Create the new columns for the duration. + new_columns = ['p_micro','no_trip','s_car','transit','car','s_micro','ridehail','walk','unknown'] + df[new_columns] = 0 + df['temp'] = 0 + + for section in df.section_mode_argmax.unique(): + X_section = df.loc[df.section_mode_argmax == section, X_features] + + # broadcast to all columns. + df.loc[df.section_mode_argmax == section, 'temp'] = model_dict[section]['model'].predict(X_section) + + for c in new_columns: + df[c] = df['av_' + c] * df['temp'] + + df.drop(columns=['temp'], inplace=True) + + df.rename(columns=dict([(x, 'tt_'+x) for x in new_columns]), inplace=True) + + # return model_dict, result_df + return model_dict, df + + + def _setup_optimizer(self): + # Define search space. + hparam_dict = { + # 10-500 + 'n_estimators': (0.25, 3), + # 5-150 + 'max_depth': (0.5, 15), + # 2-20 + 'min_samples_split': (0.2, 2.5), + # 1-20 + 'min_samples_leaf': (0.1, 2.5), + # as-is. + 'ccp_alpha': (0., 0.5), + # as-is. + 'max_features': (0.1, 0.99), + # Use clip to establish mask. + 'class_weight': (0, 1), + } + + return BayesianOptimization( + self._surrogate, + hparam_dict + ) + + + def _surrogate(self, n_estimators, max_depth, min_samples_split, min_samples_leaf, ccp_alpha, max_features, class_weight): + + cw = 'balanced_subsample' if class_weight < 0.5 else 'balanced' + + # Builds a surrogate model using the samples hparams. + model = RandomForestClassifier( + n_estimators=int(n_estimators * 100), + max_depth=int(max_depth * 10), + min_samples_split=int(min_samples_split * 10), + min_samples_leaf=int(min_samples_leaf * 10), + max_features=max(min(max_features, 0.999), 1e-3), + ccp_alpha=ccp_alpha, + bootstrap=True, + class_weight=cw, + n_jobs=os.cpu_count(), + random_state=SEED + ) + + fold_crossentropy = list() + + # Use the train split and further split in train-val. + X = self.data.drop(columns=['target']) + y = self.data.target.values.ravel() + users = X.user_id.values + + gkfold = StratifiedGroupKFold(n_splits=5, shuffle=True, random_state=SEED) + + for train_ix, test_ix in gkfold.split(X, y, users): + + X_train = X.iloc[train_ix, :] + X_test = X.iloc[test_ix, :] + + y_train = y[train_ix] + y_test = y[test_ix] + + # Re-estimate durations. + params, X_train = self._get_duration_estimate(X_train, 'train', None) + _, X_test = self._get_duration_estimate(X_test, 'test', params) + + X_train = self._drop_columns(X_train) + X_test = self._drop_columns(X_test) + + model.fit( + X_train, + y_train + ) + + # Measure performance on valid split. + ce = log_loss( + y_true=y_test, + y_pred=model.predict_proba(X_test), + labels=list(range(1, 10)) + ) + + fold_crossentropy.append(ce) + + # Return the average negative crossentropy (since bayesian optimization aims to maximize an objective). + return -np.mean(fold_crossentropy) + + + def optimize(self): + self._optimizer.maximize(n_iter=100, init_points=10) + print("Done optimizing!") + best_params = self._optimizer.max['params'] + best_loss = -self._optimizer.max['target'] + return best_loss, best_params + + +def train_final_model(params, cv_obj): + # Construct the model using the params. + model = RandomForestClassifier( + n_estimators=int(params['n_estimators'] * 100), + max_depth=int(params['max_depth'] * 10), + min_samples_split=int(params['min_samples_split'] * 10), + min_samples_leaf=int(params['min_samples_leaf'] * 10), + max_features=params['max_features'], + ccp_alpha=params['ccp_alpha'], + bootstrap=True, + class_weight='balanced_subsample', + n_jobs=os.cpu_count() + ) + + + X_tr = cv_obj.data.drop(columns=['target']) + y_tr = cv_obj.data.target.values.ravel() + + X_te = cv_obj.test.drop(columns=['target']) + y_te = cv_obj.test.target.values.ravel() + + params, X_tr = cv_obj._get_duration_estimate(X_tr, 'train', None) + + X_tr = cv_obj._drop_columns(X_tr) + X_te = cv_obj._drop_columns(X_te) + + model.fit( + X_tr, + y_tr + ) + + model.fit(X_tr, y_tr) + + print(f"Train loss: {log_loss(y_true=y_tr, y_pred=model.predict_proba(X_tr))}") + print(f"Train performance: {f1_score(y_true=y_tr, y_pred=model.predict(X_tr), average='weighted')}") + print(f"Test loss: {log_loss(y_true=y_te, y_pred=model.predict_proba(X_te))}") + print(f"Test performance: {f1_score(y_true=y_te, y_pred=model.predict(X_te), average='weighted')}") + + with open('./bayes_rf.pkl', 'wb') as f: + f.write(pickle.dumps(model)) + + +if __name__ == "__main__": + data = pd.read_csv('../data/ReplacedMode_Fix_02142024.csv') + bayes_cv = BayesianCV(data) + best_loss, best_params = bayes_cv.optimize() + print(f"Best loss: {best_loss}, best params: {str(best_params)}") + train_final_model(best_params, bayes_cv) + \ No newline at end of file diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index 5918672..4dcccb9 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -111,7 +111,7 @@ def expand_userinputs(labeled_ct): def get_section_durations(confirmed_trips: pd.DataFrame): # Initialize the parallel processing. - pandarallel.initialize(progress_bar=True) + pandarallel.initialize(progress_bar=False) """ Extract section-wise durations from trips for every trips. @@ -153,7 +153,7 @@ def get_durations(user_id, trip_id): def get_section_coordinates(confirmed_trips: pd.DataFrame): # Initialize pandarallel - pandarallel.initialize(progress_bar=True) + pandarallel.initialize(progress_bar=False) key = 'analysis/inferred_section' From 63f20c93e5483bca930da961de46312b94543cff Mon Sep 17 00:00:00 2001 From: Rahul Kulhalli Date: Wed, 20 Mar 2024 12:47:00 -0400 Subject: [PATCH 13/16] Added initial user-level modeling notebook --- rm_src/UserLevelModeling.ipynb | 869 +++++++++++++++++++++++++++++++++ 1 file changed, 869 insertions(+) create mode 100644 rm_src/UserLevelModeling.ipynb diff --git a/rm_src/UserLevelModeling.ipynb b/rm_src/UserLevelModeling.ipynb new file mode 100644 index 0000000..7fefe5a --- /dev/null +++ b/rm_src/UserLevelModeling.ipynb @@ -0,0 +1,869 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "04ccf092", + "metadata": {}, + "source": [ + "### Some important points to remember:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2985b51f", + "metadata": {}, + "outputs": [], + "source": [ + "# import warnings\n", + "\n", + "# warnings.simplefilter('ignore', Warning)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "21ef0f2e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import random\n", + "import os\n", + "import ast\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import r2_score, f1_score, log_loss\n", + "from sklearn.model_selection import train_test_split, RandomizedSearchCV, StratifiedKFold\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances\n", + "from enum import Enum\n", + "from scipy.stats import uniform\n", + "from typing import List\n", + "\n", + "pd.set_option('display.max_columns', None) " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "fef98692", + "metadata": {}, + "outputs": [], + "source": [ + "SEED = 13210\n", + "\n", + "np.random.seed(SEED)\n", + "random.seed(SEED)\n", + "\n", + "SimilarityMetric = Enum('SimilarityMetric', ['COSINE', 'EUCLIDEAN', 'KNN', 'KMEANS'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "79f8c51a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_47441/224655024.py:1: DtypeWarning: Columns (38) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df = pd.read_csv('../data/ReplacedMode_Fix_02142024.csv')\n" + ] + } + ], + "source": [ + "df = pd.read_csv('../data/ReplacedMode_Fix_02142024.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "72793473", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['source', 'end_ts', 'end_fmt_time', 'end_loc', 'raw_trip', 'start_ts', 'start_fmt_time', 'start_loc', 'duration', 'distance', 'start_place', 'end_place', 'cleaned_trip', 'inferred_labels', 'inferred_trip', 'expectation', 'confidence_threshold', 'expected_trip', 'user_input', 'start:year', 'start:month', 'start:day', 'start:hour', 'start_local_dt_minute', 'start_local_dt_second', 'start_local_dt_weekday', 'start_local_dt_timezone', 'end:year', 'end:month', 'end:day', 'end:hour', 'end_local_dt_minute', 'end_local_dt_second', 'end_local_dt_weekday', 'end_local_dt_timezone', '_id', 'user_id', 'metadata_write_ts', 'additions', 'mode_confirm', 'purpose_confirm', 'distance_miles', 'Mode_confirm', 'Trip_purpose', 'original_user_id', 'program', 'opcode', 'Timestamp', 'birth_year', 'primary_job_commute_time', 'income_category', 'n_residence_members', 'n_residents_u18', 'n_residents_with_license', 'n_motor_vehicles', 'available_modes', 'age', 'gender_Man', 'gender_Man;Nonbinary/genderqueer/genderfluid', 'gender_Nonbinary/genderqueer/genderfluid', 'gender_Prefer not to say', 'gender_Woman', 'gender_Woman;Nonbinary/genderqueer/genderfluid', 'has_drivers_license_No', 'has_drivers_license_Prefer not to say', 'has_drivers_license_Yes', 'has_multiple_jobs_No', 'has_multiple_jobs_Prefer not to say', 'has_multiple_jobs_Yes', \"highest_education_Bachelor's degree\", 'highest_education_Graduate degree or professional degree', 'highest_education_High school graduate or GED', 'highest_education_Less than a high school graduate', 'highest_education_Prefer not to say', 'highest_education_Some college or associates degree', 'primary_job_type_Full-time', 'primary_job_type_Part-time', 'primary_job_type_Prefer not to say', 'primary_job_description_Clerical or administrative support', 'primary_job_description_Custodial', 'primary_job_description_Education', 'primary_job_description_Food service', 'primary_job_description_Manufacturing, construction, maintenance, or farming', 'primary_job_description_Medical/healthcare', 'primary_job_description_Other', 'primary_job_description_Professional, managerial, or technical', 'primary_job_description_Sales or service', 'primary_job_commute_mode_Active transport', 'primary_job_commute_mode_Car transport', 'primary_job_commute_mode_Hybrid', 'primary_job_commute_mode_Public transport', 'primary_job_commute_mode_Unknown', 'primary_job_commute_mode_WFH', 'is_overnight_trip', 'n_working_residents', 'start_lat', 'start_lng', 'end_lat', 'end_lng', 'temperature_2m (°F)', 'relative_humidity_2m (%)', 'dew_point_2m (°F)', 'rain (inch)', 'snowfall (inch)', 'wind_speed_10m (mp/h)', 'wind_gusts_10m (mp/h)', 'section_distance_argmax', 'section_duration_argmax', 'section_mode_argmax', 'section_coordinates_argmax', 'mph', 'target', 'av_car', 'av_transit', 'av_ridehail', 'av_p_micro', 'av_s_micro', 'av_walk', 'av_no_trip', 'av_s_car', 'av_unknown', 'cost_p_micro', 'cost_no_trip', 'cost_s_car', 'cost_transit', 'cost_car', 'cost_s_micro', 'cost_ridehail', 'cost_walk', 'cost_unknown']\n" + ] + } + ], + "source": [ + "print(df.columns.tolist())" + ] + }, + { + "cell_type": "markdown", + "id": "85866e8a", + "metadata": {}, + "source": [ + "### We want to experiment with two types of models:\n", + "\n", + "\n", + "1. have one row per user, so that when predicting modes for a new user, we pick the \"similar user\" or users and determine the replaced mode\n", + " - In this, the traditional approach would only use demographics for the user features, we may experiment with some summaries of the trip data that will function as some level of \"fingerprint\" for the user. Ideally we would be able to show that this performs better than demographics alone\n", + " - Note also that the original method that you had outlined where the training set is a list of trips (O()) is a third approach which we will be comparing these two against" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2a9efd3e", + "metadata": {}, + "outputs": [], + "source": [ + "def encode_availability(df: pd.DataFrame):\n", + " \n", + " # Borrowed directly from the cost_time_avl_preprocessing notebook.\n", + " available = {\n", + " 'Bicycle': 'p_micro',\n", + " 'Do not have vehicle': 'unknown',\n", + " 'Get a ride from a friend or family member': 's_car',\n", + " 'None': 'no_trip',\n", + " 'Public transportation (bus, subway, light rail, etc.)': 'transit',\n", + " 'Rental car (including Zipcar/ Car2Go)': 'car',\n", + " 'Shared bicycle or scooter': 's_micro',\n", + " 'Skateboard': 'p_micro',\n", + " 'Taxi (regular taxi, Uber, Lyft, etc)': 'ridehail',\n", + " 'Walk/roll': 'walk',\n", + " 'Prefer not to say': 'unknown'\n", + " }\n", + " \n", + " newcols = list(available.values())\n", + " \n", + " # Start by creating dummy indicators.\n", + " df[newcols] = 0\n", + " \n", + " for i, row in df.iterrows():\n", + " modes = [e.strip() for e in row.available_modes.split(';')]\n", + " mapped_modes = [available[mode] for mode in modes]\n", + " df.loc[i, mapped_modes] = 1\n", + " \n", + " df.drop(columns=['available_modes'], inplace=True)\n", + " df.columns = ['av_' + str(c) if c in newcols else str(c) for c in df.columns]\n", + " \n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cfe76e8c", + "metadata": {}, + "outputs": [], + "source": [ + "def get_mode_coverage(df: pd.DataFrame):\n", + " \n", + " coverage_df = df.groupby(['user_id', 'section_mode_argmax']).size().unstack(fill_value=0)\n", + " coverage_df.columns = ['coverage_' + str(c) for c in coverage_df.columns]\n", + " \n", + " # As a preventative measure.\n", + " coverage_df.fillna(0, inplace=True)\n", + " \n", + " # Normalize over rows.\n", + " coverage_df.iloc[:, 1:] = coverage_df.iloc[:, 1:].div(coverage_df.iloc[:, 1:].sum(axis=1), axis=0)\n", + " \n", + " return coverage_df" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "75313008", + "metadata": {}, + "outputs": [], + "source": [ + "def get_trip_summaries(df: pd.DataFrame, group_key: str, feature_list: List[str]):\n", + " \n", + " def get_feature_summaries(trip_feature: str, is_ordinal: bool = False):\n", + " \n", + " if not is_ordinal:\n", + " # A mean of 0 is an actual value.\n", + " mean = df.groupby(['user_id', group_key])[trip_feature].mean().unstack(level=-1, fill_value=-1.)\n", + " mean.columns = [f'{trip_feature}_mean_' + str(c) for c in mean.columns]\n", + " \n", + " # Same with percentiles - 0 is an actual value.\n", + " median = df.groupby(['user_id', group_key])[trip_feature].median().unstack(level=-1, fill_value=-1.)\n", + " median.columns = [f'{trip_feature}_median_' + str(c) for c in median.columns]\n", + " \n", + " iqr_temp = df.groupby(['user_id', group_key])[trip_feature].quantile([0.25, 0.75]).unstack(level=-1)\n", + " iqr = (iqr_temp[0.75] - iqr_temp[0.25]).unstack(level=-1)\n", + " iqr.fillna(-1., inplace=True)\n", + " iqr.columns = [f'{trip_feature}_iqr_' + str(c) for c in iqr.columns]\n", + "\n", + " # Now merge.\n", + " merged = mean.copy()\n", + " merged = merged.merge(right=median, left_index=True, right_index=True)\n", + " merged = merged.merge(right=iqr, left_index=True, right_index=True)\n", + "\n", + " return merged\n", + " \n", + " # 0 is OK to indicate NaN values.\n", + " f_mode = df.groupby(['user_id', group_key])[trip_feature].apply(\n", + " lambda x: x.value_counts().idxmax()\n", + " ).unstack(fill_value=0.)\n", + " \n", + " f_mode.columns = [f'{trip_feature}_mode_' + str(c) for c in f_mode.columns]\n", + " \n", + " return f_mode\n", + " \n", + " # This will be the dataframe that all subsequent features will join to.\n", + " feature_df = None\n", + " \n", + " for ix, feature in enumerate(feature_list):\n", + " is_ordinal = feature == 'start:hour' or feature == 'end:hour'\n", + " if ix == 0:\n", + " feature_df = get_feature_summaries(feature, is_ordinal)\n", + " else:\n", + " next_feature_df = get_feature_summaries(feature, is_ordinal)\n", + " feature_df = feature_df.merge(right=next_feature_df, left_index=True, right_index=True)\n", + " \n", + " return feature_df" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "63617ada", + "metadata": {}, + "outputs": [], + "source": [ + "def get_demographic_data(df: pd.DataFrame, **trip_kwargs):\n", + " \n", + " '''\n", + " A method that returns a U x (D + t) matrix, where U = number of users,\n", + " D = number of demographic features, t (optional) = number of trip summary features.\n", + " \n", + " When use_trip_summaries=True, the 'available_modes' column is dropped in favor of\n", + " the already-preprocessed av_ columns. This is because we want to incorporate trip-level\n", + " information into the data. When the argument is False, we want to SOLELY use demographics.\n", + " '''\n", + " \n", + " trip_features_to_use = trip_kwargs.pop('trip_features', None)\n", + " trip_group_key = trip_kwargs.pop('trip_group_key', 'section_mode_argmax')\n", + " \n", + " demographics = [\n", + " 'user_id', 'primary_job_commute_time', 'income_category', 'n_residence_members', 'n_residents_u18', \n", + " 'n_residents_with_license', 'n_motor_vehicles', 'available_modes', 'age', 'gender_Man', \n", + " 'gender_Man;Nonbinary/genderqueer/genderfluid', 'gender_Nonbinary/genderqueer/genderfluid', \n", + " 'gender_Prefer not to say', 'gender_Woman', 'gender_Woman;Nonbinary/genderqueer/genderfluid', \n", + " 'has_drivers_license_No', 'has_drivers_license_Prefer not to say', 'has_drivers_license_Yes', \n", + " 'has_multiple_jobs_No', 'has_multiple_jobs_Prefer not to say', 'has_multiple_jobs_Yes', \n", + " \"highest_education_Bachelor's degree\", 'highest_education_Graduate degree or professional degree', \n", + " 'highest_education_High school graduate or GED', 'highest_education_Less than a high school graduate', \n", + " 'highest_education_Prefer not to say', 'highest_education_Some college or associates degree', \n", + " 'primary_job_type_Full-time', 'primary_job_type_Part-time', 'primary_job_type_Prefer not to say', \n", + " 'primary_job_description_Clerical or administrative support', 'primary_job_description_Custodial', \n", + " 'primary_job_description_Education', 'primary_job_description_Food service', \n", + " 'primary_job_description_Manufacturing, construction, maintenance, or farming', \n", + " 'primary_job_description_Medical/healthcare', 'primary_job_description_Other', \n", + " 'primary_job_description_Professional, managerial, or technical', \n", + " 'primary_job_description_Sales or service', 'primary_job_commute_mode_Active transport', \n", + " 'primary_job_commute_mode_Car transport', 'primary_job_commute_mode_Hybrid', \n", + " 'primary_job_commute_mode_Public transport', 'primary_job_commute_mode_Unknown', \n", + " 'primary_job_commute_mode_WFH', 'is_overnight_trip', 'n_working_residents'\n", + " ]\n", + " \n", + " # Retain only the first instance of each user and subset the columns.\n", + " filtered = df.groupby('user_id').first().reset_index(drop=False)[demographics]\n", + " \n", + " # Get the targets.\n", + " targets = df.groupby('user_id')['target'].apply(lambda x: x.value_counts().idxmax()).reset_index(drop=False)\n", + " \n", + " filtered = filtered.merge(right=targets, left_on='user_id', right_on='user_id')\n", + " \n", + " if not trip_features_to_use or len(trip_features_to_use) == 0:\n", + " # Use the available modes as indicators.\n", + " return encode_availability(filtered)\n", + " \n", + " # -----------------------------------------------------------\n", + " # Reaching here means that we need to include trip summaries\n", + " # -----------------------------------------------------------\n", + " \n", + " # If trip summaries are to be used, then re-use the preprocessed availability features.\n", + " availability = df[['user_id'] + [c for c in df.columns if 'av_' in c]]\n", + " availability = availability.groupby('user_id').first()\n", + " \n", + " # For every user, generate the global trip-level summaries.\n", + " global_aggs = df.groupby('user_id').agg({'duration': 'mean', 'distance': 'mean'})\n", + " \n", + " # coverage.\n", + " coverage = get_mode_coverage(df)\n", + " \n", + " # Trip-level features.\n", + " trip_features = get_trip_summaries(\n", + " df=df, \n", + " group_key=trip_group_key, \n", + " feature_list=trip_features_to_use\n", + " )\n", + " \n", + " targets = df.groupby('user_id')['target'].apply(lambda x: x.value_counts().idxmax())\n", + " \n", + " trip_features = trip_features.merge(right=coverage, left_index=True, right_index=True)\n", + " trip_features = trip_features.merge(right=global_aggs, left_index=True, right_index=True)\n", + " \n", + " # Finally, join with availability indicators and targets.\n", + " trip_features = trip_features.merge(right=availability, left_index=True, right_on='user_id')\n", + " trip_features = trip_features.merge(right=targets, left_index=True, right_index=True)\n", + " \n", + " return trip_features.reset_index(drop=False)" + ] + }, + { + "cell_type": "markdown", + "id": "fedb51e8", + "metadata": {}, + "source": [ + "## Experiment 1: Only demographics" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "66421120", + "metadata": {}, + "outputs": [], + "source": [ + "demo_df = get_demographic_data(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c023cf66", + "metadata": {}, + "outputs": [], + "source": [ + "# No stratification, pure random.\n", + "train, test = train_test_split(demo_df, test_size=0.2, random_state=SEED)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "376a4391", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "132 33\n" + ] + } + ], + "source": [ + "print(train.shape[0], test.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "ef77c9c8", + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate_using_similarity(test_df, train_df, metric=SimilarityMetric.COSINE, **metric_kwargs):\n", + " \n", + " '''\n", + " This method treats each user row as a 'fingerprint' (embedding vector). We assume that we\n", + " have no idea about the test set labels. To find which replaced mode is most likely for the test\n", + " users, we compute the cosine similarity of each test user against the users in the training set.\n", + " For the most similar user, we use their target as a proxy for the test user's replaced mode.\n", + " This operates on the following intuition: If User A and User B are similar, then their replaced\n", + " modes are also similar.\n", + " '''\n", + " \n", + " tr_targets = train_df.target.values\n", + " tr = train_df.drop(columns=['target', 'user_id'], inplace=False).reset_index(drop=True, inplace=False)\n", + " \n", + " te_targets = test_df.target.values\n", + " te = test_df.drop(columns=['target', 'user_id'], inplace=False).reset_index(drop=True, inplace=False)\n", + " \n", + " if metric == SimilarityMetric.COSINE:\n", + " # Use cosine similarity to determine which element in the train set this user is closest to.\n", + " # Offset the columns from the second entry to exclude the user_id column.\n", + " # Returns a (n_te, n_tr) matrix.\n", + " sim = cosine_similarity(te.values, tr.values)\n", + " \n", + " # Compute the argmax across the train set.\n", + " argmax = np.argmax(sim, axis=1)\n", + "\n", + " # Index into the training targets to retrieve predicted label.\n", + " y_test_pred = tr_targets[argmax]\n", + " \n", + " elif metric == SimilarityMetric.EUCLIDEAN:\n", + " \n", + " # Here, we choose the embedding with the smallest L2 distance.\n", + " distances = euclidean_distances(te.values, tr.values)\n", + " \n", + " # We choose argmin\n", + " argmin = np.argmin(distances, axis=1)\n", + " \n", + " # Index into the targets.\n", + " y_test_pred = tr_targets[argmin]\n", + " \n", + " elif metric == SimilarityMetric.KNN:\n", + " \n", + " # Build the KNN classifier. By default, let it be 3.\n", + " knn = KNeighborsClassifier(\n", + " n_neighbors=metric_kwargs.pop('n_neighbors', 3),\n", + " weights='distance',\n", + " metric=metric_kwargs.pop('knn_metric', 'cosine'),\n", + " n_jobs=os.cpu_count()\n", + " )\n", + " \n", + " # Fit the data to the KNN model\n", + " knn.fit(tr, tr_targets)\n", + " \n", + " y_test_pred = knn.predict(te)\n", + " \n", + " elif metric == SimilarityMetric.KMEANS:\n", + " \n", + " # Build the model.\n", + " kmeans = KMeans(\n", + " n_clusters=metric_kwargs.pop('n_clusters', 8),\n", + " max_iter=metric_kwargs.pop('max_iter', 300),\n", + " n_init='auto'\n", + " )\n", + " \n", + " # Fit the clustering model\n", + " kmeans.fit(tr)\n", + " \n", + " # Construct the auxiliary df and merge with the training set.\n", + " label_df = pd.DataFrame({'label': kmeans.labels_, 'target': tr_targets}, index=tr.index)\n", + " \n", + " # Now, perform an inference on the test set.\n", + " predicted_labels = kmeans.predict(te)\n", + " \n", + " y_test_pred = []\n", + " for prediction in predicted_labels:\n", + " most_likely = label_df.loc[label_df.label == prediction, 'target'].value_counts().idxmax()\n", + " y_test_pred.append(most_likely)\n", + " \n", + " else:\n", + " raise NotImplementedError(\"Unknown similarity metric\")\n", + " \n", + " \n", + " f1 = f1_score(y_true=te_targets, y_pred=y_test_pred, average='weighted')\n", + " print(f\"Test F1 score using {metric.name} = {f1}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "1a95ad5e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test F1 score using COSINE = 0.32806324110671936\n" + ] + } + ], + "source": [ + "evaluate_using_similarity(test, train, SimilarityMetric.COSINE)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "44cfbe29", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test F1 score using EUCLIDEAN = 0.2742577288031834\n" + ] + } + ], + "source": [ + "evaluate_using_similarity(test, train, SimilarityMetric.EUCLIDEAN)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "fe343c8b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test F1 score using KNN = 0.35950413223140487\n" + ] + } + ], + "source": [ + "evaluate_using_similarity(test, train, SimilarityMetric.KNN)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "d9681639", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test F1 score using KMEANS = 0.33347902097902093\n" + ] + } + ], + "source": [ + "evaluate_using_similarity(test, train, SimilarityMetric.KMEANS, n_clusters=3)" + ] + }, + { + "cell_type": "markdown", + "id": "16e435a6", + "metadata": {}, + "source": [ + "Not bad - using just a simple random split gives us the following results:\n", + "\n", + "$F1_{cosine} = 0.32$, $F1_{euclidean} = 0.27$, $F1_{KNN} = 0.36$, $F1_{kmeans} = 0.33$" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "81f0e842", + "metadata": {}, + "outputs": [], + "source": [ + "def custom_nll_scorer(clf, X, y):\n", + " y_pred = clf.predict_proba(X)\n", + " return -log_loss(y_true=y, y_pred=y_pred, labels=sorted(np.unique(y)))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "a3a6af8f", + "metadata": {}, + "outputs": [], + "source": [ + "def estimate_using_model(train, test):\n", + " \n", + " # Define the train-val splitter.\n", + " splitter = StratifiedKFold(n_splits=4, shuffle=True, random_state=SEED)\n", + " \n", + " params = {\n", + " 'n_estimators': np.arange(100, 1001, 50),\n", + " 'max_depth': [i for i in range(5, 101, 5)],\n", + " 'ccp_alpha': np.linspace(0, 1, 10),\n", + " 'class_weight': ['balanced', 'balanced_subsample', None],\n", + " 'min_samples_split': np.arange(2, 25, 2),\n", + " 'min_samples_leaf': np.arange(1, 25)\n", + " }\n", + " \n", + " rf = RandomForestClassifier(random_state=SEED)\n", + " \n", + " # Search over hparams to minimize negative log likelihood. \n", + " clf = RandomizedSearchCV(\n", + " rf, params, n_iter=500, scoring=custom_nll_scorer, \n", + " n_jobs=os.cpu_count(), cv=splitter, random_state=SEED,\n", + " verbose=0\n", + " )\n", + " \n", + " X_tr = train.drop(columns=['user_id', 'target'])\n", + " y_tr = train.target.values.ravel()\n", + " \n", + " scorer = clf.fit(X_tr, y_tr)\n", + " \n", + " best_model = scorer.best_estimator_\n", + " \n", + " print(f\"Best val score = {scorer.best_score_}\")\n", + " \n", + " X_te = test.drop(columns=['user_id', 'target'])\n", + " \n", + " # Use the best model to compute F1 on the test set.\n", + " test_f1 = f1_score(y_true=test.target.values, y_pred=best_model.predict(X_te), average='weighted')\n", + " \n", + " print(f\"Test F1 = {test_f1}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "2fab93ed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best val score = -1.7985827701200345\n", + "Test F1 = 0.32794612794612793\n" + ] + } + ], + "source": [ + "estimate_using_model(train, test)" + ] + }, + { + "cell_type": "markdown", + "id": "2988c1b2", + "metadata": {}, + "source": [ + "Interesting! The model is slightly on par with the cosine similarity, but actually worse than the KNN model!\n", + "\n", + "To recap, $F1_{cosine} = 0.32806$, $F1_{euclidean} = 0.27$, $F1_{KNN} = 0.36$, $F1_{kmeans} = 0.33$, $F1_{RF} = 0.328$" + ] + }, + { + "cell_type": "markdown", + "id": "c6b77353", + "metadata": {}, + "source": [ + "## Experiment 2: Demographics with trip summaries" + ] + }, + { + "cell_type": "markdown", + "id": "bf7753d4", + "metadata": {}, + "source": [ + "Now that we've performed experiments with solely demographic data, let's expand the feature set by including \n", + "trip summary statistics. We would like this approach to do better than the aforementioned baselines." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "1d46ab0f", + "metadata": {}, + "outputs": [], + "source": [ + "demo_plus_trips = get_demographic_data(\n", + " df, \n", + " trip_features=['mph', 'section_duration_argmax', 'section_distance_argmax', 'start:hour', 'end:hour']\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "6159c90a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "132 33\n" + ] + } + ], + "source": [ + "train, test = train_test_split(demo_plus_trips, test_size=0.2, random_state=SEED)\n", + "\n", + "print(train.shape[0], test.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "06e85bdd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test F1 score using COSINE = 0.3686868686868687\n" + ] + } + ], + "source": [ + "evaluate_using_similarity(test, train, SimilarityMetric.COSINE)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "5bc67e1d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test F1 score using EUCLIDEAN = 0.3338758428272495\n" + ] + } + ], + "source": [ + "evaluate_using_similarity(test, train, SimilarityMetric.EUCLIDEAN)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "3ea68a83", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test F1 score using KNN = 0.30201171377641967\n" + ] + } + ], + "source": [ + "evaluate_using_similarity(test, train, SimilarityMetric.KNN)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "52eb931b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test F1 score using KMEANS = 0.36007130124777187\n" + ] + } + ], + "source": [ + "evaluate_using_similarity(test, train, SimilarityMetric.KMEANS, n_clusters=3)" + ] + }, + { + "cell_type": "markdown", + "id": "ba795489", + "metadata": {}, + "source": [ + "Great! Marked improvement from last time. Simply incorporating trip-level features seems to increase the\n", + "accuracy of finding similar users. Now, $F1_{cosine} = 0.37$, $F1_{euclidean} = 0.33$, $F1_{knn} = 0.3$, $F1_{kmeans} = 0.36$" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "9acd4b0b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best val score = -1.8201049789158268\n", + "Test F1 = 0.42158426368952684\n" + ] + } + ], + "source": [ + "# Now, we try with the model\n", + "estimate_using_model(train, test)" + ] + }, + { + "cell_type": "markdown", + "id": "cd94c548", + "metadata": {}, + "source": [ + "Great! Compared to the previous model, we see definite improvements! I'm sure we can squeeze some more juice out of the models using fancy optimization, but as a baseline, these are good enough.\n", + "\n", + "\n", + "So, to recap:\n", + "$F1_{cosine} = 0.37$, $F1_{euclidean} = 0.33$, $F1_{knn} = 0.3$, $F1_{kmeans} = 0.36$, $F1_{RF} = 0.4215$" + ] + }, + { + "cell_type": "markdown", + "id": "ca54d1b2", + "metadata": {}, + "source": [ + "### Next objectives:\n", + "\n", + "1. Try grouping by other features, such as duration or distance\n", + "2. For similarity search, use other techniques such as clustering or KNN" + ] + }, + { + "cell_type": "markdown", + "id": "c339fcc6", + "metadata": {}, + "source": [ + "# Multi-level modeling" + ] + }, + { + "cell_type": "markdown", + "id": "213676ec", + "metadata": {}, + "source": [ + "In this approach, we want to piece together the similarity search and modeling processes. Here's a rough sketch of how it should be implemented:\n", + "\n", + "1. For every user in the training set, build a model using their entire trip history.\n", + "2. Consolidate these user-level models in data structure, preferably a dictionary.\n", + "3. Now, when we want to perform inference on a new user with no prior trips, we use the similarity search to get the user ID in the training set who is the most similar to the user in question.\n", + "4. We retrieve the model for this corresponding user and perform an inference. The hypothesis is that since the two users are similar, their trip substitution patterns are also similar." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca9e6e6a", + "metadata": {}, + "outputs": [], + "source": [ + "# def construct_model_dictionary(train: pd.DataFrame):\n", + " \n", + "# def train_on_user(user_id: str):\n", + "# '''\n", + "# Given the training set and the user ID to query, filter the dataset and\n", + "# retain only the relevant trips. Then, create folds and optimize a model for this user.\n", + "# Return the trained model instance.\n", + "# '''\n", + " \n", + "# user_data = train.loc[train.user_id == user_id, :].reset_index(drop=True)\n", + " \n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "emission", + "language": "python", + "name": "emission" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 5da3cdb7d375507538e8586f0db4fa617ac75fc0 Mon Sep 17 00:00:00 2001 From: Rahul Kulhalli Date: Mon, 1 Apr 2024 10:36:17 -0400 Subject: [PATCH 14/16] Added multi-source preprocessing notebooks --- .../multisource_data/01_extract_db_data.ipynb | 1008 +++++++++++++++++ .../02_extract_section_information.ipynb | 299 +++++ 2 files changed, 1307 insertions(+) create mode 100644 rm_src/multisource_data/01_extract_db_data.ipynb create mode 100644 rm_src/multisource_data/02_extract_section_information.ipynb diff --git a/rm_src/multisource_data/01_extract_db_data.ipynb b/rm_src/multisource_data/01_extract_db_data.ipynb new file mode 100644 index 0000000..c907842 --- /dev/null +++ b/rm_src/multisource_data/01_extract_db_data.ipynb @@ -0,0 +1,1008 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "38b147ff", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import pickle\n", + "import importlib\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "\n", + "from pathlib import Path\n", + "from uuid import UUID\n", + "from collections import defaultdict\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e550aa2b", + "metadata": {}, + "outputs": [], + "source": [ + "INCLUDE_TEST_USERS = False" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "39306a1d", + "metadata": {}, + "outputs": [], + "source": [ + "emission_path = Path(os.getcwd()).parent.parent / 'my_emission_server' / 'e-mission-server'\n", + "sys.path.append(str(emission_path))\n", + "\n", + "# Also add the home (viz_scripts) to the path\n", + "sys.path.append('../viz_scripts')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e171e277", + "metadata": {}, + "outputs": [], + "source": [ + "DB_SOURCE = [\n", + " \"Stage_database\",\n", + " \"openpath_prod_durham\",\n", + " \"openpath_prod_mm_masscec\",\n", + " \"openpath_prod_ride2own\",\n", + " # No replaced mode!\n", + " \"openpath_prod_uprm_civic\",\n", + " \"openpath_prod_uprm_nicr\"\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "70fa3112", + "metadata": {}, + "outputs": [], + "source": [ + "CURRENT_DB = DB_SOURCE[0]\n", + "\n", + "assert CURRENT_DB in DB_SOURCE" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "bbde79d1", + "metadata": {}, + "outputs": [], + "source": [ + "# ['p_micro', 'no_trip', 's_car', 'transit', 'car', 's_micro', 'ridehail', 'walk', 'unknown']\n", + "\n", + "REPLACED_MODE_DICT = {\n", + " \"Stage_database\": {\n", + " 'no_travel': 'no_trip',\n", + " 'Unknown': 'unknown',\n", + " 'bus': 'transit',\n", + " 'drove_alone': 'car',\n", + " 'bike': 'p_micro',\n", + " 'shared_ride': 's_car',\n", + " 'walk': 'walk',\n", + " 'train': 'transit',\n", + " 'bikeshare': 's_micro',\n", + " 'not_a trip': 'no_trip',\n", + " 'pilot_ebike': 'p_micro',\n", + " 'electric_car': 'car',\n", + " 'taxi': 'ridehail',\n", + " 'not_a_trip': 'no_trip',\n", + " 'run': 'walk',\n", + " 'scootershare': 's_micro',\n", + " 'tramway': 'transit',\n", + " 'free_shuttle': 'transit',\n", + " 'e-bike': 'p_micro',\n", + " 'rental_car': 'car',\n", + " 'train_+ bus': 'transit',\n", + " 'skateboard': 'p_micro',\n", + " 'snowboarding': 'p_micro',\n", + " 'e_bike': 'p_micro',\n", + " 'golf_cart': 'unknown',\n", + " 'emergency_vehicle with others': 's_car',\n", + " 'call_friend': 's_car',\n", + " 'no_replacement': 'no_travel',\n", + " 'doing_nothing': 'no_trip',\n", + " 'na': 'no_trip',\n", + " 'ebike': 'p_micro',\n", + " 'hiking': 'walk',\n", + " 'n/a': 'no_trip',\n", + " 'testing': 'unknown',\n", + " 'home': 'no_trip',\n", + " 'must_walk 3-5 mi a day for back': 'walk',\n", + " 'family': 's_car',\n", + " 'car': 'car',\n", + " 'pilot_e-bike': 'p_micro',\n", + " 'pilot_bike': 'p_micro',\n", + " 'time_spent on the clock at amazon': 'no_trip',\n", + " 'working': 'no_trip',\n", + " 'walk_at work': 'walk',\n", + " 'sitting_on my butt doing nothing': 'no_trip',\n", + " 'nothing._delivered food for work': 'no_trip',\n", + " 'train,_bus and walk': 'transit',\n", + " 'work_vehicle': 'car',\n", + " 'friend_picked me up': 's_car',\n", + " 'ski': 'p_micro',\n", + " 'not_accurate': 'unknown',\n", + " 'stolen_ebike': 'p_micro'\n", + " },\n", + " \"openpath_prod_durham\": {\n", + " 'Unknown': 'unknown',\n", + " 'bike': 'p_micro',\n", + " 'shared_ride': 's_car',\n", + " 'drove_alone': 'car',\n", + " 'bus': 'transit',\n", + " 'no_travel': 'no_trip',\n", + " 'scootershare': 's_micro',\n", + " 'walk': 'walk',\n", + " 'taxi': 'ridehail',\n", + " 'e_car_drove_alone': 'car',\n", + " 'bikeshare': 's_micro',\n", + " 'ebike': 'p_micro',\n", + " 'train': 'transit',\n", + " 'e_car_shared_ride': 's_car'\n", + " },\n", + " \"openpath_prod_mm_masscec\": {\n", + " 'Unknown': 'unknown',\n", + " 'drove_alone': 'car',\n", + " 'walk': 'walk',\n", + " 'shared_ride': 's_car',\n", + " 'bike': 'p_micro',\n", + " 'bikeshare': 's_micro',\n", + " 'no_travel': 'no_trip',\n", + " 'taxi': 'ridehail',\n", + " 'bus': 'transit',\n", + " 'scootershare': 's_micro',\n", + " 'train': 'transit',\n", + " 'walking': 'walk',\n", + " 'e_car_drove_alone': 'car'\n", + " },\n", + " \"openpath_prod_ride2own\": {\n", + " 'Unknown': 'unknown',\n", + " 'drove_alone': 'car',\n", + " 'walk': 'walk',\n", + " 'shared_ride': 's_car',\n", + " 'bike': 'p_micro',\n", + " 'no_travel': 'no_trip',\n", + " 'taxi': 'ridehail',\n", + " 'bus': 'transit',\n", + " 'train': 'transit',\n", + " 'e_car_drove_alone': 'car',\n", + " 'e_car_shared_ride': 's_car'\n", + " },\n", + " \"openpath_prod_uprm_nicr\": {\n", + " 'Unknown': 'unknown',\n", + " 'walk': 'walk',\n", + " 'drove_alone': 'car'\n", + " }\n", + "}\n", + "\n", + "SURVEY_DATA_DICT = {\n", + " \"Stage_database\": {\n", + " \"Unique User ID (auto-filled, do not edit)\": \"user_id\",\n", + " \"In which year were you born?\": \"birth_year\",\n", + " \"What is your gender?\": \"gender\",\n", + " \"Do you have a valid driver's license?\": \"has_drivers_license\",\n", + " \"Are you a student?\": \"is_student\",\n", + " \"What is the highest grade or degree that you have completed?\": \"highest_education\",\n", + " \"Do you work for either pay or profit?\": \"is_paid\",\n", + " \"Do you have more than one job?\": \"has_multiple_jobs\",\n", + " \"Do you work full-time or part-time at your primary job?\": \"primary_job_type\",\n", + " \"Which best describes your primary job?\": \"primary_job_description\",\n", + " \"How did you usually get to your primary job last week? \": \"primary_job_commute_mode\",\n", + " \"Thinking about your daily commute to work last week, how many minutes did it usually take to get from home to the primary job/work place?\": \"primary_job_commute_time\",\n", + " \"At your primary job, do you have the ability to set or change your own start time?\": \"is_primary_job_flexible\",\n", + " \"Do you have the option of working from home or an alternate location instead of going into your primary work place?\": \"primary_job_can_wfh\",\n", + " \"How many days per week do you usually work from home or an alternate location?\": \"wfh_days\",\n", + " \"Do you own or rent your place of residence?\": \"residence_ownership_type\",\n", + " \"What is your home type?\": \"residence_type\",\n", + " \"Please identify which category represents your total household income, before taxes, for last year.\": \"income_category\",\n", + " \"Including yourself, how many people live in your home?\": \"n_residence_members\",\n", + " \"How many children under age 18 live in your home?\": \"n_residents_u18\",\n", + " \"Including yourself, how many people have a driver's license in your household?\": \"n_residents_with_license\",\n", + " \"How many motor vehicles are owned, leased, or available for regular use by the people who currently live in your household?\": \"n_motor_vehicles\",\n", + " \"If you were unable to use your household vehicle(s), which of the following options would be available to you to get you from place to place?\": \"available_modes\",\n", + " \"Do you have a medical condition that makes it difficult to travel outside of the home?\": \"has_medical_condition\",\n", + " \"How long have you had this condition?\": \"medical_condition_duration\"\n", + " },\n", + " # Retrieved from: e-mission-phone/survey-resources/data-xls/demo-survey-v1.xlsx\n", + " \"openpath_prod_durham\": {\n", + " \"At_your_primary_job_do_you_ha\": \"is_primary_job_flexible\",\n", + " \"Which_best_describes_your_prim\": \"primary_job_description\",\n", + " \"Do_you_work_full_time_or_part_\": \"primary_job_type\",\n", + " \"Do_you_have_the_option_of_work\": \"primary_job_can_wfh\",\n", + " \"Please_describe_your_primary_job\": \"primary_job_description\",\n", + " \"Do_you_have_more_than_one_job\": \"has_multiple_jobs\",\n", + " # Two columns: how many days/week do you work & what days of the week do you work. \n", + " # the latter has only 4 NA values, the former has 45 NA values.\n", + " \"What_days_of_the_week_do_you_t\": \"wfh_days\",\n", + " \"How_many_days_do_you_usually_w_001\": \"n_wfh_days\",\n", + " # All these are NAs.\n", + " \"Which_one_below_describe_you_b\": \"description\",\n", + " \"What_is_your_race_ethnicity\": \"race_or_ethnicity\",\n", + " \"Are_you_a_student\": \"is_student\",\n", + " \"What_is_the_highest_grade_or_d\": \"highest_education\",\n", + " \"do_you_consider_yourself_to_be\": \"is_transgender\",\n", + " \"What_is_your_gender\": \"gender\",\n", + " \"How_old_are_you\": \"age\",\n", + " \"Are_you_a_paid_worker\": \"is_paid\",\n", + " \"Do_you_have_a_driver_license\": \"has_drivers_license\",\n", + " \"How_long_you_had_this_conditio\": \"medical_condition_duration\",\n", + " \"Including_yourself_how_many_w_001\": \"n_residents_u18\",\n", + " \"Including_yourself_how_many_p\": \"n_residence_members\",\n", + " \"Do_you_own_or_rent_your_home\": \"residence_ownership_type\",\n", + " \"Please_identify_which_category\": \"income_category\",\n", + " \"If_you_were_unable_to_use_your\": \"available_modes\",\n", + " \"Including_yourself_how_many_p_001\": \"n_residents_with_license\",\n", + " \"Including_yourself_how_many_w\": \"n_working_residents\",\n", + " \"What_is_your_home_type\": \"residence_type\",\n", + " \"How_many_motor_vehicles_are_ow\": \"n_motor_vehicles\",\n", + " \"Do_you_have_a_condition_or_han\": \"has_medical_condition\"\n", + " },\n", + " \"openpath_prod_mm_masscec\": {\n", + " # Same questions as Durham.\n", + " \"At_your_primary_job_do_you_ha\": \"is_primary_job_flexible\",\n", + " \"Which_best_describes_your_prim\": \"primary_job_description\",\n", + " \"Do_you_work_full_time_or_part_\": \"primary_job_type\",\n", + " \"Do_you_have_the_option_of_work\": \"primary_job_can_wfh\",\n", + " \"Please_describe_your_primary_job\": \"primary_job_description\",\n", + " \"Do_you_have_more_than_one_job\": \"has_multiple_jobs\",\n", + " # Two columns: how many days/week do you work & what days of the week do you work. \n", + " # the latter has only 4 NA values, the former has 45 NA values.\n", + " \"What_days_of_the_week_do_you_t\": \"wfh_days\",\n", + " \"How_many_days_do_you_usually_w_001\": \"n_wfh_days\",\n", + " # All these are NAs.\n", + " \"Which_one_below_describe_you_b\": \"description\",\n", + " \"What_is_your_race_ethnicity\": \"race_or_ethnicity\",\n", + " \"Are_you_a_student\": \"is_student\",\n", + " \"What_is_the_highest_grade_or_d\": \"highest_education\",\n", + " \"do_you_consider_yourself_to_be\": \"is_transgender\",\n", + " \"What_is_your_gender\": \"gender\",\n", + " \"How_old_are_you\": \"age\",\n", + " \"Are_you_a_paid_worker\": \"is_paid\",\n", + " \"Do_you_have_a_driver_license\": \"has_drivers_license\",\n", + " \"How_long_you_had_this_conditio\": \"medical_condition_duration\",\n", + " \"Including_yourself_how_many_w_001\": \"n_residents_u18\",\n", + " \"Including_yourself_how_many_p\": \"n_residence_members\",\n", + " \"Do_you_own_or_rent_your_home\": \"residence_ownership_type\",\n", + " \"Please_identify_which_category\": \"income_category\",\n", + " \"If_you_were_unable_to_use_your\": \"available_modes\",\n", + " \"Including_yourself_how_many_p_001\": \"n_residents_with_license\",\n", + " \"Including_yourself_how_many_w\": \"n_working_residents\",\n", + " \"What_is_your_home_type\": \"residence_type\",\n", + " \"How_many_motor_vehicles_are_ow\": \"n_motor_vehicles\",\n", + " \"Do_you_have_a_condition_or_han\": \"has_medical_condition\"\n", + " },\n", + " \"openpath_prod_ride2own\": {\n", + " # Same questions as Durham.\n", + " \"How_old_are_you\": \"age\",\n", + " \"What_is_your_gender\": \"gender\",\n", + " \"do_you_consider_yourself_to_be\": \"is_transgender\",\n", + " \"What_is_your_race_ethnicity\": \"race_or_ethnicity\",\n", + " \"Do_you_have_a_driver_license\": \"has_drivers_license\",\n", + " \"Are_you_a_student\": \"is_student\",\n", + " \"What_is_the_highest_grade_or_d\": \"highest_education\",\n", + " \"Are_you_a_paid_worker\": \"is_paid\",\n", + " \"Which_one_below_describe_you_b\": \"description\",\n", + " \"Do_you_own_or_rent_your_home\": \"residence_ownership_type\",\n", + " \"What_is_your_home_type\": \"residence_type\",\n", + " \"Please_identify_which_category\": \"income_category\",\n", + " \"Including_yourself_how_many_p\": \"n_residence_members\",\n", + " \"Including_yourself_how_many_w\": \"n_working_residents\",\n", + " \"Including_yourself_how_many_p_001\": \"n_residents_with_license\",\n", + " \"Including_yourself_how_many_w_001\": \"n_residents_u18\",\n", + " \"How_many_motor_vehicles_are_ow\": \"n_motor_vehicles\",\n", + " \"If_you_were_unable_to_use_your\": \"available_modes\",\n", + " \"Do_you_have_a_condition_or_han\": \"has_medical_condition\",\n", + " \"How_long_you_had_this_conditio\": \"medical_condition_duration\",\n", + " \"Do_you_have_more_than_one_job\": \"has_multiple_jobs\",\n", + " \"Do_you_work_full_time_or_part_\": \"primary_job_type\",\n", + " \"Which_best_describes_your_prim\": \"primary_job_description\",\n", + " \"Please_describe_your_primary_job\": \"primary_job_description\",\n", + " \"At_your_primary_job_do_you_ha\": \"is_primary_job_flexible\",\n", + " \"Do_you_have_the_option_of_work\": \"primary_job_can_wfh\",\n", + " \"How_many_days_do_you_usually_w_001\": \"n_wfh_days\",\n", + " \"What_days_of_the_week_do_you_t\": \"wfh_days\"\n", + " },\n", + " \"openpath_prod_uprm_nicr\": {\n", + " # Same as Durham!\n", + " \"At_your_primary_job_do_you_ha\": \"is_primary_job_flexible\",\n", + " \"Which_best_describes_your_prim\": \"primary_job_description\",\n", + " \"Do_you_work_full_time_or_part_\": \"primary_job_type\",\n", + " \"Do_you_have_the_option_of_work\": \"primary_job_can_wfh\",\n", + " \"Please_describe_your_primary_job\": \"primary_job_description\",\n", + " \"Do_you_have_more_than_one_job\": \"has_multiple_jobs\",\n", + " # Two columns: how many days/week do you work & what days of the week do you work. \n", + " # the latter has only 4 NA values, the former has 45 NA values.\n", + " \"What_days_of_the_week_do_you_t\": \"wfh_days\",\n", + " \"How_many_days_do_you_usually_w_001\": \"n_wfh_days\",\n", + " # All these are NAs.\n", + " \"Which_one_below_describe_you_b\": \"description\",\n", + " \"What_is_your_race_ethnicity\": \"race_or_ethnicity\",\n", + " \"Are_you_a_student\": \"is_student\",\n", + " \"What_is_the_highest_grade_or_d\": \"highest_education\",\n", + " \"do_you_consider_yourself_to_be\": \"is_transgender\",\n", + " \"What_is_your_gender\": \"gender\",\n", + " \"How_old_are_you\": \"age\",\n", + " \"Are_you_a_paid_worker\": \"is_paid\",\n", + " \"Do_you_have_a_driver_license\": \"has_drivers_license\",\n", + " \"How_long_you_had_this_conditio\": \"medical_condition_duration\",\n", + " \"Including_yourself_how_many_w_001\": \"n_residents_u18\",\n", + " \"Including_yourself_how_many_p\": \"n_residence_members\",\n", + " \"Do_you_own_or_rent_your_home\": \"residence_ownership_type\",\n", + " \"Please_identify_which_category\": \"income_category\",\n", + " \"If_you_were_unable_to_use_your\": \"available_modes\",\n", + " \"Including_yourself_how_many_p_001\": \"n_residents_with_license\",\n", + " \"Including_yourself_how_many_w\": \"n_working_residents\",\n", + " \"What_is_your_home_type\": \"residence_type\",\n", + " \"How_many_motor_vehicles_are_ow\": \"n_motor_vehicles\",\n", + " \"Do_you_have_a_condition_or_han\": \"has_medical_condition\"\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4df64ba8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'birth_year',\n", + " 'primary_job_commute_mode',\n", + " 'primary_job_commute_time',\n", + " 'user_id'}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set(SURVEY_DATA_DICT[\"Stage_database\"].values()).difference(\n", + " set(SURVEY_DATA_DICT[\"openpath_prod_durham\"].values())\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "69008893", + "metadata": {}, + "outputs": [], + "source": [ + "## Source: db_utils.py in op-admin-dashboard.\n", + "\n", + "BINARY_DEMOGRAPHICS_COLS = [\n", + " 'user_id',\n", + " '_id',\n", + "]\n", + "\n", + "EXCLUDED_DEMOGRAPHICS_COLS = [\n", + " 'data.xmlResponse', \n", + " 'data.name',\n", + " 'data.version',\n", + " 'data.label',\n", + " 'xmlns:jr',\n", + " 'xmlns:orx',\n", + " 'id',\n", + " 'start',\n", + " 'end',\n", + " 'attrxmlns:jr',\n", + " 'attrxmlns:orx',\n", + " 'attrid',\n", + " '__version__',\n", + " 'attrversion',\n", + " 'instanceID',\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "4357d455", + "metadata": {}, + "source": [ + "Ensure that you point the database to the appropriate name" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "255b8896", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "storage not configured, falling back to sample, default configuration\n", + "URL not formatted, defaulting to Stage_database\n", + "Connecting to database URL localhost\n" + ] + } + ], + "source": [ + "import scaffolding\n", + "import emission.core.get_database as edb\n", + "import emission.storage.timeseries.abstract_timeseries as esta" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "12cc0c54", + "metadata": {}, + "outputs": [], + "source": [ + "## Source: scaffolding.py\n", + "\n", + "def expand_userinputs(labeled_ct):\n", + " '''\n", + " param: labeled_ct: a dataframe of confirmed trips, some of which have labels\n", + " params: labels_per_trip: the number of labels for each trip.\n", + " Currently, this is 2 for studies and 3 for programs, and should be \n", + " passed in by the notebook based on the input config.\n", + " If used with a trip-level survey, it could be even larger.\n", + " '''\n", + " # CASE 1 of https://github.com/e-mission/em-public-dashboard/issues/69#issuecomment-1256835867\n", + " if len(labeled_ct) == 0:\n", + " return labeled_ct\n", + " label_only = pd.DataFrame(labeled_ct.user_input.to_list(), index=labeled_ct.index)\n", + " # disp.display(label_only.head())\n", + " labels_per_trip = len(label_only.columns)\n", + " print(\"Found %s columns of length %d\" % (label_only.columns, labels_per_trip))\n", + " expanded_ct = pd.concat([labeled_ct, label_only], axis=1)\n", + " assert len(expanded_ct) == len(labeled_ct), \\\n", + " (\"Mismatch after expanding labels, expanded_ct.rows = %s != labeled_ct.rows %s\" %\n", + " (len(expanded_ct), len(labeled_ct)))\n", + " print(\"After expanding, columns went from %s -> %s\" %\n", + " (len(labeled_ct.columns), len(expanded_ct.columns)))\n", + " assert len(expanded_ct.columns) == len(labeled_ct.columns) + labels_per_trip, \\\n", + " (\"Mismatch after expanding labels, expanded_ct.columns = %s != labeled_ct.columns %s\" %\n", + " (len(expanded_ct.columns), len(labeled_ct.columns)))\n", + " # disp.display(expanded_ct.head())\n", + " return expanded_ct" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "9a98e2fb", + "metadata": {}, + "outputs": [], + "source": [ + "## Source: scaffolding.py\n", + "\n", + "def data_quality_check(expanded_ct):\n", + " '''1. Delete rows where the mode_confirm was pilot_ebike and repalced_mode was pilot_ebike.\n", + " 2. Delete rows where the mode_confirm was pilot_ebike and repalced_mode was same_mode.\n", + " 3. Replace same_mode for the mode_confirm for Energy Impact Calcualtion.'''\n", + "\n", + " # TODO: This is only really required for the initial data collection around the minipilot\n", + " # in subsequent deployes, we removed \"same mode\" and \"pilot_ebike\" from the options, so the\n", + " # dataset did not contain of these data quality issues\n", + "\n", + " if 'replaced_mode' in expanded_ct.columns:\n", + " expanded_ct.drop(expanded_ct[(expanded_ct['mode_confirm'] == 'pilot_ebike') & (expanded_ct['replaced_mode'] == 'pilot_ebike')].index, inplace=True)\n", + " expanded_ct.drop(expanded_ct[(expanded_ct['mode_confirm'] == 'pilot_ebike') & (expanded_ct['replaced_mode'] == 'same_mode')].index, inplace=True)\n", + " expanded_ct['replaced_mode'] = np.where(expanded_ct['replaced_mode'] == 'same_mode',expanded_ct['mode_confirm'], expanded_ct['replaced_mode'])\n", + " \n", + " return expanded_ct" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "fe37bf27", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Before filtering, length=241123\n", + "After filtering, length=241123\n", + "Found Index(['mode_confirm', 'purpose_confirm', 'replaced_mode'], dtype='object') columns of length 3\n", + "After expanding, columns went from 41 -> 44\n", + "['source', 'end_ts', 'end_fmt_time', 'end_loc', 'raw_trip', 'start_ts', 'start_fmt_time', 'start_loc', 'duration', 'distance', 'start_place', 'end_place', 'cleaned_trip', 'inferred_labels', 'inferred_trip', 'expectation', 'confidence_threshold', 'expected_trip', 'user_input', 'section_modes', 'section_distances', 'start_local_dt_year', 'start_local_dt_month', 'start_local_dt_day', 'start_local_dt_hour', 'start_local_dt_minute', 'start_local_dt_second', 'start_local_dt_weekday', 'start_local_dt_timezone', 'end_local_dt_year', 'end_local_dt_month', 'end_local_dt_day', 'end_local_dt_hour', 'end_local_dt_minute', 'end_local_dt_second', 'end_local_dt_weekday', 'end_local_dt_timezone', '_id', 'user_id', 'metadata_write_ts', 'additions', 'mode_confirm', 'purpose_confirm', 'replaced_mode']\n" + ] + } + ], + "source": [ + "## Source: scaffolding.py\n", + "\n", + "uuid_df = pd.json_normalize(list(edb.get_uuid_db().find()))\n", + "\n", + "if not INCLUDE_TEST_USERS:\n", + " uuid_df = uuid_df.loc[~uuid_df.user_email.str.contains('_test_'), :]\n", + "\n", + "filtered = uuid_df.uuid.unique()\n", + "\n", + "agg = esta.TimeSeries.get_aggregate_time_series()\n", + "all_ct = agg.get_data_df(\"analysis/confirmed_trip\", None)\n", + "\n", + "print(f\"Before filtering, length={len(all_ct)}\")\n", + "participant_ct_df = all_ct.loc[all_ct.user_id.isin(filtered), :]\n", + "print(f\"After filtering, length={len(participant_ct_df)}\")\n", + "\n", + "expanded_ct = expand_userinputs(participant_ct_df)\n", + "expanded_ct = data_quality_check(expanded_ct)\n", + "print(expanded_ct.columns.tolist())\n", + "expanded_ct['replaced_mode'] = expanded_ct['replaced_mode'].fillna('Unknown')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "13536d14", + "metadata": {}, + "outputs": [], + "source": [ + "# # Additional preprocessing for replaced mode (if any)\n", + "\n", + "mode_counts = expanded_ct['replaced_mode'].value_counts()\n", + "drop_modes = mode_counts[mode_counts == 1].index.tolist()\n", + "\n", + "expanded_ct.drop(\n", + " index=expanded_ct.loc[expanded_ct.replaced_mode.isin(drop_modes)].index,\n", + " inplace=True\n", + ")\n", + "\n", + "# Additional modes to drop.\n", + "expanded_ct.drop(\n", + " index=expanded_ct.loc[expanded_ct.replaced_mode.isin(\n", + " # Remove all rows with air, boat, or weird answers.\n", + " ['houseboat', 'gondola', 'airline_flight', 'aircraft', 'zoo', 'air',\n", + " 'airplane', 'boat', 'flight', 'plane', 'meal', 'lunch']\n", + " )].index,\n", + " inplace=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2b56af00", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['no_travel' 'Unknown' 'bus' 'drove_alone' 'bike' 'shared_ride' 'walk'\n", + " 'train' 'bikeshare' 'not_a trip' 'pilot_ebike' 'electric_car' 'taxi'\n", + " 'not_a_trip' 'run' 'scootershare' 'tramway' 'free_shuttle' 'e-bike'\n", + " 'rental_car' 'train_+ bus' 'skateboard' 'e_bike' 'golf_cart'\n", + " 'emergency_vehicle with others' 'call_friend' 'no_replacement'\n", + " 'doing_nothing' 'na' 'ebike' 'hiking' 'ski' 'not_accurate' 'pilot_bike'\n", + " 'snowboarding' 'stolen_ebike' 'n/a' 'testing' 'home'\n", + " 'must_walk 3-5 mi a day for back' 'family' 'car' 'pilot_e-bike'\n", + " 'time_spent on the clock at amazon' 'working' 'walk_at work'\n", + " 'sitting_on my butt doing nothing' 'nothing._delivered food for work'\n", + " 'train,_bus and walk' 'work_vehicle' 'friend_picked me up']\n" + ] + } + ], + "source": [ + "print(expanded_ct.replaced_mode.unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "757ccbee", + "metadata": {}, + "outputs": [], + "source": [ + "expanded_ct.replaced_mode = expanded_ct.replaced_mode.apply(lambda x: REPLACED_MODE_DICT[CURRENT_DB][x])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c878ad12", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['no_trip' 'unknown' 'transit' 'car' 'p_micro' 's_car' 'walk' 's_micro'\n", + " 'ridehail' 'no_travel']\n" + ] + } + ], + "source": [ + "print(expanded_ct.replaced_mode.unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "7461a4d2", + "metadata": {}, + "outputs": [], + "source": [ + "# Demographics\n", + "\n", + "if CURRENT_DB != \"Stage_database\":\n", + "\n", + " decoded_uuids = [str(x) for x in filtered]\n", + "\n", + " ## Source: query_demographics() in op-admin-dashboard.\n", + " ts = esta.TimeSeries.get_aggregate_time_series()\n", + " entries = list(ts.find_entries([\"manual/demographic_survey\"]))\n", + "\n", + " available_key = {}\n", + " for entry in entries:\n", + " survey_key = list(entry['data']['jsonDocResponse'].keys())[0]\n", + " if survey_key not in available_key:\n", + " available_key[survey_key] = []\n", + "\n", + " # Minor modification: Added user_id check to filter users.\n", + " if str(entry['user_id']) in decoded_uuids:\n", + " available_key[survey_key].append(entry)\n", + "\n", + " dataframes = {}\n", + " for key, json_object in available_key.items():\n", + " df = pd.json_normalize(json_object)\n", + " dataframes[key] = df\n", + "\n", + " for key, df in dataframes.items():\n", + " if not df.empty:\n", + " for col in BINARY_DEMOGRAPHICS_COLS:\n", + " if col in df.columns:\n", + " df[col] = df[col].apply(str) \n", + " columns_to_drop = [col for col in df.columns if col.startswith(\"metadata\")]\n", + " df.drop(columns= columns_to_drop, inplace=True) \n", + " df.columns=[col.rsplit('.',1)[-1] if col.startswith('data.jsonDocResponse.') else col for col in df.columns]\n", + " for col in EXCLUDED_DEMOGRAPHICS_COLS:\n", + " if col in df.columns:\n", + " df.drop(columns= [col], inplace=True)\n", + "\n", + " survey_data = pd.DataFrame() \n", + " for v in dataframes.values():\n", + " survey_data = pd.concat([survey_data, v], axis=0, ignore_index=True)\n", + "else:\n", + " # Read the demographics.\n", + " survey_data = pd.read_csv('../viz_scripts/Can Do Colorado eBike Program - en.csv')\n", + " survey_data.rename(columns={'Unique User ID (auto-filled, do not edit)': 'user_id'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "a6be751e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "203 261\n" + ] + } + ], + "source": [ + "print(len(survey_data.user_id.unique()), len(expanded_ct.user_id.unique()))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "9ebc87d8", + "metadata": {}, + "outputs": [], + "source": [ + "survey_data.rename(SURVEY_DATA_DICT[CURRENT_DB], axis='columns', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "86bd8337", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " user_id\n", + "0 a2d48b05d5454d428c0841432c7467b6\n", + "1 f2799dc202bc4249b42a4fda8770d1b6\n", + "2 b2bbe715b6a14fd19f751cae8adf6b4e\n", + "3 6373dfb8cb9b47e88e8f76adcfadde20\n", + "4 93c6e0f156a44e07b920ded664419dc6" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "display(survey_data[['user_id']].head())\n", + "print(type(survey_data['user_id'][0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "189ab848", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " user_id\n", + "0 8a0473ca-e53d-4720-a99c-0696cc1fb407\n", + "1 0a093cbd-b536-43af-b03d-293425e84c76\n", + "2 0a093cbd-b536-43af-b03d-293425e84c76\n", + "3 0a093cbd-b536-43af-b03d-293425e84c76\n", + "4 0a093cbd-b536-43af-b03d-293425e84c76" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "display(expanded_ct[['user_id']].head())\n", + "print(type(expanded_ct['user_id'][0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "c7eb2e09", + "metadata": {}, + "outputs": [], + "source": [ + "# Additional preprocessing to filter unwanted users from sensed trips data.\n", + "ct_users = expanded_ct['user_id'].apply(lambda x: str(x).replace('-', ''))\n", + "survey_users = survey_data['user_id'].apply(lambda x: str(x).replace('-', ''))\n", + "\n", + "common = set(ct_users.unique()).intersection(set(survey_users.unique()))\n", + "\n", + "filtered_trips = expanded_ct.loc[ct_users.isin(common), :].reset_index(drop=True)\n", + "filtered_survey = survey_data.loc[survey_users.isin(common), :].reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "1ca22a08", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[trip data] After filtering, size goes from 240962 -> 205388\n", + "[trip data] Unique users after filtering: 184\n", + "[survey data] After filtering, size goes from 310 -> 286\n", + "[survey data] Unique users after filtering: 184\n" + ] + } + ], + "source": [ + "print(f\"[trip data] After filtering, size goes from {expanded_ct.shape[0]} -> {filtered_trips.shape[0]}\")\n", + "print(f\"[trip data] Unique users after filtering: {len(filtered_trips.user_id.unique())}\")\n", + "\n", + "print(f\"[survey data] After filtering, size goes from {survey_users.shape[0]} -> {filtered_survey.shape[0]}\")\n", + "print(f\"[survey data] Unique users after filtering: {len(filtered_survey.user_id.unique())}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "b48cd6e2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "uuid.UUID" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(filtered_trips.user_id[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86deb40c", + "metadata": {}, + "outputs": [], + "source": [ + "# raw data dump.\n", + "expanded_ct.to_csv(f'../data/raw_data/trips__{CURRENT_DB}.csv', index=False)\n", + "survey_data.to_csv(f'../data/raw_data/survey__{CURRENT_DB}.csv', index=False)\n", + "\n", + "# filtered data dump.\n", + "filtered_trips.to_csv(f'../data/filtered_data/trips__{CURRENT_DB}.csv', index=False)\n", + "filtered_survey.to_csv(f'../data/filtered_data/survey__{CURRENT_DB}.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1b140b9", + "metadata": {}, + "outputs": [], + "source": [ + "# durham has section modes and section distances.\n", + "# masscec does not have section distance and section modes.\n", + "# ride2own does not have section distances and modes.\n", + "# uprm nicr also does not have section distances and modes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "062c18c7", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea248509", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "emission", + "language": "python", + "name": "emission" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/rm_src/multisource_data/02_extract_section_information.ipynb b/rm_src/multisource_data/02_extract_section_information.ipynb new file mode 100644 index 0000000..ee5c2ac --- /dev/null +++ b/rm_src/multisource_data/02_extract_section_information.ipynb @@ -0,0 +1,299 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "e3166085", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import pickle\n", + "import ast\n", + "import importlib\n", + "# from multiprocessing import cpu_count, Pool\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "\n", + "from enum import Enum\n", + "from pathlib import Path\n", + "from uuid import UUID\n", + "from bson.objectid import ObjectId\n", + "from collections import defaultdict\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5d482225", + "metadata": {}, + "outputs": [], + "source": [ + "emission_path = Path(os.getcwd()).parent.parent / 'my_emission_server' / 'e-mission-server'\n", + "sys.path.append(str(emission_path))\n", + "\n", + "# Also add the home (viz_scripts) to the path\n", + "sys.path.append('../viz_scripts')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cf187095", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "storage not configured, falling back to sample, default configuration\n", + "URL not formatted, defaulting to openpath_prod_ride2own\n", + "Connecting to database URL localhost\n" + ] + } + ], + "source": [ + "import scaffolding\n", + "\n", + "import emission.core.get_database as edb\n", + "import emission.storage.timeseries.abstract_timeseries as esta\n", + "import emission.storage.decorations.trip_queries as esdt" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5eba5f02", + "metadata": {}, + "outputs": [], + "source": [ + "class Sections(Enum):\n", + " DURATION = 0\n", + " DISTANCE = 1\n", + " MODE = 2\n", + " \n", + "# A lightweight wrapper around the lists to ease management.\n", + "class SectionData:\n", + " def __init__(self, section_dict):\n", + " self._section_dict = section_dict\n", + " \n", + " def get_section_data(self, section: Sections):\n", + " if section not in self._section_dict:\n", + " return []\n", + " return self._section_dict[section]\n", + " \n", + "SENSED_MODE_DICT = {\n", + " 'IN_VEHICLE': 'car',\n", + " 'BICYCLING': 'p_micro',\n", + " ''\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "fcd776e5", + "metadata": {}, + "outputs": [], + "source": [ + "REQUIRED = {\n", + " \"Stage_database\": [Sections.DURATION],\n", + " \"openpath_prod_durham\": [Sections.DURATION],\n", + " \"openpath_prod_mm_masscec\": [Sections.DURATION, Sections.DISTANCE, Sections.MODE],\n", + " \"openpath_prod_ride2own\": [Sections.DURATION, Sections.DISTANCE, Sections.MODE],\n", + " \"openpath_prod_uprm_nicr\": [Sections.DURATION, Sections.DISTANCE, Sections.MODE]\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "be70ead8", + "metadata": {}, + "outputs": [], + "source": [ + "def get_section_data(df: pd.DataFrame, db_name: str):\n", + " \n", + " def _get_section_data(row):\n", + " \n", + " user_id, trip_id = UUID(row.user_id), ObjectId(row.cleaned_trip)\n", + " \n", + " section_data = esdt.get_sections_for_trip(\n", + " key='analysis/cleaned_section', user_id=user_id, trip_id=trip_id\n", + " )\n", + " \n", + " if section_data is None or len(section_data) == 0:\n", + " section_data = esdt.get_sections_for_trip(\n", + " key='analysis/inferred_section', user_id=user_id, trip_id=trip_id\n", + " )\n", + " \n", + " if section_data is None or len(section_data) == 0:\n", + " return None\n", + " \n", + " required = REQUIRED[db_name]\n", + " \n", + " section_dict = dict()\n", + " \n", + " if Sections.DURATION in required:\n", + " section_dict[Sections.DURATION] = [x.data.duration for x in section_data]\n", + " else:\n", + " section_dict[Sections.DURATION] = ast.literal_eval(x.section_durations)\n", + " \n", + " if Sections.DISTANCE in required:\n", + " section_dict[Sections.DISTANCE] = [x.data.distance for x in section_data]\n", + " else:\n", + " section_dict[Sections.DISTANCE] = ast.literal_eval(x.section_distances)\n", + " \n", + " if Sections.MODE in required:\n", + " section_dict[Sections.MODE] = [x.data.sensed_mode for x in section_data]\n", + " else:\n", + " section_dict[Sections.MODE] = ast.literal_eval(x.section_modes)\n", + " \n", + " return SectionData(section_dict)\n", + " \n", + " \n", + " section_series = df.apply(lambda x: _get_section_data(x), axis=1)\n", + " \n", + " return section_series" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "27cd85fd", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_33228/382724123.py:41: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " sections.loc[:, 'user_id'] = sections.loc[:, 'user_id'].apply(lambda x: UUID(x))\n", + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_33228/382724123.py:42: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " sections.loc[:, 'cleaned_trip'] = sections.loc[:, 'cleaned_trip'].apply(lambda x: ObjectId(x))\n" + ] + } + ], + "source": [ + "df = pd.read_csv('../data/filtered_data/trips__openpath_prod_ride2own.csv')\n", + "\n", + "section_info = get_section_data(df, 'openpath_prod_ride2own')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0ff2d17f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 <__main__.SectionData object at 0x165720d00>\n", + "1 <__main__.SectionData object at 0x165720880>\n", + "2 <__main__.SectionData object at 0x1657209d0>\n", + "3 <__main__.SectionData object at 0x165720f70>\n", + "4 <__main__.SectionData object at 0x165720fa0>\n", + " ... \n", + "5817 <__main__.SectionData object at 0x1659ebd60>\n", + "5818 <__main__.SectionData object at 0x1659eb400>\n", + "5819 <__main__.SectionData object at 0x1659ebf70>\n", + "5820 <__main__.SectionData object at 0x1659ebf10>\n", + "5821 <__main__.SectionData object at 0x1659eba60>\n", + "Length: 5822, dtype: object" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "section_info" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "938db9b9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{, , , , , }\n" + ] + } + ], + "source": [ + "modes = set()\n", + "for s in section_info:\n", + " modes.update(s.get_section_data(Sections.MODE))\n", + "\n", + "print(modes)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d30a8d58", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_argmax(db_name: str, df:pd.DataFrame, sections: pd.Series):\n", + " # Evaluate once.\n", + " distance_mask = Sections.DISTANCE in REQUIRED[db_name]\n", + " mode_mask = Sections.MODE in REQUIRED[db_name]\n", + " \n", + " for ix, section in enumerate(sections):\n", + " distance = ast.literal_eval(\n", + " section.get_section_data(Sections.DISTANCE) if distance_mask else df.loc[ix, 'section_distances']\n", + " )\n", + " \n", + " duration = ast.literal_eval(section.get_section_data(Sections.DISTANCE))\n", + " \n", + " mode = ast.literal_eval(\n", + " section.get_section_data(Sections.DISTANCE) if distance_mask else df.loc[ix, 'section_modes']\n", + " )\n", + " \n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "emission", + "language": "python", + "name": "emission" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 483ccb6e690bfd0c882faaef4e4a8b1c022f493b Mon Sep 17 00:00:00 2001 From: Rahul Kulhalli Date: Mon, 29 Apr 2024 11:30:23 -0400 Subject: [PATCH 15/16] Added all four steps for user-level modeling --- rm_src/01_extract_db_data.ipynb | 1672 ++++++++++ ...riments.py => 02_run_trip_level_models.py} | 249 +- rm_src/03_user_level_models.ipynb | 2743 +++++++++++++++++ rm_src/04_FeatureClustering.ipynb | 1419 +++++++++ rm_src/UserLevelModeling.ipynb | 869 ------ 5 files changed, 5939 insertions(+), 1013 deletions(-) create mode 100644 rm_src/01_extract_db_data.ipynb rename rm_src/{run_baseline_experiments.py => 02_run_trip_level_models.py} (55%) create mode 100644 rm_src/03_user_level_models.ipynb create mode 100644 rm_src/04_FeatureClustering.ipynb delete mode 100644 rm_src/UserLevelModeling.ipynb diff --git a/rm_src/01_extract_db_data.ipynb b/rm_src/01_extract_db_data.ipynb new file mode 100644 index 0000000..619373b --- /dev/null +++ b/rm_src/01_extract_db_data.ipynb @@ -0,0 +1,1672 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "38b147ff", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import ast\n", + "import sys\n", + "import pickle\n", + "import importlib\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "\n", + "from pandas.api.types import is_string_dtype\n", + "\n", + "from pathlib import Path\n", + "from uuid import UUID\n", + "from collections import defaultdict\n", + "\n", + "pd.set_option(\"display.max_columns\", 100)\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e550aa2b", + "metadata": {}, + "outputs": [], + "source": [ + "INCLUDE_TEST_USERS = False" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "39306a1d", + "metadata": {}, + "outputs": [], + "source": [ + "emission_path = Path(os.getcwd()).parent.parent / 'my_emission_server' / 'e-mission-server'\n", + "sys.path.append(str(emission_path))\n", + "\n", + "# Also add the home (viz_scripts) to the path\n", + "sys.path.append('../viz_scripts')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "94f673d6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "storage not configured, falling back to sample, default configuration\n", + "URL not formatted, defaulting to Stage_database\n", + "Connecting to database URL localhost\n" + ] + } + ], + "source": [ + "import scaffolding\n", + "import emission.core.get_database as edb\n", + "import emission.storage.timeseries.abstract_timeseries as esta" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e171e277", + "metadata": {}, + "outputs": [], + "source": [ + "DB_SOURCE = [\n", + " \"Stage_database\", # Does NOT have composite trips BUT has section modes and distances\n", + " \"openpath_prod_durham\", # Has composite trips\n", + " \"openpath_prod_mm_masscec\", # Has composite trips\n", + " \"openpath_prod_ride2own\", # Has composite trips\n", + "# \"openpath_prod_uprm_civic\", # No replaced mode (Excluded)\n", + " \"openpath_prod_uprm_nicr\" # Has composite trips\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "70fa3112", + "metadata": {}, + "outputs": [], + "source": [ + "CURRENT_DB = DB_SOURCE[0]\n", + "\n", + "assert CURRENT_DB in DB_SOURCE" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bbde79d1", + "metadata": {}, + "outputs": [], + "source": [ + "# ['p_micro', 'no_trip', 's_car', 'transit', 'car', 's_micro', 'ridehail', 'walk', 'unknown']\n", + "\n", + "REPLACED_MODE_DICT = {\n", + " \"Stage_database\": {\n", + " 'no_trip': 'no_trip',\n", + " 'no_travel': 'no_trip',\n", + " 'Unknown': 'unknown',\n", + " 'unknown': 'unknown',\n", + " 'bus': 'transit',\n", + " 'drove_alone': 'car',\n", + " 'bike': 'p_micro',\n", + " 'shared_ride': 's_car',\n", + " 'walk': 'walk',\n", + " 'train': 'transit',\n", + " 'bikeshare': 's_micro',\n", + " 'not_a trip': 'no_trip',\n", + " 'pilot_ebike': 'p_micro',\n", + " 'electric_car': 'car',\n", + " 'taxi': 'ridehail',\n", + " 'not_a_trip': 'no_trip',\n", + " 'run': 'walk',\n", + " 'scootershare': 's_micro',\n", + " 'tramway': 'transit',\n", + " 'free_shuttle': 'transit',\n", + " 'e-bike': 'p_micro',\n", + " 'rental_car': 'car',\n", + " 'train_+ bus': 'transit',\n", + " 'skateboard': 'p_micro',\n", + " 'snowboarding': 'p_micro',\n", + " 'e_bike': 'p_micro',\n", + " 'golf_cart': 'unknown',\n", + " 'emergency_vehicle with others': 's_car',\n", + " 'call_friend': 's_car',\n", + " 'no_replacement': 'no_travel',\n", + " 'doing_nothing': 'no_trip',\n", + " 'na': 'no_trip',\n", + " 'ebike': 'p_micro',\n", + " 'hiking': 'walk',\n", + " 'n/a': 'no_trip',\n", + " 'testing': 'unknown',\n", + " 'home': 'no_trip',\n", + " 'must_walk 3-5 mi a day for back': 'walk',\n", + " 'family': 's_car',\n", + " 'car': 'car',\n", + " 'pilot_e-bike': 'p_micro',\n", + " 'pilot_bike': 'p_micro',\n", + " 'time_spent on the clock at amazon': 'no_trip',\n", + " 'working': 'no_trip',\n", + " 'walk_at work': 'walk',\n", + " 'sitting_on my butt doing nothing': 'no_trip',\n", + " 'nothing._delivered food for work': 'no_trip',\n", + " 'train,_bus and walk': 'transit',\n", + " 'work_vehicle': 'car',\n", + " 'friend_picked me up': 's_car',\n", + " 'ski': 'p_micro',\n", + " 'not_accurate': 'unknown',\n", + " 'stolen_ebike': 'p_micro'\n", + " },\n", + " \"openpath_prod_durham\": {\n", + " 'Unknown': 'unknown',\n", + " 'bike': 'p_micro',\n", + " 'shared_ride': 's_car',\n", + " 'drove_alone': 'car',\n", + " 'bus': 'transit',\n", + " 'no_travel': 'no_trip',\n", + " 'scootershare': 's_micro',\n", + " 'walk': 'walk',\n", + " 'taxi': 'ridehail',\n", + " 'e_car_drove_alone': 'car',\n", + " 'bikeshare': 's_micro',\n", + " 'ebike': 'p_micro',\n", + " 'train': 'transit',\n", + " 'e_car_shared_ride': 's_car'\n", + " },\n", + " \"openpath_prod_mm_masscec\": {\n", + " 'Unknown': 'unknown',\n", + " 'drove_alone': 'car',\n", + " 'walk': 'walk',\n", + " 'shared_ride': 's_car',\n", + " 'bike': 'p_micro',\n", + " 'bikeshare': 's_micro',\n", + " 'no_travel': 'no_trip',\n", + " 'taxi': 'ridehail',\n", + " 'bus': 'transit',\n", + " 'scootershare': 's_micro',\n", + " 'train': 'transit',\n", + " 'walking': 'walk',\n", + " 'e_car_drove_alone': 'car'\n", + " },\n", + " \"openpath_prod_ride2own\": {\n", + " 'Unknown': 'unknown',\n", + " 'drove_alone': 'car',\n", + " 'walk': 'walk',\n", + " 'shared_ride': 's_car',\n", + " 'bike': 'p_micro',\n", + " 'no_travel': 'no_trip',\n", + " 'taxi': 'ridehail',\n", + " 'bus': 'transit',\n", + " 'train': 'transit',\n", + " 'e_car_drove_alone': 'car',\n", + " 'e_car_shared_ride': 's_car'\n", + " },\n", + " \"openpath_prod_uprm_nicr\": {\n", + " 'Unknown': 'unknown',\n", + " 'walk': 'walk',\n", + " 'drove_alone': 'car'\n", + " }\n", + "}\n", + "\n", + "SENSED_SECTION_DICT = {\n", + " \"openpath_prod_mm_masscec\": {'AIR_OR_HSR', 'BICYCLING', 'BUS', 'CAR', 'LIGHT_RAIL', 'SUBWAY', 'TRAIN', 'UNKNOWN', 'WALKING'}\n", + "}\n", + "\n", + "SURVEY_DATA_DICT = {\n", + " \"Stage_database\": {\n", + " \"Unique User ID (auto-filled, do not edit)\": \"user_id\",\n", + " \"In which year were you born?\": \"birth_year\",\n", + " \"What is your gender?\": \"gender\",\n", + " \"Do you have a valid driver's license?\": \"has_drivers_license\",\n", + " \"Are you a student?\": \"is_student\",\n", + " \"What is the highest grade or degree that you have completed?\": \"highest_education\",\n", + " \"Do you work for either pay or profit?\": \"is_paid\",\n", + " \"Do you have more than one job?\": \"has_multiple_jobs\",\n", + " \"Do you work full-time or part-time at your primary job?\": \"primary_job_type\",\n", + " \"Which best describes your primary job?\": \"primary_job_description\",\n", + " \"How did you usually get to your primary job last week? \": \"primary_job_commute_mode\",\n", + " \"Thinking about your daily commute to work last week, how many minutes did it usually take to get from home to the primary job/work place?\": \"primary_job_commute_time\",\n", + " \"At your primary job, do you have the ability to set or change your own start time?\": \"is_primary_job_flexible\",\n", + " \"Do you have the option of working from home or an alternate location instead of going into your primary work place?\": \"primary_job_can_wfh\",\n", + " \"How many days per week do you usually work from home or an alternate location?\": \"wfh_days\",\n", + " \"Do you own or rent your place of residence?\": \"residence_ownership_type\",\n", + " \"What is your home type?\": \"residence_type\",\n", + " \"Please identify which category represents your total household income, before taxes, for last year.\": \"income_category\",\n", + " \"Including yourself, how many people live in your home?\": \"n_residence_members\",\n", + " \"How many children under age 18 live in your home?\": \"n_residents_u18\",\n", + " \"Including yourself, how many people have a driver's license in your household?\": \"n_residents_with_license\",\n", + " \"How many motor vehicles are owned, leased, or available for regular use by the people who currently live in your household?\": \"n_motor_vehicles\",\n", + " \"If you were unable to use your household vehicle(s), which of the following options would be available to you to get you from place to place?\": \"available_modes\",\n", + " \"Do you have a medical condition that makes it difficult to travel outside of the home?\": \"has_medical_condition\",\n", + " \"How long have you had this condition?\": \"medical_condition_duration\"\n", + " },\n", + " # Retrieved from: e-mission-phone/survey-resources/data-xls/demo-survey-v1.xlsx\n", + " \"openpath_prod_durham\": {\n", + " \"At_your_primary_job_do_you_ha\": \"is_primary_job_flexible\",\n", + " \"Which_best_describes_your_prim\": \"primary_job_description\",\n", + " \"Do_you_work_full_time_or_part_\": \"primary_job_type\",\n", + " \"Do_you_have_the_option_of_work\": \"primary_job_can_wfh\",\n", + " \"Please_describe_your_primary_job\": \"primary_job_description_2\",\n", + " \"Do_you_have_more_than_one_job\": \"has_multiple_jobs\",\n", + " # Two columns: how many days/week do you work & what days of the week do you work. \n", + " # the latter has only 4 NA values, the former has 45 NA values.\n", + " \"What_days_of_the_week_do_you_t\": \"wfh_days\",\n", + " \"How_many_days_do_you_usually_w_001\": \"n_wfh_days\",\n", + " # All these are NAs.\n", + " \"Which_one_below_describe_you_b\": \"description\",\n", + " \"What_is_your_race_ethnicity\": \"race_or_ethnicity\",\n", + " \"Are_you_a_student\": \"is_student\",\n", + " \"What_is_the_highest_grade_or_d\": \"highest_education\",\n", + " \"do_you_consider_yourself_to_be\": \"is_transgender\",\n", + " \"What_is_your_gender\": \"gender\",\n", + " \"How_old_are_you\": \"age\",\n", + " \"Are_you_a_paid_worker\": \"is_paid\",\n", + " \"Do_you_have_a_driver_license\": \"has_drivers_license\",\n", + " \"How_long_you_had_this_conditio\": \"medical_condition_duration\",\n", + " \"Including_yourself_how_many_w_001\": \"n_residents_u18\",\n", + " \"Including_yourself_how_many_p\": \"n_residence_members\",\n", + " \"Do_you_own_or_rent_your_home\": \"residence_ownership_type\",\n", + " \"Please_identify_which_category\": \"income_category\",\n", + " \"If_you_were_unable_to_use_your\": \"available_modes\",\n", + " \"Including_yourself_how_many_p_001\": \"n_residents_with_license\",\n", + " \"Including_yourself_how_many_w\": \"n_working_residents\",\n", + " \"What_is_your_home_type\": \"residence_type\",\n", + " \"How_many_motor_vehicles_are_ow\": \"n_motor_vehicles\",\n", + " \"Do_you_have_a_condition_or_han\": \"has_medical_condition\"\n", + " },\n", + " \"openpath_prod_mm_masscec\": {\n", + " # Same questions as Durham.\n", + " \"At_your_primary_job_do_you_ha\": \"is_primary_job_flexible\",\n", + " \"Which_best_describes_your_prim\": \"primary_job_description\",\n", + " \"Do_you_work_full_time_or_part_\": \"primary_job_type\",\n", + " \"Do_you_have_the_option_of_work\": \"primary_job_can_wfh\",\n", + " \"Please_describe_your_primary_job\": \"primary_job_description_2\",\n", + " \"Do_you_have_more_than_one_job\": \"has_multiple_jobs\",\n", + " # Two columns: how many days/week do you work & what days of the week do you work. \n", + " # the latter has only 4 NA values, the former has 45 NA values.\n", + " \"What_days_of_the_week_do_you_t\": \"wfh_days\",\n", + " \"How_many_days_do_you_usually_w_001\": \"n_wfh_days\",\n", + " # All these are NAs.\n", + " \"Which_one_below_describe_you_b\": \"description\",\n", + " \"What_is_your_race_ethnicity\": \"race_or_ethnicity\",\n", + " \"Are_you_a_student\": \"is_student\",\n", + " \"What_is_the_highest_grade_or_d\": \"highest_education\",\n", + " \"do_you_consider_yourself_to_be\": \"is_transgender\",\n", + " \"What_is_your_gender\": \"gender\",\n", + " \"How_old_are_you\": \"age\",\n", + " \"Are_you_a_paid_worker\": \"is_paid\",\n", + " \"Do_you_have_a_driver_license\": \"has_drivers_license\",\n", + " \"How_long_you_had_this_conditio\": \"medical_condition_duration\",\n", + " \"Including_yourself_how_many_w_001\": \"n_residents_u18\",\n", + " \"Including_yourself_how_many_p\": \"n_residence_members\",\n", + " \"Do_you_own_or_rent_your_home\": \"residence_ownership_type\",\n", + " \"Please_identify_which_category\": \"income_category\",\n", + " \"If_you_were_unable_to_use_your\": \"available_modes\",\n", + " \"Including_yourself_how_many_p_001\": \"n_residents_with_license\",\n", + " \"Including_yourself_how_many_w\": \"n_working_residents\",\n", + " \"What_is_your_home_type\": \"residence_type\",\n", + " \"How_many_motor_vehicles_are_ow\": \"n_motor_vehicles\",\n", + " \"Do_you_have_a_condition_or_han\": \"has_medical_condition\"\n", + " },\n", + " \"openpath_prod_ride2own\": {\n", + " # Same questions as Durham.\n", + " \"How_old_are_you\": \"age\",\n", + " \"What_is_your_gender\": \"gender\",\n", + " \"do_you_consider_yourself_to_be\": \"is_transgender\",\n", + " \"What_is_your_race_ethnicity\": \"race_or_ethnicity\",\n", + " \"Do_you_have_a_driver_license\": \"has_drivers_license\",\n", + " \"Are_you_a_student\": \"is_student\",\n", + " \"What_is_the_highest_grade_or_d\": \"highest_education\",\n", + " \"Are_you_a_paid_worker\": \"is_paid\",\n", + " \"Which_one_below_describe_you_b\": \"description\",\n", + " \"Do_you_own_or_rent_your_home\": \"residence_ownership_type\",\n", + " \"What_is_your_home_type\": \"residence_type\",\n", + " \"Please_identify_which_category\": \"income_category\",\n", + " \"Including_yourself_how_many_p\": \"n_residence_members\",\n", + " \"Including_yourself_how_many_w\": \"n_working_residents\",\n", + " \"Including_yourself_how_many_p_001\": \"n_residents_with_license\",\n", + " \"Including_yourself_how_many_w_001\": \"n_residents_u18\",\n", + " \"How_many_motor_vehicles_are_ow\": \"n_motor_vehicles\",\n", + " \"If_you_were_unable_to_use_your\": \"available_modes\",\n", + " \"Do_you_have_a_condition_or_han\": \"has_medical_condition\",\n", + " \"How_long_you_had_this_conditio\": \"medical_condition_duration\",\n", + " \"Do_you_have_more_than_one_job\": \"has_multiple_jobs\",\n", + " \"Do_you_work_full_time_or_part_\": \"primary_job_type\",\n", + " \"Which_best_describes_your_prim\": \"primary_job_description\",\n", + " \"Please_describe_your_primary_job\": \"primary_job_description_2\",\n", + " \"At_your_primary_job_do_you_ha\": \"is_primary_job_flexible\",\n", + " \"Do_you_have_the_option_of_work\": \"primary_job_can_wfh\",\n", + " \"How_many_days_do_you_usually_w_001\": \"n_wfh_days\",\n", + " \"What_days_of_the_week_do_you_t\": \"wfh_days\"\n", + " },\n", + " \"openpath_prod_uprm_nicr\": {\n", + " # Same as Durham!\n", + " \"At_your_primary_job_do_you_ha\": \"is_primary_job_flexible\",\n", + " \"Which_best_describes_your_prim\": \"primary_job_description\",\n", + " \"Do_you_work_full_time_or_part_\": \"primary_job_type\",\n", + " \"Do_you_have_the_option_of_work\": \"primary_job_can_wfh\",\n", + " \"Please_describe_your_primary_job\": \"primary_job_description_2\",\n", + " \"Do_you_have_more_than_one_job\": \"has_multiple_jobs\",\n", + " # Two columns: how many days/week do you work & what days of the week do you work. \n", + " # the latter has only 4 NA values, the former has 45 NA values.\n", + " \"What_days_of_the_week_do_you_t\": \"wfh_days\",\n", + " \"How_many_days_do_you_usually_w_001\": \"n_wfh_days\",\n", + " # All these are NAs.\n", + " \"Which_one_below_describe_you_b\": \"description\",\n", + " \"What_is_your_race_ethnicity\": \"race_or_ethnicity\",\n", + " \"Are_you_a_student\": \"is_student\",\n", + " \"What_is_the_highest_grade_or_d\": \"highest_education\",\n", + " \"do_you_consider_yourself_to_be\": \"is_transgender\",\n", + " \"What_is_your_gender\": \"gender\",\n", + " \"How_old_are_you\": \"age\",\n", + " \"Are_you_a_paid_worker\": \"is_paid\",\n", + " \"Do_you_have_a_driver_license\": \"has_drivers_license\",\n", + " \"How_long_you_had_this_conditio\": \"medical_condition_duration\",\n", + " \"Including_yourself_how_many_w_001\": \"n_residents_u18\",\n", + " \"Including_yourself_how_many_p\": \"n_residence_members\",\n", + " \"Do_you_own_or_rent_your_home\": \"residence_ownership_type\",\n", + " \"Please_identify_which_category\": \"income_category\",\n", + " \"If_you_were_unable_to_use_your\": \"available_modes\",\n", + " \"Including_yourself_how_many_p_001\": \"n_residents_with_license\",\n", + " \"Including_yourself_how_many_w\": \"n_working_residents\",\n", + " \"What_is_your_home_type\": \"residence_type\",\n", + " \"How_many_motor_vehicles_are_ow\": \"n_motor_vehicles\",\n", + " \"Do_you_have_a_condition_or_han\": \"has_medical_condition\"\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "69008893", + "metadata": {}, + "outputs": [], + "source": [ + "## Source: db_utils.py in op-admin-dashboard.\n", + "\n", + "BINARY_DEMOGRAPHICS_COLS = [\n", + " 'user_id',\n", + " '_id',\n", + "]\n", + "\n", + "EXCLUDED_DEMOGRAPHICS_COLS = [\n", + " 'data.xmlResponse', \n", + " 'data.name',\n", + " 'data.version',\n", + " 'data.label',\n", + " 'xmlns:jr',\n", + " 'xmlns:orx',\n", + " 'id',\n", + " 'start',\n", + " 'end',\n", + " 'attrxmlns:jr',\n", + " 'attrxmlns:orx',\n", + " 'attrid',\n", + " '__version__',\n", + " 'attrversion',\n", + " 'instanceID',\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "12cc0c54", + "metadata": {}, + "outputs": [], + "source": [ + "## Source: scaffolding.py\n", + "\n", + "def expand_userinputs(labeled_ct):\n", + " '''\n", + " param: labeled_ct: a dataframe of confirmed trips, some of which have labels\n", + " params: labels_per_trip: the number of labels for each trip.\n", + " Currently, this is 2 for studies and 3 for programs, and should be \n", + " passed in by the notebook based on the input config.\n", + " If used with a trip-level survey, it could be even larger.\n", + " '''\n", + " # CASE 1 of https://github.com/e-mission/em-public-dashboard/issues/69#issuecomment-1256835867\n", + " if len(labeled_ct) == 0:\n", + " return labeled_ct\n", + " label_only = pd.DataFrame(labeled_ct.user_input.to_list(), index=labeled_ct.index)\n", + " # disp.display(label_only.head())\n", + " labels_per_trip = len(label_only.columns)\n", + " print(\"Found %s columns of length %d\" % (label_only.columns, labels_per_trip))\n", + " expanded_ct = pd.concat([labeled_ct, label_only], axis=1)\n", + " assert len(expanded_ct) == len(labeled_ct), \\\n", + " (\"Mismatch after expanding labels, expanded_ct.rows = %s != labeled_ct.rows %s\" %\n", + " (len(expanded_ct), len(labeled_ct)))\n", + " print(\"After expanding, columns went from %s -> %s\" %\n", + " (len(labeled_ct.columns), len(expanded_ct.columns)))\n", + " assert len(expanded_ct.columns) == len(labeled_ct.columns) + labels_per_trip, \\\n", + " (\"Mismatch after expanding labels, expanded_ct.columns = %s != labeled_ct.columns %s\" %\n", + " (len(expanded_ct.columns), len(labeled_ct.columns)))\n", + " # disp.display(expanded_ct.head())\n", + " return expanded_ct" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9a98e2fb", + "metadata": {}, + "outputs": [], + "source": [ + "## Source: scaffolding.py\n", + "\n", + "def data_quality_check(expanded_ct):\n", + " '''1. Delete rows where the mode_confirm was pilot_ebike and repalced_mode was pilot_ebike.\n", + " 2. Delete rows where the mode_confirm was pilot_ebike and repalced_mode was same_mode.\n", + " 3. Replace same_mode for the mode_confirm for Energy Impact Calcualtion.'''\n", + "\n", + " # TODO: This is only really required for the initial data collection around the minipilot\n", + " # in subsequent deployes, we removed \"same mode\" and \"pilot_ebike\" from the options, so the\n", + " # dataset did not contain of these data quality issues\n", + "\n", + " if 'replaced_mode' in expanded_ct.columns:\n", + " expanded_ct.drop(expanded_ct[(expanded_ct['mode_confirm'] == 'pilot_ebike') & (expanded_ct['replaced_mode'] == 'pilot_ebike')].index, inplace=True)\n", + " expanded_ct.drop(expanded_ct[(expanded_ct['mode_confirm'] == 'pilot_ebike') & (expanded_ct['replaced_mode'] == 'same_mode')].index, inplace=True)\n", + " expanded_ct['replaced_mode'] = np.where(expanded_ct['replaced_mode'] == 'same_mode',expanded_ct['mode_confirm'], expanded_ct['replaced_mode'])\n", + " \n", + " return expanded_ct" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fe37bf27", + "metadata": {}, + "outputs": [], + "source": [ + "if CURRENT_DB != \"Stage_database\":\n", + "\n", + " ## Source: scaffolding.py\n", + "\n", + " uuid_df = pd.json_normalize(list(edb.get_uuid_db().find()))\n", + "\n", + " if not INCLUDE_TEST_USERS:\n", + " uuid_df = uuid_df.loc[~uuid_df.user_email.str.contains('_test_'), :]\n", + "\n", + " filtered = uuid_df.uuid.unique()\n", + "\n", + " agg = esta.TimeSeries.get_aggregate_time_series()\n", + " all_ct = agg.get_data_df(\"analysis/confirmed_trip\", None)\n", + "\n", + " print(f\"Before filtering, length={len(all_ct)}\")\n", + " participant_ct_df = all_ct.loc[all_ct.user_id.isin(filtered), :]\n", + " print(f\"After filtering, length={len(participant_ct_df)}\")\n", + "\n", + " expanded_ct = expand_userinputs(participant_ct_df)\n", + " expanded_ct = data_quality_check(expanded_ct)\n", + " print(expanded_ct.columns.tolist())\n", + " expanded_ct['replaced_mode'] = expanded_ct['replaced_mode'].fillna('Unknown')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "13536d14", + "metadata": {}, + "outputs": [], + "source": [ + "# # Additional preprocessing for replaced mode (if any)\n", + "\n", + "if CURRENT_DB != \"Stage_database\":\n", + "\n", + " mode_counts = expanded_ct['replaced_mode'].value_counts()\n", + " drop_modes = mode_counts[mode_counts == 1].index.tolist()\n", + "\n", + " expanded_ct.drop(\n", + " index=expanded_ct.loc[expanded_ct.replaced_mode.isin(drop_modes)].index,\n", + " inplace=True\n", + " )\n", + "\n", + " # Additional modes to drop.\n", + " expanded_ct.drop(\n", + " index=expanded_ct.loc[expanded_ct.replaced_mode.isin(\n", + " # Remove all rows with air, boat, or weird answers.\n", + " ['houseboat', 'gondola', 'airline_flight', 'aircraft', 'zoo', 'air',\n", + " 'airplane', 'boat', 'flight', 'plane', 'meal', 'lunch']\n", + " )].index,\n", + " inplace=True\n", + " )\n", + " \n", + " expanded_ct.replaced_mode = expanded_ct.replaced_mode.apply(lambda x: REPLACED_MODE_DICT[CURRENT_DB][x])" + ] + }, + { + "cell_type": "markdown", + "id": "258844f4", + "metadata": {}, + "source": [ + "# Demographic pre-processing" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "7461a4d2", + "metadata": {}, + "outputs": [], + "source": [ + "# Demographics\n", + "\n", + "if CURRENT_DB != \"Stage_database\":\n", + "\n", + " decoded_uuids = [str(x) for x in filtered]\n", + "\n", + " ## Source: query_demographics() in op-admin-dashboard.\n", + " ts = esta.TimeSeries.get_aggregate_time_series()\n", + " entries = list(ts.find_entries([\"manual/demographic_survey\"]))\n", + "\n", + " available_key = {}\n", + " for entry in entries:\n", + " survey_key = list(entry['data']['jsonDocResponse'].keys())[0]\n", + " if survey_key not in available_key:\n", + " available_key[survey_key] = []\n", + "\n", + " # Minor modification: Added user_id check to filter users.\n", + " if str(entry['user_id']) in decoded_uuids:\n", + " available_key[survey_key].append(entry)\n", + "\n", + " dataframes = {}\n", + " for key, json_object in available_key.items():\n", + " df = pd.json_normalize(json_object)\n", + " dataframes[key] = df\n", + "\n", + " for key, df in dataframes.items():\n", + " if not df.empty:\n", + " for col in BINARY_DEMOGRAPHICS_COLS:\n", + " if col in df.columns:\n", + " df[col] = df[col].apply(str) \n", + " columns_to_drop = [col for col in df.columns if col.startswith(\"metadata\")]\n", + " df.drop(columns= columns_to_drop, inplace=True) \n", + " df.columns=[col.rsplit('.',1)[-1] if col.startswith('data.jsonDocResponse.') else col for col in df.columns]\n", + " for col in EXCLUDED_DEMOGRAPHICS_COLS:\n", + " if col in df.columns:\n", + " df.drop(columns= [col], inplace=True)\n", + "\n", + " survey_data = pd.DataFrame() \n", + " for v in dataframes.values():\n", + " survey_data = pd.concat([survey_data, v], axis=0, ignore_index=True)\n", + "else:\n", + " # Read the demographics.\n", + " survey_data = pd.read_csv('../viz_scripts/Can Do Colorado eBike Program - en.csv')\n", + " survey_data.rename(columns={'Unique User ID (auto-filled, do not edit)': 'user_id'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "fe5a9dff", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_79014/2914323964.py:6: DtypeWarning: Columns (40,41,42,47) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " expanded_ct = pd.read_csv('../data/cached_allceo_data.csv')\n" + ] + } + ], + "source": [ + "if CURRENT_DB == \"Stage_database\":\n", + " \n", + " if os.path.exists('../data/cached_allceo_data.csv'):\n", + " \n", + " # Replace current instance of dataframe with the cached dataframe.\n", + " expanded_ct = pd.read_csv('../data/cached_allceo_data.csv')\n", + " expanded_ct.loc[expanded_ct.replaced_mode == 'no_travel', 'replaced_mode'] = 'no_trip'\n", + " else:\n", + " ## NOTE: Run this cell only if the cached CSV is not already available. It will take a LOT of time.\n", + " ## Benchmark timing: ~12 hours on a MacBook Pro (2017 model) with pandarallel, 4 workers.\n", + " \n", + " importlib.reload(scaffolding)\n", + " expanded_ct = scaffolding.get_section_durations(expanded_ct)\n", + " expanded_ct.to_csv('../data/cached_allceo_data.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "a6be751e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "203 184\n" + ] + } + ], + "source": [ + "print(len(survey_data.user_id.unique()), len(expanded_ct.user_id.unique()))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9ebc87d8", + "metadata": {}, + "outputs": [], + "source": [ + "survey_data.rename(SURVEY_DATA_DICT[CURRENT_DB], axis='columns', inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "522b1362", + "metadata": {}, + "source": [ + "### Demographic data preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "336508c2", + "metadata": {}, + "outputs": [], + "source": [ + "print(survey_data.columns.tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29bc7996", + "metadata": {}, + "outputs": [], + "source": [ + "# gtg\n", + "survey_data['ft_job'] = survey_data.primary_job_type.apply(\n", + " lambda x: 1 if str(x).lower() == 'full_time' else 0\n", + ")\n", + "\n", + "# gtg\n", + "survey_data['multiple_jobs'] = survey_data.has_multiple_jobs.apply(\n", + " lambda x: 1 if str(x).lower() == 'yes' else 0\n", + ")\n", + "\n", + "# gtg\n", + "survey_data.loc[\n", + " survey_data.n_motor_vehicles.isin(\n", + " ['prefer_not_to_say', 'Prefer not to say / Prefiero no decir.']\n", + " ), 'n_motor_vehicles'\n", + "] = 0\n", + "survey_data.loc[survey_data.n_motor_vehicles.isin(['more_than_3', '4+', 'more_than_4']), 'n_motor_vehicles'] = 4\n", + "survey_data.n_motor_vehicles = survey_data.n_motor_vehicles.astype(int)\n", + "\n", + "# gtg\n", + "survey_data.has_drivers_license = survey_data.has_drivers_license.apply(\n", + " lambda x: 1 if str(x).lower() == 'yes' else 0\n", + ")\n", + "\n", + "survey_data.loc[survey_data.n_residents_u18 == 'prefer_not_to_say'] = 0\n", + "survey_data.n_residents_u18 = survey_data.n_residents_u18.astype(int)\n", + "\n", + "survey_data.loc[survey_data.n_residence_members == 'prefer_not_to_say'] = 0\n", + "survey_data.n_residence_members = survey_data.n_residence_members.astype(int)\n", + "\n", + "survey_data.loc[survey_data.n_residents_with_license == 'prefer_not_to_say'] = 0\n", + "survey_data.loc[survey_data.n_residents_with_license == 'more_than_4'] = 4\n", + "survey_data.n_residents_with_license = survey_data.n_residents_with_license.astype(int)\n", + "\n", + "# In allCEO, we see 50 & 9999. What??\n", + "survey_data = survey_data[\n", + " (survey_data.n_residence_members < 10) & (survey_data.n_residents_u18 < 10) & \n", + " (survey_data.n_residents_with_license < 10) & \n", + " (survey_data.n_residence_members - survey_data.n_residents_with_license > 0) &\n", + " (survey_data.n_residence_members - survey_data.n_residents_u18 > 0)\n", + "].reset_index(drop=True)\n", + "\n", + "# gtg\n", + "if CURRENT_DB != \"Stage_database\":\n", + " survey_data.n_working_residents = survey_data.n_working_residents.apply(\n", + " lambda x: 0 if x == 'prefer_not_to_say' else int(x)\n", + " )\n", + "else:\n", + " survey_data['n_working_residents'] = survey_data['n_residence_members'] - survey_data['n_residents_u18']\n", + " \n", + "survey_data = survey_data[survey_data.n_working_residents >= 0].reset_index(drop=True)\n", + "\n", + "# gtg\n", + "survey_data.is_paid = survey_data.is_paid.apply(lambda x: 1 if x == 'Yes' else 0)\n", + "\n", + "# gtg\n", + "survey_data.has_medical_condition = survey_data.has_medical_condition.apply(\n", + " lambda x: 1 if str(x).lower() == 'yes' else 0\n", + ")\n", + "\n", + "## gtg\n", + "survey_data.is_student.replace({\n", + " 'Not a student': 0, \n", + " 'Yes - Full Time College/University': 1,\n", + " 'Yes - Vocation/Technical/Trade School': 1,\n", + " 'Yes - K-12th Grade including GED': 1, \n", + " 'Work': 0, \n", + " 'No': 0,\n", + " 'Prefer not to say': 0,\n", + " 'Yes - Part-Time College/University': 1,\n", + " 'Taking prerequisites missing for grad program ': 1, \n", + " 'Graduate': 1,\n", + " 'Custodian': 0, \n", + " 'Work at csu': 0,\n", + " 'not_a_student': 0, \n", + " 'yes___vocation_technical_trade_school': 1,\n", + " 'yes___part_time_college_university': 1,\n", + " 'prefer_not_to_say': 0, \n", + " 'yes___k_12th_grade_including_ged': 1,\n", + " 'yes___full_time_college_university': 1\n", + "}, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "aeb85637", + "metadata": {}, + "source": [ + "### Additinal Demographic Data Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c069bd2", + "metadata": {}, + "outputs": [], + "source": [ + "if CURRENT_DB == \"Stage_database\":\n", + " age = survey_data.birth_year.apply(\n", + " lambda x: 2024 - int(x) if int(x) > 100 else int(x)\n", + " )\n", + " \n", + " upper = age - (age % 5)\n", + " lower = upper + 5\n", + " new_col = (upper + 1).astype(str) + '___' + lower.astype(str) + '_years_old'\n", + " survey_data['age'] = new_col\n", + " \n", + " survey_data.loc[survey_data.age.isin([\n", + " '66___70_years_old', '76___80_years_old', '81___85_years_old'\n", + " ]), 'age'] = '__65_years_old'\n", + " \n", + " survey_data.drop(columns=['birth_year'], inplace=True)\n", + "\n", + "else:\n", + " survey_data = survey_data[survey_data.age != 0].reset_index(drop=True)\n", + "\n", + "if survey_data.columns.isin(['primary_job_commute_mode', 'primary_job_commute_time']).all():\n", + " survey_data.drop(columns=['primary_job_commute_mode', 'primary_job_commute_time'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f094cadd", + "metadata": {}, + "outputs": [], + "source": [ + "def normalize_job_descriptions(db_name, df):\n", + " if db_name != 'Stage_database':\n", + " PRIMARY_JOB_DESCRIPTION_DICT = {\n", + " \"sales_or_service\": \"Sales or service\",\n", + " \"other\": \"Other\",\n", + " \"\": \"Other\",\n", + " \"professional__managerial__or_technical\": \"Professional, Manegerial, or Technical\",\n", + " \"manufacturing__construction__maintenance\": \"Manufacturing, construction, maintenance, or farming\",\n", + " \"clerical_or_administrative_support\": \"Clerical or administrative support\",\n", + " \"prefer_not_to_say\": \"Prefer not to say\",\n", + " }\n", + " \n", + " df.primary_job_description = df.primary_job_description.apply(\n", + " lambda x: PRIMARY_JOB_DESCRIPTION_DICT[x]\n", + " )\n", + " else:\n", + " df.primary_job_description = df.primary_job_description.str.strip()\n", + "\n", + " # Normalize the job description. Inspired from the 'e-bike trips by occupation' \n", + " # plot in the CanBikeCo full pilot paper.\n", + " df.loc[\n", + " df.primary_job_description.isin([\n", + " 'Paraprofessional', 'Education', 'education/early childhood', 'Teacher',\n", + " 'Education non-profit manager', 'Scientific research', 'Research',\n", + " 'Preschool Tracher'\n", + " ]), 'primary_job_description'\n", + " ] = 'Education'\n", + "\n", + " df.loc[\n", + " df.primary_job_description.isin([\n", + " 'Custodian', 'Custodial', 'Csu custodian', 'Janitorial',\n", + " 'Custodial Maintanace'\n", + " ]), 'primary_job_description'\n", + " ] = 'Custodial'\n", + "\n", + " df.loc[\n", + " df.primary_job_description.isin([\n", + " 'Inbound cs', 'Accounting Technician', \n", + " 'Clerical'\n", + " ]), 'primary_job_description'\n", + " ] = 'Clerical or administrative support'\n", + "\n", + " df.loc[\n", + " df.primary_job_description.isin([\n", + " 'Restaurant manager', 'Transportaion Services',\n", + " ]), 'primary_job_description'\n", + " ] = 'Sales or service'\n", + "\n", + " df.loc[\n", + " df.primary_job_description.isin([\n", + " 'Pastry chef and line cook', 'Cook', 'Chef', 'Dining Services',\n", + " 'Food Service', 'Cooking', 'Residential Dining Services', 'Line Cook'\n", + " ]), 'primary_job_description'\n", + " ] = 'Food service'\n", + "\n", + " df.loc[\n", + " df.primary_job_description.isin([\n", + " 'CNA', 'Caregiver/ Qmap', 'Health care', 'Nurse',\n", + " 'Healthcare', 'Medical', 'Medical field',\n", + " 'Family support'\n", + " ]), 'primary_job_description'\n", + " ] = 'Medical/healthcare'\n", + "\n", + " df.loc[\n", + " df.primary_job_description.isin([\n", + " 'Amazon', 'Hockey rink', 'Caregiver', 'Security', 'Nonprofit social work',\n", + " 'Therapeutic', 'Driver'\n", + " ]), 'primary_job_description'\n", + " ] = 'Other'\n", + "\n", + " df.loc[\n", + " df.primary_job_description.isin([\n", + " 'Hospital laundry', 'Matreal handler', 'Maintenance',\n", + " 'Co op laundry'\n", + " ]), 'primary_job_description'\n", + " ] = 'Manufacturing, construction, maintenance, or farming'\n", + "\n", + " df.loc[df.primary_job_description.isna(), 'primary_job_description'] = 'Other'\n", + "\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0bf37859", + "metadata": {}, + "outputs": [], + "source": [ + "INCOME_DICT = {\n", + " 'Stage_database': {\n", + " 'Prefer not to say': 0,\n", + " 'Less than $24,999': 1,\n", + " '$25,000-$49,999': 2,\n", + " '$50,000-$99,999': 3,\n", + " '$100,000 -$149,999': 4,\n", + " '$150,000-$199,999': 5,\n", + " '$150,000': 5,\n", + " '$150,000-$199,999': 6,\n", + " '$200,000 or more': 7\n", + " },\n", + " 'Others': {\n", + " 'prefer_not_to_say': 0, \n", + " 'less_than__24_999': 1,\n", + " '_25_000_to__49_999': 2,\n", + " '_50_000_to__99_999': 3,\n", + " '_100_000_to__149_999': 4,\n", + " '_150_000_to__199_999': 5\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42b3163a", + "metadata": {}, + "outputs": [], + "source": [ + "survey_data = normalize_job_descriptions(CURRENT_DB, survey_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe2b18b6", + "metadata": {}, + "outputs": [], + "source": [ + "if CURRENT_DB == 'Stage_database':\n", + " survey_data.income_category = survey_data.income_category.apply(\n", + " lambda x: INCOME_DICT['Stage_database'][x]\n", + " )\n", + "else:\n", + " survey_data.income_category = survey_data.income_category.apply(\n", + " lambda x: INCOME_DICT['Others'][x]\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b36672b9", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import OneHotEncoder\n", + "\n", + "def generate_ohe_features(df, feature_name):\n", + " ohe = OneHotEncoder()\n", + " ohe.fit(df[[feature_name]])\n", + " return pd.DataFrame(\n", + " ohe.transform(df[[feature_name]]).todense(), \n", + " columns=ohe.get_feature_names_out(),\n", + " index=df.index\n", + " ), ohe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc8d1846", + "metadata": {}, + "outputs": [], + "source": [ + "survey_data.reset_index(drop=True, inplace=True)\n", + "\n", + "ohe_features = ['highest_education', 'primary_job_description', 'gender', 'age']\n", + "\n", + "for ohe in ohe_features:\n", + " df, _ = generate_ohe_features(survey_data, ohe)\n", + " survey_data = survey_data.merge(right=df, left_index=True, right_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2d6f8c1", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "to_drop = [\n", + " 'Timestamp', 'gender', 'highest_education', 'primary_job_type', 'primary_job_description', \n", + " 'primary_job_commute_mode', 'primary_job_commute_time', 'is_primary_job_flexible', \n", + " 'primary_job_can_wfh', 'wfh_days', 'Which one below describe you best?', 'residence_ownership_type', \n", + " 'residence_type', 'medical_condition_duration', 'has_multiple_jobs', 'age', '_id', 'data.ts',\n", + " 'primary_job_description_2', 'wfh_days', 'n_wfh_days', 'description', 'race_or_ethnicity', \n", + " 'highest_education', 'is_transgender', 'medical_condition_duration'\n", + "]\n", + "\n", + "for column in to_drop:\n", + " if column in survey_data.columns:\n", + " survey_data.drop(columns=[column], inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "65039f73", + "metadata": {}, + "source": [ + "## Merge sensed data and demographics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7eb2e09", + "metadata": {}, + "outputs": [], + "source": [ + "# Additional preprocessing to filter unwanted users from sensed trips data.\n", + "expanded_ct['user_id_join'] = expanded_ct['user_id'].apply(lambda x: str(x).replace('-', ''))\n", + "survey_data['user_id_join'] = survey_data['user_id'].apply(lambda x: str(x).replace('-', ''))\n", + "\n", + "survey_data.rename(columns={'user_id': 'survey_user_id'}, inplace=True)\n", + "\n", + "common = set(expanded_ct.user_id_join.unique()).intersection(\n", + " set(survey_data.user_id_join.unique())\n", + ")\n", + "\n", + "filtered_trips = expanded_ct.loc[expanded_ct.user_id_join.isin(common), :].reset_index(drop=True)\n", + "filtered_survey = survey_data.loc[survey_data.user_id_join.isin(common), :].reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53927d5f", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Just to double-check.\n", + "print(len(filtered_trips.user_id.unique()), len(filtered_survey.survey_user_id.unique()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "daed8fb0", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the section_*_argmax.\n", + "\n", + "def compute_argmax(db: str, row):\n", + " \n", + " if db != 'Stage_database':\n", + " \n", + " sections = row['inferred_section_summary']\n", + "\n", + " if pd.isna(sections) or len(sections) == 0 or len(sections['distance']) == 0:\n", + " return row\n", + "\n", + " try:\n", + " mode = sorted(sections['distance'].items(), key=lambda x: x[-1], reverse=True)[0][0]\n", + " distance = sections['distance'][mode]\n", + " duration = sections['duration'][mode]\n", + "\n", + " row['section_mode_argmax'] = mode\n", + " row['section_distance_argmax'] = distance\n", + " row['section_duration_argmax'] = duration\n", + "\n", + " except:\n", + " row['section_mode_argmax'] = np.nan\n", + " row['section_distance_argmax'] = np.nan\n", + " row['section_duration_argmax'] = np.nan\n", + "\n", + " finally:\n", + " return row\n", + " else:\n", + " \n", + " try:\n", + " distances = ast.literal_eval(row['section_distances'])\n", + " durations = ast.literal_eval(row['section_durations'])\n", + " modes = ast.literal_eval(row['section_modes'])\n", + "\n", + " argmax = np.argmax(distances)\n", + " \n", + " row['section_distance_argmax'] = distances[argmax]\n", + " row['section_duration_argmax'] = durations[argmax]\n", + " row['section_mode_argmax'] = modes[argmax]\n", + " \n", + " except:\n", + " row['section_mode_argmax'] = np.nan\n", + " row['section_distance_argmax'] = np.nan\n", + " row['section_duration_argmax'] = np.nan\n", + " \n", + " finally:\n", + " return row" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0c008a3", + "metadata": {}, + "outputs": [], + "source": [ + "filtered_trips.reset_index(drop=True, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "7e1baa06", + "metadata": {}, + "source": [ + "### Available feature generation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de49ec4f", + "metadata": {}, + "outputs": [], + "source": [ + "available = {\n", + " # AllCEO\n", + " 'Bicycle': 'p_micro',\n", + " 'Do not have vehicle': 'unknown',\n", + " 'Do not have vehicle ': 'unknown',\n", + " 'Get a ride from a friend or family member': 's_car',\n", + " 'None': 'no_trip',\n", + " 'Public transportation (bus, subway, light rail, etc.)': 'transit',\n", + " 'Rental car (including Zipcar/ Car2Go)': 'car',\n", + " 'Shared bicycle or scooter': 's_micro',\n", + " 'Skateboard': 'p_micro',\n", + " 'Taxi (regular taxi, Uber, Lyft, etc)': 'ridehail',\n", + " 'Walk/roll': 'walk',\n", + " 'Prefer not to say': 'unknown',\n", + " # Others\n", + " 'public_transportation__bus__subway__ligh': 'transit',\n", + " 'get_a_ride_from_a_friend_or_family_membe': 's_car', \n", + " 'bicycle': 'p_micro', \n", + " 'walk': 'walk',\n", + " 'taxi__regular_taxi__uber__lyft__etc': 'ridehail',\n", + " 'rental_car__including_zipcar__car2go': 'car', \n", + " 'prefer_not_to_say': 'unknown'\n", + "}\n", + "\n", + "# We use the sensed mode to update the available modes.\n", + "# This is to account for any user data input errors. E.g.: user does not select car as available mode\n", + "# but the sensed mode is car.\n", + "section_mode_mapping = {\n", + " 'bicycling': ['p_micro', 's_micro'],\n", + " 'car': ['s_car', 'car', 'ridehail'],\n", + " 'no_sensed': ['unknown'],\n", + " 'walking': ['walk'],\n", + " 'unknown': ['unknown'],\n", + " 'transit': ['transit']\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62960039", + "metadata": {}, + "outputs": [], + "source": [ + "filtered_trips = filtered_trips.apply(lambda x: compute_argmax(CURRENT_DB, x), axis=1)\n", + "\n", + "# Drop all rows where argmax mode == air\n", + "filtered_trips.drop(\n", + " index=filtered_trips.loc[filtered_trips.section_mode_argmax.isin(['AIR_OR_HSR', 'air_or_hsr']),:].index, \n", + " inplace=True\n", + ")\n", + "\n", + "filtered_trips.section_mode_argmax.replace({\n", + " 'subway': 'transit',\n", + " 'no_sensed': 'unknown',\n", + " 'train': 'transit',\n", + " 'TRAM': 'transit',\n", + " 'LIGHT_RAIL': 'transit',\n", + " 'CAR': 'car',\n", + " 'WALKING': 'walking',\n", + " 'BICYCLING': 'bicycling',\n", + " 'UNKNOWN': 'unknown',\n", + " 'TRAIN': 'transit',\n", + " 'SUBWAY': 'transit',\n", + " 'BUS': 'transit',\n", + " 'bus': 'transit'\n", + "}, inplace=True)\n", + "\n", + "filtered_trips.dropna(subset='section_mode_argmax', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8583a709", + "metadata": {}, + "outputs": [], + "source": [ + "## Meters -> miles\n", + "filtered_trips['section_distance_argmax'] *= 0.000621371\n", + "\n", + "## Seconds -> minutes\n", + "filtered_trips['section_duration_argmax'] /= 60.\n", + "\n", + "## Total distance and duration are scaled too.\n", + "filtered_trips['distance'] *= 0.000621371\n", + "filtered_trips['duration'] /= 60." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e4d05eb", + "metadata": {}, + "outputs": [], + "source": [ + "filtered_trips = filtered_trips.merge(right=filtered_survey, left_on='user_id_join', right_on='user_id_join')" + ] + }, + { + "cell_type": "markdown", + "id": "383fe251", + "metadata": {}, + "source": [ + "## Update available indicators" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee097233", + "metadata": {}, + "outputs": [], + "source": [ + "import itertools\n", + "\n", + "new_cols = list(set(available.values()))\n", + "filtered_trips[new_cols] = 0\n", + "\n", + "for user_id, user_trips in filtered_trips.groupby('user_id'):\n", + " \n", + " if CURRENT_DB == \"Stage_database\":\n", + " \n", + " # Get the set of available modes (demographics.)\n", + " all_av_modes = user_trips['available_modes'].str.split(';').explode()\n", + " else:\n", + " # Get the set of available modes (demographics.)\n", + " all_av_modes = user_trips['available_modes'].str.split().explode()\n", + " \n", + " # Get all sensed modes.\n", + " all_sections = user_trips['section_mode_argmax'].unique()\n", + " \n", + " # Map to Common Normal Form.\n", + " mapped_sections = set(list(itertools.chain.from_iterable([section_mode_mapping[x] for x in all_sections])))\n", + " mapped_demo_av = set([available[x] for x in all_av_modes.unique()])\n", + " \n", + " # Perform a set union.\n", + " combined = list(mapped_sections.union(mapped_demo_av))\n", + " \n", + " # Update dummy indicators.\n", + " filtered_trips.loc[filtered_trips.user_id == user_id, combined] = 1\n", + "\n", + "filtered_trips.rename(columns=dict([(c, 'av_'+c) for c in new_cols]), inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "38bfcc0c", + "metadata": {}, + "source": [ + "### Cost estimation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "054a6ad1", + "metadata": {}, + "outputs": [], + "source": [ + "# All values are taken from VTPI.\n", + "# https://www.vtpi.org/tca/tca0501.pdf\n", + "mode_cost_per_mile = {\n", + " # bicycle/skateboard\n", + " 'p_micro': 0.,\n", + " 'no_trip': 0.,\n", + " # Shared car is half the cost of regular car, which is $0.6/mile.\n", + " 's_car': 0.3,\n", + " # Rental car.\n", + " 'car': 0.6,\n", + " # Average of bus and train taken.\n", + " 'transit': 0.5,\n", + " # Shared bicyle or scooter - values taken from https://nacto.org/shared-micromobility-2020-2021/ and \n", + " # https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/how-sharing-the-road-is-likely-to-transform-american-mobility\n", + " 's_micro': 0.3,\n", + " # uber/taxi/lyft\n", + " 'ridehail': 2.,\n", + " 'walk': 0.,\n", + " 'unknown': 0.\n", + "}\n", + "\n", + "# Assumptions.\n", + "mode_init_cost = {\n", + " 'p_micro': 0.,\n", + " 'no_trip': 0.,\n", + " # Shared car is half the cost of regular car, which is $0.6/mile.\n", + " 's_car': 0.,\n", + " # Rental car.\n", + " 'car': 0.,\n", + " # Average of bus and train taken.\n", + " 'transit': 0.,\n", + " # $1 unlocking cost.\n", + " 's_micro': 1.,\n", + " # uber/taxi/lyft\n", + " 'ridehail': 1.5,\n", + " 'walk': 0.,\n", + " 'unknown': 0.\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bccd3efb", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_cost_estimates(df: pd.DataFrame):\n", + " \n", + " # Create some extra colums.\n", + " columns = [c.replace('av_', '') for c in df.columns if 'av_' in c]\n", + "\n", + " # Initialize the columns to 0.\n", + " df[columns] = 0.\n", + "\n", + " rows = list()\n", + "\n", + " # Iterate over every row.\n", + " for _, row in df.iterrows():\n", + " # Check which flags are active.\n", + " row_dict = row.to_dict()\n", + "\n", + " # Access the section_distance_argmax attribute for the distance. Note that this is now in miles.\n", + " distance = row_dict['section_distance_argmax']\n", + " \n", + " # Mask using availability.\n", + " for lookup in columns:\n", + " row_dict[lookup] = row_dict['av_' + lookup] * (\n", + " mode_init_cost[lookup] + (mode_cost_per_mile[lookup] * distance)\n", + " )\n", + "\n", + " rows.append(row_dict)\n", + "\n", + " new_df = pd.DataFrame(rows)\n", + " new_df.rename(columns=dict([(c, 'cost_'+c) for c in columns]), inplace=True)\n", + "\n", + " return new_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c39f1901", + "metadata": {}, + "outputs": [], + "source": [ + "filtered_trips = compute_cost_estimates(filtered_trips)" + ] + }, + { + "cell_type": "markdown", + "id": "a6c20466", + "metadata": {}, + "source": [ + "### Outlier removal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c05071cc", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"For {CURRENT_DB=}, before outlier removal, n_rows = {filtered_trips.shape[0]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b222715f", + "metadata": {}, + "outputs": [], + "source": [ + "# Drop instances where duration/distance is unusable.\n", + "filtered_trips.drop(\n", + " index=filtered_trips.loc[(filtered_trips.section_distance_argmax <= 0) | (filtered_trips.section_duration_argmax <= 0), :].index,\n", + " inplace=False\n", + ").reset_index(drop=True, inplace=True)\n", + "\n", + "\n", + "# bus, train, bicycling, walking, car\n", + "# split-apply-combine\n", + "def drop_outliers(df: pd.DataFrame, low=0.1, high=0.9) -> pd.DataFrame:\n", + " \n", + " def filter_by_percentiles(group):\n", + " distance_low = group['section_distance_argmax'].quantile(low)\n", + " distance_high = group['section_distance_argmax'].quantile(high)\n", + " duration_low = group['section_duration_argmax'].quantile(low)\n", + " duration_high = group['section_duration_argmax'].quantile(high)\n", + " \n", + " l1_filter = group[\n", + " (group['section_distance_argmax'] >= distance_low) &\n", + " (group['section_distance_argmax'] <= distance_high)\n", + " ].reset_index(drop=True)\n", + " \n", + " l2_filter = l1_filter[\n", + " (l1_filter['section_duration_argmax'] >= duration_low) &\n", + " (l1_filter['section_duration_argmax'] <= duration_high)\n", + " ].reset_index(drop=True)\n", + " \n", + " return l2_filter\n", + " \n", + " return df.groupby('section_mode_argmax').apply(filter_by_percentiles).reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d77febb3", + "metadata": {}, + "outputs": [], + "source": [ + "filtered_trips = drop_outliers(filtered_trips, low=0.01, high=0.99)\n", + "\n", + "# Ideal speed. distance/time (in hours).\n", + "filtered_trips['mph'] = (\n", + " (filtered_trips['section_distance_argmax'] * 60.)/filtered_trips['section_duration_argmax']\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b52d5325", + "metadata": {}, + "outputs": [], + "source": [ + "filtered_trips[['section_mode_argmax', 'section_duration_argmax', 'section_distance_argmax', 'mph']].head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7ed953d", + "metadata": {}, + "outputs": [], + "source": [ + "def filter_mph(df: pd.DataFrame, low=0.1, high=0.9) -> pd.DataFrame:\n", + " \n", + " MPH_THRESHOLDS = {\n", + " # https://www.sciencedirect.com/science/article/pii/S2210670718304682\n", + " 'bicycling': 15.,\n", + " # https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806575/\n", + " 'walking': 2.93\n", + " }\n", + " \n", + " def custom_filter(group):\n", + " # Drop data specified in the dict manually.\n", + " if group.name in MPH_THRESHOLDS.keys():\n", + " f_df = group[group['mph'] <= MPH_THRESHOLDS[group.name]]\n", + " else:\n", + " mph_low = group['mph'].quantile(low)\n", + " mph_high = group['mph'].quantile(high)\n", + "\n", + " f_df = group[(group['mph'] >= mph_low) & (group['mph'] <= mph_high)]\n", + " \n", + " return f_df\n", + " \n", + " return df.groupby('section_mode_argmax').apply(custom_filter).reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c1904cd", + "metadata": {}, + "outputs": [], + "source": [ + "filtered_trips = filter_mph(filtered_trips, low=0.01, high=0.99)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3dce2b1c", + "metadata": {}, + "outputs": [], + "source": [ + "filtered_trips.groupby('section_mode_argmax')[['section_distance_argmax', 'section_duration_argmax']].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "396f196b", + "metadata": {}, + "outputs": [], + "source": [ + "filtered_trips.groupby('section_mode_argmax')[['mph']].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41109148", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"For {CURRENT_DB=}, After outlier removal, n_rows = {filtered_trips.shape[0]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ca22a08", + "metadata": {}, + "outputs": [], + "source": [ + "to_drop=[\n", + " '_id', 'additions', 'cleaned_section_summary', 'cleaned_trip', 'confidence_threshold', \n", + " 'end_fmt_time', 'end_loc', 'end_local_dt_day', 'raw_trip', 'purpose_confirm',\n", + " 'end_local_dt_minute', 'end_local_dt_month', 'end_local_dt_second', 'end_local_dt_timezone', \n", + " 'end_local_dt_weekday', 'end_local_dt_year', 'end_place', 'end_ts', 'expectation', 'expected_trip', \n", + " 'inferred_labels', 'inferred_section_summary', 'inferred_trip', 'metadata_write_ts', 'mode_confirm', \n", + " 'section_durations', 'section_modes', 'source', 'start_fmt_time', 'start_loc', 'start_local_dt_day', \n", + " 'start_local_dt_minute', 'start_local_dt_month', 'start_local_dt_second', \n", + " 'start_local_dt_timezone', 'start_local_dt_weekday', 'start_local_dt_year', 'start_place', \n", + " 'start_ts', 'user_id_join', 'user_input', 'survey_user_id', 'section_distances',\n", + " 'data.local_dt.year', 'data.local_dt.month', 'data.local_dt.day', 'data.local_dt.hour', \n", + " 'data.local_dt.minute', 'data.local_dt.second', 'data.local_dt.weekday', 'data.local_dt.timezone',\n", + " 'data.fmt_time'\n", + "]\n", + "\n", + "for col in to_drop:\n", + " if col in filtered_trips.columns:\n", + " filtered_trips.drop(columns=[col], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2937d4ef", + "metadata": {}, + "outputs": [], + "source": [ + "filtered_trips.rename({'start_local_dt_hour': 'start:hour', 'end_local_dt_hour': 'end:hour'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87c7fc92", + "metadata": {}, + "outputs": [], + "source": [ + "print(filtered_trips.columns.tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ea36cad", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "display(filtered_trips.head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7018bf4", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Done processing for {CURRENT_DB=}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0eacc539", + "metadata": {}, + "outputs": [], + "source": [ + "targets = ['p_micro', 'no_trip', 's_car', 'transit', 'car', 's_micro', 'ridehail', 'walk', 'unknown']\n", + "\n", + "# Rename and map targets.\n", + "filtered_trips.rename(columns={'replaced_mode': 'target'}, inplace=True)\n", + "filtered_trips.replace({'target': {t: ix+1 for ix, t in enumerate(targets)}}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50d3eaec", + "metadata": {}, + "outputs": [], + "source": [ + "display(filtered_trips.target.unique())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31f35a04", + "metadata": {}, + "outputs": [], + "source": [ + "filtered_trips.to_csv(f'../data/filtered_data/preprocessed_data_{CURRENT_DB}.csv', index=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "emission", + "language": "python", + "name": "emission" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/rm_src/run_baseline_experiments.py b/rm_src/02_run_trip_level_models.py similarity index 55% rename from rm_src/run_baseline_experiments.py rename to rm_src/02_run_trip_level_models.py index f876598..cd1c476 100644 --- a/rm_src/run_baseline_experiments.py +++ b/rm_src/02_run_trip_level_models.py @@ -27,21 +27,22 @@ warnings.simplefilter(action='ignore', category=Warning) # Global experiment flags and variables. -SEED = 19348 +SEED = 13210 TARGETS = ['p_micro', 'no_trip', 's_car', 'transit', 'car', 's_micro', 'ridehail', 'walk', 'unknown'] +MAP = {ix+1:t for ix, t in enumerate(TARGETS)} + CV = False # Set the Numpy seed too. random.seed(SEED) np.random.seed(SEED) - class SPLIT_TYPE(Enum): INTRA_USER = 0 INTER_USER = 1 TARGET = 2 MODE = 3 - INTER_USER_STATIC = 4 + HIDE_USER = 4 class SPLIT(Enum): @@ -68,7 +69,7 @@ def get_train_test_splits(data: pd.DataFrame, how=SPLIT_TYPE, test_ratio=0.2, sh # Iterate only once and break. break - return X_tr, X_te + return X_tr, X_te, None elif how == SPLIT_TYPE.INTRA_USER: @@ -102,7 +103,7 @@ def get_train_test_splits(data: pd.DataFrame, how=SPLIT_TYPE, test_ratio=0.2, sh ignore_index=True, axis=0 ) - return X_tr_merged, X_te_merged + return X_tr_merged, X_te_merged, None elif how == SPLIT_TYPE.TARGET: @@ -111,7 +112,7 @@ def get_train_test_splits(data: pd.DataFrame, how=SPLIT_TYPE, test_ratio=0.2, sh random_state=SEED ) - return X_tr, X_te + return X_tr, X_te, None elif how == SPLIT_TYPE.MODE: X_tr, X_te = train_test_split( @@ -119,70 +120,41 @@ def get_train_test_splits(data: pd.DataFrame, how=SPLIT_TYPE, test_ratio=0.2, sh random_state=SEED ) - return X_tr, X_te + return X_tr, X_te, None + - elif how == SPLIT_TYPE.INTER_USER_STATIC: + elif how == SPLIT_TYPE.HIDE_USER: + users = data.user_id.value_counts(normalize=True) + percentiles = users.quantile([0.25, 0.5, 0.75]) + + low_trip_users = users[users <= percentiles[0.25]].index + mid_trip_users = users[(percentiles[0.25] <= users) & (users <= percentiles[0.5])].index + high_trip_users = users[(percentiles[0.5] <= users) & (users <= percentiles[0.75])].index + + # select one from each randomly. + user1 = np.random.choice(low_trip_users) + user2 = np.random.choice(mid_trip_users) + user3 = np.random.choice(high_trip_users) - train_ids = ['810be63d084746e3b7da9d943dd88e8c', 'bf774cbe6c3040b0a022278d36a23f19', '8a8332a53a1b4cdd9f3680434e91a6ef', - '5ad862e79a6341f69f28c0096fe884da', '7f89656bd4a94d12ad8e5ad9f0afecaf', 'fbaa338d7cd7457c8cad4d0e60a44d18', - '3b25446778824941a4c70ae5774f4c68', '28cb1dde85514bbabfd42145bdaf7e0a', '3aeb5494088542fdaf798532951aebb0', - '531732fee3c24366a286d76eb534aebc', '950f4287bab5444aa0527cc23fb082b2', '737ef8494f26407b8b2a6b1b1dc631a4', - 'e06cf95717f448ecb81c440b1b2fe1ab', '7347df5e0ac94a109790b31ba2e8a02a', 'bd9cffc8dbf1402da479f9f148ec9e60', - '2f3b66a5f98546d4b7691fba57fa640f', 'f289f7001bd94db0b33a7d2e1cd28b19', '19a043d1f2414dbcafcca44ea2bd1f19', - '68788082836e4762b26ad0877643fdcf', '4e8b1b7f026c4384827f157225da13fa', '703a9cee8315441faff7eb63f2bfa93f', - 'add706b73839413da13344c355dde0bb', '47b5d57bd4354276bb6d2dcd1438901d', 'e4cfb2a8f600426897569985e234636e', - '0154d71439284c34b865e5a417cd48af', '234f4f2366244fe682dccded2fa7cc4e', '0d0ae3a556414d138c52a6040a203d24', - '44c10f66dec244d6b8644231d4a8fecb', '30e9b141d7894fbfaacecd2fa18929f9', '0eb313ab00e6469da78cc2d2e94660fb', - 'fc51d1258e4649ecbfb0e6ecdaeca454', 'a1954793b1454b2f8cf95917d7547169', '6656c04c6cba4c189fed805eaa529741', - '6a0f3653b80a4c949e127d6504debb55', 'dfe5ca1bb0854b67a6ffccad9565d669', '8b1f3ba43de945bea79de6a81716ad04', - 'cde34edb8e3a4278a18e0adb062999e5', '6d96909e5ca442ccb5679d9cdf3c8f5b', 'a60a64d82d1c439a901b683b73a74d73', - '60e6a6f6ed2e4e838f2bbed6a427028d', '88041eddad7542ea8c92b30e5c64e198', '1635c003b1f94a399ebebe21640ffced', - '1581993b404a4b9c9ca6b0e0b8212316', 'b1aed24c863949bfbfa3a844ecf60593', '4b89612d7f1f4b368635c2bc48bd7993', - 'eb2e2a5211564a9290fcb06032f9b4af', '26767f9f3da54e93b692f8be6acdac43', '8a98e383a2d143e798fc23869694934a', - 'b346b83b9f7c4536b809d5f92074fdae', 'd929e7f8b7624d76bdb0ec9ada6cc650', '863e9c6c8ec048c4b7653f73d839c85b', - 'f50537eb104e4213908f1862c8160a3e', '4a9db5a9bac046a59403b44b883cc0ba', 'cded005d5fd14c64a5bba3f5c4fe8385', - 'c7ce889c796f4e2a8859fa2d7d5068fe', '405b221abe9e43bc86a57ca7fccf2227', '0b3e78fa91d84aa6a3203440143c8c16', - 'fbff5e08b7f24a94ab4b2d7371999ef7', 'e35e65107a34496db49fa5a0b41a1e9e', 'd5137ebd4f034dc193d216128bb7fc9a', - '3f7f2e536ba9481e92f8379b796ad1d0', 'dc75e0b776214e1b9888f6abd042fd95', 'b41dd7d7c6d94fe6afe2fd26fa4ac0bd', - 'eec6936e1ac347ef9365881845ec74df', '8c7d261fe8284a42a777ffa6f380ba3b', '4baf8c8af7b7445e9067854065e3e612', - 'c6e4db31c18b4355b02a7dd97deca70b', 'f0db3b1999c2410ba5933103eca9212f', '487e20ab774742378198f94f5b5b0b43', - 'dc1ed4d71e3645d0993885398d5628ca', '8c3c63abb3ec4fc3a61e7bf316ee4efd', '15eb78dd6e104966ba6112589c29dc41', - 'c23768ccb817416eaf08be487b2e3643', 'ecd2ae17d5184807abd87a287115c299', '71f21d53b655463784f3a3c63c56707b', - '2931e0a34319495bbb5898201a54feb5', '92bde0d0662f45ac864629f486cffe77', '42b3ee0bc02a481ab1a94644a8cd7a0d', - '15aa4ba144a34b8b8079ed7e049d84df', '509b909390934e988eb120b58ed9bd8c', '14103cda12c94642974129989d39e50d', - '8b0876430c2641bcaea954ea00520e64', 'baa4ff1573ae411183e10aeb17c71c53', '14fe8002bbdc4f97acbd1a00de241bf6', - '1b7d6dfea8464bcab9321018b10ec9c9', '487ad897ba93404a8cbe5de7d1922691', '5182d93d69754d7ba06200cd1ac5980a', - '91f3ca1c278247f79a806e49e9cc236f', 'e66e63b206784a559d977d4cb5f1ec34', '840297ae39484e26bfebe83ee30c5b3e', - 'c6807997194c4c528a8fa8c1f6ee1595', '802667b6371f45b29c7abb051244836a', 'b2bbe715b6a14fd19f751cae8adf6b4e', - 'feb1d940cd3647d1a101580c2a3b3f8c', '1b9883393ab344a69bc1a0fab192a94c', 'ac604b44fdca482fb753034cb55d1351', - 'f446bf3102ff4bd99ea1c98f7d2f7af0', 'c2c5d4b9a607487ea405a99c721079d4', '85ddd3c34c58407392953c47a32f5428', - 'd51de709f95045f8bacf473574b96ba5', '6373dfb8cb9b47e88e8f76adcfadde20', '313d003df34b4bd9823b3474fc93f9f9', - '53e78583db87421f8decb529ba859ca4', '8fdc9b926a674a9ea07d91df2c5e06f2', '90480ac60a3d475a88fbdab0a003dd5d', - '7559c3f880f341e898a402eba96a855d', '19a4c2cf718d40588eb96ac25a566353', 'f4427cccaa9442b48b42bedab5ab648e', - 'e192b8a00b6c422296851c93785deaf7', '355e25bdfc244c5e85d358e39432bd44', 'a0c3a7b410b24e18995f63369a31d123', - '03a395b4d8614757bb8432b4984559b0', 'a2d48b05d5454d428c0841432c7467b6', '3d981e617b304afab0f21ce8aa6c9786', - '2cd5668ac9054e2eb2c88bb4ed94bc6d', 'd7a732f4a8644bcbb8dedfc8be242fb2', '367eb90b929d4f6e9470d15c700d2e3f', - 'e049a7b2a6cb44259f907abbb44c5abc', 'a231added8674bef95092b32bc254ac8', 'e88a8f520dde445484c0a9395e1a0599', - 'cba570ae38f341faa6257342727377b7', '97953af1b97d4e268c52e1e54dcf421a', 'd200a61757d84b1dab8fbac35ff52c28', - 'fc68a5bb0a7b4b6386b3f08a69ead36f', '4a8210aec25e443391efb924cc0e5f23', '903742c353ce42c3ad9ab039fc418816', - '2114e2a75304475fad06ad201948fbad', 'ac917eae407c4deb96625dd0dc2f2ba9', '3dddfb70e7cd40f18a63478654182e9a', - 'd3735ba212dd4c768e1675dca7bdcb6f', '7abe572148864412a33979592fa985fb', 'd3dff742d07942ca805c2f72e49e12c5' - ] + print(f"Users picked: {user1}, {user2}, {user3}") - X_tr = data.loc[data.user_id.isin(train_ids), :] - X_te = data.loc[~data.user_id.isin(train_ids), :] + # Remove these users from the entire dataset. + held_out = data.loc[data.user_id.isin([user1, user2, user3]), :].reset_index(drop=True) + remaining = data.loc[~data.user_id.isin([user1, user2, user3]), :].reset_index(drop=True) - return X_tr, X_te + # Split randomly. + X_tr, X_te = train_test_split( + remaining, test_size=test_ratio, shuffle=shuffle, random_state=SEED + ) + + return X_tr, X_te, held_out raise NotImplementedError("Unknown split type") def get_duration_estimate(df: pd.DataFrame, dset: SPLIT, model_dict: dict): - X_features = ['section_distance_argmax', 'age'] - - if 'mph' in df.columns: - X_features += ['mph'] + X_features = ['section_distance_argmax', 'mph'] if dset == SPLIT.TRAIN and model_dict is None: model_dict = dict() @@ -198,9 +170,7 @@ def get_duration_estimate(df: pd.DataFrame, dset: SPLIT, model_dict: dict): model = LinearRegression(fit_intercept=True) - X = section_data[ - X_features - ] + X = section_data[X_features] Y = section_data[['section_duration_argmax']] model.fit(X, Y.values.ravel()) @@ -213,9 +183,7 @@ def get_duration_estimate(df: pd.DataFrame, dset: SPLIT, model_dict: dict): elif dset == SPLIT.TEST: for section_mode in df.section_mode_argmax.unique(): section_data = df.loc[df.section_mode_argmax == section_mode, :] - X = section_data[ - X_features - ] + X = section_data[X_features] Y = section_data[['section_duration_argmax']] y_pred = model_dict[section_mode]['model'].predict(X) @@ -224,7 +192,7 @@ def get_duration_estimate(df: pd.DataFrame, dset: SPLIT, model_dict: dict): # Create the new columns for the duration. new_columns = ['p_micro','no_trip','s_car','transit','car','s_micro','ridehail','walk','unknown'] - df[new_columns] = 0 + df[TARGETS] = 0 df['temp'] = 0 for section in df.section_mode_argmax.unique(): @@ -233,12 +201,12 @@ def get_duration_estimate(df: pd.DataFrame, dset: SPLIT, model_dict: dict): # broadcast to all columns. df.loc[df.section_mode_argmax == section, 'temp'] = model_dict[section]['model'].predict(X_section) - for c in new_columns: + for c in TARGETS: df[c] = df['av_' + c] * df['temp'] df.drop(columns=['temp'], inplace=True) - df.rename(columns=dict([(x, 'tt_'+x) for x in new_columns]), inplace=True) + df.rename(columns=dict([(x, 'tt_'+x) for x in TARGETS]), inplace=True) # return model_dict, result_df return model_dict, df @@ -246,19 +214,7 @@ def get_duration_estimate(df: pd.DataFrame, dset: SPLIT, model_dict: dict): # Some helper functions that will help ease redundancy in the code. def drop_columns(df: pd.DataFrame): - to_drop = [ - 'source', 'end_ts', 'end_fmt_time', 'end_loc', 'raw_trip', 'start_ts', - 'start_fmt_time', 'start_loc', 'duration', 'distance', 'start_place', - 'end_place', 'cleaned_trip', 'inferred_labels', 'inferred_trip', 'expectation', - 'confidence_threshold', 'expected_trip', 'user_input', 'start:year', 'start:month', - 'start:day', 'start_local_dt_minute', 'start_local_dt_second', - 'start_local_dt_weekday', 'start_local_dt_timezone', 'end:year', 'end:month', 'end:day', - 'end_local_dt_minute', 'end_local_dt_second', 'end_local_dt_weekday', - 'end_local_dt_timezone', '_id', 'user_id', 'metadata_write_ts', 'additions', - 'mode_confirm', 'purpose_confirm', 'Mode_confirm', 'Trip_purpose', - 'original_user_id', 'program', 'opcode', 'Timestamp', 'birth_year', - 'available_modes', 'section_coordinates_argmax', 'section_mode_argmax' - ] + to_drop = ['section_mode_argmax', 'available_modes', 'user_id'] # Drop section_mode_argmax and available_modes. return df.drop( @@ -271,7 +227,7 @@ def scale_values(df: pd.DataFrame, split: SPLIT, scalers=None): # Scale costs using StandardScaler. costs = df[[c for c in df.columns if 'cost_' in c]].copy() times = df[[c for c in df.columns if 'tt_' in c or 'duration' in c]].copy() - distances = df[[c for c in df.columns if 'distance' in c]] + distances = df[[c for c in df.columns if 'distance' in c or 'mph' in c]].copy() print( "Cost columns to be scaled: ", costs.columns,"\nTime columns to be scaled: ", times.columns, \ @@ -407,24 +363,14 @@ def predict(model, X_tr, Y_tr, X_te, Y_te): def run_sampled_sweep(df: pd.DataFrame, dir_name: Path, **kwargs): targets = TARGETS.copy() - - drop_s_micro = kwargs.pop('drop_s_micro', None) - - if drop_s_micro: - df.drop( - index=df.loc[data.target == 6, :].index, - inplace=True - ) - - # Shift all values after 6 by -1 - df.loc[data.target > 5, 'target'] -= 1 - - # Update targets. - targets.pop(targets.index('s_micro')) split = kwargs.pop('split', None) - train_data, test_data = get_train_test_splits(data=df, how=split, shuffle=True) + try: + train_data, test_data, hidden_data = get_train_test_splits(data=df, how=split, shuffle=True) + except Exception as e: + print(e) + return params, train_data = get_duration_estimate(train_data, SPLIT.TRAIN, None) _, test_data = get_duration_estimate(test_data, SPLIT.TEST, params) @@ -432,12 +378,6 @@ def run_sampled_sweep(df: pd.DataFrame, dir_name: Path, **kwargs): train_data = drop_columns(train_data) test_data = drop_columns(test_data) - drop_location = kwargs.pop('drop_location', None) - - if drop_location: - train_data.drop(columns=['start_lat', 'start_lng', 'end_lat', 'end_lng'], inplace=True) - test_data.drop(columns=['start_lat', 'start_lng', 'end_lat', 'end_lng'], inplace=True) - X_tr, Y_tr = train_data.drop(columns=['target'], inplace=False), train_data.target.values.ravel() X_te, Y_te = test_data.drop(columns=['target'], inplace=False), test_data.target.values.ravel() @@ -460,8 +400,19 @@ def run_sampled_sweep(df: pd.DataFrame, dir_name: Path, **kwargs): importance_df = pd.DataFrame(importance, columns=['feature_name', 'importance']) importance_df.to_csv(dir_name / 'feature_importance.csv', index=False) + # target_names = [MAP[x] for x in np.unique(Y_te)] + with open(dir_name / 'classification_report.txt', 'w') as f: - f.write(classification_report(y_true=Y_te, y_pred=te_preds, target_names=targets)) + f.write(classification_report(y_true=Y_te, y_pred=te_preds)) + + if split == SPLIT_TYPE.HIDE_USER and hidden_data is not None: + _, hidden_data = get_duration_estimate(hidden_data, SPLIT.TEST, params) + hidden_data = drop_columns(hidden_data) + + X_hid, Y_hid = hidden_data.drop(columns=['target'], inplace=False), hidden_data.target.values.ravel() + + tr_preds, tr_f1, te_preds, te_f1 = predict(model, X_tr, Y_tr, X_hid, Y_hid) + print(f"\t\t ---> Hidden user F1: {te_f1} <---") fig, ax = plt.subplots(figsize=(7, 7)) cm = ConfusionMatrixDisplay.from_estimator( @@ -470,8 +421,8 @@ def run_sampled_sweep(df: pd.DataFrame, dir_name: Path, **kwargs): y=Y_te, ax=ax ) - ax.set_xticklabels(targets, rotation=45) - ax.set_yticklabels(targets) + # ax.set_xticklabels(target_names, rotation=45) + # ax.set_yticklabels(target_names) fig.tight_layout() plt.savefig(dir_name / 'test_confusion_matrix.png') plt.close('all') @@ -483,48 +434,58 @@ def save_metadata(dir_name: Path, **kwargs): f.write(f"{k}: {v}\n") -# def parse_args(): -# parser = argparse.ArgumentParser() -# parser.add_argument('--max-iters', default=10000, type=int) -# return parser.parse_args() - if __name__ == "__main__": - data = pd.read_csv('../data/ReplacedMode_Fix_02142024.csv') - data.drop_duplicates(inplace=True) - - print("Beginning sweeps.") - # args = parse_args() + datasets = sorted(list(Path('../data/filtered_data').glob('preprocessed_data_*.csv'))) start = perf_counter() - sweep_number = 1 - - root = Path('../benchmark_results') - if not root.exists(): - root.mkdir() - - for split in [SPLIT_TYPE.INTER_USER, SPLIT_TYPE.INTRA_USER, SPLIT_TYPE.TARGET, SPLIT_TYPE.MODE]: - for drop in [True, False]: - for location_drop in [True, False]: - kwargs = { - 'drop_s_micro': drop, - 'split': split, - 'drop_location': location_drop - } - - dir_name = root / f'benchmark_{sweep_number}' - - if not dir_name.exists(): - dir_name.mkdir() - - print(f"\t-> Running sweep #{sweep_number} with metadata={str(kwargs)}") - save_metadata(dir_name, **kwargs) - run_sampled_sweep(data.copy(), dir_name, **kwargs) - print(f"Completed benchmarking for {sweep_number} experiment.") - print(50*'-') - sweep_number += 1 + + for dataset in datasets: + name = dataset.name.replace('.csv', '') + + print(f"Starting modeling for dataset = {name}") + + data = pd.read_csv(dataset) + data.drop_duplicates(inplace=True) + data.dropna(inplace=True) + + if 'deprecatedID' in data.columns: + data.drop(columns=['deprecatedID'], inplace=True) + if 'data.key' in data.columns: + data.drop(columns=['data.key'], inplace=True) + + # These two lines make all the difference. + data.sort_values(by=['user_id'], ascending=True, inplace=True) + data = data[sorted(data.columns.tolist())] + + print("Beginning sweeps.") + + # args = parse_args() + sweep_number = 1 + + root = Path('../benchmark_results') + if not root.exists(): + root.mkdir() + + for split in [SPLIT_TYPE.INTER_USER, SPLIT_TYPE.INTRA_USER, SPLIT_TYPE.TARGET, SPLIT_TYPE.MODE, SPLIT_TYPE.HIDE_USER]: + kwargs = { + 'dataset': name, + 'split': split + } + + dir_name = root / f'benchmark_{name}_{sweep_number}' + + if not dir_name.exists(): + dir_name.mkdir() + + print(f"\t-> Running sweep #{sweep_number} with metadata={str(kwargs)}") + save_metadata(dir_name, **kwargs) + run_sampled_sweep(data.copy(), dir_name, **kwargs) + print(f"Completed benchmarking for {sweep_number} experiment.") + print(50*'-') + sweep_number += 1 elapsed = perf_counter() - start - print(f"Completed sweeps in {elapsed/60.} minutes") + print(f"Completed sweeps in {elapsed/60.} minutes") \ No newline at end of file diff --git a/rm_src/03_user_level_models.ipynb b/rm_src/03_user_level_models.ipynb new file mode 100644 index 0000000..9a31729 --- /dev/null +++ b/rm_src/03_user_level_models.ipynb @@ -0,0 +1,2743 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "04ccf092", + "metadata": {}, + "source": [ + "## Some important points to remember:\n", + "\n", + "### We want to experiment with two types of models:\n", + "\n", + "\n", + "1. have one row per user, so that when predicting modes for a new user, we pick the \"similar user\" or users and determine the replaced mode\n", + " - In this, the traditional approach would only use demographics for the user features, we may experiment with some summaries of the trip data that will function as some level of \"fingerprint\" for the user. Ideally we would be able to show that this performs better than demographics alone\n", + " - Note also that the original method that you had outlined where the training set is a list of trips (O()) is a third approach which we will be comparing these two against" + ] + }, + { + "cell_type": "markdown", + "id": "c0c1ee88", + "metadata": {}, + "source": [ + "Target order:\n", + "\n", + "```\n", + "['p_micro', 'no_trip', 's_car', 'transit', 'car', 's_micro', 'ridehail', 'walk', 'unknown']\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "21ef0f2e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import random\n", + "import os\n", + "import pickle\n", + "import ast\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import r2_score, f1_score, log_loss\n", + "from sklearn.model_selection import train_test_split, RandomizedSearchCV, StratifiedKFold, KFold\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances\n", + "from enum import Enum\n", + "from scipy.stats import uniform\n", + "from typing import List, Dict, Union\n", + "from pandas.api.types import is_numeric_dtype\n", + "from sklearn.manifold import TSNE\n", + "from multiprocessing import cpu_count\n", + "\n", + "pd.set_option('display.max_columns', 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "fef98692", + "metadata": {}, + "outputs": [], + "source": [ + "SEED = 13210\n", + "\n", + "np.random.seed(SEED)\n", + "random.seed(SEED)\n", + "\n", + "SimilarityMetric = Enum('SimilarityMetric', ['COSINE', 'EUCLIDEAN', 'KNN', 'KMEANS'])\n", + "GroupType = Enum('GroupType', ['GROUPBY', 'CUT'])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "79f8c51a", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('../data/filtered_data/preprocessed_data_Stage_database.csv')\n", + "# df = pd.read_csv('../data/filtered_data/preprocessed_data_openpath_prod_durham.csv')\n", + "# df = pd.read_csv('../data/filtered_data/preprocessed_data_openpath_prod_mm_masscec.csv')\n", + "# df = pd.read_csv('../data/filtered_data/preprocessed_data_openpath_prod_ride2own.csv')\n", + "# df = pd.read_csv('../data/filtered_data/preprocessed_data_openpath_prod_uprm_nicr.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "915e9d6f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 9, 5, 8, 3, 2, 4])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('user_id')['target'].apply(lambda x: x.value_counts().idxmax()).unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "72793473", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['distance', 'duration', 'end_local_dt_hour', 'target', 'section_distance_argmax', 'section_duration_argmax', 'section_mode_argmax', 'start_local_dt_hour', 'user_id', 'has_drivers_license', 'is_student', 'is_paid', 'income_category', 'n_residence_members', 'n_residents_u18', 'n_residents_with_license', 'n_motor_vehicles', 'available_modes', 'has_medical_condition', 'ft_job', 'multiple_jobs', 'n_working_residents', \"highest_education_Bachelor's degree\", 'highest_education_Graduate degree or professional degree', 'highest_education_High school graduate or GED', 'highest_education_Less than a high school graduate', 'highest_education_Prefer not to say', 'highest_education_Some college or associates degree', 'primary_job_description_Clerical or administrative support', 'primary_job_description_Custodial', 'primary_job_description_Education', 'primary_job_description_Food service', 'primary_job_description_Linecook', 'primary_job_description_Manufacturing, construction, maintenance, or farming', 'primary_job_description_Medical/healthcare', 'primary_job_description_Non-profit program manager', 'primary_job_description_Other', 'primary_job_description_Professional, managerial, or technical', 'primary_job_description_Sales or service', 'primary_job_description_Self employed', 'primary_job_description_food service', 'gender_Man', 'gender_Nonbinary/genderqueer/genderfluid', 'gender_Prefer not to say', 'gender_Woman', 'gender_Woman;Nonbinary/genderqueer/genderfluid', 'age_16___20_years_old', 'age_21___25_years_old', 'age_26___30_years_old', 'age_31___35_years_old', 'age_36___40_years_old', 'age_41___45_years_old', 'age_46___50_years_old', 'age_51___55_years_old', 'age_56___60_years_old', 'age_61___65_years_old', 'age___65_years_old', 'av_transit', 'av_no_trip', 'av_p_micro', 'av_s_micro', 'av_ridehail', 'av_unknown', 'av_walk', 'av_car', 'av_s_car', 'cost_transit', 'cost_no_trip', 'cost_p_micro', 'cost_s_micro', 'cost_ridehail', 'cost_unknown', 'cost_walk', 'cost_car', 'cost_s_car', 'mph']\n" + ] + } + ], + "source": [ + "print(df.columns.tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "765f08ff", + "metadata": {}, + "outputs": [], + "source": [ + "def generate_tsne_plots(df: pd.DataFrame, **kwargs):\n", + " \n", + " df = df.copy()\n", + " \n", + " # Important - if not cast as a category, seaborn considers this as a numerical value.\n", + " df.target = df.target.astype('category')\n", + " \n", + " # print(\"Unique targets: \", df.target.unique())\n", + " \n", + " # According to the docs, > consider choosing a perplexity between 5 and 50.\n", + " tsne = TSNE(\n", + " n_components=2,\n", + " perplexity=kwargs.pop('perplexity', 5),\n", + " n_iter=kwargs.pop('n_iter', 2000),\n", + " metric=kwargs.pop('metric', 'cosine'),\n", + " random_state=SEED,\n", + " n_jobs=os.cpu_count()\n", + " )\n", + " \n", + " if df.index.name == 'user_id':\n", + " df.reset_index(drop=False, inplace=True)\n", + " \n", + " if 'user_id' in df.columns:\n", + " df.drop(columns=['user_id'], inplace=True)\n", + " \n", + " targets = df.target.values\n", + " df.drop(columns=['target'], inplace=True)\n", + " \n", + " projected = tsne.fit_transform(df)\n", + " \n", + " fig, ax = plt.subplots()\n", + " sns.scatterplot(x=projected[:, 0], y=projected[:, 1], hue=targets, ax=ax)\n", + " ax.set(xlabel='Embedding dimension 1', ylabel='Embedding dimension 2', title='t-SNE plot for data')\n", + " plt.show()\n", + " \n", + " return projected" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cfe76e8c", + "metadata": {}, + "outputs": [], + "source": [ + "def get_mode_coverage(df: pd.DataFrame):\n", + " \n", + " coverage_df = df.groupby(['user_id', 'section_mode_argmax']).size().unstack(fill_value=0)\n", + " coverage_df.columns = ['coverage_' + str(c) for c in coverage_df.columns]\n", + " \n", + " # As a preventative measure.\n", + " coverage_df.fillna(0, inplace=True)\n", + " \n", + " # Normalize over rows.\n", + " coverage_df.iloc[:, 1:] = coverage_df.iloc[:, 1:].div(coverage_df.iloc[:, 1:].sum(axis=1), axis=0)\n", + " \n", + " return coverage_df" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "75313008", + "metadata": {}, + "outputs": [], + "source": [ + "def get_trip_summaries(df: pd.DataFrame, group_key: str, feature_list: List[str], **kwargs):\n", + " \n", + " def get_feature_summaries(trip_feature: str, is_ordinal: bool = False):\n", + " \n", + " if is_numeric_dtype(df[group_key]):\n", + " col_prefix = f'{trip_feature}_mean_cut'\n", + " if not use_qcut:\n", + " grouper = df.groupby(['user_id', pd.cut(df[group_key], n_cuts)])[trip_feature]\n", + " else:\n", + " grouper = df.groupby(['user_id', pd.qcut(df[group_key], n_cuts)])[trip_feature]\n", + " else:\n", + " grouper = df.groupby(['user_id', group_key])[trip_feature]\n", + " \n", + " if not is_ordinal:\n", + " # A mean of 0 is an actual value.\n", + " \n", + " mean = grouper.mean().unstack(level=-1, fill_value=-1.)\n", + " \n", + " mean.columns = [f'{trip_feature}_mean_' + str(c) for c in mean.columns]\n", + " \n", + " # Same with percentiles - 0 is an actual value.\n", + " median = grouper.median().unstack(level=-1, fill_value=-1.)\n", + " median.columns = [f'{trip_feature}_median_' + str(c) for c in median.columns]\n", + " \n", + " iqr_df = grouper.quantile([0.25, 0.75]).unstack(level=-1)\n", + " iqr = (iqr_df[0.75] - iqr_df[0.25]).unstack(level=-1)\n", + " iqr.fillna(-1., inplace=True)\n", + " iqr.columns = [f'{trip_feature}_iqr_' + str(c) for c in iqr.columns]\n", + "\n", + " # Now merge.\n", + " merged = mean.copy()\n", + " merged = merged.merge(right=median, left_index=True, right_index=True)\n", + " merged = merged.merge(right=iqr, left_index=True, right_index=True)\n", + " \n", + " merged.fillna(-1., inplace=True)\n", + "\n", + " return merged\n", + " \n", + " # 0 is OK to indicate NaN values.\n", + " f_mode = grouper.apply(\n", + " lambda x: x.value_counts().idxmax()\n", + " ).unstack(fill_value=0.)\n", + " \n", + " f_mode.columns = [f'{trip_feature}_mode_' + str(c) for c in f_mode.columns]\n", + " f_mode.fillna(0., inplace=True)\n", + " \n", + " return f_mode\n", + " \n", + " assert group_key not in feature_list, \"Cannot perform grouping and summarization of the same feature.\"\n", + " \n", + " # Optional kwarg for number of cuts for numeric dtype grouping.\n", + " # Default is 3: short, medium, long trip types:\n", + " # For e.g., if the group key is 'section_duration', it will be cut into three equally-sized bins,\n", + " # However, an alternative is also present - we could use qcut() instead, which would ensure that\n", + " # each bin has roughly the same number of samples.\n", + " n_cuts = kwargs.pop('n_cuts', 3)\n", + " use_qcut = kwargs.pop('use_qcut', False)\n", + " \n", + " # This will be the dataframe that all subsequent features will join to.\n", + " feature_df = None\n", + " \n", + " for ix, feature in enumerate(feature_list):\n", + " is_ordinal = feature == 'start_local_dt_hour' or feature == 'end_local_dt_hour'\n", + " if ix == 0:\n", + " feature_df = get_feature_summaries(feature, is_ordinal)\n", + " else:\n", + " next_feature_df = get_feature_summaries(feature, is_ordinal)\n", + " feature_df = feature_df.merge(right=next_feature_df, left_index=True, right_index=True)\n", + " \n", + " return feature_df" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "63617ada", + "metadata": {}, + "outputs": [], + "source": [ + "def get_demographic_data(df: pd.DataFrame, **trip_kwargs):\n", + " \n", + " '''\n", + " A method that returns a U x (D + t) matrix, where U = number of users,\n", + " D = number of demographic features, t (optional) = number of trip summary features.\n", + " \n", + " When use_trip_summaries=True, the 'available_modes' column is dropped in favor of\n", + " the already-preprocessed av_ columns. This is because we want to incorporate trip-level\n", + " information into the data. When the argument is False, we want to SOLELY use demographics.\n", + " '''\n", + " \n", + " trip_features_to_use = trip_kwargs.pop('trip_features', None)\n", + " trip_group_key = trip_kwargs.pop('trip_grouping', 'section_mode_argmax')\n", + " \n", + " demographics = [ \n", + " 'has_drivers_license', 'is_student', 'is_paid', 'income_category', 'n_residence_members', \n", + " 'n_residents_u18', 'n_residents_with_license', 'n_motor_vehicles',\n", + " 'has_medical_condition', 'ft_job', 'multiple_jobs', 'n_working_residents', \n", + " \"highest_education_Bachelor's degree\", 'highest_education_Graduate degree or professional degree', \n", + " 'highest_education_High school graduate or GED', 'highest_education_Less than a high school graduate', \n", + " 'highest_education_Prefer not to say', 'highest_education_Some college or associates degree', \n", + " 'primary_job_description_Clerical or administrative support', 'primary_job_description_Custodial', \n", + " 'primary_job_description_Education', 'primary_job_description_Food service', \n", + " 'primary_job_description_Linecook', \n", + " 'primary_job_description_Manufacturing, construction, maintenance, or farming', \n", + " 'primary_job_description_Medical/healthcare', 'primary_job_description_Non-profit program manager', \n", + " 'primary_job_description_Other', 'primary_job_description_Professional, managerial, or technical', \n", + " 'primary_job_description_Sales or service', 'primary_job_description_Self employed', \n", + " 'primary_job_description_food service', 'gender_Man', 'gender_Nonbinary/genderqueer/genderfluid', \n", + " 'gender_Prefer not to say', 'gender_Woman', 'gender_Woman;Nonbinary/genderqueer/genderfluid', \n", + " 'age_16___20_years_old', 'age_21___25_years_old', 'age_26___30_years_old', 'age_31___35_years_old', \n", + " 'age_36___40_years_old', 'age_41___45_years_old', 'age_46___50_years_old', 'age_51___55_years_old', \n", + " 'age_56___60_years_old', 'age_61___65_years_old', 'age___65_years_old', 'av_transit', 'av_no_trip', \n", + " 'av_p_micro', 'av_s_micro', 'av_ridehail', 'av_unknown', 'av_walk', 'av_car', 'av_s_car', \n", + " ]\n", + " \n", + " # Retain only the first instance of each user and subset the columns.\n", + " filtered = df.groupby('user_id').first()[demographics]\n", + " \n", + " # Get the targets.\n", + " targets = df.groupby('user_id')['target'].apply(lambda x: x.value_counts().idxmax())\n", + " \n", + " filtered = filtered.merge(right=targets, left_index=True, right_index=True)\n", + " \n", + " if trip_features_to_use is None or len(trip_features_to_use) == 0:\n", + "# # Use the available modes as indicators.\n", + "# return encode_availability(filtered)\n", + " return filtered\n", + " \n", + " # -----------------------------------------------------------\n", + " # Reaching here means that we need to include trip summaries\n", + " # -----------------------------------------------------------\n", + " \n", + " # If trip summaries are to be used, then re-use the preprocessed availability features.\n", + " availability = df[['user_id'] + [c for c in df.columns if 'av_' in c]]\n", + " availability = availability.groupby('user_id').first()\n", + " \n", + " # For every user, generate the global trip-level summaries.\n", + " global_aggs = df.groupby('user_id').agg({'duration': 'mean', 'distance': 'mean'})\n", + " \n", + " # coverage.\n", + " coverage = get_mode_coverage(df)\n", + " \n", + " # Trip-level features.\n", + " trip_features = get_trip_summaries(\n", + " df=df, \n", + " group_key=trip_group_key, \n", + " feature_list=trip_features_to_use,\n", + " use_qcut=trip_kwargs.pop('use_qcut', False)\n", + " )\n", + " \n", + " targets = df.groupby('user_id')['target'].apply(lambda x: x.value_counts().idxmax())\n", + " \n", + " trip_features = trip_features.merge(right=coverage, left_index=True, right_index=True)\n", + " trip_features = trip_features.merge(right=global_aggs, left_index=True, right_index=True)\n", + " \n", + " # Finally, join with availability indicators and targets.\n", + " trip_features = trip_features.merge(right=availability, left_index=True, right_on='user_id')\n", + " trip_features = trip_features.merge(right=targets, left_index=True, right_index=True)\n", + " \n", + " return trip_features.reset_index(drop=False)" + ] + }, + { + "cell_type": "markdown", + "id": "fedb51e8", + "metadata": {}, + "source": [ + "## Experiment 1: Only demographics" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "66421120", + "metadata": {}, + "outputs": [], + "source": [ + "## Educated suburban woman -> \n", + "# An embedding where:\n", + "# \"highest_education_Bachelor's degree\" == 1 or 'highest_education_Graduate degree or professional degree' == 1\n", + "# income_category >= 4 ( + more features that define 'suburban-ness')\n", + "# gender_Woman == 1\n", + "\n", + "demo_df = get_demographic_data(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "17196eaf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " has_drivers_license is_student \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 1 0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 1 0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 1 0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 1 0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 1 0 \n", + "\n", + " is_paid income_category \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0 4 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 1 3 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0 2 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 1 1 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 1 3 \n", + "\n", + " n_residence_members n_residents_u18 \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 4 2 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 5 3 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 3 0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 6 1 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 4 2 \n", + "\n", + " n_residents_with_license \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 2 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 2 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 2 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 4 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 2 \n", + "\n", + " n_motor_vehicles has_medical_condition \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 1 0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 4 0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 4 0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 1 0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 2 0 \n", + "\n", + " ft_job multiple_jobs \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0 0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0 0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0 0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0 0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0 1 \n", + "\n", + " n_working_residents \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 2 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 2 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 3 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 5 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 2 \n", + "\n", + " highest_education_Bachelor's degree \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " highest_education_Graduate degree or professional degree \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 1.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 1.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " highest_education_High school graduate or GED \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 1.0 \n", + "\n", + " highest_education_Less than a high school graduate \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " highest_education_Prefer not to say \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " highest_education_Some college or associates degree \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 1.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 1.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " primary_job_description_Clerical or administrative support \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " primary_job_description_Custodial \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " primary_job_description_Education \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 1.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " primary_job_description_Food service \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 1.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " primary_job_description_Linecook \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " primary_job_description_Manufacturing, construction, maintenance, or farming \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 1.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " primary_job_description_Medical/healthcare \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " primary_job_description_Non-profit program manager \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " primary_job_description_Other \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " primary_job_description_Professional, managerial, or technical \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 1.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " primary_job_description_Sales or service \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 1.0 \n", + "\n", + " primary_job_description_Self employed \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " primary_job_description_food service \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " gender_Man \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 1.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 1.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 1.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 1.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 1.0 \n", + "\n", + " gender_Nonbinary/genderqueer/genderfluid \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " gender_Prefer not to say gender_Woman \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 0.0 \n", + "\n", + " gender_Woman;Nonbinary/genderqueer/genderfluid \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " age_16___20_years_old \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " age_21___25_years_old \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " age_26___30_years_old \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " age_31___35_years_old \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 1.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 1.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " age_36___40_years_old \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " age_41___45_years_old \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 1.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " age_46___50_years_old \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " age_51___55_years_old \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " age_56___60_years_old \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 1.0 \n", + "\n", + " age_61___65_years_old \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 1.0 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 \n", + "\n", + " age___65_years_old av_transit \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0.0 1 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0.0 1 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0.0 1 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0.0 1 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0.0 1 \n", + "\n", + " av_no_trip av_p_micro av_s_micro \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 0 1 1 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 0 1 1 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 0 1 1 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 0 1 1 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 0 1 1 \n", + "\n", + " av_ridehail av_unknown av_walk \\\n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 1 1 1 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 1 1 1 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 1 1 1 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 1 1 1 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 1 1 1 \n", + "\n", + " av_car av_s_car target \n", + "user_id \n", + "00db212b-c8d0-44cd-8392-41ab4065e603 1 1 1 \n", + "0154d714-3928-4c34-b865-e5a417cd48af 1 1 9 \n", + "03a395b4-d861-4757-bb84-32b4984559b0 1 1 9 \n", + "0a093cbd-b536-43af-b03d-293425e84c76 1 1 9 \n", + "0d0ae3a5-5641-4d13-8c52-a6040a203d24 1 1 9 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(demo_df.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "4c458c1a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tsne_kwargs = {\n", + " 'perplexity': 6,\n", + " 'n_iter': 7500,\n", + " 'metric': 'cosine'\n", + "}\n", + "\n", + "## PLOT BY THE WAY IN WHICH PEOPLE USE THE SAME REPLACED MODE AND CHECK THE SIMILARITY.\n", + "\n", + "projections = generate_tsne_plots(demo_df, **tsne_kwargs)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "c023cf66", + "metadata": {}, + "outputs": [], + "source": [ + "# No stratification, pure random.\n", + "demo_df.reset_index(drop=False, inplace=True)\n", + "train, test = train_test_split(demo_df, test_size=0.2, random_state=SEED)\n", + "\n", + "TRAIN_USERS = train.user_id.unique().tolist()\n", + "TEST_USERS = test.user_id.unique().tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "376a4391", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "81 21\n" + ] + } + ], + "source": [ + "print(train.shape[0], test.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "630d6c08", + "metadata": {}, + "outputs": [], + "source": [ + "# Ensuring that no user information is leaked across sets.\n", + "assert train.shape[0] + test.shape[0] == len(df.user_id.unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "ef77c9c8", + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate_using_similarity(test_df, train_df, metric=SimilarityMetric.COSINE, **metric_kwargs):\n", + " \n", + " '''\n", + " This method treats each user row as a 'fingerprint' (embedding vector). We assume that we\n", + " have no idea about the test set labels. To find which replaced mode is most likely for the test\n", + " users, we compute the cosine similarity of each test user against the users in the training set.\n", + " For the most similar user, we use their target as a proxy for the test user's replaced mode.\n", + " This operates on the following intuition: If User A and User B are similar, then their replaced\n", + " modes are also similar.\n", + " '''\n", + " \n", + " tr_targets = train_df.target.values\n", + " tr = train_df.drop(columns=['target', 'user_id'], inplace=False).reset_index(drop=True, inplace=False)\n", + " \n", + " te_targets = test_df.target.values\n", + " te = test_df.drop(columns=['target', 'user_id'], inplace=False).reset_index(drop=True, inplace=False)\n", + " \n", + " if metric == SimilarityMetric.COSINE:\n", + " # Use cosine similarity to determine which element in the train set this user is closest to.\n", + " # Offset the columns from the second entry to exclude the user_id column.\n", + " # Returns a (n_te, n_tr) matrix.\n", + " sim = cosine_similarity(te.values, tr.values)\n", + " \n", + " # Compute the argmax across the train set.\n", + " argmax = np.argmax(sim, axis=1)\n", + "\n", + " # Index into the training targets to retrieve predicted label.\n", + " y_test_pred = tr_targets[argmax]\n", + " \n", + " elif metric == SimilarityMetric.EUCLIDEAN:\n", + " \n", + " # Here, we choose the embedding with the smallest L2 distance.\n", + " distances = euclidean_distances(te.values, tr.values)\n", + " \n", + " # We choose argmin\n", + " argmin = np.argmin(distances, axis=1)\n", + " \n", + " # Index into the targets.\n", + " y_test_pred = tr_targets[argmin]\n", + " \n", + " elif metric == SimilarityMetric.KNN:\n", + " \n", + " # Build the KNN classifier. By default, let it be 3.\n", + " knn = KNeighborsClassifier(\n", + " n_neighbors=metric_kwargs.pop('n_neighbors', 3),\n", + " weights='distance',\n", + " metric=metric_kwargs.pop('knn_metric', 'cosine'),\n", + " n_jobs=os.cpu_count()\n", + " )\n", + " \n", + " # Fit the data to the KNN model\n", + " knn.fit(tr, tr_targets)\n", + " \n", + " y_test_pred = knn.predict(te)\n", + " \n", + " elif metric == SimilarityMetric.KMEANS:\n", + " \n", + " # Build the model.\n", + " kmeans = KMeans(\n", + " n_clusters=metric_kwargs.pop('n_clusters', 8),\n", + " max_iter=metric_kwargs.pop('max_iter', 300),\n", + " n_init='auto',\n", + " random_state=SEED\n", + " )\n", + " \n", + " # Fit the clustering model\n", + " kmeans.fit(tr)\n", + " \n", + " # Construct the auxiliary df and merge with the training set.\n", + " label_df = pd.DataFrame({'label': kmeans.labels_, 'target': tr_targets}, index=tr.index)\n", + " \n", + " # Now, perform an inference on the test set.\n", + " predicted_labels = kmeans.predict(te)\n", + " \n", + " y_test_pred = []\n", + " for prediction in predicted_labels:\n", + " most_likely = label_df.loc[label_df.label == prediction, 'target'].value_counts().idxmax()\n", + " y_test_pred.append(most_likely)\n", + " \n", + " else:\n", + " raise NotImplementedError(\"Unknown similarity metric\")\n", + " \n", + " \n", + " f1 = f1_score(y_true=te_targets, y_pred=y_test_pred, average='weighted')\n", + " print(f\"Test F1 score using {metric.name} = {f1}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "1a95ad5e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test F1 score using COSINE = 0.42692939244663386\n", + "Test F1 score using EUCLIDEAN = 0.4126984126984127\n", + "Test F1 score using KNN = 0.4393241167434716\n", + "Test F1 score using KMEANS = 0.4733893557422969\n" + ] + } + ], + "source": [ + "for metric in [\n", + " SimilarityMetric.COSINE, SimilarityMetric.EUCLIDEAN, SimilarityMetric.KNN, SimilarityMetric.KMEANS\n", + "]:\n", + " evaluate_using_similarity(test, train, metric, n_clusters=3)" + ] + }, + { + "cell_type": "markdown", + "id": "16e435a6", + "metadata": {}, + "source": [ + "Not bad - using just a simple random split gives us the following results:\n", + "\n", + "$allCEO$:\n", + "\n", + "```\n", + "Test F1 score using COSINE = 0.42692939244663386\n", + "Test F1 score using EUCLIDEAN = 0.4126984126984127\n", + "Test F1 score using KNN = 0.4393241167434716\n", + "Test F1 score using KMEANS = 0.4733893557422969\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "81f0e842", + "metadata": {}, + "outputs": [], + "source": [ + "def custom_nll_scorer(clf, X, y):\n", + " \n", + " # [[yp1, yp2, yp3, ...], [yp1, yp3, ...]]\n", + " y_pred = clf.predict_proba(X)\n", + " \n", + " return -log_loss(y_true=y, y_pred=y_pred, labels=sorted(np.unique(y)))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "a3a6af8f", + "metadata": {}, + "outputs": [], + "source": [ + "def estimate_using_model(train, test, **model_kwargs):\n", + " \n", + " cv = model_kwargs.pop('cv', None)\n", + " n_splits = model_kwargs.pop('n_splits', 5)\n", + " n_iter = model_kwargs.pop('n_iter', 500)\n", + " \n", + " if cv is None:\n", + " # Define the train-val splitter.\n", + " cv = KFold(n_splits=n_splits, shuffle=True, random_state=SEED)\n", + " \n", + " params = {\n", + " 'n_estimators': np.arange(100, 1001, 50),\n", + " 'max_depth': [i for i in range(5, 101, 5)],\n", + " 'ccp_alpha': np.linspace(0, 1, 10),\n", + " 'class_weight': ['balanced', 'balanced_subsample', None],\n", + " 'min_samples_split': np.arange(2, 25, 2),\n", + " 'min_samples_leaf': np.arange(1, 25)\n", + " }\n", + " \n", + " rf = RandomForestClassifier(random_state=SEED)\n", + " \n", + " # Search over hparams to minimize negative log likelihood. \n", + "# clf = RandomizedSearchCV(\n", + "# rf, params, n_iter=n_iter, scoring=custom_nll_scorer, \n", + "# n_jobs=os.cpu_count(), cv=cv, random_state=SEED,\n", + "# verbose=0\n", + "# )\n", + " \n", + " clf = RandomizedSearchCV(\n", + " rf, params, n_iter=n_iter, scoring='f1_weighted', \n", + " n_jobs=cpu_count(), cv=cv, random_state=SEED,\n", + " verbose=0\n", + " )\n", + " \n", + " X_tr = train.drop(columns=['user_id', 'target'])\n", + " y_tr = train.target.values.ravel()\n", + " \n", + " scorer = clf.fit(X_tr, y_tr)\n", + " \n", + " best_model = scorer.best_estimator_\n", + " \n", + " print(f\"Best val score = {scorer.best_score_}\")\n", + " \n", + " X_te = test.drop(columns=['user_id', 'target'])\n", + " \n", + " # Use the best model to compute F1 on the test set.\n", + " test_f1 = f1_score(y_true=test.target.values, y_pred=best_model.predict(X_te), average='weighted')\n", + " \n", + " print(f\"Test F1 = {test_f1}\")\n", + " \n", + " return best_model" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "2fab93ed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best val score = 0.653870145236778\n", + "Test F1 = 0.4733893557422969\n" + ] + } + ], + "source": [ + "model = estimate_using_model(train, test)" + ] + }, + { + "cell_type": "markdown", + "id": "2988c1b2", + "metadata": {}, + "source": [ + "Interesting! The model is slightly on par with K-Means!" + ] + }, + { + "cell_type": "markdown", + "id": "c6b77353", + "metadata": {}, + "source": [ + "## Experiment 2: Demographics with trip summaries" + ] + }, + { + "cell_type": "markdown", + "id": "bf7753d4", + "metadata": {}, + "source": [ + "Now that we've performed experiments with solely demographic data, let's expand the feature set by including \n", + "trip summary statistics. We would like this approach to do better than the aforementioned baselines." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "1d46ab0f", + "metadata": {}, + "outputs": [], + "source": [ + "demo_plus_trips = get_demographic_data(\n", + " df, \n", + " trip_features=['mph', 'section_duration_argmax', 'section_distance_argmax', 'start_local_dt_hour', 'end_local_dt_hour']\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "11c1ea2c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + " user_id mph_mean_bicycling mph_mean_car \\\n", + "0 00db212b-c8d0-44cd-8392-41ab4065e603 6.239545 14.271367 \n", + "1 0154d714-3928-4c34-b865-e5a417cd48af 7.258829 16.707414 \n", + "2 03a395b4-d861-4757-bb84-32b4984559b0 8.400458 21.949909 \n", + "3 0a093cbd-b536-43af-b03d-293425e84c76 8.023209 12.519217 \n", + "4 0d0ae3a5-5641-4d13-8c52-a6040a203d24 6.533376 18.334315 \n", + "\n", + " mph_mean_transit mph_mean_unknown mph_mean_walking mph_median_bicycling \\\n", + "0 16.895797 8.795720 1.829510 6.098018 \n", + "1 7.858924 11.252165 1.461767 7.422923 \n", + "2 12.512543 15.260936 1.762294 8.725430 \n", + "3 -1.000000 8.717745 1.520088 6.855097 \n", + "4 8.428743 12.150343 1.741496 6.718031 \n", + "\n", + " mph_median_car mph_median_transit mph_median_unknown mph_median_walking \\\n", + "0 12.315223 13.748353 7.372235 2.044505 \n", + "1 14.976323 5.173881 9.457230 1.442129 \n", + "2 21.171860 11.697772 13.190136 1.767112 \n", + "3 11.158806 -1.000000 7.411870 1.635980 \n", + "4 14.397809 9.608287 10.789507 1.934605 \n", + "\n", + " mph_iqr_bicycling mph_iqr_car mph_iqr_transit mph_iqr_unknown \\\n", + "0 1.796356 7.669828 18.274283 4.166229 \n", + "1 3.577637 10.730999 7.638888 8.972405 \n", + "2 2.942594 16.747731 9.494206 11.253264 \n", + "3 3.132708 8.243087 -1.000000 4.951139 \n", + "4 2.115857 17.740953 4.817794 8.174346 \n", + "\n", + " mph_iqr_walking section_duration_argmax_mean_bicycling \\\n", + "0 1.097565 12.143593 \n", + "1 1.488708 16.175633 \n", + "2 1.390353 7.215576 \n", + "3 1.519957 16.413152 \n", + "4 1.427394 13.910655 \n", + "\n", + " section_duration_argmax_mean_car section_duration_argmax_mean_transit \\\n", + "0 16.171412 28.511443 \n", + "1 14.443387 8.807150 \n", + "2 19.063554 9.042499 \n", + "3 29.697618 -1.000000 \n", + "4 18.190569 9.022674 \n", + "\n", + " section_duration_argmax_mean_unknown section_duration_argmax_mean_walking \\\n", + "0 25.603399 16.050633 \n", + "1 29.325692 20.568486 \n", + "2 3.948607 27.637920 \n", + "3 19.780111 30.175525 \n", + "4 42.455624 17.421163 \n", + "\n", + " section_duration_argmax_median_bicycling \\\n", + "0 9.901861 \n", + "1 13.007849 \n", + "2 5.710374 \n", + "3 13.639533 \n", + "4 11.766516 \n", + "\n", + " section_duration_argmax_median_car section_duration_argmax_median_transit \\\n", + "0 12.896737 19.895491 \n", + "1 11.101905 5.928156 \n", + "2 15.811629 1.798544 \n", + "3 24.230089 -1.000000 \n", + "4 13.766647 7.504069 \n", + "\n", + " section_duration_argmax_median_unknown \\\n", + "0 18.383817 \n", + "1 21.190067 \n", + "2 1.019833 \n", + "3 16.386455 \n", + "4 29.042483 \n", + "\n", + " section_duration_argmax_median_walking \\\n", + "0 11.879527 \n", + "1 11.872480 \n", + "2 15.008960 \n", + "3 20.406550 \n", + "4 10.480811 \n", + "\n", + " section_duration_argmax_iqr_bicycling section_duration_argmax_iqr_car \\\n", + "0 7.369607 10.658638 \n", + "1 9.095686 10.245300 \n", + "2 3.155090 14.843112 \n", + "3 9.507167 24.436224 \n", + "4 6.588525 15.766817 \n", + "\n", + " section_duration_argmax_iqr_transit section_duration_argmax_iqr_unknown \\\n", + "0 42.361577 9.815050 \n", + "1 6.172666 28.133073 \n", + "2 8.897862 5.068570 \n", + "3 -1.000000 15.129912 \n", + "4 7.204236 43.098067 \n", + "\n", + " section_duration_argmax_iqr_walking \\\n", + "0 12.656333 \n", + "1 15.518311 \n", + "2 24.354036 \n", + "3 21.678383 \n", + "4 11.110703 \n", + "\n", + " section_distance_argmax_mean_bicycling section_distance_argmax_mean_car \\\n", + "0 1.324724 4.650185 \n", + "1 1.906896 4.595486 \n", + "2 1.007937 8.227581 \n", + "3 1.968588 6.684421 \n", + "4 1.516224 6.819715 \n", + "\n", + " section_distance_argmax_mean_transit section_distance_argmax_mean_unknown \\\n", + "0 10.723796 4.903672 \n", + "1 1.403190 6.730748 \n", + "2 3.100560 1.143773 \n", + "3 -1.000000 3.400954 \n", + "4 1.271131 8.299212 \n", + "\n", + " section_distance_argmax_mean_walking \\\n", + "0 0.435072 \n", + "1 0.403743 \n", + "2 0.588657 \n", + "3 0.655120 \n", + "4 0.401249 \n", + "\n", + " section_distance_argmax_median_bicycling \\\n", + "0 0.917019 \n", + "1 1.479094 \n", + "2 0.816212 \n", + "3 1.702804 \n", + "4 1.308321 \n", + "\n", + " section_distance_argmax_median_car section_distance_argmax_median_transit \\\n", + "0 2.259573 4.325583 \n", + "1 2.753808 0.389247 \n", + "2 5.555556 0.165721 \n", + "3 3.907857 -1.000000 \n", + "4 3.049242 1.253019 \n", + "\n", + " section_distance_argmax_median_unknown \\\n", + "0 2.021900 \n", + "1 2.976015 \n", + "2 0.169928 \n", + "3 1.920409 \n", + "4 11.154982 \n", + "\n", + " section_distance_argmax_median_walking \\\n", + "0 0.327231 \n", + "1 0.225779 \n", + "2 0.376107 \n", + "3 0.462168 \n", + "4 0.274414 \n", + "\n", + " section_distance_argmax_iqr_bicycling section_distance_argmax_iqr_car \\\n", + "0 0.902512 2.984623 \n", + "1 1.093852 3.797169 \n", + "2 0.097174 10.207405 \n", + "3 0.748348 5.247948 \n", + "4 0.773762 10.544007 \n", + "\n", + " section_distance_argmax_iqr_transit section_distance_argmax_iqr_unknown \\\n", + "0 15.810542 2.243013 \n", + "1 1.519272 5.904618 \n", + "2 2.961208 0.662906 \n", + "3 -1.000000 1.571451 \n", + "4 0.770136 10.829807 \n", + "\n", + " section_distance_argmax_iqr_walking start_local_dt_hour_mode_bicycling \\\n", + "0 0.317672 15.0 \n", + "1 0.229162 9.0 \n", + "2 0.433756 12.0 \n", + "3 0.696410 14.0 \n", + "4 0.310920 10.0 \n", + "\n", + " start_local_dt_hour_mode_car start_local_dt_hour_mode_transit \\\n", + "0 16.0 16.0 \n", + "1 16.0 8.0 \n", + "2 12.0 10.0 \n", + "3 17.0 0.0 \n", + "4 11.0 11.0 \n", + "\n", + " start_local_dt_hour_mode_unknown start_local_dt_hour_mode_walking \\\n", + "0 15.0 15.0 \n", + "1 8.0 8.0 \n", + "2 12.0 7.0 \n", + "3 13.0 15.0 \n", + "4 15.0 14.0 \n", + "\n", + " end_local_dt_hour_mode_bicycling end_local_dt_hour_mode_car \\\n", + "0 15.0 18.0 \n", + "1 7.0 16.0 \n", + "2 12.0 12.0 \n", + "3 15.0 8.0 \n", + "4 14.0 10.0 \n", + "\n", + " end_local_dt_hour_mode_transit end_local_dt_hour_mode_unknown \\\n", + "0 20.0 19.0 \n", + "1 8.0 8.0 \n", + "2 11.0 12.0 \n", + "3 0.0 13.0 \n", + "4 13.0 20.0 \n", + "\n", + " end_local_dt_hour_mode_walking coverage_bicycling coverage_car \\\n", + "0 15.0 168 0.577488 \n", + "1 20.0 32 0.833954 \n", + "2 7.0 507 0.696151 \n", + "3 12.0 15 0.747592 \n", + "4 14.0 71 0.840982 \n", + "\n", + " coverage_transit coverage_unknown coverage_walking duration distance \\\n", + "0 0.065253 0.021207 0.336052 17.827598 3.486762 \n", + "1 0.013837 0.044172 0.108036 15.860767 4.185754 \n", + "2 0.003276 0.054054 0.246519 24.729501 5.846063 \n", + "3 0.000000 0.111753 0.140655 31.277394 5.668017 \n", + "4 0.006403 0.052295 0.100320 21.282785 6.190271 \n", + "\n", + " av_transit av_no_trip av_p_micro av_s_micro av_ridehail av_unknown \\\n", + "0 1 0 1 1 1 1 \n", + "1 1 0 1 1 1 1 \n", + "2 1 0 1 1 1 1 \n", + "3 1 0 1 1 1 1 \n", + "4 1 0 1 1 1 1 \n", + "\n", + " av_walk av_car av_s_car target \n", + "0 1 1 1 1 \n", + "1 1 1 1 9 \n", + "2 1 1 1 9 \n", + "3 1 1 1 9 \n", + "4 1 1 1 9 " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "demo_plus_trips.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "6159c90a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "81 21\n" + ] + } + ], + "source": [ + "train = demo_plus_trips.loc[demo_plus_trips.user_id.isin(TRAIN_USERS), :]\n", + "test = demo_plus_trips.loc[demo_plus_trips.user_id.isin(TEST_USERS), :]\n", + "\n", + "print(train.shape[0], test.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "06e85bdd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test F1 score using COSINE = 0.32098765432098775\n", + "Test F1 score using EUCLIDEAN = 0.36684303350970027\n", + "Test F1 score using KNN = 0.41269841269841273\n", + "Test F1 score using KMEANS = 0.4877344877344878\n" + ] + } + ], + "source": [ + "for metric in [\n", + " SimilarityMetric.COSINE, SimilarityMetric.EUCLIDEAN, SimilarityMetric.KNN, SimilarityMetric.KMEANS\n", + "]:\n", + " evaluate_using_similarity(test, train, metric, n_clusters=4)" + ] + }, + { + "cell_type": "markdown", + "id": "ba795489", + "metadata": {}, + "source": [ + "Great! Some improvement here and there.\n", + "\n", + "$allCEO$\n", + "```\n", + "Test F1 score using COSINE = 0.32098765432098775\n", + "Test F1 score using EUCLIDEAN = 0.36684303350970027\n", + "Test F1 score using KNN = 0.41269841269841273\n", + "Test F1 score using KMEANS = 0.4877344877344878\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "9acd4b0b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best val score = 0.6326031937974128\n", + "Test F1 = 0.004329004329004328\n" + ] + }, + { + "data": { + "text/html": [ + "
RandomForestClassifier(ccp_alpha=0.2222222222222222,\n",
+       "                       class_weight='balanced_subsample', max_depth=25,\n",
+       "                       min_samples_leaf=5, min_samples_split=14,\n",
+       "                       n_estimators=700, random_state=13210)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "RandomForestClassifier(ccp_alpha=0.2222222222222222,\n", + " class_weight='balanced_subsample', max_depth=25,\n", + " min_samples_leaf=5, min_samples_split=14,\n", + " n_estimators=700, random_state=13210)" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Now, we try with the model\n", + "estimate_using_model(train, test)" + ] + }, + { + "cell_type": "markdown", + "id": "cd94c548", + "metadata": {}, + "source": [ + "Great! Compared to the previous model, we see definite improvements! I'm sure we can squeeze some more juice out of the models using fancy optimization, but as a baseline, these are good enough.\n", + "\n", + "\n", + "So, to recap:\n", + "$F1_{cosine} = 0.37$, $F1_{euclidean} = 0.33$, $F1_{knn} = 0.3$, $F1_{kmeans} = 0.36$, $F1_{RF} = 0.4215$" + ] + }, + { + "cell_type": "markdown", + "id": "8a8f6491", + "metadata": {}, + "source": [ + "### Different groupings." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ce90367", + "metadata": {}, + "outputs": [], + "source": [ + "# trip_features = ['mph', 'section_duration_argmax', 'section_distance_argmax', 'start:hour', 'end:hour']\n", + "\n", + "# for group_mode in ['section_mode_argmax', 'section_distance_argmax', 'section_duration_argmax', 'duration', 'distance']:\n", + " \n", + "# if group_mode in trip_features:\n", + "# _ = trip_features.pop(trip_features.index(group_mode))\n", + " \n", + "# exp_df = get_demographic_data(\n", + "# df, \n", + "# trip_grouping=group_mode,\n", + "# trip_features=trip_features,\n", + "# use_qcut=True\n", + "# )\n", + " \n", + "# train, test = train_test_split(exp_df, test_size=0.2, random_state=SEED)\n", + " \n", + "# for sim in [\n", + "# SimilarityMetric.COSINE, SimilarityMetric.EUCLIDEAN, SimilarityMetric.KNN, SimilarityMetric.KMEANS\n", + "# ]:\n", + "# evaluate_using_similarity(test, train, sim, n_clusters=3)\n", + " \n", + "# # estimate_using_model(train, test, n_iter=200)\n", + " \n", + "# print(50*'=')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d53f945", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "_ = generate_tsne_plots(demo_plus_trips, perplexity=6, n_iter=7500)" + ] + }, + { + "cell_type": "markdown", + "id": "c339fcc6", + "metadata": {}, + "source": [ + "# Multi-level modeling" + ] + }, + { + "cell_type": "markdown", + "id": "213676ec", + "metadata": {}, + "source": [ + "In this approach, we want to piece together the similarity search and modeling processes. Here's a rough sketch of how it should be implemented:\n", + "\n", + "1. For every user in the training set, build a model using their entire trip history.\n", + "2. Consolidate these user-level models in data structure, preferably a dictionary.\n", + "3. Now, when we want to perform inference on a new user with no prior trips, we use the similarity search to get the user ID in the training set who is the most similar to the user in question.\n", + "4. We retrieve the model for this corresponding user and perform an inference. The hypothesis is that since the two users are similar, their trip substitution patterns are also similar." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c48ee430", + "metadata": {}, + "outputs": [], + "source": [ + "def drop_columns(df: pd.DataFrame):\n", + " to_drop = [\n", + " 'source', 'end_ts', 'end_fmt_time', 'end_loc', 'raw_trip', 'start_ts', \n", + " 'start_fmt_time', 'start_loc', 'duration', 'distance', 'start_place', \n", + " 'end_place', 'cleaned_trip', 'inferred_labels', 'inferred_trip', 'expectation',\n", + " 'confidence_threshold', 'expected_trip', 'user_input', 'start:year', 'start:month', \n", + " 'start:day', 'start_local_dt_minute', 'start_local_dt_second', \n", + " 'start_local_dt_weekday', 'start_local_dt_timezone', 'end:year', 'end:month', 'end:day', \n", + " 'end_local_dt_minute', 'end_local_dt_second', 'end_local_dt_weekday', \n", + " 'end_local_dt_timezone', '_id', 'metadata_write_ts', 'additions', \n", + " 'mode_confirm', 'purpose_confirm', 'Mode_confirm', 'Trip_purpose', \n", + " 'original_user_id', 'program', 'opcode', 'Timestamp', 'birth_year', \n", + " 'available_modes', 'section_coordinates_argmax', 'section_mode_argmax'\n", + " ]\n", + " \n", + " # Drop section_mode_argmax and available_modes.\n", + " return df.drop(\n", + " columns=to_drop, \n", + " inplace=False\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca9e6e6a", + "metadata": {}, + "outputs": [], + "source": [ + "def construct_model_dictionary(train: pd.DataFrame):\n", + " \n", + " def train_on_user(user_id: str):\n", + " '''\n", + " Given the training set and the user ID to query, filter the dataset and\n", + " retain only the relevant trips. Then, create folds and optimize a model for this user.\n", + " Return the trained model instance.\n", + " '''\n", + " \n", + " user_data = train.loc[train.user_id == user_id, :].reset_index(drop=True)\n", + " \n", + " # Split user trips into train-test folds.\n", + " u_train, u_test = train_test_split(user_data, test_size=0.2, shuffle=True, random_state=SEED)\n", + " \n", + " user_model = estimate_using_model(\n", + " u_train, u_test, \n", + " n_iter=100\n", + " )\n", + " \n", + " return user_model\n", + " \n", + " for user in train.user_id.unique():\n", + " MODEL_DICT[user]['warm_start'] = train_on_user(user)\n", + " print(50*'=')\n", + " \n", + " print(\"\\nDone!\")" + ] + }, + { + "cell_type": "markdown", + "id": "2a035c16", + "metadata": {}, + "source": [ + "## Warm start:\n", + "\n", + "If the queried user has prior trips, we know that we we can harness the additional information. So if we encounter such a user, we will first find the most similar user (using only demographics). Once the most similar user is found, we query the trip model for the user and run inference through it.\n", + "\n", + "## Cold start:\n", + "\n", + "If the queried user has no prior trips, we will use the demo-only model. We first perform a similarity search and then run user inference through the demo-only model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "082c4e39", + "metadata": {}, + "outputs": [], + "source": [ + "class MultiLevelModel:\n", + " def __init__(self, model_dict: Dict, train: pd.DataFrame, test: pd.DataFrame, **model_kwargs):\n", + " \n", + " self._demographics = [\n", + " 'primary_job_commute_time', 'income_category', 'n_residence_members', 'n_residents_u18', \n", + " 'n_residents_with_license', 'n_motor_vehicles', 'available_modes', 'age', 'gender_Man', \n", + " 'gender_Man;Nonbinary/genderqueer/genderfluid', 'gender_Nonbinary/genderqueer/genderfluid', \n", + " 'gender_Prefer not to say', 'gender_Woman', 'gender_Woman;Nonbinary/genderqueer/genderfluid', \n", + " 'has_drivers_license_No', 'has_drivers_license_Prefer not to say', 'has_drivers_license_Yes', \n", + " 'has_multiple_jobs_No', 'has_multiple_jobs_Prefer not to say', 'has_multiple_jobs_Yes', \n", + " \"highest_education_Bachelor's degree\", 'highest_education_Graduate degree or professional degree', \n", + " 'highest_education_High school graduate or GED', 'highest_education_Less than a high school graduate', \n", + " 'highest_education_Prefer not to say', 'highest_education_Some college or associates degree', \n", + " 'primary_job_type_Full-time', 'primary_job_type_Part-time', 'primary_job_type_Prefer not to say', \n", + " 'primary_job_description_Clerical or administrative support', 'primary_job_description_Custodial', \n", + " 'primary_job_description_Education', 'primary_job_description_Food service', \n", + " 'primary_job_description_Manufacturing, construction, maintenance, or farming', \n", + " 'primary_job_description_Medical/healthcare', 'primary_job_description_Other', \n", + " 'primary_job_description_Professional, managerial, or technical', \n", + " 'primary_job_description_Sales or service', 'primary_job_commute_mode_Active transport', \n", + " 'primary_job_commute_mode_Car transport', 'primary_job_commute_mode_Hybrid', \n", + " 'primary_job_commute_mode_Public transport', 'primary_job_commute_mode_Unknown', \n", + " 'primary_job_commute_mode_WFH', 'is_overnight_trip', 'n_working_residents'\n", + " ]\n", + " \n", + " assert all([c in test.columns for c in self._demographics]), \"[test] Demographic features are missing!\"\n", + " assert all([c in train.columns for c in self._demographics]), \"[train] Demographic features are missing!\"\n", + " \n", + " self._mdict = model_dict\n", + " self._train = train\n", + " self._test = test\n", + " self.metric = model_kwargs.pop('metric', SimilarityMetric.COSINE)\n", + " \n", + " \n", + " def _phase1(self):\n", + " \n", + " tr = self._train.copy()\n", + " te = self._test.copy()\n", + " \n", + " if tr.columns.isin(['user_id', 'target']).sum() == 2:\n", + " tr = tr.drop(columns=['user_id', 'target']).reset_index(drop=True)\n", + " \n", + " if te.columns.isin(['user_id', 'target']).sum() == 2:\n", + " te = te.drop(columns=['user_id', 'target']).reset_index(drop=True)\n", + "\n", + " te_users = self._test.user_id.tolist()\n", + "\n", + " if self.metric == SimilarityMetric.COSINE:\n", + "\n", + " sim = cosine_similarity(te.values, tr.values)\n", + "\n", + " # Compute the argmax across the train set.\n", + " argmax = np.argmax(sim, axis=1)\n", + "\n", + " # Retrieve the user_id at these indices.\n", + " train_users = self._train.loc[argmax, 'user_id']\n", + "\n", + " elif self.metric == SimilarityMetric.EUCLIDEAN:\n", + "\n", + " sim = euclidean_distances(te.values, tr.values)\n", + "\n", + " # Compute the argmin here!\n", + " argmin = np.argmin(sim, axis=1)\n", + "\n", + " # Retrieve the train user_ids.\n", + " train_users = self._train.loc[argmin, 'user_id']\n", + "\n", + " return pd.DataFrame({'test_user_id': te_users, 'train_user_id': train_users})\n", + " \n", + " \n", + " def _phase2(self, sim_df: pd.DataFrame, cold_start: bool):\n", + " \n", + " prediction_df = list()\n", + " \n", + " # Now, we use the sim_df to run inference based on whether \n", + " for ix, row in sim_df.iterrows():\n", + " train_user = row['train_user_id']\n", + " \n", + " # Retrieve the appropriate model.\n", + " user_models = self._mdict.get(train_user, None)\n", + " \n", + " start_type = 'cold_start' if cold_start else 'warm_start'\n", + " \n", + " # which specific model?\n", + " sp_model = user_models.get(start_type, None)\n", + " \n", + " # Now get the test user data.\n", + " test_user = row['test_user_id']\n", + " \n", + " if cold_start:\n", + " test_data = self._test.loc[self._test.user_id == test_user, self._demographics]\n", + " test_data = test_data.iloc[0, :]\n", + " else:\n", + " test_data = self._test.loc[self._test.user_id == test_user, :]\n", + " \n", + " predictions = sp_model.predict(test_data)\n", + " \n", + " print(f\"test: [{test_user}], predictions: {predictions}\")\n", + " \n", + " \n", + " def execute_pipeline(self, cold_start: bool = False):\n", + " # For each test user, get the most similar train user.\n", + " sim_df = self._phase1()\n", + " \n", + " predictions = self._phase2(sim_df, cold_start)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb63632d", + "metadata": {}, + "outputs": [], + "source": [ + "# FULL DATA.\n", + "train = df.loc[df.user_id.isin(TRAIN_USERS), :]\n", + "test = df.loc[df.user_id.isin(TEST_USERS), :]\n", + "\n", + "train_counts = train.user_id.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2528eaa", + "metadata": {}, + "outputs": [], + "source": [ + "## We only want to train on users who have a good number of trips.\n", + "good_users = train_counts[train_counts >= 100].index\n", + "\n", + "bad_users = train_counts[train_counts < 100].index\n", + "\n", + "print(f\"Number of users filtered out of training: {len(bad_users)}\")\n", + "\n", + "filtered_train = train.loc[train.user_id.isin(good_users), :]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bae55b21", + "metadata": {}, + "outputs": [], + "source": [ + "# Full data.\n", + "\n", + "train_df = drop_columns(filtered_train)\n", + "test_df = drop_columns(test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88d0e2d2", + "metadata": {}, + "outputs": [], + "source": [ + "print(train_df.shape, test_df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37febd6d", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "model_dict = construct_model_dictionary(train_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1249925", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "emission", + "language": "python", + "name": "emission" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/rm_src/04_FeatureClustering.ipynb b/rm_src/04_FeatureClustering.ipynb new file mode 100644 index 0000000..31c1191 --- /dev/null +++ b/rm_src/04_FeatureClustering.ipynb @@ -0,0 +1,1419 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "aa5d3c5f", + "metadata": {}, + "source": [ + "To determine which non-demographic trip-level characteristics we should stratify by, we want to do some high level clustering.\n", + "\n", + "we already have clustering based on demographics (which corresponds to the demographic stratification) and can show that the argmax replaced mode is not the same\n", + "\n", + "we should now try two clusterings:\n", + "- based on trip summaries\n", + " - % of trips in each primary sensed mode\n", + " - number of trips\n", + " - % of distance in each primary sensed mode\n", + " - most common start time\n", + " - most common end time\n", + "\n", + "we can see whether the replaced mode argmax is now consistent in each cluster\n", + "\n", + "- cluster based on the replaced mode distributions. features are:\n", + " - % of trips with x replaced mode for x in set of unique replaced modes (filtered to top 75%)\n", + " - distance of trips with x replaced mode for x in set of unique replaced modes (filtered to top 75%)\n", + "\n", + "see how the demographics and trip summaries vary per cluster and see if there is a strong correlation between certain characteristics and being in the same cluster.\n", + "\n", + "\n", + "- Chart 1 will visualize the clustering with trip-summary features\n", + "- Chart 2 will visualize the clustering with replaced mode features.\n", + "\n", + "Note that clustering is unsupervised and does not have a target variable.\n", + "\n", + "For chart 1, for $n$ users, our input matrix will be $O(n \\times 15)$ since there are ~ 6 sensed modes. After the clustering, we will check the replaced mode argmax distribution in each cluster.\n", + "\n", + "For chart 2, for $n$ users where the 75th percentile of the replaced modes has modes, our input matrix will be $O(n \\times 2 \\times r)$. After the clustering, we will check the distribution of demographics and trip characteristics in each cluster." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "1db71454", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9 72866\n", + "2 18633\n", + "5 13834\n", + "3 9455\n", + "8 7908\n", + "1 6539\n", + "4 1630\n", + "7 1469\n", + "6 189\n", + "Name: target, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "\n", + "df = pd.read_csv('../data/filtered_data/preprocessed_data_Stage_database.csv')\n", + "df.drop_duplicates(inplace=True)\n", + "display(df.target.value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "789df947", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import random\n", + "import os\n", + "import itertools\n", + "import pickle\n", + "import ast\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as mcolors\n", + "import seaborn as sns\n", + "\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances\n", + "from sklearn.metrics import davies_bouldin_score, calinski_harabasz_score, silhouette_score\n", + "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n", + "from typing import List, Dict, Union\n", + "from pandas.api.types import is_numeric_dtype\n", + "from sklearn.cluster import DBSCAN, KMeans\n", + "from collections import Counter\n", + "\n", + "pd.set_option('display.max_columns', None)\n", + "\n", + "%matplotlib inline\n", + "\n", + "SEED = 13210\n", + "\n", + "np.random.seed(SEED)\n", + "random.seed(SEED)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "33ef3275", + "metadata": {}, + "outputs": [], + "source": [ + "# Data is located at\n", + "CURRENT_DB = 'ride2own'\n", + "\n", + "df = pd.read_csv('../data/filtered_data/preprocessed_data_openpath_prod_ride2own.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d0d884a3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9 800\n", + "4 33\n", + "8 6\n", + "7 5\n", + "2 1\n", + "1 1\n", + "3 1\n", + "5 1\n", + "Name: target, dtype: int64" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.target.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b2281bdc", + "metadata": {}, + "outputs": [], + "source": [ + "df.rename(\n", + " columns={'end_local_dt_hour': 'end:hour', 'start_local_dt_hour': 'start:hour', 'replaced_mode': 'target'}, \n", + " inplace=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9c22d6ac", + "metadata": {}, + "outputs": [], + "source": [ + "TARGETS = ['p_micro', 'no_trip', 's_car', 'transit', 'car', 's_micro', 'ridehail', 'walk', 'unknown']\n", + "MAP = {ix+1: t for (ix, t) in enumerate(TARGETS)}\n", + "TARGET_MAP = {v:k for k, v in MAP.items()}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "063f6124", + "metadata": {}, + "outputs": [], + "source": [ + "df.replace({'target': TARGET_MAP}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cef8d45b", + "metadata": {}, + "outputs": [], + "source": [ + "# % of trips per mode.\n", + "trip_percents = df.groupby(['user_id'])['section_mode_argmax'].apply(lambda x: x.value_counts(normalize=True)).unstack(level=-1)\n", + "trip_percents.fillna(0., inplace=True)\n", + "\n", + "trip_percents.columns = ['coverage_'+x for x in trip_percents.columns]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "68c6af2d", + "metadata": {}, + "outputs": [], + "source": [ + "n_trips = pd.DataFrame(df.groupby('user_id').size(), columns=['n_trips'])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "eff378a7", + "metadata": {}, + "outputs": [], + "source": [ + "most_common_start = df.groupby('user_id')['start:hour'].apply(lambda x: x.value_counts().idxmax())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "cffbd401", + "metadata": {}, + "outputs": [], + "source": [ + "most_common_end = df.groupby('user_id')['end:hour'].apply(lambda x: x.value_counts().idxmax())" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f1eb1633", + "metadata": {}, + "outputs": [], + "source": [ + "# % of distance in each primary sensed mode.\n", + "total_distance = df.groupby(['user_id', 'section_mode_argmax'])['section_distance_argmax'].sum().unstack(level=-1)\n", + "total_distance = total_distance.div(total_distance.sum(axis=1), axis=0)\n", + "total_distance.fillna(0., inplace=True)\n", + "total_distance.columns = ['pct_distance_' + x for x in total_distance.columns]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d9cc0a0f", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "figure1_df = trip_percents.merge(right=total_distance, left_index=True, right_index=True).merge(\n", + " right=n_trips, left_index=True, right_index=True\n", + ").merge(\n", + " right=most_common_start, left_index=True, right_index=True\n", + ").merge(right=most_common_end, left_index=True, right_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "750fbd0c", + "metadata": {}, + "outputs": [], + "source": [ + "# Normalize the last three columns.\n", + "\n", + "def min_max_normalize(col: pd.Series):\n", + " _max, _min = col.max(), col.min()\n", + " return pd.Series((col - _min)/(_max - _min))\n", + "\n", + "figure1_df['n_trips'] = min_max_normalize(figure1_df['n_trips'])\n", + "figure1_df['start:hour'] = np.sin(figure1_df['start:hour'].values)\n", + "figure1_df['end:hour'] = np.sin(figure1_df['end:hour'].values)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": 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" + ], + "text/plain": [ + " coverage_bicycling coverage_car \\\n", + "user_id \n", + "258a8119-a5c2-4d38-875d-a9a9f7ed2d7d 0.166667 0.333333 \n", + "7ac8a16d-bafe-4bef-a5b5-43b64beb97a6 0.033113 0.443709 \n", + "87e12281-655e-4b7f-9dce-67b42217a5e8 0.300000 0.233333 \n", + "bc08c318-5a56-4e11-9f69-9c48468b7571 0.067935 0.739130 \n", + "be2a3c5c-1db0-4736-8304-a1c705da4cd6 0.009174 0.220183 \n", + "\n", + " coverage_transit coverage_unknown \\\n", + "user_id \n", + "258a8119-a5c2-4d38-875d-a9a9f7ed2d7d 0.166667 0.166667 \n", + "7ac8a16d-bafe-4bef-a5b5-43b64beb97a6 0.119205 0.013245 \n", + "87e12281-655e-4b7f-9dce-67b42217a5e8 0.033333 0.033333 \n", + "bc08c318-5a56-4e11-9f69-9c48468b7571 0.048913 0.027174 \n", + "be2a3c5c-1db0-4736-8304-a1c705da4cd6 0.055046 0.073394 \n", + "\n", + " coverage_walking \\\n", + "user_id \n", + "258a8119-a5c2-4d38-875d-a9a9f7ed2d7d 0.166667 \n", + "7ac8a16d-bafe-4bef-a5b5-43b64beb97a6 0.390728 \n", + "87e12281-655e-4b7f-9dce-67b42217a5e8 0.400000 \n", + "bc08c318-5a56-4e11-9f69-9c48468b7571 0.116848 \n", + "be2a3c5c-1db0-4736-8304-a1c705da4cd6 0.642202 \n", + "\n", + " pct_distance_bicycling \\\n", + "user_id \n", + "258a8119-a5c2-4d38-875d-a9a9f7ed2d7d 0.172218 \n", + "7ac8a16d-bafe-4bef-a5b5-43b64beb97a6 0.023119 \n", + "87e12281-655e-4b7f-9dce-67b42217a5e8 0.277527 \n", + "bc08c318-5a56-4e11-9f69-9c48468b7571 0.038010 \n", + "be2a3c5c-1db0-4736-8304-a1c705da4cd6 0.012170 \n", + "\n", + " pct_distance_car pct_distance_transit \\\n", + "user_id \n", + "258a8119-a5c2-4d38-875d-a9a9f7ed2d7d 0.668293 0.017667 \n", + "7ac8a16d-bafe-4bef-a5b5-43b64beb97a6 0.862170 0.086055 \n", + "87e12281-655e-4b7f-9dce-67b42217a5e8 0.515470 0.037176 \n", + "bc08c318-5a56-4e11-9f69-9c48468b7571 0.914976 0.022974 \n", + "be2a3c5c-1db0-4736-8304-a1c705da4cd6 0.416293 0.092856 \n", + "\n", + " pct_distance_unknown \\\n", + "user_id \n", + "258a8119-a5c2-4d38-875d-a9a9f7ed2d7d 0.134188 \n", + "7ac8a16d-bafe-4bef-a5b5-43b64beb97a6 0.005839 \n", + "87e12281-655e-4b7f-9dce-67b42217a5e8 0.037151 \n", + "bc08c318-5a56-4e11-9f69-9c48468b7571 0.009626 \n", + "be2a3c5c-1db0-4736-8304-a1c705da4cd6 0.080060 \n", + "\n", + " pct_distance_walking n_trips \\\n", + "user_id \n", + "258a8119-a5c2-4d38-875d-a9a9f7ed2d7d 0.007634 0.008219 \n", + "7ac8a16d-bafe-4bef-a5b5-43b64beb97a6 0.022817 0.819178 \n", + "87e12281-655e-4b7f-9dce-67b42217a5e8 0.132676 0.073973 \n", + "bc08c318-5a56-4e11-9f69-9c48468b7571 0.014414 1.000000 \n", + "be2a3c5c-1db0-4736-8304-a1c705da4cd6 0.398620 0.290411 \n", + "\n", + " start:hour end:hour \n", + "user_id \n", + "258a8119-a5c2-4d38-875d-a9a9f7ed2d7d -0.287903 -0.287903 \n", + "7ac8a16d-bafe-4bef-a5b5-43b64beb97a6 0.650288 0.420167 \n", + "87e12281-655e-4b7f-9dce-67b42217a5e8 -0.544021 -0.544021 \n", + "bc08c318-5a56-4e11-9f69-9c48468b7571 0.656987 -0.961397 \n", + "be2a3c5c-1db0-4736-8304-a1c705da4cd6 -0.287903 0.656987 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "figure1_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "598d82bc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-inf\n" + ] + } + ], + "source": [ + "epsilons = np.linspace(1e-3, 1., 1000)\n", + "\n", + "best_eps = -np.inf\n", + "best_score = -np.inf\n", + "\n", + "for eps in epsilons:\n", + " model = DBSCAN(eps=eps).fit(figure1_df)\n", + " \n", + " if len(np.unique(model.labels_)) < 2:\n", + " continue\n", + " \n", + " score = silhouette_score(figure1_df, model.labels_)\n", + " if score > best_score:\n", + " best_eps = eps\n", + " best_score = score\n", + "\n", + "print(best_eps)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "bc89a42d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Counter({-1: 7})\n" + ] + } + ], + "source": [ + "'''\n", + "AlLCEO: eps=0.542\n", + "durham: eps=0.661\n", + "masscec: eps=0.64\n", + "'''\n", + "\n", + "clustering = DBSCAN(eps=0.8).fit(figure1_df)\n", + "\n", + "print(Counter(clustering.labels_))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "05c9a7c4", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7 users in cluster -1\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# After clustering, we would like to see what the replaced mode argmax distribution in each cluster is.\n", + "\n", + "labels = clustering.labels_\n", + "\n", + "for cix in np.unique(labels):\n", + " cluster_users = figure1_df.iloc[labels == cix,:].index\n", + " \n", + " print(f\"{len(cluster_users)} users in cluster {cix}\")\n", + " \n", + " # Now, for each user, look at the actual data and determine the replaced mode argmax distribution.\n", + " sub_df = df.loc[df.user_id.isin(cluster_users), :].reset_index(drop=True)\n", + " \n", + " sub_df['target'] = sub_df['target'].apply(lambda x: MAP[x])\n", + " \n", + " rm_argmax = sub_df.groupby('user_id')['target'].apply(lambda x: x.value_counts().idxmax())\n", + " fig, ax = plt.subplots()\n", + " rm_argmax.hist(ax=ax)\n", + " ax.set_title(f\"Replaced mode argmax distribution for users in cluster {cix}\")\n", + " ax.set_xlabel(\"Target\")\n", + " \n", + " plt.savefig(f'./plots_for_paper/{CURRENT_DB}__FIG1_cluster_{cix}_target_dist.png', dpi=300)\n", + " \n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2e8e117", + "metadata": {}, + "outputs": [], + "source": [ + "user_target_pct = pd.DataFrame()\n", + "\n", + "# For every user, compute the replaced mode distribution.\n", + "for user_id, user_data in df.groupby('user_id'):\n", + " \n", + " target_distribution = user_data['target'].value_counts(normalize=True)\n", + " target_distribution.rename(index=MAP, inplace=True)\n", + " user_target_pct = pd.concat([user_target_pct, target_distribution.to_frame(user_id).T])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "99369dba", + "metadata": {}, + "outputs": [], + "source": [ + "user_target_pct.columns = ['pct_trips_' + str(x) for x in user_target_pct.columns]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6cca3671", + "metadata": {}, + "outputs": [], + "source": [ + "target_distance = pd.DataFrame()\n", + "\n", + "# For every user, compute the replaced mode distribution.\n", + "for user_id, user_data in df.groupby('user_id'):\n", + " \n", + " # total_distance = user_data['distance'].sum()\n", + " distance_per_target = user_data.groupby('target')['section_distance_argmax'].sum()\n", + " distance_per_target.rename(index=MAP, inplace=True)\n", + " row = distance_per_target.to_frame(user_id).T\n", + " target_distance = pd.concat([target_distance, row])\n", + " \n", + "target_distance.columns = ['distance_' + str(x) for x in target_distance.columns]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18093734", + "metadata": {}, + "outputs": [], + "source": [ + "target_duration = df.groupby(['user_id', 'target'])['section_duration_argmax'].sum().unstack()\n", + "target_duration.rename(columns=MAP, inplace=True)\n", + "target_duration.fillna(0., inplace=True)\n", + "target_duration.columns = ['duration_' + str(x) for x in target_duration.columns]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8001a140", + "metadata": {}, + "outputs": [], + "source": [ + "target_df = user_target_pct.merge(right=target_distance, left_index=True, right_index=True).merge(\n", + " right=target_duration, left_index=True, right_index=True\n", + ")\n", + "\n", + "target_df.fillna(0., inplace=True)\n", + "\n", + "target_df = pd.DataFrame(\n", + " MinMaxScaler().fit_transform(target_df),\n", + " columns=target_df.columns,\n", + " index=target_df.index\n", + ")\n", + "\n", + "display(target_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31fecc00", + "metadata": {}, + "outputs": [], + "source": [ + "epsilons = np.linspace(5e-3, 1., 1500)\n", + "best_score = -np.inf\n", + "best_eps = None\n", + "best_n = None\n", + "# alpha = 0.7\n", + "beta = 0.05\n", + "\n", + "for eps in epsilons:\n", + " for n in range(2, 30):\n", + " labels = DBSCAN(eps=eps, min_samples=n).fit(target_df).labels_\n", + " \n", + " n_unique = np.unique(labels)\n", + " n_outliers = len(labels[labels == -1])\n", + " \n", + " if n_outliers == len(labels) or len(n_unique) < 2:\n", + " continue\n", + " \n", + " # Encourage more clustering and discourage more outliers.\n", + " score = silhouette_score(target_df, labels) + (len(labels) - n_outliers)/n_outliers\n", + " \n", + " if score > best_score:\n", + " best_score = score\n", + " best_eps = eps\n", + " best_n = n\n", + "\n", + "print(f\"{best_score=}, {best_n=}, {best_eps=}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e39b41ba", + "metadata": {}, + "outputs": [], + "source": [ + "# 0.35 is a good value\n", + "\n", + "'''\n", + "allCEO = DBSCAN(eps=0.52, min_samples=2)\n", + "durham: DBSCAN(eps=best_eps, min_samples=2)\n", + "masscec: min_samples=2, eps=0.986724482988659\n", + "'''\n", + "\n", + "cl2 = DBSCAN(eps=best_eps, min_samples=2).fit(target_df)\n", + "# cl2 = KMeans(n_clusters=5).fit(target_df)\n", + "\n", + "Counter(cl2.labels_)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1dbf8763", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA\n", + "\n", + "tsfm = PCA(n_components=2).fit_transform(target_df)\n", + "\n", + "fig, ax = plt.subplots()\n", + "sns.scatterplot(x=tsfm[:,0], y=tsfm[:,1], c=cl2.labels_)\n", + "ax.set(xlabel='Latent Dim 0', ylabel='Latent Dim 1')\n", + "plt.savefig(f'./plots_for_paper/{CURRENT_DB}__Fig2__PCA_w_colors.png', dpi=300)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e444316", + "metadata": {}, + "outputs": [], + "source": [ + "print(df.columns.tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0bc09b9", + "metadata": {}, + "outputs": [], + "source": [ + "# Per-cluster users.\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.ensemble import IsolationForest\n", + "from sklearn.svm import OneClassSVM\n", + "from sklearn.neighbors import LocalOutlierFactor\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "\n", + "\n", + "demographic_cols = {\n", + " 'Stage_database': [\n", + " 'has_drivers_license', 'is_student', 'is_paid', \n", + " 'income_category', 'n_residence_members', 'n_residents_u18', 'n_residents_with_license', \n", + " 'n_motor_vehicles', 'has_medical_condition', 'ft_job', 'multiple_jobs', \n", + " 'n_working_residents', \"highest_education_Bachelor's degree\", \n", + " 'highest_education_Graduate degree or professional degree', \n", + " 'highest_education_High school graduate or GED', 'highest_education_Less than a high school graduate', \n", + " 'highest_education_Prefer not to say', 'highest_education_Some college or associates degree', \n", + " 'primary_job_description_Clerical or administrative support', 'primary_job_description_Custodial', \n", + " 'primary_job_description_Education', 'primary_job_description_Food service', \n", + " 'primary_job_description_Linecook', \n", + " 'primary_job_description_Manufacturing, construction, maintenance, or farming', \n", + " 'primary_job_description_Medical/healthcare', 'primary_job_description_Non-profit program manager', \n", + " 'primary_job_description_Other', 'primary_job_description_Professional, managerial, or technical', \n", + " 'primary_job_description_Sales or service', 'primary_job_description_Self employed', \n", + " 'primary_job_description_food service', 'gender_Man', 'gender_Nonbinary/genderqueer/genderfluid', \n", + " 'gender_Prefer not to say', 'gender_Woman', 'gender_Woman;Nonbinary/genderqueer/genderfluid', \n", + " 'av_transit', 'av_no_trip', 'av_p_micro', 'av_s_micro', 'av_ridehail', 'av_unknown', 'av_walk', 'av_car', \n", + " 'av_s_car'\n", + " ] + [c for c in df.columns if 'age' in c],\n", + " 'durham': [\n", + " 'is_student', 'is_paid', 'has_drivers_license', \n", + " 'n_residents_u18', 'n_residence_members', 'income_category',\n", + " 'n_residents_with_license', 'n_working_residents', 'n_motor_vehicles', 'has_medical_condition', \n", + " 'ft_job', 'multiple_jobs', 'highest_education_bachelor_s_degree', \n", + " 'highest_education_graduate_degree_or_professional_degree', \n", + " 'highest_education_high_school_graduate_or_ged', 'highest_education_less_than_a_high_school_graduate', \n", + " 'highest_education_some_college_or_associates_degree', \n", + " 'primary_job_description_Clerical or administrative support', \n", + " 'primary_job_description_Manufacturing, construction, maintenance, or farming', \n", + " 'primary_job_description_Other', 'primary_job_description_Professional, Manegerial, or Technical', \n", + " 'primary_job_description_Sales or service', 'gender_man', \n", + " 'gender_non_binary_genderqueer_gender_non_confor', 'gender_woman', \n", + " 'av_walk', 'av_unknown', 'av_no_trip', 'av_p_micro', 'av_transit', 'av_car', 'av_ridehail', \n", + " 'av_s_micro', 'av_s_car'\n", + " ] + [c for c in df.columns if 'age' in c],\n", + " 'masscec': [\n", + " 'is_student', 'is_paid', 'has_drivers_license', 'n_residents_u18', 'n_residence_members', \n", + " 'income_category', 'n_residents_with_license', 'n_working_residents', 'n_motor_vehicles', \n", + " 'has_medical_condition', 'ft_job', 'multiple_jobs', 'highest_education_bachelor_s_degree', \n", + " 'highest_education_graduate_degree_or_professional_degree', \n", + " 'highest_education_high_school_graduate_or_ged', 'highest_education_less_than_a_high_school_graduate', \n", + " 'highest_education_prefer_not_to_say', 'highest_education_some_college_or_associates_degree', \n", + " 'primary_job_description_Clerical or administrative support', \n", + " 'primary_job_description_Manufacturing, construction, maintenance, or farming', \n", + " 'primary_job_description_Other', 'primary_job_description_Prefer not to say', \n", + " 'primary_job_description_Professional, Manegerial, or Technical', \n", + " 'primary_job_description_Sales or service', 'gender_man', 'gender_prefer_not_to_say', 'gender_woman', \n", + " 'av_p_micro', 'av_s_car', 'av_s_micro', 'av_transit', 'av_car', 'av_no_trip', 'av_unknown', \n", + " 'av_ridehail', 'av_walk'\n", + " ] + [c for c in df.columns if 'age' in c],\n", + "}\n", + "\n", + "\n", + "cluster_labels = cl2.labels_\n", + "demographics = df.groupby('user_id').first()[demographic_cols[CURRENT_DB]]\n", + "demographics = demographics.loc[target_df.index, :]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a3c6355", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "### DEMOGRAPHICS\n", + "\n", + "def entropy(x):\n", + " # Compute bincount, normalize over the entire size. Gives us probabilities.\n", + " p = np.unique(x, return_counts=True)[1]/len(x)\n", + " # Compute the enropy usnig the probabilities.\n", + " return -np.sum(p * np.log2(p))\n", + "\n", + "def preprocess_demo_data(df: pd.DataFrame):\n", + " return df\n", + "\n", + "\n", + "within_cluster_homogeneity = dict()\n", + "other_cluster_homogeneity = dict()\n", + "labels = cl2.labels_\n", + "\n", + "for cix in np.unique(labels):\n", + " within_cluster_homogeneity[cix] = dict()\n", + " users = target_df[labels == cix].index\n", + " data = demographics.loc[demographics.index.isin(users), :].reset_index(drop=True)\n", + " processed = preprocess_demo_data(data)\n", + " \n", + " for col in processed.columns:\n", + " # Numeric/ordinal values. Use std. to measure homogeneity.\n", + " if col in [\n", + " 'n_residence_members', 'n_residents_u18', 'n_working_residents', 'n_motor_vehicles',\n", + " 'n_residents_with_license', 'income_category'\n", + " ]:\n", + " within_cluster_homogeneity[cix][col] = processed[col].std()\n", + " else:\n", + " within_cluster_homogeneity[cix][col] = entropy(processed[col])\n", + "\n", + "# Compute average homogeneity across other clusters.\n", + "for cix in within_cluster_homogeneity.keys():\n", + " other_cluster_homogeneity[cix] = dict()\n", + " other_clusters = set(within_cluster_homogeneity.keys()) - set([cix])\n", + " for feature in within_cluster_homogeneity[cix].keys():\n", + " homogeneity_in_others = [within_cluster_homogeneity[x][feature] for x in other_clusters]\n", + " other_cluster_homogeneity[cix][feature] = np.mean(homogeneity_in_others)\n", + "\n", + " \n", + "# Compute contrastive homogeneity\n", + "# CH = homogeneity within cluster / average homogeneity across other clusters\n", + "for cix in within_cluster_homogeneity.keys():\n", + " ch_scores = list()\n", + " print(f\"For cluster {cix}:\")\n", + " for feature in within_cluster_homogeneity[cix].keys():\n", + " feature_ch = within_cluster_homogeneity[cix][feature]/(other_cluster_homogeneity[cix][feature] + 1e-6)\n", + " ch_scores.append((feature, feature_ch))\n", + " \n", + " ch_df = pd.DataFrame(ch_scores, columns=['feature', 'ch']).sort_values(by=['ch']).head(4)\n", + " \n", + " # Display actual values.\n", + " users = target_df[labels == cix].index\n", + " data = demographics.loc[demographics.index.isin(users), :].reset_index(drop=True)\n", + " processed = preprocess_demo_data(data)\n", + " \n", + " display(ch_df)\n", + " print()\n", + " filtered = processed.loc[:, processed.columns.isin(ch_df.feature)][ch_df.feature]\n", + " filtered_features = ch_df.feature.tolist()\n", + " \n", + " fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))\n", + " for i, a in enumerate(ax.flatten()):\n", + " sns.histplot(filtered[filtered_features[i]], ax=a, stat=\"percent\")\n", + " plt.tight_layout()\n", + " plt.savefig(f\"{CURRENT_DB}_{cix}_Demographic_consistency.png\", dpi=300)\n", + " plt.show()\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "580bbd86", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import iqr\n", + "\n", + "def get_trip_summary_df(users, df):\n", + " '''\n", + " 1. df = a huge dataframe of user-trips. Each row is a trip.\n", + " 2. every trip is divided into sections: [walk, transit, walk]\n", + " 3. Each section has a corresponding distance and duration: [m1, m2, m3], [t1, t2, t3], [d1, d2, d3]\n", + " 4. What we are doing is only considering the mode, distance, and duration of the section with the largest distance\n", + " '''\n", + " \n", + " costs = [c for c in df.columns if 'av_' in c]\n", + " \n", + " mode_coverage = df.groupby(['user_id', 'section_mode_argmax'])[\n", + " ['section_duration_argmax', 'section_distance_argmax', 'mph'] + costs\n", + " ].agg(['mean', 'median']).unstack()\n", + " \n", + " global_stats = df.groupby('user_id')[['duration', 'distance']].agg(\n", + " ['mean', 'median']\n", + " )\n", + "\n", + " mode_coverage.columns = mode_coverage.columns.map('_'.join)\n", + " global_stats.columns = global_stats.columns.map('_'.join)\n", + " \n", + " # return mode_coverage\n", + " return mode_coverage.merge(right=global_stats, left_index=True, right_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92ad2485", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "## TRIP SUMMARIES\n", + "\n", + "# Per-cluster users.\n", + "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n", + "from sklearn.ensemble import IsolationForest\n", + "from sklearn.svm import OneClassSVM\n", + "from sklearn.neighbors import LocalOutlierFactor\n", + "from sklearn.feature_selection import SelectKBest, mutual_info_classif\n", + "\n", + "labels = cl2.labels_\n", + "\n", + "def get_data(cix):\n", + " users = target_df.iloc[labels == cix, :].index\n", + " \n", + " # Compute trip summaries.\n", + " X = df.loc[df.user_id.isin(users), [\n", + " 'section_distance_argmax', 'duration', 'distance', 'section_mode_argmax',\n", + " 'section_duration_argmax', 'mph', 'target', 'user_id'\n", + " ] + [c for c in df.columns if 'cost_' in c]].reset_index(drop=True)\n", + " \n", + " # Compute the target distribution and select the argmax.\n", + " target_distribution = X.target.value_counts(ascending=False, normalize=True)\n", + " target_distribution.rename(index=MAP, inplace=True)\n", + " \n", + " # Caution - this summary df has NaNs. Use nanstd() to compute nan-aware std.\n", + " subset = get_trip_summary_df(users, X)\n", + " \n", + " norm_subset = pd.DataFrame(\n", + " MinMaxScaler().fit_transform(subset),\n", + " columns=subset.columns, index=subset.index\n", + " )\n", + " \n", + " return norm_subset, target_distribution\n", + "\n", + "\n", + "in_cluster_homogeneity = dict()\n", + "out_cluster_homogeneity = dict()\n", + "\n", + "for cluster_ix in np.unique(labels):\n", + " in_cluster_homogeneity[cluster_ix] = dict()\n", + " norm_subset, _ = get_data(cluster_ix)\n", + " for feature in norm_subset.columns:\n", + " in_cluster_homogeneity[cluster_ix][feature] = np.nanstd(norm_subset[feature])\n", + "\n", + "for cix in in_cluster_homogeneity.keys():\n", + " out_cluster_homogeneity[cix] = dict()\n", + " oix = set(labels) - set([cix])\n", + " for feature in norm_subset.columns:\n", + " out_cluster_homogeneity[cix][feature] = np.nanmean([in_cluster_homogeneity[x].get(feature, np.nan) for x in oix])\n", + "\n", + "# Now, compute the per-cluster homogeneity.\n", + "for cix in in_cluster_homogeneity.keys():\n", + " ch = list()\n", + " for feature in in_cluster_homogeneity[cix].keys():\n", + " if feature in in_cluster_homogeneity[cix] and feature in out_cluster_homogeneity[cix]:\n", + " ratio = in_cluster_homogeneity[cix][feature] / (out_cluster_homogeneity[cix][feature] + 1e-6)\n", + " ch.append([feature, ratio])\n", + " \n", + " ch_df = pd.DataFrame(ch, columns=['feature', 'ch']).sort_values(by=['ch']).head(4)\n", + " data, target_dist = get_data(cix)\n", + " \n", + " features = ch_df.feature.tolist()\n", + " \n", + " print(f\"For cluster {cix}:\")\n", + " display(target_dist)\n", + " display(ch_df)\n", + " \n", + " fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))\n", + " for i, a in enumerate(ax.flatten()):\n", + " sns.histplot(data[features[i]], ax=a, stat=\"percent\")\n", + " plt.tight_layout()\n", + " plt.savefig(f\"{CURRENT_DB}_{cix}_Trip_consistency.png\", dpi=300)\n", + " plt.show()\n", + " print()\n", + " \n", + " print(50*'=')" + ] + }, + { + "cell_type": "markdown", + "id": "4992ff45", + "metadata": {}, + "source": [ + "## Now check the combined homogeneity score" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8723e3d", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "ic, oc = dict(), dict()\n", + "\n", + "labels = cl2.labels_\n", + "TOP_K = 3\n", + "\n", + "\n", + "for cix in np.unique(labels):\n", + " ic[cix] = dict()\n", + " \n", + " # Trip characteristics.\n", + " norm_subset, _ = get_data(cix)\n", + " for feature in norm_subset.columns:\n", + " ic[cix][feature] = np.nanstd(norm_subset[feature])\n", + " \n", + " # Demographics.\n", + " users = target_df[labels == cix].index\n", + " data = demographics.loc[demographics.index.isin(users), :].reset_index(drop=True)\n", + " processed = preprocess_demo_data(data)\n", + " \n", + " for col in processed.columns:\n", + " # Numeric/ordinal values. Use std. to measure homogeneity.\n", + " if col in [\n", + " 'n_residence_members', 'n_residents_u18', 'n_working_residents', 'n_motor_vehicles',\n", + " 'n_residents_with_license', 'income_category'\n", + " ]:\n", + " ic[cix][col] = np.nanstd(processed[col])\n", + " else:\n", + " ic[cix][col] = entropy(processed[col])\n", + "\n", + "for cix in ic.keys():\n", + " oc[cix] = dict()\n", + " oix = set(labels) - set([cix])\n", + " for feature in ic[cix].keys():\n", + " oc[cix][feature] = np.nanmean([ic[x].get(feature, np.nan) for x in oix])\n", + "\n", + "per_cluster_most_homogeneous = dict()\n", + "\n", + "# Now, compute the per-cluster homogeneity.\n", + "ax_ix = 0\n", + "for cix in ic.keys():\n", + "\n", + " print(f\"For cluster {cix}:\")\n", + "\n", + " # For each, cluster, we will have (TOP_K x n_clusters) figures.\n", + " fig, ax = plt.subplots(nrows=TOP_K, ncols=len(ic.keys()), figsize=(12, 8))\n", + "\n", + " other_ix = set(ic.keys()) - set([cix])\n", + " \n", + " ch = list()\n", + " for feature in ic[cix].keys():\n", + " if feature in oc[cix]:\n", + " ratio = ic[cix][feature] / (oc[cix][feature] + 1e-6)\n", + " ch.append([feature, ratio])\n", + " \n", + " # Just the top k.\n", + " ch_df = pd.DataFrame(ch, columns=['feature', 'ch']).sort_values(by=['ch']).reset_index(drop=True).head(TOP_K)\n", + "\n", + " figure_data = dict()\n", + " \n", + " # Get the actual trip summary data.\n", + " trip_summary_data, target_dist = get_data(cix)\n", + " \n", + " # Get the actual demographic data.\n", + " users = target_df[labels == cix].index\n", + " data = demographics.loc[demographics.index.isin(users), :].reset_index(drop=True)\n", + " processed = preprocess_demo_data(data)\n", + "\n", + " # Left-most subplot will be that of the current cluster's feature.\n", + " for row_ix, row in ch_df.iterrows():\n", + " if row.feature in trip_summary_data.columns:\n", + " sns.histplot(trip_summary_data[row.feature], ax=ax[row_ix][0], stat='percent').set_title(\"Current cluster\")\n", + " else:\n", + " sns.histplot(processed[row.feature], ax=ax[row_ix][0], stat='percent').set_title(\"Current cluster\")\n", + " ax[row_ix][0].set_xlabel(ax[row_ix][0].get_xlabel(), fontsize=8)\n", + " ax[row_ix][0].set_ylim(0., 100.)\n", + "\n", + " offset_col_ix = 1\n", + " ## Now, others.\n", + " for oix in other_ix:\n", + " # Get the actual trip summary data.\n", + " other_summary_data, _ = get_data(oix)\n", + " \n", + " # Get the actual demographic data.\n", + " users = target_df[labels == oix].index\n", + " data = demographics.loc[demographics.index.isin(users), :].reset_index(drop=True)\n", + " other_demo = preprocess_demo_data(data)\n", + "\n", + " for row_ix, row in ch_df.iterrows():\n", + " if row.feature in other_summary_data.columns:\n", + " sns.histplot(other_summary_data[row.feature], ax=ax[row_ix][offset_col_ix], stat='percent').set_title(f\"Cluster {oix}\")\n", + " else:\n", + " sns.histplot(other_demo[row.feature], ax=ax[row_ix][offset_col_ix], stat='percent').set_title(f\"Cluster {oix}\")\n", + " ax[row_ix][offset_col_ix].set_xlabel(ax[row_ix][offset_col_ix].get_xlabel(), fontsize=8)\n", + " ax[row_ix][offset_col_ix].set_ylim(0., 100.)\n", + " \n", + " offset_col_ix += 1\n", + " \n", + " plt.tight_layout()\n", + " plt.savefig(f\"./plots_for_paper/{CURRENT_DB}_cluster{cix}_combined_features.png\", dpi=300)\n", + " plt.show()\n", + " print(50 * '=')" + ] + }, + { + "cell_type": "markdown", + "id": "24a80f68", + "metadata": {}, + "source": [ + "## Try a different clustering technique?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d0288db8", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.cluster import AffinityPropagation\n", + "\n", + "best_score = -np.inf\n", + "best_params = None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b14ad0c", + "metadata": {}, + "outputs": [], + "source": [ + "cls = AffinityPropagation(random_state=13210).fit(target_df)\n", + "labels = cls.labels_\n", + "\n", + "print(labels)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2562bbb6-66eb-4283-8c08-6e20a0b2ade5", + "metadata": {}, + "outputs": [], + "source": [ + "center_embeddings = cls.cluster_centers_\n", + "centers_proj = PCA(n_components=2).fit_transform(center_embeddings)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7aad38a", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "sns.scatterplot(x=tsfm[:,0], y=tsfm[:,1], c=cls.labels_, ax=ax)\n", + "ax.scatter(x=centers_proj[:,0], y=centers_proj[:,1], marker='X', c='red', alpha=0.5)\n", + "ax.set(xlabel='Latent Dim 0', ylabel='Latent Dim 1')\n", + "# plt.legend([str(x) for x in ap_labels], loc='best')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39ce0238-b3f2-4f46-a52f-13e3160cc52f", + "metadata": {}, + "outputs": [], + "source": [ + "def get_data2(cix, labels):\n", + " users = target_df.iloc[labels == cix, :].index\n", + " \n", + " # Compute trip summaries.\n", + " X = df.loc[df.user_id.isin(users), [\n", + " 'section_distance_argmax', 'section_duration_argmax',\n", + " 'section_mode_argmax', 'distance',\n", + " 'duration', 'mph', 'user_id', 'target'\n", + " ]]\n", + " \n", + " # Compute the target distribution and select the argmax.\n", + " target_distribution = X.target.value_counts(ascending=False, normalize=True)\n", + " target_distribution.rename(index=MAP, inplace=True)\n", + " \n", + " # Caution - this summary df has NaNs. Use nanstd() to compute nan-aware std.\n", + " subset = get_trip_summary_df(users, X)\n", + " \n", + " norm_subset = pd.DataFrame(\n", + " MinMaxScaler().fit_transform(subset),\n", + " columns=subset.columns, index=subset.index\n", + " )\n", + " \n", + " return norm_subset, target_distribution" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec27cf29", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "## Analaysis for this data.\n", + "\n", + "ic, oc = dict(), dict()\n", + "labels = cls.labels_\n", + "\n", + "for cix in np.unique(labels):\n", + " users = target_df[labels == cix].index\n", + " \n", + " ic[cix] = dict()\n", + " \n", + " # Trip characteristics.\n", + " norm_subset, _ = get_data2(cix, labels)\n", + " for feature in norm_subset.columns:\n", + " ic[cix][feature] = np.nanstd(norm_subset[feature])\n", + " \n", + " # Demographics.\n", + " data = demographics.loc[demographics.index.isin(users), :].reset_index(drop=True)\n", + " processed = preprocess_demo_data(data)\n", + " \n", + " for col in processed.columns:\n", + " # Numeric/ordinal values. Use std. to measure homogeneity.\n", + " if col == 'age' or col == 'income_category' or col == 'n_working_residents':\n", + " ic[cix][col] = np.nanstd(processed[col])\n", + " else:\n", + " ic[cix][col] = entropy(processed[col])\n", + "\n", + "for cix in ic.keys():\n", + " oc[cix] = dict()\n", + " oix = set(labels) - set([cix])\n", + " for feature in ic[cix].keys():\n", + " oc[cix][feature] = np.nanmean([ic[x].get(feature, np.nan) for x in oix])\n", + "\n", + "# # Now, compute the per-cluster homogeneity.\n", + "# for cix in ic.keys():\n", + " \n", + "# users = users = target_df[labels == cix].index\n", + "# norm_subset, target_dist = get_data(cix, labels)\n", + "# data = demographics.loc[demographics.index.isin(users), :].reset_index(drop=True)\n", + "# processed = preprocess_demo_data(data)\n", + " \n", + "# concat = processed.merge(norm_subset, left_index=True, right_index=True)\n", + " \n", + "# ch = list()\n", + "# for feature in ic[cix].keys():\n", + "# ratio = ic[cix][feature] / (oc[cix][feature] + 1e-6)\n", + "# ch.append([feature, ratio])\n", + " \n", + "# ch_df = pd.DataFrame(ch, columns=['feature', 'ch']).sort_values(by=['ch']).head(TOP_K).reset_index(drop=True)\n", + "\n", + "\n", + "# Now, compute the per-cluster homogeneity.\n", + "ax_ix = 0\n", + "for cix in ic.keys():\n", + "\n", + " print(f\"For cluster {cix}:\")\n", + "\n", + " # For each, cluster, we will have (TOP_K x n_clusters) figures.\n", + " fig, ax = plt.subplots(nrows=5, ncols=len(ic.keys()), figsize=(12, 8))\n", + "\n", + " other_ix = set(ic.keys()) - set([cix])\n", + " \n", + " ch = list()\n", + " for feature in ic[cix].keys():\n", + " ratio = ic[cix][feature] / (oc[cix][feature] + 1e-6)\n", + " ch.append([feature, ratio])\n", + " \n", + " # Just the top k.\n", + " ch_df = pd.DataFrame(ch, columns=['feature', 'ch']).sort_values(by=['ch']).reset_index(drop=True).head(5)\n", + " figure_data = dict()\n", + " \n", + " # Get the actual trip summary data.\n", + " trip_summary_data, target_dist = get_data(cix)\n", + "\n", + " display(target_dist)\n", + " \n", + " # Get the actual demographic data.\n", + " users = target_df[labels == cix].index\n", + " data = demographics.loc[demographics.index.isin(users), :].reset_index(drop=True)\n", + " processed = preprocess_demo_data(data)\n", + "\n", + " # Left-most subplot will be that of the current cluster's feature.\n", + " for row_ix, row in ch_df.iterrows():\n", + " if row.feature in trip_summary_data.columns:\n", + " sns.histplot(trip_summary_data[row.feature], ax=ax[row_ix][0], stat='percent').set_title(\"Current cluster\")\n", + " else:\n", + " sns.histplot(processed[row.feature], ax=ax[row_ix][0], stat='percent').set_title(\"Current cluster\")\n", + " ax[row_ix][0].set_xlabel(ax[row_ix][0].get_xlabel(), fontsize=6)\n", + " ax[row_ix][0].set_ylim(0., 100.)\n", + "\n", + " offset_col_ix = 1\n", + " ## Now, others.\n", + " for oix in other_ix:\n", + " # Get the actual trip summary data.\n", + " other_summary_data, _ = get_data(oix)\n", + " \n", + " # Get the actual demographic data.\n", + " users = target_df[labels == oix].index\n", + " data = demographics.loc[demographics.index.isin(users), :].reset_index(drop=True)\n", + " other_demo = preprocess_demo_data(data)\n", + "\n", + " for row_ix, row in ch_df.iterrows():\n", + " if row.feature in other_summary_data.columns:\n", + " sns.histplot(other_summary_data[row.feature], ax=ax[row_ix][offset_col_ix], stat='percent').set_title(f\"Cluster {oix}\")\n", + " else:\n", + " sns.histplot(other_demo[row.feature], ax=ax[row_ix][offset_col_ix], stat='percent').set_title(f\"Cluster {oix}\")\n", + " ax[row_ix][offset_col_ix].set_xlabel(ax[row_ix][offset_col_ix].get_xlabel(), fontsize=6)\n", + " ax[row_ix][offset_col_ix].set_ylim(0., 100.)\n", + " \n", + " offset_col_ix += 1\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + " print(50 * '=')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0b642db", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "emission", + "language": "python", + "name": "emission" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/rm_src/UserLevelModeling.ipynb b/rm_src/UserLevelModeling.ipynb deleted file mode 100644 index 7fefe5a..0000000 --- a/rm_src/UserLevelModeling.ipynb +++ /dev/null @@ -1,869 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "04ccf092", - "metadata": {}, - "source": [ - "### Some important points to remember:\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "2985b51f", - "metadata": {}, - "outputs": [], - "source": [ - "# import warnings\n", - "\n", - "# warnings.simplefilter('ignore', Warning)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "21ef0f2e", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import random\n", - "import os\n", - "import ast\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from sklearn.linear_model import LinearRegression\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import r2_score, f1_score, log_loss\n", - "from sklearn.model_selection import train_test_split, RandomizedSearchCV, StratifiedKFold\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "from sklearn.cluster import KMeans\n", - "from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances\n", - "from enum import Enum\n", - "from scipy.stats import uniform\n", - "from typing import List\n", - "\n", - "pd.set_option('display.max_columns', None) " - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "fef98692", - "metadata": {}, - "outputs": [], - "source": [ - "SEED = 13210\n", - "\n", - "np.random.seed(SEED)\n", - "random.seed(SEED)\n", - "\n", - "SimilarityMetric = Enum('SimilarityMetric', ['COSINE', 'EUCLIDEAN', 'KNN', 'KMEANS'])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "79f8c51a", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_47441/224655024.py:1: DtypeWarning: Columns (38) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " df = pd.read_csv('../data/ReplacedMode_Fix_02142024.csv')\n" - ] - } - ], - "source": [ - "df = pd.read_csv('../data/ReplacedMode_Fix_02142024.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "72793473", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['source', 'end_ts', 'end_fmt_time', 'end_loc', 'raw_trip', 'start_ts', 'start_fmt_time', 'start_loc', 'duration', 'distance', 'start_place', 'end_place', 'cleaned_trip', 'inferred_labels', 'inferred_trip', 'expectation', 'confidence_threshold', 'expected_trip', 'user_input', 'start:year', 'start:month', 'start:day', 'start:hour', 'start_local_dt_minute', 'start_local_dt_second', 'start_local_dt_weekday', 'start_local_dt_timezone', 'end:year', 'end:month', 'end:day', 'end:hour', 'end_local_dt_minute', 'end_local_dt_second', 'end_local_dt_weekday', 'end_local_dt_timezone', '_id', 'user_id', 'metadata_write_ts', 'additions', 'mode_confirm', 'purpose_confirm', 'distance_miles', 'Mode_confirm', 'Trip_purpose', 'original_user_id', 'program', 'opcode', 'Timestamp', 'birth_year', 'primary_job_commute_time', 'income_category', 'n_residence_members', 'n_residents_u18', 'n_residents_with_license', 'n_motor_vehicles', 'available_modes', 'age', 'gender_Man', 'gender_Man;Nonbinary/genderqueer/genderfluid', 'gender_Nonbinary/genderqueer/genderfluid', 'gender_Prefer not to say', 'gender_Woman', 'gender_Woman;Nonbinary/genderqueer/genderfluid', 'has_drivers_license_No', 'has_drivers_license_Prefer not to say', 'has_drivers_license_Yes', 'has_multiple_jobs_No', 'has_multiple_jobs_Prefer not to say', 'has_multiple_jobs_Yes', \"highest_education_Bachelor's degree\", 'highest_education_Graduate degree or professional degree', 'highest_education_High school graduate or GED', 'highest_education_Less than a high school graduate', 'highest_education_Prefer not to say', 'highest_education_Some college or associates degree', 'primary_job_type_Full-time', 'primary_job_type_Part-time', 'primary_job_type_Prefer not to say', 'primary_job_description_Clerical or administrative support', 'primary_job_description_Custodial', 'primary_job_description_Education', 'primary_job_description_Food service', 'primary_job_description_Manufacturing, construction, maintenance, or farming', 'primary_job_description_Medical/healthcare', 'primary_job_description_Other', 'primary_job_description_Professional, managerial, or technical', 'primary_job_description_Sales or service', 'primary_job_commute_mode_Active transport', 'primary_job_commute_mode_Car transport', 'primary_job_commute_mode_Hybrid', 'primary_job_commute_mode_Public transport', 'primary_job_commute_mode_Unknown', 'primary_job_commute_mode_WFH', 'is_overnight_trip', 'n_working_residents', 'start_lat', 'start_lng', 'end_lat', 'end_lng', 'temperature_2m (°F)', 'relative_humidity_2m (%)', 'dew_point_2m (°F)', 'rain (inch)', 'snowfall (inch)', 'wind_speed_10m (mp/h)', 'wind_gusts_10m (mp/h)', 'section_distance_argmax', 'section_duration_argmax', 'section_mode_argmax', 'section_coordinates_argmax', 'mph', 'target', 'av_car', 'av_transit', 'av_ridehail', 'av_p_micro', 'av_s_micro', 'av_walk', 'av_no_trip', 'av_s_car', 'av_unknown', 'cost_p_micro', 'cost_no_trip', 'cost_s_car', 'cost_transit', 'cost_car', 'cost_s_micro', 'cost_ridehail', 'cost_walk', 'cost_unknown']\n" - ] - } - ], - "source": [ - "print(df.columns.tolist())" - ] - }, - { - "cell_type": "markdown", - "id": "85866e8a", - "metadata": {}, - "source": [ - "### We want to experiment with two types of models:\n", - "\n", - "\n", - "1. have one row per user, so that when predicting modes for a new user, we pick the \"similar user\" or users and determine the replaced mode\n", - " - In this, the traditional approach would only use demographics for the user features, we may experiment with some summaries of the trip data that will function as some level of \"fingerprint\" for the user. Ideally we would be able to show that this performs better than demographics alone\n", - " - Note also that the original method that you had outlined where the training set is a list of trips (O()) is a third approach which we will be comparing these two against" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "2a9efd3e", - "metadata": {}, - "outputs": [], - "source": [ - "def encode_availability(df: pd.DataFrame):\n", - " \n", - " # Borrowed directly from the cost_time_avl_preprocessing notebook.\n", - " available = {\n", - " 'Bicycle': 'p_micro',\n", - " 'Do not have vehicle': 'unknown',\n", - " 'Get a ride from a friend or family member': 's_car',\n", - " 'None': 'no_trip',\n", - " 'Public transportation (bus, subway, light rail, etc.)': 'transit',\n", - " 'Rental car (including Zipcar/ Car2Go)': 'car',\n", - " 'Shared bicycle or scooter': 's_micro',\n", - " 'Skateboard': 'p_micro',\n", - " 'Taxi (regular taxi, Uber, Lyft, etc)': 'ridehail',\n", - " 'Walk/roll': 'walk',\n", - " 'Prefer not to say': 'unknown'\n", - " }\n", - " \n", - " newcols = list(available.values())\n", - " \n", - " # Start by creating dummy indicators.\n", - " df[newcols] = 0\n", - " \n", - " for i, row in df.iterrows():\n", - " modes = [e.strip() for e in row.available_modes.split(';')]\n", - " mapped_modes = [available[mode] for mode in modes]\n", - " df.loc[i, mapped_modes] = 1\n", - " \n", - " df.drop(columns=['available_modes'], inplace=True)\n", - " df.columns = ['av_' + str(c) if c in newcols else str(c) for c in df.columns]\n", - " \n", - " return df" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "cfe76e8c", - "metadata": {}, - "outputs": [], - "source": [ - "def get_mode_coverage(df: pd.DataFrame):\n", - " \n", - " coverage_df = df.groupby(['user_id', 'section_mode_argmax']).size().unstack(fill_value=0)\n", - " coverage_df.columns = ['coverage_' + str(c) for c in coverage_df.columns]\n", - " \n", - " # As a preventative measure.\n", - " coverage_df.fillna(0, inplace=True)\n", - " \n", - " # Normalize over rows.\n", - " coverage_df.iloc[:, 1:] = coverage_df.iloc[:, 1:].div(coverage_df.iloc[:, 1:].sum(axis=1), axis=0)\n", - " \n", - " return coverage_df" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "75313008", - "metadata": {}, - "outputs": [], - "source": [ - "def get_trip_summaries(df: pd.DataFrame, group_key: str, feature_list: List[str]):\n", - " \n", - " def get_feature_summaries(trip_feature: str, is_ordinal: bool = False):\n", - " \n", - " if not is_ordinal:\n", - " # A mean of 0 is an actual value.\n", - " mean = df.groupby(['user_id', group_key])[trip_feature].mean().unstack(level=-1, fill_value=-1.)\n", - " mean.columns = [f'{trip_feature}_mean_' + str(c) for c in mean.columns]\n", - " \n", - " # Same with percentiles - 0 is an actual value.\n", - " median = df.groupby(['user_id', group_key])[trip_feature].median().unstack(level=-1, fill_value=-1.)\n", - " median.columns = [f'{trip_feature}_median_' + str(c) for c in median.columns]\n", - " \n", - " iqr_temp = df.groupby(['user_id', group_key])[trip_feature].quantile([0.25, 0.75]).unstack(level=-1)\n", - " iqr = (iqr_temp[0.75] - iqr_temp[0.25]).unstack(level=-1)\n", - " iqr.fillna(-1., inplace=True)\n", - " iqr.columns = [f'{trip_feature}_iqr_' + str(c) for c in iqr.columns]\n", - "\n", - " # Now merge.\n", - " merged = mean.copy()\n", - " merged = merged.merge(right=median, left_index=True, right_index=True)\n", - " merged = merged.merge(right=iqr, left_index=True, right_index=True)\n", - "\n", - " return merged\n", - " \n", - " # 0 is OK to indicate NaN values.\n", - " f_mode = df.groupby(['user_id', group_key])[trip_feature].apply(\n", - " lambda x: x.value_counts().idxmax()\n", - " ).unstack(fill_value=0.)\n", - " \n", - " f_mode.columns = [f'{trip_feature}_mode_' + str(c) for c in f_mode.columns]\n", - " \n", - " return f_mode\n", - " \n", - " # This will be the dataframe that all subsequent features will join to.\n", - " feature_df = None\n", - " \n", - " for ix, feature in enumerate(feature_list):\n", - " is_ordinal = feature == 'start:hour' or feature == 'end:hour'\n", - " if ix == 0:\n", - " feature_df = get_feature_summaries(feature, is_ordinal)\n", - " else:\n", - " next_feature_df = get_feature_summaries(feature, is_ordinal)\n", - " feature_df = feature_df.merge(right=next_feature_df, left_index=True, right_index=True)\n", - " \n", - " return feature_df" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "63617ada", - "metadata": {}, - "outputs": [], - "source": [ - "def get_demographic_data(df: pd.DataFrame, **trip_kwargs):\n", - " \n", - " '''\n", - " A method that returns a U x (D + t) matrix, where U = number of users,\n", - " D = number of demographic features, t (optional) = number of trip summary features.\n", - " \n", - " When use_trip_summaries=True, the 'available_modes' column is dropped in favor of\n", - " the already-preprocessed av_ columns. This is because we want to incorporate trip-level\n", - " information into the data. When the argument is False, we want to SOLELY use demographics.\n", - " '''\n", - " \n", - " trip_features_to_use = trip_kwargs.pop('trip_features', None)\n", - " trip_group_key = trip_kwargs.pop('trip_group_key', 'section_mode_argmax')\n", - " \n", - " demographics = [\n", - " 'user_id', 'primary_job_commute_time', 'income_category', 'n_residence_members', 'n_residents_u18', \n", - " 'n_residents_with_license', 'n_motor_vehicles', 'available_modes', 'age', 'gender_Man', \n", - " 'gender_Man;Nonbinary/genderqueer/genderfluid', 'gender_Nonbinary/genderqueer/genderfluid', \n", - " 'gender_Prefer not to say', 'gender_Woman', 'gender_Woman;Nonbinary/genderqueer/genderfluid', \n", - " 'has_drivers_license_No', 'has_drivers_license_Prefer not to say', 'has_drivers_license_Yes', \n", - " 'has_multiple_jobs_No', 'has_multiple_jobs_Prefer not to say', 'has_multiple_jobs_Yes', \n", - " \"highest_education_Bachelor's degree\", 'highest_education_Graduate degree or professional degree', \n", - " 'highest_education_High school graduate or GED', 'highest_education_Less than a high school graduate', \n", - " 'highest_education_Prefer not to say', 'highest_education_Some college or associates degree', \n", - " 'primary_job_type_Full-time', 'primary_job_type_Part-time', 'primary_job_type_Prefer not to say', \n", - " 'primary_job_description_Clerical or administrative support', 'primary_job_description_Custodial', \n", - " 'primary_job_description_Education', 'primary_job_description_Food service', \n", - " 'primary_job_description_Manufacturing, construction, maintenance, or farming', \n", - " 'primary_job_description_Medical/healthcare', 'primary_job_description_Other', \n", - " 'primary_job_description_Professional, managerial, or technical', \n", - " 'primary_job_description_Sales or service', 'primary_job_commute_mode_Active transport', \n", - " 'primary_job_commute_mode_Car transport', 'primary_job_commute_mode_Hybrid', \n", - " 'primary_job_commute_mode_Public transport', 'primary_job_commute_mode_Unknown', \n", - " 'primary_job_commute_mode_WFH', 'is_overnight_trip', 'n_working_residents'\n", - " ]\n", - " \n", - " # Retain only the first instance of each user and subset the columns.\n", - " filtered = df.groupby('user_id').first().reset_index(drop=False)[demographics]\n", - " \n", - " # Get the targets.\n", - " targets = df.groupby('user_id')['target'].apply(lambda x: x.value_counts().idxmax()).reset_index(drop=False)\n", - " \n", - " filtered = filtered.merge(right=targets, left_on='user_id', right_on='user_id')\n", - " \n", - " if not trip_features_to_use or len(trip_features_to_use) == 0:\n", - " # Use the available modes as indicators.\n", - " return encode_availability(filtered)\n", - " \n", - " # -----------------------------------------------------------\n", - " # Reaching here means that we need to include trip summaries\n", - " # -----------------------------------------------------------\n", - " \n", - " # If trip summaries are to be used, then re-use the preprocessed availability features.\n", - " availability = df[['user_id'] + [c for c in df.columns if 'av_' in c]]\n", - " availability = availability.groupby('user_id').first()\n", - " \n", - " # For every user, generate the global trip-level summaries.\n", - " global_aggs = df.groupby('user_id').agg({'duration': 'mean', 'distance': 'mean'})\n", - " \n", - " # coverage.\n", - " coverage = get_mode_coverage(df)\n", - " \n", - " # Trip-level features.\n", - " trip_features = get_trip_summaries(\n", - " df=df, \n", - " group_key=trip_group_key, \n", - " feature_list=trip_features_to_use\n", - " )\n", - " \n", - " targets = df.groupby('user_id')['target'].apply(lambda x: x.value_counts().idxmax())\n", - " \n", - " trip_features = trip_features.merge(right=coverage, left_index=True, right_index=True)\n", - " trip_features = trip_features.merge(right=global_aggs, left_index=True, right_index=True)\n", - " \n", - " # Finally, join with availability indicators and targets.\n", - " trip_features = trip_features.merge(right=availability, left_index=True, right_on='user_id')\n", - " trip_features = trip_features.merge(right=targets, left_index=True, right_index=True)\n", - " \n", - " return trip_features.reset_index(drop=False)" - ] - }, - { - "cell_type": "markdown", - "id": "fedb51e8", - "metadata": {}, - "source": [ - "## Experiment 1: Only demographics" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "66421120", - "metadata": {}, - "outputs": [], - "source": [ - "demo_df = get_demographic_data(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "c023cf66", - "metadata": {}, - "outputs": [], - "source": [ - "# No stratification, pure random.\n", - "train, test = train_test_split(demo_df, test_size=0.2, random_state=SEED)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "376a4391", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "132 33\n" - ] - } - ], - "source": [ - "print(train.shape[0], test.shape[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "ef77c9c8", - "metadata": {}, - "outputs": [], - "source": [ - "def evaluate_using_similarity(test_df, train_df, metric=SimilarityMetric.COSINE, **metric_kwargs):\n", - " \n", - " '''\n", - " This method treats each user row as a 'fingerprint' (embedding vector). We assume that we\n", - " have no idea about the test set labels. To find which replaced mode is most likely for the test\n", - " users, we compute the cosine similarity of each test user against the users in the training set.\n", - " For the most similar user, we use their target as a proxy for the test user's replaced mode.\n", - " This operates on the following intuition: If User A and User B are similar, then their replaced\n", - " modes are also similar.\n", - " '''\n", - " \n", - " tr_targets = train_df.target.values\n", - " tr = train_df.drop(columns=['target', 'user_id'], inplace=False).reset_index(drop=True, inplace=False)\n", - " \n", - " te_targets = test_df.target.values\n", - " te = test_df.drop(columns=['target', 'user_id'], inplace=False).reset_index(drop=True, inplace=False)\n", - " \n", - " if metric == SimilarityMetric.COSINE:\n", - " # Use cosine similarity to determine which element in the train set this user is closest to.\n", - " # Offset the columns from the second entry to exclude the user_id column.\n", - " # Returns a (n_te, n_tr) matrix.\n", - " sim = cosine_similarity(te.values, tr.values)\n", - " \n", - " # Compute the argmax across the train set.\n", - " argmax = np.argmax(sim, axis=1)\n", - "\n", - " # Index into the training targets to retrieve predicted label.\n", - " y_test_pred = tr_targets[argmax]\n", - " \n", - " elif metric == SimilarityMetric.EUCLIDEAN:\n", - " \n", - " # Here, we choose the embedding with the smallest L2 distance.\n", - " distances = euclidean_distances(te.values, tr.values)\n", - " \n", - " # We choose argmin\n", - " argmin = np.argmin(distances, axis=1)\n", - " \n", - " # Index into the targets.\n", - " y_test_pred = tr_targets[argmin]\n", - " \n", - " elif metric == SimilarityMetric.KNN:\n", - " \n", - " # Build the KNN classifier. By default, let it be 3.\n", - " knn = KNeighborsClassifier(\n", - " n_neighbors=metric_kwargs.pop('n_neighbors', 3),\n", - " weights='distance',\n", - " metric=metric_kwargs.pop('knn_metric', 'cosine'),\n", - " n_jobs=os.cpu_count()\n", - " )\n", - " \n", - " # Fit the data to the KNN model\n", - " knn.fit(tr, tr_targets)\n", - " \n", - " y_test_pred = knn.predict(te)\n", - " \n", - " elif metric == SimilarityMetric.KMEANS:\n", - " \n", - " # Build the model.\n", - " kmeans = KMeans(\n", - " n_clusters=metric_kwargs.pop('n_clusters', 8),\n", - " max_iter=metric_kwargs.pop('max_iter', 300),\n", - " n_init='auto'\n", - " )\n", - " \n", - " # Fit the clustering model\n", - " kmeans.fit(tr)\n", - " \n", - " # Construct the auxiliary df and merge with the training set.\n", - " label_df = pd.DataFrame({'label': kmeans.labels_, 'target': tr_targets}, index=tr.index)\n", - " \n", - " # Now, perform an inference on the test set.\n", - " predicted_labels = kmeans.predict(te)\n", - " \n", - " y_test_pred = []\n", - " for prediction in predicted_labels:\n", - " most_likely = label_df.loc[label_df.label == prediction, 'target'].value_counts().idxmax()\n", - " y_test_pred.append(most_likely)\n", - " \n", - " else:\n", - " raise NotImplementedError(\"Unknown similarity metric\")\n", - " \n", - " \n", - " f1 = f1_score(y_true=te_targets, y_pred=y_test_pred, average='weighted')\n", - " print(f\"Test F1 score using {metric.name} = {f1}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "1a95ad5e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test F1 score using COSINE = 0.32806324110671936\n" - ] - } - ], - "source": [ - "evaluate_using_similarity(test, train, SimilarityMetric.COSINE)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "44cfbe29", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test F1 score using EUCLIDEAN = 0.2742577288031834\n" - ] - } - ], - "source": [ - "evaluate_using_similarity(test, train, SimilarityMetric.EUCLIDEAN)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "fe343c8b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test F1 score using KNN = 0.35950413223140487\n" - ] - } - ], - "source": [ - "evaluate_using_similarity(test, train, SimilarityMetric.KNN)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "d9681639", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test F1 score using KMEANS = 0.33347902097902093\n" - ] - } - ], - "source": [ - "evaluate_using_similarity(test, train, SimilarityMetric.KMEANS, n_clusters=3)" - ] - }, - { - "cell_type": "markdown", - "id": "16e435a6", - "metadata": {}, - "source": [ - "Not bad - using just a simple random split gives us the following results:\n", - "\n", - "$F1_{cosine} = 0.32$, $F1_{euclidean} = 0.27$, $F1_{KNN} = 0.36$, $F1_{kmeans} = 0.33$" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "81f0e842", - "metadata": {}, - "outputs": [], - "source": [ - "def custom_nll_scorer(clf, X, y):\n", - " y_pred = clf.predict_proba(X)\n", - " return -log_loss(y_true=y, y_pred=y_pred, labels=sorted(np.unique(y)))" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "a3a6af8f", - "metadata": {}, - "outputs": [], - "source": [ - "def estimate_using_model(train, test):\n", - " \n", - " # Define the train-val splitter.\n", - " splitter = StratifiedKFold(n_splits=4, shuffle=True, random_state=SEED)\n", - " \n", - " params = {\n", - " 'n_estimators': np.arange(100, 1001, 50),\n", - " 'max_depth': [i for i in range(5, 101, 5)],\n", - " 'ccp_alpha': np.linspace(0, 1, 10),\n", - " 'class_weight': ['balanced', 'balanced_subsample', None],\n", - " 'min_samples_split': np.arange(2, 25, 2),\n", - " 'min_samples_leaf': np.arange(1, 25)\n", - " }\n", - " \n", - " rf = RandomForestClassifier(random_state=SEED)\n", - " \n", - " # Search over hparams to minimize negative log likelihood. \n", - " clf = RandomizedSearchCV(\n", - " rf, params, n_iter=500, scoring=custom_nll_scorer, \n", - " n_jobs=os.cpu_count(), cv=splitter, random_state=SEED,\n", - " verbose=0\n", - " )\n", - " \n", - " X_tr = train.drop(columns=['user_id', 'target'])\n", - " y_tr = train.target.values.ravel()\n", - " \n", - " scorer = clf.fit(X_tr, y_tr)\n", - " \n", - " best_model = scorer.best_estimator_\n", - " \n", - " print(f\"Best val score = {scorer.best_score_}\")\n", - " \n", - " X_te = test.drop(columns=['user_id', 'target'])\n", - " \n", - " # Use the best model to compute F1 on the test set.\n", - " test_f1 = f1_score(y_true=test.target.values, y_pred=best_model.predict(X_te), average='weighted')\n", - " \n", - " print(f\"Test F1 = {test_f1}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "2fab93ed", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best val score = -1.7985827701200345\n", - "Test F1 = 0.32794612794612793\n" - ] - } - ], - "source": [ - "estimate_using_model(train, test)" - ] - }, - { - "cell_type": "markdown", - "id": "2988c1b2", - "metadata": {}, - "source": [ - "Interesting! The model is slightly on par with the cosine similarity, but actually worse than the KNN model!\n", - "\n", - "To recap, $F1_{cosine} = 0.32806$, $F1_{euclidean} = 0.27$, $F1_{KNN} = 0.36$, $F1_{kmeans} = 0.33$, $F1_{RF} = 0.328$" - ] - }, - { - "cell_type": "markdown", - "id": "c6b77353", - "metadata": {}, - "source": [ - "## Experiment 2: Demographics with trip summaries" - ] - }, - { - "cell_type": "markdown", - "id": "bf7753d4", - "metadata": {}, - "source": [ - "Now that we've performed experiments with solely demographic data, let's expand the feature set by including \n", - "trip summary statistics. We would like this approach to do better than the aforementioned baselines." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "1d46ab0f", - "metadata": {}, - "outputs": [], - "source": [ - "demo_plus_trips = get_demographic_data(\n", - " df, \n", - " trip_features=['mph', 'section_duration_argmax', 'section_distance_argmax', 'start:hour', 'end:hour']\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "6159c90a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "132 33\n" - ] - } - ], - "source": [ - "train, test = train_test_split(demo_plus_trips, test_size=0.2, random_state=SEED)\n", - "\n", - "print(train.shape[0], test.shape[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "06e85bdd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test F1 score using COSINE = 0.3686868686868687\n" - ] - } - ], - "source": [ - "evaluate_using_similarity(test, train, SimilarityMetric.COSINE)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "5bc67e1d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test F1 score using EUCLIDEAN = 0.3338758428272495\n" - ] - } - ], - "source": [ - "evaluate_using_similarity(test, train, SimilarityMetric.EUCLIDEAN)" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "3ea68a83", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test F1 score using KNN = 0.30201171377641967\n" - ] - } - ], - "source": [ - "evaluate_using_similarity(test, train, SimilarityMetric.KNN)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "52eb931b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test F1 score using KMEANS = 0.36007130124777187\n" - ] - } - ], - "source": [ - "evaluate_using_similarity(test, train, SimilarityMetric.KMEANS, n_clusters=3)" - ] - }, - { - "cell_type": "markdown", - "id": "ba795489", - "metadata": {}, - "source": [ - "Great! Marked improvement from last time. Simply incorporating trip-level features seems to increase the\n", - "accuracy of finding similar users. Now, $F1_{cosine} = 0.37$, $F1_{euclidean} = 0.33$, $F1_{knn} = 0.3$, $F1_{kmeans} = 0.36$" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "9acd4b0b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best val score = -1.8201049789158268\n", - "Test F1 = 0.42158426368952684\n" - ] - } - ], - "source": [ - "# Now, we try with the model\n", - "estimate_using_model(train, test)" - ] - }, - { - "cell_type": "markdown", - "id": "cd94c548", - "metadata": {}, - "source": [ - "Great! Compared to the previous model, we see definite improvements! I'm sure we can squeeze some more juice out of the models using fancy optimization, but as a baseline, these are good enough.\n", - "\n", - "\n", - "So, to recap:\n", - "$F1_{cosine} = 0.37$, $F1_{euclidean} = 0.33$, $F1_{knn} = 0.3$, $F1_{kmeans} = 0.36$, $F1_{RF} = 0.4215$" - ] - }, - { - "cell_type": "markdown", - "id": "ca54d1b2", - "metadata": {}, - "source": [ - "### Next objectives:\n", - "\n", - "1. Try grouping by other features, such as duration or distance\n", - "2. For similarity search, use other techniques such as clustering or KNN" - ] - }, - { - "cell_type": "markdown", - "id": "c339fcc6", - "metadata": {}, - "source": [ - "# Multi-level modeling" - ] - }, - { - "cell_type": "markdown", - "id": "213676ec", - "metadata": {}, - "source": [ - "In this approach, we want to piece together the similarity search and modeling processes. Here's a rough sketch of how it should be implemented:\n", - "\n", - "1. For every user in the training set, build a model using their entire trip history.\n", - "2. Consolidate these user-level models in data structure, preferably a dictionary.\n", - "3. Now, when we want to perform inference on a new user with no prior trips, we use the similarity search to get the user ID in the training set who is the most similar to the user in question.\n", - "4. We retrieve the model for this corresponding user and perform an inference. The hypothesis is that since the two users are similar, their trip substitution patterns are also similar." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ca9e6e6a", - "metadata": {}, - "outputs": [], - "source": [ - "# def construct_model_dictionary(train: pd.DataFrame):\n", - " \n", - "# def train_on_user(user_id: str):\n", - "# '''\n", - "# Given the training set and the user ID to query, filter the dataset and\n", - "# retain only the relevant trips. Then, create folds and optimize a model for this user.\n", - "# Return the trained model instance.\n", - "# '''\n", - " \n", - "# user_data = train.loc[train.user_id == user_id, :].reset_index(drop=True)\n", - " \n", - " " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "emission", - "language": "python", - "name": "emission" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 03d1d6cb60ce8b83413d56ed6d22dfa60469485f Mon Sep 17 00:00:00 2001 From: Rahul Kulhalli Date: Mon, 29 Apr 2024 13:25:17 -0400 Subject: [PATCH 16/16] Cleaned commit with initial readme --- .DS_Store | Bin 6148 -> 10244 bytes README.md | 130 +- data/README.md | 1 + docker-compose.dev.yml | 47 - docker-compose.yml | 44 - frontend/Dockerfile | 14 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If you have a question, as suggested by the open source guide, please file an issue instead of sending an email. Since issues are public, other contributors can try to answer the question and benefit from the answer. +## Prerequisites: +- These experiments were conducted on top of the `emission` anaconda environment. Please ensure that this environment is available to you before re-running the code. +- In addition, the script uses `seaborn` for plotting and `pandarallel` for parallel pandas processing. +- Ensure you have the following data sources loaded in your MongoDB Docker container: + - Stage_database (All CEO) + - Durham + - Masscec + - Ride2own + - UPRM NICR +- Once these data sources are procured and loaded in your Mongo container, you will need to add the inferred sections to the data. To do this, please run the [add_sections_and_summaries_to_trips.py](https://github.com/e-mission/e-mission-server/blob/master/bin/historical/migrations/add_sections_and_summaries_to_trips.py) script. **NOTE**: If you see a lot of errors in the log, try to re-run the script by modifying the following line from: -## Development - -We use docker images for the software dependencies since we will not be modifying them here. - -So the steps are: - -#### Launch dev environment - -``` -$ docker-compose -f docker-compose.dev.yml up -Creating network "em-public-dashboard_emission" with the default driver -Creating em-public-dashboard_db_1 ... done -Creating em-public-dashboard_plot-gen_1 ... done -Creating em-public-dashboard_dashboard_1 ... done -... -dashboard_1 | Starting up http-server, serving ./ -dashboard_1 | Available on: -dashboard_1 | http://127.0.0.1:8080 -dashboard_1 | http://172.25.0.3:8080 -dashboard_1 | Hit CTRL-C to stop the server -... -notebook-server_1 | -notebook-server_1 | To access the notebook, open this file in a browser: -notebook-server_1 | file:///root/.local/share/jupyter/runtime/nbserver-22-open.html -notebook-server_1 | Or copy and paste one of these URLs: -notebook-server_1 | http://f8317197efaf:8888/?token=5cfd541b7461a47310c9c8aaa4114f921457a6f17b8ca159 -notebook-server_1 | or http://127.0.0.1:8888/?token=5cfd541b7461a47310c9c8aaa4114f921457a6f17b8ca159 -... -``` - -#### Test the frontend install - -Go to http://localhost:3274/ to see the front-end. Note that the port is *3274* -instead of the *8080* in the logs, since we remap it as part of the docker-compose. - -#### Test the notebook install - -Use the notebook URL from the console but change `8888` to `47962` - -``` -http://127.0.0.1:8888/?token= -``` - -becomes - -``` -http://127.0.0.1:47962/?token= -``` - -#### Load some data - -https://github.com/e-mission/e-mission-server/#quick-start - -There are multiple sources listed there, or you can use the mongodump from: -https://github.com/asiripanich/emdash#loading-test-data - -#### Loading data from a mongodump - -We have a helper script to load data directly from a mongodump. +```language=python +# Before +eps.dispatch(split_lists, skip_if_no_new_data=False, target_fn=add_sections_to_trips) +# After +eps.dispatch(split_lists, skip_if_no_new_data=False, target_fn=None) ``` -$ bash viz_scripts/docker/load_mongodump.sh -``` - -Note that this expects a standard setup with: -- this repository checked out under the `em-public-dashboard` directory, which makes the database name `em-public-dashboard_db_1` -- the incoming mongodump is in tar gz format. This should be true of all canbikeco dumps, you may need to change the `tar xvf` to `unzip` otherwise. The mongo container typically doesn't have zip installed, so using tar is more portable. - -**If you have a non-standard setup, please use your expertise to change the script appropriately.** - -#### Happy visualizations! - -Look at the existing notebooks for examples on how to start. -In particular, before you check in, please make sure that you are reading -inputs correctly, because otherwise, no metrics will be generated. - -### Design decisions - -Dashboards! They are fairly essential for user acceptance, but there are many options to build them. -And the choice of the technology stack for them is particularly fraught. -And for community projects, especially outside computer stack, choosing a technology stack ensures that half your collaborators cannot access it. -For example, choosing python will cause R users to balk and vice versa. -And there is although contributors can write some javascript, picking a charting library again steepens the learning curve. - -So we are going to use a simple and stupid dashboard. -This will consist of a reactive grid layout -(e.g. https://strml.github.io/react-grid-layout/examples/15-drag-from-outside.html) -served by a simple static express server following the instructions at -https://www.thoughts-in-motion.com/articles/creating-a-static-web-server-with-node-js-and-express/ - -The grid layout will display static, pre-generated images using whatever program the user wishes. -The program should take the time range as input and generate a static image shared with the express server. -We have included python examples using ipython notebook and simple python scripts for the following metrics: - -- mode share (notebook) -- purpose share (notebook) -- total number of trips per day (python) -In order to get the prototype out, there are a lot of shortcuts. We can revisit -this later if there is sufficient interest/funding. +This will trigger the intake pipeline for the current db and add the inferred section. -- Using gridster (https://github.com/dsmorse/gridster.js/) and bootstrap instead of react -- Using the pre-built (https://hub.docker.com/r/danjellz/http-server) instead of express -- Using a mounted volume instead of building a custom docker image to make deployment easier -- Using the e-mission server codebase to generate graphs instead of a REST API +- Note 2: The script above did not work for the All CEO data for me. Therefore, I obtained the section durations using the `get_section_durations` method I've written in `scaffolding.py` (you do not have to call this method, it is already handled in the notebooks). Please note that running this script takes a long time and it is advised to cache the generated output. -The one part where we are NOT cutting corners is in the parts where we expect -contributions from others. We are going to build in automated tests for that -part to ensure non-bitrotted code. +## Running the experiments +The order in which the experiments are to be run are denoted by the preceding number. The following is a brief summary about each notebook: +1. `01_extract_db_data.ipynb`: This notebook extracts the data, performs the necessary preprocessing, updates availability indicators, computes cost estimates, and stores the preprocessed data in `data/filtered_trips`. +2. `02_run_trip_level_models.py`: This script reads all the preprocessed data, fits trip-level models with different stratitifications, generates the outputs, and stores them in `outputs/benchmark_results/`. +3. `03_user_level_models.ipynb`: This notebook explores user fingerprints, similarity searching, and naive user-level models. +4. `04_FeatureClustering.ipynb`: This notebook performs two functions: (a) Cluster users based on demographics/trip feature summaries and check for target distributions across clusters, and (b) Cluster users by grouping w.r.t. the target and checking for feature homogeneity within clusters diff --git a/data/README.md b/data/README.md new file mode 100644 index 0000000..222add2 --- /dev/null +++ b/data/README.md @@ -0,0 +1 @@ +Placeholder folder to store data \ No newline at end of file diff --git a/docker-compose.dev.yml b/docker-compose.dev.yml deleted file mode 100644 index b04d63e..0000000 --- a/docker-compose.dev.yml +++ /dev/null @@ -1,47 +0,0 @@ -version: "3" -services: - dashboard: - image: danjellz/http-server:1.4 - depends_on: - - db - ports: - # DASH in numbers - - "3274:8080" - volumes: - - ./frontend:/public - - ./plots:/public/plots - networks: - - emission - notebook-server: - image: em-pub-dash-dev/viz-scripts - build: - context: viz_scripts - dockerfile: docker/Dockerfile.dev - depends_on: - - db - environment: - - DB_HOST=db - - WEB_SERVER_HOST=0.0.0.0 - - CRON_MODE= - - STUDY_CONFIG=stage-program - ports: - # ipynb in numbers - - "47962:8888" - networks: - - emission - volumes: - - ./viz_scripts:/usr/src/app/saved-notebooks - - ./plots:/plots - db: - image: mongo:4.4.0 - volumes: - - mongo-data:/data/db - networks: - - emission - -networks: - emission: - -volumes: - mongo-data: - diff --git a/docker-compose.yml b/docker-compose.yml deleted file mode 100644 index 73a9875..0000000 --- a/docker-compose.yml +++ /dev/null @@ -1,44 +0,0 @@ -version: "3" -services: - dashboard: - image: em-pub-dash-prod/frontend - build: frontend - depends_on: - - db - ports: - # DASH in numbers - - "3274:6060" - volumes: - - ./plots:/public/plots - networks: - - emission - notebook-server: - image: em-pub-dash-prod/viz-scripts - build: viz_scripts - depends_on: - - db - environment: - - DB_HOST=db - - WEB_SERVER_HOST=0.0.0.0 - - CRON_MODE=TRUE - - STUDY_CONFIG=stage-program - ports: - # ipynb in numbers - - "47962:8888" - networks: - - emission - volumes: - - ./plots:/plots - db: - image: mongo:4.4.0 - volumes: - - mongo-data:/data/db - networks: - - emission - -networks: - emission: - -volumes: - mongo-data: - diff --git a/frontend/Dockerfile b/frontend/Dockerfile deleted file mode 100644 index 69321ca..0000000 --- a/frontend/Dockerfile +++ /dev/null @@ -1,14 +0,0 @@ -FROM node:18-alpine -VOLUME /public/plots - -RUN mkdir -p /public -WORKDIR /public - -RUN npm install -g npm@8.14.0 && npm i -g http-server - -COPY client /public/client -COPY *.html /public/ - -EXPOSE 6060 -CMD ["http-server", "-p", "6060"] - diff --git a/frontend/client/css/bootstrap.min.css b/frontend/client/css/bootstrap.min.css deleted file mode 100644 index 286cde4..0000000 --- a/frontend/client/css/bootstrap.min.css +++ /dev/null @@ -1,7 +0,0 @@ -/*! - * Bootstrap v4.5.3 (https://getbootstrap.com/) - * Copyright 2011-2020 The Bootstrap Authors - * Copyright 2011-2020 Twitter, Inc. - * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) - 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-.gridster{position:relative}.gridster>*{-webkit-transition:height .4s,width .4s;-moz-transition:height .4s,width .4s;-o-transition:height .4s,width .4s;-ms-transition:height .4s,width .4s;transition:height .4s,width .4s}.gridster .gs-w{z-index:2;position:absolute}.gridster .preview-holder{z-index:1;position:absolute;background-color:#fff;border-color:#fff;opacity:.3}.gridster .player-revert{z-index:10!important;-webkit-transition:left .3s,top .3s!important;-moz-transition:left .3s,top .3s!important;-o-transition:left .3s,top .3s!important;transition:left .3s,top .3s!important}.gridster.collapsed{height:auto!important}.gridster.collapsed .gs-w{position:static!important}.ready .gs-w:not(.preview-holder),.ready .resize-preview-holder{-webkit-transition:opacity .3s,left .3s,top .3s,width .3s,height .3s;-moz-transition:opacity .3s,left .3s,top .3s,width .3s,height .3s;-o-transition:opacity .3s,left .3s,top .3s,width .3s,height .3s;transition:opacity .3s,left .3s,top .3s,width .3s,height 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- background-position:center right - } - .navbar-wrapper .container.app{ - border-top:none - } -} -header a.app-name,header a.app-name:hover,header a.app-name:visited{ - color:#4B545A; - display:block; - font-family:Roboto; - font-size:1.5rem; - line-height:1.2em; - margin:0 0 .3em; - text-align:center; - text-decoration:none -} -@media (min-width:768px){ - header a.app-name,header a.app-name:hover,header a.app-name:visited { - font-size:1.85rem; - margin-top:28px; - text-align:left - } -} -/* Interior page header */ -header .logo.interior { - height: 65px; - background-size: 137px; -} -#interior.program-header .navbar-light .navbar-nav > li > a {padding: 5px 15px;} -@media (min-width: 768px) { - .banner-logo { - min-height: 75px; - } -} -/* Search */ -.site-searchbar-toggle { - border: 1px solid #5e6a71; - border-radius: 5px; - color: #5e6a71; - margin: 15px; - padding: 8px 6px 8px 6px; - position: absolute; - right: 0; - top: 0; -} -.site-searchbar-toggle.int {margin:0;} -.site-navbar-toggler { - display: inline-block; -} -.application .navbar-light .site-navbar-toggler, -.program-header .navbar-light .side-navbar-toggler { - border: none; -} -.site-navbar-toggler { - font-size: 1.25rem; - line-height: 1; - background-color: transparent; -} -.site-search-bar { - margin: 0; -} -.site-searchbar-form { - font-family: Roboto, "Helvetica Neue", Helvetica, sans-serif; - padding-top: 7px; - padding-bottom: 7px; - background-color: transparent; -} -.site-searchbar-form .form-control { - display: inline-block; - width: 200px; - vertical-align: middle; - box-shadow: none; - height: 40px; -} -@media (max-width: 350px) { - .site-searchbar-form .form-control { - width: 160px; - } -} -.site-searchbar-form .form-group {margin-bottom:0;} -#searchbar-collapse-mobile button { - background-color: #D1D5D8; - border-color: #D1D5D8; - color: #333; - line-height:1.7; -} - -@media (min-width: 576px) { - #searchbar-collapse-mobile button {margin-left:3px;} -} - -/* Medium devices (tablets, 768px and up) The navbar toggle appears at this breakpoint */ -@media (min-width: 768px) { - .site-searchbar-toggle { - position: relative; - } - .site-searchbar-form {float:right;} - .site-search-bar { - background-color: #e3e3e3; - } -} diff --git a/frontend/client/css/nrel.application.min.css b/frontend/client/css/nrel.application.min.css deleted file mode 100755 index 93f25d5..0000000 --- a/frontend/client/css/nrel.application.min.css +++ /dev/null @@ -1 +0,0 @@ -body, html {overflow-x:hidden;}#content {margin-top:.5em }header .logo{background-position:center center }@media (min-width:768px){#content {margin-top:1em }header .logo{background-position:center right }.navbar-wrapper .container.app{border-top:none }}header a.app-name,header a.app-name:hover,header a.app-name:visited{color:#4B545A;display:block;font-family:Roboto;font-size:1.5rem;line-height:1.2em;margin:0 0 .3em;text-align:center;text-decoration:none }@media (min-width:768px){header a.app-name,header a.app-name:hover,header a.app-name:visited {font-size:1.85rem;margin-top:28px;text-align:left }}header .logo.interior {height: 65px;background-size: 137px;}#interior.program-header .navbar-light .navbar-nav > li > a {padding: 5px 15px;}@media (min-width: 768px) {.banner-logo {min-height: 75px;}}.site-searchbar-toggle {border: 1px solid #5e6a71;border-radius: 5px;color: #5e6a71;margin: 15px;padding: 8px 6px 8px 6px;position: absolute;right: 0;top: 0;}.site-searchbar-toggle.int {margin:0;}.site-navbar-toggler {display: inline-block;}.application .navbar-light .site-navbar-toggler, .program-header .navbar-light .side-navbar-toggler {border: none;}.site-navbar-toggler {font-size: 1.25rem;line-height: 1;background-color: transparent;}.site-search-bar {margin: 0;}.site-searchbar-form {font-family: Roboto, "Helvetica Neue", Helvetica, sans-serif;padding-top: 7px;padding-bottom: 7px;background-color: transparent;}.site-searchbar-form .form-control {display: inline-block;width: 200px;vertical-align: middle;box-shadow: none;height: 40px;}@media (max-width: 350px) {.site-searchbar-form .form-control {width: 160px;}}.site-searchbar-form .form-group {margin-bottom:0;}#searchbar-collapse-mobile button {background-color: #D1D5D8;border-color: #D1D5D8;color: #333;line-height:1.7;}@media (min-width: 576px) {#searchbar-collapse-mobile button {margin-left:3px;}}@media (min-width: 768px) {.site-searchbar-toggle {position: relative;}.site-searchbar-form {float:right;}.site-search-bar {background-color: #e3e3e3;}} \ No newline at end of file diff --git a/frontend/client/css/nrel.complete.css b/frontend/client/css/nrel.complete.css deleted file mode 100755 index 0f7f852..0000000 --- a/frontend/client/css/nrel.complete.css +++ /dev/null @@ -1,2651 +0,0 @@ -@charset "UTF-8"; -/* CSS Document */ - -body, html {overflow-x:hidden;} -body { - background-color: transparent; - color: #222; - font-family:'Roboto', 'Helvetica Neue', Helvetica, sans-serif; - font-weight: 400; - line-height: 1.65; - padding: 0; -} -h1 { - color:#222; - font-weight: 300; - font-size: 2em; - margin-top: 0; -} -@media (min-width: 768px) { - h1 {font-size: 2.15em;} -} -h2 { - color: #e07700; - font-weight: 300; - font-size: 1.75em; -} -h3 { - color:#222; - font-weight: 400; - font-size: 1.5em; -} -h4 { - color: #222; - font-size: 1.375em; - font-weight: 400; -} -h5 { - color:#222; - font-weight: 400; - font-size: 1.25em; -} -h6 { - color:#222; - font-weight: 400; - font-size: 1.125em; -} -a { - color: #0071b8; -} -a:visited { - color: #9650b9; -} -a:hover, a:active { - color: #0071b8; - text-decoration: underline; -} -.blue a:visited { - color: #0071b8; -} -.green a:visited { - color: #5D9732; -} -a.tile { - display: block; -} -a.tile:hover { - text-decoration: none; -} -a.fa { - text-decoration: none; -} -a.fa:hover { - text-decoration: underline; -} -h1 a[href]::after, -h2 a[href]::after, -h3 a[href]::after, -h4 a[href]::after, -h5 a[href]::after, -h6 a[href]::after { - font-family: FontAwesome; - content: "\00a0\f054"; - display: inline; - font-size: 0.65em; -} -a[href]::after { - font-family: FontAwesome; - font-size: 0.85em; -} - -h1 a[href$=".pdf"]::after, -h2 a[href$=".pdf"]::after, -h3 a[href$=".pdf"]::after, -h4 a[href$=".pdf"]::after, -h5 a[href$=".pdf"]::after, -h6 a[href$=".pdf"]::after, -a[href$=".pdf"]::after { - content: "\00a0\f1c1"; - display: inline; -} - -h1 a[href$=".doc"]::after, -h2 a[href$=".doc"]::after, -h3 a[href$=".doc"]::after, -h4 a[href$=".doc"]::after, -h5 a[href$=".doc"]::after, -h6 a[href$=".doc"]::after, -a[href$=".doc"]::after { - content: "\00a0\f1c2"; - display: inline; -} - -h1 a[href$=".xls"]::after, -h2 a[href$=".xls"]::after, -h3 a[href$=".xls"]::after, -h4 a[href$=".xls"]::after, -h5 a[href$=".xls"]::after, -h6 a[href$=".xls"]::after, -a[href$=".xls"]::after{ - content: "\00a0\f1c3"; - display: inline; -} - -h1 a[href$=".ppt"]::after, -h2 a[href$=".ppt"]::after, -h3 a[href$=".ppt"]::after, -h4 a[href$=".ppt"]::after, -h5 a[href$=".ppt"]::after, -h6 a[href$=".ppt"]::after, -a[href$=".ppt"]::after { - content: "\00a0\f1c4"; - display: inline; -} - -h1 a[href$=".jpg"]::after, -h2 a[href$=".jpg"]::after, -h3 a[href$=".jpg"]::after, -h4 a[href$=".jpg"]::after, -h5 a[href$=".jpg"]::after, -h6 a[href$=".jpg"]::after, -h1 a[href$=".png"]::after, -h2 a[href$=".png"]::after, -h3 a[href$=".png"]::after, -h4 a[href$=".png"]::after, -h5 a[href$=".png"]::after, -h6 a[href$=".png"]::after, -h1 a[href$=".gif"]::after, -h2 a[href$=".gif"]::after, -h3 a[href$=".gif"]::after, -h4 a[href$=".gif"]::after, -h5 a[href$=".gif"]::after, -h6 a[href$=".gif"]::after, -a[href$=".jpg"]::after, a[href$=".png"]::after, a[href$=".gif"]::after { - content: "\00a0\f1c5"; - display: inline; -} - -h1 a[href$=".mp4"]::after, -h2 a[href$=".mp4"]::after, -h3 a[href$=".mp4"]::after, -h4 a[href$=".mp4"]::after, -h5 a[href$=".mp4"]::after, -h6 a[href$=".mp4"]::after, -a[href$=".mp4"]::after { - content: "\00a0\f03d"; - display: inline; -} - -h1 a[href$=".txt"]::after, -h2 a[href$=".txt"]::after, -h3 a[href$=".txt"]::after, -h4 a[href$=".txt"]::after, -h5 a[href$=".txt"]::after, -h6 a[href$=".txt"]::after, -a[href$=".txt"]::after { - content: "\00a0\f15b"; - display: inline; -} - -h1 a[href$=".zip"]::after, -h2 a[href$=".zip"]::after, -h3 a[href$=".zip"]::after, -h4 a[href$=".zip"]::after, -h5 a[href$=".zip"]::after, -h6 a[href$=".zip"]::after, -a[href$=".zip"]::after { - content: "\00a0\f1c6"; - display: inline; -} -img a[href]::after, -img a[href$=".pdf"]::after, -img a[href$=".doc"]::after, -img a[href$=".xls"]::after, -img a[href$=".ppt"]::after, -img a[href$=".jpg"]::after, -img a[href$=".png"]::after, -img a[href$=".gif"]::after, -img a[href$=".mp4"]::after, -img a[href$=".txt"]::after, -img a[href$=".zip"]::after {content:none;} -a.noicon::after {content:none;} -#content { - padding-bottom: 5em; -} -@media (min-width: 768px) { - body.layout-12 > #page-tools .row > .col-sm-9 { - width: 100%; - } - body.layout-12 > #page-tools .row > .col-sm-offset-3 { - margin-left:0; - } -} -.list-headline { - border-bottom: 1px solid #000; - padding-bottom: 10px; -} -.lead { - font-weight: 300; - font-size: 1.15em; - line-height: 1.4em; -} -.banner-site { - font-size: 1.5rem; - line-height: 1.2em; - font-weight: 400; - margin: 0 0 .3em; - text-align: center; - text-decoration: none; -} -@media (min-width: 768px) { - .banner-site { - font-size: 2rem; - font-weight: 300; - text-align: left; - margin: 5px 0; - } -} -.banner-site a, .banner-site a:hover { - color: #000; - text-decoration: none; -} -.banner-site a:visited { - color: #4B545A; - text-decoration: none; -} -.navbar-light .navbar-brand {color:#4B545A;} -.breadcrumb { - background-color: transparent; - font-size: 14px; - margin-bottom: 1em; - padding-left:0; -} -.breadcrumb > li + li:before { - content: "\00BB"; - padding: 0 5px; - color: #000; -} -.breadcrumb a, .breadcrumb a:visited { - color: #0071b8; - text-decoration: none; -} -nav.breadcrumb.invisible { - margin-top: -50px; -} -.hide { - display: none !important; -} -.sidenav { - margin: 2em 0; -} -.sidenav ul.nav li { - font-weight: 400; - line-height: 1.3; - width:100%; -} -.sidenav a, .sidenav a:visited { - color:#0079C2; - text-decoration: none; -} -.nav>li>a:hover, .nav>li>a:focus { - background-color: transparent; -} -@media (min-width: 992px) { - .sidenav { - margin: 0.6em 0; - } -} -.sidenav > ul.nav > li:first-child > a { - border-top: 3px solid #D1D5D8; -} -.sidenav > ul.nav > li > a { - background-color: transparent; - border-bottom:1px solid #d1d5d8; - border-top: 0; -} -.sidenav > ul.nav > li > a:hover, .sidenav > ul.nav > li > a:focus, .sidenav > ul.nav > li.active > a, .sidenav > ul.nav > li.active > a:visited { - background-color: #0079c2; - color: #fff; -} -.sidenav > ul.nav ul.nav li > a { - padding-left: 30px; - border-bottom: 1px dashed #d1d5d8; -} -.sidenav > ul.nav ul.nav li:last-child > a { - border-bottom: 1px solid #d1d5d8; -} -.sidenav > ul.nav ul.nav li > a:hover, .sidenav > ul.nav ul.nav li > a:focus, .sidenav > ul.nav ul.nav li.active > a, .sidenav > ul.nav ul.nav li.active > a:visited { - background-color: #E5F1F9; - color: #0064A2; -} -.sidenav ul.nav ul.nav ul.nav li { - color: #0079c2; - display: list-item; -} -.sidenav ul.nav ul.nav ul.nav li > a:before { - content: '\25A0\00a0'; - vertical-align: text-bottom; -} -.sidenav ul.nav ul.nav ul.nav li > a { - border-bottom: none; - margin-left: 1em; - text-indent: -0.85em; -} -.sidenav ul.nav ul.nav ul.nav li:last-child > a { - border-bottom: 1px dashed #d1d5d8; -} -.sidenav ul.nav ul.nav ul.nav li:hover, .sidenav ul.nav ul.nav ul.nav li:focus, .sidenav ul.nav ul.nav ul.nav li.active { - background-color: #E5F1F9; - color: #0064A2; -} -h1.green, h2.green, h3.green, h4.green, h5.green, h6.green, p.green { - color: #5D9732; -} -div.green { - background-color: #5D9732; -} -h1.grey, h1.gray, h2.grey, h2.gray, h3.grey, h3.gray, h4.grey, h4.gray, h5.grey, h5.gray, h6.grey, h6.gray, p.grey, p.gray { - color: #5E6A71; -} -div.grey, div.gray { - background-color: #5E6A71; - color: #fff; -} -h1.blue, h2.blue, h3.blue, h4.blue, h5.blue, h6.blue, p.blue { - color: #0079c2; -} -div.blue { - background-color: #0079C2; - color: #fff; -} -h1.black, h2.black, h3.black, h4.black, h5.black, h6.black, p.black { - color: #000; -} -div.black { - background-color: #000; - color: #fff; -} -h1 a, h2 a, h3 a, h4 a, h5 a, h6 a { - text-decoration: none; -} -.allcaps { - text-transform: uppercase; -} -.nomargin + h2, .nomargin + h3, .nomargin + h4, .nomargin + h5, .nomargin + h6 { - margin-top: 0; -} -.smaller {font-size:90%;} - -/* CARDS */ -.card {display:block;} -.bg-blue .card-title { - color:#fff; -} -.headerlink-cr::after { - font-family: FontAwesome; - content: "\00a0\f054"; - display: inline; - font-size: 0.65em; -} -.card-header { - background-color: #fff; - border-bottom: 6px solid #E66D0A; -} -.card-header h2 { - font-size: 1.5em; - font-weight: 400; - margin:0; -} -.card-header h2 a:link, .card-header h2 a:visited, -.card-header h3 a:link, .card-header h3 a:visited { - color:#212121; -} -.card-header h2 a:hover, .card-header h2 a:focus, -.card-header h3 a:hover, .card-header h3 a:focus { - color:#cc6600; -} -.card-header.orange { - background-color: #fff; - border-bottom: 6px solid #E66D0A; -} -.card-text { - line-height: 1.35em; -} -.card-title {margin-bottom:0;} -.card-footer {background-color: #fff;} -.fw-feat { - background-color: #fff; - background-clip: border-box; - border: 1px solid rgba(0,0,0,.125); - border-radius: .25rem; - width:100%; -} -.fw-feat h3 a:link, .fw-feat h2 a:link, -.fw-feat h3 a:visited, .fw-feat h2 a:visited { - color:#000; - text-decoration: none; -} -.fw-feat h3 a:hover, .fw-feat h2 a:hover, -.fw-feat h3 a:active, .fw-feat h2 a:active { - color:#e07700; - text-decoration: none; -} -.fw-feat-info {padding:1em 1em 0 1em; border-bottom: 6px solid #E66D0A; border-top:none;} -/* Large Devices, Wide Screens */ -@media only screen and (min-width : 1200px) { - .overlay-text { - top: 65px; - } - .fw-feat-info {border-bottom:none; border-top: 6px solid #E66D0A;} -} - -/* secondary page features */ -.card.bg-secondary .card-body {border-top: 6px solid #e07700;} -.card.bg-secondary .card-body h2, -.card.bg-secondary .card-body h3, -.card.bg-secondary .card-body h4 { - color:#F5F5F5; -} - -/* orange navigation cards/blocks */ -.orange-card .card-body { - border-top: 6px solid #e07700; - font-size: 1rem; -} -.orange-card h3 {font-weight:400;} -.orange-card h4 a:link, .orange-card h4 a:visited {color:#e07700;font-weight:300;} -.orange-card h4 a:hover, .orange-card h4 a:active {color:#0071b8;font-weight:300;} -.img-feat-orange { - background: rgba(255, 255, 255, 0.8); - border-bottom: 6px solid #E66D0A; - position: absolute; - bottom: 0; - left: 0; - padding: 1em; - margin:0; - width: 100%; -} -.img-feat-orange a:link, -.img-feat-orange a:visited { - color:#000; -} -.img-feat-orange a:active, -.img-feat-orange a:hover { - color:#e07700; -} - -/* cards with color borders and icons */ -.border-danger {border-color:#D9531E !important;} -.border-danger .card-header { - background-color: #D9531E; - border-bottom:none; -} -.border-primary {border-color:#0079C2 !important;} -.border-primary .card-header { - background-color: #0079C2; - border-bottom:none; -} -.border-success {border-color:#5D9732 !important;} -.border-success .card-header { - background-color: #5D9732; - border-bottom:none; -} -.border-warning {border-color:#F7A11A !important;} -.border-warning .card-header { - background-color: #F7A11A; - border-bottom:none; -} -.border-danger .card-header h3, .border-primary .card-header h3, .border-success .card-header h3, .border-warning .card-header h3 {color:#fff;} -.card-title img {width: 30px; margin-bottom:5px; margin-right: 6px;} - -/* Vision callout boxes */ -/* snippets with images */ -.visionbox.main {background-color:#0079C2;color:#fff;} -.visionbox.ceem {background-color:#FFC432;color:#3A4246;} -.visionbox.e2m {background-color:#933C06;color:#fff;} -.visionbox.iep {background-color:#5D9732;color:#fff;} -.visiontext {padding:1em;} -.visionlink {font-weight: 300;font-size:1.35em;line-height:1.1em;} -.main .visionlink a:link, .main .visionlink a:visited, -.e2m .visionlink a:link, .e2m .visionlink a:visited, -.iep .visionlink a:link, .iep .visionlink a:visited {color:#fff;} -.ceem .visionlink a:link, .ceem .visionlink a:visited {color:#3A4246;} -/* assets with icons */ -.visionicon {height:90px; margin:0.35em auto;} -.vision h3 a:link, .vision h3 a:visited {color:#333;} -.vision h3 a:hover, .vision h3 a:active {color:#333;text-decoration:underline;} -.iep-top {background-color:#8CC63F;text-align:center;} -.iep-text {border:1px solid #8CC63F;padding:1em;} -.e2m-top {background-color:#D9531E;text-align:center;} -.e2m-text {border:1px solid #D9531E;padding:1em;} -.ceem-top {background-color:#F7A11A;text-align:center;} -.ceem-text {border:1px solid #F7A11A;padding:1em;} -.nrelvision-top {background-color:#E9ECEF;text-align:center;} -.nrelvision-text {border:1px solid #E9ECEF;padding:1em;} - - -/* Set width to make card deck cards 100% width */ -@media (max-width: 768px) { - - .card-deck-wrapper {margin-right:0;margin-left:0;} - .card-deck { - display: block; - width: 100%; - margin-bottom: .75rem; - table-layout: fixed; - border-spacing: 1.25rem 0; - } - .card-deck .card { - display: block; - margin-bottom: .75rem; - vertical-align: top; - } -} - -/* CARDS/ACCORDIONS */ - -#accordion .card, .accordion .card, -[id^="accordion_"] .card, .accordion .card { - border: 1px solid #0079C2; - border-radius: 0; - margin: -1px -1px 0.5rem -1px; -} -#accordion .card-header, .accordion .card-header, -[id^="accordion_"] .card-header, .accordion .card-header { - padding: 0; - margin: 0; - border-radius: 0; - border-bottom: none; -} -#accordion .card-header .btn, .accordion .card-header .btn, -[id^="accordion_"] .card-header .btn, .accordion .card-header .btn { - text-transform: none; - font-size: 1.3rem; - font-weight: 300; - width:100%; - text-align: left; - border-radius: 0; - line-height: 1; - padding: 0.65rem 0.65rem 0.65rem 40px; -} -#accordion .card-header .btn-link:hover, -[id^="accordion_"] .card-header .btn-link:hover, -#accordion .card-header .btn-link:active, -[id^="accordion_"] .card-header .btn-link:active, -#accordion .card-header .btn-link:focus, -[id^="accordion_"] .card-header .btn-link:focus, -.accordion .card-header .btn-link:hover, -.accordion .card-header .btn-link:active, -.accordion .card-header .btn-link:focus { - color: #0079C2; - text-decoration: none; -} -/* #accordion .card-header [data-toggle="collapse"] .fa:before { -content: "\f056"; -} -#accordion .card-header [data-toggle="collapse"].collapsed .fa:before { -content: "\f055"; -} */ -#accordion .card-header .btn[aria-expanded="true"], -[id^="accordion_"] .card-header .btn[aria-expanded="true"], -.accordion .card-header .btn[aria-expanded="true"] { - background-color: #0079C2; - color: #fff; - background-image: url('client/img/icon_minus.svg'); - background-repeat: no-repeat; - background-position: 1% center; - background-size: 25px 25px; - margin-left: 0px; - padding-left: 40px; - display: block; -} -#accordion .card-header .btn[aria-expanded="false"], -[id^="accordion_"] .card-header .btn[aria-expanded="false"], -.accordion .card-header .btn[aria-expanded="false"] { - background-color: #fff; - color: #0079C2; - background-image: url('client/img/icon_plus_blue.svg'); - background-repeat: no-repeat; - background-position: 1% center; - background-size: 25px 25px; - margin-left: 0px; - padding-left: 40px; - display: block; -} -#accordion .card-header .btn:hover, #accordion .card-header .btn:focus, -[id^="accordion_"] .card-header .btn:hover, [id^="accordion_"] .card-header .btn:focus { - background-color: #0079C2; - color: #fff; - background-image: url('/_resources/images/icon_minus.svg'); - background-repeat: no-repeat; - background-position: 1% center; - background-size: 25px 25px; -} -.accordion > .card:not(:first-of-type):not(:last-of-type), -.accordion > .card:first-of-type {border-bottom: 1px solid #0079C2;} - -/* striped list cards */ -.nrel-list .card-header h3 a:hover, .nrel-list .card-header h3 a:focus { - color: #303030; - text-decoration:underline; -} -.nrel-list .card-header span a:link, .nrel-list .card-header span a:visited { - color:#212121; - text-decoration:underline; - font-size: 0.9em; -} -.nrel-list .card-header span a:hover, .nrel-list .card-header span a:focus { - color:#F5F5F5; -} -ul.list-group>li:nth-child(odd){ - background-color:#F5F5F5; -} -.nrel-list .card-header { - background-color: #80D0FF; - border-bottom: 1px solid rgba(0,0,0,.125); -} -.nrel-list .card-header h3 { - font-size: 1.35em; - font-weight:400; - margin:0; -} -.nrel-list .list-group-item .date { - font-size: 0.85em; - margin: 0.3em 0 0 0; - font-weight: 300; -} -.nrel-list .list-group-item p, -.list-group-item { - font-weight: 400; - line-height: 1.2; - margin:0; -} - -/* Program site home page features */ -.feature-teaser { - background-color: #F5F5F5; - padding: 1.5em; - height:100%; -} -.feature-teaser.ft-blue { - background-color: #52BFFF; - color:#333; -} -.feature-teaser h1 { - font-size: 2em; -} -.feature-teaser h1 strong { - font-weight: bold; -} -.feature-teaser p.lead { - font-size: 1rem; -} -.feature-teaser.ft-blue a:link, -.feature-teaser.ft-blue a:visited { - color: #000; -} -@media (min-width: 768px) { - .feature-teaser h1{ - font-size: 3em; - } - .feature-teaser p.lead { - font-size: 1.25rem; - } -} -@media (min-width: 992px) { - .feature-teaser h1{ - font-size: 2em; - } - .feature-teaser p.lead { - font-size: 1rem; - } -} -@media (min-width: 1200px) { - .feature-teaser { - padding: 2em; - } - .feature-teaser h1 { - font-size: 2.5rem; - } - .feature-teaser p.lead { - font-size: 1.25rem; - } -} - -.big-quote::before { - color: #ccc; - font-family: serif; - font-size: 3em; - line-height: 0; - vertical-align: text-bottom; - content:"\201C"; - margin-right:3px; - float: left; - margin-top: -25px; - padding-top: 45px; -} -.header-description { - color:#757575; - font-size: 1rem; - font-weight: 400; - margin-top: 0; -} -h2 .fa, h3 .fa, h4 .fa, h5 .fa, h6 .fa, .header .fa { - font-size: 80%; - vertical-align:middle; -} -.headerlink:after { - font-family: FontAwesome; - content: "\00a0\f054"; - display: inline; - font-size: 0.65em; -} -.more, .learn-more { - font-weight: 400; -} -.more a, .more a:visited, .learn-more a, .learn-more a:visited { - color:#0071b8; - text-decoration: none; -} -.more a:hover, .learn-more a:hover { - text-decoration: underline; -} -.nav-tabs { - margin-top:1em; -} -.nav-tabs { - font-size: 21px; - font-weight: 300; -} -.nav-tabs .nav-link { - margin-bottom: -2px; - border-bottom: 1px solid #fff; - border-radius: 7px 7px 0 0; - margin-right:0; - background-color: #fff; - border-color: #e9ecef; -} -.nav-tabs .nav-link.active, -.nav-tabs .nav-link:hover { - border-color: #dee2e6 #dee2e6 #fff; -} -.nav-tabs .nav-item { - margin-bottom: 0; -} -.tab-pane { - padding:2em 1em; -} -.panel { - -webkit-box-shadow: none; - box-shadow: none; -} -.panel-default { - border: 1px solid #127BBF; -} -.panel-body { - padding: 15px; -} -.collapse.in { - display: block; -} -.panel-heading > .panel-title > .accordion-toggle { - margin-left:0px; - padding-left:40px; - display: block; -} -.panel-heading > .panel-title > .accordion-toggle, .panel-heading > .panel-title > .accordion-toggle:visited { - color:#fff; -} -.panel-heading > .panel-title > .accordion-toggle { - background-image: url('client/img/icon_minus.svg'); - background-repeat: no-repeat; - background-position: 1% center; - background-size: 25px 25px; -} -.panel-heading > .panel-title > .accordion-toggle.collapsed { - background-image: url('client/img/icon_plus_blue.svg'); -} -.panel-group .panel { - margin-bottom: 12px; -} -.panel-group .panel + .panel { - margin-top:1px; -} -.panel-group .panel, .panel-group .panel-heading { - border-radius: 1px; -} -.panel-group .panel:first-child { - border-top-left-radius: 4px; - border-top-right-radius: 4px; -} -.panel-group .panel:last-child { - border-bottom-left-radius: 4px; - border-bottom-right-radius: 4px; -} -.panel-heading { - padding: 0; - cursor: pointer; -} -.panel-heading h4 { - margin: 0; -} -h4.panel-title a[href]::after {content:"";} -.panel-default > .panel-heading { - background-color: transparent; - color:#fff; -} -.panel-default > .panel-heading a { - display: block; - padding: 10px 15px; - text-decoration: none; -} -.panel-default > .panel-heading a:hover { - text-decoration: none; - color:#fff; -} - -.panel-group.inverse .panel-heading > .panel-title > a.accordion-toggle, .panel-group.inverse .panel-heading > .panel-title > a.accordion-toggle:visited { - background-color: #127BBF; - color:#fff; -} -.panel-group.inverse .panel-heading > .panel-title > a.accordion-toggle.collapsed, .panel-group.inverse .panel-heading > .panel-title > a.accordion-toggle.collapsed:visited { - background-color: transparent; - color: #127BBF; -} -.content-sandwich:before, .content-sandwich:after { - content: " "; - display: table; -} -.content-sandwich:after { - clear:both; -} -.content-sandwich { - border-top: 5px solid #ccc; - border-bottom: 5px solid #ccc; - margin-top: 1.65em; - margin-bottom: 1.65em; - padding: 1em 0; -} -.content-sandwich-topper + .content-sandwich { - margin-top: 0; -} -.content-sandwich p { - font-size: 16px; - font-weight: normal; -} -.content-sandwich .headline, .content-sandwich .header { - font-size: 18px; - font-weight: 600; - margin-bottom: 0; - margin-top: 0; -} -.content-sandwich .headline ~ p, .content-sandwich .header ~ p, .content-sandwich h1 ~ p, .content-sandwich h2 ~ p, .content-sandwich h3 ~ p, .content-sandwich h4 ~ p, .content-sandwich h5 ~ p, .content-sandwich h6 ~ p { - margin-top: 0; -} -.content-sandwich .item { - border-bottom: 1px solid #ccc; - padding: 1em 0; -} -.content-sandwich .item:last-child { - border-bottom: none; -} -.content-sandwich .item p { - margin-bottom: 0; -} -.content-sandwich ul { - font-size: 16px; - font-weight: normal; -} -.content-sandwich hr { - border-top: 1px solid #ccc; -} -.content-sandwich img { - margin: 0 auto; -} -.content-sandwich.vertical-aligned { -} -.content-sandwich.vertical-aligned [class*="col-"] .icon, .content-sandwich.vertical-aligned [class*="col-"] > *:only-child { - margin-top: 1.5em; -} -@media(min-width:768px) { - .content-sandwich.vertical-aligned [class*="col-"] .icon, .content-sandwich.vertical-aligned [class*="col-"] > *:only-child { - margin-top: .75em; - } -} -@media(min-width:1200px) { - .content-sandwich.vertical-aligned [class*="col-"] .icon, .content-sandwich.vertical-aligned [class*="col-"] > *:only-child { - margin-top: 1.5em; - } -} -.content-sandwich.quicklinks .icon { - margin-top: 1em; -} -.content-sandwich.quicklinks .header { - margin-top: 1em; -} -@media (min-width: 992px) { - .content-sandwich.quicklinks .header { - margin-top: 1.65em; - } -} -.bio { - padding-bottom: 2em; -} -.bio:last-child { - border-bottom: none; -} -.bio h3.header-description, .bio h4.header-description, .bio p.header-description { - font-size: 1rem; - color:#757575; - font-weight:400; - margin: 0 0 0.25em 0; -} -.bio p.header {font-size:1.3em;font-weight:300;line-height: 1.2;margin:0 0 0.25em 0;} -.bio p.header a[href]::after { - font-family: FontAwesome; - content: "\00a0\f054"; - display: inline; - font-size: 0.65em; -} -#staff_wrapper input { - margin-bottom:0.5em; -} -#staff .bg-blue { - color: #fff; - background-color: #0079C2; -} -table#staff td { - text-align: left; -} -table#staff td.child ul { - padding-left: 1.2em; -} -table#staff td.child ul li { - text-align: left; - list-style: none; - margin-left: 0; -} -.feature { - border-bottom: 1px solid #000; - display: block; - margin-bottom:30px; - min-height: 250px; - text-decoration: none; -} -@media(min-width:768px){ - .feature { - margin-bottom:12px; - } -} -.row .col-sm-6:last-child .feature.last { - border-bottom: none; -} -@media(min-width:768px){ - .feature.last { - border-bottom: none; - } -} -@media (min-width: 992px){ - .feature { - min-height: 260px; - } -} -@media (min-width: 1200px){ - .feature { - min-height: 290px; - } -} -.feature:hover { - text-decoration: none; -} -.feature:hover .headline { - color:#e07700; -} -.feature .category { - color: #5E6A71; - font-size: 16px; - font-weight: 700; - margin-bottom: 5px; - margin-top: 10px; - text-transform: uppercase; -} -.feature .headline { - color: #0079C2; - font-weight: 400; - font-size: 1.4em; - line-height: 1.2; - margin-top:0; -} -.feature-secondary .link-tile { - margin-top: 24px; -} -.feature-secondary .link-tile + .link-tile { - margin-top:24px; -} -.showcase { - opacity: 1; - padding-bottom: 2em; - padding-top: 2em; -} -.showcase .caption { - background-color: rgba(0,0,0,.8); - box-sizing: border-box; - color: #fff; - font-weight: 300; - line-height: 1.1; - padding: .5em 1em; - position:relative; -} -.showcase .link-tile, .showcase .link-tile { - position: relative; -} -.showcase .feature-primary .caption, .showcase .feature-secondary .caption { - font-size: 30px; -} -.modal-footer { - text-align: left; -} -@media(min-width:768px){ - .showcase { - height:431px; - } - .showcase .caption { - left: 0; - right: 0; - bottom: 0; - position: absolute; - } - .feature-secondary .link-tile { - margin-top: 0px; - } - .showcase .feature-primary .caption { - font-size: 20px; - } - .showcase .feature-secondary .caption { - font-size: 14px; - } -} -@media(min-width:992px){ - .showcase { - height:545px; - } -} -@media(min-width:1200px){ - .showcase { - height:649px; - } - .showcase .feature-primary .caption { - font-size: 30px; - } - .showcase .feature-secondary .caption { - font-size: 20px; - } -} -.modal.fade .modal-dialog { - transform: none !important; - -webkit-transition: -webkit-transform 0.3s ease-out; - -moz-transition: -moz-transform 0.3s ease-out; - -o-transition: -o-transform 0.3s ease-out; - transition: transform 0.3s ease-out; -} -.modal.in .modal-dialog { - transform: none !important; -} -span.required { - font-weight: normal; -} -select, select.form-control { - background-image: linear-gradient(#FFF,#E6E6E6); - box-shadow: inset 0 1px #FFF,inset 0 0 0 1px rgba(255, 255, 255, 0.5),0 1px 2px rgba(0, 0, 0, 0.1); -} -.dropdown-menu .divider { - width: auto; - border-top: none; - margin: 9px auto; - padding: 0; -} -a.btn, a.btn:visited, .btn a:hover { - color: #fff; - text-decoration: none; -} -a.btn-link, a.btn-link:visited { - color: #0079C2; - text-decoration: none; -} -.btn { - font-weight: 400; - min-height:40px; - text-transform: uppercase; - line-height: 1.8; -} -a.btn-default { - color:#333; -} -.btn-default { - color: #333; - background-color: #fff; - border-color: #ccc; -} -.btn-default:hover, .btn-default:focus, .btn-default:active, .btn-default.active, .open > .dropdown-toggle.btn-default { - color: #333; - background-color: #e6e6e6; - border-color: #adadad; - -webkit-box-shadow: inset 0px 2px 2px 0px rgba(50, 50, 50, 0.4); - -moz-box-shadow: inset 0px 2px 2px 0px rgba(50, 50, 50, 0.4); - box-shadow: inset 0px 2px 2px 0px rgba(50, 50, 50, 0.4); -} -.btn-primary { - color: #fff; - background-color: #0079c2; - border-color: #0079c2; -} -.btn-primary:hover, .btn-primary:focus, .btn-primary:active, .btn-primary.active, .open > .dropdown-toggle.btn-primary { - color: #b7d6e9; - background-color: #3071a9; - border-color: #285e8e; - -webkit-box-shadow: inset 0px 2px 2px 0px rgba(50, 50, 50, 0.4); - -moz-box-shadow: inset 0px 2px 2px 0px rgba(50, 50, 50, 0.4); - box-shadow: inset 0px 2px 2px 0px rgba(50, 50, 50, 0.4); -} -.btn-success { - color: #fff; - background-color: #4c8224; - border-color: #4c8224; -} -.btn-success:hover, .btn-success:focus, .btn-success:active, .btn-success.active, .open > .dropdown-toggle.btn-success { - color: #d2e3c5; - background-color: #3b651c; - border-color: #3b651c; - -webkit-box-shadow: inset 0px 2px 2px 0px rgba(50, 50, 50, 0.4); - -moz-box-shadow: inset 0px 2px 2px 0px rgba(50, 50, 50, 0.4); - box-shadow: inset 0px 2px 2px 0px rgba(50, 50, 50, 0.4); -} -.btn-info { - color: #fff; - background-color: #5e6a71; - border-color: #5e6a71; -} -.btn-info:hover, .btn-info:focus, .btn-info:active, .btn-info.active, .open > .dropdown-toggle.btn-info { - color: #cccccc; - background-color: #3d4449; - border-color: #3d4449; - -webkit-box-shadow: inset 0px 2px 2px 0px rgba(50, 50, 50, 0.4); - -moz-box-shadow: inset 0px 2px 2px 0px rgba(50, 50, 50, 0.4); - box-shadow: inset 0px 2px 2px 0px rgba(50, 50, 50, 0.4); -} -.btn-warning { - color: #000; - background-color: #f7a11a; - border-color: #f7a11a; -} -.btn-warning:hover, .btn-warning:focus, .btn-warning:active, .btn-warning.active, .open > .dropdown-toggle.btn-warning { - color: #2d1c00; - background-color: #b87813; - border-color: #b87813; - -webkit-box-shadow: inset 0px 2px 2px 0px rgba(50, 50, 50, 0.3); - -moz-box-shadow: inset 0px 2px 2px 0px rgba(50, 50, 50, 0.3); - box-shadow: inset 0px 2px 2px 0px rgba(50, 50, 50, 0.3); -} -.btn-danger { - color: #fff; - background-color: #933c06; - border-color: #933c06; -} -.btn-danger:hover, .btn-danger:focus, .btn-danger:active, .btn-danger.active, .open > .dropdown-toggle.btn-danger { - color: #dac4b7; - background-color: #702e05; - border-color: #702e05; - -webkit-box-shadow: inset 0px 2px 2px 0px rgba(50, 50, 50, 0.4); - -moz-box-shadow: inset 0px 2px 2px 0px rgba(50, 50, 50, 0.4); - box-shadow: inset 0px 2px 2px 0px rgba(50, 50, 50, 0.4); -} -.btn-long { - font-size:16px; - height: 38px; - line-height: 1.3em; - white-space: normal; -} -.ou-form .btn { - display:inline-block; - padding:6px 12px; - margin-bottom:0; - font-weight:400; - text-align:center; - white-space:nowrap; - vertical-align:middle; - cursor:pointer; - border:1px solid transparent; - border-radius:4px; - color:#fff; - text-transform: uppercase; -} -.ou-form .btn-success:active,.ou-form .btn-success:hover { - color: #d2e3c5; - background-color: #3b651c; - border-color: #3b651c; - -webkit-box-shadow: inset 0px 2px 2px 0px rgba(50, 50, 50, 0.4); - -moz-box-shadow: inset 0px 2px 2px 0px rgba(50, 50, 50, 0.4); - box-shadow: inset 0px 2px 2px 0px rgba(50, 50, 50, 0.4); -} -@media only screen and (min-width : 992px) { - .btn-long { - height: 80px; - } -} -@media only screen and (min-width : 1200px) { - .btn-long { - height: 60px; - } -} -select.form-control, input.form-control { - height: 40px; -} -@media(min-width:768px){ - .row-eq-height { - display: -webkit-box; - display: -webkit-flex; - display: -ms-flexbox; - display: flex; - } - .row-eq-height [class^="col-"] { - min-height: 270px; - } - .row-eq-height .sink { - bottom:0; - left: 15px; - right: 15px; - position: absolute; - } -} -.row-highlighted { - background-color: #F5F5F5; - margin-bottom:2em; - margin-top:2em; - padding-bottom:2em; - padding-top:1em; -} -.marketing { - text-align: center; -} -.marketing .img-circle { - margin-top:2em; -} -.marketing h2 { - padding-top:1em; -} -.marketing .btn { - margin-bottom:2em; -} -iframe { - border:0; -} -.video { - margin: 1em 0; -} -.play-overlay { - position: absolute; - top: 50%; - left: 50%; - transform: translate(-50%, -50%); -} -.container-xs-height { - display:table; - padding-left:0px; - padding-right:0px; -} -.row-xs-height { - display:table-row; -} -.col-xs-height { - display:table-cell; - float:none; -} -@media (min-width: 768px) { - .container-sm-height { - display:table; - padding-left:0px; - padding-right:0px; - } - .row-sm-height { - display:table-row; - } - .col-sm-height { - display:table-cell; - float:none; - } -} -@media (min-width: 992px) { - .container-md-height { - display:table; - padding-left:0px; - padding-right:0px; - } - .row-md-height { - display:table-row; - } - .col-md-height { - display:table-cell; - float:none; - } -} -@media (min-width: 1200px) { - .container-lg-height { - display:table; - padding-left:0px; - padding-right:0px; - } - .row-lg-height { - display:table-row; - } - .col-lg-height { - display:table-cell; - float:none; - } -} -.col-top { - vertical-align:top; -} -.col-middle { - vertical-align:middle; -} -.col-bottom { - vertical-align:bottom; -} -ul.fa-blue-arrow { - list-style-type: none; -} -ul.fa-blue-arrow li { - margin-bottom: 1em; -} -.fa-blue-arrow a { - margin-left: -5px; - color: #000; -} -.fa-blue-arrow a:visited { - color: #000; -} -.fa-blue-arrow a::before { - font-family: 'FontAwesome'; - content: '\f138'; - margin:0 5px 0 -15px; - color: #0071b8; - text-indent:15px; -} -.fa-blue-arrow a:hover { - text-decoration: none; - color: #0071b8; -} -ul.fa-check-circle:before { - content: ''; -} -ul.fa-check-circle { - list-style-type: none; -} -ul.fa-check-circle li { - margin-bottom: 1em; -} -ul.fa-check-circle li { - margin-left: -5px; - color:#000; -} -ul.fa-check-circle li:before { - color: #0071b8; - content: "\f058"; - font-family: 'FontAwesome'; - margin:0 5px 0 -15px; - text-indent:15px; -} -.list-pipes li + li:before { - content: " | "; -} -.list-pipes li { - padding-left:0 !important; - padding-right:0 !important; -} -.list-links.list-unstyled li { - padding-bottom: 10px; -} -ul.fa-blue-arrow-text { - list-style-type: none; -} -ul.fa-blue-arrow-text li { - margin-bottom: 1em; -} -ul.padded-list li, -ol.padded-list li { - margin-bottom:1.75em; -} -.fa-blue-arrow-text a { - margin-left: -17px; -} -.fa-blue-arrow-text a:before { - font-family: 'FontAwesome'; - content: '\f138'; - margin:0 5px 0 -15px; - color: #0071b8; - text-indent:15px; - display: inline-block; -} -.hero { - margin-bottom: 1em; -} -figcaption, .caption { - font-size: 12px; - color: #757575; -} -.credit { - font-style: italic; -} -a[data-toggle=lightbox] { - text-decoration: none; -} -.enlarge { - display: block; - font-size: 12px; - text-align: right; - text-decoration: none; -} -.enlarge:before { - content: '\f002'; - font-family: 'FontAwesome'; - padding-right: 3px; -} -.border { - border:1px solid #666; -} -a.singleLightbox { - text-decoration: none; -} -a.singleLightbox:visited { - color: #0071b8; -} -a.singleLightbox:hover { - color: #0071b8; - text-decoration: underline; -} -.icon-circle { - height: 100px; - width: 100px; - border-radius: 100px; - background-color: #0071b8; - line-height:100px; - text-align: center; - vertical-align: middle; - color: #FFF; - font-weight: 100; - font-size: 2.2em; - margin:0 auto; -} -@media (min-width: 768px) { - .icon-circle { - margin: auto; - } -} -.icon-circle.green { - background-color: #4c8224; -} -.divider { - width: 12%; - border-top: 1px solid #5D9732; - margin: 0 auto; - padding: 0 0 1.4em 0; -} -.cf:before, .cf:after { - content: " "; - display: table; -} -.cf:after { - clear:both; -} -.clearboth { - clear:both; -} -.wide-80 { - min-width: 80%; -} -.wide-60 { - min-width: 60%; -} -.wide-40 { - min-width: 40%; -} -.wide-20 { - min-width: 20%; -} -.nomargin { - margin-top: 0; - margin-bottom: 0; -} -.notop { - margin-top:0; -} -.padtop { - padding-top: 1em; -} -.padbottom { - padding-bottom: 1em; -} -.clear { - clear: both; -} -.kbd { - padding: 2px 4px; - font-size: 90%; - color: #252525; - background-color: #F5F5F5; - border-radius: 3px; - box-shadow: none; - font-family: Menlo,Monaco,Consolas,"Courier New",monospace; -} -.pull-right { - float: right; -} -.well { - background-color: #F5F5F5; - border-radius: 0; - border:none; - box-shadow: none; - padding: 20px; - margin-bottom: 20px; -} -.well.pull-right { - margin-left:0; - margin-bottom:1em; -} -.well h2 {color:#000;} -@media (min-width: 768px) { - .well.pull-right { - margin-left:1em; - } -} -.content-box { - border: 1px solid #D1D5D8; - padding: 1em; - margin: 0; -} -.actionbox { - border-radius: 0; - border:none; - box-shadow: none; - margin-bottom:2em; - margin-top: 2em; - padding-bottom:1.5em; - padding-top:1.5em; -} -.actionbox.pull-right { - margin-top:0; -} -.actionbox > h2, .actionbox > h3, .actionbox .header { - margin-top:0; -} -.actionbox .btn { - margin-top:.5em; - margin-bottom:.5em; -} -.actionbox.inline { - padding-top:1em; - padding-bottom:1em; -} -.actionbox.inline .header { - margin-top:0; - margin-bottom:.5em; -} -@media (min-width: 768px) { - .actionbox.inline .header { - display: inline; - } -} -.actionbox.inline .btn { - display: block; - margin-bottom:0; - margin-top:0; - max-width: 320px; - margin: 0 auto; -} -@media (min-width: 768px) { - .actionbox.inline .btn { - margin-left:2em; - display: inline; - vertical-align: top; - line-height: 33px; - } -} -.maintenancebox { - border-radius: 0; - border:none; - box-shadow: none; - margin-bottom:1.5em; - margin-top: 1.5em; - padding:1em; -} -.maintenancebox p { - margin:0; - text-align: center; -} -.graphic-box { - position: relative; -} -.caption-box { - background: #000; - background-color: rgba(0, 0, 0, 0.8); - box-sizing: border-box; - color: #fff; - padding:.5em; - text-align: left; - left: 0; - right:0; - bottom:0; - width: 100%; - position: relative; -} -@media (min-width:768px) { - .caption-box { - position: absolute; - padding:1em; - } -} -.caption-box .header, .caption-box h3 { - font-weight: 300; -} -.caption-box .teaser { - line-height: 34px; - margin:0; -} -.caption-box .btn { - text-transform: uppercase; -} -.caption-box a, .caption-box a:visited { - color: #fff; -} -.caption-box .small { - color: inherit; - line-height: inherit; -} -.fileIcon { - margin-left: 5px; -} -.lg-icon { - width:6.5em; -} -.fs-icon, .md-icon { - width:4.5em; -} -.sm-icon { - width:3.5em; -} -.sm-icon-h {height:3.5em;} -.icon-block:hover svg path, -.icon-block:hover h4 a, -.icon-block:hover { - fill: #cc6600; - color: #cc6600; - text-decoration: none; -} -.icon-block h4 {margin-top:0.5em;} -.v-center .row { - display: table; - margin: 0; - table-layout: fixed; - width: 100%; -} -.v-center [class^="col-"] { - display: table-cell; - vertical-align: middle; - float: none; -} -.v-center [class^="col-"] *:first-child { - margin-top: 0; -} -.v-center [class^="col-"] *:last-child { - margin-bottom: 0; -} -.img-center { - margin:0 auto; -} -.img-border{ - border: 1px solid #bebebe; -} -a > .fa:first-child { - margin-right: 5px; - padding-bottom: 3px; -} -a > .fa-ml { - margin-left: 5px; - margin-right:0!important; -} -.addthis_toolbar { - text-align: center; -} -.addthis_toolbar a, .addthis_toolbar a:visited { - color:#000; - line-height: 26px; - text-decoration: none; -} -#feedback-container { - margin-top:3em; - text-align: center; -} -#feedback-container .button-submit { - float:none; -} -#feedback-container textarea { - width: 50%; - margin: 0 auto; -} -.addtocalendar var{ - display: none; -} -.addtocalendar { - position: relative; - display: inline-block; - background: transparent!important; -} -.atcb-link { - display: block; - outline: none!important; - cursor: pointer; -} -.atcb-link:focus~ul, .atcb-link:active~ul, .atcb-list:hover{ - visibility:visible; -} -.atcb-list { - visibility: hidden; - position: absolute; - top: 100%; - left: 0; - width: 170px; - z-index: 900; -} -.atcb-list, .atcb-item { - list-style: none; - margin: 0; - padding: 0; - background: #fff; -} -.atcb-item { - float: none; - text-align: left; -} -.atcb-item-link { - text-decoration: none; - outline: none; - display: block; -} -.atcb-item.hover, .atcb-item:hover { - position: relative; - z-index: 900; - cursor: pointer; - text-decoration: none; - outline: none; -} -.atc-style-menu-wb .atcb-list { - width: 170px; - border: 1px solid rgb(186,186,186); - border-radius: 2px; - box-shadow: 0 0 5px #AAA; -} -.atc-style-menu-wb .atcb-list, .atc-style-menu-wb .atcb-item { - background: #fff; - color: #000; -} -.atc-style-menu-wb .atcb-item, .atc-style-menu-wb .atcb-item-link { - line-height: 1.3em; - vertical-align: middle; - zoom: 1; -} -.atc-style-menu-wb .atcb-item-link, .atc-style-menu-wb .atcb-item-link:hover, .atc-style-menu-wb .atcb-item-link:active, .atc-style-menu-wb .atcb-item-link:focus { - color: #000; - font-family: "Verdana"; - font-size: 14px; - text-decoration: none; - outline: none; - padding: 5px 15px; -} -.atc-style-menu-wb .atcb-item-link:hover, .atc-style-menu-wb .atcb-item-link:active, .atc-style-menu-wb .atcb-item-link:focus { - color: #fff; -} -.atc-style-menu-wb .atcb-item.hover, .atc-style-menu-wb .atcb-item:hover { - background: rgb(66,129,244); -} -.press { - line-height: 1.3; -} -.press .header { - display: table; - border-bottom: 1px solid #000; - margin-bottom: 7px; - width: 100%; -} -.press .more-link { - display: table-cell; - font-size:14px; - text-align: right; -} -.press .more-link a { - text-decoration: none; -} -.press .headline { - color: #0079C2; - font-weight: 400; - line-height: 1.2; - margin: 0; -} -.press .headline + .headline { - margin-top: 10px; -} -.date {margin-bottom:0;} -.press .date { - font-size: 0.85em; - font-weight: 300; - margin: 0.3em 0 1.5em 0; -} -.event .date { - margin-bottom:0; -} -.press .more, .press img { - display: none; -} -.press img.rss { - display: inline; -} -.item .date { - font-size: 1.1em; - font-weight: 500; - margin-top: 2em; -} -.item .date + .headline { - margin-top: .25em; -} -.event-list { - list-style-type: none; - margin-left: 0; - padding-left: 0; -} -.event-list > li { - margin-bottom:1.5em; -} -.event-list .date { - font-weight: bold; - margin-bottom: 0; - text-transform: uppercase; -} -.event-list .event { - font-weight: bold; - line-height:1.3; - margin-bottom: 0; -} -.event-list .location { - line-height:1.3; - margin-bottom: 0; -} -.event-list .addtocalendar > a { - text-transform: uppercase; - font-size: small; -} -div.event { - margin-bottom:10px; -} -.marketing-stack { - display: table; - margin-bottom: 2em; - min-height:555px; - text-align: center; -} -.marketing-stack > div { - display: table-row; -} -.marketing-stack > div > div { - display: table-cell; -} -.marketing-stack > div:first-child > div { - vertical-align: top; -} -.marketing-stack > div:last-child > div { - vertical-align: bottom; -} -@media(min-width:768px) { - .marketing-stack .img-fluid { - width:100%; - } -} -.media-block { - margin-top: 1em; - margin-bottom: 1em; - font-size:1rem; -} -.media-block:after { - clear:both; -} -.media-block h4 { - margin:0.25em 0; -} -.media-block .header, .media-block h2.header, .media-block h3.header, .media-block h4.header, .media-block h5.header, .media-block h6.header { - margin-top:0; -} -.media-block img + .header, .media-block img + h2.header, .media-block img + h3.header, .media-block img + h4.header { - margin-top:1em; -} -.media-block .more a { - text-decoration: none; -} -.media-block .more a:hover { - text-decoration: underline; -} -.media-block img + ul, .media-block img + ol, .media-block img + p { - margin-top: 0.5em; -} -.link-tile { - text-decoration: none; -} -.link-tile { - display: block; -} -.link-tile:hover { - text-decoration: none; -} -.link-tile a { - text-decoration: none; -} -.link-tile a:hover { - text-decoration: none; -} -.link-tile:hover h2, .link-tile:hover h4 { - color:#e07700; -} -.footnote { - border-top: 1px solid #ccc; - font-style: italic; - margin-top: 1.62em; - padding-top: 1em; -} -.table-sm-data, .table-data, .table.data { - font-size: 80%; -} -.table-inline, .table.inline { - display: inline; -} -.table-striped > tbody > tr:nth-child(2n+1) > td, .table-striped > tbody > tr:nth-child(2n+1) > th { - background-color: #F5F5F5; -} -caption { - font-size:22px; - text-align:left; - border-bottom: 3px solid #52BFFF; - color: #004677; - caption-side: top; - padding:0; -} -table.dataTable.dtr-inline.collapsed > tbody > tr[role="row"] > td:first-child::before, table.dataTable.dtr-inline.collapsed > tbody > tr[role="row"] > th:first-child::before { - background-color:#0079c2 !important; -} -#scrollUp { - bottom: 0; - right: 0; - padding: 10px 20px; - background-color: #0079c2; - color: #fff; - text-decoration: none; -} -.navbar { - padding:0; -} -.dropdown-toggle::after { - display:none; -} -@media (min-width:768px) { - .searchbar-toggle { - display: none; - } -} -.program-header .navbar .navbar-collapse { - border: none; -} -@media (min-width: 768px) { - .program-header .navbar .navbar-collapse { - border-top: 1px solid #d1d5d8; - } -} -@media (min-width: 992px) { - .program-header .navbar .navbar-collapse { - border:none; - } - .program-header .navbar .navbar-collapse.xl-row { - border-top: 1px solid #d1d5d8; - } - .program-header .navbar .navbar-collapse.rows { - border-top: 1px solid #d1d5d8; - } -} -@media (min-width: 1200px) { - .program-header .navbar .navbar-collapse.xl-row { - border-top:none - } -} -.application .navbar-light .navbar-nav .dropdown-menu, .program-header .navbar-light .navbar-nav .dropdown-menu { - left:auto; - right:0; - white-space: nowrap; -} -.application .navbar-light .navbar-toggler, .program-header .navbar-light .navbar-toggler { - border: none; -} -.navbar-toggle-label { - font-size: 0.8em; - color: #fff; -} -#app-menu, #program-menu { - background-color: #5e6a71; - color:#fff; - width: 100%; - position: relative; -} -#app-menu, #program-menu:hover { - background-color: #0079c2; -} -@media (min-width:768px) { - #app-menu, #program-menu { - width:auto - } -} -#app-menu, #program-menu:before { - content:""; - background-color: #5e6a71; - position: absolute; - height: 100%; - width: 4000px; - left: -2000px; - z-index: -1; -} -#app-menu, #program-menu:hover:before { - background-color: #0079c2; -} -.custom-toggler .navbar-toggler-icon { - background-image: url("data:image/svg+xml;charset=utf8,%3Csvg viewBox='0 0 32 32' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath stroke='rgb(255,255,255)' stroke-width='2' stroke-linecap='round' stroke-miterlimit='10' d='M4 8h24M4 16h24M4 24h24'/%3E%3C/svg%3E"); -} -.navbar-wrapper { - min-height: 50px; -} -.navbar-wrapper a { - text-decoration: none; -} -.navbar-wrapper.corporate-topnav .container { - border-top:none; -} -@media (min-width:768px) { - .navbar-wrapper .container { - border-top: 1px solid #d1d5d8; - } - .navbar-toggler { - display: none; - } -} -.navbar-light .navbar-nav > li > a { - border-top:3px solid transparent; - color: #5e6a71; - padding-top: 10px; - padding-bottom: 10px; - line-height:30px; -} -@media (min-width:768px) { - .navbar-light .navbar-nav > li > a { - text-transform: none; - border-top:3px solid transparent; - background-color: transparent; - } -} -.application .navbar-light .navbar-nav > li > a, .program-header .navbar-light .navbar-nav > li > a { - padding-top: 15px; - padding-bottom: 15px; - border-top:3px solid transparent; -} -.application .navbar-light .navbar-nav > li > a:hover, .application .navbar-light .navbar-nav > li > a:focus, .application .navbar-light .navbar-nav > li.active > a:hover, .application .navbar-light .navbar-nav > li.active > a:focus, .program-header .navbar-light .navbar-nav > li > a:hover, .program-header .navbar-light .navbar-nav > li > a:focus, .program-header .navbar-light .navbar-nav > li.active > a:hover, .program-header .navbar-light .navbar-nav > li.active > a:focus { - background-color: #0B5E90; - border-top: 3px solid transparent; - color: #fff; -} -@media (min-width:768px) { - .application .navbar-light .navbar-nav > li.active > a, .application .navbar-light .navbar-nav > li > a:hover, .application .navbar-light .navbar-nav > li > a:focus, .application .navbar-light .navbar-nav > li.active > a:hover, .application .navbar-light .navbar-nav > li.active > a:focus, .program-header .navbar-light .navbar-nav > li.active > a, .program-header .navbar-light .navbar-nav > li > a:hover, .program-header .navbar-light .navbar-nav > li > a:focus, .program-header .navbar-light .navbar-nav > li.active > a:hover, .program-header .navbar-light .navbar-nav > li.active > a:focus { - background-color: #5E6A71; - } -} -.navbar-light .navbar-nav > li > a .fa-angle-down:before { - content: "\f107"; -} -.navbar-light .navbar-nav > li.show > a .fa-angle-down:before { - content: "\f106"; -} -@media(min-width:768px) { - .application .navbar-light .navbar-nav > li.show > a, .program-header .navbar-light .navbar-nav > li.show > a { - border-top:3px solid transparent; - } -} -.application .navbar-light .navbar-nav > li.show > a:hover, .application .navbar-light .navbar-nav > li.show > a:focus, .program-header .navbar-light .navbar-nav > li.show > a:hover, .program-header .navbar-light .navbar-nav > li.show > a:focus { - background-color: #0B5E90; - color: #fff; -} -@media(min-width:768px) { - .application .navbar-light .navbar-nav > li.show > a:hover, .application .navbar-light .navbar-nav > li.show > a:focus, .program-header .navbar-light .navbar-nav > li.show > a:hover, .program-header .navbar-light .navbar-nav > li.show > a:focus { - background-color: #5E6A71; - } -} -.application .navbar-light .navbar-nav .show .dropdown-menu > li > a, .program-header .navbar-light .navbar-nav .show .dropdown-menu > li > a { - background-color: #282e2e; - border-bottom: 1px solid #000; - color: #62d2ff; - padding-top:10px; - padding-bottom:10px; -} -@media(min-width:768px) { - .application .navbar-light .navbar-nav .show .dropdown-menu > li > a, .program-header .navbar-light .navbar-nav .show .dropdown-menu > li > a { - background-color: #5E6A71; - border-bottom: 1px solid #4B545A; - color: #fff; - } -} -.application .navbar-light .navbar-nav .show .dropdown-menu > li > a:hover, .application .navbar-light .navbar-nav .show .dropdown-menu > li > a:focus, .program-header .navbar-light .navbar-nav .show .dropdown-menu > li > a:hover, .program-header .navbar-light .navbar-nav .show .dropdown-menu > li > a:focus { - background-color: #0B5E90; - color: #fff; -} -.application .navbar-light .navbar-nav .show .dropdown-menu > li > a .fa-home, .program-header .navbar-light .navbar-nav .show .dropdown-menu > li > a .fa-home { - position: absolute; - right:10px; - top:15px; -} -.application .navbar-light .navbar-nav .show .dropdown-menu > li > a, .program-header .navbar-light .navbar-nav .show .dropdown-menu > li > a { - position: relative; -} -.application .navbar-light .navbar-nav .dropdown > ul ul, .program-header .navbar-light .navbar-nav .dropdown > ul ul { - padding-left:0; - list-style-type: none; -} -.application .navbar-light .navbar-nav .dropdown > ul ul li, .program-header .navbar-light .navbar-nav .dropdown > ul ul li { - background-color: #282e2e; -} -.application .navbar-light .navbar-nav .dropdown > ul ul a, .application .navbar-light .navbar-nav .dropdown > ul ul a:visited, .program-header .navbar-light .navbar-nav .dropdown > ul ul a, .program-header .navbar-light .navbar-nav .dropdown > ul ul a:visited { - color: #62d2ff; - display: block; - text-decoration: none; - padding-left:40px; - padding-top:10px; - padding-bottom:10px; -} -.application .navbar-light .navbar-nav .dropdown > ul ul a:hover, 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/foo/bar/baz/boink.html - pv.siteurl = location.pathname.substr(0,slash); // /foo/bar/baz/ - pv.filename = location.pathname.substr(slash) ; // boink.html - - // catch situations where the url ends in a slash, with index.html implied - if( ! pv.filename.length) { - pv.filename = 'index.html'; // this could be index.php or index.cfm, or ... - } - - - /* - * Contact Us footer link - * if the site doesn't defer to the globalwebmaster, use the local one - */ - if( !pv.globalwebmaster && pv.sitename ) { - $('#contact-link').attr( 'href', pv.siteurl + 'contacts.html' ); - } else { - $('#contact-link').attr( 'href', '/webmaster.html' ); - } - -}); diff --git a/frontend/client/js/app.min.js b/frontend/client/js/app.min.js deleted file mode 100755 index 206289e..0000000 --- a/frontend/client/js/app.min.js +++ /dev/null @@ -1,2 +0,0 @@ -/*! application template scripts Mon June 22 2015 07:48:15 */ -"use strict";$(document).ready(function(){window.nrel=$.extend({},window.nrel),window.nrel.pagevars=$.extend({},window.nrel.pagevars),window.nrel.pagevars.sitename="AppName";var e,a,n;a=window.nrel||{},n=a.pagevars||{},n.pagename=$("h1").text(),e=location.pathname.lastIndexOf("/")+1,n.pageurl=location.pathname,n.siteurl=location.pathname.substr(0,e),n.filename=location.pathname.substr(e),n.filename.length||(n.filename="index.html"),!n.globalwebmaster&&n.sitename?$("#contact-link").attr("href",n.siteurl+"contacts.html"):$("#contact-link").attr("href","/webmaster.html")}); \ No newline at end of file diff --git a/frontend/client/js/iconomatic/.gitignore b/frontend/client/js/iconomatic/.gitignore deleted file mode 100755 index f97c964..0000000 --- a/frontend/client/js/iconomatic/.gitignore +++ /dev/null @@ -1,4 +0,0 @@ -# -# NPM -# -node_modules diff --git a/frontend/client/js/iconomatic/.jshintrc b/frontend/client/js/iconomatic/.jshintrc deleted file mode 100755 index e51ca6c..0000000 --- a/frontend/client/js/iconomatic/.jshintrc +++ /dev/null @@ -1,7 +0,0 @@ -{ - "strict" : true, // Require `use strict` pragma in every file. - "white" : false, // Check against strict whitespace and indentation rules. - "jquery" : true, - "browser" : true, // Standard browser globals e.g. `window`, `document`. - "laxcomma" : true // Suppress warnings about comma-first coding style. -} diff --git a/frontend/client/js/iconomatic/jquery.iconomatic.js b/frontend/client/js/iconomatic/jquery.iconomatic.js deleted file mode 100755 index 6079239..0000000 --- a/frontend/client/js/iconomatic/jquery.iconomatic.js +++ /dev/null @@ -1,198 +0,0 @@ -;(function ( $, window, document, undefined ) { - - 'use strict'; - - // Default file types to label with icons - var fileTypes = { - 'avi' : { 'class' : 'fa-file-video-o', 'type' : 'Video'} - , 'doc' : { 'class' : 'fa-file-word-o', 'type' : 'Microsoft Word'} - , 'docx': { 'class' : 'fa-file-word-o', 'type' : 'Microsoft Word'} - , 'gif' : { 'class' : 'fa-file-image-o', 'type' : 'GIF'} - , 'jpg' : { 'class' : 'fa-file-image-o', 'type' : 'JPG'} - , 'm3u' : { 'class' : 'fa-file-audio-o', 'type' : 'Audio'} - , 'mov' : { 'class' : 'fa-file-video-o', 'type' : 'Video'} - , 'mp3' : { 'class' : 'fa-file-audio-o', 'type' : 'Audio'} - , 'mp4' : { 'class' : 'fa-file-video-o', 'type' : 'Video'} - , 'mpg' : { 'class' : 'fa-file-video-o', 'type' : 'Video'} - , 'mpeg': { 'class' : 'fa-file-video-o', 'type' : 'Video'} - , 'pdf' : { 'class' : 'fa-file-pdf-o', 'type' : 'PDF'} - , 'ppt' : { 'class' : 'fa-file-powerpoint-o', 'type' : 'Microsoft PowerPoint'} - , 'pptx': { 'class' : 'fa-file-powerpoint-o', 'type' : 'Microsoft PowerPoint'} - , 'wmv' : { 'class' : 'fa-file-video-o', 'type' : 'Video'} - , 'txt' : { 'class' : 'fa-file-text-o', 'type' : 'Text'} - , 'xls' : { 'class' : 'fa-file-excel-o', 'type' : 'Microsoft Excel'} - , 'xlsb': { 'class' : 'fa-file-excel-o', 'type' : 'Microsoft Excel'} - , 'xlsx': { 'class' : 'fa-file-excel-o', 'type' : 'Microsoft Excel'} - , 'xlsm': { 'class' : 'fa-file-excel-o', 'type' : 'Microsoft Excel'} - , 'zip' : { 'class' : 'fa-file-archive-o', 'type' : 'ZIP Archive'} - }; - - - var pluginName = 'iconomatic'; - - // Plugin defaults - var defaults = { - ajax: false - ,dataMode: false - ,dataAttr: 'iconomatic' - ,iconClass: 'fileIcon' - ,filesObj: fileTypes - }; - - // Constructor - function Iconomatic( element, options ) { - this.element = element; - - this.options = $.extend( {}, defaults, options ); - - this._defaults = defaults; - this._name = pluginName; - - this.init(); - } - - Iconomatic.prototype = { - - init: function() { - var links, - context; - - context = $('body'); // todo: move this into settings - - links = this.getLinks( context ); - this.addIcons( links ); - - if( this.options.ajax ) { - this.enableAjax(); - } - }, - - /* - * Inspect all tags within our region - * Return the ones with relevant file extensions or data attributes - * - */ - getLinks: function( region ) { - var opts, - types, - dataAttr, - links; - - opts = this.options; - types = opts.filesObj; - dataAttr = 'data-' + opts.dataAttr; - - links = []; - - $(region).find('a').filter(function(){ - return !$(this).attr('data-iconomatic-tagged'); // remove any previously tagged - }).each( function( idx, link ){ - var href, - ext; - - - href = $(link).attr('href'); - - if( typeof href !== 'undefined' && href !== null && href !== '' ) { - - ext = href.toLowerCase().split('.').splice( -1, 1 ).toString(); // this could be more elegant - - if( ext in types ) { - $(link).attr('data-iconomatic-tagged', ext); // tag our valid links - links.push( link ); - } - } - if( opts.dataMode ) { - - // copy the users data attribute to our data attribute - if( $(link).attr(dataAttr) ) { - - $(link).attr('data-iconomatic-tagged', function(){ // tag our valid links - return $(this).attr( dataAttr ); - }); - - links.push( link ); - } - } - }); - - return links; - }, - - /* - * Use the mutation observer to watch for changes in our doc. - * Find the Links and add the icons in the changed region. - * - */ - enableAjax: function(){ - var links, - MutationObserver, - region, - observer, - self = this; - - if( window.MutationObserver || window.WebKitMutationObserver || window.MozMutationObserver ) { - MutationObserver = window.MutationObserver || window.WebKitMutationObserver || window.MozMutationObserver; - } else { - return true; // BAIL ON ', attrs ).appendTo(link); // finally do the work! - } - }); - } - - }; - - $.fn[pluginName] = function ( options ) { - return this.each(function () { - if (!$.data(this, 'plugin_' + pluginName)) { - $.data(this, 'plugin_' + pluginName, new Iconomatic( this, options )); - } - }); - }; - - -})( jQuery, window, document ); diff --git a/frontend/client/js/iconomatic/jquery.iconomatic.min.js b/frontend/client/js/iconomatic/jquery.iconomatic.min.js deleted file mode 100755 index 2644eb2..0000000 --- a/frontend/client/js/iconomatic/jquery.iconomatic.min.js +++ /dev/null @@ -1,10 +0,0 @@ -/*! -* Project: Iconomatic -* Version: 2.0.0 -* Description: Detect links to native files and append the proper icon image after the link -* Author: Michael Oakley -* Build Date: 2016-05-12 -*/ - -(function(a,b,c,d){"use strict";var e={avi:{"class":"fa-file-video-o",type:"Video"},doc:{"class":"fa-file-word-o",type:"Microsoft Word"},docx:{"class":"fa-file-word-o",type:"Microsoft Word"},gif:{"class":"fa-file-image-o",type:"GIF"},jpg:{"class":"fa-file-image-o",type:"JPG"},m3u:{"class":"fa-file-audio-o",type:"Audio"},mov:{"class":"fa-file-video-o",type:"Video"},mp3:{"class":"fa-file-audio-o",type:"Audio"},mp4:{"class":"fa-file-video-o",type:"Video"},mpg:{"class":"fa-file-video-o",type:"Video"},mpeg:{"class":"fa-file-video-o",type:"Video"},pdf:{"class":"fa-file-pdf-o",type:"PDF"},ppt:{"class":"fa-file-powerpoint-o",type:"Microsoft PowerPoint"},pptx:{"class":"fa-file-powerpoint-o",type:"Microsoft 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this.each(function(){if(!a.data(this,"plugin_"+f)){a.data(this,"plugin_"+f,new h(this,b))}})}})(jQuery,window,document); -//# sourceMappingURL=jquery.iconomatic.min.map \ No newline at end of file diff --git a/frontend/client/js/jquery.gridster.min.js b/frontend/client/js/jquery.gridster.min.js deleted file mode 100644 index 0c7b018..0000000 --- a/frontend/client/js/jquery.gridster.min.js +++ /dev/null @@ -1,2 +0,0 @@ -/*! gridster.js - v0.7.0 - 2016-02-26 - * https://dsmorse.github.io/gridster.js/ - Copyright (c) 2016 ducksboard; Licensed MIT */ !function(a,b){"use strict";"object"==typeof exports?module.exports=b(require("jquery")):"function"==typeof define&&define.amd?define("gridster-coords",["jquery"],b):a.GridsterCoords=b(a.$||a.jQuery)}(this,function(a){"use strict";function b(b){return b[0]&&a.isPlainObject(b[0])?this.data=b[0]:this.el=b,this.isCoords=!0,this.coords={},this.init(),this}var c=b.prototype;return 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b.row||(b=a(b).coords().grid,c=a(c).coords().grid),b=e(b),c=e(c),b.row>c.row?1:-1})},d.sort_by_row_and_col_asc=function(a){return a=a.sort(function(a,b){return a=e(a),b=e(b),a.row>b.row||a.row===b.row&&a.col>b.col?1:-1})},d.sort_by_col_asc=function(a){return a=a.sort(function(a,b){return a=e(a),b=e(b),a.col>b.col?1:-1})},d.sort_by_row_desc=function(a){return a=a.sort(function(a,b){return a=e(a),b=e(b),a.row+a.size_yi&&this.add_faux_rows(Math.max(d-i,0));var k={col:j,row:g.row,size_x:c,size_y:d};return this.mutate_widget_in_gridmap(b,g,k),this.set_dom_grid_height(),this.set_dom_grid_width(),f&&f.call(this,k.size_x,k.size_y),b},h.collapse_widget=function(a,b){var c=a.coords().grid,d=parseInt(a.attr("pre_expand_sizex")),e=parseInt(a.attr("pre_expand_sizey")),f=parseInt(a.attr("pre_expand_col")),g={col:f,row:c.row,size_x:d,size_y:e};return this.mutate_widget_in_gridmap(a,c,g),this.set_dom_grid_height(),this.set_dom_grid_width(),b&&b.call(this,g.size_x,g.size_y),a},h.fit_to_content=function(a,b,c,d){var e=a.coords().grid,f=this.$wrapper.width(),g=this.$wrapper.height(),h=this.options.widget_base_dimensions[0]+2*this.options.widget_margins[0],i=this.options.widget_base_dimensions[1]+2*this.options.widget_margins[1],j=Math.ceil((f+2*this.options.widget_margins[0])/h),k=Math.ceil((g+2*this.options.widget_margins[1])/i),l={col:e.col,row:e.row,size_x:Math.min(b,j),size_y:Math.min(c,k)};return this.mutate_widget_in_gridmap(a,e,l),this.set_dom_grid_height(),this.set_dom_grid_width(),d&&d.call(this,l.size_x,l.size_y),a},h.center_widgets=debounce(function(){var b,c=this.$wrapper.width();b=this.is_responsive()?this.get_responsive_col_width():this.options.widget_base_dimensions[0]+2*this.options.widget_margins[0];var d=2*Math.floor(Math.max(Math.floor(c/b),this.min_col_count)/2);this.options.min_cols=d,this.options.max_cols=d,this.options.extra_cols=0,this.set_dom_grid_width(d),this.cols=d;var e=(d-this.prev_col_count)/2;return 0>e?(this.get_min_col()>-1*e?this.shift_cols(e):this.resize_widget_dimensions(this.options),setTimeout(a.proxy(function(){this.resize_widget_dimensions(this.options)},this),0)):e>0?(this.resize_widget_dimensions(this.options),setTimeout(a.proxy(function(){this.shift_cols(e)},this),0)):(this.resize_widget_dimensions(this.options),setTimeout(a.proxy(function(){this.resize_widget_dimensions(this.options)},this),0)),this.prev_col_count=d,this},200),h.get_min_col=function(){return Math.min.apply(Math,this.$widgets.map(a.proxy(function(b,c){return this.get_cells_occupied(a(c).coords().grid).cols},this)).get())},h.shift_cols=function(b){var c=this.$widgets.map(a.proxy(function(b,c){var d=a(c);return this.dom_to_coords(d)},this));c=d.sort_by_row_and_col_asc(c),c.each(a.proxy(function(c,d){var e=a(d.el),f=e.coords().grid,g=parseInt(e.attr("data-col")),h={col:Math.max(Math.round(g+b),1),row:f.row,size_x:f.size_x,size_y:f.size_y};setTimeout(a.proxy(function(){this.mutate_widget_in_gridmap(e,f,h)},this),0)},this))},h.mutate_widget_in_gridmap=function(b,c,d){var e=c.size_y,f=this.get_cells_occupied(c),g=this.get_cells_occupied(d),h=[];a.each(f.cols,function(b,c){-1===a.inArray(c,g.cols)&&h.push(c)});var i=[];a.each(g.cols,function(b,c){-1===a.inArray(c,f.cols)&&i.push(c)});var j=[];a.each(f.rows,function(b,c){-1===a.inArray(c,g.rows)&&j.push(c)});var k=[];if(a.each(g.rows,function(b,c){-1===a.inArray(c,f.rows)&&k.push(c)}),this.remove_from_gridmap(c),i.length){var l=[d.col,d.row,d.size_x,Math.min(e,d.size_y),b];this.empty_cells.apply(this,l)}if(k.length){var m=[d.col,d.row,d.size_x,d.size_y,b];this.empty_cells.apply(this,m)}if(c.col=d.col,c.row=d.row,c.size_x=d.size_x,c.size_y=d.size_y,this.add_to_gridmap(d,b),b.removeClass("player-revert"),this.update_widget_dimensions(b,d),h.length){var n=[h[0],d.row,h[h.length-1]-h[0]+1,Math.min(e,d.size_y),b];this.remove_empty_cells.apply(this,n)}if(j.length){var o=[d.col,d.row,d.size_x,d.size_y,b];this.remove_empty_cells.apply(this,o)}return this.move_widget_up(b),this},h.empty_cells=function(b,c,d,e,f){var g=this.widgets_below({col:b,row:c-e,size_x:d,size_y:e});return g.not(f).each(a.proxy(function(b,d){var f=a(d),g=f.coords().grid;if(g.row<=c+e-1){var h=c+e-g.row;this.move_widget_down(f,h)}},this)),this.is_resizing||this.set_dom_grid_height(),this},h.remove_empty_cells=function(b,c,d,e,f){var g=this.widgets_below({col:b,row:c,size_x:d,size_y:e});return g.not(f).each(a.proxy(function(b,c){this.move_widget_up(a(c),e)},this)),this.set_dom_grid_height(),this},h.next_position=function(a,b){a||(a=1),b||(b=1);for(var c,e=this.gridmap,f=e.length,g=[],h=1;f>h;h++){c=e[h].length;for(var i=1;c>=i;i++){var j=this.can_move_to({size_x:a,size_y:b},h,i);j&&g.push({col:h,row:i,size_y:b,size_x:a})}}return g.length?d.sort_by_row_and_col_asc(g)[0]:!1},h.remove_by_grid=function(a,b){var c=this.is_widget(a,b);c&&this.remove_widget(c)},h.remove_widget=function(b,c,d){var e=b instanceof a?b:a(b);if(0===e.length)return this;var f=e.coords().grid;if(void 0===f)return this;a.isFunction(c)&&(d=c,c=!1),this.cells_occupied_by_placeholder={},this.$widgets=this.$widgets.not(e);var g=this.widgets_below(e);return this.remove_from_gridmap(f),this.options.hide_element.call(this,e,a.proxy(function(){e.remove(),c||g.each(a.proxy(function(b,c){this.move_widget_up(a(c),f.size_y)},this)),this.set_dom_grid_height(),d&&d.call(this,b)},this)),this},h.remove_all_widgets=function(b){return this.$widgets.each(a.proxy(function(a,c){this.remove_widget(c,!0,b)},this)),this},h.serialize=function(b){b||(b=this.$widgets);var c=[];return b.each(a.proxy(function(b,d){var e=a(d);"undefined"!=typeof e.coords().grid&&c.push(this.options.serialize_params(e,e.coords().grid))},this)),c},h.serialize_changed=function(){return this.serialize(this.$changed)},h.dom_to_coords=function(a){return{col:parseInt(a.attr("data-col"),10),row:parseInt(a.attr("data-row"),10),size_x:parseInt(a.attr("data-sizex"),10)||1,size_y:parseInt(a.attr("data-sizey"),10)||1,max_size_x:parseInt(a.attr("data-max-sizex"),10)||!1,max_size_y:parseInt(a.attr("data-max-sizey"),10)||!1,min_size_x:parseInt(a.attr("data-min-sizex"),10)||!1,min_size_y:parseInt(a.attr("data-min-sizey"),10)||!1,el:a}},h.register_widget=function(b){var c=b instanceof a,d=c?this.dom_to_coords(b):b,e=!1;c||(b=d.el);var f=this.can_go_widget_up(d);return this.options.shift_widgets_up&&f&&(d.row=f,b.attr("data-row",f),this.$el.trigger("gridster:positionchanged",[d]),e=!0),this.options.avoid_overlapped_widgets&&!this.can_move_to({size_x:d.size_x,size_y:d.size_y},d.col,d.row)&&(a.extend(d,this.next_position(d.size_x,d.size_y)),b.attr({"data-col":d.col,"data-row":d.row,"data-sizex":d.size_x,"data-sizey":d.size_y}),e=!0),b.data("coords",b.coords()),b.data("coords").grid=d,this.add_to_gridmap(d,b),this.update_widget_dimensions(b,d),this.options.resize.enabled&&this.add_resize_handle(b),e},h.update_widget_position=function(a,b){return this.for_each_cell_occupied(a,function(a,c){return this.gridmap[a]?void(this.gridmap[a][c]=b):this}),this},h.update_widget_dimensions=function(a,b){var c=b.size_x*(this.is_responsive()?this.get_responsive_col_width():this.options.widget_base_dimensions[0])+(b.size_x-1)*this.options.widget_margins[0],d=b.size_y*this.options.widget_base_dimensions[1]+(b.size_y-1)*this.options.widget_margins[1];return a.data("coords").update({width:c,height:d}),a.attr({"data-col":b.col,"data-row":b.row,"data-sizex":b.size_x,"data-sizey":b.size_y}),this},h.update_widgets_dimensions=function(){return a.each(this.$widgets,a.proxy(function(b,c){var d=a(c).coords().grid;"object"==typeof d&&this.update_widget_dimensions(a(c),d)},this)),this},h.remove_from_gridmap=function(a){return this.update_widget_position(a,!1)},h.add_to_gridmap=function(a,b){this.update_widget_position(a,b||a.el)},h.draggable=function(){var b=this,c=a.extend(!0,{},this.options.draggable,{offset_left:this.options.widget_margins[0],offset_top:this.options.widget_margins[1],container_width:this.cols*this.min_widget_width+(this.cols+1)*this.options.widget_margins[0],limit:!0,start:function(c,d){b.$widgets.filter(".player-revert").removeClass("player-revert"),b.$player=a(this),b.$helper=a(d.$helper),b.helper=!b.$helper.is(b.$player),b.on_start_drag.call(b,c,d),b.$el.trigger("gridster:dragstart")},stop:function(a,c){b.on_stop_drag.call(b,a,c),b.$el.trigger("gridster:dragstop")},drag:throttle(function(a,c){b.on_drag.call(b,a,c),b.$el.trigger("gridster:drag")},60)});this.drag_api=this.$el.dragg(c).data("drag")},h.resizable=function(){return this.resize_api=this.$el.gridDraggable({items:"."+this.options.resize.handle_class,offset_left:this.options.widget_margins[0],container_width:this.container_width,move_element:!1,resize:!0,limit:this.options.max_cols!==1/0,scroll_container:this.options.scroll_container,start:a.proxy(this.on_start_resize,this),stop:a.proxy(function(b,c){delay(a.proxy(function(){this.on_stop_resize(b,c)},this),120)},this),drag:throttle(a.proxy(this.on_resize,this),60)}),this},h.setup_resize=function(){this.resize_handle_class=this.options.resize.handle_class;var b=this.options.resize.axes,c='';return this.resize_handle_tpl=a.map(b,function(a){return c.replace("{type}",a)}).join(""),a.isArray(this.options.draggable.ignore_dragging)&&this.options.draggable.ignore_dragging.push("."+this.resize_handle_class),this},h.on_start_drag=function(b,c){this.$helper.add(this.$player).add(this.$wrapper).addClass("dragging"),this.highest_col=this.get_highest_occupied_cell().col,this.$player.addClass("player"),this.player_grid_data=this.$player.coords().grid,this.placeholder_grid_data=a.extend({},this.player_grid_data),this.set_dom_grid_height(this.$el.height()+this.player_grid_data.size_y*this.min_widget_height),this.set_dom_grid_width(this.cols);var d=this.player_grid_data.size_x,e=this.cols-this.highest_col;this.options.max_cols===1/0&&d>=e&&this.add_faux_cols(Math.min(d-e,1));var f=this.faux_grid,g=this.$player.data("coords").coords;this.cells_occupied_by_player=this.get_cells_occupied(this.player_grid_data),this.cells_occupied_by_placeholder=this.get_cells_occupied(this.placeholder_grid_data),this.last_cols=[],this.last_rows=[],this.collision_api=this.$helper.collision(f,this.options.collision),this.$preview_holder=a("<"+this.$player.get(0).tagName+" />",{"class":"preview-holder","data-row":this.$player.attr("data-row"),"data-col":this.$player.attr("data-col"),css:{width:g.width,height:g.height}}).appendTo(this.$el),this.options.draggable.start&&this.options.draggable.start.call(this,b,c)},h.on_drag=function(a,b){if(null===this.$player)return!1;var c=this.options.widget_margins,d=this.$preview_holder.attr("data-col"),e=this.$preview_holder.attr("data-row"),f={left:b.position.left+this.baseX-c[0]*d,top:b.position.top+this.baseY-c[1]*e};if(this.options.max_cols===1/0){var g=this.placeholder_grid_data.col+this.placeholder_grid_data.size_x-1;g>=this.cols-1&&this.options.max_cols>=this.cols+1&&(this.add_faux_cols(1),this.set_dom_grid_width(this.cols+1),this.drag_api.set_limits(this.cols*this.min_widget_width+(this.cols+1)*this.options.widget_margins[0])),this.collision_api.set_colliders(this.faux_grid)}this.colliders_data=this.collision_api.get_closest_colliders(f),this.on_overlapped_column_change(this.on_start_overlapping_column,this.on_stop_overlapping_column),this.on_overlapped_row_change(this.on_start_overlapping_row,this.on_stop_overlapping_row),this.helper&&this.$player&&this.$player.css({left:b.position.left,top:b.position.top}),this.options.draggable.drag&&this.options.draggable.drag.call(this,a,b)},h.on_stop_drag=function(a,b){this.$helper.add(this.$player).add(this.$wrapper).removeClass("dragging");var c=this.options.widget_margins,d=this.$preview_holder.attr("data-col"),e=this.$preview_holder.attr("data-row");b.position.left=b.position.left+this.baseX-c[0]*d,b.position.top=b.position.top+this.baseY-c[1]*e,this.colliders_data=this.collision_api.get_closest_colliders(b.position),this.on_overlapped_column_change(this.on_start_overlapping_column,this.on_stop_overlapping_column),this.on_overlapped_row_change(this.on_start_overlapping_row,this.on_stop_overlapping_row),this.$changed=this.$changed.add(this.$player),this.placeholder_grid_data.el.coords().grid.col===this.placeholder_grid_data.col&&this.placeholder_grid_data.el.coords().grid.row===this.placeholder_grid_data.row||(this.update_widget_position(this.placeholder_grid_data.el.coords().grid,!1),this.options.collision.wait_for_mouseup&&this.for_each_cell_occupied(this.placeholder_grid_data,function(a,b){if(this.is_widget(a,b)){var c=this.placeholder_grid_data.row+this.placeholder_grid_data.size_y,d=parseInt(this.gridmap[a][b][0].getAttribute("data-row")),e=c-d;this.move_widget_down(this.is_widget(a,b),e)}})),this.cells_occupied_by_player=this.get_cells_occupied(this.placeholder_grid_data);var f=this.placeholder_grid_data.col,g=this.placeholder_grid_data.row;this.set_cells_player_occupies(f,g),this.$player.coords().grid.row=g,this.$player.coords().grid.col=f,this.$player.addClass("player-revert").removeClass("player").attr({"data-col":f,"data-row":g}).css({left:"",top:""}),this.options.draggable.stop&&this.options.draggable.stop.call(this,a,b),this.$preview_holder.remove(),this.$player=null,this.$helper=null,this.placeholder_grid_data={},this.player_grid_data={},this.cells_occupied_by_placeholder={},this.cells_occupied_by_player={},this.w_queue={},this.set_dom_grid_height(),this.set_dom_grid_width(),this.options.max_cols===1/0&&this.drag_api.set_limits(this.cols*this.min_widget_width+(this.cols+1)*this.options.widget_margins[0])},h.on_start_resize=function(b,c){this.$resized_widget=c.$player.closest(".gs-w"),this.resize_coords=this.$resized_widget.coords(),this.resize_wgd=this.resize_coords.grid,this.resize_initial_width=this.resize_coords.coords.width,this.resize_initial_height=this.resize_coords.coords.height,this.resize_initial_sizex=this.resize_coords.grid.size_x,this.resize_initial_sizey=this.resize_coords.grid.size_y,this.resize_initial_col=this.resize_coords.grid.col,this.resize_last_sizex=this.resize_initial_sizex,this.resize_last_sizey=this.resize_initial_sizey,this.resize_max_size_x=Math.min(this.resize_wgd.max_size_x||this.options.resize.max_size[0],this.options.max_cols-this.resize_initial_col+1),this.resize_max_size_y=this.resize_wgd.max_size_y||this.options.resize.max_size[1],this.resize_min_size_x=this.resize_wgd.min_size_x||this.options.resize.min_size[0]||1,this.resize_min_size_y=this.resize_wgd.min_size_y||this.options.resize.min_size[1]||1,this.resize_initial_last_col=this.get_highest_occupied_cell().col,this.set_dom_grid_width(this.cols),this.resize_dir={right:c.$player.is("."+this.resize_handle_class+"-x"),bottom:c.$player.is("."+this.resize_handle_class+"-y")},this.is_responsive()||this.$resized_widget.css({"min-width":this.options.widget_base_dimensions[0],"min-height":this.options.widget_base_dimensions[1]});var d=this.$resized_widget.get(0).tagName;this.$resize_preview_holder=a("<"+d+" />",{"class":"preview-holder resize-preview-holder","data-row":this.$resized_widget.attr("data-row"),"data-col":this.$resized_widget.attr("data-col"), -css:{width:this.resize_initial_width,height:this.resize_initial_height}}).appendTo(this.$el),this.$resized_widget.addClass("resizing"),this.options.resize.start&&this.options.resize.start.call(this,b,c,this.$resized_widget),this.$el.trigger("gridster:resizestart")},h.on_stop_resize=function(b,c){this.$resized_widget.removeClass("resizing").css({width:"",height:"","min-width":"","min-height":""}),delay(a.proxy(function(){this.$resize_preview_holder.remove().css({"min-width":"","min-height":""}),this.options.resize.stop&&this.options.resize.stop.call(this,b,c,this.$resized_widget),this.$el.trigger("gridster:resizestop")},this),300),this.set_dom_grid_width(),this.set_dom_grid_height(),this.options.max_cols===1/0&&this.drag_api.set_limits(this.cols*this.min_widget_width)},h.on_resize=function(a,b){var c,d=b.pointer.diff_left,e=b.pointer.diff_top,f=this.is_responsive()?this.get_responsive_col_width():this.options.widget_base_dimensions[0],g=this.options.widget_base_dimensions[1],h=this.options.widget_margins[0],i=this.options.widget_margins[1],j=this.resize_max_size_x,k=this.resize_min_size_x,l=this.resize_max_size_y,m=this.resize_min_size_y,n=this.options.max_cols===1/0,o=Math.ceil(d/(f+2*h)-.2),p=Math.ceil(e/(g+2*i)-.2),q=Math.max(1,this.resize_initial_sizex+o),r=Math.max(1,this.resize_initial_sizey+p),s=Math.floor(this.container_width/this.min_widget_width-this.resize_initial_col+1),t=s*this.min_widget_width+(s-1)*h;q=Math.max(Math.min(q,j),k),q=Math.min(s,q),c=j*f+(q-1)*h;var u=Math.min(c,t),v=k*f+(q-1)*h;r=Math.max(Math.min(r,l),m);var w=l*g+(r-1)*i,x=m*g+(r-1)*i;if(this.resize_dir.right?r=this.resize_initial_sizey:this.resize_dir.bottom&&(q=this.resize_initial_sizex),n){var y=this.resize_initial_col+q-1;n&&this.resize_initial_last_col<=y&&(this.set_dom_grid_width(Math.max(y+1,this.cols)),this.colsd;d++)-1===a.inArray(e[d],this.last_cols)&&(b||a.noop).call(this,e[d]);for(d=0;f>d;d++)-1===a.inArray(this.last_cols[d],e)&&(c||a.noop).call(this,this.last_cols[d]);return this.last_cols=e,this},h.on_overlapped_row_change=function(b,c){if(!this.colliders_data.length)return this;var d,e=this.get_targeted_rows(this.colliders_data[0].el.data.row),f=this.last_rows.length,g=e.length;for(d=0;g>d;d++)-1===a.inArray(e[d],this.last_rows)&&(b||a.noop).call(this,e[d]);for(d=0;f>d;d++)-1===a.inArray(this.last_rows[d],e)&&(c||a.noop).call(this,this.last_rows[d]);this.last_rows=e},h.set_player=function(b,c,d){var e=this,f=!1,g=d?{col:b}:e.colliders_data[0].el.data,h=g.col,i=g.row||c;this.player_grid_data={col:h,row:i,size_y:this.player_grid_data.size_y,size_x:this.player_grid_data.size_x},this.cells_occupied_by_player=this.get_cells_occupied(this.player_grid_data),this.cells_occupied_by_placeholder=this.get_cells_occupied(this.placeholder_grid_data);var j=this.get_widgets_overlapped(this.player_grid_data),k=this.player_grid_data.size_y,l=this.player_grid_data.size_x,m=this.cells_occupied_by_placeholder,n=this;if(j.each(a.proxy(function(b,c){var d=a(c),e=d.coords().grid,g=m.cols[0]+l-1,o=m.rows[0]+k-1;if(d.hasClass(n.options.static_class))return!0;if(n.options.collision.wait_for_mouseup&&n.drag_api.is_dragging)n.placeholder_grid_data.col=h,n.placeholder_grid_data.row=i,n.cells_occupied_by_placeholder=n.get_cells_occupied(n.placeholder_grid_data),n.$preview_holder.attr({"data-row":i,"data-col":h});else if(e.size_x<=l&&e.size_y<=k)if(n.is_swap_occupied(m.cols[0],e.row,e.size_x,e.size_y)||n.is_player_in(m.cols[0],e.row)||n.is_in_queue(m.cols[0],e.row,d))if(n.is_swap_occupied(g,e.row,e.size_x,e.size_y)||n.is_player_in(g,e.row)||n.is_in_queue(g,e.row,d))if(n.is_swap_occupied(e.col,m.rows[0],e.size_x,e.size_y)||n.is_player_in(e.col,m.rows[0])||n.is_in_queue(e.col,m.rows[0],d))if(n.is_swap_occupied(e.col,o,e.size_x,e.size_y)||n.is_player_in(e.col,o)||n.is_in_queue(e.col,o,d))if(n.is_swap_occupied(m.cols[0],m.rows[0],e.size_x,e.size_y)||n.is_player_in(m.cols[0],m.rows[0])||n.is_in_queue(m.cols[0],m.rows[0],d))for(var p=0;l>p;p++)for(var q=0;k>q;q++){var r=m.cols[0]+p,s=m.rows[0]+q;if(!n.is_swap_occupied(r,s,e.size_x,e.size_y)&&!n.is_player_in(r,s)&&!n.is_in_queue(r,s,d)){f=n.queue_widget(r,s,d),p=l;break}}else n.options.move_widgets_down_only?j.each(a.proxy(function(b,c){var d=a(c);n.can_go_down(d)&&d.coords().grid.row===n.player_grid_data.row&&!n.is_in_queue(g,e.row,d)&&(n.move_widget_down(d,n.player_grid_data.size_y),n.set_placeholder(h,i))})):f=n.queue_widget(m.cols[0],m.rows[0],d);else f=n.queue_widget(e.col,o,d);else f=n.queue_widget(e.col,m.rows[0],d);else f=n.queue_widget(g,e.row,d);else n.options.move_widgets_down_only?j.each(a.proxy(function(b,c){var d=a(c);n.can_go_down(d)&&d.coords().grid.row===n.player_grid_data.row&&!n.is_in_queue(d.coords().grid.col,e.row,d)&&(n.move_widget_down(d,n.player_grid_data.size_y),n.set_placeholder(h,i))})):f=n.queue_widget(m.cols[0],e.row,d);else n.options.shift_larger_widgets_down&&!f&&j.each(a.proxy(function(b,c){var d=a(c);n.can_go_down(d)&&d.coords().grid.row===n.player_grid_data.row&&(n.move_widget_down(d,n.player_grid_data.size_y),n.set_placeholder(h,i))}));n.clean_up_changed()})),f&&this.can_placeholder_be_set(h,i,l,k)){for(var o in this.w_queue){var p=parseInt(o.split("_")[0]),q=parseInt(o.split("_")[1]);"full"!==this.w_queue[o]&&this.new_move_widget_to(this.w_queue[o],p,q)}this.set_placeholder(h,i)}if(!j.length){if(this.options.shift_widgets_up){var r=this.can_go_player_up(this.player_grid_data);r!==!1&&(i=r)}this.can_placeholder_be_set(h,i,l,k)&&this.set_placeholder(h,i)}return this.w_queue={},{col:h,row:i}},h.is_swap_occupied=function(a,b,c,d){for(var e=!1,f=0;c>f;f++)for(var g=0;d>g;g++){var h=a+f,i=b+g,j=h+"_"+i;if(this.is_occupied(h,i))e=!0;else if(j in this.w_queue){if("full"===this.w_queue[j]){e=!0;continue}var k=this.w_queue[j],l=k.coords().grid;this.is_widget_under_player(l.col,l.row)||delete this.w_queue[j]}i>parseInt(this.options.max_rows)&&(e=!0),h>parseInt(this.options.max_cols)&&(e=!0),this.is_player_in(h,i)&&(e=!0)}return e},h.can_placeholder_be_set=function(a,b,c,d){for(var e=!0,f=0;c>f;f++)for(var g=0;d>g;g++){var h=a+f,i=b+g,j=this.is_widget(h,i);i>parseInt(this.options.max_rows)&&(e=!1),h>parseInt(this.options.max_cols)&&(e=!1),this.is_occupied(h,i)&&!this.is_widget_queued_and_can_move(j)&&(e=!1)}return e},h.queue_widget=function(a,b,c){var d=c,e=d.coords().grid,f=a+"_"+b;if(f in this.w_queue)return!1;this.w_queue[f]=d;for(var g=0;g=0&&a.inArray(c,d.rows)>=0},h.is_placeholder_in=function(b,c){var d=this.cells_occupied_by_placeholder||{};return this.is_placeholder_in_col(b)&&a.inArray(c,d.rows)>=0},h.is_placeholder_in_col=function(b){var c=this.cells_occupied_by_placeholder||[];return a.inArray(b,c.cols)>=0},h.is_empty=function(a,b){return"undefined"!=typeof this.gridmap[a]?"undefined"!=typeof this.gridmap[a][b]&&this.gridmap[a][b]===!1:!0},h.is_valid_col=function(a,b){return this.options.max_cols===1/0?!0:this.cols>=this.calculate_highest_col(a,b)},h.is_valid_row=function(a,b){return this.rows>=this.calculate_highest_row(a,b)},h.calculate_highest_col=function(a,b){return a+(b||1)-1},h.calculate_highest_row=function(a,b){return a+(b||1)-1},h.is_occupied=function(a,b){return this.gridmap[a]?!!this.gridmap[a][b]:!1},h.is_widget=function(a,b){var c=this.gridmap[a];return c?(c=c[b],c?c:!1):!1},h.is_static=function(a,b){var c=this.gridmap[a];return c?(c=c[b],!(!c||!c.hasClass(this.options.static_class))):!1},h.is_widget_under_player=function(a,b){return this.is_widget(a,b)?this.is_player_in(a,b):!1},h.get_widgets_under_player=function(b){b||(b=this.cells_occupied_by_player||{cols:[],rows:[]});var c=a([]);return a.each(b.cols,a.proxy(function(d,e){a.each(b.rows,a.proxy(function(a,b){this.is_widget(e,b)&&(c=c.add(this.gridmap[e][b]))},this))},this)),c},h.set_placeholder=function(b,c){var d=a.extend({},this.placeholder_grid_data),e=b+d.size_x-1;e>this.cols&&(b-=e-b);var f=this.placeholder_grid_data.row0&&(this.is_empty(a,h)||this.is_player(a,h)||this.is_widget(a,h)&&g[h].is(f));)d[a].push(h),e=e>h?h:e;return 0===d[a].length?(c=!1,!0):void d[a].sort(function(a,b){return a-b})}),c?this.get_valid_rows(a,d,e):!1},h.can_go_widget_up=function(a){var b=a.row+a.size_y-1,c=!0,d=[],e=1e4;return this.for_each_column_occupied(a,function(f){var g=this.gridmap[f];d[f]=[];for(var h=b+1;--h>0&&(!this.is_widget(f,h)||this.is_player_in(f,h)||g[h].is(a.el));)this.is_player(f,h)||this.is_placeholder_in(f,h)||this.is_player_in(f,h)||d[f].push(h),e>h&&(e=h);return 0===d[f].length?(c=!1,!0):void d[f].sort(function(a,b){return a-b})}),c?this.get_valid_rows(a,d,e):!1},h.get_valid_rows=function(b,c,d){for(var e=b.row,f=b.row+b.size_y-1,g=b.size_y,h=d-1,i=[];++h<=f;){var j=!0;if(a.each(c,function(b,c){a.isArray(c)&&-1===a.inArray(h,c)&&(j=!1)}),j===!0&&(i.push(h),i.length===g))break}var k=!1;return 1===g?i[0]!==e&&(k=i[0]||!1):i[0]!==e&&(k=this.get_consecutive_numbers_index(i,g)),k},h.get_consecutive_numbers_index=function(a,b){for(var c=a.length,d=[],e=!0,f=-1,g=0;c>g;g++){if(e||a[g]===f+1){if(d.push(g),d.length===b)break;e=!1}else d=[],e=!0;f=a[g]}return d.length>=b?a[d[0]]:!1},h.get_widgets_overlapped=function(){var b=a([]),c=[],d=this.cells_occupied_by_player.rows.slice(0);return d.reverse(),a.each(this.cells_occupied_by_player.cols,a.proxy(function(e,f){a.each(d,a.proxy(function(d,e){if(!this.gridmap[f])return!0;var g=this.gridmap[f][e];this.is_occupied(f,e)&&!this.is_player(g)&&-1===a.inArray(g,c)&&(b=b.add(g),c.push(g))},this))},this)),b},h.on_start_overlapping_column=function(a){this.set_player(a,void 0,!1)},h.on_start_overlapping_row=function(a){this.set_player(void 0,a,!1)},h.on_stop_overlapping_column=function(a){var b=this;this.options.shift_larger_widgets_down&&this.for_each_widget_below(a,this.cells_occupied_by_player.rows[0],function(a,c){b.move_widget_up(this,b.player_grid_data.size_y)})},h.on_stop_overlapping_row=function(a){var b=this,c=this.cells_occupied_by_player.cols;if(this.options.shift_larger_widgets_down)for(var d=0,e=c.length;e>d;d++)this.for_each_widget_below(c[d],a,function(a,c){b.move_widget_up(this,b.player_grid_data.size_y)})},h.new_move_widget_to=function(a,b,c){var d=a.coords().grid;return this.remove_from_gridmap(d),d.row=c,d.col=b,this.add_to_gridmap(d),a.attr("data-row",c),a.attr("data-col",b),this.update_widget_position(d,a),this.$changed=this.$changed.add(a),this},h.move_widget=function(a,b,c,d){var e=a.coords().grid,f={col:b,row:c,size_x:e.size_x,size_y:e.size_y};return this.mutate_widget_in_gridmap(a,e,f),this.set_dom_grid_height(),this.set_dom_grid_width(),d&&d.call(this,f.col,f.row),a},h.move_widget_to=function(b,c){var d=this,e=b.coords().grid,f=this.widgets_below(b),g=this.can_move_to(e,e.col,c);return g===!1?!1:(this.remove_from_gridmap(e),e.row=c,this.add_to_gridmap(e),b.attr("data-row",c),this.$changed=this.$changed.add(b),f.each(function(b,c){var e=a(c),f=e.coords().grid,g=d.can_go_widget_up(f);g&&g!==f.row&&d.move_widget_to(e,g)}),this)},h.move_widget_up=function(b,c){if(void 0===c)return!1;var d=b.coords().grid,e=d.row,f=[];return c||(c=1),this.can_go_up(b)?void this.for_each_column_occupied(d,function(d){if(-1===a.inArray(b,f)){var g=b.coords().grid,h=e-c;if(h=this.can_go_up_to_row(g,d,h),!h)return!0;this.remove_from_gridmap(g),g.row=h,this.add_to_gridmap(g),b.attr("data-row",g.row),this.$changed=this.$changed.add(b),f.push(b)}}):!1},h.move_widget_down=function(b,c){var d,e,f,g;if(0>=c)return!1;if(d=b.coords().grid,e=d.row,f=[],g=c,!b)return!1;if(-1===a.inArray(b,f)){var h=b.coords().grid,i=e+c,j=this.widgets_below(b);this.remove_from_gridmap(h),j.each(a.proxy(function(b,c){var d=a(c),e=d.coords().grid,f=this.displacement_diff(e,h,g);f>0&&this.move_widget_down(d,f)},this)),h.row=i,this.update_widget_position(h,b),b.attr("data-row",h.row),this.$changed=this.$changed.add(b),f.push(b)}},h.can_go_up_to_row=function(b,c,d){var e,f=!0,g=[],h=b.row;if(this.for_each_column_occupied(b,function(a){for(g[a]=[],e=h;e--&&this.is_empty(a,e)&&!this.is_placeholder_in(a,e);)g[a].push(e);return g[a].length?void 0:(f=!1,!0)}),!f)return!1;for(e=d,e=1;h>e;e++){for(var i=!0,j=0,k=g.length;k>j;j++)g[j]&&-1===a.inArray(e,g[j])&&(i=!1);if(i===!0){f=e;break}}return f},h.displacement_diff=function(a,b,c){var d=a.row,e=[],f=b.row+b.size_y;this.for_each_column_occupied(a,function(a){for(var b=0,c=f;d>c;c++)this.is_empty(a,c)&&(b+=1);e.push(b)});var g=Math.max.apply(Math,e);return c-=g,c>0?c:0},h.widgets_below=function(b){var c=a([]),e=a.isPlainObject(b)?b:b.coords().grid;if(void 0===e)return c;var f=this,g=e.row+e.size_y-1;return this.for_each_column_occupied(e,function(b){f.for_each_widget_below(b,g,function(b,d){return f.is_player(this)||-1!==a.inArray(this,c)?void 0:(c=c.add(this),!0)})}),d.sort_by_row_asc(c)},h.set_cells_player_occupies=function(a,b){return this.remove_from_gridmap(this.placeholder_grid_data),this.placeholder_grid_data.col=a,this.placeholder_grid_data.row=b,this.add_to_gridmap(this.placeholder_grid_data,this.$player),this},h.empty_cells_player_occupies=function(){return this.remove_from_gridmap(this.placeholder_grid_data),this},h.can_go_down=function(b){var c=!0,d=this;return b.hasClass(this.options.static_class)&&(c=!1),this.widgets_below(b).each(function(){a(this).hasClass(d.options.static_class)&&(c=!1)}),c},h.can_go_up=function(a){var b=a.coords().grid,c=b.row,d=c-1,e=!0;return 1===c?!1:(this.for_each_column_occupied(b,function(a){return this.is_occupied(a,d)||this.is_player(a,d)||this.is_placeholder_in(a,d)||this.is_player_in(a,d)?(e=!1,!0):void 0}),e)},h.can_move_to=function(a,b,c){var d=a.el,e={size_y:a.size_y,size_x:a.size_x,col:b,row:c},f=!0;if(this.options.max_cols!==1/0){var g=b+a.size_x-1;if(g>this.cols)return!1}return this.options.max_rows=d;d++)c.push(d);return c},h.get_targeted_rows=function(a){for(var b=(a||this.player_grid_data.row)+(this.player_grid_data.size_y-1),c=[],d=a;b>=d;d++)c.push(d);return c},h.get_cells_occupied=function(b){var c,d={cols:[],rows:[]};for(arguments[1]instanceof a&&(b=arguments[1].coords().grid),c=0;c0&&this.is_widget(d,m)&&-1===a.inArray(g[d][m],l)&&(h=f.call(g[d][m],d,m),l.push(g[d][m]),h)););},"for_each/below":function(){for(m=e+1,i=g[d].length;i>m;m++)this.is_widget(d,m)&&-1===a.inArray(g[d][m],l)&&(h=f.call(g[d][m],d,m),l.push(g[d][m]))}};n[j]&&n[j].call(this)}},h.for_each_widget_above=function(a,b,c){return this._traversing_widgets("for_each","above",a,b,c),this},h.for_each_widget_below=function(a,b,c){return this._traversing_widgets("for_each","below",a,b,c),this},h.get_highest_occupied_cell=function(){for(var a,b=this.gridmap,c=b[1].length,d=[],e=[],f=b.length-1;f>=1;f--)for(a=c-1;a>=1;a--)if(this.is_widget(f,a)){d.push(a),e.push(f);break}return{col:Math.max.apply(Math,e),row:Math.max.apply(Math,d)}},h.get_widgets_in_range=function(b,c,d,e){var f,g,h,i,j=a([]);for(f=d;f>=b;f--)for(g=e;g>=c;g--)h=this.is_widget(f,g),h!==!1&&(i=h.data("coords").grid,i.col>=b&&i.col<=d&&i.row>=c&&i.row<=e&&(j=j.add(h)));return j},h.get_widgets_at_cell=function(a,b){return this.get_widgets_in_range(a,b,a,b)},h.get_widgets_from=function(b,c){var d=a();return b&&(d=d.add(this.$widgets.filter(function(){var c=parseInt(a(this).attr("data-col"));return c===b||c>b}))),c&&(d=d.add(this.$widgets.filter(function(){var b=parseInt(a(this).attr("data-row"));return b===c||b>c}))),d},h.set_dom_grid_height=function(a){if("undefined"==typeof a){var b=this.get_highest_occupied_cell().row;a=(b+1)*this.options.widget_margins[1]+b*this.min_widget_height}return this.container_height=a,this.$el.css("height",this.container_height),this},h.set_dom_grid_width=function(a){"undefined"==typeof a&&(a=this.get_highest_occupied_cell().col);var b=this.options.max_cols===1/0?this.options.max_cols:this.cols;return a=Math.min(b,Math.max(a,this.options.min_cols)),this.container_width=(a+1)*this.options.widget_margins[0]+a*this.min_widget_width,this.is_responsive()?(this.$el.css({"min-width":"100%","max-width":"100%"}),this):(this.$el.css("width",this.container_width),this)},h.is_responsive=function(){return this.options.autogenerate_stylesheet&&"auto"===this.options.widget_base_dimensions[0]&&this.options.max_cols!==1/0},h.get_responsive_col_width=function(){var a=this.cols||this.options.max_cols;return(this.$el[0].clientWidth-3-(a+1)*this.options.widget_margins[0])/a},h.resize_responsive_layout=function(){return this.min_widget_width=this.get_responsive_col_width(),this.generate_stylesheet(),this.update_widgets_dimensions(),this.drag_api.set_limits(this.cols*this.min_widget_width+(this.cols+1)*this.options.widget_margins[0]),this},h.toggle_collapsed_grid=function(a,b){return 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Developed with funding from the Vehicle Technologies Office, Office of Energy Efficiency and Renewable Energy, U.S. Department of Energy.

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- U.S. Department of Energy Office of Energy Efficiency and Renewable Energy -
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- - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/frontend/metrics_program.html b/frontend/metrics_program.html deleted file mode 100644 index 7574620..0000000 --- a/frontend/metrics_program.html +++ /dev/null @@ -1,30 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/frontend/metrics_study.html b/frontend/metrics_study.html deleted file mode 100644 index 9657483..0000000 --- a/frontend/metrics_study.html +++ /dev/null @@ -1,15 +0,0 @@ - - - - - - - - - - - - - - - diff --git a/frontend/options.html b/frontend/options.html deleted file mode 100644 index 5c08b8c..0000000 --- a/frontend/options.html +++ /dev/null @@ -1,3 +0,0 @@ - - - diff --git a/frontend/programs.html b/frontend/programs.html deleted file mode 100644 index b78603b..0000000 --- a/frontend/programs.html +++ /dev/null @@ -1 +0,0 @@ - diff --git a/outputs/README.md b/outputs/README.md new file mode 100644 index 0000000..bd2374a --- /dev/null +++ b/outputs/README.md @@ -0,0 +1 @@ +Placeholder directory to hold outputs \ No newline at end of file diff --git a/viz_scripts/docker/.DS_Store b/rm_src/.DS_Store similarity index 79% rename from viz_scripts/docker/.DS_Store rename to rm_src/.DS_Store index c95f7deb550b627d9b6bb5d4e1c68872e1d6e433..e6699c11d32c180eae581f3e2673895bab1bece9 100644 GIT binary patch delta 292 zcmZoMXfc=|#>B`mu~2NHo}wrV0|Nsi1A_nqLncEpLq0<(LlHyr#KPtEAPE76T%bS> zLkY4ZLn=c&LkdG8P&6?o-7q*gKeqs=76u6F$<23hNy^Dj0&3t;C_Z?+?(R`XARA3z z5X2?OmgVNB5TOxMUqJ@Mk((bftFdhUz|6|DnVo~50~iKCk?+it`9&-_K&}S{1JmXJ HkuA&sqsl`~ literal 6148 zcmeHKJ5Iwu5PfSRB|sBspmc*og@ll3Xsjp{)Epq#u?Z1QY~_PQ=Z3>@8Vt7%a+4S1ByU8k z<1aEG@2-n67AP>sxA#{hd?H_qE9&Wo6^?*C3>iDdBQj>)V~QM^d*_>+*ILdAW9N8c ztm`wg^A#>|i8G}9)-3UWgmq&^r1ZtKDdSgES=o}lXUvdqcQQw{P6wFS+OoEo*%?MC zxx#{Dslh(A3NMWc5-t5l>?ge^v?pp_FS`AKBlmTTx8Tf$#p4lS3eTQn50cAiL*f1c!4n^v zKOHpo3P9}8ZG~g`(S#@|qk z?aun44Tp&ws#OM*fo%qM+_W#}|Mk!J|Lr85DFe#Dzhc02hPT53x8%>(#^&U#4XJlj p5s9lDZc;d~M=@jNC_biIp+A%XG4fbBq=%w^1S}0|l!0Gm;1d#Zgunm* diff --git a/rm_src/02_run_trip_level_models.py b/rm_src/02_run_trip_level_models.py index cd1c476..16567ec 100644 --- a/rm_src/02_run_trip_level_models.py +++ b/rm_src/02_run_trip_level_models.py @@ -464,7 +464,7 @@ def save_metadata(dir_name: Path, **kwargs): # args = parse_args() sweep_number = 1 - root = Path('../benchmark_results') + root = Path('../outputs/benchmark_results') if not root.exists(): root.mkdir() diff --git a/rm_src/04_FeatureClustering.ipynb b/rm_src/04_FeatureClustering.ipynb index 31c1191..e61668c 100644 --- a/rm_src/04_FeatureClustering.ipynb +++ b/rm_src/04_FeatureClustering.ipynb @@ -1,78 +1,8 @@ { "cells": [ - { - "cell_type": "markdown", - "id": "aa5d3c5f", - "metadata": {}, - "source": [ - "To determine which non-demographic trip-level characteristics we should stratify by, we want to do some high level clustering.\n", - "\n", - "we already have clustering based on demographics (which corresponds to the demographic stratification) and can show that the argmax replaced mode is not the same\n", - "\n", - "we should now try two clusterings:\n", - "- based on trip summaries\n", - " - % of trips in each primary sensed mode\n", - " - number of trips\n", - " - % of distance in each primary sensed mode\n", - " - most common start time\n", - " - most common end time\n", - "\n", - "we can see whether the replaced mode argmax is now consistent in each cluster\n", - "\n", - "- cluster based on the replaced mode distributions. features are:\n", - " - % of trips with x replaced mode for x in set of unique replaced modes (filtered to top 75%)\n", - " - distance of trips with x replaced mode for x in set of unique replaced modes (filtered to top 75%)\n", - "\n", - "see how the demographics and trip summaries vary per cluster and see if there is a strong correlation between certain characteristics and being in the same cluster.\n", - "\n", - "\n", - "- Chart 1 will visualize the clustering with trip-summary features\n", - "- Chart 2 will visualize the clustering with replaced mode features.\n", - "\n", - "Note that clustering is unsupervised and does not have a target variable.\n", - "\n", - "For chart 1, for $n$ users, our input matrix will be $O(n \\times 15)$ since there are ~ 6 sensed modes. After the clustering, we will check the replaced mode argmax distribution in each cluster.\n", - "\n", - "For chart 2, for $n$ users where the 75th percentile of the replaced modes has modes, our input matrix will be $O(n \\times 2 \\times r)$. After the clustering, we will check the distribution of demographics and trip characteristics in each cluster." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "1db71454", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "9 72866\n", - "2 18633\n", - "5 13834\n", - "3 9455\n", - "8 7908\n", - "1 6539\n", - "4 1630\n", - "7 1469\n", - "6 189\n", - "Name: target, dtype: int64" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "\n", - "df = pd.read_csv('../data/filtered_data/preprocessed_data_Stage_database.csv')\n", - "df.drop_duplicates(inplace=True)\n", - "display(df.target.value_counts())" - ] - }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "789df947", "metadata": {}, "outputs": [], @@ -109,49 +39,50 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, + "id": "aea4dda7", + "metadata": {}, + "outputs": [], + "source": [ + "DATA_SOURCES = [\n", + " ('../data/filtered_data/preprocessed_data_Stage_database.csv', 'allceo'),\n", + " ('../data/filtered_data/preprocessed_data_openpath_prod_durham.csv', 'durham'),\n", + " ('../data/filtered_data/preprocessed_data_openpath_prod_ride2own.csv', 'ride2own'),\n", + " ('../data/filtered_data/preprocessed_data_openpath_prod_mm_masscec.csv', 'masscec'),\n", + " ('../data/filtered_data/preprocessed_data_openpath_prod_uprm_nicr.csv', 'nicr')\n", + "]\n", + "\n", + "# Switch between 0-4\n", + "DB_NUMBER = 0" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "33ef3275", "metadata": {}, "outputs": [], "source": [ - "# Data is located at\n", - "CURRENT_DB = 'ride2own'\n", + "# Change this name to something unique\n", + "CURRENT_DB = DATA_SOURCES[DB_NUMBER][1]\n", + "PATH = DATA_SOURCES[DB_NUMBER][0]\n", "\n", - "df = pd.read_csv('../data/filtered_data/preprocessed_data_openpath_prod_ride2own.csv')" + "df = pd.read_csv(PATH)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "d0d884a3", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "9 800\n", - "4 33\n", - "8 6\n", - "7 5\n", - "2 1\n", - "1 1\n", - "3 1\n", - "5 1\n", - "Name: target, dtype: int64" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.target.value_counts()" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "b2281bdc", "metadata": {}, "outputs": [], @@ -164,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "9c22d6ac", "metadata": {}, "outputs": [], @@ -176,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "063f6124", "metadata": {}, "outputs": [], @@ -186,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "cef8d45b", "metadata": {}, "outputs": [], @@ -200,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "68c6af2d", "metadata": {}, "outputs": [], @@ -210,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "eff378a7", "metadata": {}, "outputs": [], @@ -220,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "cffbd401", "metadata": {}, "outputs": [], @@ -230,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "f1eb1633", "metadata": {}, "outputs": [], @@ -244,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "d9cc0a0f", "metadata": { "scrolled": true @@ -260,7 +191,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "750fbd0c", "metadata": {}, "outputs": [], @@ -278,236 +209,20 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "1c3d1849", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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coverage_bicyclingcoverage_carcoverage_transitcoverage_unknowncoverage_walkingpct_distance_bicyclingpct_distance_carpct_distance_transitpct_distance_unknownpct_distance_walkingn_tripsstart:hourend:hour
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" - ], - "text/plain": [ - " coverage_bicycling coverage_car \\\n", - "user_id \n", - "258a8119-a5c2-4d38-875d-a9a9f7ed2d7d 0.166667 0.333333 \n", - "7ac8a16d-bafe-4bef-a5b5-43b64beb97a6 0.033113 0.443709 \n", - "87e12281-655e-4b7f-9dce-67b42217a5e8 0.300000 0.233333 \n", - "bc08c318-5a56-4e11-9f69-9c48468b7571 0.067935 0.739130 \n", - "be2a3c5c-1db0-4736-8304-a1c705da4cd6 0.009174 0.220183 \n", - "\n", - " coverage_transit coverage_unknown \\\n", - "user_id \n", - "258a8119-a5c2-4d38-875d-a9a9f7ed2d7d 0.166667 0.166667 \n", - "7ac8a16d-bafe-4bef-a5b5-43b64beb97a6 0.119205 0.013245 \n", - "87e12281-655e-4b7f-9dce-67b42217a5e8 0.033333 0.033333 \n", - "bc08c318-5a56-4e11-9f69-9c48468b7571 0.048913 0.027174 \n", - "be2a3c5c-1db0-4736-8304-a1c705da4cd6 0.055046 0.073394 \n", - "\n", - " coverage_walking \\\n", - "user_id \n", - "258a8119-a5c2-4d38-875d-a9a9f7ed2d7d 0.166667 \n", - "7ac8a16d-bafe-4bef-a5b5-43b64beb97a6 0.390728 \n", - "87e12281-655e-4b7f-9dce-67b42217a5e8 0.400000 \n", - "bc08c318-5a56-4e11-9f69-9c48468b7571 0.116848 \n", - "be2a3c5c-1db0-4736-8304-a1c705da4cd6 0.642202 \n", - "\n", - " pct_distance_bicycling \\\n", - "user_id \n", - "258a8119-a5c2-4d38-875d-a9a9f7ed2d7d 0.172218 \n", - "7ac8a16d-bafe-4bef-a5b5-43b64beb97a6 0.023119 \n", - "87e12281-655e-4b7f-9dce-67b42217a5e8 0.277527 \n", - "bc08c318-5a56-4e11-9f69-9c48468b7571 0.038010 \n", - "be2a3c5c-1db0-4736-8304-a1c705da4cd6 0.012170 \n", - "\n", - " pct_distance_car pct_distance_transit \\\n", - "user_id \n", - "258a8119-a5c2-4d38-875d-a9a9f7ed2d7d 0.668293 0.017667 \n", - "7ac8a16d-bafe-4bef-a5b5-43b64beb97a6 0.862170 0.086055 \n", - "87e12281-655e-4b7f-9dce-67b42217a5e8 0.515470 0.037176 \n", - "bc08c318-5a56-4e11-9f69-9c48468b7571 0.914976 0.022974 \n", - "be2a3c5c-1db0-4736-8304-a1c705da4cd6 0.416293 0.092856 \n", - "\n", - " pct_distance_unknown \\\n", - "user_id \n", - "258a8119-a5c2-4d38-875d-a9a9f7ed2d7d 0.134188 \n", - "7ac8a16d-bafe-4bef-a5b5-43b64beb97a6 0.005839 \n", - "87e12281-655e-4b7f-9dce-67b42217a5e8 0.037151 \n", - "bc08c318-5a56-4e11-9f69-9c48468b7571 0.009626 \n", - "be2a3c5c-1db0-4736-8304-a1c705da4cd6 0.080060 \n", - "\n", - " pct_distance_walking n_trips \\\n", - "user_id \n", - "258a8119-a5c2-4d38-875d-a9a9f7ed2d7d 0.007634 0.008219 \n", - "7ac8a16d-bafe-4bef-a5b5-43b64beb97a6 0.022817 0.819178 \n", - "87e12281-655e-4b7f-9dce-67b42217a5e8 0.132676 0.073973 \n", - "bc08c318-5a56-4e11-9f69-9c48468b7571 0.014414 1.000000 \n", - "be2a3c5c-1db0-4736-8304-a1c705da4cd6 0.398620 0.290411 \n", - "\n", - " start:hour end:hour \n", - "user_id \n", - "258a8119-a5c2-4d38-875d-a9a9f7ed2d7d -0.287903 -0.287903 \n", - "7ac8a16d-bafe-4bef-a5b5-43b64beb97a6 0.650288 0.420167 \n", - "87e12281-655e-4b7f-9dce-67b42217a5e8 -0.544021 -0.544021 \n", - "bc08c318-5a56-4e11-9f69-9c48468b7571 0.656987 -0.961397 \n", - "be2a3c5c-1db0-4736-8304-a1c705da4cd6 -0.287903 0.656987 " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "figure1_df.head()" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "598d82bc", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-inf\n" - ] - } - ], + "outputs": [], "source": [ "epsilons = np.linspace(1e-3, 1., 1000)\n", "\n", @@ -530,18 +245,10 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "bc89a42d", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Counter({-1: 7})\n" - ] - } - ], + "outputs": [], "source": [ "'''\n", "AlLCEO: eps=0.542\n", @@ -556,30 +263,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "05c9a7c4", "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7 users in cluster -1\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# After clustering, we would like to see what the replaced mode argmax distribution in each cluster is.\n", "\n", @@ -601,7 +290,7 @@ " ax.set_title(f\"Replaced mode argmax distribution for users in cluster {cix}\")\n", " ax.set_xlabel(\"Target\")\n", " \n", - " plt.savefig(f'./plots_for_paper/{CURRENT_DB}__FIG1_cluster_{cix}_target_dist.png', dpi=300)\n", + " plt.savefig(f'../outputs/{CURRENT_DB}__FIG1_cluster_{cix}_target_dist.png', dpi=300)\n", " \n", " plt.show()" ] @@ -759,7 +448,7 @@ "fig, ax = plt.subplots()\n", "sns.scatterplot(x=tsfm[:,0], y=tsfm[:,1], c=cl2.labels_)\n", "ax.set(xlabel='Latent Dim 0', ylabel='Latent Dim 1')\n", - "plt.savefig(f'./plots_for_paper/{CURRENT_DB}__Fig2__PCA_w_colors.png', dpi=300)\n", + "plt.savefig(f'../outputs/{CURRENT_DB}__Fig2__PCA_w_colors.png', dpi=300)\n", "plt.show()" ] }, @@ -1165,7 +854,7 @@ " offset_col_ix += 1\n", " \n", " plt.tight_layout()\n", - " plt.savefig(f\"./plots_for_paper/{CURRENT_DB}_cluster{cix}_combined_features.png\", dpi=300)\n", + " plt.savefig(f\"../outputs/{CURRENT_DB}_cluster{cix}_combined_features.png\", dpi=300)\n", " plt.show()\n", " print(50 * '=')" ] @@ -1175,7 +864,7 @@ "id": "24a80f68", "metadata": {}, "source": [ - "## Try a different clustering technique?" + "## Try a different clustering technique? (Unexplored)" ] }, { diff --git a/rm_src/baseline_modeling0.ipynb b/rm_src/baseline_modeling0.ipynb deleted file mode 100644 index 8e623b4..0000000 --- a/rm_src/baseline_modeling0.ipynb +++ /dev/null @@ -1,1431 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### All experiments are logged in Notion [here](https://www.notion.so/Replacement-mode-modeling-257c2f460377498d921e6b167f465945)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from enum import Enum\n", - "import random\n", - "\n", - "# Math and graphing.\n", - "import pandas as pd\n", - "import numpy as np\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# sklearn imports.\n", - "from sklearn.model_selection import train_test_split, StratifiedGroupKFold, GroupKFold\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.linear_model import LinearRegression\n", - "from sklearn.metrics import f1_score, r2_score, ConfusionMatrixDisplay\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Global experiment flags and variables.\n", - "SEED = 19348\n", - "TARGETS = ['p_micro', 'no_trip', 's_car', 'transit', 'car', 's_micro', 'ridehail', 'walk', 'unknown']\n", - "\n", - "DROP_S_MICRO = True\n", - "\n", - "# Set the Numpy seed too.\n", - "random.seed(SEED)\n", - "np.random.seed(SEED)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "class SPLIT_TYPE(Enum):\n", - " INTRA_USER = 0\n", - " INTER_USER = 1\n", - " TARGET = 2\n", - " MODE = 3\n", - " INTER_USER_STATIC = 4\n", - " \n", - "\n", - "class SPLIT(Enum):\n", - " TRAIN = 0\n", - " TEST = 1\n", - "\n", - "\n", - "def get_train_test_splits(data: pd.DataFrame, how=SPLIT_TYPE, test_ratio=0.2, shuffle=True):\n", - " \n", - " if how == SPLIT_TYPE.INTER_USER:\n", - "\n", - " X = data.drop(columns=['target'])\n", - " y = data['target'].values\n", - " groups = data.user_id.values\n", - " \n", - " splitter = StratifiedGroupKFold(n_splits=5, shuffle=shuffle, random_state=SEED)\n", - " # splitter = GroupKFold(n_splits=5)\n", - " \n", - " for train_index, test_index in splitter.split(X, y, groups):\n", - " X_tr = data.iloc[train_index, :]\n", - " X_te = data.iloc[test_index, :]\n", - " \n", - " # Iterate only once and break.\n", - " break\n", - "\n", - " return X_tr, X_te\n", - " \n", - " elif how == SPLIT_TYPE.INTRA_USER:\n", - " \n", - " # There are certain users with only one observation. What do we do with those?\n", - " # As per the mobilitynet modeling pipeline, we randomly assign them to either the\n", - " # training or test set.\n", - " \n", - " value_counts = data.user_id.value_counts()\n", - " single_count_ids = value_counts[value_counts == 1].index\n", - " \n", - " data_filtered = data.loc[~data.user_id.isin(single_count_ids), :].reset_index(drop=True)\n", - " data_single_counts = data.loc[data.user_id.isin(single_count_ids), :].reset_index(drop=True)\n", - " \n", - " X_tr, X_te = train_test_split(\n", - " data_filtered, test_size=test_ratio, shuffle=shuffle, stratify=data_filtered.user_id,\n", - " random_state=SEED\n", - " )\n", - " \n", - " data_single_counts['assigned'] = np.random.choice(['train', 'test'], len(data_single_counts))\n", - " X_tr_merged = pd.concat(\n", - " [X_tr, data_single_counts.loc[data_single_counts.assigned == 'train', :].drop(\n", - " columns=['assigned'], inplace=False\n", - " )],\n", - " ignore_index=True, axis=0\n", - " )\n", - " \n", - " X_te_merged = pd.concat(\n", - " [X_te, data_single_counts.loc[data_single_counts.assigned == 'test', :].drop(\n", - " columns=['assigned'], inplace=False\n", - " )],\n", - " ignore_index=True, axis=0\n", - " )\n", - " \n", - " return X_tr_merged, X_te_merged\n", - " \n", - " elif how == SPLIT_TYPE.TARGET:\n", - " \n", - " X_tr, X_te = train_test_split(\n", - " data, test_size=test_ratio, shuffle=shuffle, stratify=data.target,\n", - " random_state=SEED\n", - " )\n", - " \n", - " return X_tr, X_te\n", - " \n", - " elif how == SPLIT_TYPE.MODE:\n", - " X_tr, X_te = train_test_split(\n", - " data, test_size=test_ratio, shuffle=shuffle, stratify=data.section_mode_argmax,\n", - " random_state=SEED\n", - " )\n", - " \n", - " return X_tr, X_te\n", - " \n", - " elif how == SPLIT_TYPE.INTER_USER_STATIC:\n", - " \n", - " train_ids = ['810be63d084746e3b7da9d943dd88e8c', 'bf774cbe6c3040b0a022278d36a23f19', '8a8332a53a1b4cdd9f3680434e91a6ef', \n", - " '5ad862e79a6341f69f28c0096fe884da', '7f89656bd4a94d12ad8e5ad9f0afecaf', 'fbaa338d7cd7457c8cad4d0e60a44d18', \n", - " '3b25446778824941a4c70ae5774f4c68', '28cb1dde85514bbabfd42145bdaf7e0a', '3aeb5494088542fdaf798532951aebb0', \n", - " '531732fee3c24366a286d76eb534aebc', '950f4287bab5444aa0527cc23fb082b2', '737ef8494f26407b8b2a6b1b1dc631a4', \n", - " 'e06cf95717f448ecb81c440b1b2fe1ab', '7347df5e0ac94a109790b31ba2e8a02a', 'bd9cffc8dbf1402da479f9f148ec9e60', \n", - " '2f3b66a5f98546d4b7691fba57fa640f', 'f289f7001bd94db0b33a7d2e1cd28b19', '19a043d1f2414dbcafcca44ea2bd1f19', \n", - " '68788082836e4762b26ad0877643fdcf', '4e8b1b7f026c4384827f157225da13fa', '703a9cee8315441faff7eb63f2bfa93f', \n", - " 'add706b73839413da13344c355dde0bb', '47b5d57bd4354276bb6d2dcd1438901d', 'e4cfb2a8f600426897569985e234636e', \n", - " '0154d71439284c34b865e5a417cd48af', '234f4f2366244fe682dccded2fa7cc4e', '0d0ae3a556414d138c52a6040a203d24', \n", - " '44c10f66dec244d6b8644231d4a8fecb', '30e9b141d7894fbfaacecd2fa18929f9', '0eb313ab00e6469da78cc2d2e94660fb', \n", - " 'fc51d1258e4649ecbfb0e6ecdaeca454', 'a1954793b1454b2f8cf95917d7547169', '6656c04c6cba4c189fed805eaa529741', \n", - " '6a0f3653b80a4c949e127d6504debb55', 'dfe5ca1bb0854b67a6ffccad9565d669', '8b1f3ba43de945bea79de6a81716ad04', \n", - " 'cde34edb8e3a4278a18e0adb062999e5', '6d96909e5ca442ccb5679d9cdf3c8f5b', 'a60a64d82d1c439a901b683b73a74d73', \n", - " '60e6a6f6ed2e4e838f2bbed6a427028d', '88041eddad7542ea8c92b30e5c64e198', '1635c003b1f94a399ebebe21640ffced', \n", - " '1581993b404a4b9c9ca6b0e0b8212316', 'b1aed24c863949bfbfa3a844ecf60593', '4b89612d7f1f4b368635c2bc48bd7993', \n", - " 'eb2e2a5211564a9290fcb06032f9b4af', '26767f9f3da54e93b692f8be6acdac43', '8a98e383a2d143e798fc23869694934a', \n", - " 'b346b83b9f7c4536b809d5f92074fdae', 'd929e7f8b7624d76bdb0ec9ada6cc650', '863e9c6c8ec048c4b7653f73d839c85b', \n", - " 'f50537eb104e4213908f1862c8160a3e', '4a9db5a9bac046a59403b44b883cc0ba', 'cded005d5fd14c64a5bba3f5c4fe8385', \n", - " 'c7ce889c796f4e2a8859fa2d7d5068fe', '405b221abe9e43bc86a57ca7fccf2227', '0b3e78fa91d84aa6a3203440143c8c16', \n", - " 'fbff5e08b7f24a94ab4b2d7371999ef7', 'e35e65107a34496db49fa5a0b41a1e9e', 'd5137ebd4f034dc193d216128bb7fc9a', \n", - " '3f7f2e536ba9481e92f8379b796ad1d0', 'dc75e0b776214e1b9888f6abd042fd95', 'b41dd7d7c6d94fe6afe2fd26fa4ac0bd', \n", - " 'eec6936e1ac347ef9365881845ec74df', '8c7d261fe8284a42a777ffa6f380ba3b', '4baf8c8af7b7445e9067854065e3e612', \n", - " 'c6e4db31c18b4355b02a7dd97deca70b', 'f0db3b1999c2410ba5933103eca9212f', '487e20ab774742378198f94f5b5b0b43', \n", - " 'dc1ed4d71e3645d0993885398d5628ca', '8c3c63abb3ec4fc3a61e7bf316ee4efd', '15eb78dd6e104966ba6112589c29dc41', \n", - " 'c23768ccb817416eaf08be487b2e3643', 'ecd2ae17d5184807abd87a287115c299', '71f21d53b655463784f3a3c63c56707b', \n", - " '2931e0a34319495bbb5898201a54feb5', '92bde0d0662f45ac864629f486cffe77', '42b3ee0bc02a481ab1a94644a8cd7a0d', \n", - " '15aa4ba144a34b8b8079ed7e049d84df', '509b909390934e988eb120b58ed9bd8c', '14103cda12c94642974129989d39e50d', \n", - " '8b0876430c2641bcaea954ea00520e64', 'baa4ff1573ae411183e10aeb17c71c53', '14fe8002bbdc4f97acbd1a00de241bf6', \n", - " '1b7d6dfea8464bcab9321018b10ec9c9', '487ad897ba93404a8cbe5de7d1922691', '5182d93d69754d7ba06200cd1ac5980a', \n", - " '91f3ca1c278247f79a806e49e9cc236f', 'e66e63b206784a559d977d4cb5f1ec34', '840297ae39484e26bfebe83ee30c5b3e', \n", - " 'c6807997194c4c528a8fa8c1f6ee1595', '802667b6371f45b29c7abb051244836a', 'b2bbe715b6a14fd19f751cae8adf6b4e', \n", - " 'feb1d940cd3647d1a101580c2a3b3f8c', '1b9883393ab344a69bc1a0fab192a94c', 'ac604b44fdca482fb753034cb55d1351', \n", - " 'f446bf3102ff4bd99ea1c98f7d2f7af0', 'c2c5d4b9a607487ea405a99c721079d4', '85ddd3c34c58407392953c47a32f5428', \n", - " 'd51de709f95045f8bacf473574b96ba5', '6373dfb8cb9b47e88e8f76adcfadde20', '313d003df34b4bd9823b3474fc93f9f9', \n", - " '53e78583db87421f8decb529ba859ca4', '8fdc9b926a674a9ea07d91df2c5e06f2', '90480ac60a3d475a88fbdab0a003dd5d', \n", - " '7559c3f880f341e898a402eba96a855d', '19a4c2cf718d40588eb96ac25a566353', 'f4427cccaa9442b48b42bedab5ab648e', \n", - " 'e192b8a00b6c422296851c93785deaf7', '355e25bdfc244c5e85d358e39432bd44', 'a0c3a7b410b24e18995f63369a31d123', \n", - " '03a395b4d8614757bb8432b4984559b0', 'a2d48b05d5454d428c0841432c7467b6', '3d981e617b304afab0f21ce8aa6c9786', \n", - " '2cd5668ac9054e2eb2c88bb4ed94bc6d', 'd7a732f4a8644bcbb8dedfc8be242fb2', '367eb90b929d4f6e9470d15c700d2e3f', \n", - " 'e049a7b2a6cb44259f907abbb44c5abc', 'a231added8674bef95092b32bc254ac8', 'e88a8f520dde445484c0a9395e1a0599',\n", - " 'cba570ae38f341faa6257342727377b7', '97953af1b97d4e268c52e1e54dcf421a', 'd200a61757d84b1dab8fbac35ff52c28', \n", - " 'fc68a5bb0a7b4b6386b3f08a69ead36f', '4a8210aec25e443391efb924cc0e5f23', '903742c353ce42c3ad9ab039fc418816', \n", - " '2114e2a75304475fad06ad201948fbad', 'ac917eae407c4deb96625dd0dc2f2ba9', '3dddfb70e7cd40f18a63478654182e9a', \n", - " 'd3735ba212dd4c768e1675dca7bdcb6f', '7abe572148864412a33979592fa985fb', 'd3dff742d07942ca805c2f72e49e12c5' \n", - " ]\n", - " \n", - " X_tr = data.loc[data.user_id.isin(train_ids), :]\n", - " X_te = data.loc[~data.user_id.isin(train_ids), :]\n", - " \n", - " return X_tr, X_te\n", - " \n", - " raise NotImplementedError(\"Unknown split type\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Modeling" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/2274972291.py:4: DtypeWarning: Columns (38) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " data = pd.read_csv('../data/ReplacedMode_Fix_02142024.csv')\n" - ] - } - ], - "source": [ - "# Read the data.\n", - "# data = pd.read_csv('../data/FULL_preprocessed_data_RM_weather.csv')\n", - "# data = pd.read_csv('../data/ReplacedMode_Fix.csv')\n", - "data = pd.read_csv('../data/ReplacedMode_Fix_02142024.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "if DROP_S_MICRO:\n", - " data.drop(\n", - " index=data.loc[data.target == 6, :].index,\n", - " inplace=True\n", - " )\n", - " \n", - " # Shift all values after 6 by -1\n", - " data.loc[data.target > 5, 'target'] -= 1" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "data.drop_duplicates(inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_hist(df, features=None):\n", - " if not features:\n", - " # All features.\n", - " features = df.columns.tolist()\n", - " \n", - " n_features = len(features)\n", - " \n", - " ncols = 6\n", - " nrows = n_features//ncols if n_features%ncols == 0 else (n_features//ncols) + 1\n", - " \n", - " fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(10, 10))\n", - " for ix, ax in enumerate(axes.flatten()):\n", - " \n", - " if ix > n_features:\n", - " break\n", - " \n", - " df[features[ix]].hist(ax=ax)\n", - " ax.set(title=features[ix])\n", - " \n", - " plt.tight_layout()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# First, we map the user IDs to ints.\n", - "\n", - "# USERS = list(data.user_id.unique())\n", - "\n", - "# USER_MAP = {\n", - "# u: i+1 for (i, u) in enumerate(USERS)\n", - "# }\n", - "\n", - "# data['user_id'] = data['user_id'].apply(lambda x: USER_MAP[x])\n", - "\n", - "# data.rename(\n", - "# columns={'start_local_dt_weekday': 'start:DOW', 'end_local_dt_weekday': 'end:DOW'},\n", - "# inplace=True\n", - "# )\n", - "\n", - "# Drop the samples with chosen == no trip or chosen == unknown\n", - "# data.drop(index=data.loc[data.chosen.isin([2, 9])].index, inplace=True)\n", - "\n", - "# data.n_working_residents = data.n_working_residents.apply(lambda x: 0 if x < 0 else x)\n", - "\n", - "# Fix some age preprocessing issues.\n", - "# data.age = data.age.apply(lambda x: x if x < 100 else 2024-x)\n", - "\n", - "# Collapse 'train' and 'bus' into 'transit'\n", - "# data.loc[\n", - "# data.section_mode_argmax.isin(['train', 'bus']), 'section_mode_argmax'\n", - "# ] = 'transit'" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# display(data.section_mode_argmax.value_counts())" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# transit = data.loc[data.section_mode_argmax == 'transit', :].copy()\n", - "# transit['section_duration_argmax'] /= 60.\n", - "\n", - "# transit['mph'] = transit['section_distance_argmax']/transit['section_duration_argmax']\n", - "\n", - "# display(transit[['section_duration_argmax', 'section_distance_argmax', 'mph']].describe())" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# import plotly.express as px\n", - "\n", - "# sp = data.loc[data.section_mode_argmax.isin(['car', 'transit', 'walking']), :]\n", - "# fig = px.line(sp, y='section_distance_argmax', color='section_mode_argmax')\n", - "# fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Close the figure above.\n", - "# plt.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "def get_duration_estimate(df: pd.DataFrame, dset: SPLIT, model_dict: dict):\n", - " \n", - " X_features = ['section_distance_argmax', 'age']\n", - " \n", - " if 'mph' in df.columns:\n", - " X_features += ['mph']\n", - " \n", - " if dset == SPLIT.TRAIN and model_dict is None:\n", - " model_dict = dict()\n", - " \n", - " if dset == SPLIT.TEST and model_dict is None:\n", - " raise AttributeError(\"Expected model dict for testing.\")\n", - " \n", - " if dset == SPLIT.TRAIN:\n", - " for section_mode in df.section_mode_argmax.unique():\n", - " section_data = df.loc[df.section_mode_argmax == section_mode, :]\n", - " if section_mode not in model_dict:\n", - " model_dict[section_mode] = dict()\n", - "\n", - " model = LinearRegression(fit_intercept=True)\n", - "\n", - " X = section_data[\n", - " X_features\n", - " ]\n", - " Y = section_data[['section_duration_argmax']]\n", - "\n", - " model.fit(X, Y.values.ravel())\n", - "\n", - " r2 = r2_score(y_pred=model.predict(X), y_true=Y.values.ravel())\n", - " print(f\"Train R2 for {section_mode}: {r2}\")\n", - "\n", - " model_dict[section_mode]['model'] = model\n", - " \n", - " elif dset == SPLIT.TEST:\n", - " for section_mode in df.section_mode_argmax.unique():\n", - " section_data = df.loc[df.section_mode_argmax == section_mode, :]\n", - " X = section_data[\n", - " X_features\n", - " ]\n", - " Y = section_data[['section_duration_argmax']]\n", - " \n", - " y_pred = model_dict[section_mode]['model'].predict(X)\n", - " r2 = r2_score(y_pred=y_pred, y_true=Y.values.ravel())\n", - " print(f\"Test R2 for {section_mode}: {r2}\")\n", - " \n", - " # Create the new columns for the duration.\n", - " new_columns = ['p_micro','no_trip','s_car','transit','car','s_micro','ridehail','walk','unknown']\n", - " df[new_columns] = 0\n", - " df['temp'] = 0\n", - " \n", - " for section in df.section_mode_argmax.unique():\n", - " X_section = df.loc[df.section_mode_argmax == section, X_features]\n", - " \n", - " # broadcast to all columns.\n", - " df.loc[df.section_mode_argmax == section, 'temp'] = model_dict[section]['model'].predict(X_section)\n", - " \n", - " for c in new_columns:\n", - " df[c] = df['av_' + c] * df['temp']\n", - " \n", - " df.drop(columns=['temp'], inplace=True)\n", - " \n", - " df.rename(columns=dict([(x, 'tt_'+x) for x in new_columns]), inplace=True)\n", - " \n", - " # return model_dict, result_df\n", - " return model_dict, df" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Now, we split the data.\n", - "train_data, test_data = get_train_test_splits(data=data, how=SPLIT_TYPE.INTER_USER)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7757575757575758\n", - "0.8380486968449932\n" - ] - } - ], - "source": [ - "# If split is inter-user, we should verify test size.\n", - "\n", - "n_tr, n_te = len(train_data.user_id.unique()), len(test_data.user_id.unique())\n", - "n_ex_tr, n_ex_te = train_data.shape[0], test_data.shape[0]\n", - "\n", - "print(n_tr/(n_tr+n_te))\n", - "print(n_ex_tr/(n_ex_tr+n_ex_te))" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['source', 'end_ts', 'end_fmt_time', 'end_loc', 'raw_trip', 'start_ts', 'start_fmt_time', 'start_loc', 'duration', 'distance', 'start_place', 'end_place', 'cleaned_trip', 'inferred_labels', 'inferred_trip', 'expectation', 'confidence_threshold', 'expected_trip', 'user_input', 'start:year', 'start:month', 'start:day', 'start:hour', 'start_local_dt_minute', 'start_local_dt_second', 'start_local_dt_weekday', 'start_local_dt_timezone', 'end:year', 'end:month', 'end:day', 'end:hour', 'end_local_dt_minute', 'end_local_dt_second', 'end_local_dt_weekday', 'end_local_dt_timezone', '_id', 'user_id', 'metadata_write_ts', 'additions', 'mode_confirm', 'purpose_confirm', 'distance_miles', 'Mode_confirm', 'Trip_purpose', 'original_user_id', 'program', 'opcode', 'Timestamp', 'birth_year', 'primary_job_commute_time', 'income_category', 'n_residence_members', 'n_residents_u18', 'n_residents_with_license', 'n_motor_vehicles', 'available_modes', 'age', 'gender_Man', 'gender_Man;Nonbinary/genderqueer/genderfluid', 'gender_Nonbinary/genderqueer/genderfluid', 'gender_Prefer not to say', 'gender_Woman', 'gender_Woman;Nonbinary/genderqueer/genderfluid', 'has_drivers_license_No', 'has_drivers_license_Prefer not to say', 'has_drivers_license_Yes', 'has_multiple_jobs_No', 'has_multiple_jobs_Prefer not to say', 'has_multiple_jobs_Yes', \"highest_education_Bachelor's degree\", 'highest_education_Graduate degree or professional degree', 'highest_education_High school graduate or GED', 'highest_education_Less than a high school graduate', 'highest_education_Prefer not to say', 'highest_education_Some college or associates degree', 'primary_job_type_Full-time', 'primary_job_type_Part-time', 'primary_job_type_Prefer not to say', 'primary_job_description_Clerical or administrative support', 'primary_job_description_Custodial', 'primary_job_description_Education', 'primary_job_description_Food service', 'primary_job_description_Manufacturing, construction, maintenance, or farming', 'primary_job_description_Medical/healthcare', 'primary_job_description_Other', 'primary_job_description_Professional, managerial, or technical', 'primary_job_description_Sales or service', 'primary_job_commute_mode_Active transport', 'primary_job_commute_mode_Car transport', 'primary_job_commute_mode_Hybrid', 'primary_job_commute_mode_Public transport', 'primary_job_commute_mode_Unknown', 'primary_job_commute_mode_WFH', 'is_overnight_trip', 'n_working_residents', 'start_lat', 'start_lng', 'end_lat', 'end_lng', 'temperature_2m (°F)', 'relative_humidity_2m (%)', 'dew_point_2m (°F)', 'rain (inch)', 'snowfall (inch)', 'wind_speed_10m (mp/h)', 'wind_gusts_10m (mp/h)', 'section_distance_argmax', 'section_duration_argmax', 'section_mode_argmax', 'section_coordinates_argmax', 'mph', 'target', 'av_s_micro', 'av_ridehail', 'av_unknown', 'av_car', 'av_transit', 'av_walk', 'av_s_car', 'av_no_trip', 'av_p_micro', 'cost_p_micro', 'cost_no_trip', 'cost_s_car', 'cost_transit', 'cost_car', 'cost_s_micro', 'cost_ridehail', 'cost_walk', 'cost_unknown']\n" - ] - } - ], - "source": [ - "print(train_data.columns.tolist())" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train R2 for bicycling: 0.9131217438269534\n", - "Train R2 for transit: 0.7868655990732462\n", - "Train R2 for car: 0.7673262331496208\n", - "Train R2 for no_sensed: 0.6274006368569693\n", - "Train R2 for walking: 0.6356687086293809\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[new_columns] = 0\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[new_columns] = 0\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[new_columns] = 0\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[new_columns] = 0\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[new_columns] = 0\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[new_columns] = 0\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[new_columns] = 0\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[new_columns] = 0\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[new_columns] = 0\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:49: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df['temp'] = 0\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:58: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[c] = df['av_' + c] * df['temp']\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:60: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df.drop(columns=['temp'], inplace=True)\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:62: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df.rename(columns=dict([(x, 'tt_'+x) for x in new_columns]), inplace=True)\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[new_columns] = 0\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[new_columns] = 0\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[new_columns] = 0\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[new_columns] = 0\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[new_columns] = 0\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[new_columns] = 0\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[new_columns] = 0\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[new_columns] = 0\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:48: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[new_columns] = 0\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:49: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df['temp'] = 0\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:58: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df[c] = df['av_' + c] * df['temp']\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:60: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df.drop(columns=['temp'], inplace=True)\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_76325/1465244600.py:62: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " df.rename(columns=dict([(x, 'tt_'+x) for x in new_columns]), inplace=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "----------\n", - "Test R2 for bicycling: 0.9035643287707561\n", - "Test R2 for transit: 0.41808445188600185\n", - "Test R2 for car: 0.729524725154663\n", - "Test R2 for no_sensed: 0.708585489321987\n", - "Test R2 for walking: 0.6801138679986298\n" - ] - } - ], - "source": [ - "params, train_data = get_duration_estimate(train_data, SPLIT.TRAIN, None)\n", - "print(10 * \"-\")\n", - "_, test_data = get_duration_estimate(test_data, SPLIT.TEST, params)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((58650, 139), (11334, 139))" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_data.shape, test_data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Some helper functions that will help ease redundancy in the code.\n", - "\n", - "def drop_columns(df: pd.DataFrame):\n", - " to_drop = [\n", - " 'source', 'end_ts', 'end_fmt_time', 'end_loc', 'raw_trip', 'start_ts', \n", - " 'start_fmt_time', 'start_loc', 'duration', 'distance', 'start_place', \n", - " 'end_place', 'cleaned_trip', 'inferred_labels', 'inferred_trip', 'expectation',\n", - " 'confidence_threshold', 'expected_trip', 'user_input', 'start:year', 'start:month', \n", - " 'start:day', 'start_local_dt_minute', 'start_local_dt_second', \n", - " 'start_local_dt_weekday', 'start_local_dt_timezone', 'end:year', 'end:month', 'end:day', \n", - " 'end_local_dt_minute', 'end_local_dt_second', 'end_local_dt_weekday', \n", - " 'end_local_dt_timezone', '_id', 'user_id', 'metadata_write_ts', 'additions', \n", - " 'mode_confirm', 'purpose_confirm', 'Mode_confirm', 'Trip_purpose', \n", - " 'original_user_id', 'program', 'opcode', 'Timestamp', 'birth_year', \n", - " 'available_modes', 'section_coordinates_argmax', 'section_mode_argmax'\n", - " ]\n", - " \n", - " # Drop section_mode_argmax and available_modes.\n", - " return df.drop(\n", - " columns=to_drop, \n", - " inplace=False\n", - " )\n", - "\n", - "\n", - "def scale_values(df: pd.DataFrame, split: SPLIT, scalers=None):\n", - " # Scale costs using StandardScaler.\n", - " costs = df[[c for c in df.columns if 'cost_' in c]].copy()\n", - " times = df[[c for c in df.columns if 'tt_' in c or 'duration' in c]].copy()\n", - " distances = df[[c for c in df.columns if 'distance' in c]]\n", - " \n", - " print(\n", - " \"Cost columns to be scaled: \", costs.columns,\"\\nTime columns to be scaled: \", times.columns, \\\n", - " \"\\nDistance columns to be scaled: \", distances.columns\n", - " )\n", - " \n", - " if split == SPLIT.TRAIN and scalers is None:\n", - " cost_scaler = StandardScaler()\n", - " tt_scaler = StandardScaler()\n", - " dist_scaler = StandardScaler()\n", - " \n", - " cost_scaled = pd.DataFrame(\n", - " cost_scaler.fit_transform(costs), \n", - " columns=costs.columns, \n", - " index=costs.index\n", - " )\n", - " \n", - " tt_scaled = pd.DataFrame(\n", - " tt_scaler.fit_transform(times),\n", - " columns=times.columns,\n", - " index=times.index\n", - " )\n", - " \n", - " dist_scaled = pd.DataFrame(\n", - " dist_scaler.fit_transform(distances),\n", - " columns=distances.columns,\n", - " index=distances.index\n", - " )\n", - " \n", - " elif split == SPLIT.TEST and scalers is not None:\n", - " \n", - " cost_scaler, tt_scaler, dist_scaler = scalers\n", - " \n", - " cost_scaled = pd.DataFrame(\n", - " cost_scaler.transform(costs), \n", - " columns=costs.columns, \n", - " index=costs.index\n", - " )\n", - " \n", - " tt_scaled = pd.DataFrame(\n", - " tt_scaler.transform(times), \n", - " columns=times.columns, \n", - " index=times.index\n", - " )\n", - " \n", - " dist_scaled = pd.DataFrame(\n", - " dist_scaler.transform(distances),\n", - " columns=distances.columns,\n", - " index=distances.index\n", - " )\n", - " \n", - " else:\n", - " raise NotImplementedError(\"Unknown split\")\n", - " \n", - " # Drop the original columns.\n", - " df.drop(\n", - " columns=costs.columns.tolist() + times.columns.tolist() + distances.columns.tolist(), \n", - " inplace=True\n", - " )\n", - " \n", - " df = df.merge(right=cost_scaled, left_index=True, right_index=True)\n", - " df = df.merge(right=tt_scaled, left_index=True, right_index=True)\n", - " df = df.merge(right=dist_scaled, left_index=True, right_index=True)\n", - " \n", - " return df, (cost_scaler, tt_scaler, dist_scaler)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# First, drop columns.\n", - "\n", - "train_data = drop_columns(train_data)\n", - "\n", - "# Scale cost.\n", - "# train_data, scalers = scale_values(train_data, SPLIT.TRAIN, None)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "test_data = drop_columns(test_data)\n", - "\n", - "# Scale cost.\n", - "# test_data, _ = scale_values(test_data, SPLIT.TEST, scalers)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index(['start:hour', 'end:hour', 'distance_miles', 'primary_job_commute_time',\n", - " 'income_category', 'n_residence_members', 'n_residents_u18',\n", - " 'n_residents_with_license', 'n_motor_vehicles', 'age', 'gender_Man',\n", - " 'gender_Man;Nonbinary/genderqueer/genderfluid',\n", - " 'gender_Nonbinary/genderqueer/genderfluid', 'gender_Prefer not to say',\n", - " 'gender_Woman', 'gender_Woman;Nonbinary/genderqueer/genderfluid',\n", - " 'has_drivers_license_No', 'has_drivers_license_Prefer not to say',\n", - " 'has_drivers_license_Yes', 'has_multiple_jobs_No',\n", - " 'has_multiple_jobs_Prefer not to say', 'has_multiple_jobs_Yes',\n", - " 'highest_education_Bachelor's degree',\n", - " 'highest_education_Graduate degree or professional degree',\n", - " 'highest_education_High school graduate or GED',\n", - " 'highest_education_Less than a high school graduate',\n", - " 'highest_education_Prefer not to say',\n", - " 'highest_education_Some college or associates degree',\n", - " 'primary_job_type_Full-time', 'primary_job_type_Part-time',\n", - " 'primary_job_type_Prefer not to say',\n", - " 'primary_job_description_Clerical or administrative support',\n", - " 'primary_job_description_Custodial',\n", - " 'primary_job_description_Education',\n", - " 'primary_job_description_Food service',\n", - " 'primary_job_description_Manufacturing, construction, maintenance, or farming',\n", - " 'primary_job_description_Medical/healthcare',\n", - " 'primary_job_description_Other',\n", - " 'primary_job_description_Professional, managerial, or technical',\n", - " 'primary_job_description_Sales or service',\n", - " 'primary_job_commute_mode_Active transport',\n", - " 'primary_job_commute_mode_Car transport',\n", - " 'primary_job_commute_mode_Hybrid',\n", - " 'primary_job_commute_mode_Public transport',\n", - " 'primary_job_commute_mode_Unknown', 'primary_job_commute_mode_WFH',\n", - " 'is_overnight_trip', 'n_working_residents', 'start_lat', 'start_lng',\n", - " 'end_lat', 'end_lng', 'temperature_2m (°F)', 'relative_humidity_2m (%)',\n", - " 'dew_point_2m (°F)', 'rain (inch)', 'snowfall (inch)',\n", - " 'wind_speed_10m (mp/h)', 'wind_gusts_10m (mp/h)',\n", - " 'section_distance_argmax', 'section_duration_argmax', 'mph', 'target',\n", - " 'av_s_micro', 'av_ridehail', 'av_unknown', 'av_car', 'av_transit',\n", - " 'av_walk', 'av_s_car', 'av_no_trip', 'av_p_micro', 'cost_p_micro',\n", - " 'cost_no_trip', 'cost_s_car', 'cost_transit', 'cost_car',\n", - " 'cost_s_micro', 'cost_ridehail', 'cost_walk', 'cost_unknown',\n", - " 'tt_p_micro', 'tt_no_trip', 'tt_s_car', 'tt_transit', 'tt_car',\n", - " 'tt_s_micro', 'tt_ridehail', 'tt_walk', 'tt_unknown'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "print(train_data.columns)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "8" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(train_data.target.unique())" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# train_data.to_csv('../data/train.csv', index=False)\n", - "# test_data.to_csv('../data/test.csv', index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import classification_report\n", - "from sklearn.model_selection import GridSearchCV, StratifiedKFold\n", - "from pprint import pprint\n", - "from sklearn.inspection import permutation_importance\n", - "from time import perf_counter" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Random Forest classifier" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "CV = False\n", - "SAVE_MODEL = True" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.ensemble import RandomForestClassifier\n", - "\n", - "# exp question - compute sample weights using user_id.\n", - "\n", - "rf_train = train_data.drop(columns=['target', \n", - " 'start_lat', 'start_lng', 'end_lat', 'end_lng'\n", - " ])\n", - "rf_test = test_data.drop(columns=['target', \n", - " 'start_lat', 'start_lng', 'end_lat', 'end_lng'\n", - " ])\n", - "\n", - "if CV:\n", - "\n", - " model = RandomForestClassifier(random_state=SEED)\n", - "\n", - " # We want to build bootstrapped trees that would not always use all the features.\n", - "\n", - " param_set2 = {\n", - " 'n_estimators': [150, 200, 250, 300],\n", - " 'min_samples_split': [2, 3, 4],\n", - " 'min_samples_leaf': [1, 2, 3],\n", - " 'class_weight': ['balanced_subsample'],\n", - " 'max_features': [None, 'sqrt'],\n", - " 'bootstrap': [True]\n", - " }\n", - "\n", - " cv_set2 = StratifiedKFold(n_splits=3, shuffle=True, random_state=SEED)\n", - "\n", - " clf_set2 = GridSearchCV(model, param_set2, cv=cv_set2, n_jobs=-1, scoring='f1_weighted', verbose=1)\n", - "\n", - " start = perf_counter()\n", - "\n", - " clf_set2.fit(\n", - " rf_train,\n", - " train_data.target.values.ravel()\n", - " )\n", - "\n", - " time_req = (perf_counter() - start)/60.\n", - "\n", - " best_model = clf_set2.best_estimator_\n", - "else:\n", - " best_model = RandomForestClassifier(\n", - " n_estimators=150,\n", - " max_depth=None,\n", - " min_samples_leaf=2,\n", - " bootstrap=True,\n", - " class_weight='balanced_subsample',\n", - " random_state=SEED,\n", - " n_jobs=-1\n", - " ).fit(rf_train, train_data.target.values.ravel())" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
RandomForestClassifier(class_weight='balanced_subsample', min_samples_leaf=2,\n",
-       "                       n_estimators=150, n_jobs=-1, random_state=19348)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "RandomForestClassifier(class_weight='balanced_subsample', min_samples_leaf=2,\n", - " n_estimators=150, n_jobs=-1, random_state=19348)" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "best_model" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "tr_f1_set2 = f1_score(\n", - " y_true=train_data.target.values,\n", - " y_pred=best_model.predict(rf_train),\n", - " average='weighted'\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "te_f1_set2 = f1_score(\n", - " y_true=test_data.target.values,\n", - " y_pred=best_model.predict(rf_test),\n", - " average='weighted'\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[BOOTSTRAPPED] | Train F1: 0.9429078702826239, Test F1: 0.3260387751184679\n" - ] - } - ], - "source": [ - "# Without location:\n", - "#. intra-user split:\n", - "# [BOOTSTRAPPED] | Train F1: 0.9983454261487021, Test F1: 0.7192048995905516\n", - "# if stratified by section_mode_argmax:\n", - "# [BOOTSTRAPPED] | Train F1: 0.9987250576328509, Test F1: 0.7242573620109232\n", - "\n", - "# With location:\n", - "# [BOOTSTRAPPED] | Train F1: 0.9992402006853468, Test F1: 0.7654135199070202\n", - "\n", - "print(f\"[BOOTSTRAPPED] | Train F1: {tr_f1_set2}, Test F1: {te_f1_set2}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "if SAVE_MODEL:\n", - "\n", - " import pickle\n", - "\n", - " with open('../models/tuned_rf_model.pkl', 'wb') as f:\n", - " f.write(pickle.dumps(best_model))" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[('primary_job_commute_time', 0.03656116744063983),\n", - " ('distance_miles', 0.03384896885706385),\n", - " ('mph', 0.03365824428294463),\n", - " ('cost_transit', 0.03351839132356644),\n", - " ('income_category', 0.03327663593086925),\n", - " ('age', 0.03320140844541059),\n", - " ('dew_point_2m (°F)', 0.03174850829935579),\n", - " ('section_distance_argmax', 0.0316844184072864),\n", - " ('temperature_2m (°F)', 0.031062635841221802),\n", - " ('n_motor_vehicles', 0.02809262054525041),\n", - " ('cost_s_car', 0.02590076039294508),\n", - " ('cost_ridehail', 0.02390316297823517),\n", - " ('section_duration_argmax', 0.023124663088762328),\n", - " ('n_residents_with_license', 0.022957697145547393),\n", - " ('tt_p_micro', 0.022799905074206473),\n", - " ('tt_transit', 0.02231532316999558),\n", - " ('relative_humidity_2m (%)', 0.02223392911205542),\n", - " ('tt_walk', 0.02156031560995313),\n", - " ('end:hour', 0.02127261764701738),\n", - " ('start:hour', 0.02113307125915479),\n", - " ('cost_car', 0.02069943868032831),\n", - " ('primary_job_commute_mode_Car transport', 0.020695999999399473),\n", - " ('n_residence_members', 0.02039769915552014),\n", - " ('wind_gusts_10m (mp/h)', 0.020371151744672206),\n", - " ('wind_speed_10m (mp/h)', 0.0202171294903448),\n", - " ('n_working_residents', 0.019856756085641247),\n", - " ('tt_s_car', 0.018478958407386708),\n", - " ('tt_car', 0.017174957078456614),\n", - " ('tt_ridehail', 0.01676576006316021),\n", - " ('cost_s_micro', 0.014284462066383688),\n", - " ('primary_job_description_Sales or service', 0.012984251205446487),\n", - " ('n_residents_u18', 0.011854045241279415),\n", - " ('tt_s_micro', 0.011691299016354224),\n", - " ('primary_job_commute_mode_Public transport', 0.011604917446563265),\n", - " ('primary_job_commute_mode_Active transport', 0.011563480793645827),\n", - " ('gender_Woman', 0.011263214031603247),\n", - " ('highest_education_High school graduate or GED', 0.010576049151755485),\n", - " ('highest_education_Some college or associates degree', 0.01020534553535622),\n", - " ('av_transit', 0.009289915096852685),\n", - " ('primary_job_type_Full-time', 0.009228727317286755),\n", - " ('primary_job_description_Clerical or administrative support',\n", - " 0.009038264768764997),\n", - " ('primary_job_type_Part-time', 0.008895929760766197),\n", - " ('av_p_micro', 0.00863641893397233),\n", - " ('gender_Man', 0.008452778636530149),\n", - " ('primary_job_description_Professional, managerial, or technical',\n", - " 0.008401553611939554),\n", - " ('has_drivers_license_Yes', 0.007324804786898662),\n", - " ('primary_job_description_Manufacturing, construction, maintenance, or '\n", - " 'farming',\n", - " 0.007086900844546432),\n", - " ('av_walk', 0.0065497221649774935),\n", - " ('primary_job_description_Education', 0.0062592730468462535),\n", - " ('has_drivers_license_No', 0.0061996065304084184),\n", - " (\"highest_education_Bachelor's degree\", 0.006032319692096047),\n", - " ('has_multiple_jobs_Yes', 0.005882443681020677),\n", - " ('has_multiple_jobs_No', 0.005335658287831361),\n", - " ('av_s_micro', 0.005050550371666917),\n", - " ('av_car', 0.004728243532015504),\n", - " ('highest_education_Graduate degree or professional degree',\n", - " 0.004642958375833981),\n", - " ('gender_Woman;Nonbinary/genderqueer/genderfluid', 0.00421330092341653),\n", - " ('primary_job_description_Custodial', 0.003419042865152697),\n", - " ('av_s_car', 0.0033783136347606295),\n", - " ('av_ridehail', 0.0027666404305310257),\n", - " ('gender_Nonbinary/genderqueer/genderfluid', 0.0026293269089904046),\n", - " ('highest_education_Prefer not to say', 0.002504632146302557),\n", - " ('primary_job_description_Food service', 0.002417226793032642),\n", - " ('primary_job_description_Medical/healthcare', 0.002318202110078709),\n", - " ('primary_job_type_Prefer not to say', 0.0018874661300219261),\n", - " ('tt_unknown', 0.0017189591933169184),\n", - " ('primary_job_description_Other', 0.0016913655476757901),\n", - " ('rain (inch)', 0.0014573980261950155),\n", - " ('has_drivers_license_Prefer not to say', 0.0012507320988533646),\n", - " ('primary_job_commute_mode_WFH', 0.0010339629984452642),\n", - " ('av_unknown', 0.0009490470975242473),\n", - " ('highest_education_Less than a high school graduate', 0.0009240966799280463),\n", - " ('has_multiple_jobs_Prefer not to say', 0.0009222062920680996),\n", - " ('snowfall (inch)', 0.0009015378230446266),\n", - " ('primary_job_commute_mode_Hybrid', 0.0008805253765039911),\n", - " ('gender_Man;Nonbinary/genderqueer/genderfluid', 0.0005723629022594494),\n", - " ('primary_job_commute_mode_Unknown', 0.0005385851614091888),\n", - " ('gender_Prefer not to say', 3.0396144453361146e-05),\n", - " ('tt_no_trip', 1.0172431126169753e-05),\n", - " ('av_no_trip', 9.070799875677394e-06),\n", - " ('is_overnight_trip', 0.0),\n", - " ('cost_p_micro', 0.0),\n", - " ('cost_no_trip', 0.0),\n", - " ('cost_walk', 0.0),\n", - " ('cost_unknown', 0.0)]\n" - ] - } - ], - "source": [ - "# Feature importances - gini entropy\n", - "\n", - "pprint(\n", - " sorted(\n", - " zip(\n", - " best_model.feature_names_in_, \n", - " best_model.feature_importances_\n", - " ), \n", - " key=lambda x: x[-1], reverse=True\n", - " )\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# importance = permutation_importance(\n", - "# best_model,\n", - "# rf_test,\n", - "# test_data.target.values,\n", - "# n_repeats=5,\n", - "# random_state=SEED,\n", - "# n_jobs=-1,\n", - "# scoring='f1_weighted'\n", - "# )" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "# pd.DataFrame(\n", - "# {\n", - "# 'feature names': test_data.columns.delete(\n", - "# test_data.columns.isin(['target'])\n", - "# ),\n", - "# 'imp_mean': importance.importances_mean, \n", - "# 'imp_std': importance.importances_std\n", - "# }\n", - "# ).sort_values(by=['imp_mean'], axis='rows', ascending=False).head(20)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "# fig, ax = plt.subplots(nrows=1, ncols=2)\n", - "y_pred = best_model.predict(rf_test)\n", - "pred_df = pd.DataFrame(\n", - " {\n", - " 'y_pred': y_pred.ravel(),\n", - " 'y_true': test_data.target.values.ravel()\n", - " }\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(7, 7))\n", - "cm = ConfusionMatrixDisplay.from_estimator(\n", - " best_model,\n", - " X=rf_test,\n", - " y=test_data[['target']],\n", - " ax=ax\n", - ")\n", - "# ax.set_xticklabels(TARGETS, rotation=45)\n", - "# ax.set_yticklabels(TARGETS)\n", - "fig.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 1 0.71 0.73 0.72 1087\n", - " 2 0.78 0.74 0.76 3358\n", - " 3 0.61 0.59 0.60 1690\n", - " 4 0.53 0.61 0.57 565\n", - " 5 0.79 0.76 0.77 4288\n", - " 6 0.57 0.65 0.61 348\n", - " 7 0.65 0.73 0.69 1351\n", - " 8 0.77 0.82 0.79 1310\n", - "\n", - " accuracy 0.73 13997\n", - " macro avg 0.68 0.70 0.69 13997\n", - "weighted avg 0.73 0.73 0.73 13997\n", - "\n" - ] - } - ], - "source": [ - "# print(classification_report(y_true=pred_df.y_true, y_pred=pred_df.y_pred, target_names=TARGETS))\n", - "print(classification_report(y_true=pred_df.y_true, y_pred=pred_df.y_pred))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## XGBoost" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# from sklearn.utils.class_weight import compute_sample_weight\n", - "\n", - "# sample_weights = compute_sample_weight(class_weight='balanced', y=train_data.user_id.values.ravel())" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "# from xgboost import XGBClassifier\n", - "\n", - "# y_train = train_data.target.values.ravel() - 1\n", - "# y_test = test_data.target.values.ravel() - 1\n", - "\n", - "# # weights = compute_class_weight(class_weight='balanced', classes=np.unique(y_pred), y_pred)\n", - "\n", - "# xgm = XGBClassifier(\n", - "# n_estimators=300,\n", - "# max_depth=None,\n", - "# tree_method='hist',\n", - "# objective='multi:softmax',\n", - "# num_class=9\n", - "# ).fit(rf_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "# preds = xgm.predict(rf_test)\n", - "\n", - "# print(classification_report(y_true=y_test, y_pred=preds, target_names=TARGETS))" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "# import pickle\n", - "\n", - "# # RF_RM.pkl = 0.8625 on test.\n", - "# # RF_RM_1.pkl = 0.77 on test.\n", - "# with open('../models/RF_RM_1.pkl', 'wb') as f:\n", - "# f.write(pickle.dumps(model))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## TODO:\n", - "\n", - "\n", - "- Explain why location might not be a good feature to add (plot start and end on map and explain how model might just overfit to the raw coordinates)\n", - "- Merge `unknown` and `no_trip` into one category and validate against models trained on (a) separate labels (b) dropped labels\n", - "- Explore more of the abnormal `walking` trips" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "interpreter": { - "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" - }, - "kernelspec": { - "display_name": "emission", - "language": "python", - "name": "emission" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/rm_src/benchmark_analysis.ipynb b/rm_src/benchmark_analysis.ipynb deleted file mode 100644 index 104dd51..0000000 --- a/rm_src/benchmark_analysis.ipynb +++ /dev/null @@ -1,381 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "269547d8", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "28343f2b", - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_csv('../data/FULL_preprocessed_data_RM_weather.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "fa60582f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "E-bike 20029\n", - "Gas Car, drove alone 18412\n", - "Gas Car, with others 16684\n", - "Walk 10857\n", - "Not a Trip 2420\n", - "Regular Bike 1703\n", - "Bus 1363\n", - "Other 1145\n", - "Taxi/Uber/Lyft 310\n", - "Bikeshare 84\n", - "Train 67\n", - "Scooter share 52\n", - "Free Shuttle 50\n", - "Skate board 41\n", - "Name: Mode_confirm, dtype: int64" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.Mode_confirm.value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "7e380c56", - "metadata": {}, - "outputs": [], - "source": [ - "# hyp = df[df.Mode_confirm == 'E-bike']\n", - "hyp = df.copy()\n", - "hyp = hyp[['income_category', 'n_motor_vehicles',\n", - " 'n_residence_members', 'n_residents_u18', 'is_student',\n", - " 'n_residents_with_license', 'duration', 'distance_miles',\n", - " 'age', 'is_overnight_trip', 'n_working_residents', 'is_male',\n", - " 'start:sin_HOD', \n", - " 'start:cos_HOD',\n", - " 'end:sin_HOD', 'end:cos_HOD', 'temperature_2m (°F)',\n", - " 'relative_humidity_2m (%)', 'dew_point_2m (°F)', 'rain (inch)',\n", - " 'snowfall (inch)', 'cloud_cover (%)', 'wind_speed_10m (mp/h)',\n", - " 'wind_gusts_10m (mp/h)', 'section_distance_argmax',\n", - " 'section_duration_argmax', 'mph', 'chosen', 'av_no_trip', 'av_s_car',\n", - " 'av_p_micro', 'av_walk', 'av_s_micro', 'av_transit', 'av_ridehail',\n", - " 'av_car', 'av_unknown', 'cost_p_micro', 'cost_no_trip', 'cost_s_car',\n", - " 'cost_transit', 'cost_car', 'cost_s_micro', 'cost_ridehail',\n", - " 'cost_walk', 'cost_unknown']]" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "77e331a5", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import f1_score\n", - "from sklearn.ensemble import RandomForestClassifier" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "1ccfe253", - "metadata": {}, - "outputs": [], - "source": [ - "X_tr, X_te, Y_tr, Y_te = train_test_split(\n", - " hyp.drop(columns=['chosen']), hyp[['chosen']], test_size=0.2, shuffle=True, stratify=hyp[['chosen']]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "2ea90f37", - "metadata": {}, - "outputs": [], - "source": [ - "model = RandomForestClassifier().fit(X_tr, Y_tr.values.ravel())" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "46c50e0d", - "metadata": {}, - "outputs": [], - "source": [ - "y_tr_pred = model.predict(X_tr)\n", - "y_te_pred = model.predict(X_te)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "77f9f005", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "f1_score(y_true=Y_tr.values.ravel(), y_pred=y_tr_pred, average='weighted')" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "c08ccd5a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.7247243014745804" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "f1_score(y_true=Y_te.values.ravel(), y_pred=y_te_pred, average='weighted')" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "bb5be29d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[('age', 0.07034490454052576), ('mph', 0.046077458861916464), ('distance_miles', 0.04227922185622416), ('income_category', 0.042045300984451986), ('dew_point_2m (°F)', 0.040326971736931075), ('temperature_2m (°F)', 0.04026013502695752), ('n_residence_members', 0.04011768641381229), ('duration', 0.038452743580322295), ('section_distance_argmax', 0.038170155240570436), ('cost_transit', 0.03781163632826536), ('section_duration_argmax', 0.035462758618913716), ('cost_s_car', 0.034390094889762884), ('n_motor_vehicles', 0.03379247604608051), ('n_residents_with_license', 0.03070869700490467), ('cost_ridehail', 0.03048846642958717), ('relative_humidity_2m (%)', 0.030090075033090597), ('wind_gusts_10m (mp/h)', 0.029081664383436266), ('n_working_residents', 0.028816144221162413), ('wind_speed_10m (mp/h)', 0.02858556819484081), ('cloud_cover (%)', 0.02495871571321774), ('cost_car', 0.023892323875139673), ('end:cos_HOD', 0.023765393583672044), ('start:cos_HOD', 0.023437391395931484), ('start:sin_HOD', 0.022230720826376518), ('end:sin_HOD', 0.02207708217592035), ('n_residents_u18', 0.021771464179907447), ('is_male', 0.02009990945010731), ('cost_s_micro', 0.01639305932751749), ('av_p_micro', 0.015927295338181373), ('av_walk', 0.012539972617736221), ('av_transit', 0.011339815089503233), ('is_student', 0.010405977662256977), ('av_s_micro', 0.007322014585269799), ('av_car', 0.006833582392128755), ('av_ridehail', 0.006552208955349027), ('av_s_car', 0.006497932334664742), ('rain (inch)', 0.0026762209888453866), ('av_unknown', 0.0016465804016971863), ('snowfall (inch)', 0.0016132591409545494), ('av_no_trip', 0.0007168051104359397), ('is_overnight_trip', 1.1546343053889719e-07), ('cost_p_micro', 0.0), ('cost_no_trip', 0.0), ('cost_walk', 0.0), ('cost_unknown', 0.0)]\n" - ] - } - ], - "source": [ - "print(\n", - " sorted(list(zip(model.feature_names_in_, model.feature_importances_)), key=lambda x: x[-1], reverse=True)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "7c886ef1", - "metadata": {}, - "outputs": [], - "source": [ - "survey_data = pd.read_csv('../viz_scripts/Can Do Colorado eBike Program - en.csv')\n", - "df = pd.read_csv('../data/FULL_preprocessed_data_RM_weather.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "88434a04", - "metadata": {}, - "outputs": [], - "source": [ - "# column renaming here!\n", - "\n", - "survey_data.rename(\n", - " {\n", - " \"Unique User ID (auto-filled, do not edit)\": \"user_id\",\n", - " \"In which year were you born?\": \"birth_year\",\n", - " \"What is your gender?\": \"gender\",\n", - " \"Do you have a valid driver's license?\": \"has_drivers_license\",\n", - " \"Are you a student?\": \"is_student\",\n", - " \"What is the highest grade or degree that you have completed?\": \"highest_education\",\n", - " \"Do you work for either pay or profit?\": \"is_paid\",\n", - " \"Do you have more than one job?\": \"has_multiple_jobs\",\n", - " \"Do you work full-time or part-time at your primary job?\": \"primary_job_type\",\n", - " \"Which best describes your primary job?\": \"primary_job_description\",\n", - " \"How did you usually get to your primary job last week? \": \"primary_job_commute_mode\",\n", - " \"Thinking about your daily commute to work last week, how many minutes did it usually take to get from home to the primary job/work place?\": \"primary_job_commute_time\",\n", - " \"At your primary job, do you have the ability to set or change your own start time?\": \"is_primary_job_flexible\",\n", - " \"Do you have the option of working from home or an alternate location instead of going into your primary work place?\": \"primary_job_can_wfh\",\n", - " \"How many days per week do you usually work from home or an alternate location?\": \"wfh_days\",\n", - " \"Do you own or rent your place of residence?\": \"residence_ownership_type\",\n", - " \"What is your home type?\": \"residence_type\",\n", - " \"Please identify which category represents your total household income, before taxes, for last year.\": \"income_category\",\n", - " \"Including yourself, how many people live in your home?\": \"n_residence_members\",\n", - " \"How many children under age 18 live in your home?\": \"n_residents_u18\",\n", - " \"Including yourself, how many people have a driver's license in your household?\": \"n_residents_with_license\",\n", - " \"How many motor vehicles are owned, leased, or available for regular use by the people who currently live in your household?\": \"n_motor_vehicles\",\n", - " \"If you were unable to use your household vehicle(s), which of the following options would be available to you to get you from place to place?\": \"available_modes\",\n", - " \"Do you have a medical condition that makes it difficult to travel outside of the home?\": \"has_medical_condition\",\n", - " \"How long have you had this condition?\": \"medical_condition_duration\"\n", - " },\n", - " axis='columns',\n", - " inplace=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "48a2ece6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 a2d48b05d5454d428c0841432c7467b6\n", - "1 f2799dc202bc4249b42a4fda8770d1b6\n", - "2 b2bbe715b6a14fd19f751cae8adf6b4e\n", - "3 6373dfb8cb9b47e88e8f76adcfadde20\n", - "4 93c6e0f156a44e07b920ded664419dc6\n", - "Name: user_id, dtype: object" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(survey_data.user_id.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "f88441fe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 6373dfb8cb9b47e88e8f76adcfadde20\n", - "1 6373dfb8cb9b47e88e8f76adcfadde20\n", - "2 6373dfb8cb9b47e88e8f76adcfadde20\n", - "3 6373dfb8cb9b47e88e8f76adcfadde20\n", - "4 6373dfb8cb9b47e88e8f76adcfadde20\n", - "Name: user_id, dtype: object" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(df.user_id.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "2af9b36f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "203" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(survey_data.user_id.unique())" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "de46b68c", - "metadata": {}, - "outputs": [], - "source": [ - "f_survey = survey_data.loc[survey_data.user_id.isin(df.user_id), :]" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "9b77fd2f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "156" - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(f_survey.user_id.unique())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e23fa31b", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "emission", - "language": "python", - "name": "emission" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/rm_src/cost_time_avl_preprocessing.ipynb b/rm_src/cost_time_avl_preprocessing.ipynb deleted file mode 100644 index 9ed8ed4..0000000 --- a/rm_src/cost_time_avl_preprocessing.ipynb +++ /dev/null @@ -1,1865 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import ast\n", - "from traceback import print_exception\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_62467/3166671539.py:3: DtypeWarning: Columns (40) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " df = pd.read_csv('../data/final_modeling_data_02142024.csv')\n" - ] - } - ], - "source": [ - "# Data loading. Refer to the mnl_exploration.ipynb file for details on how user-level features are generated.\n", - "\n", - "df = pd.read_csv('../data/final_modeling_data_02142024.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape: (80691, 103)\n" - ] - } - ], - "source": [ - "print(\"Shape: \", df.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "df.drop_duplicates(inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Add weather info" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "df.start_fmt_time = pd.to_datetime(df.start_fmt_time, utc=True).dt.tz_convert('America/Denver')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(Timestamp('2020-11-02 18:48:13.450000-0700', tz='America/Denver'),\n", - " Timestamp('2022-12-30 23:33:27.147785-0700', tz='America/Denver'))" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# We now need weather data. To get that, we download data from OpenMeteo for the given dates.\n", - "df.start_fmt_time.min(), df.start_fmt_time.max()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Downloaded using OpenMeteo API.\n", - "weather_df = pd.read_csv('../data/denver_weather_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "weather_df.time = pd.to_datetime(weather_df.time, utc=True).dt.tz_convert('America/Denver')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "original_shape = df.shape[0]\n", - "\n", - "# Round start time to nearest hour.\n", - "df['start_time_rounded'] = df.start_fmt_time.dt.round(\n", - " 'H', ambiguous='infer', nonexistent='shift_backward'\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# True intersection\n", - "df = df.merge(right=weather_df, left_on='start_time_rounded', right_on='time', how='left')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Drop rows where weather data is NA.\n", - "df.dropna(subset=['temperature_2m (°F)','relative_humidity_2m (%)', 'dew_point_2m (°F)', 'rain (inch)',\n", - " 'snowfall (inch)', 'wind_speed_10m (mp/h)', 'wind_gusts_10m (mp/h)'], inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Old shape: 80691. new shape: 80689\n" - ] - } - ], - "source": [ - "print(f\"Old shape: {original_shape}. new shape: {df.shape[0]}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "df.drop(columns=['start_time_rounded', 'time'], inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating the dummy variables for the available modes" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'source': 0,\n", - " 'end_ts': 0,\n", - " 'end_fmt_time': 0,\n", - " 'end_loc': 0,\n", - " 'raw_trip': 0,\n", - " 'start_ts': 0,\n", - " 'start_fmt_time': 0,\n", - " 'start_loc': 0,\n", - " 'duration': 0,\n", - " 'distance': 0,\n", - " 'start_place': 0,\n", - " 'end_place': 0,\n", - " 'cleaned_trip': 0,\n", - " 'inferred_labels': 217,\n", - " 'inferred_trip': 217,\n", - " 'expectation': 217,\n", - " 'confidence_threshold': 217,\n", - " 'expected_trip': 217,\n", - " 'user_input': 0,\n", - " 'section_modes': 0,\n", - " 'section_distances': 0,\n", - " 'start:year': 0,\n", - " 'start:month': 0,\n", - " 'start:day': 0,\n", - " 'start:hour': 0,\n", - " 'start_local_dt_minute': 0,\n", - " 'start_local_dt_second': 0,\n", - " 'start_local_dt_weekday': 0,\n", - " 'start_local_dt_timezone': 0,\n", - " 'end:year': 0,\n", - " 'end:month': 0,\n", - " 'end:day': 0,\n", - " 'end:hour': 0,\n", - " 'end_local_dt_minute': 0,\n", - " 'end_local_dt_second': 0,\n", - " 'end_local_dt_weekday': 0,\n", - " 'end_local_dt_timezone': 0,\n", - " '_id': 0,\n", - " 'user_id': 0,\n", - " 'metadata_write_ts': 0,\n", - " 'additions': 80687,\n", - " 'mode_confirm': 337,\n", - " 'purpose_confirm': 527,\n", - " 'distance_miles': 0,\n", - " 'Mode_confirm': 0,\n", - " 'Replaced_mode': 0,\n", - " 'Trip_purpose': 0,\n", - " 'original_user_id': 0,\n", - " 'program': 0,\n", - " 'opcode': 0,\n", - " 'Timestamp': 0,\n", - " 'birth_year': 0,\n", - " 'primary_job_commute_time': 0,\n", - " 'income_category': 0,\n", - " 'n_residence_members': 0,\n", - " 'n_residents_u18': 0,\n", - " 'n_residents_with_license': 0,\n", - " 'n_motor_vehicles': 0,\n", - " 'available_modes': 0,\n", - " 'age': 0,\n", - " 'gender_Man': 0,\n", - " 'gender_Man;Nonbinary/genderqueer/genderfluid': 0,\n", - " 'gender_Nonbinary/genderqueer/genderfluid': 0,\n", - " 'gender_Prefer not to say': 0,\n", - " 'gender_Woman': 0,\n", - " 'gender_Woman;Nonbinary/genderqueer/genderfluid': 0,\n", - " 'has_drivers_license_No': 0,\n", - " 'has_drivers_license_Prefer not to say': 0,\n", - " 'has_drivers_license_Yes': 0,\n", - " 'has_multiple_jobs_No': 0,\n", - " 'has_multiple_jobs_Prefer not to say': 0,\n", - " 'has_multiple_jobs_Yes': 0,\n", - " \"highest_education_Bachelor's degree\": 0,\n", - " 'highest_education_Graduate degree or professional degree': 0,\n", - " 'highest_education_High school graduate or GED': 0,\n", - " 'highest_education_Less than a high school graduate': 0,\n", - " 'highest_education_Prefer not to say': 0,\n", - " 'highest_education_Some college or associates degree': 0,\n", - " 'primary_job_type_Full-time': 0,\n", - " 'primary_job_type_Part-time': 0,\n", - " 'primary_job_type_Prefer not to say': 0,\n", - " 'primary_job_description_Clerical or administrative support': 0,\n", - " 'primary_job_description_Custodial': 0,\n", - " 'primary_job_description_Education': 0,\n", - " 'primary_job_description_Food service': 0,\n", - " 'primary_job_description_Manufacturing, construction, maintenance, or farming': 0,\n", - " 'primary_job_description_Medical/healthcare': 0,\n", - " 'primary_job_description_Other': 0,\n", - " 'primary_job_description_Professional, managerial, or technical': 0,\n", - " 'primary_job_description_Sales or service': 0,\n", - " 'primary_job_commute_mode_Active transport': 0,\n", - " 'primary_job_commute_mode_Car transport': 0,\n", - " 'primary_job_commute_mode_Hybrid': 0,\n", - " 'primary_job_commute_mode_Public transport': 0,\n", - " 'primary_job_commute_mode_Unknown': 0,\n", - " 'primary_job_commute_mode_WFH': 0,\n", - " 'is_overnight_trip': 0,\n", - " 'n_working_residents': 0,\n", - " 'start_lat': 0,\n", - " 'start_lng': 0,\n", - " 'end_lat': 0,\n", - " 'end_lng': 0,\n", - " 'section_durations': 0,\n", - " 'temperature_2m (°F)': 0,\n", - " 'relative_humidity_2m (%)': 0,\n", - " 'dew_point_2m (°F)': 0,\n", - " 'rain (inch)': 0,\n", - " 'snowfall (inch)': 0,\n", - " 'wind_speed_10m (mp/h)': 0,\n", - " 'wind_gusts_10m (mp/h)': 0}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.isna().sum().to_dict()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# | [d1, d2, d3] | [t1, t2, t3] | [m1, m2, m3] |\n", - "\n", - "\n", - "def compute_argmax(df: pd.DataFrame):\n", - " # Create the two columns.\n", - " df[['section_distance_argmax', 'section_duration_argmax', 'section_mode_argmax', 'section_coordinates_argmax']] = None\n", - " rows = list()\n", - "\n", - " for ix, row in df.iterrows():\n", - " row_dict = row.to_dict()\n", - " \n", - " try:\n", - " parsed_distances = ast.literal_eval(row_dict['section_distances'])\n", - " parsed_durations = ast.literal_eval(row_dict['section_durations'])\n", - " parsed_modes = ast.literal_eval(row_dict['section_modes'])\n", - "\n", - " argmax_ix = np.argmax(parsed_distances)\n", - " \n", - " # Pick the argmax and scale to miles. (meters -> miles)\n", - " row_dict['section_distance_argmax'] = parsed_distances[argmax_ix] * 0.0006213712\n", - " \n", - " # Pick the argmax and scale to minutes.\n", - " row_dict['section_duration_argmax'] = parsed_durations[argmax_ix] / 60\n", - " \n", - " # Pick the argmax mode.\n", - " row_dict['section_mode_argmax'] = parsed_modes[argmax_ix]\n", - " \n", - " row_dict['mark'] = False\n", - "\n", - " except Exception as e:\n", - " row_dict['mark'] = True\n", - "\n", - " rows.append(row_dict)\n", - "\n", - " return pd.DataFrame(rows)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Let's plot the mode-wise durations as a function of distance.\n", - "df = compute_argmax(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "df.drop(columns=['section_distances', 'section_durations', 'section_modes'], inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "df = df.loc[~df.mark, :].reset_index(drop=True, inplace=False).drop(columns=['mark'], inplace=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Drop instances where duration/distance is unusable.\n", - "df.drop(\n", - " index=df.loc[(df.section_distance_argmax <= 0) | (df.section_duration_argmax <= 0), :].index,\n", - " inplace=False\n", - ").reset_index(drop=True, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# bus, train, bicycling, walking, car\n", - "# split-apply-combine\n", - "def drop_outliers(df: pd.DataFrame, low=0.1, high=0.9) -> pd.DataFrame:\n", - " def filter_by_percentiles(group):\n", - " distance_low = group['section_distance_argmax'].quantile(low)\n", - " distance_high = group['section_distance_argmax'].quantile(high)\n", - " duration_low = group['section_duration_argmax'].quantile(low)\n", - " duration_high = group['section_duration_argmax'].quantile(high)\n", - " \n", - " l1_filter = group[\n", - " (group['section_distance_argmax'] >= distance_low) &\n", - " (group['section_distance_argmax'] <= distance_high)\n", - " ].reset_index(drop=True)\n", - " \n", - " l2_filter = l1_filter[\n", - " (l1_filter['section_duration_argmax'] >= duration_low) &\n", - " (l1_filter['section_duration_argmax'] <= duration_high)\n", - " ].reset_index(drop=True)\n", - " \n", - " return l2_filter\n", - " \n", - " return df.groupby('section_mode_argmax').apply(filter_by_percentiles).reset_index(drop=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "filtered_df = drop_outliers(df, low=0.01, high=0.99)\n", - "\n", - "# Ideal speed. distance/time.\n", - "filtered_df['mph'] = (\n", - " (filtered_df['section_distance_argmax'] * 60.)/filtered_df['section_duration_argmax']\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "def filter_mph(df: pd.DataFrame, low=0.1, high=0.9) -> pd.DataFrame:\n", - " \n", - " MPH_THRESHOLDS = {\n", - " # https://www.sciencedirect.com/science/article/pii/S2210670718304682\n", - " 'bicycling': 15.,\n", - " # https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806575/\n", - " 'walking': 2.93\n", - " }\n", - " \n", - " def custom_filter(group):\n", - " # Drop data specified in the dict manually.\n", - " if group.name in MPH_THRESHOLDS.keys():\n", - " f_df = group[group['mph'] <= MPH_THRESHOLDS[group.name]]\n", - " else:\n", - " mph_low = group['mph'].quantile(low)\n", - " mph_high = group['mph'].quantile(high)\n", - "\n", - " f_df = group[(group['mph'] >= mph_low) & (group['mph'] <= mph_high)]\n", - " \n", - " return f_df\n", - " \n", - " return df.groupby('section_mode_argmax').apply(custom_filter).reset_index(drop=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "filtered_df = filter_mph(filtered_df, low=0.01, high=0.99)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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section_distance_argmaxsection_duration_argmax
countmeanstdmin25%50%75%maxcountmeanstdmin25%50%75%max
section_mode_argmax
bicycling9330.02.3445441.5918890.2375421.3568891.9442322.84951111.0639289330.015.47328610.2903711.9445019.03576612.60526818.45200373.866648
bus478.01.5522301.4132800.0907730.6462641.2944771.96049311.592445478.010.0174397.3021100.5182575.8019838.16453012.26903959.147400
car40885.05.9405427.9846880.1961411.7135573.3460606.76201374.46435240885.016.71742713.2743852.0895678.29183912.63890120.472570110.945333
no_sensed2730.04.6353807.4772240.1038540.7519981.9925924.91712071.8569712730.021.32137926.6386770.2629415.85108212.44090226.501255215.273083
subway9.022.42263712.1751413.25961114.51015123.58765527.75658342.8919059.043.06526923.00917313.10361627.56275934.40000058.71400081.366026
train96.014.30393613.0792180.8863428.64742910.57257615.51667067.98700996.030.68719517.1348546.33244220.76354925.69510640.19015888.009998
walking16608.00.5455240.7196910.0712370.1730100.3097770.6048848.70644316608.027.73044935.4896952.2836178.55144715.59037631.206353295.377052
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" - ], - "text/plain": [ - " section_distance_argmax \\\n", - " count mean std min \n", - "section_mode_argmax \n", - "bicycling 9330.0 2.344544 1.591889 0.237542 \n", - "bus 478.0 1.552230 1.413280 0.090773 \n", - "car 40885.0 5.940542 7.984688 0.196141 \n", - "no_sensed 2730.0 4.635380 7.477224 0.103854 \n", - "subway 9.0 22.422637 12.175141 3.259611 \n", - "train 96.0 14.303936 13.079218 0.886342 \n", - "walking 16608.0 0.545524 0.719691 0.071237 \n", - "\n", - " \\\n", - " 25% 50% 75% max \n", - "section_mode_argmax \n", - "bicycling 1.356889 1.944232 2.849511 11.063928 \n", - "bus 0.646264 1.294477 1.960493 11.592445 \n", - "car 1.713557 3.346060 6.762013 74.464352 \n", - "no_sensed 0.751998 1.992592 4.917120 71.856971 \n", - "subway 14.510151 23.587655 27.756583 42.891905 \n", - "train 8.647429 10.572576 15.516670 67.987009 \n", - "walking 0.173010 0.309777 0.604884 8.706443 \n", - "\n", - " section_duration_argmax \\\n", - " count mean std min \n", - "section_mode_argmax \n", - "bicycling 9330.0 15.473286 10.290371 1.944501 \n", - "bus 478.0 10.017439 7.302110 0.518257 \n", - "car 40885.0 16.717427 13.274385 2.089567 \n", - "no_sensed 2730.0 21.321379 26.638677 0.262941 \n", - "subway 9.0 43.065269 23.009173 13.103616 \n", - "train 96.0 30.687195 17.134854 6.332442 \n", - "walking 16608.0 27.730449 35.489695 2.283617 \n", - "\n", - " \n", - " 25% 50% 75% max \n", - "section_mode_argmax \n", - "bicycling 9.035766 12.605268 18.452003 73.866648 \n", - "bus 5.801983 8.164530 12.269039 59.147400 \n", - "car 8.291839 12.638901 20.472570 110.945333 \n", - "no_sensed 5.851082 12.440902 26.501255 215.273083 \n", - "subway 27.562759 34.400000 58.714000 81.366026 \n", - "train 20.763549 25.695106 40.190158 88.009998 \n", - "walking 8.551447 15.590376 31.206353 295.377052 " - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "filtered_df.groupby('section_mode_argmax')[['section_distance_argmax', 'section_duration_argmax']].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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mph
countmeanstdmin25%50%75%max
section_mode_argmax
bicycling9330.09.2473912.5968762.5792017.3631369.29994011.14917414.998472
bus478.09.8021044.9857440.5411395.8717609.73236713.05967228.627234
car40885.018.58825710.3420231.64471911.09961516.53874024.23853355.344377
no_sensed2730.013.0426749.2094853.8911096.28956610.04212116.65885853.570005
subway9.031.47696012.52411314.92539825.30840427.72687535.93073059.570373
train96.025.33505010.2497564.68230021.23773924.78125728.72312862.081000
walking16608.01.4947370.8070070.0225500.8041961.4750822.1925422.929953
\n", - "
" - ], - "text/plain": [ - " mph \\\n", - " count mean std min 25% \n", - "section_mode_argmax \n", - "bicycling 9330.0 9.247391 2.596876 2.579201 7.363136 \n", - "bus 478.0 9.802104 4.985744 0.541139 5.871760 \n", - "car 40885.0 18.588257 10.342023 1.644719 11.099615 \n", - "no_sensed 2730.0 13.042674 9.209485 3.891109 6.289566 \n", - "subway 9.0 31.476960 12.524113 14.925398 25.308404 \n", - "train 96.0 25.335050 10.249756 4.682300 21.237739 \n", - "walking 16608.0 1.494737 0.807007 0.022550 0.804196 \n", - "\n", - " \n", - " 50% 75% max \n", - "section_mode_argmax \n", - "bicycling 9.299940 11.149174 14.998472 \n", - "bus 9.732367 13.059672 28.627234 \n", - "car 16.538740 24.238533 55.344377 \n", - "no_sensed 10.042121 16.658858 53.570005 \n", - "subway 27.726875 35.930730 59.570373 \n", - "train 24.781257 28.723128 62.081000 \n", - "walking 1.475082 2.192542 2.929953 " - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "filtered_df.groupby('section_mode_argmax')[['mph']].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dropped 10460 rows.\n" - ] - } - ], - "source": [ - "print(f\"Dropped {df.shape[0] - filtered_df.shape[0]} rows.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.12963353121243293" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(80689 - 70229)/80689" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Regular Bike', 'Other', 'No Travel', 'Gas Car, drove alone',\n", - " 'Gas Car, with others', 'Walk', 'Taxi/Uber/Lyft', 'Train',\n", - " 'Bikeshare', 'Bus', 'Skate board', 'Free Shuttle', 'Scooter share',\n", - " 'E-bike', 'Not a Trip'], dtype=object)" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "filtered_df.Replaced_mode.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'p_micro': 1, 'no_trip': 2, 's_car': 3, 'transit': 4, 'car': 5, 's_micro': 6, 'ridehail': 7, 'walk': 8, 'unknown': 9}\n" - ] - } - ], - "source": [ - "## Define the mapping strategy\n", - "\n", - "\"\"\"\n", - "p_micro: Personal micromobility\n", - "s_micro: Shared micromobility\n", - "s_car: Shared car\n", - "car: Car/rental car\n", - "transit: train + bus\n", - "no_trip: No vehicle / None\n", - "ridehail: Uber/Lyft\n", - "\"\"\"\n", - "\n", - "available = {\n", - " 'Bicycle': 'p_micro',\n", - " 'Do not have vehicle': 'unknown',\n", - " 'Get a ride from a friend or family member': 's_car',\n", - " 'None': 'no_trip',\n", - " 'Public transportation (bus, subway, light rail, etc.)': 'transit',\n", - " 'Rental car (including Zipcar/ Car2Go)': 'car',\n", - " 'Shared bicycle or scooter': 's_micro',\n", - " 'Skateboard': 'p_micro',\n", - " 'Taxi (regular taxi, Uber, Lyft, etc)': 'ridehail',\n", - " 'Walk/roll': 'walk',\n", - " 'Prefer not to say': 'unknown'\n", - "}\n", - "\n", - "# Map each sensed mode to the binary indicators.\n", - "section_mode_mapping = {\n", - " 'bicycling': ['p_micro', 's_micro'],\n", - " 'bus': ['transit'],\n", - " 'car': ['s_car', 'car', 'ridehail'],\n", - " 'no_sensed': ['unknown'],\n", - " 'train': ['transit'],\n", - " 'walking': ['walk'],\n", - " 'subway': ['transit']\n", - "}\n", - "\n", - "# For target - mode_confirm\n", - "# target_mapping = {\n", - "# 'Regular Bike': 'p_micro',\n", - "# 'Walk': 'walk',\n", - "# 'Gas Car, with others': 's_car',\n", - "# 'Gas Car, drove alone': 'car',\n", - "# 'Bikeshare': 's_micro',\n", - "# 'Other': 'unknown',\n", - "# 'Bus': 'transit',\n", - "# 'Not a Trip': 'no_trip',\n", - "# 'E-bike': 'p_micro',\n", - "# 'Train': 'transit',\n", - "# 'Taxi/Uber/Lyft': 'ridehail',\n", - "# 'Free Shuttle': 'transit',\n", - "# 'Scooter share': 's_micro',\n", - "# 'Skate board': 'p_micro'\n", - "# }\n", - "\n", - "# For target - replaced_mode\n", - "target_mapping = {\n", - " 'Regular Bike': 'p_micro', \n", - " 'Walk': 'walk', \n", - " 'No Travel': 'no_trip', \n", - " 'Other': 'unknown',\n", - " 'Gas Car, with others': 's_car',\n", - " 'Gas Car, drove alone': 'car',\n", - " 'Train': 'transit',\n", - " 'Scooter share': 's_micro', \n", - " 'Taxi/Uber/Lyft': 'ridehail', \n", - " 'Free Shuttle': 'transit', \n", - " 'Skate board': 'p_micro',\n", - " 'Bikeshare': 's_micro',\n", - " 'Bus': 'transit',\n", - " 'Not a Trip': 'no_trip',\n", - " 'E-bike': 'p_micro'\n", - "}\n", - "\n", - "# # These values were obtained using a simple linear regression model. Specifics can be found in time_distance_estimation.ipynb\n", - "# duration_dict = {\n", - "# 'walk': {\n", - "# 'intercept': 14.09698152519682,\n", - "# 'coef': 19.153802211061134\n", - "# },\n", - "# 'p_micro': {\n", - "# 'intercept': 4.753590860550791,\n", - "# 'coef': 4.2723965916811935\n", - "# },\n", - "# 's_micro': {\n", - "# 'intercept': 4.753590860550791,\n", - "# 'coef': 4.2723965916811935\n", - "# },\n", - "# 's_car': {\n", - "# 'intercept': 10.515751350707365, \n", - "# 'coef': 1.112111276629702\n", - "# },\n", - "# 'car': {\n", - "# 'intercept': 10.515751350707365, \n", - "# 'coef': 1.112111276629702\n", - "# },\n", - "# 'ridehail': {\n", - "# 'intercept': 10.515751350707365, \n", - "# 'coef': 1.112111276629702\n", - "# },\n", - "# 'transit': {\n", - "# 'intercept': 7.5000351741492395,\n", - "# 'coef': 1.839562017986485\n", - "# },\n", - "# 'unknown': {\n", - "# 'intercept': 9.423026094931313,\n", - "# 'coef': 2.640429444466706\n", - "# },\n", - "# 'no_trip': {\n", - "# 'intercept': 0.,\n", - "# 'coef': 0.\n", - "# }\n", - "# }\n", - "\n", - "ordinal_mapping = {'p_micro': 1, 'no_trip': 2, 's_car': 3, 'transit': 4, 'car': 5, 's_micro': 6, 'ridehail': 7, 'walk': 8, 'unknown': 9}\n", - "reverse_ordinal_mapping = {v:k for k,v in ordinal_mapping.items()}\n", - "\n", - "print(ordinal_mapping)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_available_features(\n", - " df: pd.DataFrame, av: dict, sm: dict, target_mapper: dict\n", - "):\n", - " \"\"\"\n", - " This method generates the wide-form dummy features for available modes.\n", - " The current implementation uses (section_mode_argmax OR available_modes) to indicate mode availability.\n", - " \"\"\"\n", - "\n", - " # Reset indices.\n", - " df = df.reset_index(drop=True, inplace=False)\n", - "\n", - " columns = list(set(av.values())) + ['unknown']\n", - "\n", - " # Create the columns along with the 'chosen' column.\n", - " df[columns] = 0\n", - "\n", - " row_dicts = list()\n", - "\n", - " for i, row in df.iterrows():\n", - " row_dict = row.to_dict()\n", - "\n", - " # Access the available modes. Split on ; and strip all the elements.\n", - " available_modes = [x.strip() for x in str(row_dict['available_modes']).split(';')]\n", - " \n", - " # Set all the available modes associated with the chosen modes too.\n", - " # Toggle this flag off if you don't want the target to be considered as an available mode.\n", - " # row_dict[row_dict['chosen']] = 1\n", - " \n", - " row_dict['target'] = target_mapper[row_dict['target']]\n", - " \n", - " # Update the available modes.\n", - " for available_mode in available_modes:\n", - " row_dict[av[available_mode]] = 1\n", - " \n", - " # Now, look at the section_mode_argmax as a proxy for the confirmed mode.\n", - " chosen_modes = sm[row_dict['section_mode_argmax']]\n", - "\n", - " # Determine the length of the mapping.\n", - " n = len(chosen_modes)\n", - "\n", - " # # Go ahead and modify the current dict with the first value.\n", - " # row_dict['chosen'] = ordinal_mapper[chosen_modes[0]]\n", - " # row_dict['primary_mode'] = 1\n", - "\n", - " # Also update the available modes.\n", - " for chosen_mode in chosen_modes:\n", - " row_dict[chosen_mode] = 1\n", - "\n", - " # # Append the first row to the list.\n", - " row_dicts.append(row_dict)\n", - " \n", - " constructed = pd.DataFrame(row_dicts)\n", - " constructed.rename(columns=dict([(c, 'av_'+c) for c in target_mapper.keys()]), inplace=True)\n", - "\n", - " return constructed" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```language=python\n", - "{'p_micro': 1, 'no_trip': 2, 's_car': 3, 'transit': 4, 'car': 5, 's_micro': 6, 'ridehail': 7, 'walk': 8, 'unknown': 9}\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_target_variable(df: pd.DataFrame, mapper: dict):\n", - " df['target'] = df['Replaced_mode'].apply(lambda x: mapper[x])\n", - " df.drop(columns=['Replaced_mode'], inplace=True)\n", - " return df\n", - "\n", - "filtered_df = generate_target_variable(filtered_df, target_mapping)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate wide-format availability indicators..\n", - "av_df = generate_available_features(filtered_df, available, section_mode_mapping, ordinal_mapping)\n", - "\n", - "# Collapse train and bus into 'transit'.\n", - "av_df.loc[av_df.section_mode_argmax.isin(['bus', 'train', 'subway']), 'section_mode_argmax'] = 'transit'" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['bicycling', 'transit', 'car', 'no_sensed', 'walking'],\n", - " dtype=object)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "av_df.section_mode_argmax.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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5 rows × 121 columns

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" - ], - "text/plain": [ - " source end_ts end_fmt_time \\\n", - "0 DwellSegmentationTimeFilter 1.619140e+09 2021-04-22 18:59:39-06:00 \n", - "1 DwellSegmentationTimeFilter 1.619197e+09 2021-04-23 11:04:53-06:00 \n", - "2 DwellSegmentationTimeFilter 1.619662e+09 2021-04-28 19:58:51-06:00 \n", - "3 DwellSegmentationTimeFilter 1.619728e+09 2021-04-29 14:23:25-06:00 \n", - "4 DwellSegmentationTimeFilter 1.619794e+09 2021-04-30 08:54:03-06:00 \n", - "\n", - " end_loc \\\n", - "0 {'type': 'Point', 'coordinates': [-105.1002447... \n", - "1 {'type': 'Point', 'coordinates': [-105.0776493... \n", - "2 {'type': 'Point', 'coordinates': [-105.0854639... \n", - "3 {'type': 'Point', 'coordinates': [-105.0776309... \n", - "4 {'type': 'Point', 'coordinates': [-105.0775022... \n", - "\n", - " raw_trip start_ts start_fmt_time \\\n", - "0 6082e47f27dcf1f393b70dd8 1.619139e+09 2021-04-22 18:48:48.365291-06:00 \n", - "1 60830eac2b9762e085f774e8 1.619196e+09 2021-04-23 10:45:48.219950-06:00 \n", - "2 608adb94b706ddbc669dabb5 1.619658e+09 2021-04-28 19:07:47.510844-06:00 \n", - "3 608b21db654c4a5ab2fa435a 1.619727e+09 2021-04-29 14:05:50.979267-06:00 \n", - "4 608c1f002f548f5db71d16ef 1.619793e+09 2021-04-30 08:34:49.162054-06:00 \n", - "\n", - " start_loc duration distance \\\n", - "0 {'type': 'Point', 'coordinates': [-105.0772567... 10.843912 1995.541857 \n", - "1 {'type': 'Point', 'coordinates': [-105.1228294... 19.079667 4764.133777 \n", - "2 {'type': 'Point', 'coordinates': [-105.1227172... 51.058153 9049.744908 \n", - "3 {'type': 'Point', 'coordinates': [-105.1228283... 17.567012 4731.604069 \n", - "4 {'type': 'Point', 'coordinates': [-105.1226652... 19.230632 4675.329725 \n", - "\n", - " ... target av_s_micro av_ridehail av_unknown av_car av_transit av_walk \\\n", - "0 ... 1 1 0 0 0 0 0 \n", - "1 ... 1 1 0 0 0 0 0 \n", - "2 ... 1 1 0 0 0 0 0 \n", - "3 ... 9 1 0 0 0 0 0 \n", - "4 ... 9 1 0 0 0 0 0 \n", - "\n", - " av_s_car av_no_trip av_p_micro \n", - "0 0 0 1 \n", - "1 0 0 1 \n", - "2 0 0 1 \n", - "3 0 0 1 \n", - "4 0 0 1 \n", - "\n", - "[5 rows x 121 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(av_df.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "# def compute_alt_durations(df: pd.DataFrame, reverse_mapper: dict, duration_mapper: dict):\n", - "# '''\n", - "# We re-iterate over the generated available mode df and populate the modes.\n", - "# We are only concerned with the rows that have primary_mode = 0, which indicates that they are alternate mode rows.\n", - "# '''\n", - "\n", - "# column_names = list(reverse_mapper.values())\n", - "\n", - "# # Start with a 0 cost for everyone.\n", - "# df[column_names] = 0\n", - "\n", - "# rows = list()\n", - "# for ix, row in df.iterrows():\n", - " \n", - "# # Convert to a dict\n", - "# row_dict = row.to_dict()\n", - "\n", - "# for mode in column_names:\n", - "# if mode == reverse_mapper[row['chosen']]:\n", - "# row_dict[mode] = row_dict['section_distance_argmax']\n", - "# continue\n", - " \n", - "# mode_params = duration_mapper[mode]\n", - "# # use availability as a mask.\n", - "# row_dict[mode] = mode_params['intercept'] + (mode_params['coef'] * row_dict['section_distance_argmax'])\n", - " \n", - "# rows.append(row_dict)\n", - " \n", - "# return pd.DataFrame(rows).rename(columns=dict([(c, 'tt_'+c) for c in column_names]), inplace=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "# av_time_df = compute_alt_durations(av_df, reverse_ordinal_mapping, duration_dict)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cost estimation\n", - "\n", - "```\n", - "{'p_micro': 1, 'no_trip': 2, 's_car': 3, 'transit': 4, 'car': 5, 's_micro': 6, 'ridehail': 7, 'walk': 8, 'unknown': 9}\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# All values are taken from VTPI.\n", - "# https://www.vtpi.org/tca/tca0501.pdf\n", - "mode_cost_per_mile = {\n", - " # bicycle/skateboard\n", - " 'p_micro': 0.,\n", - " 'no_trip': 0.,\n", - " # Shared car is half the cost of regular car, which is $0.6/mile.\n", - " 's_car': 0.3,\n", - " # Rental car.\n", - " 'car': 0.6,\n", - " # Average of bus and train taken.\n", - " 'transit': 0.5,\n", - " # Shared bicyle or scooter - values taken from https://nacto.org/shared-micromobility-2020-2021/ and \n", - " # https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/how-sharing-the-road-is-likely-to-transform-american-mobility\n", - " 's_micro': 0.3,\n", - " # uber/taxi/lyft\n", - " 'ridehail': 2.,\n", - " 'walk': 0.,\n", - " 'unknown': 0.\n", - "}\n", - "\n", - "# Assumptions.\n", - "mode_init_cost = {\n", - " 'p_micro': 0.,\n", - " 'no_trip': 0.,\n", - " # Shared car is half the cost of regular car, which is $0.6/mile.\n", - " 's_car': 0.,\n", - " # Rental car.\n", - " 'car': 0.,\n", - " # Average of bus and train taken.\n", - " 'transit': 0.,\n", - " # $1 unlocking cost.\n", - " 's_micro': 1.,\n", - " # uber/taxi/lyft\n", - " 'ridehail': 1.5,\n", - " 'walk': 0.,\n", - " 'unknown': 0.\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "def compute_cost_estimates(df: pd.DataFrame, cost_mapper: dict, init_cost_mapper: dict, ordinal_mapper: dict):\n", - " \n", - " # Create some extra colums.\n", - " columns = list(ordinal_mapper.keys())\n", - "\n", - " # Initialize the columns to 0.\n", - " df[columns] = 0.\n", - "\n", - " rows = list()\n", - "\n", - " # Iterate over every row.\n", - " for _, row in df.iterrows():\n", - " # Check which flags are active.\n", - " row_dict = row.to_dict()\n", - "\n", - " # Access the section_distance_argmax attribute for the distance. Note that this is now in miles.\n", - " distance = row_dict['section_distance_argmax']\n", - " \n", - " # Mask using availability.\n", - " for lookup in columns:\n", - " row_dict[lookup] = row_dict['av_' + lookup] * (init_cost_mapper[lookup] + (cost_mapper[lookup] * distance))\n", - "\n", - " rows.append(row_dict)\n", - "\n", - " new_df = pd.DataFrame(rows)\n", - " new_df.rename(columns=dict([(c, 'cost_'+c) for c in ordinal_mapper.keys()]), inplace=True)\n", - "\n", - " return new_df" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "cost_df = compute_cost_estimates(\n", - " av_df, cost_mapper=mode_cost_per_mile, \n", - " init_cost_mapper=mode_init_cost, \n", - " ordinal_mapper=ordinal_mapping\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cost_p_microcost_no_tripcost_s_carcost_transitcost_carcost_s_microcost_ridehailcost_walkcost_unknown
count70136.070136.070136.00000070136.00000070136.00000070136.00000070136.00000070136.070136.0
mean0.00.01.1411061.3771552.1056480.4241358.4993170.00.0
std0.00.02.0364752.8822274.0725741.09201913.7634830.00.0
min0.00.00.0000000.0000000.0000000.0000000.0000000.00.0
25%0.00.00.0724390.0000000.0000000.0000000.0000000.00.0
50%0.00.00.5073840.4143110.7847330.0000004.6687140.00.0
75%0.00.01.2541981.4195532.3882350.0000009.7276180.00.0
max0.00.022.33930637.23217644.67861123.262930150.4287030.00.0
\n", - "
" - ], - "text/plain": [ - " cost_p_micro cost_no_trip cost_s_car cost_transit cost_car \\\n", - "count 70136.0 70136.0 70136.000000 70136.000000 70136.000000 \n", - "mean 0.0 0.0 1.141106 1.377155 2.105648 \n", - "std 0.0 0.0 2.036475 2.882227 4.072574 \n", - "min 0.0 0.0 0.000000 0.000000 0.000000 \n", - "25% 0.0 0.0 0.072439 0.000000 0.000000 \n", - "50% 0.0 0.0 0.507384 0.414311 0.784733 \n", - "75% 0.0 0.0 1.254198 1.419553 2.388235 \n", - "max 0.0 0.0 22.339306 37.232176 44.678611 \n", - "\n", - " cost_s_micro cost_ridehail cost_walk cost_unknown \n", - "count 70136.000000 70136.000000 70136.0 70136.0 \n", - "mean 0.424135 8.499317 0.0 0.0 \n", - "std 1.092019 13.763483 0.0 0.0 \n", - "min 0.000000 0.000000 0.0 0.0 \n", - "25% 0.000000 0.000000 0.0 0.0 \n", - "50% 0.000000 4.668714 0.0 0.0 \n", - "75% 0.000000 9.727618 0.0 0.0 \n", - "max 23.262930 150.428703 0.0 0.0 " - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cost_df[[c for c in cost_df.columns if 'cost_' in c]].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "# student_status = {\n", - "# 'Not a student': 0,\n", - "# 'Yes - Full Time College/University': 1,\n", - "# 'Yes - Vocation/Technical/Trade School': 1,\n", - "# 'Yes - Part-Time College/University': 1,\n", - "# 'Fire Fighter 2 Training': 0,\n", - "# 'Taking prerequisites missing for grad program ': 1,\n", - "# 'Work': 0,\n", - "# 'Graduate': 1,\n", - "# 'Work at csu': 0,\n", - "# 'Custodian': 0, \n", - "# 'taking classes toward early childhood licensure': 1,\n", - "# 'Prefer not to say': 0\n", - "# }\n", - "\n", - "\n", - "# cost_df['is_student'] = cost_df['is_student'].apply(lambda x: student_status[x])" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "# cost_df['age'] = cost_df['age'].apply(lambda x: x if x < 100 else 2024 - x)\n", - "# cost_df['n_working_residents'] = cost_df['n_working_residents'].apply(lambda x: 0 if x < 0 else x)\n", - "# cost_df.rename(\n", - "# columns={'start_local_dt_weekday': 'start:DOW', 'end_local_dt_weekday': 'end:DOW'},\n", - "# inplace=True\n", - "# )" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "cost_df.to_csv('../data/ReplacedMode_Fix_02142024.csv', index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "interpreter": { - "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" - }, - "kernelspec": { - "display_name": "emission", - "language": "python", - "name": "emission" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/rm_src/env.txt b/rm_src/env.txt deleted file mode 100644 index b2d31ea..0000000 --- a/rm_src/env.txt +++ /dev/null @@ -1,214 +0,0 @@ -# This file may be used to create an environment using: -# $ conda create --name --file -# platform: osx-64 -@EXPLICIT -https://conda.anaconda.org/conda-forge/osx-64/_py-xgboost-mutex-2.0-cpu_0.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/bzip2-1.0.8-h0d85af4_4.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/ca-certificates-2024.2.2-h8857fd0_0.conda -https://conda.anaconda.org/conda-forge/osx-64/libbrotlicommon-1.0.9-hb7f2c08_9.conda -https://conda.anaconda.org/conda-forge/osx-64/libcxx-16.0.6-hd57cbcb_0.conda -https://conda.anaconda.org/conda-forge/osx-64/libdeflate-1.18-hac1461d_0.conda -https://conda.anaconda.org/conda-forge/osx-64/libffi-3.4.2-h0d85af4_5.tar.bz2 -https://conda.anaconda.org/conda-forge/osx-64/libjpeg-turbo-2.1.5.1-hb7f2c08_0.conda 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a/rm_src/mnl_exploration.ipynb +++ /dev/null @@ -1,1213 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "import pickle\n", - "import importlib\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "\n", - "from pathlib import Path\n", - "from uuid import UUID\n", - "from collections import defaultdict\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "emission_path = Path(os.getcwd()).parent.parent / 'my_emission_server' / 'e-mission-server'\n", - "sys.path.append(str(emission_path))\n", - "\n", - "# Also add the home (viz_scripts) to the path\n", - "sys.path.append('../viz_scripts')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "storage not configured, falling back to sample, default configuration\n", - "URL not formatted, defaulting to \"Stage_database\"\n", - "Connecting to database URL localhost\n" - ] - } - ], - "source": [ - "import scaffolding\n", - "import emission.core.get_database as edb" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "importlib.reload(scaffolding)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def r(df: pd.DataFrame) -> pd.DataFrame:\n", - " return df.reset_index(drop=True, inplace=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "with open('../viz_scripts/auxiliary_files/dic_re.pkl', 'rb') as f:\n", - " dic_re = pickle.loads(f.read())\n", - "\n", - "with open('../viz_scripts/auxiliary_files/dic_pur.pkl', 'rb') as f:\n", - " dic_pur = pickle.loads(f.read())\n", - "\n", - "# convert a dictionary to a defaultdict\n", - "dic_re = defaultdict(lambda: 'Other', dic_re)\n", - "dic_pur = defaultdict(lambda: 'Other', dic_pur)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found new program stage, creating new list\n", - "Found new program 4c, creating new list\n", - "Found new program cc, creating new list\n", - "Found new program fc, creating new list\n", - "Found new program pc, creating new list\n", - "Found new program sc, creating new list\n", - "Found new program vail, creating new list\n", - "Found new program prepilot, creating new list\n" - ] - } - ], - "source": [ - "# Split UUIDs by program\n", - "program_uuid_map = {}\n", - "for ue in edb.get_uuid_db().find():\n", - " uuid = str(ue['uuid'])\n", - " # uuid = str(ue['uuid'])\n", - " program = ue['user_email'].split(\"_\")[0]\n", - " if program in program_uuid_map.keys():\n", - " program_uuid_map[program].append(uuid)\n", - " else:\n", - " print(f\"Found new program {program}, creating new list\")\n", - " program_uuid_map[program] = []\n", - " program_uuid_map[program].append(uuid)\n", - "\n", - "uuid_program_list = []\n", - "for ue in edb.get_uuid_db().find():\n", - " # uuid = str(ue['uuid'].as_uuid(3))\n", - " uuid = str(ue['uuid'])\n", - " program = ue['user_email'].split(\"_\")[0]\n", - " uuid_program_list.append({\"program\": program, \"opcode\": ue[\"user_email\"], \"user_id\": uuid})" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "uuid_program_df = pd.DataFrame(uuid_program_list)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded all confirmed trips of length 241123\n", - "After filtering, found 241123 participant trips \n", - "After filtering, found 92446 labeled trips\n", - "Found Index(['mode_confirm', 'purpose_confirm', 'replaced_mode'], dtype='object') columns of length 3\n", - "After expanding, columns went from 41 -> 44\n", - "_prepilot\n", - "Based on 92395 confirmed trips from 235 users\n", - "of 241123 total trips from 261 users (38.32%)\n" - ] - } - ], - "source": [ - "# %%capture\n", - "\n", - "# for program in uuid_program_df.program.unique():\n", - "expanded_ct, file_suffix, quality_text, debug_df = scaffolding.load_viz_notebook_data(None,\n", - " None,\n", - " 'prepilot',\n", - " 'program',\n", - " dic_re,\n", - " dic_pur=dic_pur)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Join to the program df to get each user's program\n", - "expanded_ct['original_user_id'] = expanded_ct['user_id'].copy()\n", - "expanded_ct['user_id'] = expanded_ct['user_id'].apply(lambda x: str(x))\n", - "expanded_ct = expanded_ct.merge(uuid_program_df, on='user_id')\n", - "expanded_ct['user_id'] = expanded_ct['user_id'].apply(lambda x: str(x).replace(\"-\", \"\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['source', 'end_ts', 'end_fmt_time', 'end_loc', 'raw_trip', 'start_ts',\n", - " 'start_fmt_time', 'start_loc', 'duration', 'distance', 'start_place',\n", - " 'end_place', 'cleaned_trip', 'inferred_labels', 'inferred_trip',\n", - " 'expectation', 'confidence_threshold', 'expected_trip', 'user_input',\n", - " 'section_modes', 'section_distances', 'start_local_dt_year',\n", - " 'start_local_dt_month', 'start_local_dt_day', 'start_local_dt_hour',\n", - " 'start_local_dt_minute', 'start_local_dt_second',\n", - " 'start_local_dt_weekday', 'start_local_dt_timezone',\n", - " 'end_local_dt_year', 'end_local_dt_month', 'end_local_dt_day',\n", - " 'end_local_dt_hour', 'end_local_dt_minute', 'end_local_dt_second',\n", - " 'end_local_dt_weekday', 'end_local_dt_timezone', '_id', 'user_id',\n", - " 'metadata_write_ts', 'additions', 'mode_confirm', 'purpose_confirm',\n", - " 'replaced_mode', 'distance_miles', 'Mode_confirm', 'Replaced_mode',\n", - " 'Trip_purpose', 'original_user_id', 'program', 'opcode'],\n", - " dtype='object')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "expanded_ct.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Rename the target column.\n", - "expanded_ct.drop(columns=['replaced_mode'], axis='rows', inplace=True)\n", - "expanded_ct['Replaced_mode'] = expanded_ct['Replaced_mode'].fillna('Unlabeled')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "92383 trips across 235 users\n" - ] - } - ], - "source": [ - "print(\n", - " \"{n_trips} trips across {n_users} users\".format(\n", - " n_trips=len(expanded_ct.cleaned_trip.unique()),\n", - " n_users=len(expanded_ct.user_id.unique())\n", - " )\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "dates = pd.to_datetime(\n", - " expanded_ct.start_fmt_time, utc=True\n", - ").dt.tz_convert('America/Denver')" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Date range from 2016-08-15 07:59:32.418000-06:00 to 2022-12-30 23:33:27.147785-07:00\n" - ] - } - ], - "source": [ - "print(\n", - " \"Date range from {min_dt} to {max_dt}\".format(\n", - " min_dt=dates.min(),\n", - " max_dt=dates.max()\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Survey Data preprocessing" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Read the Denver dedmographic info\n", - "survey_data = pd.read_csv('../viz_scripts/Can Do Colorado eBike Program - en.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data.rename(\n", - " {\n", - " \"Unique User ID (auto-filled, do not edit)\": \"user_id\",\n", - " \"In which year were you born?\": \"birth_year\",\n", - " \"What is your gender?\": \"gender\",\n", - " \"Do you have a valid driver's license?\": \"has_drivers_license\",\n", - " \"Are you a student?\": \"is_student\",\n", - " \"What is the highest grade or degree that you have completed?\": \"highest_education\",\n", - " \"Do you work for either pay or profit?\": \"is_paid\",\n", - " \"Do you have more than one job?\": \"has_multiple_jobs\",\n", - " \"Do you work full-time or part-time at your primary job?\": \"primary_job_type\",\n", - " \"Which best describes your primary job?\": \"primary_job_description\",\n", - " \"How did you usually get to your primary job last week? \": \"primary_job_commute_mode\",\n", - " \"Thinking about your daily commute to work last week, how many minutes did it usually take to get from home to the primary job/work place?\": \"primary_job_commute_time\",\n", - " \"At your primary job, do you have the ability to set or change your own start time?\": \"is_primary_job_flexible\",\n", - " \"Do you have the option of working from home or an alternate location instead of going into your primary work place?\": \"primary_job_can_wfh\",\n", - " \"How many days per week do you usually work from home or an alternate location?\": \"wfh_days\",\n", - " \"Do you own or rent your place of residence?\": \"residence_ownership_type\",\n", - " \"What is your home type?\": \"residence_type\",\n", - " \"Please identify which category represents your total household income, before taxes, for last year.\": \"income_category\",\n", - " \"Including yourself, how many people live in your home?\": \"n_residence_members\",\n", - " \"How many children under age 18 live in your home?\": \"n_residents_u18\",\n", - " \"Including yourself, how many people have a driver's license in your household?\": \"n_residents_with_license\",\n", - " \"How many motor vehicles are owned, leased, or available for regular use by the people who currently live in your household?\": \"n_motor_vehicles\",\n", - " \"If you were unable to use your household vehicle(s), which of the following options would be available to you to get you from place to place?\": \"available_modes\",\n", - " \"Do you have a medical condition that makes it difficult to travel outside of the home?\": \"has_medical_condition\",\n", - " \"How long have you had this condition?\": \"medical_condition_duration\"\n", - " },\n", - " axis='columns',\n", - " inplace=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data['Timestamp'] = pd.to_datetime(survey_data['Timestamp'])\n", - "survey_data['Timestamp'] = survey_data['Timestamp'].dt.tz_localize('America/Los_Angeles', ambiguous='infer')\n", - "survey_data['Timestamp'] = survey_data['Timestamp'].dt.tz_convert('America/Denver')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "survey_data.Timestamp.min(), survey_data.Timestamp.max()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "f_survey = survey_data.loc[survey_data.user_id.isin(expanded_ct.user_id), :]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(f_survey.user_id.unique()), len(survey_data.user_id.unique())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "f_survey = f_survey.sort_values(\n", - " by=['user_id', 'Timestamp'], ascending=True\n", - ").drop_duplicates(\n", - " subset=['user_id'], keep='last', ignore_index=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "assert f_survey.user_id.value_counts().max() == 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Remove outliers.\n", - "\n", - "bad_ix = list()\n", - "\n", - "# WFH is not a good feature to include because of its high sparsity.\n", - "# wfh_mask = f_survey.wfh_days > 7\n", - "\n", - "# Computed by calculating 99th percentile.\n", - "commute_time_mask = f_survey.primary_job_commute_time > 75\n", - "\n", - "# Computed by calculating 99th percentile.\n", - "residence_members_mask = f_survey.n_residence_members > 8\n", - "\n", - "for mask in [commute_time_mask, residence_members_mask]:\n", - " bad_ix += f_survey[mask].index.tolist()\n", - "\n", - "f_survey.drop(index=set(bad_ix), inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Find age at the time of the timestamp. subtract birth year from timestamp year.\n", - "def compute_age(row):\n", - " if row['birth_year'] < 100:\n", - " return row['birth_year']\n", - " return row['Timestamp'].year - row['birth_year']\n", - "\n", - "f_survey['age'] = f_survey.apply(lambda x: compute_age(x), axis=1)\n", - "# f_survey.drop(columns=['birth_year'], inplace=True)\n", - "\n", - "# For those who leave it NA, categorize them as 'prefer not to say'.\n", - "f_survey.loc[f_survey['has_multiple_jobs'].isna(), 'has_multiple_jobs'] = 'Prefer not to say'\n", - "\n", - "# Fix primary commute mode.\n", - "\n", - "f_survey.loc[\n", - " f_survey.primary_job_commute_mode.isna(), 'primary_job_commute_mode'\n", - "] = 'Unknown'\n", - "\n", - "# Remove all leading and trailing whitespace.\n", - "f_survey.primary_job_commute_mode = f_survey.primary_job_commute_mode.str.strip()\n", - "\n", - "f_survey.loc[\n", - " f_survey.primary_job_commute_mode.isin([\n", - " 'Car', 'SUV', 'Pickup truck', 'Taxi/Limo (including services like Uber or Lyft)', 'Van',\n", - " 'Shared vehicle'\n", - " ]), 'primary_job_commute_mode'\n", - "] = 'Car transport'\n", - "\n", - "f_survey.loc[\n", - " f_survey.primary_job_commute_mode.isin([\n", - " 'Public or commuter bus', 'Buss'\n", - " ]), 'primary_job_commute_mode'\n", - "] = 'Public transport'\n", - "\n", - "f_survey.loc[\n", - " f_survey.primary_job_commute_mode.isin([\n", - " 'Walk', 'Skateboard', 'Bicycle'\n", - " ]), 'primary_job_commute_mode'\n", - "] = 'Active transport'\n", - "\n", - "f_survey.loc[\n", - " f_survey.primary_job_commute_mode.isin([\n", - " 'Telecommute', 'Work from home', 'I work from home'\n", - " ]), 'primary_job_commute_mode'\n", - "] = 'WFH'\n", - "\n", - "f_survey.loc[\n", - " f_survey.primary_job_commute_mode.isin([\n", - " 'Walk, bike, bus, uber or lyft.', 'Car and E-Bike'\n", - " ]), 'primary_job_commute_mode'\n", - "] = 'Hybrid'\n", - "\n", - "# Fix primary job type\n", - "f_survey.loc[f_survey.primary_job_type.isna(), 'primary_job_type'] = 'Prefer not to say'\n", - "\n", - "# Remove whitespace.\n", - "f_survey.primary_job_description = f_survey.primary_job_description.str.strip()\n", - "\n", - "# Normalize the job description. Inspired from the 'e-bike trips by occupation' \n", - "# plot in the CanBikeCo full pilot paper.\n", - "f_survey.loc[\n", - " f_survey.primary_job_description.isin([\n", - " 'Paraprofessional', 'Education', 'education/early childhood', 'Teacher',\n", - " 'Education non-profit manager', 'Scientific research', 'Research',\n", - " 'Preschool Tracher'\n", - " ]), 'primary_job_description'\n", - "] = 'Education'\n", - "\n", - "f_survey.loc[\n", - " f_survey.primary_job_description.isin([\n", - " 'Custodian', 'Custodial', 'Csu custodian', 'Janitorial',\n", - " 'Custodial Maintanace'\n", - " ]), 'primary_job_description'\n", - "] = 'Custodial'\n", - "\n", - "f_survey.loc[\n", - " f_survey.primary_job_description.isin([\n", - " 'Inbound cs', 'Accounting Technician', \n", - " 'Clerical'\n", - " ]), 'primary_job_description'\n", - "] = 'Clerical or administrative support'\n", - "\n", - "f_survey.loc[\n", - " f_survey.primary_job_description.isin([\n", - " 'Restaurant manager', 'Transportaion Services',\n", - " ]), 'primary_job_description'\n", - "] = 'Sales or service'\n", - "\n", - "f_survey.loc[\n", - " f_survey.primary_job_description.isin([\n", - " 'Pastry chef and line cook', 'Cook', 'Chef', 'Dining Services',\n", - " 'Food Service', 'Cooking', 'Residential Dining Services', 'Line Cook'\n", - " ]), 'primary_job_description'\n", - "] = 'Food service'\n", - "\n", - "f_survey.loc[\n", - " f_survey.primary_job_description.isin([\n", - " 'CNA', 'Caregiver/ Qmap', 'Health care', 'Nurse',\n", - " 'Healthcare', 'Medical', 'Medical field',\n", - " 'Family support'\n", - " ]), 'primary_job_description'\n", - "] = 'Medical/healthcare'\n", - "\n", - "f_survey.loc[\n", - " f_survey.primary_job_description.isin([\n", - " 'Amazon', 'Hockey rink', 'Caregiver', 'Security', 'Nonprofit social work',\n", - " 'Therapeutic', 'Driver'\n", - " ]), 'primary_job_description'\n", - "] = 'Other'\n", - "\n", - "f_survey.loc[\n", - " f_survey.primary_job_description.isin([\n", - " 'Hospital laundry', 'Matreal handler', 'Maintenance',\n", - " 'Co op laundry'\n", - " ]), 'primary_job_description'\n", - "] = 'Manufacturing, construction, maintenance, or farming'\n", - "\n", - "f_survey.loc[f_survey.primary_job_description.isna(), 'primary_job_description'] = 'Other'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "f_survey.primary_job_description.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Impute the commute time by grouping by occupation type and using median.\n", - "job_commutes = f_survey.groupby('primary_job_description')['primary_job_commute_time'].median()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for job_type in job_commutes.index:\n", - " f_survey.loc[\n", - " (f_survey.primary_job_description == job_type)&(f_survey.primary_job_commute_time.isna()), \n", - " 'primary_job_commute_time'\n", - " ] = job_commutes[job_type]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix motor vehicles. Keep this ordinal.\n", - "f_survey.loc[f_survey.n_motor_vehicles == '4+', 'n_motor_vehicles'] = 4\n", - "f_survey.loc[f_survey.n_motor_vehicles == 'Prefer not to say / Prefiero no decir.', 'n_motor_vehicles'] = 0\n", - "f_survey.n_motor_vehicles = f_survey.n_motor_vehicles.astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "to_drop = [\n", - " 'Which one below describe you best?', 'wfh_days', 'primary_job_can_wfh', 'is_primary_job_flexible',\n", - " 'medical_condition_duration', 'has_medical_condition', 'residence_type', 'residence_ownership_type',\n", - " 'is_paid', 'is_student'\n", - "]\n", - "\n", - "f_survey.drop(columns=to_drop, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "display(f_survey.isna().sum())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "'''\n", - "Let us think about this. Given that the income category is not defined, it does not make sense\n", - "to encode the factor as a non-zero ordinal number. 9.6% of the people in the dataset choose to\n", - "not disclose their income. We could just drop them, but then we risk losing quite a lot of information.\n", - "'''\n", - "\n", - "f_survey.loc[f_survey.income_category == 'Prefer not to say', 'income_category'] = 0\n", - "f_survey.loc[f_survey.income_category == 'Less than $24,999', 'income_category'] = 1\n", - "f_survey.loc[f_survey.income_category == '$25,000-$49,999', 'income_category'] = 2\n", - "f_survey.loc[f_survey.income_category == '$50,000-$99,999', 'income_category'] = 3\n", - "f_survey.loc[f_survey.income_category == '$100,000 -$149,999', 'income_category'] = 4\n", - "f_survey.loc[f_survey.income_category == '$150,000-$199,999', 'income_category'] = 5\n", - "f_survey.loc[f_survey.income_category == '$150,000', 'income_category'] = 5\n", - "f_survey.loc[f_survey.income_category == '$150,000-$199,999', 'income_category'] = 6\n", - "f_survey.loc[f_survey.income_category == '$200,000 or more', 'income_category'] = 7\n", - "f_survey.income_category = f_survey.income_category.astype(int)\n", - "\n", - "# TODO: Think of a strategy here." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "f_survey.primary_job_description.value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "display(f_survey.info())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.preprocessing import OneHotEncoder\n", - "\n", - "def generate_ohe_features(df, feature_name):\n", - " ohe = OneHotEncoder()\n", - " ohe.fit(df[[feature_name]])\n", - " return pd.DataFrame(\n", - " ohe.transform(df[[feature_name]]).todense(), \n", - " columns=ohe.get_feature_names_out(),\n", - " index=df.index\n", - " ), ohe" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ohe_features = [\n", - " 'gender', 'has_drivers_license', 'has_multiple_jobs', 'highest_education', \n", - " 'primary_job_type', 'primary_job_description', 'primary_job_commute_mode'\n", - "]\n", - "\n", - "for feature in ohe_features:\n", - " df, _ = generate_ohe_features(f_survey, feature)\n", - " f_survey = f_survey.merge(right=df, left_index=True, right_index=True)\n", - "\n", - "f_survey.drop(columns=ohe_features, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "display(f_survey.info())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from typing import List\n", - "\n", - "def plot_survey_distribution(df: pd.DataFrame, feature_names: List[str]):\n", - " \n", - " n = len(feature_names)\n", - " \n", - " # No more than 4 plots in on subplot.\n", - " assert n <= 4, \"Only 4 features can be visualized at a time.\"\n", - " \n", - " ncols = 2\n", - " nrows = n//ncols if n%ncols == 0 else (n//ncols) + 1\n", - " \n", - " h_scale = 4*nrows\n", - " \n", - " fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(8, h_scale))\n", - " for ix, ax in enumerate(axes.flatten()):\n", - " if ix < n:\n", - " sns.histplot(data=df, y=feature_names[ix], ax=ax)\n", - " else:\n", - " ax.set_axis_off()\n", - " plt.tight_layout()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot_survey_distribution(\n", - "# f_survey, ['income_category', 'n_motor_vehicles', 'primary_job_commute_mode']\n", - "# )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(len(f_survey.user_id.unique()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## GPS Data Preprocessing" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# We only wish to focus on Denver data for now.\n", - "# denver_data = r(expanded_ct.loc[\n", - "# (expanded_ct.start_local_dt_timezone == \"America/Denver\") & (expanded_ct.end_local_dt_timezone == \"America/Denver\"), \n", - "# :])\n", - "\n", - "denver_data = expanded_ct.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "denver_data['start_fmt_time'] = pd.to_datetime(\n", - " denver_data['start_fmt_time'], utc=True\n", - ").dt.tz_convert('America/Denver')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(denver_data.start_fmt_time.min(), denver_data.start_fmt_time.max())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Parse the datetime to Denver time.\n", - "denver_data['start_fmt_time'] = pd.to_datetime(\n", - " denver_data['start_fmt_time'], utc=True\n", - ").dt.tz_convert('America/Denver')\n", - "\n", - "# Re-compute all the start variables.\n", - "# denver_data['start_local_dt_year'] = denver_data['start_fmt_time'].dt.year\n", - "# denver_data['start_local_dt_month'] = denver_data['start_fmt_time'].dt.month\n", - "# denver_data['start_local_dt_day'] = denver_data['start_fmt_time'].dt.day\n", - "# denver_data['start_local_dt_hour'] = denver_data['start_fmt_time'].dt.hour\n", - "# denver_data['start_local_dt_weekday'] = denver_data['start_fmt_time'].dt.weekday\n", - "\n", - "# ## Do the same with the end time.\n", - "denver_data['end_fmt_time'] = pd.to_datetime(\n", - " denver_data['end_fmt_time'], utc=True\n", - ").dt.tz_convert('America/Denver')\n", - "\n", - "# # Re-compute all the end variables.\n", - "# denver_data['end_local_dt_year'] = denver_data['end_fmt_time'].dt.year\n", - "# denver_data['end_local_dt_month'] = denver_data['end_fmt_time'].dt.month\n", - "# denver_data['end_local_dt_day'] = denver_data['end_fmt_time'].dt.day\n", - "# denver_data['end_local_dt_hour'] = denver_data['end_fmt_time'].dt.hour\n", - "# denver_data['end_local_dt_weekday'] = denver_data['end_fmt_time'].dt.weekday" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Merge the trip data with the survey data.\n", - "\n", - "# inner join.\n", - "merged_data = denver_data.merge(\n", - " f_survey, left_on='user_id', right_on='user_id'\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(len(denver_data.user_id.unique()), len(merged_data.user_id.unique()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Let's start choosing features for modeling.\n", - "\n", - "# base_time_features = ['fmt_time', 'local_dt_year', 'local_dt_month', 'local_dt_day', 'local_dt_hour', 'local_dt_weekday']\n", - "# time_features = ['start_' + x for x in base_time_features] + ['end_' + x for x in base_time_features]\n", - "\n", - "# demographic_features = ['available_modes',\n", - "# 'birth_year', 'income_category', 'n_motor_vehicles', 'n_residence_members', 'n_residents_u18', 'gender', \n", - "# 'is_student', 'n_residents_with_license']\n", - "\n", - "# sensed_features = ['duration', 'distance_miles', 'cleaned_trip', 'start_loc', 'end_loc', 'section_modes', 'section_distances']\n", - "\n", - "# modeling_data = merged_data[['user_id', '_id', 'original_user_id', 'cleaned_trip', 'Replaced_mode', 'Mode_confirm'] + time_features + demographic_features + sensed_features].copy()\n", - "modeling_data = merged_data.copy()\n", - "\n", - "# Rename columns in-place.\n", - "modeling_data.rename(columns={\n", - " 'start_local_dt_year': 'start:year', 'start_local_dt_month': 'start:month', 'start_local_dt_day': 'start:day', 'start_local_dt_hour': 'start:hour',\n", - " 'end_local_dt_year': 'end:year', 'end_local_dt_month': 'end:month', 'end_local_dt_day': 'end:day', 'end_local_dt_hour': 'end:hour'\n", - " }, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def is_overnight_trip(start_date, end_date):\n", - " return int((end_date - start_date).days > 0)\n", - "\n", - "# overnight trips may be more likely taken by car.\n", - "modeling_data['is_overnight_trip'] = modeling_data.apply(\n", - " lambda x: is_overnight_trip(x.start_fmt_time, x.end_fmt_time), axis=1\n", - ")\n", - "\n", - "# Number of working individuals in the household = number of individuals in the house - number of children.\n", - "modeling_data['n_working_residents'] = (modeling_data['n_residence_members'] - modeling_data['n_residents_u18']).astype(int)\n", - "\n", - "# Convert the total duration of the trip into minutes.\n", - "modeling_data[['duration']] = modeling_data[['duration']]/60\n", - "\n", - "# Extract start and end latitudes and longitudes.\n", - "modeling_data['start_lat'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][1])\n", - "modeling_data['start_lng'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][0])\n", - "\n", - "modeling_data['end_lat'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][1])\n", - "modeling_data['end_lng'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# from calendar import monthrange\n", - "\n", - "# # Find day of month: use monthrange with (mm, yyyy) args and find how many days that month had (leap years are supported).\n", - "# def get_num_days_in_month(yyyy, mm):\n", - "# return monthrange(yyyy, mm)[1]\n", - "\n", - "# def is_overnight_trip(start_date, end_date):\n", - "# return int((end_date - start_date).days > 0)\n", - "\n", - "# # get the number of days for the start and end times.\n", - "# modeling_data['start:n_days_in_month'] = modeling_data.apply(lambda x: get_num_days_in_month(x['start:year'], x['start:month']), axis=1)\n", - "# modeling_data['end:n_days_in_month'] = modeling_data.apply(lambda x: get_num_days_in_month(x['end:year'], x['end:month']), axis=1)\n", - "\n", - "# # age = current year - year of birth\n", - "# modeling_data['age'] = 2023 - modeling_data['birth_year']\n", - "\n", - "# # overnight trips may be more likely taken by car.\n", - "# modeling_data['is_overnight_trip'] = modeling_data.apply(lambda x: is_overnight_trip(x.start_fmt_time, x.end_fmt_time), axis=1)\n", - "\n", - "# # Number of working individuals in the household = number of individuals in the house - number of children.\n", - "# modeling_data['n_working_residents'] = (modeling_data['n_residence_members'] - modeling_data['n_residents_u18']).astype(int)\n", - "\n", - "# # Create a binary indicator.\n", - "# modeling_data['is_male'] = modeling_data.gender.apply(lambda x: 1 if x==\"Male\" else 0)\n", - "\n", - "# # Bin the number of vehicles owned.\n", - "# # Drop the observations with (Prefer not to say)\n", - "# modeling_data = modeling_data.loc[~modeling_data['n_motor_vehicles'].isin(['Prefer not to say / Prefiero no decir.']), :]\n", - "# modeling_data.loc[modeling_data['n_motor_vehicles'].isin(['4+']), 'n_motor_vehicles'] = 4\n", - "# modeling_data['n_motor_vehicles'] = modeling_data['n_motor_vehicles'].astype(int)\n", - "\n", - "# # Convert the total duration of the trip into minutes.\n", - "# modeling_data[['duration']] = modeling_data[['duration']]/60\n", - "\n", - "# # Extract start and end latitudes and longitudes.\n", - "# modeling_data['start_lat'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][1])\n", - "# modeling_data['start_lng'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][0])\n", - "\n", - "# modeling_data['end_lat'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][1])\n", - "# modeling_data['end_lng'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# # Time-related feature engineeering:\n", - "# '''\n", - "# HOD: hour of day\n", - "# DOM: day of month\n", - "# MOY: month of year\n", - "# '''\n", - "\n", - "# def get_HOD(hour, how='sin'):\n", - "# if how == 'sin':\n", - "# return np.sin(2 * np.pi * (hour/24))\n", - "# return np.cos(2 * np.pi * (hour/24))\n", - "\n", - "# def get_DOM(day, n_days, how='sin'):\n", - "# if how == 'sin':\n", - "# return np.sin(2 * np.pi * (day/n_days))\n", - "# return np.cos(2 * np.pi * (day/n_days))\n", - "\n", - "# def get_MOY(month, how='sin'):\n", - "# if how == 'sin':\n", - "# return np.sin(2 * np.pi * (month/12))\n", - "# return np.cos(2 * np.pi * (month/12))\n", - "\n", - "# # Start - sin\n", - "# modeling_data['start:sin_HOD'] = modeling_data.apply(lambda x: get_HOD(x['start:hour']), axis=1)\n", - "# modeling_data['start:sin_DOM'] = modeling_data.apply(lambda x: get_DOM(x['start:day'], x['start:n_days_in_month']), axis=1)\n", - "# modeling_data['start:sin_MOY'] = modeling_data.apply(lambda x: get_MOY(x['start:year']), axis=1)\n", - "\n", - "# # Start - cos\n", - "# modeling_data['start:cos_HOD'] = modeling_data.apply(lambda x: get_HOD(x['start:hour'], how='cos'), axis=1)\n", - "# modeling_data['start:cos_DOM'] = modeling_data.apply(lambda x: get_DOM(x['start:day'], x['start:n_days_in_month'], how='cos'), axis=1)\n", - "# modeling_data['start:cos_MOY'] = modeling_data.apply(lambda x: get_MOY(x['start:year'], how='cos'), axis=1)\n", - "\n", - "# # End - sin\n", - "# modeling_data['end:sin_HOD'] = modeling_data.apply(lambda x: get_HOD(x['end:hour']), axis=1)\n", - "# modeling_data['end:sin_DOM'] = modeling_data.apply(lambda x: get_DOM(x['end:day'], x['end:n_days_in_month']), axis=1)\n", - "# modeling_data['end:sin_MOY'] = modeling_data.apply(lambda x: get_MOY(x['end:year']), axis=1)\n", - "\n", - "# # End - cos\n", - "# modeling_data['end:cos_HOD'] = modeling_data.apply(lambda x: get_HOD(x['end:hour'], how='cos'), axis=1)\n", - "# modeling_data['end:cos_DOM'] = modeling_data.apply(lambda x: get_DOM(x['end:day'], x['end:n_days_in_month'], how='cos'), axis=1)\n", - "# modeling_data['end:cos_MOY'] = modeling_data.apply(lambda x: get_MOY(x['end:year'], how='cos'), axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "modeling_data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def remove_air_or_hsr(df):\n", - "\n", - " df['mark'] = 0\n", - "\n", - " for ix, row in df.iterrows():\n", - " sections = row['section_modes']\n", - " if 'air_or_hsr' in sections:\n", - " df.loc[ix, 'mark'] = 1\n", - " \n", - " df = r(df.loc[df.mark == 0, :])\n", - " df.drop(columns=['mark'], inplace=True)\n", - "\n", - " return df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "modeling_data = remove_air_or_hsr(modeling_data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "importlib.reload(scaffolding)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results = list()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "modeling_data = r(modeling_data.loc[:,~modeling_data.columns.duplicated()].copy())\n", - "subset = modeling_data[['original_user_id', 'cleaned_trip']]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "EXTRACT_DURATIONS = False" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Takes ~150 minutes if n=5.\n", - "num_splits = 5\n", - "samples_per_split = modeling_data.shape[0]//num_splits" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "from time import perf_counter\n", - "\n", - "if EXTRACT_DURATIONS:\n", - "\n", - " for split_ix in range(num_splits):\n", - " low = samples_per_split * split_ix\n", - "\n", - " # -1 since .loc is index-inclusive.\n", - " high = samples_per_split * (split_ix + 1) - 1\n", - "\n", - " # For last split, include last index too.\n", - " if split_ix < num_splits - 1:\n", - " split = subset.loc[low:high, :]\n", - " else:\n", - " split = subset.loc[low:, :]\n", - "\n", - " print(f\"Split {split_ix} size: {split.shape[0]}\")\n", - "\n", - " now = perf_counter()\n", - " result = scaffolding.get_section_durations(split)\n", - " end = perf_counter() - now\n", - "\n", - " print(f\"Took {end/60} minutes to complete\")\n", - "\n", - " results.append(result)\n", - "\n", - " print(50*'-')\n", - "\n", - " cat = pd.concat(results, axis=0)\n", - " \n", - " # This will save a LOT of time.\n", - " # cat.to_csv('../data/section_durations.csv', index=False)\n", - "else:\n", - " cat = pd.read_csv('../data/section_durations.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(modeling_data.shape[0], cat.shape[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "modeling_data = pd.concat([modeling_data, cat[['section_durations']]], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "modeling_data.to_csv('../data/final_modeling_data_02142024.csv', index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "interpreter": { - "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" - }, - "kernelspec": { - "display_name": "emission", - "language": "python", - "name": "emission" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/rm_src/multisource_data/01_extract_db_data.ipynb b/rm_src/multisource_data/01_extract_db_data.ipynb deleted file mode 100644 index c907842..0000000 --- a/rm_src/multisource_data/01_extract_db_data.ipynb +++ /dev/null @@ -1,1008 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "38b147ff", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "import pickle\n", - "import importlib\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "\n", - "from pathlib import Path\n", - "from uuid import UUID\n", - "from collections import defaultdict\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "e550aa2b", - "metadata": {}, - "outputs": [], - "source": [ - "INCLUDE_TEST_USERS = False" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "39306a1d", - "metadata": {}, - "outputs": [], - "source": [ - "emission_path = Path(os.getcwd()).parent.parent / 'my_emission_server' / 'e-mission-server'\n", - "sys.path.append(str(emission_path))\n", - "\n", - "# Also add the home (viz_scripts) to the path\n", - "sys.path.append('../viz_scripts')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "e171e277", - "metadata": {}, - "outputs": [], - "source": [ - "DB_SOURCE = [\n", - " \"Stage_database\",\n", - " \"openpath_prod_durham\",\n", - " \"openpath_prod_mm_masscec\",\n", - " \"openpath_prod_ride2own\",\n", - " # No replaced mode!\n", - " \"openpath_prod_uprm_civic\",\n", - " \"openpath_prod_uprm_nicr\"\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "70fa3112", - "metadata": {}, - "outputs": [], - "source": [ - "CURRENT_DB = DB_SOURCE[0]\n", - "\n", - "assert CURRENT_DB in DB_SOURCE" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "bbde79d1", - "metadata": {}, - "outputs": [], - "source": [ - "# ['p_micro', 'no_trip', 's_car', 'transit', 'car', 's_micro', 'ridehail', 'walk', 'unknown']\n", - "\n", - "REPLACED_MODE_DICT = {\n", - " \"Stage_database\": {\n", - " 'no_travel': 'no_trip',\n", - " 'Unknown': 'unknown',\n", - " 'bus': 'transit',\n", - " 'drove_alone': 'car',\n", - " 'bike': 'p_micro',\n", - " 'shared_ride': 's_car',\n", - " 'walk': 'walk',\n", - " 'train': 'transit',\n", - " 'bikeshare': 's_micro',\n", - " 'not_a trip': 'no_trip',\n", - " 'pilot_ebike': 'p_micro',\n", - " 'electric_car': 'car',\n", - " 'taxi': 'ridehail',\n", - " 'not_a_trip': 'no_trip',\n", - " 'run': 'walk',\n", - " 'scootershare': 's_micro',\n", - " 'tramway': 'transit',\n", - " 'free_shuttle': 'transit',\n", - " 'e-bike': 'p_micro',\n", - " 'rental_car': 'car',\n", - " 'train_+ bus': 'transit',\n", - " 'skateboard': 'p_micro',\n", - " 'snowboarding': 'p_micro',\n", - " 'e_bike': 'p_micro',\n", - " 'golf_cart': 'unknown',\n", - " 'emergency_vehicle with others': 's_car',\n", - " 'call_friend': 's_car',\n", - " 'no_replacement': 'no_travel',\n", - " 'doing_nothing': 'no_trip',\n", - " 'na': 'no_trip',\n", - " 'ebike': 'p_micro',\n", - " 'hiking': 'walk',\n", - " 'n/a': 'no_trip',\n", - " 'testing': 'unknown',\n", - " 'home': 'no_trip',\n", - " 'must_walk 3-5 mi a day for back': 'walk',\n", - " 'family': 's_car',\n", - " 'car': 'car',\n", - " 'pilot_e-bike': 'p_micro',\n", - " 'pilot_bike': 'p_micro',\n", - " 'time_spent on the clock at amazon': 'no_trip',\n", - " 'working': 'no_trip',\n", - " 'walk_at work': 'walk',\n", - " 'sitting_on my butt doing nothing': 'no_trip',\n", - " 'nothing._delivered food for work': 'no_trip',\n", - " 'train,_bus and walk': 'transit',\n", - " 'work_vehicle': 'car',\n", - " 'friend_picked me up': 's_car',\n", - " 'ski': 'p_micro',\n", - " 'not_accurate': 'unknown',\n", - " 'stolen_ebike': 'p_micro'\n", - " },\n", - " \"openpath_prod_durham\": {\n", - " 'Unknown': 'unknown',\n", - " 'bike': 'p_micro',\n", - " 'shared_ride': 's_car',\n", - " 'drove_alone': 'car',\n", - " 'bus': 'transit',\n", - " 'no_travel': 'no_trip',\n", - " 'scootershare': 's_micro',\n", - " 'walk': 'walk',\n", - " 'taxi': 'ridehail',\n", - " 'e_car_drove_alone': 'car',\n", - " 'bikeshare': 's_micro',\n", - " 'ebike': 'p_micro',\n", - " 'train': 'transit',\n", - " 'e_car_shared_ride': 's_car'\n", - " },\n", - " \"openpath_prod_mm_masscec\": {\n", - " 'Unknown': 'unknown',\n", - " 'drove_alone': 'car',\n", - " 'walk': 'walk',\n", - " 'shared_ride': 's_car',\n", - " 'bike': 'p_micro',\n", - " 'bikeshare': 's_micro',\n", - " 'no_travel': 'no_trip',\n", - " 'taxi': 'ridehail',\n", - " 'bus': 'transit',\n", - " 'scootershare': 's_micro',\n", - " 'train': 'transit',\n", - " 'walking': 'walk',\n", - " 'e_car_drove_alone': 'car'\n", - " },\n", - " \"openpath_prod_ride2own\": {\n", - " 'Unknown': 'unknown',\n", - " 'drove_alone': 'car',\n", - " 'walk': 'walk',\n", - " 'shared_ride': 's_car',\n", - " 'bike': 'p_micro',\n", - " 'no_travel': 'no_trip',\n", - " 'taxi': 'ridehail',\n", - " 'bus': 'transit',\n", - " 'train': 'transit',\n", - " 'e_car_drove_alone': 'car',\n", - " 'e_car_shared_ride': 's_car'\n", - " },\n", - " \"openpath_prod_uprm_nicr\": {\n", - " 'Unknown': 'unknown',\n", - " 'walk': 'walk',\n", - " 'drove_alone': 'car'\n", - " }\n", - "}\n", - "\n", - "SURVEY_DATA_DICT = {\n", - " \"Stage_database\": {\n", - " \"Unique User ID (auto-filled, do not edit)\": \"user_id\",\n", - " \"In which year were you born?\": \"birth_year\",\n", - " \"What is your gender?\": \"gender\",\n", - " \"Do you have a valid driver's license?\": \"has_drivers_license\",\n", - " \"Are you a student?\": \"is_student\",\n", - " \"What is the highest grade or degree that you have completed?\": \"highest_education\",\n", - " \"Do you work for either pay or profit?\": \"is_paid\",\n", - " \"Do you have more than one job?\": \"has_multiple_jobs\",\n", - " \"Do you work full-time or part-time at your primary job?\": \"primary_job_type\",\n", - " \"Which best describes your primary job?\": \"primary_job_description\",\n", - " \"How did you usually get to your primary job last week? \": \"primary_job_commute_mode\",\n", - " \"Thinking about your daily commute to work last week, how many minutes did it usually take to get from home to the primary job/work place?\": \"primary_job_commute_time\",\n", - " \"At your primary job, do you have the ability to set or change your own start time?\": \"is_primary_job_flexible\",\n", - " \"Do you have the option of working from home or an alternate location instead of going into your primary work place?\": \"primary_job_can_wfh\",\n", - " \"How many days per week do you usually work from home or an alternate location?\": \"wfh_days\",\n", - " \"Do you own or rent your place of residence?\": \"residence_ownership_type\",\n", - " \"What is your home type?\": \"residence_type\",\n", - " \"Please identify which category represents your total household income, before taxes, for last year.\": \"income_category\",\n", - " \"Including yourself, how many people live in your home?\": \"n_residence_members\",\n", - " \"How many children under age 18 live in your home?\": \"n_residents_u18\",\n", - " \"Including yourself, how many people have a driver's license in your household?\": \"n_residents_with_license\",\n", - " \"How many motor vehicles are owned, leased, or available for regular use by the people who currently live in your household?\": \"n_motor_vehicles\",\n", - " \"If you were unable to use your household vehicle(s), which of the following options would be available to you to get you from place to place?\": \"available_modes\",\n", - " \"Do you have a medical condition that makes it difficult to travel outside of the home?\": \"has_medical_condition\",\n", - " \"How long have you had this condition?\": \"medical_condition_duration\"\n", - " },\n", - " # Retrieved from: e-mission-phone/survey-resources/data-xls/demo-survey-v1.xlsx\n", - " \"openpath_prod_durham\": {\n", - " \"At_your_primary_job_do_you_ha\": \"is_primary_job_flexible\",\n", - " \"Which_best_describes_your_prim\": \"primary_job_description\",\n", - " \"Do_you_work_full_time_or_part_\": \"primary_job_type\",\n", - " \"Do_you_have_the_option_of_work\": \"primary_job_can_wfh\",\n", - " \"Please_describe_your_primary_job\": \"primary_job_description\",\n", - " \"Do_you_have_more_than_one_job\": \"has_multiple_jobs\",\n", - " # Two columns: how many days/week do you work & what days of the week do you work. \n", - " # the latter has only 4 NA values, the former has 45 NA values.\n", - " \"What_days_of_the_week_do_you_t\": \"wfh_days\",\n", - " \"How_many_days_do_you_usually_w_001\": \"n_wfh_days\",\n", - " # All these are NAs.\n", - " \"Which_one_below_describe_you_b\": \"description\",\n", - " \"What_is_your_race_ethnicity\": \"race_or_ethnicity\",\n", - " \"Are_you_a_student\": \"is_student\",\n", - " \"What_is_the_highest_grade_or_d\": \"highest_education\",\n", - " \"do_you_consider_yourself_to_be\": \"is_transgender\",\n", - " \"What_is_your_gender\": \"gender\",\n", - " \"How_old_are_you\": \"age\",\n", - " \"Are_you_a_paid_worker\": \"is_paid\",\n", - " \"Do_you_have_a_driver_license\": \"has_drivers_license\",\n", - " \"How_long_you_had_this_conditio\": \"medical_condition_duration\",\n", - " \"Including_yourself_how_many_w_001\": \"n_residents_u18\",\n", - " \"Including_yourself_how_many_p\": \"n_residence_members\",\n", - " \"Do_you_own_or_rent_your_home\": \"residence_ownership_type\",\n", - " \"Please_identify_which_category\": \"income_category\",\n", - " \"If_you_were_unable_to_use_your\": \"available_modes\",\n", - " \"Including_yourself_how_many_p_001\": \"n_residents_with_license\",\n", - " \"Including_yourself_how_many_w\": \"n_working_residents\",\n", - " \"What_is_your_home_type\": \"residence_type\",\n", - " \"How_many_motor_vehicles_are_ow\": \"n_motor_vehicles\",\n", - " \"Do_you_have_a_condition_or_han\": \"has_medical_condition\"\n", - " },\n", - " \"openpath_prod_mm_masscec\": {\n", - " # Same questions as Durham.\n", - " \"At_your_primary_job_do_you_ha\": \"is_primary_job_flexible\",\n", - " \"Which_best_describes_your_prim\": \"primary_job_description\",\n", - " \"Do_you_work_full_time_or_part_\": \"primary_job_type\",\n", - " \"Do_you_have_the_option_of_work\": \"primary_job_can_wfh\",\n", - " \"Please_describe_your_primary_job\": \"primary_job_description\",\n", - " \"Do_you_have_more_than_one_job\": \"has_multiple_jobs\",\n", - " # Two columns: how many days/week do you work & what days of the week do you work. \n", - " # the latter has only 4 NA values, the former has 45 NA values.\n", - " \"What_days_of_the_week_do_you_t\": \"wfh_days\",\n", - " \"How_many_days_do_you_usually_w_001\": \"n_wfh_days\",\n", - " # All these are NAs.\n", - " \"Which_one_below_describe_you_b\": \"description\",\n", - " \"What_is_your_race_ethnicity\": \"race_or_ethnicity\",\n", - " \"Are_you_a_student\": \"is_student\",\n", - " \"What_is_the_highest_grade_or_d\": \"highest_education\",\n", - " \"do_you_consider_yourself_to_be\": \"is_transgender\",\n", - " \"What_is_your_gender\": \"gender\",\n", - " \"How_old_are_you\": \"age\",\n", - " \"Are_you_a_paid_worker\": \"is_paid\",\n", - " \"Do_you_have_a_driver_license\": \"has_drivers_license\",\n", - " \"How_long_you_had_this_conditio\": \"medical_condition_duration\",\n", - " \"Including_yourself_how_many_w_001\": \"n_residents_u18\",\n", - " \"Including_yourself_how_many_p\": \"n_residence_members\",\n", - " \"Do_you_own_or_rent_your_home\": \"residence_ownership_type\",\n", - " \"Please_identify_which_category\": \"income_category\",\n", - " \"If_you_were_unable_to_use_your\": \"available_modes\",\n", - " \"Including_yourself_how_many_p_001\": \"n_residents_with_license\",\n", - " \"Including_yourself_how_many_w\": \"n_working_residents\",\n", - " \"What_is_your_home_type\": \"residence_type\",\n", - " \"How_many_motor_vehicles_are_ow\": \"n_motor_vehicles\",\n", - " \"Do_you_have_a_condition_or_han\": \"has_medical_condition\"\n", - " },\n", - " \"openpath_prod_ride2own\": {\n", - " # Same questions as Durham.\n", - " \"How_old_are_you\": \"age\",\n", - " \"What_is_your_gender\": \"gender\",\n", - " \"do_you_consider_yourself_to_be\": \"is_transgender\",\n", - " \"What_is_your_race_ethnicity\": \"race_or_ethnicity\",\n", - " \"Do_you_have_a_driver_license\": \"has_drivers_license\",\n", - " \"Are_you_a_student\": \"is_student\",\n", - " \"What_is_the_highest_grade_or_d\": \"highest_education\",\n", - " \"Are_you_a_paid_worker\": \"is_paid\",\n", - " \"Which_one_below_describe_you_b\": \"description\",\n", - " \"Do_you_own_or_rent_your_home\": \"residence_ownership_type\",\n", - " \"What_is_your_home_type\": \"residence_type\",\n", - " \"Please_identify_which_category\": \"income_category\",\n", - " \"Including_yourself_how_many_p\": \"n_residence_members\",\n", - " \"Including_yourself_how_many_w\": \"n_working_residents\",\n", - " \"Including_yourself_how_many_p_001\": \"n_residents_with_license\",\n", - " \"Including_yourself_how_many_w_001\": \"n_residents_u18\",\n", - " \"How_many_motor_vehicles_are_ow\": \"n_motor_vehicles\",\n", - " \"If_you_were_unable_to_use_your\": \"available_modes\",\n", - " \"Do_you_have_a_condition_or_han\": \"has_medical_condition\",\n", - " \"How_long_you_had_this_conditio\": \"medical_condition_duration\",\n", - " \"Do_you_have_more_than_one_job\": \"has_multiple_jobs\",\n", - " \"Do_you_work_full_time_or_part_\": \"primary_job_type\",\n", - " \"Which_best_describes_your_prim\": \"primary_job_description\",\n", - " \"Please_describe_your_primary_job\": \"primary_job_description\",\n", - " \"At_your_primary_job_do_you_ha\": \"is_primary_job_flexible\",\n", - " \"Do_you_have_the_option_of_work\": \"primary_job_can_wfh\",\n", - " \"How_many_days_do_you_usually_w_001\": \"n_wfh_days\",\n", - " \"What_days_of_the_week_do_you_t\": \"wfh_days\"\n", - " },\n", - " \"openpath_prod_uprm_nicr\": {\n", - " # Same as Durham!\n", - " \"At_your_primary_job_do_you_ha\": \"is_primary_job_flexible\",\n", - " \"Which_best_describes_your_prim\": \"primary_job_description\",\n", - " \"Do_you_work_full_time_or_part_\": \"primary_job_type\",\n", - " \"Do_you_have_the_option_of_work\": \"primary_job_can_wfh\",\n", - " \"Please_describe_your_primary_job\": \"primary_job_description\",\n", - " \"Do_you_have_more_than_one_job\": \"has_multiple_jobs\",\n", - " # Two columns: how many days/week do you work & what days of the week do you work. \n", - " # the latter has only 4 NA values, the former has 45 NA values.\n", - " \"What_days_of_the_week_do_you_t\": \"wfh_days\",\n", - " \"How_many_days_do_you_usually_w_001\": \"n_wfh_days\",\n", - " # All these are NAs.\n", - " \"Which_one_below_describe_you_b\": \"description\",\n", - " \"What_is_your_race_ethnicity\": \"race_or_ethnicity\",\n", - " \"Are_you_a_student\": \"is_student\",\n", - " \"What_is_the_highest_grade_or_d\": \"highest_education\",\n", - " \"do_you_consider_yourself_to_be\": \"is_transgender\",\n", - " \"What_is_your_gender\": \"gender\",\n", - " \"How_old_are_you\": \"age\",\n", - " \"Are_you_a_paid_worker\": \"is_paid\",\n", - " \"Do_you_have_a_driver_license\": \"has_drivers_license\",\n", - " \"How_long_you_had_this_conditio\": \"medical_condition_duration\",\n", - " \"Including_yourself_how_many_w_001\": \"n_residents_u18\",\n", - " \"Including_yourself_how_many_p\": \"n_residence_members\",\n", - " \"Do_you_own_or_rent_your_home\": \"residence_ownership_type\",\n", - " \"Please_identify_which_category\": \"income_category\",\n", - " \"If_you_were_unable_to_use_your\": \"available_modes\",\n", - " \"Including_yourself_how_many_p_001\": \"n_residents_with_license\",\n", - " \"Including_yourself_how_many_w\": \"n_working_residents\",\n", - " \"What_is_your_home_type\": \"residence_type\",\n", - " \"How_many_motor_vehicles_are_ow\": \"n_motor_vehicles\",\n", - " \"Do_you_have_a_condition_or_han\": \"has_medical_condition\"\n", - " }\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "4df64ba8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'birth_year',\n", - " 'primary_job_commute_mode',\n", - " 'primary_job_commute_time',\n", - " 'user_id'}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "set(SURVEY_DATA_DICT[\"Stage_database\"].values()).difference(\n", - " set(SURVEY_DATA_DICT[\"openpath_prod_durham\"].values())\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "69008893", - "metadata": {}, - "outputs": [], - "source": [ - "## Source: db_utils.py in op-admin-dashboard.\n", - "\n", - "BINARY_DEMOGRAPHICS_COLS = [\n", - " 'user_id',\n", - " '_id',\n", - "]\n", - "\n", - "EXCLUDED_DEMOGRAPHICS_COLS = [\n", - " 'data.xmlResponse', \n", - " 'data.name',\n", - " 'data.version',\n", - " 'data.label',\n", - " 'xmlns:jr',\n", - " 'xmlns:orx',\n", - " 'id',\n", - " 'start',\n", - " 'end',\n", - " 'attrxmlns:jr',\n", - " 'attrxmlns:orx',\n", - " 'attrid',\n", - " '__version__',\n", - " 'attrversion',\n", - " 'instanceID',\n", - "]" - ] - }, - { - "cell_type": "markdown", - "id": "4357d455", - "metadata": {}, - "source": [ - "Ensure that you point the database to the appropriate name" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "255b8896", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "storage not configured, falling back to sample, default configuration\n", - "URL not formatted, defaulting to Stage_database\n", - "Connecting to database URL localhost\n" - ] - } - ], - "source": [ - "import scaffolding\n", - "import emission.core.get_database as edb\n", - "import emission.storage.timeseries.abstract_timeseries as esta" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "12cc0c54", - "metadata": {}, - "outputs": [], - "source": [ - "## Source: scaffolding.py\n", - "\n", - "def expand_userinputs(labeled_ct):\n", - " '''\n", - " param: labeled_ct: a dataframe of confirmed trips, some of which have labels\n", - " params: labels_per_trip: the number of labels for each trip.\n", - " Currently, this is 2 for studies and 3 for programs, and should be \n", - " passed in by the notebook based on the input config.\n", - " If used with a trip-level survey, it could be even larger.\n", - " '''\n", - " # CASE 1 of https://github.com/e-mission/em-public-dashboard/issues/69#issuecomment-1256835867\n", - " if len(labeled_ct) == 0:\n", - " return labeled_ct\n", - " label_only = pd.DataFrame(labeled_ct.user_input.to_list(), index=labeled_ct.index)\n", - " # disp.display(label_only.head())\n", - " labels_per_trip = len(label_only.columns)\n", - " print(\"Found %s columns of length %d\" % (label_only.columns, labels_per_trip))\n", - " expanded_ct = pd.concat([labeled_ct, label_only], axis=1)\n", - " assert len(expanded_ct) == len(labeled_ct), \\\n", - " (\"Mismatch after expanding labels, expanded_ct.rows = %s != labeled_ct.rows %s\" %\n", - " (len(expanded_ct), len(labeled_ct)))\n", - " print(\"After expanding, columns went from %s -> %s\" %\n", - " (len(labeled_ct.columns), len(expanded_ct.columns)))\n", - " assert len(expanded_ct.columns) == len(labeled_ct.columns) + labels_per_trip, \\\n", - " (\"Mismatch after expanding labels, expanded_ct.columns = %s != labeled_ct.columns %s\" %\n", - " (len(expanded_ct.columns), len(labeled_ct.columns)))\n", - " # disp.display(expanded_ct.head())\n", - " return expanded_ct" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "9a98e2fb", - "metadata": {}, - "outputs": [], - "source": [ - "## Source: scaffolding.py\n", - "\n", - "def data_quality_check(expanded_ct):\n", - " '''1. Delete rows where the mode_confirm was pilot_ebike and repalced_mode was pilot_ebike.\n", - " 2. Delete rows where the mode_confirm was pilot_ebike and repalced_mode was same_mode.\n", - " 3. Replace same_mode for the mode_confirm for Energy Impact Calcualtion.'''\n", - "\n", - " # TODO: This is only really required for the initial data collection around the minipilot\n", - " # in subsequent deployes, we removed \"same mode\" and \"pilot_ebike\" from the options, so the\n", - " # dataset did not contain of these data quality issues\n", - "\n", - " if 'replaced_mode' in expanded_ct.columns:\n", - " expanded_ct.drop(expanded_ct[(expanded_ct['mode_confirm'] == 'pilot_ebike') & (expanded_ct['replaced_mode'] == 'pilot_ebike')].index, inplace=True)\n", - " expanded_ct.drop(expanded_ct[(expanded_ct['mode_confirm'] == 'pilot_ebike') & (expanded_ct['replaced_mode'] == 'same_mode')].index, inplace=True)\n", - " expanded_ct['replaced_mode'] = np.where(expanded_ct['replaced_mode'] == 'same_mode',expanded_ct['mode_confirm'], expanded_ct['replaced_mode'])\n", - " \n", - " return expanded_ct" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "fe37bf27", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Before filtering, length=241123\n", - "After filtering, length=241123\n", - "Found Index(['mode_confirm', 'purpose_confirm', 'replaced_mode'], dtype='object') columns of length 3\n", - "After expanding, columns went from 41 -> 44\n", - "['source', 'end_ts', 'end_fmt_time', 'end_loc', 'raw_trip', 'start_ts', 'start_fmt_time', 'start_loc', 'duration', 'distance', 'start_place', 'end_place', 'cleaned_trip', 'inferred_labels', 'inferred_trip', 'expectation', 'confidence_threshold', 'expected_trip', 'user_input', 'section_modes', 'section_distances', 'start_local_dt_year', 'start_local_dt_month', 'start_local_dt_day', 'start_local_dt_hour', 'start_local_dt_minute', 'start_local_dt_second', 'start_local_dt_weekday', 'start_local_dt_timezone', 'end_local_dt_year', 'end_local_dt_month', 'end_local_dt_day', 'end_local_dt_hour', 'end_local_dt_minute', 'end_local_dt_second', 'end_local_dt_weekday', 'end_local_dt_timezone', '_id', 'user_id', 'metadata_write_ts', 'additions', 'mode_confirm', 'purpose_confirm', 'replaced_mode']\n" - ] - } - ], - "source": [ - "## Source: scaffolding.py\n", - "\n", - "uuid_df = pd.json_normalize(list(edb.get_uuid_db().find()))\n", - "\n", - "if not INCLUDE_TEST_USERS:\n", - " uuid_df = uuid_df.loc[~uuid_df.user_email.str.contains('_test_'), :]\n", - "\n", - "filtered = uuid_df.uuid.unique()\n", - "\n", - "agg = esta.TimeSeries.get_aggregate_time_series()\n", - "all_ct = agg.get_data_df(\"analysis/confirmed_trip\", None)\n", - "\n", - "print(f\"Before filtering, length={len(all_ct)}\")\n", - "participant_ct_df = all_ct.loc[all_ct.user_id.isin(filtered), :]\n", - "print(f\"After filtering, length={len(participant_ct_df)}\")\n", - "\n", - "expanded_ct = expand_userinputs(participant_ct_df)\n", - "expanded_ct = data_quality_check(expanded_ct)\n", - "print(expanded_ct.columns.tolist())\n", - "expanded_ct['replaced_mode'] = expanded_ct['replaced_mode'].fillna('Unknown')" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "13536d14", - "metadata": {}, - "outputs": [], - "source": [ - "# # Additional preprocessing for replaced mode (if any)\n", - "\n", - "mode_counts = expanded_ct['replaced_mode'].value_counts()\n", - "drop_modes = mode_counts[mode_counts == 1].index.tolist()\n", - "\n", - "expanded_ct.drop(\n", - " index=expanded_ct.loc[expanded_ct.replaced_mode.isin(drop_modes)].index,\n", - " inplace=True\n", - ")\n", - "\n", - "# Additional modes to drop.\n", - "expanded_ct.drop(\n", - " index=expanded_ct.loc[expanded_ct.replaced_mode.isin(\n", - " # Remove all rows with air, boat, or weird answers.\n", - " ['houseboat', 'gondola', 'airline_flight', 'aircraft', 'zoo', 'air',\n", - " 'airplane', 'boat', 'flight', 'plane', 'meal', 'lunch']\n", - " )].index,\n", - " inplace=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "2b56af00", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['no_travel' 'Unknown' 'bus' 'drove_alone' 'bike' 'shared_ride' 'walk'\n", - " 'train' 'bikeshare' 'not_a trip' 'pilot_ebike' 'electric_car' 'taxi'\n", - " 'not_a_trip' 'run' 'scootershare' 'tramway' 'free_shuttle' 'e-bike'\n", - " 'rental_car' 'train_+ bus' 'skateboard' 'e_bike' 'golf_cart'\n", - " 'emergency_vehicle with others' 'call_friend' 'no_replacement'\n", - " 'doing_nothing' 'na' 'ebike' 'hiking' 'ski' 'not_accurate' 'pilot_bike'\n", - " 'snowboarding' 'stolen_ebike' 'n/a' 'testing' 'home'\n", - " 'must_walk 3-5 mi a day for back' 'family' 'car' 'pilot_e-bike'\n", - " 'time_spent on the clock at amazon' 'working' 'walk_at work'\n", - " 'sitting_on my butt doing nothing' 'nothing._delivered food for work'\n", - " 'train,_bus and walk' 'work_vehicle' 'friend_picked me up']\n" - ] - } - ], - "source": [ - "print(expanded_ct.replaced_mode.unique())" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "757ccbee", - "metadata": {}, - "outputs": [], - "source": [ - "expanded_ct.replaced_mode = expanded_ct.replaced_mode.apply(lambda x: REPLACED_MODE_DICT[CURRENT_DB][x])" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "c878ad12", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['no_trip' 'unknown' 'transit' 'car' 'p_micro' 's_car' 'walk' 's_micro'\n", - " 'ridehail' 'no_travel']\n" - ] - } - ], - "source": [ - "print(expanded_ct.replaced_mode.unique())" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "7461a4d2", - "metadata": {}, - "outputs": [], - "source": [ - "# Demographics\n", - "\n", - "if CURRENT_DB != \"Stage_database\":\n", - "\n", - " decoded_uuids = [str(x) for x in filtered]\n", - "\n", - " ## Source: query_demographics() in op-admin-dashboard.\n", - " ts = esta.TimeSeries.get_aggregate_time_series()\n", - " entries = list(ts.find_entries([\"manual/demographic_survey\"]))\n", - "\n", - " available_key = {}\n", - " for entry in entries:\n", - " survey_key = list(entry['data']['jsonDocResponse'].keys())[0]\n", - " if survey_key not in available_key:\n", - " available_key[survey_key] = []\n", - "\n", - " # Minor modification: Added user_id check to filter users.\n", - " if str(entry['user_id']) in decoded_uuids:\n", - " available_key[survey_key].append(entry)\n", - "\n", - " dataframes = {}\n", - " for key, json_object in available_key.items():\n", - " df = pd.json_normalize(json_object)\n", - " dataframes[key] = df\n", - "\n", - " for key, df in dataframes.items():\n", - " if not df.empty:\n", - " for col in BINARY_DEMOGRAPHICS_COLS:\n", - " if col in df.columns:\n", - " df[col] = df[col].apply(str) \n", - " columns_to_drop = [col for col in df.columns if col.startswith(\"metadata\")]\n", - " df.drop(columns= columns_to_drop, inplace=True) \n", - " df.columns=[col.rsplit('.',1)[-1] if col.startswith('data.jsonDocResponse.') else col for col in df.columns]\n", - " for col in EXCLUDED_DEMOGRAPHICS_COLS:\n", - " if col in df.columns:\n", - " df.drop(columns= [col], inplace=True)\n", - "\n", - " survey_data = pd.DataFrame() \n", - " for v in dataframes.values():\n", - " survey_data = pd.concat([survey_data, v], axis=0, ignore_index=True)\n", - "else:\n", - " # Read the demographics.\n", - " survey_data = pd.read_csv('../viz_scripts/Can Do Colorado eBike Program - en.csv')\n", - " survey_data.rename(columns={'Unique User ID (auto-filled, do not edit)': 'user_id'}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "a6be751e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "203 261\n" - ] - } - ], - "source": [ - "print(len(survey_data.user_id.unique()), len(expanded_ct.user_id.unique()))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "9ebc87d8", - "metadata": {}, - "outputs": [], - "source": [ - "survey_data.rename(SURVEY_DATA_DICT[CURRENT_DB], axis='columns', inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "86bd8337", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - "
" - ], - "text/plain": [ - " user_id\n", - "0 8a0473ca-e53d-4720-a99c-0696cc1fb407\n", - "1 0a093cbd-b536-43af-b03d-293425e84c76\n", - "2 0a093cbd-b536-43af-b03d-293425e84c76\n", - "3 0a093cbd-b536-43af-b03d-293425e84c76\n", - "4 0a093cbd-b536-43af-b03d-293425e84c76" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "display(expanded_ct[['user_id']].head())\n", - "print(type(expanded_ct['user_id'][0]))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "c7eb2e09", - "metadata": {}, - "outputs": [], - "source": [ - "# Additional preprocessing to filter unwanted users from sensed trips data.\n", - "ct_users = expanded_ct['user_id'].apply(lambda x: str(x).replace('-', ''))\n", - "survey_users = survey_data['user_id'].apply(lambda x: str(x).replace('-', ''))\n", - "\n", - "common = set(ct_users.unique()).intersection(set(survey_users.unique()))\n", - "\n", - "filtered_trips = expanded_ct.loc[ct_users.isin(common), :].reset_index(drop=True)\n", - "filtered_survey = survey_data.loc[survey_users.isin(common), :].reset_index(drop=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "1ca22a08", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[trip data] After filtering, size goes from 240962 -> 205388\n", - "[trip data] Unique users after filtering: 184\n", - "[survey data] After filtering, size goes from 310 -> 286\n", - "[survey data] Unique users after filtering: 184\n" - ] - } - ], - "source": [ - "print(f\"[trip data] After filtering, size goes from {expanded_ct.shape[0]} -> {filtered_trips.shape[0]}\")\n", - "print(f\"[trip data] Unique users after filtering: {len(filtered_trips.user_id.unique())}\")\n", - "\n", - "print(f\"[survey data] After filtering, size goes from {survey_users.shape[0]} -> {filtered_survey.shape[0]}\")\n", - "print(f\"[survey data] Unique users after filtering: {len(filtered_survey.user_id.unique())}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "b48cd6e2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "uuid.UUID" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(filtered_trips.user_id[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "86deb40c", - "metadata": {}, - "outputs": [], - "source": [ - "# raw data dump.\n", - "expanded_ct.to_csv(f'../data/raw_data/trips__{CURRENT_DB}.csv', index=False)\n", - "survey_data.to_csv(f'../data/raw_data/survey__{CURRENT_DB}.csv', index=False)\n", - "\n", - "# filtered data dump.\n", - "filtered_trips.to_csv(f'../data/filtered_data/trips__{CURRENT_DB}.csv', index=False)\n", - "filtered_survey.to_csv(f'../data/filtered_data/survey__{CURRENT_DB}.csv', index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a1b140b9", - "metadata": {}, - "outputs": [], - "source": [ - "# durham has section modes and section distances.\n", - "# masscec does not have section distance and section modes.\n", - "# ride2own does not have section distances and modes.\n", - "# uprm nicr also does not have section distances and modes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "062c18c7", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ea248509", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "emission", - "language": "python", - "name": "emission" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/rm_src/multisource_data/02_extract_section_information.ipynb b/rm_src/multisource_data/02_extract_section_information.ipynb deleted file mode 100644 index ee5c2ac..0000000 --- a/rm_src/multisource_data/02_extract_section_information.ipynb +++ /dev/null @@ -1,299 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "e3166085", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "import pickle\n", - "import ast\n", - "import importlib\n", - "# from multiprocessing import cpu_count, Pool\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "\n", - "from enum import Enum\n", - "from pathlib import Path\n", - "from uuid import UUID\n", - "from bson.objectid import ObjectId\n", - "from collections import defaultdict\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5d482225", - "metadata": {}, - "outputs": [], - "source": [ - "emission_path = Path(os.getcwd()).parent.parent / 'my_emission_server' / 'e-mission-server'\n", - "sys.path.append(str(emission_path))\n", - "\n", - "# Also add the home (viz_scripts) to the path\n", - "sys.path.append('../viz_scripts')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "cf187095", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "storage not configured, falling back to sample, default configuration\n", - "URL not formatted, defaulting to openpath_prod_ride2own\n", - "Connecting to database URL localhost\n" - ] - } - ], - "source": [ - "import scaffolding\n", - "\n", - "import emission.core.get_database as edb\n", - "import emission.storage.timeseries.abstract_timeseries as esta\n", - "import emission.storage.decorations.trip_queries as esdt" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "5eba5f02", - "metadata": {}, - "outputs": [], - "source": [ - "class Sections(Enum):\n", - " DURATION = 0\n", - " DISTANCE = 1\n", - " MODE = 2\n", - " \n", - "# A lightweight wrapper around the lists to ease management.\n", - "class SectionData:\n", - " def __init__(self, section_dict):\n", - " self._section_dict = section_dict\n", - " \n", - " def get_section_data(self, section: Sections):\n", - " if section not in self._section_dict:\n", - " return []\n", - " return self._section_dict[section]\n", - " \n", - "SENSED_MODE_DICT = {\n", - " 'IN_VEHICLE': 'car',\n", - " 'BICYCLING': 'p_micro',\n", - " ''\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "fcd776e5", - "metadata": {}, - "outputs": [], - "source": [ - "REQUIRED = {\n", - " \"Stage_database\": [Sections.DURATION],\n", - " \"openpath_prod_durham\": [Sections.DURATION],\n", - " \"openpath_prod_mm_masscec\": [Sections.DURATION, Sections.DISTANCE, Sections.MODE],\n", - " \"openpath_prod_ride2own\": [Sections.DURATION, Sections.DISTANCE, Sections.MODE],\n", - " \"openpath_prod_uprm_nicr\": [Sections.DURATION, Sections.DISTANCE, Sections.MODE]\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "be70ead8", - "metadata": {}, - "outputs": [], - "source": [ - "def get_section_data(df: pd.DataFrame, db_name: str):\n", - " \n", - " def _get_section_data(row):\n", - " \n", - " user_id, trip_id = UUID(row.user_id), ObjectId(row.cleaned_trip)\n", - " \n", - " section_data = esdt.get_sections_for_trip(\n", - " key='analysis/cleaned_section', user_id=user_id, trip_id=trip_id\n", - " )\n", - " \n", - " if section_data is None or len(section_data) == 0:\n", - " section_data = esdt.get_sections_for_trip(\n", - " key='analysis/inferred_section', user_id=user_id, trip_id=trip_id\n", - " )\n", - " \n", - " if section_data is None or len(section_data) == 0:\n", - " return None\n", - " \n", - " required = REQUIRED[db_name]\n", - " \n", - " section_dict = dict()\n", - " \n", - " if Sections.DURATION in required:\n", - " section_dict[Sections.DURATION] = [x.data.duration for x in section_data]\n", - " else:\n", - " section_dict[Sections.DURATION] = ast.literal_eval(x.section_durations)\n", - " \n", - " if Sections.DISTANCE in required:\n", - " section_dict[Sections.DISTANCE] = [x.data.distance for x in section_data]\n", - " else:\n", - " section_dict[Sections.DISTANCE] = ast.literal_eval(x.section_distances)\n", - " \n", - " if Sections.MODE in required:\n", - " section_dict[Sections.MODE] = [x.data.sensed_mode for x in section_data]\n", - " else:\n", - " section_dict[Sections.MODE] = ast.literal_eval(x.section_modes)\n", - " \n", - " return SectionData(section_dict)\n", - " \n", - " \n", - " section_series = df.apply(lambda x: _get_section_data(x), axis=1)\n", - " \n", - " return section_series" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "27cd85fd", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_33228/382724123.py:41: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " sections.loc[:, 'user_id'] = sections.loc[:, 'user_id'].apply(lambda x: UUID(x))\n", - "/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_33228/382724123.py:42: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " sections.loc[:, 'cleaned_trip'] = sections.loc[:, 'cleaned_trip'].apply(lambda x: ObjectId(x))\n" - ] - } - ], - "source": [ - "df = pd.read_csv('../data/filtered_data/trips__openpath_prod_ride2own.csv')\n", - "\n", - "section_info = get_section_data(df, 'openpath_prod_ride2own')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "0ff2d17f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 <__main__.SectionData object at 0x165720d00>\n", - "1 <__main__.SectionData object at 0x165720880>\n", - "2 <__main__.SectionData object at 0x1657209d0>\n", - "3 <__main__.SectionData object at 0x165720f70>\n", - "4 <__main__.SectionData object at 0x165720fa0>\n", - " ... \n", - "5817 <__main__.SectionData object at 0x1659ebd60>\n", - "5818 <__main__.SectionData object at 0x1659eb400>\n", - "5819 <__main__.SectionData object at 0x1659ebf70>\n", - "5820 <__main__.SectionData object at 0x1659ebf10>\n", - "5821 <__main__.SectionData object at 0x1659eba60>\n", - "Length: 5822, dtype: object" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "section_info" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "938db9b9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{, , , , , }\n" - ] - } - ], - "source": [ - "modes = set()\n", - "for s in section_info:\n", - " modes.update(s.get_section_data(Sections.MODE))\n", - "\n", - "print(modes)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d30a8d58", - "metadata": {}, - "outputs": [], - "source": [ - "def compute_argmax(db_name: str, df:pd.DataFrame, sections: pd.Series):\n", - " # Evaluate once.\n", - " distance_mask = Sections.DISTANCE in REQUIRED[db_name]\n", - " mode_mask = Sections.MODE in REQUIRED[db_name]\n", - " \n", - " for ix, section in enumerate(sections):\n", - " distance = ast.literal_eval(\n", - " section.get_section_data(Sections.DISTANCE) if distance_mask else df.loc[ix, 'section_distances']\n", - " )\n", - " \n", - " duration = ast.literal_eval(section.get_section_data(Sections.DISTANCE))\n", - " \n", - " mode = ast.literal_eval(\n", - " section.get_section_data(Sections.DISTANCE) if distance_mask else df.loc[ix, 'section_modes']\n", - " )\n", - " \n", - " " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "emission", - "language": "python", - "name": "emission" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/rm_src/rf_bayesian_optim.py b/rm_src/rf_bayesian_optim.py deleted file mode 100644 index 6c911bd..0000000 --- a/rm_src/rf_bayesian_optim.py +++ /dev/null @@ -1,280 +0,0 @@ -import warnings -warnings.simplefilter(action='ignore', category=Warning) - -import os -import numpy as np -import pandas as pd -import pickle -from bayes_opt import BayesianOptimization -from sklearn.linear_model import LinearRegression -from sklearn.ensemble import RandomForestClassifier -from sklearn.model_selection import StratifiedGroupKFold -from sklearn.metrics import f1_score, log_loss, r2_score - -SEED = 13210 - -class BayesianCV: - def __init__(self, data): - - init_splitter = StratifiedGroupKFold(n_splits=5, shuffle=True, random_state=SEED) - X = data.drop(columns=['target']) - groups = data.user_id.values - y = data.target.values - - for train_ix, test_ix in init_splitter.split(X, y, groups): - train = data.iloc[train_ix, :] - test = data.iloc[test_ix, :] - - break - - # Can't have split, so let it happen for two times. - # train, test = train_test_split(data, test_size=0.2, shuffle=True, stratify=data.target) - - print("Train-test split done.") - - # Estimate the test durations using the train data. - params, train = self._get_duration_estimate(train, 'train', None) - _, test = self._get_duration_estimate(test, 'test', params) - - # We drop the training duration estimates since we will be re-computing them during CV. - train.drop(columns=[c for c in train.columns if 'tt_' in c], inplace=True) - - # This is out final train and test data. - self.data = train.reset_index(drop=True) - self.test = test.reset_index(drop=True) - - self._optimizer = self._setup_optimizer() - - - def _drop_columns(self, df: pd.DataFrame): - to_drop = [ - 'source', 'end_ts', 'end_fmt_time', 'end_loc', 'raw_trip', 'start_ts', - 'start_fmt_time', 'start_loc', 'duration', 'distance', 'start_place', - 'end_place', 'cleaned_trip', 'inferred_labels', 'inferred_trip', 'expectation', - 'confidence_threshold', 'expected_trip', 'user_input', 'start:year', 'start:month', - 'start:day', 'start_local_dt_minute', 'start_local_dt_second', - 'start_local_dt_weekday', 'start_local_dt_timezone', 'end:year', 'end:month', 'end:day', - 'end_local_dt_minute', 'end_local_dt_second', 'end_local_dt_weekday', - 'end_local_dt_timezone', '_id', 'user_id', 'metadata_write_ts', 'additions', - 'mode_confirm', 'purpose_confirm', 'Mode_confirm', 'Trip_purpose', - 'original_user_id', 'program', 'opcode', 'Timestamp', 'birth_year', - 'available_modes', 'section_coordinates_argmax', 'section_mode_argmax', - 'start_lat', 'start_lng', 'end_lat', 'end_lng' - ] - - # Drop section_mode_argmax and available_modes. - return df.drop( - columns=to_drop, - inplace=False - ) - - - def _get_duration_estimate(self, df: pd.DataFrame, dset: str, model_dict: dict): - - X_features = ['section_distance_argmax', 'age'] - - if 'mph' in df.columns: - X_features += ['mph'] - - if dset == 'train' and model_dict is None: - model_dict = dict() - - if dset == 'test' and model_dict is None: - raise AttributeError("Expected model dict for testing.") - - if dset == 'train': - for section_mode in df.section_mode_argmax.unique(): - section_data = df.loc[df.section_mode_argmax == section_mode, :] - if section_mode not in model_dict: - model_dict[section_mode] = dict() - - model = LinearRegression(fit_intercept=True) - - X = section_data[ - X_features - ] - Y = section_data[['section_duration_argmax']] - - model.fit(X, Y.values.ravel()) - - r2 = r2_score(y_pred=model.predict(X), y_true=Y.values.ravel()) - # print(f"Train R2 for {section_mode}: {r2}") - - model_dict[section_mode]['model'] = model - - elif dset == 'test': - for section_mode in df.section_mode_argmax.unique(): - section_data = df.loc[df.section_mode_argmax == section_mode, :] - X = section_data[ - X_features - ] - Y = section_data[['section_duration_argmax']] - - y_pred = model_dict[section_mode]['model'].predict(X) - r2 = r2_score(y_pred=y_pred, y_true=Y.values.ravel()) - # print(f"Test R2 for {section_mode}: {r2}") - - # Create the new columns for the duration. - new_columns = ['p_micro','no_trip','s_car','transit','car','s_micro','ridehail','walk','unknown'] - df[new_columns] = 0 - df['temp'] = 0 - - for section in df.section_mode_argmax.unique(): - X_section = df.loc[df.section_mode_argmax == section, X_features] - - # broadcast to all columns. - df.loc[df.section_mode_argmax == section, 'temp'] = model_dict[section]['model'].predict(X_section) - - for c in new_columns: - df[c] = df['av_' + c] * df['temp'] - - df.drop(columns=['temp'], inplace=True) - - df.rename(columns=dict([(x, 'tt_'+x) for x in new_columns]), inplace=True) - - # return model_dict, result_df - return model_dict, df - - - def _setup_optimizer(self): - # Define search space. - hparam_dict = { - # 10-500 - 'n_estimators': (0.25, 3), - # 5-150 - 'max_depth': (0.5, 15), - # 2-20 - 'min_samples_split': (0.2, 2.5), - # 1-20 - 'min_samples_leaf': (0.1, 2.5), - # as-is. - 'ccp_alpha': (0., 0.5), - # as-is. - 'max_features': (0.1, 0.99), - # Use clip to establish mask. - 'class_weight': (0, 1), - } - - return BayesianOptimization( - self._surrogate, - hparam_dict - ) - - - def _surrogate(self, n_estimators, max_depth, min_samples_split, min_samples_leaf, ccp_alpha, max_features, class_weight): - - cw = 'balanced_subsample' if class_weight < 0.5 else 'balanced' - - # Builds a surrogate model using the samples hparams. - model = RandomForestClassifier( - n_estimators=int(n_estimators * 100), - max_depth=int(max_depth * 10), - min_samples_split=int(min_samples_split * 10), - min_samples_leaf=int(min_samples_leaf * 10), - max_features=max(min(max_features, 0.999), 1e-3), - ccp_alpha=ccp_alpha, - bootstrap=True, - class_weight=cw, - n_jobs=os.cpu_count(), - random_state=SEED - ) - - fold_crossentropy = list() - - # Use the train split and further split in train-val. - X = self.data.drop(columns=['target']) - y = self.data.target.values.ravel() - users = X.user_id.values - - gkfold = StratifiedGroupKFold(n_splits=5, shuffle=True, random_state=SEED) - - for train_ix, test_ix in gkfold.split(X, y, users): - - X_train = X.iloc[train_ix, :] - X_test = X.iloc[test_ix, :] - - y_train = y[train_ix] - y_test = y[test_ix] - - # Re-estimate durations. - params, X_train = self._get_duration_estimate(X_train, 'train', None) - _, X_test = self._get_duration_estimate(X_test, 'test', params) - - X_train = self._drop_columns(X_train) - X_test = self._drop_columns(X_test) - - model.fit( - X_train, - y_train - ) - - # Measure performance on valid split. - ce = log_loss( - y_true=y_test, - y_pred=model.predict_proba(X_test), - labels=list(range(1, 10)) - ) - - fold_crossentropy.append(ce) - - # Return the average negative crossentropy (since bayesian optimization aims to maximize an objective). - return -np.mean(fold_crossentropy) - - - def optimize(self): - self._optimizer.maximize(n_iter=100, init_points=10) - print("Done optimizing!") - best_params = self._optimizer.max['params'] - best_loss = -self._optimizer.max['target'] - return best_loss, best_params - - -def train_final_model(params, cv_obj): - # Construct the model using the params. - model = RandomForestClassifier( - n_estimators=int(params['n_estimators'] * 100), - max_depth=int(params['max_depth'] * 10), - min_samples_split=int(params['min_samples_split'] * 10), - min_samples_leaf=int(params['min_samples_leaf'] * 10), - max_features=params['max_features'], - ccp_alpha=params['ccp_alpha'], - bootstrap=True, - class_weight='balanced_subsample', - n_jobs=os.cpu_count() - ) - - - X_tr = cv_obj.data.drop(columns=['target']) - y_tr = cv_obj.data.target.values.ravel() - - X_te = cv_obj.test.drop(columns=['target']) - y_te = cv_obj.test.target.values.ravel() - - params, X_tr = cv_obj._get_duration_estimate(X_tr, 'train', None) - - X_tr = cv_obj._drop_columns(X_tr) - X_te = cv_obj._drop_columns(X_te) - - model.fit( - X_tr, - y_tr - ) - - model.fit(X_tr, y_tr) - - print(f"Train loss: {log_loss(y_true=y_tr, y_pred=model.predict_proba(X_tr))}") - print(f"Train performance: {f1_score(y_true=y_tr, y_pred=model.predict(X_tr), average='weighted')}") - print(f"Test loss: {log_loss(y_true=y_te, y_pred=model.predict_proba(X_te))}") - print(f"Test performance: {f1_score(y_true=y_te, y_pred=model.predict(X_te), average='weighted')}") - - with open('./bayes_rf.pkl', 'wb') as f: - f.write(pickle.dumps(model)) - - -if __name__ == "__main__": - data = pd.read_csv('../data/ReplacedMode_Fix_02142024.csv') - bayes_cv = BayesianCV(data) - best_loss, best_params = bayes_cv.optimize() - print(f"Best loss: {best_loss}, best params: {str(best_params)}") - train_final_model(best_params, bayes_cv) - \ No newline at end of file diff --git a/viz_scripts/.DS_Store b/viz_scripts/.DS_Store index 147fc0ae208e4357c20fe72befadf1956a5d71e7..cea56581763453b1f7d758e2a09ed44dab75bcdf 100644 GIT binary patch literal 10244 zcmeHMU2GIp6h3EL%FICNv=&)P7dBM1)BJqiFnj&_qno=z|*KgNYauA50U)pBH0%F;O0jsL|lL_s%xE3&!}MkT|!w zbMCqKoO|zl_d9ob?*agJW%X8oC;&*5nN%0j;|htjvwT?!`8Aa!k^BKHFhPS1OhV!^ z>!2b)AV45MAV45MAVA<+K!Cp4EJ+K544wf30RjO6a|p2gAxfFam@j99nqbR3JBmO+v_wM#mDm0Air^sNdVD?kSaQg9dy$P~Nd@e=nU+vJ2h?$7<>Z?(ZB~vsC83QYg*ekw>3vMwrrekj!tc9+teK0 zu%&H!T9%fqYTed>b~h?&oHoP5A*jrTAqlP=;8Am);8g-qF z*Ub$_O!8%Q302Ly#+?Q^m^q_|+}v84bYVjI^@WS;Rx~uOY1_25v$ucOY~`XVRjE;G zhjXTDCM{!V!qmO7y_%acY~8dEjnZ=1o_WwTGP3%bJZNiaV^&>MReAlAaCjt}H1Ext z-lWEm; zv#h%E;9;VMa9yXgN~$goSz6MtMAZhRT~c?-!#M$?sNAA-NY&UZkEEP*nx^fF>RZDx zNj)ds&Bnf6bG?Ca!_Lp@)^Lv`jk;r6#u%6&ZstAuB#AE#)iak~-SnIcfAJml;qYG8 zRi~~Sy8qtcf$$xY`YHLSrkyh>GH7i@fJhCoCb0$_yoOkd&}5?AQleT6`d~lBC{Yeh zz$thRPQ%-97CwMa;5>W*U%}V#4SWwj!Ow6JenG?#mg79E!DU#F5nPRJxE?oRJ9c0X z_TqNz!(F%shjA~C;sG@A9<(rxSv-dK#ZDLjc!<12Uu-@rHVEPjaR@FV;Z zzr`Q$N4$W);&1pn{t;3`)uFo3icq7NbSNKQ!-ce>|7li}UK$eqE)p8QQb$@df+)>O$e6N&?C0l(ZZP{6{Y4bivC5Lb0VEOB#!C8FmyX2l z+M|>YP-cnQ%?Qa|=)p`_J-?18gCP1>`wd7?K(C z8S)s?CVvzZo_tY2a`Qd`B}QvyhIEEhpl~Wf5kn?WNjyU?kX-`h6$AOH3`szlY#<%a zPyiIqXUGDwlNm}T+et{9p9fl~@gEF;ECvQCp#DUlx&ok{JfPX}3@Ko9OMoQE`b4nF zsgrFaWXu?>ff{BYX_f$L&If9MSXByCS^(r1gZae_8DPz+Kx?J=7*--{oqSi&c=Bm+ zD_uUI;$)z*G$71p$N}38a)lnypi0l2{N$vZ{3M`GU@$R$p8QRG&SG{B4nbz12uP3{ hNVtN+Z)4$i=E?jjo}kcVV1k4!D6kne$Mei#1^~%pT0sB+ diff --git a/viz_scripts/Dockerfile b/viz_scripts/Dockerfile deleted file mode 100644 index e4c3c37..0000000 --- a/viz_scripts/Dockerfile +++ /dev/null @@ -1,42 +0,0 @@ -# python 3 -FROM emission/e-mission-server.dev.server-only:4.0.0 -ENV SERVER_REPO=https://github.com/e-mission/e-mission-server.git -ENV SERVER_BRANCH=join_redirect_to_static - -VOLUME /plots - -ADD docker/environment36.dashboard.additions.yml / - -RUN /bin/bash -c "/clone_server.sh" - -WORKDIR /usr/src/app - -RUN /bin/bash -c "cd e-mission-server && source setup/activate.sh && conda env update --name emission --file setup/environment36.notebook.additions.yml" -RUN /bin/bash -c "cd e-mission-server && source setup/activate.sh && conda env update --name emission --file /environment36.dashboard.additions.yml" - -RUN mkdir -p /usr/src/app/saved-notebooks -WORKDIR /usr/src/app/saved-notebooks - -COPY auxiliary_files ./auxiliary_files -COPY bin ./bin -COPY *.ipynb . -COPY *.py . - -# Delete all test packages since they generate false positives in the vulnerability scan -# e.g. -# root/miniconda-4.12.0/pkgs/conda-4.12.0-py38h06a4308_0/info/test/tests/data/env_metadata/py27-osx-no-binary/lib/python2.7/site-packages/requests-2.19.1-py2.7.egg-info/PKG-INFO -# root/miniconda-4.12.0/pkgs/conda-4.12.0-py38h06a4308_0/info/test/tests/data/env_metadata/py36-osx-whl/lib/python3.6/site-packages/Django-2.1.dist-info/METADATA -# root/miniconda-4.12.0/pkgs/conda-4.12.0-py38h06a4308_0/info/test/tests/data/env_metadata/py36-osx-whl/lib/python3.6/site-packages/Scrapy-1.5.1.dist-info/METADATA - -RUN /bin/bash -c "find /root/miniconda-*/pkgs -wholename \*info/test\* -type d | xargs rm -rf" - -WORKDIR /usr/src/app - -ADD docker/start_notebook.sh /usr/src/app/.docker/start_notebook.sh -RUN chmod u+x /usr/src/app/.docker/start_notebook.sh - -ADD docker/crontab /usr/src/app/crontab - -EXPOSE 8888 - -CMD ["/bin/bash", "/usr/src/app/.docker/start_notebook.sh"] diff --git a/viz_scripts/auxiliary_files/energy_intensity.csv b/viz_scripts/auxiliary_files/energy_intensity.csv deleted file mode 100644 index c203241..0000000 --- a/viz_scripts/auxiliary_files/energy_intensity.csv +++ /dev/null @@ -1,17 +0,0 @@ -mode,fuel,(kWH)/trip,EI(kWH/PMT),energy_intensity_factor,energy_intensity_units,CO2_factor,CO2_factor_units -"Gas Car, drove alone",gasoline,0,,5170,BTU/PMT,157.2,lb_CO2/MMBTU -"Gas Car, with others",gasoline,0,,2585,BTU/PMT,157.2,lb_CO2/MMBTU -"E-car, drove alone",electric,0,0.25,0.25,kWH/PMT,1166,lb_CO2/MWH -"E-car, with others",electric,0,0.125,0.125,kWH/PMT,1166,lb_CO2/MWH -Taxi/Uber/Lyft,gasoline,0,,7214,BTU/PMT,157.2,lb_CO2/MMBTU -Bus,diesel,0,,4560,BTU/PMT,161.3,lb_CO2/MMBTU -Free Shuttle,diesel,0,,4560,BTU/PMT,161.3,lb_CO2/MMBTU -Train,electric,0,0.37,0.37,kWH/PMT,1166,lb_CO2/MWH -Scooter share,electric,0.0041,0.027,0.027,kWH/PMT,1166,lb_CO2/MWH -E-bike,electric,0,0.022,0.022,kWH/PMT,1166,lb_CO2/MWH -Bikeshare,human_powered,0.09,0,0,kWH/PMT,1166,lb_CO2/MWH -Walk,human_powered,0,,0,,0,0 -Skate board,human_powered,0,,0,,0,0 -Regular Bike,human_powered,0,,0,,0,0 -Not a Trip,none,0,,0,,0,0 -No Travel,none,0,,0,,0,0 diff --git a/viz_scripts/auxiliary_files/mode_labels.csv b/viz_scripts/auxiliary_files/mode_labels.csv deleted file mode 100644 index 9397d75..0000000 --- a/viz_scripts/auxiliary_files/mode_labels.csv +++ /dev/null @@ -1,32 +0,0 @@ -replaced_mode,mode_confirm,mode_clean -drove_alone,drove_alone,"Gas Car, drove alone" -e_car_drove_alone,e_car_drove_alone,"E-car, drove alone" -work_vehicle,work_vehicle,"Gas Car, drove alone" -bus,bus,Bus -train,train,Train -free_shuttle,free_shuttle,Free Shuttle -"train,_bus and walk","train,_bus and walk",Train -train_and pilot e-bike,train_and pilot e-bike,Train -taxi,taxi,Taxi/Uber/Lyft -friend_picked me up,friend_picked me up,"Gas Car, with others" -carpool_w/ friend to work,carpool_w/ friend to work,"Gas Car, with others" -friend_carpool to work,friend_carpool to work,"Gas Car, with others" -carpool_to work,carpool_to work,"Gas Car, with others" -friend/co_worker carpool,friend/co_worker carpool,"Gas Car, with others" -carpool_to lunch,carpool_to lunch,"Gas Car, with others" -carpool,carpool,"Gas Car, with others" -carpool_for lunch,carpool_for lunch,"Gas Car, with others" -carpool_lunch,carpool_lunch,"Gas Car, with others" -shared_ride,shared_ride,"Gas Car, with others" -e_car_shared_ride,e_car_shared_ride,"E-car, with others" -bikeshare,bikeshare,Bikeshare -scootershare,scootershare,Scooter share -pilot_ebike,pilot_ebike,E-bike -e-bike,e-bike,E-bike -walk,walk,Walk -skateboard,skateboard,Skate board -bike,bike,Regular Bike -the_friend who drives us to work was running errands after the shift before dropping me off. not a trip of mine.,the_friend who drives us to work was running errands after the shift before dropping me off. not a trip of mine.,Not a Trip -not_a_trip,not_a_trip,Not a Trip -no_travel,,No Travel -same_mode,,Same Mode diff --git a/viz_scripts/auxiliary_files/purpose_labels.csv b/viz_scripts/auxiliary_files/purpose_labels.csv deleted file mode 100644 index 298e8aa..0000000 --- a/viz_scripts/auxiliary_files/purpose_labels.csv +++ /dev/null @@ -1,47 +0,0 @@ -purpose_confirm,bin_purpose -work_travel,Work -work,Work -home,Home -meal,Meal -shopping,Shopping -personal_med,Personal/Medical -exercise,Recreation/Exercise -transit_transfer,Transit transfer -pick_drop,Pick-up/Drop off -entertainment,Entertainment/Social -car_mechanic,Other -school,School -revisado_bike,Other -placas_de carro,Other -community_walk,Entertainment/Social -gardening,Entertainment/Social -visiting,Entertainment/Social -church,Religious -community_garden,Entertainment/Social -community_meeting,Entertainment/Social -visit_a friend,Entertainment/Social -aseguranza,Other -meeting_bike,Entertainment/Social -gas_station,Other -iglesia,Religious -curso,School -mi_hija recién aliviada,Entertainment/Social -servicio_comunitario,Entertainment/Social -pago_de aseguranza,Other -grupo_comunitario,Entertainment/Social -caminata_comunitaria,Entertainment/Social -bank,Other -religious,Religious -no_travel,No travel -work_break - short walk,Entertainment/Social -work_- lunch break,Meal -friend_was running errands before dropping me off after work,Other -"multiple_errands, etc.",Other -lunch_break,Meal -break,Entertainment/Social -pet,Entertainment/Social -recording_performance at park,Entertainment/Social -not_a trip,not_a_trip -on_the way home,Home -other,Other -nan,nan \ No newline at end of file diff --git a/viz_scripts/bin/generate_plots.py b/viz_scripts/bin/generate_plots.py deleted file mode 100644 index 966e5fd..0000000 --- a/viz_scripts/bin/generate_plots.py +++ /dev/null @@ -1,87 +0,0 @@ -import nbclient -import nbformat -import nbparameterise as nbp -import argparse -import arrow -import requests -import json -import os -import sys - - -# Configuration settings to use for all generated plots by this instance -# This could also be specified as a parser argument, if we want to generate plots for all programs from one instance -# Full list is at -# https://github.com/e-mission/nrel-openpath-deploy-configs/tree/main/configs -STUDY_CONFIG = os.getenv('STUDY_CONFIG', "stage-program") - -parser = argparse.ArgumentParser(prog="generate_metrics") -parser.add_argument("plot_notebook", help="the notebook the generates the plot") -parser.add_argument("program", help="the program for the plot") -parser.add_argument("-d", "--date", nargs=2, type=int, - help="the year and month for the plot. Default: all previous days and months since program start + one combined for the program as a whole") - -args = parser.parse_args() - -# Read and use parameters from the unified config file on the e-mission Github page -download_url = "https://raw.githubusercontent.com/e-mission/nrel-openpath-deploy-configs/main/configs/" + STUDY_CONFIG + ".nrel-op.json" -print("About to download config from %s" % download_url) -r = requests.get(download_url) -if r.status_code is not 200: - print(f"Unable to download study config, status code: {r.status_code}") - sys.exit(1) -else: - dynamic_config = json.loads(r.text) - print(f"Successfully downloaded config with version {dynamic_config['version']} "\ - f"for {dynamic_config['intro']['translated_text']['en']['deployment_name']} "\ - f"and data collection URL {dynamic_config['server']['connectUrl']}") - -if dynamic_config['intro']['program_or_study'] == 'program': - mode_studied = dynamic_config['intro']['mode_studied'] -else: - mode_studied = None - -if args.date is None: - start_date = arrow.get(int(dynamic_config['intro']['start_year']), - int(dynamic_config['intro']['start_month']), 1) - end_date = arrow.get() -else: - start_date = arrow.get() - end_date = start_date - -compute_range = list(arrow.Arrow.range('month', start_date, end_date)) - -print(f"Running at {arrow.get()} with args {args} for range {compute_range[0], compute_range[-1]}") - -with open(args.plot_notebook) as f: - nb = nbformat.read(f, as_version=4) - -# Get a list of Parameter objects -orig_parameters = nbp.extract_parameters(nb) - -# We will be recomputing values for multiple months -# So let's make a common function to invoke -def compute_for_date(month, year): - params = nbp.parameter_values( - orig_parameters, - year=year, - month=month, - program=args.program, - study_type=dynamic_config['intro']['program_or_study'], - mode_of_interest=mode_studied, - include_test_users=dynamic_config.get('metrics', {}).get('include_test_users', False)) - - print(f"Running at {arrow.get()} with params {params}") - - # Make a notebook object with these definitions - new_nb = nbp.replace_definitions(nb, params, execute=False) - - # Execute the notebook with the new parameters - nbclient.execute(new_nb) - -# Compute the overall metrics -compute_for_date(None, None) - -# Compute for every month until now -for month_year in compute_range: - compute_for_date(month_year.month, month_year.year) diff --git a/viz_scripts/bin/update_mappings.py b/viz_scripts/bin/update_mappings.py deleted file mode 100644 index 906bfb2..0000000 --- a/viz_scripts/bin/update_mappings.py +++ /dev/null @@ -1,16 +0,0 @@ -# In a docker setup, run as -# sudo run_from_host/update_mappings.sh -# -# -import nbclient -import nbformat -import argparse - -parser = argparse.ArgumentParser(prog="update_mappings") -parser.add_argument("mapping_notebook", help="the notebook the stores the mappings") - -args = parser.parse_args() - -with open(args.mapping_notebook) as f: - nb = nbformat.read(f, as_version=4) - nbclient.execute(nb) diff --git a/viz_scripts/denver_weather_data.csv b/viz_scripts/denver_weather_data.csv deleted file mode 100644 index 77def6f..0000000 --- a/viz_scripts/denver_weather_data.csv +++ /dev/null @@ -1,26329 +0,0 @@ -time,temperature_2m (°F),relative_humidity_2m (%),dew_point_2m (°F),rain (inch),snowfall (inch),wind_speed_10m (mp/h),wind_gusts_10m (mp/h) -2020-01-01T00:00,24.1,56,10.8,0,0,7.3,15 -2020-01-01T01:00,24,58,11.3,0,0,6,12.5 -2020-01-01T02:00,26.7,57,13.5,0,0,7.7,14.1 -2020-01-01T03:00,27.1,57,13.7,0,0,6.5,13.2 -2020-01-01T04:00,24.9,61,13.3,0,0,5.6,11.2 -2020-01-01T05:00,30.8,57,17.3,0,0,8.9,8.9 -2020-01-01T06:00,28.7,61,16.9,0,0,10.6,17.7 -2020-01-01T07:00,27.1,68,18,0,0,5.5,17.4 -2020-01-01T08:00,30.7,67,21.1,0,0,5.6,9.8 -2020-01-01T09:00,33.6,65,23.3,0,0,5.1,9.2 -2020-01-01T10:00,38.3,59,25.3,0,0,8.1,13.2 -2020-01-01T11:00,42,54,26.7,0,0,9.5,16.8 -2020-01-01T12:00,39.5,67,29.3,0,0,5.1,16.8 -2020-01-01T13:00,38.3,72,30,0.008,0,6,42.1 -2020-01-01T14:00,39,75,31.6,0,0,5.8,10.1 -2020-01-01T15:00,42.8,59,29.7,0,0,3.2,9.4 -2020-01-01T16:00,38.1,65,27.5,0,0,7,8.5 -2020-01-01T17:00,31.2,67,21.5,0,0,7.4,11.2 -2020-01-01T18:00,30,70,21.6,0,0,6.3,10.5 -2020-01-01T19:00,29.3,73,21.6,0,0,8.2,13.9 -2020-01-01T20:00,26.9,71,18.9,0,0,6.7,13.6 -2020-01-01T21:00,23.7,65,13.7,0,0,10.4,13.2 -2020-01-01T22:00,24.5,58,11.9,0,0,10.1,13.2 -2020-01-01T23:00,34.7,43,14.4,0,0,6.5,12.8 -2020-01-02T00:00,32.2,47,14.4,0,0,3.6,8.5 -2020-01-02T01:00,28.3,52,13.1,0,0,6.9,8.5 -2020-01-02T02:00,26.1,51,10.6,0,0,8.9,11.2 -2020-01-02T03:00,18.3,57,5.6,0,0,12.2,14.8 -2020-01-02T04:00,32.3,36,8.1,0,0,4.3,16.1 -2020-01-02T05:00,24.9,69,16.1,0,0,6,10.5 -2020-01-02T06:00,24.9,63,14.1,0,0,7.5,11.2 -2020-01-02T07:00,22.2,72,14.5,0,0,8.4,12.1 -2020-01-02T08:00,20.1,73,12.9,0,0,7.9,11 -2020-01-02T09:00,30.7,62,19.3,0,0,5.3,11 -2020-01-02T10:00,36.2,53,20.9,0,0,6.5,12.1 -2020-01-02T11:00,37.7,50,20.8,0,0,5.7,12.5 -2020-01-02T12:00,38.6,51,22.1,0,0,3.4,11.2 -2020-01-02T13:00,42.6,30,13.2,0,0,4.8,11.6 -2020-01-02T14:00,42.4,30,13.4,0,0,8.9,15.7 -2020-01-02T15:00,39.8,35,14.5,0,0,11.6,19.7 -2020-01-02T16:00,36.4,39,13.8,0,0,10.7,19.9 -2020-01-02T17:00,33.9,52,18.3,0,0,4.8,17 -2020-01-02T18:00,29.7,62,18.1,0,0,3.8,5.6 -2020-01-02T19:00,30.1,53,15.2,0,0,1.8,6.3 -2020-01-02T20:00,26.3,61,14.6,0,0,4.6,5.4 -2020-01-02T21:00,29.2,46,10.9,0,0,9.6,13.2 -2020-01-02T22:00,28.2,48,11.2,0,0,6,17.4 -2020-01-02T23:00,24.3,57,11.3,0,0,4.7,9.4 -2020-01-03T00:00,22.6,58,10,0,0,4.1,6.7 -2020-01-03T01:00,22.6,53,8.2,0,0,4.3,6.9 -2020-01-03T02:00,22.8,51,7.3,0,0,3.6,6.9 -2020-01-03T03:00,22.3,51,6.9,0,0,4.3,6.3 -2020-01-03T04:00,22.6,49,6.4,0,0,3.3,6.5 -2020-01-03T05:00,24.9,49,8.5,0,0,6.4,5.6 -2020-01-03T06:00,25.3,50,9.1,0,0,5.3,8.3 -2020-01-03T07:00,23,55,9.2,0,0,9.8,14.3 -2020-01-03T08:00,23,53,8.3,0,0,9.6,14.8 -2020-01-03T09:00,28.8,44,9.7,0,0,9.4,16.3 -2020-01-03T10:00,34.2,38,10.9,0,0,8.4,16.1 -2020-01-03T11:00,40.2,29,10.6,0,0,7.1,15.7 -2020-01-03T12:00,42.5,27,11,0,0,0.9,14.1 -2020-01-03T13:00,43.3,31,14.7,0,0,2.7,8.5 -2020-01-03T14:00,43.3,33,16.3,0,0,5.8,10.3 -2020-01-03T15:00,41,42,19.9,0,0,9.1,16.6 -2020-01-03T16:00,37,49,19.7,0,0,5.9,14.3 -2020-01-03T17:00,42.6,34,16.4,0,0,4.8,8.7 -2020-01-03T18:00,36.5,46,17.9,0,0,3.2,6.5 -2020-01-03T19:00,32,60,19.6,0,0,4.1,5.6 -2020-01-03T20:00,32.1,60,19.7,0,0,3.6,6 -2020-01-03T21:00,31.2,63,20.1,0,0,4.5,9.4 -2020-01-03T22:00,30.7,64,19.9,0,0,1.6,7.6 -2020-01-03T23:00,30.3,66,20.2,0,0,2.7,4.5 -2020-01-04T00:00,29.8,67,20.1,0,0,5.9,9.6 -2020-01-04T01:00,29.8,64,19.1,0,0,3.7,9.6 -2020-01-04T02:00,28.6,67,18.9,0,0,5.2,6 -2020-01-04T03:00,28.2,66,18.1,0,0,4.4,6 -2020-01-04T04:00,29.9,59,17.5,0,0,5.5,8.9 -2020-01-04T05:00,29.6,63,18.5,0,0,5.1,8.9 -2020-01-04T06:00,30.2,61,18.3,0,0,4.6,8.3 -2020-01-04T07:00,29.9,61,18.2,0,0,7.4,8.7 -2020-01-04T08:00,33.7,53,18.5,0,0,5.9,9.2 -2020-01-04T09:00,40.9,44,20.4,0,0,5.9,9.2 -2020-01-04T10:00,45.2,45,25.3,0,0,7.9,13.9 -2020-01-04T11:00,49.6,41,26.6,0,0,8.6,15.2 -2020-01-04T12:00,50.6,39,26.3,0,0,10.3,18.1 -2020-01-04T13:00,53.1,35,26.2,0,0,10.3,18.1 -2020-01-04T14:00,54.4,33,26.2,0,0,7.6,18.1 -2020-01-04T15:00,54.1,38,29,0,0,3.2,13.2 -2020-01-04T16:00,49.7,49,31.6,0,0,4.5,5.6 -2020-01-04T17:00,50.5,43,28.8,0,0,1.6,5.6 -2020-01-04T18:00,35.5,73,27.8,0,0,5.6,6.5 -2020-01-04T19:00,39.7,55,24.8,0,0,3.8,8.1 -2020-01-04T20:00,38.7,54,23.4,0,0,4,5.4 -2020-01-04T21:00,35.3,53,19.8,0,0,6.5,9.8 -2020-01-04T22:00,39.2,39,16.3,0,0,9.8,15 -2020-01-04T23:00,43.2,30,13.6,0,0,15.2,24.2 -2020-01-05T00:00,44.2,29,13.7,0,0,22.1,36.2 -2020-01-05T01:00,43.6,30,14.1,0,0,22.2,38.9 -2020-01-05T02:00,41.7,31,13.2,0,0,6.4,37.1 -2020-01-05T03:00,32.6,55,18.1,0,0,9.1,13.4 -2020-01-05T04:00,27.7,71,19.6,0,0,5.6,16.1 -2020-01-05T05:00,25.9,73,18.3,0,0,6.3,9.2 -2020-01-05T06:00,25.9,67,16.4,0,0,5.4,10.1 -2020-01-05T07:00,24.5,68,15.6,0,0,4.8,8.7 -2020-01-05T08:00,26.8,61,15.2,0,0,2.9,7.2 -2020-01-05T09:00,31.3,49,14.5,0,0,4.1,8.7 -2020-01-05T10:00,36.3,43,16,0,0,5.6,10.7 -2020-01-05T11:00,38.5,37,14.4,0,0,9.2,17.4 -2020-01-05T12:00,41.3,35,15.7,0,0,7.2,17.7 -2020-01-05T13:00,43.5,36,18.1,0,0,4.1,15.2 -2020-01-05T14:00,44.2,36,18.9,0,0,4.2,11 -2020-01-05T15:00,43.9,38,19.7,0,0,6.6,12.3 -2020-01-05T16:00,39.9,48,21.6,0,0,10.3,16.3 -2020-01-05T17:00,30.9,60,18.8,0,0,11.1,18.8 -2020-01-05T18:00,29.1,61,17.4,0,0,5.6,18.3 -2020-01-05T19:00,26,68,17,0,0,6.5,8.9 -2020-01-05T20:00,25.4,67,16.1,0,0,2.9,7.8 -2020-01-05T21:00,27.6,58,14.9,0,0,2.4,4 -2020-01-05T22:00,28,54,13.4,0,0,1.4,5.4 -2020-01-05T23:00,23.4,66,13.7,0,0,3.4,5.6 -2020-01-06T00:00,22.6,72,14.8,0,0,2.9,7.2 -2020-01-06T01:00,23.1,68,14.1,0,0,5.4,7.4 -2020-01-06T02:00,30.4,41,9.6,0,0,12.3,18.3 -2020-01-06T03:00,30.9,35,6.3,0,0,15.7,25.9 -2020-01-06T04:00,30.2,33,4.2,0,0,16.4,27.5 -2020-01-06T05:00,23.8,46,6.1,0,0,7.1,27.3 -2020-01-06T06:00,23.4,52,8.2,0,0,3.8,9.2 -2020-01-06T07:00,23,49,6.5,0,0,6.2,10.1 -2020-01-06T08:00,24.6,45,6.1,0,0,6.1,10.3 -2020-01-06T09:00,29.2,39,7.3,0,0,6.8,12.1 -2020-01-06T10:00,33.7,35,8.9,0,0,6.3,13.9 -2020-01-06T11:00,37.9,29,8.2,0,0,6.5,15 -2020-01-06T12:00,40.1,23,5.5,0,0,14.9,23.9 -2020-01-06T13:00,39.9,24,6.4,0,0,14.1,27.3 -2020-01-06T14:00,40.2,25,6.7,0,0,10.8,24.8 -2020-01-06T15:00,40.1,25,6.6,0,0,5.3,19.2 -2020-01-06T16:00,36.9,38,13.6,0,0,4.2,15.7 -2020-01-06T17:00,29.4,42,9,0,0,7.9,7.4 -2020-01-06T18:00,34.8,31,7.3,0,0,3.4,9.2 -2020-01-06T19:00,26.4,47,8.7,0,0,4.7,7.2 -2020-01-06T20:00,24.1,54,9.9,0,0,6.1,8.1 -2020-01-06T21:00,23.9,54,9.5,0,0,6.6,8.3 -2020-01-06T22:00,25.3,47,8,0,0,5.8,8.3 -2020-01-06T23:00,26.2,42,6.4,0,0,9.7,15 -2020-01-07T00:00,26.2,43,7,0,0,10.9,17.4 -2020-01-07T01:00,25.9,46,7.8,0,0,11.1,17.4 -2020-01-07T02:00,23.6,50,7.9,0,0,9.7,18.6 -2020-01-07T03:00,25.5,47,8.2,0,0,2.7,14.8 -2020-01-07T04:00,23.2,54,8.9,0,0,3.6,5.6 -2020-01-07T05:00,24.2,61,12.5,0,0,5.9,7.6 -2020-01-07T06:00,26.1,58,13.3,0,0,4.5,7.2 -2020-01-07T07:00,26.2,57,13.2,0,0,5.2,7.8 -2020-01-07T08:00,28,56,14.4,0,0,3.7,7.6 -2020-01-07T09:00,33.2,49,15.9,0,0,5.7,10.1 -2020-01-07T10:00,39.2,41,17.7,0,0,6.7,13.4 -2020-01-07T11:00,44.9,34,18.5,0,0,5.2,13.4 -2020-01-07T12:00,48.2,31,18.9,0,0,6,13.2 -2020-01-07T13:00,50,28,18.1,0,0,6.9,14.1 -2020-01-07T14:00,50.9,27,18.2,0,0,6.6,14.3 -2020-01-07T15:00,50.5,29,19.8,0,0,5.9,13.2 -2020-01-07T16:00,45.1,55,29.9,0,0,4.5,10.1 -2020-01-07T17:00,36.8,59,23.6,0,0,7.4,5.4 -2020-01-07T18:00,34.7,60,22.4,0,0,4.3,8.9 -2020-01-07T19:00,30.7,67,21,0,0,5,8.9 -2020-01-07T20:00,27.6,69,18.6,0,0,4.4,7.6 -2020-01-07T21:00,26.4,66,16.6,0,0,5.5,7.2 -2020-01-07T22:00,25.6,64,15.2,0,0,6.7,8.3 -2020-01-07T23:00,25.5,62,14.5,0,0,6.5,8.1 -2020-01-08T00:00,25.4,62,14.3,0,0,6,8.1 -2020-01-08T01:00,25.8,62,14.6,0,0,5.9,7.6 -2020-01-08T02:00,25.4,62,14.2,0,0,6.2,7.8 -2020-01-08T03:00,25.9,58,13.3,0,0,4.5,7.6 -2020-01-08T04:00,25.9,57,12.7,0,0,4.3,6.7 -2020-01-08T05:00,24.5,61,13,0,0,6.3,6.7 -2020-01-08T06:00,25.2,59,12.7,0,0,6.6,7.8 -2020-01-08T07:00,25.8,55,11.9,0,0,6.2,8.1 -2020-01-08T08:00,29.5,47,11.5,0,0,4.5,7.8 -2020-01-08T09:00,35.6,42,14.5,0,0,5.8,10.5 -2020-01-08T10:00,42.6,35,16.6,0,0,6.6,12.8 -2020-01-08T11:00,47.5,29,16.9,0,0,9.1,16.6 -2020-01-08T12:00,51.2,25,16.5,0,0,10.6,19 -2020-01-08T13:00,53,24,16.7,0,0,13.5,23.3 -2020-01-08T14:00,53.2,23,16.5,0,0,12.1,23.7 -2020-01-08T15:00,52.3,27,19,0,0,8,21 -2020-01-08T16:00,44.9,51,27.6,0,0,6.3,12.8 -2020-01-08T17:00,43.5,31,14.5,0,0,8.3,7.6 -2020-01-08T18:00,36.6,40,14.4,0,0,5.9,13.9 -2020-01-08T19:00,35.7,41,14.5,0,0,1.4,9.4 -2020-01-08T20:00,33.4,51,17.2,0,0,1.4,4 -2020-01-08T21:00,28.5,50,12.4,0,0,5.2,8.1 -2020-01-08T22:00,28.8,43,8.9,0,0,6,9.4 -2020-01-08T23:00,27.7,48,10.5,0,0,6,11 -2020-01-09T00:00,26,55,11.9,0,0,5,9.8 -2020-01-09T01:00,23.4,65,13.2,0,0,6.5,8.3 -2020-01-09T02:00,22.3,68,13.4,0,0,3.2,8.1 -2020-01-09T03:00,22.5,67,13.2,0,0,4.3,5.4 -2020-01-09T04:00,24.6,56,11.3,0,0,2.3,5.4 -2020-01-09T05:00,22.6,63,12.1,0,0,3.5,4.7 -2020-01-09T06:00,23.2,63,12.5,0,0,3.4,4.7 -2020-01-09T07:00,24.6,58,12,0,0,3.9,5.8 -2020-01-09T08:00,25.3,56,11.9,0,0,4.7,6.3 -2020-01-09T09:00,30.7,46,12.6,0,0,1.6,7.6 -2020-01-09T10:00,35.2,40,13.4,0,0,3.5,8.7 -2020-01-09T11:00,37.9,33,11.3,0,0,4.3,11.6 -2020-01-09T12:00,40.1,29,10.6,0,0,1.6,11.2 -2020-01-09T13:00,40.7,29,11.1,0,0,4.1,11 -2020-01-09T14:00,41.3,28,10.9,0,0,6.9,14.1 -2020-01-09T15:00,40.9,29,10.9,0,0,7.7,15 -2020-01-09T16:00,38.7,46,19.5,0,0,2.8,13.9 -2020-01-09T17:00,36.2,49,18.9,0,0,5.1,8.7 -2020-01-09T18:00,35.6,49,18.1,0,0,4,7.4 -2020-01-09T19:00,31.3,59,18.8,0,0,4.1,6.3 -2020-01-09T20:00,30.8,66,20.9,0,0,4.7,6 -2020-01-09T21:00,31.1,68,21.9,0,0.028,0.4,5.6 -2020-01-09T22:00,30,72,22.2,0,0.028,1.8,2.9 -2020-01-09T23:00,27.5,79,21.9,0,0,3.4,4.7 -2020-01-10T00:00,29.1,72,21.4,0,0,1,4 -2020-01-10T01:00,27.2,76,20.8,0,0,0.8,1.6 -2020-01-10T02:00,22.2,85,18.4,0,0,4.6,4.9 -2020-01-10T03:00,22.3,73,15,0,0,4.5,6.3 -2020-01-10T04:00,21.7,67,12.3,0,0,3.3,6 -2020-01-10T05:00,23.5,58,10.9,0,0,4.7,4.5 -2020-01-10T06:00,23.5,63,12.9,0,0,5.2,7.4 -2020-01-10T07:00,21.9,67,12.7,0,0.028,5.6,7.6 -2020-01-10T08:00,25.1,59,12.9,0,0.028,1.6,6.7 -2020-01-10T09:00,28.3,55,14.1,0,0,3.2,7.4 -2020-01-10T10:00,30.7,48,13.5,0,0,5.4,12.1 -2020-01-10T11:00,32.2,42,11.8,0,0,7.5,15.7 -2020-01-10T12:00,32.9,41,11.5,0,0,8.2,17.2 -2020-01-10T13:00,32.1,43,12.3,0,0,8.3,17.2 -2020-01-10T14:00,31.6,46,13,0,0,7.7,17.4 -2020-01-10T15:00,30.8,44,11.6,0,0,6.5,15.9 -2020-01-10T16:00,29.1,44,9.9,0,0,5.6,13.2 -2020-01-10T17:00,22.6,58,10,0,0,6,9.4 -2020-01-10T18:00,19.9,65,10.1,0,0,3.2,9.8 -2020-01-10T19:00,18.2,71,10.5,0,0,3.6,5.4 -2020-01-10T20:00,18.1,70,10,0,0,3.2,4.7 -2020-01-10T21:00,18.3,70,10.2,0,0,1.8,3.6 -2020-01-10T22:00,18,71,10,0,0,3,4.3 -2020-01-10T23:00,17.9,72,10.4,0,0,3.3,5.6 -2020-01-11T00:00,17.7,74,10.8,0,0,2.8,5.6 -2020-01-11T01:00,19.2,70,10.9,0,0,0.5,3.8 -2020-01-11T02:00,17.7,74,10.8,0,0,3,3.6 -2020-01-11T03:00,16.6,72,9.2,0,0,2.6,3.4 -2020-01-11T04:00,15.8,65,6.1,0,0,1.8,2.9 -2020-01-11T05:00,15.1,47,-1.7,0,0,2.5,2.7 -2020-01-11T06:00,16.2,38,-5.1,0,0,2.7,6 -2020-01-11T07:00,16.8,37,-4.9,0,0,1.7,4.3 -2020-01-11T08:00,18.6,41,-1.5,0,0,5.6,9.4 -2020-01-11T09:00,19.5,48,2.8,0,0,5,11 -2020-01-11T10:00,21.9,39,0.9,0,0,3.3,12.5 -2020-01-11T11:00,26.3,32,0.4,0,0,4.1,12.5 -2020-01-11T12:00,31.3,26,0.6,0,0,5.4,13.2 -2020-01-11T13:00,35.9,23,1.4,0,0,6.3,14.5 -2020-01-11T14:00,38.9,21,2.7,0,0,5.7,14.5 -2020-01-11T15:00,39.5,22,3.7,0,0,3.2,12.8 -2020-01-11T16:00,34.4,29,5.1,0,0,4.1,7.8 -2020-01-11T17:00,30.5,54,15.6,0,0,6.2,5.8 -2020-01-11T18:00,32.5,54,17.7,0,0,4,7.6 -2020-01-11T19:00,30.2,66,20.1,0,0.028,4.9,6.7 -2020-01-11T20:00,30.7,58,17.5,0,0,4.5,6.7 -2020-01-11T21:00,30.7,53,15.4,0,0,5.5,8.9 -2020-01-11T22:00,26.8,60,14.6,0,0,4.9,8.9 -2020-01-11T23:00,24,62,13,0,0,4.3,7.8 -2020-01-12T00:00,21.9,63,11.2,0,0,3.6,7.4 -2020-01-12T01:00,19.9,64,9.7,0,0,3.8,5.8 -2020-01-12T02:00,19.9,62,9.1,0,0,3.8,6 -2020-01-12T03:00,19.5,57,6.9,0,0,4.4,7.6 -2020-01-12T04:00,20.2,52,5.5,0,0,4,7.6 -2020-01-12T05:00,19.7,53,5.5,0,0,2.8,8.9 -2020-01-12T06:00,20.7,49,4.6,0,0,3.6,5.6 -2020-01-12T07:00,19.9,47,2.8,0,0,6,9.6 -2020-01-12T08:00,20.9,43,1.8,0,0,8.1,12.1 -2020-01-12T09:00,28,31,1,0,0,6.5,13 -2020-01-12T10:00,35.2,23,1.3,0,0,5.8,12.5 -2020-01-12T11:00,37.5,21,1.5,0,0,3.8,12.5 -2020-01-12T12:00,39.7,18,-0.8,0,0,2.6,11 -2020-01-12T13:00,41.1,15,-3.5,0,0,2.5,9.6 -2020-01-12T14:00,41.8,14,-4.1,0,0,6.5,13 -2020-01-12T15:00,41,16,-2.4,0,0,7.1,13.9 -2020-01-12T16:00,37.3,23,3.1,0,0,10.3,17 -2020-01-12T17:00,33.8,34,8.3,0,0,6.5,17 -2020-01-12T18:00,31.3,37,7.7,0,0,7.6,10.3 -2020-01-12T19:00,25.3,45,7,0,0,3.2,8.9 -2020-01-12T20:00,21.7,58,9.1,0,0,2,4.7 -2020-01-12T21:00,20.1,62,9.3,0,0,4.7,5.4 -2020-01-12T22:00,19.8,60,8.1,0,0,4.8,6.3 -2020-01-12T23:00,19.6,57,7,0,0,5.1,5.8 -2020-01-13T00:00,20.8,50,5.1,0,0,4.4,6.7 -2020-01-13T01:00,19,53,4.6,0,0,4.5,6.7 -2020-01-13T02:00,21.1,47,4.2,0,0,3.8,6.3 -2020-01-13T03:00,19.5,48,3,0,0,4.6,6.3 -2020-01-13T04:00,19.6,46,2.2,0,0,4.3,6.3 -2020-01-13T05:00,19.9,46,2.6,0,0,6,6.5 -2020-01-13T06:00,19,47,2,0,0,4.1,7.4 -2020-01-13T07:00,18.1,49,1.9,0,0,4.7,5.8 -2020-01-13T08:00,21.6,42,2,0,0,3.2,6.3 -2020-01-13T09:00,28,32,2.1,0,0,0.8,5.6 -2020-01-13T10:00,32.9,27,2.3,0,0,1.3,7.6 -2020-01-13T11:00,37.6,23,3.3,0,0,1.6,7.8 -2020-01-13T12:00,40.1,22,3.8,0,0,5.3,12.8 -2020-01-13T13:00,38.9,24,5,0,0,8.1,15 -2020-01-13T14:00,40.3,25,7.3,0,0,6,17.4 -2020-01-13T15:00,41.8,23,6.8,0,0,1.8,12.5 -2020-01-13T16:00,38.9,23,4.3,0,0,5.1,7.8 -2020-01-13T17:00,29.7,45,10.7,0,0,7,10.5 -2020-01-13T18:00,32.5,41,11.4,0,0,2.2,7.8 -2020-01-13T19:00,23.5,61,11.9,0,0,6.5,7.8 -2020-01-13T20:00,24,60,12.1,0,0,4.9,8.1 -2020-01-13T21:00,25.4,50,9.1,0,0,7,11.2 -2020-01-13T22:00,24.4,48,7.3,0,0,6,11.2 -2020-01-13T23:00,23.4,51,8,0,0,6.5,8.7 -2020-01-14T00:00,23.6,52,8.6,0,0,5.2,8.1 -2020-01-14T01:00,22.6,53,7.9,0,0,5.5,6.5 -2020-01-14T02:00,21.7,54,7.4,0,0,5.6,7.4 -2020-01-14T03:00,21.3,54,7.1,0,0,5.5,6.9 -2020-01-14T04:00,22.4,50,6.5,0,0,4.2,6.7 -2020-01-14T05:00,21.2,51,5.7,0,0,5.4,7.2 -2020-01-14T06:00,20.9,50,5.2,0,0,5.4,7.2 -2020-01-14T07:00,21.2,49,4.9,0,0,5.6,6.7 -2020-01-14T08:00,25.5,41,4.9,0,0,3.7,7.6 -2020-01-14T09:00,35.3,27,5,0,0,3.5,7.2 -2020-01-14T10:00,45.5,19,5.6,0,0,6.9,12.3 -2020-01-14T11:00,51.3,16,6.3,0,0,14,23.7 -2020-01-14T12:00,52.7,16,7.2,0,0,17.7,30.2 -2020-01-14T13:00,52.9,16,8,0,0,19.2,32.9 -2020-01-14T14:00,53.1,17,9.8,0,0,18.4,32.9 -2020-01-14T15:00,52.3,21,13.7,0,0,15.4,30.6 -2020-01-14T16:00,47.8,27,15.1,0,0,7.1,25.5 -2020-01-14T17:00,37.3,34,11.4,0,0,2.7,11.6 -2020-01-14T18:00,32.3,44,12.7,0,0,2.7,3.4 -2020-01-14T19:00,26.7,59,14.1,0,0,4.4,6.7 -2020-01-14T20:00,24.9,65,14.8,0,0,4.2,6.7 -2020-01-14T21:00,24,66,14.2,0,0,4.5,6.5 -2020-01-14T22:00,24.4,62,13.1,0,0,3.2,6 -2020-01-14T23:00,23.5,61,12.2,0,0,4,5.4 -2020-01-15T00:00,27.6,51,11.8,0,0,2.6,5.1 -2020-01-15T01:00,26.8,57,13.7,0,0,9.3,16.3 -2020-01-15T02:00,23.6,70,15.4,0,0,4,15 -2020-01-15T03:00,23.3,70,14.9,0,0,5.8,6.9 -2020-01-15T04:00,23.5,67,14,0,0,5.2,7.6 -2020-01-15T05:00,23.3,70,14.9,0,0,5,7.4 -2020-01-15T06:00,21.6,74,14.5,0,0,4.8,7.6 -2020-01-15T07:00,22.3,69,13.7,0,0,4.8,7.4 -2020-01-15T08:00,24.4,62,13,0,0,4.5,7.8 -2020-01-15T09:00,28.4,52,13,0,0,4.1,9.6 -2020-01-15T10:00,33.9,40,11.8,0,0,2.9,9.6 -2020-01-15T11:00,40.5,28,10,0,0,4,11.2 -2020-01-15T12:00,44.1,24,9.6,0,0,7.3,14.3 -2020-01-15T13:00,41.8,31,13.3,0,0,15.4,27.3 -2020-01-15T14:00,43.8,30,14,0,0,13.9,27.5 -2020-01-15T15:00,39.3,38,15.9,0,0,16.3,28.6 -2020-01-15T16:00,35.8,47,17.3,0,0,14.3,27.7 -2020-01-15T17:00,30.8,47,13,0,0,9.8,23.9 -2020-01-15T18:00,29.2,47,11.5,0,0,12.3,20.1 -2020-01-15T19:00,26.7,52,11.5,0,0,7.5,19.9 -2020-01-15T20:00,25,58,12.3,0,0,4.7,8.7 -2020-01-15T21:00,26.1,58,13.4,0,0,5.4,6.5 -2020-01-15T22:00,23.6,63,13,0,0,6,8.3 -2020-01-15T23:00,22.8,64,12.6,0,0,4.5,7.6 -2020-01-16T00:00,22.7,61,11.1,0,0,5.7,7.6 -2020-01-16T01:00,22.5,51,7.2,0,0,6.9,8.5 -2020-01-16T02:00,23.6,49,7.1,0,0,5,8.7 -2020-01-16T03:00,22,54,8,0,0,3.8,6.5 -2020-01-16T04:00,21.3,55,7.8,0,0,4.1,6.5 -2020-01-16T05:00,19,56,5.8,0,0,6.7,10.7 -2020-01-16T06:00,17.4,59,5.6,0,0,6.1,10.5 -2020-01-16T07:00,16.7,61,5.7,0,0,5.2,9.4 -2020-01-16T08:00,17.3,61,6.2,0,0,3.5,7.8 -2020-01-16T09:00,20.4,55,6.8,0,0,3.5,9.4 -2020-01-16T10:00,24.9,46,6.8,0,0,3.7,10.7 -2020-01-16T11:00,30.4,36,6.7,0,0,5.1,12.8 -2020-01-16T12:00,34.5,31,7,0,0,7.5,15.7 -2020-01-16T13:00,36.8,29,7.4,0,0,9,17.4 -2020-01-16T14:00,37.8,29,8.3,0,0,9,17.4 -2020-01-16T15:00,36.2,32,9.4,0,0,9.2,17 -2020-01-16T16:00,32.6,39,10.3,0,0,9.4,16.3 -2020-01-16T17:00,24.2,47,6.7,0,0,4.9,15.2 -2020-01-16T18:00,23.4,49,6.9,0,0,5.8,9.2 -2020-01-16T19:00,23.1,50,7.3,0,0,4.9,9.2 -2020-01-16T20:00,23.9,50,7.9,0,0,6.1,9.6 -2020-01-16T21:00,24.4,51,8.7,0,0,4.9,10.1 -2020-01-16T22:00,25.3,50,9.2,0,0,5.6,8.9 -2020-01-16T23:00,24.8,50,8.7,0,0,7.9,13 -2020-01-17T00:00,23.3,52,8.2,0,0,6.1,13 -2020-01-17T01:00,22.8,52,7.9,0,0,4.5,9.8 -2020-01-17T02:00,22,54,8,0,0,5,8.7 -2020-01-17T03:00,21.9,55,8.1,0,0,3,8.1 -2020-01-17T04:00,21.1,57,8.1,0,0,2.6,4.9 -2020-01-17T05:00,21.3,54,7.3,0,0,2.8,4.5 -2020-01-17T06:00,20.9,58,8.3,0,0,5,7.4 -2020-01-17T07:00,20.4,64,10.3,0,0,5.1,6.5 -2020-01-17T08:00,24.9,61,13.4,0,0,4.3,6.3 -2020-01-17T09:00,33.7,46,15.3,0,0,4.9,9.8 -2020-01-17T10:00,45.2,31,16.2,0,0,6.2,12.1 -2020-01-17T11:00,51.7,27,18.5,0,0,13.8,23.9 -2020-01-17T12:00,52.1,21,13.6,0,0,10.5,24.8 -2020-01-17T13:00,52.1,15,6,0,0,12.5,22.4 -2020-01-17T14:00,50.5,19,9.5,0,0,11.6,23.7 -2020-01-17T15:00,47.4,21,9.1,0,0,10.5,21.9 -2020-01-17T16:00,39.7,33,12.9,0,0,19.7,34 -2020-01-17T17:00,35.4,39,12.7,0,0,8.3,33.6 -2020-01-17T18:00,35.2,37,11.4,0,0,9.6,15.9 -2020-01-17T19:00,35,35,10.1,0,0,10.5,20.4 -2020-01-17T20:00,30.8,40,9.3,0,0,7.9,17.2 -2020-01-17T21:00,32.3,35,7.5,0,0,4.5,12.3 -2020-01-17T22:00,30.4,37,7.3,0,0,1.9,6.3 -2020-01-17T23:00,27.5,41,6.8,0,0,6.4,8.7 -2020-01-18T00:00,29.1,36,5.2,0,0,10.3,17.2 -2020-01-18T01:00,26.9,37,3.9,0,0,6.4,16.8 -2020-01-18T02:00,28,35,3.9,0,0,9.7,16.3 -2020-01-18T03:00,22.3,45,4.3,0,0,6.5,15.9 -2020-01-18T04:00,19.8,52,4.8,0,0,5.4,8.1 -2020-01-18T05:00,19.3,54,5.5,0,0,4,6.7 -2020-01-18T06:00,20.9,50,5.3,0,0,3.6,4.9 -2020-01-18T07:00,19,53,4.8,0,0,5.9,6.9 -2020-01-18T08:00,22.9,44,4,0,0,1.9,6.9 -2020-01-18T09:00,27.5,34,2.8,0,0,1.8,6.7 -2020-01-18T10:00,32,26,1,0,0,2.9,9.2 -2020-01-18T11:00,36.4,20,-1.3,0,0,3.4,10.7 -2020-01-18T12:00,39.8,16,-3.2,0,0,1.5,10.7 -2020-01-18T13:00,41.8,14,-4.1,0,0,4.6,11.6 -2020-01-18T14:00,41.8,14,-4.1,0,0,8.4,16.6 -2020-01-18T15:00,42.2,16,-1.2,0,0,8,16.8 -2020-01-18T16:00,38.8,20,1.1,0,0,4.7,14.5 -2020-01-18T17:00,30.5,42,10,0,0,5.7,8.5 -2020-01-18T18:00,27.3,49,10.5,0,0,5.3,8.3 -2020-01-18T19:00,28,47,10.3,0,0,4.6,8.1 -2020-01-18T20:00,28.9,46,10.9,0,0,4.4,5.6 -2020-01-18T21:00,30,45,11.1,0,0,1.7,5.4 -2020-01-18T22:00,28,48,10.9,0,0,2.2,2.5 -2020-01-18T23:00,22.6,58,10,0,0,3.8,4.7 -2020-01-19T00:00,21.5,60,9.9,0,0,3.4,4.5 -2020-01-19T01:00,21,63,10.5,0,0,4.1,5.6 -2020-01-19T02:00,21.1,64,10.8,0,0,3.5,5.4 -2020-01-19T03:00,20.1,67,10.9,0,0,3.9,5.1 -2020-01-19T04:00,20.1,67,10.9,0,0,4.1,5.1 -2020-01-19T05:00,20,65,10.3,0,0,4.7,5.8 -2020-01-19T06:00,19.9,63,9.3,0,0,4.9,5.8 -2020-01-19T07:00,20,60,8.4,0,0,4.3,5.8 -2020-01-19T08:00,22.5,53,7.8,0,0,3.5,5.1 -2020-01-19T09:00,27.3,42,7.3,0,0,2,6.7 -2020-01-19T10:00,33.9,33,7.7,0,0,4.3,10.5 -2020-01-19T11:00,39.9,26,8.1,0,0,6,13.6 -2020-01-19T12:00,43.7,23,8.7,0,0,5.6,13.6 -2020-01-19T13:00,44.8,23,9.1,0,0,4.7,13.4 -2020-01-19T14:00,44.9,23,9.6,0,0,4.3,11.9 -2020-01-19T15:00,44,25,10.4,0,0,5.9,11.2 -2020-01-19T16:00,39.9,31,11.4,0,0,5.1,11.6 -2020-01-19T17:00,31.3,50,14.6,0,0,6.5,8.3 -2020-01-19T18:00,27.8,58,15.1,0,0,5.5,8.7 -2020-01-19T19:00,27.1,61,15.5,0,0,3.8,7.6 -2020-01-19T20:00,25.6,63,14.8,0,0,4.2,5.6 -2020-01-19T21:00,23.6,70,15.4,0,0,4,6 -2020-01-19T22:00,24.2,66,14.5,0,0,5.2,8.7 -2020-01-19T23:00,22.9,64,12.7,0,0,4.5,8.7 -2020-01-20T00:00,21.9,63,11.4,0,0,5.6,6.9 -2020-01-20T01:00,22,59,9.9,0,0,4.6,7.2 -2020-01-20T02:00,21,57,8,0,0,4.6,6.5 -2020-01-20T03:00,20.8,52,6.1,0,0,4.6,6.5 -2020-01-20T04:00,20.6,49,4.6,0,0,4.7,6.5 -2020-01-20T05:00,20.8,43,1.7,0,0,3.7,6.5 -2020-01-20T06:00,21.7,38,0.1,0,0,3,4.5 -2020-01-20T07:00,23.1,34,-1,0,0,1.6,3.6 -2020-01-20T08:00,22.2,35,-1.1,0,0,1.4,1.8 -2020-01-20T09:00,27,28,-1.7,0,0,1.5,6 -2020-01-20T10:00,32.8,21,-2.6,0,0,2.8,8.9 -2020-01-20T11:00,39,16,-3.7,0,0,2.6,9.6 -2020-01-20T12:00,44.5,11,-6.2,0,0,2.7,9.6 -2020-01-20T13:00,48.2,10,-6.7,0,0,2.8,9.8 -2020-01-20T14:00,49.5,10,-4,0,0,3.2,9.4 -2020-01-20T15:00,48.7,12,-1.6,0,0,4.9,9.2 -2020-01-20T16:00,42.4,17,0.3,0,0,4.7,9.4 -2020-01-20T17:00,32.5,31,5.1,0,0,7.6,9.2 -2020-01-20T18:00,31.3,34,5.8,0,0,5.9,9.2 -2020-01-20T19:00,33.6,29,4.4,0,0,5.2,7.6 -2020-01-20T20:00,32.1,32,5.5,0,0,5,6 -2020-01-20T21:00,27.1,46,9.1,0,0,6.6,8.1 -2020-01-20T22:00,32,39,10,0,0,2.9,7.8 -2020-01-20T23:00,31.3,43,11.6,0,0,1.4,3.6 -2020-01-21T00:00,31.1,45,12,0,0,0.5,2.2 -2020-01-21T01:00,31.2,43,11.4,0,0,1.6,2.2 -2020-01-21T02:00,31,41,10,0,0,3.4,3.4 -2020-01-21T03:00,30.1,41,9.2,0,0,4.1,6.9 -2020-01-21T04:00,28,43,8.6,0,0,2.4,6.7 -2020-01-21T05:00,30.5,54,15.6,0,0,2.9,4 -2020-01-21T06:00,31.7,51,15.8,0,0,4.5,5.8 -2020-01-21T07:00,33.4,58,20.2,0,0,4.2,7.4 -2020-01-21T08:00,33.5,63,22.1,0,0,3.5,6.3 -2020-01-21T09:00,37.1,58,23.7,0,0,3.6,5.6 -2020-01-21T10:00,41,53,25.2,0,0,0.2,6 -2020-01-21T11:00,44.2,46,24.7,0,0,4.5,8.9 -2020-01-21T12:00,45.6,42,23.7,0,0,5.7,13 -2020-01-21T13:00,43.7,45,23.9,0,0,2.6,12.5 -2020-01-21T14:00,45.4,44,24.8,0,0,3.4,6.7 -2020-01-21T15:00,44.9,49,26.7,0,0,3.4,7.8 -2020-01-21T16:00,42.7,55,27.6,0,0,3.3,6.3 -2020-01-21T17:00,41.1,53,25.2,0,0,3.5,6.5 -2020-01-21T18:00,35.6,65,25.2,0,0,5.6,6.5 -2020-01-21T19:00,31.3,65,21,0,0,6.1,8.1 -2020-01-21T20:00,35.4,55,20.8,0,0,2.4,7.2 -2020-01-21T21:00,33.1,67,23.4,0,0,4.3,6.5 -2020-01-21T22:00,33.7,72,25.7,0,0,5.2,6.9 -2020-01-21T23:00,33.6,73,25.8,0,0,3.6,6.3 -2020-01-22T00:00,34.6,68,25,0,0,3.4,5.8 -2020-01-22T01:00,35.2,66,24.8,0,0,1.5,4.5 -2020-01-22T02:00,28,83,23.5,0,0,5.4,6 -2020-01-22T03:00,26.9,84,22.6,0,0,6,7.8 -2020-01-22T04:00,28,81,22.7,0,0,5.8,8.1 -2020-01-22T05:00,35.8,55,21,0,0,7.6,7.6 -2020-01-22T06:00,36.2,54,21.2,0,0,6.7,12.8 -2020-01-22T07:00,36.1,57,22.1,0,0,5.6,11.4 -2020-01-22T08:00,34,63,22.6,0,0,4.8,9.2 -2020-01-22T09:00,39.4,53,23.5,0,0,6.8,11.2 -2020-01-22T10:00,46.3,40,23.5,0,0,8.8,15 -2020-01-22T11:00,47,36,21.2,0,0,11,19.5 -2020-01-22T12:00,47.8,32,19,0,0,12.9,23.5 -2020-01-22T13:00,49.6,29,18.2,0,0,10.6,23.5 -2020-01-22T14:00,49.6,30,19.3,0,0,10.1,20.1 -2020-01-22T15:00,48.6,30,18.7,0,0,10,19 -2020-01-22T16:00,45.7,36,20.1,0,0,7.2,17.9 -2020-01-22T17:00,40.6,37,16.5,0,0,6,16.3 -2020-01-22T18:00,43.3,40,20.4,0,0,16.9,26.8 -2020-01-22T19:00,39.3,42,18,0,0,9,28.2 -2020-01-22T20:00,37.4,45,17.9,0,0,5,14.8 -2020-01-22T21:00,33.1,54,18.4,0,0,4.1,6.5 -2020-01-22T22:00,33,55,18.7,0,0,7.1,11.2 -2020-01-22T23:00,32.9,55,18.3,0,0,6.5,12.3 -2020-01-23T00:00,32.4,54,17.6,0,0,6.7,10.7 -2020-01-23T01:00,32.6,46,14.3,0,0,8.6,13.9 -2020-01-23T02:00,31.6,47,13.5,0,0,7.8,14.1 -2020-01-23T03:00,31.6,45,12.7,0,0,8.7,14.1 -2020-01-23T04:00,31.4,40,10,0,0,9.5,16.3 -2020-01-23T05:00,31.5,49,14.5,0,0,1,17 -2020-01-23T06:00,25.8,64,15.2,0,0,6.6,6.9 -2020-01-23T07:00,25.9,59,13.5,0,0,5.8,8.1 -2020-01-23T08:00,28.7,50,12.4,0,0,3.1,7.4 -2020-01-23T09:00,31.8,43,11.8,0,0,3.1,6.7 -2020-01-23T10:00,33.8,41,12.4,0,0,5.5,11.9 -2020-01-23T11:00,35.6,40,13.6,0,0,7.4,15 -2020-01-23T12:00,38.4,38,14.9,0,0,9,17.9 -2020-01-23T13:00,40.3,37,16,0,0,7.1,17.9 -2020-01-23T14:00,42.4,36,17.2,0,0,4.2,15 -2020-01-23T15:00,43.3,37,18.5,0,0,5.8,11.6 -2020-01-23T16:00,41.4,42,19.9,0,0,6.5,12.1 -2020-01-23T17:00,36.1,42,15,0,0,5.5,10.7 -2020-01-23T18:00,35.3,43,14.9,0,0,5.5,10.3 -2020-01-23T19:00,34.7,45,15.6,0,0,3.9,9.2 -2020-01-23T20:00,34,49,17,0,0,3.8,6.3 -2020-01-23T21:00,32.2,56,18.2,0,0,4.1,6.5 -2020-01-23T22:00,29.5,66,19.4,0,0,4.5,7.2 -2020-01-23T23:00,28.5,71,20.3,0,0,3.6,7.2 -2020-01-24T00:00,30.4,67,20.7,0,0,3.3,6.3 -2020-01-24T01:00,30.5,67,20.9,0,0,3.7,5.6 -2020-01-24T02:00,30.7,67,21,0,0,3.9,4.7 -2020-01-24T03:00,30.2,67,20.4,0,0,3.4,5.4 -2020-01-24T04:00,30.4,63,19.4,0,0,3.7,6 -2020-01-24T05:00,29.7,62,18.1,0,0,3.8,5.8 -2020-01-24T06:00,30.8,58,17.9,0,0,2.1,4.7 -2020-01-24T07:00,29,59,16.5,0,0,3.7,5.4 -2020-01-24T08:00,31.1,54,16.3,0,0,7,10.1 -2020-01-24T09:00,37.3,43,16.9,0,0,7.4,13.2 -2020-01-24T10:00,45.1,32,17.2,0,0,4.9,13 -2020-01-24T11:00,49.3,32,20.8,0,0,3.3,11 -2020-01-24T12:00,51.4,31,21.6,0,0,1.2,8.9 -2020-01-24T13:00,52.4,30,22.2,0,0,0.6,6.9 -2020-01-24T14:00,52.8,31,22.9,0,0,2.4,7.6 -2020-01-24T15:00,52.1,32,23.4,0,0,3.5,8.1 -2020-01-24T16:00,48.7,42,26.8,0,0,4,8.1 -2020-01-24T17:00,41.1,50,23.7,0,0,7.1,10.3 -2020-01-24T18:00,40.2,51,23.7,0,0,5.8,8.5 -2020-01-24T19:00,43.9,42,22.4,0,0,3.5,7.2 -2020-01-24T20:00,38.6,55,24,0,0,4.6,4.9 -2020-01-24T21:00,41.1,50,23.8,0,0,2,5.8 -2020-01-24T22:00,40,52,23.9,0,0,1.3,2.7 -2020-01-24T23:00,35.5,65,25,0,0,0.9,2 -2020-01-25T00:00,32.5,74,25.1,0,0,2.5,3.8 -2020-01-25T01:00,30.8,80,25.3,0,0,2.7,4.9 -2020-01-25T02:00,29.6,85,25.7,0,0,3.8,6.7 -2020-01-25T03:00,31.6,78,25.4,0,0,5.4,8.9 -2020-01-25T04:00,30,80,24.7,0,0,5.1,9.2 -2020-01-25T05:00,35.2,61,23.1,0,0,1,8.5 -2020-01-25T06:00,32.3,72,24.3,0,0,3.4,4 -2020-01-25T07:00,32.2,71,23.9,0,0,3.5,4.9 -2020-01-25T08:00,35.2,67,25.2,0,0,1.8,4.7 -2020-01-25T09:00,41,51,24.3,0,0,4.5,7.8 -2020-01-25T10:00,44.8,40,21.7,0,0,3.8,8.5 -2020-01-25T11:00,47.4,34,20.3,0,0,4.5,11 -2020-01-25T12:00,48.9,31,19.4,0,0,4.1,11 -2020-01-25T13:00,50.4,29,19.5,0,0,5.2,12.5 -2020-01-25T14:00,51.5,26,18.1,0,0,8.2,16.1 -2020-01-25T15:00,50.9,28,19.1,0,0,9.2,17.7 -2020-01-25T16:00,48.9,32,20.7,0,0,4.5,17.2 -2020-01-25T17:00,44.2,38,20.5,0,0,8.6,7.6 -2020-01-25T18:00,41.1,43,20.4,0,0,10.3,17 -2020-01-25T19:00,36,55,21.2,0,0,5.8,17.2 -2020-01-25T20:00,31.8,67,22.1,0,0,3.7,9.4 -2020-01-25T21:00,31.2,71,22.9,0,0,2.4,7.2 -2020-01-25T22:00,31.8,73,24.1,0,0,2.4,4.9 -2020-01-25T23:00,30.7,73,23,0,0,2.2,3.1 -2020-01-26T00:00,33.5,63,22.2,0,0,4.3,3.8 -2020-01-26T01:00,31.2,69,22.1,0,0,4.6,6.7 -2020-01-26T02:00,29,74,21.7,0,0,3.5,6 -2020-01-26T03:00,28.7,75,21.8,0,0,3.6,4.3 -2020-01-26T04:00,26.3,79,20.7,0,0,5.4,6 -2020-01-26T05:00,29.3,67,19.6,0,0,5.7,6.3 -2020-01-26T06:00,28.1,67,18.5,0,0,5.6,6.7 -2020-01-26T07:00,27.5,65,17.4,0,0,4.1,7.4 -2020-01-26T08:00,28.9,63,17.9,0,0,4.1,6.3 -2020-01-26T09:00,33.7,53,18.3,0,0,2.8,7.6 -2020-01-26T10:00,38.9,44,19,0,0,3.4,9.4 -2020-01-26T11:00,44.8,36,19.3,0,0,3.8,10.7 -2020-01-26T12:00,49,30,19,0,0,4.4,12.1 -2020-01-26T13:00,51.5,25,17.1,0,0,3.3,11.9 -2020-01-26T14:00,52.8,23,16.4,0,0,2.3,10.5 -2020-01-26T15:00,53.1,23,16.7,0,0,3.2,8.5 -2020-01-26T16:00,51,29,19.9,0,0,6.3,10.5 -2020-01-26T17:00,44.2,43,23.2,0,0,4.6,10.3 -2020-01-26T18:00,41,47,22.5,0,0,5.6,6.9 -2020-01-26T19:00,40.9,46,21.8,0,0,3.8,6.9 -2020-01-26T20:00,33.4,63,22.3,0,0,3.4,4.9 -2020-01-26T21:00,31.6,68,22.3,0,0,2.5,4.9 -2020-01-26T22:00,29.6,74,22.5,0,0,3,5.1 -2020-01-26T23:00,27.6,80,22.4,0,0,4,5.6 -2020-01-27T00:00,27.4,79,21.8,0,0,4,5.6 -2020-01-27T01:00,28,73,20.7,0,0,4.5,6 -2020-01-27T02:00,30.9,64,20.3,0,0,2.3,5.8 -2020-01-27T03:00,31.1,64,20.3,0,0,0.9,2.5 -2020-01-27T04:00,31.2,64,20.3,0,0,3,2.9 -2020-01-27T05:00,31,66,20.8,0,0,1.5,6.7 -2020-01-27T06:00,29.9,72,21.9,0,0,2.1,2.9 -2020-01-27T07:00,30.7,70,21.9,0,0,2.1,2.5 -2020-01-27T08:00,32.2,65,21.7,0,0,2.8,4 -2020-01-27T09:00,32.5,67,22.7,0,0.028,4.1,8.7 -2020-01-27T10:00,33.7,67,23.9,0.004,0.138,2.7,8.9 -2020-01-27T11:00,36.2,62,24.4,0,0,4.4,10.7 -2020-01-27T12:00,36.2,63,25,0,0.028,5.4,11.9 -2020-01-27T13:00,36.1,68,26.6,0,0.11,5.7,12.5 -2020-01-27T14:00,33.7,80,28.3,0,0.276,2,12.1 -2020-01-27T15:00,32.9,86,29,0,0.165,1.2,6.3 -2020-01-27T16:00,33.7,85,29.8,0,0.028,0.5,6.5 -2020-01-27T17:00,33.5,98,33,0.004,0,6.2,4.5 -2020-01-27T18:00,33.1,99,32.9,0.004,0,4.7,11.4 -2020-01-27T19:00,32.9,100,32.8,0.004,0,5.1,8.1 -2020-01-27T20:00,33.4,99,33,0.004,0,3.8,8.3 -2020-01-27T21:00,33.1,94,31.6,0,0,2.8,6 -2020-01-27T22:00,34.4,66,24.2,0,0,6.3,9.6 -2020-01-27T23:00,32.6,63,21.3,0,0,5.2,9.8 -2020-01-28T00:00,32.5,58,19.1,0,0,5.1,7.2 -2020-01-28T01:00,29.2,64,18.4,0,0,6.2,7.6 -2020-01-28T02:00,31.4,56,17.5,0,0,4.4,7.8 -2020-01-28T03:00,29.7,62,18.3,0,0,5.2,6.5 -2020-01-28T04:00,32.8,52,17,0,0,3.4,6.5 -2020-01-28T05:00,27.5,71,19.3,0,0,3.4,4.3 -2020-01-28T06:00,31.3,58,18.1,0,0,1.5,4.5 -2020-01-28T07:00,26.6,65,16.3,0,0,4.2,5.1 -2020-01-28T08:00,28.7,55,14.5,0,0,3.3,6.5 -2020-01-28T09:00,33.9,46,15.2,0,0,1.4,7.2 -2020-01-28T10:00,37.2,38,14.1,0,0,0.5,5.8 -2020-01-28T11:00,40,32,12.2,0,0,1,7.2 -2020-01-28T12:00,42.5,26,9.9,0,0,1.7,8.5 -2020-01-28T13:00,44.3,24,9.4,0,0,1.8,8.9 -2020-01-28T14:00,45.6,23,9.5,0,0,1.7,8.5 -2020-01-28T15:00,46,23,9.9,0,0,2.1,8.1 -2020-01-28T16:00,45.2,26,12.4,0,0,2.9,7.4 -2020-01-28T17:00,34.7,49,17.3,0,0,8.5,6.7 -2020-01-28T18:00,32,48,14.4,0,0,7.4,10.1 -2020-01-28T19:00,32.7,43,12.7,0,0,4.1,9.8 -2020-01-28T20:00,27.7,55,13.6,0,0,4.4,7.2 -2020-01-28T21:00,24.4,65,14.3,0,0,6,7.2 -2020-01-28T22:00,25.3,61,13.7,0,0,4.3,7.2 -2020-01-28T23:00,26.9,57,13.6,0,0,4.3,6.9 -2020-01-29T00:00,25.4,60,13.4,0,0,4.1,6.9 -2020-01-29T01:00,25.6,59,13.4,0,0,3.1,6.7 -2020-01-29T02:00,25.3,63,14.4,0,0,2.9,4.7 -2020-01-29T03:00,27,60,15,0,0,3.3,5.6 -2020-01-29T04:00,28.3,63,17.2,0,0,3,6.3 -2020-01-29T05:00,31.4,50,14.9,0,0,1.6,3.8 -2020-01-29T06:00,29.6,60,17.4,0,0,4.1,4.7 -2020-01-29T07:00,29,61,17.3,0,0,3.6,5.6 -2020-01-29T08:00,30.3,57,16.9,0,0,4.6,6.5 -2020-01-29T09:00,32.3,54,17.3,0,0.028,4.4,8.7 -2020-01-29T10:00,35.5,52,19.6,0,0,4.3,10.1 -2020-01-29T11:00,37.9,47,19.7,0,0,3.8,10.5 -2020-01-29T12:00,40.2,45,20.3,0,0,3.3,10.7 -2020-01-29T13:00,41.7,43,20.8,0,0,3.1,10.3 -2020-01-29T14:00,42.5,43,21.4,0,0,2.8,10.3 -2020-01-29T15:00,42.6,43,21.8,0,0,3,9.4 -2020-01-29T16:00,42,45,22,0,0,4.2,8.7 -2020-01-29T17:00,35,69,25.8,0,0,5.4,8.1 -2020-01-29T18:00,32.7,79,26.9,0,0,4.9,7.6 -2020-01-29T19:00,31.7,91,29.4,0,0.083,5.3,9.4 -2020-01-29T20:00,32.1,90,29.5,0,0.11,2.9,9.4 -2020-01-29T21:00,31.6,90,29,0,0.055,2.6,4.5 -2020-01-29T22:00,30,93,28.2,0,0.083,4.3,6.7 -2020-01-29T23:00,27.1,90,24.5,0,0.055,2.7,6.9 -2020-01-30T00:00,27.7,77,21.4,0,0,4.2,6.7 -2020-01-30T01:00,25.4,67,16.2,0,0,4.1,6.9 -2020-01-30T02:00,23.9,70,15.4,0,0,5.3,8.3 -2020-01-30T03:00,23.4,70,14.9,0,0,5.7,8.7 -2020-01-30T04:00,22.7,61,11.1,0,0,5.4,9.8 -2020-01-30T05:00,22.9,56,9.4,0,0,3.7,8.5 -2020-01-30T06:00,25,50,9.1,0,0,5.2,9.6 -2020-01-30T07:00,25.7,49,9,0,0,5.1,8.9 -2020-01-30T08:00,28.7,41,7.8,0,0,4.7,8.5 -2020-01-30T09:00,33.5,33,7.8,0,0,1.6,7.6 -2020-01-30T10:00,38.7,29,9.1,0,0,3.4,9.2 -2020-01-30T11:00,40.7,29,10.6,0,0,7,14.5 -2020-01-30T12:00,41.6,31,12.8,0,0,6.8,15.2 -2020-01-30T13:00,41.5,34,15.1,0,0,9,17.4 -2020-01-30T14:00,38.9,43,18.5,0,0.028,6.2,17.7 -2020-01-30T15:00,37.8,48,20,0,0,8.4,13.6 -2020-01-30T16:00,36.4,55,21.6,0,0,9.8,16.6 -2020-01-30T17:00,33,64,22.1,0,0.055,5.4,16.1 -2020-01-30T18:00,31.2,84,27,0,0.193,5.8,12.5 -2020-01-30T19:00,30.2,84,26,0,0.165,4,10.7 -2020-01-30T20:00,26.5,94,25.1,0,0.138,3.4,11.6 -2020-01-30T21:00,27.1,92,25.1,0,0.083,1.8,10.5 -2020-01-30T22:00,25.9,91,23.5,0,0,4.1,6.5 -2020-01-30T23:00,23.5,88,20.5,0,0,2.9,6.9 -2020-01-31T00:00,23.3,76,16.8,0,0,4.3,6.9 -2020-01-31T01:00,23,60,11.2,0,0,4.3,6.9 -2020-01-31T02:00,22.9,50,7.1,0,0,5.3,7.6 -2020-01-31T03:00,27.4,41,6.5,0,0,9.6,14.5 -2020-01-31T04:00,28.9,38,6.6,0,0,13.2,20.8 -2020-01-31T05:00,28.4,39,6.4,0,0,10.8,23.7 -2020-01-31T06:00,29.4,34,4.6,0,0,9.4,18.3 -2020-01-31T07:00,27.1,34,2.8,0,0,7.5,14.8 -2020-01-31T08:00,32.3,28,2.8,0,0,5.4,10.3 -2020-01-31T09:00,38.8,23,3.7,0,0,7.2,12.3 -2020-01-31T10:00,43.1,19,4.1,0,0,6.6,12.8 -2020-01-31T11:00,46.6,17,3.7,0,0,9.2,17 -2020-01-31T12:00,48.9,15,3.7,0,0,12.9,22.6 -2020-01-31T13:00,49.7,15,4,0,0,12.9,23.9 -2020-01-31T14:00,50,17,7.1,0,0,12.3,23 -2020-01-31T15:00,48.9,21,10.8,0,0,10.3,21.3 -2020-01-31T16:00,45.1,25,11.2,0,0,9.5,17 -2020-01-31T17:00,39.6,32,12.1,0,0,13.3,15.2 -2020-01-31T18:00,38,35,13,0,0,14.2,23.3 -2020-01-31T19:00,37.2,37,13.6,0,0,13,22.8 -2020-01-31T20:00,37.1,39,14.5,0,0,13.1,20.8 -2020-01-31T21:00,37,39,14.3,0,0,13.4,22.1 -2020-01-31T22:00,37.5,40,15.4,0,0,13.5,21.9 -2020-01-31T23:00,38.1,41,16.6,0,0,11.6,22.1 -2020-02-01T00:00,36.3,47,17.8,0,0,7.7,19 -2020-02-01T01:00,33.2,55,18.6,0,0,8.8,12.1 -2020-02-01T02:00,35,51,18.7,0,0,8.8,10.7 -2020-02-01T03:00,33.6,56,19.5,0,0,9.3,11.4 -2020-02-01T04:00,34.1,56,20,0,0,8.6,11.4 -2020-02-01T05:00,35.1,56,21.1,0,0,9,11.2 -2020-02-01T06:00,36.9,56,22.7,0,0,5.4,10.7 -2020-02-01T07:00,33.5,64,22.7,0,0,4.4,11.4 -2020-02-01T08:00,37.9,63,26.3,0,0,4.2,6.9 -2020-02-01T09:00,46.7,41,24.4,0,0,5.6,7.4 -2020-02-01T10:00,54.1,31,24.2,0,0,10.4,15 -2020-02-01T11:00,57.9,25,22.3,0,0,15.4,24.2 -2020-02-01T12:00,60.8,20,19.3,0,0,19.1,31.5 -2020-02-01T13:00,61.4,18,17.8,0,0,21.3,35.1 -2020-02-01T14:00,61.3,19,18.1,0,0,21.9,36 -2020-02-01T15:00,60.6,18,16.3,0,0,22.5,37.6 -2020-02-01T16:00,58.1,17,13.7,0,0,22.6,38 -2020-02-01T17:00,49.3,28,17.6,0,0,7,37.1 -2020-02-01T18:00,44.2,31,15.7,0,0,5.4,8.3 -2020-02-01T19:00,40.4,39,17.6,0,0,5.3,8.5 -2020-02-01T20:00,38.3,49,20.6,0,0,7.8,11.2 -2020-02-01T21:00,39.8,43,19.3,0,0,7.1,14.3 -2020-02-01T22:00,38.8,44,18.7,0,0,8.4,13.2 -2020-02-01T23:00,37.5,43,17.2,0,0,6.8,13.4 -2020-02-02T00:00,34.5,46,15.7,0,0,6,11 -2020-02-02T01:00,34.4,40,12.7,0,0,7.6,9.8 -2020-02-02T02:00,35.2,36,10.9,0,0,7.5,10.3 -2020-02-02T03:00,34.3,37,10.8,0,0,7.3,10.3 -2020-02-02T04:00,33.8,38,10.8,0,0,8.1,10.7 -2020-02-02T05:00,32.5,42,12.1,0,0,10.3,10.7 -2020-02-02T06:00,37.3,32,10,0,0,8,13.2 -2020-02-02T07:00,36.1,34,10.6,0,0,8.3,11.2 -2020-02-02T08:00,42.3,30,13.3,0,0,7.7,11.6 -2020-02-02T09:00,50,27,17.2,0,0,10.8,16.1 -2020-02-02T10:00,57.4,22,19.3,0,0,12,19.9 -2020-02-02T11:00,61.6,19,19.2,0,0,12.4,21.9 -2020-02-02T12:00,63.5,18,19.2,0,0,15,25.7 -2020-02-02T13:00,64.2,17,18.6,0,0,17.3,29.8 -2020-02-02T14:00,65,16,18.3,0,0,17.8,30.6 -2020-02-02T15:00,64.4,19,21.1,0,0,16.5,30.4 -2020-02-02T16:00,62,21,21.3,0,0,14,27.7 -2020-02-02T17:00,57.6,22,18.9,0,0,10.8,23 -2020-02-02T18:00,51.4,24,15.9,0,0,8.7,17.9 -2020-02-02T19:00,46.6,27,14.2,0,0,7.7,14.1 -2020-02-02T20:00,41.3,34,15,0,0,6.2,12.3 -2020-02-02T21:00,39.9,37,15.5,0,0,7,11.4 -2020-02-02T22:00,37.9,43,17.6,0,0,7.3,12.8 -2020-02-02T23:00,34.1,66,23.7,0,0,12.8,36 -2020-02-03T00:00,33.1,70,24.3,0,0,11.9,22.4 -2020-02-03T01:00,31.8,74,24.5,0,0,10.6,21.7 -2020-02-03T02:00,32.1,73,24.5,0,0,9.8,18.6 -2020-02-03T03:00,31.6,75,24.7,0,0,7.9,16.8 -2020-02-03T04:00,30.7,80,25.1,0,0,7.6,13.6 -2020-02-03T05:00,28.5,80,23,0,0.028,7.6,15 -2020-02-03T06:00,27.7,82,22.9,0,0.055,6.7,13.2 -2020-02-03T07:00,26.1,84,21.8,0,0.055,7.9,14.1 -2020-02-03T08:00,24.7,81,19.6,0,0.055,6.9,14.1 -2020-02-03T09:00,24.6,77,18.6,0,0.028,5.6,13.2 -2020-02-03T10:00,24.4,76,18,0,0.028,6.6,13 -2020-02-03T11:00,24.4,73,16.9,0,0.055,8.1,15.4 -2020-02-03T12:00,24.3,70,15.9,0,0.028,10,18.6 -2020-02-03T13:00,24.1,66,14.4,0,0.028,11,20.4 -2020-02-03T14:00,24.2,62,13.1,0,0.028,10.7,20.8 -2020-02-03T15:00,23.9,61,12.3,0,0.028,9.1,19.9 -2020-02-03T16:00,23.4,61,12,0,0.028,7.8,17.2 -2020-02-03T17:00,18.8,74,11.8,0,0.055,6.5,15 -2020-02-03T18:00,19,76,12.6,0,0.083,5.6,12.1 -2020-02-03T19:00,19.1,73,11.8,0,0.165,7.6,13.6 -2020-02-03T20:00,18.7,73,11.4,0,0.138,8.2,14.5 -2020-02-03T21:00,18,74,11,0,0.11,8.5,15.7 -2020-02-03T22:00,17.5,71,9.8,0,0.11,7.9,15.7 -2020-02-03T23:00,17.1,72,9.5,0,0.083,7.8,14.1 -2020-02-04T00:00,16.8,71,9.1,0,0.055,7.8,14.3 -2020-02-04T01:00,16.5,71,8.7,0,0.028,7.3,14.1 -2020-02-04T02:00,16.3,71,8.7,0,0.028,7.6,13.4 -2020-02-04T03:00,16.2,73,9,0,0.028,7.3,13.4 -2020-02-04T04:00,16.2,73,9.1,0,0.028,7.1,13.2 -2020-02-04T05:00,15.5,76,9.4,0,0.028,8.5,13 -2020-02-04T06:00,14.4,75,7.9,0,0.055,6,14.1 -2020-02-04T07:00,13.4,75,6.8,0,0.028,4.6,11 -2020-02-04T08:00,13.5,76,7.2,0,0.028,6.1,10.7 -2020-02-04T09:00,14.1,73,7,0,0.028,7.2,12.8 -2020-02-04T10:00,15.2,66,5.9,0,0.028,6.8,12.8 -2020-02-04T11:00,16,63,5.6,0,0,5.1,12.1 -2020-02-04T12:00,17,61,5.7,0,0,5.4,11 -2020-02-04T13:00,17.9,61,6.6,0,0,5.9,11.6 -2020-02-04T14:00,18.1,56,4.8,0,0,5.7,11.9 -2020-02-04T15:00,17.8,53,3.5,0,0,3.9,11 -2020-02-04T16:00,16.2,58,3.8,0,0,2.9,8.3 -2020-02-04T17:00,13.3,63,3.2,0,0,5.5,9.6 -2020-02-04T18:00,8.5,69,0.4,0,0,4.7,9.6 -2020-02-04T19:00,8.9,64,-0.7,0,0,5.6,10.3 -2020-02-04T20:00,10.6,61,-0.3,0,0,3.8,7.8 -2020-02-04T21:00,3.5,65,-5.6,0,0,4.5,6.7 -2020-02-04T22:00,-3.2,67,-11.6,0,0,7.3,10.3 -2020-02-04T23:00,-2.3,60,-12.8,0,0,6.3,10.5 -2020-02-05T00:00,3,47,-12.6,0,0,4.8,8.9 -2020-02-05T01:00,2.5,46,-13.5,0,0,3.7,6.9 -2020-02-05T02:00,-4,54,-16.5,0,0,5.3,7.6 -2020-02-05T03:00,-5.7,56,-17.5,0,0,5.9,8.7 -2020-02-05T04:00,-7.7,58,-18.4,0,0,6.4,8.9 -2020-02-05T05:00,-4.1,57,-15.4,0,0,5.5,12.3 -2020-02-05T06:00,-10.1,58,-20.9,0,0,8.9,13.6 -2020-02-05T07:00,2.4,40,-16.4,0,0,5.2,12.3 -2020-02-05T08:00,7.4,34,-15.2,0,0,0.5,7.2 -2020-02-05T09:00,9.1,35,-13.3,0,0,4.3,6.7 -2020-02-05T10:00,11.7,49,-3.9,0,0,4.4,7.6 -2020-02-05T11:00,18.8,50,3.1,0,0,2.6,7.2 -2020-02-05T12:00,23.5,39,2.3,0,0,1.4,5.1 -2020-02-05T13:00,25.3,40,4.1,0,0,2.1,4.7 -2020-02-05T14:00,25.3,40,4.5,0,0,3.3,7.4 -2020-02-05T15:00,24,44,5.4,0,0,3,7.6 -2020-02-05T16:00,22.3,38,0.2,0,0,3.9,7.4 -2020-02-05T17:00,24.2,29,-3.8,0,0,2.3,6.5 -2020-02-05T18:00,22.7,31,-3.3,0,0,3.5,3.8 -2020-02-05T19:00,12.7,52,-1.6,0,0,8.8,13.9 -2020-02-05T20:00,15.7,51,0.7,0,0,7.4,13 -2020-02-05T21:00,14.8,56,2.1,0,0,9.4,12.8 -2020-02-05T22:00,22.4,38,0.7,0,0,1.4,12.5 -2020-02-05T23:00,20.6,39,-0.4,0,0,2.2,4.3 -2020-02-06T00:00,19.3,39,-1.4,0,0,2.4,3.1 -2020-02-06T01:00,15.4,44,-2.6,0,0,3.7,4.9 -2020-02-06T02:00,18.6,39,-2.2,0,0,2.1,5.4 -2020-02-06T03:00,21.4,36,-1.3,0,0,0.2,2.7 -2020-02-06T04:00,11.4,54,-2.2,0,0,3.3,4.9 -2020-02-06T05:00,17,53,2.8,0,0,4.2,8.7 -2020-02-06T06:00,18.6,53,4.5,0,0,6.9,11.6 -2020-02-06T07:00,19.1,59,7,0,0,7.1,12.8 -2020-02-06T08:00,21.4,59,9.3,0,0,6,12.8 -2020-02-06T09:00,26.2,53,11.2,0,0,5.8,10.7 -2020-02-06T10:00,31,44,11.8,0,0,7.2,13.2 -2020-02-06T11:00,33.2,51,16.9,0,0,2.8,13 -2020-02-06T12:00,34.3,57,20.4,0,0,1.8,5.6 -2020-02-06T13:00,35.4,49,18.3,0,0,3.3,6.7 -2020-02-06T14:00,35.4,51,19.2,0,0,3.5,7.4 -2020-02-06T15:00,35.6,55,21.1,0,0,3.2,7.4 -2020-02-06T16:00,34.6,58,21.3,0,0,4.1,7.2 -2020-02-06T17:00,33.3,55,18.9,0,0,6.1,8.9 -2020-02-06T18:00,27.9,73,20.2,0,0.028,5.4,10.5 -2020-02-06T19:00,25.5,84,21.4,0,0.083,5.4,8.5 -2020-02-06T20:00,26.2,87,22.9,0,0.193,3.7,6.3 -2020-02-06T21:00,27.4,89,24.6,0.004,0.358,3.6,5.8 -2020-02-06T22:00,27.5,90,25,0,0.303,4.8,8.1 -2020-02-06T23:00,27.1,90,24.6,0,0.248,4.8,8.1 -2020-02-07T00:00,26.3,90,23.9,0,0.248,5.1,8.3 -2020-02-07T01:00,26,89,23.2,0,0.193,6.1,9.6 -2020-02-07T02:00,25.8,88,22.6,0,0.165,6.9,11.2 -2020-02-07T03:00,25.2,88,22.2,0,0.11,9.1,14.5 -2020-02-07T04:00,24.5,87,21.3,0,0.083,8.9,14.8 -2020-02-07T05:00,22.4,80,17.2,0,0.083,8.1,14.3 -2020-02-07T06:00,22.1,81,17.1,0,0.055,9.9,15.9 -2020-02-07T07:00,21.4,80,16.3,0,0.055,8,16.3 -2020-02-07T08:00,21.6,80,16.4,0,0.11,7.2,13 -2020-02-07T09:00,23.1,79,17.6,0,0.138,6.3,11.9 -2020-02-07T10:00,23.8,80,18.5,0,0.22,4.6,10.3 -2020-02-07T11:00,25.2,78,19.2,0,0.165,6.3,11.6 -2020-02-07T12:00,26.1,76,19.4,0,0.055,6.9,13.9 -2020-02-07T13:00,27.4,76,20.9,0,0.11,4,11.6 -2020-02-07T14:00,28.9,76,22.1,0,0.028,4.8,7.8 -2020-02-07T15:00,28.4,78,22.5,0,0,3.6,8.1 -2020-02-07T16:00,28.1,80,22.8,0,0.028,2.9,6.3 -2020-02-07T17:00,26.2,92,24.1,0,0.083,2.4,5.4 -2020-02-07T18:00,26,92,24,0,0.055,2.2,3.8 -2020-02-07T19:00,25.9,92,24,0,0.11,2.7,2.7 -2020-02-07T20:00,22.8,91,20.7,0,0.055,4.7,5.1 -2020-02-07T21:00,22.3,86,18.9,0,0.028,4.2,6.7 -2020-02-07T22:00,20.7,86,17.1,0,0,5.1,6.7 -2020-02-07T23:00,23.5,82,18.9,0,0,4.9,8.5 -2020-02-08T00:00,20.8,85,16.9,0,0,5.1,8.3 -2020-02-08T01:00,20.2,83,15.9,0,0,5.2,6.9 -2020-02-08T02:00,17.8,83,13.6,0,0,6.3,8.5 -2020-02-08T03:00,13.2,86,9.7,0,0,6.8,7.8 -2020-02-08T04:00,11.3,82,6.9,0,0,8.5,10.1 -2020-02-08T05:00,16.8,77,10.8,0,0,3.3,10.1 -2020-02-08T06:00,17.3,73,10.1,0,0,1.7,4.3 -2020-02-08T07:00,18.1,70,10,0,0,2.6,2.5 -2020-02-08T08:00,14.5,80,9.5,0,0,4.3,5.8 -2020-02-08T09:00,17.7,85,14,0,0,1.3,5.6 -2020-02-08T10:00,21,80,15.8,0,0,1.8,3.6 -2020-02-08T11:00,27.1,76,20.4,0,0,0.5,4.3 -2020-02-08T12:00,32.5,73,24.8,0,0,2.1,3.4 -2020-02-08T13:00,34.5,73,26.6,0,0,3.6,6.3 -2020-02-08T14:00,32.7,79,27,0,0,5.4,8.7 -2020-02-08T15:00,31.6,77,25.3,0,0,5,9.8 -2020-02-08T16:00,33.6,63,22.6,0,0,4,7.8 -2020-02-08T17:00,28.7,81,23.5,0,0,5.5,4.9 -2020-02-08T18:00,28.9,71,20.7,0,0,5.4,6.5 -2020-02-08T19:00,26.8,74,19.8,0,0,6.9,9.4 -2020-02-08T20:00,20,88,17,0,0,4.7,8.3 -2020-02-08T21:00,19.9,83,15.5,0,0,7.3,11.2 -2020-02-08T22:00,28.3,69,19.6,0,0.028,1.6,11.2 -2020-02-08T23:00,24.1,79,18.4,0,0,7.4,11 -2020-02-09T00:00,25,84,20.8,0,0,6.7,14.1 -2020-02-09T01:00,25.2,86,21.5,0,0.055,4.2,10.5 -2020-02-09T02:00,24.8,85,21,0.004,0.055,6.1,9.2 -2020-02-09T03:00,25.2,86,21.5,0,0.138,6.7,11.4 -2020-02-09T04:00,24.7,90,22.2,0,0.165,3.8,10.5 -2020-02-09T05:00,25.3,86,21.7,0,0.11,1.3,4.9 -2020-02-09T06:00,24.7,88,21.7,0,0.055,3.5,3.8 -2020-02-09T07:00,25.2,91,22.8,0,0.248,3.1,4.9 -2020-02-09T08:00,25.5,92,23.6,0,0.276,5.2,7.8 -2020-02-09T09:00,25.4,89,22.7,0,0.11,7.2,12.1 -2020-02-09T10:00,25.3,87,21.9,0,0.055,7.7,12.8 -2020-02-09T11:00,24.6,83,20.2,0,0.028,9.3,15.4 -2020-02-09T12:00,24,83,19.6,0,0.028,8.6,16.1 -2020-02-09T13:00,24.2,84,20,0,0.028,7,15 -2020-02-09T14:00,24.2,85,20.4,0,0.028,6.1,12.5 -2020-02-09T15:00,23.3,88,20.1,0,0,5.7,10.7 -2020-02-09T16:00,21.2,89,18.6,0,0,4,9.6 -2020-02-09T17:00,22.6,88,19.5,0,0,3.8,6.5 -2020-02-09T18:00,21.9,84,18,0,0,4.9,8.3 -2020-02-09T19:00,19.9,83,15.6,0,0,5.4,7.6 -2020-02-09T20:00,17.3,82,12.8,0,0,6.8,8.3 -2020-02-09T21:00,12.6,85,8.9,0,0,7.5,8.9 -2020-02-09T22:00,9.3,88,6.5,0,0,7.2,8.9 -2020-02-09T23:00,4.8,87,1.8,0,0,8.1,9.6 -2020-02-10T00:00,9.4,78,3.8,0,0,3.4,9.6 -2020-02-10T01:00,9.3,74,2.6,0,0,2.7,4.3 -2020-02-10T02:00,11,69,2.8,0,0,1.5,3.4 -2020-02-10T03:00,10.8,71,3.3,0,0,0.5,2.2 -2020-02-10T04:00,9.6,80,4.6,0,0,1.6,2.2 -2020-02-10T05:00,5.5,92,3.7,0,0,1.9,5.6 -2020-02-10T06:00,4.8,93,3.3,0,0,4,5.4 -2020-02-10T07:00,10.1,93,8.4,0,0,3.8,6.9 -2020-02-10T08:00,9.4,87,6.3,0,0,2.9,4.5 -2020-02-10T09:00,10.2,88,7.4,0,0,5.1,8.9 -2020-02-10T10:00,14,85,10.3,0,0,2.9,8.5 -2020-02-10T11:00,16.9,84,12.9,0,0,1.8,5.8 -2020-02-10T12:00,19.8,84,15.7,0,0,3.4,7.4 -2020-02-10T13:00,22.7,83,18.3,0,0,5.4,9.4 -2020-02-10T14:00,24.4,83,20,0,0,5.9,10.1 -2020-02-10T15:00,24.4,82,19.8,0,0,3.5,9.6 -2020-02-10T16:00,22.6,87,19.3,0,0.028,5.6,8.5 -2020-02-10T17:00,23.3,85,19.4,0,0.028,4.2,10.5 -2020-02-10T18:00,22,87,18.9,0,0.083,3.3,6.9 -2020-02-10T19:00,21.7,87,18.4,0,0.11,5.8,8.5 -2020-02-10T20:00,21.3,88,18.4,0,0.193,8.1,12.5 -2020-02-10T21:00,21.2,90,18.7,0,0.193,9.6,15 -2020-02-10T22:00,21.1,91,19,0,0.138,9.8,17 -2020-02-10T23:00,20.5,90,18.1,0,0.055,6.3,15.7 -2020-02-11T00:00,20,88,17,0,0.055,5.6,10.1 -2020-02-11T01:00,19.6,87,16.3,0,0.028,4.3,8.9 -2020-02-11T02:00,17.5,87,14.4,0,0,4.4,6.9 -2020-02-11T03:00,15.4,89,12.8,0,0,4.9,5.8 -2020-02-11T04:00,10.7,94,9.2,0,0,5.2,6.3 -2020-02-11T05:00,11.1,93,9.6,0,0,6.6,8.1 -2020-02-11T06:00,14.1,89,11.6,0,0,4.7,8.7 -2020-02-11T07:00,13.9,94,12.6,0,0,4.9,8.1 -2020-02-11T08:00,5.6,92,3.8,0,0,4.5,6 -2020-02-11T09:00,9,79,3.8,0,0,4.1,6.3 -2020-02-11T10:00,14.8,71,7,0,0,4.9,6.7 -2020-02-11T11:00,20.1,68,11.4,0,0,6,7.2 -2020-02-11T12:00,22.7,70,14.4,0,0,2.3,7.4 -2020-02-11T13:00,21.8,73,14.5,0,0,5.4,8.7 -2020-02-11T14:00,19,77,13,0,0,6.7,11.4 -2020-02-11T15:00,16.9,77,10.9,0,0,2.9,10.7 -2020-02-11T16:00,13.5,76,7.4,0,0,2.7,4.7 -2020-02-11T17:00,11.2,85,7.5,0,0,4.7,3.6 -2020-02-11T18:00,16.3,74,9.3,0,0,1,5.6 -2020-02-11T19:00,17.6,69,9.2,0,0,2.6,2.7 -2020-02-11T20:00,6.4,87,3.3,0,0,5.8,6.9 -2020-02-11T21:00,2,87,-0.8,0,0,6,7.2 -2020-02-11T22:00,2.5,81,-1.9,0,0,5.1,7.8 -2020-02-11T23:00,0.6,76,-5.2,0,0,7.4,8.9 -2020-02-12T00:00,6.9,68,-1.3,0,0,6.2,8.7 -2020-02-12T01:00,8.3,70,0.5,0,0,6.9,8.3 -2020-02-12T02:00,13.6,61,2.7,0,0,3.1,9.2 -2020-02-12T03:00,9.2,66,0.2,0,0,2.8,3.8 -2020-02-12T04:00,15.3,57,2.8,0,0,4.3,4.7 -2020-02-12T05:00,27.3,51,11.7,0,0,12.3,7.6 -2020-02-12T06:00,26.2,51,10.5,0,0,13.6,21.9 -2020-02-12T07:00,26.7,51,11.1,0,0,14.1,23.3 -2020-02-12T08:00,25.6,58,12.9,0,0,11.4,22.6 -2020-02-12T09:00,25.6,64,15.3,0,0,5.4,18.3 -2020-02-12T10:00,29,67,19.4,0,0,2.3,8.5 -2020-02-12T11:00,27.6,72,19.9,0,0,7.6,11.6 -2020-02-12T12:00,27.8,68,18.5,0,0,2.9,12.1 -2020-02-12T13:00,29.5,65,19.1,0,0.028,3.2,5.8 -2020-02-12T14:00,28.9,69,20.1,0,0.028,3.4,6.5 -2020-02-12T15:00,28.3,74,21.1,0,0.028,2.5,6 -2020-02-12T16:00,27.2,80,22,0,0.028,3,5.4 -2020-02-12T17:00,23.8,82,19.1,0,0.055,4,6 -2020-02-12T18:00,21.1,85,17.3,0,0.055,5.4,6.7 -2020-02-12T19:00,23.2,85,19.2,0,0.138,4.3,6.9 -2020-02-12T20:00,23.3,91,20.9,0,0.276,8.6,13.9 -2020-02-12T21:00,22.6,92,20.7,0,0.138,4.5,13.9 -2020-02-12T22:00,22.2,92,20.1,0,0.055,3.8,7.6 -2020-02-12T23:00,21.1,92,19.2,0,0,2.8,6 -2020-02-13T00:00,20.6,92,18.7,0,0,3.1,3.6 -2020-02-13T01:00,19.6,92,17.6,0,0,2.9,4 -2020-02-13T02:00,18.2,88,15.3,0,0,3.3,5.6 -2020-02-13T03:00,14.3,87,11,0,0,2.6,5.6 -2020-02-13T04:00,13.2,89,10.6,0,0,1.6,3.4 -2020-02-13T05:00,16.5,88,13.6,0,0,6.6,2.9 -2020-02-13T06:00,15.7,87,12.5,0,0.028,3,10.7 -2020-02-13T07:00,16.3,83,12,0,0.028,1.8,4.9 -2020-02-13T08:00,15.6,85,12,0,0.028,2.1,2.9 -2020-02-13T09:00,16,85,12.3,0,0,3,5.4 -2020-02-13T10:00,16.5,83,12.3,0,0,1.8,6.7 -2020-02-13T11:00,16.3,86,12.9,0,0,0.8,5.1 -2020-02-13T12:00,16.4,89,13.6,0,0,2.8,6.7 -2020-02-13T13:00,16.5,91,14.5,0,0,4,8.7 -2020-02-13T14:00,17.5,89,14.9,0,0,1.3,8.3 -2020-02-13T15:00,18.5,86,15.1,0,0,0.9,4.5 -2020-02-13T16:00,18.3,86,14.9,0,0,1.9,2.7 -2020-02-13T17:00,16.4,91,14.3,0,0,1.3,2.7 -2020-02-13T18:00,14.6,90,12.1,0,0,2,4 -2020-02-13T19:00,16.3,89,13.6,0,0,2.2,3.1 -2020-02-13T20:00,11.9,90,9.7,0,0,5.5,6.5 -2020-02-13T21:00,9.3,86,6,0,0,5.4,6.5 -2020-02-13T22:00,3.7,83,-0.3,0,0,7.3,8.7 -2020-02-13T23:00,3.8,76,-1.9,0,0,5.8,8.9 -2020-02-14T00:00,2.7,75,-3.5,0,0,8.2,9.8 -2020-02-14T01:00,4.4,72,-2.6,0,0,8.1,10.1 -2020-02-14T02:00,9.2,69,1.1,0,0,6.5,10.1 -2020-02-14T03:00,12.9,67,3.9,0,0,6.4,9.6 -2020-02-14T04:00,15.5,65,6,0,0,6.5,10.1 -2020-02-14T05:00,18.3,69,9.9,0,0,7.1,10.7 -2020-02-14T06:00,18.4,69,10,0,0,7.8,12.1 -2020-02-14T07:00,17.7,70,9.7,0,0,7.4,13 -2020-02-14T08:00,19.6,69,11.2,0,0,6.3,11.4 -2020-02-14T09:00,20.8,72,13.1,0,0,6.8,9.6 -2020-02-14T10:00,28.2,67,18.8,0,0,5,8.3 -2020-02-14T11:00,33.4,66,23.4,0,0,4.5,7.8 -2020-02-14T12:00,39.1,47,20.6,0,0,2,7.6 -2020-02-14T13:00,41.9,41,19.9,0,0,1.1,2.2 -2020-02-14T14:00,44.6,34,17.8,0,0,1,2.5 -2020-02-14T15:00,38.2,60,25.4,0,0,4.7,5.6 -2020-02-14T16:00,35.2,58,22,0,0,6.5,8.1 -2020-02-14T17:00,44.9,38,20.8,0,0,2,7.8 -2020-02-14T18:00,39.6,43,19.1,0,0,2.5,3.6 -2020-02-14T19:00,26.8,73,19.2,0,0,7,7.8 -2020-02-14T20:00,28,69,19.1,0,0,7.1,9.8 -2020-02-14T21:00,29.5,63,18.6,0,0,2.7,8.5 -2020-02-14T22:00,23.7,71,15.6,0,0,3.4,3.4 -2020-02-14T23:00,17.5,83,13.3,0,0,5.5,8.3 -2020-02-15T00:00,20.8,76,14.3,0,0,2.8,9.2 -2020-02-15T01:00,19.9,74,12.9,0,0,2.9,2.9 -2020-02-15T02:00,19.6,70,11.5,0,0,2.6,5.1 -2020-02-15T03:00,14,74,7.2,0,0,4.2,4.7 -2020-02-15T04:00,11.9,71,4.3,0,0,5.8,6.9 -2020-02-15T05:00,7.2,77,1.4,0,0,8.3,8.3 -2020-02-15T06:00,12.1,71,4.4,0,0,5.8,10.1 -2020-02-15T07:00,10,70,2.3,0,0,6.8,7.6 -2020-02-15T08:00,15,61,4.1,0,0,4.7,8.3 -2020-02-15T09:00,16.3,63,5.7,0,0,4.7,5.6 -2020-02-15T10:00,22.6,58,9.9,0,0,4.5,6.3 -2020-02-15T11:00,27.6,57,14.2,0,0,2.7,5.4 -2020-02-15T12:00,29.5,63,18.3,0,0,3,4.9 -2020-02-15T13:00,29,70,20.3,0,0,6.4,10.3 -2020-02-15T14:00,27.8,74,20.5,0,0,8.2,13.4 -2020-02-15T15:00,26.6,73,19.1,0,0,7,13.6 -2020-02-15T16:00,23.5,78,17.8,0,0,3.8,11.4 -2020-02-15T17:00,22.9,78,17,0,0,1.3,4.7 -2020-02-15T18:00,16.3,87,13.3,0,0,4.8,6.5 -2020-02-15T19:00,16.7,89,14,0,0,5.1,7.8 -2020-02-15T20:00,21.3,84,17.2,0,0,4.2,6.7 -2020-02-15T21:00,22.2,81,17.3,0,0,4.3,5.1 -2020-02-15T22:00,22.6,79,17.1,0,0,4.2,5.8 -2020-02-15T23:00,20.8,77,14.6,0,0,2.7,4.9 -2020-02-16T00:00,12.8,83,8.6,0,0,4.5,5.4 -2020-02-16T01:00,13.9,80,8.9,0,0,2.8,5.4 -2020-02-16T02:00,15.4,74,8.7,0,0,3.4,5.1 -2020-02-16T03:00,10.4,75,4.2,0,0,4.7,5.8 -2020-02-16T04:00,10.3,72,3.1,0,0,7.2,8.1 -2020-02-16T05:00,20.4,54,6.5,0,0,3.8,8.7 -2020-02-16T06:00,17.5,62,6.5,0,0,4.3,5.1 -2020-02-16T07:00,14.1,74,7.3,0,0,5.8,7.4 -2020-02-16T08:00,14.4,77,8.6,0,0,5.5,8.1 -2020-02-16T09:00,21.2,74,14.4,0,0,3.1,6.7 -2020-02-16T10:00,27.4,72,19.5,0,0,3.8,6 -2020-02-16T11:00,31.3,71,22.9,0,0,4.1,5.8 -2020-02-16T12:00,33.8,76,27.1,0,0,2.4,4.9 -2020-02-16T13:00,34.9,73,27.1,0,0,2.9,4.9 -2020-02-16T14:00,35,76,28.3,0,0,5.1,8.1 -2020-02-16T15:00,34.4,81,29.1,0,0,5.2,8.5 -2020-02-16T16:00,33,79,27.1,0,0,4,9.2 -2020-02-16T17:00,37.3,56,23,0,0,3.9,7.8 -2020-02-16T18:00,29.4,74,22,0,0,5.7,6.7 -2020-02-16T19:00,30.7,72,22.7,0,0,6,7.4 -2020-02-16T20:00,27.2,76,20.7,0,0,5.9,7.6 -2020-02-16T21:00,33.4,65,23,0,0,2.8,7.4 -2020-02-16T22:00,28.4,78,22.5,0,0,1.8,3.8 -2020-02-16T23:00,24.7,84,20.7,0,0,1.8,4 -2020-02-17T00:00,23.2,81,18.2,0,0,7.4,11.6 -2020-02-17T01:00,27.5,78,21.5,0,0,5.2,11.4 -2020-02-17T02:00,22.2,80,17,0,0,5.2,7.6 -2020-02-17T03:00,18.6,81,13.7,0,0,6.8,8.3 -2020-02-17T04:00,18.2,79,12.8,0,0,6.1,9.4 -2020-02-17T05:00,20.9,67,11.6,0,0,4.6,8.5 -2020-02-17T06:00,12.6,78,7.1,0,0,7.2,7.8 -2020-02-17T07:00,10.1,83,5.9,0,0,7.1,9.4 -2020-02-17T08:00,13.1,81,8.4,0,0,5.9,8.3 -2020-02-17T09:00,21.5,76,15,0,0,3.1,7.2 -2020-02-17T10:00,28,74,20.8,0,0,3.6,5.1 -2020-02-17T11:00,32.5,78,26.4,0,0,6.7,9.6 -2020-02-17T12:00,33.6,80,28,0,0,5.7,11.4 -2020-02-17T13:00,34.9,80,29.3,0,0,5.8,9.4 -2020-02-17T14:00,35.1,80,29.4,0,0,8.2,13 -2020-02-17T15:00,34.3,80,28.7,0,0,7.6,13.2 -2020-02-17T16:00,32.7,74,25.3,0,0,7.3,12.1 -2020-02-17T17:00,30.9,68,21.6,0,0,7.2,11.9 -2020-02-17T18:00,29.4,73,21.7,0,0,3.8,11.6 -2020-02-17T19:00,28.4,69,19.6,0,0.028,8.9,12.8 -2020-02-17T20:00,25.8,80,20.6,0,0.083,3.1,15 -2020-02-17T21:00,25.3,85,21.6,0,0.138,2.5,4.7 -2020-02-17T22:00,24.9,87,21.5,0,0.11,3.9,6 -2020-02-17T23:00,23.9,87,20.6,0,0.055,3.5,6.7 -2020-02-18T00:00,21.1,89,18.4,0,0.028,4.5,5.8 -2020-02-18T01:00,21.6,91,19.3,0,0.028,3.3,5.1 -2020-02-18T02:00,21.3,93,19.7,0,0.055,3.5,6.5 -2020-02-18T03:00,19.7,94,18.3,0,0.055,3,5.6 -2020-02-18T04:00,18.5,95,17.2,0,0.028,3.2,4.7 -2020-02-18T05:00,20.8,90,18.4,0,0.055,1.5,3.8 -2020-02-18T06:00,20.3,90,17.9,0,0.028,3.5,3.8 -2020-02-18T07:00,14.4,93,12.7,0,0,7,7.8 -2020-02-18T08:00,17.1,89,14.4,0,0,4.8,9.2 -2020-02-18T09:00,20.8,84,16.6,0,0,3.8,7.2 -2020-02-18T10:00,25.1,78,19.2,0,0,5.7,8.5 -2020-02-18T11:00,26.2,75,19.4,0,0,8.2,13 -2020-02-18T12:00,27.1,61,15.4,0,0,8.1,14.1 -2020-02-18T13:00,27.6,51,12,0,0,7.4,13.4 -2020-02-18T14:00,27.2,58,14.3,0,0,5.9,12.1 -2020-02-18T15:00,27.3,65,17.1,0,0,4,9.6 -2020-02-18T16:00,25.7,76,19.3,0,0,2.6,6.5 -2020-02-18T17:00,21.7,78,15.9,0,0,4.7,4.7 -2020-02-18T18:00,25.9,69,17.1,0,0,3.5,5.6 -2020-02-18T19:00,22.2,78,16.4,0,0,6,6.7 -2020-02-18T20:00,16.2,86,12.7,0,0,7.2,8.7 -2020-02-18T21:00,14.3,86,10.8,0,0,6.8,8.7 -2020-02-18T22:00,12.2,84,8.4,0,0,6.4,8.7 -2020-02-18T23:00,15.5,78,10,0,0,2.1,7.6 -2020-02-19T00:00,12.2,78,6.5,0,0,4,4.3 -2020-02-19T01:00,10.8,82,6.3,0,0,5.9,7.2 -2020-02-19T02:00,14.3,85,10.5,0,0,3.8,7.2 -2020-02-19T03:00,14,86,10.5,0,0,0.7,4.5 -2020-02-19T04:00,10.1,89,7.4,0,0,3.4,4 -2020-02-19T05:00,15.4,82,11,0,0,1.4,4.5 -2020-02-19T06:00,12.1,78,6.4,0,0,4.5,5.6 -2020-02-19T07:00,11.1,70,3.4,0,0,3.8,5.4 -2020-02-19T08:00,11.7,72,4.3,0,0,5.2,6.7 -2020-02-19T09:00,17.4,73,10.2,0,0,5.4,7.8 -2020-02-19T10:00,19,79,13.5,0,0,9.7,15.4 -2020-02-19T11:00,19,78,13.1,0,0.028,10.4,17.2 -2020-02-19T12:00,19.5,77,13.4,0,0.028,10,17.2 -2020-02-19T13:00,20.2,77,14.2,0,0,9.8,17.2 -2020-02-19T14:00,20.8,78,14.9,0,0.028,10.2,17.2 -2020-02-19T15:00,20.3,82,15.6,0,0.028,9.3,17.4 -2020-02-19T16:00,19.6,84,15.6,0.004,0.055,9.1,15.4 -2020-02-19T17:00,19.7,85,16,0,0.083,7.2,14.5 -2020-02-19T18:00,18.7,89,15.9,0,0.165,9.4,14.1 -2020-02-19T19:00,17.4,87,14.3,0,0.083,7,14.8 -2020-02-19T20:00,16.7,87,13.5,0,0.055,4.3,11.2 -2020-02-19T21:00,15.6,88,12.7,0,0.055,2.1,6.7 -2020-02-19T22:00,15.1,88,12.2,0,0.028,2,4.7 -2020-02-19T23:00,15,89,12.4,0,0.028,0.7,2.7 -2020-02-20T00:00,14.4,91,12.2,0,0.028,2.1,2 -2020-02-20T01:00,10.9,93,9.4,0,0,3.6,4.3 -2020-02-20T02:00,11.7,96,10.9,0,0,1.7,4.3 -2020-02-20T03:00,6.5,94,5.2,0,0,4.5,4.9 -2020-02-20T04:00,6.7,90,4.4,0,0,5.2,6.5 -2020-02-20T05:00,4.5,91,2.4,0,0,4.9,6.3 -2020-02-20T06:00,1.9,89,-0.5,0,0,5.7,6.7 -2020-02-20T07:00,-0.1,87,-3.1,0,0,5,6.7 -2020-02-20T08:00,-1.9,83,-5.8,0,0,5.5,6.5 -2020-02-20T09:00,3.6,74,-3,0,0,2.7,6.5 -2020-02-20T10:00,8.5,66,-0.4,0,0,3.2,5.6 -2020-02-20T11:00,12.9,70,5,0,0,3.2,6.5 -2020-02-20T12:00,16.3,73,9.1,0,0,2.7,6.5 -2020-02-20T13:00,18.1,74,11.1,0,0,3.5,7.2 -2020-02-20T14:00,19.1,75,12.6,0,0,4.1,7.8 -2020-02-20T15:00,19.6,79,14,0,0,2.8,7.8 -2020-02-20T16:00,19.1,79,13.6,0,0,2.3,4.9 -2020-02-20T17:00,19.2,73,11.9,0,0,1.9,3.6 -2020-02-20T18:00,20.9,64,10.5,0,0,3.2,4.3 -2020-02-20T19:00,12.2,74,5.4,0,0,5.3,6.3 -2020-02-20T20:00,11.8,74,5.1,0,0,5.7,6.9 -2020-02-20T21:00,8.8,73,1.9,0,0,5.6,6.9 -2020-02-20T22:00,2,76,-3.7,0,0,5.5,6.7 -2020-02-20T23:00,-0.5,74,-6.8,0,0,6.7,7.8 -2020-02-21T00:00,-0.5,69,-8.2,0,0,8,9.4 -2020-02-21T01:00,0,66,-8.8,0,0,8.1,9.6 -2020-02-21T02:00,1.9,62,-8.2,0,0,7.1,9.6 -2020-02-21T03:00,1.9,63,-7.8,0,0,7.8,9.2 -2020-02-21T04:00,2.1,64,-7.4,0,0,6.8,9.2 -2020-02-21T05:00,2.1,65,-6.9,0,0,7.8,8.3 -2020-02-21T06:00,-1.7,70,-9.1,0,0,8.6,10.3 -2020-02-21T07:00,-1.7,69,-9.3,0,0,8.1,10.1 -2020-02-21T08:00,3.8,62,-6.3,0,0,7.4,9.6 -2020-02-21T09:00,12,55,-1.1,0,0,7.1,8.7 -2020-02-21T10:00,20.6,56,7.4,0,0,7.2,8.3 -2020-02-21T11:00,30.2,54,15.4,0,0,5.9,8.9 -2020-02-21T12:00,35.2,56,21.2,0,0,4.9,9.6 -2020-02-21T13:00,37.7,59,24.8,0,0,4.9,8.5 -2020-02-21T14:00,40.3,55,25.3,0,0,5.4,6.9 -2020-02-21T15:00,39.4,52,23.2,0,0,5.1,6.3 -2020-02-21T16:00,35.5,63,24,0,0,5.8,7.2 -2020-02-21T17:00,40.2,34,14.2,0,0,3.1,6.9 -2020-02-21T18:00,37.5,34,11.8,0,0,0.8,4 -2020-02-21T19:00,29.9,48,12.6,0,0,5.8,6.3 -2020-02-21T20:00,22.8,68,14,0,0,6.1,8.1 -2020-02-21T21:00,16.8,85,13.2,0,0,5.9,7.4 -2020-02-21T22:00,14.5,85,10.7,0,0,8.2,9.4 -2020-02-21T23:00,14.9,78,9.3,0,0,8.6,10.1 -2020-02-22T00:00,17.4,74,10.7,0,0,5.7,10.1 -2020-02-22T01:00,15.4,75,9,0,0,5.9,6.7 -2020-02-22T02:00,12.7,78,7.2,0,0,7.9,9.6 -2020-02-22T03:00,14.9,76,8.6,0,0,6.7,9.6 -2020-02-22T04:00,17.7,72,10.3,0,0,6,9.2 -2020-02-22T05:00,14.8,77,9.1,0,0,9.1,9.2 -2020-02-22T06:00,18.9,75,12.4,0,0,7.4,11 -2020-02-22T07:00,19.9,75,13.2,0,0,7.4,9.6 -2020-02-22T08:00,24.4,72,16.7,0,0,5.8,9.6 -2020-02-22T09:00,28.9,72,21,0,0,4.8,8.7 -2020-02-22T10:00,32.6,72,24.5,0,0,5.3,8.3 -2020-02-22T11:00,37.5,70,28.5,0,0,6.9,8.1 -2020-02-22T12:00,46.9,41,24.6,0,0,4.5,8.7 -2020-02-22T13:00,45,52,28.4,0,0,3.5,5.6 -2020-02-22T14:00,42.7,58,28.9,0,0,5,7.2 -2020-02-22T15:00,39.3,71,30.8,0,0,4.9,8.5 -2020-02-22T16:00,40.5,63,28.9,0,0,3.2,6.9 -2020-02-22T17:00,43.4,51,26.3,0,0,1.1,4.3 -2020-02-22T18:00,42.6,52,26.3,0,0,5.1,5.8 -2020-02-22T19:00,35.3,65,24.9,0,0,3.8,6 -2020-02-22T20:00,34.4,69,25.4,0,0,2.9,4.9 -2020-02-22T21:00,29.8,87,26.2,0,0,4,5.6 -2020-02-22T22:00,28.7,91,26.4,0,0,3.3,4.9 -2020-02-22T23:00,29.9,90,27.2,0,0,3.5,4.5 -2020-02-23T00:00,32.7,80,27.1,0,0,0.7,5.8 -2020-02-23T01:00,31.2,83,26.5,0,0,1.2,2.9 -2020-02-23T02:00,30.3,79,24.4,0,0,6.6,8.7 -2020-02-23T03:00,28.3,73,20.8,0,0,9.1,11 -2020-02-23T04:00,32.2,72,24.2,0,0,5.5,11.2 -2020-02-23T05:00,33,70,24.3,0,0,7.9,11.2 -2020-02-23T06:00,33.5,72,25.6,0,0,7.9,14.1 -2020-02-23T07:00,33.7,75,26.7,0,0.055,5.9,12.5 -2020-02-23T08:00,33.9,82,29,0,0.11,6.2,9.6 -2020-02-23T09:00,33.6,86,30,0,0.138,5.9,10.5 -2020-02-23T10:00,33.7,87,30.4,0.004,0.083,5.2,10.5 -2020-02-23T11:00,34.2,89,31.2,0.004,0.055,6.3,9.8 -2020-02-23T12:00,33.9,89,31.1,0,0.138,7.4,11.6 -2020-02-23T13:00,33.6,90,31.1,0,0.138,7.9,12.5 -2020-02-23T14:00,33.8,90,31.1,0,0.083,7.2,12.5 -2020-02-23T15:00,34.1,87,30.7,0.004,0.028,5.6,11.4 -2020-02-23T16:00,34,85,29.9,0,0,4.3,9.6 -2020-02-23T17:00,32.5,87,29,0,0,4.6,6.9 -2020-02-23T18:00,25.8,93,24,0,0,8.1,9.4 -2020-02-23T19:00,26.2,91,24,0,0,7.1,9.6 -2020-02-23T20:00,30.7,88,27.6,0,0,4.2,8.7 -2020-02-23T21:00,30.5,86,26.7,0,0,5.9,7.4 -2020-02-23T22:00,26.7,84,22.6,0,0,7.7,10.1 -2020-02-23T23:00,24.3,88,21.2,0,0,6.6,9.4 -2020-02-24T00:00,21.3,89,18.7,0,0,6.2,9.2 -2020-02-24T01:00,19.6,88,16.7,0,0,6.9,9.2 -2020-02-24T02:00,16.9,85,13.3,0,0,8.9,10.5 -2020-02-24T03:00,17.7,77,11.8,0,0,9.3,12.3 -2020-02-24T04:00,19,73,11.9,0,0,5.6,11 -2020-02-24T05:00,12.7,81,8.1,0,0,7.1,7.4 -2020-02-24T06:00,11.7,81,7.1,0,0,7.4,8.7 -2020-02-24T07:00,11.4,81,6.8,0,0,9.4,11.2 -2020-02-24T08:00,20.8,70,12.4,0,0,7.7,11.4 -2020-02-24T09:00,33,54,18.3,0,0.028,22.4,33.1 -2020-02-24T10:00,32.4,51,16.3,0,0,15.1,36.7 -2020-02-24T11:00,33,49,15.8,0,0,12.5,24.6 -2020-02-24T12:00,32.9,50,16.4,0,0,14.2,24.6 -2020-02-24T13:00,33.3,49,16.1,0,0,14.9,23.7 -2020-02-24T14:00,32.3,49,15.3,0,0,18.3,29.8 -2020-02-24T15:00,31.6,50,14.9,0,0,14.9,30 -2020-02-24T16:00,30.2,50,14,0,0,11.6,24.4 -2020-02-24T17:00,28.1,50,11.8,0,0,6.5,19 -2020-02-24T18:00,25.4,52,10.2,0,0,8.5,13.2 -2020-02-24T19:00,22.6,57,9.5,0,0,7.4,14.5 -2020-02-24T20:00,19.6,60,7.8,0,0,6.8,12.1 -2020-02-24T21:00,20.3,61,8.9,0,0,7.5,11.9 -2020-02-24T22:00,20.3,59,8.4,0,0,9.2,14.3 -2020-02-24T23:00,19.9,59,7.7,0,0,9.6,15.4 -2020-02-25T00:00,20.5,58,7.9,0,0,9.5,15.7 -2020-02-25T01:00,19.2,58,6.9,0,0,7.9,15.4 -2020-02-25T02:00,17.2,60,5.7,0,0,6.4,12.5 -2020-02-25T03:00,18.2,55,4.9,0,0,5.4,9.8 -2020-02-25T04:00,18.3,56,5.3,0,0,4,6.7 -2020-02-25T05:00,13.9,57,1.3,0,0,7.7,6.7 -2020-02-25T06:00,18.2,61,7.2,0,0,11.8,16.1 -2020-02-25T07:00,20,62,9.1,0,0,20.9,33.3 -2020-02-25T08:00,20.1,64,10,0,0,23.1,37.1 -2020-02-25T09:00,20.8,62,9.9,0,0,21.3,37.6 -2020-02-25T10:00,21.5,63,10.7,0,0.028,21.8,35.1 -2020-02-25T11:00,22.5,67,13,0,0.028,19.3,35.3 -2020-02-25T12:00,23.5,59,11,0,0.028,21.6,34.9 -2020-02-25T13:00,24.2,60,12.3,0,0.028,21.3,35.1 -2020-02-25T14:00,24.3,60,12.2,0,0.055,20.8,34.4 -2020-02-25T15:00,23.8,65,13.8,0,0,17.7,33.6 -2020-02-25T16:00,23,67,13.7,0,0,15.5,28.9 -2020-02-25T17:00,20.7,67,11.6,0,0,9.4,25.1 -2020-02-25T18:00,15.9,71,8.2,0,0,5.7,15.2 -2020-02-25T19:00,13.2,66,4,0,0,6.3,10.3 -2020-02-25T20:00,11,66,2,0,0,6,9.6 -2020-02-25T21:00,6.4,68,-1.8,0,0,8.1,10.3 -2020-02-25T22:00,4.3,67,-4.2,0,0,5.7,9.8 -2020-02-25T23:00,1.4,71,-5.9,0,0,5.1,7.2 -2020-02-26T00:00,-0.4,73,-7.1,0,0,5.7,7.2 -2020-02-26T01:00,-4.5,77,-10,0,0,6.3,7.4 -2020-02-26T02:00,-6.8,76,-12.5,0,0,8.6,9.8 -2020-02-26T03:00,-5.2,71,-12.3,0,0,7.2,9.8 -2020-02-26T04:00,-2.9,66,-11.6,0,0,6,9.2 -2020-02-26T05:00,-3.5,64,-12.5,0,0,7.5,8.7 -2020-02-26T06:00,-1.7,63,-11.4,0,0,6.1,9.4 -2020-02-26T07:00,-0.1,63,-9.7,0,0,5.5,8.5 -2020-02-26T08:00,6.1,63,-3.9,0,0,5.3,9.2 -2020-02-26T09:00,14,63,3.7,0,0,4.3,8.5 -2020-02-26T10:00,19.1,70,10.9,0,0,6.9,11.4 -2020-02-26T11:00,23.2,66,13.6,0,0,7.1,12.3 -2020-02-26T12:00,26.3,55,12.4,0,0,6.7,12.1 -2020-02-26T13:00,28.9,57,15.4,0,0,6.8,12.1 -2020-02-26T14:00,29.9,55,15.7,0,0,4.6,11.6 -2020-02-26T15:00,29.7,56,15.8,0,0,2.5,8.3 -2020-02-26T16:00,29.9,70,21.5,0,0,1.9,4.5 -2020-02-26T17:00,32.4,59,19.5,0,0,2.2,6.7 -2020-02-26T18:00,23,74,15.9,0,0,6.9,7.8 -2020-02-26T19:00,20,72,12.5,0,0,4.6,8.3 -2020-02-26T20:00,16.5,79,11,0,0,5.4,7.4 -2020-02-26T21:00,15.4,75,9.1,0,0,6,8.1 -2020-02-26T22:00,14.7,72,7.3,0,0,6,8.1 -2020-02-26T23:00,16.7,68,7.9,0,0,5.9,8.9 -2020-02-27T00:00,17.9,69,9.3,0,0,7.9,12.5 -2020-02-27T01:00,17.5,76,11.2,0,0,5.8,13.2 -2020-02-27T02:00,17.2,75,10.8,0,0,5,8.5 -2020-02-27T03:00,15.9,72,8.5,0,0,5.8,9.4 -2020-02-27T04:00,16,72,8.4,0,0,5.7,8.7 -2020-02-27T05:00,22.9,57,10.1,0,0,0.4,8.7 -2020-02-27T06:00,18.6,68,9.8,0,0,5.5,7.4 -2020-02-27T07:00,29.5,47,11.8,0,0,6.6,8.3 -2020-02-27T08:00,30.1,49,13,0,0,4.2,8.3 -2020-02-27T09:00,30.8,61,19,0,0,2.3,5.4 -2020-02-27T10:00,32.5,77,26.1,0,0,1.5,5.4 -2020-02-27T11:00,35.7,67,25.9,0,0,2.3,3.6 -2020-02-27T12:00,36.6,62,24.8,0,0,5.1,7.6 -2020-02-27T13:00,36.3,63,25,0,0,6.3,10.3 -2020-02-27T14:00,36.1,64,25.2,0,0,4.6,10.1 -2020-02-27T15:00,36,65,25.3,0,0,2.9,7.2 -2020-02-27T16:00,36.1,53,20.8,0,0,2.3,4.9 -2020-02-27T17:00,39.3,45,19.6,0,0,3.4,6.7 -2020-02-27T18:00,29.2,63,18.3,0,0,8.8,10.3 -2020-02-27T19:00,28.9,52,13.5,0,0,7.9,11.9 -2020-02-27T20:00,26.7,54,12.5,0,0,3.6,9.8 -2020-02-27T21:00,23.7,72,15.9,0,0,4.8,7.4 -2020-02-27T22:00,21,74,14.2,0,0,6.8,9.2 -2020-02-27T23:00,15.8,80,10.8,0,0,8.2,10.3 -2020-02-28T00:00,13.5,81,8.8,0,0,6.2,10.1 -2020-02-28T01:00,12.2,77,6.4,0,0,6.9,8.9 -2020-02-28T02:00,12.7,74,5.9,0,0,6.9,9.4 -2020-02-28T03:00,12,74,5.4,0,0,7,9.2 -2020-02-28T04:00,10.6,75,4.3,0,0,7.8,9.6 -2020-02-28T05:00,14.7,69,6.4,0,0,6.1,9.6 -2020-02-28T06:00,14,69,5.5,0,0,8.1,9.6 -2020-02-28T07:00,14.2,71,6.4,0,0,7.3,9.8 -2020-02-28T08:00,19.7,69,11.3,0,0,4.4,9.4 -2020-02-28T09:00,28.3,65,18.1,0,0,3.1,6.3 -2020-02-28T10:00,35.5,57,21.8,0,0,4.9,6.7 -2020-02-28T11:00,38.7,60,26,0,0,2.5,7.8 -2020-02-28T12:00,42.4,43,21.3,0,0,2.6,3.1 -2020-02-28T13:00,42.1,55,27.2,0,0,4.3,4.7 -2020-02-28T14:00,41.4,59,28,0,0,5.9,7.4 -2020-02-28T15:00,41.1,57,27.1,0,0,6.5,8.5 -2020-02-28T16:00,45.8,34,19,0,0,3.8,8.1 -2020-02-28T17:00,38.5,58,24.9,0,0,6.2,4.5 -2020-02-28T18:00,45.8,33,18.5,0,0,1.7,7.4 -2020-02-28T19:00,38,45,18.4,0,0,2.5,2.9 -2020-02-28T20:00,21.9,83,17.7,0,0,4.8,6 -2020-02-28T21:00,19,91,16.7,0,0,7.3,8.7 -2020-02-28T22:00,18.7,84,14.7,0,0,8.1,9.8 -2020-02-28T23:00,17.1,83,12.8,0,0,8.1,9.8 -2020-02-29T00:00,15.1,83,10.9,0,0,8.7,10.7 -2020-02-29T01:00,13.9,83,9.7,0,0,8.5,10.7 -2020-02-29T02:00,13.4,82,8.9,0,0,8.5,11 -2020-02-29T03:00,12.5,82,8.1,0,0,8.3,10.5 -2020-02-29T04:00,11.8,81,7.1,0,0,8.7,11 -2020-02-29T05:00,16,75,9.6,0,0,7.6,11 -2020-02-29T06:00,18.5,74,11.6,0,0,6.2,8.7 -2020-02-29T07:00,19.9,74,12.8,0,0,6,7.6 -2020-02-29T08:00,24.8,72,17.2,0,0,4.9,7.6 -2020-02-29T09:00,30.5,79,24.6,0,0,2.8,6.5 -2020-02-29T10:00,35.3,73,27.7,0,0,3.5,5.1 -2020-02-29T11:00,39.1,64,27.9,0,0,3.8,6 -2020-02-29T12:00,44.6,38,20.8,0,0,1.4,5.1 -2020-02-29T13:00,46.1,37,21,0,0,3.4,4.5 -2020-02-29T14:00,48.4,33,20.6,0,0,1.6,4.9 -2020-02-29T15:00,49.1,36,23,0,0,2.3,3.8 -2020-02-29T16:00,41.2,61,28.7,0,0,3.7,5.6 -2020-02-29T17:00,39.4,64,28.1,0,0,6.1,6 -2020-02-29T18:00,42.6,40,20.2,0,0,5,7.8 -2020-02-29T19:00,29.2,70,20.8,0,0,6.7,9.4 -2020-02-29T20:00,28,78,22,0,0,5.7,9.2 -2020-02-29T21:00,25.3,80,19.9,0,0,5.2,9.2 -2020-02-29T22:00,22.6,84,18.4,0,0,6.6,9.4 -2020-02-29T23:00,20.7,81,15.6,0,0,8.1,11 -2020-03-01T00:00,19.7,81,14.9,0,0,7.9,10.1 -2020-03-01T01:00,17.4,83,13,0,0,8.6,10.5 -2020-03-01T02:00,19.1,79,13.6,0,0,7.9,10.7 -2020-03-01T03:00,20.3,76,14.1,0,0,7.3,9.8 -2020-03-01T04:00,20.1,77,14,0,0,7,8.9 -2020-03-01T05:00,19.1,82,14.5,0,0,4,9.4 -2020-03-01T06:00,19.9,80,14.6,0,0,4.9,6.5 -2020-03-01T07:00,14.8,84,10.9,0,0,6.4,8.5 -2020-03-01T08:00,23.2,81,18.1,0,0,3.4,8.9 -2020-03-01T09:00,30.7,70,22.2,0,0,9.3,13.9 -2020-03-01T10:00,30.2,73,22.6,0,0,10.1,18.1 -2020-03-01T11:00,34.1,68,24.7,0,0,7.9,17.7 -2020-03-01T12:00,35.9,66,25.6,0,0,5.8,14.3 -2020-03-01T13:00,36.3,68,26.6,0,0,7.2,13.2 -2020-03-01T14:00,36.3,69,27,0,0,7.1,13.6 -2020-03-01T15:00,36.4,69,27.2,0,0.083,6.5,13.9 -2020-03-01T16:00,36.2,71,27.6,0,0.028,4.1,12.3 -2020-03-01T17:00,32.8,81,27.6,0,0.028,6.6,11 -2020-03-01T18:00,31.3,84,26.9,0,0.11,7.5,12.3 -2020-03-01T19:00,30.4,86,26.6,0,0.138,9.1,15.4 -2020-03-01T20:00,28.9,88,25.7,0,0.138,7.6,15 -2020-03-01T21:00,27.9,90,25.3,0,0.138,4.7,12.5 -2020-03-01T22:00,26.5,91,24.3,0,0.055,2.8,8.1 -2020-03-01T23:00,25.6,92,23.7,0,0.028,3,4.9 -2020-03-02T00:00,26,91,23.8,0,0,1.4,4 -2020-03-02T01:00,25.3,92,23.3,0,0,3,4 -2020-03-02T02:00,24.7,95,23.5,0,0,2.5,4 -2020-03-02T03:00,22,98,21.5,0,0,3,3.4 -2020-03-02T04:00,22,96,20.9,0,0,4.1,6.5 -2020-03-02T05:00,23.3,93,21.7,0,0.028,4.3,7.2 -2020-03-02T06:00,20.8,93,19.1,0,0,3.7,6.9 -2020-03-02T07:00,21.7,96,20.7,0,0,1.4,5.4 -2020-03-02T08:00,22.2,97,21.5,0,0,1.6,4.9 -2020-03-02T09:00,22.2,98,21.8,0,0,0.7,6 -2020-03-02T10:00,24.5,95,23.3,0,0,3.4,9.2 -2020-03-02T11:00,30.1,81,24.9,0,0,2.5,8.9 -2020-03-02T12:00,35.2,69,26.2,0,0,4.1,8.5 -2020-03-02T13:00,39.7,66,29.3,0,0,2.7,7.6 -2020-03-02T14:00,40.6,65,29.9,0,0,4.1,6.9 -2020-03-02T15:00,41.6,55,26.6,0,0,4.2,8.3 -2020-03-02T16:00,40.4,56,25.8,0,0,5.2,9.2 -2020-03-02T17:00,38.7,59,25.7,0,0,4.8,9.6 -2020-03-02T18:00,29.9,61,18,0,0,5.1,10.3 -2020-03-02T19:00,36.3,46,17.3,0,0,10.5,15.4 -2020-03-02T20:00,35.5,50,18.9,0,0,10.9,22.1 -2020-03-02T21:00,30.6,49,13.8,0,0,11,18.3 -2020-03-02T22:00,24.3,62,13.2,0,0,9.6,14.5 -2020-03-02T23:00,24.7,63,13.9,0,0,5.9,12.1 -2020-03-03T00:00,24.1,63,13.4,0,0,6.7,11.4 -2020-03-03T01:00,17.6,70,9.3,0,0,8.1,11.2 -2020-03-03T02:00,17.2,69,8.8,0,0,7.7,10.1 -2020-03-03T03:00,13.2,78,7.5,0,0,8.2,10.5 -2020-03-03T04:00,11.6,81,6.9,0,0,7.3,11 -2020-03-03T05:00,16.9,77,10.9,0,0,4.7,10.3 -2020-03-03T06:00,14.4,76,8.2,0,0,6.8,10.3 -2020-03-03T07:00,16.8,74,10,0,0,5.8,10.3 -2020-03-03T08:00,24.9,68,16,0,0,5.8,10.1 -2020-03-03T09:00,33.3,61,21.2,0,0,4.8,11.4 -2020-03-03T10:00,38.8,58,25.4,0,0,3.2,8.7 -2020-03-03T11:00,47.6,37,22.3,0,0,6.5,14.8 -2020-03-03T12:00,51.6,29,20.4,0,0,5,14.8 -2020-03-03T13:00,54.5,25,19.1,0,0,4.5,13.2 -2020-03-03T14:00,56.2,22,18.3,0,0,10.5,19 -2020-03-03T15:00,55,25,19.8,0,0,9.8,21.3 -2020-03-03T16:00,53.2,31,22.9,0,0,9,17.9 -2020-03-03T17:00,49.5,47,30.1,0,0,6,18.1 -2020-03-03T18:00,41.1,58,27.3,0,0,6.7,10.5 -2020-03-03T19:00,39.7,44,19.8,0,0,9.6,18.6 -2020-03-03T20:00,37.2,48,19.2,0,0,5.1,15.2 -2020-03-03T21:00,29.5,63,18.6,0,0,6.7,8.1 -2020-03-03T22:00,27.5,67,18,0,0,5.5,8.9 -2020-03-03T23:00,23.1,80,17.9,0,0,4.3,8.3 -2020-03-04T00:00,24.3,78,18.5,0,0,6.2,9.2 -2020-03-04T01:00,24.6,74,17.5,0,0,5.7,9.4 -2020-03-04T02:00,24.2,70,15.7,0,0,5.8,9.2 -2020-03-04T03:00,22.9,70,14.5,0,0,5.6,9.2 -2020-03-04T04:00,23.2,66,13.6,0,0,6.1,9.8 -2020-03-04T05:00,20,67,10.9,0,0,6.5,9.6 -2020-03-04T06:00,23.2,60,11.5,0,0,5,8.1 -2020-03-04T07:00,24.6,58,12.1,0,0,5.4,7.8 -2020-03-04T08:00,29.8,50,13.6,0,0,4.5,10.1 -2020-03-04T09:00,38.8,44,18.5,0,0,2.3,10.1 -2020-03-04T10:00,46.5,39,23.1,0,0,2.3,8.5 -2020-03-04T11:00,52.7,28,20.7,0,0,7.8,15.2 -2020-03-04T12:00,56,24,19.9,0,0,10.3,19.9 -2020-03-04T13:00,58.2,22,19.6,0,0,10.1,19.9 -2020-03-04T14:00,59.6,21,19.1,0,0,10.2,19.7 -2020-03-04T15:00,59.5,22,20.2,0,0,9.2,19.2 -2020-03-04T16:00,58.6,25,23.1,0,0,6.5,16.8 -2020-03-04T17:00,52.3,24,16.4,0,0,8.7,11 -2020-03-04T18:00,46.9,23,11.1,0,0,7,15.2 -2020-03-04T19:00,40.3,28,10,0,0,5.6,11.4 -2020-03-04T20:00,32.1,56,18.2,0,0,7.5,11.4 -2020-03-04T21:00,30.7,60,18.4,0,0,9.3,15 -2020-03-04T22:00,31.1,50,14.8,0,0,5.6,14.8 -2020-03-04T23:00,31.6,43,11.7,0,0,8.9,12.8 -2020-03-05T00:00,29.6,48,12.3,0,0,8.3,15.2 -2020-03-05T01:00,27.8,58,14.7,0,0,6.2,13.2 -2020-03-05T02:00,26.2,62,15,0,0,6.5,11 -2020-03-05T03:00,24.8,68,15.6,0,0,5.5,10.3 -2020-03-05T04:00,26.7,58,13.9,0,0,5.8,9.4 -2020-03-05T05:00,28,62,16.8,0,0,3,9.2 -2020-03-05T06:00,23.8,81,18.8,0,0,5.3,4.9 -2020-03-05T07:00,27,79,21.2,0,0,4.1,6.7 -2020-03-05T08:00,29.9,70,21.5,0,0,5,10.3 -2020-03-05T09:00,33.4,63,22.2,0,0,7.2,15.2 -2020-03-05T10:00,37.1,56,23,0,0,4.7,15.7 -2020-03-05T11:00,39.8,52,23.5,0,0,1.7,12.8 -2020-03-05T12:00,42.9,47,24.2,0,0,3.2,11.9 -2020-03-05T13:00,46.1,44,25.6,0,0,2.6,11.6 -2020-03-05T14:00,48.5,42,26.2,0,0,3.3,11.2 -2020-03-05T15:00,49.6,42,27.3,0,0,4.8,12.3 -2020-03-05T16:00,49.5,45,28.8,0,0,6.6,13.4 -2020-03-05T17:00,47.2,53,31.1,0,0,6.3,14.1 -2020-03-05T18:00,39.1,71,30.4,0,0,5,11.2 -2020-03-05T19:00,34.9,78,28.8,0,0,5,9.2 -2020-03-05T20:00,32,82,27.2,0,0,3.4,8.5 -2020-03-05T21:00,29.1,87,25.8,0,0,3.7,4.9 -2020-03-05T22:00,27.1,90,24.6,0,0,3.7,5.6 -2020-03-05T23:00,25.1,92,23,0,0,3,5.8 -2020-03-06T00:00,23.7,91,21.5,0,0,3.5,4.9 -2020-03-06T01:00,23.4,90,20.8,0,0,3.3,5.1 -2020-03-06T02:00,22.6,89,19.9,0,0,2.9,4.9 -2020-03-06T03:00,22.9,85,19,0,0,3.4,5.1 -2020-03-06T04:00,24,75,17.2,0,0,3.8,5.8 -2020-03-06T05:00,23.7,74,16.5,0,0,4,6.3 -2020-03-06T06:00,24.7,69,16,0,0,4.2,6.9 -2020-03-06T07:00,26.5,66,16.6,0,0,4.1,6.7 -2020-03-06T08:00,33.1,59,20.1,0,0,4.5,9.6 -2020-03-06T09:00,43.1,50,25.5,0,0,3.1,9.6 -2020-03-06T10:00,52.1,36,26,0,0,4.3,10.5 -2020-03-06T11:00,58.1,26,23.5,0,0,7.4,15.4 -2020-03-06T12:00,61.6,21,21.4,0,0,8.5,17.7 -2020-03-06T13:00,63.3,19,20.5,0,0,7.9,17.7 -2020-03-06T14:00,64,19,21.5,0,0,7.1,16.6 -2020-03-06T15:00,63.9,21,22.6,0,0,6.8,15 -2020-03-06T16:00,62.2,25,26.1,0,0,6.3,13.6 -2020-03-06T17:00,58.7,50,40,0,0,4.8,11.4 -2020-03-06T18:00,57.5,36,30.7,0,0,2.8,5.8 -2020-03-06T19:00,41.4,65,30.4,0,0,5.9,7.4 -2020-03-06T20:00,37.2,72,29,0,0,5.4,7.4 -2020-03-06T21:00,34.7,76,27.9,0,0,4.1,6.9 -2020-03-06T22:00,34.7,72,26.5,0,0,3.1,6.7 -2020-03-06T23:00,30.7,80,25.4,0,0,5.4,5.8 -2020-03-07T00:00,32,72,24,0,0,4.9,6.7 -2020-03-07T01:00,30.5,74,23.2,0,0,6.5,7.8 -2020-03-07T02:00,30.3,72,22.5,0,0,6.3,7.6 -2020-03-07T03:00,29.2,73,21.7,0,0,6.8,8.1 -2020-03-07T04:00,28,76,21.5,0,0,6.3,8.3 -2020-03-07T05:00,27.8,78,21.7,0,0,5.9,7.8 -2020-03-07T06:00,29.2,73,21.5,0,0,5.2,7.2 -2020-03-07T07:00,31.6,67,21.9,0,0,4.6,7.4 -2020-03-07T08:00,38.8,57,25,0,0,5.5,10.5 -2020-03-07T09:00,49,46,29.2,0,0,4,10.5 -2020-03-07T10:00,57.7,32,28.1,0,0,6.5,13.4 -2020-03-07T11:00,61.9,22,23.1,0,0,11.2,20.8 -2020-03-07T12:00,62.5,22,23.3,0,0,11.3,22.4 -2020-03-07T13:00,63,22,23.5,0,0,10.1,21.7 -2020-03-07T14:00,63.3,22,24.1,0,0,9.4,19.5 -2020-03-07T15:00,63,23,24.9,0,0,8.6,18.6 -2020-03-07T16:00,62,25,25.9,0,0,8.3,16.8 -2020-03-07T17:00,59.5,43,37.2,0,0,4.9,15.2 -2020-03-07T18:00,51.9,37,26.2,0,0,7,10.3 -2020-03-07T19:00,44.4,53,28.1,0,0,6.5,10.7 -2020-03-07T20:00,39.8,61,27.6,0,0,6.5,11.6 -2020-03-07T21:00,39,60,26.4,0,0,5.9,10.7 -2020-03-07T22:00,38.1,62,26.1,0,0,5.9,10.1 -2020-03-07T23:00,38.8,58,25.5,0,0,7.1,11.2 -2020-03-08T00:00,40.4,54,24.9,0,0,8.5,14.1 -2020-03-08T01:00,39.7,55,24.8,0,0,8.9,14.5 -2020-03-08T02:00,37.8,59,24.7,0,0,7.9,14.5 -2020-03-08T03:00,35.5,65,24.8,0,0,7.6,13 -2020-03-08T04:00,34.5,66,24.4,0,0,6.5,12.3 -2020-03-08T05:00,31.7,77,25.3,0,0,4.7,10.7 -2020-03-08T06:00,30.7,80,25.1,0,0,4.9,6.7 -2020-03-08T07:00,33.7,72,25.7,0,0,3.4,6.5 -2020-03-08T08:00,41.8,59,28.6,0,0,4.3,9.2 -2020-03-08T09:00,51.2,47,31.4,0,0,3.7,10.1 -2020-03-08T10:00,58,36,31.4,0,0,5.4,12.5 -2020-03-08T11:00,61.7,29,28.9,0,0,5.1,14.1 -2020-03-08T12:00,63.2,25,27.1,0,0,2.6,13.2 -2020-03-08T13:00,64.5,23,26.1,0,0,3.4,10.5 -2020-03-08T14:00,64.8,23,25.6,0,0,8.7,17.7 -2020-03-08T15:00,62.5,24,25.3,0,0,4.7,17.9 -2020-03-08T16:00,61.1,28,27.5,0,0,10.5,18.8 -2020-03-08T17:00,58.4,34,30.1,0,0,11,18.8 -2020-03-08T18:00,51.3,45,30.7,0,0,6.8,19.2 -2020-03-08T19:00,46.2,73,37.9,0.016,0,6.9,12.5 -2020-03-08T20:00,45.3,85,41,0.051,0,8.4,14.5 -2020-03-08T21:00,43.3,88,40,0.079,0,6.2,13.6 -2020-03-08T22:00,43.3,89,40.3,0.079,0,7.5,11.9 -2020-03-08T23:00,38.8,98,38.3,0.067,0,9.8,16.8 -2020-03-09T00:00,36.7,99,36.4,0.024,0,6.9,16.3 -2020-03-09T01:00,36.5,98,35.9,0.008,0,0.9,12.1 -2020-03-09T02:00,36.9,94,35.3,0.004,0,2.8,6.5 -2020-03-09T03:00,37.6,92,35.4,0,0,1.4,5.8 -2020-03-09T04:00,36.3,95,35,0,0,2.3,3.1 -2020-03-09T05:00,37.9,93,36.1,0,0,3.6,3.1 -2020-03-09T06:00,38.1,93,36.2,0,0,1.8,6 -2020-03-09T07:00,38.3,93,36.4,0,0,3,5.6 -2020-03-09T08:00,38.4,92,36.3,0,0,0,6.5 -2020-03-09T09:00,39.5,88,36.2,0,0,3.6,8.7 -2020-03-09T10:00,44.1,73,36.1,0,0,4.3,11.9 -2020-03-09T11:00,49.6,52,32.8,0,0,3.8,12.3 -2020-03-09T12:00,54.1,35,26.9,0,0,4,12.5 -2020-03-09T13:00,56.6,29,25.1,0,0,4.5,13.4 -2020-03-09T14:00,57.7,26,23.3,0,0,5.7,13.6 -2020-03-09T15:00,59.2,24,21.9,0,0,6.4,13.9 -2020-03-09T16:00,58.4,24,21.7,0,0,7.6,15.2 -2020-03-09T17:00,55,45,34.2,0,0,3.2,14.5 -2020-03-09T18:00,51.4,58,37,0.004,0,0.9,6.5 -2020-03-09T19:00,45.2,70,36.1,0,0,4.5,7.4 -2020-03-09T20:00,39.6,83,35,0,0,3.9,8.3 -2020-03-09T21:00,36.2,91,34,0,0,2.9,6.7 -2020-03-09T22:00,34.5,92,32.4,0,0,3.2,5.4 -2020-03-09T23:00,32.1,93,30.3,0,0,4,6.9 -2020-03-10T00:00,30.7,91,28.4,0,0,2.8,6.9 -2020-03-10T01:00,31.5,81,26.2,0,0,3.4,5.8 -2020-03-10T02:00,29.6,81,24.5,0,0,4.1,5.6 -2020-03-10T03:00,28.3,82,23.5,0,0,4.8,6.3 -2020-03-10T04:00,28.9,75,22,0,0,4.3,6.7 -2020-03-10T05:00,29.4,68,20.3,0,0,5.2,7.8 -2020-03-10T06:00,29.1,68,19.9,0,0,4.8,9.4 -2020-03-10T07:00,30.7,67,21,0,0,5.1,7.8 -2020-03-10T08:00,38.5,56,24,0,0,4.1,10.1 -2020-03-10T09:00,46.6,44,25.9,0,0,2.8,9.2 -2020-03-10T10:00,53,32,23.7,0,0,5.7,13.2 -2020-03-10T11:00,56.1,27,22.5,0,0,6.9,15.4 -2020-03-10T12:00,57.8,27,24.2,0,0,7.5,16.6 -2020-03-10T13:00,58.5,28,25.5,0,0,8.9,19 -2020-03-10T14:00,58.8,29,26.7,0,0,9.5,20.4 -2020-03-10T15:00,60.6,27,26.6,0,0,5.4,19.2 -2020-03-10T16:00,60.1,22,21.1,0,0,10.7,19 -2020-03-10T17:00,57.1,30,26.2,0,0,7.4,20.8 -2020-03-10T18:00,47.2,45,27.1,0,0,6.5,12.8 -2020-03-10T19:00,48.6,40,25.4,0,0,4.4,8.7 -2020-03-10T20:00,42.7,54,27.3,0,0,4.8,6.9 -2020-03-10T21:00,40.7,62,28.7,0,0,13.1,17.9 -2020-03-10T22:00,40.6,59,27.3,0,0,9.2,21 -2020-03-10T23:00,36.3,69,27.1,0,0,5.6,14.8 -2020-03-11T00:00,35.9,65,25.2,0,0,6.3,9.8 -2020-03-11T01:00,35,66,24.8,0,0,5.8,10.7 -2020-03-11T02:00,33.1,66,23.1,0,0,5.1,9.4 -2020-03-11T03:00,29.5,76,22.7,0,0,6.3,7.8 -2020-03-11T04:00,28.4,78,22.4,0,0,5.7,8.1 -2020-03-11T05:00,28.3,76,21.7,0,0,5.3,7.6 -2020-03-11T06:00,27.9,73,20.5,0,0,4.7,8.1 -2020-03-11T07:00,32.3,61,20.2,0,0,6,9.2 -2020-03-11T08:00,41.5,48,23.5,0,0,6.2,12.3 -2020-03-11T09:00,48.6,41,26.2,0,0,5.2,11.9 -2020-03-11T10:00,54.3,33,26,0,0,5.4,13.4 -2020-03-11T11:00,59.1,27,25.3,0,0,5.4,13.6 -2020-03-11T12:00,62.1,24,24.7,0,0,6.1,14.8 -2020-03-11T13:00,63.8,24,26.6,0,0,5.2,14.8 -2020-03-11T14:00,64.9,24,27.3,0,0,4.7,13.6 -2020-03-11T15:00,65,24,27.6,0,0,7.1,14.1 -2020-03-11T16:00,63.8,25,27,0,0,8.6,16.6 -2020-03-11T17:00,61.8,53,44.4,0,0,2.9,15.7 -2020-03-11T18:00,60.3,34,31.5,0,0,4.7,6.7 -2020-03-11T19:00,48.5,53,32.3,0,0,5.4,5.4 -2020-03-11T20:00,49.1,45,28.6,0,0,9.2,15.2 -2020-03-11T21:00,45.1,50,27.8,0,0,6.5,15.2 -2020-03-11T22:00,40.5,57,26.6,0,0,7.6,11 -2020-03-11T23:00,42.2,54,26.7,0,0,7.8,10.1 -2020-03-12T00:00,43,44,22.5,0,0,13,24.4 -2020-03-12T01:00,43.8,43,22.5,0,0,9.9,23 -2020-03-12T02:00,39.8,53,24.1,0,0,6.2,15.9 -2020-03-12T03:00,36,64,24.9,0,0,7.8,9.4 -2020-03-12T04:00,36.5,62,24.5,0,0,4,9.2 -2020-03-12T05:00,36.4,70,27.4,0,0,4.6,7.2 -2020-03-12T06:00,37.3,67,27.3,0,0,4.5,8.5 -2020-03-12T07:00,39.4,63,27.9,0,0,4,7.2 -2020-03-12T08:00,43.5,57,29.4,0,0,9.1,13 -2020-03-12T09:00,46.8,51,29.5,0,0,9.7,22.1 -2020-03-12T10:00,48.6,49,30.2,0,0,6.5,18.6 -2020-03-12T11:00,49.8,48,30.9,0,0,8.8,17.9 -2020-03-12T12:00,50.5,48,31.7,0,0,11.9,23.3 -2020-03-12T13:00,51.6,45,30.8,0,0,8.4,23.5 -2020-03-12T14:00,49.7,47,30.1,0,0,10.3,18.3 -2020-03-12T15:00,44.5,58,30.7,0,0,12.7,25.7 -2020-03-12T16:00,46.1,54,30.3,0,0,8.6,22.8 -2020-03-12T17:00,42,56,27.5,0,0,12.3,17 -2020-03-12T18:00,38.5,65,27.6,0,0,8.5,21.9 -2020-03-12T19:00,37.5,68,27.8,0,0,5.1,13.9 -2020-03-12T20:00,37.5,67,27.5,0,0,4.4,9.8 -2020-03-12T21:00,37.2,68,27.6,0,0,2.2,7.8 -2020-03-12T22:00,36.8,70,27.9,0,0,1.5,4.9 -2020-03-12T23:00,36.1,72,28,0,0,2.2,4.7 -2020-03-13T00:00,36.6,71,28.2,0,0,3.4,6.3 -2020-03-13T01:00,36.4,71,28,0,0,7.9,12.8 -2020-03-13T02:00,34.5,76,27.9,0,0,11.1,17.9 -2020-03-13T03:00,32.2,84,27.8,0,0,11.4,19.9 -2020-03-13T04:00,31.5,86,27.7,0,0,9.6,19.2 -2020-03-13T05:00,31,84,26.8,0,0,6.5,16.1 -2020-03-13T06:00,31.6,81,26.5,0,0,6,11.4 -2020-03-13T07:00,31,82,26.2,0,0,7,12.5 -2020-03-13T08:00,30.7,84,26.5,0,0.11,6.2,12.8 -2020-03-13T09:00,31.4,83,26.8,0,0.138,3.7,12.1 -2020-03-13T10:00,33.8,76,27.1,0,0.028,1.8,9.2 -2020-03-13T11:00,38.6,60,25.9,0,0,5.5,12.3 -2020-03-13T12:00,41.8,50,24.6,0,0,9.6,17.4 -2020-03-13T13:00,44.1,47,25.1,0,0,10.8,20.8 -2020-03-13T14:00,45.8,44,25.2,0,0,13.2,23 -2020-03-13T15:00,42.1,51,25.2,0,0,18.6,32.2 -2020-03-13T16:00,40.3,54,25.2,0,0,17.6,32.2 -2020-03-13T17:00,36.8,56,22.5,0,0,10.8,29.8 -2020-03-13T18:00,36.6,58,23.3,0,0,10,18.3 -2020-03-13T19:00,35.1,63,23.8,0,0,3,16.8 -2020-03-13T20:00,33.3,70,24.4,0,0,2,5.1 -2020-03-13T21:00,30.9,74,23.6,0,0,2.6,4.7 -2020-03-13T22:00,31.4,71,23.2,0,0,0.7,4.5 -2020-03-13T23:00,29.3,89,26.3,0,0,4,6 -2020-03-14T00:00,28.8,92,26.8,0,0,5.6,9.2 -2020-03-14T01:00,28.4,88,25.4,0,0,4.7,10.5 -2020-03-14T02:00,28.1,87,24.8,0,0,4.8,8.9 -2020-03-14T03:00,27.8,86,24,0,0,4.9,9.2 -2020-03-14T04:00,27.2,86,23.5,0,0,4.5,8.5 -2020-03-14T05:00,26.8,95,25.5,0,0,6.1,7.8 -2020-03-14T06:00,27.1,94,25.6,0,0,3.8,10.5 -2020-03-14T07:00,27.8,90,25.3,0,0,2.3,7.4 -2020-03-14T08:00,29,85,25.1,0,0,2,7.2 -2020-03-14T09:00,30.5,80,25,0,0,1.9,8.3 -2020-03-14T10:00,33.5,72,25.6,0,0,2.3,9.4 -2020-03-14T11:00,38.2,64,27.2,0,0,2.9,11.6 -2020-03-14T12:00,42.5,57,28.3,0,0,4,13.2 -2020-03-14T13:00,46.6,51,29.5,0,0,4.7,14.1 -2020-03-14T14:00,49.4,47,30.2,0,0,6.5,15.7 -2020-03-14T15:00,51.4,45,30.5,0,0,8.6,17.9 -2020-03-14T16:00,50.1,47,30.4,0,0,9.6,19 -2020-03-14T17:00,43.2,60,30,0,0,11.3,18.6 -2020-03-14T18:00,41.5,63,29.8,0,0,8.8,19 -2020-03-14T19:00,40.6,66,30,0,0,8.9,15.4 -2020-03-14T20:00,38.2,73,30.2,0,0,8.2,14.8 -2020-03-14T21:00,34.7,82,29.8,0,0,5.2,13.4 -2020-03-14T22:00,33.1,87,29.6,0,0,4.2,8.5 -2020-03-14T23:00,30.5,94,29,0,0,4.2,6.7 -2020-03-15T00:00,29.1,94,27.6,0,0,3.7,4.7 -2020-03-15T01:00,28.7,96,27.6,0,0,4.1,6 -2020-03-15T02:00,28.3,99,28,0,0,3.5,8.3 -2020-03-15T03:00,29.4,95,28.1,0,0,4.3,7.2 -2020-03-15T04:00,29.4,93,27.5,0,0,4.3,8.7 -2020-03-15T05:00,29.1,93,27.2,0,0,1.5,7.2 -2020-03-15T06:00,29.4,93,27.5,0,0,2.8,5.1 -2020-03-15T07:00,29.8,92,27.8,0,0,5.1,9.8 -2020-03-15T08:00,31,88,27.8,0,0,4.5,10.5 -2020-03-15T09:00,35.2,76,28.2,0,0,2.5,10.7 -2020-03-15T10:00,40.5,65,29.7,0,0,5.2,13.4 -2020-03-15T11:00,45.1,57,31,0,0,4.8,14.5 -2020-03-15T12:00,49.4,53,32.7,0,0,4.8,14.1 -2020-03-15T13:00,52.1,50,33.9,0,0,6.1,15.4 -2020-03-15T14:00,53.1,49,34.3,0,0,6.6,15.7 -2020-03-15T15:00,54,47,34.1,0,0,6.3,16.1 -2020-03-15T16:00,54.1,45,33.4,0,0,6.4,15 -2020-03-15T17:00,52.1,47,32.5,0,0,7.8,13.6 -2020-03-15T18:00,46.3,62,34.1,0,0,4.4,13.2 -2020-03-15T19:00,43.7,69,34.3,0,0,4.3,8.3 -2020-03-15T20:00,39.8,81,34.6,0,0,4.7,6.7 -2020-03-15T21:00,36.8,90,34.1,0,0,3.2,6 -2020-03-15T22:00,34,93,32.3,0,0,1.6,4.9 -2020-03-15T23:00,31.9,95,30.7,0,0,3.2,4.9 -2020-03-16T00:00,30.4,93,28.6,0,0,4.4,5.6 -2020-03-16T01:00,31.3,85,27.3,0,0,2.6,6 -2020-03-16T02:00,28.8,87,25.3,0,0,3.3,3.8 -2020-03-16T03:00,28.1,88,25,0,0,2.8,4.5 -2020-03-16T04:00,27.3,89,24.5,0,0,4.2,6.7 -2020-03-16T05:00,28.1,80,22.8,0,0,4.9,6.7 -2020-03-16T06:00,27.1,82,22.3,0,0,5.5,7.2 -2020-03-16T07:00,30.9,73,23.3,0,0,5.9,8.7 -2020-03-16T08:00,39.9,57,25.7,0,0,4.1,10.3 -2020-03-16T09:00,48.2,44,27.3,0,0,3.6,10.3 -2020-03-16T10:00,54.3,35,27.1,0,0,5.1,12.8 -2020-03-16T11:00,58.6,26,24.1,0,0,6.2,14.8 -2020-03-16T12:00,61.4,21,20.9,0,0,5.4,14.8 -2020-03-16T13:00,63,17,17.4,0,0,6.8,15.2 -2020-03-16T14:00,64.3,15,15.9,0,0,8.1,16.8 -2020-03-16T15:00,64.5,13,12.4,0,0,7.8,17.7 -2020-03-16T16:00,59.2,37,33.1,0,0,12,20.4 -2020-03-16T17:00,49.6,52,32.6,0,0,12.4,24.6 -2020-03-16T18:00,43.7,64,32.4,0,0,13.6,23 -2020-03-16T19:00,41.2,69,31.9,0,0,9.6,22.8 -2020-03-16T20:00,39.6,74,32.1,0,0,10.1,17.4 -2020-03-16T21:00,38.1,80,32.6,0,0,7,17.4 -2020-03-16T22:00,37.9,81,32.7,0,0,5.4,12.5 -2020-03-16T23:00,37.8,82,32.6,0,0,5.2,10.3 -2020-03-17T00:00,37.9,82,32.7,0,0,4.7,9.4 -2020-03-17T01:00,37.6,83,32.8,0,0,6.5,11.4 -2020-03-17T02:00,37.7,83,32.9,0,0,3.6,11.4 -2020-03-17T03:00,37.9,82,33,0,0,0.8,6.7 -2020-03-17T04:00,37,86,33.3,0,0,4.9,8.5 -2020-03-17T05:00,37.2,84,32.8,0,0,5.6,10.7 -2020-03-17T06:00,36.6,86,32.8,0,0,5.4,11 -2020-03-17T07:00,36,88,32.8,0,0,4,9.6 -2020-03-17T08:00,36.7,86,33,0,0,2.9,8.5 -2020-03-17T09:00,38.2,83,33.4,0,0,3.3,9.4 -2020-03-17T10:00,41.4,74,33.6,0,0,3.8,11.4 -2020-03-17T11:00,43.9,68,34.2,0,0,7.5,16.8 -2020-03-17T12:00,45.7,65,34.6,0,0,9.1,20.4 -2020-03-17T13:00,50.4,56,35.2,0,0,7.1,20.1 -2020-03-17T14:00,54.8,50,36.2,0,0,6.5,17 -2020-03-17T15:00,55.7,49,37,0,0,6.9,16.6 -2020-03-17T16:00,55.9,50,37.5,0,0,3.8,15.4 -2020-03-17T17:00,50.7,54,34.7,0,0,9,10.1 -2020-03-17T18:00,47.3,64,35.7,0,0,2.1,14.8 -2020-03-17T19:00,45.1,72,36.5,0,0,2.9,4.5 -2020-03-17T20:00,43.3,77,36.4,0,0,3.1,3.6 -2020-03-17T21:00,40.1,85,36.1,0,0,4,6.3 -2020-03-17T22:00,37,86,33.2,0,0,5.3,6.5 -2020-03-17T23:00,39.8,70,30.7,0,0,1.9,6.5 -2020-03-18T00:00,35.3,83,30.6,0,0,2.7,2.7 -2020-03-18T01:00,33.4,83,28.7,0,0,3.1,4.5 -2020-03-18T02:00,31.2,86,27.4,0,0,3.2,3.8 -2020-03-18T03:00,30.3,85,26.3,0,0,4.1,4.7 -2020-03-18T04:00,29.1,84,25,0,0,4.6,5.4 -2020-03-18T05:00,27.5,85,23.7,0,0,4.8,5.8 -2020-03-18T06:00,27.5,80,22.3,0,0,4.2,6 -2020-03-18T07:00,31.8,69,22.7,0,0,4.7,8.1 -2020-03-18T08:00,40.1,54,24.6,0,0,4.1,9.6 -2020-03-18T09:00,49.4,40,25.9,0,0,4.6,11.6 -2020-03-18T10:00,57.7,31,27.2,0,0,4.7,11.9 -2020-03-18T11:00,62.2,20,21,0,0,10.7,20.1 -2020-03-18T12:00,63.5,20,21.4,0,0,13.5,24.6 -2020-03-18T13:00,63.9,18,20.1,0,0,15.7,31.8 -2020-03-18T14:00,64,18,19.3,0,0,15.6,28.9 -2020-03-18T15:00,63.2,20,20.9,0,0,14.2,28.4 -2020-03-18T16:00,62.4,20,21.2,0,0,11.4,25.3 -2020-03-18T17:00,56.2,44,34.7,0,0,8.4,19.7 -2020-03-18T18:00,51.6,48,32.6,0,0,10.9,20.4 -2020-03-18T19:00,48.6,63,36.6,0,0,19.6,33.6 -2020-03-18T20:00,47.2,69,37.7,0,0,17.9,32.4 -2020-03-18T21:00,45.4,78,39.1,0,0,15.6,30 -2020-03-18T22:00,43.5,83,38.8,0.004,0,16.2,28 -2020-03-18T23:00,41.5,85,37.2,0.004,0,13.7,27.1 -2020-03-19T00:00,40.6,85,36.4,0,0,13.2,23.3 -2020-03-19T01:00,40.6,84,36.2,0,0,12.5,22.1 -2020-03-19T02:00,40.4,84,36.1,0,0,12.7,21.9 -2020-03-19T03:00,39.8,87,36.1,0.004,0,10.5,21.5 -2020-03-19T04:00,38.8,94,37.2,0.024,0,11,18.3 -2020-03-19T05:00,36.2,100,36.2,0.043,0,11.4,18.6 -2020-03-19T06:00,36,100,35.9,0.047,0.028,11.5,19 -2020-03-19T07:00,35.7,100,35.6,0.047,0.276,11.7,18.3 -2020-03-19T08:00,35.5,100,35.4,0.039,0.386,13.7,21 -2020-03-19T09:00,35.4,100,35.3,0.039,0.579,18.1,27.3 -2020-03-19T10:00,35.5,100,35.4,0.039,0.772,16.9,28.4 -2020-03-19T11:00,34.6,99,34.3,0.039,0.661,18.3,29.5 -2020-03-19T12:00,34.7,97,33.9,0.028,0.661,18.1,29.8 -2020-03-19T13:00,34.5,95,33.4,0.016,0.551,16.2,30.2 -2020-03-19T14:00,33.9,93,32.2,0.008,0.524,16.7,29.1 -2020-03-19T15:00,33.2,92,31.1,0.004,0.441,15.2,28.4 -2020-03-19T16:00,32.5,88,29.3,0,0.248,13.7,25.1 -2020-03-19T17:00,31.3,91,28.9,0,0.193,13.1,22.6 -2020-03-19T18:00,29.5,93,27.7,0,0.11,8.7,21.7 -2020-03-19T19:00,29.2,94,27.8,0,0.083,5.7,14.3 -2020-03-19T20:00,28.8,94,27.1,0,0.083,5.5,9.2 -2020-03-19T21:00,28.4,90,26,0,0.083,4.2,9.6 -2020-03-19T22:00,27.9,91,25.7,0,0.083,3.8,7.2 -2020-03-19T23:00,27,94,25.3,0,0.083,3.4,6 -2020-03-20T00:00,26.2,93,24.4,0,0.11,2.9,5.6 -2020-03-20T01:00,25.7,92,23.6,0,0.083,3.8,6 -2020-03-20T02:00,25.3,90,22.8,0,0.028,3.6,6.5 -2020-03-20T03:00,25,91,22.6,0,0.028,4,6.3 -2020-03-20T04:00,24.7,91,22.5,0,0.028,4.9,7.6 -2020-03-20T05:00,22.1,88,19.2,0,0,6.1,9.4 -2020-03-20T06:00,21.3,88,18.2,0,0.055,5.3,9.6 -2020-03-20T07:00,20.9,87,17.8,0,0.028,5.6,8.9 -2020-03-20T08:00,21.1,86,17.6,0,0,6.5,11.2 -2020-03-20T09:00,21.6,85,17.8,0,0,7.2,12.5 -2020-03-20T10:00,22.1,82,17.4,0,0,6.7,13.2 -2020-03-20T11:00,22.9,77,16.8,0,0,4.4,12.1 -2020-03-20T12:00,23.7,78,18,0,0,7.3,12.8 -2020-03-20T13:00,25.1,85,21.2,0,0,8.8,15.7 -2020-03-20T14:00,24.7,90,22.2,0,0,9,15.9 -2020-03-20T15:00,24.1,90,21.6,0,0,6.8,15.9 -2020-03-20T16:00,24.4,88,21.3,0,0,5.6,12.3 -2020-03-20T17:00,20.8,88,17.7,0,0,5.5,9.8 -2020-03-20T18:00,20.3,88,17.2,0,0,1.8,8.7 -2020-03-20T19:00,19.9,88,17,0,0.165,2.8,4.7 -2020-03-20T20:00,18,92,16,0,0,5.7,9.2 -2020-03-20T21:00,16.1,92,14.3,0,0,4.6,9.6 -2020-03-20T22:00,16.3,94,15,0,0,4.5,6.7 -2020-03-20T23:00,17.8,94,16.4,0,0,2.1,5.4 -2020-03-21T00:00,16.4,95,15.4,0,0,1.8,2.7 -2020-03-21T01:00,14.4,97,13.7,0,0,2.2,2.7 -2020-03-21T02:00,8.6,91,6.5,0,0,3.6,4.5 -2020-03-21T03:00,7.3,86,4,0,0,4.4,5.4 -2020-03-21T04:00,8.1,83,3.9,0,0,4.6,6.5 -2020-03-21T05:00,8.4,78,3,0,0,6.5,8.3 -2020-03-21T06:00,8.7,80,3.7,0,0,7.2,8.5 -2020-03-21T07:00,9.8,80,4.9,0,0,6.8,9.2 -2020-03-21T08:00,16.2,76,10,0,0,6,8.1 -2020-03-21T09:00,25.5,71,17.5,0,0,4.2,7.4 -2020-03-21T10:00,32.7,71,24.3,0,0,4.1,6.7 -2020-03-21T11:00,35.1,73,27.2,0,0,3.9,7.8 -2020-03-21T12:00,37,72,28.8,0,0,3.6,6.3 -2020-03-21T13:00,38.1,68,28.5,0,0,3.8,5.8 -2020-03-21T14:00,36.7,76,29.8,0,0,4.1,6 -2020-03-21T15:00,36.7,77,30.2,0,0,3.4,6 -2020-03-21T16:00,35.9,80,30.4,0,0,3.8,7.6 -2020-03-21T17:00,34.6,87,31,0,0,3.9,6.3 -2020-03-21T18:00,33.4,77,26.9,0.004,0.055,6.5,8.5 -2020-03-21T19:00,31.6,80,26.2,0.004,0,6.2,7.8 -2020-03-21T20:00,31.3,85,27.3,0,0,5.1,7.6 -2020-03-21T21:00,30.2,88,27.1,0,0,4.9,8.3 -2020-03-21T22:00,25.6,91,23.4,0,0,7.7,9.6 -2020-03-21T23:00,21.7,93,20.1,0,0,6.8,9.6 -2020-03-22T00:00,20,89,17.2,0,0,6.8,8.3 -2020-03-22T01:00,18.2,86,14.6,0,0,6.9,8.7 -2020-03-22T02:00,18,85,14.3,0,0,6.4,8.5 -2020-03-22T03:00,18.1,85,14.4,0,0,5.3,7.8 -2020-03-22T04:00,13.5,89,10.9,0,0,6.3,7.8 -2020-03-22T05:00,13.5,86,10,0,0,7.3,8.1 -2020-03-22T06:00,11.5,88,8.5,0,0,7.6,9.4 -2020-03-22T07:00,15,86,11.5,0,0,5.8,9.4 -2020-03-22T08:00,21.7,83,17.4,0,0,3.9,7.2 -2020-03-22T09:00,30.7,78,24.7,0,0,2.4,6.9 -2020-03-22T10:00,32.2,78,26.2,0,0,7.8,11.6 -2020-03-22T11:00,31.5,78,25.5,0,0,4.5,14.8 -2020-03-22T12:00,33.9,74,26.6,0,0,3,8.1 -2020-03-22T13:00,35.3,74,28,0,0,1.6,5.4 -2020-03-22T14:00,37,76,29.9,0,0,2.8,4.9 -2020-03-22T15:00,37.9,74,30.5,0,0,3.7,6.5 -2020-03-22T16:00,37.9,75,30.8,0,0,5,7.6 -2020-03-22T17:00,37.9,80,32.3,0,0,5.7,8.1 -2020-03-22T18:00,40.7,70,31.8,0,0,4.1,7.8 -2020-03-22T19:00,33.6,83,29,0,0,4.2,4.9 -2020-03-22T20:00,24.2,95,23,0,0,6.3,8.5 -2020-03-22T21:00,22.5,90,20,0,0,6.7,8.3 -2020-03-22T22:00,20,84,16.1,0,0,7,8.5 -2020-03-22T23:00,17,84,13,0,0,7.6,9.4 -2020-03-23T00:00,16,83,11.8,0,0,6.8,10.3 -2020-03-23T01:00,12.5,87,9.3,0,0,7.9,10.1 -2020-03-23T02:00,11.3,89,8.7,0,0,7.7,10.1 -2020-03-23T03:00,13.5,87,10.4,0,0,6,9.8 -2020-03-23T04:00,10.9,88,8.1,0,0,6.5,8.3 -2020-03-23T05:00,14.4,83,10.1,0,0,7.2,8.3 -2020-03-23T06:00,12.6,85,8.9,0,0,5.9,9.8 -2020-03-23T07:00,13.9,82,9.5,0,0,5.1,7.8 -2020-03-23T08:00,22.2,78,16.5,0,0,4.5,7.2 -2020-03-23T09:00,32.4,70,23.5,0,0,2.6,6.5 -2020-03-23T10:00,37.3,70,28.6,0,0,3.4,6.7 -2020-03-23T11:00,41,64,29.8,0,0,4.3,6.9 -2020-03-23T12:00,44.9,54,29,0,0,4.2,7.4 -2020-03-23T13:00,44.6,59,31.1,0,0,4.3,6.5 -2020-03-23T14:00,44.8,63,32.8,0,0,4.6,9.6 -2020-03-23T15:00,46.4,56,31.6,0,0,4.5,9.6 -2020-03-23T16:00,51.1,43,29.3,0,0,0.9,6.9 -2020-03-23T17:00,45.5,68,35.6,0,0,6.4,5.6 -2020-03-23T18:00,38.8,74,31.2,0,0,5.9,11 -2020-03-23T19:00,37.5,78,31.2,0,0,5.4,11.2 -2020-03-23T20:00,33.3,87,29.8,0,0,6.6,8.9 -2020-03-23T21:00,35.6,85,31.6,0,0,4.1,8.7 -2020-03-23T22:00,27.8,96,26.9,0,0,4,5.1 -2020-03-23T23:00,23.8,96,22.8,0,0,5,6.5 -2020-03-24T00:00,24.8,92,22.7,0,0,5.2,7.8 -2020-03-24T01:00,23,92,20.9,0,0,6,8.3 -2020-03-24T02:00,19.8,93,18.1,0,0,7.5,10.1 -2020-03-24T03:00,26.2,83,21.6,0,0,5.4,9.6 -2020-03-24T04:00,23.4,81,18.3,0,0,5.7,7.2 -2020-03-24T05:00,25.7,84,21.5,0,0,6,6.9 -2020-03-24T06:00,26.8,82,22,0,0,6.8,8.3 -2020-03-24T07:00,31.7,81,26.4,0,0,4.8,8.3 -2020-03-24T08:00,37.2,78,31.1,0,0,1.1,6.3 -2020-03-24T09:00,39.7,76,32.7,0,0,3,5.1 -2020-03-24T10:00,46.9,54,31,0,0,2,5.6 -2020-03-24T11:00,51.7,44,30.7,0,0,2.9,10.3 -2020-03-24T12:00,54.9,30,24.2,0,0,6,14.8 -2020-03-24T13:00,56.4,29,24.5,0,0,4.7,14.8 -2020-03-24T14:00,57.3,28,24.4,0,0,6.5,14.8 -2020-03-24T15:00,57.7,27,23.8,0,0,6.9,15.2 -2020-03-24T16:00,57.5,27,23.8,0,0,6,14.5 -2020-03-24T17:00,54.5,49,36,0,0,4,12.5 -2020-03-24T18:00,47.4,61,34.7,0,0,7.2,8.1 -2020-03-24T19:00,42.2,65,31.4,0,0,4.5,8.5 -2020-03-24T20:00,36.1,77,29.7,0,0,5.8,8.1 -2020-03-24T21:00,34.2,81,29,0,0,4.1,7.2 -2020-03-24T22:00,32.5,84,28.3,0,0,4.4,7.2 -2020-03-24T23:00,32.4,81,27.1,0,0,4.8,7.6 -2020-03-25T00:00,30.4,83,26,0,0,4.3,7.6 -2020-03-25T01:00,29.5,81,24.4,0,0,4.9,7.8 -2020-03-25T02:00,29,77,22.6,0,0,5,8.7 -2020-03-25T03:00,28.8,75,21.9,0,0,4.4,8.1 -2020-03-25T04:00,28.9,75,22.1,0,0,4.4,7.2 -2020-03-25T05:00,28,77,21.8,0,0,6,7.2 -2020-03-25T06:00,30.6,70,22.1,0,0,3.7,7.2 -2020-03-25T07:00,36.3,62,24.4,0,0,5.8,9.2 -2020-03-25T08:00,44.9,53,28.6,0,0,5.6,11.2 -2020-03-25T09:00,50.4,43,28.7,0,0,4.5,11.2 -2020-03-25T10:00,55.1,33,26.8,0,0,3.1,11.2 -2020-03-25T11:00,57.3,32,27.7,0,0,5.1,12.8 -2020-03-25T12:00,58.7,29,26.3,0,0,6.9,15.2 -2020-03-25T13:00,60.4,26,25.1,0,0,4.5,15.9 -2020-03-25T14:00,61.7,22,22,0,0,4,12.3 -2020-03-25T15:00,62.2,20,20.7,0,0,3.5,11.2 -2020-03-25T16:00,61.7,20,20.5,0,0,5.6,11 -2020-03-25T17:00,55.5,42,32.8,0,0,3.7,11.2 -2020-03-25T18:00,50.7,51,33.3,0,0,4.1,6.7 -2020-03-25T19:00,45.1,60,32.1,0,0,5,6 -2020-03-25T20:00,42.6,67,32.5,0,0,3.1,6.3 -2020-03-25T21:00,40.5,71,31.6,0,0,4.6,5.6 -2020-03-25T22:00,37.7,72,29.5,0,0,5.6,8.3 -2020-03-25T23:00,38,68,28.3,0,0,7.3,11.2 -2020-03-26T00:00,38.1,62,26.4,0,0,6.3,13 -2020-03-26T01:00,36.1,65,25.3,0,0,4.5,10.3 -2020-03-26T02:00,34.8,67,25.1,0,0,4.4,6.7 -2020-03-26T03:00,33.9,70,25.1,0,0,4,6.9 -2020-03-26T04:00,31,77,24.7,0,0,2.5,6.9 -2020-03-26T05:00,29,82,24.4,0,0,6.7,3.6 -2020-03-26T06:00,29.1,82,24.3,0,0,5.4,8.1 -2020-03-26T07:00,33.5,72,25.4,0,0,1.1,6.7 -2020-03-26T08:00,38.1,66,27.8,0,0,2.2,7.6 -2020-03-26T09:00,40.3,69,30.8,0,0,3.8,11.4 -2020-03-26T10:00,43.2,66,32.5,0,0,4.1,12.3 -2020-03-26T11:00,46.8,61,33.9,0,0,5.6,14.8 -2020-03-26T12:00,50.7,56,35.5,0,0,7.7,17.4 -2020-03-26T13:00,54.7,51,37,0,0,8.4,19.2 -2020-03-26T14:00,58,49,39,0,0,6.7,18.6 -2020-03-26T15:00,58.7,51,40.6,0,0,7.4,16.3 -2020-03-26T16:00,57.1,54,40.6,0,0,10.7,19.9 -2020-03-26T17:00,50,57,35.2,0,0,7.3,20.8 -2020-03-26T18:00,46.1,67,35.6,0,0,3.9,12.5 -2020-03-26T19:00,41.1,79,35.1,0,0,6.5,7.6 -2020-03-26T20:00,40.9,79,34.9,0,0,4.9,7.6 -2020-03-26T21:00,38.8,85,34.6,0,0,5.1,6.3 -2020-03-26T22:00,39.4,81,34.1,0,0,3.8,6.3 -2020-03-26T23:00,39,83,34.2,0,0,3.8,4.5 -2020-03-27T00:00,38.1,85,34.1,0,0,2.9,4.7 -2020-03-27T01:00,38.3,82,33.4,0,0,1.8,4.7 -2020-03-27T02:00,37.6,85,33.4,0,0,1.6,2.2 -2020-03-27T03:00,35.9,90,33.4,0,0,2.7,3.8 -2020-03-27T04:00,34.4,92,32.5,0,0,4.8,6.9 -2020-03-27T05:00,32.2,93,30.3,0,0,5.1,10.1 -2020-03-27T06:00,31,96,30,0,0,3.6,8.9 -2020-03-27T07:00,33.4,91,31.1,0,0,5.8,11.2 -2020-03-27T08:00,35.2,81,29.9,0,0,5.9,12.5 -2020-03-27T09:00,37,74,29.4,0,0,5.4,12.8 -2020-03-27T10:00,39.3,68,29.6,0,0,5.9,14.3 -2020-03-27T11:00,41.3,61,28.9,0,0,4.9,14.5 -2020-03-27T12:00,44.2,53,28,0,0,2.1,13.6 -2020-03-27T13:00,46.4,50,28.8,0,0,4.6,13.4 -2020-03-27T14:00,47.5,48,28.7,0,0,6.4,15.9 -2020-03-27T15:00,48.3,46,28.6,0,0,6,15.9 -2020-03-27T16:00,45.4,52,28.7,0.004,0,13,22.4 -2020-03-27T17:00,34.9,80,29.3,0.028,0,12.7,24.2 -2020-03-27T18:00,34.3,81,29,0,0.22,9.7,21.5 -2020-03-27T19:00,33.6,86,29.9,0,0.248,6.8,17 -2020-03-27T20:00,34.1,89,31.3,0,0.22,5.5,12.1 -2020-03-27T21:00,32.9,91,30.6,0.004,0.193,6.2,11.6 -2020-03-27T22:00,32.2,91,29.8,0,0.083,3.2,9.8 -2020-03-27T23:00,32.5,89,29.6,0,0.22,5.2,12.8 -2020-03-28T00:00,31.8,91,29.6,0,0.083,4.7,13.9 -2020-03-28T01:00,31.4,86,27.7,0,0,3.2,7.6 -2020-03-28T02:00,33.1,67,23.4,0,0,2.9,5.1 -2020-03-28T03:00,30.9,58,18.1,0,0,5,8.1 -2020-03-28T04:00,31.9,42,11.6,0,0,8.3,12.8 -2020-03-28T05:00,30.1,39,8,0,0,10.1,18.1 -2020-03-28T06:00,30.8,38,8.4,0,0,17,26.4 -2020-03-28T07:00,32.4,38,9.8,0,0,17.7,29.8 -2020-03-28T08:00,35.9,37,11.8,0,0,11.4,28.9 -2020-03-28T09:00,38.8,33,12.1,0,0,13,23.3 -2020-03-28T10:00,41.4,30,11.9,0,0,14.5,26.4 -2020-03-28T11:00,44,26,11.6,0,0,15.5,28.6 -2020-03-28T12:00,45.8,23,10.3,0,0,17.6,32 -2020-03-28T13:00,46.6,22,9.8,0,0,17.6,32.4 -2020-03-28T14:00,46.9,20,7.9,0,0,15.2,32 -2020-03-28T15:00,46.3,24,11.8,0,0,16.2,29.1 -2020-03-28T16:00,45.4,28,14.3,0,0,15.6,29.1 -2020-03-28T17:00,44.7,31,15.8,0,0,8.9,27.7 -2020-03-28T18:00,40.8,36,15.8,0,0,7.2,15.7 -2020-03-28T19:00,35.2,43,14.7,0,0,5,11.6 -2020-03-28T20:00,31.3,54,16.4,0,0,6.3,8.3 -2020-03-28T21:00,29.4,58,16.3,0,0,4.7,8.1 -2020-03-28T22:00,27.9,62,16.6,0,0,3.9,5.8 -2020-03-28T23:00,27.4,61,15.8,0,0,3.5,5.6 -2020-03-29T00:00,26.2,61,14.5,0,0,4.3,5.8 -2020-03-29T01:00,25.6,60,13.6,0,0,4.1,6 -2020-03-29T02:00,25.4,58,12.8,0,0,3.9,5.8 -2020-03-29T03:00,25.2,58,12.5,0,0,4.2,7.2 -2020-03-29T04:00,24.6,59,12.5,0,0,3.9,6.7 -2020-03-29T05:00,23.5,64,13.3,0,0,4.5,8.5 -2020-03-29T06:00,23.5,63,12.8,0,0,6.4,9.8 -2020-03-29T07:00,27.6,56,14,0,0,8.5,14.5 -2020-03-29T08:00,35.1,49,17.8,0,0,7.4,14.8 -2020-03-29T09:00,42.2,37,17.9,0,0,4.9,14.8 -2020-03-29T10:00,45.2,34,18.4,0,0,4.3,12.5 -2020-03-29T11:00,47.8,30,17.7,0,0,5.1,13.9 -2020-03-29T12:00,50,29,19.1,0,0,5.6,14.8 -2020-03-29T13:00,51.6,27,19,0,0,6.3,15.7 -2020-03-29T14:00,52.8,24,17.2,0,0,7.3,16.1 -2020-03-29T15:00,53.2,23,16.2,0,0,7.1,16.6 -2020-03-29T16:00,53.8,22,15.8,0,0,5.9,15.2 -2020-03-29T17:00,52,28,19.8,0,0,3.4,12.5 -2020-03-29T18:00,47.8,35,21.7,0,0,5.2,7.8 -2020-03-29T19:00,42.8,46,23.2,0,0,4.8,8.5 -2020-03-29T20:00,37.8,60,25,0,0,4.5,9.2 -2020-03-29T21:00,33.6,74,26.2,0,0,4.3,6.5 -2020-03-29T22:00,32.7,75,25.7,0,0,2.7,6 -2020-03-29T23:00,33.3,71,24.8,0.004,0,2.9,7.4 -2020-03-30T00:00,32.2,76,25.4,0,0,3.1,5.4 -2020-03-30T01:00,30.8,79,25.2,0,0,2.5,5.4 -2020-03-30T02:00,30.4,82,25.6,0,0,1.1,9.8 -2020-03-30T03:00,29.7,82,25,0,0.028,1.3,11.4 -2020-03-30T04:00,28.5,88,25.3,0,0.055,1.8,12.8 -2020-03-30T05:00,30,85,26,0,0.055,0.8,13 -2020-03-30T06:00,29.9,88,26.7,0,0,2.7,3.1 -2020-03-30T07:00,33.9,75,26.7,0,0,1.5,4.5 -2020-03-30T08:00,38.7,58,25.2,0,0,0.8,5.6 -2020-03-30T09:00,41.9,49,24,0,0,1.8,8.5 -2020-03-30T10:00,45.9,43,24.6,0,0,1.5,8.5 -2020-03-30T11:00,49.9,40,26.3,0,0,3.4,11 -2020-03-30T12:00,52.5,37,26.9,0,0,5.2,18.6 -2020-03-30T13:00,55.4,34,27.2,0,0,6.6,17.4 -2020-03-30T14:00,51.4,50,33.4,0.031,0,10.9,21.3 -2020-03-30T15:00,52.9,37,27.1,0,0,5.4,21.7 -2020-03-30T16:00,53.5,33,25.4,0,0,5.1,14.5 -2020-03-30T17:00,50.7,43,29.1,0,0,7.3,11.4 -2020-03-30T18:00,47.5,51,30.3,0,0,4.2,13.4 -2020-03-30T19:00,40.1,68,30.4,0,0,3.1,7.2 -2020-03-30T20:00,34.9,85,30.9,0,0,3.4,5.6 -2020-03-30T21:00,31.6,93,29.8,0,0,3.5,5.6 -2020-03-30T22:00,31.2,90,28.7,0,0,4.1,6 -2020-03-30T23:00,30.3,87,27,0,0,5.7,7.2 -2020-03-31T00:00,29.9,85,25.9,0,0,4.7,7.2 -2020-03-31T01:00,29.2,77,22.8,0,0,4.6,7.2 -2020-03-31T02:00,29.2,68,20.1,0,0,4.9,7.4 -2020-03-31T03:00,29.2,64,18.4,0,0,5,7.8 -2020-03-31T04:00,28.9,65,18.4,0,0,6.3,7.6 -2020-03-31T05:00,28.8,66,18.7,0,0,4.5,8.5 -2020-03-31T06:00,28.3,69,19.5,0,0,4.1,5.8 -2020-03-31T07:00,34.3,61,22,0,0,3.2,6.7 -2020-03-31T08:00,43.1,47,24.4,0,0,2.9,8.7 -2020-03-31T09:00,50.8,37,25.7,0,0,6.4,13.2 -2020-03-31T10:00,55.9,29,24,0,0,8,17.2 -2020-03-31T11:00,58.5,26,23.9,0,0,10.2,20.4 -2020-03-31T12:00,60,25,24,0,0,9.3,20.8 -2020-03-31T13:00,62.1,23,24.3,0,0,8.1,19.7 -2020-03-31T14:00,63,23,25,0,0,7,17.7 -2020-03-31T15:00,63.4,23,25.2,0,0,5.3,15.7 -2020-03-31T16:00,63.6,23,25.1,0,0,3,12.8 -2020-03-31T17:00,61.6,29,29.4,0,0,4.1,8.1 -2020-03-31T18:00,53.9,46,33.7,0,0,6.5,7.6 -2020-03-31T19:00,47.9,49,29.5,0,0,8.1,9.8 -2020-03-31T20:00,47.8,46,28.3,0,0,1.8,9.6 -2020-03-31T21:00,43.1,53,27.3,0,0,6.2,9.8 -2020-03-31T22:00,40.6,56,26.1,0,0,4.3,10.1 -2020-03-31T23:00,36.4,70,27.6,0,0,4.8,6.7 -2020-04-01T00:00,36.6,69,27.2,0,0,4.9,7.6 -2020-04-01T01:00,37.8,61,25.5,0,0,1.8,7.6 -2020-04-01T02:00,36.2,65,25.5,0,0,0.7,2.2 -2020-04-01T03:00,31.6,76,25.1,0,0,3.7,4.3 -2020-04-01T04:00,29.9,78,23.8,0,0,4.7,6.3 -2020-04-01T05:00,31.1,74,23.7,0,0,4.2,6.9 -2020-04-01T06:00,32.3,71,23.8,0,0,4.7,7.6 -2020-04-01T07:00,38.4,60,25.6,0,0,4.9,9.2 -2020-04-01T08:00,47,50,29.4,0,0,2.7,8.9 -2020-04-01T09:00,55.8,38,30.7,0,0,4.5,11 -2020-04-01T10:00,62.2,25,26.2,0,0,10.7,19.7 -2020-04-01T11:00,64.6,20,22.4,0,0,10.3,22.8 -2020-04-01T12:00,65.7,20,23.5,0,0,7.9,20.6 -2020-04-01T13:00,66.8,20,24,0,0,8.1,17.7 -2020-04-01T14:00,67.3,19,24,0,0,7.9,17.7 -2020-04-01T15:00,66.9,19,22.9,0,0,4.9,17 -2020-04-01T16:00,66.1,19,22.3,0,0,2,11.9 -2020-04-01T17:00,64.5,31,33.1,0,0,2.9,9.2 -2020-04-01T18:00,57.8,37,31.6,0,0,4.8,7.2 -2020-04-01T19:00,51,44,29.8,0,0,4.6,7.6 -2020-04-01T20:00,50.5,44,29.4,0,0,8.9,15.2 -2020-04-01T21:00,48.5,59,34.8,0,0,11.7,23.3 -2020-04-01T22:00,44.9,78,38.3,0,0,11.3,22.1 -2020-04-01T23:00,46,76,38.8,0,0,11,18.6 -2020-04-02T00:00,44.2,84,39.7,0.004,0,8.7,18.8 -2020-04-02T01:00,42.8,88,39.6,0.004,0,7.9,14.8 -2020-04-02T02:00,41.1,90,38.5,0.004,0,7.6,13.6 -2020-04-02T03:00,39.7,92,37.4,0,0,7.5,13.2 -2020-04-02T04:00,38.5,91,36.1,0,0,7.3,13.2 -2020-04-02T05:00,33.7,75,26.8,0.004,0,8.2,13 -2020-04-02T06:00,32.1,74,24.8,0,0,9.1,15.9 -2020-04-02T07:00,29.9,73,22.4,0,0,11.1,19.7 -2020-04-02T08:00,28.4,71,20.3,0,0,10.8,20.6 -2020-04-02T09:00,25.5,72,17.6,0,0,10.3,19.9 -2020-04-02T10:00,25,71,16.9,0,0,8,19.2 -2020-04-02T11:00,24.9,70,16.6,0,0,8,16.8 -2020-04-02T12:00,25.7,66,15.8,0,0,9.2,18.1 -2020-04-02T13:00,26.9,61,15.4,0,0.028,6.9,18.6 -2020-04-02T14:00,27.2,62,16,0,0.028,4.2,15.7 -2020-04-02T15:00,27.8,63,17,0,0,1.4,11.6 -2020-04-02T16:00,28.1,64,17.4,0,0.028,3.2,9.2 -2020-04-02T17:00,24.3,76,17.8,0,0.11,2.7,9.6 -2020-04-02T18:00,24.9,75,18.2,0,0.083,0.3,7.8 -2020-04-02T19:00,24.6,79,19.1,0,0.11,6.5,10.7 -2020-04-02T20:00,22.9,68,14,0,0.083,9.8,17.7 -2020-04-02T21:00,22,70,13.7,0,0.055,7.8,17.2 -2020-04-02T22:00,21.3,72,13.6,0,0.028,6.8,14.1 -2020-04-02T23:00,20.9,73,13.6,0,0.055,6,12.5 -2020-04-03T00:00,20.6,74,13.7,0,0.083,5.8,11.4 -2020-04-03T01:00,20.5,74,13.6,0,0.055,5.9,11.4 -2020-04-03T02:00,20.7,74,13.8,0,0.055,5.1,11 -2020-04-03T03:00,20.9,74,14,0,0.028,3.7,9.8 -2020-04-03T04:00,21.3,74,14.3,0,0.028,4.2,8.3 -2020-04-03T05:00,19.9,79,14.3,0,0.028,6,9.8 -2020-04-03T06:00,19.6,81,14.7,0,0,4.4,10.1 -2020-04-03T07:00,23.1,72,15.5,0,0,3.1,8.5 -2020-04-03T08:00,26.1,66,16.3,0,0,4.3,11 -2020-04-03T09:00,29.6,59,17.1,0,0,6.3,14.5 -2020-04-03T10:00,33.3,52,17.4,0,0,6.8,16.3 -2020-04-03T11:00,37.9,41,16,0,0,8.2,18.1 -2020-04-03T12:00,40.2,36,15.4,0,0,7.6,18.1 -2020-04-03T13:00,42.5,35,16.8,0,0,6.1,17 -2020-04-03T14:00,43.3,35,17.5,0,0,4.8,15 -2020-04-03T15:00,45.7,34,18.6,0,0,3.7,12.5 -2020-04-03T16:00,46,33,18.8,0,0,3.2,11.6 -2020-04-03T17:00,39.2,47,20.8,0,0,6.3,10.1 -2020-04-03T18:00,38.7,48,20.6,0,0,5.4,11.9 -2020-04-03T19:00,36.4,52,20.6,0,0,10,15 -2020-04-03T20:00,34.5,56,20.5,0,0,9.4,19 -2020-04-03T21:00,33.5,60,21.1,0,0,3.6,14.5 -2020-04-03T22:00,32.7,64,21.7,0,0,3.4,6 -2020-04-03T23:00,30.8,69,22,0,0,2.1,5.6 -2020-04-04T00:00,28.4,78,22.3,0,0,2.2,4.5 -2020-04-04T01:00,26.7,82,21.8,0,0,1.8,4 -2020-04-04T02:00,25.1,86,21.4,0,0,3,4.5 -2020-04-04T03:00,23.8,87,20.4,0,0,2.5,5.1 -2020-04-04T04:00,23.3,87,19.9,0,0,1.8,3.4 -2020-04-04T05:00,20.7,92,18.6,0,0,3.8,4.3 -2020-04-04T06:00,22.6,92,20.6,0,0,1.7,4.7 -2020-04-04T07:00,25.6,85,21.7,0,0,1.6,5.8 -2020-04-04T08:00,29.5,78,23.5,0,0,3.5,10.3 -2020-04-04T09:00,33.8,70,25.2,0,0,4,12.5 -2020-04-04T10:00,38.5,61,26.3,0,0,2.8,12.5 -2020-04-04T11:00,43.9,52,27.2,0,0,4.1,13 -2020-04-04T12:00,49.1,44,28,0,0,5.6,15.7 -2020-04-04T13:00,52.5,39,28.4,0,0,5.9,15.4 -2020-04-04T14:00,55.6,34,27.5,0,0,6,15.4 -2020-04-04T15:00,57.2,29,25.5,0,0,6.7,15 -2020-04-04T16:00,57.2,27,23.6,0,0,7.2,15.4 -2020-04-04T17:00,55.8,35,28.2,0,0,3.3,14.3 -2020-04-04T18:00,50.6,50,32.6,0,0,7,10.7 -2020-04-04T19:00,45.3,63,33.4,0,0,6.8,12.1 -2020-04-04T20:00,42.1,69,32.5,0,0,4.1,11.2 -2020-04-04T21:00,37.9,86,34,0,0,3.5,6.7 -2020-04-04T22:00,35.1,92,33,0,0,3.8,6.3 -2020-04-04T23:00,33.2,91,30.8,0,0,3.4,6 -2020-04-05T00:00,32.1,87,28.7,0,0,4.1,6.3 -2020-04-05T01:00,31.4,82,26.4,0,0,4.6,7.6 -2020-04-05T02:00,30.4,80,25.1,0,0,4,7.6 -2020-04-05T03:00,29.2,82,24.4,0,0,4.3,6.3 -2020-04-05T04:00,30.4,76,23.7,0,0,3.1,5.4 -2020-04-05T05:00,29,78,23.1,0,0,4,5.1 -2020-04-05T06:00,29.3,79,23.7,0,0,3.6,4.9 -2020-04-05T07:00,36.2,65,25.5,0,0,3.4,7.6 -2020-04-05T08:00,45.1,53,28.9,0,0,1.1,7.6 -2020-04-05T09:00,54.1,42,31.6,0,0,2.9,9.6 -2020-04-05T10:00,61.3,30,29.9,0,0,7,15 -2020-04-05T11:00,63.7,26,28,0,0,9.2,18.8 -2020-04-05T12:00,64.8,25,27.8,0,0,8.9,19.2 -2020-04-05T13:00,66,23,27,0,0,10,20.4 -2020-04-05T14:00,66.6,21,25.5,0,0,10.2,20.6 -2020-04-05T15:00,67.3,19,23.6,0,0,11.4,21.9 -2020-04-05T16:00,67.2,18,22.2,0,0,11.4,22.1 -2020-04-05T17:00,63.3,38,37,0,0,6.6,20.6 -2020-04-05T18:00,59,45,37.9,0,0,5.3,11.2 -2020-04-05T19:00,52.2,50,34.3,0,0,7.4,10.3 -2020-04-05T20:00,46.2,52,29.8,0,0,4.8,13.9 -2020-04-05T21:00,44.3,52,27.7,0,0,6.3,10.7 -2020-04-05T22:00,40.7,60,28,0,0,4.5,10.5 -2020-04-05T23:00,39.4,64,28.4,0,0,4.4,7.2 -2020-04-06T00:00,37.7,65,27.1,0,0,4.1,6.7 -2020-04-06T01:00,35.9,65,25.2,0,0,5.2,7.4 -2020-04-06T02:00,36.2,59,23.2,0,0,4.9,7.6 -2020-04-06T03:00,35.5,60,23,0,0,5.2,8.3 -2020-04-06T04:00,34.4,62,22.8,0,0,4.8,8.3 -2020-04-06T05:00,35.1,60,22.4,0,0,5.1,7.8 -2020-04-06T06:00,34,65,23.4,0,0,3.8,8.3 -2020-04-06T07:00,40.8,55,26.1,0,0,3.5,8.1 -2020-04-06T08:00,50.8,45,30.3,0,0,1.7,7.6 -2020-04-06T09:00,59.8,33,30.8,0,0,3.5,10.1 -2020-04-06T10:00,64.9,20,22.7,0,0,6.3,14.3 -2020-04-06T11:00,66.7,15,17.8,0,0,9.7,19.5 -2020-04-06T12:00,67.1,16,19.5,0,0,9.8,21.3 -2020-04-06T13:00,68,16,20.1,0,0,8.4,20.4 -2020-04-06T14:00,68.7,15,19.8,0,0,7,18.3 -2020-04-06T15:00,68.9,15,19.6,0,0,6.2,15.9 -2020-04-06T16:00,68.8,15,18.6,0,0,4.7,13.9 -2020-04-06T17:00,67.2,22,27,0,0,4.7,10.7 -2020-04-06T18:00,58.6,39,33.7,0,0,5.9,8.1 -2020-04-06T19:00,56.6,32,27.1,0,0,4,8.5 -2020-04-06T20:00,46.4,48,28,0,0,4.3,7.2 -2020-04-06T21:00,44.1,50,26.4,0,0,5.5,8.7 -2020-04-06T22:00,43.8,44,23.5,0,0,5.7,9.8 -2020-04-06T23:00,41.3,46,21.8,0,0,5.4,9.4 -2020-04-07T00:00,39.1,49,21.6,0,0,5,8.7 -2020-04-07T01:00,37.4,54,22.1,0,0,5.3,7.8 -2020-04-07T02:00,35.9,59,22.9,0,0,4.9,7.4 -2020-04-07T03:00,35.2,61,23.1,0,0,4.4,7.2 -2020-04-07T04:00,34.8,59,21.9,0,0,6.5,8.7 -2020-04-07T05:00,40.1,43,19.2,0,0,4.7,7.8 -2020-04-07T06:00,38.7,46,19.6,0,0,3.9,5.8 -2020-04-07T07:00,42.3,50,24.9,0,0,1.2,5.1 -2020-04-07T08:00,50.4,37,25.3,0,0,4.3,10.5 -2020-04-07T09:00,58.2,28,25.3,0,0,4,11.2 -2020-04-07T10:00,63.1,22,23.7,0,0,3.8,11.9 -2020-04-07T11:00,64.7,18,20.7,0,0,1.9,11.6 -2020-04-07T12:00,65.8,18,21.4,0,0,4.3,12.5 -2020-04-07T13:00,66.6,18,21.2,0,0,6.4,15.4 -2020-04-07T14:00,67.1,17,20.9,0,0,6.2,15.7 -2020-04-07T15:00,67.4,16,20.3,0,0,6.4,15 -2020-04-07T16:00,67.4,16,19.2,0,0,7.4,15.7 -2020-04-07T17:00,67.1,21,25.9,0,0,7.9,15 -2020-04-07T18:00,61.1,39,35.7,0,0,5.1,13 -2020-04-07T19:00,51.4,47,31.9,0,0,6.2,6.7 -2020-04-07T20:00,48,51,30.8,0,0,4.7,7.8 -2020-04-07T21:00,44.9,47,25.6,0,0,4.8,7.6 -2020-04-07T22:00,41.9,45,22.1,0,0,4.8,7.6 -2020-04-07T23:00,41.7,49,23.7,0,0,5.9,9.8 -2020-04-08T00:00,39.9,54,24.7,0,0,4.7,9.8 -2020-04-08T01:00,38.4,56,24.3,0,0,4.4,7.6 -2020-04-08T02:00,37,57,23,0,0,4.8,7.8 -2020-04-08T03:00,35.3,61,23.3,0,0,3.9,7.6 -2020-04-08T04:00,34.2,63,22.7,0,0,4.3,5.8 -2020-04-08T05:00,34.4,65,24,0,0,3.2,7.8 -2020-04-08T06:00,35.6,61,23.4,0,0,4.6,6.9 -2020-04-08T07:00,41.8,53,25.7,0,0,4.5,9.6 -2020-04-08T08:00,50.5,39,26.7,0,0,5,11.4 -2020-04-08T09:00,57.6,24,21,0,0,4.1,11.9 -2020-04-08T10:00,60.9,20,19.9,0,0,4.9,13.4 -2020-04-08T11:00,62.5,20,21.1,0,0,4,13.6 -2020-04-08T12:00,64.3,20,21.9,0,0,3.4,12.8 -2020-04-08T13:00,65.8,20,23.3,0,0,5.3,13.6 -2020-04-08T14:00,66,21,24.5,0,0,9.1,18.8 -2020-04-08T15:00,66.2,22,26.1,0,0,8.5,19.9 -2020-04-08T16:00,65.8,23,26.7,0,0,8.9,17.7 -2020-04-08T17:00,66.5,19,22.8,0,0,5.6,20.4 -2020-04-08T18:00,60.4,31,29.2,0,0,4.9,10.7 -2020-04-08T19:00,52.4,43,30.4,0,0,7.4,8.7 -2020-04-08T20:00,50.8,42,28.4,0,0,5.1,9.6 -2020-04-08T21:00,47.4,49,29,0,0,6.4,10.3 -2020-04-08T22:00,45.7,53,29.4,0,0,7.2,11.9 -2020-04-08T23:00,42.4,61,29.7,0,0,4.3,11.9 -2020-04-09T00:00,40.6,67,30.3,0,0,5,6.7 -2020-04-09T01:00,39.8,69,30.5,0,0,3.6,7.2 -2020-04-09T02:00,39.9,67,29.8,0,0,5.4,8.9 -2020-04-09T03:00,38.1,71,29.5,0,0,4.7,8.9 -2020-04-09T04:00,36.4,76,29.6,0,0,4.2,7.4 -2020-04-09T05:00,35.1,80,29.4,0,0,3.9,4.9 -2020-04-09T06:00,36.5,77,29.9,0,0,3,6.7 -2020-04-09T07:00,39.2,70,30.2,0,0,5.9,10.1 -2020-04-09T08:00,41.9,61,29.5,0,0,11.6,20.8 -2020-04-09T09:00,45.2,53,28.9,0,0,10.3,22.1 -2020-04-09T10:00,48,48,29.2,0,0,10.1,21.5 -2020-04-09T11:00,49.8,44,28.9,0,0,11.1,23.5 -2020-04-09T12:00,52.5,41,29.3,0,0,9.3,23.3 -2020-04-09T13:00,54.5,39,30.1,0,0,6.9,20.8 -2020-04-09T14:00,55.9,38,30.3,0,0,6.4,17.4 -2020-04-09T15:00,56.8,37,30.7,0,0,5.1,16.3 -2020-04-09T16:00,56.4,37,30.3,0,0,4.8,13.9 -2020-04-09T17:00,55.4,42,32.8,0,0,7.3,13.4 -2020-04-09T18:00,53.3,46,32.9,0,0,5.1,14.5 -2020-04-09T19:00,50.5,51,33.2,0,0,2.3,8.1 -2020-04-09T20:00,47.8,61,35.2,0,0,4.1,4.9 -2020-04-09T21:00,46.4,62,34.2,0,0,3.4,4.9 -2020-04-09T22:00,45.3,61,32.5,0,0,3.8,6.3 -2020-04-09T23:00,41.1,74,33.4,0,0,3.9,6.5 -2020-04-10T00:00,38.2,86,34.4,0,0,2.7,5.6 -2020-04-10T01:00,36.6,93,34.8,0,0,2.7,4.9 -2020-04-10T02:00,35.1,95,33.8,0,0,3.2,4.3 -2020-04-10T03:00,35.1,92,32.9,0,0,3.3,5.1 -2020-04-10T04:00,34.4,90,31.8,0,0,5,6 -2020-04-10T05:00,37.8,91,35.4,0,0,4.4,6.3 -2020-04-10T06:00,37,92,34.8,0,0,4.1,6.9 -2020-04-10T07:00,42.4,80,36.9,0,0,4.3,9.2 -2020-04-10T08:00,51.2,63,39.1,0,0,5,11.4 -2020-04-10T09:00,60.6,38,34.9,0,0,6.7,15 -2020-04-10T10:00,65.8,28,31.4,0,0,4.2,19.7 -2020-04-10T11:00,67.6,22,27.2,0,0,5.7,15.9 -2020-04-10T12:00,68.7,19,24.8,0,0,6.7,17 -2020-04-10T13:00,70.3,18,25.4,0,0,8.1,18.1 -2020-04-10T14:00,65.2,29,31.8,0.024,0,7.4,18.1 -2020-04-10T15:00,62.6,29,29.5,0,0,11.2,21.7 -2020-04-10T16:00,66,19,23.1,0,0,5.5,20.4 -2020-04-10T17:00,64.6,22,25.1,0,0,10.2,15 -2020-04-10T18:00,60.1,32,29.8,0,0,10.3,17.9 -2020-04-10T19:00,59.3,26,24.3,0,0,13.6,22.8 -2020-04-10T20:00,56.7,29,25.1,0,0,10.8,22.8 -2020-04-10T21:00,52.4,38,27.7,0,0,8.1,17.7 -2020-04-10T22:00,49.8,43,28.2,0,0,5.8,13.4 -2020-04-10T23:00,46.7,47,27.6,0,0,4.9,9.2 -2020-04-11T00:00,43,54,27.5,0,0,6.6,8.7 -2020-04-11T01:00,40.3,58,26.9,0,0,5.3,8.3 -2020-04-11T02:00,39,61,26.9,0,0,4.3,6.9 -2020-04-11T03:00,38.2,58,24.9,0,0,4.7,6.9 -2020-04-11T04:00,38.3,50,21.4,0,0,5.6,8.5 -2020-04-11T05:00,38.8,47,20,0,0,5.6,11 -2020-04-11T06:00,39.5,46,20.2,0,0,6.5,10.5 -2020-04-11T07:00,45.1,41,23.1,0,0,6,12.8 -2020-04-11T08:00,53.3,33,24.7,0,0,2.9,11.2 -2020-04-11T09:00,60.2,20,19,0,0,5.4,13.4 -2020-04-11T10:00,62.6,17,17.5,0,0,2.9,13.2 -2020-04-11T11:00,64.5,16,18,0,0,0.5,11.2 -2020-04-11T12:00,66,15,16.7,0,0,2.7,11.4 -2020-04-11T13:00,67.4,13,14,0,0,3.3,12.5 -2020-04-11T14:00,69,11,11.4,0,0,2.7,12.3 -2020-04-11T15:00,69.7,8,6.4,0,0,7.7,16.3 -2020-04-11T16:00,68.1,9,8.2,0,0,9.4,19.5 -2020-04-11T17:00,65,16,17.4,0,0,11.7,18.3 -2020-04-11T18:00,60.9,19,18.6,0,0,9,20.4 -2020-04-11T19:00,54,30,23.3,0,0,7.8,14.5 -2020-04-11T20:00,53.9,31,23.6,0,0,4.6,9.4 -2020-04-11T21:00,52.2,34,24.6,0,0,4.3,8.5 -2020-04-11T22:00,51,39,26.9,0,0,10.5,17 -2020-04-11T23:00,47.7,57,33.3,0,0,12.3,19.7 -2020-04-12T00:00,40.6,83,35.8,0.008,0,17,28 -2020-04-12T01:00,36.3,99,36.1,0.028,0.055,13.8,29.3 -2020-04-12T02:00,34.8,96,33.7,0.012,0.11,12.1,22.6 -2020-04-12T03:00,30.7,91,28.2,0.004,0.11,14.4,26.2 -2020-04-12T04:00,27.9,87,24.4,0,0.11,12.1,24.8 -2020-04-12T05:00,23.1,84,19,0,0.193,10.6,21 -2020-04-12T06:00,22.6,83,18.3,0,0.055,9.7,18.3 -2020-04-12T07:00,22.6,80,17.4,0,0.055,10.1,18.3 -2020-04-12T08:00,23.4,75,16.5,0,0.055,10.4,19.9 -2020-04-12T09:00,24.1,66,14.5,0,0.028,10.3,20.1 -2020-04-12T10:00,25.4,63,14.6,0,0.055,10.2,21 -2020-04-12T11:00,26.1,62,14.8,0,0.028,9.3,20.8 -2020-04-12T12:00,27.1,64,16.7,0,0.055,7.4,19.5 -2020-04-12T13:00,28,64,17.5,0,0.028,8.1,18.6 -2020-04-12T14:00,29.6,59,17,0,0.028,9,19.9 -2020-04-12T15:00,30,46,11.5,0,0,10,20.8 -2020-04-12T16:00,29.2,50,12.7,0,0,10.2,21 -2020-04-12T17:00,24.4,58,11.7,0,0.028,7.7,19.9 -2020-04-12T18:00,22.9,61,11.6,0,0,7.6,15.7 -2020-04-12T19:00,21.7,66,12.2,0,0.028,6.7,15 -2020-04-12T20:00,21.9,66,12.3,0,0.055,5.9,13.2 -2020-04-12T21:00,21.7,67,12.3,0,0.055,3.8,11.9 -2020-04-12T22:00,21.3,71,13.4,0,0.083,2.6,8.7 -2020-04-12T23:00,21.2,77,15.1,0,0.055,3,7.2 -2020-04-13T00:00,21.4,77,15.4,0,0.028,3.9,8.1 -2020-04-13T01:00,21.4,75,14.7,0,0.028,4.5,8.7 -2020-04-13T02:00,21.4,75,14.6,0,0.028,5.4,10.1 -2020-04-13T03:00,21.2,75,14.5,0,0.028,6.3,11.9 -2020-04-13T04:00,20.6,78,14.9,0,0.028,8.1,13.9 -2020-04-13T05:00,17.3,77,11.5,0,0.028,10.2,16.3 -2020-04-13T06:00,17.1,76,10.7,0,0.055,8.9,17.9 -2020-04-13T07:00,17,74,10.2,0,0.055,9.4,17.9 -2020-04-13T08:00,17.2,73,10.1,0,0.055,8.2,17.4 -2020-04-13T09:00,17.3,76,11.1,0,0.028,7.6,17 -2020-04-13T10:00,19.3,64,9.2,0,0.028,6.7,14.1 -2020-04-13T11:00,23.2,56,10,0,0.028,6.7,17.4 -2020-04-13T12:00,22,68,13.1,0,0.028,8.2,18.8 -2020-04-13T13:00,22.2,67,13,0,0.028,8.2,17.7 -2020-04-13T14:00,23.5,64,13.3,0,0.028,8,15.7 -2020-04-13T15:00,23.4,68,14.5,0,0.028,9.5,17.4 -2020-04-13T16:00,22.2,68,13.4,0,0.055,9.2,17.9 -2020-04-13T17:00,21.2,67,12,0,0.083,3.6,16.8 -2020-04-13T18:00,19.8,75,13.2,0,0.028,1.7,6.9 -2020-04-13T19:00,19.9,69,11.5,0,0.028,2,4.5 -2020-04-13T20:00,17.2,79,11.8,0,0.028,3.4,5.6 -2020-04-13T21:00,15.2,79,9.8,0,0,5.4,7.6 -2020-04-13T22:00,19.4,72,11.8,0,0,4.2,7.8 -2020-04-13T23:00,16.9,76,10.6,0,0,4.1,5.8 -2020-04-14T00:00,12,82,7.6,0,0,4.5,7.2 -2020-04-14T01:00,12.9,76,6.7,0,0,3.7,6.7 -2020-04-14T02:00,11.8,75,5.3,0,0,4.8,7.4 -2020-04-14T03:00,6.2,83,2.1,0,0,6.4,8.1 -2020-04-14T04:00,9.4,79,4.3,0,0,3.4,9.2 -2020-04-14T05:00,5.2,75,-1,0,0,6.1,10.3 -2020-04-14T06:00,0.6,75,-5.5,0,0,7.4,10.3 -2020-04-14T07:00,6.1,62,-4.2,0,0,6.5,10.5 -2020-04-14T08:00,13.6,56,0.7,0,0,5.9,9.8 -2020-04-14T09:00,25.4,43,5.9,0,0,5.2,10.5 -2020-04-14T10:00,29.6,38,6.8,0,0,4.5,11.6 -2020-04-14T11:00,33.7,43,13.6,0,0,2.3,9.6 -2020-04-14T12:00,35.1,56,20.8,0,0,2.8,5.8 -2020-04-14T13:00,37.4,36,12.6,0,0,4.6,9.6 -2020-04-14T14:00,37.7,38,14.2,0,0,4.4,11.4 -2020-04-14T15:00,37.1,48,19,0,0,4.1,9.8 -2020-04-14T16:00,36.9,40,14.6,0,0,4,8.5 -2020-04-14T17:00,42.4,36,17.3,0,0,4.3,9.6 -2020-04-14T18:00,30.9,48,13.4,0,0,8.4,12.1 -2020-04-14T19:00,37.1,30,8.3,0,0,6.1,11.6 -2020-04-14T20:00,30.7,40,9.1,0,0,4,6.9 -2020-04-14T21:00,20.9,66,11.4,0,0,6.3,8.7 -2020-04-14T22:00,14.6,78,9,0,0,7.3,10.1 -2020-04-14T23:00,16.9,75,10.3,0,0,6.3,10.1 -2020-04-15T00:00,20.7,68,11.8,0,0,5.1,8.5 -2020-04-15T01:00,18.5,72,11.1,0,0,4.8,6.9 -2020-04-15T02:00,16.8,75,10.2,0,0,5.4,7.6 -2020-04-15T03:00,15.7,78,10.1,0,0,5.9,8.1 -2020-04-15T04:00,13.1,79,7.7,0,0,7.5,10.1 -2020-04-15T05:00,17.6,77,11.7,0,0,5.6,10.1 -2020-04-15T06:00,16.1,75,9.4,0,0,6.2,11.4 -2020-04-15T07:00,20.9,68,11.9,0,0,6.5,10.7 -2020-04-15T08:00,30.7,55,16.6,0,0,5,10.3 -2020-04-15T09:00,39.1,43,18.4,0,0,5.5,11 -2020-04-15T10:00,41.3,39,18.1,0,0,6.5,13 -2020-04-15T11:00,42,43,21.1,0,0,4.4,13.2 -2020-04-15T12:00,49.8,21,11.7,0,0,7.7,17.4 -2020-04-15T13:00,50.5,21,12.5,0,0,8.6,20.1 -2020-04-15T14:00,51.5,23,14.5,0,0,7.4,19.2 -2020-04-15T15:00,51.7,24,16,0,0,9.3,19.2 -2020-04-15T16:00,50.9,26,17.3,0,0,10.5,19.5 -2020-04-15T17:00,42.8,61,30.3,0,0,13,24.6 -2020-04-15T18:00,40.8,68,31.2,0,0,9.9,24.6 -2020-04-15T19:00,37.6,80,31.8,0.004,0,10.5,17 -2020-04-15T20:00,35.1,86,31.4,0.012,0.028,7.9,17.2 -2020-04-15T21:00,34.6,88,31.5,0.004,0.083,5.6,13.6 -2020-04-15T22:00,33.9,90,31.3,0.004,0.11,4.8,9.6 -2020-04-15T23:00,33.5,90,31,0.004,0.083,2.2,12.1 -2020-04-16T00:00,31.6,97,30.9,0.004,0.193,2,5.1 -2020-04-16T01:00,31.6,97,31,0,0.193,2.1,4 -2020-04-16T02:00,31,99,30.7,0,0.193,4,14.5 -2020-04-16T03:00,31.2,97,30.4,0,0.248,4.7,19 -2020-04-16T04:00,31.2,97,30.5,0,0.138,3.3,8.3 -2020-04-16T05:00,30,97,29.3,0,0.11,8.7,6.9 -2020-04-16T06:00,28.9,95,27.5,0,0.579,10.2,16.6 -2020-04-16T07:00,27.3,95,26,0,0.469,12.4,20.4 -2020-04-16T08:00,27.1,95,25.8,0,0.276,9.3,19.9 -2020-04-16T09:00,27.7,92,25.8,0,0.165,9.3,15.9 -2020-04-16T10:00,27.6,94,26.1,0,0.055,11.3,19 -2020-04-16T11:00,26.7,94,25.1,0,0.11,11,19.5 -2020-04-16T12:00,27.4,92,25.4,0,0.303,10.1,18.6 -2020-04-16T13:00,26.7,90,24.2,0,0.331,10.7,17.4 -2020-04-16T14:00,26.2,90,23.6,0,0.22,11.5,20.1 -2020-04-16T15:00,26.2,89,23.5,0,0.11,11.7,19.2 -2020-04-16T16:00,25.8,91,23.6,0,0.165,13.2,21.5 -2020-04-16T17:00,22.6,92,20.6,0,0.138,10.1,21.7 -2020-04-16T18:00,22.4,91,20.1,0,0.055,8.1,16.3 -2020-04-16T19:00,21.4,89,18.8,0,0.028,7.4,13.4 -2020-04-16T20:00,20.2,90,17.8,0,0,3.6,11.6 -2020-04-16T21:00,19.5,90,17.2,0,0,2.3,5.6 -2020-04-16T22:00,18.7,90,16.2,0,0,2.4,4 -2020-04-16T23:00,18,88,15,0,0,3.6,6 -2020-04-17T00:00,13.2,88,10.2,0,0,5.8,6.9 -2020-04-17T01:00,15.4,85,11.8,0,0,5.1,6.9 -2020-04-17T02:00,15.4,87,12.3,0,0,5.3,6.5 -2020-04-17T03:00,13.2,89,10.6,0,0,5.8,6.9 -2020-04-17T04:00,10.7,90,8.2,0,0,5.4,7.4 -2020-04-17T05:00,14.3,82,9.7,0,0,5.7,8.3 -2020-04-17T06:00,12.3,79,7.1,0,0,4.9,9.2 -2020-04-17T07:00,13.6,77,7.8,0,0,6,9.2 -2020-04-17T08:00,18.7,75,12.1,0,0,7.5,11.6 -2020-04-17T09:00,23.9,77,17.7,0,0,7.5,12.5 -2020-04-17T10:00,29.5,71,21.4,0,0,4.1,11 -2020-04-17T11:00,32.5,79,26.8,0,0,2.2,7.2 -2020-04-17T12:00,34.5,67,24.5,0,0,2.2,3.8 -2020-04-17T13:00,35.2,67,25.5,0,0,3.6,6.7 -2020-04-17T14:00,35.6,71,27.2,0,0,3.2,6.9 -2020-04-17T15:00,35.8,75,28.6,0,0,2.6,6 -2020-04-17T16:00,35.5,76,28.7,0,0,3.2,5.1 -2020-04-17T17:00,32.4,80,26.8,0,0,5.2,5.6 -2020-04-17T18:00,34.5,66,24.4,0,0,1.9,6.3 -2020-04-17T19:00,32.3,71,23.8,0,0,3.7,4.7 -2020-04-17T20:00,29,75,22.2,0,0,3.2,6.3 -2020-04-17T21:00,20.8,90,18.4,0,0,7.1,8.7 -2020-04-17T22:00,18.4,88,15.5,0,0,7.3,9.2 -2020-04-17T23:00,16.4,84,12.5,0,0,8.1,10.1 -2020-04-18T00:00,17.8,76,11.6,0,0,7.3,9.4 -2020-04-18T01:00,18.9,72,11.4,0,0,6.9,8.7 -2020-04-18T02:00,18.5,71,10.8,0,0,7.3,8.7 -2020-04-18T03:00,18.9,71,11.1,0,0,6.7,8.7 -2020-04-18T04:00,19,72,11.5,0,0,6.1,8.1 -2020-04-18T05:00,14,83,9.9,0,0,7.1,7.2 -2020-04-18T06:00,15.3,83,11.1,0,0,6.3,9.2 -2020-04-18T07:00,21.5,80,16.2,0,0,4.6,8.1 -2020-04-18T08:00,29.7,79,23.9,0,0,4.3,6.3 -2020-04-18T09:00,35,73,27.1,0,0,4.9,5.8 -2020-04-18T10:00,41.4,52,25.2,0,0,1.6,5.8 -2020-04-18T11:00,40.3,66,29.8,0,0,3.8,5.4 -2020-04-18T12:00,36.9,77,30.4,0,0,6.3,11.6 -2020-04-18T13:00,37.4,73,29.6,0,0,5.9,11.9 -2020-04-18T14:00,38.5,71,30,0,0,3.5,11 -2020-04-18T15:00,37.5,73,29.6,0,0,5.1,8.7 -2020-04-18T16:00,38,70,29,0.004,0.028,4.7,8.7 -2020-04-18T17:00,37.1,84,32.7,0,0,3.3,8.1 -2020-04-18T18:00,34.5,89,31.6,0,0,5.7,7.2 -2020-04-18T19:00,31.1,87,27.8,0,0,6.3,8.3 -2020-04-18T20:00,31.4,89,28.4,0,0,4.5,8.1 -2020-04-18T21:00,31.3,89,28.5,0,0,5.4,8.5 -2020-04-18T22:00,30.4,91,28,0,0,4.6,8.5 -2020-04-18T23:00,32.3,89,29.3,0,0,5.5,8.7 -2020-04-19T00:00,35.2,84,30.9,0.004,0,7.1,11.4 -2020-04-19T01:00,35.1,86,31.3,0.004,0,5,11.4 -2020-04-19T02:00,34,87,30.4,0.004,0,6.2,8.3 -2020-04-19T03:00,34.3,85,30.3,0.004,0,5,7.6 -2020-04-19T04:00,38.1,75,30.8,0.004,0,0.2,6.9 -2020-04-19T05:00,28.9,87,25.4,0,0,5.2,4.9 -2020-04-19T06:00,27.4,89,24.6,0,0,4.7,7.8 -2020-04-19T07:00,30.4,89,27.6,0,0,4.3,8.5 -2020-04-19T08:00,32.5,87,29,0,0,4,7.8 -2020-04-19T09:00,33.1,87,29.6,0,0,1.1,6.9 -2020-04-19T10:00,35,84,30.7,0,0,1.6,4 -2020-04-19T11:00,37.6,85,33.6,0,0,1.1,3.4 -2020-04-19T12:00,39.9,81,34.7,0,0,2.2,4.7 -2020-04-19T13:00,42.5,74,34.8,0,0,3.2,6.9 -2020-04-19T14:00,41.8,73,33.8,0,0,10.1,17.7 -2020-04-19T15:00,44.9,59,31.3,0,0,8.4,23.3 -2020-04-19T16:00,48.3,50,30.5,0,0,6.2,18.1 -2020-04-19T17:00,51.9,47,32.5,0,0,1,14.1 -2020-04-19T18:00,49.1,62,36.8,0,0,4.2,7.4 -2020-04-19T19:00,43,63,31.2,0,0,4.9,8.1 -2020-04-19T20:00,38,68,28.3,0,0,3.5,7.6 -2020-04-19T21:00,34.4,84,30,0,0,4.6,5.4 -2020-04-19T22:00,33.8,84,29.4,0,0,3.8,6 -2020-04-19T23:00,33.9,77,27.4,0,0,5.6,9.4 -2020-04-20T00:00,31.3,85,27.4,0,0,4.7,8.7 -2020-04-20T01:00,30.5,88,27.2,0,0,4.8,6.7 -2020-04-20T02:00,29,88,25.8,0,0,3,6 -2020-04-20T03:00,28,85,24.1,0,0,3.6,3.8 -2020-04-20T04:00,27.2,82,22.6,0,0,3.8,4.9 -2020-04-20T05:00,25.2,87,21.7,0,0,5.6,5.8 -2020-04-20T06:00,29.4,79,23.7,0,0,5.7,8.7 -2020-04-20T07:00,35.9,68,26.3,0,0,4.3,10.3 -2020-04-20T08:00,44.2,57,30.1,0,0,3,9.8 -2020-04-20T09:00,51.2,46,30.9,0,0,1.4,9.6 -2020-04-20T10:00,54.9,38,30,0,0,0,8.7 -2020-04-20T11:00,56.8,34,28.9,0,0,3,11 -2020-04-20T12:00,58.6,27,24.4,0,0,4.6,13.4 -2020-04-20T13:00,60.3,24,23.3,0,0,4.2,13.6 -2020-04-20T14:00,61.4,24,24.6,0,0,4.3,12.8 -2020-04-20T15:00,61.3,25,24.9,0,0,5.3,13.2 -2020-04-20T16:00,60.3,28,26.8,0,0,8.5,15.7 -2020-04-20T17:00,58.7,44,36.6,0,0,8.6,22.6 -2020-04-20T18:00,54.1,53,37.5,0,0,7.2,16.1 -2020-04-20T19:00,46.8,61,34.2,0,0,4.1,11.4 -2020-04-20T20:00,42.4,72,34.1,0,0,3.6,6.9 -2020-04-20T21:00,38.9,82,33.9,0,0,2.1,6 -2020-04-20T22:00,37,85,33.1,0,0,2.1,4 -2020-04-20T23:00,34.9,89,32,0,0,3,5.6 -2020-04-21T00:00,33.2,93,31.5,0,0,1.5,4.9 -2020-04-21T01:00,32.9,91,30.7,0,0,3.8,5.8 -2020-04-21T02:00,31.8,94,30.3,0,0,3.1,7.4 -2020-04-21T03:00,31.6,92,29.5,0,0,1.8,4.9 -2020-04-21T04:00,30.4,93,28.6,0,0,2.6,3.4 -2020-04-21T05:00,29.1,97,28.5,0,0,1.7,4.3 -2020-04-21T06:00,30.3,97,29.6,0,0,3,6.7 -2020-04-21T07:00,37.4,83,32.6,0,0,1.8,7.2 -2020-04-21T08:00,45.3,63,33.4,0,0,0.7,7.6 -2020-04-21T09:00,49.9,53,33.6,0,0,2.9,10.5 -2020-04-21T10:00,54.1,47,34.1,0,0,4.3,13 -2020-04-21T11:00,57.5,43,35.2,0,0,4.7,14.1 -2020-04-21T12:00,60,41,36.3,0,0,6,15.4 -2020-04-21T13:00,60.4,40,36.1,0,0,6.1,17 -2020-04-21T14:00,62.4,34,33.7,0,0,2.6,15.9 -2020-04-21T15:00,62.1,35,34.4,0,0,5.4,13.4 -2020-04-21T16:00,62.1,36,34.7,0,0,6.1,13.6 -2020-04-21T17:00,54,66,42.7,0,0,12.2,13.4 -2020-04-21T18:00,54.8,64,42.7,0,0,2.2,22.4 -2020-04-21T19:00,50.5,73,42.1,0,0,4.3,6.7 -2020-04-21T20:00,48.1,69,38.3,0,0,6.5,10.7 -2020-04-21T21:00,44.8,71,36,0,0,5.1,10.7 -2020-04-21T22:00,42.1,75,34.8,0,0,5.1,8.5 -2020-04-21T23:00,40,74,32.3,0,0,4.4,8.7 -2020-04-22T00:00,38.8,73,30.8,0,0,3.8,7.2 -2020-04-22T01:00,37.5,73,29.7,0,0,5.4,6.7 -2020-04-22T02:00,37.1,70,28.3,0,0,6.8,8.3 -2020-04-22T03:00,36.9,67,27,0,0,7.3,8.7 -2020-04-22T04:00,37.6,61,25.3,0,0,7.8,9.4 -2020-04-22T05:00,45.2,42,23.3,0,0,9.6,10.5 -2020-04-22T06:00,47.8,40,24.6,0,0,10.3,18.8 -2020-04-22T07:00,51.5,43,29.5,0,0,5.2,16.8 -2020-04-22T08:00,55,41,31.9,0,0,5.4,12.3 -2020-04-22T09:00,57.6,38,32,0,0,7.2,15.7 -2020-04-22T10:00,60.1,31,29.4,0,0,7.7,17 -2020-04-22T11:00,60.6,37,34.3,0.02,0,7.5,20.1 -2020-04-22T12:00,60.8,38,34.8,0.031,0,1.7,17.7 -2020-04-22T13:00,61.2,38,35.2,0,0,5.9,14.1 -2020-04-22T14:00,58.1,47,38.2,0.043,0,1.4,19.2 -2020-04-22T15:00,61.3,36,33.8,0,0,2.7,11 -2020-04-22T16:00,61.8,34,33.2,0,0,2.9,10.1 -2020-04-22T17:00,60.3,39,35.3,0,0,2.4,9.4 -2020-04-22T18:00,57.7,50,39.2,0,0,5.5,10.1 -2020-04-22T19:00,50.5,65,39,0,0,5.4,9.4 -2020-04-22T20:00,47.7,72,39,0,0,4.3,6.9 -2020-04-22T21:00,45.1,71,36.1,0,0,6.1,9.6 -2020-04-22T22:00,44.6,52,27.9,0,0,7,11.9 -2020-04-22T23:00,42.4,51,25.7,0,0,6.9,11.9 -2020-04-23T00:00,41.5,54,26.1,0,0,7.1,12.3 -2020-04-23T01:00,39.2,60,26.6,0,0,5.3,12.1 -2020-04-23T02:00,35.5,74,28,0,0,3.6,8.9 -2020-04-23T03:00,33.7,82,28.7,0,0,3.5,6.3 -2020-04-23T04:00,34.6,79,28.7,0,0,3.4,6.7 -2020-04-23T05:00,36.5,71,27.9,0,0,5.7,11.9 -2020-04-23T06:00,42.8,56,28.2,0,0,5.8,10.7 -2020-04-23T07:00,49.5,49,30.9,0,0,5.4,10.1 -2020-04-23T08:00,55.4,38,30.1,0,0,3.4,10.7 -2020-04-23T09:00,58.7,34,30.2,0,0,10.6,20.4 -2020-04-23T10:00,59,34,30.4,0,0,5.4,20.8 -2020-04-23T11:00,61.3,32,31.3,0,0,6.1,17.2 -2020-04-23T12:00,62.8,30,30.7,0,0,8.4,20.6 -2020-04-23T13:00,64.2,27,29.7,0,0,10,23 -2020-04-23T14:00,65.6,23,26.2,0,0,10.4,23.3 -2020-04-23T15:00,65.2,22,25,0,0,12.4,24.2 -2020-04-23T16:00,64.9,22,24.9,0,0,9.7,24.6 -2020-04-23T17:00,64.7,23,26.5,0,0,8.6,18.3 -2020-04-23T18:00,59.5,34,31.2,0,0,7.3,14.8 -2020-04-23T19:00,54,45,33.4,0,0,7.6,14.5 -2020-04-23T20:00,50.8,64,39.1,0.016,0,9.4,13.4 -2020-04-23T21:00,48.8,86,44.7,0.02,0,5.2,15.4 -2020-04-23T22:00,48.2,87,44.5,0.008,0,3.4,9.2 -2020-04-23T23:00,46.8,91,44.2,0,0,6.4,10.3 -2020-04-24T00:00,43.3,90,40.7,0,0,7.4,12.1 -2020-04-24T01:00,43.2,73,35.2,0,0,10.1,16.8 -2020-04-24T02:00,44.1,66,33.4,0,0,6.3,17.2 -2020-04-24T03:00,41.9,68,32.1,0,0,4,10.5 -2020-04-24T04:00,39.7,74,32,0,0,4.6,7.4 -2020-04-24T05:00,38.1,97,37.3,0,0,5.5,6 -2020-04-24T06:00,37.9,98,37.2,0.063,0.055,4.1,14.3 -2020-04-24T07:00,39.8,92,37.7,0.004,0,5.4,11.4 -2020-04-24T08:00,42.5,82,37.5,0.004,0,5.7,13.4 -2020-04-24T09:00,46,69,36.3,0,0,2.5,13.4 -2020-04-24T10:00,49.3,56,34.3,0,0,2.3,10.5 -2020-04-24T11:00,53.2,45,32.5,0,0,3.9,21.5 -2020-04-24T12:00,54.9,39,30.7,0,0,5,21.5 -2020-04-24T13:00,56.3,36,29.4,0,0,6.9,18.1 -2020-04-24T14:00,58.4,31,28.1,0,0,9.1,20.4 -2020-04-24T15:00,57.5,32,28.1,0,0,7.5,20.6 -2020-04-24T16:00,57.1,35,29.7,0,0,7.6,17 -2020-04-24T17:00,52.3,47,32.8,0,0,2.3,16.3 -2020-04-24T18:00,51.4,49,32.8,0,0,1.3,7.6 -2020-04-24T19:00,46.1,65,35.1,0,0,3.8,5.8 -2020-04-24T20:00,45.5,71,36.6,0,0,0.9,6.7 -2020-04-24T21:00,42.4,81,37,0,0,2.6,3.1 -2020-04-24T22:00,40.8,86,36.9,0,0,3.5,5.1 -2020-04-24T23:00,38.8,91,36.2,0,0,3.3,5.6 -2020-04-25T00:00,37,94,35.3,0,0,3,5.1 -2020-04-25T01:00,38.1,91,35.8,0,0,1.7,5.6 -2020-04-25T02:00,37.3,94,35.7,0.004,0,3.6,4 -2020-04-25T03:00,38.4,92,36.2,0.012,0,1.6,8.5 -2020-04-25T04:00,38.6,93,36.7,0,0,1.8,4 -2020-04-25T05:00,40.6,89,37.7,0,0,0.9,4.3 -2020-04-25T06:00,40.6,90,37.9,0,0,2.2,4.3 -2020-04-25T07:00,42.7,82,37.6,0,0,3.8,8.5 -2020-04-25T08:00,47.9,66,37,0,0,6.3,13.6 -2020-04-25T09:00,52.5,47,32.9,0,0,2.9,19.9 -2020-04-25T10:00,55,38,29.7,0,0,2.7,18.8 -2020-04-25T11:00,56.2,36,29.3,0.02,0,5.3,14.8 -2020-04-25T12:00,56.9,33,28.4,0.02,0,7.7,17.9 -2020-04-25T13:00,57.7,33,28.8,0,0,6.4,18.3 -2020-04-25T14:00,58.2,34,29.8,0,0,6.7,16.3 -2020-04-25T15:00,59.6,31,29.3,0,0,4.6,16.6 -2020-04-25T16:00,59.5,31,28.9,0,0,4.2,13 -2020-04-25T17:00,60.5,29,28.2,0,0,7,11.2 -2020-04-25T18:00,58.5,51,40.3,0,0,2.5,13 -2020-04-25T19:00,57.7,33,28.9,0,0,3,3.4 -2020-04-25T20:00,49.5,49,31.2,0,0,4.6,6.9 -2020-04-25T21:00,46.7,61,33.9,0,0,4.9,8.5 -2020-04-25T22:00,44.5,70,35.3,0,0,4.8,8.1 -2020-04-25T23:00,44.1,70,34.9,0,0,5.1,8.3 -2020-04-26T00:00,41.8,72,33.4,0,0,4,8.3 -2020-04-26T01:00,39.3,73,31.4,0,0,3.8,6.7 -2020-04-26T02:00,37.6,74,30,0,0,5.3,6.5 -2020-04-26T03:00,37.5,69,28.1,0,0,5.2,6.5 -2020-04-26T04:00,39,60,26.2,0,0,4,6.5 -2020-04-26T05:00,37.9,69,28.6,0,0,3.7,4.9 -2020-04-26T06:00,41.6,60,28.6,0,0,3.6,5.1 -2020-04-26T07:00,48.6,50,30.8,0,0,2.5,7.2 -2020-04-26T08:00,57.6,41,33.8,0,0,1.6,7.4 -2020-04-26T09:00,64.5,32,34.2,0,0,3.2,9.6 -2020-04-26T10:00,68.5,23,29.6,0,0,4.2,11.6 -2020-04-26T11:00,70.2,21,28,0,0,2.9,11.6 -2020-04-26T12:00,72.1,19,27.6,0,0,0,11.6 -2020-04-26T13:00,72.1,19,27.2,0,0,1.8,10.3 -2020-04-26T14:00,72.6,19,28,0,0,8.4,15.7 -2020-04-26T15:00,66.6,30,34.3,0,0,15.4,26.4 -2020-04-26T16:00,62.7,37,36.1,0,0,15.7,27.3 -2020-04-26T17:00,60.4,45,38.8,0,0,2.5,26.4 -2020-04-26T18:00,59.1,50,40.6,0,0,5.4,11.4 -2020-04-26T19:00,56.1,54,39.6,0,0,4.2,8.5 -2020-04-26T20:00,56.7,51,38.8,0,0,2.9,6.7 -2020-04-26T21:00,53.6,57,38.8,0,0,3.8,5.8 -2020-04-26T22:00,50,60,36.9,0,0,3.9,7.6 -2020-04-26T23:00,45.2,67,35,0,0,5.4,6.5 -2020-04-27T00:00,43.3,69,33.9,0,0,5.1,6.5 -2020-04-27T01:00,42.4,70,33.2,0,0,3.8,6.3 -2020-04-27T02:00,40,76,33.2,0,0,4.2,6.3 -2020-04-27T03:00,38.9,80,33.2,0,0,3.6,5.8 -2020-04-27T04:00,39.7,76,32.9,0,0,4.5,7.4 -2020-04-27T05:00,40,72,31.8,0,0,4.1,7.6 -2020-04-27T06:00,42.5,68,32.6,0,0,5.9,8.7 -2020-04-27T07:00,49.6,57,34.9,0,0,2.6,9.4 -2020-04-27T08:00,58,44,36.1,0,0,3.7,9.6 -2020-04-27T09:00,64.9,32,34,0,0,5.4,13.2 -2020-04-27T10:00,68.6,25,31.3,0,0,4.4,14.1 -2020-04-27T11:00,70.3,24,31.8,0,0,1.7,12.8 -2020-04-27T12:00,71.7,24,33.4,0,0,2.3,10.5 -2020-04-27T13:00,73.1,23,33.3,0,0,2.7,11.4 -2020-04-27T14:00,73.9,22,32.5,0,0,4.6,12.8 -2020-04-27T15:00,74.4,21,32.1,0,0,4.6,13.2 -2020-04-27T16:00,74.1,20,30.7,0,0,2.9,12.3 -2020-04-27T17:00,73.9,18,27.3,0,0,6,8.7 -2020-04-27T18:00,68.5,46,46.9,0,0,4.7,9.6 -2020-04-27T19:00,64.9,28,31.3,0,0,1.9,5.6 -2020-04-27T20:00,57.2,41,33.6,0,0,5.4,8.7 -2020-04-27T21:00,53.1,54,36.9,0,0,3.6,9.6 -2020-04-27T22:00,50,63,37.9,0,0,4.3,6.7 -2020-04-27T23:00,50.3,61,37.1,0,0,3.1,8.1 -2020-04-28T00:00,46.8,69,37,0,0,4.6,5.4 -2020-04-28T01:00,51.8,55,36,0,0,2.5,8.1 -2020-04-28T02:00,47.3,59,33.9,0,0,7.6,12.1 -2020-04-28T03:00,45.9,62,33.5,0,0,5.4,12.3 -2020-04-28T04:00,45.7,63,33.7,0,0,4.7,8.9 -2020-04-28T05:00,47.2,64,35.6,0,0,1.1,7.6 -2020-04-28T06:00,48.7,63,36.7,0,0,0.3,2.9 -2020-04-28T07:00,53.8,56,38.3,0,0,3.4,8.5 -2020-04-28T08:00,61.5,37,35.2,0,0,1.2,8.9 -2020-04-28T09:00,63.5,29,31,0,0,5.1,13 -2020-04-28T10:00,65.6,29,32.6,0,0,7.4,16.6 -2020-04-28T11:00,69.4,17,23.2,0,0,12.5,23.7 -2020-04-28T12:00,70.3,16,22.6,0,0,16.8,32.2 -2020-04-28T13:00,71.1,16,23.2,0,0,16,31.3 -2020-04-28T14:00,70.9,16,22.6,0,0,16.7,30.6 -2020-04-28T15:00,70.2,16,22.3,0,0,16.8,30.9 -2020-04-28T16:00,68.5,18,23.4,0,0,16.2,30.4 -2020-04-28T17:00,65.5,22,26.1,0,0,13.1,28.9 -2020-04-28T18:00,57.3,36,30.3,0,0,13.5,25.9 -2020-04-28T19:00,53.8,40,30.2,0,0,3,21.9 -2020-04-28T20:00,51,49,32.3,0,0,4,7.4 -2020-04-28T21:00,50.2,52,33.2,0,0,3.7,6.7 -2020-04-28T22:00,46.3,61,33.7,0,0,5.2,6 -2020-04-28T23:00,45.1,63,33.2,0,0,4.2,6.3 -2020-04-29T00:00,44,66,33.4,0,0,4.8,6.5 -2020-04-29T01:00,42.1,71,33.4,0,0,5.8,6.9 -2020-04-29T02:00,40.6,76,33.5,0,0,5.2,7.4 -2020-04-29T03:00,39,81,33.6,0,0,4.3,6 -2020-04-29T04:00,38.7,83,33.8,0,0,2.2,5.4 -2020-04-29T05:00,36.7,89,33.7,0,0,3.3,4.3 -2020-04-29T06:00,40.1,79,34,0,0,5.4,8.7 -2020-04-29T07:00,46.9,54,31.3,0,0,5.4,11.2 -2020-04-29T08:00,52.6,29,21.3,0,0,6.6,14.8 -2020-04-29T09:00,57,27,23.3,0,0,5.1,14.8 -2020-04-29T10:00,61,25,24.9,0,0,4.5,13.6 -2020-04-29T11:00,64.5,22,24.4,0,0,4.8,14.1 -2020-04-29T12:00,66.5,20,23.7,0,0,6.5,16.3 -2020-04-29T13:00,68,19,24.5,0,0,6.8,17.2 -2020-04-29T14:00,68.7,20,26,0,0,6.5,17 -2020-04-29T15:00,68.6,21,27,0,0,6.4,16.1 -2020-04-29T16:00,67.8,22,27.5,0,0,5.9,15.2 -2020-04-29T17:00,68.6,20,26,0,0,4,13.6 -2020-04-29T18:00,65.6,28,31.5,0,0,3.7,9.8 -2020-04-29T19:00,60.9,31,29.8,0,0,5,6.5 -2020-04-29T20:00,61.9,25,25.5,0,0,3.5,5.8 -2020-04-29T21:00,58.4,28,25.4,0,0,0.9,4.3 -2020-04-29T22:00,48.1,54,32.2,0,0,3.2,5.4 -2020-04-29T23:00,45,70,35.9,0,0,3.3,5.6 -2020-04-30T00:00,43.3,77,36.7,0,0,3,5.6 -2020-04-30T01:00,42.6,77,36.1,0,0,3.7,4.7 -2020-04-30T02:00,42,75,34.7,0,0,3.8,5.1 -2020-04-30T03:00,40.8,76,34,0,0,4.1,5.4 -2020-04-30T04:00,41.1,75,33.9,0,0,3.8,5.4 -2020-04-30T05:00,42,70,33.1,0,0,4,5.6 -2020-04-30T06:00,45.9,61,33.2,0,0,3.5,5.1 -2020-04-30T07:00,52.1,52,35,0,0,4,8.9 -2020-04-30T08:00,61,37,34.4,0,0,3.9,10.5 -2020-04-30T09:00,68.6,27,33.7,0,0,2.3,10.5 -2020-04-30T10:00,76.2,21,32.9,0,0,3.8,11.6 -2020-04-30T11:00,80.2,15,27.9,0,0,3.1,11.6 -2020-04-30T12:00,82,14,27.8,0,0,3.6,11 -2020-04-30T13:00,83.3,13,28.1,0,0,3.3,11 -2020-04-30T14:00,83,13,27.2,0,0,3.2,10.5 -2020-04-30T15:00,81.3,17,32.5,0,0,3.3,8.9 -2020-04-30T16:00,81.7,16,31.8,0,0,6.8,13.2 -2020-04-30T17:00,79.5,21,36.1,0,0,5.8,15.7 -2020-04-30T18:00,75.7,32,43.7,0,0,4.4,8.9 -2020-04-30T19:00,66.8,40,41.5,0,0,5.5,7.8 -2020-04-30T20:00,62.5,47,41.9,0,0,4.1,9.6 -2020-04-30T21:00,55.9,56,40.3,0,0,4.8,7.8 -2020-04-30T22:00,55.8,49,37,0,0,6.2,10.7 -2020-04-30T23:00,56,47,36.2,0,0,6.5,13.9 -2020-05-01T00:00,53.9,49,35.2,0,0,4.8,10.5 -2020-05-01T01:00,51.2,50,33.4,0,0,5.7,12.5 -2020-05-01T02:00,50.5,50,32.3,0,0,5.5,9.6 -2020-05-01T03:00,47.4,55,32.1,0,0,4.6,8.7 -2020-05-01T04:00,47,55,31.6,0,0,4.1,6.7 -2020-05-01T05:00,46.3,58,32.4,0,0,4.2,7.6 -2020-05-01T06:00,51.2,50,33.2,0,0,6.5,9.6 -2020-05-01T07:00,59.1,42,36,0,0,3.3,10.5 -2020-05-01T08:00,66.1,36,38.4,0,0,4,9.6 -2020-05-01T09:00,70.3,28,35.4,0,0,8.9,18.1 -2020-05-01T10:00,74.9,23,34.3,0,0,8.9,19.2 -2020-05-01T11:00,78.4,21,34.9,0,0,6.5,18.8 -2020-05-01T12:00,80.1,19,33.9,0,0,4.5,19.7 -2020-05-01T13:00,80.1,19,34.2,0,0,7,16.1 -2020-05-01T14:00,80.4,18,32.7,0,0,7.1,19.7 -2020-05-01T15:00,81.8,16,31,0,0,8.8,19.9 -2020-05-01T16:00,80.1,19,34.3,0,0,10.2,19 -2020-05-01T17:00,74.3,40,48.1,0,0,15.1,19 -2020-05-01T18:00,71.8,42,47.4,0,0,10.1,25.7 -2020-05-01T19:00,66.1,49,46.2,0,0,6.9,16.8 -2020-05-01T20:00,64,53,46.5,0,0,14.9,24.4 -2020-05-01T21:00,61.4,61,48,0,0,14,25.3 -2020-05-01T22:00,58.6,73,50,0,0,10.2,23 -2020-05-01T23:00,57.3,79,51,0,0,10.3,17.4 -2020-05-02T00:00,56.7,83,51.5,0,0,7.6,16.8 -2020-05-02T01:00,55.4,88,51.9,0.004,0,7.3,12.5 -2020-05-02T02:00,54.7,90,51.9,0.004,0,6.4,12.5 -2020-05-02T03:00,54.2,91,51.8,0.004,0,4.5,10.7 -2020-05-02T04:00,53.2,95,51.8,0,0,1.6,7.6 -2020-05-02T05:00,51.8,89,48.7,0,0,4.7,2.7 -2020-05-02T06:00,52.1,81,46.4,0,0,6.2,10.7 -2020-05-02T07:00,52.6,76,45.3,0,0,6.6,12.3 -2020-05-02T08:00,55.3,68,44.9,0,0,6.3,13.2 -2020-05-02T09:00,58.9,57,43.9,0,0,5.7,15 -2020-05-02T10:00,61.4,51,43.3,0,0,5.5,14.1 -2020-05-02T11:00,62.9,48,42.8,0,0,4.5,14.1 -2020-05-02T12:00,66.9,41,42.5,0,0,2.1,12.8 -2020-05-02T13:00,68.5,38,42,0,0,6.6,16.1 -2020-05-02T14:00,67,40,42,0,0,8.1,18.3 -2020-05-02T15:00,64.9,52,46.9,0,0,16.4,28 -2020-05-02T16:00,63.4,58,48.3,0,0,19.1,33.1 -2020-05-02T17:00,57.3,77,50.1,0.055,0,8.3,35.3 -2020-05-02T18:00,57.9,74,49.7,0,0,7.8,15 -2020-05-02T19:00,55.3,83,50.2,0.004,0,8.1,14.1 -2020-05-02T20:00,53.4,93,51.4,0.098,0,8.1,16.3 -2020-05-02T21:00,52.4,93,50.5,0.02,0,3.3,14.1 -2020-05-02T22:00,50.6,98,50,0,0,5.4,9.4 -2020-05-02T23:00,51.1,95,49.8,0,0,6.8,11 -2020-05-03T00:00,49.5,95,48.2,0,0,4.3,11.2 -2020-05-03T01:00,47.7,97,46.9,0,0,2.2,7.4 -2020-05-03T02:00,45.4,98,45,0,0,2.9,5.6 -2020-05-03T03:00,43.8,97,42.9,0,0,3.3,6 -2020-05-03T04:00,43.9,96,42.8,0,0,3.2,6.7 -2020-05-03T05:00,45.6,94,44.1,0,0,3.5,6 -2020-05-03T06:00,47,96,46,0,0,2.8,6.3 -2020-05-03T07:00,49.6,92,47.3,0,0,1,7.2 -2020-05-03T08:00,53,80,46.9,0,0,2.4,9.4 -2020-05-03T09:00,58.3,64,46.1,0,0,2.2,10.3 -2020-05-03T10:00,63.3,55,47,0,0,4.5,13.2 -2020-05-03T11:00,67.4,49,47.4,0,0,5.1,14.8 -2020-05-03T12:00,70.9,45,48.6,0.016,0,7.9,18.6 -2020-05-03T13:00,71.2,43,47.3,0.02,0,7.7,18.6 -2020-05-03T14:00,71.6,35,42.5,0.008,0,9.6,20.4 -2020-05-03T15:00,70.3,42,46,0,0,14.4,26.6 -2020-05-03T16:00,70.4,39,44.5,0,0,8.8,26.6 -2020-05-03T17:00,67.9,47,47.2,0,0,9.6,20.4 -2020-05-03T18:00,64.9,55,48.2,0,0,11.8,19 -2020-05-03T19:00,60.3,66,48.9,0,0,13.1,22.8 -2020-05-03T20:00,56.3,81,50.6,0.012,0,10.8,23.3 -2020-05-03T21:00,53.6,84,49,0,0,10.3,18.8 -2020-05-03T22:00,53,86,48.9,0,0,6.9,16.8 -2020-05-03T23:00,52.1,88,48.6,0,0,5.3,11.4 -2020-05-04T00:00,52.9,85,48.7,0,0,5.1,10.1 -2020-05-04T01:00,52.6,86,48.6,0,0,1.7,8.5 -2020-05-04T02:00,51.4,92,49.2,0,0,4.2,6.3 -2020-05-04T03:00,51.9,89,48.7,0,0,6.7,10.7 -2020-05-04T04:00,51.4,87,47.8,0,0,8.7,13.9 -2020-05-04T05:00,49.9,85,45.5,0,0,7.8,14.1 -2020-05-04T06:00,49.8,76,42.7,0,0,4.6,13 -2020-05-04T07:00,51.3,68,41,0,0,1.1,8.9 -2020-05-04T08:00,53.4,60,39.9,0,0,1.6,8.9 -2020-05-04T09:00,55.9,54,39.5,0,0,2.6,10.5 -2020-05-04T10:00,58.7,50,40.1,0,0,4.1,13.2 -2020-05-04T11:00,64.4,36,36.8,0.004,0,8.6,24.2 -2020-05-04T12:00,68.7,21,26.7,0,0,12.4,24.4 -2020-05-04T13:00,69.4,21,27.7,0,0,14.1,27.7 -2020-05-04T14:00,68,23,28.9,0,0,22.4,38.7 -2020-05-04T15:00,68.1,20,25.2,0,0,22.5,42.3 -2020-05-04T16:00,67,17,21.1,0,0,19,39.8 -2020-05-04T17:00,65.6,16,17.5,0,0,18.7,33.8 -2020-05-04T18:00,60.4,23,22.1,0,0,17.6,33.1 -2020-05-04T19:00,56.7,22,18.6,0,0,15.6,30.9 -2020-05-04T20:00,53.7,26,19.3,0,0,10.1,25.9 -2020-05-04T21:00,50.6,32,21.5,0,0,6.2,16.8 -2020-05-04T22:00,47.4,40,24.4,0,0,6.2,9.8 -2020-05-04T23:00,44.6,51,27.8,0,0,3.3,10.1 -2020-05-05T00:00,42,57,27.9,0,0,4.8,6.3 -2020-05-05T01:00,43.3,52,26.7,0,0,2.5,6 -2020-05-05T02:00,37.9,69,28.6,0,0,5.4,6.9 -2020-05-05T03:00,36.5,72,28.2,0,0,5.4,6.7 -2020-05-05T04:00,35.7,70,27,0,0,4.6,6.7 -2020-05-05T05:00,34.8,76,28,0,0,4.7,6.3 -2020-05-05T06:00,38.5,62,26.4,0,0,5,8.7 -2020-05-05T07:00,44.5,50,27,0,0,4.3,10.7 -2020-05-05T08:00,52.7,33,24.4,0,0,4,11.2 -2020-05-05T09:00,58.2,24,21.2,0,0,1.9,11.9 -2020-05-05T10:00,61,19,18.6,0,0,3,11.9 -2020-05-05T11:00,63.3,19,20,0,0,2.5,12.1 -2020-05-05T12:00,65.8,18,20.6,0,0,4.5,13.9 -2020-05-05T13:00,67.1,17,21.3,0,0,6.2,16.1 -2020-05-05T14:00,67.5,18,22.9,0,0,6.9,16.8 -2020-05-05T15:00,66.9,20,24,0,0,10.9,21.5 -2020-05-05T16:00,66.3,19,22.8,0,0,11.4,22.6 -2020-05-05T17:00,66.6,17,21,0,0,10.4,21.5 -2020-05-05T18:00,64.7,21,24.4,0,0,4.6,17.7 -2020-05-05T19:00,62.3,24,25.2,0,0,2.9,7.4 -2020-05-05T20:00,59.7,26,25.1,0,0,2.8,3.6 -2020-05-05T21:00,52.9,37,27.4,0,0,4.5,7.2 -2020-05-05T22:00,51.4,40,27.7,0,0,5.1,11.9 -2020-05-05T23:00,49.2,46,29.5,0,0,8.1,12.5 -2020-05-06T00:00,47.4,46,27.8,0,0,8.1,14.1 -2020-05-06T01:00,45.2,50,27.9,0,0,6,13.2 -2020-05-06T02:00,42.2,60,29.3,0,0,3.9,9.8 -2020-05-06T03:00,40.7,66,30.2,0,0,3.5,5.8 -2020-05-06T04:00,40.3,68,30.6,0,0,1.8,4.9 -2020-05-06T05:00,38.8,69,29.6,0,0,4.9,3.1 -2020-05-06T06:00,42.1,63,30.3,0,0,2.7,6.3 -2020-05-06T07:00,46.6,51,29.3,0,0,0.9,6.5 -2020-05-06T08:00,51,44,29.8,0,0,1.6,8.7 -2020-05-06T09:00,55.8,37,29.9,0,0,0.7,8.9 -2020-05-06T10:00,60.3,32,30.2,0,0,2.7,11.4 -2020-05-06T11:00,64.1,28,30.6,0,0,5.4,15.2 -2020-05-06T12:00,67.4,25,30.4,0,0,6.5,16.8 -2020-05-06T13:00,69.8,22,28.7,0,0,7.2,17.7 -2020-05-06T14:00,71.2,20,27.8,0,0,7,17.7 -2020-05-06T15:00,71.2,20,28,0,0,8.8,18.1 -2020-05-06T16:00,69.1,28,34.4,0,0,13.7,25.1 -2020-05-06T17:00,65.4,33,35.8,0,0,9.1,25.5 -2020-05-06T18:00,63.8,34,35.2,0,0,6.7,17 -2020-05-06T19:00,61.7,39,36.3,0,0,2.9,11 -2020-05-06T20:00,55.9,43,33.5,0,0,5.6,8.3 -2020-05-06T21:00,52.5,49,34.1,0,0,3.8,7.8 -2020-05-06T22:00,50.3,53,33.6,0,0,7.6,11.6 -2020-05-06T23:00,48.7,54,32.7,0,0,6.2,12.3 -2020-05-07T00:00,48.7,51,31.5,0,0,9.2,15 -2020-05-07T01:00,46.5,57,32,0,0,5.9,15 -2020-05-07T02:00,44.6,69,35,0,0,5.6,11.2 -2020-05-07T03:00,44.4,64,32.9,0,0,3,6.9 -2020-05-07T04:00,42.4,64,31.1,0,0,2.9,4.7 -2020-05-07T05:00,39.9,72,31.5,0,0,4.3,4.3 -2020-05-07T06:00,44.3,61,31.6,0,0,2.1,5.1 -2020-05-07T07:00,51.8,49,33,0,0,3.5,8.9 -2020-05-07T08:00,59.1,37,32.8,0,0,6.6,14.1 -2020-05-07T09:00,62.2,33,32.6,0,0,11.2,21.7 -2020-05-07T10:00,64.8,27,30.3,0,0,9.4,22.1 -2020-05-07T11:00,66.7,26,30.6,0,0,7.4,19.9 -2020-05-07T12:00,67.8,26,32.1,0,0,8.1,18.8 -2020-05-07T13:00,68.3,26,31.7,0,0,10.7,21.9 -2020-05-07T14:00,70.2,21,28.2,0,0,13.7,25.7 -2020-05-07T15:00,72.1,15,22.6,0,0,9.9,27.3 -2020-05-07T16:00,66.6,13,14,0,0,26.6,44.7 -2020-05-07T17:00,65.1,11,8.9,0,0,28.1,47.4 -2020-05-07T18:00,62.6,12,9.1,0,0,19.4,47.6 -2020-05-07T19:00,57.2,17,12.4,0,0,14.8,33.1 -2020-05-07T20:00,54.1,22,15.9,0,0,14.7,24.8 -2020-05-07T21:00,48.7,38,24.5,0,0,17.5,31.3 -2020-05-07T22:00,45.9,46,26.1,0,0,10.6,29.3 -2020-05-07T23:00,42.4,60,29.3,0,0,5.5,17.7 -2020-05-08T00:00,41.1,66,30.7,0,0,4,9.2 -2020-05-08T01:00,42.3,59,28.8,0,0,5.1,10.7 -2020-05-08T02:00,41.8,54,26.3,0,0,5,9.8 -2020-05-08T03:00,40.8,55,25.8,0,0,6.3,11.4 -2020-05-08T04:00,39.4,63,27.9,0,0,6.7,12.8 -2020-05-08T05:00,37.4,78,31.2,0,0,4.2,12.1 -2020-05-08T06:00,37.7,78,31.6,0.004,0,1.6,8.5 -2020-05-08T07:00,38.4,76,31.6,0,0,2.3,7.2 -2020-05-08T08:00,39.6,71,31.1,0,0,3.7,9.8 -2020-05-08T09:00,42.4,58,28.5,0,0,4.9,12.1 -2020-05-08T10:00,47.1,46,27.3,0,0,6.6,16.1 -2020-05-08T11:00,53.4,33,25.3,0,0,3.6,16.6 -2020-05-08T12:00,57.3,29,25.2,0,0,4.7,14.8 -2020-05-08T13:00,60.2,26,25.1,0,0,5.5,15.7 -2020-05-08T14:00,63.6,22,24.3,0,0,5.7,15.7 -2020-05-08T15:00,64.3,22,24.3,0,0,5.1,15.4 -2020-05-08T16:00,63.1,23,24.9,0,0,1.1,14.1 -2020-05-08T17:00,59.9,28,27,0,0,8.7,13 -2020-05-08T18:00,57.8,32,27.9,0,0,6.2,17.2 -2020-05-08T19:00,54.1,38,29.4,0,0,3.6,11.9 -2020-05-08T20:00,52,39,28,0,0,5.2,8.5 -2020-05-08T21:00,48.9,43,27.5,0,0,4.5,8.7 -2020-05-08T22:00,47.9,41,25.4,0,0,5.5,8.7 -2020-05-08T23:00,46,42,24,0,0,4.7,8.7 -2020-05-09T00:00,43.6,55,28.4,0,0,4.8,7.4 -2020-05-09T01:00,45.3,53,29,0,0,11.9,19.2 -2020-05-09T02:00,44.9,48,26.5,0,0,9.4,21 -2020-05-09T03:00,45.1,51,28,0,0,5,15.7 -2020-05-09T04:00,42.5,59,29.3,0,0,6.2,10.3 -2020-05-09T05:00,42.8,68,33.1,0,0,5.9,10.3 -2020-05-09T06:00,45.3,63,33.5,0,0,7.8,13.4 -2020-05-09T07:00,48.4,56,33.4,0,0,9.4,17.9 -2020-05-09T08:00,50.6,49,32.1,0,0,13.1,23.7 -2020-05-09T09:00,53.1,41,29.8,0,0,13.3,26.2 -2020-05-09T10:00,55.5,35,28,0,0,9.9,24.8 -2020-05-09T11:00,56.8,32,27.5,0,0,9.2,20.1 -2020-05-09T12:00,58.6,31,27.8,0,0,9,19.7 -2020-05-09T13:00,58.6,32,28.9,0,0,10.3,21.5 -2020-05-09T14:00,59.9,32,29.8,0,0,7.8,22.4 -2020-05-09T15:00,60.1,31,29.7,0,0,6.8,18.3 -2020-05-09T16:00,60.3,30,28.8,0,0,7.6,16.8 -2020-05-09T17:00,55.7,38,30.3,0,0,11.9,23.5 -2020-05-09T18:00,52.3,39,27.9,0,0,13.3,23 -2020-05-09T19:00,51.2,39,27.2,0,0,8.1,23.3 -2020-05-09T20:00,50.4,38,26,0,0,6.7,14.1 -2020-05-09T21:00,48.7,37,23.6,0,0,8.3,14.5 -2020-05-09T22:00,48.1,38,23.5,0,0,5.8,13.9 -2020-05-09T23:00,47.7,39,23.7,0,0,4.2,9.8 -2020-05-10T00:00,46.2,40,23.5,0,0,5.4,7.8 -2020-05-10T01:00,45,39,21.7,0,0,7.1,11.2 -2020-05-10T02:00,43.3,40,20.7,0,0,6.7,11.2 -2020-05-10T03:00,40.5,49,22.8,0,0,5.9,10.7 -2020-05-10T04:00,42.6,42,21.3,0,0,0.8,6.9 -2020-05-10T05:00,38.9,57,25.1,0,0,5.8,5.8 -2020-05-10T06:00,39.7,56,25.3,0,0,13.3,21.5 -2020-05-10T07:00,41.3,51,24.4,0,0,12.8,23.5 -2020-05-10T08:00,43.3,47,24.3,0,0,10.2,23.7 -2020-05-10T09:00,46.2,43,24.7,0,0,7.2,20.8 -2020-05-10T10:00,49.6,41,26.5,0,0,6.6,17.4 -2020-05-10T11:00,52.9,37,27.6,0,0,6.7,17.4 -2020-05-10T12:00,56,34,28,0,0,7.1,17.9 -2020-05-10T13:00,58.6,31,28.1,0,0,7.7,18.6 -2020-05-10T14:00,60.3,30,28.3,0,0,6.9,18.6 -2020-05-10T15:00,61.3,28,28.3,0,0,8.4,18.8 -2020-05-10T16:00,61.5,29,28.8,0,0,7.9,18.8 -2020-05-10T17:00,60,31,28.9,0,0,8.4,17.7 -2020-05-10T18:00,59,32,28.9,0,0,10.6,18.6 -2020-05-10T19:00,54.9,39,30.2,0,0,9.1,18.8 -2020-05-10T20:00,52.3,43,30.4,0,0,10.1,17.2 -2020-05-10T21:00,49.5,44,28.3,0,0,10.6,19 -2020-05-10T22:00,47,50,29.1,0,0,8.5,17.7 -2020-05-10T23:00,45.8,52,29.1,0,0,6.8,14.1 -2020-05-11T00:00,43.7,54,28,0,0,7.1,11.4 -2020-05-11T01:00,40.5,62,28.3,0,0,8.3,13.6 -2020-05-11T02:00,39.3,64,28.3,0,0,7.5,13.6 -2020-05-11T03:00,38.2,67,28.1,0,0,6.9,12.3 -2020-05-11T04:00,36.9,70,28,0,0,6.8,11.2 -2020-05-11T05:00,36,71,27.6,0,0,8.3,12.3 -2020-05-11T06:00,35.1,76,28.1,0.004,0,8.6,19.5 -2020-05-11T07:00,36.5,71,28.1,0,0,5.6,15.7 -2020-05-11T08:00,38.9,65,28,0,0.138,1.8,12.1 -2020-05-11T09:00,40.3,62,28.3,0,0,0.7,8.5 -2020-05-11T10:00,41.9,60,29.2,0.004,0.028,3.4,10.5 -2020-05-11T11:00,41.3,64,29.9,0.02,0.028,3.4,11.2 -2020-05-11T12:00,40.4,71,31.8,0.02,0,2.4,10.3 -2020-05-11T13:00,41.4,74,33.5,0.004,0,1.6,8.5 -2020-05-11T14:00,44.2,70,35.2,0.004,0,0.5,8.9 -2020-05-11T15:00,46.2,68,36.1,0,0,1.8,9.6 -2020-05-11T16:00,50.4,61,37.6,0,0,2.7,10.1 -2020-05-11T17:00,53,62,40.5,0,0,7.2,10.3 -2020-05-11T18:00,53,63,40.6,0,0,7.7,13.2 -2020-05-11T19:00,50.2,71,41.1,0,0,9.9,17.7 -2020-05-11T20:00,48.2,77,41.2,0.012,0,5.2,15.9 -2020-05-11T21:00,46.8,83,41.8,0.004,0,2.9,8.5 -2020-05-11T22:00,45.3,93,43.3,0.016,0,1.6,5.1 -2020-05-11T23:00,44.9,93,43.1,0.035,0,2.5,9.2 -2020-05-12T00:00,44.7,94,43.2,0.035,0,5,7.8 -2020-05-12T01:00,43.7,96,42.5,0,0,2.3,8.1 -2020-05-12T02:00,41.5,98,40.9,0,0,9.4,14.8 -2020-05-12T03:00,39.9,100,39.8,0,0,11,17.9 -2020-05-12T04:00,40.3,98,39.7,0,0,6.8,17.2 -2020-05-12T05:00,37.8,97,37,0,0,4.1,11.2 -2020-05-12T06:00,38.2,95,36.9,0,0,2.6,7.2 -2020-05-12T07:00,38.6,95,37.2,0,0,5.2,11 -2020-05-12T08:00,41.1,87,37.6,0,0,5.7,13.4 -2020-05-12T09:00,46.1,79,40.1,0,0,2.2,14.1 -2020-05-12T10:00,52.1,69,42.3,0,0,4.3,13.4 -2020-05-12T11:00,57.6,60,43.9,0,0,6.5,16.8 -2020-05-12T12:00,64,51,45.5,0,0,6.5,16.6 -2020-05-12T13:00,68.4,45,46.3,0,0,9.2,19.5 -2020-05-12T14:00,70.7,43,46.9,0,0,10,21.3 -2020-05-12T15:00,71.4,41,46.7,0,0,12.2,23.9 -2020-05-12T16:00,72,40,46.1,0,0,9.9,23.5 -2020-05-12T17:00,68.7,45,46.8,0,0,3,19.2 -2020-05-12T18:00,66.1,50,46.9,0,0,4.7,8.3 -2020-05-12T19:00,60.4,53,43.4,0,0,4.7,8.7 -2020-05-12T20:00,55.5,63,43.3,0,0,2.9,7.8 -2020-05-12T21:00,53.3,70,43.6,0,0,6.7,7.6 -2020-05-12T22:00,50.7,76,43.3,0,0,4.3,8.9 -2020-05-12T23:00,48.1,76,40.9,0,0,5.3,8.3 -2020-05-13T00:00,47.7,63,35.9,0,0,7.2,11.2 -2020-05-13T01:00,48.6,52,31.8,0,0,6.2,11.9 -2020-05-13T02:00,50.5,45,30.1,0,0,8.1,12.3 -2020-05-13T03:00,51.7,42,29.1,0,0,10.1,16.8 -2020-05-13T04:00,51.4,39,27.3,0,0,10,16.8 -2020-05-13T05:00,44.7,45,24.7,0,0,5.9,16.3 -2020-05-13T06:00,49.7,44,28.9,0,0,6.2,9.6 -2020-05-13T07:00,58.9,39,34.3,0,0,6,11.6 -2020-05-13T08:00,66.9,30,34.1,0,0,4.1,11.4 -2020-05-13T09:00,70.3,18,25.1,0,0,2.1,10.1 -2020-05-13T10:00,71.5,18,26.2,0,0,2.7,10.3 -2020-05-13T11:00,73,18,27.1,0,0,0.9,10.7 -2020-05-13T12:00,75.9,15,24.9,0,0,3.7,11.6 -2020-05-13T13:00,76.8,14,23.2,0,0,6.1,14.3 -2020-05-13T14:00,75.8,17,28.6,0,0,8.8,17.4 -2020-05-13T15:00,73.4,19,28.9,0,0,16,27.7 -2020-05-13T16:00,73.4,17,25.3,0,0,11.1,27.7 -2020-05-13T17:00,73,20,29.1,0,0,7.2,19.2 -2020-05-13T18:00,70.1,33,39.4,0,0,4.1,13 -2020-05-13T19:00,68.1,29,34.3,0,0,1.4,6.7 -2020-05-13T20:00,63.1,34,34.2,0,0,1.8,3.6 -2020-05-13T21:00,57.2,48,37.5,0,0,12.3,18.8 -2020-05-13T22:00,55,62,42.2,0,0,9,20.6 -2020-05-13T23:00,53.9,63,41.6,0,0,9.7,15.7 -2020-05-14T00:00,51.6,70,42,0,0,5.3,16.1 -2020-05-14T01:00,49.9,77,43,0,0,4.5,8.7 -2020-05-14T02:00,52.3,71,43,0,0,4.4,6.7 -2020-05-14T03:00,51.7,83,46.7,0.02,0,7.7,15.9 -2020-05-14T04:00,51.2,88,47.8,0,0,4.4,12.8 -2020-05-14T05:00,49.4,76,42.2,0,0,3,7.2 -2020-05-14T06:00,51,81,45.2,0,0,4,7.8 -2020-05-14T07:00,52.8,85,48.3,0,0,5.7,12.1 -2020-05-14T08:00,56.1,73,47.5,0,0,3.4,12.5 -2020-05-14T09:00,59.8,59,45.4,0,0,2.2,11 -2020-05-14T10:00,64,47,43.1,0,0,5.6,14.3 -2020-05-14T11:00,67.6,35,38.8,0,0,4.4,15.9 -2020-05-14T12:00,68.9,32,37.6,0,0,6.5,15.2 -2020-05-14T13:00,64.3,50,45.5,0,0,17.9,30.4 -2020-05-14T14:00,64.3,50,45.3,0,0,11.1,33.1 -2020-05-14T15:00,65.5,50,46.5,0,0,6.1,21.9 -2020-05-14T16:00,62,61,48.3,0.02,0,4.4,17.2 -2020-05-14T17:00,60.5,59,46,0,0,1.7,19.2 -2020-05-14T18:00,58.3,68,47.9,0.016,0,7.4,13.2 -2020-05-14T19:00,55.3,82,50,0.004,0,2.5,13.2 -2020-05-14T20:00,54.4,86,50.5,0,0,1.4,4.5 -2020-05-14T21:00,53.2,89,49.9,0.012,0,2.8,3.6 -2020-05-14T22:00,51.7,90,48.8,0.016,0,4.1,8.5 -2020-05-14T23:00,50.1,94,48.4,0,0,4.4,5.1 -2020-05-15T00:00,49.3,96,48.2,0,0,3.6,5.8 -2020-05-15T01:00,49.7,95,48.4,0,0,3.8,6.5 -2020-05-15T02:00,50.3,95,48.9,0,0,1,4.9 -2020-05-15T03:00,48.9,96,47.9,0,0,2,2.2 -2020-05-15T04:00,49.1,96,47.9,0,0,2.8,3.1 -2020-05-15T05:00,47.7,95,46.3,0,0,4.4,4.3 -2020-05-15T06:00,49.6,90,46.9,0,0,3.8,8.7 -2020-05-15T07:00,51.4,84,46.9,0,0,3.1,7.8 -2020-05-15T08:00,53.2,78,46.7,0,0,2.1,8.3 -2020-05-15T09:00,56.4,69,46.2,0,0,2.4,8.7 -2020-05-15T10:00,59,61,45.4,0,0,3.4,11 -2020-05-15T11:00,62.1,51,43.7,0,0,3.6,11 -2020-05-15T12:00,63.1,48,42.9,0,0,7.8,15.7 -2020-05-15T13:00,65.3,46,44.2,0,0,3.1,16.8 -2020-05-15T14:00,63.3,52,45.1,0.004,0,8.9,19.7 -2020-05-15T15:00,62.7,53,45.4,0.004,0,3.8,20.1 -2020-05-15T16:00,60.8,60,46.8,0.008,0,7.3,19.7 -2020-05-15T17:00,60.8,70,50.8,0.02,0,8.1,20.6 -2020-05-15T18:00,56.2,86,52.2,0.118,0,12,21.5 -2020-05-15T19:00,53.7,92,51.5,0.083,0,10.3,21 -2020-05-15T20:00,52.8,93,50.8,0.028,0,6.7,17 -2020-05-15T21:00,52.6,87,48.9,0,0,3.2,10.7 -2020-05-15T22:00,51.7,85,47.2,0,0,2.1,6 -2020-05-15T23:00,49.5,93,47.4,0,0,2.5,4.3 -2020-05-16T00:00,49.1,94,47.4,0,0,5.1,8.7 -2020-05-16T01:00,48,94,46.4,0,0,4.7,8.7 -2020-05-16T02:00,46.7,94,45.1,0,0,4.4,8.3 -2020-05-16T03:00,45.2,95,43.9,0,0,4.2,7.4 -2020-05-16T04:00,43.3,96,42.3,0,0,3.9,6.9 -2020-05-16T05:00,46.4,91,44,0,0,2.1,6.3 -2020-05-16T06:00,48.5,88,45,0,0,2.2,4.9 -2020-05-16T07:00,51.9,76,44.5,0,0,1,5.8 -2020-05-16T08:00,54.6,57,39.7,0,0,1.1,6.7 -2020-05-16T09:00,57.8,44,35.9,0,0,3.2,10.1 -2020-05-16T10:00,60.6,36,33.4,0,0,5.4,14.3 -2020-05-16T11:00,62.8,35,34.6,0,0,4,14.8 -2020-05-16T12:00,64.9,34,35.8,0,0,5.1,13.9 -2020-05-16T13:00,66.6,33,36.4,0,0,9.4,19.9 -2020-05-16T14:00,67.1,33,36.7,0,0,9.9,20.6 -2020-05-16T15:00,66.8,33,36.6,0,0,7.8,20.6 -2020-05-16T16:00,67,32,36.1,0,0,6,17.2 -2020-05-16T17:00,66.1,29,32.9,0,0,4.9,13.9 -2020-05-16T18:00,64.8,31,33.6,0,0,5.3,10.3 -2020-05-16T19:00,59.8,44,37.9,0,0,3.6,9.4 -2020-05-16T20:00,57.3,41,33.8,0,0,6.6,12.3 -2020-05-16T21:00,56.8,41,33.6,0,0,1.5,10.5 -2020-05-16T22:00,52.8,53,36.1,0,0,2.9,4.5 -2020-05-16T23:00,50.9,61,37.9,0,0,3.5,6 -2020-05-17T00:00,51.6,57,37,0,0,0.3,4.3 -2020-05-17T01:00,48.4,67,37.8,0,0,1.3,1.8 -2020-05-17T02:00,46.8,70,37.6,0,0,1.8,2.5 -2020-05-17T03:00,43.7,79,37.8,0,0,3.2,4.5 -2020-05-17T04:00,43.1,80,37.3,0,0,3,5.4 -2020-05-17T05:00,43.3,79,37,0,0,3.8,5.4 -2020-05-17T06:00,46,74,38.1,0,0,3.8,8.3 -2020-05-17T07:00,49.6,67,39.2,0,0,4.3,10.5 -2020-05-17T08:00,52.8,61,39.6,0,0,4.6,12.8 -2020-05-17T09:00,56.1,55,40.3,0,0,3.8,12.8 -2020-05-17T10:00,60.1,51,42,0,0,5,14.3 -2020-05-17T11:00,64.2,47,43.6,0,0,6.2,16.3 -2020-05-17T12:00,67.6,43,44.2,0,0,6.9,17.2 -2020-05-17T13:00,70.3,40,44.9,0,0,6.8,17.2 -2020-05-17T14:00,72.2,38,45.4,0,0,7.6,17.9 -2020-05-17T15:00,73,36,44.8,0,0,6.5,17.7 -2020-05-17T16:00,73.9,35,44.2,0,0,6.5,15.7 -2020-05-17T17:00,73.3,32,41.9,0,0,8.5,15.4 -2020-05-17T18:00,71.1,37,43.3,0,0,6.3,15.7 -2020-05-17T19:00,64.9,48,44.9,0,0,4.4,10.5 -2020-05-17T20:00,61.2,58,46.3,0,0,4.3,7.4 -2020-05-17T21:00,59.6,63,47,0,0,6.3,9.8 -2020-05-17T22:00,57.7,68,47.2,0,0,4.4,10.3 -2020-05-17T23:00,54.5,77,47.3,0,0,4.7,7.2 -2020-05-18T00:00,55.5,72,46.7,0,0,2.7,5.6 -2020-05-18T01:00,54.6,75,46.9,0,0,2.6,3.1 -2020-05-18T02:00,49.3,89,46.1,0,0,4.6,5.6 -2020-05-18T03:00,51.1,80,45.1,0,0,3,5.6 -2020-05-18T04:00,50.2,83,45.3,0,0,1.9,3.4 -2020-05-18T05:00,49.4,87,45.8,0,0,2.5,2.5 -2020-05-18T06:00,50.2,85,45.8,0,0,0.7,3.8 -2020-05-18T07:00,52.6,77,45.6,0,0,0.8,6.3 -2020-05-18T08:00,55.6,71,46.3,0,0,1.6,8.7 -2020-05-18T09:00,60.2,62,47.2,0,0,1.7,9.8 -2020-05-18T10:00,66.3,51,47.7,0,0,3.2,11.9 -2020-05-18T11:00,72.3,43,48.8,0,0,4.3,13.4 -2020-05-18T12:00,77.7,39,50.5,0,0,6.6,15.9 -2020-05-18T13:00,80.7,34,49.6,0,0,8.2,18.1 -2020-05-18T14:00,83.2,28,46.3,0,0,7.6,18.1 -2020-05-18T15:00,84.4,24,43.6,0,0,7.6,16.6 -2020-05-18T16:00,84.4,24,44,0,0,9.2,17.7 -2020-05-18T17:00,84.2,23,42.6,0,0,4.7,19.2 -2020-05-18T18:00,77.5,35,47.8,0,0,12.9,20.1 -2020-05-18T19:00,69.4,45,47.1,0,0,6.5,21.9 -2020-05-18T20:00,66,51,47.4,0,0,4,10.7 -2020-05-18T21:00,62.1,56,46,0,0,4.5,8.5 -2020-05-18T22:00,59.2,57,44.2,0,0,5.7,7.8 -2020-05-18T23:00,57.7,57,42.4,0,0,4.6,8.1 -2020-05-19T00:00,55.9,57,40.9,0,0,5.2,8.7 -2020-05-19T01:00,52.3,64,40.5,0,0,5.2,8.5 -2020-05-19T02:00,51.5,67,41,0,0,5.7,8.1 -2020-05-19T03:00,53.4,61,40.3,0,0,1.5,6.9 -2020-05-19T04:00,49.5,67,39,0,0,2.1,2.5 -2020-05-19T05:00,49.6,75,41.9,0,0,4.2,5.4 -2020-05-19T06:00,52.8,72,43.9,0,0,5,9.6 -2020-05-19T07:00,58.2,65,46.5,0,0,2.6,9.4 -2020-05-19T08:00,62.9,60,48.7,0,0,2.8,9.6 -2020-05-19T09:00,70.3,40,45,0,0,3.4,11.2 -2020-05-19T10:00,81,23,40,0,0,3.9,12.3 -2020-05-19T11:00,86.7,11,26.8,0,0,10,18.6 -2020-05-19T12:00,89.2,10,24.4,0,0,12.8,23.9 -2020-05-19T13:00,86.3,16,34.2,0,0,16.1,29.1 -2020-05-19T14:00,86.3,14,31.3,0,0,14.2,29.3 -2020-05-19T15:00,84.5,16,32.8,0,0,20.3,34.2 -2020-05-19T16:00,85.9,14,31.7,0,0,18.5,34.7 -2020-05-19T17:00,85.1,15,31.7,0,0,13.9,35.1 -2020-05-19T18:00,79.3,25,40.6,0,0,20.2,34.7 -2020-05-19T19:00,75.7,29,41.4,0,0,20.4,34 -2020-05-19T20:00,72,43,48,0,0,17.8,34.7 -2020-05-19T21:00,69,47,47.9,0,0,13.2,29.3 -2020-05-19T22:00,67.1,50,47.7,0,0,11.2,22.1 -2020-05-19T23:00,66.1,52,47.8,0,0,10.6,19 -2020-05-20T00:00,63.9,56,47.9,0,0,8.7,17.7 -2020-05-20T01:00,61.5,60,47.4,0,0,6.9,14.5 -2020-05-20T02:00,58.4,65,46.5,0,0,5.9,11.4 -2020-05-20T03:00,56.9,66,45.6,0,0,6.5,10.7 -2020-05-20T04:00,55.2,67,44.5,0,0,6.5,11 -2020-05-20T05:00,54.3,67,43.7,0,0,5.9,11.2 -2020-05-20T06:00,57.7,61,44.2,0,0,6.5,11.6 -2020-05-20T07:00,65,47,44.1,0,0,7.3,13.9 -2020-05-20T08:00,72.9,33,42.4,0,0,10,18.6 -2020-05-20T09:00,78.1,20,33.7,0,0,13.9,25.5 -2020-05-20T10:00,81,12,24.2,0,0,15.9,29.1 -2020-05-20T11:00,82.9,10,21.4,0,0,16.4,29.8 -2020-05-20T12:00,84,10,22.7,0,0,17.5,32.2 -2020-05-20T13:00,85.6,8,16.7,0,0,19.2,34.4 -2020-05-20T14:00,85.5,9,20.2,0,0,18.6,34.7 -2020-05-20T15:00,86.5,7,15.1,0,0,19.6,33.8 -2020-05-20T16:00,83.9,9,18,0,0,15,34.7 -2020-05-20T17:00,83.1,8,15.8,0,0,14.5,26.8 -2020-05-20T18:00,80.1,10,18,0,0,8.6,24.6 -2020-05-20T19:00,71.2,18,25,0,0,10.5,14.5 -2020-05-20T20:00,64.9,15,15.7,0,0,16,28.6 -2020-05-20T21:00,62.4,12,9.8,0,0,6.5,26.6 -2020-05-20T22:00,59.2,22,20.8,0,0,5.5,11 -2020-05-20T23:00,54.1,37,28.7,0,0,5.5,6.7 -2020-05-21T00:00,53.2,49,34.7,0,0,3.6,7.2 -2020-05-21T01:00,49.6,57,35.2,0,0,4.5,6.5 -2020-05-21T02:00,48.5,52,31.7,0,0,2,5.8 -2020-05-21T03:00,45.9,47,27,0,0,2.9,5.6 -2020-05-21T04:00,43.9,48,25.4,0,0,3.8,7.2 -2020-05-21T05:00,44.1,45,24.2,0,0,2.9,6.9 -2020-05-21T06:00,48.8,45,28.4,0,0,3.1,7.4 -2020-05-21T07:00,55.7,37,29.9,0,0,1.9,7.4 -2020-05-21T08:00,58.6,34,30.2,0,0,2.7,9.8 -2020-05-21T09:00,62.3,27,27.6,0,0,5,13.6 -2020-05-21T10:00,66,22,25.7,0,0,6.3,16.1 -2020-05-21T11:00,69.2,18,24.3,0,0,5.2,16.3 -2020-05-21T12:00,71.2,17,24.2,0,0,4.7,15.2 -2020-05-21T13:00,72.7,16,24.2,0,0,5.1,14.8 -2020-05-21T14:00,73.2,15,22.6,0,0,5.6,15 -2020-05-21T15:00,73.3,14,21.4,0,0,6.3,14.3 -2020-05-21T16:00,72.1,15,22.1,0,0,11.1,20.1 -2020-05-21T17:00,72.1,15,21.8,0,0,6.1,22.6 -2020-05-21T18:00,70.3,17,23.7,0,0,5.6,9.8 -2020-05-21T19:00,65,26,29.2,0,0,5.4,12.8 -2020-05-21T20:00,59.5,35,32.2,0,0,4.8,8.9 -2020-05-21T21:00,55.1,46,34.9,0,0,5.7,9.8 -2020-05-21T22:00,57.4,40,33,0,0,1.2,7.2 -2020-05-21T23:00,53.5,48,34.3,0,0,2.2,3.4 -2020-05-22T00:00,50.8,46,30.7,0,0,5.2,8.7 -2020-05-22T01:00,48.9,44,28.1,0,0,5,8.7 -2020-05-22T02:00,47.1,46,27.4,0,0,4.1,8.3 -2020-05-22T03:00,45,52,28.4,0,0,5.3,6.5 -2020-05-22T04:00,43.9,55,28.9,0,0,5.3,6.7 -2020-05-22T05:00,44.6,68,34.8,0,0,2.8,6.5 -2020-05-22T06:00,49,54,33.1,0,0,2.2,5.8 -2020-05-22T07:00,55,44,33.4,0,0,2.7,8.7 -2020-05-22T08:00,61.4,33,31.8,0,0,2.2,9.2 -2020-05-22T09:00,68.1,24,30.3,0,0,4.3,12.3 -2020-05-22T10:00,73.1,16,24.7,0,0,6,15 -2020-05-22T11:00,75.9,13,21.1,0,0,7,17.2 -2020-05-22T12:00,78.3,10,17.9,0,0,7.4,17.7 -2020-05-22T13:00,80.2,8,13.7,0,0,8.5,18.8 -2020-05-22T14:00,81.2,7,11.8,0,0,9.6,20.4 -2020-05-22T15:00,81,7,12.1,0,0,9,20.1 -2020-05-22T16:00,80.8,7,11.8,0,0,4.9,18.8 -2020-05-22T17:00,79.9,10,18.6,0,0,12.2,12.1 -2020-05-22T18:00,78.6,9,15.1,0,0,9.4,20.8 -2020-05-22T19:00,69.3,15,20.3,0,0,5.2,15.7 -2020-05-22T20:00,63.4,23,24.5,0,0,6.1,8.5 -2020-05-22T21:00,65.8,19,22.1,0,0,1.3,7.2 -2020-05-22T22:00,58.6,28,25.6,0,0,5.6,7.6 -2020-05-22T23:00,56.1,33,27.7,0,0,5.9,7.8 -2020-05-23T00:00,55.8,34,28,0,0,4.3,7.6 -2020-05-23T01:00,52.9,42,30.6,0,0,5.4,5.6 -2020-05-23T02:00,50.5,52,33.7,0,0,5.6,7.4 -2020-05-23T03:00,48.8,54,33.1,0,0,2.8,7.2 -2020-05-23T04:00,47.3,56,32.5,0,0,2.5,4.9 -2020-05-23T05:00,49.9,57,35.2,0,0,2.2,4.5 -2020-05-23T06:00,53.2,51,35.6,0,0,3.8,7.2 -2020-05-23T07:00,56.6,53,39.7,0,0,1.3,8.1 -2020-05-23T08:00,60.3,45,39,0,0,1.6,8.5 -2020-05-23T09:00,65.4,36,37.4,0,0,3.2,11.4 -2020-05-23T10:00,71.8,24,33.1,0,0,6,15.4 -2020-05-23T11:00,75.8,18,29.3,0,0,5.2,16.6 -2020-05-23T12:00,78.3,13,23.4,0,0,2.9,15 -2020-05-23T13:00,79.5,10,18.4,0,0,1.7,12.1 -2020-05-23T14:00,79.7,10,18.1,0,0,4.4,12.8 -2020-05-23T15:00,76.3,19,31.3,0,0,14.4,25.1 -2020-05-23T16:00,75.3,15,24.4,0,0,13.2,27.7 -2020-05-23T17:00,73.9,21,31.6,0,0,13.1,23.3 -2020-05-23T18:00,71.9,23,31.6,0,0,7.2,22.6 -2020-05-23T19:00,67.6,28,33.2,0,0,6.7,11.9 -2020-05-23T20:00,65.5,30,33.2,0,0,12.2,20.1 -2020-05-23T21:00,61.2,42,37.8,0,0,13.1,21.5 -2020-05-23T22:00,57.7,59,43.4,0,0,9.1,21.9 -2020-05-23T23:00,55.8,65,44.2,0,0,5.5,15.2 -2020-05-24T00:00,52.6,75,45,0,0,5.1,9.4 -2020-05-24T01:00,52.2,76,44.9,0,0,2.8,6.5 -2020-05-24T02:00,50.6,79,44.2,0,0,3.2,4.9 -2020-05-24T03:00,50.6,78,44.1,0,0,3.5,5.1 -2020-05-24T04:00,49.5,82,44.1,0,0,3.3,5.4 -2020-05-24T05:00,49.7,80,43.8,0,0,2.1,5.6 -2020-05-24T06:00,52,72,43.3,0,0,3.4,7.2 -2020-05-24T07:00,53.6,65,42.2,0,0,2.9,9.2 -2020-05-24T08:00,55,64,42.8,0.004,0,3,9.6 -2020-05-24T09:00,57,60,43.1,0,0,4,14.1 -2020-05-24T10:00,58.4,53,41.5,0,0,4.4,13.9 -2020-05-24T11:00,59.5,46,38.6,0,0,3.7,14.1 -2020-05-24T12:00,62.9,39,37.6,0,0,3.5,13.9 -2020-05-24T13:00,65,37,38.1,0,0,5.9,16.1 -2020-05-24T14:00,56.6,63,44.2,0.047,0,10.1,18.6 -2020-05-24T15:00,53.6,80,47.7,0.154,0,5.1,18.1 -2020-05-24T16:00,50.6,82,45.2,0.118,0,15.9,22.1 -2020-05-24T17:00,46,79,39.7,0.268,0,8.6,35.6 -2020-05-24T18:00,45.3,85,41,0.079,0,6.2,15.4 -2020-05-24T19:00,45.1,82,40.1,0.024,0,3.5,11 -2020-05-24T20:00,44.5,84,40.1,0.004,0,5.2,8.7 -2020-05-24T21:00,44.2,90,41.4,0,0,4.4,10.1 -2020-05-24T22:00,43.6,93,41.6,0,0,3.1,7.8 -2020-05-24T23:00,42,95,40.6,0,0,3.7,6.5 -2020-05-25T00:00,41.5,93,39.7,0,0,3.8,6.7 -2020-05-25T01:00,40.3,93,38.5,0,0,3.6,6.7 -2020-05-25T02:00,38.6,95,37.2,0,0,3.3,6.7 -2020-05-25T03:00,37.7,94,36.2,0,0,3.4,6 -2020-05-25T04:00,37,92,35.1,0,0,3.7,6.5 -2020-05-25T05:00,38.4,97,37.7,0,0,3.2,6.9 -2020-05-25T06:00,41.8,88,38.6,0,0,3.4,8.3 -2020-05-25T07:00,46.7,70,37.5,0,0,4,9.6 -2020-05-25T08:00,49.8,60,36.4,0,0,5.4,13.9 -2020-05-25T09:00,52.3,55,36.7,0,0,3.1,13.6 -2020-05-25T10:00,55.7,49,37,0,0,3.3,11.6 -2020-05-25T11:00,57.8,42,34.7,0.012,0,7.2,17 -2020-05-25T12:00,58.6,41,34.7,0.02,0,7.6,19 -2020-05-25T13:00,60,39,34.7,0,0,7.7,19 -2020-05-25T14:00,61.3,35,33.6,0,0,7.4,18.6 -2020-05-25T15:00,61.4,33,32.4,0,0,5.5,17.9 -2020-05-25T16:00,61.5,31,30.8,0,0,4.1,14.1 -2020-05-25T17:00,59.2,45,37.6,0,0,6.1,11.4 -2020-05-25T18:00,58.6,47,38.6,0,0,4.8,13.4 -2020-05-25T19:00,57.7,51,39.7,0,0,3,9.2 -2020-05-25T20:00,55.9,55,40,0,0,1.6,3.8 -2020-05-25T21:00,49.6,74,41.5,0,0,4,4.9 -2020-05-25T22:00,46.9,77,40.1,0,0,3.5,6 -2020-05-25T23:00,44.3,82,39.3,0,0,4.6,7.4 -2020-05-26T00:00,43.4,81,37.9,0,0,4.3,7.6 -2020-05-26T01:00,41.9,75,34.6,0,0,3.6,7.2 -2020-05-26T02:00,39.7,73,31.8,0,0,3.5,6.5 -2020-05-26T03:00,38.7,71,29.9,0,0,4.4,6 -2020-05-26T04:00,38.9,62,27.1,0,0,4.1,6 -2020-05-26T05:00,40.1,59,26.9,0,0,3.8,6.7 -2020-05-26T06:00,44.7,54,28.9,0,0,4.2,8.1 -2020-05-26T07:00,51.7,51,34,0,0,3.2,8.9 -2020-05-26T08:00,59.9,41,36,0,0,2.1,9.4 -2020-05-26T09:00,65.1,33,35.3,0,0,0.6,8.7 -2020-05-26T10:00,68.3,27,33.2,0,0,2.4,10.1 -2020-05-26T11:00,70.3,26,34,0,0,3.7,12.1 -2020-05-26T12:00,71.9,26,35.2,0,0,5,13.6 -2020-05-26T13:00,73,26,36.1,0,0,3.7,14.5 -2020-05-26T14:00,75.3,24,36.5,0,0,7.8,15.7 -2020-05-26T15:00,75.1,24,36.2,0,0,8.9,20.8 -2020-05-26T16:00,74.4,25,36.8,0,0,7.8,18.1 -2020-05-26T17:00,73.6,29,39.3,0,0,4.5,15.2 -2020-05-26T18:00,72.1,41,46.9,0,0,2,9.2 -2020-05-26T19:00,66.6,40,41.6,0,0,9.9,15.7 -2020-05-26T20:00,62.4,46,41.5,0,0,9.2,17 -2020-05-26T21:00,61.7,44,39.6,0,0,12.1,19.2 -2020-05-26T22:00,60.8,39,36,0,0,10.6,21.3 -2020-05-26T23:00,55.3,53,38.5,0,0,4,17.4 -2020-05-27T00:00,53.1,60,39.7,0,0,3.2,7.6 -2020-05-27T01:00,53.6,56,38.1,0,0,6.2,11 -2020-05-27T02:00,50.9,63,38.8,0,0,5.1,9.8 -2020-05-27T03:00,52,64,40.2,0,0,4.3,6.7 -2020-05-27T04:00,53.4,63,41.2,0,0,3,6 -2020-05-27T05:00,54.5,54,38.4,0,0,2.6,3.6 -2020-05-27T06:00,55.6,58,41.2,0,0,3.3,6 -2020-05-27T07:00,60.7,53,43.3,0,0,1.1,6.5 -2020-05-27T08:00,66.2,45,44.2,0,0,2.8,8.9 -2020-05-27T09:00,71,35,41.7,0,0,2.7,9.8 -2020-05-27T10:00,73.8,30,40.7,0,0,3.8,12.1 -2020-05-27T11:00,74.8,30,41.4,0,0,3.1,12.1 -2020-05-27T12:00,75.9,29,41.5,0,0,1.9,11 -2020-05-27T13:00,76.8,28,41.6,0,0,2.6,10.3 -2020-05-27T14:00,77.7,28,41.8,0,0,4.3,12.1 -2020-05-27T15:00,76.6,29,41.7,0,0,3,12.3 -2020-05-27T16:00,76.5,29,42.2,0,0,4,10.3 -2020-05-27T17:00,75.2,34,44.9,0,0,4.8,12.5 -2020-05-27T18:00,71.5,44,48.2,0,0,5.6,8.9 -2020-05-27T19:00,66,56,49.9,0,0,7.7,19.5 -2020-05-27T20:00,62.8,67,51.8,0,0,2.6,17 -2020-05-27T21:00,60.7,70,50.8,0,0,4.9,7.2 -2020-05-27T22:00,58.9,73,50.3,0,0,6.7,11.2 -2020-05-27T23:00,57.1,73,48.7,0,0,4.9,11 -2020-05-28T00:00,54.9,75,47.2,0,0,3.5,8.1 -2020-05-28T01:00,55.2,66,43.9,0,0,6.6,11 -2020-05-28T02:00,53.3,73,44.9,0,0,6.8,11.4 -2020-05-28T03:00,55.9,66,44.5,0,0,4,8.5 -2020-05-28T04:00,51.7,78,45,0,0,5.3,7.2 -2020-05-28T05:00,54.5,78,47.9,0,0,5.7,8.7 -2020-05-28T06:00,56.8,75,48.9,0.004,0,6.4,11.2 -2020-05-28T07:00,58.8,71,49.3,0,0,7.8,15.2 -2020-05-28T08:00,60.4,67,49.5,0,0,7,15.2 -2020-05-28T09:00,62.9,61,49.4,0,0,6.7,15.4 -2020-05-28T10:00,66.1,55,49.6,0,0,6.4,15.7 -2020-05-28T11:00,69.1,50,49.6,0,0,5.8,15.7 -2020-05-28T12:00,70.7,46,49.2,0,0,5,15.2 -2020-05-28T13:00,72.8,44,49.4,0,0,4.2,13.9 -2020-05-28T14:00,73.9,42,49.1,0,0,3.4,13 -2020-05-28T15:00,74.3,41,49.1,0,0,1.8,12.5 -2020-05-28T16:00,73.9,40,48.3,0.008,0,3.5,11.9 -2020-05-28T17:00,70.7,53,52.5,0,0,3.9,11.6 -2020-05-28T18:00,69.6,55,52.8,0,0,3.9,10.1 -2020-05-28T19:00,66.5,62,53.2,0,0,6.4,10.7 -2020-05-28T20:00,62.7,71,53.2,0,0,4,10.7 -2020-05-28T21:00,61.2,77,53.9,0,0,3.1,6.7 -2020-05-28T22:00,60.2,78,53.2,0,0,3,5.1 -2020-05-28T23:00,57.4,83,52.2,0,0,4.5,5.4 -2020-05-29T00:00,56.5,83,51.5,0,0,3.7,5.1 -2020-05-29T01:00,55.7,86,51.6,0,0,3.4,5.4 -2020-05-29T02:00,54.2,89,51.2,0,0,3.2,5.4 -2020-05-29T03:00,52.4,92,50.3,0,0,3.4,3.8 -2020-05-29T04:00,50.5,96,49.4,0,0,4.1,5.4 -2020-05-29T05:00,51,90,48.1,0,0,3.2,5.4 -2020-05-29T06:00,54.7,76,47.4,0,0,1.4,4.5 -2020-05-29T07:00,58.4,74,50.1,0,0,2.8,8.7 -2020-05-29T08:00,62.7,66,51,0,0,2.4,9.8 -2020-05-29T09:00,67.6,56,51.4,0,0,2.6,10.5 -2020-05-29T10:00,72.1,50,52.7,0,0,4.8,13.2 -2020-05-29T11:00,76.7,46,54.2,0,0,6.1,15.9 -2020-05-29T12:00,80.8,41,54.9,0,0,7.5,17 -2020-05-29T13:00,80.4,41,54.6,0,0,3,17.4 -2020-05-29T14:00,80.5,38,52.5,0,0,0.9,11.2 -2020-05-29T15:00,84.4,24,44.2,0,0,4.7,11.9 -2020-05-29T16:00,84.5,21,40.7,0,0,1.4,12.3 -2020-05-29T17:00,82.5,26,44.6,0,0,7.8,12.5 -2020-05-29T18:00,78.6,34,48.3,0,0,10.7,17.9 -2020-05-29T19:00,73.9,42,49,0,0,8.9,17.7 -2020-05-29T20:00,67.4,55,50.7,0,0,5.4,14.8 -2020-05-29T21:00,65.6,59,50.7,0,0,5.4,8.3 -2020-05-29T22:00,64.2,58,49.3,0,0,5.5,10.3 -2020-05-29T23:00,61,56,45.2,0,0,5.2,8.9 -2020-05-30T00:00,58.5,56,42.7,0,0,5.6,7.4 -2020-05-30T01:00,57.7,57,42.7,0,0,5.7,7.4 -2020-05-30T02:00,58.7,60,44.8,0,0,4.3,6.9 -2020-05-30T03:00,57.7,66,46.4,0,0,3.3,6.5 -2020-05-30T04:00,56.9,68,46.6,0,0,5.5,6 -2020-05-30T05:00,59.6,60,45.9,0,0,3.9,7.2 -2020-05-30T06:00,61.3,60,47.1,0,0,3.4,6 -2020-05-30T07:00,66.9,50,47.7,0,0,4.6,10.3 -2020-05-30T08:00,72.1,43,48.7,0,0,3.4,10.7 -2020-05-30T09:00,76.4,40,50.3,0,0,3.8,10.5 -2020-05-30T10:00,78.4,38,50.6,0,0,8.9,18.1 -2020-05-30T11:00,78.6,37,50.1,0,0,7.2,19.5 -2020-05-30T12:00,77.1,38,49.3,0,0,3.3,16.6 -2020-05-30T13:00,79.7,34,48.7,0,0,1.2,11.4 -2020-05-30T14:00,79.7,31,46.2,0,0,2.2,10.1 -2020-05-30T15:00,79.1,33,47.8,0,0,5.4,13 -2020-05-30T16:00,77.3,39,50.4,0,0,8.2,16.3 -2020-05-30T17:00,74.3,50,54.6,0,0,13.7,16.1 -2020-05-30T18:00,71.2,59,56.2,0,0,8.4,23.5 -2020-05-30T19:00,69.4,63,56.3,0,0,5.6,15 -2020-05-30T20:00,67.4,70,57.2,0,0,2.5,9.6 -2020-05-30T21:00,65.9,77,58.4,0,0,1.8,4 -2020-05-30T22:00,64.9,81,58.8,0.004,0,4.6,10.3 -2020-05-30T23:00,63.5,84,58.6,0,0,0.7,7.6 -2020-05-31T00:00,60.5,91,58,0,0,4.7,8.1 -2020-05-31T01:00,58.8,93,56.8,0,0,2.8,7.6 -2020-05-31T02:00,57.4,95,55.9,0,0,1.8,4.9 -2020-05-31T03:00,57.1,91,54.5,0,0,1.6,2.9 -2020-05-31T04:00,56.3,87,52.3,0,0,2.1,3.1 -2020-05-31T05:00,56.8,87,53,0,0,3.5,4 -2020-05-31T06:00,59.5,77,52.3,0,0,2,5.8 -2020-05-31T07:00,62.6,70,52.8,0,0,0.9,6.3 -2020-05-31T08:00,66.8,61,53.1,0,0,2.3,9.2 -2020-05-31T09:00,72.3,48,51.7,0,0,1.7,9.6 -2020-05-31T10:00,77.9,37,49.6,0,0,2.9,10.5 -2020-05-31T11:00,82.2,30,47.7,0,0,3.8,11.9 -2020-05-31T12:00,84.7,27,46.9,0,0,4.6,13.9 -2020-05-31T13:00,85,26,46.6,0.004,0,1.6,13 -2020-05-31T14:00,85,23,42.8,0,0,2.3,9.4 -2020-05-31T15:00,83.9,26,45.1,0,0,5.5,12.8 -2020-05-31T16:00,82.6,26,44.4,0,0,5.7,12.5 -2020-05-31T17:00,78.4,37,50.1,0,0,7.6,12.1 -2020-05-31T18:00,76.1,44,52.5,0,0,6.9,12.8 -2020-05-31T19:00,72.7,48,51.8,0,0,7,12.3 -2020-05-31T20:00,68.6,54,51.1,0,0,4.3,11.6 -2020-05-31T21:00,64.7,63,52,0,0,5.1,6.7 -2020-05-31T22:00,63.4,62,50.3,0,0,4,6.5 -2020-05-31T23:00,63,56,47.1,0,0,5.1,7.8 -2020-06-01T00:00,62.2,52,44.2,0,0,4.2,8.5 -2020-06-01T01:00,59.8,58,44.9,0,0,5.7,6.7 -2020-06-01T02:00,59.2,56,43.5,0,0,5.2,6.9 -2020-06-01T03:00,57.7,58,43.2,0,0,5.9,7.2 -2020-06-01T04:00,57.7,55,41.5,0,0,5.6,7.2 -2020-06-01T05:00,56.9,65,45.2,0,0,4.8,7.2 -2020-06-01T06:00,62.7,51,44.4,0,0,4.7,8.1 -2020-06-01T07:00,68.8,46,47,0,0,4.2,9.4 -2020-06-01T08:00,75.5,36,46.5,0,0,4.7,11.9 -2020-06-01T09:00,80.7,29,45.8,0,0,3,11.6 -2020-06-01T10:00,84.1,26,45.2,0,0,3.5,11 -2020-06-01T11:00,86,20,39.7,0,0,3.8,12.1 -2020-06-01T12:00,87.4,19,39.5,0,0,6.9,16.6 -2020-06-01T13:00,87.6,18,39,0,0,2.2,16.3 -2020-06-01T14:00,87.8,17,37.8,0,0,2.5,10.3 -2020-06-01T15:00,87.6,17,37.5,0,0,3,9.8 -2020-06-01T16:00,87.2,17,37.8,0,0,5.4,11.2 -2020-06-01T17:00,87.3,20,40.8,0,0,7.6,13.9 -2020-06-01T18:00,82.6,28,46.4,0,0,5.1,13.6 -2020-06-01T19:00,78.2,32,45.9,0,0,9.6,14.8 -2020-06-01T20:00,75.2,35,45.8,0,0,11.3,18.3 -2020-06-01T21:00,73.9,34,43.5,0,0,12.6,21.3 -2020-06-01T22:00,71.2,36,42.6,0,0,9.5,20.8 -2020-06-01T23:00,67.7,42,43.5,0,0,5.1,15.7 -2020-06-02T00:00,64.7,48,44.6,0,0,4.6,8.1 -2020-06-02T01:00,62.6,53,45.4,0,0,5.5,6.7 -2020-06-02T02:00,62,51,43.6,0,0,4.4,6.7 -2020-06-02T03:00,61.1,53,43.6,0,0,5.4,6.7 -2020-06-02T04:00,60.7,50,42.1,0,0,5.4,6.9 -2020-06-02T05:00,61.2,54,44.2,0,0,2.8,7.2 -2020-06-02T06:00,67.2,43,44.2,0,0,0.9,4.7 -2020-06-02T07:00,72.2,36,43.8,0,0,1.7,6.3 -2020-06-02T08:00,76.6,31,44.1,0,0,0.7,6.3 -2020-06-02T09:00,79.3,24,39.8,0,0,2.7,9.4 -2020-06-02T10:00,81.8,20,36.9,0,0,2.9,9.4 -2020-06-02T11:00,83.5,18,35.7,0,0,4.2,12.1 -2020-06-02T12:00,84.8,17,35.2,0,0,4.5,13 -2020-06-02T13:00,86.4,16,34.8,0,0,6,14.8 -2020-06-02T14:00,87,15,34.3,0,0,7.6,17 -2020-06-02T15:00,87.3,15,33.5,0,0,8.3,17.9 -2020-06-02T16:00,86.5,15,33.4,0,0,9.3,18.6 -2020-06-02T17:00,79.3,24,39.2,0,0,8.3,20.4 -2020-06-02T18:00,79.9,24,40.2,0,0,4.4,14.1 -2020-06-02T19:00,74,42,49.5,0,0,4.1,8.1 -2020-06-02T20:00,70.8,41,45.8,0,0,4.1,5.1 -2020-06-02T21:00,68.3,44,45.2,0,0,4.7,6.9 -2020-06-02T22:00,65.1,55,48.7,0,0,3.6,8.5 -2020-06-02T23:00,63.1,67,51.8,0,0,4.1,5.6 -2020-06-03T00:00,61.4,75,53.4,0,0,4.7,5.6 -2020-06-03T01:00,59.9,87,55.9,0,0,9.4,15.4 -2020-06-03T02:00,58.4,86,54.2,0,0,5.9,15.7 -2020-06-03T03:00,58,80,51.7,0,0,3.2,9.6 -2020-06-03T04:00,57.9,85,53.5,0,0,3.8,6.5 -2020-06-03T05:00,55,76,47.6,0,0,3.9,6.7 -2020-06-03T06:00,60.2,57,45,0,0,2.2,5.6 -2020-06-03T07:00,65.1,52,46.9,0,0,0.9,6.7 -2020-06-03T08:00,69.4,50,50,0,0,1.7,8.3 -2020-06-03T09:00,74.5,42,49.9,0,0,1.4,9.6 -2020-06-03T10:00,79.9,27,42.9,0,0,2.1,9.6 -2020-06-03T11:00,82.8,20,37.4,0,0,5.1,13.2 -2020-06-03T12:00,84.4,18,36.5,0,0,8.5,18.1 -2020-06-03T13:00,86.2,17,36.1,0,0,4,18.6 -2020-06-03T14:00,84.6,19,37.1,0,0,10.9,21.9 -2020-06-03T15:00,85,15,31.6,0,0,3.5,21.5 -2020-06-03T16:00,84,15,32,0,0,8.3,15.4 -2020-06-03T17:00,83.9,17,33.7,0,0,15.9,18.1 -2020-06-03T18:00,77.4,31,44.6,0,0,12.6,28.4 -2020-06-03T19:00,74.5,43,50.3,0,0,1.8,20.1 -2020-06-03T20:00,69.6,47,48.6,0,0,5.1,8.3 -2020-06-03T21:00,64.7,61,51.1,0,0,2.6,8.3 -2020-06-03T22:00,62.9,62,49.8,0,0,5.7,8.7 -2020-06-03T23:00,61.5,52,43.7,0,0,5.8,11 -2020-06-04T00:00,60.5,51,42.4,0,0,5.2,9.8 -2020-06-04T01:00,59.5,51,41.1,0,0,5.1,8.7 -2020-06-04T02:00,58,48,38.7,0,0,5.3,8.9 -2020-06-04T03:00,56.5,49,37.6,0,0,4.4,8.7 -2020-06-04T04:00,55.4,50,37.2,0,0,4.2,7.2 -2020-06-04T05:00,54.7,60,41,0,0,3.9,6.7 -2020-06-04T06:00,59.5,48,39.8,0,0,3.2,6.9 -2020-06-04T07:00,66.6,41,42.4,0,0,3.6,9.6 -2020-06-04T08:00,73.9,32,42.2,0,0,1.9,9.6 -2020-06-04T09:00,79.7,22,37,0,0,3.3,10.5 -2020-06-04T10:00,82.7,17,33.7,0,0,3.8,11.6 -2020-06-04T11:00,83.6,18,35.1,0,0,5.9,15.9 -2020-06-04T12:00,82.5,20,37.3,0,0,4.7,21.7 -2020-06-04T13:00,82.8,19,36.1,0,0,4.1,22.1 -2020-06-04T14:00,85.2,19,37.9,0,0,8.6,18.6 -2020-06-04T15:00,79.8,29,44.3,0.004,0,11,22.8 -2020-06-04T16:00,82.6,20,37.4,0,0,6.8,21.3 -2020-06-04T17:00,82.5,19,36.4,0,0,6.4,17.2 -2020-06-04T18:00,81.6,23,40.1,0,0,4.8,12.5 -2020-06-04T19:00,75,34,44.4,0,0,5.8,8.3 -2020-06-04T20:00,70.7,33,39.9,0,0,3,7.4 -2020-06-04T21:00,66.9,38,40.6,0,0,2.7,4.3 -2020-06-04T22:00,62.8,47,42.1,0,0,4.1,6.5 -2020-06-04T23:00,61.4,47,41.2,0,0,5,8.9 -2020-06-05T00:00,59.5,49,40.3,0,0,4.6,8.3 -2020-06-05T01:00,58.2,51,40.3,0,0,4.3,7.8 -2020-06-05T02:00,56.6,53,39.8,0,0,4,6.9 -2020-06-05T03:00,55.2,55,39.3,0,0,5,6.7 -2020-06-05T04:00,55.7,50,37.4,0,0,4.5,6.9 -2020-06-05T05:00,56.6,56,41.1,0,0,3.6,7.4 -2020-06-05T06:00,60.4,51,42,0,0,2.4,5.8 -2020-06-05T07:00,65,43,42.3,0,0,1.2,6.3 -2020-06-05T08:00,71.7,34,41.8,0,0,2.9,9.6 -2020-06-05T09:00,78.4,27,41.5,0,0,1.8,9.8 -2020-06-05T10:00,83.6,23,41.4,0,0,0.9,9.2 -2020-06-05T11:00,87.4,18,38.9,0,0,2.5,9.6 -2020-06-05T12:00,90.1,14,34.3,0,0,6.5,16.6 -2020-06-05T13:00,91.9,13,33.6,0,0,6.9,17.2 -2020-06-05T14:00,92.6,13,33.6,0,0,6.3,16.3 -2020-06-05T15:00,91.8,14,35.1,0,0,8.8,17.7 -2020-06-05T16:00,91.9,13,33.4,0,0,7.8,17.7 -2020-06-05T17:00,89.8,14,34.6,0,0,15.6,21 -2020-06-05T18:00,84.1,22,41.2,0,0,12,26.2 -2020-06-05T19:00,79.8,27,42.7,0,0,10.3,19.7 -2020-06-05T20:00,75.5,33,44.3,0,0,9.6,17.2 -2020-06-05T21:00,74.2,35,44.6,0,0,7.9,15.7 -2020-06-05T22:00,72.1,37,44.2,0,0,6.2,13.2 -2020-06-05T23:00,68.8,43,45.6,0,0,5.1,10.3 -2020-06-06T00:00,66.5,51,47.6,0,0,4.8,8.5 -2020-06-06T01:00,65.9,52,47.8,0,0,3.8,6 -2020-06-06T02:00,64.8,52,46.7,0,0,2.8,5.1 -2020-06-06T03:00,63.1,53,46,0,0,3.6,5.8 -2020-06-06T04:00,60.5,57,45.3,0,0,4.6,7.2 -2020-06-06T05:00,64,46,42.6,0,0,5.3,7.6 -2020-06-06T06:00,67.7,41,43.4,0,0,4.5,20.1 -2020-06-06T07:00,71.4,35,42.4,0,0,10.2,15.2 -2020-06-06T08:00,74.4,35,45.1,0,0,16.2,28.4 -2020-06-06T09:00,72.4,43,48.7,0.008,0,17,33.8 -2020-06-06T10:00,74,42,49.6,0.008,0,13.8,41.2 -2020-06-06T11:00,72.4,46,50.5,0.004,0,10.3,36 -2020-06-06T12:00,81.1,32,48.4,0,0,15.8,30.6 -2020-06-06T13:00,84.5,26,46,0,0,24.6,45.4 -2020-06-06T14:00,82.9,26,44.9,0,0,25.7,45.9 -2020-06-06T15:00,81,30,46.6,0,0,26.6,49.9 -2020-06-06T16:00,79.1,31,46.1,0,0,20.2,44.7 -2020-06-06T17:00,78.9,29,44.3,0,0,13.9,35.3 -2020-06-06T18:00,79.4,42,54.2,0,0,2.8,23 -2020-06-06T19:00,71.7,43,48.3,0,0,14,23 -2020-06-06T20:00,68.1,48,47.4,0,0,3.5,23 -2020-06-06T21:00,67,50,47.6,0,0,1.1,4.5 -2020-06-06T22:00,61.7,62,48.7,0,0,4.1,5.1 -2020-06-06T23:00,59,70,49.1,0,0,2.9,5.1 -2020-06-07T00:00,57.8,73,49.1,0,0,2.9,5.1 -2020-06-07T01:00,55.9,75,48,0,0,3.1,6 -2020-06-07T02:00,53.2,79,46.9,0,0,3.4,5.4 -2020-06-07T03:00,52,76,44.8,0,0,3.4,4.9 -2020-06-07T04:00,50.8,70,41.3,0,0,3.2,4.9 -2020-06-07T05:00,51.4,61,38.3,0,0,3.8,5.1 -2020-06-07T06:00,56.7,48,37,0,0,3.6,7.8 -2020-06-07T07:00,66.2,34,37,0,0,4.5,9.4 -2020-06-07T08:00,76.2,17,27.8,0,0,11.6,20.1 -2020-06-07T09:00,78.8,16,29.3,0,0,14.6,26.8 -2020-06-07T10:00,82.4,15,30.3,0,0,12,26.8 -2020-06-07T11:00,84.3,14,29.8,0,0,16.1,29.1 -2020-06-07T12:00,85.6,13,29.8,0,0,17.3,31.3 -2020-06-07T13:00,86.8,13,29.4,0,0,19.2,34.2 -2020-06-07T14:00,88.7,10,25.7,0,0,21.3,35.3 -2020-06-07T15:00,89.1,10,25.6,0,0,21.8,37.4 -2020-06-07T16:00,88.8,10,24.7,0,0,20.1,38.7 -2020-06-07T17:00,88.3,10,23.7,0,0,16.9,34.7 -2020-06-07T18:00,84.4,14,30.1,0,0,10,28.4 -2020-06-07T19:00,80.4,15,28.6,0,0,10.9,20.4 -2020-06-07T20:00,72.6,21,29.8,0,0,5.5,17.7 -2020-06-07T21:00,70.2,21,28.9,0,0,7.9,12.3 -2020-06-07T22:00,66.3,24,28.3,0,0,6.7,13.2 -2020-06-07T23:00,61.4,31,30.7,0,0,4.4,11 -2020-06-08T00:00,59.2,37,32.7,0,0,4.8,9.4 -2020-06-08T01:00,60.2,52,42.4,0,0,13.2,25.9 -2020-06-08T02:00,56.1,64,44.1,0,0,5.6,21.7 -2020-06-08T03:00,57.6,59,43.3,0,0,1.7,7.4 -2020-06-08T04:00,54.2,69,44.2,0,0,4.5,5.1 -2020-06-08T05:00,53.5,74,45.6,0,0,2.4,5.8 -2020-06-08T06:00,58.3,57,43,0,0,6.2,11 -2020-06-08T07:00,60.5,51,42.4,0,0,5.5,13.9 -2020-06-08T08:00,63.1,46,41.7,0,0,4.7,13 -2020-06-08T09:00,66.3,37,39.3,0,0,5.7,14.8 -2020-06-08T10:00,69.4,30,36.8,0,0,8.2,18.1 -2020-06-08T11:00,72,27,36.3,0,0,10.5,22.1 -2020-06-08T12:00,73,26,35.9,0,0,9.5,22.1 -2020-06-08T13:00,74.3,25,36.1,0,0,8.6,20.6 -2020-06-08T14:00,75,25,36.6,0,0,7.8,19.2 -2020-06-08T15:00,76.7,25,37.8,0.008,0,8.4,17.4 -2020-06-08T16:00,76.6,25,37.9,0,0,10.4,21.7 -2020-06-08T17:00,75.8,29,41.5,0,0,18.4,21.7 -2020-06-08T18:00,66,43,43,0,0,24.1,40.7 -2020-06-08T19:00,61.7,51,43.2,0,0,18.4,55 -2020-06-08T20:00,58.6,52,40.9,0,0,9.9,31.1 -2020-06-08T21:00,56.2,56,40.6,0,0,12.2,21 -2020-06-08T22:00,52,74,43.9,0.055,0,7.2,26.4 -2020-06-08T23:00,49.3,86,45.4,0.079,0,7.7,25.3 -2020-06-09T00:00,46.5,86,42.6,0.059,0,11.7,23.9 -2020-06-09T01:00,45.8,82,40.6,0.067,0,9.8,19.5 -2020-06-09T02:00,45.3,82,40,0.035,0,7.6,16.6 -2020-06-09T03:00,45.1,87,41.6,0.024,0,5.1,13 -2020-06-09T04:00,46.3,85,42,0.016,0,3.3,9.2 -2020-06-09T05:00,44,79,37.9,0.028,0,2.2,9.8 -2020-06-09T06:00,44.7,82,39.6,0.031,0,8.2,12.5 -2020-06-09T07:00,44.5,84,39.8,0.055,0,10.5,18.8 -2020-06-09T08:00,46.9,76,39.7,0.039,0,7,17.7 -2020-06-09T09:00,47.7,77,40.6,0.008,0,3.4,13.6 -2020-06-09T10:00,50,71,41.1,0,0,5.7,20.1 -2020-06-09T11:00,55.4,54,38.8,0,0,9.2,23.9 -2020-06-09T12:00,59.2,41,35.3,0,0,8.1,20.4 -2020-06-09T13:00,64.1,32,33.9,0,0,11,21.9 -2020-06-09T14:00,66.2,22,26.6,0,0,18.9,32.7 -2020-06-09T15:00,67.2,20,25,0,0,17.9,35.3 -2020-06-09T16:00,66.4,21,25.4,0,0,14,32.2 -2020-06-09T17:00,66.5,21,25.1,0,0,10.2,25.5 -2020-06-09T18:00,62.9,24,25.1,0,0,10.9,22.6 -2020-06-09T19:00,59.4,27,25,0,0,10.5,18.6 -2020-06-09T20:00,55.9,31,25.3,0,0,8.7,17.7 -2020-06-09T21:00,52.7,37,27.1,0,0,6.9,14.5 -2020-06-09T22:00,51.7,40,27.9,0,0,9.4,15.4 -2020-06-09T23:00,48.7,49,30.5,0,0,3.6,15.4 -2020-06-10T00:00,47.3,58,33.3,0,0,3.1,6.3 -2020-06-10T01:00,49.9,54,34,0,0,1.6,6.3 -2020-06-10T02:00,45.1,69,35.3,0,0,3.1,2.9 -2020-06-10T03:00,43.9,70,34.8,0,0,5.9,6.7 -2020-06-10T04:00,44.7,69,35.1,0,0,3.6,6.9 -2020-06-10T05:00,48.9,57,34.5,0,0,1.3,5.4 -2020-06-10T06:00,49.7,60,36.4,0,0,1.8,5.1 -2020-06-10T07:00,54,46,33.9,0,0,4.8,10.7 -2020-06-10T08:00,59.7,37,33.1,0,0,7.2,15.4 -2020-06-10T09:00,63.1,34,34.3,0,0,5.7,15.4 -2020-06-10T10:00,66.3,32,35.2,0,0,3.4,14.5 -2020-06-10T11:00,69.4,29,35.3,0,0,1.7,12.1 -2020-06-10T12:00,73,22,32.1,0,0,3.8,11.9 -2020-06-10T13:00,74.9,19,30.3,0,0,8.7,18.6 -2020-06-10T14:00,75.2,19,30.4,0,0,9.4,22.1 -2020-06-10T15:00,74.8,20,30.7,0,0,8.2,19.9 -2020-06-10T16:00,74,23,33.4,0,0,7,17.2 -2020-06-10T17:00,74.9,19,30.4,0,0,4.7,15 -2020-06-10T18:00,72.9,25,34.7,0,0,2.4,10.3 -2020-06-10T19:00,68.8,37,41.5,0,0,3.6,3.8 -2020-06-10T20:00,61.8,46,41,0,0,6.6,8.3 -2020-06-10T21:00,57.7,52,40.4,0,0,4.8,8.1 -2020-06-10T22:00,55.7,56,40.4,0,0,4.6,7.6 -2020-06-10T23:00,53.7,59,39.7,0,0,4.2,7.6 -2020-06-11T00:00,51.9,63,39.7,0,0,5,8.3 -2020-06-11T01:00,50.9,61,38.1,0,0,4.5,8.3 -2020-06-11T02:00,48.7,65,37.3,0,0,4.3,7.4 -2020-06-11T03:00,47.2,70,37.9,0,0,4.4,6 -2020-06-11T04:00,46.7,71,37.7,0,0,4.1,5.8 -2020-06-11T05:00,48.4,63,36.5,0,0,3.6,5.8 -2020-06-11T06:00,52.7,59,38.8,0,0,2.4,6.3 -2020-06-11T07:00,58.8,52,41.5,0,0,2.7,8.3 -2020-06-11T08:00,65.8,38,39.7,0,0,3,10.1 -2020-06-11T09:00,70.4,28,36.1,0,0,2.8,10.1 -2020-06-11T10:00,73.6,23,33,0,0,4.2,12.8 -2020-06-11T11:00,75.7,21,33,0,0,3.8,12.8 -2020-06-11T12:00,77.4,21,33.8,0,0,5,14.1 -2020-06-11T13:00,78.1,20,34,0,0,5.5,14.8 -2020-06-11T14:00,78.9,19,33.6,0,0,3,14.5 -2020-06-11T15:00,79.6,19,33.5,0,0,3.2,11.2 -2020-06-11T16:00,79.7,19,34.2,0,0,6.4,13.6 -2020-06-11T17:00,79.6,19,33.5,0,0,7,14.1 -2020-06-11T18:00,77.4,22,36,0,0,6.3,14.5 -2020-06-11T19:00,71.2,40,45.9,0,0,4.7,10.1 -2020-06-11T20:00,71.5,32,40.1,0,0,1.8,5.8 -2020-06-11T21:00,62.2,47,41.5,0,0,5.3,6.3 -2020-06-11T22:00,60.6,46,39.8,0,0,4.6,7.2 -2020-06-11T23:00,56.4,56,40.7,0,0,5.2,7.2 -2020-06-12T00:00,53.8,61,40.6,0,0,2.6,6.5 -2020-06-12T01:00,51.5,70,42,0,0,4.5,5.8 -2020-06-12T02:00,50.8,71,41.8,0,0,2.2,6.3 -2020-06-12T03:00,49.8,74,41.7,0,0,2.7,5.1 -2020-06-12T04:00,47.8,77,41,0,0,2.2,5.1 -2020-06-12T05:00,48.7,76,41.5,0,0,3.1,4.7 -2020-06-12T06:00,54.4,63,42,0,0,3.6,8.3 -2020-06-12T07:00,62.7,48,42.8,0,0,2.7,8.3 -2020-06-12T08:00,70.6,36,42.4,0,0,4.5,11.6 -2020-06-12T09:00,76.9,28,41.1,0,0,5.2,13 -2020-06-12T10:00,82,22,39.5,0,0,5.4,14.1 -2020-06-12T11:00,85.2,17,35.5,0,0,4.2,15 -2020-06-12T12:00,87.1,14,33,0,0,5.4,14.1 -2020-06-12T13:00,88,14,32.4,0,0,5.4,15 -2020-06-12T14:00,88.3,14,32.3,0,0,7.2,15.4 -2020-06-12T15:00,87.5,14,32.5,0,0,10.1,19.5 -2020-06-12T16:00,87.4,14,31.6,0,0,9.7,19.9 -2020-06-12T17:00,87.5,14,31.9,0,0,6.2,22.8 -2020-06-12T18:00,82,24,41.5,0,0,10.2,19.7 -2020-06-12T19:00,77.8,30,43.8,0,0,7.7,18.1 -2020-06-12T20:00,72.7,31,40.6,0,0,8.8,15.4 -2020-06-12T21:00,68.7,37,41.4,0,0,5.6,14.5 -2020-06-12T22:00,65.7,42,41.8,0,0,5.2,9.2 -2020-06-12T23:00,64.5,44,42,0,0,5.6,8.5 -2020-06-13T00:00,63,47,42.4,0,0,5.2,9.8 -2020-06-13T01:00,60.6,51,42.4,0,0,4.9,8.7 -2020-06-13T02:00,57.1,59,43.1,0,0,4.6,7.8 -2020-06-13T03:00,55,64,43.2,0,0,4.3,6.5 -2020-06-13T04:00,54.6,61,41.5,0,0,3.6,5.8 -2020-06-13T05:00,54.8,67,43.9,0,0,3.3,5.8 -2020-06-13T06:00,59.3,56,43.6,0,0,1,4.5 -2020-06-13T07:00,64,45,42.2,0,0,1.6,6.7 -2020-06-13T08:00,67.3,41,43.1,0,0,3.1,10.7 -2020-06-13T09:00,72.7,35,43.5,0,0,3.8,11.4 -2020-06-13T10:00,78.5,28,42.3,0,0,3.6,12.8 -2020-06-13T11:00,83.6,24,43,0,0,7.3,18.6 -2020-06-13T12:00,84.7,19,38.4,0,0,4.7,17.4 -2020-06-13T13:00,87.8,19,39.9,0,0,2.7,16.3 -2020-06-13T14:00,78.4,34,47.5,0,0,15.7,34 -2020-06-13T15:00,79.5,33,47.6,0,0,8.1,28.2 -2020-06-13T16:00,81.9,30,47,0,0,5.6,17 -2020-06-13T17:00,79.6,34,49.1,0,0,8.2,16.3 -2020-06-13T18:00,78.1,38,50.1,0,0,3.1,14.1 -2020-06-13T19:00,75.4,35,46.1,0,0,6.9,13.6 -2020-06-13T20:00,71.3,43,47.8,0,0,4.2,11.4 -2020-06-13T21:00,68.7,49,48.7,0,0,4,6.9 -2020-06-13T22:00,66.6,51,48,0,0,5.8,9.4 -2020-06-13T23:00,65.1,52,47.2,0,0,6.3,10.1 -2020-06-14T00:00,65.6,52,47.3,0,0,7.6,12.3 -2020-06-14T01:00,65.8,49,45.9,0,0,8,13.4 -2020-06-14T02:00,64.3,47,43.7,0,0,7.1,13.4 -2020-06-14T03:00,62.2,48,42.4,0,0,6.7,11.9 -2020-06-14T04:00,59.8,50,41.2,0,0,6.9,11.6 -2020-06-14T05:00,62.6,49,43.3,0,0,12.3,12.3 -2020-06-14T06:00,65.5,46,44.4,0,0,11.9,20.4 -2020-06-14T07:00,71.6,39,45.2,0,0,12,21.3 -2020-06-14T08:00,76.5,32,44.4,0,0,14,24.8 -2020-06-14T09:00,79.5,26,42,0,0,13.7,26.8 -2020-06-14T10:00,82.4,21,38.2,0,0,12.2,25.5 -2020-06-14T11:00,85.1,17,35.7,0,0,12.5,25.7 -2020-06-14T12:00,87.4,14,32.8,0,0,10.5,25.7 -2020-06-14T13:00,89.2,12,30.7,0,0,10.8,23.3 -2020-06-14T14:00,90.3,11,29.4,0,0,12.4,24.8 -2020-06-14T15:00,90,11,29,0,0,13.2,26.4 -2020-06-14T16:00,89.8,11,28.7,0,0,7.6,25.5 -2020-06-14T17:00,89.2,11,27.2,0,0,10.3,17.2 -2020-06-14T18:00,82.8,18,35.6,0,0,15.2,24.8 -2020-06-14T19:00,79.3,21,35.5,0,0,4.7,24.8 -2020-06-14T20:00,72.6,27,37.1,0,0,4.5,7.6 -2020-06-14T21:00,67.8,36,40.2,0,0,4,6.9 -2020-06-14T22:00,65.8,40,41.1,0,0,5.1,7.4 -2020-06-14T23:00,62.3,44,40,0,0,5.7,10.7 -2020-06-15T00:00,59.8,46,38.9,0,0,3.8,9.4 -2020-06-15T01:00,58.4,46,37.6,0,0,3.5,6.3 -2020-06-15T02:00,55.9,48,36.3,0,0,4.2,6.3 -2020-06-15T03:00,54,48,34.9,0,0,4.2,6.5 -2020-06-15T04:00,52.8,47,33.2,0,0,4.2,6 -2020-06-15T05:00,53.4,42,30.8,0,0,3.8,6.3 -2020-06-15T06:00,57.5,39,32.6,0,0,3.9,8.1 -2020-06-15T07:00,64,33,34.3,0,0,0.6,8.1 -2020-06-15T08:00,68.7,26,32.3,0,0,3.7,11.2 -2020-06-15T09:00,74.9,22,33,0,0,2.7,11.4 -2020-06-15T10:00,80.4,17,32.1,0,0,2.9,11.2 -2020-06-15T11:00,84.8,13,29.4,0,0,4.4,13.4 -2020-06-15T12:00,88,11,26.5,0,0,6.4,15.7 -2020-06-15T13:00,90.4,10,25.5,0,0,8.3,18.6 -2020-06-15T14:00,90.7,10,25.8,0,0,6.5,18.6 -2020-06-15T15:00,90.9,10,26.2,0,0,4,15.7 -2020-06-15T16:00,90.8,9,25.1,0,0,2.2,11.9 -2020-06-15T17:00,90.1,10,26.5,0,0,5.2,14.3 -2020-06-15T18:00,88.1,12,29,0,0,1.4,10.7 -2020-06-15T19:00,80.8,22,38.4,0,0,4.1,7.2 -2020-06-15T20:00,74.4,27,38.6,0,0,5.4,7.2 -2020-06-15T21:00,71,31,38.8,0,0,4,7.6 -2020-06-15T22:00,68.5,33,38.2,0,0,5,8.3 -2020-06-15T23:00,65.3,37,38.3,0,0,3.9,8.1 -2020-06-16T00:00,63.3,40,38.8,0,0,4.1,6.7 -2020-06-16T01:00,61.3,43,38.4,0,0,4.3,6.7 -2020-06-16T02:00,59.9,44,37.9,0,0,4.7,7.6 -2020-06-16T03:00,59.6,43,36.9,0,0,6,9.6 -2020-06-16T04:00,59.6,39,34.5,0,0,6.5,10.5 -2020-06-16T05:00,60.6,34,31.9,0,0,5.9,12.8 -2020-06-16T06:00,65.8,31,34.1,0,0,5.8,9.4 -2020-06-16T07:00,73.1,26,36.1,0,0,4.3,9.8 -2020-06-16T08:00,80.5,21,36.4,0,0,6,12.8 -2020-06-16T09:00,86.1,14,31.6,0,0,9,17.7 -2020-06-16T10:00,88.7,12,29.8,0,0,14.5,26.4 -2020-06-16T11:00,89.5,12,29.7,0,0,12.9,29.1 -2020-06-16T12:00,90.4,12,31.1,0,0,11.1,25.3 -2020-06-16T13:00,92.4,11,29.8,0,0,12.1,24.8 -2020-06-16T14:00,92.9,10,29.3,0,0,15,28.6 -2020-06-16T15:00,91.6,12,31.7,0,0,12.9,28.9 -2020-06-16T16:00,91.2,13,33.7,0,0,13.4,25.1 -2020-06-16T17:00,92.6,11,29.3,0,0,11.1,25.5 -2020-06-16T18:00,90.1,12,30.4,0,0,10.2,19.5 -2020-06-16T19:00,85.2,14,30.5,0,0,10.8,17.9 -2020-06-16T20:00,79.6,16,30.1,0,0,9.8,18.3 -2020-06-16T21:00,72.9,22,32.3,0,0,5.3,16.3 -2020-06-16T22:00,68.8,27,33.4,0,0,5.9,10.1 -2020-06-16T23:00,65.9,31,34.6,0,0,5.8,9.8 -2020-06-17T00:00,64.4,34,35.2,0,0,6.8,12.1 -2020-06-17T01:00,61.3,38,35.8,0,0,5.4,11.6 -2020-06-17T02:00,60.4,39,35,0,0,6.5,12.5 -2020-06-17T03:00,58.6,42,35.7,0,0,5.1,11.6 -2020-06-17T04:00,59.6,40,35.3,0,0,1.3,7.8 -2020-06-17T05:00,55.5,56,39.8,0,0,2.9,3.6 -2020-06-17T06:00,60.1,44,38.1,0,0,4.1,8.5 -2020-06-17T07:00,64,38,37.7,0,0,4,10.7 -2020-06-17T08:00,68.1,32,37.3,0,0,2.7,11 -2020-06-17T09:00,76.4,24,36.4,0,0,1.6,10.3 -2020-06-17T10:00,84.5,15,31.8,0,0,3,11.4 -2020-06-17T11:00,88.7,9,22.8,0,0,4.3,13 -2020-06-17T12:00,91,9,23.6,0,0,6.2,15.7 -2020-06-17T13:00,93,8,22.2,0,0,6,16.6 -2020-06-17T14:00,94.3,7,20.4,0,0,3,15.7 -2020-06-17T15:00,93.4,9,25.4,0,0,9.3,18.1 -2020-06-17T16:00,90.1,11,29.4,0,0,18.4,33.1 -2020-06-17T17:00,88.5,12,30.2,0,0,9.8,32.7 -2020-06-17T18:00,87.4,14,32.1,0,0,12.5,19.5 -2020-06-17T19:00,81.4,17,32.1,0,0,17.8,27.7 -2020-06-17T20:00,74.7,24,36,0,0,13.3,37.6 -2020-06-17T21:00,72.1,27,36.5,0,0,7.6,21.7 -2020-06-17T22:00,70.1,29,36.5,0,0,7.2,12.8 -2020-06-17T23:00,67,29,33.4,0,0,8.3,13.6 -2020-06-18T00:00,63.7,32,33.5,0,0,5.1,13.9 -2020-06-18T01:00,62,36,34.6,0,0,5.1,8.5 -2020-06-18T02:00,60.3,25,24.4,0,0,6.3,11.4 -2020-06-18T03:00,58,25,22.7,0,0,4.7,10.5 -2020-06-18T04:00,55.7,28,22.9,0,0,5.2,7.6 -2020-06-18T05:00,55,39,30.7,0,0,3.2,7.6 -2020-06-18T06:00,56.1,36,29.6,0,0,4.9,9.6 -2020-06-18T07:00,58,34,30.1,0,0,6.2,13.9 -2020-06-18T08:00,59.9,34,31.2,0,0,6.5,15.7 -2020-06-18T09:00,62,33,32.2,0,0,5.1,15.7 -2020-06-18T10:00,64.5,31,33.2,0,0,4,14.5 -2020-06-18T11:00,66.8,30,34.3,0,0,4.7,14.8 -2020-06-18T12:00,69.3,29,35.5,0,0,5.1,15.4 -2020-06-18T13:00,71.7,27,36.1,0,0,3.7,15.4 -2020-06-18T14:00,73.4,26,36.1,0,0,3.6,13.6 -2020-06-18T15:00,73.9,25,36.1,0,0,6.9,15.2 -2020-06-18T16:00,72.2,26,35.1,0,0,5.9,15.4 -2020-06-18T17:00,66.8,36,38.7,0,0,4.9,13 -2020-06-18T18:00,62.6,46,41.6,0.008,0,6,13.6 -2020-06-18T19:00,60.3,59,45.9,0.02,0,3.5,14.5 -2020-06-18T20:00,58.4,76,50.7,0.016,0,4,10.7 -2020-06-18T21:00,57.3,79,50.9,0.024,0,1.1,8.9 -2020-06-18T22:00,56.2,87,52.3,0.028,0,3.4,10.5 -2020-06-18T23:00,55,91,52.4,0.012,0,4.4,8.5 -2020-06-19T00:00,53.2,92,50.8,0,0,2.6,7.8 -2020-06-19T01:00,53.1,91,50.5,0,0,2.2,4.7 -2020-06-19T02:00,52.3,95,51.1,0,0,2.7,3.8 -2020-06-19T03:00,53.9,92,51.6,0,0,2.5,4.5 -2020-06-19T04:00,53.9,92,51.7,0,0,1.4,4.5 -2020-06-19T05:00,54.3,85,50,0.047,0,8.7,15 -2020-06-19T06:00,54.1,81,48.2,0.008,0,7.9,19 -2020-06-19T07:00,55.5,74,47.2,0,0,7.7,15.7 -2020-06-19T08:00,57.4,67,46.6,0.039,0,3.9,20.6 -2020-06-19T09:00,58.6,66,47.3,0.02,0,0.9,20.8 -2020-06-19T10:00,61.4,60,47.6,0.004,0,1.7,20.1 -2020-06-19T11:00,67.6,43,44.4,0,0,1.3,10.3 -2020-06-19T12:00,71.1,34,41.4,0,0,2.6,15.2 -2020-06-19T13:00,74.1,31,41.5,0,0,10.6,19.5 -2020-06-19T14:00,67,47,46.1,0.004,0,15.7,27.5 -2020-06-19T15:00,65.2,52,47.4,0.004,0,14.4,28.6 -2020-06-19T16:00,66.8,44,44.4,0,0,9,29.1 -2020-06-19T17:00,67.3,44,44.4,0,0,2.9,18.6 -2020-06-19T18:00,64.3,56,48.4,0,0,10,17.2 -2020-06-19T19:00,61.4,66,50.1,0,0,7.3,18.1 -2020-06-19T20:00,58.9,73,50.3,0,0,5.3,12.1 -2020-06-19T21:00,55.6,85,51,0,0,4.3,8.7 -2020-06-19T22:00,55.1,85,50.7,0,0,2.3,5.1 -2020-06-19T23:00,54.1,87,50.2,0,0,4.6,7.8 -2020-06-20T00:00,51.9,91,49.3,0,0,3.8,7.6 -2020-06-20T01:00,50.4,90,47.5,0,0,3.4,6.3 -2020-06-20T02:00,49.5,86,45.5,0,0,3.6,6 -2020-06-20T03:00,48.2,82,43,0,0,4,6.9 -2020-06-20T04:00,46.5,77,39.6,0,0,3.2,6.7 -2020-06-20T05:00,46.6,74,38.7,0,0,4.7,5.8 -2020-06-20T06:00,51.3,59,37.6,0,0,5,9.6 -2020-06-20T07:00,60.4,45,39.1,0,0,3.4,9.8 -2020-06-20T08:00,69.2,32,38.1,0,0,3.2,10.3 -2020-06-20T09:00,75,21,32.3,0,0,2.6,10.1 -2020-06-20T10:00,77.5,17,28.5,0,0,2.4,10.7 -2020-06-20T11:00,79.3,16,29.1,0,0,4.8,13.2 -2020-06-20T12:00,81,15,29.1,0,0,5.1,14.5 -2020-06-20T13:00,81.6,15,28.8,0,0,5.5,15 -2020-06-20T14:00,82,15,29.8,0,0,5.6,15 -2020-06-20T15:00,82.6,16,31.4,0,0,7.1,16.3 -2020-06-20T16:00,83.5,14,30,0,0,7.2,16.1 -2020-06-20T17:00,83.6,14,29.2,0,0,6.3,17 -2020-06-20T18:00,79.3,20,34.3,0,0,9.3,15.7 -2020-06-20T19:00,74.3,27,38.5,0,0,4.5,15.2 -2020-06-20T20:00,71.5,32,40.5,0,0,5.5,9.8 -2020-06-20T21:00,68.6,34,39,0,0,4.9,8.9 -2020-06-20T22:00,67.6,34,38.1,0,0,3.1,6.9 -2020-06-20T23:00,66.9,38,40.4,0,0,4.7,8.7 -2020-06-21T00:00,65.2,40,40.6,0,0,3.8,7.4 -2020-06-21T01:00,64.4,38,38,0,0,6,10.3 -2020-06-21T02:00,62.2,40,37.2,0,0,4.6,10.1 -2020-06-21T03:00,59.5,46,38.9,0,0,3.4,7.6 -2020-06-21T04:00,59,46,38.2,0,0,4,6.7 -2020-06-21T05:00,58.9,59,44.7,0,0,3.5,6.7 -2020-06-21T06:00,61,44,39.1,0,0,3.4,6 -2020-06-21T07:00,64,41,39.7,0,0,4,8.3 -2020-06-21T08:00,68.6,34,39.4,0,0,5.2,11 -2020-06-21T09:00,73.6,27,37.1,0,0,3.5,11.4 -2020-06-21T10:00,77.5,22,35.4,0,0,3.1,10.7 -2020-06-21T11:00,80.1,22,37.3,0,0,3.1,11.2 -2020-06-21T12:00,81.6,21,38.4,0,0,5.4,15.4 -2020-06-21T13:00,81.4,21,37.5,0,0,6.8,16.3 -2020-06-21T14:00,81.5,21,37.9,0,0,10,19.9 -2020-06-21T15:00,79.4,24,40,0,0,7.7,21.3 -2020-06-21T16:00,79.4,24,39.1,0,0,7,16.8 -2020-06-21T17:00,67.1,61,53.1,0,0,17.3,14.3 -2020-06-21T18:00,68,55,51.4,0,0,9.1,28.6 -2020-06-21T19:00,67.9,53,50,0,0,2.1,16.6 -2020-06-21T20:00,64.9,61,51.1,0,0,3.3,5.1 -2020-06-21T21:00,63.4,65,51.4,0,0,4.5,6.5 -2020-06-21T22:00,61.2,71,51.6,0,0,3.5,9.2 -2020-06-21T23:00,59.5,76,51.9,0,0,3.4,6 -2020-06-22T00:00,59.1,74,50.8,0,0,2.9,4.7 -2020-06-22T01:00,57.9,73,49.4,0,0,4.7,8.3 -2020-06-22T02:00,57.9,66,46.6,0,0,3.6,8.7 -2020-06-22T03:00,57.8,71,48.6,0,0,12.6,20.1 -2020-06-22T04:00,56.4,78,49.7,0,0,8.2,21.3 -2020-06-22T05:00,56,70,46.4,0,0,4.1,13.6 -2020-06-22T06:00,60.4,58,45.5,0,0,2.7,7.2 -2020-06-22T07:00,64.6,49,44.8,0,0,2.9,8.7 -2020-06-22T08:00,67.1,45,44.9,0,0,2.7,10.1 -2020-06-22T09:00,70,43,46.5,0,0,4,12.3 -2020-06-22T10:00,73.1,39,46.9,0,0,6,15.4 -2020-06-22T11:00,74.3,37,46.4,0,0,5.7,15.9 -2020-06-22T12:00,76.1,34,45.3,0,0,5.5,15.4 -2020-06-22T13:00,77.4,31,44.2,0,0,5.7,15.9 -2020-06-22T14:00,78.5,29,43.8,0,0,5.8,15.4 -2020-06-22T15:00,78.8,29,43.6,0,0,6,16.3 -2020-06-22T16:00,79.3,27,42.6,0,0,6.6,15 -2020-06-22T17:00,77.2,35,47.7,0,0,10.4,16.1 -2020-06-22T18:00,76.8,33,45.1,0,0,7.2,19.7 -2020-06-22T19:00,73.9,37,46.2,0,0,5.4,13.2 -2020-06-22T20:00,68,53,50.4,0,0,11,18.3 -2020-06-22T21:00,65.2,63,52.3,0,0,7.1,18.1 -2020-06-22T22:00,63,69,52.7,0,0,4.3,12.1 -2020-06-22T23:00,61.9,71,52.5,0.004,0,2.4,6.9 -2020-06-23T00:00,61.8,75,53.7,0,0,3.9,6.3 -2020-06-23T01:00,63.1,67,52,0,0,8.3,13.2 -2020-06-23T02:00,61.5,61,47.9,0,0,7.3,13.9 -2020-06-23T03:00,58.8,64,46.8,0,0,4.2,12.1 -2020-06-23T04:00,56.2,75,48.4,0,0,2.9,6.7 -2020-06-23T05:00,56,82,50.6,0,0,4.6,4.7 -2020-06-23T06:00,58.5,76,51.1,0,0,0.2,7.6 -2020-06-23T07:00,62.9,62,49.5,0,0,0.9,6.7 -2020-06-23T08:00,66.7,53,49,0,0,0.8,7.6 -2020-06-23T09:00,70.2,48,49.3,0,0,1.6,9.4 -2020-06-23T10:00,73.8,43,49.7,0,0,3.1,11.9 -2020-06-23T11:00,76.9,39,50.1,0,0,4.7,13.6 -2020-06-23T12:00,79.3,33,47.7,0,0,5.3,15.2 -2020-06-23T13:00,82,28,45.8,0,0,7.8,16.1 -2020-06-23T14:00,77.1,33,46,0,0,9.3,23.9 -2020-06-23T15:00,80.7,28,44.3,0,0,8.5,19 -2020-06-23T16:00,79.4,29,44.8,0,0,9,19 -2020-06-23T17:00,81.2,27,43.6,0,0,4.9,17.9 -2020-06-23T18:00,79.7,29,45.1,0,0,5.6,10.3 -2020-06-23T19:00,74.5,45,51.8,0,0,4.1,9.8 -2020-06-23T20:00,69.7,51,50.5,0,0,4.6,6.7 -2020-06-23T21:00,68.5,50,49.2,0,0,5.4,8.7 -2020-06-23T22:00,64.9,58,49.7,0,0,4.7,8.9 -2020-06-23T23:00,62.6,63,49.8,0,0,4.5,7.8 -2020-06-24T00:00,60.5,66,49,0,0,3.6,7.4 -2020-06-24T01:00,58.4,69,48.1,0,0,3.8,5.6 -2020-06-24T02:00,57,70,47.4,0,0,4.4,5.6 -2020-06-24T03:00,56.7,66,45.4,0,0,3.8,5.6 -2020-06-24T04:00,55.5,66,44.2,0,0,4,5.6 -2020-06-24T05:00,55.9,66,44.8,0,0,3.6,5.8 -2020-06-24T06:00,60,60,46,0,0,4.4,8.9 -2020-06-24T07:00,67.6,48,47.4,0,0,2.8,8.9 -2020-06-24T08:00,74.7,37,46.4,0,0,2.2,8.7 -2020-06-24T09:00,80.1,29,44.8,0,0,2.5,9.8 -2020-06-24T10:00,83.9,24,43.5,0,0,1.7,10.5 -2020-06-24T11:00,85.6,18,36.7,0,0,2.6,12.1 -2020-06-24T12:00,87.4,16,36.2,0,0,6.3,15 -2020-06-24T13:00,87.6,17,37.5,0,0,10.1,20.8 -2020-06-24T14:00,88,16,36.5,0,0,8.1,21 -2020-06-24T15:00,87.8,16,36.5,0,0,4.5,17.9 -2020-06-24T16:00,87.3,16,36.3,0,0,5,13 -2020-06-24T17:00,87.1,18,37.9,0,0,7.5,12.3 -2020-06-24T18:00,79.8,30,45.2,0.004,0,5.4,13.2 -2020-06-24T19:00,75.5,35,45.7,0,0,10.6,18.1 -2020-06-24T20:00,72.4,42,48.2,0,0,3.4,17.2 -2020-06-24T21:00,70.2,51,51.3,0,0,3.5,5.4 -2020-06-24T22:00,68.7,53,51,0,0,4.8,7.2 -2020-06-24T23:00,66.4,58,51.2,0,0,5.2,9.4 -2020-06-25T00:00,63.8,64,51.4,0,0,4.1,8.9 -2020-06-25T01:00,60.6,73,51.9,0,0,5.1,8.3 -2020-06-25T02:00,59.7,70,49.8,0,0,4.5,8.5 -2020-06-25T03:00,58.6,65,46.9,0,0,4.6,7.6 -2020-06-25T04:00,61.3,55,45,0,0,3.6,7.4 -2020-06-25T05:00,58.2,66,46.9,0,0,5.5,4.5 -2020-06-25T06:00,64.3,53,46.6,0,0,4.5,7.4 -2020-06-25T07:00,71.1,38,44.5,0,0,4.9,10.5 -2020-06-25T08:00,78.6,28,42.8,0,0,2.9,10.7 -2020-06-25T09:00,85.6,21,41.5,0,0,1.9,9.4 -2020-06-25T10:00,87.5,17,37.7,0,0,7.9,17.2 -2020-06-25T11:00,86,21,41.9,0,0,13,24.2 -2020-06-25T12:00,88.6,14,34.1,0,0,4.6,24.8 -2020-06-25T13:00,86.6,19,39.1,0,0,12.1,23.3 -2020-06-25T14:00,87.4,17,37.8,0,0,3.9,23.3 -2020-06-25T15:00,87.5,18,38.9,0,0,8.6,14.8 -2020-06-25T16:00,84.9,23,42.5,0,0,9.7,23.5 -2020-06-25T17:00,84,23,41.7,0,0,7.5,19.2 -2020-06-25T18:00,83.6,22,40.7,0,0,10.6,17.4 -2020-06-25T19:00,78.4,29,43.3,0,0,14.8,23.9 -2020-06-25T20:00,74.7,35,44.9,0,0,9.2,25.3 -2020-06-25T21:00,73,38,45.8,0,0,4.3,15 -2020-06-25T22:00,71.9,43,48,0,0,10,16.8 -2020-06-25T23:00,68.2,55,51.5,0,0,8.6,16.8 -2020-06-26T00:00,67.6,55,50.8,0,0,7.1,14.3 -2020-06-26T01:00,64.1,64,51.6,0,0,3.5,11.9 -2020-06-26T02:00,62.4,66,50.9,0,0,2,4.7 -2020-06-26T03:00,59.5,73,50.8,0,0,1.8,5.1 -2020-06-26T04:00,57.8,79,51.4,0,0,1.1,3.6 -2020-06-26T05:00,58.4,81,52.7,0,0,4.5,2.7 -2020-06-26T06:00,61.8,71,52.4,0,0,3,7.4 -2020-06-26T07:00,66.7,59,52.2,0,0,3.6,9.8 -2020-06-26T08:00,71.2,48,50.5,0,0,3.4,10.7 -2020-06-26T09:00,74.6,43,50.4,0,0,4.7,13 -2020-06-26T10:00,76.6,41,51.2,0,0,4.7,14.3 -2020-06-26T11:00,78.7,39,52,0,0,4.2,14.1 -2020-06-26T12:00,77.5,42,52.6,0.008,0,1.2,14.5 -2020-06-26T13:00,78.2,41,52.3,0,0,3.4,13 -2020-06-26T14:00,76.7,45,53.5,0,0,8.8,17.2 -2020-06-26T15:00,74.8,48,54,0.004,0,6.3,22.6 -2020-06-26T16:00,70.9,60,56.3,0,0,5.7,17 -2020-06-26T17:00,67.3,77,59.8,0,0,8.7,18.6 -2020-06-26T18:00,67.8,81,61.8,0.016,0,8.5,22.1 -2020-06-26T19:00,68.5,64,55.9,0,0,4.1,17.2 -2020-06-26T20:00,65.3,82,59.8,0,0,7.2,11.4 -2020-06-26T21:00,63.9,88,60.4,0,0,5.5,11.6 -2020-06-26T22:00,62.9,79,56.2,0,0,5.9,10.5 -2020-06-26T23:00,62.2,74,53.8,0,0,5.3,9.6 -2020-06-27T00:00,59.9,74,51.5,0,0,4.6,8.7 -2020-06-27T01:00,58.6,70,48.8,0,0,4.9,7.8 -2020-06-27T02:00,57.2,72,48.1,0,0,4.4,7.8 -2020-06-27T03:00,56.5,68,46.2,0,0,4.3,7.2 -2020-06-27T04:00,55.2,66,44.1,0,0,6,7.8 -2020-06-27T05:00,59.4,51,41,0,0,7.4,7.2 -2020-06-27T06:00,64.9,42,40.9,0,0,4.5,11.9 -2020-06-27T07:00,70.1,36,41.6,0,0,3.3,8.1 -2020-06-27T08:00,75.3,31,42.4,0,0,3.2,10.3 -2020-06-27T09:00,79.9,27,43.1,0,0,2.7,10.1 -2020-06-27T10:00,82.7,22,40.4,0,0,5.1,13.6 -2020-06-27T11:00,84.7,19,37.8,0,0,5.2,15.9 -2020-06-27T12:00,86.6,17,36,0,0,6.5,17 -2020-06-27T13:00,87.7,15,34.4,0,0,7.6,18.6 -2020-06-27T14:00,87.8,15,34.4,0,0,4.4,18.3 -2020-06-27T15:00,85.3,18,37.5,0,0,9.7,17.7 -2020-06-27T16:00,85.1,17,35.1,0,0,4.5,21 -2020-06-27T17:00,85.2,18,37,0,0,10.7,19.5 -2020-06-27T18:00,82.3,20,37.1,0,0,8.7,19.7 -2020-06-27T19:00,77.8,41,52.3,0,0,2.1,14.8 -2020-06-27T20:00,73.8,30,40.2,0,0,5.4,8.7 -2020-06-27T21:00,71.5,32,40.1,0,0,5.8,8.7 -2020-06-27T22:00,69.3,35,40.6,0,0,6,10.1 -2020-06-27T23:00,66,38,39.5,0,0,5.2,9.6 -2020-06-28T00:00,64.2,38,38.4,0,0,4.9,8.3 -2020-06-28T01:00,63.2,37,36.7,0,0,5.5,8.5 -2020-06-28T02:00,62.2,35,34.3,0,0,6.2,10.1 -2020-06-28T03:00,61.8,33,32.1,0,0,6,10.1 -2020-06-28T04:00,61.9,30,30.1,0,0,6.6,11.2 -2020-06-28T05:00,59.5,40,35.2,0,0,4.3,10.7 -2020-06-28T06:00,65.5,35,36.8,0,0,6,10.1 -2020-06-28T07:00,73,30,40,0,0,3.5,10.1 -2020-06-28T08:00,80.5,24,40.6,0,0,4,9.6 -2020-06-28T09:00,85.8,17,35.5,0,0,4.7,14.3 -2020-06-28T10:00,88.3,15,34.6,0,0,6.5,15.4 -2020-06-28T11:00,86,18,37.4,0,0,10.4,21.3 -2020-06-28T12:00,89.3,14,34.6,0,0,4.3,21 -2020-06-28T13:00,85.1,20,40.1,0.004,0,12.1,25.5 -2020-06-28T14:00,87.6,16,35.2,0,0,2.7,23 -2020-06-28T15:00,85.7,19,38.1,0,0,5.2,14.5 -2020-06-28T16:00,87.7,17,37,0,0,10,19.2 -2020-06-28T17:00,81.4,28,45,0,0,4,30.2 -2020-06-28T18:00,82.4,27,44.7,0,0,2.4,10.1 -2020-06-28T19:00,78.3,37,49.6,0,0,4.5,6.7 -2020-06-28T20:00,72.8,38,45.8,0,0,6.1,7.6 -2020-06-28T21:00,69.1,44,46.1,0,0,4.7,7.6 -2020-06-28T22:00,66.4,50,47.2,0,0,3.6,6.7 -2020-06-28T23:00,63.8,54,46.9,0,0,4.6,6.5 -2020-06-29T00:00,62.9,52,45,0,0,4,6.5 -2020-06-29T01:00,61.9,53,44.3,0,0,4.7,7.6 -2020-06-29T02:00,61.5,53,44.2,0,0,6.2,9.8 -2020-06-29T03:00,60.8,51,42.4,0,0,6.8,11.2 -2020-06-29T04:00,59.8,49,40.5,0,0,7.1,11.9 -2020-06-29T05:00,56.8,63,44.2,0,0,4.6,12.8 -2020-06-29T06:00,63.2,45,41.4,0,0,6.1,10.1 -2020-06-29T07:00,71.6,33,40.8,0,0,6.9,13 -2020-06-29T08:00,80.1,22,37.6,0,0,8.1,16.1 -2020-06-29T09:00,84,16,33.4,0,0,7.6,17.4 -2020-06-29T10:00,86.1,15,33.5,0,0,6.1,16.8 -2020-06-29T11:00,87.8,15,34.1,0,0,5.6,15.4 -2020-06-29T12:00,89.6,14,34.3,0,0,5.2,15.2 -2020-06-29T13:00,90.9,14,34.6,0,0,4.3,14.8 -2020-06-29T14:00,92.1,13,33.4,0,0,3.5,13.2 -2020-06-29T15:00,93.7,12,34,0,0,7.8,17.2 -2020-06-29T16:00,92.8,13,34.3,0,0,4.9,18.3 -2020-06-29T17:00,90,14,34.5,0,0,8.8,13.6 -2020-06-29T18:00,88.8,15,35.7,0,0,7.9,16.1 -2020-06-29T19:00,83.1,20,38.3,0,0,6.8,13.4 -2020-06-29T20:00,79.4,21,35.5,0,0,9,14.8 -2020-06-29T21:00,77.4,20,33.5,0,0,9.2,15.4 -2020-06-29T22:00,73.7,25,36.1,0,0,7.9,15.2 -2020-06-29T23:00,68.4,38,41.5,0,0,4.8,13 -2020-06-30T00:00,64.6,46,43.3,0,0,3.4,7.8 -2020-06-30T01:00,64,51,45.7,0,0,6.7,10.1 -2020-06-30T02:00,63.2,43,40.6,0,0,6.8,12.3 -2020-06-30T03:00,62.4,45,41,0,0,3.7,8.5 -2020-06-30T04:00,62.9,44,40.6,0,0,2.9,5.6 -2020-06-30T05:00,60.3,51,42.2,0,0,4.3,3.8 -2020-06-30T06:00,64.1,44,41.5,0,0,5,8.5 -2020-06-30T07:00,67.2,39,41.5,0,0,7.1,14.3 -2020-06-30T08:00,69.9,35,41.2,0,0,7.3,17 -2020-06-30T09:00,70.7,34,40.9,0,0,0.7,16.3 -2020-06-30T10:00,69.5,38,42.9,0,0,6.7,17.2 -2020-06-30T11:00,72.9,31,40.3,0,0,1.6,19.7 -2020-06-30T12:00,76.8,18,30.3,0,0,4.6,20.4 -2020-06-30T13:00,79.9,10,18.5,0,0,6.3,17.4 -2020-06-30T14:00,81,8,15.2,0,0,8.5,19.2 -2020-06-30T15:00,82,8,16.2,0,0,8.8,19.9 -2020-06-30T16:00,81.3,9,16.8,0,0,9.4,19.7 -2020-06-30T17:00,82.8,8,14.6,0,0,7.1,21.5 -2020-06-30T18:00,78.4,14,25.4,0,0,10.8,21.7 -2020-06-30T19:00,71.8,28,37.2,0,0,11.3,19 -2020-06-30T20:00,69.4,31,37.3,0,0,6.9,18.6 -2020-06-30T21:00,66.7,35,38.3,0,0,4.2,10.7 -2020-06-30T22:00,64.9,38,38.8,0,0,5.8,12.1 -2020-06-30T23:00,62,43,39.4,0,0,7.4,12.5 -2020-07-01T00:00,59.4,48,39.5,0,0,4.5,12.1 -2020-07-01T01:00,58.2,56,42.4,0,0,2.7,8.3 -2020-07-01T02:00,57.2,58,42.7,0,0,5.8,9.6 -2020-07-01T03:00,55.2,59,41.1,0,0,3.3,9.6 -2020-07-01T04:00,53.6,67,42.9,0,0,2.6,4.9 -2020-07-01T05:00,53.9,56,38.6,0,0,4.3,4.3 -2020-07-01T06:00,58.4,43,36.1,0,0,3.2,9.8 -2020-07-01T07:00,64,41,39.7,0,0,5.4,11.9 -2020-07-01T08:00,71.4,27,35.2,0,0,8.8,17.4 -2020-07-01T09:00,77.3,17,29,0,0,10.9,21.9 -2020-07-01T10:00,80.6,13,25.2,0,0,9.4,21.9 -2020-07-01T11:00,83.3,11,23.6,0,0,9.5,20.6 -2020-07-01T12:00,85.2,13,28,0,0,13.9,26.6 -2020-07-01T13:00,85.8,13,30.2,0,0,15.5,29.5 -2020-07-01T14:00,86.2,13,30,0,0,15.8,31.1 -2020-07-01T15:00,85.8,13,29.3,0,0,16.8,30.6 -2020-07-01T16:00,86.1,12,28,0,0,18.6,32.9 -2020-07-01T17:00,85,14,30.7,0,0,15.7,32.9 -2020-07-01T18:00,84.5,14,30.6,0,0,14.7,28.4 -2020-07-01T19:00,80.2,17,30.9,0,0,16.1,26.2 -2020-07-01T20:00,76.7,18,30,0,0,14.6,26.6 -2020-07-01T21:00,73.6,21,30.9,0,0,11.8,23.9 -2020-07-01T22:00,69.5,25,31.9,0,0,6.5,19.5 -2020-07-01T23:00,65.3,31,33.7,0,0,4.3,11 -2020-07-02T00:00,62.9,36,35.4,0,0,3.3,6.5 -2020-07-02T01:00,61.8,40,37,0,0,4.5,7.4 -2020-07-02T02:00,61.9,39,36.7,0,0,4.7,8.5 -2020-07-02T03:00,60.8,42,37.6,0,0,5.8,7.6 -2020-07-02T04:00,60,46,39.1,0,0,5.5,8.1 -2020-07-02T05:00,58.7,53,41.6,0,0,3.9,7.6 -2020-07-02T06:00,63.1,42,39.8,0,0,3.1,6.7 -2020-07-02T07:00,68.2,33,38.2,0,0,3.4,8.9 -2020-07-02T08:00,72.5,29,38,0,0,3.4,11 -2020-07-02T09:00,77.6,23,37.4,0,0,2.8,11.2 -2020-07-02T10:00,83.8,17,34.3,0,0,4.1,12.8 -2020-07-02T11:00,88.6,13,30.7,0,0,6,18.1 -2020-07-02T12:00,90.9,10,27.3,0,0,3.6,17.4 -2020-07-02T13:00,92.4,9,25.9,0,0,5.2,15.4 -2020-07-02T14:00,92.8,8,24.4,0,0,5.8,16.3 -2020-07-02T15:00,92.4,9,24.3,0,0,6.2,15.9 -2020-07-02T16:00,92.3,9,25.3,0,0,5.3,15.7 -2020-07-02T17:00,90.9,11,28.9,0,0,15,13.4 -2020-07-02T18:00,85.8,17,35.5,0,0,9.4,26.8 -2020-07-02T19:00,80.7,30,46.5,0,0,15.4,25.1 -2020-07-02T20:00,74.5,41,48.9,0,0,6.2,25.5 -2020-07-02T21:00,75.3,37,47.5,0,0,9.2,15.4 -2020-07-02T22:00,73.1,39,46.9,0,0,2.9,15 -2020-07-02T23:00,74.2,37,46.2,0,0,4.1,11.2 -2020-07-03T00:00,70.4,37,43,0,0,5.6,9.6 -2020-07-03T01:00,68.5,39,42.5,0,0,3,9.4 -2020-07-03T02:00,67.6,43,44,0,0,2.1,4 -2020-07-03T03:00,63.9,43,40.8,0,0,5.6,9.2 -2020-07-03T04:00,61.3,43,38.5,0,0,4.9,9.4 -2020-07-03T05:00,61.1,56,45.1,0,0,3.5,6.3 -2020-07-03T06:00,64.7,49,45.1,0,0,2,5.6 -2020-07-03T07:00,69.1,50,49.8,0,0,3.8,9.8 -2020-07-03T08:00,73.9,43,49.9,0,0,6.7,14.1 -2020-07-03T09:00,77.4,37,48.9,0,0,6,17.9 -2020-07-03T10:00,82,31,48.1,0,0,3.8,15 -2020-07-03T11:00,84.9,26,46.1,0,0,2.1,13.4 -2020-07-03T12:00,87.2,21,42.9,0,0,3.2,12.3 -2020-07-03T13:00,89.6,18,39.9,0,0,7.7,18.3 -2020-07-03T14:00,90,16,38.4,0,0,4.4,18.3 -2020-07-03T15:00,90.8,17,39.3,0,0,9.7,19.2 -2020-07-03T16:00,89.4,18,40.6,0,0,7.6,21.5 -2020-07-03T17:00,83.5,23,41.9,0,0,16.6,16.1 -2020-07-03T18:00,82.2,23,40.8,0,0,10.6,28.4 -2020-07-03T19:00,80.7,25,41.5,0,0,5.1,18.6 -2020-07-03T20:00,76.9,29,42.4,0,0,12.7,19 -2020-07-03T21:00,74.9,43,51.2,0,0,13,24.6 -2020-07-03T22:00,73,49,52.4,0,0,9.7,21.3 -2020-07-03T23:00,71.7,54,54,0,0,7.5,15.9 -2020-07-04T00:00,68.9,54,51.4,0,0,4.5,12.5 -2020-07-04T01:00,68.1,52,50,0,0,4,7.6 -2020-07-04T02:00,67.8,52,49.4,0,0,4.9,8.3 -2020-07-04T03:00,65.6,52,47.5,0,0,2.6,8.1 -2020-07-04T04:00,64,53,46.8,0,0,3.3,4.7 -2020-07-04T05:00,64.3,62,50.9,0,0,5.1,4.3 -2020-07-04T06:00,68.3,51,49.3,0,0,1.2,6 -2020-07-04T07:00,71.2,44,47.9,0,0,2.9,8.5 -2020-07-04T08:00,74.3,39,47.6,0,0,5.7,13.9 -2020-07-04T09:00,78.7,33,47.3,0,0,6.2,15.7 -2020-07-04T10:00,84.2,27,46.1,0,0,6,15.7 -2020-07-04T11:00,86.8,21,42,0,0,4.7,19.5 -2020-07-04T12:00,87.7,21,42.4,0,0,11.7,23 -2020-07-04T13:00,88.6,21,44.1,0,0,11.3,25.7 -2020-07-04T14:00,87.7,23,45.2,0,0,9.3,23.9 -2020-07-04T15:00,86.7,24,45.4,0,0,6.1,20.1 -2020-07-04T16:00,87,22,43.5,0,0,8,16.3 -2020-07-04T17:00,75,44,51.6,0,0,4.6,19.9 -2020-07-04T18:00,70.2,64,57.3,0.02,0,8.6,15.7 -2020-07-04T19:00,69.1,70,58.7,0,0,7,15.9 -2020-07-04T20:00,67.8,75,59.7,0,0,8.3,13.2 -2020-07-04T21:00,67.4,76,59.5,0,0,6.5,13.4 -2020-07-04T22:00,66.1,79,59.3,0,0,4.8,10.3 -2020-07-04T23:00,64.9,82,59.3,0,0,4,7.8 -2020-07-05T00:00,63.3,84,58.6,0,0,4.4,7.2 -2020-07-05T01:00,61.6,83,56.5,0,0,2.7,7.4 -2020-07-05T02:00,59.9,88,56.4,0,0,3.4,5.4 -2020-07-05T03:00,58.1,92,55.9,0,0,3.9,6.7 -2020-07-05T04:00,57.1,92,54.7,0,0,2.6,6.5 -2020-07-05T05:00,57.7,87,53.7,0,0,1.3,4.3 -2020-07-05T06:00,61.6,70,51.8,0,0,0.8,5.1 -2020-07-05T07:00,66.8,58,51.8,0,0,1.3,7.4 -2020-07-05T08:00,73.6,43,49.7,0,0,1.1,7.8 -2020-07-05T09:00,80,32,47.1,0,0,4.3,12.8 -2020-07-05T10:00,84.7,23,43.2,0,0,5.9,15.7 -2020-07-05T11:00,88.1,19,40.1,0,0,3.5,15.9 -2020-07-05T12:00,90.2,16,38.5,0,0,4,13.2 -2020-07-05T13:00,90.3,16,37.5,0,0,4.6,14.5 -2020-07-05T14:00,91.1,15,36.6,0,0,7.9,17.7 -2020-07-05T15:00,92,16,38.8,0,0,10.4,20.8 -2020-07-05T16:00,85.3,24,44.5,0,0,14.6,27.7 -2020-07-05T17:00,85.6,21,41.7,0,0,8.1,26.8 -2020-07-05T18:00,84.7,23,42.6,0,0,6.6,16.6 -2020-07-05T19:00,81.6,27,44.3,0,0,3.1,12.5 -2020-07-05T20:00,77.5,31,44.9,0,0,6.9,11.2 -2020-07-05T21:00,74.5,37,46.5,0,0,6.9,11.2 -2020-07-05T22:00,70.7,43,47.3,0,0,4.6,11.6 -2020-07-05T23:00,67.8,48,47.2,0,0,3.8,7.4 -2020-07-06T00:00,65.5,52,47.3,0,0,4.6,8.1 -2020-07-06T01:00,63.7,55,47.2,0,0,3.2,7.8 -2020-07-06T02:00,62.1,57,46.9,0,0,4.7,5.8 -2020-07-06T03:00,61.2,59,46.7,0,0,4.5,6.3 -2020-07-06T04:00,60.1,59,45.9,0,0,3.8,6.3 -2020-07-06T05:00,59.3,58,44.6,0,0,3,5.8 -2020-07-06T06:00,63.3,51,44.7,0,0,3.8,7.8 -2020-07-06T07:00,70.1,41,45,0,0,2.8,8.5 -2020-07-06T08:00,76.3,32,44.2,0,0,2.7,10.1 -2020-07-06T09:00,81.7,26,43.2,0,0,1.6,9.8 -2020-07-06T10:00,86.2,22,43,0,0,4.3,13.6 -2020-07-06T11:00,89.8,19,41.5,0,0,4.3,14.3 -2020-07-06T12:00,91.9,14,35.7,0,0,3,14.1 -2020-07-06T13:00,92.7,12,31.6,0,0,2.3,12.1 -2020-07-06T14:00,94,10,29.9,0,0,3.1,12.1 -2020-07-06T15:00,94,10,30.2,0,0,5.6,15 -2020-07-06T16:00,93.5,10,29.5,0,0,5.5,15 -2020-07-06T17:00,91.1,12,32.5,0,0,3.1,13.9 -2020-07-06T18:00,88.4,16,36.9,0,0,6,11.6 -2020-07-06T19:00,85.3,23,43.3,0,0,3.4,11.4 -2020-07-06T20:00,82,24,41.1,0,0,2.9,6.9 -2020-07-06T21:00,77.7,26,40.1,0,0,4.3,5.4 -2020-07-06T22:00,75.3,30,41.7,0,0,11.7,17.7 -2020-07-06T23:00,75.4,28,39.6,0,0,13.1,22.8 -2020-07-07T00:00,74.6,27,38.5,0,0,12.2,21.7 -2020-07-07T01:00,71.7,30,38.9,0,0,9.2,20.1 -2020-07-07T02:00,70.1,34,40.3,0,0,8.4,15.2 -2020-07-07T03:00,69.6,35,40.6,0,0,9.7,15.9 -2020-07-07T04:00,69.1,35,40,0,0,10.2,16.8 -2020-07-07T05:00,69.6,32,38.8,0,0,9.7,16.8 -2020-07-07T06:00,71.7,32,40.5,0,0,8.8,16.1 -2020-07-07T07:00,77.9,26,40.6,0,0,6.8,14.1 -2020-07-07T08:00,84.2,21,40.2,0,0,7,14.5 -2020-07-07T09:00,90.4,16,38,0,0,9.6,19 -2020-07-07T10:00,94.1,12,34.3,0,0,10.2,20.6 -2020-07-07T11:00,93.2,12,32.5,0,0,14.3,28 -2020-07-07T12:00,95.4,9,28.7,0,0,10.5,27.1 -2020-07-07T13:00,97.4,9,29.4,0,0,9.6,22.4 -2020-07-07T14:00,96.1,12,35.1,0,0,16.3,28.4 -2020-07-07T15:00,92.5,14,36.3,0,0,19.4,40.7 -2020-07-07T16:00,95.1,12,33.8,0,0,12.3,34 -2020-07-07T17:00,88.8,19,40.6,0,0,11.4,23.9 -2020-07-07T18:00,88.2,19,40.9,0,0,6.5,19.5 -2020-07-07T19:00,85.1,21,41.4,0,0,6.2,12.3 -2020-07-07T20:00,80.1,24,40.6,0,0,5.6,11.6 -2020-07-07T21:00,78.8,32,46.9,0,0,15,25.5 -2020-07-07T22:00,76.3,36,47.2,0,0,9.1,24.8 -2020-07-07T23:00,72.6,37,44.6,0,0,7.3,15 -2020-07-08T00:00,69,35,39.8,0,0,6,12.1 -2020-07-08T01:00,67.8,23,29.1,0,0,5,10.1 -2020-07-08T02:00,65.8,20,23.5,0,0,5.3,8.7 -2020-07-08T03:00,63.7,21,23.4,0,0,5.1,8.9 -2020-07-08T04:00,61.3,22,22,0,0,4.9,8.7 -2020-07-08T05:00,59.6,21,20.1,0,0,4.7,8.5 -2020-07-08T06:00,64.5,18,20.2,0,0,6,10.3 -2020-07-08T07:00,73.4,15,23.3,0,0,3.3,10.1 -2020-07-08T08:00,80.7,12,22.9,0,0,0.2,8.9 -2020-07-08T09:00,84.4,11,23.2,0,0,1.7,9.2 -2020-07-08T10:00,87.9,8,19.3,0,0,3.8,12.5 -2020-07-08T11:00,91.5,7,19,0,0,5.4,15.4 -2020-07-08T12:00,93.3,7,18.8,0,0,5.4,15.2 -2020-07-08T13:00,94.6,7,19.6,0,0,7.5,18.6 -2020-07-08T14:00,94.8,7,19.5,0,0,5.7,18.6 -2020-07-08T15:00,94.9,6,17.9,0,0,2.3,15.4 -2020-07-08T16:00,94.7,6,16.3,0,0,1.1,10.3 -2020-07-08T17:00,92.8,6,17,0,0,9.2,7.6 -2020-07-08T18:00,91.9,6,16.2,0,0,5.7,15.4 -2020-07-08T19:00,87.4,8,19,0,0,4.9,9.2 -2020-07-08T20:00,83.5,9,17.4,0,0,4.6,9.2 -2020-07-08T21:00,76.9,13,23.2,0,0,4.3,7.6 -2020-07-08T22:00,76.3,16,26.6,0,0,3.1,7.4 -2020-07-08T23:00,73.3,25,35,0,0,9.4,13.9 -2020-07-09T00:00,70,29,36.2,0,0,4.4,16.1 -2020-07-09T01:00,67.8,30,35.2,0,0,3.9,6.7 -2020-07-09T02:00,64.2,34,35.5,0,0,5,6.3 -2020-07-09T03:00,63.6,34,34.9,0,0,4.3,6.3 -2020-07-09T04:00,61.2,40,36.5,0,0,4.7,5.8 -2020-07-09T05:00,59.5,39,34.8,0,0,5.1,6.3 -2020-07-09T06:00,64,33,34,0,0,1.4,6.7 -2020-07-09T07:00,72.1,23,31.7,0,0,4,9.8 -2020-07-09T08:00,76.5,18,30.5,0,0,1.8,10.3 -2020-07-09T09:00,80.2,16,30.3,0,0,1.4,9.2 -2020-07-09T10:00,84.2,14,29,0,0,3.5,12.3 -2020-07-09T11:00,87.5,12,28.9,0,0,4.7,14.8 -2020-07-09T12:00,89.7,12,29.5,0,0,5.7,15.7 -2020-07-09T13:00,91.3,11,29,0,0,7.2,18.1 -2020-07-09T14:00,91.6,11,29.9,0,0,8.1,18.1 -2020-07-09T15:00,91.6,11,30.4,0,0,7,20.8 -2020-07-09T16:00,92.7,10,28.6,0,0,3.4,17 -2020-07-09T17:00,86.1,23,44.1,0,0,11.2,14.1 -2020-07-09T18:00,85.4,30,50.8,0,0,13.1,21 -2020-07-09T19:00,72.1,66,60.1,0.091,0,7.3,24.8 -2020-07-09T20:00,67.4,77,60.1,0.02,0,4.6,13.9 -2020-07-09T21:00,67.6,79,60.7,0,0,4.6,9.4 -2020-07-09T22:00,67.8,80,61.3,0,0,0.9,8.1 -2020-07-09T23:00,67.1,83,61.6,0,0,1.9,3.4 -2020-07-10T00:00,67,82,61.4,0,0,3.8,6.3 -2020-07-10T01:00,65.7,83,60.4,0,0,4,7.2 -2020-07-10T02:00,64.1,85,59.6,0,0,4.4,7.2 -2020-07-10T03:00,63,79,56.4,0,0,3.3,7.2 -2020-07-10T04:00,61.8,65,50,0,0,3.4,5.6 -2020-07-10T05:00,61.9,44,39.7,0,0,4.1,6.5 -2020-07-10T06:00,64.5,49,45.1,0,0,5.5,11.6 -2020-07-10T07:00,67.6,58,52.2,0,0,0.3,11 -2020-07-10T08:00,72.3,47,50.8,0,0,4.3,12.3 -2020-07-10T09:00,80.4,26,42.5,0,0,3.8,12.8 -2020-07-10T10:00,89.2,11,27.3,0,0,4.1,13.4 -2020-07-10T11:00,93.6,6,16.6,0,0,4.8,14.3 -2020-07-10T12:00,95.6,6,17.1,0,0,4.1,14.5 -2020-07-10T13:00,97.4,6,17.9,0,0,4.7,14.3 -2020-07-10T14:00,98.7,5,17.9,0,0,6.5,16.6 -2020-07-10T15:00,99.1,5,17.8,0,0,7.6,17.9 -2020-07-10T16:00,98.9,5,17.8,0,0,6.6,17.7 -2020-07-10T17:00,96.1,6,19.6,0,0,0.6,17 -2020-07-10T18:00,95.7,6,19.2,0,0,3.2,6.7 -2020-07-10T19:00,89.5,10,26.3,0,0,4.9,7.4 -2020-07-10T20:00,90.1,9,22.6,0,0,0.9,7.2 -2020-07-10T21:00,77.6,16,27.1,0,0,4.1,6.9 -2020-07-10T22:00,76.1,17,27.6,0,0,4.4,7.2 -2020-07-10T23:00,74.8,13,21.4,0,0,6.3,7.8 -2020-07-11T00:00,71.9,15,21.7,0,0,6.5,8.7 -2020-07-11T01:00,71.1,18,25.8,0,0,5.1,8.9 -2020-07-11T02:00,70.4,21,28.1,0,0,7.3,12.3 -2020-07-11T03:00,68.9,24,30.8,0,0,6.5,12.1 -2020-07-11T04:00,66.7,28,32.8,0,0,4.8,11 -2020-07-11T05:00,64.8,37,38.2,0,0,4.1,7.6 -2020-07-11T06:00,69.3,37,41.8,0,0,4.8,8.9 -2020-07-11T07:00,72.3,37,44.7,0,0,6.3,13.4 -2020-07-11T08:00,74.7,37,46.9,0,0,4.1,13.6 -2020-07-11T09:00,77.5,34,46.9,0,0,3.6,12.5 -2020-07-11T10:00,81,30,46.2,0,0,3.7,13.2 -2020-07-11T11:00,85.1,25,45.2,0,0,4.1,13.9 -2020-07-11T12:00,89.5,19,42.3,0,0,5,15.2 -2020-07-11T13:00,91.6,16,39.4,0,0,6.6,17 -2020-07-11T14:00,92.7,14,37.2,0,0,7.5,17.9 -2020-07-11T15:00,93.7,12,34.2,0,0,7.7,18.3 -2020-07-11T16:00,93.5,12,33.3,0,0,8.6,18.3 -2020-07-11T17:00,91.5,13,34,0,0,12.2,18.3 -2020-07-11T18:00,90.6,13,32.5,0,0,9.6,21.7 -2020-07-11T19:00,86.9,13,30.7,0,0,8.3,17.2 -2020-07-11T20:00,80.7,20,35.3,0,0,4.7,13.6 -2020-07-11T21:00,79.3,23,38.4,0,0,5.4,8.5 -2020-07-11T22:00,79.4,30,45.1,0,0,11.9,19.5 -2020-07-11T23:00,75.8,31,42.6,0,0,8.8,19.9 -2020-07-12T00:00,72.1,33,41.2,0,0,6.8,14.3 -2020-07-12T01:00,70,34,40.6,0,0,7.6,12.1 -2020-07-12T02:00,70.1,37,42.3,0,0,9.2,15.4 -2020-07-12T03:00,69,40,43.6,0,0,6.3,15.2 -2020-07-12T04:00,67.6,44,45.1,0,0,4.5,10.3 -2020-07-12T05:00,66.5,54,49.1,0,0,5.2,7.6 -2020-07-12T06:00,71.1,44,48.1,0,0,3.8,6.9 -2020-07-12T07:00,77.3,35,47.4,0,0,4.3,10.1 -2020-07-12T08:00,83.9,27,46.6,0,0,3.3,10.7 -2020-07-12T09:00,88.4,19,41.3,0,0,3.6,11.6 -2020-07-12T10:00,91.6,14,36.2,0,0,5.5,15.9 -2020-07-12T11:00,95.2,10,30.3,0,0,6,15 -2020-07-12T12:00,95.2,10,30.4,0,0,5,17.7 -2020-07-12T13:00,95.9,9,28.7,0,0,4.9,14.8 -2020-07-12T14:00,90.9,13,33.9,0,0,10.9,18.6 -2020-07-12T15:00,92.2,12,32.6,0,0,8.7,24.4 -2020-07-12T16:00,92.6,13,34.5,0,0,10.6,22.6 -2020-07-12T17:00,88.7,18,40,0,0,8.8,20.4 -2020-07-12T18:00,90.8,14,35.7,0,0,6.7,17.2 -2020-07-12T19:00,86.7,16,35.2,0,0,4.5,12.5 -2020-07-12T20:00,80.8,21,36.8,0,0,4,7.8 -2020-07-12T21:00,80.6,24,40.3,0,0,7.9,13 -2020-07-12T22:00,77.5,28,41.6,0,0,5.7,13.2 -2020-07-12T23:00,74.6,32,42.5,0,0,5.1,9.2 -2020-07-13T00:00,72.6,34,42.6,0,0,4.6,8.5 -2020-07-13T01:00,70.7,36,42.1,0,0,5.2,7.4 -2020-07-13T02:00,70.7,34,41.3,0,0,4.5,7.4 -2020-07-13T03:00,69.9,35,40.7,0,0,5,7.8 -2020-07-13T04:00,68.7,36,40.5,0,0,4.8,7.8 -2020-07-13T05:00,65.9,42,42.3,0,0,5.6,7.4 -2020-07-13T06:00,72.5,32,41.4,0,0,2.6,6.9 -2020-07-13T07:00,78.9,27,42.1,0,0,3.8,9.2 -2020-07-13T08:00,84.5,22,42,0,0,4.3,11.4 -2020-07-13T09:00,89.4,18,40.7,0,0,5.4,13.2 -2020-07-13T10:00,92.9,17,41.4,0,0,12.2,24.8 -2020-07-13T11:00,92.4,16,39.1,0,0,4.1,24.4 -2020-07-13T12:00,96.1,14,38.3,0,0,5.4,15.9 -2020-07-13T13:00,87.2,28,50.5,0.016,0,14.6,26.8 -2020-07-13T14:00,92.4,17,41.3,0,0,7.9,27.3 -2020-07-13T15:00,88.3,20,41.6,0,0,2.9,18.1 -2020-07-13T16:00,88.9,19,40.9,0,0,6.5,15.4 -2020-07-13T17:00,93.3,17,41.8,0,0,16.1,15.9 -2020-07-13T18:00,78.1,44,54.3,0,0,16.7,30 -2020-07-13T19:00,75.9,50,56.2,0,0,6.4,28.6 -2020-07-13T20:00,74.5,53,56.2,0,0,2.8,11.9 -2020-07-13T21:00,73.9,53,55.8,0,0,5.7,8.9 -2020-07-13T22:00,71.9,54,54.4,0,0,5.4,9.4 -2020-07-13T23:00,70.5,57,54.3,0,0,5.9,9.6 -2020-07-14T00:00,69.4,57,53.8,0,0,4.9,9.8 -2020-07-14T01:00,68.4,54,51.3,0,0,4.6,8.5 -2020-07-14T02:00,67.6,51,49,0,0,4.6,7.6 -2020-07-14T03:00,68.2,46,46.9,0,0,5.2,9.2 -2020-07-14T04:00,68,43,45,0,0,5.1,8.7 -2020-07-14T05:00,66.5,46,45.2,0,0,5.2,8.3 -2020-07-14T06:00,70,41,45.2,0,0,7.9,11 -2020-07-14T07:00,68.9,50,49.6,0,0,8.6,21 -2020-07-14T08:00,70.3,42,46.1,0,0,10.1,21.3 -2020-07-14T09:00,72.5,36,44.2,0,0,6.5,20.8 -2020-07-14T10:00,75.3,34,45.1,0,0,6,16.3 -2020-07-14T11:00,78.8,33,47.6,0,0,6.8,18.1 -2020-07-14T12:00,80.2,33,48.4,0,0,6.4,18.3 -2020-07-14T13:00,81.3,32,48.5,0,0,3.1,16.8 -2020-07-14T14:00,85.6,27,48.1,0,0,2.9,12.5 -2020-07-14T15:00,82.9,30,48,0.016,0,7.5,18.3 -2020-07-14T16:00,83.2,30,48.7,0,0,5.6,17.2 -2020-07-14T17:00,72.5,58,57.1,0,0,6.4,13.6 -2020-07-14T18:00,70.2,70,59.9,0.012,0,9.2,17.2 -2020-07-14T19:00,70.8,72,61.3,0,0,11.4,19.9 -2020-07-14T20:00,70.9,67,59.5,0,0,9.4,19.9 -2020-07-14T21:00,69.4,70,59.1,0,0,4,15.7 -2020-07-14T22:00,68.1,74,59.4,0,0,7.1,11.2 -2020-07-14T23:00,66.8,76,59,0,0,4.3,13 -2020-07-15T00:00,66,75,57.7,0,0,4.9,10.3 -2020-07-15T01:00,64.5,77,57.2,0,0,1.9,8.1 -2020-07-15T02:00,63.8,79,57.2,0,0,4,5.8 -2020-07-15T03:00,62.2,81,56.1,0,0,3.3,6.9 -2020-07-15T04:00,60.5,78,53.7,0,0,3.8,4.5 -2020-07-15T05:00,58.9,81,53.2,0,0,3.6,9.6 -2020-07-15T06:00,62.4,67,51.2,0,0,0.2,4.3 -2020-07-15T07:00,65.4,64,52.8,0,0,1.8,7.4 -2020-07-15T08:00,70.6,57,54.9,0,0,9.2,17 -2020-07-15T09:00,72.7,55,55.6,0,0,10.7,24.6 -2020-07-15T10:00,75.3,49,54.6,0,0,8.6,22.4 -2020-07-15T11:00,77.5,43,53.4,0,0,8.7,19.9 -2020-07-15T12:00,77.4,41,52.1,0,0,3.7,20.1 -2020-07-15T13:00,79.1,38,51.1,0,0,4.6,13.9 -2020-07-15T14:00,80.8,34,50.1,0,0,2.5,14.1 -2020-07-15T15:00,81.1,34,49.7,0,0,10.1,20.8 -2020-07-15T16:00,82.2,31,48.4,0.004,0,4.3,20.6 -2020-07-15T17:00,71.3,67,59.6,0,0,4.3,14.3 -2020-07-15T18:00,73.3,61,59.2,0,0,2.8,10.7 -2020-07-15T19:00,73.9,58,58.4,0,0,1.6,9.6 -2020-07-15T20:00,73.6,55,56.3,0,0,4,6 -2020-07-15T21:00,72.6,47,51.4,0,0,4.5,8.1 -2020-07-15T22:00,69.7,50,50.1,0,0,4.1,7.4 -2020-07-15T23:00,69.6,51,50.5,0,0,1.7,6.7 -2020-07-16T00:00,67.2,56,51,0,0,1.5,2.5 -2020-07-16T01:00,63.6,64,51.4,0,0,2.9,3.6 -2020-07-16T02:00,62.4,65,50.5,0,0,4.5,5.4 -2020-07-16T03:00,64,59,49.4,0,0,3.6,5.4 -2020-07-16T04:00,61.6,62,48.7,0,0,4.3,4.9 -2020-07-16T05:00,62,63,49.3,0,0,4.3,5.1 -2020-07-16T06:00,66.8,47,45.9,0,0,5.2,8.5 -2020-07-16T07:00,73.9,35,44.7,0,0,3.3,8.9 -2020-07-16T08:00,80.9,27,44,0,0,2.1,8.9 -2020-07-16T09:00,86.5,20,40.9,0,0,3.7,11 -2020-07-16T10:00,88.6,19,41,0,0,4.9,12.8 -2020-07-16T11:00,91.9,15,37,0,0,5.8,17.9 -2020-07-16T12:00,92.9,14,37.5,0,0,7.8,18.1 -2020-07-16T13:00,88.8,19,41.7,0,0,13.7,23.7 -2020-07-16T14:00,90.3,16,38,0,0,6.3,30.4 -2020-07-16T15:00,91.1,15,36.3,0,0,6.5,15.2 -2020-07-16T16:00,90.1,17,39.7,0,0,7.6,18.8 -2020-07-16T17:00,81.1,28,44.8,0,0,15.1,17 -2020-07-16T18:00,79.9,31,46.7,0,0,12.3,26.6 -2020-07-16T19:00,78.9,34,47.8,0,0,7.8,21 -2020-07-16T20:00,76.7,36,48,0,0,8.1,13 -2020-07-16T21:00,73.2,42,48.7,0,0,6.9,13.2 -2020-07-16T22:00,72.1,43,48.6,0,0,6.7,11.2 -2020-07-16T23:00,70.4,46,48.7,0,0,6.1,11.2 -2020-07-17T00:00,69.1,47,48,0,0,5.1,10.1 -2020-07-17T01:00,68.3,45,46.4,0,0,5.3,8.7 -2020-07-17T02:00,66.7,46,45.5,0,0,4.9,8.3 -2020-07-17T03:00,65.2,49,45.3,0,0,6,7.6 -2020-07-17T04:00,64.9,48,44.9,0,0,6.9,8.3 -2020-07-17T05:00,65.5,44,43,0,0,6.4,8.7 -2020-07-17T06:00,70.4,39,44.2,0,0,3.2,8.5 -2020-07-17T07:00,75.6,33,44.6,0,0,2.2,7.4 -2020-07-17T08:00,81,27,44,0,0,1.3,7.4 -2020-07-17T09:00,86.1,22,42.4,0,0,4.5,12.3 -2020-07-17T10:00,89.8,17,39.3,0,0,8,18.3 -2020-07-17T11:00,90.4,17,38.9,0,0,11.7,23.5 -2020-07-17T12:00,91.9,15,38.1,0,0,8.5,23.9 -2020-07-17T13:00,92.3,15,37.8,0,0,5.7,19.5 -2020-07-17T14:00,92.8,15,37.5,0,0,4.6,15.9 -2020-07-17T15:00,93.4,14,37.2,0,0,4.2,13.9 -2020-07-17T16:00,86.9,27,49.1,0,0,18.5,31.5 -2020-07-17T17:00,89.9,16,37.7,0,0,11.1,32.9 -2020-07-17T18:00,83.2,25,44.2,0,0,17.5,30 -2020-07-17T19:00,80.3,33,48.5,0.004,0,14.3,29.5 -2020-07-17T20:00,76.6,42,52.1,0.016,0,13.3,23.7 -2020-07-17T21:00,73.5,50,53.7,0.008,0,11.4,21 -2020-07-17T22:00,74.3,44,51,0,0,9.7,18.3 -2020-07-17T23:00,73,45,50.4,0,0,6.1,15.9 -2020-07-18T00:00,69.3,55,52.3,0,0,4.8,10.3 -2020-07-18T01:00,66.8,61,52.8,0,0,4.3,8.1 -2020-07-18T02:00,66.2,55,49.5,0,0,5.4,9.6 -2020-07-18T03:00,65.8,49,46.1,0,0,5.6,9.2 -2020-07-18T04:00,65.2,50,46,0,0,6.5,7.8 -2020-07-18T05:00,69.7,41,45,0,0,5.2,7.8 -2020-07-18T06:00,72.8,38,45.6,0,0,1.3,6.3 -2020-07-18T07:00,78.4,31,45.1,0,0,2.7,7.6 -2020-07-18T08:00,85.9,23,43.5,0,0,3.9,10.5 -2020-07-18T09:00,91,15,37,0,0,2.7,11 -2020-07-18T10:00,93.4,11,31.3,0,0,5.6,13.6 -2020-07-18T11:00,94.7,10,30.3,0,0,11,22.1 -2020-07-18T12:00,96.3,10,30.5,0,0,11,23.7 -2020-07-18T13:00,97,10,31.2,0,0,11.7,23.7 -2020-07-18T14:00,96.1,10,30.4,0,0,11.6,24.6 -2020-07-18T15:00,95.2,11,31.6,0,0,9.6,23.5 -2020-07-18T16:00,94.3,11,31.9,0,0,8.7,20.1 -2020-07-18T17:00,89.3,17,38.8,0,0,11.4,19.9 -2020-07-18T18:00,87.6,15,35.1,0,0,15.1,25.5 -2020-07-18T19:00,85.9,19,39.4,0,0,7.2,25.7 -2020-07-18T20:00,81.8,23,40.7,0,0,4.5,12.1 -2020-07-18T21:00,79,29,44.4,0,0,6.5,11.2 -2020-07-18T22:00,76.4,35,46.6,0,0,3.9,11 -2020-07-18T23:00,73.3,40,47.8,0,0,2.4,6.5 -2020-07-19T00:00,70.6,44,47.4,0,0,4.1,6.3 -2020-07-19T01:00,67.5,49,47.6,0,0,3.3,7.2 -2020-07-19T02:00,68.9,48,48.3,0,0,2.6,4.9 -2020-07-19T03:00,67.7,54,50.3,0,0,5.7,8.7 -2020-07-19T04:00,67.1,58,51.6,0,0,6.3,10.7 -2020-07-19T05:00,62.4,76,54.8,0,0,3.6,10.3 -2020-07-19T06:00,64.1,64,51.5,0,0,2.5,7.2 -2020-07-19T07:00,66.5,60,52.3,0,0,3,8.7 -2020-07-19T08:00,69.5,57,53.7,0,0,3,10.7 -2020-07-19T09:00,73.9,51,54.5,0,0,2.9,11.9 -2020-07-19T10:00,79.4,40,52.8,0,0,3,11.6 -2020-07-19T11:00,84.5,32,51.1,0,0,5.2,14.8 -2020-07-19T12:00,86.7,25,46.4,0,0,6.4,15.2 -2020-07-19T13:00,89.7,19,41.7,0,0,4.9,18.8 -2020-07-19T14:00,89.8,19,41.9,0,0,7.8,17.7 -2020-07-19T15:00,90.1,16,37.9,0,0,2.7,18.8 -2020-07-19T16:00,89.6,17,39.5,0,0,10.4,18.3 -2020-07-19T17:00,87.2,19,39.5,0,0,7.2,23.3 -2020-07-19T18:00,86.6,19,39.4,0,0,8,15 -2020-07-19T19:00,83,22,40.8,0,0,4.1,14.5 -2020-07-19T20:00,78.1,28,42.4,0,0,5.2,7.8 -2020-07-19T21:00,75.2,33,44,0,0,6.7,10.5 -2020-07-19T22:00,74.2,35,44.7,0,0,6.8,13 -2020-07-19T23:00,72,39,45.8,0,0,5.9,11.4 -2020-07-20T00:00,69.9,41,45.2,0,0,5.6,9.8 -2020-07-20T01:00,68.5,41,43.9,0,0,5.3,9.2 -2020-07-20T02:00,67.8,40,42.5,0,0,5.4,8.7 -2020-07-20T03:00,66.3,40,41.5,0,0,4.9,8.7 -2020-07-20T04:00,64.2,42,40.6,0,0,4.8,7.8 -2020-07-20T05:00,66.4,37,39.7,0,0,1.2,6.9 -2020-07-20T06:00,66.5,40,41.4,0,0,2.1,3.8 -2020-07-20T07:00,69.5,35,40.8,0,0,3.5,9.2 -2020-07-20T08:00,74.9,30,41.3,0,0,3,10.1 -2020-07-20T09:00,81.3,24,40.7,0,0,4,12.5 -2020-07-20T10:00,86.5,19,40,0,0,8.5,17.9 -2020-07-20T11:00,88.7,20,42.5,0,0,8.6,22.8 -2020-07-20T12:00,89.2,19,41.2,0,0,8.4,21.3 -2020-07-20T13:00,89.4,19,42.1,0,0,4.6,21 -2020-07-20T14:00,90.3,19,42,0,0,8.7,17.9 -2020-07-20T15:00,90.1,17,40,0,0,8,20.4 -2020-07-20T16:00,89.1,17,38.9,0,0,6.7,17.9 -2020-07-20T17:00,89.6,17,39.1,0,0,7.8,15.2 -2020-07-20T18:00,87.1,22,43.5,0,0,9.2,16.6 -2020-07-20T19:00,78.4,37,50.3,0,0,16.1,27.1 -2020-07-20T20:00,75.4,43,51.4,0,0,6.3,26.8 -2020-07-20T21:00,73.4,43,49.8,0,0,3.6,10.7 -2020-07-20T22:00,71.5,48,50.6,0,0,3.3,6 -2020-07-20T23:00,71.6,51,52.4,0,0,5.2,7.8 -2020-07-21T00:00,69.4,56,52.9,0,0,4.1,9.8 -2020-07-21T01:00,67.4,58,52.1,0,0,4,6 -2020-07-21T02:00,66.2,60,51.7,0,0,3,5.6 -2020-07-21T03:00,63.6,65,51.6,0,0,2.9,3.8 -2020-07-21T04:00,62.7,65,50.7,0,0,2.6,4 -2020-07-21T05:00,63.6,68,53,0,0,2.9,4.3 -2020-07-21T06:00,64.8,69,54.2,0,0,3.2,7.4 -2020-07-21T07:00,66.3,70,56,0,0,6.1,13 -2020-07-21T08:00,69.2,65,57,0,0,6.5,15.4 -2020-07-21T09:00,72.6,58,57.1,0,0,5,15.2 -2020-07-21T10:00,76.3,51,56.8,0,0,2.8,14.3 -2020-07-21T11:00,80.8,43,56.5,0,0,3.6,13 -2020-07-21T12:00,84.2,38,55.8,0,0,5.9,15.9 -2020-07-21T13:00,82.1,38,54.1,0,0,6.8,18.1 -2020-07-21T14:00,83.8,32,51,0,0,1.8,17.2 -2020-07-21T15:00,84,29,48,0,0,9.7,22.6 -2020-07-21T16:00,86.6,24,46,0,0,5.9,20.6 -2020-07-21T17:00,81.4,36,52,0,0,0.6,20.6 -2020-07-21T18:00,84.6,24,43.3,0,0,10.1,16.1 -2020-07-21T19:00,81.7,27,44.2,0,0,7.5,19 -2020-07-21T20:00,77.7,30,43.4,0,0,7.8,12.8 -2020-07-21T21:00,74.6,32,43.1,0,0,7.8,13.4 -2020-07-21T22:00,70.9,39,44.6,0,0,4.5,12.8 -2020-07-21T23:00,69.4,44,46.3,0,0,3.6,5.6 -2020-07-22T00:00,67.5,49,47.7,0,0,4.2,7.6 -2020-07-22T01:00,64.5,55,47.8,0,0,3.9,7.2 -2020-07-22T02:00,63.9,55,47.4,0,0,4,7.6 -2020-07-22T03:00,62.8,55,46.4,0,0,4.1,6.7 -2020-07-22T04:00,61.7,56,46,0,0,3.8,6.9 -2020-07-22T05:00,60.5,65,48.8,0,0,2.1,6 -2020-07-22T06:00,62.2,61,48.7,0,0,1.4,4.5 -2020-07-22T07:00,66.3,54,49,0,0,3.8,9.4 -2020-07-22T08:00,72.1,45,49.4,0,0,3.8,11.4 -2020-07-22T09:00,77.6,35,47.6,0,0,2.7,11 -2020-07-22T10:00,83.8,26,45.2,0,0,2.6,11 -2020-07-22T11:00,89,20,42.3,0,0,3.6,12.1 -2020-07-22T12:00,91.5,16,39.7,0,0,3.6,12.1 -2020-07-22T13:00,92.8,15,38.3,0,0,2.7,12.1 -2020-07-22T14:00,93.5,14,37.3,0,0,2.7,11.2 -2020-07-22T15:00,94.3,14,37.8,0,0,4.4,11.9 -2020-07-22T16:00,88.7,21,44,0,0,13.2,22.6 -2020-07-22T17:00,85.8,24,45.1,0,0,12.8,23.9 -2020-07-22T18:00,82.6,29,47.1,0,0,16.4,28 -2020-07-22T19:00,79.6,35,49.6,0,0,9.3,27.7 -2020-07-22T20:00,78.7,37,50,0,0,2.7,15.2 -2020-07-22T21:00,75.4,42,50.9,0,0,3.3,6 -2020-07-22T22:00,72.5,51,53.2,0,0,5.3,8.3 -2020-07-22T23:00,70.3,56,54.1,0,0,4.3,9.4 -2020-07-23T00:00,71.8,52,53.1,0,0,9.2,15.2 -2020-07-23T01:00,72.1,49,52,0,0,6.5,15.4 -2020-07-23T02:00,71.3,47,50.1,0,0,5.9,11 -2020-07-23T03:00,70.3,47,48.9,0,0,3.8,10.1 -2020-07-23T04:00,70,45,47.8,0,0,4.4,8.1 -2020-07-23T05:00,66.2,60,51.8,0,0,3.2,7.4 -2020-07-23T06:00,68.6,53,51,0,0,5.5,9.4 -2020-07-23T07:00,74.9,42,50.1,0,0,3.1,9.6 -2020-07-23T08:00,79.2,34,48.7,0,0,3.6,10.1 -2020-07-23T09:00,82.4,30,48,0,0,5.1,13.2 -2020-07-23T10:00,85.2,26,46.9,0,0,5.1,14.1 -2020-07-23T11:00,87.2,24,46.5,0,0,4.6,14.1 -2020-07-23T12:00,88.5,23,46.2,0,0,4.5,14.1 -2020-07-23T13:00,89.5,23,46.6,0,0,3.2,13.9 -2020-07-23T14:00,89.8,23,46.9,0,0,1.1,12.3 -2020-07-23T15:00,91,22,47.4,0,0,2.4,10.7 -2020-07-23T16:00,89.8,23,47.4,0,0,2.5,10.7 -2020-07-23T17:00,85.6,27,47.4,0,0,9.5,9.4 -2020-07-23T18:00,85.7,25,45.9,0,0,11.4,19.7 -2020-07-23T19:00,83,28,46.6,0,0,8.1,19.7 -2020-07-23T20:00,79.4,33,47.9,0,0,4.7,13.6 -2020-07-23T21:00,76.6,38,49.1,0,0,3.7,8.1 -2020-07-23T22:00,73.6,41,48.4,0,0,4.7,6.9 -2020-07-23T23:00,71.7,42,47.3,0,0,5.1,8.5 -2020-07-24T00:00,70.6,45,48.1,0,0,4,8.5 -2020-07-24T01:00,70.3,46,48.6,0,0,3.3,7.2 -2020-07-24T02:00,68.7,50,49.5,0,0,3,5.6 -2020-07-24T03:00,65.8,57,50,0,0,3.8,5.6 -2020-07-24T04:00,65.2,58,50.1,0,0,3.5,5.1 -2020-07-24T05:00,66.6,59,52,0,0,1.4,4.5 -2020-07-24T06:00,67.9,59,52.9,0,0,1.8,5.4 -2020-07-24T07:00,71.6,52,52.8,0,0,0.7,5.6 -2020-07-24T08:00,76.9,42,51.8,0,0,1.4,7.6 -2020-07-24T09:00,81.8,34,50.4,0,0,3.9,11.4 -2020-07-24T10:00,85.6,28,49.1,0,0,3.8,13.2 -2020-07-24T11:00,87.2,28,49.8,0,0,3.8,12.8 -2020-07-24T12:00,85,30,50.2,0,0,8.3,17.4 -2020-07-24T13:00,87.1,26,47.8,0,0,4.8,19.7 -2020-07-24T14:00,88.3,25,47.8,0,0,5.6,15.9 -2020-07-24T15:00,83.5,31,49.8,0,0,4.5,14.3 -2020-07-24T16:00,83.6,33,51.6,0,0,7.1,18.1 -2020-07-24T17:00,79.7,41,53.6,0,0,2.8,15.2 -2020-07-24T18:00,75.7,53,57.2,0.004,0,6.7,12.3 -2020-07-24T19:00,73,61,58.8,0,0,7.4,13.4 -2020-07-24T20:00,70.9,68,59.8,0.008,0,6.3,13 -2020-07-24T21:00,69.1,81,62.9,0.02,0,4.8,10.7 -2020-07-24T22:00,68.8,83,63.3,0.012,0,1.6,8.5 -2020-07-24T23:00,67.7,88,64.1,0,0,3.4,5.4 -2020-07-25T00:00,67.2,94,65.3,0,0,3.9,6.9 -2020-07-25T01:00,65.8,96,64.8,0,0,3.8,6.7 -2020-07-25T02:00,63.6,97,62.8,0,0,4,7.2 -2020-07-25T03:00,62.4,96,61.3,0,0,4.3,7.6 -2020-07-25T04:00,62.3,88,58.7,0,0,4.8,8.1 -2020-07-25T05:00,62.7,69,52.5,0,0,4.7,8.1 -2020-07-25T06:00,67.7,56,51.4,0,0,3.5,6.5 -2020-07-25T07:00,72.9,46,50.7,0,0,1.1,6.5 -2020-07-25T08:00,76.8,40,50.9,0,0,1.8,7.4 -2020-07-25T09:00,81,34,49.6,0,0,4.1,11.4 -2020-07-25T10:00,83.9,30,48.8,0,0,6,15.2 -2020-07-25T11:00,85.9,25,46.3,0,0,8.8,19.2 -2020-07-25T12:00,86.6,24,45.8,0,0,7.6,19.7 -2020-07-25T13:00,87.1,24,46,0,0,7,17.7 -2020-07-25T14:00,84.4,28,47.6,0,0,13.2,23.5 -2020-07-25T15:00,82.8,30,48.4,0,0,11.6,28 -2020-07-25T16:00,83.7,28,47.5,0,0,4.3,21.7 -2020-07-25T17:00,68.5,76,60.5,0,0,4.1,11.4 -2020-07-25T18:00,70.9,64,58.3,0.004,0,5.5,12.3 -2020-07-25T19:00,68.9,76,61.1,0.012,0,7.9,13.6 -2020-07-25T20:00,68.4,78,61.3,0.004,0,6.8,13.4 -2020-07-25T21:00,68.4,77,60.8,0,0,5.9,11.2 -2020-07-25T22:00,67.4,79,60.5,0.016,0,7.8,12.5 -2020-07-25T23:00,65.8,90,62.9,0.043,0,5.4,12.5 -2020-07-26T00:00,65.8,92,63.4,0.024,0,3.7,9.2 -2020-07-26T01:00,65.3,91,62.5,0,0,2.1,6.3 -2020-07-26T02:00,65,92,62.6,0,0,2.3,4 -2020-07-26T03:00,62.4,97,61.6,0,0,3.4,4.9 -2020-07-26T04:00,61.9,98,61.3,0,0,2.6,4.9 -2020-07-26T05:00,64,82,58.2,0,0,4.2,5.1 -2020-07-26T06:00,66,75,57.9,0,0,1.8,7.4 -2020-07-26T07:00,68.5,70,58.4,0,0,0.8,4.9 -2020-07-26T08:00,71.9,60,57.3,0,0,1.3,6.5 -2020-07-26T09:00,76.5,45,53.5,0,0,4.5,11 -2020-07-26T10:00,80.1,37,51.4,0,0,8.9,18.3 -2020-07-26T11:00,81.5,35,51.2,0,0,10,20.4 -2020-07-26T12:00,81.1,35,51.3,0,0,9.9,20.6 -2020-07-26T13:00,79.3,40,53.2,0,0,12.3,23.5 -2020-07-26T14:00,78.4,43,54,0.004,0,7.9,23.3 -2020-07-26T15:00,80,41,54,0,0,6.4,17.9 -2020-07-26T16:00,80.6,39,53.6,0,0,3.9,15 -2020-07-26T17:00,72.4,59,57.3,0,0,0.7,11.2 -2020-07-26T18:00,73.6,54,55.7,0,0,4.8,10.3 -2020-07-26T19:00,71.8,55,54.5,0,0,5.1,10.5 -2020-07-26T20:00,69.8,58,54.6,0,0,3.7,8.5 -2020-07-26T21:00,67.8,65,55.7,0,0,2.6,6.3 -2020-07-26T22:00,67.6,66,55.9,0,0,0.5,5.1 -2020-07-26T23:00,66,74,57.4,0,0,2.5,3.4 -2020-07-27T00:00,66.1,75,58.1,0,0,1.6,3.4 -2020-07-27T01:00,64.9,78,58,0,0,1.9,2.7 -2020-07-27T02:00,64,76,56.4,0,0,3.2,5.1 -2020-07-27T03:00,62.3,79,55.7,0,0,3.1,5.4 -2020-07-27T04:00,62.7,77,55.4,0,0,1.8,3.8 -2020-07-27T05:00,60.9,82,55.4,0,0,2.2,3.4 -2020-07-27T06:00,63,75,55,0,0,2.9,6.3 -2020-07-27T07:00,66.2,73,57.1,0,0,4.1,10.3 -2020-07-27T08:00,69.7,65,57.6,0,0,2.5,10.3 -2020-07-27T09:00,73.6,57,57.4,0,0,2.7,10.7 -2020-07-27T10:00,77.5,48,55.9,0,0,3.9,12.8 -2020-07-27T11:00,80.6,40,54.1,0,0,4.9,14.3 -2020-07-27T12:00,82.7,36,53,0,0,5.6,15.7 -2020-07-27T13:00,83.6,32,50.5,0,0,5.8,15.7 -2020-07-27T14:00,83.7,30,48.7,0,0,6.2,15.7 -2020-07-27T15:00,84.7,28,47.6,0,0,5.8,15.4 -2020-07-27T16:00,84.3,28,47.2,0,0,6.5,14.5 -2020-07-27T17:00,84.5,28,47.4,0,0,6,15 -2020-07-27T18:00,79.3,41,53.4,0,0,13.5,22.6 -2020-07-27T19:00,76.6,46,54.3,0,0,10.4,23.9 -2020-07-27T20:00,74.6,54,56.8,0,0,10,17 -2020-07-27T21:00,72.9,54,55.4,0,0,13.3,21.9 -2020-07-27T22:00,70,62,56.2,0,0,9.4,22.4 -2020-07-27T23:00,68.2,64,55.7,0,0,6.3,15.4 -2020-07-28T00:00,66.8,65,54.9,0,0,5.9,10.5 -2020-07-28T01:00,64.8,66,53.2,0,0,4.5,9.8 -2020-07-28T02:00,63.2,67,52.2,0,0,3.2,7.6 -2020-07-28T03:00,61.7,70,51.8,0,0,3.8,4.9 -2020-07-28T04:00,61.2,69,51.1,0,0,4,4.9 -2020-07-28T05:00,63.2,68,52.5,0,0,3.6,4.9 -2020-07-28T06:00,65.5,66,53.9,0,0,2.2,5.6 -2020-07-28T07:00,67.8,67,56.3,0,0,1.6,6.7 -2020-07-28T08:00,70.3,62,56.5,0,0,2.2,8.9 -2020-07-28T09:00,73.1,55,56.2,0,0,1.6,8.9 -2020-07-28T10:00,76.9,46,54.7,0,0,3.7,11.4 -2020-07-28T11:00,80.2,38,52.6,0,0,4.8,13.6 -2020-07-28T12:00,81.6,36,51.9,0,0,6.2,17.4 -2020-07-28T13:00,81.3,39,53.8,0.008,0,5.8,19.2 -2020-07-28T14:00,79.7,39,52.5,0.004,0,7.6,18.1 -2020-07-28T15:00,82.3,31,49,0,0,5.4,16.3 -2020-07-28T16:00,82,31,48.4,0,0,2.8,15.2 -2020-07-28T17:00,77.5,43,53.2,0,0,8.9,13 -2020-07-28T18:00,73.6,56,56.8,0,0,8.4,20.6 -2020-07-28T19:00,70.1,63,57,0.008,0,8.1,16.6 -2020-07-28T20:00,67.5,79,60.7,0.031,0,4.5,13.6 -2020-07-28T21:00,66.6,80,60.3,0,0,4.3,9.2 -2020-07-28T22:00,65,82,59.5,0,0,5.2,8.5 -2020-07-28T23:00,64.1,83,58.8,0,0,5.4,8.9 -2020-07-29T00:00,62.8,85,58.1,0,0,5.5,9.2 -2020-07-29T01:00,61.8,84,56.8,0,0,4.7,9.2 -2020-07-29T02:00,60.4,84,55.4,0,0,4.3,8.1 -2020-07-29T03:00,58.1,83,52.9,0,0,3.3,7.2 -2020-07-29T04:00,57.2,75,49.3,0,0,3.7,6.3 -2020-07-29T05:00,60,50,41.5,0,0,4.6,6.5 -2020-07-29T06:00,63.1,44,41.1,0,0,6.3,9.8 -2020-07-29T07:00,69.9,38,43.1,0,0,4.3,10.3 -2020-07-29T08:00,78.7,28,43.3,0,0,4,10.7 -2020-07-29T09:00,84.6,19,37.6,0,0,5.4,13.4 -2020-07-29T10:00,87.1,16,35.5,0,0,6.7,17 -2020-07-29T11:00,89.9,13,31.8,0,0,17.1,32 -2020-07-29T12:00,89.9,14,33.8,0,0,14.3,35.1 -2020-07-29T13:00,92,10,28.6,0,0,16.1,29.8 -2020-07-29T14:00,92.6,11,30.2,0,0,15.1,32.7 -2020-07-29T15:00,92,11,30.4,0,0,11,29.8 -2020-07-29T16:00,92,11,29.2,0,0,16.9,29.3 -2020-07-29T17:00,87.3,19,40.5,0,0,11.5,30.9 -2020-07-29T18:00,84.8,20,39.7,0,0,12.7,19.7 -2020-07-29T19:00,77.4,37,49,0,0,13.5,27.7 -2020-07-29T20:00,74.3,42,49.7,0,0,4.9,22.4 -2020-07-29T21:00,72.2,47,50.7,0,0,5.1,8.5 -2020-07-29T22:00,70.3,51,51.4,0,0,3.6,8.7 -2020-07-29T23:00,69,54,51.8,0,0,4.3,6.5 -2020-07-30T00:00,66.4,62,52.8,0,0,4.1,6.9 -2020-07-30T01:00,64.8,67,53.6,0,0,4.4,5.8 -2020-07-30T02:00,63.8,69,53.5,0,0,3.4,5.4 -2020-07-30T03:00,62.8,69,52.6,0,0,1.6,4.5 -2020-07-30T04:00,60.7,71,51.1,0,0,3.4,5.8 -2020-07-30T05:00,59.5,69,49.3,0,0,0.3,6.3 -2020-07-30T06:00,60.5,67,49.5,0,0,2.7,5.6 -2020-07-30T07:00,65.9,53,48.6,0,0,2.7,9.4 -2020-07-30T08:00,70.2,48,49.4,0,0,3.9,11 -2020-07-30T09:00,74.5,41,49,0,0,5.8,14.1 -2020-07-30T10:00,76.5,37,48.5,0,0,11.9,23.7 -2020-07-30T11:00,74.8,42,50,0,0,12.9,25.3 -2020-07-30T12:00,75,43,51.1,0,0,12.5,25.1 -2020-07-30T13:00,76.3,42,51.7,0,0,12.5,25.9 -2020-07-30T14:00,77.5,42,52.7,0,0,12.3,25.9 -2020-07-30T15:00,77.2,42,52.6,0,0,10.6,25.1 -2020-07-30T16:00,74,50,54.3,0.02,0,10.4,22.1 -2020-07-30T17:00,70.2,59,55.1,0.012,0,6.3,20.1 -2020-07-30T18:00,68.9,64,56.3,0,0,12.2,21.3 -2020-07-30T19:00,65.9,76,58,0.004,0,7.2,22.4 -2020-07-30T20:00,65.2,78,58.2,0,0,4,13.4 -2020-07-30T21:00,64.5,83,59.1,0,0,1.4,7.2 -2020-07-30T22:00,63.3,86,58.9,0,0,2.2,3.4 -2020-07-30T23:00,62.3,87,58.6,0,0,2.2,3.6 -2020-07-31T00:00,60.7,92,58.3,0,0,2.9,4 -2020-07-31T01:00,59.8,93,57.9,0,0,3.2,4.7 -2020-07-31T02:00,58.2,96,57,0,0,1.7,3.8 -2020-07-31T03:00,57.1,94,55.4,0,0,1.5,3.8 -2020-07-31T04:00,55.9,92,53.5,0,0,1.6,3.1 -2020-07-31T05:00,56.8,86,52.7,0,0,3,3.6 -2020-07-31T06:00,59.5,74,51.3,0,0,2.6,6.3 -2020-07-31T07:00,65,61,51.2,0,0,2.1,7.6 -2020-07-31T08:00,70.6,51,51.4,0,0,1.4,8.5 -2020-07-31T09:00,75.1,43,51.3,0,0,1.2,8.7 -2020-07-31T10:00,79.3,31,45.8,0,0,4,13 -2020-07-31T11:00,82.4,25,42.8,0,0,5.2,14.8 -2020-07-31T12:00,84.5,22,41.2,0,0,7.4,18.8 -2020-07-31T13:00,85.3,21,40.6,0,0,11,22.1 -2020-07-31T14:00,85.6,20,39.9,0,0,10.2,22.6 -2020-07-31T15:00,85.4,21,40.9,0,0,7,21.3 -2020-07-31T16:00,85.1,21,41.4,0,0,6.7,17 -2020-07-31T17:00,83.1,25,43.9,0,0,6.5,18.1 -2020-07-31T18:00,82,27,44.6,0,0,2.6,13.6 -2020-07-31T19:00,77.9,31,44.4,0,0,10.3,16.6 -2020-07-31T20:00,73.1,39,47,0,0,3.8,17.4 -2020-07-31T21:00,70.6,47,49.5,0,0,6.7,11.2 -2020-07-31T22:00,67,55,50.3,0,0,3.9,11 -2020-07-31T23:00,65,61,51.1,0,0,3.6,5.6 -2020-08-01T00:00,63.7,62,50.5,0,0,4.4,7.4 -2020-08-01T01:00,61.7,65,50,0,0,3.5,7.6 -2020-08-01T02:00,60.2,69,50.1,0,0,2.9,5.8 -2020-08-01T03:00,58.8,74,50.6,0,0,2.8,4.7 -2020-08-01T04:00,57.4,80,51.3,0,0,3.2,4.5 -2020-08-01T05:00,57.7,64,45.5,0,0,3.6,4.3 -2020-08-01T06:00,59.8,59,45.2,0,0,3,6.7 -2020-08-01T07:00,65.7,48,45.7,0,0,1.6,6.9 -2020-08-01T08:00,72.1,38,45.2,0,0,1.4,8.3 -2020-08-01T09:00,77.9,30,43.8,0,0,1.6,8.9 -2020-08-01T10:00,82.5,25,43,0,0,2.6,11 -2020-08-01T11:00,85.8,19,39.2,0,0,5.2,14.3 -2020-08-01T12:00,87.7,17,37,0,0,6.3,16.8 -2020-08-01T13:00,88.8,16,37.1,0,0,9,19.5 -2020-08-01T14:00,88.3,17,37.3,0,0,8.5,20.8 -2020-08-01T15:00,86,21,41.1,0,0,12,23.3 -2020-08-01T16:00,84.9,20,39.8,0,0,14,25.9 -2020-08-01T17:00,80.7,28,44.6,0,0,10.4,25.7 -2020-08-01T18:00,80.1,27,43.3,0,0,9.3,18.1 -2020-08-01T19:00,77.3,30,43.8,0,0,4.7,16.1 -2020-08-01T20:00,73.8,36,45.1,0,0,3.7,7.8 -2020-08-01T21:00,70.3,41,45.8,0,0,4.3,6 -2020-08-01T22:00,69.5,43,45.8,0,0,5.1,8.9 -2020-08-01T23:00,65.8,51,46.9,0,0,3.7,8.1 -2020-08-02T00:00,64,55,47.7,0,0,2.7,5.4 -2020-08-02T01:00,62.5,59,48,0,0,3,5.1 -2020-08-02T02:00,60.8,63,48,0,0,2.9,5.1 -2020-08-02T03:00,60,64,47.8,0,0,4.2,7.2 -2020-08-02T04:00,57.8,69,47.8,0,0,3.5,7.2 -2020-08-02T05:00,60,64,47.7,0,0,3.7,4.5 -2020-08-02T06:00,62.6,60,48.4,0,0,5.9,8.9 -2020-08-02T07:00,66.8,57,51,0,0,6.3,13 -2020-08-02T08:00,71,51,51.7,0,0,5.1,13.4 -2020-08-02T09:00,74.5,45,51.6,0,0,6.4,15.4 -2020-08-02T10:00,77.4,40,51,0,0,5.5,16.3 -2020-08-02T11:00,80.2,34,49.5,0,0,5.8,15.7 -2020-08-02T12:00,82.7,30,48.2,0,0,6.3,17 -2020-08-02T13:00,82.5,29,47.3,0.008,0,6.4,17.2 -2020-08-02T14:00,84.7,24,44,0,0,6.2,16.8 -2020-08-02T15:00,84.7,23,42.8,0,0,8.4,19.2 -2020-08-02T16:00,84.3,24,44.1,0,0,6.2,18.1 -2020-08-02T17:00,83.9,26,45.7,0,0,4.2,14.8 -2020-08-02T18:00,80.5,33,48.6,0,0,8.6,16.3 -2020-08-02T19:00,77.3,37,48.7,0,0,2.9,15.2 -2020-08-02T20:00,77,37,48.6,0,0,2.8,6.5 -2020-08-02T21:00,70.4,48,49.8,0,0,4.1,6 -2020-08-02T22:00,67.9,54,50.6,0,0,4.8,7.8 -2020-08-02T23:00,65.7,60,51.3,0,0,3,8.1 -2020-08-03T00:00,64.6,63,51.8,0,0,3.4,5.1 -2020-08-03T01:00,62,70,52.2,0,0,3.3,6 -2020-08-03T02:00,62.2,72,53.1,0,0,6.7,10.3 -2020-08-03T03:00,60.9,76,53.4,0,0,4.6,11.9 -2020-08-03T04:00,58.7,81,52.9,0,0,5,7.4 -2020-08-03T05:00,60.9,76,53.4,0,0,1.5,6.3 -2020-08-03T06:00,60.6,78,53.8,0,0,1.1,3.4 -2020-08-03T07:00,63.9,69,53.5,0,0,0.4,6.3 -2020-08-03T08:00,67.7,62,54.2,0,0,2.7,9.6 -2020-08-03T09:00,71.8,55,54.7,0,0,2.6,11 -2020-08-03T10:00,75.6,48,54.3,0,0,2.8,11.9 -2020-08-03T11:00,79.4,41,53.8,0,0,3.7,13 -2020-08-03T12:00,83.7,35,52.8,0,0,3.7,13.6 -2020-08-03T13:00,85.6,31,51.9,0,0,1.4,13.4 -2020-08-03T14:00,84.7,33,52.2,0.02,0,1.5,10.7 -2020-08-03T15:00,86.1,28,49,0.004,0,4.9,14.3 -2020-08-03T16:00,85.1,27,47.4,0,0,4.3,14.8 -2020-08-03T17:00,82.3,37,53.5,0,0,8.5,11 -2020-08-03T18:00,80.5,40,53.7,0,0,10.4,22.1 -2020-08-03T19:00,78.7,39,51.9,0,0,9.9,18.3 -2020-08-03T20:00,75.7,41,50.4,0,0,7.6,16.6 -2020-08-03T21:00,74.2,44,50.9,0,0,6.3,12.8 -2020-08-03T22:00,72.7,52,54,0,0,18.6,25.7 -2020-08-03T23:00,68.5,64,55.7,0,0,8.8,32.7 -2020-08-04T00:00,66.9,67,55.7,0,0,7.3,15 -2020-08-04T01:00,65.6,69,55,0,0,3.4,13.2 -2020-08-04T02:00,63.3,75,55.4,0,0,3.8,5.4 -2020-08-04T03:00,63.1,75,54.9,0,0,2.8,4.9 -2020-08-04T04:00,62.1,68,51.4,0,0,3.8,5.8 -2020-08-04T05:00,62.6,69,52.3,0,0,3.2,6.5 -2020-08-04T06:00,65,62,51.5,0,0,3.8,5.4 -2020-08-04T07:00,68.5,54,51.4,0,0,4.5,7.8 -2020-08-04T08:00,75.9,39,49.1,0,0,4.8,15.9 -2020-08-04T09:00,80.1,35,49.8,0,0,5.9,13.9 -2020-08-04T10:00,83.8,30,49.3,0,0,3.4,15 -2020-08-04T11:00,89.5,24,47.6,0,0,6.4,14.8 -2020-08-04T12:00,86.7,28,49.3,0,0,6.1,23.5 -2020-08-04T13:00,89.8,24,48.4,0,0,7.8,21.3 -2020-08-04T14:00,87,25,46.6,0.012,0,7.8,23.9 -2020-08-04T15:00,84.8,25,45.2,0,0,10.1,22.6 -2020-08-04T16:00,83,29,47.4,0,0,17.2,29.5 -2020-08-04T17:00,81.1,42,55.9,0,0,18.7,31.5 -2020-08-04T18:00,79,41,53.1,0.004,0,4.9,31.8 -2020-08-04T19:00,73.6,52,54.7,0.004,0,2,9.6 -2020-08-04T20:00,71.4,58,55.8,0,0,5.8,9.8 -2020-08-04T21:00,70,60,55.7,0,0,6.8,11.2 -2020-08-04T22:00,68.9,63,55.6,0,0,7.3,11.9 -2020-08-04T23:00,68,63,55.1,0,0,6,12.3 -2020-08-05T00:00,66.3,67,55,0,0,4.2,10.1 -2020-08-05T01:00,63.9,73,55.1,0,0,2.8,7.2 -2020-08-05T02:00,63,75,54.9,0,0,3.3,4.7 -2020-08-05T03:00,62,75,53.9,0,0,3.6,5.6 -2020-08-05T04:00,61.5,69,51.4,0,0,4.2,6.7 -2020-08-05T05:00,61.1,81,55.1,0,0,3.4,6.7 -2020-08-05T06:00,62.3,74,54,0,0,3.7,7.4 -2020-08-05T07:00,66.7,65,54.4,0,0,4.3,9.4 -2020-08-05T08:00,73.3,48,52.2,0,0,5.4,13 -2020-08-05T09:00,79.3,36,50.2,0,0,4.2,13.2 -2020-08-05T10:00,83.8,29,48.3,0,0,1.4,12.5 -2020-08-05T11:00,87.2,24,46.3,0,0,3.4,12.5 -2020-08-05T12:00,85,29,49.2,0.008,0,4.3,18.3 -2020-08-05T13:00,86.2,24,45.1,0,0,2.7,13.6 -2020-08-05T14:00,85.2,27,47.1,0.008,0,1.8,11.4 -2020-08-05T15:00,84.4,28,47.8,0.004,0,7.5,14.5 -2020-08-05T16:00,84.2,27,46.4,0,0,5.8,18.3 -2020-08-05T17:00,83.1,37,54.1,0,0,10.4,15.7 -2020-08-05T18:00,79.1,42,54.1,0.008,0,4.1,19 -2020-08-05T19:00,66.8,79,60.3,0.122,0,13.2,26.4 -2020-08-05T20:00,68.6,71,58.7,0,0,6.4,22.4 -2020-08-05T21:00,68.5,71,58.8,0,0,5.2,11.6 -2020-08-05T22:00,67.3,74,58.8,0,0,4,8.7 -2020-08-05T23:00,67.6,73,58.6,0,0,5.5,9.4 -2020-08-06T00:00,65.6,74,56.9,0,0,3.2,9.2 -2020-08-06T01:00,63.8,80,57.6,0,0,4.2,5.4 -2020-08-06T02:00,64,78,56.8,0,0,4.2,7.2 -2020-08-06T03:00,63.3,72,54,0,0,3.6,6.7 -2020-08-06T04:00,62.8,70,52.9,0,0,1.7,4.3 -2020-08-06T05:00,60.8,73,52,0,0,4.4,4 -2020-08-06T06:00,63.6,59,48.9,0,0,2.2,5.4 -2020-08-06T07:00,67.4,52,49.1,0,0,2,7.2 -2020-08-06T08:00,72.9,43,49.2,0,0,4.5,11.9 -2020-08-06T09:00,82.5,25,43.6,0,0,3.4,12.1 -2020-08-06T10:00,88.3,19,40.9,0,0,3.4,14.1 -2020-08-06T11:00,85.1,24,44.1,0,0,5.2,13.6 -2020-08-06T12:00,85.6,21,41.8,0,0,4.2,17.7 -2020-08-06T13:00,89,18,40.3,0,0,10,20.4 -2020-08-06T14:00,88.5,19,40.7,0,0,11.5,26.2 -2020-08-06T15:00,88.5,18,39.6,0,0,2.8,22.8 -2020-08-06T16:00,87.5,19,40,0,0,10.6,19 -2020-08-06T17:00,86.5,21,41.6,0,0,6.8,21 -2020-08-06T18:00,83.1,25,43.8,0,0,5.6,15.4 -2020-08-06T19:00,79.9,30,45.6,0,0,5.2,9.8 -2020-08-06T20:00,76.4,35,46.4,0,0,4.8,13.6 -2020-08-06T21:00,72.8,42,48.1,0,0,3.8,7.8 -2020-08-06T22:00,69.4,50,50,0,0,4,6.5 -2020-08-06T23:00,66.9,55,50.2,0,0,4.2,6.5 -2020-08-07T00:00,65.8,57,50.3,0,0,3.4,5.6 -2020-08-07T01:00,64.3,57,48.9,0,0,4,6.7 -2020-08-07T02:00,63.1,55,46.4,0,0,5.2,8.9 -2020-08-07T03:00,62.3,48,42.5,0,0,6.1,10.3 -2020-08-07T04:00,62.3,41,38,0,0,6.2,10.7 -2020-08-07T05:00,59.1,40,34.9,0,0,5.4,10.5 -2020-08-07T06:00,63.4,33,34,0,0,5.1,7.6 -2020-08-07T07:00,70.9,26,34.3,0,0,4.6,10.1 -2020-08-07T08:00,80.4,20,35.2,0,0,3.2,10.1 -2020-08-07T09:00,87.1,13,29.6,0,0,1.4,9.8 -2020-08-07T10:00,89.7,9,23.5,0,0,2.3,10.1 -2020-08-07T11:00,91.3,9,24.4,0,0,4.6,13.4 -2020-08-07T12:00,93.2,9,25.4,0,0,6.5,16.3 -2020-08-07T13:00,93.7,9,25.3,0,0,6.3,16.6 -2020-08-07T14:00,94.8,8,24.4,0,0,4.5,15.7 -2020-08-07T15:00,95.5,8,23.5,0,0,3.6,13.2 -2020-08-07T16:00,94.7,7,22.7,0,0,3.8,11.4 -2020-08-07T17:00,94.6,7,22.6,0,0,5.8,15.7 -2020-08-07T18:00,91.6,9,26,0,0,6.2,11.4 -2020-08-07T19:00,82,14,27.8,0,0,5.9,10.3 -2020-08-07T20:00,80.3,15,29.1,0,0,6.2,9.4 -2020-08-07T21:00,74.8,18,28.9,0,0,5.4,8.7 -2020-08-07T22:00,71,26,34.7,0,0,4.6,8.9 -2020-08-07T23:00,66.4,38,40.1,0,0,4.6,7.6 -2020-08-08T00:00,66.4,37,39.6,0,0,4.1,6.5 -2020-08-08T01:00,68.5,23,29.7,0,0,7,11.4 -2020-08-08T02:00,67.3,16,19.2,0,0,5.8,11.6 -2020-08-08T03:00,70.3,13,17.6,0,0,4.5,10.5 -2020-08-08T04:00,61.8,26,26.2,0,0,6.2,7.4 -2020-08-08T05:00,61.7,31,30.7,0,0,4.7,7.4 -2020-08-08T06:00,63.1,32,32.6,0,0,4.7,7.4 -2020-08-08T07:00,69.2,26,32.4,0,0,2.4,8.3 -2020-08-08T08:00,76.6,19,31.4,0,0,1.5,7.2 -2020-08-08T09:00,84.3,12,25.6,0,0,0.9,7.6 -2020-08-08T10:00,88,9,20.9,0,0,5.1,13.9 -2020-08-08T11:00,89.2,9,22.4,0,0,2.1,13.9 -2020-08-08T12:00,91,8,22.2,0,0,6.3,15.2 -2020-08-08T13:00,92.1,8,23.6,0,0,8.9,19.5 -2020-08-08T14:00,92.5,9,24.7,0,0,9,19.7 -2020-08-08T15:00,92.4,8,23.9,0,0,8.8,19.2 -2020-08-08T16:00,92.5,8,23.5,0,0,10.6,20.1 -2020-08-08T17:00,91.1,8,22.1,0,0,9.3,22.4 -2020-08-08T18:00,88.3,11,28,0,0,12.5,20.8 -2020-08-08T19:00,83,14,28.9,0,0,10.2,20.8 -2020-08-08T20:00,79.7,15,28.4,0,0,8.9,17 -2020-08-08T21:00,76.5,19,31.1,0,0,9.5,15.9 -2020-08-08T22:00,72.1,25,34.3,0,0,7.9,15.9 -2020-08-08T23:00,70,28,35.4,0,0,6.5,13.2 -2020-08-09T00:00,68.3,30,35.5,0,0,5.4,11 -2020-08-09T01:00,67.6,29,34.5,0,0,6.7,11.4 -2020-08-09T02:00,63.9,32,33.8,0,0,5.3,11 -2020-08-09T03:00,61.4,34,32.9,0,0,6.8,8.1 -2020-08-09T04:00,60.7,33,31.3,0,0,6.8,8.3 -2020-08-09T05:00,61.4,27,27.4,0,0,6.1,8.5 -2020-08-09T06:00,65,26,29.8,0,0,4.5,8.7 -2020-08-09T07:00,72.5,22,31.3,0,0,3.8,9.2 -2020-08-09T08:00,81.7,17,31.9,0,0,2.3,8.5 -2020-08-09T09:00,89.2,10,24.7,0,0,5.8,13 -2020-08-09T10:00,91.7,8,21.7,0,0,6.5,16.1 -2020-08-09T11:00,92.8,8,22.1,0,0,2.8,15.7 -2020-08-09T12:00,93.8,8,22.2,0,0,1,11.6 -2020-08-09T13:00,94.6,7,21.9,0,0,4.8,13.4 -2020-08-09T14:00,94.9,7,21.7,0,0,6.3,15.7 -2020-08-09T15:00,94.5,7,20.8,0,0,7.7,17 -2020-08-09T16:00,93.7,7,20.2,0,0,9.1,17.4 -2020-08-09T17:00,93.1,7,18.8,0,0,5.4,18.6 -2020-08-09T18:00,91,9,25.4,0,0,3.7,10.5 -2020-08-09T19:00,87.1,11,25.3,0,0,1.7,6.7 -2020-08-09T20:00,78,16,28.5,0,0,6.5,8.7 -2020-08-09T21:00,74.5,17,27.4,0,0,5.3,8.7 -2020-08-09T22:00,71.5,21,29.2,0,0,4.3,7.6 -2020-08-09T23:00,69.9,23,30,0,0,7.1,11.9 -2020-08-10T00:00,66.3,26,30.3,0,0,3.8,11.9 -2020-08-10T01:00,63.8,31,32.3,0,0,5,6.3 -2020-08-10T02:00,64.1,26,28.9,0,0,6.2,7.6 -2020-08-10T03:00,64.9,26,29.4,0,0,7.1,8.7 -2020-08-10T04:00,64.7,28,30.7,0,0,5.3,9.4 -2020-08-10T05:00,62.1,53,44.5,0,0,6.9,8.1 -2020-08-10T06:00,61.9,58,47.1,0,0,6.9,11.4 -2020-08-10T07:00,64,58,49.1,0,0,5.3,13.4 -2020-08-10T08:00,66.7,55,49.8,0,0,0.7,11.6 -2020-08-10T09:00,71.2,46,49.6,0,0,2.3,10.3 -2020-08-10T10:00,76.9,36,47.9,0,0,3.5,12.3 -2020-08-10T11:00,82.1,28,46.1,0,0,3.5,13.9 -2020-08-10T12:00,86.9,22,43.7,0,0,6.5,16.1 -2020-08-10T13:00,89.2,20,42.8,0,0,8.6,19.7 -2020-08-10T14:00,88.9,16,36.5,0,0,3.7,19.2 -2020-08-10T15:00,89.2,14,33.8,0,0,3.8,13.6 -2020-08-10T16:00,88.8,14,34.3,0,0,9.3,17.7 -2020-08-10T17:00,87.4,22,43.3,0,0,6.7,22.1 -2020-08-10T18:00,85,23,43.3,0,0,2.3,12.1 -2020-08-10T19:00,80.7,26,42.8,0,0,3.5,5.8 -2020-08-10T20:00,76.1,33,44.8,0,0,4.1,6.5 -2020-08-10T21:00,72.1,41,46.8,0,0,5.9,7.8 -2020-08-10T22:00,68,49,48.2,0,0,5.1,7.6 -2020-08-10T23:00,65.3,55,48.7,0,0,2.6,6.3 -2020-08-11T00:00,63.4,56,47.4,0,0,3.2,5.1 -2020-08-11T01:00,62.4,54,45.7,0,0,3.8,5.8 -2020-08-11T02:00,62.3,46,41.2,0,0,4.3,7.4 -2020-08-11T03:00,60.8,45,39.3,0,0,3.8,7.2 -2020-08-11T04:00,59.4,46,38.5,0,0,3,6.5 -2020-08-11T05:00,61.3,45,39.7,0,0,3,5.8 -2020-08-11T06:00,63.1,46,42,0,0,1.3,6 -2020-08-11T07:00,66.8,36,39,0,0,2.1,7.2 -2020-08-11T08:00,72.8,28,38,0,0,1.3,7.8 -2020-08-11T09:00,80.6,20,35.9,0,0,1.3,8.9 -2020-08-11T10:00,87.9,14,32.2,0,0,3.5,11 -2020-08-11T11:00,90.7,10,25.4,0,0,3.7,13.9 -2020-08-11T12:00,92.3,9,24.4,0,0,5.4,13.9 -2020-08-11T13:00,93.2,8,23.5,0,0,6.6,15.9 -2020-08-11T14:00,93.1,8,23,0,0,5.5,15.9 -2020-08-11T15:00,93.4,8,22.6,0,0,2.4,14.1 -2020-08-11T16:00,92.8,8,22.6,0,0,3.9,9.8 -2020-08-11T17:00,90.4,10,25.9,0,0,11.9,13.2 -2020-08-11T18:00,89,10,25.6,0,0,11.1,21.3 -2020-08-11T19:00,84.2,12,25.6,0,0,4.9,18.6 -2020-08-11T20:00,80.9,15,28.6,0,0,8.9,13.9 -2020-08-11T21:00,74.6,23,34.2,0,0,4.7,15 -2020-08-11T22:00,73.6,23,33.8,0,0,3.9,6 -2020-08-11T23:00,71.2,27,35.6,0,0,4.7,8.1 -2020-08-12T00:00,67.3,35,38.7,0,0,6.4,7.6 -2020-08-12T01:00,66,37,38.7,0,0,6,7.8 -2020-08-12T02:00,64.1,36,36.5,0,0,5.3,7.4 -2020-08-12T03:00,62.9,34,34.3,0,0,4,6.7 -2020-08-12T04:00,61.7,34,33.2,0,0,5.2,8.7 -2020-08-12T05:00,59.4,70,49.6,0,0,4.1,8.7 -2020-08-12T06:00,61.5,59,47.1,0,0,5.4,9.4 -2020-08-12T07:00,66.5,50,47.1,0,0,4.3,8.5 -2020-08-12T08:00,72.1,40,46.7,0,0,6.6,15.4 -2020-08-12T09:00,79.3,25,40.2,0,0,4.7,14.8 -2020-08-12T10:00,86.2,15,33.5,0,0,4,13.4 -2020-08-12T11:00,90.1,11,27.6,0,0,4.2,13.4 -2020-08-12T12:00,90.4,10,25.8,0,0,5.3,14.3 -2020-08-12T13:00,90.9,10,27.4,0,0,3,14.5 -2020-08-12T14:00,91.4,11,28.4,0,0,2.4,12.1 -2020-08-12T15:00,91.6,11,28.8,0,0,5.2,13.2 -2020-08-12T16:00,90.9,11,28.9,0,0,8.3,17 -2020-08-12T17:00,91,13,33.4,0,0,7.4,22.1 -2020-08-12T18:00,85.9,17,35.8,0,0,7.8,18.8 -2020-08-12T19:00,81.6,19,35.5,0,0,5.4,13 -2020-08-12T20:00,75.9,24,36.8,0,0,4.4,8.9 -2020-08-12T21:00,73,26,36.2,0,0,5.2,8.9 -2020-08-12T22:00,71.6,21,29.8,0,0,5.4,8.5 -2020-08-12T23:00,70.5,21,28.1,0,0,6.2,10.1 -2020-08-13T00:00,67.6,24,29.8,0,0,5.6,10.1 -2020-08-13T01:00,64,31,32.8,0,0,4.3,8.9 -2020-08-13T02:00,61.8,37,35.4,0,0,3.8,6 -2020-08-13T03:00,61.9,36,34.8,0,0,4.1,6.9 -2020-08-13T04:00,60,36,33.1,0,0,4,6.9 -2020-08-13T05:00,58.7,27,25.1,0,0,4.6,6.3 -2020-08-13T06:00,61.5,26,26.6,0,0,4.8,6.7 -2020-08-13T07:00,68.7,23,29.7,0,0,4.7,10.3 -2020-08-13T08:00,77.5,17,29,0,0,4.1,11 -2020-08-13T09:00,84.1,11,25,0,0,2.7,10.7 -2020-08-13T10:00,88.5,9,22.4,0,0,1.6,10.3 -2020-08-13T11:00,90.6,8,21.8,0,0,4,12.5 -2020-08-13T12:00,92.2,8,22.3,0,0,3.2,14.1 -2020-08-13T13:00,93.2,8,22.1,0,0,1.6,12.3 -2020-08-13T14:00,94.3,7,21.5,0,0,5.5,14.8 -2020-08-13T15:00,94.5,7,20.5,0,0,7.3,17.2 -2020-08-13T16:00,94.7,7,19.5,0,0,3.4,16.8 -2020-08-13T17:00,93.3,8,23.4,0,0,4.3,15.4 -2020-08-13T18:00,90.2,22,46.1,0,0,2.7,8.9 -2020-08-13T19:00,85.6,12,27.1,0,0,5.5,5.4 -2020-08-13T20:00,81.2,14,27,0,0,5.5,8.9 -2020-08-13T21:00,76.5,18,30.5,0,0,5.7,10.5 -2020-08-13T22:00,70.8,26,34.3,0,0,5.1,9.4 -2020-08-13T23:00,67.5,32,36.2,0,0,4.7,6.9 -2020-08-14T00:00,66.1,32,35.7,0,0,3.9,6.3 -2020-08-14T01:00,66.6,29,33.1,0,0,6.5,10.5 -2020-08-14T02:00,65.1,28,31.6,0,0,6.5,11.2 -2020-08-14T03:00,63.5,29,30.6,0,0,6.1,11.2 -2020-08-14T04:00,62.7,29,29.8,0,0,5.5,10.5 -2020-08-14T05:00,63.5,23,25.5,0,0,6.6,8.7 -2020-08-14T06:00,64.7,28,30.5,0,0,3.6,8.1 -2020-08-14T07:00,71.6,23,32.2,0,0,5.4,11 -2020-08-14T08:00,79.1,19,32.7,0,0,4,11 -2020-08-14T09:00,85.7,13,28.9,0,0,1.8,10.7 -2020-08-14T10:00,89.9,9,22.9,0,0,7.2,16.3 -2020-08-14T11:00,90.7,9,24.4,0,0,11.2,22.4 -2020-08-14T12:00,92.3,9,24.8,0,0,10.7,24.8 -2020-08-14T13:00,94,8,24.4,0,0,9,22.4 -2020-08-14T14:00,93.9,8,24.8,0,0,9.2,20.1 -2020-08-14T15:00,93.8,9,26.1,0,0,8.9,19.9 -2020-08-14T16:00,92.3,11,29.2,0,0,11.1,21.3 -2020-08-14T17:00,88.8,15,34.8,0,0,8.8,21.9 -2020-08-14T18:00,86.3,17,35.6,0,0,9.8,16.3 -2020-08-14T19:00,79.5,27,42.3,0,0,11.1,20.6 -2020-08-14T20:00,76.1,31,43.6,0,0,5.2,18.1 -2020-08-14T21:00,75.9,32,43.9,0,0,1.3,8.3 -2020-08-14T22:00,71.7,38,45.1,0,0,2.9,3.4 -2020-08-14T23:00,72.2,37,44.5,0,0,1.2,3.4 -2020-08-15T00:00,65.4,49,46,0,0,3.2,4.5 -2020-08-15T01:00,63.1,55,46.5,0,0,2.6,4.5 -2020-08-15T02:00,61.2,58,46.5,0,0,2.4,4.9 -2020-08-15T03:00,59.6,60,45.7,0,0,1.2,4.5 -2020-08-15T04:00,59.6,55,43.6,0,0,3.5,4.7 -2020-08-15T05:00,61.3,49,42,0,0,4.7,6.5 -2020-08-15T06:00,62.1,47,41.7,0,0,4.9,7.8 -2020-08-15T07:00,67.7,39,42.1,0,0,2.6,7.6 -2020-08-15T08:00,72.9,35,43.3,0,0,3.2,10.3 -2020-08-15T09:00,78.7,28,42.9,0,0,1.4,10.5 -2020-08-15T10:00,84.5,20,39,0,0,4.1,12.5 -2020-08-15T11:00,89.1,15,35.7,0,0,5.6,15 -2020-08-15T12:00,91.9,13,34.5,0,0,6.7,17 -2020-08-15T13:00,92.6,13,34.8,0,0,9,19.7 -2020-08-15T14:00,92.7,14,35.7,0,0,8.6,20.4 -2020-08-15T15:00,92.8,13,35.2,0,0,8.6,19.2 -2020-08-15T16:00,91.7,14,35,0,0,9.8,19.2 -2020-08-15T17:00,89.5,15,35.1,0,0,11.4,19.7 -2020-08-15T18:00,87.7,15,35.2,0,0,6.8,20.4 -2020-08-15T19:00,81.9,20,37,0,0,5.5,11.2 -2020-08-15T20:00,82.7,19,35.7,0,0,3.2,6.7 -2020-08-15T21:00,80.8,20,36.1,0,0,1.1,3.8 -2020-08-15T22:00,74.1,26,37.1,0,0,4.1,5.1 -2020-08-15T23:00,68.8,32,37.8,0,0,3.3,5.1 -2020-08-16T00:00,65.9,38,39.5,0,0,4.3,5.6 -2020-08-16T01:00,64,42,40.2,0,0,4.7,6 -2020-08-16T02:00,63.6,40,38.7,0,0,3.7,6 -2020-08-16T03:00,61.6,43,39,0,0,3.8,5.8 -2020-08-16T04:00,61,45,39.7,0,0,3.8,5.8 -2020-08-16T05:00,61.9,47,41.6,0,0,3,6.3 -2020-08-16T06:00,63.9,46,42.5,0,0,5.1,7.4 -2020-08-16T07:00,70.6,38,43.4,0,0,3,8.3 -2020-08-16T08:00,78.4,28,42.4,0,0,2.6,8.9 -2020-08-16T09:00,84.2,22,41.5,0,0,2.1,9.8 -2020-08-16T10:00,88.5,18,39.4,0,0,1.6,9.6 -2020-08-16T11:00,91.9,13,33.4,0,0,6.2,15.4 -2020-08-16T12:00,93.4,10,28.9,0,0,7.8,18.8 -2020-08-16T13:00,94.4,10,28.8,0,0,7.3,18.6 -2020-08-16T14:00,94.8,10,29.3,0,0,8.1,18.6 -2020-08-16T15:00,94.4,11,31.1,0,0,9.9,19.9 -2020-08-16T16:00,87.4,23,45.5,0,0,19.1,33.6 -2020-08-16T17:00,93,12,33.5,0,0,10.7,33.8 -2020-08-16T18:00,89.9,14,35,0,0,12.7,21.5 -2020-08-16T19:00,85.1,18,37,0,0,10.5,21.7 -2020-08-16T20:00,80.1,24,40.1,0,0,4.2,17 -2020-08-16T21:00,78.5,26,41.3,0,0,4.7,6.7 -2020-08-16T22:00,77,33,45.6,0,0,4.7,10.1 -2020-08-16T23:00,72.7,40,46.7,0,0,4.1,7.4 -2020-08-17T00:00,70.1,41,45,0,0,3.6,5.6 -2020-08-17T01:00,69,38,42.4,0,0,4.2,6.3 -2020-08-17T02:00,67.5,48,46.9,0,0,5.9,7.4 -2020-08-17T03:00,66.7,50,47.8,0,0,4.7,7.6 -2020-08-17T04:00,64,52,45.8,0,0,3.2,5.6 -2020-08-17T05:00,63.6,53,46,0,0,2.7,4.3 -2020-08-17T06:00,64.5,50,45.4,0,0,2.4,5.1 -2020-08-17T07:00,70.1,46,48.2,0,0,1.2,5.8 -2020-08-17T08:00,74,44,50.8,0,0,2,8.7 -2020-08-17T09:00,78.9,37,50.5,0,0,2.8,11 -2020-08-17T10:00,83.6,31,49.7,0,0,3.2,11.9 -2020-08-17T11:00,88.1,25,48.3,0,0,3.7,13.2 -2020-08-17T12:00,91.9,19,43.7,0,0,3.8,13.4 -2020-08-17T13:00,93.8,16,40.2,0,0,10.7,20.8 -2020-08-17T14:00,91.9,13,34.3,0,0,9.7,24.8 -2020-08-17T15:00,93.7,11,32.1,0,0,8.8,19.9 -2020-08-17T16:00,92.8,14,36.3,0,0,7.4,18.8 -2020-08-17T17:00,92.8,15,38.6,0,0,9.8,15.9 -2020-08-17T18:00,89.4,33,56.3,0,0,2.2,17.4 -2020-08-17T19:00,85.3,20,40,0,0,5,9.6 -2020-08-17T20:00,80.9,25,41.6,0,0,4.5,8.5 -2020-08-17T21:00,77.5,28,41.5,0,0,4.4,7.6 -2020-08-17T22:00,73.9,34,43.9,0,0,6.5,7.8 -2020-08-17T23:00,71.7,36,43.3,0,0,4.6,7.6 -2020-08-18T00:00,69.9,39,43.8,0,0,4,6.3 -2020-08-18T01:00,67.7,42,43.9,0,0,3.7,6.9 -2020-08-18T02:00,65.7,44,43.4,0,0,4,6.3 -2020-08-18T03:00,64.6,45,43,0,0,3.5,6 -2020-08-18T04:00,63.7,46,42.6,0,0,3.8,6 -2020-08-18T05:00,62.8,49,43.3,0,0,5.4,6 -2020-08-18T06:00,65.6,43,42.4,0,0,3.9,6.7 -2020-08-18T07:00,72.1,35,42.5,0,0,3.5,8.7 -2020-08-18T08:00,80.7,26,42.4,0,0,1.1,8.7 -2020-08-18T09:00,86.8,20,40.9,0,0,3.4,10.5 -2020-08-18T10:00,91.3,16,39.6,0,0,3.2,11.9 -2020-08-18T11:00,93.9,13,35.8,0,0,2.8,13 -2020-08-18T12:00,95.2,12,33.8,0,0,4.5,14.5 -2020-08-18T13:00,96.3,11,32.1,0,0,8.5,19 -2020-08-18T14:00,96.6,11,33.3,0,0,10.4,21.3 -2020-08-18T15:00,95.6,11,33.4,0,0,9.7,21.5 -2020-08-18T16:00,93.7,13,34.5,0,0,12,21 -2020-08-18T17:00,93.7,11,30.7,0,0,8.2,24.8 -2020-08-18T18:00,90.1,14,33.6,0,0,10.9,18.3 -2020-08-18T19:00,86.5,16,35.2,0,0,5.4,18.3 -2020-08-18T20:00,81.5,19,35.5,0,0,3.2,8.9 -2020-08-18T21:00,80.2,21,36.2,0,0,4.5,5.4 -2020-08-18T22:00,79.2,22,36.6,0,0,1.8,6 -2020-08-18T23:00,73.1,29,38.6,0,0,3.2,3.6 -2020-08-19T00:00,72.3,30,38.9,0,0,4.8,7.4 -2020-08-19T01:00,69,35,40,0,0,6.4,7.8 -2020-08-19T02:00,68.4,35,39.6,0,0,5,7.8 -2020-08-19T03:00,66.1,37,39.1,0,0,4.6,7.2 -2020-08-19T04:00,64.7,38,38.7,0,0,3.7,5.6 -2020-08-19T05:00,65.5,45,43.3,0,0,4.3,5.8 -2020-08-19T06:00,66.9,42,43.1,0,0,4.3,7.4 -2020-08-19T07:00,71.7,35,42.6,0,0,0.7,7.6 -2020-08-19T08:00,75.6,32,43.4,0,0,2.4,8.9 -2020-08-19T09:00,80.1,28,43.7,0,0,2.5,10.5 -2020-08-19T10:00,85,23,43,0,0,1.6,10.7 -2020-08-19T11:00,89.2,19,41.7,0,0,1.5,10.5 -2020-08-19T12:00,92.3,16,39.6,0,0,1.6,14.8 -2020-08-19T13:00,93.5,14,37.5,0,0,3.2,13 -2020-08-19T14:00,93.1,14,37.7,0,0,12.8,23.5 -2020-08-19T15:00,91.4,14,36.1,0,0,7.1,25.1 -2020-08-19T16:00,88.4,20,42.8,0,0,17.7,32 -2020-08-19T17:00,87,19,39.1,0,0,11.8,31.3 -2020-08-19T18:00,85.5,21,41.4,0,0,2.1,20.1 -2020-08-19T19:00,82.7,21,38.9,0,0,6.8,11.4 -2020-08-19T20:00,79.4,26,41.4,0,0,4.9,11.6 -2020-08-19T21:00,78.2,32,45.9,0,0,3.7,7.8 -2020-08-19T22:00,73.5,38,46.3,0,0,2.9,4.3 -2020-08-19T23:00,72.8,40,47.2,0,0,4.2,7.6 -2020-08-20T00:00,70.3,48,49.9,0,0,5.1,7.6 -2020-08-20T01:00,69,53,51.1,0,0,2.9,9.2 -2020-08-20T02:00,67.7,54,50.4,0,0,1.4,3.6 -2020-08-20T03:00,64.4,53,47.1,0,0,2.7,5.4 -2020-08-20T04:00,62.2,52,44.3,0,0,2.1,5.1 -2020-08-20T05:00,60.9,60,47,0,0,2.2,3.6 -2020-08-20T06:00,62.3,58,47.6,0,0,2.3,3.6 -2020-08-20T07:00,67,51,48.5,0,0,2.1,7.2 -2020-08-20T08:00,71.8,44,48.5,0,0,4.1,11.6 -2020-08-20T09:00,79.2,31,45.8,0,0,3.1,11.6 -2020-08-20T10:00,86.6,22,42.7,0,0,7.5,16.8 -2020-08-20T11:00,90.6,19,42.8,0,0,7.9,21.5 -2020-08-20T12:00,91.1,19,43.1,0,0,8.1,20.8 -2020-08-20T13:00,91.9,18,41.8,0,0,6.7,19.5 -2020-08-20T14:00,90.8,18,41.2,0,0,13.3,27.7 -2020-08-20T15:00,90.1,17,39,0,0,10.6,25.7 -2020-08-20T16:00,89.2,18,40,0,0,9.1,21.9 -2020-08-20T17:00,85.5,23,43.9,0,0,6.5,18.3 -2020-08-20T18:00,84.3,25,44.2,0,0,5.8,13.9 -2020-08-20T19:00,78.6,31,45.8,0,0,4,10.5 -2020-08-20T20:00,79.1,31,45.7,0,0,5,8.5 -2020-08-20T21:00,76.4,37,48.2,0,0,12.3,19.9 -2020-08-20T22:00,75.1,38,48.1,0,0,11.5,20.4 -2020-08-20T23:00,73.7,41,48.7,0,0,3.3,19.2 -2020-08-21T00:00,71.4,44,48.5,0,0,5.9,9.8 -2020-08-21T01:00,68.9,47,47.9,0,0,5.9,10.1 -2020-08-21T02:00,67.5,49,47.8,0,0,6.5,10.7 -2020-08-21T03:00,66.6,50,47.1,0,0,5.2,10.7 -2020-08-21T04:00,65.4,49,46,0,0,4.5,8.9 -2020-08-21T05:00,63,51,44.4,0,0,4.3,7.4 -2020-08-21T06:00,65.6,48,45.1,0,0,2.9,6.9 -2020-08-21T07:00,72.5,36,43.8,0,0,2.1,6.9 -2020-08-21T08:00,79.5,27,42.4,0,0,2.2,8.3 -2020-08-21T09:00,84.9,20,40,0,0,3,10.5 -2020-08-21T10:00,88.3,17,38.7,0,0,5.8,14.5 -2020-08-21T11:00,90.2,16,37.5,0,0,6.6,17.2 -2020-08-21T12:00,91.6,15,36.7,0,0,5.9,17.2 -2020-08-21T13:00,93.3,13,35.2,0,0,5.8,16.6 -2020-08-21T14:00,94.1,12,34.3,0,0,7.8,18.1 -2020-08-21T15:00,93.8,12,33.4,0,0,7.9,18.8 -2020-08-21T16:00,93.4,12,32.3,0,0,6.5,17.4 -2020-08-21T17:00,90.8,16,39.2,0,0,5.9,14.8 -2020-08-21T18:00,88.9,18,40.2,0,0,5.9,14.3 -2020-08-21T19:00,86.8,19,40.3,0,0,1.4,9.8 -2020-08-21T20:00,78.9,27,41.9,0,0,5.2,6.7 -2020-08-21T21:00,76.7,28,41.5,0,0,5.6,8.3 -2020-08-21T22:00,73,34,42.7,0,0,4.2,10.3 -2020-08-21T23:00,69.8,39,43.7,0,0,4.5,6.9 -2020-08-22T00:00,67.7,42,43.6,0,0,4.7,6 -2020-08-22T01:00,67,40,41.8,0,0,4,6.3 -2020-08-22T02:00,66,37,38.8,0,0,4.9,8.1 -2020-08-22T03:00,64,34,35.2,0,0,4.7,8.1 -2020-08-22T04:00,61.8,34,33.4,0,0,5.5,6.9 -2020-08-22T05:00,63.4,30,31.6,0,0,3.8,7.4 -2020-08-22T06:00,64.7,28,30.9,0,0,4.3,7.2 -2020-08-22T07:00,72.3,22,31,0,0,3,7.8 -2020-08-22T08:00,81.7,16,31.6,0,0,2.2,8.1 -2020-08-22T09:00,89.5,11,28.9,0,0,1.4,8.5 -2020-08-22T10:00,93,9,26.1,0,0,5.6,13.9 -2020-08-22T11:00,94.8,8,25.4,0,0,7.5,17.4 -2020-08-22T12:00,96.1,8,25.7,0,0,8.8,19.7 -2020-08-22T13:00,97,8,26.2,0,0,9.2,21.3 -2020-08-22T14:00,97.6,7,23.8,0,0,10.5,21.5 -2020-08-22T15:00,97,7,21.6,0,0,9.8,21.9 -2020-08-22T16:00,96.3,6,20.4,0,0,9.7,20.1 -2020-08-22T17:00,93.9,8,22.5,0,0,7.1,19.2 -2020-08-22T18:00,90.6,11,29.3,0,0,3.5,13 -2020-08-22T19:00,87.4,10,24.5,0,0,2.3,6.9 -2020-08-22T20:00,80.6,16,29.9,0,0,3.8,5.1 -2020-08-22T21:00,75.1,18,29,0,0,4.1,6.3 -2020-08-22T22:00,72.1,21,30.4,0,0,3.8,6 -2020-08-22T23:00,70.3,23,31,0,0,3.4,5.6 -2020-08-23T00:00,67.5,28,33.5,0,0,4.2,6 -2020-08-23T01:00,65.8,32,34.8,0,0,5,6 -2020-08-23T02:00,68.4,28,33.6,0,0,2.5,6.3 -2020-08-23T03:00,63.9,33,34.3,0,0,4.3,4.9 -2020-08-23T04:00,61.8,35,33.8,0,0,4.3,5.8 -2020-08-23T05:00,61.3,38,35.4,0,0,5,5.4 -2020-08-23T06:00,63.1,37,36.1,0,0,3.6,7.4 -2020-08-23T07:00,70.2,28,35.8,0,0,3.9,9.4 -2020-08-23T08:00,77.6,21,34.8,0,0,2,8.7 -2020-08-23T09:00,85.1,14,31.3,0,0,2.8,10.3 -2020-08-23T10:00,91.1,11,29.8,0,0,3.8,11.9 -2020-08-23T11:00,94.7,10,29.8,0,0,4.2,13.2 -2020-08-23T12:00,96.7,9,28.9,0,0,4.8,14.1 -2020-08-23T13:00,97.2,9,28.5,0,0,5.9,15.2 -2020-08-23T14:00,97.3,9,28.7,0,0,7.4,17.2 -2020-08-23T15:00,96.9,9,28.4,0,0,6.8,17.2 -2020-08-23T16:00,96.3,9,28,0,0,6.3,15.9 -2020-08-23T17:00,92.8,12,32,0,0,13.8,13.9 -2020-08-23T18:00,90.6,12,30.8,0,0,4.8,22.8 -2020-08-23T19:00,86,16,35.2,0,0,8.1,13.6 -2020-08-23T20:00,79.7,22,37.7,0,0,4.5,13.4 -2020-08-23T21:00,77.3,26,39.7,0,0,5.4,7.2 -2020-08-23T22:00,74.8,28,39.4,0,0,5.2,8.7 -2020-08-23T23:00,71.6,30,38.4,0,0,4.1,8.5 -2020-08-24T00:00,69.4,34,39.9,0,0,5.1,8.7 -2020-08-24T01:00,68.1,36,39.9,0,0,5.7,9.4 -2020-08-24T02:00,67.4,33,37.2,0,0,4.1,10.3 -2020-08-24T03:00,66.7,36,39.1,0,0,4,6.5 -2020-08-24T04:00,64.4,41,40.4,0,0,3.8,6.7 -2020-08-24T05:00,65.8,38,39.1,0,0,4,5.1 -2020-08-24T06:00,66.5,35,37.9,0,0,4.6,7.8 -2020-08-24T07:00,73,28,38.2,0,0,3.6,8.9 -2020-08-24T08:00,81.9,21,38,0,0,2.3,8.5 -2020-08-24T09:00,87.1,16,35.9,0,0,0.9,8.5 -2020-08-24T10:00,90.4,14,35.4,0,0,4,12.3 -2020-08-24T11:00,93.4,13,34.9,0,0,6,15.4 -2020-08-24T12:00,95.5,12,34.7,0,0,5.7,16.3 -2020-08-24T13:00,96.4,11,33.9,0,0,5.9,15.7 -2020-08-24T14:00,97.3,11,33.5,0,0,7.1,16.3 -2020-08-24T15:00,94.2,13,36.8,0,0,11.8,23.3 -2020-08-24T16:00,93.7,13,35.3,0,0,8.4,23.3 -2020-08-24T17:00,90.7,16,37.8,0,0,18.3,16.8 -2020-08-24T18:00,88.5,19,40.6,0,0,13.6,31.1 -2020-08-24T19:00,83.8,24,43.2,0,0,10.3,22.8 -2020-08-24T20:00,81.6,26,43.3,0,0,7.1,17.2 -2020-08-24T21:00,79.2,28,43.6,0,0,4,11.9 -2020-08-24T22:00,76.4,32,44.6,0,0,6,10.3 -2020-08-24T23:00,73.3,36,44.7,0,0,6.4,11.2 -2020-08-25T00:00,71.6,37,44.2,0,0,4.8,10.7 -2020-08-25T01:00,69.4,40,43.9,0,0,3.6,8.3 -2020-08-25T02:00,69.7,38,43.1,0,0,5.3,8.9 -2020-08-25T03:00,68.6,39,42.6,0,0,4.5,9.2 -2020-08-25T04:00,67.4,40,41.9,0,0,4.3,7.6 -2020-08-25T05:00,66.5,40,41.5,0,0,5.4,6.7 -2020-08-25T06:00,68.8,38,42.4,0,0,3.8,6.9 -2020-08-25T07:00,74.6,30,41,0,0,3.1,7.4 -2020-08-25T08:00,81.7,24,41.3,0,0,1.9,7.8 -2020-08-25T09:00,87.9,18,39.4,0,0,2.5,9.4 -2020-08-25T10:00,91,16,38.5,0,0,3.8,11.9 -2020-08-25T11:00,94.5,14,37.5,0,0,3.9,13.6 -2020-08-25T12:00,95.6,13,37.4,0,0,3.3,13.4 -2020-08-25T13:00,95.8,13,37.6,0,0,3.1,12.1 -2020-08-25T14:00,96,13,37.5,0,0,4.9,13.2 -2020-08-25T15:00,96.1,13,37.4,0,0,5.6,15.4 -2020-08-25T16:00,93.2,17,41.2,0,0,15.6,25.7 -2020-08-25T17:00,90.7,18,41.3,0,0,4.5,31.5 -2020-08-25T18:00,86.8,25,46.4,0,0,13.2,21.7 -2020-08-25T19:00,81.8,31,47.9,0,0,14.2,27.5 -2020-08-25T20:00,80.9,32,48.1,0,0,7.9,23.3 -2020-08-25T21:00,78.1,36,48.8,0,0,5.8,13.2 -2020-08-25T22:00,75.6,40,49.7,0,0,6.1,10.5 -2020-08-25T23:00,73.4,44,50.2,0,0,4.9,10.3 -2020-08-26T00:00,70.8,48,50.2,0,0,3.6,8.1 -2020-08-26T01:00,69.5,47,48.6,0,0,3.9,6.3 -2020-08-26T02:00,69.1,45,47.1,0,0,4.3,7.4 -2020-08-26T03:00,67.9,47,46.7,0,0,3.7,7.2 -2020-08-26T04:00,66.9,46,45.7,0,0,3.8,5.8 -2020-08-26T05:00,72.9,35,43.3,0,0,4.4,6.5 -2020-08-26T06:00,73.3,33,42.8,0,0,1.6,5.6 -2020-08-26T07:00,75.3,32,43.5,0,0,2,6 -2020-08-26T08:00,78.9,29,43.6,0,0,2.8,9.6 -2020-08-26T09:00,84,24,43.3,0,0,1.4,9.2 -2020-08-26T10:00,87.9,21,42.6,0,0,6.9,15.9 -2020-08-26T11:00,88.7,21,43.3,0,0,10.1,21.7 -2020-08-26T12:00,88.7,20,42.6,0,0,3.8,21 -2020-08-26T13:00,91.6,18,41.7,0,0,3.5,13.2 -2020-08-26T14:00,92.8,17,41.7,0,0,5.4,14.5 -2020-08-26T15:00,92.5,18,42.2,0,0,6.7,15.2 -2020-08-26T16:00,90.5,19,42.4,0,0,10.8,19.7 -2020-08-26T17:00,80.5,35,50.5,0,0,6.6,21 -2020-08-26T18:00,76.3,50,56.2,0.028,0,6.3,11.6 -2020-08-26T19:00,70.6,75,62.2,0.035,0,11.7,18.8 -2020-08-26T20:00,69.9,72,60.3,0.008,0,15,24.6 -2020-08-26T21:00,69.7,67,58.4,0.004,0,11.6,24.6 -2020-08-26T22:00,69.6,66,57.7,0,0,5.9,18.6 -2020-08-26T23:00,70,57,54.2,0,0,4.6,9.2 -2020-08-27T00:00,69.4,55,52.3,0,0,5.8,9.8 -2020-08-27T01:00,67.3,57,51.6,0,0,5.9,9.8 -2020-08-27T02:00,66.2,59,51.4,0,0,5,9.8 -2020-08-27T03:00,64.3,64,51.9,0,0,3.9,8.3 -2020-08-27T04:00,63.2,64,51,0,0,3.5,6.5 -2020-08-27T05:00,61.9,74,53.6,0,0,3.9,5.6 -2020-08-27T06:00,64.1,58,48.8,0,0,4.1,6 -2020-08-27T07:00,70.4,41,45.6,0,0,2.4,8.7 -2020-08-27T08:00,77.1,31,44.3,0,0,3.5,9.8 -2020-08-27T09:00,82,26,43.9,0,0,4.1,12.3 -2020-08-27T10:00,86.1,22,43.3,0,0,4.4,13.4 -2020-08-27T11:00,89.7,19,41.6,0,0,3.8,13.4 -2020-08-27T12:00,92.4,16,39.6,0,0,5.6,15.4 -2020-08-27T13:00,94.3,14,38.1,0,0,6.9,17 -2020-08-27T14:00,94.6,13,36.3,0,0,6.8,17.4 -2020-08-27T15:00,94.5,12,34.8,0,0,6,16.8 -2020-08-27T16:00,94,13,34.8,0,0,6.3,14.8 -2020-08-27T17:00,88.4,20,42.7,0,0,15.4,14.8 -2020-08-27T18:00,85.6,24,44.7,0,0,10,26.6 -2020-08-27T19:00,82.1,26,44,0,0,6.7,16.6 -2020-08-27T20:00,79.3,29,44.1,0,0,6.1,11.9 -2020-08-27T21:00,76.1,34,45.9,0,0,4,10.3 -2020-08-27T22:00,73.2,39,46.6,0,0,3.4,6.7 -2020-08-27T23:00,71.2,42,47.1,0,0,3.7,5.8 -2020-08-28T00:00,69.8,44,46.7,0,0,5.5,9.4 -2020-08-28T01:00,68.3,45,46.1,0,0,4.4,9.4 -2020-08-28T02:00,67.2,45,45,0,0,4.3,7.6 -2020-08-28T03:00,65.8,44,43.5,0,0,3.7,7.2 -2020-08-28T04:00,64.5,45,42.8,0,0,4.6,5.8 -2020-08-28T05:00,65.4,53,47.7,0,0,8.8,17.9 -2020-08-28T06:00,65.5,56,49.4,0,0,5,14.5 -2020-08-28T07:00,68.9,49,49.2,0,0,5,9.8 -2020-08-28T08:00,71.8,46,50.1,0,0,10.8,20.6 -2020-08-28T09:00,74.1,43,50.2,0,0,9.9,21 -2020-08-28T10:00,76.7,38,49.6,0,0,9.5,20.6 -2020-08-28T11:00,78.4,36,49,0,0,9,20.4 -2020-08-28T12:00,79.9,33,48.3,0,0,9.6,21 -2020-08-28T13:00,80.6,30,46.6,0,0,7.9,21.3 -2020-08-28T14:00,81.1,29,45.6,0,0,6.5,18.1 -2020-08-28T15:00,80.4,29,45.1,0,0,4.5,15.9 -2020-08-28T16:00,78.4,33,46.5,0.004,0,4.6,12.3 -2020-08-28T17:00,67.3,78,60.1,0.008,0,5.4,14.5 -2020-08-28T18:00,65.9,82,60.2,0.067,0,4.5,9.4 -2020-08-28T19:00,64.6,88,61.1,0.067,0,7.7,11.9 -2020-08-28T20:00,64,89,60.8,0.02,0,8.5,15.4 -2020-08-28T21:00,63.2,89,59.9,0,0,7.4,13.6 -2020-08-28T22:00,62.6,87,58.7,0,0,7.4,12.3 -2020-08-28T23:00,63.1,83,57.8,0,0,3.9,12.1 -2020-08-29T00:00,62.6,81,56.8,0,0,3.6,6.7 -2020-08-29T01:00,60.3,93,58.2,0,0,3.8,6 -2020-08-29T02:00,60,96,58.9,0,0,4.9,8.7 -2020-08-29T03:00,58.5,98,57.8,0,0,3.9,8.3 -2020-08-29T04:00,58.8,99,58.5,0,0,1.9,5.1 -2020-08-29T05:00,61.5,89,58.4,0,0,1.5,6 -2020-08-29T06:00,61.6,87,57.8,0,0,0.9,4.5 -2020-08-29T07:00,62.6,87,58.6,0,0,1.8,5.8 -2020-08-29T08:00,64.4,79,57.7,0,0,0.9,6.5 -2020-08-29T09:00,67.1,67,55.9,0,0,1.6,8.7 -2020-08-29T10:00,70.3,56,54,0,0,2.1,9.4 -2020-08-29T11:00,71.9,49,51.9,0,0,3.5,10.7 -2020-08-29T12:00,74.8,41,49.7,0,0,2.7,10.7 -2020-08-29T13:00,80.3,32,47.8,0,0,3.2,10.7 -2020-08-29T14:00,79.7,30,46,0,0,1.8,14.8 -2020-08-29T15:00,80.9,26,43.3,0,0,6,12.8 -2020-08-29T16:00,81.7,26,43,0,0,7.2,16.8 -2020-08-29T17:00,77.4,31,44.1,0,0,9.2,15.9 -2020-08-29T18:00,74.1,36,45.4,0,0,7.5,16.8 -2020-08-29T19:00,67.5,47,46.4,0,0,5.5,12.1 -2020-08-29T20:00,69.2,46,47.8,0,0,11.2,15.7 -2020-08-29T21:00,64.7,62,51.5,0,0,3.8,19.2 -2020-08-29T22:00,65.2,62,52.1,0,0,3,5.4 -2020-08-29T23:00,61.5,69,51.3,0,0,3.8,6 -2020-08-30T00:00,59.6,71,50.2,0,0,4,6.5 -2020-08-30T01:00,58.7,71,49.2,0,0,3.7,6.3 -2020-08-30T02:00,59,67,48,0,0,4.6,7.4 -2020-08-30T03:00,59.5,58,44.5,0,0,5.2,8.1 -2020-08-30T04:00,60.6,49,41.5,0,0,5.3,9.8 -2020-08-30T05:00,60.6,45,39,0,0,5.9,8.7 -2020-08-30T06:00,59.6,48,39.7,0,0,4.5,9.6 -2020-08-30T07:00,65.4,41,41,0,0,1.8,7.6 -2020-08-30T08:00,74.4,30,40.6,0,0,3.8,9.8 -2020-08-30T09:00,80.5,20,35.8,0,0,1.2,11 -2020-08-30T10:00,82.4,19,35.6,0,0,1,10.1 -2020-08-30T11:00,85.5,17,35.2,0,0,2.1,14.8 -2020-08-30T12:00,89.5,11,28.8,0,0,9.9,20.1 -2020-08-30T13:00,88.3,12,30.2,0,0,13.1,27.3 -2020-08-30T14:00,88.4,13,31,0,0,7.7,25.9 -2020-08-30T15:00,88.9,13,30.9,0,0,6.2,18.3 -2020-08-30T16:00,88.7,12,29.7,0,0,8.5,17.7 -2020-08-30T17:00,83.5,17,34.8,0,0,13.1,21.5 -2020-08-30T18:00,81.8,20,36.7,0,0,7.6,22.8 -2020-08-30T19:00,74.2,28,39.2,0,0,3.8,13 -2020-08-30T20:00,70.9,35,42,0,0,6.4,9.8 -2020-08-30T21:00,67.9,41,43.5,0,0,6.3,11.2 -2020-08-30T22:00,67.4,38,40.6,0,0,21,31.5 -2020-08-30T23:00,64.5,35,35.9,0,0,14.8,35.3 -2020-08-31T00:00,61.9,38,36,0,0,12.6,24.8 -2020-08-31T01:00,58.4,43,36.2,0,0,11.9,23.3 -2020-08-31T02:00,56.3,48,36.7,0,0,9,19.9 -2020-08-31T03:00,54.5,51,36.9,0,0,5.6,15 -2020-08-31T04:00,52.4,56,37.4,0,0,3.4,9.4 -2020-08-31T05:00,51.6,63,39.3,0,0,2.1,4.5 -2020-08-31T06:00,52.1,62,39.7,0,0,2,4.3 -2020-08-31T07:00,54.6,55,38.7,0,0,6.1,12.1 -2020-08-31T08:00,58.1,48,38.8,0,0,5.6,14.3 -2020-08-31T09:00,63.1,41,38.8,0,0,4.9,14.1 -2020-08-31T10:00,67.7,35,39.1,0,0,0.6,14.1 -2020-08-31T11:00,73,30,39.4,0,0,4.1,13.4 -2020-08-31T12:00,77.3,26,39.4,0,0,6,17.2 -2020-08-31T13:00,79.8,24,39.8,0,0,3.3,17.9 -2020-08-31T14:00,81.5,23,39.7,0,0,4,16.1 -2020-08-31T15:00,81.2,23,40.2,0,0,3.1,13.2 -2020-08-31T16:00,79.1,27,42.2,0,0,4.3,18.6 -2020-08-31T17:00,76.1,26,38.6,0,0,7.6,11.4 -2020-08-31T18:00,74.1,29,39.8,0,0,8.8,14.5 -2020-08-31T19:00,69.3,40,43.8,0,0,6.9,16.6 -2020-08-31T20:00,67.1,42,43.3,0,0,6.5,11.2 -2020-08-31T21:00,60.8,64,48.6,0.031,0,15,30 -2020-08-31T22:00,60.2,66,48.9,0.008,0,8.3,23.9 -2020-08-31T23:00,59.2,70,49.4,0,0,1,13.2 -2020-09-01T00:00,58.6,77,51.4,0.004,0,5.4,7.8 -2020-09-01T01:00,57.8,76,50.5,0,0,5.2,10.1 -2020-09-01T02:00,56,80,50.1,0,0,4.1,8.5 -2020-09-01T03:00,57,77,49.9,0,0,2.3,7.6 -2020-09-01T04:00,56.9,76,49.5,0,0,2.1,3.8 -2020-09-01T05:00,54.7,87,50.9,0,0,3.7,4.7 -2020-09-01T06:00,55.9,79,49.5,0,0,1.9,4.9 -2020-09-01T07:00,58.2,69,47.9,0,0,2.2,6.7 -2020-09-01T08:00,61.2,57,45.8,0,0,3.4,10.1 -2020-09-01T09:00,65.6,45,43.8,0,0,6.9,15.7 -2020-09-01T10:00,71,36,42.9,0,0,7.9,18.6 -2020-09-01T11:00,74.8,30,41.3,0,0,4.2,18.6 -2020-09-01T12:00,77.7,25,39.3,0,0,3.1,14.3 -2020-09-01T13:00,80,22,37.4,0,0,6.2,16.3 -2020-09-01T14:00,81.2,18,33.4,0,0,8.5,19.7 -2020-09-01T15:00,81.8,18,33.7,0,0,6,19.5 -2020-09-01T16:00,81.7,18,34.7,0,0,7.6,16.1 -2020-09-01T17:00,79.6,20,34.6,0,0,6.2,18.3 -2020-09-01T18:00,77.1,22,35.7,0,0,5.7,13 -2020-09-01T19:00,72.1,28,37.2,0,0,4.4,9.2 -2020-09-01T20:00,72.4,27,36.9,0,0,1.8,5.4 -2020-09-01T21:00,65.4,38,39.4,0,0,3.3,5.1 -2020-09-01T22:00,62.4,46,41.5,0,0,4.1,6.5 -2020-09-01T23:00,60.9,45,39.6,0,0,4.8,8.3 -2020-09-02T00:00,59.5,42,36.6,0,0,3.6,8.3 -2020-09-02T01:00,58.5,42,35.5,0,0,4.3,6.9 -2020-09-02T02:00,57.1,42,34.3,0,0,3.8,6.9 -2020-09-02T03:00,55.9,46,35.5,0,0,3.8,6.7 -2020-09-02T04:00,54.8,48,35.2,0,0,4.5,6.5 -2020-09-02T05:00,55.9,38,30.9,0,0,4,5.6 -2020-09-02T06:00,54.9,40,31.1,0,0,3.1,5.8 -2020-09-02T07:00,60.5,32,30.8,0,0,2.4,7.2 -2020-09-02T08:00,65.2,29,32.2,0,0,1.3,7.4 -2020-09-02T09:00,70.2,25,32.4,0,0,2.8,10.7 -2020-09-02T10:00,76.2,19,31.3,0,0,3.4,12.1 -2020-09-02T11:00,81.5,16,31.7,0,0,4.3,13.6 -2020-09-02T12:00,85.2,14,31.1,0,0,4.1,14.1 -2020-09-02T13:00,87.8,13,30.7,0,0,4.3,14.1 -2020-09-02T14:00,89.2,12,29.8,0,0,5.1,14.5 -2020-09-02T15:00,89.6,11,29.2,0,0,5.6,14.8 -2020-09-02T16:00,89.5,11,28.6,0,0,5,14.5 -2020-09-02T17:00,89,12,28.9,0,0,4.4,12.3 -2020-09-02T18:00,83.9,17,34.2,0,0,4,9.2 -2020-09-02T19:00,77.4,19,32,0,0,5,5.6 -2020-09-02T20:00,74.8,23,34.5,0,0,6.2,9.6 -2020-09-02T21:00,71.2,27,35.4,0,0,7.8,13.9 -2020-09-02T22:00,68.5,28,34,0,0,5.5,12.8 -2020-09-02T23:00,66.9,25,30.2,0,0,5.3,8.9 -2020-09-03T00:00,67.1,22,27.2,0,0,6.4,9.8 -2020-09-03T01:00,65.8,23,27.2,0,0,6.3,9.8 -2020-09-03T02:00,63.3,26,28,0,0,8.3,10.3 -2020-09-03T03:00,65.1,25,28.2,0,0,6.3,9.6 -2020-09-03T04:00,63.3,34,34.3,0,0,13.1,23.9 -2020-09-03T05:00,56,51,38.4,0,0,5.4,21.7 -2020-09-03T06:00,55.9,54,39.6,0,0,4.3,8.9 -2020-09-03T07:00,60.4,45,38.9,0,0,1.8,7.8 -2020-09-03T08:00,63.3,39,37.8,0,0,3,9.4 -2020-09-03T09:00,66.9,34,37.6,0,0,6.1,15 -2020-09-03T10:00,71.6,28,36.6,0,0,7,17.2 -2020-09-03T11:00,76.6,22,35.2,0,0,6.5,17.4 -2020-09-03T12:00,81.2,18,33.9,0,0,6.5,17 -2020-09-03T13:00,85.2,15,32.9,0,0,6.5,17 -2020-09-03T14:00,87.5,14,32.8,0,0,6.4,16.8 -2020-09-03T15:00,88.6,14,34,0,0,5.5,16.1 -2020-09-03T16:00,88.6,15,34.4,0,0,5,14.3 -2020-09-03T17:00,86.4,14,31.9,0,0,4.3,12.3 -2020-09-03T18:00,82.2,17,33.4,0,0,6.8,11.9 -2020-09-03T19:00,76.9,22,35.1,0,0,6.8,12.1 -2020-09-03T20:00,72,27,36.3,0,0,6.3,11.4 -2020-09-03T21:00,67.7,33,37.6,0,0,2.7,7.6 -2020-09-03T22:00,65.9,37,38.9,0,0,4.1,6 -2020-09-03T23:00,63.2,42,39.7,0,0,4.2,6.7 -2020-09-04T00:00,61.2,47,40.8,0,0,4.5,5.8 -2020-09-04T01:00,62.4,45,40.9,0,0,0.5,5.6 -2020-09-04T02:00,59.5,50,40.9,0,0,4.9,6.7 -2020-09-04T03:00,58.2,50,39.4,0,0,3.6,6.5 -2020-09-04T04:00,56.9,48,37.4,0,0,3.5,5.1 -2020-09-04T05:00,54.7,57,39.6,0,0,2.9,5.6 -2020-09-04T06:00,54.5,58,40.2,0,0,3.3,4.9 -2020-09-04T07:00,62,44,39.6,0,0,3.7,8.7 -2020-09-04T08:00,70.4,30,37.2,0,0,2.9,9.4 -2020-09-04T09:00,77.5,23,36.4,0,0,1,9.4 -2020-09-04T10:00,82.7,19,36,0,0,5.4,13.9 -2020-09-04T11:00,86,17,35.8,0,0,8.1,18.3 -2020-09-04T12:00,88.3,15,34.5,0,0,8.2,19.5 -2020-09-04T13:00,89.9,13,33.3,0,0,6.5,19 -2020-09-04T14:00,90.6,13,32.9,0,0,6.7,16.6 -2020-09-04T15:00,90.6,13,32.7,0,0,7.6,16.8 -2020-09-04T16:00,90,13,32.6,0,0,7.3,17 -2020-09-04T17:00,87.3,14,31.9,0,0,9.9,16.3 -2020-09-04T18:00,83.8,16,32.7,0,0,4.8,17.2 -2020-09-04T19:00,78.5,20,34.1,0,0,4.6,7.8 -2020-09-04T20:00,76.7,22,34.6,0,0,3,5.6 -2020-09-04T21:00,71.5,27,35.8,0,0,4.5,5.6 -2020-09-04T22:00,67.6,33,37.5,0,0,5.1,6 -2020-09-04T23:00,67.8,33,37.6,0,0,5.1,8.3 -2020-09-05T00:00,65,37,38,0,0,5.2,8.3 -2020-09-05T01:00,63.1,40,38.5,0,0,5.4,7.2 -2020-09-05T02:00,60.4,45,38.8,0,0,3,7.2 -2020-09-05T03:00,58.8,48,39.4,0,0,4.9,7.6 -2020-09-05T04:00,58.4,50,39.6,0,0,4,7.6 -2020-09-05T05:00,57.9,50,39.5,0,0,3.4,6.3 -2020-09-05T06:00,58,49,39,0,0,3.1,5.8 -2020-09-05T07:00,64.1,39,38.9,0,0,3.6,8.5 -2020-09-05T08:00,73.5,29,39.1,0,0,3.3,9.6 -2020-09-05T09:00,82.8,19,36.4,0,0,3.9,11.4 -2020-09-05T10:00,89.7,14,34.5,0,0,6.1,14.5 -2020-09-05T11:00,94.3,11,30.8,0,0,5.4,15.7 -2020-09-05T12:00,96.4,9,27.9,0,0,3.3,14.8 -2020-09-05T13:00,97.4,8,27.1,0,0,3.4,12.1 -2020-09-05T14:00,98.3,8,25.7,0,0,5.2,13.9 -2020-09-05T15:00,98.5,7,24.8,0,0,6.5,15.2 -2020-09-05T16:00,97.4,7,24.4,0,0,2.3,15 -2020-09-05T17:00,95,10,30.1,0,0,11.2,16.6 -2020-09-05T18:00,89.2,12,30.3,0,0,7.9,19 -2020-09-05T19:00,82.2,15,30.4,0,0,5.8,13 -2020-09-05T20:00,77.3,19,32.4,0,0,5.2,9.2 -2020-09-05T21:00,73,24,34,0,0,4.9,7.2 -2020-09-05T22:00,71.3,24,32.5,0,0,6.3,7.8 -2020-09-05T23:00,68.8,24,30.4,0,0,6.2,7.8 -2020-09-06T00:00,68.8,23,29.8,0,0,6.2,9.8 -2020-09-06T01:00,69.1,22,28.4,0,0,7.9,13 -2020-09-06T02:00,67.5,22,27.1,0,0,7.6,13 -2020-09-06T03:00,65,25,28,0,0,7.9,13.2 -2020-09-06T04:00,65.8,23,26.5,0,0,10.2,16.6 -2020-09-06T05:00,67.8,21,26.2,0,0,10.5,17.4 -2020-09-06T06:00,68.9,20,26.1,0,0,11.2,18.3 -2020-09-06T07:00,76.1,16,27,0,0,13,20.8 -2020-09-06T08:00,85.1,13,28.1,0,0,13.7,23.5 -2020-09-06T09:00,91.7,9,25.3,0,0,13.2,25.1 -2020-09-06T10:00,94.4,8,23.7,0,0,11.3,24.6 -2020-09-06T11:00,96.1,7,21.9,0,0,12,23.9 -2020-09-06T12:00,96.8,7,23.3,0,0,12.1,24.2 -2020-09-06T13:00,96.7,7,24.2,0,0,10.7,24.4 -2020-09-06T14:00,96.8,6,20.8,0,0,7.7,21.9 -2020-09-06T15:00,96.7,7,20.8,0,0,7.6,16.6 -2020-09-06T16:00,94.3,7,22.3,0,0,8.1,15.4 -2020-09-06T17:00,92.8,7,20.4,0,0,6.4,15.7 -2020-09-06T18:00,90.6,8,22.2,0,0,2.1,10.5 -2020-09-06T19:00,79.9,13,24.4,0,0,4.6,6.3 -2020-09-06T20:00,75.4,16,26.5,0,0,4,6.7 -2020-09-06T21:00,73.7,18,27.8,0,0,7.6,13 -2020-09-06T22:00,72,22,30.5,0,0,5.2,12.3 -2020-09-06T23:00,71,22,29.9,0,0,5.4,9.2 -2020-09-07T00:00,65.6,29,32.7,0,0,6.8,8.9 -2020-09-07T01:00,63.9,32,33.5,0,0,5.1,8.3 -2020-09-07T02:00,61.7,34,33,0,0,3,6.5 -2020-09-07T03:00,61.6,34,32.6,0,0,4.7,7.2 -2020-09-07T04:00,59.8,35,32.3,0,0,5.7,7.8 -2020-09-07T05:00,57.8,39,33,0,0,2.6,6.9 -2020-09-07T06:00,56.4,43,34.2,0,0,2.8,4.7 -2020-09-07T07:00,62.8,35,34.5,0,0,3.4,8.1 -2020-09-07T08:00,70.6,25,32.8,0,0,6,13.2 -2020-09-07T09:00,81,15,28.9,0,0,8.3,16.8 -2020-09-07T10:00,88.5,8,19.7,0,0,6.5,17.9 -2020-09-07T11:00,90.8,7,18.3,0,0,5.7,15.7 -2020-09-07T12:00,92.6,5,11.8,0,0,7.5,18.1 -2020-09-07T13:00,94,5,11.6,0,0,8.1,18.8 -2020-09-07T14:00,94.8,5,13.2,0,0,8.6,18.8 -2020-09-07T15:00,94.7,6,15.7,0,0,9.2,19.7 -2020-09-07T16:00,94.4,6,15.5,0,0,4.8,19.2 -2020-09-07T17:00,76.6,18,30.4,0,0,23,11.6 -2020-09-07T18:00,73.9,26,36.8,0,0,18.2,38 -2020-09-07T19:00,68.5,44,45.7,0,0,23.9,39.6 -2020-09-07T20:00,64,56,47.8,0,0,17.5,40 -2020-09-07T21:00,60.8,72,51.6,0.039,0,17.4,29.3 -2020-09-07T22:00,50.8,76,43.4,0.039,0,17.9,35.3 -2020-09-07T23:00,49.5,74,41.6,0.024,0,16.4,30.2 -2020-09-08T00:00,47,88,43.8,0.028,0,14.9,27.3 -2020-09-08T01:00,46.5,93,44.6,0.043,0,14.5,26.4 -2020-09-08T02:00,45.4,92,43.2,0.024,0,12.7,24.6 -2020-09-08T03:00,44.2,85,40,0.012,0,11.4,22.1 -2020-09-08T04:00,43.3,92,41,0.016,0,12,20.4 -2020-09-08T05:00,37.5,100,37.5,0.039,0,13.2,21.3 -2020-09-08T06:00,35.3,98,34.9,0.031,0.11,15.4,24.6 -2020-09-08T07:00,35.9,100,35.9,0.016,0.22,12.5,25.7 -2020-09-08T08:00,35.9,100,35.9,0.02,0.165,12.4,22.6 -2020-09-08T09:00,35.2,100,35.2,0.016,0.138,14.3,23.3 -2020-09-08T10:00,35.1,94,33.5,0.008,0.138,12.8,23.7 -2020-09-08T11:00,35.4,87,31.9,0.008,0.11,12.7,22.6 -2020-09-08T12:00,36.5,78,30.4,0.004,0.083,12.6,23.3 -2020-09-08T13:00,36.5,74,29,0.004,0.083,12.9,23 -2020-09-08T14:00,36.9,73,29.1,0,0.083,12.2,23.9 -2020-09-08T15:00,36.5,73,28.7,0,0.138,11.3,21.9 -2020-09-08T16:00,35.4,79,29.7,0,0.579,10.3,20.4 -2020-09-08T17:00,34.5,88,31.4,0,0.303,9.5,20.1 -2020-09-08T18:00,34.5,87,31,0,0.331,9.1,18.1 -2020-09-08T19:00,33.2,90,30.7,0,0.248,6,16.3 -2020-09-08T20:00,32.8,90,30.2,0,0.303,6.5,11.2 -2020-09-08T21:00,32.6,92,30.6,0,0.386,3.8,11.6 -2020-09-08T22:00,32.4,92,30.4,0,0.22,2.9,7.2 -2020-09-08T23:00,31.6,93,29.8,0,0.083,2.6,5.4 -2020-09-09T00:00,31.1,92,29.1,0,0.028,2.9,4.5 -2020-09-09T01:00,32,90,29.4,0,0.028,3.2,5.4 -2020-09-09T02:00,32.1,88,29,0,0.028,4.3,7.6 -2020-09-09T03:00,31.5,90,28.8,0,0.028,2.9,7.6 -2020-09-09T04:00,31.6,90,28.9,0,0.055,3.3,6.3 -2020-09-09T05:00,31.4,91,28.9,0,0.11,5.4,6.9 -2020-09-09T06:00,31.1,90,28.5,0,0.055,3.7,8.9 -2020-09-09T07:00,31.6,89,28.7,0,0.055,3.4,6.5 -2020-09-09T08:00,32.2,86,28.5,0,0.055,5.1,8.5 -2020-09-09T09:00,32.8,83,28.1,0,0.055,4.9,8.9 -2020-09-09T10:00,33.8,80,28.2,0,0.028,4.3,9.4 -2020-09-09T11:00,35.2,75,28.2,0,0,3.6,8.3 -2020-09-09T12:00,36.1,75,28.9,0,0,2.2,6.9 -2020-09-09T13:00,37.3,71,28.8,0,0,2.5,5.8 -2020-09-09T14:00,37.7,71,29,0,0,2.8,6.5 -2020-09-09T15:00,37.7,70,28.8,0,0,3.5,7.6 -2020-09-09T16:00,37.4,70,28.5,0,0,4.8,9.8 -2020-09-09T17:00,37.9,72,29.7,0,0,5.3,11.2 -2020-09-09T18:00,34,81,28.7,0,0,4,9.6 -2020-09-09T19:00,37.1,69,27.8,0,0,3.5,6.7 -2020-09-09T20:00,33.3,75,26.2,0,0,4.5,6.3 -2020-09-09T21:00,27.6,85,23.8,0,0,6.7,9.2 -2020-09-09T22:00,33.2,82,28.2,0,0,5.1,9.2 -2020-09-09T23:00,33.2,83,28.6,0,0,4.1,7.2 -2020-09-10T00:00,30.9,87,27.4,0,0,2,4.9 -2020-09-10T01:00,26.8,90,24.2,0,0,3.6,5.6 -2020-09-10T02:00,29.8,89,27,0,0,3.5,5.8 -2020-09-10T03:00,30.7,87,27.1,0,0.055,3.4,6.3 -2020-09-10T04:00,31.4,91,29,0,0.193,2.1,6 -2020-09-10T05:00,34.4,91,32.1,0,0.028,2.2,4 -2020-09-10T06:00,35.2,89,32.2,0,0,2.9,4.9 -2020-09-10T07:00,34,93,32.3,0,0,2.8,4.5 -2020-09-10T08:00,37.5,85,33.4,0,0,1.2,4.5 -2020-09-10T09:00,37.1,89,34.1,0,0,2.9,4.9 -2020-09-10T10:00,40.1,79,34.3,0,0,2.8,5.4 -2020-09-10T11:00,44.6,66,33.8,0,0,1.6,4.5 -2020-09-10T12:00,48.4,59,34.7,0,0,0.9,3.4 -2020-09-10T13:00,49.8,58,35.6,0,0,4.9,14.1 -2020-09-10T14:00,51.7,56,36.6,0,0,4.5,14.8 -2020-09-10T15:00,52.3,55,36.8,0,0,7.4,16.6 -2020-09-10T16:00,51.7,55,35.9,0,0,8.4,17.2 -2020-09-10T17:00,48,67,37.4,0,0,12.4,17 -2020-09-10T18:00,46.6,68,36.5,0,0,10.6,21.3 -2020-09-10T19:00,47.7,66,36.7,0,0,5.6,17.2 -2020-09-10T20:00,48.1,64,36.5,0,0,4.2,9.6 -2020-09-10T21:00,46.9,68,37,0,0,1.9,7.4 -2020-09-10T22:00,44.6,81,39,0,0,2.6,4 -2020-09-10T23:00,43.4,82,38.4,0,0,2.5,4.3 -2020-09-11T00:00,40.6,92,38.4,0,0,3.9,6.3 -2020-09-11T01:00,38,96,37,0,0,3.8,6.5 -2020-09-11T02:00,37.3,94,35.9,0,0,3.5,6.3 -2020-09-11T03:00,37.1,93,35.3,0,0,3.3,5.8 -2020-09-11T04:00,35.4,96,34.3,0,0,2.8,5.6 -2020-09-11T05:00,34,96,32.9,0,0,4.6,4.7 -2020-09-11T06:00,36.1,89,33,0,0,4.5,7.2 -2020-09-11T07:00,39.6,79,33.7,0,0,1.4,7.6 -2020-09-11T08:00,43.5,70,34.4,0,0,1.6,7.8 -2020-09-11T09:00,48,62,35.8,0,0,4.3,12.5 -2020-09-11T10:00,53.2,53,36.7,0,0,4,13 -2020-09-11T11:00,56.7,50,38,0,0,4.6,13.9 -2020-09-11T12:00,60.1,43,37.5,0,0,3,13.6 -2020-09-11T13:00,61.3,38,35.7,0,0,1.8,11.6 -2020-09-11T14:00,63.2,37,36.7,0,0,1.8,10.1 -2020-09-11T15:00,66.2,34,36.7,0,0,7.2,14.8 -2020-09-11T16:00,67.6,30,35.2,0,0,5.2,15.9 -2020-09-11T17:00,62.7,43,40.1,0,0,2.9,11.2 -2020-09-11T18:00,56.5,60,42.8,0,0,4.4,8.3 -2020-09-11T19:00,51.4,74,43.4,0,0,2.9,6 -2020-09-11T20:00,49.6,75,42,0,0,5.1,8.1 -2020-09-11T21:00,48,69,38.2,0,0,5.7,10.1 -2020-09-11T22:00,46,68,36,0,0,5.9,9.6 -2020-09-11T23:00,46.5,61,33.9,0,0,5.2,9.8 -2020-09-12T00:00,46.8,60,33.6,0,0,3.8,8.3 -2020-09-12T01:00,47.6,58,33.7,0,0,5.6,8.7 -2020-09-12T02:00,46.8,60,33.4,0,0,6.2,10.7 -2020-09-12T03:00,43.9,67,33.7,0,0,4.1,9.8 -2020-09-12T04:00,43.3,69,34,0,0,4.3,6.7 -2020-09-12T05:00,41.9,75,34.7,0,0,4.3,7.8 -2020-09-12T06:00,44,70,34.7,0,0,4.3,6.5 -2020-09-12T07:00,49.9,59,36.2,0,0,6.2,11 -2020-09-12T08:00,58.6,47,38.2,0,0,6.5,13.2 -2020-09-12T09:00,65.8,34,37,0,0,4.5,13.4 -2020-09-12T10:00,70.2,31,37.8,0,0,2.2,11.6 -2020-09-12T11:00,72.9,24,34.3,0,0,1.8,9.4 -2020-09-12T12:00,74.9,19,30.3,0,0,1.9,9.6 -2020-09-12T13:00,76.2,17,28.5,0,0,2.6,10.1 -2020-09-12T14:00,76.7,18,29.5,0,0,4.5,12.1 -2020-09-12T15:00,76.7,19,31,0,0,5.2,12.8 -2020-09-12T16:00,76.3,20,32.2,0,0,5.2,12.5 -2020-09-12T17:00,74.8,30,41,0,0,3.3,11.2 -2020-09-12T18:00,70.2,34,40.1,0,0,4.7,6.3 -2020-09-12T19:00,70.3,30,37.8,0,0,1.8,5.6 -2020-09-12T20:00,64.3,38,38,0,0,2.9,2.7 -2020-09-12T21:00,60.2,45,39,0,0,3.7,5.8 -2020-09-12T22:00,53.3,61,40.1,0,0,4,6 -2020-09-12T23:00,51.2,65,39.9,0,0,4.4,6 -2020-09-13T00:00,49.1,70,39.8,0,0,4.1,5.8 -2020-09-13T01:00,47.7,74,39.9,0,0,5.2,6.3 -2020-09-13T02:00,47.1,74,39.3,0,0,4.9,6 -2020-09-13T03:00,46.4,73,38.4,0,0,4.8,6.5 -2020-09-13T04:00,46.1,71,37.2,0,0,3.9,6.3 -2020-09-13T05:00,45.9,68,36.1,0,0,3.9,6 -2020-09-13T06:00,45.8,68,35.8,0,0,3.4,6.5 -2020-09-13T07:00,51.4,59,37.3,0,0,4.9,9.6 -2020-09-13T08:00,60.4,48,40.4,0,0,2.7,9.6 -2020-09-13T09:00,68.3,37,41.3,0,0,1.9,8.5 -2020-09-13T10:00,74,27,38.1,0,0,1.4,8.7 -2020-09-13T11:00,78.1,22,35.9,0,0,2.7,10.3 -2020-09-13T12:00,80.4,17,32.1,0,0,4.8,13 -2020-09-13T13:00,81.7,15,28.8,0,0,5.5,14.1 -2020-09-13T14:00,82.1,13,27.1,0,0,5.3,14.3 -2020-09-13T15:00,82,13,26.4,0,0,4.5,13.2 -2020-09-13T16:00,81.3,13,26.4,0,0,4.9,11.4 -2020-09-13T17:00,79,20,34.3,0,0,4.3,10.5 -2020-09-13T18:00,69.5,30,37,0,0,6.2,7.6 -2020-09-13T19:00,64.4,39,38.8,0,0,5.9,8.3 -2020-09-13T20:00,60.8,48,40.7,0,0,4.1,7.4 -2020-09-13T21:00,56.9,57,41.9,0,0,1.3,5.6 -2020-09-13T22:00,54,62,41.1,0,0,2.4,3.6 -2020-09-13T23:00,52.1,64,40.4,0,0,2.9,4.7 -2020-09-14T00:00,50,70,40.6,0,0,2.9,5.1 -2020-09-14T01:00,48.7,75,41,0,0,2.9,4.7 -2020-09-14T02:00,47.6,78,40.9,0,0,3.2,5.4 -2020-09-14T03:00,46.6,78,40.2,0,0,2.9,5.4 -2020-09-14T04:00,46.5,74,38.7,0,0,4.3,5.6 -2020-09-14T05:00,47.4,66,36.5,0,0,4.5,6 -2020-09-14T06:00,46.9,69,37.3,0,0,4.5,5.1 -2020-09-14T07:00,53.5,54,37.1,0,0,3.3,7.4 -2020-09-14T08:00,61.3,43,38.8,0,0,1.6,7.6 -2020-09-14T09:00,68.5,32,37.5,0,0,1.5,8.3 -2020-09-14T10:00,74.5,24,35.8,0,0,0.7,8.5 -2020-09-14T11:00,78.9,20,34.3,0,0,2.5,10.3 -2020-09-14T12:00,82,17,33,0,0,3,11.4 -2020-09-14T13:00,83.7,15,31.8,0,0,3,11.4 -2020-09-14T14:00,84.5,15,31,0,0,2,11.2 -2020-09-14T15:00,84.6,14,29.8,0,0,0.6,9.6 -2020-09-14T16:00,84.2,13,28.5,0,0,2.8,7.8 -2020-09-14T17:00,82.3,39,54.9,0,0,2.9,8.5 -2020-09-14T18:00,73.2,30,40.2,0,0,5.3,7.4 -2020-09-14T19:00,67.1,39,41.2,0,0,6.6,8.1 -2020-09-14T20:00,68.3,30,35.3,0,0,8.3,13.6 -2020-09-14T21:00,63.2,39,37.6,0,0,6.7,13.6 -2020-09-14T22:00,59.6,46,38.8,0,0,4.9,11.2 -2020-09-14T23:00,58.1,47,37.8,0,0,5.8,9.2 -2020-09-15T00:00,57.3,44,35.7,0,0,5.4,9.4 -2020-09-15T01:00,54.3,48,35.2,0,0,6.1,8.7 -2020-09-15T02:00,53.1,49,34.2,0,0,5.2,7.2 -2020-09-15T03:00,52.9,46,32.8,0,0,5.2,6.9 -2020-09-15T04:00,52.3,46,32.2,0,0,5.4,6.9 -2020-09-15T05:00,51.9,46,31.6,0,0,5.5,7.2 -2020-09-15T06:00,51.4,50,33.4,0,0,5.8,7.8 -2020-09-15T07:00,59.1,39,34,0,0,3.6,7.2 -2020-09-15T08:00,67.2,32,36.6,0,0,0.9,7.2 -2020-09-15T09:00,75.6,25,37.2,0,0,1.8,7.8 -2020-09-15T10:00,81.4,15,30,0,0,4.7,12.3 -2020-09-15T11:00,84,11,24.1,0,0,7.4,16.6 -2020-09-15T12:00,85.2,10,23,0,0,7.4,17.2 -2020-09-15T13:00,86,10,23.2,0,0,6.9,17 -2020-09-15T14:00,86.3,11,25.3,0,0,7,16.6 -2020-09-15T15:00,86.1,11,24.6,0,0,7.4,16.1 -2020-09-15T16:00,85.4,10,23.6,0,0,6,15.9 -2020-09-15T17:00,83.3,11,23.3,0,0,7.8,12.5 -2020-09-15T18:00,75.9,20,31.4,0,0,6.6,13.2 -2020-09-15T19:00,71,29,37.1,0,0,6.6,11 -2020-09-15T20:00,67,32,36.2,0,0,4.6,10.7 -2020-09-15T21:00,64,34,34.9,0,0,5.9,7.8 -2020-09-15T22:00,61.5,37,35,0,0,6.3,7.8 -2020-09-15T23:00,59.5,40,35.2,0,0,5.1,7.6 -2020-09-16T00:00,57.5,43,35.2,0,0,5.5,6.7 -2020-09-16T01:00,57.4,42,34.4,0,0,4.2,6.5 -2020-09-16T02:00,57.6,41,33.8,0,0,3.3,5.4 -2020-09-16T03:00,55.4,45,34.3,0,0,3.8,4.9 -2020-09-16T04:00,55.4,47,35.4,0,0,3.8,4.9 -2020-09-16T05:00,54.3,45,33.4,0,0,6.7,5.6 -2020-09-16T06:00,55,44,33.5,0,0,6.3,9.6 -2020-09-16T07:00,61.5,41,37.6,0,0,9,14.1 -2020-09-16T08:00,65.6,42,41.6,0,0,9.9,18.3 -2020-09-16T09:00,67.5,39,41.9,0,0,8.3,19 -2020-09-16T10:00,69.8,35,41,0,0,7.6,17.9 -2020-09-16T11:00,72.1,31,40.2,0,0,7.6,17.9 -2020-09-16T12:00,74.2,27,38.4,0,0,7.2,18.1 -2020-09-16T13:00,75.7,25,37.3,0,0,6.1,17.4 -2020-09-16T14:00,76.6,24,37.2,0,0,4.8,16.1 -2020-09-16T15:00,77.2,24,37.5,0,0,3.9,14.1 -2020-09-16T16:00,76.8,25,37.9,0,0,3.6,11.9 -2020-09-16T17:00,74,26,37,0,0,3.1,10.3 -2020-09-16T18:00,70.1,33,39.3,0,0,3.6,6.5 -2020-09-16T19:00,65.8,41,41.4,0,0,4.7,7.6 -2020-09-16T20:00,63.3,49,43.7,0,0,6.4,10.3 -2020-09-16T21:00,60.3,55,44.2,0,0,4.5,10.5 -2020-09-16T22:00,57.3,62,44.2,0,0,3.6,7.2 -2020-09-16T23:00,54.5,69,44.5,0,0,3.1,5.6 -2020-09-17T00:00,53.2,72,44.3,0,0,3.2,5.4 -2020-09-17T01:00,52.1,72,43.3,0,0,3,5.6 -2020-09-17T02:00,50.9,73,42.4,0,0,3.8,4.7 -2020-09-17T03:00,50.3,71,41.2,0,0,3.8,4.9 -2020-09-17T04:00,49,74,40.9,0,0,4.3,5.1 -2020-09-17T05:00,48.4,71,39.3,0,0,4.7,5.6 -2020-09-17T06:00,48.1,70,38.8,0,0,4.5,6.3 -2020-09-17T07:00,54.8,56,39.3,0,0,3.6,7.6 -2020-09-17T08:00,63.2,43,40.3,0,0,3.5,9.6 -2020-09-17T09:00,72.1,29,37.7,0,0,2.9,10.1 -2020-09-17T10:00,78.8,19,33,0,0,2.9,10.1 -2020-09-17T11:00,82.9,13,27.9,0,0,6.9,15.9 -2020-09-17T12:00,84.9,11,24.7,0,0,9,19.7 -2020-09-17T13:00,86.1,10,23.4,0,0,8.9,19.7 -2020-09-17T14:00,86,10,22.4,0,0,9.9,20.4 -2020-09-17T15:00,85.7,10,23.5,0,0,8.7,20.1 -2020-09-17T16:00,84.9,11,24.8,0,0,7.6,18.1 -2020-09-17T17:00,82,16,30.8,0,0,2.2,15 -2020-09-17T18:00,78.8,15,27.1,0,0,3.4,4.7 -2020-09-17T19:00,75.7,16,26.5,0,0,3.1,4.5 -2020-09-17T20:00,69.2,22,28.8,0,0,2.5,3.8 -2020-09-17T21:00,66,32,35.7,0,0,10.3,15 -2020-09-17T22:00,63.7,36,36.5,0,0,10.4,17.9 -2020-09-17T23:00,62.2,41,38.2,0,0,6.3,17.9 -2020-09-18T00:00,59.4,49,40.3,0,0,4.7,10.1 -2020-09-18T01:00,57.5,55,41.5,0,0,4.2,7.4 -2020-09-18T02:00,53.5,64,41.5,0,0,2.3,5.1 -2020-09-18T03:00,51.7,67,41.3,0,0,1.9,3.6 -2020-09-18T04:00,49.8,73,41.4,0,0,2.9,4 -2020-09-18T05:00,48.7,76,41.6,0,0,2.4,4.5 -2020-09-18T06:00,48,78,41.4,0,0,2.4,4.3 -2020-09-18T07:00,53.4,64,41.6,0,0,2.5,6.7 -2020-09-18T08:00,59.5,50,41,0,0,1.3,7.6 -2020-09-18T09:00,64.9,39,39.5,0,0,2.4,9.8 -2020-09-18T10:00,71.2,31,38.8,0,0,2.7,11 -2020-09-18T11:00,77,26,39.4,0,0,4.7,13.6 -2020-09-18T12:00,81.8,22,38.8,0,0,5.4,15 -2020-09-18T13:00,85.1,15,32,0,0,4,15 -2020-09-18T14:00,86.3,12,27.6,0,0,4.3,13 -2020-09-18T15:00,86.2,13,30,0,0,7.3,15.9 -2020-09-18T16:00,84.5,16,33.4,0,0,9.5,18.3 -2020-09-18T17:00,76.2,27,39.4,0,0,7.1,17.9 -2020-09-18T18:00,73,32,41.5,0,0,2.1,11.4 -2020-09-18T19:00,72.8,33,41.8,0,0,2.8,3.4 -2020-09-18T20:00,67.9,42,43.8,0,0,4.3,4.9 -2020-09-18T21:00,66.9,43,43.8,0,0,2.1,4.9 -2020-09-18T22:00,62.1,50,43.2,0,0,3.6,5.4 -2020-09-18T23:00,60.1,57,44.7,0,0,1.8,5.1 -2020-09-19T00:00,58,63,45.3,0,0,2.4,3.8 -2020-09-19T01:00,57.8,61,44.4,0,0,3.5,5.8 -2020-09-19T02:00,56.7,55,40.6,0,0,3.6,5.8 -2020-09-19T03:00,56.2,50,37.9,0,0,3.5,5.8 -2020-09-19T04:00,55.4,49,36.5,0,0,3.5,5.6 -2020-09-19T05:00,56.6,45,35.2,0,0,3.8,5.8 -2020-09-19T06:00,54.4,45,33.6,0,0,4.3,6.9 -2020-09-19T07:00,59.9,37,33.6,0,0,5.4,9.6 -2020-09-19T08:00,68.8,30,36.1,0,0,3.4,9.6 -2020-09-19T09:00,78.5,21,35.3,0,0,1.8,9.2 -2020-09-19T10:00,83.8,13,28.5,0,0,2.1,9.2 -2020-09-19T11:00,85.7,12,27.5,0,0,2.3,10.5 -2020-09-19T12:00,86.7,11,26.3,0,0,2.1,10.5 -2020-09-19T13:00,87.8,10,25.4,0,0,4.6,12.5 -2020-09-19T14:00,87.8,10,25.4,0,0,5.9,14.8 -2020-09-19T15:00,85.8,11,26.2,0,0,8.5,16.6 -2020-09-19T16:00,84.3,13,28.9,0,0,13.2,23.3 -2020-09-19T17:00,82.6,15,30,0,0,7.5,24.6 -2020-09-19T18:00,75.8,23,35.2,0,0,5.3,14.3 -2020-09-19T19:00,71,29,37,0,0,5.6,8.5 -2020-09-19T20:00,65.9,37,39,0,0,4.4,9.4 -2020-09-19T21:00,62.8,43,40,0,0,4.7,9.6 -2020-09-19T22:00,60.6,51,42.2,0,0,6.1,10.3 -2020-09-19T23:00,58.2,54,41.8,0,0,4,10.7 -2020-09-20T00:00,57.3,52,39.7,0,0,4.5,6.9 -2020-09-20T01:00,55.4,54,39.2,0,0,4.7,6 -2020-09-20T02:00,56.8,47,36.6,0,0,5.6,9.4 -2020-09-20T03:00,55.1,49,36.1,0,0,4.7,9.2 -2020-09-20T04:00,56.8,45,36,0,0,1.6,5.6 -2020-09-20T05:00,52.6,55,36.9,0,0,4.5,4.9 -2020-09-20T06:00,52.2,56,37.1,0,0,4.7,6 -2020-09-20T07:00,58.6,44,36.6,0,0,2.6,6.7 -2020-09-20T08:00,66.2,34,37.2,0,0,3.6,8.9 -2020-09-20T09:00,72.1,25,34.4,0,0,7.4,15 -2020-09-20T10:00,76.6,21,34,0,0,8,17.9 -2020-09-20T11:00,79.2,21,35.6,0,0,4.9,17.4 -2020-09-20T12:00,82.3,17,33.7,0,0,1.7,13.6 -2020-09-20T13:00,83.6,16,32.6,0,0,1.4,9.8 -2020-09-20T14:00,85.3,14,31.3,0,0,1.1,9.6 -2020-09-20T15:00,84.8,14,30.8,0,0,7.9,15.9 -2020-09-20T16:00,82.5,18,34.5,0,0,9.7,18.3 -2020-09-20T17:00,80.2,20,35.7,0,0,2.9,18.1 -2020-09-20T18:00,78.1,22,36,0,0,3,5.1 -2020-09-20T19:00,69.7,31,37.9,0,0,3.8,4.5 -2020-09-20T20:00,65.4,39,40.1,0,0,3.5,6 -2020-09-20T21:00,61.8,44,39.9,0,0,6.2,9.8 -2020-09-20T22:00,60.9,43,38.1,0,0,6.1,10.3 -2020-09-20T23:00,59,46,38.4,0,0,4.7,10.1 -2020-09-21T00:00,57.7,51,39.5,0,0,5.5,7.4 -2020-09-21T01:00,57.1,49,38.3,0,0,3.4,6.9 -2020-09-21T02:00,54.9,52,37.7,0,0,4.7,7.8 -2020-09-21T03:00,53.6,53,37,0,0,3.9,8.1 -2020-09-21T04:00,52.4,56,37,0,0,3.8,6.5 -2020-09-21T05:00,52.1,60,38.7,0,0,4.3,4.9 -2020-09-21T06:00,50.9,66,39.8,0,0,4.1,6.7 -2020-09-21T07:00,56.8,54,40.5,0,0,4.8,9.2 -2020-09-21T08:00,64.1,41,40.2,0,0,3,9.4 -2020-09-21T09:00,71.6,28,36.8,0,0,2.4,9.6 -2020-09-21T10:00,78.1,21,34.4,0,0,2.4,10.1 -2020-09-21T11:00,82.8,16,31.7,0,0,3.1,11.4 -2020-09-21T12:00,84.7,14,29.7,0,0,2.7,11.4 -2020-09-21T13:00,85.6,13,29.1,0,0,3.6,12.1 -2020-09-21T14:00,86.2,13,28.9,0,0,6.7,15.2 -2020-09-21T15:00,85.9,13,29.2,0,0,7,16.1 -2020-09-21T16:00,84.7,14,29.7,0,0,4.8,15.2 -2020-09-21T17:00,79.2,20,35.1,0,0,9.9,10.1 -2020-09-21T18:00,73.9,27,37.5,0,0,3.1,16.3 -2020-09-21T19:00,69.6,32,38.6,0,0,6.3,9.8 -2020-09-21T20:00,67.3,34,37.9,0,0,4.6,10.3 -2020-09-21T21:00,66.4,35,37.9,0,0,6.6,10.3 -2020-09-21T22:00,63.8,38,37.9,0,0,5.8,10.7 -2020-09-21T23:00,61,43,38.6,0,0,4.6,9.4 -2020-09-22T00:00,59.1,47,39,0,0,4.1,7.2 -2020-09-22T01:00,55.9,54,39.7,0,0,4.9,6.9 -2020-09-22T02:00,54.2,58,39.6,0,0,4,5.8 -2020-09-22T03:00,53.9,57,39,0,0,4.1,5.1 -2020-09-22T04:00,53.2,57,38.2,0,0,4.1,4.9 -2020-09-22T05:00,54,53,37.3,0,0,3.8,5.8 -2020-09-22T06:00,52.7,59,38.8,0,0,3.8,5.4 -2020-09-22T07:00,58.6,48,38.8,0,0,6.4,11 -2020-09-22T08:00,66.7,37,39.9,0,0,3.4,11.2 -2020-09-22T09:00,73,28,37.7,0,0,5.8,13 -2020-09-22T10:00,79.4,21,35.8,0,0,7.2,15.7 -2020-09-22T11:00,82.6,18,34.3,0,0,8.2,18.6 -2020-09-22T12:00,83.3,16,33.1,0,0,5.9,18.1 -2020-09-22T13:00,82.3,18,34.1,0,0,10.3,21.3 -2020-09-22T14:00,82.9,16,31.6,0,0,4.3,20.8 -2020-09-22T15:00,83.5,14,29,0,0,1.8,12.1 -2020-09-22T16:00,82.9,13,27.9,0,0,1.8,9.2 -2020-09-22T17:00,80.8,16,29.9,0,0,3.6,7.8 -2020-09-22T18:00,75.1,22,34.2,0,0,4,6.5 -2020-09-22T19:00,72.9,27,37,0,0,7.8,12.3 -2020-09-22T20:00,69.2,31,37.4,0,0,9.5,15.4 -2020-09-22T21:00,67.9,32,37.1,0,0,7.6,15.7 -2020-09-22T22:00,65.1,36,37.8,0,0,4.3,12.8 -2020-09-22T23:00,62.5,40,37.7,0,0,4.9,7.8 -2020-09-23T00:00,59.8,42,37,0,0,7.2,8.1 -2020-09-23T01:00,61.6,40,36.9,0,0,4.3,8.5 -2020-09-23T02:00,58,46,37.1,0,0,6,8.1 -2020-09-23T03:00,58.7,44,37,0,0,5.2,7.8 -2020-09-23T04:00,57.2,47,37.2,0,0,8.1,9.6 -2020-09-23T05:00,60.1,44,38,0,0,4.7,9.8 -2020-09-23T06:00,57.8,49,38.6,0,0,4,6.7 -2020-09-23T07:00,61.2,44,38.8,0,0,2.4,6 -2020-09-23T08:00,67.2,37,39.7,0,0,2.7,8.5 -2020-09-23T09:00,71.7,31,39.2,0,0,6.3,12.8 -2020-09-23T10:00,77,21,34.3,0,0,4.5,14.5 -2020-09-23T11:00,79.5,17,31.4,0,0,0.8,12.8 -2020-09-23T12:00,80.7,15,29.5,0,0,2.3,11 -2020-09-23T13:00,82.1,14,28.3,0,0,2,11.2 -2020-09-23T14:00,82.5,14,28.1,0,0,2.7,10.7 -2020-09-23T15:00,82.4,13,27.1,0,0,4.7,12.8 -2020-09-23T16:00,82,13,26,0,0,3.2,12.8 -2020-09-23T17:00,80.9,15,28.9,0,0,2.2,9.2 -2020-09-23T18:00,75.7,19,31,0,0,4.3,5.1 -2020-09-23T19:00,70.3,24,31.6,0,0,5.1,6.5 -2020-09-23T20:00,68.2,27,32.8,0,0,4,6.5 -2020-09-23T21:00,64.9,32,34.4,0,0,4.5,7.2 -2020-09-23T22:00,61.6,35,33.7,0,0,5.2,7.2 -2020-09-23T23:00,59.7,34,31.1,0,0,5.7,7.8 -2020-09-24T00:00,58.5,34,30.1,0,0,4.5,7.4 -2020-09-24T01:00,56.2,36,29.9,0,0,6.7,8.1 -2020-09-24T02:00,55.4,37,29.7,0,0,6.4,8.3 -2020-09-24T03:00,54.1,40,30.1,0,0,6.5,8.1 -2020-09-24T04:00,54.7,39,30.4,0,0,4.9,7.8 -2020-09-24T05:00,54.1,43,32.3,0,0,4.4,7.4 -2020-09-24T06:00,52.7,45,32.1,0,0,5,6.9 -2020-09-24T07:00,59.5,36,32.8,0,0,5.2,9.2 -2020-09-24T08:00,67.8,28,33.4,0,0,5,11.2 -2020-09-24T09:00,75.9,20,31.5,0,0,5,12.8 -2020-09-24T10:00,80.5,16,29.6,0,0,4.1,12.5 -2020-09-24T11:00,83.6,14,28.5,0,0,3.9,12.8 -2020-09-24T12:00,85.9,12,27.5,0,0,3.2,12.5 -2020-09-24T13:00,87.8,11,26.6,0,0,2.7,11.9 -2020-09-24T14:00,89,10,25.8,0,0,2.6,11 -2020-09-24T15:00,89.5,10,25.3,0,0,3.4,10.5 -2020-09-24T16:00,89,10,24.5,0,0,4.7,10.7 -2020-09-24T17:00,85.2,16,34.7,0,0,2.9,10.5 -2020-09-24T18:00,83.7,12,25.3,0,0,3.4,5.1 -2020-09-24T19:00,73.5,18,27.2,0,0,5.4,5.6 -2020-09-24T20:00,67.4,25,30.6,0,0,3.8,7.6 -2020-09-24T21:00,63.1,33,33.7,0,0,4.1,6.9 -2020-09-24T22:00,62.8,35,34.5,0,0,4.6,7.8 -2020-09-24T23:00,60.7,37,34.3,0,0,4.1,8.1 -2020-09-25T00:00,59.4,38,33.5,0,0,4.3,7.4 -2020-09-25T01:00,58.6,37,32.6,0,0,4.4,7.2 -2020-09-25T02:00,57.2,39,32.3,0,0,5.4,7.2 -2020-09-25T03:00,55.5,40,31.6,0,0,6.4,7.8 -2020-09-25T04:00,54,42,31.4,0,0,6.5,8.1 -2020-09-25T05:00,53.7,44,32.5,0,0,5.1,7.8 -2020-09-25T06:00,53.1,45,32.3,0,0,4.9,7.2 -2020-09-25T07:00,60.5,35,32.5,0,0,5.1,8.5 -2020-09-25T08:00,69.4,27,33.9,0,0,2,9.8 -2020-09-25T09:00,71.6,24,33.4,0,0,12,22.1 -2020-09-25T10:00,75.6,19,30.7,0,0,6.6,22.4 -2020-09-25T11:00,79.5,17,30.7,0,0,6.7,15.9 -2020-09-25T12:00,82.6,15,29.7,0,0,3.2,15.9 -2020-09-25T13:00,85.6,13,28.5,0,0,2.8,11 -2020-09-25T14:00,88.8,11,28.1,0,0,4.6,12.5 -2020-09-25T15:00,89.3,11,27.4,0,0,3,12.8 -2020-09-25T16:00,89.3,11,27.1,0,0,3.1,9.8 -2020-09-25T17:00,83.9,16,32.9,0,0,9.8,9.2 -2020-09-25T18:00,76.4,21,34,0,0,6.4,16.3 -2020-09-25T19:00,71.6,25,34.2,0,0,4.7,7.4 -2020-09-25T20:00,70.2,27,34.8,0,0,4.6,6.9 -2020-09-25T21:00,66,32,35.6,0,0,4.5,6.7 -2020-09-25T22:00,64.9,31,33.7,0,0,6.8,11.2 -2020-09-25T23:00,63.2,31,32.2,0,0,5.9,11.6 -2020-09-26T00:00,62.7,30,31,0,0,6.7,11.2 -2020-09-26T01:00,61.8,31,30.7,0,0,6.1,11.2 -2020-09-26T02:00,61,32,30.8,0,0,4.8,10.3 -2020-09-26T03:00,61.2,31,30.5,0,0,6,9.6 -2020-09-26T04:00,60.7,31,29.8,0,0,5.1,9.8 -2020-09-26T05:00,59.4,34,30.7,0,0,5.9,9.6 -2020-09-26T06:00,58.8,34,30.4,0,0,4.9,8.5 -2020-09-26T07:00,64.1,28,30.4,0,0,7.8,11.9 -2020-09-26T08:00,74.7,20,30.4,0,0,8.9,15.9 -2020-09-26T09:00,81.4,14,28.4,0,0,12.6,22.4 -2020-09-26T10:00,84.2,11,24.9,0,0,13.4,26.2 -2020-09-26T11:00,86.5,10,24.3,0,0,6.9,25.3 -2020-09-26T12:00,88.3,9,22.6,0,0,9,19 -2020-09-26T13:00,89.7,9,22.3,0,0,9.7,20.6 -2020-09-26T14:00,89.8,9,22.6,0,0,11.2,21.7 -2020-09-26T15:00,89.2,9,22.7,0,0,11,22.1 -2020-09-26T16:00,87.9,9,23,0,0,12.2,22.4 -2020-09-26T17:00,79,15,26.8,0,0,13.9,21.9 -2020-09-26T18:00,73.8,18,27.8,0,0,6.6,22.8 -2020-09-26T19:00,71.3,20,28.2,0,0,6.5,12.3 -2020-09-26T20:00,68,24,29.9,0,0,5,10.5 -2020-09-26T21:00,64.3,28,30.4,0,0,5.7,8.5 -2020-09-26T22:00,63.3,32,33.2,0,0,4.5,10.5 -2020-09-26T23:00,62.4,35,34.6,0,0,3.9,7.2 -2020-09-27T00:00,58.7,41,35.1,0,0,5,5.6 -2020-09-27T01:00,56.7,44,34.8,0,0,5.2,6.9 -2020-09-27T02:00,56.3,45,35,0,0,4.8,6.7 -2020-09-27T03:00,57.6,42,34.6,0,0,2.4,6 -2020-09-27T04:00,57,43,34.8,0,0,2.3,3.1 -2020-09-27T05:00,53.1,59,38.9,0,0,5.1,3.8 -2020-09-27T06:00,52.6,63,40.4,0,0,4.9,6.7 -2020-09-27T07:00,54.8,59,40.6,0,0,11.2,19 -2020-09-27T08:00,54.8,55,39.1,0,0,15.5,25.7 -2020-09-27T09:00,50.2,49,31.8,0,0,15.5,31.5 -2020-09-27T10:00,48.7,53,32.2,0,0,8.5,27.3 -2020-09-27T11:00,49.8,51,32.2,0,0,4.5,16.1 -2020-09-27T12:00,53.4,45,32.5,0,0,4.7,13.2 -2020-09-27T13:00,56.3,39,31.9,0,0,6.3,15.4 -2020-09-27T14:00,57.5,37,31.2,0,0,11.4,21.3 -2020-09-27T15:00,54.3,42,31.6,0,0,13.8,26.4 -2020-09-27T16:00,52.4,45,31.6,0,0,10.1,25.3 -2020-09-27T17:00,48.5,63,36.3,0,0,14.8,18.8 -2020-09-27T18:00,47.2,62,34.8,0.012,0,8.1,24.6 -2020-09-27T19:00,46.9,66,36.2,0.02,0,4.3,14.8 -2020-09-27T20:00,46.1,71,37.1,0.016,0,4.8,8.7 -2020-09-27T21:00,45.3,78,38.8,0,0,5.8,9.8 -2020-09-27T22:00,45.9,76,38.8,0,0,6.3,11 -2020-09-27T23:00,45.9,72,37.5,0,0,5.4,10.5 -2020-09-28T00:00,44.2,69,34.6,0,0,5,9.2 -2020-09-28T01:00,41.6,68,31.8,0,0,3.9,8.5 -2020-09-28T02:00,40.2,71,31.5,0,0,4.4,6 -2020-09-28T03:00,40.7,68,30.8,0,0,4.1,6 -2020-09-28T04:00,39.6,65,28.9,0,0,4.4,6 -2020-09-28T05:00,42.5,59,29,0,0,4.3,5.6 -2020-09-28T06:00,42.4,60,29.3,0,0,3.6,6.9 -2020-09-28T07:00,44.5,57,30.2,0,0,3.8,8.5 -2020-09-28T08:00,49,40,25.6,0,0,2,8.9 -2020-09-28T09:00,51.7,30,21.1,0,0,0.4,8.5 -2020-09-28T10:00,54.2,24,18.5,0,0,2.5,10.7 -2020-09-28T11:00,57,20,16.6,0,0,6,15.9 -2020-09-28T12:00,59.7,17,14.4,0,0,9,20.1 -2020-09-28T13:00,61.2,16,15.2,0,0,9.2,21 -2020-09-28T14:00,61.7,16,14.5,0,0,6.8,20.4 -2020-09-28T15:00,61.9,15,13,0,0,5.6,17 -2020-09-28T16:00,61.6,13,10.9,0,0,5.6,14.3 -2020-09-28T17:00,59.8,15,12.4,0,0,4.1,13 -2020-09-28T18:00,58.3,17,13.3,0,0,1.6,7.2 -2020-09-28T19:00,53.2,24,17.6,0,0,4.6,6.7 -2020-09-28T20:00,50.8,28,19.2,0,0,6.6,11.4 -2020-09-28T21:00,47.8,35,21.2,0,0,4.5,11.2 -2020-09-28T22:00,45.5,42,23.5,0,0,5.1,8.5 -2020-09-28T23:00,45.5,40,22.5,0,0,5.1,9.2 -2020-09-29T00:00,45.8,33,18.4,0,0,4.8,8.1 -2020-09-29T01:00,44.9,30,15.4,0,0,6,7.6 -2020-09-29T02:00,44,31,15.5,0,0,5.9,7.8 -2020-09-29T03:00,43.2,34,16.5,0,0,6,7.4 -2020-09-29T04:00,42.8,34,16.6,0,0,6.4,7.6 -2020-09-29T05:00,45.4,32,16.9,0,0,7.8,8.1 -2020-09-29T06:00,44.5,35,18.6,0,0,7.9,9.6 -2020-09-29T07:00,52.6,28,20.7,0,0,5.4,9.4 -2020-09-29T08:00,60.8,23,22.9,0,0,5.2,11.2 -2020-09-29T09:00,69,18,24.3,0,0,4.3,11.9 -2020-09-29T10:00,73.8,17,25.9,0,0,4.1,12.3 -2020-09-29T11:00,76,16,26.4,0,0,5.2,14.1 -2020-09-29T12:00,78,15,25.8,0,0,5.4,15.2 -2020-09-29T13:00,79.7,13,24.4,0,0,4.1,14.8 -2020-09-29T14:00,80.7,12,23.9,0,0,2.8,12.8 -2020-09-29T15:00,80.8,12,23.9,0,0,1.6,11 -2020-09-29T16:00,80.1,12,23.7,0,0,2.6,8.5 -2020-09-29T17:00,75.2,18,28.1,0,0,2.7,8.5 -2020-09-29T18:00,70,19,25.9,0,0,2.9,5.1 -2020-09-29T19:00,62.4,28,28.9,0,0,3.7,5.1 -2020-09-29T20:00,58.1,34,30.1,0,0,4.8,7.6 -2020-09-29T21:00,59.8,24,23,0,0,7.2,11.6 -2020-09-29T22:00,61.8,16,15.4,0,0,6.9,11.6 -2020-09-29T23:00,56.8,20,16.1,0,0,8.9,11.2 -2020-09-30T00:00,62.8,16,15.6,0,0,5.6,11 -2020-09-30T01:00,52.5,23,15.6,0,0,8,9.4 -2020-09-30T02:00,51.4,23,15.3,0,0,8.5,10.5 -2020-09-30T03:00,52.3,23,15.2,0,0,7.9,11.2 -2020-09-30T04:00,56.9,19,15.5,0,0,1.8,9.4 -2020-09-30T05:00,53.2,26,19.1,0,0,5.4,4.3 -2020-09-30T06:00,49.7,32,21.1,0,0,3.8,6.5 -2020-09-30T07:00,53.5,29,22,0,0,2.6,4.7 -2020-09-30T08:00,60.9,22,21.3,0,0,1.6,6.9 -2020-09-30T09:00,65.1,17,19.8,0,0,7,13.9 -2020-09-30T10:00,67.3,21,26.3,0,0,7.4,17.2 -2020-09-30T11:00,69.4,20,26.5,0,0,7.1,17.4 -2020-09-30T12:00,71.2,19,26.2,0,0,5.9,17.2 -2020-09-30T13:00,72.7,18,27.1,0,0,6.2,16.1 -2020-09-30T14:00,73.2,19,28.4,0,0,6.3,16.1 -2020-09-30T15:00,72.9,20,29.2,0,0,6.5,15.7 -2020-09-30T16:00,72.1,21,30,0,0,6.4,15.2 -2020-09-30T17:00,69.3,25,31.6,0,0,5,13.4 -2020-09-30T18:00,64.1,31,33,0,0,5.8,8.5 -2020-09-30T19:00,61.7,35,33.8,0,0,6,11.2 -2020-09-30T20:00,59.7,38,34,0,0,4,9.6 -2020-09-30T21:00,58.8,39,34.2,0,0,2.4,4.7 -2020-09-30T22:00,56.9,44,35.2,0,0,3.4,3.8 -2020-09-30T23:00,53.7,50,35.7,0,0,4.5,5.1 -2020-10-01T00:00,52.5,54,36.5,0,0,5.2,7.6 -2020-10-01T01:00,50.5,54,34.5,0,0,6.5,11.4 -2020-10-01T02:00,49,54,33.2,0,0,7.9,13 -2020-10-01T03:00,47,56,32.3,0,0,4.8,13 -2020-10-01T04:00,45,62,32.5,0,0,4.7,7.8 -2020-10-01T05:00,45.3,61,32.6,0,0,1.8,5.8 -2020-10-01T06:00,45.1,61,32.6,0,0,0.5,2 -2020-10-01T07:00,46,57,31.6,0,0,0.4,3.1 -2020-10-01T08:00,49.8,44,28.6,0,0,1.6,7.2 -2020-10-01T09:00,53.2,37,27.4,0,0,2.9,10.7 -2020-10-01T10:00,56.4,33,27.3,0,0,4.1,13 -2020-10-01T11:00,60.1,29,27.4,0,0,3.7,13.2 -2020-10-01T12:00,63.6,25,27.5,0,0,2.8,13.2 -2020-10-01T13:00,65.9,23,26.9,0,0,5.2,14.8 -2020-10-01T14:00,67.1,21,25.9,0,0,5.3,15.4 -2020-10-01T15:00,67.6,20,25.6,0,0,4.3,14.5 -2020-10-01T16:00,67,21,25.5,0,0,4.5,12.5 -2020-10-01T17:00,64.4,23,26.2,0,0,0.4,11.2 -2020-10-01T18:00,60.5,28,27.3,0,0,2.8,3.4 -2020-10-01T19:00,59.2,30,28,0,0,3.3,4.3 -2020-10-01T20:00,59.5,29,27.6,0,0,2.1,4 -2020-10-01T21:00,54.1,39,29.4,0,0,2,3.4 -2020-10-01T22:00,51.1,48,32.2,0,0,2.6,4.9 -2020-10-01T23:00,49.6,53,33.3,0,0,3.6,5.6 -2020-10-02T00:00,49,51,31.7,0,0,3.5,6 -2020-10-02T01:00,49,46,29.1,0,0,2.9,6.3 -2020-10-02T02:00,46.9,46,27.2,0,0,3.5,5.6 -2020-10-02T03:00,45.3,50,28,0,0,4,5.6 -2020-10-02T04:00,44.4,53,28.4,0,0,5,6.3 -2020-10-02T05:00,46,47,27.1,0,0,3.5,6.3 -2020-10-02T06:00,46.8,43,25.5,0,0,2.7,4.5 -2020-10-02T07:00,49.6,37,24.5,0,0,2.8,4.5 -2020-10-02T08:00,56.5,26,22.2,0,0,2.7,8.5 -2020-10-02T09:00,63.7,20,21.6,0,0,3.6,10.5 -2020-10-02T10:00,68.5,17,21.4,0,0,5.1,13.6 -2020-10-02T11:00,71.3,15,21.6,0,0,5.3,15 -2020-10-02T12:00,73.4,14,21.6,0,0,3.3,14.5 -2020-10-02T13:00,75,13,21.2,0,0,3.5,12.3 -2020-10-02T14:00,75.8,13,20.8,0,0,3.2,12.3 -2020-10-02T15:00,75.7,13,20.9,0,0,5.1,13.4 -2020-10-02T16:00,74.2,13,20.8,0,0,1.3,13.2 -2020-10-02T17:00,72.3,14,19.6,0,0,1.7,8.1 -2020-10-02T18:00,64.7,20,22.2,0,0,5.2,5.8 -2020-10-02T19:00,63.4,22,23.4,0,0,4.5,6.3 -2020-10-02T20:00,63.2,23,24.4,0,0,4.8,7.8 -2020-10-02T21:00,59.8,28,26.3,0,0,4.3,10.1 -2020-10-02T22:00,57.2,35,30.1,0,0,6.8,25.5 -2020-10-02T23:00,52.2,46,32.1,0,0,4.8,11.2 -2020-10-03T00:00,53.5,44,32.4,0,0,2.7,6 -2020-10-03T01:00,49.9,52,33.1,0,0,3.8,5.6 -2020-10-03T02:00,49.2,56,34,0,0,3.6,5.6 -2020-10-03T03:00,48.1,58,34,0,0,2.1,4 -2020-10-03T04:00,44.9,65,34,0,0,3.2,4 -2020-10-03T05:00,43.1,70,33.9,0,0,1.5,4 -2020-10-03T06:00,41.3,74,33.7,0,0,1.9,3.4 -2020-10-03T07:00,45.7,63,33.8,0,0,3.5,7.6 -2020-10-03T08:00,53.2,46,32.9,0,0,1.8,7.6 -2020-10-03T09:00,57.5,39,32.6,0,0,2.8,10.1 -2020-10-03T10:00,60.2,35,32.7,0,0,5,13.9 -2020-10-03T11:00,62.8,32,32.5,0,0,5.2,14.8 -2020-10-03T12:00,64.7,29,32.1,0,0,7,17 -2020-10-03T13:00,65.7,28,31.5,0,0,8,18.8 -2020-10-03T14:00,66.2,27,31.2,0,0,6.4,18.6 -2020-10-03T15:00,66.6,27,31.1,0,0,4.3,15.9 -2020-10-03T16:00,66,27,30.9,0,0,3.8,12.3 -2020-10-03T17:00,63.5,30,31.6,0,0,0.7,10.3 -2020-10-03T18:00,58.3,39,33.4,0,0,4.5,5.6 -2020-10-03T19:00,55.6,45,34.6,0,0,3.9,5.8 -2020-10-03T20:00,53.5,50,35.3,0,0,3.7,5.4 -2020-10-03T21:00,51.6,54,35.6,0,0,4.4,5.6 -2020-10-03T22:00,50.7,56,35.6,0,0,3.6,6 -2020-10-03T23:00,50.5,56,35.4,0,0,2.4,4.5 -2020-10-04T00:00,47.2,63,35.2,0,0,2.8,4.9 -2020-10-04T01:00,46.5,62,34.1,0,0,3.7,5.6 -2020-10-04T02:00,46,58,31.8,0,0,4,6 -2020-10-04T03:00,44.5,58,30.8,0,0,4.6,6 -2020-10-04T04:00,45.6,52,29.1,0,0,3.4,5.6 -2020-10-04T05:00,43.5,55,28.4,0,0,3.9,4.3 -2020-10-04T06:00,41.3,63,29.7,0,0,5.3,6.5 -2020-10-04T07:00,47,52,30.1,0,0,4.1,7.4 -2020-10-04T08:00,55.1,39,30.7,0,0,3.3,8.9 -2020-10-04T09:00,64,26,28.2,0,0,3.9,11 -2020-10-04T10:00,71.9,16,23.6,0,0,5.3,13.4 -2020-10-04T11:00,76.9,12,19.6,0,0,6.8,16.1 -2020-10-04T12:00,79.5,10,18.5,0,0,7,16.6 -2020-10-04T13:00,81.1,9,17.4,0,0,5.8,16.6 -2020-10-04T14:00,82,9,16.8,0,0,4.7,15 -2020-10-04T15:00,82.5,8,16.5,0,0,4.2,13 -2020-10-04T16:00,81.7,9,17.3,0,0,6.4,11.9 -2020-10-04T17:00,76.3,12,20.1,0,0,6.3,12.8 -2020-10-04T18:00,70,16,22.3,0,0,6.4,10.5 -2020-10-04T19:00,64.6,20,23.1,0,0,5.3,8.9 -2020-10-04T20:00,59.4,27,25,0,0,4.6,8.3 -2020-10-04T21:00,55.6,32,26.5,0,0,3.9,7.4 -2020-10-04T22:00,55.8,32,26.7,0,0,4.8,7.4 -2020-10-04T23:00,56.8,31,26.1,0,0,6.1,9.8 -2020-10-05T00:00,53.8,34,26.3,0,0,4.6,10.1 -2020-10-05T01:00,50.8,39,26.9,0,0,4.4,7.6 -2020-10-05T02:00,48.2,44,27.1,0,0,5,6.9 -2020-10-05T03:00,47.6,45,27.1,0,0,4.7,6.9 -2020-10-05T04:00,47.6,44,26.5,0,0,4,5.6 -2020-10-05T05:00,47.2,39,23.5,0,0,5.9,7.2 -2020-10-05T06:00,46,40,22.9,0,0,4.6,7.6 -2020-10-05T07:00,52.8,31,23.1,0,0,7,11.2 -2020-10-05T08:00,64.2,22,24.3,0,0,5.9,12.5 -2020-10-05T09:00,71.3,14,19.9,0,0,2,11.9 -2020-10-05T10:00,74.8,12,19.2,0,0,0.9,8.7 -2020-10-05T11:00,78.6,10,18.5,0,0,3.4,11 -2020-10-05T12:00,80.6,10,18.2,0,0,7.5,17 -2020-10-05T13:00,81.5,10,19.4,0,0,8.1,18.6 -2020-10-05T14:00,82.9,8,16.9,0,0,8,17.9 -2020-10-05T15:00,83.1,8,15,0,0,8.4,18.3 -2020-10-05T16:00,81.1,10,18.8,0,0,8.4,17.2 -2020-10-05T17:00,74.3,13,20.6,0,0,12.5,17.2 -2020-10-05T18:00,64.8,23,25.6,0,0,4.8,23.3 -2020-10-05T19:00,61.8,26,27,0,0,4,7.2 -2020-10-05T20:00,58,32,28.5,0,0,3.9,6.7 -2020-10-05T21:00,55.1,37,29.3,0,0,3.3,6 -2020-10-05T22:00,52.3,42,29.8,0,0,3,5.1 -2020-10-05T23:00,50.5,46,30.3,0,0,3.7,5.6 -2020-10-06T00:00,49.3,46,29.2,0,0,4.1,6.7 -2020-10-06T01:00,47,48,28.1,0,0,5,6.7 -2020-10-06T02:00,45.9,48,27.3,0,0,5.1,6.3 -2020-10-06T03:00,45.1,45,25.2,0,0,5.1,6.5 -2020-10-06T04:00,44.8,40,22.2,0,0,3.8,6.5 -2020-10-06T05:00,45.9,39,22,0,0,4.4,6.3 -2020-10-06T06:00,44.9,39,21.6,0,0,5,7.4 -2020-10-06T07:00,51.4,30,21,0,0,5.9,8.9 -2020-10-06T08:00,60.8,22,21.4,0,0,3.8,10.7 -2020-10-06T09:00,69,17,21.7,0,0,2.7,9.6 -2020-10-06T10:00,73.6,14,20.7,0,0,2.4,9.6 -2020-10-06T11:00,77.5,10,16.9,0,0,4.1,12.5 -2020-10-06T12:00,81,8,13.6,0,0,4.4,13 -2020-10-06T13:00,83.8,7,11.9,0,0,4.5,13.2 -2020-10-06T14:00,84.8,6,11.3,0,0,3.9,13 -2020-10-06T15:00,84.7,6,9.6,0,0,4.5,11.4 -2020-10-06T16:00,83.5,6,8.8,0,0,6.2,12.1 -2020-10-06T17:00,79.1,11,19.2,0,0,4.1,11.2 -2020-10-06T18:00,76.6,9,13.9,0,0,2.1,4.7 -2020-10-06T19:00,70.9,12,15.9,0,0,1.3,2.7 -2020-10-06T20:00,61.7,19,19.6,0,0,4,4.7 -2020-10-06T21:00,55,29,23.6,0,0,4,5.4 -2020-10-06T22:00,54,31,24.3,0,0,4.6,6.9 -2020-10-06T23:00,52.8,32,24.1,0,0,4.3,6.9 -2020-10-07T00:00,51.1,34,23.7,0,0,4.3,6.9 -2020-10-07T01:00,50.1,34,23,0,0,5.7,7.4 -2020-10-07T02:00,49.2,34,21.7,0,0,7.3,8.7 -2020-10-07T03:00,52.9,27,19.9,0,0,5.5,8.7 -2020-10-07T04:00,54.9,24,19,0,0,3.5,6.7 -2020-10-07T05:00,47.8,36,21.9,0,0,5.2,5.6 -2020-10-07T06:00,46.4,37,21.7,0,0,6.5,7.6 -2020-10-07T07:00,52.3,31,22.3,0,0,3.5,7.6 -2020-10-07T08:00,60.4,25,24.3,0,0,4.7,10.1 -2020-10-07T09:00,70.2,16,21.6,0,0,4.4,11.2 -2020-10-07T10:00,77.2,11,19.4,0,0,3.7,11.2 -2020-10-07T11:00,80.2,9,16,0,0,6.5,15 -2020-10-07T12:00,81.3,8,15.5,0,0,7.8,17.4 -2020-10-07T13:00,81.9,8,16,0,0,7,17.7 -2020-10-07T14:00,82.3,9,16.5,0,0,5.6,16.6 -2020-10-07T15:00,82.2,9,17.2,0,0,5.1,13.9 -2020-10-07T16:00,81.4,9,18.3,0,0,4.9,12.1 -2020-10-07T17:00,75.2,14,22.9,0,0,5.5,9.8 -2020-10-07T18:00,67.7,17,21,0,0,6.7,8.9 -2020-10-07T19:00,66.2,19,23,0,0,4.8,8.3 -2020-10-07T20:00,64.6,22,24.5,0,0,4.4,5.6 -2020-10-07T21:00,64.8,22,24.5,0,0,0.6,5.1 -2020-10-07T22:00,55.7,31,25.6,0,0,3.6,4.5 -2020-10-07T23:00,54.5,33,26.2,0,0,3.6,4.7 -2020-10-08T00:00,51.8,37,26.5,0,0,1.1,4.5 -2020-10-08T01:00,48.9,43,27.3,0,0,2.3,3.8 -2020-10-08T02:00,46.5,48,28,0,0,3,4.7 -2020-10-08T03:00,45.1,52,28.4,0,0,3.5,4.7 -2020-10-08T04:00,44.1,54,28.5,0,0,3.4,4.9 -2020-10-08T05:00,43.9,52,27.3,0,0,2.6,4.5 -2020-10-08T06:00,42.2,55,27.2,0,0,3.4,4 -2020-10-08T07:00,47.4,46,27.8,0,0,4,6.7 -2020-10-08T08:00,56,32,26.4,0,0,4.5,10.3 -2020-10-08T09:00,65,21,24.2,0,0,3.9,11.2 -2020-10-08T10:00,73.9,13,20.2,0,0,3.3,11.2 -2020-10-08T11:00,79.4,10,17.2,0,0,1.9,11 -2020-10-08T12:00,82.7,8,16,0,0,2.2,10.1 -2020-10-08T13:00,84.7,7,15.3,0,0,3.5,11.4 -2020-10-08T14:00,85.7,7,14.8,0,0,5.1,13 -2020-10-08T15:00,85.5,7,13.8,0,0,8.7,16.6 -2020-10-08T16:00,82.9,8,14.5,0,0,11.5,19.9 -2020-10-08T17:00,80.6,8,13.6,0,0,1.3,19.7 -2020-10-08T18:00,73.7,11,16.3,0,0,2.9,3.8 -2020-10-08T19:00,63.9,17,18.6,0,0,4.9,6.9 -2020-10-08T20:00,60.6,21,20.4,0,0,4.5,7.6 -2020-10-08T21:00,59,20,17.5,0,0,5.9,9.4 -2020-10-08T22:00,57.5,18,14.8,0,0,6,10.1 -2020-10-08T23:00,56.8,19,14.9,0,0,5.9,10.3 -2020-10-09T00:00,55,21,16.2,0,0,5.3,9.6 -2020-10-09T01:00,53.7,24,17.6,0,0,5.2,8.5 -2020-10-09T02:00,52.4,26,18.7,0,0,5,8.1 -2020-10-09T03:00,50.5,30,19.9,0,0,6,7.8 -2020-10-09T04:00,51.2,30,20.6,0,0,4.7,7.8 -2020-10-09T05:00,50.1,34,22.9,0,0,5.9,11 -2020-10-09T06:00,50,34,22.6,0,0,5.4,8.1 -2020-10-09T07:00,54,31,23.9,0,0,3.7,7.6 -2020-10-09T08:00,61.5,25,25.8,0,0,2.7,8.1 -2020-10-09T09:00,70.5,19,26.2,0,0,3.4,9.6 -2020-10-09T10:00,77.9,13,23.3,0,0,3.6,11.6 -2020-10-09T11:00,81.1,11,22.2,0,0,1.1,11.2 -2020-10-09T12:00,82.6,11,22.3,0,0,1.4,9.8 -2020-10-09T13:00,82.3,11,22.2,0,0,1,9.2 -2020-10-09T14:00,82.6,11,21.9,0,0,2.9,10.1 -2020-10-09T15:00,82.8,10,21.2,0,0,5.8,12.5 -2020-10-09T16:00,81.7,11,21.7,0,0,8.1,14.3 -2020-10-09T17:00,74.9,17,27.1,0,0,6.3,14.3 -2020-10-09T18:00,74.3,14,22.6,0,0,6.8,10.7 -2020-10-09T19:00,69.8,17,22.6,0,0,4.7,11.2 -2020-10-09T20:00,67.8,19,23.5,0,0,3.7,7.2 -2020-10-09T21:00,64.3,24,26.5,0,0,4.7,7.6 -2020-10-09T22:00,58.8,31,28.6,0,0,5.4,8.1 -2020-10-09T23:00,62.7,26,27.7,0,0,3.3,6.3 -2020-10-10T00:00,56.2,34,28,0,0,3.5,4 -2020-10-10T01:00,51.7,42,29.1,0,0,3.9,6.3 -2020-10-10T02:00,49.2,46,29,0,0,3.2,5.4 -2020-10-10T03:00,47.7,49,29.3,0,0,3.5,4.9 -2020-10-10T04:00,48,46,28.3,0,0,4.9,6.5 -2020-10-10T05:00,46.3,50,28.7,0,0,5.1,7.4 -2020-10-10T06:00,44.9,53,28.6,0,0,2.9,6.3 -2020-10-10T07:00,48.4,48,29.6,0,0,2.7,5.4 -2020-10-10T08:00,56.6,34,28.7,0,0,1.5,6.5 -2020-10-10T09:00,62.7,24,25.8,0,0,2.2,9.2 -2020-10-10T10:00,68.2,19,24.3,0,0,1.9,9.6 -2020-10-10T11:00,73.6,15,23.3,0,0,1.4,9.6 -2020-10-10T12:00,78.2,13,22.6,0,0,1.8,9.8 -2020-10-10T13:00,81.1,11,21.3,0,0,3,10.7 -2020-10-10T14:00,82.6,10,20.8,0,0,7.9,15.7 -2020-10-10T15:00,80.6,13,24.4,0,0,15,26.2 -2020-10-10T16:00,79.4,13,24.7,0,0,11.8,26.6 -2020-10-10T17:00,75.8,16,26.6,0,0,2.4,19.7 -2020-10-10T18:00,71.5,19,27.4,0,0,4.2,5.4 -2020-10-10T19:00,65.5,25,28.4,0,0,4.5,4.9 -2020-10-10T20:00,62.1,29,29.5,0,0,4.4,7.2 -2020-10-10T21:00,57.7,34,29.2,0,0,4.2,6.7 -2020-10-10T22:00,56.4,35,29.1,0,0,5.4,8.7 -2020-10-10T23:00,55.4,35,28.1,0,0,4.9,8.9 -2020-10-11T00:00,54.4,35,27.2,0,0,5.6,9.2 -2020-10-11T01:00,53.7,34,26.1,0,0,6.2,10.3 -2020-10-11T02:00,55.6,30,24.4,0,0,6.9,10.7 -2020-10-11T03:00,56.3,28,23.2,0,0,8.2,13 -2020-10-11T04:00,56.5,27,22.9,0,0,10.4,16.8 -2020-10-11T05:00,53.9,32,24.6,0,0,9.4,17.7 -2020-10-11T06:00,51.5,35,24.7,0,0,8.1,15.4 -2020-10-11T07:00,54.8,32,25.2,0,0,8.9,14.3 -2020-10-11T08:00,64.5,23,25.7,0,0,8.6,15.7 -2020-10-11T09:00,72.3,16,23.5,0,0,9,17.4 -2020-10-11T10:00,74.1,15,23,0,0,10.7,19.7 -2020-10-11T11:00,74.5,16,26,0,0,12.7,24.6 -2020-10-11T12:00,73.5,18,27.6,0,0,15.9,28.9 -2020-10-11T13:00,61.5,31,30.9,0.004,0,24.4,45.9 -2020-10-11T14:00,60.2,29,28,0.008,0,24.7,43 -2020-10-11T15:00,59.1,28,25.7,0.004,0,21.5,42.7 -2020-10-11T16:00,55.3,26,21.3,0,0,21.9,38.9 -2020-10-11T17:00,52.3,28,20.5,0,0,16.3,37.6 -2020-10-11T18:00,50.5,32,21.5,0,0,12,28.4 -2020-10-11T19:00,48.5,37,23.5,0,0,7.9,20.4 -2020-10-11T20:00,46.2,41,24.1,0,0,4.3,13.2 -2020-10-11T21:00,46.7,40,23.5,0,0,2.5,7.2 -2020-10-11T22:00,44.6,42,22.6,0,0,8.5,11.9 -2020-10-11T23:00,44.2,39,20.8,0,0,10,16.3 -2020-10-12T00:00,43,41,20.8,0,0,9.9,15.9 -2020-10-12T01:00,42.5,42,20.9,0,0,11.5,18.3 -2020-10-12T02:00,41.5,43,20.6,0,0,10,18.3 -2020-10-12T03:00,40.1,45,20.5,0,0,5.9,15.9 -2020-10-12T04:00,38,51,21.7,0,0,4.1,9.2 -2020-10-12T05:00,39,48,20.9,0,0,4.4,7.8 -2020-10-12T06:00,36.7,51,20.4,0,0,5.1,6.7 -2020-10-12T07:00,41.6,38,17.9,0,0,7.6,10.5 -2020-10-12T08:00,48.5,26,15.3,0,0,4.1,12.5 -2020-10-12T09:00,55.2,19,13.3,0,0,4,10.7 -2020-10-12T10:00,59.5,16,13.7,0,0,4.7,13.2 -2020-10-12T11:00,64.5,15,15.4,0,0,2.7,13.2 -2020-10-12T12:00,69.5,13,16.4,0,0,2.9,11.2 -2020-10-12T13:00,73.6,11,15.4,0,0,5.2,13.6 -2020-10-12T14:00,75.8,9,11.8,0,0,9.5,19 -2020-10-12T15:00,75.7,8,9.7,0,0,11,21 -2020-10-12T16:00,74.3,8,9.5,0,0,8.9,20.8 -2020-10-12T17:00,68.9,12,14.3,0,0,7.2,15.9 -2020-10-12T18:00,63.3,15,14.8,0,0,7.1,12.8 -2020-10-12T19:00,56.5,22,18.1,0,0,4.1,11.4 -2020-10-12T20:00,51.9,31,22,0,0,3.7,6.7 -2020-10-12T21:00,55.3,27,21.7,0,0,1.9,4.7 -2020-10-12T22:00,55.2,27,21.7,0,0,1.4,2.5 -2020-10-12T23:00,50.9,31,21.1,0,0,3.5,4.3 -2020-10-13T00:00,46.8,34,19.8,0,0,6,7.8 -2020-10-13T01:00,49,31,19.4,0,0,4.1,8.3 -2020-10-13T02:00,43.6,39,20,0,0,5.8,7.6 -2020-10-13T03:00,42.6,42,21.1,0,0,4.4,7.4 -2020-10-13T04:00,42.1,44,21.7,0,0,4.5,7.2 -2020-10-13T05:00,42.3,45,22.6,0,0,5.2,7.4 -2020-10-13T06:00,42.5,43,21.7,0,0,5.3,8.7 -2020-10-13T07:00,46.4,41,24.2,0,0,1.6,8.3 -2020-10-13T08:00,54.3,28,22.2,0,0,1.4,6 -2020-10-13T09:00,58.4,25,22.9,0,0,10.4,19.5 -2020-10-13T10:00,61.3,21,21,0,0,8,20.1 -2020-10-13T11:00,66.3,17,20.6,0,0,1.6,17.7 -2020-10-13T12:00,73.5,13,20.4,0,0,5.9,14.1 -2020-10-13T13:00,77.7,11,20,0,0,3.5,15.7 -2020-10-13T14:00,78.5,11,20.3,0,0,5.4,13.2 -2020-10-13T15:00,79.6,11,20.9,0,0,3.8,13.4 -2020-10-13T16:00,79,11,20.9,0,0,3.6,10.1 -2020-10-13T17:00,68.5,21,27.1,0,0,5.8,8.3 -2020-10-13T18:00,64.2,21,23.9,0,0,6.3,6.7 -2020-10-13T19:00,61.3,23,23.4,0,0,5.1,8.5 -2020-10-13T20:00,56.8,28,23.9,0,0,5.2,8.3 -2020-10-13T21:00,55.9,30,25,0,0,5.2,8.5 -2020-10-13T22:00,55.5,30,24.8,0,0,7,11 -2020-10-13T23:00,59.4,23,21.5,0,0,10.1,15.9 -2020-10-14T00:00,60.9,18,17.5,0,0,11.7,19.2 -2020-10-14T01:00,59.7,19,17,0,0,11.4,19.2 -2020-10-14T02:00,58.1,20,17.2,0,0,10.3,18.3 -2020-10-14T03:00,55.6,23,18.3,0,0,7.8,16.8 -2020-10-14T04:00,52.1,28,20.1,0,0,5.6,12.5 -2020-10-14T05:00,51.4,30,21.3,0,0,5,9.2 -2020-10-14T06:00,52.6,28,20.5,0,0,6.5,10.1 -2020-10-14T07:00,55.5,26,21.5,0,0,5.6,12.5 -2020-10-14T08:00,62.7,23,24.7,0,0,3.4,9.2 -2020-10-14T09:00,69.5,17,22.5,0,0,3.8,10.3 -2020-10-14T10:00,73.8,14,20.9,0,0,1.4,10.3 -2020-10-14T11:00,78.2,13,23.8,0,0,4.7,11.4 -2020-10-14T12:00,80.5,12,23.6,0,0,10.1,21.5 -2020-10-14T13:00,80.4,12,22.9,0,0,14,25.9 -2020-10-14T14:00,82.4,11,22.3,0,0,11.4,26.6 -2020-10-14T15:00,71.4,21,29.8,0,0,16.4,27.7 -2020-10-14T16:00,65.6,27,30.9,0,0,16.7,31.8 -2020-10-14T17:00,55.6,48,36.3,0,0,12.2,28.2 -2020-10-14T18:00,52.5,54,36.4,0,0,9.7,20.6 -2020-10-14T19:00,48.8,59,35.3,0,0,13.1,21.9 -2020-10-14T20:00,46.6,54,31,0,0,7.6,22.1 -2020-10-14T21:00,46.5,56,31.7,0,0,7.4,13.6 -2020-10-14T22:00,45.2,63,33.3,0,0,4,13.2 -2020-10-14T23:00,44.4,66,33.8,0,0,2.1,8.5 -2020-10-15T00:00,44.3,66,33.6,0,0,0.9,5.6 -2020-10-15T01:00,43.7,63,32.1,0,0,0.5,4.3 -2020-10-15T02:00,43.4,57,29,0,0,2.9,2.9 -2020-10-15T03:00,43.3,46,24.2,0,0,2.9,3.4 -2020-10-15T04:00,42.4,48,23.9,0,0,1.6,3.1 -2020-10-15T05:00,41.5,53,25.5,0,0,7.4,3.6 -2020-10-15T06:00,40.2,54,24.9,0,0,6.4,12.3 -2020-10-15T07:00,40.5,54,25.3,0,0,5.5,11 -2020-10-15T08:00,41.8,51,25.1,0,0,4.8,11 -2020-10-15T09:00,44.4,46,25.1,0,0,2.2,11.6 -2020-10-15T10:00,47.6,41,25,0,0,1.5,9.2 -2020-10-15T11:00,50.5,36,24.6,0,0,4.3,13 -2020-10-15T12:00,53.2,32,24.2,0,0,6.3,16.3 -2020-10-15T13:00,54.6,29,23,0,0,5.3,16.8 -2020-10-15T14:00,56.1,27,22.5,0,0,5.5,14.8 -2020-10-15T15:00,55.9,28,22.9,0,0,7.1,15.9 -2020-10-15T16:00,54.5,30,23.5,0,0,8,15.9 -2020-10-15T17:00,51.3,29,19.9,0,0,6.3,15.9 -2020-10-15T18:00,47.6,35,21,0,0,9.5,12.3 -2020-10-15T19:00,42.1,49,24.4,0,0,6.3,22.6 -2020-10-15T20:00,41.5,52,25,0,0,3.5,10.3 -2020-10-15T21:00,39.5,59,26.2,0,0,3,6 -2020-10-15T22:00,38.8,61,26.4,0,0,2,4.3 -2020-10-15T23:00,37.6,64,26.4,0,0,3.5,5.4 -2020-10-16T00:00,37,65,26.3,0,0,4.3,7.6 -2020-10-16T01:00,36.1,69,26.7,0,0,4.7,8.1 -2020-10-16T02:00,34.3,74,27.1,0,0,3.8,7.8 -2020-10-16T03:00,35.2,72,27.1,0,0,2,4.9 -2020-10-16T04:00,32.5,81,27.3,0,0,3,4 -2020-10-16T05:00,31.6,88,28.5,0,0,3,4.3 -2020-10-16T06:00,30.5,91,28,0,0,3.4,5.1 -2020-10-16T07:00,32.8,80,27.4,0,0,3.7,7.4 -2020-10-16T08:00,38.2,60,25.4,0,0,1.1,7.4 -2020-10-16T09:00,44.1,44,23.5,0,0,2,8.7 -2020-10-16T10:00,50.8,29,19.9,0,0,2.3,10.1 -2020-10-16T11:00,56.5,21,17.4,0,0,2.7,12.1 -2020-10-16T12:00,61,17,15.8,0,0,2,11.2 -2020-10-16T13:00,66,15,17,0,0,3.3,11.6 -2020-10-16T14:00,70.3,13,17.2,0,0,2.7,11.6 -2020-10-16T15:00,72,13,18.1,0,0,2.9,10.1 -2020-10-16T16:00,71.6,14,19.1,0,0,0.8,8.9 -2020-10-16T17:00,62.6,24,25.2,0,0,5.6,6.9 -2020-10-16T18:00,59.5,27,25.3,0,0,4.8,8.7 -2020-10-16T19:00,54.6,35,27.1,0,0,4.3,8.1 -2020-10-16T20:00,52.5,34,25.2,0,0,5.1,7.6 -2020-10-16T21:00,51.9,30,21.7,0,0,5.6,8.7 -2020-10-16T22:00,52.8,26,18.9,0,0,6.1,10.5 -2020-10-16T23:00,51.3,27,18.1,0,0,4.9,9.6 -2020-10-17T00:00,50.4,28,18.5,0,0,4.8,7.4 -2020-10-17T01:00,50.1,28,18.5,0,0,7.1,11.4 -2020-10-17T02:00,48.7,31,19.1,0,0,7.7,12.8 -2020-10-17T03:00,45.6,40,22.4,0,0,5.1,12.3 -2020-10-17T04:00,47.2,40,23.9,0,0,2.4,6.5 -2020-10-17T05:00,51.6,33,23.4,0,0,2.3,7.2 -2020-10-17T06:00,45.6,41,23,0,0,4.7,5.8 -2020-10-17T07:00,50.5,32,21.5,0,0,3,6 -2020-10-17T08:00,56.1,29,24.4,0,0,2.1,5.6 -2020-10-17T09:00,62.1,22,23.2,0,0,2.4,7.8 -2020-10-17T10:00,66.4,18,21.5,0,0,5.4,12.3 -2020-10-17T11:00,69.2,16,21.7,0,0,7.5,16.3 -2020-10-17T12:00,72,15,22.2,0,0,6.5,17 -2020-10-17T13:00,74.4,15,23,0,0,2.9,15.4 -2020-10-17T14:00,74.5,15,23.4,0,0,0.3,10.7 -2020-10-17T15:00,74.5,15,23.2,0,0,3.7,9.6 -2020-10-17T16:00,73,18,26.6,0,0,2.1,9.8 -2020-10-17T17:00,62,28,28.3,0,0,6.8,4.5 -2020-10-17T18:00,62.5,24,24.8,0,0,4.2,8.1 -2020-10-17T19:00,61.3,24,24.6,0,0,4.7,5.6 -2020-10-17T20:00,54.1,33,25.9,0,0,3.4,5.6 -2020-10-17T21:00,52.8,43,30.7,0,0,17,28.2 -2020-10-17T22:00,46.9,62,34.6,0,0,12.4,28.9 -2020-10-17T23:00,43.3,73,35.2,0,0,3.1,20.6 -2020-10-18T00:00,42.1,76,35,0,0,3.2,5.1 -2020-10-18T01:00,40.8,80,35.1,0,0,3,4.5 -2020-10-18T02:00,40.7,80,35.1,0,0,2.6,3.6 -2020-10-18T03:00,39.5,83,34.8,0,0,5,8.1 -2020-10-18T04:00,39.4,82,34.3,0,0,5.6,10.1 -2020-10-18T05:00,38.9,77,32.2,0,0,6.3,12.1 -2020-10-18T06:00,37.9,76,30.8,0,0,9.4,16.6 -2020-10-18T07:00,37.7,77,31.1,0,0,6.4,16.8 -2020-10-18T08:00,38.5,75,31.2,0.004,0,4.8,12.3 -2020-10-18T09:00,39.7,70,30.8,0,0,6.4,13.4 -2020-10-18T10:00,41.1,66,30.7,0,0,5.2,13.4 -2020-10-18T11:00,43.3,60,30.4,0,0,4.8,12.8 -2020-10-18T12:00,46.8,52,30.2,0,0,3.1,12.8 -2020-10-18T13:00,50.3,46,30.2,0,0,4.9,13.6 -2020-10-18T14:00,52.7,42,30.4,0,0,6.4,15.2 -2020-10-18T15:00,53.6,41,30.7,0,0,4.5,15.4 -2020-10-18T16:00,54.5,41,31.1,0,0,4.7,11.9 -2020-10-18T17:00,46,54,30.5,0,0,4.3,12.3 -2020-10-18T18:00,43.2,62,30.8,0,0,1.8,7.2 -2020-10-18T19:00,41.3,66,30.7,0,0,3.4,5.6 -2020-10-18T20:00,38.8,73,30.7,0,0,5,8.3 -2020-10-18T21:00,38.8,75,31.4,0,0,4,8.9 -2020-10-18T22:00,38.1,76,31.3,0,0,5.2,9.4 -2020-10-18T23:00,36.9,82,31.8,0,0,4.5,8.9 -2020-10-19T00:00,37.6,81,32.2,0,0,1.2,7.8 -2020-10-19T01:00,36,87,32.4,0,0,3.4,6.3 -2020-10-19T02:00,34.5,91,32.3,0,0,3,5.8 -2020-10-19T03:00,37,82,32.1,0,0,2.9,3.8 -2020-10-19T04:00,35.2,89,32.2,0,0,3,4 -2020-10-19T05:00,35.2,84,30.8,0,0,5.7,5.8 -2020-10-19T06:00,35.7,81,30.5,0,0,5.9,7.2 -2020-10-19T07:00,41.5,59,28.4,0,0,5.2,7.6 -2020-10-19T08:00,49.7,40,26.2,0,0,5.2,11.6 -2020-10-19T09:00,57.7,30,26.3,0,0,2.5,10.3 -2020-10-19T10:00,62.8,25,26.2,0,0,4.2,10.1 -2020-10-19T11:00,66,21,24.7,0,0,6.8,17 -2020-10-19T12:00,69.1,20,26.2,0,0,6.8,15.9 -2020-10-19T13:00,69.4,22,29.1,0,0,8.5,18.1 -2020-10-19T14:00,69.7,22,29.5,0,0,7,18.1 -2020-10-19T15:00,70.1,22,29.1,0,0,4.7,15 -2020-10-19T16:00,68.5,23,29.7,0,0,3.3,10.7 -2020-10-19T17:00,60.3,31,29.1,0,0,7.5,6.7 -2020-10-19T18:00,54.8,41,31.6,0,0,4.7,12.3 -2020-10-19T19:00,54.6,42,32.1,0,0,2.7,7.8 -2020-10-19T20:00,48.7,54,32.6,0,0,3.8,4.3 -2020-10-19T21:00,48.7,52,31.9,0,0,4,5.8 -2020-10-19T22:00,45.6,56,30.7,0,0,4.1,5.1 -2020-10-19T23:00,42.7,59,29.3,0,0,1.7,5.1 -2020-10-20T00:00,40.3,62,28.4,0,0,2.6,4.3 -2020-10-20T01:00,39.9,59,26.6,0,0,3.4,5.1 -2020-10-20T02:00,42.7,52,26.2,0,0,0.9,4.3 -2020-10-20T03:00,40.4,55,25.3,0,0,4.1,4.7 -2020-10-20T04:00,39.9,47,21.5,0,0,7.4,8.7 -2020-10-20T05:00,52,23,15.7,0,0,10,11 -2020-10-20T06:00,49.5,26,16.1,0,0,6.9,16.3 -2020-10-20T07:00,47.7,32,19.4,0,0,5.2,11 -2020-10-20T08:00,51,37,25.3,0,0,3.9,8.9 -2020-10-20T09:00,55.9,32,26.3,0,0,3.2,8.5 -2020-10-20T10:00,62.8,21,22.1,0,0,1.2,10.3 -2020-10-20T11:00,67.4,17,20.5,0,0,3.2,11 -2020-10-20T12:00,70.6,16,22.6,0,0,2.2,11 -2020-10-20T13:00,72.7,16,24.3,0,0,6,13.9 -2020-10-20T14:00,72.6,17,25.7,0,0,7.3,16.1 -2020-10-20T15:00,72.5,17,25.6,0,0,5,15.2 -2020-10-20T16:00,70.1,20,27.1,0,0,7.1,11.6 -2020-10-20T17:00,56.5,40,32.2,0,0,9.1,17.2 -2020-10-20T18:00,53.4,45,32.5,0,0,5,15 -2020-10-20T19:00,50.8,50,33,0,0,4.3,8.1 -2020-10-20T20:00,52.5,47,32.8,0,0,0.9,5.8 -2020-10-20T21:00,49.6,53,33.4,0,0,2.2,3.1 -2020-10-20T22:00,49,55,33.6,0,0,0.5,2.9 -2020-10-20T23:00,44.1,67,33.8,0,0,4.8,5.6 -2020-10-21T00:00,42.4,73,34.3,0,0,4.1,6.5 -2020-10-21T01:00,41.2,76,34.3,0,0,2.8,5.4 -2020-10-21T02:00,39.3,82,34.2,0,0,2.1,4.7 -2020-10-21T03:00,38.2,81,32.8,0,0,2.8,4.3 -2020-10-21T04:00,40.6,68,30.8,0,0,2.4,5.6 -2020-10-21T05:00,37.1,85,33.1,0,0,3.2,3.4 -2020-10-21T06:00,34.9,95,33.6,0,0,3.4,7.2 -2020-10-21T07:00,36.2,93,34.5,0,0,2.9,5.6 -2020-10-21T08:00,41.2,76,34.2,0,0,3.4,8.3 -2020-10-21T09:00,47.6,58,33.6,0,0,3.7,10.5 -2020-10-21T10:00,53.4,47,33.5,0,0,7.2,15.4 -2020-10-21T11:00,58.2,33,28.9,0,0,2,16.6 -2020-10-21T12:00,67.1,23,27.6,0,0,3.7,11 -2020-10-21T13:00,73.3,16,24.2,0,0,5.5,13.2 -2020-10-21T14:00,76,13,21,0,0,5,13.2 -2020-10-21T15:00,76.5,13,22,0,0,4.1,12.1 -2020-10-21T16:00,73.9,19,29.6,0,0,2.8,9.4 -2020-10-21T17:00,65.1,21,24.4,0,0,5.6,9.2 -2020-10-21T18:00,57.7,32,27.9,0,0,5.8,8.9 -2020-10-21T19:00,54.6,37,29.1,0,0,4.7,7.6 -2020-10-21T20:00,51.3,42,28.9,0,0,3.8,6.7 -2020-10-21T21:00,48.6,46,28.8,0,0,5.9,7.4 -2020-10-21T22:00,45.6,51,28.7,0,0,3,7.8 -2020-10-21T23:00,44.4,51,27.3,0,0,4.4,6 -2020-10-22T00:00,44.3,40,21.6,0,0,5,6.9 -2020-10-22T01:00,43.7,36,18.8,0,0,6.8,8.5 -2020-10-22T02:00,43.3,35,17.3,0,0,6.2,8.5 -2020-10-22T03:00,42.2,35,16.3,0,0,7.1,8.7 -2020-10-22T04:00,42.7,33,15.4,0,0,8.3,10.1 -2020-10-22T05:00,36.6,98,36.1,0,0,9.8,10.1 -2020-10-22T06:00,35.3,97,34.5,0,0,10.4,17.9 -2020-10-22T07:00,36.4,88,33.3,0,0,8.3,17 -2020-10-22T08:00,37.1,79,31.4,0,0,9.1,17 -2020-10-22T09:00,37.1,76,30.1,0,0,6.7,17 -2020-10-22T10:00,38.8,69,29.4,0,0,6.3,14.1 -2020-10-22T11:00,41.3,61,28.9,0,0,7.4,15.7 -2020-10-22T12:00,44.2,54,28.8,0,0,12.2,22.4 -2020-10-22T13:00,44.6,53,28.3,0,0,13.1,24.6 -2020-10-22T14:00,43.9,52,27.6,0,0,9,24.6 -2020-10-22T15:00,44.7,50,27.1,0,0,10.3,18.8 -2020-10-22T16:00,42.8,55,27.7,0,0,13.6,24.2 -2020-10-22T17:00,35.2,60,22.6,0,0,16.9,23.9 -2020-10-22T18:00,32.5,58,19.4,0,0.028,12.7,28.4 -2020-10-22T19:00,30.4,57,16.9,0,0.028,11.7,22.6 -2020-10-22T20:00,28.3,60,16.3,0,0,11.7,21.5 -2020-10-22T21:00,27.1,64,16.3,0,0,9.2,20.8 -2020-10-22T22:00,26,66,16.3,0,0,7.2,16.8 -2020-10-22T23:00,25,68,15.8,0,0,7.1,13.9 -2020-10-23T00:00,23.4,70,15.1,0,0,7.9,14.8 -2020-10-23T01:00,22.7,71,14.7,0,0,7.4,15 -2020-10-23T02:00,22.6,71,14.5,0,0,6.1,13.9 -2020-10-23T03:00,22.1,72,14.5,0,0,6.5,12.3 -2020-10-23T04:00,21.7,74,14.7,0,0,6.3,12.3 -2020-10-23T05:00,20.3,78,14.5,0,0,6.1,12.1 -2020-10-23T06:00,20,79,14.5,0,0,5.9,12.5 -2020-10-23T07:00,19.9,77,13.8,0,0,3.8,11.4 -2020-10-23T08:00,20.8,73,13.5,0,0,3.7,10.1 -2020-10-23T09:00,23.3,65,13.2,0,0,2.9,10.3 -2020-10-23T10:00,25.3,58,12.7,0,0,2.3,10.3 -2020-10-23T11:00,28.6,52,13,0,0,2.9,11.6 -2020-10-23T12:00,34.1,44,14.5,0,0,3.7,14.1 -2020-10-23T13:00,36.1,41,14.8,0,0,2.9,13 -2020-10-23T14:00,37.7,39,15.1,0,0,6.4,15.4 -2020-10-23T15:00,38.7,37,14.9,0,0,6.6,16.1 -2020-10-23T16:00,38.7,38,15,0,0,5.2,15 -2020-10-23T17:00,33.7,54,19,0,0,1.8,11.4 -2020-10-23T18:00,32.1,60,19.7,0,0,4.7,7.6 -2020-10-23T19:00,29.9,64,19.3,0,0,1.4,8.3 -2020-10-23T20:00,28,66,18.2,0,0,0.9,3.1 -2020-10-23T21:00,27.8,68,18.5,0,0,3.1,5.1 -2020-10-23T22:00,27.6,72,19.7,0,0,3.4,7.4 -2020-10-23T23:00,28.6,69,19.9,0,0,2.1,5.6 -2020-10-24T00:00,26.9,75,19.9,0,0,3.6,6.9 -2020-10-24T01:00,25.3,79,19.6,0,0,3,7.4 -2020-10-24T02:00,23.9,80,18.7,0,0,4.8,8.3 -2020-10-24T03:00,21.9,84,17.9,0,0,5.9,11 -2020-10-24T04:00,20.9,87,17.8,0,0,5,10.5 -2020-10-24T05:00,20.4,92,18.5,0,0,4.6,12.5 -2020-10-24T06:00,21.7,89,18.9,0,0,3.6,11.6 -2020-10-24T07:00,24.1,82,19.4,0,0,5.9,11.9 -2020-10-24T08:00,32.8,61,20.7,0,0,5.1,11.2 -2020-10-24T09:00,41.1,47,22.5,0,0,5.5,12.1 -2020-10-24T10:00,46.3,41,23.6,0,0,8.1,18.3 -2020-10-24T11:00,52.7,34,25.1,0,0,3.5,17.4 -2020-10-24T12:00,56.8,29,24.6,0,0,7,16.3 -2020-10-24T13:00,59.4,27,25.1,0,0,7.3,16.8 -2020-10-24T14:00,59.5,27,25.4,0,0,7.1,17.4 -2020-10-24T15:00,59.2,27,25.6,0,0,7.3,15.7 -2020-10-24T16:00,57.5,30,25.9,0,0,5.4,14.8 -2020-10-24T17:00,47.1,39,23.6,0,0,4.7,9.6 -2020-10-24T18:00,44.9,45,24.7,0,0,7.9,12.5 -2020-10-24T19:00,42.7,49,24.9,0,0,10.1,15.9 -2020-10-24T20:00,38.8,56,24.3,0,0,11.9,19.5 -2020-10-24T21:00,36.5,60,24.1,0,0,11.4,19.9 -2020-10-24T22:00,34.2,65,23.5,0,0,9,19 -2020-10-24T23:00,32.5,68,23.1,0,0,5.9,15 -2020-10-25T00:00,32.2,67,22.6,0,0,5.7,10.5 -2020-10-25T01:00,31.6,68,22.2,0,0,6.6,11.2 -2020-10-25T02:00,31.2,69,22.4,0,0,7.1,12.5 -2020-10-25T03:00,28.4,76,21.7,0,0.055,12.8,20.6 -2020-10-25T04:00,23.3,76,17,0,0.083,13,25.9 -2020-10-25T05:00,17.7,70,9.7,0,0.055,13.1,23 -2020-10-25T06:00,16.3,72,8.8,0,0.055,12.3,23.5 -2020-10-25T07:00,15.1,71,7.3,0,0.055,12.2,23.7 -2020-10-25T08:00,15.3,65,5.6,0.004,0.028,8.4,21.7 -2020-10-25T09:00,15.7,62,5.1,0,0.028,9.2,19.7 -2020-10-25T10:00,16.3,62,5.6,0,0.055,6.3,18.1 -2020-10-25T11:00,17.2,62,6.3,0,0.11,7.3,16.1 -2020-10-25T12:00,17.5,62,6.5,0,0.083,9,17.2 -2020-10-25T13:00,17.8,61,6.6,0,0.083,11.2,22.6 -2020-10-25T14:00,17,64,6.9,0,0.138,12.8,23 -2020-10-25T15:00,16.1,66,6.8,0,0.138,11.4,23.3 -2020-10-25T16:00,15.3,68,6.6,0,0.11,11,21.7 -2020-10-25T17:00,11.2,76,5.2,0,0.11,10.2,21 -2020-10-25T18:00,10.8,73,3.9,0,0.22,9.5,17.2 -2020-10-25T19:00,10.6,75,4.2,0,0.165,9.2,17.7 -2020-10-25T20:00,10.1,83,6.1,0,0.11,8.1,15.9 -2020-10-25T21:00,9.5,83,5.5,0,0.11,7.4,14.1 -2020-10-25T22:00,9.4,83,5.3,0,0.11,6,12.5 -2020-10-25T23:00,8.6,84,4.8,0,0.11,6.2,11.9 -2020-10-26T00:00,8.2,85,4.7,0,0.11,5.4,10.5 -2020-10-26T01:00,8,87,4.8,0,0.055,4.6,9.4 -2020-10-26T02:00,7.6,88,4.8,0,0.055,6.5,10.7 -2020-10-26T03:00,7.1,88,4.3,0,0.11,6.8,10.3 -2020-10-26T04:00,6.5,88,3.7,0,0.083,7.8,11.6 -2020-10-26T05:00,6.5,89,3.9,0,0.055,4.8,11.9 -2020-10-26T06:00,6,89,3.5,0,0.055,5.8,8.9 -2020-10-26T07:00,6.3,89,3.7,0,0.055,4.5,8.7 -2020-10-26T08:00,7.1,88,4.3,0,0.028,4.5,7.6 -2020-10-26T09:00,7.3,88,4.5,0,0,4.8,8.5 -2020-10-26T10:00,7.6,86,4.2,0,0,2.8,9.2 -2020-10-26T11:00,8.5,77,2.7,0,0,1.3,6.7 -2020-10-26T12:00,9.2,75,2.8,0,0.028,2.8,6.9 -2020-10-26T13:00,10.4,72,3.3,0,0.028,3.8,8.5 -2020-10-26T14:00,10.5,74,3.7,0,0,2.7,8.7 -2020-10-26T15:00,9.9,77,4.1,0,0,1.8,6.3 -2020-10-26T16:00,7.1,79,1.9,0,0,4,5.6 -2020-10-26T17:00,6.2,89,3.6,0,0,2.9,5.1 -2020-10-26T18:00,6.6,88,3.7,0,0,4.1,4.3 -2020-10-26T19:00,1.8,92,0,0,0,5.7,6.3 -2020-10-26T20:00,-1.4,95,-2.5,0,0,5.5,7.6 -2020-10-26T21:00,-2.5,91,-4.4,0,0,4.7,6.7 -2020-10-26T22:00,-5.3,89,-7.9,0,0,5.7,6.5 -2020-10-26T23:00,-7.8,83,-11.6,0,0,7.7,8.7 -2020-10-27T00:00,-4.4,77,-9.8,0,0,8.6,10.3 -2020-10-27T01:00,-1.8,77,-7.4,0,0,7.7,10.1 -2020-10-27T02:00,-2.6,78,-7.6,0,0,8.5,9.6 -2020-10-27T03:00,1,75,-5.3,0,0,7,10.3 -2020-10-27T04:00,-1.4,78,-6.6,0,0,7.5,8.9 -2020-10-27T05:00,-0.3,73,-7,0,0,7.5,9.2 -2020-10-27T06:00,-4.1,73,-10.7,0,0,8.4,9.8 -2020-10-27T07:00,-5.6,72,-12.2,0,0,9.8,12.1 -2020-10-27T08:00,0.4,67,-8,0,0,8.1,11.9 -2020-10-27T09:00,8.4,63,-1.5,0,0,7,9.6 -2020-10-27T10:00,21.4,44,2.9,0,0,3,8.5 -2020-10-27T11:00,22.7,59,10.7,0,0,4.4,6 -2020-10-27T12:00,29,60,16.8,0,0,5.8,11 -2020-10-27T13:00,33.8,47,15.8,0,0,5.8,8.7 -2020-10-27T14:00,37.9,34,12,0,0,3.8,7.2 -2020-10-27T15:00,31,56,17.1,0,0,5.1,7.8 -2020-10-27T16:00,24.4,69,15.7,0,0,5.6,10.3 -2020-10-27T17:00,23,71,14.9,0,0,2.1,9.6 -2020-10-27T18:00,22.4,73,14.9,0,0,1.4,2.5 -2020-10-27T19:00,15.9,75,9.2,0,0,6.7,7.2 -2020-10-27T20:00,14,80,9,0,0,5.9,9.4 -2020-10-27T21:00,7.6,87,4.5,0,0,6.2,7.6 -2020-10-27T22:00,7.2,81,2.7,0,0,5.7,7.4 -2020-10-27T23:00,7.2,75,1,0,0,7.2,8.7 -2020-10-28T00:00,7.5,79,2.4,0,0,6.3,8.5 -2020-10-28T01:00,9,78,3.5,0,0,5.9,7.8 -2020-10-28T02:00,9.5,76,3.5,0,0,7.2,8.7 -2020-10-28T03:00,10.3,77,4.5,0,0,7.1,8.7 -2020-10-28T04:00,13.3,74,6.5,0,0,8.2,9.8 -2020-10-28T05:00,18,76,11.6,0,0,7.2,9.8 -2020-10-28T06:00,18.9,74,11.8,0,0,6.9,9.8 -2020-10-28T07:00,23,71,14.9,0,0,4.2,8.1 -2020-10-28T08:00,22.2,74,15.1,0,0,7.4,10.3 -2020-10-28T09:00,36.1,52,20.4,0,0,2.6,9.2 -2020-10-28T10:00,35.2,68,25.7,0,0,6.3,7.8 -2020-10-28T11:00,40,63,28.3,0,0,4.3,8.7 -2020-10-28T12:00,41.2,64,29.8,0,0,5.2,8.3 -2020-10-28T13:00,41.7,60,28.7,0,0,4.6,8.7 -2020-10-28T14:00,41.2,60,28.4,0,0,4.8,9.4 -2020-10-28T15:00,39.2,65,28.6,0,0,5.4,8.7 -2020-10-28T16:00,44.1,48,25.5,0,0,4,8.5 -2020-10-28T17:00,35.7,71,27.1,0,0,7.5,6 -2020-10-28T18:00,40.5,58,26.8,0,0,4,8.9 -2020-10-28T19:00,33.5,68,24.3,0,0,4.9,5.6 -2020-10-28T20:00,28.3,80,22.8,0,0,6.2,8.7 -2020-10-28T21:00,24.1,87,20.7,0,0,6,8.5 -2020-10-28T22:00,21.6,88,18.7,0,0,6.1,8.5 -2020-10-28T23:00,19.2,88,16.3,0,0,7,9.2 -2020-10-29T00:00,18.3,87,15.1,0,0,8.6,10.7 -2020-10-29T01:00,19.7,84,15.5,0,0,8.3,11.2 -2020-10-29T02:00,18.9,85,15,0,0,8.4,11.4 -2020-10-29T03:00,21.2,82,16.7,0,0,7,10.5 -2020-10-29T04:00,20.4,82,15.8,0,0,7.1,8.9 -2020-10-29T05:00,20.4,84,16.3,0,0,7.9,9.6 -2020-10-29T06:00,19.3,84,15.2,0,0,8.1,10.7 -2020-10-29T07:00,21.6,81,16.7,0,0,7.3,10.7 -2020-10-29T08:00,27.5,75,20.7,0,0,6.5,10.3 -2020-10-29T09:00,37.5,65,26.8,0,0,4.6,9.6 -2020-10-29T10:00,41.9,59,28.8,0,0,5.4,9.2 -2020-10-29T11:00,44.6,56,30,0,0,6.3,9.8 -2020-10-29T12:00,56.3,31,26,0,0,4.6,10.3 -2020-10-29T13:00,54.1,36,28,0,0,4.9,6.9 -2020-10-29T14:00,46.8,53,30.6,0,0,6.8,10.5 -2020-10-29T15:00,49.8,45,29.1,0,0,6.1,10.3 -2020-10-29T16:00,56.6,34,28.3,0,0,5.3,9.2 -2020-10-29T17:00,51.6,48,32.6,0,0,3.4,6.7 -2020-10-29T18:00,50,50,32.1,0,0,5,7.6 -2020-10-29T19:00,45.1,56,30.4,0,0,6.1,6.7 -2020-10-29T20:00,33.7,76,26.9,0,0,8.9,11.9 -2020-10-29T21:00,33.1,76,26.3,0,0,8.2,11.6 -2020-10-29T22:00,32.5,72,24.6,0,0,8.5,11.6 -2020-10-29T23:00,34.6,63,23.2,0,0,7.4,12.1 -2020-10-30T00:00,36.3,60,23.8,0,0,5.5,8.9 -2020-10-30T01:00,34.1,64,23.1,0,0,5.7,6.7 -2020-10-30T02:00,28.8,70,20.3,0,0,6.8,8.1 -2020-10-30T03:00,27,71,18.8,0,0,7.4,8.9 -2020-10-30T04:00,26.6,70,18.2,0,0,7.3,9.2 -2020-10-30T05:00,26,76,19.6,0,0,7.4,8.9 -2020-10-30T06:00,24.4,74,17.4,0,0,7.6,10.1 -2020-10-30T07:00,25.8,72,18,0,0,6.5,9.4 -2020-10-30T08:00,30.4,69,21.3,0,0,5.4,8.9 -2020-10-30T09:00,42.6,48,24.2,0,0,3.3,8.1 -2020-10-30T10:00,50.2,35,23.7,0,0,3.1,5.6 -2020-10-30T11:00,56.1,29,24.6,0,0,4.1,11.4 -2020-10-30T12:00,59.5,27,25.2,0,0,4.8,12.5 -2020-10-30T13:00,61.9,25,25.8,0,0,4.9,12.8 -2020-10-30T14:00,62.7,25,26.5,0,0,5.6,12.3 -2020-10-30T15:00,61.5,28,27.9,0,0,7.2,13.4 -2020-10-30T16:00,57.8,36,30.8,0,0,5.4,13.2 -2020-10-30T17:00,46,56,30.9,0,0,7.9,8.5 -2020-10-30T18:00,47.6,53,31.4,0,0,2.1,9.4 -2020-10-30T19:00,42.4,63,30.5,0,0,4.5,6.5 -2020-10-30T20:00,38.2,69,29,0,0,4.4,6.9 -2020-10-30T21:00,35.2,73,27.5,0,0,4.8,7.8 -2020-10-30T22:00,33.8,72,25.7,0,0,4.6,7.4 -2020-10-30T23:00,34.1,67,24.3,0,0,5.7,8.3 -2020-10-31T00:00,37.2,54,21.9,0,0,7.2,11 -2020-10-31T01:00,39.1,45,19.5,0,0,7.5,11.9 -2020-10-31T02:00,36.6,48,18.7,0,0,6.4,11.4 -2020-10-31T03:00,32.6,56,18.7,0,0,8,9.8 -2020-10-31T04:00,31.1,59,18.6,0,0,6.9,9.8 -2020-10-31T05:00,28.6,68,19.5,0,0,7,8.7 -2020-10-31T06:00,31.2,63,19.9,0,0,4.3,8.3 -2020-10-31T07:00,34.3,57,20.8,0,0,4.3,7.4 -2020-10-31T08:00,40.7,51,23.9,0,0,4.7,9.2 -2020-10-31T09:00,49.2,41,26.5,0,0,3.6,9.4 -2020-10-31T10:00,56.6,27,22.7,0,0,4.7,10.7 -2020-10-31T11:00,59.8,22,20.7,0,0,5.5,12.8 -2020-10-31T12:00,60.4,25,24.2,0,0,11.1,20.4 -2020-10-31T13:00,55.9,33,27.1,0,0,15.3,27.7 -2020-10-31T14:00,55.7,33,26.7,0,0,9.9,27.3 -2020-10-31T15:00,54.3,35,27,0,0,8.2,19 -2020-10-31T16:00,52.6,39,28,0,0,4.7,15.4 -2020-10-31T17:00,51.3,37,25.8,0,0,2.1,7.6 -2020-10-31T18:00,48.3,41,25.8,0,0,1.1,2.9 -2020-10-31T19:00,43.3,52,26.8,0,0,2.4,2.9 -2020-10-31T20:00,36.1,74,28.6,0,0,3.4,5.1 -2020-10-31T21:00,34,82,29,0,0,3.4,4.9 -2020-10-31T22:00,31.9,88,28.9,0,0,4.4,5.8 -2020-10-31T23:00,31.1,88,27.9,0,0,4.9,6.5 -2020-11-01T00:00,29.8,87,26.4,0,0,4.3,6.7 -2020-11-01T01:00,29.7,82,24.9,0,0,4,5.1 -2020-11-01T02:00,28.1,82,23.5,0,0,5.1,6 -2020-11-01T03:00,28.9,74,21.7,0,0,3.6,6 -2020-11-01T04:00,31.1,66,21.1,0,0,1.2,4.5 -2020-11-01T05:00,31.3,67,21.7,0,0,1,1.8 -2020-11-01T06:00,26.3,79,20.6,0,0,4.6,4.9 -2020-11-01T07:00,28.2,72,20.2,0,0,4,6.7 -2020-11-01T08:00,36,54,21.1,0,0,6.3,11.2 -2020-11-01T09:00,45.3,40,22.4,0,0,6.6,13.2 -2020-11-01T10:00,53.5,28,21.2,0,0,6.5,14.1 -2020-11-01T11:00,60.7,22,21.4,0,0,5.1,14.1 -2020-11-01T12:00,64.9,18,20.6,0,0,4.4,12.1 -2020-11-01T13:00,62.4,26,26.9,0,0,11.1,20.6 -2020-11-01T14:00,61.2,28,27.8,0,0,9,20.8 -2020-11-01T15:00,60.5,29,28,0,0,6.9,17.4 -2020-11-01T16:00,57.5,38,31.7,0,0,3.9,13.2 -2020-11-01T17:00,49.8,45,29.6,0,0,6.1,7.6 -2020-11-01T18:00,45.7,56,30.8,0,0,6,8.5 -2020-11-01T19:00,51.1,42,28.7,0,0,2.9,7.8 -2020-11-01T20:00,52.1,37,26.8,0,0,1.6,3.6 -2020-11-01T21:00,45.6,47,26.4,0,0,1.6,2.2 -2020-11-01T22:00,37.6,63,26,0,0,3.6,4.5 -2020-11-01T23:00,34.8,67,24.8,0,0,4,4.7 -2020-11-02T00:00,33.7,65,23.4,0,0,5.2,6.5 -2020-11-02T01:00,34.1,61,21.8,0,0,5.5,7.4 -2020-11-02T02:00,33.8,62,22,0,0,6.5,8.1 -2020-11-02T03:00,33.4,62,21.6,0,0,6.9,8.3 -2020-11-02T04:00,33,61,20.8,0,0,7.1,8.5 -2020-11-02T05:00,35.3,53,20,0,0,6.1,8.5 -2020-11-02T06:00,34.2,56,19.9,0,0,6.1,7.4 -2020-11-02T07:00,34.6,60,22.2,0,0,5.9,7.6 -2020-11-02T08:00,43.5,43,22.6,0,0,4.5,8.3 -2020-11-02T09:00,51.7,36,25.8,0,0,3.6,9.2 -2020-11-02T10:00,59,29,26.8,0,0,4.4,11 -2020-11-02T11:00,65.8,19,22.4,0,0,4.8,11.6 -2020-11-02T12:00,69.5,15,20.1,0,0,3.1,11.4 -2020-11-02T13:00,71.2,15,20.5,0,0,3,9.6 -2020-11-02T14:00,71.6,15,21.9,0,0,3.4,9.2 -2020-11-02T15:00,70.4,18,25.1,0,0,4.5,8.9 -2020-11-02T16:00,64.4,31,33.4,0,0,4.4,8.5 -2020-11-02T17:00,57.3,33,28.7,0,0,6.9,8.5 -2020-11-02T18:00,53.5,41,30.2,0,0,5.2,8.3 -2020-11-02T19:00,46.9,53,30.7,0,0,3.4,6.5 -2020-11-02T20:00,42.9,61,30.2,0,0,3.8,5.4 -2020-11-02T21:00,41.7,59,28.3,0,0,5.7,7.4 -2020-11-02T22:00,41,55,25.9,0,0,5.4,7.4 -2020-11-02T23:00,39.7,53,23.8,0,0,5.7,7.4 -2020-11-03T00:00,38.9,53,23.1,0,0,7,8.7 -2020-11-03T01:00,38.2,56,23.7,0,0,7.8,9.2 -2020-11-03T02:00,37.4,58,24.1,0,0,7.7,9.4 -2020-11-03T03:00,37.4,58,23.9,0,0,7.9,9.2 -2020-11-03T04:00,37.8,56,23.6,0,0,7.5,9.6 -2020-11-03T05:00,37.1,58,23.5,0,0,7.9,8.9 -2020-11-03T06:00,38.2,55,23.5,0,0,6.8,9.2 -2020-11-03T07:00,40.1,54,24.9,0,0,5.7,8.7 -2020-11-03T08:00,48.7,42,26.4,0,0,5.4,7.8 -2020-11-03T09:00,57.3,34,29.5,0,0,3.9,9.2 -2020-11-03T10:00,64.9,28,31.5,0,0,4,9.8 -2020-11-03T11:00,69.7,23,29.8,0,0,3.4,10.3 -2020-11-03T12:00,71.3,22,30.1,0,0,3,9.8 -2020-11-03T13:00,71.4,21,29.9,0,0,3.4,9.2 -2020-11-03T14:00,70.8,20,28,0,0,3.6,9.2 -2020-11-03T15:00,70.8,19,26.7,0,0,2.9,8.7 -2020-11-03T16:00,65.3,42,41.8,0,0,4.8,6.3 -2020-11-03T17:00,59.6,29,27.2,0,0,6.3,8.7 -2020-11-03T18:00,54.4,39,29.8,0,0,5.6,9.2 -2020-11-03T19:00,50.4,49,31.7,0,0,6,7.6 -2020-11-03T20:00,50.7,42,28.4,0,0,6.1,9.8 -2020-11-03T21:00,51.4,39,27.1,0,0,6.3,10.3 -2020-11-03T22:00,48.8,39,25.1,0,0,6.5,10.1 -2020-11-03T23:00,45.7,42,24,0,0,8.7,10.3 -2020-11-04T00:00,45.3,44,24.8,0,0,7,10.5 -2020-11-04T01:00,42.8,55,27.7,0,0,6.5,8.5 -2020-11-04T02:00,41.3,62,29.1,0,0,6.3,7.8 -2020-11-04T03:00,41,62,29.1,0,0,5.2,7.6 -2020-11-04T04:00,41.2,61,28.9,0,0,4.2,6.9 -2020-11-04T05:00,40.6,61,28.3,0,0,5,7.4 -2020-11-04T06:00,40.6,60,27.7,0,0,4.6,7.8 -2020-11-04T07:00,42.3,56,27.7,0,0,4.6,7.4 -2020-11-04T08:00,50.4,45,29.7,0,0,5.9,9.8 -2020-11-04T09:00,59,36,32,0,0,7.1,13.2 -2020-11-04T10:00,66.1,27,30.8,0,0,5.8,14.3 -2020-11-04T11:00,70.2,24,31.9,0,0,1.6,12.5 -2020-11-04T12:00,72.6,22,31.9,0,0,4.5,11 -2020-11-04T13:00,73.8,17,26.6,0,0,7.8,15.7 -2020-11-04T14:00,73.7,17,26.1,0,0,10.3,18.8 -2020-11-04T15:00,71.6,20,28.5,0,0,12,21.5 -2020-11-04T16:00,67.7,27,32.1,0,0,5.7,20.6 -2020-11-04T17:00,61.8,32,31.5,0,0,7.3,10.5 -2020-11-04T18:00,59.4,32,29.8,0,0,7.7,13.6 -2020-11-04T19:00,54.7,39,30.4,0,0,6.4,12.5 -2020-11-04T20:00,56,37,30.4,0,0,6.4,10.3 -2020-11-04T21:00,56.8,36,29.8,0,0,1.8,8.1 -2020-11-04T22:00,47.9,53,31.8,0,0,4.5,6.9 -2020-11-04T23:00,46.4,58,32.5,0,0,5,8.3 -2020-11-05T00:00,44,63,32.2,0,0,5.4,7.8 -2020-11-05T01:00,43.1,64,31.6,0,0,6,7.6 -2020-11-05T02:00,42.1,63,30.5,0,0,6.7,8.1 -2020-11-05T03:00,41.4,62,29.2,0,0,7,8.3 -2020-11-05T04:00,41.2,60,28.4,0,0,6.3,8.5 -2020-11-05T05:00,42,58,28.3,0,0,5,8.1 -2020-11-05T06:00,41.2,59,28,0,0,5,7.8 -2020-11-05T07:00,41.6,57,27.7,0,0,5,8.1 -2020-11-05T08:00,48.7,45,28.4,0,0,5.6,9.8 -2020-11-05T09:00,56.4,36,29.8,0,0,4.5,10.5 -2020-11-05T10:00,63.2,27,28.7,0,0,4.1,11 -2020-11-05T11:00,68.1,21,26.4,0,0,4,11.2 -2020-11-05T12:00,70.9,17,24.5,0,0,3.5,11.2 -2020-11-05T13:00,72.2,17,24.9,0,0,2.1,10.5 -2020-11-05T14:00,73,16,24.6,0,0,2.3,8.7 -2020-11-05T15:00,72.9,16,24.4,0,0,2.5,8.1 -2020-11-05T16:00,67.5,36,39.7,0,0,3.2,6.3 -2020-11-05T17:00,62.1,28,28.3,0,0,5.8,6.7 -2020-11-05T18:00,65.1,24,27.1,0,0,1.6,6.9 -2020-11-05T19:00,51.8,41,28.6,0,0,5.2,6.3 -2020-11-05T20:00,47.4,51,30.2,0,0,4.9,6.9 -2020-11-05T21:00,46.4,52,29.7,0,0,4.4,6.9 -2020-11-05T22:00,44.7,54,29,0,0,5.2,7.2 -2020-11-05T23:00,44.1,53,28,0,0,4.6,7.4 -2020-11-06T00:00,43.3,50,26.1,0,0,5,7.8 -2020-11-06T01:00,42.2,49,24.2,0,0,5.3,7.8 -2020-11-06T02:00,42.4,45,22.8,0,0,5.4,8.3 -2020-11-06T03:00,42.5,41,20.4,0,0,6.1,8.9 -2020-11-06T04:00,39.9,43,19.4,0,0,8.1,9.8 -2020-11-06T05:00,43.4,35,17.9,0,0,6.2,9.8 -2020-11-06T06:00,45.1,34,18.2,0,0,5.4,7.2 -2020-11-06T07:00,42.5,42,20.8,0,0,6.1,6.9 -2020-11-06T08:00,49.7,33,21.6,0,0,3.4,6.9 -2020-11-06T09:00,58.2,26,23.2,0,0,4,8.9 -2020-11-06T10:00,65.9,20,24,0,0,4.6,11 -2020-11-06T11:00,71.1,16,22.1,0,0,3.3,11.2 -2020-11-06T12:00,73.1,15,22,0,0,3.8,10.5 -2020-11-06T13:00,74,15,23.5,0,0,7.2,14.8 -2020-11-06T14:00,73.7,15,23.5,0,0,7.8,15.9 -2020-11-06T15:00,72.3,16,24.1,0,0,7.4,15.2 -2020-11-06T16:00,67.1,23,27.9,0,0,8.1,13 -2020-11-06T17:00,57.4,33,28.9,0,0,7.4,13 -2020-11-06T18:00,61.7,27,27.3,0,0,2.7,8.3 -2020-11-06T19:00,61.1,28,27.3,0,0,1,2.9 -2020-11-06T20:00,59.2,30,27.4,0,0,1.7,2 -2020-11-06T21:00,53.5,40,29.8,0,0,4.3,6.5 -2020-11-06T22:00,54.3,39,30.1,0,0,6,9.8 -2020-11-06T23:00,55,37,28.9,0,0,7.6,12.3 -2020-11-07T00:00,52.8,39,28.8,0,0,9.2,15 -2020-11-07T01:00,52.2,41,28.9,0,0,10.4,17 -2020-11-07T02:00,52.3,41,29.1,0,0,11.4,18.8 -2020-11-07T03:00,53,40,29.5,0,0,10.8,19.2 -2020-11-07T04:00,53.9,39,29.8,0,0,11.5,18.8 -2020-11-07T05:00,53,41,29.9,0,0,9.9,18.8 -2020-11-07T06:00,51.6,43,30.1,0,0,11.5,19 -2020-11-07T07:00,50.5,45,29.8,0,0,9.6,19 -2020-11-07T08:00,55.7,38,30.2,0,0,10.1,16.1 -2020-11-07T09:00,62.2,30,30.7,0,0,11.3,18.3 -2020-11-07T10:00,67.2,25,30,0,0,19.2,32.2 -2020-11-07T11:00,67.8,25,31.1,0,0,22.4,38.5 -2020-11-07T12:00,71.2,23,31,0,0,19.7,38.5 -2020-11-07T13:00,70.5,23,31.3,0,0,17.9,34.2 -2020-11-07T14:00,71.2,23,31.3,0,0,18.3,31.3 -2020-11-07T15:00,70.6,23,31.1,0,0,18.8,32.2 -2020-11-07T16:00,68,25,31.2,0,0,20.1,33.1 -2020-11-07T17:00,63.1,29,30.1,0,0,8.9,33.6 -2020-11-07T18:00,56.2,38,30.8,0,0,5.9,15 -2020-11-07T19:00,54.5,41,31.6,0,0,6.4,10.1 -2020-11-07T20:00,58.4,36,31.3,0,0,13.7,20.6 -2020-11-07T21:00,57.5,40,33.4,0,0,14.8,25.7 -2020-11-07T22:00,55,45,33.9,0,0,11.6,24.8 -2020-11-07T23:00,53.4,48,34.1,0,0,10.7,19.5 -2020-11-08T00:00,54,45,33.1,0,0,16,25.5 -2020-11-08T01:00,50.4,69,40.6,0.024,0,15.5,27.3 -2020-11-08T02:00,51,70,41.7,0.047,0,14.7,28 -2020-11-08T03:00,48.7,73,40.6,0.004,0,10,23.7 -2020-11-08T04:00,49.4,71,40.4,0,0,9.8,16.6 -2020-11-08T05:00,48.9,71,40,0,0,5.2,16.1 -2020-11-08T06:00,48.1,69,38.4,0.004,0,5.7,9.2 -2020-11-08T07:00,45.7,63,34,0,0,6.1,9.8 -2020-11-08T08:00,49.5,36,23.4,0,0,10.3,16.3 -2020-11-08T09:00,51.6,19,10.4,0,0,15.4,26.4 -2020-11-08T10:00,52.6,16,8.1,0,0,12.3,27.5 -2020-11-08T11:00,54.3,15,7,0,0,9.9,23.3 -2020-11-08T12:00,56.2,13,5.1,0,0,10.3,20.8 -2020-11-08T13:00,57.7,10,2.3,0,0,12.8,23.7 -2020-11-08T14:00,57.6,13,6.5,0,0,15.2,27.3 -2020-11-08T15:00,57.7,13,7.3,0,0,13.6,27.3 -2020-11-08T16:00,55.4,17,11.3,0,0,14.1,25.1 -2020-11-08T17:00,51.4,20,12,0,0,17.7,25.7 -2020-11-08T18:00,49.5,24,13.9,0,0,12.3,30 -2020-11-08T19:00,43.3,34,16.5,0,0,4.6,20.1 -2020-11-08T20:00,39.8,43,19.1,0,0,5.3,8.3 -2020-11-08T21:00,43.5,40,21,0,0,5.7,17.9 -2020-11-08T22:00,41.5,47,22.7,0,0,2.7,9.4 -2020-11-08T23:00,39.5,54,24.3,0,0,2.5,3.8 -2020-11-09T00:00,37.6,61,25.3,0,0,4.9,4.5 -2020-11-09T01:00,40.5,35,15,0,0,7.6,12.8 -2020-11-09T02:00,39.1,38,15.5,0,0,8.6,13.2 -2020-11-09T03:00,38.1,43,17.3,0,0,9.4,15.9 -2020-11-09T04:00,37.2,47,19,0,0,6.4,15.4 -2020-11-09T05:00,37.3,54,22.2,0,0,6.3,10.5 -2020-11-09T06:00,35.4,60,22.8,0,0,6.1,10.1 -2020-11-09T07:00,34.5,65,24,0,0,5.4,10.7 -2020-11-09T08:00,36.4,72,28.1,0,0,8.7,15.4 -2020-11-09T09:00,35.6,75,28.5,0.004,0,9.6,17.4 -2020-11-09T10:00,35.2,72,27,0,0.028,10.2,19.7 -2020-11-09T11:00,36,68,26.5,0,0.028,9,19 -2020-11-09T12:00,34.6,72,26.6,0,0.11,8.9,17.7 -2020-11-09T13:00,34.8,70,26.1,0,0.028,7.6,16.8 -2020-11-09T14:00,36.6,64,25.4,0.004,0,7.1,16.1 -2020-11-09T15:00,35.8,67,25.9,0,0.055,6.1,15.2 -2020-11-09T16:00,35.1,69,26,0,0.165,6.9,13 -2020-11-09T17:00,29.8,81,24.7,0,0.331,7,13.6 -2020-11-09T18:00,29.2,80,23.9,0,0.055,5.6,12.8 -2020-11-09T19:00,28.9,79,23.4,0,0.055,3.4,10.5 -2020-11-09T20:00,29.1,79,23.3,0,0.055,2.1,7.6 -2020-11-09T21:00,28.8,80,23.3,0,0.055,2.4,6.3 -2020-11-09T22:00,28.6,80,23.2,0,0.028,1.6,6 -2020-11-09T23:00,27.8,83,23.2,0,0,1.4,5.1 -2020-11-10T00:00,26.8,86,23.1,0,0,1.3,4.5 -2020-11-10T01:00,26.2,88,23.3,0,0,5.2,7.2 -2020-11-10T02:00,26.9,89,24,0,0,8.4,12.3 -2020-11-10T03:00,27.8,69,19,0,0,8.6,14.5 -2020-11-10T04:00,27.1,49,10.4,0,0,7.7,13.6 -2020-11-10T05:00,27.5,45,9.1,0,0,7.5,14.5 -2020-11-10T06:00,26.2,48,9.2,0,0,6.4,12.1 -2020-11-10T07:00,25.2,50,9.1,0,0,6.6,10.3 -2020-11-10T08:00,31.3,39,9.4,0,0,7.4,12.3 -2020-11-10T09:00,38.6,27,7.8,0,0,8.1,15.9 -2020-11-10T10:00,40.6,24,6.8,0,0,4.8,15.4 -2020-11-10T11:00,42.5,21,5.5,0,0,3.4,12.8 -2020-11-10T12:00,44.4,19,4.8,0,0,3.5,11.9 -2020-11-10T13:00,46,18,5.4,0,0,5.7,13.9 -2020-11-10T14:00,46.3,18,5.1,0,0,10.7,19.7 -2020-11-10T15:00,43.4,21,6.1,0,0,13.6,25.1 -2020-11-10T16:00,40.7,27,9,0,0,14.6,24.4 -2020-11-10T17:00,37,36,12.7,0,0,10.6,25.7 -2020-11-10T18:00,35.9,38,12.7,0,0,7.8,17.4 -2020-11-10T19:00,35.1,36,10.9,0,0,6,12.8 -2020-11-10T20:00,34.6,36,10.6,0,0,2.2,10.7 -2020-11-10T21:00,29.1,52,13.5,0,0,3.7,4.7 -2020-11-10T22:00,27.7,57,14.3,0,0,2.7,4.9 -2020-11-10T23:00,28.8,54,14.2,0,0,2.3,3.6 -2020-11-11T00:00,24.2,70,15.9,0,0,5.6,6.3 -2020-11-11T01:00,22.8,72,15.2,0,0,4.4,6.9 -2020-11-11T02:00,23.2,67,13.8,0,0,3.6,6 -2020-11-11T03:00,21.3,71,13.3,0,0,4.3,5.1 -2020-11-11T04:00,21.1,69,12.6,0,0,3.2,5.4 -2020-11-11T05:00,20.7,77,14.5,0,0,5.2,5.6 -2020-11-11T06:00,20,74,13,0,0,5.4,6.3 -2020-11-11T07:00,21.7,67,12.3,0,0,3.9,6.9 -2020-11-11T08:00,27.6,49,11,0,0,4.7,9.6 -2020-11-11T09:00,33.9,35,9.1,0,0,4.5,10.7 -2020-11-11T10:00,37.9,27,6.6,0,0,5,12.8 -2020-11-11T11:00,40.9,23,6.1,0,0,4.3,12.5 -2020-11-11T12:00,43.9,20,5.6,0,0,5.6,14.1 -2020-11-11T13:00,46,18,5.4,0,0,6.1,14.5 -2020-11-11T14:00,47.2,17,4.3,0,0,7,15.7 -2020-11-11T15:00,47.2,16,3.4,0,0,6.3,15 -2020-11-11T16:00,44.7,19,5.2,0,0,9.4,15.7 -2020-11-11T17:00,35,41,13.6,0,0,5.9,15.4 -2020-11-11T18:00,34,43,13.6,0,0,4.9,8.3 -2020-11-11T19:00,29.8,53,14.7,0,0,4.5,8.1 -2020-11-11T20:00,30.9,50,14.6,0,0,1.8,5.8 -2020-11-11T21:00,26.2,62,14.8,0,0,3.4,5.6 -2020-11-11T22:00,26,62,14.7,0,0,1.4,5.6 -2020-11-11T23:00,26.9,60,14.7,0,0,2.9,5.4 -2020-11-12T00:00,23.6,68,14.7,0,0,3.3,5.6 -2020-11-12T01:00,22.7,68,13.9,0,0,4.3,5.6 -2020-11-12T02:00,22.6,69,13.9,0,0,4.7,6 -2020-11-12T03:00,22.5,70,14.1,0,0,4.7,6 -2020-11-12T04:00,23.6,66,13.9,0,0,4.3,6.5 -2020-11-12T05:00,25.5,59,13.3,0,0,0.9,6.3 -2020-11-12T06:00,23.8,64,13.5,0,0,3.7,3.4 -2020-11-12T07:00,26.2,62,15.1,0,0,5.2,7.4 -2020-11-12T08:00,28.7,54,14.1,0,0,6.8,12.3 -2020-11-12T09:00,33.1,44,13.3,0,0,8.3,15.4 -2020-11-12T10:00,35,41,13.6,0,0,11.4,20.1 -2020-11-12T11:00,36.4,37,12.7,0,0,8.3,22.1 -2020-11-12T12:00,38.8,32,11.6,0,0,3.1,17.4 -2020-11-12T13:00,42.4,26,10.1,0,0,1.6,11 -2020-11-12T14:00,44.2,25,10.8,0,0,8.8,15.7 -2020-11-12T15:00,41.3,27,10.1,0,0,11.2,23.9 -2020-11-12T16:00,40.2,28,10,0,0,8.1,19.9 -2020-11-12T17:00,36.1,45,16.9,0,0,8.8,13 -2020-11-12T18:00,31.4,53,16.2,0,0,6.9,14.3 -2020-11-12T19:00,34,47,15.6,0,0,1.6,8.5 -2020-11-12T20:00,27.1,63,16.1,0,0,4.7,6 -2020-11-12T21:00,25.3,63,14.6,0,0,3.7,6.3 -2020-11-12T22:00,23,65,13.1,0,0,4.9,6.3 -2020-11-12T23:00,22.1,62,11,0,0,4.7,6.5 -2020-11-13T00:00,21.5,55,7.9,0,0,4.9,6.3 -2020-11-13T01:00,21.2,49,5,0,0,5.6,6.7 -2020-11-13T02:00,21.8,44,3.3,0,0,4.6,6.9 -2020-11-13T03:00,23.5,39,2.3,0,0,3.3,5.6 -2020-11-13T04:00,23,40,2.3,0,0,3.6,4.3 -2020-11-13T05:00,20.9,46,3.4,0,0,4.4,4.5 -2020-11-13T06:00,19.7,47,2.5,0,0,3.8,5.4 -2020-11-13T07:00,21.7,39,0.5,0,0,4.1,6.7 -2020-11-13T08:00,31.6,23,-1.7,0,0,10.4,15.2 -2020-11-13T09:00,43.6,15,-0.8,0,0,14.8,23.7 -2020-11-13T10:00,51.7,13,2.6,0,0,15,25.9 -2020-11-13T11:00,55.9,13,5.1,0,0,9,25.5 -2020-11-13T12:00,58.8,12,6.4,0,0,6,17 -2020-11-13T13:00,61,11,6.9,0,0,7.6,14.5 -2020-11-13T14:00,60.7,13,9.9,0,0,10.3,17.9 -2020-11-13T15:00,58.5,17,14.3,0,0,11.1,19.7 -2020-11-13T16:00,53.4,23,17,0,0,9.9,18.1 -2020-11-13T17:00,44.7,33,17.6,0,0,7.8,15.9 -2020-11-13T18:00,44.9,35,18.8,0,0,7.1,11.9 -2020-11-13T19:00,43.5,37,18.8,0,0,9.4,14.1 -2020-11-13T20:00,44.8,33,17.1,0,0,14.8,24.2 -2020-11-13T21:00,42.4,34,15.9,0,0,12.5,24.4 -2020-11-13T22:00,41.5,34,15,0,0,14.9,23.7 -2020-11-13T23:00,41.9,31,13.6,0,0,15,24.8 -2020-11-14T00:00,40.4,32,12.8,0,0,9.7,23.9 -2020-11-14T01:00,39.4,35,13.7,0,0,5.1,15.2 -2020-11-14T02:00,38.3,38,15.2,0,0,4.6,8.3 -2020-11-14T03:00,39.5,44,19.1,0.004,0,6.4,9.8 -2020-11-14T04:00,39.5,49,22.1,0,0,8,13 -2020-11-14T05:00,35.1,61,22.9,0,0,7.4,17.4 -2020-11-14T06:00,38.6,61,26.2,0,0,6,10.1 -2020-11-14T07:00,38.8,65,27.9,0,0,7.2,11 -2020-11-14T08:00,42,61,29.6,0,0,9.8,15 -2020-11-14T09:00,48,38,23.9,0.004,0,13.4,21.3 -2020-11-14T10:00,43.1,29,13.3,0.008,0,23.8,39.4 -2020-11-14T11:00,41.8,27,10.3,0,0.055,22.5,40.5 -2020-11-14T12:00,42.1,21,4.7,0,0,23.8,42.7 -2020-11-14T13:00,42.4,19,3.4,0,0,22.4,41.4 -2020-11-14T14:00,41.2,19,2.5,0,0,22.4,39.1 -2020-11-14T15:00,39.1,21,2.3,0,0,21.5,38.7 -2020-11-14T16:00,37.2,24,3.5,0,0,15.5,36.7 -2020-11-14T17:00,34.6,27,4.2,0,0,12.5,29.5 -2020-11-14T18:00,32.1,30,4.4,0,0,10.1,21.3 -2020-11-14T19:00,31.5,33,5.6,0,0,9.8,18.3 -2020-11-14T20:00,29.1,34,4.5,0,0,5.6,16.8 -2020-11-14T21:00,28.1,33,2.8,0,0,5.4,9.8 -2020-11-14T22:00,28.9,32,2.7,0,0,7.2,10.7 -2020-11-14T23:00,29.8,31,2.9,0,0,12.6,20.6 -2020-11-15T00:00,28.2,33,2.8,0,0,6.4,20.6 -2020-11-15T01:00,23.7,39,2.5,0,0,4.7,10.3 -2020-11-15T02:00,20.8,41,1,0,0,9.5,15.4 -2020-11-15T03:00,22.6,40,1.9,0,0,6.3,15 -2020-11-15T04:00,22,41,1.8,0,0,4.3,10.3 -2020-11-15T05:00,23.9,40,3.3,0,0,4.8,8.1 -2020-11-15T06:00,24.6,36,1.4,0,0,6.8,9.6 -2020-11-15T07:00,26.6,30,-0.7,0,0,14.7,22.1 -2020-11-15T08:00,35.3,25,3.4,0,0,9.8,22.6 -2020-11-15T09:00,42.5,24,8.2,0,0,7.4,14.8 -2020-11-15T10:00,47.8,23,11.6,0,0,7.6,14.3 -2020-11-15T11:00,52.6,22,14.5,0,0,10.7,19.5 -2020-11-15T12:00,56.5,21,16.5,0,0,6.3,19.9 -2020-11-15T13:00,57.6,21,17.4,0,0,2.4,13.9 -2020-11-15T14:00,57.8,20,17.4,0,0,3.2,8.9 -2020-11-15T15:00,57.2,22,18.6,0,0,2.7,8.9 -2020-11-15T16:00,53.2,32,23.9,0,0,2.3,6 -2020-11-15T17:00,45.5,37,20.6,0,0,7.2,8.5 -2020-11-15T18:00,40.7,46,21.7,0,0,6.1,11.4 -2020-11-15T19:00,40.6,47,22.1,0,0,2.7,7.2 -2020-11-15T20:00,37.5,53,21.8,0,0,5.1,7.2 -2020-11-15T21:00,36.1,57,22.4,0,0,5,7.2 -2020-11-15T22:00,35.4,60,22.8,0,0,4.4,6.7 -2020-11-15T23:00,33.7,66,23.5,0,0,4.5,6.9 -2020-11-16T00:00,32,70,23.3,0,0,4.5,6.9 -2020-11-16T01:00,30.5,72,22.6,0,0,6.9,7.8 -2020-11-16T02:00,30.2,71,21.9,0,0,5.2,8.1 -2020-11-16T03:00,31.4,66,21.4,0,0,4.8,7.8 -2020-11-16T04:00,31,67,21.3,0,0,5.6,9.4 -2020-11-16T05:00,32.2,65,21.6,0,0,6.3,9.2 -2020-11-16T06:00,37.8,51,21.4,0,0,4.3,8.7 -2020-11-16T07:00,34.4,59,21.7,0,0,4.6,5.4 -2020-11-16T08:00,37.2,55,22.5,0,0,5.7,9.8 -2020-11-16T09:00,44.8,43,23.9,0,0,3.2,10.1 -2020-11-16T10:00,52.3,34,24.6,0,0,3.8,10.3 -2020-11-16T11:00,57.8,27,24.2,0,0,1.6,10.1 -2020-11-16T12:00,61.3,23,23.1,0,0,4.5,11.2 -2020-11-16T13:00,63.6,20,22.1,0,0,4.3,11.9 -2020-11-16T14:00,64.8,19,22.1,0,0,3.5,11.2 -2020-11-16T15:00,64.4,20,22.4,0,0,3.1,9.2 -2020-11-16T16:00,57.5,35,30.1,0,0,4.7,6.5 -2020-11-16T17:00,53.9,35,27.1,0,0,5,7.8 -2020-11-16T18:00,48.9,43,27.3,0,0,3.3,5.8 -2020-11-16T19:00,45.6,49,27.6,0,0,3.6,5.6 -2020-11-16T20:00,39.7,63,28,0,0,4.6,5.4 -2020-11-16T21:00,37.8,66,27.6,0,0,4.5,6.9 -2020-11-16T22:00,37.2,61,25.2,0,0,4.6,7.8 -2020-11-16T23:00,34.3,66,24.3,0,0,5.1,7.4 -2020-11-17T00:00,33.6,65,23,0,0,5.7,7.4 -2020-11-17T01:00,33.4,62,21.8,0,0,6.5,8.1 -2020-11-17T02:00,32.5,63,21.4,0,0,6.1,8.1 -2020-11-17T03:00,33,60,20.8,0,0,5.2,7.2 -2020-11-17T04:00,32.2,61,20.1,0,0,6.9,8.3 -2020-11-17T05:00,31.4,63,20.3,0,0,7.3,8.5 -2020-11-17T06:00,30.6,65,20.2,0,0,7.5,8.9 -2020-11-17T07:00,30,66,20,0,0,6.9,8.9 -2020-11-17T08:00,38.5,47,20.1,0,0,4.4,8.5 -2020-11-17T09:00,46.7,36,21.4,0,0,4.1,9.2 -2020-11-17T10:00,55.4,25,20.3,0,0,5.1,11.6 -2020-11-17T11:00,62.2,19,19.3,0,0,4,11.6 -2020-11-17T12:00,66.7,16,18.6,0,0,2.6,10.5 -2020-11-17T13:00,69.2,14,17.2,0,0,2.6,8.9 -2020-11-17T14:00,69.7,13,17.2,0,0,4.5,10.1 -2020-11-17T15:00,67.9,16,20.2,0,0,5.6,10.5 -2020-11-17T16:00,59.6,28,26.6,0,0,4.9,9.2 -2020-11-17T17:00,53.2,38,28.5,0,0,6.5,8.1 -2020-11-17T18:00,47.6,52,30.8,0,0,7,10.3 -2020-11-17T19:00,46.7,49,28.7,0,0,2,8.9 -2020-11-17T20:00,40.4,58,26.6,0,0,4.5,6.7 -2020-11-17T21:00,38.1,58,24.7,0,0,5.6,7.4 -2020-11-17T22:00,36.4,60,23.8,0,0,7.5,8.5 -2020-11-17T23:00,37.1,51,20.7,0,0,6.5,9.4 -2020-11-18T00:00,38.3,45,19,0,0,5.9,8.9 -2020-11-18T01:00,37.8,46,18.9,0,0,6.1,9.4 -2020-11-18T02:00,38.8,45,19.2,0,0,6.5,10.3 -2020-11-18T03:00,40.4,44,19.9,0,0,6.7,10.7 -2020-11-18T04:00,41.5,42,20.1,0,0,6.8,10.7 -2020-11-18T05:00,38.3,50,21.2,0,0,8.5,10.7 -2020-11-18T06:00,41.5,44,21.4,0,0,5.4,10.5 -2020-11-18T07:00,42,43,21.3,0,0,5.4,8.1 -2020-11-18T08:00,48.9,34,21.7,0,0,7.2,10.5 -2020-11-18T09:00,56.3,27,22.5,0,0,10.7,17 -2020-11-18T10:00,60.9,24,23.6,0,0,12.2,19.2 -2020-11-18T11:00,65.6,20,23.8,0,0,13.2,22.6 -2020-11-18T12:00,66.7,20,24.6,0,0,12.6,22.4 -2020-11-18T13:00,68.5,20,25.5,0,0,11.9,22.1 -2020-11-18T14:00,69.3,20,26.4,0,0,12.5,21.3 -2020-11-18T15:00,67.1,24,29.1,0,0,13.2,23.5 -2020-11-18T16:00,63.5,30,31.1,0,0,7.5,21.7 -2020-11-18T17:00,59.5,26,24.9,0,0,4.7,12.1 -2020-11-18T18:00,53.2,34,25.6,0,0,6.7,9.8 -2020-11-18T19:00,46.6,44,25.7,0,0,4.1,8.1 -2020-11-18T20:00,42.5,57,28.2,0,0,4.4,6.5 -2020-11-18T21:00,42,60,28.9,0,0,5.6,7.6 -2020-11-18T22:00,44.2,52,27.7,0,0,6.2,9.8 -2020-11-18T23:00,43,52,26.5,0,0,6.8,10.7 -2020-11-19T00:00,41.2,54,25.8,0,0,7,11.2 -2020-11-19T01:00,40.6,54,25.2,0,0,7.4,11.6 -2020-11-19T02:00,40.6,53,24.9,0,0,7.4,12.3 -2020-11-19T03:00,40,55,25.2,0,0,7.9,13 -2020-11-19T04:00,38.1,61,25.9,0,0,5.8,13 -2020-11-19T05:00,37,62,25.2,0,0,4.3,9.4 -2020-11-19T06:00,35.3,67,25.5,0,0,3,6.5 -2020-11-19T07:00,39.9,56,25.4,0,0,1.8,4 -2020-11-19T08:00,40.6,57,26.5,0,0,0.7,2.9 -2020-11-19T09:00,47.7,46,27.8,0,0,2.3,6.9 -2020-11-19T10:00,54.5,35,27.3,0,0,6.2,12.8 -2020-11-19T11:00,59.9,27,25.5,0,0,7.8,15.4 -2020-11-19T12:00,62.3,24,24.8,0,0,11.3,21 -2020-11-19T13:00,63.1,24,25.5,0,0,11.1,22.1 -2020-11-19T14:00,63.8,25,26.9,0,0,9.6,20.8 -2020-11-19T15:00,62.4,28,29.2,0,0,9.5,18.1 -2020-11-19T16:00,57.8,37,31.6,0,0,5,16.8 -2020-11-19T17:00,49.1,50,31.1,0,0,5.8,8.3 -2020-11-19T18:00,46.1,57,31.6,0,0,5.9,7.8 -2020-11-19T19:00,47.7,52,30.8,0,0,4.3,7.2 -2020-11-19T20:00,44.1,59,30.7,0,0,8,9.2 -2020-11-19T21:00,45.3,53,29.1,0,0,7.4,11 -2020-11-19T22:00,43.5,49,25.6,0,0,9.5,15.9 -2020-11-19T23:00,40.9,52,24.6,0,0,6.7,15.4 -2020-11-20T00:00,38,57,24.3,0,0,5.3,10.5 -2020-11-20T01:00,35.8,62,24,0,0,7.4,8.9 -2020-11-20T02:00,38.3,54,23,0,0,6.8,9.2 -2020-11-20T03:00,37,54,21.7,0,0,7.2,8.9 -2020-11-20T04:00,41.5,42,20.1,0,0,4.9,8.9 -2020-11-20T05:00,39.2,44,19.1,0,0,3.9,6.3 -2020-11-20T06:00,38.2,43,17.7,0,0,2.8,5.1 -2020-11-20T07:00,37,44,17.3,0,0,3,4 -2020-11-20T08:00,36.6,54,21.4,0,0,2.1,4 -2020-11-20T09:00,41.2,44,20.7,0,0,3,8.1 -2020-11-20T10:00,44.6,38,20.8,0,0,3.5,10.5 -2020-11-20T11:00,47.8,37,23,0,0,2.7,10.5 -2020-11-20T12:00,50.3,34,22.6,0,0,0.9,10.1 -2020-11-20T13:00,51.6,31,22.1,0,0,6.2,13.9 -2020-11-20T14:00,51.3,31,21.6,0,0,10.6,19.9 -2020-11-20T15:00,51.8,25,17.4,0,0,12.8,22.1 -2020-11-20T16:00,48.2,28,16.8,0,0,15.8,25.9 -2020-11-20T17:00,46,28,14.7,0,0,15.6,26.4 -2020-11-20T18:00,43.8,31,14.8,0,0,17.5,28.9 -2020-11-20T19:00,40.6,31,12.1,0,0,14.7,29.1 -2020-11-20T20:00,39.9,32,12.7,0,0,11.5,24.2 -2020-11-20T21:00,39.6,34,13.2,0,0,7.3,19 -2020-11-20T22:00,37.1,38,13.8,0,0,4.8,12.1 -2020-11-20T23:00,35.7,41,14.5,0,0,5.1,7.8 -2020-11-21T00:00,34.7,44,15.2,0,0,2.7,6.3 -2020-11-21T01:00,33.4,50,16.7,0,0,3.4,4.5 -2020-11-21T02:00,32.4,54,17.7,0,0,4.7,5.6 -2020-11-21T03:00,35.1,46,16.5,0,0,2.7,5.8 -2020-11-21T04:00,33,53,17.9,0,0,5,7.8 -2020-11-21T05:00,31.1,56,17.2,0,0,4.7,9.6 -2020-11-21T06:00,28.9,61,17.2,0,0,4.1,7.8 -2020-11-21T07:00,27.9,65,17.7,0,0,4.1,6.3 -2020-11-21T08:00,30.8,57,17.2,0,0,3.6,7.6 -2020-11-21T09:00,35.2,48,17.4,0,0,3.1,9.2 -2020-11-21T10:00,39,42,17.7,0,0,4.6,12.1 -2020-11-21T11:00,42.6,37,18.2,0,0,5.6,14.1 -2020-11-21T12:00,44.2,37,19.4,0,0,4.7,14.8 -2020-11-21T13:00,46.4,36,20.8,0,0,3.4,13.2 -2020-11-21T14:00,47.8,35,21.7,0,0,3.8,10.7 -2020-11-21T15:00,47.5,36,21.7,0,0,4.8,11 -2020-11-21T16:00,45.6,39,22.3,0,0,3.8,10.7 -2020-11-21T17:00,39.7,49,22.1,0,0,5.4,6.9 -2020-11-21T18:00,37.3,61,25,0,0,4.9,6.5 -2020-11-21T19:00,37.2,66,27.1,0,0,2.3,6 -2020-11-21T20:00,38.7,62,26.9,0,0,2.7,3.1 -2020-11-21T21:00,38.5,62,26.4,0,0,2.3,3.1 -2020-11-21T22:00,37.8,61,25.7,0,0,4.1,5.4 -2020-11-21T23:00,37.1,60,24.6,0,0,1.7,5.1 -2020-11-22T00:00,30.4,73,22.9,0,0,5.1,5.8 -2020-11-22T01:00,29.1,76,22.4,0,0,6,8.1 -2020-11-22T02:00,27.1,79,21.6,0,0,5.6,8.1 -2020-11-22T03:00,25.5,84,21.4,0,0,5.8,6.7 -2020-11-22T04:00,25.9,83,21.4,0,0,4.5,6.7 -2020-11-22T05:00,28,71,19.9,0,0,3.9,5.6 -2020-11-22T06:00,26.3,62,15,0,0,5.1,6.3 -2020-11-22T07:00,24.5,60,12.7,0,0,4.1,5.8 -2020-11-22T08:00,29.3,50,13,0,0,2.5,5.4 -2020-11-22T09:00,32.3,38,9.4,0,0,3,8.5 -2020-11-22T10:00,35.2,30,7,0,0,3,9.8 -2020-11-22T11:00,38.2,26,6.7,0,0,3.4,10.5 -2020-11-22T12:00,41.5,24,7.3,0,0,3.3,11 -2020-11-22T13:00,44,23,8.8,0,0,4.1,11.2 -2020-11-22T14:00,44.9,23,9.3,0,0,7.4,15.2 -2020-11-22T15:00,43.3,25,9.6,0,0,10.6,19 -2020-11-22T16:00,40.4,30,11,0,0,7.5,19.2 -2020-11-22T17:00,36.1,34,10.3,0,0,6.3,12.3 -2020-11-22T18:00,32.2,46,13.6,0,0,5.4,10.3 -2020-11-22T19:00,32.6,43,12.5,0,0,3.8,6.3 -2020-11-22T20:00,33.4,40,11.6,0,0,3.7,6.5 -2020-11-22T21:00,35.2,35,10.3,0,0,2.7,5.6 -2020-11-22T22:00,31.9,39,9.7,0,0,1.9,3.8 -2020-11-22T23:00,29.4,44,10,0,0,3.2,4 -2020-11-23T00:00,30.3,42,10.1,0,0,3.4,4.9 -2020-11-23T01:00,27.9,49,11,0,0,4.5,5.4 -2020-11-23T02:00,29.3,46,11.2,0,0,2.7,5.1 -2020-11-23T03:00,27.4,54,12.8,0,0,2.7,3.8 -2020-11-23T04:00,28.9,55,14.8,0,0,2.1,3.6 -2020-11-23T05:00,29.5,55,15.4,0,0,2.5,3.4 -2020-11-23T06:00,27.7,56,14.2,0,0,3.6,4.3 -2020-11-23T07:00,30.8,52,15.1,0,0,1.6,4.7 -2020-11-23T08:00,33.8,51,17.8,0,0,2.7,4.5 -2020-11-23T09:00,42.2,48,24,0,0,4.1,8.5 -2020-11-23T10:00,51.3,44,29.8,0,0,8.3,14.8 -2020-11-23T11:00,58.3,35,30.6,0,0,6.2,16.8 -2020-11-23T12:00,60.6,31,29.8,0,0,4.1,13 -2020-11-23T13:00,59.7,31,29,0,0,10.4,19.2 -2020-11-23T14:00,57.5,32,27.5,0,0,12.8,23 -2020-11-23T15:00,56.2,33,27.3,0,0,10.1,22.8 -2020-11-23T16:00,52.2,40,28.5,0,0,5.2,17.7 -2020-11-23T17:00,43.7,56,28.9,0,0,7,8.7 -2020-11-23T18:00,43.7,53,27.6,0,0,5.4,8.7 -2020-11-23T19:00,41.8,54,26.4,0,0,5.7,9.2 -2020-11-23T20:00,40.5,58,27,0,0,8.3,12.3 -2020-11-23T21:00,41.1,66,30.6,0,0,8.8,15.9 -2020-11-23T22:00,40.6,69,31.3,0,0,7.2,14.5 -2020-11-23T23:00,38.6,72,30.3,0,0,4.7,11.9 -2020-11-24T00:00,39.8,67,29.8,0,0,4.9,7.6 -2020-11-24T01:00,41,64,29.8,0,0,3.8,5.8 -2020-11-24T02:00,37.3,74,29.8,0,0,4.7,8.1 -2020-11-24T03:00,37.3,76,30.4,0.004,0,9,15.9 -2020-11-24T04:00,34.8,93,32.9,0.012,0.028,6.1,14.8 -2020-11-24T05:00,33.6,98,33.1,0.012,0.138,6.7,9.8 -2020-11-24T06:00,32.9,96,31.9,0.004,0.579,8.6,13.9 -2020-11-24T07:00,31,93,29.1,0,0.606,9.6,16.6 -2020-11-24T08:00,29.7,86,25.9,0,0.413,5,16.6 -2020-11-24T09:00,29.8,88,26.5,0,0.276,3.1,9.4 -2020-11-24T10:00,31.2,80,25.6,0,0.11,2.6,6.7 -2020-11-24T11:00,32.4,75,25.3,0,0.028,1.6,5.6 -2020-11-24T12:00,33.3,71,24.8,0,0,1.6,4.5 -2020-11-24T13:00,34.1,67,24.4,0,0,2.1,4.5 -2020-11-24T14:00,37.1,40,15.2,0,0,0.2,4.3 -2020-11-24T15:00,37.9,37,14.2,0,0,0.8,1.8 -2020-11-24T16:00,37.8,37,14,0,0,2.4,4 -2020-11-24T17:00,32,73,24.2,0,0,4.1,8.1 -2020-11-24T18:00,30.7,72,22.7,0,0,4.5,7.2 -2020-11-24T19:00,29.1,72,21.4,0,0,4.3,8.1 -2020-11-24T20:00,26.2,76,19.7,0,0,5.7,8.5 -2020-11-24T21:00,25.3,70,16.9,0,0,5.7,9.4 -2020-11-24T22:00,24,69,15.4,0,0,4.8,9.2 -2020-11-24T23:00,21.9,75,15.4,0,0,5.3,7.8 -2020-11-25T00:00,21.3,76,15,0,0,6,9.2 -2020-11-25T01:00,22.8,68,13.8,0,0,5,9.2 -2020-11-25T02:00,23.8,62,12.8,0,0,5.4,8.5 -2020-11-25T03:00,24.3,59,12,0,0,6,9.6 -2020-11-25T04:00,23.9,58,11.4,0,0,6.4,9.8 -2020-11-25T05:00,21.3,68,12.3,0,0,5.2,9.8 -2020-11-25T06:00,23.1,61,11.5,0,0,6.4,10.1 -2020-11-25T07:00,23.1,59,10.8,0,0,5.9,10.3 -2020-11-25T08:00,27.3,49,10.5,0,0,7.4,11.9 -2020-11-25T09:00,34.4,36,10.1,0,0,5.8,12.1 -2020-11-25T10:00,41.5,24,7.5,0,0,5.4,12.3 -2020-11-25T11:00,46.5,18,5.1,0,0,5.6,13 -2020-11-25T12:00,49.5,17,6.4,0,0,3.7,13 -2020-11-25T13:00,51.1,17,7.7,0,0,2.2,10.3 -2020-11-25T14:00,52.2,17,9.1,0,0,1.5,7.8 -2020-11-25T15:00,51.4,21,12.7,0,0,3.8,6 -2020-11-25T16:00,43.9,27,12.4,0,0,4.8,7.8 -2020-11-25T17:00,32.9,52,17,0,0,6.6,7.8 -2020-11-25T18:00,31.5,60,19.3,0,0,4.4,8.3 -2020-11-25T19:00,28,77,21.7,0,0,3.9,6.3 -2020-11-25T20:00,25.4,83,21.1,0,0,3,5.8 -2020-11-25T21:00,22.9,87,19.6,0,0,4.3,5.1 -2020-11-25T22:00,24.8,77,18.7,0,0,3.3,5.1 -2020-11-25T23:00,26.3,70,17.9,0,0,4.5,5.4 -2020-11-26T00:00,25.1,67,15.8,0,0,4,6 -2020-11-26T01:00,22.6,71,14.7,0,0,3.7,6 -2020-11-26T02:00,21.1,73,13.8,0,0,5.5,8.3 -2020-11-26T03:00,22,67,12.7,0,0,4.1,8.5 -2020-11-26T04:00,21.7,64,11.4,0,0,5.1,6.5 -2020-11-26T05:00,20.4,64,10,0,0,7,6.5 -2020-11-26T06:00,22.6,57,9.7,0,0,4.7,8.5 -2020-11-26T07:00,23.9,58,11.3,0,0,4.9,7.4 -2020-11-26T08:00,29.5,48,12.3,0,0,6.7,10.1 -2020-11-26T09:00,35.3,42,14.3,0,0,4.9,10.5 -2020-11-26T10:00,39.6,37,15.5,0,0,5.5,12.3 -2020-11-26T11:00,41.6,35,15.7,0,0,6.3,13.6 -2020-11-26T12:00,43.4,33,16.1,0,0,7.7,15.7 -2020-11-26T13:00,44.6,31,16.2,0,0,7.9,15.9 -2020-11-26T14:00,45.1,31,16.3,0,0,7.2,15.4 -2020-11-26T15:00,44.9,32,16.8,0,0,8.9,14.5 -2020-11-26T16:00,40,40,17.8,0,0,9.1,15.9 -2020-11-26T17:00,36.4,54,21.4,0,0,11.9,14.3 -2020-11-26T18:00,34.1,63,22.7,0,0,9,21 -2020-11-26T19:00,33.2,66,23,0,0,8.4,14.5 -2020-11-26T20:00,33,64,22.2,0,0,4.7,13.4 -2020-11-26T21:00,32.6,70,24,0,0.028,1.9,7.4 -2020-11-26T22:00,31.9,72,23.8,0,0.028,2.3,3.6 -2020-11-26T23:00,29.1,82,24.3,0,0,3.4,4.7 -2020-11-27T00:00,27.4,86,23.8,0,0,3.5,5.6 -2020-11-27T01:00,25.4,88,22.5,0,0,3.7,5.8 -2020-11-27T02:00,24,87,20.6,0,0,3.8,6 -2020-11-27T03:00,22.6,84,18.4,0,0,3.2,6 -2020-11-27T04:00,21.4,77,15.2,0,0,2.5,5.1 -2020-11-27T05:00,18.7,78,12.8,0,0,3.9,4 -2020-11-27T06:00,20.2,62,9.2,0,0,2,4.7 -2020-11-27T07:00,20.1,54,6.1,0,0,3.6,6 -2020-11-27T08:00,22.2,49,6,0,0,3.2,7.6 -2020-11-27T09:00,27.6,39,5.7,0,0,0.5,7.6 -2020-11-27T10:00,32.3,38,9.5,0,0,3.3,9.8 -2020-11-27T11:00,35.3,38,11.9,0,0,4.5,12.3 -2020-11-27T12:00,37.8,35,12.7,0,0,2.9,12.3 -2020-11-27T13:00,39.9,32,12.7,0,0,0.7,10.3 -2020-11-27T14:00,41.5,30,12.3,0,0,2,8.1 -2020-11-27T15:00,41.3,30,12.4,0,0,2.2,7.8 -2020-11-27T16:00,38.3,38,15.1,0,0,2.7,6 -2020-11-27T17:00,35.1,43,14.7,0,0,3.8,4.5 -2020-11-27T18:00,28.3,56,14.7,0,0,6.4,7.8 -2020-11-27T19:00,25,61,13.5,0,0,3.7,7.8 -2020-11-27T20:00,21.5,69,12.9,0,0,3,5.1 -2020-11-27T21:00,21.1,68,12.2,0,0,3.6,5.1 -2020-11-27T22:00,21.8,60,10.1,0,0,4,6 -2020-11-27T23:00,22.4,52,7.3,0,0,4.1,6.5 -2020-11-28T00:00,22.2,49,6.1,0,0,4.1,6.5 -2020-11-28T01:00,21.9,49,5.8,0,0,3.8,6.3 -2020-11-28T02:00,21.4,52,6.4,0,0,3.8,5.8 -2020-11-28T03:00,20.9,53,6.6,0,0,3.9,5.4 -2020-11-28T04:00,21.3,52,6.4,0,0,3.8,5.6 -2020-11-28T05:00,18.8,57,6.2,0,0,7,6.3 -2020-11-28T06:00,21.7,51,6.4,0,0,4.2,8.3 -2020-11-28T07:00,21.6,53,7,0,0,4.8,6.5 -2020-11-28T08:00,27,44,8.1,0,0,6.1,9.6 -2020-11-28T09:00,34.5,34,8.8,0,0,4.4,10.5 -2020-11-28T10:00,43,24,8.3,0,0,2.2,9.8 -2020-11-28T11:00,48.5,18,6.3,0,0,1.9,8.1 -2020-11-28T12:00,51.5,15,5.6,0,0,2.3,8.5 -2020-11-28T13:00,53.1,16,7.4,0,0,3.5,9.2 -2020-11-28T14:00,53.2,17,9.4,0,0,2.8,9.2 -2020-11-28T15:00,50.5,45,30.2,0,0,2.2,6.7 -2020-11-28T16:00,43.8,34,17,0,0,3.7,4.7 -2020-11-28T17:00,37.5,35,12.2,0,0,4.9,5.1 -2020-11-28T18:00,31.8,50,15.1,0,0,5.8,7.4 -2020-11-28T19:00,32.1,50,15.6,0,0,6.2,8.7 -2020-11-28T20:00,35.2,41,13.8,0,0,7.2,11 -2020-11-28T21:00,32.9,44,13.1,0,0,7.8,11 -2020-11-28T22:00,30.2,49,13.2,0,0,9.9,11.6 -2020-11-28T23:00,30.9,47,13.2,0,0,8.5,11.9 -2020-11-29T00:00,30.4,52,14.6,0,0,7,11 -2020-11-29T01:00,25.6,65,15.4,0,0,6.2,11.4 -2020-11-29T02:00,27.1,62,15.7,0,0,6.4,10.1 -2020-11-29T03:00,28.9,53,13.8,0,0,5.5,13 -2020-11-29T04:00,24.9,62,13.6,0,0,6.5,8.9 -2020-11-29T05:00,29.1,51,13.1,0,0,2.7,8.3 -2020-11-29T06:00,29.6,50,13,0,0,2.9,4 -2020-11-29T07:00,29.6,50,13.1,0,0,1.7,4 -2020-11-29T08:00,29.1,51,13.1,0,0,1.2,2.7 -2020-11-29T09:00,32.9,43,12.9,0,0,2.4,7.2 -2020-11-29T10:00,34.8,40,12.8,0,0,1.9,8.3 -2020-11-29T11:00,35.7,39,13.1,0,0,3.8,11.2 -2020-11-29T12:00,36.1,39,13.5,0,0,3.7,11.9 -2020-11-29T13:00,36.7,38,13.6,0,0,3.1,11.4 -2020-11-29T14:00,37.1,38,13.6,0,0,2.5,10.3 -2020-11-29T15:00,36.9,38,13.6,0,0,2.1,8.9 -2020-11-29T16:00,33.7,48,16.3,0,0,2.7,6.9 -2020-11-29T17:00,31.9,48,14.3,0,0,2.8,5.6 -2020-11-29T18:00,26.3,61,14.8,0,0,4.2,6 -2020-11-29T19:00,24.1,67,14.8,0,0,4.1,6.7 -2020-11-29T20:00,22,73,14.7,0,0,4.3,6.5 -2020-11-29T21:00,20.8,74,13.9,0,0,3.7,6 -2020-11-29T22:00,19.6,73,12.5,0,0,2.7,6 -2020-11-29T23:00,20.1,68,11.4,0,0,3.2,4.7 -2020-11-30T00:00,20.2,64,9.9,0,0,3.4,5.1 -2020-11-30T01:00,20.4,57,7.5,0,0,3.7,5.8 -2020-11-30T02:00,20.2,51,5.1,0,0,4.2,6.9 -2020-11-30T03:00,19.4,50,3.6,0,0,4.1,7.2 -2020-11-30T04:00,19.6,45,1.7,0,0,4,6.9 -2020-11-30T05:00,17.2,48,1,0,0,7.1,6.5 -2020-11-30T06:00,20.6,41,0.8,0,0,4.7,8.3 -2020-11-30T07:00,21.1,41,1,0,0,4.5,7.2 -2020-11-30T08:00,26.7,32,0.7,0,0,6.7,10.1 -2020-11-30T09:00,35.8,22,0.6,0,0,6.5,12.3 -2020-11-30T10:00,44.8,16,1.2,0,0,5.9,12.3 -2020-11-30T11:00,51.6,12,0.1,0,0,4.7,12.3 -2020-11-30T12:00,55,10,-1.5,0,0,4.2,11 -2020-11-30T13:00,56.1,9,-0.9,0,0,5.8,11.6 -2020-11-30T14:00,55.1,11,1.9,0,0,5.9,12.1 -2020-11-30T15:00,50.5,15,4.6,0,0,4.8,10.5 -2020-11-30T16:00,48.8,17,5.3,0,0,7.2,10.5 -2020-11-30T17:00,42.7,25,9.3,0,0,3.4,8.9 -2020-11-30T18:00,29.5,44,10.1,0,0,5,8.5 -2020-11-30T19:00,25,65,14.8,0,0,4.1,7.2 -2020-11-30T20:00,27.5,49,10.9,0,0,6.1,9.2 -2020-11-30T21:00,26.5,48,9.2,0,0,4.7,10.1 -2020-11-30T22:00,26.4,47,9,0,0,7,11 -2020-11-30T23:00,28.3,41,7.5,0,0,6.9,11.9 -2020-12-01T00:00,27.9,41,7.3,0,0,5.1,11 -2020-12-01T01:00,27.8,42,7.9,0,0,4.1,7.8 -2020-12-01T02:00,28.5,42,8.2,0,0,5.4,8.9 -2020-12-01T03:00,28.4,43,8.9,0,0,5,9.2 -2020-12-01T04:00,29.4,45,10.5,0,0,4,7.8 -2020-12-01T05:00,38.8,26,7,0,0,2.8,6.3 -2020-12-01T06:00,30.4,37,7.3,0,0,6.6,8.5 -2020-12-01T07:00,31.1,38,8.6,0,0,5.8,8.5 -2020-12-01T08:00,34.7,37,11,0,0,7,10.5 -2020-12-01T09:00,36.4,40,14.2,0,0,9.2,14.5 -2020-12-01T10:00,35.2,48,17.4,0,0.055,4.1,15 -2020-12-01T11:00,38.7,37,14.5,0,0.028,6.4,11.6 -2020-12-01T12:00,40.3,33,13.2,0,0,7.2,16.6 -2020-12-01T13:00,41.1,33,13.9,0,0,8.9,17 -2020-12-01T14:00,37.9,49,20.6,0,0,12.3,22.1 -2020-12-01T15:00,38.7,41,16.9,0,0,9.5,21.5 -2020-12-01T16:00,36.8,39,14.4,0,0,8.1,15.9 -2020-12-01T17:00,33.3,57,19.7,0,0,13.3,27.7 -2020-12-01T18:00,31.3,64,20.6,0,0,11.2,22.1 -2020-12-01T19:00,28.7,72,20.9,0,0,8.5,18.3 -2020-12-01T20:00,28.2,74,20.9,0,0,6.5,14.1 -2020-12-01T21:00,27.1,73,19.5,0,0,7.6,12.1 -2020-12-01T22:00,26.9,61,15.3,0,0,9.1,15 -2020-12-01T23:00,26.2,60,14.3,0,0,7.8,14.8 -2020-12-02T00:00,25.8,59,13.4,0,0,7,12.8 -2020-12-02T01:00,24.8,58,12.1,0,0,5.4,11.6 -2020-12-02T02:00,24.6,58,11.8,0,0,5.7,9.2 -2020-12-02T03:00,22.8,61,11.2,0,0,5.1,9.2 -2020-12-02T04:00,21.2,63,10.4,0,0,3.6,8.3 -2020-12-02T05:00,19.9,59,7.8,0,0,4.7,6 -2020-12-02T06:00,18.9,61,7.6,0,0,4.4,7.2 -2020-12-02T07:00,18.7,64,8.4,0,0,3.7,6.3 -2020-12-02T08:00,22.1,56,8.9,0,0,1.4,5.1 -2020-12-02T09:00,24.8,47,7.4,0,0,3.4,8.7 -2020-12-02T10:00,27.7,35,3.7,0,0,9.4,17.2 -2020-12-02T11:00,29.3,32,2.8,0,0,11.2,21.3 -2020-12-02T12:00,30.3,30,2.8,0,0,11.8,21.7 -2020-12-02T13:00,31.6,29,3.3,0,0,12.5,23.7 -2020-12-02T14:00,31.9,30,4.1,0,0,11.9,23.3 -2020-12-02T15:00,31.4,32,4.7,0,0,9.1,21.3 -2020-12-02T16:00,29,37,6,0,0,6.1,15.7 -2020-12-02T17:00,23.2,57,10.2,0,0,6.8,9.6 -2020-12-02T18:00,22.7,61,11.3,0,0,5.1,8.5 -2020-12-02T19:00,23.8,59,11.4,0,0,3.8,6.5 -2020-12-02T20:00,25.3,54,10.9,0,0,1.6,6.5 -2020-12-02T21:00,22.9,60,11,0,0,2,2.7 -2020-12-02T22:00,20.4,60,8.9,0,0,3.4,4.7 -2020-12-02T23:00,19.6,57,7,0,0,4.2,5.4 -2020-12-03T00:00,19.5,54,5.6,0,0,4.6,5.6 -2020-12-03T01:00,19.3,52,4.4,0,0,4,5.6 -2020-12-03T02:00,19,50,3.3,0,0,4.5,5.4 -2020-12-03T03:00,19.1,47,2.3,0,0,4.5,5.8 -2020-12-03T04:00,19.4,45,1.5,0,0,4.5,5.8 -2020-12-03T05:00,19.7,44,1.2,0,0,5.4,6.5 -2020-12-03T06:00,18.6,45,1,0,0,5.9,6.9 -2020-12-03T07:00,18.3,46,1,0,0,6,7.6 -2020-12-03T08:00,23.9,36,0.9,0,0,5.9,8.5 -2020-12-03T09:00,30.4,27,0.7,0,0,3.4,9.4 -2020-12-03T10:00,37,20,-0.8,0,0,3,8.9 -2020-12-03T11:00,41.5,15,-3.2,0,0,3.6,10.5 -2020-12-03T12:00,43.8,12,-5.6,0,0,2.5,10.5 -2020-12-03T13:00,45,9,-11.4,0,0,1.6,8.9 -2020-12-03T14:00,45.1,7,-16.2,0,0,3.9,8.9 -2020-12-03T15:00,43.4,7,-15.5,0,0,3,9.2 -2020-12-03T16:00,41.8,9,-13.2,0,0,2.2,5.1 -2020-12-03T17:00,39.1,13,-7.3,0,0,3.4,2.7 -2020-12-03T18:00,27.3,32,1,0,0,5.9,7.6 -2020-12-03T19:00,26.1,43,6.8,0,0,4.7,7.6 -2020-12-03T20:00,25.2,49,8.7,0,0,5.2,7.2 -2020-12-03T21:00,25.3,48,8.5,0,0,5.4,7.4 -2020-12-03T22:00,25.2,49,8.7,0,0,6.2,7.8 -2020-12-03T23:00,24.6,51,9.1,0,0,7.3,8.5 -2020-12-04T00:00,24.8,51,9.4,0,0,7,8.5 -2020-12-04T01:00,25.3,52,10.2,0,0,6,8.3 -2020-12-04T02:00,25.1,55,11.1,0,0,6.4,7.8 -2020-12-04T03:00,25.3,55,11.5,0,0,5.9,7.8 -2020-12-04T04:00,24.9,57,11.7,0,0,6.7,8.1 -2020-12-04T05:00,24.1,57,11.1,0,0,6.9,8.3 -2020-12-04T06:00,24.1,58,11.3,0,0,7.1,8.3 -2020-12-04T07:00,24.4,57,11.5,0,0,6.5,8.3 -2020-12-04T08:00,30,46,11.6,0,0,5.2,8.1 -2020-12-04T09:00,37.6,34,11.9,0,0,5,10.3 -2020-12-04T10:00,45.9,25,12.2,0,0,3.1,9.8 -2020-12-04T11:00,51.8,20,11.4,0,0,3.5,9.4 -2020-12-04T12:00,55,16,10,0,0,3.2,9.6 -2020-12-04T13:00,55.9,17,11.9,0,0,2.6,8.9 -2020-12-04T14:00,55.4,19,13.2,0,0,2.3,7.6 -2020-12-04T15:00,54.1,21,15.4,0,0,1.1,4.9 -2020-12-04T16:00,51.3,25,16.2,0,0,1.4,1.8 -2020-12-04T17:00,48,28,16.1,0,0,1.6,2 -2020-12-04T18:00,36.5,44,16.8,0,0,3,3.6 -2020-12-04T19:00,28,67,18.6,0,0,3.3,5.4 -2020-12-04T20:00,24.6,79,19,0,0,3.8,6 -2020-12-04T21:00,23.3,81,18.2,0,0,5.1,8.3 -2020-12-04T22:00,22.9,78,17.2,0,0,3.2,8.1 -2020-12-04T23:00,22.1,80,16.9,0,0,2.8,5.4 -2020-12-05T00:00,22.1,77,16.1,0,0,2.1,3.6 -2020-12-05T01:00,23.1,69,14.5,0,0,3.6,5.8 -2020-12-05T02:00,24.1,61,12.5,0,0,5.5,6.7 -2020-12-05T03:00,24.3,58,11.7,0,0,5.4,7.2 -2020-12-05T04:00,24.5,56,11.2,0,0,5.1,6.9 -2020-12-05T05:00,23.3,60,11.4,0,0,6.9,7.4 -2020-12-05T06:00,23.5,59,11.1,0,0,6.1,8.1 -2020-12-05T07:00,22.8,60,10.9,0,0,5,7.4 -2020-12-05T08:00,26,52,10.8,0,0,6.2,9.6 -2020-12-05T09:00,35.9,35,10.7,0,0,6,11.4 -2020-12-05T10:00,46.1,23,10.7,0,0,5.1,11.4 -2020-12-05T11:00,50.2,20,10.8,0,0,5.6,12.3 -2020-12-05T12:00,50.5,23,13.8,0,0,7.7,15 -2020-12-05T13:00,50.5,24,14.7,0,0,6.1,15.2 -2020-12-05T14:00,50.3,25,15.4,0,0,2.7,12.5 -2020-12-05T15:00,49.1,32,20.6,0,0,1.3,7.2 -2020-12-05T16:00,42.2,41,20.3,0,0,4.2,4.5 -2020-12-05T17:00,38.8,43,18.3,0,0,4.5,9.4 -2020-12-05T18:00,33.4,56,19.5,0,0,6.5,7.8 -2020-12-05T19:00,29,69,20,0,0,4.6,7.6 -2020-12-05T20:00,27.8,70,19.2,0,0,4.4,6.7 -2020-12-05T21:00,27.5,63,16.5,0,0,4.3,6.9 -2020-12-05T22:00,26.2,62,15,0,0,4.3,6.9 -2020-12-05T23:00,25.5,61,13.8,0,0,4.5,6.3 -2020-12-06T00:00,25.5,59,13,0,0,5.1,6.5 -2020-12-06T01:00,25.3,58,12.7,0,0,5.5,7.4 -2020-12-06T02:00,25.4,57,12.2,0,0,5.4,7.2 -2020-12-06T03:00,25.8,55,11.7,0,0,6,7.8 -2020-12-06T04:00,25.6,53,10.9,0,0,6.7,7.8 -2020-12-06T05:00,24.9,53,10.3,0,0,6.7,8.1 -2020-12-06T06:00,29.1,43,9.1,0,0,4.5,7.8 -2020-12-06T07:00,28.7,42,8.7,0,0,4.7,5.4 -2020-12-06T08:00,32.2,37,8.6,0,0,4.2,6.9 -2020-12-06T09:00,41.1,26,9.1,0,0,3.7,8.1 -2020-12-06T10:00,47.4,24,12.6,0,0,2.1,7.6 -2020-12-06T11:00,51.9,22,14.5,0,0,2.9,8.3 -2020-12-06T12:00,54.8,21,15.5,0,0,2.7,8.7 -2020-12-06T13:00,56.9,19,15.3,0,0,2.7,8.3 -2020-12-06T14:00,57.8,19,15.9,0,0,3.2,7.8 -2020-12-06T15:00,55.9,26,21.5,0,0,3.3,7.6 -2020-12-06T16:00,46.9,34,19.6,0,0,6.4,7.8 -2020-12-06T17:00,50.4,21,12.2,0,0,5.8,9.4 -2020-12-06T18:00,38.1,37,14.3,0,0,5,6.7 -2020-12-06T19:00,32.9,50,16.3,0,0,4.9,8.1 -2020-12-06T20:00,30,54,15.4,0,0,5.2,7.4 -2020-12-06T21:00,28.8,53,13.6,0,0,5.2,7.4 -2020-12-06T22:00,27.3,52,12,0,0,5.4,7.4 -2020-12-06T23:00,26.2,53,11.2,0,0,5.9,7.6 -2020-12-07T00:00,25.8,53,11,0,0,6.6,8.1 -2020-12-07T01:00,26.6,50,10.6,0,0,5.2,7.8 -2020-12-07T02:00,25,54,10.5,0,0,6.1,6.7 -2020-12-07T03:00,25.2,52,10.1,0,0,5.4,7.2 -2020-12-07T04:00,24.4,54,10.1,0,0,5.1,6.7 -2020-12-07T05:00,23.8,56,10.3,0,0,5.9,6.3 -2020-12-07T06:00,24.4,53,9.5,0,0,5.7,7.2 -2020-12-07T07:00,25.5,49,9,0,0,5,7.2 -2020-12-07T08:00,30.8,39,8.8,0,0,5.7,8.9 -2020-12-07T09:00,38.3,31,10.2,0,0,5.1,10.5 -2020-12-07T10:00,46.5,27,14.1,0,0,4.3,9.8 -2020-12-07T11:00,53.1,20,13.2,0,0,5,10.7 -2020-12-07T12:00,56.6,15,9.5,0,0,5,11.2 -2020-12-07T13:00,58.1,14,8.9,0,0,4.4,11 -2020-12-07T14:00,58,14,9.2,0,0,3.6,9.8 -2020-12-07T15:00,55.3,27,21.8,0,0,2.4,8.1 -2020-12-07T16:00,45.8,35,19.9,0,0,6.7,7.8 -2020-12-07T17:00,38.8,35,13.6,0,0,6.8,9.8 -2020-12-07T18:00,36.2,42,15.3,0,0,5.9,8.9 -2020-12-07T19:00,33.7,46,14.9,0,0,5.2,8.9 -2020-12-07T20:00,31,50,14.7,0,0,4.8,8.1 -2020-12-07T21:00,29,49,12.1,0,0,6.7,8.3 -2020-12-07T22:00,33.4,37,9.9,0,0,4.6,8.9 -2020-12-07T23:00,28,45,9.3,0,0,6.2,6.9 -2020-12-08T00:00,30.3,39,8.1,0,0,5,7.6 -2020-12-08T01:00,27.1,44,8.1,0,0,5.7,6.5 -2020-12-08T02:00,24.8,50,8.9,0,0,6.6,7.8 -2020-12-08T03:00,24.5,52,9.5,0,0,6.1,7.8 -2020-12-08T04:00,24.1,53,9.5,0,0,6.2,7.8 -2020-12-08T05:00,24.4,49,7.9,0,0,6.8,7.8 -2020-12-08T06:00,24.9,44,6.2,0,0,6.4,8.5 -2020-12-08T07:00,24.2,42,4.1,0,0,7.5,8.9 -2020-12-08T08:00,31,30,3,0,0,5.2,8.9 -2020-12-08T09:00,40.3,21,3.8,0,0,7.1,12.3 -2020-12-08T10:00,48.8,20,9.2,0,0,5.9,12.1 -2020-12-08T11:00,55.3,18,12.9,0,0,5.5,11.9 -2020-12-08T12:00,59.5,16,13.7,0,0,4.5,11.2 -2020-12-08T13:00,61.4,15,13.8,0,0,4.2,9.8 -2020-12-08T14:00,62.2,17,16.6,0,0,2.8,9.2 -2020-12-08T15:00,60,32,29.9,0,0,1.6,6 -2020-12-08T16:00,57.7,24,21.4,0,0,4.4,5.1 -2020-12-08T17:00,51.5,24,15.8,0,0,3.7,12.1 -2020-12-08T18:00,49.1,24,14.3,0,0,6.9,6.9 -2020-12-08T19:00,37.5,28,7,0,0,9.7,12.3 -2020-12-08T20:00,35.3,27,4.6,0,0,10.4,12.5 -2020-12-08T21:00,32.8,33,6.5,0,0,9.2,12.3 -2020-12-08T22:00,30.8,40,9.4,0,0,7.6,11.2 -2020-12-08T23:00,29.6,41,8.7,0,0,7.8,9.4 -2020-12-09T00:00,29.8,38,7.4,0,0,6.7,9.2 -2020-12-09T01:00,28.9,40,7.4,0,0,6,8.1 -2020-12-09T02:00,27.8,41,7.1,0,0,7.7,8.9 -2020-12-09T03:00,28.3,38,5.9,0,0,7.6,9.4 -2020-12-09T04:00,28.7,37,5.7,0,0,6.4,9.2 -2020-12-09T05:00,27.3,36,4,0,0,7.8,8.5 -2020-12-09T06:00,28,36,4.2,0,0,5.7,9.6 -2020-12-09T07:00,27,37,3.9,0,0,5.4,8.5 -2020-12-09T08:00,30.5,30,2.8,0,0,4.2,8.3 -2020-12-09T09:00,36.8,22,1.6,0,0,4.9,9.8 -2020-12-09T10:00,44.6,17,2.8,0,0,3.4,9.6 -2020-12-09T11:00,51.1,16,6.9,0,0,3.1,8.9 -2020-12-09T12:00,55.2,15,7.9,0,0,4.5,9.8 -2020-12-09T13:00,58.3,11,4.7,0,0,4.6,10.7 -2020-12-09T14:00,59.3,11,4.9,0,0,3.2,9.6 -2020-12-09T15:00,55.5,40,31.4,0,0,4,6.5 -2020-12-09T16:00,55.9,15,8.3,0,0,2.9,5.4 -2020-12-09T17:00,46,26,13.1,0,0,5.7,4.5 -2020-12-09T18:00,39.3,36,14.4,0,0,7.2,8.5 -2020-12-09T19:00,36.5,35,11.7,0,0,7.9,9.2 -2020-12-09T20:00,32.2,43,11.9,0,0,7.9,10.3 -2020-12-09T21:00,29.7,49,12.8,0,0,7.6,9.6 -2020-12-09T22:00,28.2,52,12.8,0,0,7.4,9.2 -2020-12-09T23:00,27.3,53,12.3,0,0,7.4,8.9 -2020-12-10T00:00,26.9,51,11.2,0,0,6.8,8.7 -2020-12-10T01:00,27,49,10.1,0,0,6.4,8.5 -2020-12-10T02:00,26.7,48,9.7,0,0,6.9,8.1 -2020-12-10T03:00,27.2,47,9.4,0,0,6.9,8.3 -2020-12-10T04:00,30.2,41,9.2,0,0,5.2,8.3 -2020-12-10T05:00,31.2,40,9.4,0,0,5.6,7.8 -2020-12-10T06:00,29.5,49,12.5,0,0,5.4,9.6 -2020-12-10T07:00,26.7,57,13.5,0,0,7.6,9.2 -2020-12-10T08:00,31.1,55,16.7,0,0,5.7,9.2 -2020-12-10T09:00,37,39,14.2,0,0,4.8,9.4 -2020-12-10T10:00,40.1,40,17.4,0,0,6.7,12.5 -2020-12-10T11:00,40.4,43,19.9,0,0,7.1,14.5 -2020-12-10T12:00,40.6,43,19.9,0,0,4.7,14.5 -2020-12-10T13:00,40.5,44,20.3,0,0,4.2,10.7 -2020-12-10T14:00,40.3,46,20.9,0,0,4,10.1 -2020-12-10T15:00,40.1,47,21.6,0,0,4.7,9.6 -2020-12-10T16:00,37.8,55,23,0,0,3.4,9.2 -2020-12-10T17:00,35.6,56,21.3,0,0,6.5,6.3 -2020-12-10T18:00,33.7,62,22.2,0,0,6.3,11.2 -2020-12-10T19:00,32.1,68,22.8,0,0,5.7,10.3 -2020-12-10T20:00,30.3,77,23.8,0,0.028,5.9,9.8 -2020-12-10T21:00,29.4,82,24.5,0,0.083,5.8,10.5 -2020-12-10T22:00,28.5,85,24.6,0,0.11,6.4,11.2 -2020-12-10T23:00,27.9,86,24.1,0,0.055,5.9,11.2 -2020-12-11T00:00,27.2,85,23.3,0,0.028,4.8,10.3 -2020-12-11T01:00,26.8,84,22.7,0,0.028,4.4,8.7 -2020-12-11T02:00,26.3,84,22.3,0,0.028,3.8,8.3 -2020-12-11T03:00,26.1,84,21.8,0,0,3.2,7.2 -2020-12-11T04:00,26,83,21.4,0,0,2.9,6.5 -2020-12-11T05:00,25.6,83,21.3,0,0,3.5,6.3 -2020-12-11T06:00,25.8,82,21.1,0,0.028,3,7.4 -2020-12-11T07:00,25.8,81,20.8,0,0.028,2.4,6.5 -2020-12-11T08:00,26,81,20.9,0,0.028,2.7,6.3 -2020-12-11T09:00,26.9,79,21.3,0,0.028,3.3,8.5 -2020-12-11T10:00,27.4,79,21.7,0,0.028,4,9.6 -2020-12-11T11:00,28,77,21.6,0,0.055,3.8,10.3 -2020-12-11T12:00,28.3,76,21.7,0,0.055,3.2,10.1 -2020-12-11T13:00,28.8,72,20.9,0,0.055,4.1,10.3 -2020-12-11T14:00,29.1,70,20.6,0,0.055,4.8,11 -2020-12-11T15:00,29.5,68,20.2,0,0.028,4.8,11.4 -2020-12-11T16:00,28.6,69,19.8,0,0,4.9,10.7 -2020-12-11T17:00,23.1,79,17.6,0,0,4.4,8.7 -2020-12-11T18:00,22.7,81,17.7,0,0,3.7,8.1 -2020-12-11T19:00,22.8,80,17.7,0,0,1.1,6.3 -2020-12-11T20:00,23.2,79,17.5,0,0,0.6,3.8 -2020-12-11T21:00,22.7,76,16.3,0,0,0.5,3.8 -2020-12-11T22:00,21.7,78,15.8,0,0,1.4,3.4 -2020-12-11T23:00,20.6,82,15.9,0,0,1.6,3.4 -2020-12-12T00:00,19.2,88,16.2,0,0,2.6,4.3 -2020-12-12T01:00,19,89,16.3,0,0,2.7,4.5 -2020-12-12T02:00,18.3,90,15.8,0,0,3.6,4.7 -2020-12-12T03:00,18.1,90,15.7,0,0,2.9,4.7 -2020-12-12T04:00,18.5,88,15.4,0,0,2.6,4 -2020-12-12T05:00,16.2,92,14.4,0,0,3.6,4.5 -2020-12-12T06:00,17.2,94,15.8,0,0,3.8,6 -2020-12-12T07:00,17.7,91,15.6,0,0,5.3,9.2 -2020-12-12T08:00,18.2,88,15.4,0,0,4.5,9.8 -2020-12-12T09:00,19.2,85,15.4,0,0,3.2,9.2 -2020-12-12T10:00,20.8,78,15.1,0,0,3.8,9.6 -2020-12-12T11:00,22.7,72,14.9,0,0,4.7,11.6 -2020-12-12T12:00,25.7,63,14.9,0,0,5.5,13.6 -2020-12-12T13:00,27.3,60,15.3,0,0,6.2,14.5 -2020-12-12T14:00,26.8,61,15.3,0,0,8.2,16.1 -2020-12-12T15:00,25.9,65,15.7,0,0,9.1,16.3 -2020-12-12T16:00,24.8,69,16.2,0,0,9.4,18.1 -2020-12-12T17:00,20.7,77,14.5,0,0.028,6.5,15.7 -2020-12-12T18:00,20.7,78,14.8,0,0.055,8,14.3 -2020-12-12T19:00,21.3,78,15.4,0,0.055,7.3,14.3 -2020-12-12T20:00,21.3,78,15.6,0,0.028,8.3,14.1 -2020-12-12T21:00,21.6,77,15.5,0,0.055,7.9,14.8 -2020-12-12T22:00,22.2,80,17,0,0.11,9,15 -2020-12-12T23:00,22.9,84,18.8,0,0.138,9.3,16.6 -2020-12-13T00:00,23.5,85,19.8,0,0.083,10.6,17.4 -2020-12-13T01:00,24.4,80,19,0,0.028,10.7,17.9 -2020-12-13T02:00,23.2,76,16.8,0,0,6.7,17.7 -2020-12-13T03:00,22.2,77,16.2,0,0,1.9,10.7 -2020-12-13T04:00,20.7,81,15.8,0,0,1.6,3.8 -2020-12-13T05:00,16,86,12.7,0,0,3.4,4 -2020-12-13T06:00,16.5,87,13.4,0,0,4.7,7.2 -2020-12-13T07:00,15.9,82,11.4,0,0,4.4,7.6 -2020-12-13T08:00,17.2,64,6.9,0,0,6.3,9.8 -2020-12-13T09:00,21.8,48,5.2,0,0,6.3,12.1 -2020-12-13T10:00,27.1,42,7,0,0,10.6,18.8 -2020-12-13T11:00,30.6,31,3.2,0,0,12.5,22.8 -2020-12-13T12:00,34.6,26,2.9,0,0,11.9,22.8 -2020-12-13T13:00,37.9,20,0.8,0,0,9.8,22.1 -2020-12-13T14:00,39.5,18,-0.5,0,0,7.8,18.3 -2020-12-13T15:00,39.3,18,-1,0,0,5.1,14.5 -2020-12-13T16:00,35.1,21,-0.8,0,0,3.3,7.8 -2020-12-13T17:00,22.4,52,7.3,0,0,6.3,8.1 -2020-12-13T18:00,22.7,52,7.7,0,0,4.6,8.3 -2020-12-13T19:00,27.2,43,7.5,0,0,2.3,6.7 -2020-12-13T20:00,20.8,55,7.1,0,0,4.5,5.1 -2020-12-13T21:00,18.4,59,6.4,0,0,4.4,6 -2020-12-13T22:00,19.2,52,4.6,0,0,3.6,5.6 -2020-12-13T23:00,20.9,46,3.3,0,0,1.9,4.7 -2020-12-14T00:00,20.9,45,2.8,0,0,0.9,2.5 -2020-12-14T01:00,17.8,52,2.9,0,0,3,3.8 -2020-12-14T02:00,17.3,57,4.6,0,0,3,4 -2020-12-14T03:00,17.6,61,6.4,0,0,3.4,4.5 -2020-12-14T04:00,16.7,64,6.6,0,0,6.9,12.8 -2020-12-14T05:00,13.6,80,8.6,0,0,3.7,11.4 -2020-12-14T06:00,13.6,75,7.1,0,0,2.6,4.7 -2020-12-14T07:00,14.2,68,5.6,0,0,2.5,4.3 -2020-12-14T08:00,14.8,66,5.4,0,0,4.6,8.3 -2020-12-14T09:00,19.1,52,4.3,0,0,4.3,10.3 -2020-12-14T10:00,25.2,40,4.1,0,0,3.7,10.5 -2020-12-14T11:00,30.5,34,5.2,0,0,2,10.5 -2020-12-14T12:00,34.3,32,7.5,0,0,5.1,11.6 -2020-12-14T13:00,33.9,32,7.3,0,0,5.9,12.3 -2020-12-14T14:00,33.9,34,8.3,0,0,5.1,12.5 -2020-12-14T15:00,32.9,36,9,0,0,6.5,11.9 -2020-12-14T16:00,28.9,44,10,0,0,3.6,11.6 -2020-12-14T17:00,21.5,68,12.4,0,0,6.5,6 -2020-12-14T18:00,25.3,59,12.9,0,0,3.8,8.1 -2020-12-14T19:00,26.4,60,14.5,0,0.028,4.1,6 -2020-12-14T20:00,25.7,70,17.2,0,0.055,5.1,8.3 -2020-12-14T21:00,25.2,77,19,0,0.055,5.1,8.1 -2020-12-14T22:00,24.5,81,19.6,0,0.055,6.5,10.5 -2020-12-14T23:00,23.9,81,19,0,0.055,6.3,11.6 -2020-12-15T00:00,23.7,83,19.2,0,0.055,5.4,10.3 -2020-12-15T01:00,23.2,83,18.9,0,0.055,4,8.9 -2020-12-15T02:00,23.1,83,18.6,0,0.028,2.2,7.2 -2020-12-15T03:00,22.7,83,18.3,0,0,2.3,4.5 -2020-12-15T04:00,22.2,84,18.1,0,0,1.1,4 -2020-12-15T05:00,19.5,89,16.8,0,0,5.4,2.9 -2020-12-15T06:00,22.2,83,17.9,0,0,4.7,7.4 -2020-12-15T07:00,20.4,81,15.4,0,0,5.5,7.8 -2020-12-15T08:00,23.5,76,16.9,0,0.028,4.5,7.8 -2020-12-15T09:00,22.9,78,17.2,0,0.028,3.6,7.4 -2020-12-15T10:00,26.8,67,17.2,0,0.028,4.1,7.8 -2020-12-15T11:00,27.9,62,16.4,0,0.028,5.8,10.7 -2020-12-15T12:00,28.8,60,16.4,0,0,5,11.6 -2020-12-15T13:00,29.4,57,15.9,0,0,3.9,9.4 -2020-12-15T14:00,29.6,56,16,0,0,2.1,7.4 -2020-12-15T15:00,27.3,66,17.4,0,0,2.7,4.7 -2020-12-15T16:00,21.1,68,12.3,0,0,6.8,8.9 -2020-12-15T17:00,27.9,56,14,0,0,2.1,10.7 -2020-12-15T18:00,23.7,66,14.2,0,0,3.6,4.7 -2020-12-15T19:00,21.2,73,13.8,0,0,8.5,12.3 -2020-12-15T20:00,19.8,72,12.1,0,0,10.6,16.6 -2020-12-15T21:00,16.1,70,8.2,0,0,5.2,16.6 -2020-12-15T22:00,16.1,63,5.8,0,0,10.6,16.1 -2020-12-15T23:00,15.4,60,3.8,0,0,11.9,18.1 -2020-12-16T00:00,15.7,59,3.8,0,0,11.3,18.1 -2020-12-16T01:00,16.9,58,4.5,0,0,11.6,17.7 -2020-12-16T02:00,17,57,4.3,0,0,12,17.7 -2020-12-16T03:00,16.5,59,4.8,0,0,13.6,20.4 -2020-12-16T04:00,16.6,61,5.4,0,0,12.5,20.6 -2020-12-16T05:00,20.3,60,8.7,0,0,5.5,19.9 -2020-12-16T06:00,21.7,60,10.1,0,0,14.3,22.8 -2020-12-16T07:00,21,64,10.8,0,0,7.6,21.5 -2020-12-16T08:00,25.3,53,10.7,0,0,2.5,8.5 -2020-12-16T09:00,27.9,48,10.6,0,0,1.4,5.8 -2020-12-16T10:00,33.5,39,11,0,0,2.6,7.2 -2020-12-16T11:00,36.6,36,12.2,0,0,4.5,11.2 -2020-12-16T12:00,38.5,33,11.7,0,0,4.8,11 -2020-12-16T13:00,40.4,29,10.8,0,0,4,11.6 -2020-12-16T14:00,39.9,29,10,0,0,2.4,9.8 -2020-12-16T15:00,38.8,31,10.7,0,0,6.3,9.8 -2020-12-16T16:00,34.2,40,12.4,0,0,6.7,11.4 -2020-12-16T17:00,30,55,15.6,0,0,5.9,10.3 -2020-12-16T18:00,30.3,53,15.1,0,0,4.7,8.1 -2020-12-16T19:00,31.6,44,12,0,0,6.3,8.3 -2020-12-16T20:00,33.4,39,11.4,0,0,2.7,9.2 -2020-12-16T21:00,27.8,56,14.2,0,0,4.9,6.7 -2020-12-16T22:00,24.7,66,14.8,0,0,9.9,14.8 -2020-12-16T23:00,25.2,61,13.5,0,0,8.5,15.4 -2020-12-17T00:00,25.4,58,12.7,0,0,5.8,13.2 -2020-12-17T01:00,25.5,56,11.9,0,0,4.8,9.2 -2020-12-17T02:00,24.4,57,11.1,0,0,6.6,10.3 -2020-12-17T03:00,23.5,56,10.1,0,0,7.2,11.2 -2020-12-17T04:00,22.6,55,9,0,0,6.5,11.2 -2020-12-17T05:00,20.3,61,9,0,0,7.3,11 -2020-12-17T06:00,21.4,57,8.4,0,0,5.9,12.1 -2020-12-17T07:00,21,58,8.5,0,0,4.5,9.2 -2020-12-17T08:00,23.7,52,8.6,0,0,6.1,8.5 -2020-12-17T09:00,29.5,42,9.1,0,0,4.8,10.7 -2020-12-17T10:00,36.3,34,10.5,0,0,5.8,11.9 -2020-12-17T11:00,42.1,29,12.3,0,0,6.4,13.2 -2020-12-17T12:00,41.7,33,14.8,0,0,6.9,13.2 -2020-12-17T13:00,43.2,32,15.3,0,0,5.9,11.2 -2020-12-17T14:00,42.4,34,15.7,0,0,3.2,9.4 -2020-12-17T15:00,39.5,39,16.7,0,0,4.6,5.8 -2020-12-17T16:00,35.3,47,17.2,0,0,6.1,6.9 -2020-12-17T17:00,33.5,52,18,0,0,4.3,7.8 -2020-12-17T18:00,28.5,69,19.7,0,0,5.5,8.1 -2020-12-17T19:00,29,70,20.5,0,0,5.8,6.9 -2020-12-17T20:00,25.7,78,19.9,0,0,5,6.9 -2020-12-17T21:00,25.4,76,19,0,0,5.7,7.2 -2020-12-17T22:00,24.4,75,17.5,0,0,5.2,6.9 -2020-12-17T23:00,24.3,72,16.4,0,0,3.5,6 -2020-12-18T00:00,22.1,76,15.6,0,0,2.1,4.7 -2020-12-18T01:00,19.7,81,14.8,0,0,2.7,3.6 -2020-12-18T02:00,19.6,79,14,0,0,3.2,5.1 -2020-12-18T03:00,18.8,78,13.1,0,0,2.6,5.4 -2020-12-18T04:00,18,79,12.7,0,0,1.4,4.5 -2020-12-18T05:00,16.6,79,11.2,0,0,4.5,6.9 -2020-12-18T06:00,20.8,70,12.7,0,0,2.6,5.8 -2020-12-18T07:00,23.2,65,13.2,0,0,3.4,4 -2020-12-18T08:00,27.7,55,13.6,0,0,2.4,4.7 -2020-12-18T09:00,30.1,53,15.2,0,0,7.3,12.8 -2020-12-18T10:00,33.1,55,18.9,0,0,2.2,12.1 -2020-12-18T11:00,34.9,63,23.5,0,0.055,5.2,11.9 -2020-12-18T12:00,33.5,77,27.1,0,0.11,3.6,11.9 -2020-12-18T13:00,33.4,79,27.6,0,0.055,4.2,8.3 -2020-12-18T14:00,33.7,78,27.6,0,0.028,5,9.2 -2020-12-18T15:00,33.8,77,27.4,0,0,3.7,9.4 -2020-12-18T16:00,33.4,74,26,0,0,2.9,6.9 -2020-12-18T17:00,27.7,86,24,0,0,4.7,3.6 -2020-12-18T18:00,25.8,86,22.2,0,0,5.2,7.6 -2020-12-18T19:00,24.1,86,20.6,0,0,6,8.1 -2020-12-18T20:00,22.8,86,19.1,0,0,6.5,8.9 -2020-12-18T21:00,19.9,85,16.1,0,0,7.4,9.8 -2020-12-18T22:00,16.2,87,13.1,0,0,7.6,10.5 -2020-12-18T23:00,13.5,87,10.4,0,0,7.6,11 -2020-12-19T00:00,12.8,84,8.9,0,0,7.6,10.3 -2020-12-19T01:00,12.7,81,7.9,0,0,6.9,10.7 -2020-12-19T02:00,15,74,8.2,0,0,5.4,10.1 -2020-12-19T03:00,15.8,72,8.2,0,0,5.6,9.2 -2020-12-19T04:00,15.9,70,7.9,0,0,6,9.4 -2020-12-19T05:00,19.9,68,10.9,0,0,8.1,9.6 -2020-12-19T06:00,18.8,66,9.1,0,0,7.2,13.2 -2020-12-19T07:00,16.5,66,7.3,0,0,6.1,12.3 -2020-12-19T08:00,20.1,63,9.5,0,0,4.6,8.9 -2020-12-19T09:00,27.2,59,14.7,0,0,3.8,8.5 -2020-12-19T10:00,34.3,54,19.5,0,0,5,10.3 -2020-12-19T11:00,38.4,48,20.5,0,0,8.3,15.4 -2020-12-19T12:00,39.7,44,19.6,0,0,16,27.7 -2020-12-19T13:00,43.5,30,14.2,0,0,13.2,28.4 -2020-12-19T14:00,43.8,31,15.3,0,0,13.5,22.1 -2020-12-19T15:00,41.5,35,16,0,0,10.6,22.4 -2020-12-19T16:00,36.7,39,14.2,0,0,5.4,15.9 -2020-12-19T17:00,32.5,46,13.9,0,0,7.8,9.6 -2020-12-19T18:00,31.6,52,15.9,0,0,6.8,13.2 -2020-12-19T19:00,29.1,57,15.8,0,0,4.9,11.4 -2020-12-19T20:00,28.8,55,14.5,0,0,6,8.9 -2020-12-19T21:00,29.8,52,14.5,0,0,7.2,10.7 -2020-12-19T22:00,28.3,56,14.5,0,0,6.7,10.1 -2020-12-19T23:00,29.8,52,14.4,0,0,3.1,8.7 -2020-12-20T00:00,25,60,13.1,0,0,3.7,6.3 -2020-12-20T01:00,23.3,58,10.9,0,0,8.5,12.1 -2020-12-20T02:00,23.1,56,9.9,0,0,8.9,13.4 -2020-12-20T03:00,23.1,56,9.9,0,0,9,13.6 -2020-12-20T04:00,22.4,57,9.6,0,0,8.7,14.1 -2020-12-20T05:00,22.5,61,11.2,0,0,7.8,13.9 -2020-12-20T06:00,23.2,60,11.2,0,0,6.8,14.1 -2020-12-20T07:00,24.7,56,11.2,0,0,5.7,10.3 -2020-12-20T08:00,27.4,51,11.5,0,0,7.4,9.8 -2020-12-20T09:00,32.8,40,11.3,0,0,9.9,13.9 -2020-12-20T10:00,38,38,14.6,0,0,11.9,18.8 -2020-12-20T11:00,44,32,16.2,0,0,13.5,22.1 -2020-12-20T12:00,47.9,30,18.1,0,0,15.4,23.9 -2020-12-20T13:00,51,28,18.7,0,0,13.8,26.8 -2020-12-20T14:00,52.4,29,20.8,0,0,4.7,23 -2020-12-20T15:00,50.2,32,21.7,0,0,1.4,9.6 -2020-12-20T16:00,48.3,36,22.6,0,0,4.1,5.8 -2020-12-20T17:00,38.8,52,22.7,0,0,7.6,6.9 -2020-12-20T18:00,37,57,23,0,0,7.3,9.6 -2020-12-20T19:00,33.3,67,23.4,0,0,7.5,8.5 -2020-12-20T20:00,39.1,52,22.8,0,0,7.7,11.2 -2020-12-20T21:00,40.3,50,23.2,0,0,8.8,14.3 -2020-12-20T22:00,36.1,58,22.9,0,0,6.5,14.1 -2020-12-20T23:00,31.7,68,22.4,0,0,7.7,9.6 -2020-12-21T00:00,31.6,68,22.4,0,0,6.7,9.4 -2020-12-21T01:00,35.3,59,22.6,0,0,6.8,10.1 -2020-12-21T02:00,36.4,58,23.1,0,0,7.7,12.1 -2020-12-21T03:00,35.8,60,23.4,0,0,7.5,12.1 -2020-12-21T04:00,34.5,64,23.6,0,0,5.9,11.6 -2020-12-21T05:00,30.8,72,22.7,0,0,7.2,8.9 -2020-12-21T06:00,30.4,72,22.6,0,0,5.9,8.7 -2020-12-21T07:00,30,75,22.9,0,0,5.8,7.2 -2020-12-21T08:00,35.3,62,23.5,0,0,5.9,8.3 -2020-12-21T09:00,43.1,47,24.1,0,0,7.4,11.2 -2020-12-21T10:00,49.7,39,25.5,0,0,2.3,11.2 -2020-12-21T11:00,52,39,27.6,0,0,2.9,6 -2020-12-21T12:00,53.4,38,28.5,0,0,6.8,11.9 -2020-12-21T13:00,54.2,36,27.9,0,0,10.4,16.8 -2020-12-21T14:00,54.1,35,27.1,0,0,12,20.1 -2020-12-21T15:00,53,37,27.4,0,0,8.9,19.7 -2020-12-21T16:00,46.1,50,28.5,0,0,6,13.6 -2020-12-21T17:00,39.7,55,24.9,0,0,9.5,9.6 -2020-12-21T18:00,37.9,64,26.8,0,0,6.3,11.4 -2020-12-21T19:00,34.1,76,27.4,0,0,6.7,11.4 -2020-12-21T20:00,30.9,80,25.6,0,0,5.8,10.5 -2020-12-21T21:00,29.9,81,24.8,0,0,4.7,8.1 -2020-12-21T22:00,29.7,78,23.7,0,0,5.2,8.1 -2020-12-21T23:00,29.5,74,22.1,0,0,5,8.5 -2020-12-22T00:00,28,75,20.9,0,0,4.8,8.1 -2020-12-22T01:00,26.2,77,20,0,0,5.9,7.4 -2020-12-22T02:00,26.3,75,19.5,0,0,4.7,7.2 -2020-12-22T03:00,25.8,76,19.2,0,0,5.8,6.9 -2020-12-22T04:00,26.1,75,19.1,0,0,6.2,7.4 -2020-12-22T05:00,24.9,77,18.8,0,0,7.1,7.4 -2020-12-22T06:00,25.8,76,19.2,0,0,6.9,8.5 -2020-12-22T07:00,26.1,73,18.5,0,0,8.2,9.4 -2020-12-22T08:00,33,52,17.3,0,0,6.4,9.8 -2020-12-22T09:00,40,40,17.8,0,0,11.6,17.4 -2020-12-22T10:00,45.9,34,18.8,0,0,12.4,20.8 -2020-12-22T11:00,51,29,20.2,0,0,15.1,24.8 -2020-12-22T12:00,53.8,29,22,0,0,17.7,29.5 -2020-12-22T13:00,55.8,28,23.3,0,0,15.4,29.5 -2020-12-22T14:00,56.4,29,24.5,0,0,13,25.5 -2020-12-22T15:00,54.9,32,25.2,0,0,9.9,21 -2020-12-22T16:00,48.7,39,24.8,0,0,6.9,15.9 -2020-12-22T17:00,43.2,42,21.7,0,0,8.2,11 -2020-12-22T18:00,40.7,54,25.5,0,0,8.5,15 -2020-12-22T19:00,42.2,52,25.6,0,0,11,20.6 -2020-12-22T20:00,39,54,23.7,0,0,10.6,17.9 -2020-12-22T21:00,36,40,14.2,0,0,19.3,31.5 -2020-12-22T22:00,30.7,32,4.5,0,0,20.7,37.6 -2020-12-22T23:00,24.9,35,1,0,0,6.6,33.8 -2020-12-23T00:00,24.1,37,1.6,0,0,4.1,8.7 -2020-12-23T01:00,24.9,37,2,0,0,5,8.1 -2020-12-23T02:00,26.3,36,2.8,0,0,7.6,14.8 -2020-12-23T03:00,25.3,37,2.5,0,0,7.4,14.1 -2020-12-23T04:00,24.6,38,2.7,0,0,7.1,12.3 -2020-12-23T05:00,25.3,35,1.4,0,0,8.4,15.4 -2020-12-23T06:00,23.1,38,1.4,0,0,4.7,14.3 -2020-12-23T07:00,21.4,38,-0.1,0,0,5.3,8.7 -2020-12-23T08:00,22.6,35,-1.3,0,0,5.1,8.9 -2020-12-23T09:00,25.4,32,-0.1,0,0,6.7,11.9 -2020-12-23T10:00,27.1,34,2.1,0,0,8.6,17.2 -2020-12-23T11:00,29.4,21,-5.6,0,0,12.5,22.1 -2020-12-23T12:00,29.8,19,-7.3,0,0,16.9,29.5 -2020-12-23T13:00,29.1,21,-6.2,0,0,17.7,32.2 -2020-12-23T14:00,28.6,21,-6.6,0,0,16.7,31.5 -2020-12-23T15:00,28,19,-9.1,0,0,14.2,29.1 -2020-12-23T16:00,25.8,21,-8.4,0,0,12.7,24.8 -2020-12-23T17:00,21.1,29,-6.2,0,0,4.9,21.5 -2020-12-23T18:00,23.4,27,-5.7,0,0,0.8,8.3 -2020-12-23T19:00,19.6,36,-3,0,0,3.6,5.4 -2020-12-23T20:00,22.8,30,-4.3,0,0,9.8,14.3 -2020-12-23T21:00,23.8,29,-4.1,0,0,9.7,19.7 -2020-12-23T22:00,22.7,29,-4.7,0,0,5,16.1 -2020-12-23T23:00,21,30,-5.6,0,0,6.9,9.8 -2020-12-24T00:00,23.9,26,-5.7,0,0,2.1,9.2 -2020-12-24T01:00,20,33,-4.6,0,0,2.6,4.5 -2020-12-24T02:00,19.9,33,-4.9,0,0,3.6,6.5 -2020-12-24T03:00,19.9,33,-4.9,0,0,3.1,6.5 -2020-12-24T04:00,20.8,32,-4.4,0,0,2.7,5.4 -2020-12-24T05:00,24.9,29,-3.2,0,0,4.5,4.9 -2020-12-24T06:00,28.3,25,-2.7,0,0,4.3,6.7 -2020-12-24T07:00,27.2,30,-0.3,0,0,7.8,11.6 -2020-12-24T08:00,30.8,29,2.1,0,0,9.4,16.6 -2020-12-24T09:00,35.1,25,2.8,0,0,4.5,15.2 -2020-12-24T10:00,39.4,22,3.2,0,0,3.2,9.2 -2020-12-24T11:00,44.4,20,6.2,0,0,11.2,20.1 -2020-12-24T12:00,47.5,20,7.9,0,0,10.1,20.8 -2020-12-24T13:00,49,19,8.8,0,0,7.6,18.6 -2020-12-24T14:00,49.2,20,9.6,0,0,8,15.2 -2020-12-24T15:00,47.2,23,11.1,0,0,8.6,15 -2020-12-24T16:00,41.3,29,11.7,0,0,8.5,14.3 -2020-12-24T17:00,37.1,34,11.3,0,0,10.2,16.1 -2020-12-24T18:00,37.1,34,11.2,0,0,13.2,21 -2020-12-24T19:00,37.3,33,10.9,0,0,13.4,22.6 -2020-12-24T20:00,36.7,34,11,0,0,10.1,21.9 -2020-12-24T21:00,34.7,37,11.2,0,0,7.7,16.6 -2020-12-24T22:00,32.6,41,11.6,0,0,6.5,12.3 -2020-12-24T23:00,29.9,47,12.1,0,0,7.3,9.6 -2020-12-25T00:00,28.2,51,12.6,0,0,7.3,9.8 -2020-12-25T01:00,27.5,53,12.6,0,0,6.5,9.2 -2020-12-25T02:00,26.6,54,12.3,0,0,5.6,8.5 -2020-12-25T03:00,26.9,53,11.8,0,0,5.4,8.1 -2020-12-25T04:00,27.2,49,10.5,0,0,6.3,9.8 -2020-12-25T05:00,27.7,45,8.9,0,0,6.2,10.5 -2020-12-25T06:00,26.7,44,7.8,0,0,5.2,9.8 -2020-12-25T07:00,25.6,45,7.4,0,0,5.3,8.1 -2020-12-25T08:00,28.7,40,7.5,0,0,5.4,8.3 -2020-12-25T09:00,35.2,32,8,0,0,6.5,11.9 -2020-12-25T10:00,43,24,8.5,0,0,6.2,12.5 -2020-12-25T11:00,49.6,19,8.9,0,0,6.2,13 -2020-12-25T12:00,53.2,20,13.5,0,0,5.7,13 -2020-12-25T13:00,55.1,19,13.3,0,0,5.6,11.9 -2020-12-25T14:00,55.7,18,12.6,0,0,5.1,11.4 -2020-12-25T15:00,52.9,33,24.9,0,0,3.3,9.4 -2020-12-25T16:00,46,35,20.1,0,0,5.8,6.9 -2020-12-25T17:00,46.6,33,18.9,0,0,3.1,6.9 -2020-12-25T18:00,31.8,62,20.3,0,0,6.1,7.6 -2020-12-25T19:00,27.5,75,20.7,0,0,6.7,7.6 -2020-12-25T20:00,28,65,17.6,0,0,5.8,8.1 -2020-12-25T21:00,27.8,54,13.4,0,0,7.4,9.4 -2020-12-25T22:00,27.4,52,11.9,0,0,8.3,10.1 -2020-12-25T23:00,28.2,50,11.9,0,0,5.2,9.8 -2020-12-26T00:00,27.1,51,11.2,0,0,5.4,9.4 -2020-12-26T01:00,24.3,55,10.4,0,0,7,8.9 -2020-12-26T02:00,25.2,49,8.8,0,0,6.4,8.3 -2020-12-26T03:00,26.5,48,9.2,0,0,5.1,8.3 -2020-12-26T04:00,25.9,47,8.6,0,0,8,13.2 -2020-12-26T05:00,25.3,51,9.7,0,0,5,14.3 -2020-12-26T06:00,27.2,47,9.4,0,0,6.4,10.1 -2020-12-26T07:00,30,43,10.4,0,0,6.2,11.2 -2020-12-26T08:00,31.6,42,11.3,0,0,7.6,12.1 -2020-12-26T09:00,37.8,37,14,0,0,7.7,12.8 -2020-12-26T10:00,45.2,32,17.2,0,0,2.5,12.5 -2020-12-26T11:00,50.1,29,18.7,0,0,0.5,6.9 -2020-12-26T12:00,53.8,23,17.1,0,0,4.2,9.6 -2020-12-26T13:00,55.9,20,15.5,0,0,2.3,10.1 -2020-12-26T14:00,56.9,21,17,0,0,6.5,11.4 -2020-12-26T15:00,56.7,30,25.6,0,0,2,11.9 -2020-12-26T16:00,47.7,44,26.6,0,0,6.9,8.3 -2020-12-26T17:00,40.2,44,19.9,0,0,7.4,8.1 -2020-12-26T18:00,39.4,46,20.1,0,0,8.4,10.1 -2020-12-26T19:00,42.9,40,20.5,0,0,0.9,11 -2020-12-26T20:00,32.4,59,19.5,0,0,5.1,7.6 -2020-12-26T21:00,33.7,48,16,0,0,7.1,11 -2020-12-26T22:00,37.3,41,16,0,0,12,18.8 -2020-12-26T23:00,37.8,47,19.5,0,0,15.2,24.6 -2020-12-27T00:00,37,51,20.5,0,0,12.2,25.1 -2020-12-27T01:00,35.2,58,22,0,0,10.4,19.9 -2020-12-27T02:00,36.3,56,22.1,0,0,10.4,17.7 -2020-12-27T03:00,36.4,53,20.8,0,0,12.7,19.9 -2020-12-27T04:00,37.8,45,18.5,0,0,14.8,24.8 -2020-12-27T05:00,36.6,46,17.8,0,0,12.8,24.8 -2020-12-27T06:00,35.5,39,12.7,0,0,13,22.1 -2020-12-27T07:00,33.8,40,12,0,0,7.7,21.7 -2020-12-27T08:00,33.2,46,14.7,0,0,6.2,12.8 -2020-12-27T09:00,36.9,40,14.7,0,0,4.1,9.8 -2020-12-27T10:00,40.5,33,13.6,0,0,4,9.4 -2020-12-27T11:00,43.1,27,11,0,0,5.4,12.3 -2020-12-27T12:00,45,23,9.2,0,0,5.1,13.4 -2020-12-27T13:00,46.2,21,8.1,0,0,8.2,15.7 -2020-12-27T14:00,45.7,21,8.1,0,0,10.1,19.7 -2020-12-27T15:00,44.2,23,8.4,0,0,8.1,19.2 -2020-12-27T16:00,40.9,30,11.7,0,0,6.7,14.8 -2020-12-27T17:00,32.8,42,12.5,0,0,6.6,14.3 -2020-12-27T18:00,30.2,51,14.3,0,0,6.2,8.3 -2020-12-27T19:00,29.2,54,14.7,0,0,5.1,7.6 -2020-12-27T20:00,28.7,51,12.7,0,0,4,6.3 -2020-12-27T21:00,27.1,51,11.3,0,0,3.3,5.1 -2020-12-27T22:00,26.6,49,9.9,0,0,4.1,6.9 -2020-12-27T23:00,25.4,50,9.5,0,0,4.4,6.7 -2020-12-28T00:00,24.1,60,12.1,0,0,4.5,6.5 -2020-12-28T01:00,24.6,61,13,0,0,2.9,5.4 -2020-12-28T02:00,23.6,66,14.1,0,0,3.1,4.9 -2020-12-28T03:00,26.2,62,14.7,0,0,1.8,3.6 -2020-12-28T04:00,25.3,66,15.7,0,0,2.1,2.5 -2020-12-28T05:00,25,74,17.8,0,0,2.8,3.4 -2020-12-28T06:00,24.6,75,17.8,0,0,2.6,3.8 -2020-12-28T07:00,24,76,17.6,0,0,1.9,3.4 -2020-12-28T08:00,24,76,17.5,0,0,0.5,2.7 -2020-12-28T09:00,27.5,63,16.4,0,0,2.4,6.7 -2020-12-28T10:00,29.7,54,15,0,0.028,5.2,11 -2020-12-28T11:00,29.7,62,18.1,0,0.028,6.4,13 -2020-12-28T12:00,30.5,68,21.2,0,0,5.3,13.6 -2020-12-28T13:00,30.7,72,22.7,0,0.028,5.1,12.1 -2020-12-28T14:00,30.5,75,23.4,0,0.028,5.1,11.6 -2020-12-28T15:00,30,77,23.7,0,0.028,4.5,11 -2020-12-28T16:00,28.9,82,24.3,0,0.083,4.6,9.4 -2020-12-28T17:00,24.9,90,22.3,0,0.055,3.2,8.7 -2020-12-28T18:00,26.3,85,22.6,0,0.028,3.6,8.1 -2020-12-28T19:00,26.2,86,22.7,0,0.11,5.6,10.1 -2020-12-28T20:00,25.7,89,22.9,0,0.138,5.3,19.2 -2020-12-28T21:00,25.6,89,22.7,0,0.22,5.8,21 -2020-12-28T22:00,25.8,88,22.6,0,0.11,3.7,10.1 -2020-12-28T23:00,25.3,89,22.5,0,0.055,3.9,7.2 -2020-12-29T00:00,24,94,22.5,0,0.028,4,7.2 -2020-12-29T01:00,24.1,93,22.3,0,0.028,3.7,8.3 -2020-12-29T02:00,24.6,91,22.4,0,0.028,4.1,7.4 -2020-12-29T03:00,24.7,89,21.9,0,0.055,2.8,7.8 -2020-12-29T04:00,23.3,93,21.6,0,0,1.5,5.8 -2020-12-29T05:00,23.6,91,21.4,0,0,5,5.6 -2020-12-29T06:00,23.5,91,21.2,0,0,0.9,8.7 -2020-12-29T07:00,21,97,20.2,0,0,3,5.4 -2020-12-29T08:00,21.2,95,20,0,0,8.5,11.2 -2020-12-29T09:00,21.2,94,19.7,0,0,8.6,20.6 -2020-12-29T10:00,26.1,72,18.4,0,0,2.8,15.7 -2020-12-29T11:00,29.5,63,18.5,0,0,1.3,8.9 -2020-12-29T12:00,31.2,59,18.5,0,0,3.5,10.7 -2020-12-29T13:00,32.9,57,19.4,0,0,4.3,11.4 -2020-12-29T14:00,33.7,56,19.5,0,0,5.8,12.8 -2020-12-29T15:00,33.4,54,18.4,0,0,3.4,12.8 -2020-12-29T16:00,32.6,53,17.2,0,0,0.9,8.3 -2020-12-29T17:00,25.1,72,17.3,0,0,3.4,2 -2020-12-29T18:00,24.2,80,19,0,0,4.5,6 -2020-12-29T19:00,25.9,73,18.5,0,0,3.9,6 -2020-12-29T20:00,22.7,78,17,0,0,4.3,6.5 -2020-12-29T21:00,21.1,74,14,0,0,4.1,7.2 -2020-12-29T22:00,20,71,12,0,0,3.7,6.3 -2020-12-29T23:00,19.5,64,9.2,0,0,5.1,8.1 -2020-12-30T00:00,18.9,61,7.6,0,0,4.6,8.1 -2020-12-30T01:00,18.1,61,6.9,0,0,4.7,7.4 -2020-12-30T02:00,17.5,60,5.8,0,0,6,9.4 -2020-12-30T03:00,17.7,49,1.8,0,0,5.3,9.4 -2020-12-30T04:00,18.1,40,-2.1,0,0,4,8.5 -2020-12-30T05:00,14.2,44,-3.5,0,0,7.3,6.7 -2020-12-30T06:00,17.6,38,-3.6,0,0,5,8.5 -2020-12-30T07:00,17.5,37,-4.6,0,0,4.5,6.7 -2020-12-30T08:00,20.4,31,-5.4,0,0,4.5,6.7 -2020-12-30T09:00,26.7,24,-5.3,0,0,5.1,10.5 -2020-12-30T10:00,33.2,19,-4.5,0,0,3.4,10.3 -2020-12-30T11:00,37.9,17,-3.7,0,0,1.7,9.6 -2020-12-30T12:00,40.3,15,-3.3,0,0,1.6,8.1 -2020-12-30T13:00,42.3,14,-3.3,0,0,1.1,8.1 -2020-12-30T14:00,43.2,14,-3,0,0,0.9,7.4 -2020-12-30T15:00,42.3,15,-2.2,0,0,2.9,6 -2020-12-30T16:00,33.4,25,1.6,0,0,6.9,8.1 -2020-12-30T17:00,37.3,24,4,0,0,3.6,8.7 -2020-12-30T18:00,31.8,32,5,0,0,3.8,4.9 -2020-12-30T19:00,24.8,46,6.8,0,0,4.8,6.7 -2020-12-30T20:00,23.4,53,8.6,0,0,6.3,9.8 -2020-12-30T21:00,22.6,51,7.2,0,0,4.9,9.8 -2020-12-30T22:00,23.6,46,6,0,0,4.1,7.8 -2020-12-30T23:00,24.7,43,5.2,0,0,4.6,7.4 -2020-12-31T00:00,24.3,43,5,0,0,5.7,8.9 -2020-12-31T01:00,23.5,46,5.7,0,0,7.1,11.2 -2020-12-31T02:00,23.1,49,6.8,0,0,6.8,11.9 -2020-12-31T03:00,22.4,53,7.7,0,0,3.6,10.7 -2020-12-31T04:00,21.9,56,8.5,0,0,4.7,5.6 -2020-12-31T05:00,23.3,58,10.8,0,0,4.7,6.7 -2020-12-31T06:00,23.5,57,10.3,0,0,4,8.5 -2020-12-31T07:00,23.6,56,10.2,0,0,4.7,6.9 -2020-12-31T08:00,24.7,53,10,0,0,6.7,10.3 -2020-12-31T09:00,30,43,10.2,0,0,2.7,10.3 -2020-12-31T10:00,35.1,35,10.2,0,0,2.2,7.8 -2020-12-31T11:00,39.1,30,10,0,0,4.5,11 -2020-12-31T12:00,41.3,28,10.4,0,0,6.7,14.5 -2020-12-31T13:00,42.2,27,10.6,0,0,6.9,15 -2020-12-31T14:00,42.4,27,10.6,0,0,6,14.5 -2020-12-31T15:00,41.6,28,10.7,0,0,5.2,12.8 -2020-12-31T16:00,35.2,37,11.2,0,0,5.1,9.6 -2020-12-31T17:00,29.6,50,13.1,0,0,6.3,8.9 -2020-12-31T18:00,28.9,49,12.3,0,0,6.3,9.2 -2020-12-31T19:00,27,52,11.7,0,0,4.5,7.4 -2020-12-31T20:00,28.8,51,13,0,0,4.1,5.6 -2020-12-31T21:00,26.1,57,12.7,0,0,5.7,6.7 -2020-12-31T22:00,25.5,57,12.4,0,0,5.5,6.7 -2020-12-31T23:00,28.3,50,12,0,0,3.5,6.5 -2021-01-01T00:00,23.5,61,11.9,0,0,3.2,4 -2021-01-01T01:00,21.4,66,11.8,0,0,2.7,4.3 -2021-01-01T02:00,20.8,64,10.6,0,0,2.5,4.3 -2021-01-01T03:00,19.9,64,9.8,0,0,3.5,5.8 -2021-01-01T04:00,20,61,8.9,0,0,2.9,5.8 -2021-01-01T05:00,18.8,64,8.6,0,0,4,5.1 -2021-01-01T06:00,21,57,8.2,0,0,2.1,5.4 -2021-01-01T07:00,21.5,56,8.2,0,0,4,4.7 -2021-01-01T08:00,25.3,48,8.4,0,0,2.2,5.4 -2021-01-01T09:00,31.2,39,9.1,0,0,0.8,5.4 -2021-01-01T10:00,35.2,36,11.2,0,0,1.6,6.9 -2021-01-01T11:00,37.2,41,15.4,0,0,2.7,9.6 -2021-01-01T12:00,38.1,40,16.1,0,0,1.4,9.8 -2021-01-01T13:00,38.6,40,16.3,0,0,2.5,8.9 -2021-01-01T14:00,38.4,43,17.6,0,0,4.1,10.5 -2021-01-01T15:00,37.9,44,17.9,0,0,3.2,10.3 -2021-01-01T16:00,36.9,46,17.9,0,0,2.1,7.4 -2021-01-01T17:00,35.8,49,18.3,0,0,3.7,2.7 -2021-01-01T18:00,33.4,54,18.4,0,0,1.4,4.3 -2021-01-01T19:00,30.4,60,18.2,0,0,1.7,2.2 -2021-01-01T20:00,27.5,66,17.8,0,0,1.5,2.2 -2021-01-01T21:00,21.8,82,17.1,0,0,3.4,4.7 -2021-01-01T22:00,21.5,77,15.4,0,0,3.3,4.9 -2021-01-01T23:00,21.9,67,12.7,0,0,3.5,5.8 -2021-01-02T00:00,21.5,61,10,0,0,3.3,5.8 -2021-01-02T01:00,21.3,57,8.3,0,0,4,6.3 -2021-01-02T02:00,21.9,54,7.6,0,0,3.9,6.5 -2021-01-02T03:00,22,53,7.6,0,0,4.4,7.2 -2021-01-02T04:00,21.4,54,7.3,0,0,3.8,6.9 -2021-01-02T05:00,19.9,56,6.5,0,0,4.3,7.2 -2021-01-02T06:00,21.6,49,5.2,0,0,4.3,8.3 -2021-01-02T07:00,21.5,47,4.6,0,0,6.2,7.4 -2021-01-02T08:00,26.2,38,4.2,0,0,4.2,8.5 -2021-01-02T09:00,33.8,28,3.7,0,0,4.9,8.9 -2021-01-02T10:00,39.6,22,3.9,0,0,2.9,8.9 -2021-01-02T11:00,43.9,19,4.4,0,0,4.5,10.5 -2021-01-02T12:00,46.6,17,3.6,0,0,3.5,11.6 -2021-01-02T13:00,47.9,15,2.9,0,0,3,10.1 -2021-01-02T14:00,47.8,15,2.2,0,0,6.6,12.3 -2021-01-02T15:00,44.8,22,8.1,0,0,9.2,14.3 -2021-01-02T16:00,39.6,29,9.9,0,0,5.7,16.6 -2021-01-02T17:00,34.1,38,11.2,0,0,5.7,10.3 -2021-01-02T18:00,37.5,30,9,0,0,3.5,9.4 -2021-01-02T19:00,29.6,46,11.3,0,0,5.9,9.6 -2021-01-02T20:00,30.9,42,10.3,0,0,8.9,13.2 -2021-01-02T21:00,28.5,47,10.9,0,0,5.5,15.4 -2021-01-02T22:00,27,51,11,0,0,5.4,7.8 -2021-01-02T23:00,25.3,52,10,0,0,8.7,13.9 -2021-01-03T00:00,25.7,48,8.5,0,0,8.3,15.4 -2021-01-03T01:00,26,47,8.5,0,0,3.9,13.4 -2021-01-03T02:00,25.1,49,8.5,0,0,3.9,6.5 -2021-01-03T03:00,25.5,47,8.2,0,0,4.6,7.4 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b/viz_scripts/docker/Dockerfile.dev deleted file mode 100644 index f6c10b1..0000000 --- a/viz_scripts/docker/Dockerfile.dev +++ /dev/null @@ -1,24 +0,0 @@ -# python 3 -FROM emission/e-mission-server.dev.server-only:4.0.0 -ARG SERVER_REPO=https://github.com/aGuttman/e-mission-server.git -ARG SERVER_BRANCH=dashboard-dependencies - -VOLUME /plots - -ADD docker/environment36.dashboard.additions.yml / - -RUN /bin/bash -c "/clone_server.sh" - -WORKDIR /usr/src/app - -RUN /bin/bash -c "cd e-mission-server && source setup/activate.sh && conda env update --name emission --file setup/environment36.notebook.additions.yml" -RUN /bin/bash -c "cd e-mission-server && source setup/activate.sh && conda env update --name emission --file /environment36.dashboard.additions.yml" - -ADD docker/start_notebook.sh /usr/src/app/.docker/start_notebook.sh -RUN chmod u+x /usr/src/app/.docker/start_notebook.sh - -ADD docker/crontab /usr/src/app/crontab - -EXPOSE 8888 - -CMD ["/bin/bash", "/usr/src/app/.docker/start_notebook.sh"] diff --git a/viz_scripts/docker/crontab b/viz_scripts/docker/crontab deleted file mode 100644 index d02d3d5..0000000 --- a/viz_scripts/docker/crontab +++ /dev/null @@ -1,8 +0,0 @@ -0 7 * * * python bin/update_mappings.py mapping_dictionaries.ipynb >> /var/log/intake.stdinout 2>&1 -0 8 * * * python bin/generate_plots.py generic_metrics.ipynb default >> /var/log/intake.stdinout 2>&1 -0 8 * * * python bin/generate_plots.py generic_timeseries.ipynb default >> /var/log/intake.stdinout 2>&1 -0 8 * * * python bin/generate_plots.py mode_specific_metrics.ipynb default >> /var/log/intake.stdinout 2>&1 -0 8 * * * python bin/generate_plots.py mode_specific_timeseries.ipynb default >> /var/log/intake.stdinout 2>&1 -0 8 * * * python bin/generate_plots.py energy_calculations.ipynb default >> /var/log/intake.stdinout 2>&1 -# For testing only -# */5 * * * * python bin/generate_plots.py mode_purpose_share.ipynb default >> /var/log/intake.stdinout 2>&1 diff --git a/viz_scripts/docker/environment36.dashboard.additions.yml b/viz_scripts/docker/environment36.dashboard.additions.yml deleted file mode 100644 index 76d37e7..0000000 --- a/viz_scripts/docker/environment36.dashboard.additions.yml +++ /dev/null @@ -1,9 +0,0 @@ -name: emission -channels: -- conda-forge -- defaults -dependencies: -- seaborn=0.11.1 -- pip: - - nbparameterise - - devcron diff --git a/viz_scripts/docker/generate_plots.sh b/viz_scripts/docker/generate_plots.sh deleted file mode 100755 index 6674f4a..0000000 --- a/viz_scripts/docker/generate_plots.sh +++ /dev/null @@ -1,5 +0,0 @@ -cd e-mission-server -source setup/activate.sh -cd ../saved-notebooks - -PYTHONPATH=/usr/src/app/e-mission-server python bin/generate_plots.py $* diff --git a/viz_scripts/docker/load_mongodump.sh b/viz_scripts/docker/load_mongodump.sh deleted file mode 100644 index fb17cd7..0000000 --- a/viz_scripts/docker/load_mongodump.sh +++ /dev/null @@ -1,9 +0,0 @@ -MONGODUMP_FILE=$1 - -echo "Copying file to docker container" -docker cp $MONGODUMP_FILE em-public-dashboard_db_1:/tmp - -FILE_NAME=`basename $MONGODUMP_FILE` - -echo "Restoring the dump from $FILE_NAME" -docker exec -e MONGODUMP_FILE=$FILE_NAME em-public-dashboard_db_1 bash -c 'cd /tmp && tar xvf $MONGODUMP_FILE && mongorestore' diff --git a/viz_scripts/docker/start_notebook.sh b/viz_scripts/docker/start_notebook.sh deleted file mode 100755 index d08a5e9..0000000 --- a/viz_scripts/docker/start_notebook.sh +++ /dev/null @@ -1,45 +0,0 @@ -#!/usr/bin/env bash -#Configure web server - -pushd /usr/src/app/e-mission-server - -#set database URL using environment variable -echo "DB host = "${DB_HOST} -if [ -z ${DB_HOST} ] ; then - local_host=`hostname -i` - sed "s_localhost_${local_host}_" conf/storage/db.conf.sample > conf/storage/db.conf -else - sed "s_localhost_${DB_HOST}_" conf/storage/db.conf.sample > conf/storage/db.conf -fi -popd - -### configure the saved-notebooks directory for persistent notebooks - -# Ensure that the database config is available so that we can connect to it -mkdir -p saved-notebooks/conf/storage -cp e-mission-server/conf/storage/db.conf saved-notebooks/conf/storage/db.conf -cat saved-notebooks/conf/storage/db.conf - -#set Web Server host using environment variable -echo "Web host = "${WEB_SERVER_HOST} - -# change python environment -pushd e-mission-server -pwd -source setup/activate.sh -conda env list -popd - -cd saved-notebooks - -# launch the notebook server -# tail -f /dev/null -if [ -z ${CRON_MODE} ] ; then - echo "Running notebook in docker, change host:port to localhost:47962 in the URL below" - PYTHONPATH=/usr/src/app/e-mission-server jupyter notebook --no-browser --ip=${WEB_SERVER_HOST} --allow-root -else - echo "Running crontab without user interaction, setting python path" - export PYTHONPATH=/usr/src/app/e-mission-server - # tail -f /dev/null - devcron ../crontab >> /var/log/cron.console.stdinout 2>&1 -fi diff --git a/viz_scripts/docker/update_mappings.sh b/viz_scripts/docker/update_mappings.sh deleted file mode 100755 index 866bfb7..0000000 --- a/viz_scripts/docker/update_mappings.sh +++ /dev/null @@ -1,5 +0,0 @@ -cd e-mission-server -source setup/activate.sh -cd ../saved-notebooks - -PYTHONPATH=/usr/src/app/e-mission-server python bin/update_mappings.py $* diff --git a/viz_scripts/energy_calculations.ipynb b/viz_scripts/energy_calculations.ipynb deleted file mode 100644 index a8efd6b..0000000 --- a/viz_scripts/energy_calculations.ipynb +++ /dev/null @@ -1,260 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "united-feeding", - "metadata": {}, - "source": [ - "## Generate Static Graphs" - ] - }, - { - "cell_type": "markdown", - "id": "outdoor-celebrity", - "metadata": {}, - "source": [ - "These are the input parameters for the notebook. They will be automatically changed when the scripts to generate monthly statistics are run. You can modify them manually to generate multiple plots locally as well.\n", - "\n", - "Pass in `None` to remove the filters and plot all data. This is not recommended for production settings, but might be useful for reports based on data snapshots." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "determined-matrix", - "metadata": {}, - "outputs": [], - "source": [ - "year = 2020\n", - "month = 11\n", - "program = \"default\"\n", - "study_type = \"program\"\n", - "mode_of_interest = \"e-bike\"\n", - "include_test_users = False" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "pharmaceutical-survival", - "metadata": {}, - "outputs": [], - "source": [ - "from collections import defaultdict\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "from plots import *\n", - "import scaffolding\n", - "\n", - "sns.set_style(\"whitegrid\")\n", - "sns.set()\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "furnished-silicon", - "metadata": {}, - "outputs": [], - "source": [ - "# Do not run this notebook at all unless it is for a program; nbclient will run up through this cell\n", - "if study_type != \"program\":\n", - " ipython = get_ipython()\n", - " ipython._showtraceback = scaffolding.no_traceback_handler\n", - " raise Exception(\"The plots in this notebook are only relevant to programs\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "nearby-fruit", - "metadata": {}, - "outputs": [], - "source": [ - "# Loading mapping dictionaries from mapping_dictionaries notebook\n", - "%store -r df_ei\n", - "%store -r dic_re\n", - "%store -r dic_pur\n", - "%store -r dic_fuel\n", - "\n", - "# convert a dictionary to a defaultdict\n", - "dic_re = defaultdict(lambda: 'Other',dic_re)\n", - "dic_pur = defaultdict(lambda: 'Other',dic_pur)\n", - "dic_fuel = defaultdict(lambda: 'Other',dic_fuel)" - ] - }, - { - "cell_type": "markdown", - "id": "parallel-patch", - "metadata": {}, - "source": [ - "## Collect Data From Database" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "sufficient-kingston", - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "expanded_ct, file_suffix, quality_text, debug_df = scaffolding.load_viz_notebook_data(year,\n", - " month,\n", - " program,\n", - " study_type,\n", - " dic_re,\n", - " dic_pur=dic_pur,\n", - " include_test_users=include_test_users)\n", - "# CASE 1 of https://github.com/e-mission/em-public-dashboard/issues/69#issuecomment-1256835867 \n", - "expanded_ct = scaffolding.add_energy_impact(expanded_ct, df_ei, dic_fuel) if len(expanded_ct) > 0 else expanded_ct" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "625872b0", - "metadata": {}, - "outputs": [], - "source": [ - "if 'mode_confirm' in expanded_ct.columns:\n", - " mode_of_interest_df = expanded_ct.query(f\"mode_confirm == '{mode_of_interest}'\")\n", - " debug_df.loc[f\"{mode_of_interest}_trips\"] = len(mode_of_interest_df)\n", - " debug_df.loc[f\"{mode_of_interest}_trips_with_replaced_mode\"] = scaffolding.trip_label_count(\"Replaced_mode\", mode_of_interest_df)" - ] - }, - { - "cell_type": "markdown", - "id": "caring-aruba", - "metadata": {}, - "source": [ - "## Energy Impacts Relative to Specific Mode" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "blessed-leader", - "metadata": {}, - "outputs": [], - "source": [ - "# CASE 2 of https://github.com/e-mission/em-public-dashboard/issues/69#issuecomment-1256835867\n", - "data_eb = expanded_ct.query(f\"mode_confirm == '{mode_of_interest}'\") if \"mode_confirm\" in expanded_ct.columns else expanded_ct" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "seeing-court", - "metadata": {}, - "outputs": [], - "source": [ - "quality_text_mode = scaffolding.get_quality_text(expanded_ct, data_eb, mode_of_interest)" - ] - }, - { - "cell_type": "markdown", - "id": "advanced-complexity", - "metadata": {}, - "source": [ - "### Sketch of energy impact by trips for specified mode" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dense-programmer", - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "# ebei : ebike energy impact\n", - "plot_title_no_quality=f\"Sketch of Energy Impact of {mode_of_interest} trips\"\n", - "file_name =f'sketch_energy_impact_{mode_of_interest}%s' % file_suffix\n", - " \n", - "try:\n", - " ebei=data_eb.groupby('Replaced_mode').agg({'Energy_Impact(kWH)': ['sum', 'mean']},)\n", - " ebei.columns = ['Sketch of Total Energy_Impact(kWH)', 'Sketch of Average Energy_Impact(kWH)']\n", - " ebei= ebei.reset_index()\n", - " ebei = ebei.sort_values(by=['Sketch of Total Energy_Impact(kWH)'], ascending=False)\n", - " ebei['boolean'] = ebei['Sketch of Total Energy_Impact(kWH)'] > 0\n", - " net_energy_saved = round(sum(ebei['Sketch of Total Energy_Impact(kWH)']), 2)\n", - "\n", - " x = ebei['Sketch of Total Energy_Impact(kWH)']\n", - " y = ebei['Replaced_mode']\n", - " color =ebei['boolean']\n", - "\n", - " plot_title= plot_title_no_quality+f\"\\n Contribution by replaced mode towards a total of {net_energy_saved}(kWH)\\n\"+quality_text\n", - " energy_impact(x,y,color,plot_title,file_name)\n", - " alt_text = store_alt_text_bar(pd.DataFrame(x.values,y), file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality)" - ] - }, - { - "cell_type": "markdown", - "id": "strategic-sheet", - "metadata": {}, - "source": [ - "### Sketch of CO2 emissions impact by trips of specified mode" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "animated-place", - "metadata": {}, - "outputs": [], - "source": [ - "plot_title_no_quality=f\"Sketch of Total Pounds of CO2 Emissions of {mode_of_interest} trips\"\n", - "file_name =f'sketch_CO2impact_{mode_of_interest}%s' % file_suffix\n", - "\n", - "try:\n", - " ebco2=data_eb.groupby('Replaced_mode').agg({'CO2_Impact(lb)': ['sum', 'mean']},)\n", - " ebco2.columns = ['total_lb_CO2_emissions', 'average_lb_CO2_emission']\n", - " ebco2 = ebco2.reset_index()\n", - " ebco2 = ebco2.sort_values(by=['total_lb_CO2_emissions'], ascending=False)\n", - " ebco2['boolean'] = ebco2['total_lb_CO2_emissions'] > 0\n", - " net_CO2_emissions = round(sum(ebco2['total_lb_CO2_emissions']), 2)\n", - "\n", - " x = ebco2['total_lb_CO2_emissions']\n", - " y = ebco2['Replaced_mode']\n", - " color = ebco2['boolean']\n", - "\n", - " plot_title= plot_title_no_quality+f\"\\n Contribution by replaced mode towards a total of {net_CO2_emissions}(lb CO2 Emissions )\\n\"+quality_text\n", - " CO2_impact(x,y,color,plot_title,file_name)\n", - " alt_text = store_alt_text_bar(pd.DataFrame(x.values,y), file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/viz_scripts/generic_metrics.ipynb b/viz_scripts/generic_metrics.ipynb deleted file mode 100644 index b59a6e4..0000000 --- a/viz_scripts/generic_metrics.ipynb +++ /dev/null @@ -1,400 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "academic-context", - "metadata": {}, - "source": [ - "## Generate Static Graphs" - ] - }, - { - "cell_type": "markdown", - "id": "medium-siemens", - "metadata": {}, - "source": [ - "These are the input parameters for the notebook. They will be automatically changed when the scripts to generate monthly statistics are run. You can modify them manually to generate multiple plots locally as well.\n", - "\n", - "Pass in `None` to remove the filters and plot all data. This is not recommended for production settings, but might be useful for reports based on data snapshots." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "narrative-hunter", - "metadata": {}, - "outputs": [], - "source": [ - "year = 2020\n", - "month = 11\n", - "program = \"default\"\n", - "study_type = \"study\"\n", - "mode_of_interest = None\n", - "include_test_users = False" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "activated-portugal", - "metadata": {}, - "outputs": [], - "source": [ - "from collections import defaultdict\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "from plots import *\n", - "import scaffolding\n", - "\n", - "sns.set_style(\"whitegrid\")\n", - "sns.set()\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "alternative-voltage", - "metadata": {}, - "outputs": [], - "source": [ - "# Loading mapping dictionaries from mapping_dictionaries notebook\n", - "%store -r dic_re\n", - "%store -r dic_pur\n", - "\n", - "# convert a dictionary to a defaultdict\n", - "dic_re = defaultdict(lambda: 'Other',dic_re)\n", - "dic_pur = defaultdict(lambda: 'Other',dic_pur)" - ] - }, - { - "cell_type": "markdown", - "id": "intellectual-columbus", - "metadata": {}, - "source": [ - "## Collect Data From Database" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "organic-pitch", - "metadata": {}, - "outputs": [], - "source": [ - "expanded_ct, file_suffix, quality_text, debug_df = scaffolding.load_viz_notebook_data(year,\n", - " month,\n", - " program,\n", - " study_type,\n", - " dic_re,\n", - " dic_pur=dic_pur,\n", - " include_test_users=include_test_users)" - ] - }, - { - "cell_type": "markdown", - "id": "modified-skiing", - "metadata": {}, - "source": [ - "## Generic Metrics" - ] - }, - { - "cell_type": "markdown", - "id": "distributed-peace", - "metadata": {}, - "source": [ - "### Distribution of Mode_confirm attribute" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "tracked-serbia", - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "file_name='ntrips_mode_confirm%s' % file_suffix\n", - "plot_title_no_quality= \"Number of trips for each mode (selected by users)\"\n", - "try:\n", - " labels_mc = expanded_ct['Mode_confirm'].value_counts(dropna=True).keys().tolist()\n", - " values_mc = expanded_ct['Mode_confirm'].value_counts(dropna=True).tolist() \n", - " plot_title = plot_title_no_quality+\"\\n\"+quality_text\n", - " pie_chart_mode(plot_title,labels_mc,values_mc,file_name)\n", - " alt_text = store_alt_text_pie(pd.DataFrame(values_mc, labels_mc), file_name, plot_title)\n", - " print(expanded_ct['Mode_confirm'].value_counts(dropna=True))\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "about-seafood", - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "plot_title_no_quality= \"Number of commute trips for each mode (selected by users)\"\n", - "file_name= 'ntrips_commute_mode_confirm%s' % file_suffix\n", - "\n", - "try:\n", - " labels_mc = expanded_ct.query(\"Trip_purpose == 'Work'\").Mode_confirm.value_counts(dropna=True).keys().tolist()\n", - " values_mc = expanded_ct.query(\"Trip_purpose == 'Work'\").Mode_confirm.value_counts(dropna=True).tolist()\n", - " commute_quality_text = scaffolding.get_quality_text(expanded_ct, expanded_ct.query(\"Trip_purpose == 'Work'\"), \"commute\", include_test_users)\n", - " plot_title= plot_title_no_quality+\"\\n\"+commute_quality_text\n", - " pie_chart_mode(plot_title,labels_mc,values_mc,file_name)\n", - " alt_text = store_alt_text_pie(pd.DataFrame(values_mc, labels_mc), file_name, plot_title)\n", - " print(expanded_ct.query(\"Trip_purpose == 'Work'\").Mode_confirm.value_counts(dropna=True))\n", - "except:\n", - " debug_df.loc[\"Commute_trips\"] = len(expanded_ct.query(\"Trip_purpose == 'Work'\")) if \"Trip_purpose\" in expanded_ct.columns else 0\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality)" - ] - }, - { - "cell_type": "markdown", - "id": "careful-spencer", - "metadata": {}, - "source": [ - "### Distribution of Trip_purpose attribute" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "conservative-september", - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "plot_title_no_quality=\"Number of trips for each purposes (selected by users)\"\n", - "file_name= 'ntrips_purpose%s' % file_suffix\n", - "\n", - "try:\n", - " labels_tp = expanded_ct['Trip_purpose'].value_counts(dropna=True).keys().tolist()\n", - " values_tp = expanded_ct['Trip_purpose'].value_counts(dropna=True).tolist()\n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - " pie_chart_purpose(plot_title,labels_tp,values_tp,file_name)\n", - " alt_text = store_alt_text_pie(pd.DataFrame(values_tp, labels_tp), file_name, plot_title)\n", - " print(expanded_ct['Trip_purpose'].value_counts(dropna=True))\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality)" - ] - }, - { - "cell_type": "markdown", - "id": "crucial-keyboard", - "metadata": {}, - "source": [ - "### Mode choice for trips under 10 miles" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "identified-replica", - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "plot_title_no_quality=\"Mode confirmations for trips under 10 Miles\"\n", - "file_name ='ntrips_under10miles_mode_confirm%s' % file_suffix\n", - "try:\n", - " labels_d10 = expanded_ct.loc[(expanded_ct['distance_miles'] <= 10)].Mode_confirm.value_counts(dropna=True).keys().tolist()\n", - " values_d10 = expanded_ct.loc[(expanded_ct['distance_miles'] <= 10)].Mode_confirm.value_counts(dropna=True).tolist()\n", - " d10_quality_text = scaffolding.get_quality_text(expanded_ct, expanded_ct[expanded_ct['distance_miles'] <= 10], \"< 10 mile\", include_test_users)\n", - " plot_title= plot_title_no_quality+\"\\n\"+d10_quality_text\n", - " pie_chart_mode(plot_title,labels_d10,values_d10,file_name)\n", - " alt_text = store_alt_text_pie(pd.DataFrame(values_d10, labels_d10), file_name, plot_title)\n", - " print(expanded_ct.loc[(expanded_ct['distance_miles'] <= 10)].Mode_confirm.value_counts(dropna=True))\n", - "except:\n", - " d10_df = expanded_ct.query(\"distance_miles <= 10\") if \"distance_miles\" in expanded_ct.columns else expanded_ct\n", - " debug_df.loc[\"Trips_less_than_10_miles\"] = scaffolding.trip_label_count(\"Mode_confirm\", d10_df)\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality)" - ] - }, - { - "cell_type": "markdown", - "id": "dominant-company", - "metadata": {}, - "source": [ - "### Miles per chosen transport mode" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "satisfied-sharing", - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "plot_title_no_quality=\"Miles for each mode (selected by users)\"\n", - "file_name ='miles_mode_confirm%s' % file_suffix\n", - "\n", - "try:\n", - " miles = expanded_ct.groupby('Mode_confirm').agg({'distance_miles': ['sum', 'count' , 'mean']})\n", - " miles.columns = ['Total (miles)', 'Count', 'Average (miles)']\n", - " miles = miles.reset_index()\n", - " miles =miles.sort_values(by=['Total (miles)'], ascending=False)\n", - "\n", - " #data\n", - " miles_dict = dict(zip(miles['Mode_confirm'], miles['Total (miles)']))\n", - "\n", - " labels_m = []\n", - " values_m = []\n", - "\n", - " for x, y in miles_dict.items():\n", - " labels_m.append(x)\n", - " values_m.append(y)\n", - " \n", - " plot_title=\"Miles for each mode (selected by users)\\n%s\" % quality_text\n", - "\n", - " pie_chart_mode(plot_title,labels_m,values_m,file_name)\n", - " alt_text = store_alt_text_pie(pd.DataFrame(values_m, labels_m), file_name, plot_title)\n", - " print(miles)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality) " - ] - }, - { - "cell_type": "markdown", - "id": "demanding-franklin", - "metadata": {}, - "source": [ - "### Average miles per transport mode selected (Mode_confirm)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "patent-cliff", - "metadata": {}, - "outputs": [], - "source": [ - "plot_title_no_quality=\" Average Miles for each mode with > 3 entries\"\n", - "file_name ='average_miles_mode_confirm%s' % file_suffix\n", - "\n", - "try:\n", - " data = miles.drop((miles.query(\"Count < 3\").index)).sort_values(by=['Average (miles)'], ascending=False)\n", - " x='Mode_confirm'\n", - " y='Average (miles)'\n", - " \n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - "\n", - " barplot_mode(data,x,y,plot_title,file_name)\n", - " alt_text = store_alt_text_bar(pd.DataFrame(data['Average (miles)'].values, data['Mode_confirm']), file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality) " - ] - }, - { - "cell_type": "markdown", - "id": "sound-bradley", - "metadata": {}, - "source": [ - "### Number of trips by day¶" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "alleged-subsection", - "metadata": {}, - "outputs": [], - "source": [ - "plot_title_no_quality=\"Number of trips by day\"\n", - "file_name ='ntrips_per_day%s' % file_suffix\n", - "\n", - "try:\n", - " fq_days = expanded_ct.groupby(['start_local_dt_day']).agg({'start_local_dt_day': ['sum', 'count']})\n", - " fq_days = fq_days.reset_index()\n", - " fq_days.columns = ['Day of the Month', 'Total', 'Number of Trips']\n", - "\n", - " data = fq_days\n", - " x = 'Day of the Month'\n", - " y = 'Number of Trips'\n", - " \n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - "\n", - " barplot_day(data,x,y,plot_title,file_name)\n", - " alt_text = store_alt_text_bar(pd.DataFrame(data['Number of Trips'].values, data['Day of the Month']), file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality) " - ] - }, - { - "cell_type": "markdown", - "id": "challenging-julian", - "metadata": {}, - "source": [ - "### Number of trips by day of week¶" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "hollywood-optimization", - "metadata": {}, - "outputs": [], - "source": [ - "plot_title_no_quality=\"Number of trips by weekday\"\n", - "file_name ='ntrips_per_weekday%s' % file_suffix\n", - "try:\n", - " fq_weekdays = expanded_ct.groupby(['start_local_dt_weekday']).agg({'start_local_dt_weekday': ['sum', 'count']})\n", - " fq_weekdays = fq_weekdays.reset_index()\n", - " fq_weekdays.columns = ['Weekday', 'Total', 'Number of Trips']\n", - " weekday_labels = [\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"]\n", - " fq_weekdays[\"Weekday\"] = fq_weekdays.Weekday.apply(lambda x: weekday_labels[x])\n", - "\n", - " data = fq_weekdays\n", - " x = 'Weekday'\n", - " y = 'Number of Trips'\n", - "\n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - " \n", - " barplot_day(data,x,y,plot_title,file_name)\n", - " alt_text = store_alt_text_bar(pd.DataFrame(data['Number of Trips'].values, data['Weekday']), file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality) " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/viz_scripts/generic_timeseries.ipynb b/viz_scripts/generic_timeseries.ipynb deleted file mode 100644 index ca012da..0000000 --- a/viz_scripts/generic_timeseries.ipynb +++ /dev/null @@ -1,372 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate Static Graphs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These are the input parameters for the notebook. They will be automatically changed when the scripts to generate monthly statistics are run. You can modify them manually to generate multiple plots locally as well.\n", - "\n", - "Pass in `None` to remove the filters and plot all data. This is not recommended for production settings, but might be useful for reports based on data snapshots." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "year = 2020\n", - "month = 11\n", - "program = \"default\"\n", - "study_type = \"study\"\n", - "mode_of_interest = None\n", - "include_test_users = False" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from collections import defaultdict\n", - "import datetime\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "from plots import *\n", - "import scaffolding\n", - "\n", - "sns.set_style(\"whitegrid\")\n", - "sns.set()\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Loading mapping dictionaries from mapping_dictionaries notebook\n", - "%store -r df_ei\n", - "%store -r dic_re\n", - "%store -r dic_fuel\n", - "\n", - "# convert a dictionary to a defaultdict\n", - "dic_re = defaultdict(lambda: 'Other',dic_re)\n", - "dic_fuel = defaultdict(lambda: 'Other',dic_fuel)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Collect Data From Database" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "expanded_ct, file_suffix, quality_text, debug_df = scaffolding.load_viz_notebook_data(year,\n", - " month,\n", - " program,\n", - " study_type,\n", - " dic_re,\n", - " include_test_users=include_test_users)\n", - "expanded_ct = scaffolding.add_energy_labels(expanded_ct, df_ei, dic_fuel) if \"mode_confirm\" in expanded_ct.columns else expanded_ct" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Preprocessing" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get timestamp from known year/month/day aggregated to days\n", - "sel_cols_no_label_dep = ['user_id','start_local_dt_year','start_local_dt_month','start_local_dt_day','distance_miles']\n", - "sel_cols_with_label_dep = sel_cols_no_label_dep + ['Mode_confirm','Mode_confirm_EI(kWH)','Mode_confirm_lb_CO2']\n", - "if len(expanded_ct) == 0:\n", - " data = expanded_ct.copy()\n", - "elif \"Mode_confirm\" not in expanded_ct.columns:\n", - " data = expanded_ct[sel_cols_no_label_dep].copy()\n", - "else:\n", - " data = expanded_ct[sel_cols_with_label_dep].copy()\n", - " \n", - "if len(expanded_ct) > 0:\n", - " data.rename(columns={'start_local_dt_year':'year','start_local_dt_month':'month','start_local_dt_day':'day'}, inplace=True)\n", - " data['date_time'] = pd.to_datetime(data[['year','month','day']])\n", - " data = data.drop(columns=['year','month','day'])\n", - "\n", - " # Categorical type will include all days/modes in groupby even if there is no data for a particular tabulation\n", - " data.user_id = pd.Categorical(data.user_id)\n", - " data.date_time = pd.Categorical(data.date_time)\n", - " \n", - " if \"Mode_confirm\" in expanded_ct.columns:\n", - " data.Mode_confirm = pd.Categorical(data.Mode_confirm, ordered=True, categories=np.unique(list(dic_re.values())))\n", - "\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "if len(expanded_ct) > 0:\n", - " # Get the count of unique users that were active on each given date\n", - " active_users = pd.DataFrame(data.groupby(['date_time'], as_index=False)['user_id'].nunique())\n", - " active_users.rename(columns={'user_id':'active_users'}, inplace=True)\n", - "\n", - " if \"Mode_confirm\" in expanded_ct.columns:\n", - " # Count the number of trips for each confirmed mode\n", - " mode_counts = data.groupby(['user_id','date_time','Mode_confirm'], as_index=False).size()\n", - " mode_counts.rename(columns={'size':'trip_count'}, inplace=True)\n", - "\n", - " # Sum daily distance traveled for each mode\n", - " mode_distance = data.groupby(['user_id','date_time','Mode_confirm'], as_index=False)[['distance_miles']].sum()\n", - " mode_distance.rename(columns={'sum':'distance_miles'}, inplace=True)\n", - " mode_distance['distance_miles'] = mode_distance['distance_miles'].fillna(0)\n", - "\n", - " # Sum daily emissions for each user\n", - " emissions = data.groupby(['user_id','date_time'], as_index=False)[['Mode_confirm_lb_CO2', 'distance_miles']].sum()\n", - " emissions['Mode_confirm_lb_CO2'] = emissions['Mode_confirm_lb_CO2'].fillna(0)\n", - " emissions['distance_miles'] = emissions['Mode_confirm_lb_CO2'].fillna(0)\n", - "\n", - " # Sum daily energy for each user\n", - " energy = data.groupby(['user_id','date_time'], as_index=False)[['Mode_confirm_EI(kWH)', 'distance_miles']].sum()\n", - " energy['Mode_confirm_EI(kWH)'] = energy['Mode_confirm_EI(kWH)'].fillna(0)\n", - " energy['distance_miles'] = energy['Mode_confirm_EI(kWH)'].fillna(0)\n", - "\n", - " # Add 7-day rolling avg smoothing to better see trends\n", - " mode_counts['trip_count_smooth'] = mode_counts.groupby(['user_id','Mode_confirm'])['trip_count'].apply(lambda x: x.rolling(7,1).mean())\n", - " mode_distance['distance_miles_smooth'] = mode_distance.groupby(['user_id','Mode_confirm'])['distance_miles'].apply(lambda x: x.rolling(7,1).mean())\n", - " emissions['distance_miles_smooth'] = emissions.groupby(['user_id'])['distance_miles'].apply(lambda x: x.rolling(7,1).mean())\n", - " energy['distance_miles_smooth'] = energy.groupby(['user_id'])['distance_miles'].apply(lambda x: x.rolling(7,1).mean())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate Timeseries Plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Emissions per week" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_title_no_quality = 'Net Daily Emissions (All Users, excluding air)'\n", - "file_name = \"ts_emissions_user%s\"%file_suffix\n", - "\n", - "try:\n", - " # Emissions per week across all users (net impact)\n", - " plot_data = emissions.groupby(['date_time'], as_index=False)['Mode_confirm_lb_CO2'].agg(['sum'])\n", - " plot_data = plot_data.merge(active_users, on='date_time')\n", - " plot_data['sum'] = plot_data['sum'] / plot_data['active_users']\n", - "\n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - " ylab = 'Emissions (lb CO2/day/user)'\n", - " timeseries_plot(plot_data['date_time'], plot_data['sum'], plot_title, ylab, file_name)\n", - " alt_text = store_alt_text_timeseries(plot_data, file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Energy per week" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_title_no_quality = 'Net Daily Energy (All Users, excluding air)'\n", - "file_name = \"ts_energy_user%s\"%file_suffix\n", - "\n", - "try:\n", - " # Energy per week across all users (net impact)\n", - " plot_data = energy.groupby(['date_time'], as_index=False)['Mode_confirm_EI(kWH)'].agg(['sum'])\n", - " plot_data = plot_data.merge(active_users, on='date_time')\n", - " plot_data['sum'] = plot_data['sum'] / plot_data['active_users']\n", - "\n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - " ylab = 'Energy (kWH/day/user)'\n", - " timeseries_plot(plot_data['date_time'], plot_data['sum'], plot_title, ylab, file_name)\n", - " alt_text = store_alt_text_timeseries(plot_data, file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Emissions per mile per day" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_title_no_quality = 'Average Daily Emission Rate (All Users, excluding air)'\n", - "file_name = \"ts_emissions_vmt%s\"%file_suffix\n", - "\n", - "try:\n", - " # Emissions per mile per day across all users (travel efficiency)\n", - " # Note that the energy plot will be identical to this one since scale factor is divided out\n", - " emissions['CO2_per_mile'] = emissions['Mode_confirm_lb_CO2'] / emissions['distance_miles_smooth']\n", - " emissions['CO2_per_mile'] = emissions['CO2_per_mile'].fillna(0)\n", - " plot_data = emissions.groupby(['date_time'])['CO2_per_mile'].agg(['mean']).reset_index()\n", - "\n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - " ylab = 'Emissions (lb CO2/mile/day)'\n", - " timeseries_plot(plot_data['date_time'], plot_data['mean'], plot_title, ylab, file_name)\n", - " alt_text = store_alt_text_timeseries(plot_data, file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Number of active users" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_title_no_quality = 'Number of Active Users'\n", - "file_name = \"ts_users%s\"%file_suffix\n", - "\n", - "try:\n", - " # Plot of active users\n", - " plot_data = active_users\n", - "\n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - " ylab = 'Unique IDs'\n", - " timeseries_plot(plot_data['date_time'], plot_data['active_users'], plot_title, ylab, file_name)\n", - " alt_text = store_alt_text_timeseries(plot_data, file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily Mode share" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_title_no_quality = 'Daily Aggregate Mode Share (excluding \"Other\" and \"Not a trip\"'\n", - "file_name = \"ts_all_modes%s\"%file_suffix\n", - "\n", - "try:\n", - " # Plot of mode share proportions across all users\n", - " # Consolidate modes\n", - " plot_data = mode_counts.replace('Bikeshare', 'Shared Micromobility')\n", - " plot_data = plot_data.replace('Scooter share', 'Shared Micromobility')\n", - " plot_data = plot_data.replace('Regular Bike', 'Personal Micromobility')\n", - " plot_data = plot_data.replace('Skate board', 'Personal Micromobility')\n", - " plot_data = plot_data.replace('Train', 'Transit')\n", - " plot_data = plot_data.replace('Free Shuttle', 'Transit')\n", - " plot_data = plot_data.replace('Bus', 'Transit')\n", - " plot_data = plot_data.replace('Walk', 'Walk')\n", - " plot_data = plot_data.replace('Taxi/Uber/Lyft', 'Ridehail')\n", - " plot_data = plot_data.replace('Pilot ebike', 'E-Bike')\n", - "\n", - " plot_data = plot_data.groupby(['date_time','Mode_confirm'], as_index=False)['trip_count_smooth'].sum()\n", - " total_trips = plot_data.groupby(['date_time'], as_index=False).sum()\n", - " plot_data = plot_data.merge(total_trips, on='date_time')\n", - " plot_data['trip_proportion'] = plot_data['trip_count_smooth_x'] / plot_data['trip_count_smooth_y']\n", - "\n", - " # Re-establish categorical variable to not include Other and Non-trips\n", - " plot_data = plot_data[~plot_data['Mode_confirm'].isin(['Not a Trip','Other'])]\n", - " plot_data.Mode_confirm = pd.Categorical(plot_data.Mode_confirm, ordered=True, categories=np.unique(list(dic_re.values())))\n", - "\n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - " ylab = 'Proportion of All Trips'\n", - " legend_title = 'Confirmed Mode'\n", - " timeseries_multi_plot(plot_data, 'date_time','trip_proportion','Mode_confirm', plot_title, ylab, legend_title, file_name)\n", - " alt_text = store_alt_text_generic('multivariate timeseries', file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality) " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/viz_scripts/mapping_dictionaries.ipynb b/viz_scripts/mapping_dictionaries.ipynb deleted file mode 100644 index 1252544..0000000 --- a/viz_scripts/mapping_dictionaries.ipynb +++ /dev/null @@ -1,89 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 4, - "id": "available-fusion", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import pickle" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "obvious-chapter", - "metadata": {}, - "outputs": [], - "source": [ - "df_pur = pd.read_csv(r'auxiliary_files/purpose_labels.csv')\n", - "df_re = pd.read_csv(r'auxiliary_files/mode_labels.csv')\n", - "df_ei = pd.read_csv(r'auxiliary_files/energy_intensity.csv')\n", - "\n", - "#dictionaries:\n", - "dic_pur = dict(zip(df_pur['purpose_confirm'],df_pur['bin_purpose'])) # bin purpose\n", - "dic_re = dict(zip(df_re['replaced_mode'],df_re['mode_clean'])) # bin modes\n", - "dic_fuel = dict(zip(df_ei['mode'],df_ei['fuel']))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "younger-indication", - "metadata": {}, - "outputs": [], - "source": [ - "# %store df_ei\n", - "# %store dic_re\n", - "# %store dic_pur\n", - "# %store dic_fuel\n", - "\n", - "with open('auxiliary_files/df_ei.pkl', 'wb') as f:\n", - " f.write(pickle.dumps(df_ei))\n", - "\n", - "with open('auxiliary_files/dic_re.pkl', 'wb') as f:\n", - " f.write(pickle.dumps(dic_re))\n", - "\n", - "with open('auxiliary_files/dic_pur.pkl', 'wb') as f:\n", - " f.write(pickle.dumps(dic_pur))\n", - "\n", - "with open('auxiliary_files/dic_fuel.pkl', 'wb') as f:\n", - " f.write(pickle.dumps(dic_fuel))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8e389b3d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "interpreter": { - "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" - }, - "kernelspec": { - "display_name": "Python 3.9.16 ('emission')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/viz_scripts/mode_specific_metrics.ipynb b/viz_scripts/mode_specific_metrics.ipynb deleted file mode 100644 index 4e9da95..0000000 --- a/viz_scripts/mode_specific_metrics.ipynb +++ /dev/null @@ -1,373 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "worldwide-portuguese", - "metadata": {}, - "source": [ - "## Generate Static Graphs" - ] - }, - { - "cell_type": "markdown", - "id": "alive-integration", - "metadata": {}, - "source": [ - "These are the input parameters for the notebook. They will be automatically changed when the scripts to generate monthly statistics are run. You can modify them manually to generate multiple plots locally as well.\n", - "\n", - "Pass in `None` to remove the filters and plot all data. This is not recommended for production settings, but might be useful for reports based on data snapshots." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "former-luther", - "metadata": {}, - "outputs": [], - "source": [ - "year = 2020\n", - "month = 11\n", - "program = \"default\"\n", - "study_type = \"program\"\n", - "mode_of_interest = \"e-bike\"\n", - "include_test_users = False" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dated-compromise", - "metadata": {}, - "outputs": [], - "source": [ - "from collections import defaultdict\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "from plots import *\n", - "import scaffolding\n", - "\n", - "sns.set_style(\"whitegrid\")\n", - "sns.set()\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "assisted-mathematics", - "metadata": {}, - "outputs": [], - "source": [ - "# Do not run this notebook at all unless it is for a program; nbclient will run up through this cell\n", - "if study_type != \"program\":\n", - " ipython = get_ipython()\n", - " ipython._showtraceback = scaffolding.no_traceback_handler\n", - " raise Exception(\"The plots in this notebook are only relevant to programs\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cathedral-scanning", - "metadata": {}, - "outputs": [], - "source": [ - "# Loading mapping dictionaries from mapping_dictionaries notebook\n", - "%store -r dic_re\n", - "%store -r dic_pur\n", - "\n", - "# convert a dictionary to a defaultdict\n", - "dic_re = defaultdict(lambda: 'Other',dic_re)\n", - "dic_pur = defaultdict(lambda: 'Other',dic_pur)" - ] - }, - { - "cell_type": "markdown", - "id": "built-occupation", - "metadata": {}, - "source": [ - "## Collect Data From Database" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "empty-intensity", - "metadata": {}, - "outputs": [], - "source": [ - "expanded_ct, file_suffix, quality_text, debug_df = scaffolding.load_viz_notebook_data(year,\n", - " month,\n", - " program,\n", - " study_type,\n", - " dic_re,\n", - " dic_pur=dic_pur,\n", - " include_test_users=include_test_users)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "77eedae6", - "metadata": {}, - "outputs": [], - "source": [ - "if 'mode_confirm' in expanded_ct.columns:\n", - " mode_of_interest_df = expanded_ct.query(f\"mode_confirm == '{mode_of_interest}'\")\n", - " debug_df.loc[f\"{mode_of_interest}_trips\"] = len(mode_of_interest_df)\n", - " debug_df.loc[f\"{mode_of_interest}_trips_with_replaced_mode\"] = scaffolding.trip_label_count(\"Replaced_mode\", mode_of_interest_df)" - ] - }, - { - "cell_type": "markdown", - "id": "surgical-continuity", - "metadata": {}, - "source": [ - "## Metrics for Specific Mode" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "romance-green", - "metadata": {}, - "outputs": [], - "source": [ - "data_eb = expanded_ct.query(f\"mode_confirm == '{mode_of_interest}'\") if \"mode_confirm\" in expanded_ct.columns else expanded_ct" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "seeing-court", - "metadata": {}, - "outputs": [], - "source": [ - "quality_text = scaffolding.get_quality_text(expanded_ct, data_eb, mode_of_interest, include_test_users)" - ] - }, - { - "cell_type": "markdown", - "id": "loaded-expert", - "metadata": {}, - "source": [ - "### Trips by purpose for specified mode" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "respiratory-breach", - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "plot_title_no_quality=f\"Number of trips for each purpose for {mode_of_interest} only\"\n", - "file_name= f'ntrips_{mode_of_interest}_purpose%s' % file_suffix\n", - "\n", - "try:\n", - " labels_tp = data_eb['Trip_purpose'].value_counts(dropna=True).keys().tolist()\n", - " values_tp = data_eb['Trip_purpose'].value_counts(dropna=True).tolist()\n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - " pie_chart_purpose(plot_title,labels_tp,values_tp,file_name)\n", - " alt_text = store_alt_text_pie(pd.DataFrame(values_tp, labels_tp), file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "thermal-midnight", - "metadata": {}, - "outputs": [], - "source": [ - "plot_title_no_quality=f\"Number of trips for each replaced transport mode for {mode_of_interest} only\"\n", - "file_name =f'ntrips_{mode_of_interest}_replaced_mode%s' % file_suffix\n", - "\n", - "try:\n", - " labels_eb = data_eb.Replaced_mode.value_counts(dropna=True).keys().tolist()\n", - " values_eb = data_eb.Replaced_mode.value_counts(dropna=True).tolist()\n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - " pie_chart_mode(plot_title,labels_eb,values_eb,file_name)\n", - " alt_text = store_alt_text_pie(pd.DataFrame(values_eb, labels_eb), file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality)" - ] - }, - { - "cell_type": "markdown", - "id": "dependent-reservoir", - "metadata": {}, - "source": [ - "### Miles for each mode replaced by the specified mode" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "pointed-velvet", - "metadata": {}, - "outputs": [], - "source": [ - "plot_title_no_quality=f\"Distribution of Miles Replaced by {mode_of_interest}\"\n", - "file_name =f'miles_{mode_of_interest}_replaced_mode%s' % file_suffix\n", - "\n", - "try:\n", - " dg=data_eb.groupby('Replaced_mode').agg({'distance_miles': ['sum', 'count' , 'mean']},)\n", - " dg.columns = ['Total (miles)', 'Count' ,'Average (miles)']\n", - " dg = dg.reset_index()\n", - " dg = dg.sort_values(by=['Total (miles)'], ascending=False)\n", - "\n", - " dg_dict = dict(zip(dg['Replaced_mode'], dg['Total (miles)']))\n", - " labels_m = []\n", - " values_m = []\n", - "\n", - " for x, y in dg_dict.items():\n", - " labels_m.append(x)\n", - " values_m.append(y)\n", - "\n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - " pie_chart_mode(plot_title,labels_m,values_m,file_name)\n", - " alt_text = store_alt_text_pie(pd.DataFrame(values_m, labels_m), file_name, plot_title)\n", - " print(dg)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality)" - ] - }, - { - "cell_type": "markdown", - "id": "honest-dylan", - "metadata": {}, - "source": [ - "### Average miles per trip for specified mode" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "binary-program", - "metadata": {}, - "outputs": [], - "source": [ - "plot_title_no_quality=\"Average Miles for each replaced mode with > 3 entries\\n'Other' represents trips with a non-standard or missing replacement\"\n", - "file_name ='average_miles_replaced_mode%s' % file_suffix\n", - "\n", - "try:\n", - " data = dg.drop((dg.query(\"Count < 3\").index)).sort_values(by=['Average (miles)'], ascending=False)\n", - " x='Replaced_mode'\n", - " y='Average (miles)'\n", - " y2 = \"Count\"\n", - "\n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - " barplot_mode(data,x,y,plot_title,file_name)\n", - " alt_text = store_alt_text_bar(pd.DataFrame(data['Average (miles)'].values, data.Replaced_mode), file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality)" - ] - }, - { - "cell_type": "markdown", - "id": "committed-favorite", - "metadata": {}, - "source": [ - "### Number of trips by day for specified mode" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "limiting-handling", - "metadata": {}, - "outputs": [], - "source": [ - "plot_title_no_quality=f\"Number of {mode_of_interest} trips by day\"\n", - "file_name =f'ntrips_{mode_of_interest}_per_day%s' % file_suffix\n", - "\n", - "\n", - "try:\n", - " fq_days = data_eb.groupby(['start_local_dt_day']).agg({'start_local_dt_day': ['sum', 'count']})\n", - " fq_days = fq_days.reset_index()\n", - " fq_days.columns = ['Day of the Month', 'Total', 'Number of Trips']\n", - "\n", - " data = fq_days\n", - " x = 'Day of the Month'\n", - " y = 'Number of Trips'\n", - "\n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - " barplot_day(data,x,y,plot_title,file_name)\n", - " alt_text = store_alt_text_bar(pd.DataFrame(data['Number of Trips'].values, data['Day of the Month'].values), file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality)" - ] - }, - { - "cell_type": "markdown", - "id": "pediatric-cowboy", - "metadata": {}, - "source": [ - "### Number of trips by day of week¶" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "metropolitan-musical", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "plot_title_no_quality=f\"Number of {mode_of_interest} trips by weekday\"\n", - "file_name =f'ntrips_{mode_of_interest}_per_weekday%s' % file_suffix\n", - "\n", - "try:\n", - " fq_weekdays = data_eb.groupby(['start_local_dt_weekday']).agg({'start_local_dt_weekday': ['sum', 'count']})\n", - " fq_weekdays = fq_weekdays.reset_index()\n", - " fq_weekdays.columns = ['Weekday', 'Total', 'Number of Trips']\n", - " weekday_labels = [\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"]\n", - " fq_weekdays[\"Weekday\"] = fq_weekdays.Weekday.apply(lambda x: weekday_labels[x])\n", - "\n", - " data = fq_weekdays\n", - " x = 'Weekday'\n", - " y = 'Number of Trips'\n", - "\n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - " barplot_day(data,x,y,plot_title,file_name)\n", - " alt_text = store_alt_text_bar(pd.DataFrame(data['Number of Trips'].values, data['Weekday'].values), file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/viz_scripts/mode_specific_timeseries.ipynb b/viz_scripts/mode_specific_timeseries.ipynb deleted file mode 100644 index a157bcc..0000000 --- a/viz_scripts/mode_specific_timeseries.ipynb +++ /dev/null @@ -1,491 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate Static Graphs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These are the input parameters for the notebook. They will be automatically changed when the scripts to generate monthly statistics are run. You can modify them manually to generate multiple plots locally as well.\n", - "\n", - "Pass in `None` to remove the filters and plot all data. This is not recommended for production settings, but might be useful for reports based on data snapshots." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "year = 2020\n", - "month = 11\n", - "program = \"default\"\n", - "study_type = \"program\"\n", - "mode_of_interest = \"e-bike\"\n", - "include_test_users = False" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from collections import defaultdict\n", - "import datetime\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "from plots import *\n", - "import scaffolding\n", - "\n", - "sns.set_style(\"whitegrid\")\n", - "sns.set()\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Do not run this notebook at all unless it is for a program; nbclient will run up through this cell\n", - "if study_type != \"program\":\n", - " ipython = get_ipython()\n", - " ipython._showtraceback = scaffolding.no_traceback_handler\n", - " raise Exception(\"The plots in this notebook are only relevant to programs\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Loading mapping dictionaries from mapping_dictionaries notebook\n", - "%store -r dic_re\n", - "%store -r dic_pur\n", - "\n", - "# convert a dictionary to a defaultdict\n", - "dic_re = defaultdict(lambda: 'Other',dic_re)\n", - "dic_pur = defaultdict(lambda: 'Other',dic_pur)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Collect Data From Database" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "expanded_ct, file_suffix, quality_text, debug_df = scaffolding.load_viz_notebook_data(year,\n", - " month,\n", - " program,\n", - " study_type,\n", - " dic_re,\n", - " dic_pur=dic_pur,\n", - " include_test_users=include_test_users)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if 'mode_confirm' in expanded_ct.columns:\n", - " mode_of_interest_df = expanded_ct.query(f\"mode_confirm == '{mode_of_interest}'\")\n", - " debug_df.loc[f\"{mode_of_interest}_trips\"] = len(mode_of_interest_df)\n", - " debug_df.loc[f\"{mode_of_interest}_trips_with_replaced_mode\"] = scaffolding.trip_label_count(\"Replaced_mode\", mode_of_interest_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Preprocessing" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get timestamp from known year/month/day aggregated to days\n", - "sel_cols_no_label_dep = ['user_id','start_local_dt_year','start_local_dt_month','start_local_dt_day','distance_miles']\n", - "sel_cols_with_label_dep = sel_cols_no_label_dep + ['mode_confirm']\n", - "if len(expanded_ct) == 0:\n", - " data = expanded_ct.copy()\n", - "elif \"mode_confirm\" not in expanded_ct.columns:\n", - " data = expanded_ct[sel_cols_no_label_dep].copy()\n", - "else:\n", - " data = expanded_ct[sel_cols_with_label_dep].copy()\n", - " \n", - "if len(expanded_ct) > 0:\n", - " data.rename(columns={'start_local_dt_year':'year','start_local_dt_month':'month','start_local_dt_day':'day'}, inplace=True)\n", - " data['date_time'] = pd.to_datetime(data[['year','month','day']])\n", - " data = data.drop(columns=['year','month','day'])\n", - "\n", - " # Categorical type will include all days/modes in groupby even if there is no data for a particular tabulation\n", - " data.user_id = pd.Categorical(data.user_id)\n", - " data.date_time = pd.Categorical(data.date_time)\n", - " \n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "if len(expanded_ct) > 0:\n", - " # Get the count of unique users that were active on each given date\n", - " active_users = pd.DataFrame(data.groupby(['date_time'], as_index=False)['user_id'].nunique())\n", - " active_users.rename(columns={'user_id':'active_users'}, inplace=True)\n", - "\n", - " if \"mode_confirm\" in expanded_ct.columns:\n", - " # Count the number of trips for each confirmed mode\n", - " mode_counts = data.groupby(['user_id','date_time','mode_confirm'], as_index=False).size()\n", - " mode_counts.rename(columns={'size':'trip_count'}, inplace=True)\n", - "\n", - " # Sum daily distance traveled for each mode\n", - " mode_distance = data.groupby(['user_id','date_time','mode_confirm'], as_index=False)[['distance_miles']].sum()\n", - " mode_distance.rename(columns={'sum':'distance_miles'}, inplace=True)\n", - " mode_distance['distance_miles'] = mode_distance['distance_miles'].fillna(0)\n", - "\n", - " # Add 7-day rolling avg smoothing to better see trends\n", - " mode_counts['trip_count_smooth'] = mode_counts.groupby(['user_id','mode_confirm'])['trip_count'].apply(lambda x: x.rolling(7,1).mean())\n", - " mode_distance['distance_miles_smooth'] = mode_distance.groupby(['user_id','mode_confirm'])['distance_miles'].apply(lambda x: x.rolling(7,1).mean())\n", - " \n", - " # This is the mode specific part\n", - " mode_counts_interest = mode_counts[mode_counts['mode_confirm']==mode_of_interest].copy()\n", - " mode_distance_interest = mode_distance[mode_distance['mode_confirm']==mode_of_interest].copy()\n", - " \n", - " # Mapping new mode labels with dictionaries\n", - " mode_counts['Mode_confirm'] = mode_counts['mode_confirm'].map(dic_re)\n", - " mode_counts_interest['Mode_confirm'] = mode_counts_interest['mode_confirm'].map(dic_re)\n", - " mode_distance_interest['Mode_confirm'] = mode_distance_interest['mode_confirm'].map(dic_re)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "quality_text = scaffolding.get_quality_text(expanded_ct, mode_counts_interest, mode_of_interest, include_test_users)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate Timeseries Plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily trips for mode of interest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot of total ebikeshare trips across all users\n", - "plot_title_no_quality = f'Daily {mode_of_interest} Trips'\n", - "file_name = f\"ts_{mode_of_interest}_share%s\"%file_suffix\n", - "\n", - "try:\n", - " if len(mode_counts_interest) == 0:\n", - " # force error generation so that we will go into the \"missing\" data code path\n", - " raise RuntimeError(f\"No {mode_of_interest} trips found\")\n", - " plot_data = mode_counts_interest.groupby(['date_time'])['trip_count_smooth'].agg(['sum']).reset_index()\n", - " \n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - " ylab = 'Trip Count'\n", - "\n", - " timeseries_plot(plot_data['date_time'], plot_data['sum'], plot_title, ylab, file_name)\n", - " alt_text = store_alt_text_timeseries(plot_data, file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily mileage for mode of interest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot of total ebikeshare mileage across all users\n", - "plot_title_no_quality = f'Daily {mode_of_interest} Mileage'\n", - "file_name = f\"ts_{mode_of_interest}_miles%s\"%file_suffix\n", - "\n", - "try:\n", - " if len(mode_counts_interest) == 0:\n", - " # force error generation so that we will go into the \"missing\" data code path\n", - " raise RuntimeError(f\"No {mode_of_interest} trips found\")\n", - "\n", - " plot_data = mode_distance_interest.groupby(['date_time'])['distance_miles'].agg(['sum']).reset_index()\n", - "\n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - " ylab = 'Miles'\n", - "\n", - " timeseries_plot(plot_data['date_time'], plot_data['sum'], plot_title, ylab, file_name)\n", - " alt_text = store_alt_text_timeseries(plot_data, file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily mileage per user for mode of interest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot of total ebikeshare mileage normalized by number of users\n", - "plot_title_no_quality = f'Daily {mode_of_interest} Mileage per Active User'\n", - "file_name = f\"ts_{mode_of_interest}_miles_user%s\"%file_suffix\n", - "\n", - "try:\n", - " if len(mode_counts_interest) == 0:\n", - " # force error generation so that we will go into the \"missing\" data code path\n", - " raise RuntimeError(f\"No {mode_of_interest} trips found\")\n", - "\n", - " plot_data = mode_distance_interest.groupby(['date_time'])['distance_miles'].agg(['sum']).reset_index()\n", - " plot_data = plot_data.merge(active_users, on='date_time')\n", - " plot_data['mileage_per_user'] = plot_data['sum'] / plot_data['active_users']\n", - "\n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - " ylab = 'miles/user'\n", - "\n", - " timeseries_plot(plot_data['date_time'], plot_data['mileage_per_user'], plot_title, ylab, file_name)\n", - " alt_text = store_alt_text_timeseries(plot_data, file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Proportion of total daily mileage for mode of interest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot of ebike mileage share proportion across all users\n", - "plot_title_no_quality = f'Daily {mode_of_interest} Mileage Proportion (All Users, excluding air)'\n", - "file_name = f\"ts_{mode_of_interest}_miles_proportion%s\"%file_suffix\n", - "\n", - "try:\n", - " if len(mode_counts_interest) == 0:\n", - " # force error generation so that we will go into the \"missing\" data code path\n", - " raise RuntimeError(f\"No {mode_of_interest} trips found\")\n", - "\n", - " plot_data = mode_distance_interest.groupby(['date_time'], as_index=False)['distance_miles_smooth'].sum()\n", - " total_miles = mode_distance.groupby(['date_time'], as_index=False)['distance_miles_smooth'].sum()\n", - " plot_data = plot_data.merge(total_miles, on=['date_time'])\n", - " plot_data['miles_proportion'] = plot_data['distance_miles_smooth_x'] / plot_data['distance_miles_smooth_y']\n", - "\n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - " ylab = 'Proportion of Daily Miles'\n", - "\n", - " timeseries_plot(plot_data['date_time'], plot_data['miles_proportion'], plot_title, ylab, file_name)\n", - " alt_text = store_alt_text_timeseries(plot_data, file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Proportion of total daily trips for mode of interest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot of ebike trip share proportion across all users\n", - "plot_title_no_quality = f'Daily {mode_of_interest} Trip Proportion (All Users, excluding air)'\n", - "file_name = f\"ts_{mode_of_interest}_trips_proportion%s\"%file_suffix\n", - "\n", - "try:\n", - " if len(mode_counts_interest) == 0:\n", - " # force error generation so that we will go into the \"missing\" data code path\n", - " raise RuntimeError(f\"No {mode_of_interest} trips found\")\n", - "\n", - " plot_data = mode_counts.groupby(['date_time','mode_confirm'], as_index=False)['trip_count_smooth'].sum()\n", - " total_trips = plot_data.groupby(['date_time'], as_index=False).sum()\n", - " plot_data = plot_data.merge(total_trips, on='date_time')\n", - " plot_data['trip_proportion'] = plot_data['trip_count_smooth_x'] / plot_data['trip_count_smooth_y']\n", - " plot_data = plot_data[plot_data['mode_confirm']==mode_of_interest]\n", - "\n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - " ylab = 'Proportion of Daily Trips'\n", - "\n", - " timeseries_plot(plot_data['date_time'], plot_data['trip_proportion'], plot_title, ylab, file_name)\n", - " alt_text = store_alt_text_timeseries(plot_data.drop(columns=['mode_confirm','trip_count_smooth_x','trip_count_smooth_y']), file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily trip proportion for mode of interest with error bounds" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plots the number of trips per user with error bars\n", - "# from the seaborn documentation:\n", - "# By default, the plot aggregates over multiple y values at each value of x and \n", - "# shows an estimate of the central tendency and a confidence interval for that estimate.\n", - "# In our case, we have multiple trip proportions (one per user) for each day\n", - "# so the band represents the variation of the number of trips and the thick line represents the mean/median (unsure which)\n", - "# but this still doesn't tell us which users have dropped their ridership\n", - "\n", - "plot_title_no_quality = f'Daily {mode_of_interest} Trip Proportion for Individual users (Running average and variation)'\n", - "file_name = f\"ts_{mode_of_interest}_trip_individual_variation%s\"%file_suffix\n", - "\n", - "try:\n", - " if len(mode_counts_interest) == 0:\n", - " # force error generation so that we will go into the \"missing\" data code path\n", - " raise RuntimeError(f\"No {mode_of_interest} trips found\")\n", - "\n", - " plot_data_mode = mode_counts_interest.groupby(['date_time', 'user_id'], as_index=False)[\"trip_count_smooth\"].sum()\n", - " plot_data = mode_counts.groupby(['date_time','user_id'], as_index=False)['trip_count_smooth'].sum()\n", - " plot_data = plot_data.merge(plot_data_mode, on=['date_time', 'user_id'])\n", - " plot_data['mode_trip_proportion'] = plot_data['trip_count_smooth_y'] / plot_data['trip_count_smooth_x']\n", - "\n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - " ylab = 'Proportion of Daily Trips'\n", - "\n", - " timeseries_plot(plot_data['date_time'], plot_data['mode_trip_proportion'], plot_title, ylab, file_name)\n", - " alt_text = store_alt_text_timeseries(plot_data.drop(columns=['user_id','trip_count_smooth_x','trip_count_smooth_y']), file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daily mileage proportion for mode of interest with error bounds" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plots the number of trips per user with error bars\n", - "# from the seaborn documentation:\n", - "# By default, the plot aggregates over multiple y values at each value of x and \n", - "# shows an estimate of the central tendency and a confidence interval for that estimate.\n", - "# In our case, we have multiple trip proportions (one per user) for each day\n", - "# so the band represents the variation of the number of trips and the thick line represents the mean/median (unsure which)\n", - "# but this still doesn't tell us which users have dropped their ridership\n", - "\n", - "plot_title = f'Daily {mode_of_interest} Mileage Proportion for Individual users (Running average and variation)'\n", - "file_name = f\"ts_{mode_of_interest}_mile_individual_variation%s\"%file_suffix\n", - "\n", - "try:\n", - " if len(mode_distance_interest) == 0:\n", - " # force error generation so that we will go into the \"missing\" data code path\n", - " raise RuntimeError(f\"No {mode_of_interest} trips found\")\n", - "\n", - " plot_data_mode = mode_distance_interest.groupby(['date_time', 'user_id'], as_index=False)[\"distance_miles_smooth\"].sum()\n", - " plot_data = mode_distance.groupby(['date_time','user_id'], as_index=False)['distance_miles_smooth'].sum()\n", - " plot_data = plot_data.merge(plot_data_mode, on=['date_time', 'user_id'])\n", - " plot_data['mode_miles_proportion'] = plot_data['distance_miles_smooth_y'] / plot_data['distance_miles_smooth_x']\n", - "\n", - " plot_title= plot_title_no_quality+\"\\n\"+quality_text\n", - " ylab = 'Proportion of Daily Miles'\n", - "\n", - " timeseries_plot(plot_data['date_time'], plot_data['mode_miles_proportion'], plot_title, ylab, file_name)\n", - " alt_text = store_alt_text_timeseries(plot_data.drop(columns=['user_id','distance_miles_smooth_x','distance_miles_smooth_y']), file_name, plot_title)\n", - "except:\n", - " generate_missing_plot(plot_title_no_quality,debug_df,file_name)\n", - " alt_text = store_alt_text_missing(debug_df, file_name, plot_title_no_quality)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/viz_scripts/plots.py b/viz_scripts/plots.py deleted file mode 100644 index fa88b9b..0000000 --- a/viz_scripts/plots.py +++ /dev/null @@ -1,437 +0,0 @@ -import pandas as pd -import numpy as np -import arrow -import itertools -import matplotlib.pyplot as plt -import seaborn as sns -from matplotlib.patches import Patch - -sns.set_style("whitegrid") -sns.set() -get_ipython().run_line_magic('matplotlib', 'inline') - -# Module for pretty-printing outputs (e.g. head) to help users -# understand what is going on -# However, this means that this module can only be used in an ipython notebook - -import IPython.display as disp - - -SAVE_DIR="/plots/" - - -def merge_small_entries(labels, values): - v2l_df = pd.DataFrame({"vals": values}, index=labels) - - # Calculate % for all the values - vs = v2l_df.vals.sum() - v2l_df["pct"] = v2l_df.vals.apply(lambda x: (x/vs) * 100) - disp.display(v2l_df) - - # Find small chunks to combine - small_chunk = v2l_df.where(lambda x: x.pct <= 2).dropna() - misc_count = small_chunk.sum() - - v2l_df = v2l_df.drop(small_chunk.index) - disp.display(v2l_df) - - # This part if a bit tricky - # We could have already had a non-zero other, and it could be small or large - if "Other" not in v2l_df.index: - # zero other will end up with misc_count - v2l_df.loc["Other"] = misc_count - elif "Other" in small_chunk.index: - # non-zero small other will already be in misc_count - v2l_df.loc["Other"] = misc_count - else: - # non-zero large other, will not already be in misc_count - v2l_df.loc["Other"] = v2l_df.loc["Other"] + misc_count - disp.display(v2l_df) - - return (v2l_df.index.to_list(),v2l_df.vals.to_list()) - -def pie_chart_mode(plot_title,labels,values,file_name): - all_labels= ['Gas Car, drove alone', - 'Bus', - 'Train', - 'Free Shuttle', - 'Taxi/Uber/Lyft', - 'Gas Car, with others', - 'Bikeshare', - 'Scooter share', - 'E-bike', - 'Walk', - 'Skate board', - 'Regular Bike', - 'Not a Trip', - 'No Travel', - 'Same Mode', - 'E-car, drove alone', - 'E-car, with others', - 'Air', - 'Other'] - - val2labeldf = pd.DataFrame({"labels": labels, "values": values}) - - colours = dict(zip(all_labels, plt.cm.tab20.colors[:len(all_labels)])) - fig, ax = plt.subplots(figsize=(10, 10), subplot_kw=dict(aspect="equal")) - - m_labels, m_values = merge_small_entries(labels, values) - - def func(pct, values): - total = sum(values) - absolute = int(round(pct*total/100.0)) - return "{:.1f}%\n({:d})".format(pct, absolute) if pct > 4 else'' - - wedges, texts, autotexts = ax.pie(m_values, - labels = m_labels, - colors=[colours[key] for key in labels], - pctdistance=0.75, - autopct= lambda pct: func(pct, values), - textprops={'size': 23}) - - ax.set_title(plot_title, size=25) - plt.text(-1.3,-1.3,f"Last updated {arrow.get()}", fontsize=10) - plt.setp(autotexts, **{'color':'white', 'weight':'bold', 'fontsize':20}) - plt.savefig(SAVE_DIR+file_name+".png", bbox_inches='tight') - plt.show() - -def pie_chart_purpose(plot_title,labels,values,file_name): - labels_trip= ['Work', - 'Home', - 'Meal', - 'Shopping', - 'Personal/Medical', - 'Recreation/Exercise', - 'Transit transfer', - 'Pick-up/Drop off', - 'Entertainment/Social', - 'Other', - 'School', - 'Religious', - 'No travel', - 'not_a_trip'] - - colours = dict(zip(labels_trip, plt.cm.tab20.colors[:len(labels_trip)])) - fig, ax = plt.subplots(figsize=(10, 10), subplot_kw=dict(aspect="equal")) - - m_labels, m_values = merge_small_entries(labels, values) - - def func(pct, values): - total = sum(values) - absolute = int(round(pct*total/100.0)) - return "{:.1f}%\n({:d})".format(pct, absolute) if pct > 3 else'' - - wedges, texts, autotexts = ax.pie(m_values, - labels = m_labels, - colors=[colours[key] for key in labels], - pctdistance=0.85, - autopct=lambda pct: func(pct, values), - textprops={'size': 23}) - - ax.set_title(plot_title, size=25) - plt.text(-1.3,-1.3,f"Last updated {arrow.get()}", fontsize=10) - plt.setp(autotexts, **{'color':'white', 'weight':'bold', 'fontsize':20}) - plt.savefig(SAVE_DIR+file_name+".png", bbox_inches='tight') - plt.show() - -def energy_impact(x,y,color,plot_title,file_name): - color = color.map({True: 'green', False: 'red'}) - objects = ('Energy Savings', 'Energy Loss') - - y_labels = y - plt.figure(figsize=(15, 8)) - width = 0.8 - ax = x.plot(kind='barh',width=width, color=color) - ax.set_title(plot_title, fontsize=18) - ax.set_xlabel('Energy_Impact(kWH)', fontsize=18) - ax.set_ylabel('Replaced Mode',fontsize=18) - ax.set_yticklabels(y_labels) - ax.xaxis.set_tick_params(labelsize=15) - ax.yaxis.set_tick_params(labelsize=15) - ax.relim() - ax.autoscale_view() - - rects = ax.patches - for rect in rects: - x_value = rect.get_width() - y_value = rect.get_y() + rect.get_height() / 2 - space = 5 - ha = 'left' - - if x_value < 0: - space *= -1 - ha = 'right' - - label = "{:.1f}".format(x_value) - - # Create annotation - plt.annotate( - label, - (x_value, y_value), - xytext=(space, 0), - textcoords="offset points", - va='center', - ha=ha, fontsize=12, color='black', fontweight='bold') - - # map names to colors - cmap = {True: 'green', False: 'red'} - patches = [Patch(color=v, label=k) for k, v in cmap.items()] - plt.text(0,-1.5,f"Last updated {arrow.get()}", fontsize=10) - plt.legend(labels=objects, handles=patches, loc='upper right', borderaxespad=0, fontsize=15, frameon=True) - plt.savefig(SAVE_DIR+file_name+".png", bbox_inches='tight') - -def barplot_mode(data,x,y,plot_title,file_name): - all_labels= ['Gas Car, drove alone', - 'Bus', - 'Train', - 'Free Shuttle', - 'Taxi/Uber/Lyft', - 'Gas Car, with others', - 'Bikeshare', - 'Scooter share', - 'E-bike', - 'Walk', - 'Skate board', - 'Regular Bike', - 'Not a Trip', - 'No Travel', - 'Same Mode', - 'E-car, drove alone', - 'E-car, with others', - 'Air', - 'Other'] - - colours = dict(zip(all_labels, plt.cm.tab20.colors[:len(all_labels)])) - sns.set(font_scale=1.5) - f = plt.subplots(figsize=(15, 6)) - sns.set(style='whitegrid') - ax = sns.barplot(x=x, y=y, palette=colours,data=data, ci=None) - plt.xlabel(x, fontsize=23) - plt.ylabel(y, fontsize=23) - plt.title(plot_title, fontsize=25) - # y should be based on the max range + the biggest label ("Gas Car, with others") - plt.text(0,-(data[y].max()/8 + 3.3),f"Last updated {arrow.get()}", fontsize=10) - plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right') - plt.savefig(SAVE_DIR+file_name+".png", bbox_inches='tight') - -def barplot_mode2(data,x,y,y2,plot_title,file_name): - all_labels= ['Gas Car, drove alone', - 'Bus', - 'Train', - 'Free Shuttle', - 'Taxi/Uber/Lyft', - 'Gas Car, with others', - 'Bikeshare', - 'Scooter share', - 'E-bike', - 'Walk', - 'Skate board', - 'Regular Bike', - 'Not a Trip', - 'No Travel', - 'Same Mode', - 'E-car, drove alone', - 'E-car, with others', - 'Air', - 'Other'] - - colours = dict(zip(all_labels, plt.cm.tab20.colors[:len(all_labels)])) - sns.set(font_scale=1.5) - - fig, ax1 = plt.subplots(figsize=(15,6)) - #bar plot creation - ax1.set_title(plot_title, fontsize=16) - plt.text(0,-2,f"Last updated {arrow.get()}", fontsize=10) - ax1.set_xlabel(x, fontsize=16) - ax1.set_ylabel(y, fontsize=16) - ax1 = sns.barplot(x=x, y=y, data = data, palette=colours, ci=None) - ax1.grid(False) - - #specify we want to share the same x-axis - ax2 = ax1.twinx() - color = 'tab:red' - #line plot creation - ax2.set_ylabel('Count', fontsize=16) - ax2 = sns.pointplot(x=x, y=y2, data = data, sort=False, color=color) - ax2.grid(False) - plt.setp(ax2.get_xticklabels(), rotation=45, ha='right') - plt.setp(ax1.get_xticklabels(), rotation=45, ha='right') - plt.savefig(SAVE_DIR+file_name+".png", bbox_inches='tight') - -def barplot_day(data,x,y,plot_title,file_name): - sns.set(font_scale=1.5) - f = plt.subplots(figsize=(15, 6)) - sns.set(style='whitegrid') - ax = sns.barplot(x=x, y=y,data=data, ci=None, color='blue') - plt.xlabel(x, fontsize=16) - plt.ylabel(y, fontsize=16) - plt.title(plot_title, fontsize=16) - # heuristic where we take the max value and divide it by 8 to get the scale - # the 8 is heuristic based on experimentation with the CanBikeCO data - plt.text(0,-(data[y].max())/8,f"Last updated {arrow.get()}", fontsize=10) - plt.savefig(SAVE_DIR+file_name+".png", bbox_inches='tight') - -def CO2_impact(x,y,color,plot_title,file_name): - color = color.map({True: 'green', False: 'red'}) - objects = ('CO2 Reduction', 'CO2 Increase') - - y_labels = y - plt.figure(figsize=(15, 8)) - width = 0.8 - ax = x.plot(kind='barh',width=width, color=color) - ax.set_title(plot_title, fontsize=18) - ax.set_xlabel('CO2 Emissions (lb)', fontsize=18) - ax.set_ylabel('Replaced Mode',fontsize=18) - ax.set_yticklabels(y_labels) - ax.xaxis.set_tick_params(labelsize=15) - ax.yaxis.set_tick_params(labelsize=15) - ax.relim() - ax.autoscale_view() - - rects = ax.patches - for rect in rects: - x_value = rect.get_width() - y_value = rect.get_y() + rect.get_height() / 2 - space = 5 - ha = 'left' - - if x_value < 0: - space *= -1 - ha = 'right' - - label = "{:.1f}".format(x_value) - - # Create annotation - plt.annotate( - label, - (x_value, y_value), - xytext=(space, 0), - textcoords="offset points", - va='center', - ha=ha, fontsize=12, color='black', fontweight='bold') - - # map names to colors - cmap = {True: 'green', False: 'red'} - patches = [Patch(color=v, label=k) for k, v in cmap.items()] - plt.text(0,-1.5,f"Last updated {arrow.get()}", fontsize=10) - plt.legend(labels=objects, handles=patches, loc='upper right', borderaxespad=0, fontsize=15, frameon=True) - plt.savefig(SAVE_DIR+file_name+".png", bbox_inches='tight') - -def timeseries_plot(x,y,plot_title,ylab,file_name): - fig, ax = plt.subplots(figsize=(16,4)) - sns.lineplot(ax=ax, x=x, y=y).set(title=plot_title, xlabel='Date', ylabel=ylab) - plt.xticks(rotation=45) - plt.subplots_adjust(bottom=0.25) - ax.figure.savefig(SAVE_DIR+file_name+".png", bbox_inches='tight') - -def timeseries_multi_plot(data,x,y,hue,plot_title,ylab,legend_title,file_name): - fig, ax = plt.subplots(figsize=(16,4)) - sns.lineplot(ax=ax, data=data, x=x, y=y, hue=hue).set(title=plot_title, xlabel='Date', ylabel=ylab) - plt.xticks(rotation=45) - plt.subplots_adjust(bottom=0.25) - plt.legend(bbox_to_anchor=(1.02, 1), loc='best', borderaxespad=0, title=legend_title) - ax.figure.savefig(SAVE_DIR+file_name+".png", bbox_inches='tight') - -def access_alt_text(alt_text, chart_name): - """ Inputs: - alt_text = the text describing the chart - chart_name = the alt text file to save or update - """ - f = open(SAVE_DIR+chart_name+".txt",'w') - f.write(alt_text) - f.close() - return alt_text - -def store_alt_text_generic(chart_description, chart_name, var_name): - """ Inputs: - chart_description = what type of chart is it - chart_name = what to label chart by in the dictionary - var_name = the variable being analyzed across the chart - """ - # Fill out the alt text based on components of the chart and passed data - alt_text = f"{chart_description} chart of {var_name}." - alt_text = access_alt_text(alt_text, chart_name) - return alt_text - -def store_alt_text_bar(df, chart_name, var_name): - """ Inputs: - df = dataframe with index of item names, first column is counts - chart_name = what to label chart by in the dictionary - var_name = the variable being analyzed across pie slices - """ - # Fill out the alt text based on components of the chart and passed data - alt_text = f"Bar chart of {var_name}." - for i in range(0,len(df)): - alt_text += f" {df.index[i]} is {np.round(df.iloc[i,0], 1)}." - alt_text = access_alt_text(alt_text, chart_name) - return alt_text - -def store_alt_text_pie(df, chart_name, var_name): - """ Inputs: - df = dataframe with index of item names, first column is counts - chart_name = what to label chart by in the dictionary - var_name = the variable being analyzed across pie slices - """ - # Fill out the alt text based on components of the chart and passed data - alt_text = f"Pie chart of {var_name}." - for i in range(0,len(df)): - alt_text += f" {df.index[i]} is {np.round(df.iloc[i,0] / np.sum(df.iloc[:,0]) * 100, 1)}%." - alt_text = access_alt_text(alt_text, chart_name) - return alt_text - -def store_alt_text_timeseries(df, chart_name, var_name): - """ Inputs: - df = dataframe with first col of dates, second column is values - chart_name = what to label chart by in the dictionary - var_name = the variable being analyzed across pie slices - """ - # Fill out the alt text based on components of the chart and passed data - alt_text = f"Scatter chart of {var_name}." - arg_min = np.argmin(df.iloc[:,1]) - arg_max = np.argmax(df.iloc[:,1]) - alt_text += f" First minimum is {np.round(df.iloc[arg_min,1], 1)} on {df.iloc[arg_min,0]}. First maximum is {np.round(df.iloc[arg_max,1], 1)} on {df.iloc[arg_max,0]}." - alt_text = access_alt_text(alt_text, chart_name) - return alt_text - -def generate_missing_plot(plot_title,debug_df,file_name): - f, ax = plt.subplots(figsize=(10,10)) - - plt.title("Unable to generate plot\n"+plot_title+"\n Reason:", fontsize=25, color="red") - # Must keep the patch visible; otherwise the entire figure becomes transparent - # f.patch.set_visible(False) - ax.axis('off') - ax.axis('tight') - # ax = sns.barplot(x=debug_df['count'],y=debug_df.index, palette=sns.color_palette("Reds",n_colors=10)) - # ax.set_xlim(0, None) - # for i in ax.containers: - # ax.bar_label(i,) - the_table = plt.table(cellText=debug_df.values, - rowLabels=debug_df.index, - colLabels=debug_df.columns, - loc="center") - the_table.auto_set_font_size(False) - the_table.set_fontsize(20) - the_table.scale(1, 4) - cellDict = the_table.get_celld() - for i in range(1,len(debug_df)+1): - currCellTextStr = cellDict[(i,0)].get_text().get_text() - currCellTextFloat = float(currCellTextStr) - if np.isnan(currCellTextFloat): - cellDict[(i,0)].get_text().set_text("None") - if np.isnan(currCellTextFloat) or currCellTextFloat == 0: - cellDict[(i, 0)].get_text().set_color("red") - plt.savefig(SAVE_DIR+file_name+".png", bbox_inches='tight') - -def store_alt_text_missing(df, chart_name, var_name): - """ Inputs: - df = dataframe with index of debug information, first column is counts - chart_name = what to label chart by in the dictionary - var_name = the variable being analyzed across pie slices - """ - # Fill out the alt text based on components of the chart and passed data - alt_text = f"Unable to generate\nBar chart of {var_name}.\nReason:" - for i in range(0,len(df)): - alt_text += f" {df.index[i]} is {np.round(df.iloc[i,0], 1)}." - alt_text = access_alt_text(alt_text, chart_name) - return alt_text diff --git a/viz_scripts/replacement_model_data_processing.ipynb b/viz_scripts/replacement_model_data_processing.ipynb deleted file mode 100644 index 19684a3..0000000 --- a/viz_scripts/replacement_model_data_processing.ipynb +++ /dev/null @@ -1,580 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "year = None\n", - "month = None\n", - "program = \"prepilot\"\n", - "study_type = \"program\"\n", - "mode_of_interest = \"pilot_ebike\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SAVE_DIR = '/plots/'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from collections import defaultdict\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "from sklearn import linear_model\n", - "\n", - "from plots import *\n", - "import scaffolding\n", - "\n", - "sns.set_style(\"whitegrid\")\n", - "sns.set()\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Settings and imports specific to this notebook\n", - "\n", - "include_replaced_modes_as_valid = True # Flip this when we want to get results versus generate the replaced_mode correction graphs\n", - "model_with_sensed = False\n", - "input_dataset = \"ONLY_LABELED\" # \"ONLY_LABELED\", \"ONLY_SENSED\" or \"BEST_AVAILABLE\" for sensitivity analysis\n", - "LABEL_ASSIST_THRESHOLD = 0.3\n", - "\n", - "# For reloading modules from Jupyter\n", - "# import importlib\n", - "# importlib.reload(replacement_models)\n", - "\n", - "import datetime\n", - "import pickle\n", - "import sklearn.metrics\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.model_selection import KFold\n", - "from sklearn.model_selection import train_test_split\n", - "import emission.core.get_database as edb\n", - "import emission.core.wrapper.entry as ecwe\n", - "import emission.storage.decorations.analysis_timeseries_queries as esda\n", - "import emission.storage.decorations.trip_queries as esdt\n", - "import emission.storage.decorations.timeline as esdl\n", - "import emission.storage.timeseries.abstract_timeseries as esta\n", - "import emission.storage.timeseries.timequery as estt\n", - "from uuid import UUID" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Do not run this notebook at all unless it is for a program; nbclient will run up through this cell\n", - "if study_type != \"program\":\n", - " raise Exception(\"The plots in this notebook are only relevant to programs\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Loading mapping dictionaries from mapping_dictionaries notebook\n", - "%store -r dic_re\n", - "%store -r dic_pur\n", - "\n", - "# convert a dictionary to a defaultdict\n", - "dic_re = defaultdict(lambda: 'Other',dic_re)\n", - "dic_pur = defaultdict(lambda: 'Other',dic_pur)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get UUIDs by Program" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Split UUIDs by program\n", - "program_uuid_map = {}\n", - "for ue in edb.get_uuid_db().find():\n", - " program = ue['user_email'].split(\"_\")[0]\n", - " if program in program_uuid_map.keys():\n", - " program_uuid_map[program].append(str(ue['uuid']))\n", - " else:\n", - " print(f\"Found new program {program}, creating new list\")\n", - " program_uuid_map[program] = []\n", - " program_uuid_map[program].append(str(ue['uuid']))\n", - "\n", - "uuid_program_list = []\n", - "for ue in edb.get_uuid_db().find():\n", - " program = ue['user_email'].split(\"_\")[0]\n", - " uuid_program_list.append({\"program\": program, \"opcode\": ue[\"user_email\"], \"user_id_str\": str(ue['uuid'])})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "uuid_program_df = pd.DataFrame.from_dict(uuid_program_list)\n", - "uuid_program_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Collect Data From Database" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "expanded_ct, file_suffix, quality_text, debug_df = scaffolding.load_viz_notebook_data(year,\n", - " month,\n", - " program,\n", - " study_type,\n", - " dic_re,\n", - " dic_pur=dic_pur)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Join to the program df to get each user's program\n", - "expanded_ct['user_id_str'] = expanded_ct['user_id'].astype(str)\n", - "expanded_ct = expanded_ct.merge(uuid_program_df, on='user_id_str')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Add non-label category\n", - "expanded_ct['replaced_mode'] = expanded_ct['replaced_mode'].fillna('Unlabeled')\n", - "expanded_ct.loc[expanded_ct['replaced_mode'] == 'Unlabeled', 'Replaced_mode'] = \"Unlabeled\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Join the expanded database data to socioeconomic data\n", - "socio_data = pd.read_csv('./Can Do Colorado eBike Program - en.csv')\n", - "socio_data.rename(columns={'Unique User ID (auto-filled, do not edit)':'user_id',\n", - " 'Please identify which category represents your total household income, before taxes, for last year.':'HHINC',\n", - " 'How many motor vehicles are owned, leased, or available for regular use by the people who currently live in your household?':'VEH',\n", - " 'In which year were you born?':'AGE',\n", - " 'Including yourself, how many people live in your home?':'HHSIZE',\n", - " 'How many children under age 18 live in your home?':'CHILDREN',\n", - " 'What is your gender?':'GENDER',\n", - " 'If you were unable to use your household vehicle(s), which of the following options would be available to you to get you from place to place?':'available_modes',\n", - " 'Are you a student?':'STUDENT',\n", - " \"Including yourself, how many people have a driver's license in your household?\":'DRIVERS'}, inplace=True)\n", - "socio_data = socio_data[~socio_data.user_id.isnull()]\n", - "\n", - "# Deal with people who have multiple responses by using most recent\n", - "socio_data = socio_data.sort_values(by=['user_id', 'Timestamp'])\n", - "socio_data.drop_duplicates(subset=['user_id'], keep='last', inplace=True)\n", - "socio_data['user_id_socio'] = socio_data.user_id\n", - "socio_data = socio_data.drop(labels='user_id', axis=1)\n", - "\n", - "# Lose some trips due to people with no survey responses\n", - "expanded_ct['user_id_socio'] = expanded_ct.user_id.astype(str)\n", - "expanded_ct.user_id_socio = [i.replace('-','') for i in expanded_ct.user_id_socio] # remove all dashes from strings\n", - "expanded_ct = expanded_ct.merge(socio_data, on='user_id_socio')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Preprocessing" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "## Filter database to variables of modeling interest\n", - "data = expanded_ct[['Mode_confirm','Replaced_mode','replaced_mode','Trip_purpose','duration','distance_miles','start_local_dt_weekday','available_modes','AGE','HHINC','VEH','HHSIZE','CHILDREN','GENDER','STUDENT','DRIVERS','user_id','_id','start_local_dt_year','start_local_dt_month','start_local_dt_day','cleaned_trip','start_fmt_time','start_loc','end_loc']].copy()\n", - "\n", - "\n", - "## Pre-filter round of variable creation\n", - "# Make copy of user_id to be categorized since both versions are needed\n", - "data['user_id_int'] = data['user_id']\n", - "\n", - "# Get timestamp from known year/month/day aggregated to days\n", - "data.rename(columns={'start_local_dt_year':'year','start_local_dt_month':'month','start_local_dt_day':'day'}, inplace=True)\n", - "data['date_time'] = pd.to_datetime(data[['year','month','day']])\n", - "data = data.drop(columns=['year','day'])\n", - "\n", - "# Get time of day\n", - "data['hour'] = [int(x[1][:2]) for x in expanded_ct.start_fmt_time.str.split('T')]\n", - "\n", - "# Fix age\n", - "data['AGE'] = 2022 - data['AGE']\n", - "\n", - "# Number of workers\n", - "data['WORKERS'] = (data['HHSIZE'] - data['CHILDREN']).astype(int)\n", - "\n", - "# Vehicles per driver\n", - "data['VEH'] = data['VEH'].replace('4+', '4')\n", - "\n", - "# Recoded Cyclical Time of Day\n", - "hours_in_day = 24\n", - "months_in_year = 12\n", - "data['sin_time'] = np.sin(2*np.pi*data.hour/hours_in_day)\n", - "data['cos_time'] = np.cos(2*np.pi*data.hour/hours_in_day)\n", - "data['sin_month'] = np.sin(2*np.pi*data.month/months_in_year)\n", - "data['cos_month'] = np.cos(2*np.pi*data.month/months_in_year)\n", - "\n", - "# Duration in minutes\n", - "data['duration'] = data['duration'] / 60\n", - "\n", - "# Add coordinates to the data\n", - "z = pd.json_normalize(data.start_loc)['coordinates']\n", - "olon = [str(x[0]) for x in z]\n", - "olat = [str(x[1]) for x in z]\n", - "data['olat'] = olat\n", - "data['olon'] = olon\n", - "z = pd.json_normalize(data.end_loc)['coordinates']\n", - "dlon = [str(x[0]) for x in z]\n", - "dlat = [str(x[1]) for x in z]\n", - "data['dlat'] = dlat\n", - "data['dlon'] = dlon\n", - "\n", - "# Recode variables\n", - "data.Mode_confirm = data.Mode_confirm.replace(\n", - " ['Gas Car, drove alone',\n", - " 'Gas Car, with others',\n", - " 'Bikeshare',\n", - " 'Scooter share',\n", - " 'Regular Bike',\n", - " 'Skate board',\n", - " 'Train',\n", - " 'Free Shuttle',\n", - " 'Bus',\n", - " 'Walk',\n", - " 'Taxi/Uber/Lyft',\n", - " 'E-bike'],\n", - " ['car',\n", - " 's_car',\n", - " 's_micro',\n", - " 's_micro',\n", - " 'p_micro',\n", - " 'p_micro',\n", - " 'transit',\n", - " 'transit',\n", - " 'transit',\n", - " 'walk',\n", - " 'ridehail',\n", - " 'ebike']\n", - ")\n", - "data.Replaced_mode = data.Replaced_mode.replace(\n", - " ['Gas Car, drove alone',\n", - " 'Gas Car, with others',\n", - " 'Bikeshare',\n", - " 'Scooter share',\n", - " 'Regular Bike',\n", - " 'Skate board',\n", - " 'Train',\n", - " 'Free Shuttle',\n", - " 'Bus',\n", - " 'Walk',\n", - " 'Taxi/Uber/Lyft',\n", - " 'E-bike',\n", - " 'No Travel'],\n", - " ['car',\n", - " 's_car',\n", - " 's_micro',\n", - " 's_micro',\n", - " 'p_micro',\n", - " 'p_micro',\n", - " 'transit',\n", - " 'transit',\n", - " 'transit',\n", - " 'walk',\n", - " 'ridehail',\n", - " 'ebike',\n", - " 'no_travel']\n", - ")\n", - "data.Trip_purpose = data.Trip_purpose.replace(\n", - " ['Work',\n", - " 'School',\n", - " 'Recreation/Exercise',\n", - " 'Transit transfer',\n", - " 'Meal',\n", - " 'Entertainment/Social',\n", - " 'Shopping',\n", - " 'Personal/Medical',\n", - " 'Religious',\n", - " 'Pick-up/Drop off'],\n", - " ['commute',\n", - " 'commute',\n", - " 'recreation',\n", - " 'transit_transfer',\n", - " 'discretionary',\n", - " 'discretionary',\n", - " 'discretionary',\n", - " 'discretionary',\n", - " 'discretionary',\n", - " 'pudo']\n", - ")\n", - "data['is_weekend'] = 0\n", - "data.loc[data['start_local_dt_weekday'].isin(['0','6']), 'is_weekend'] = 1\n", - "data['is_male'] = 0\n", - "data.loc[data['GENDER'].isin(['Man']), 'is_male'] = 1\n", - "\n", - "## Filter data\n", - "# Filter out responses to data that are not workable\n", - "data = data[~data['Mode_confirm'].isin(['Not a Trip','Other'])]\n", - "data = data[~data['Replaced_mode'].isin(['Not a Trip','Other','Unlabeled'])]\n", - "data = data[~data['available_modes'].isin(['None', 'Prefer not to say'])]\n", - "data = data[~data['Trip_purpose'].isin(['not_a_trip','Other'])]\n", - "data = data[~data['HHINC'].isin(['Prefer not to say','$100,000 -$149,999','$150,000','$150,000-$199,999','$200,000 or more'])] # Side note why is 150k (n=7) its own bin?\n", - "data = data[~data['VEH'].isin(['Prefer not to say / Prefiero no decir.'])]\n", - "data = data[data['distance_miles']<50]\n", - "data = data[data['AGE']<100]\n", - "data = data[data['HHSIZE']<10]\n", - "data = data[data['HHSIZE']>data['CHILDREN']]\n", - "\n", - "\n", - "## Post-filter round of variable creation\n", - "# OHE any categorical, non-ordinal variables\n", - "ohe_vars = ['Trip_purpose','HHINC','STUDENT']\n", - "ohe_prefixes = ['purp','hhinc','student']\n", - "data = pd.get_dummies(data, columns=ohe_vars, prefix=ohe_prefixes)\n", - "\n", - "# Calculate travel times for each trip, across every mode\n", - "def add_all_mode_tt(data, mode_col, duration_col, dist_col):\n", - " wait_times_init = {'car':0.00,\n", - " 's_car':0.00,\n", - " 'ridehail':5.00,\n", - " 's_micro':5.00,\n", - " 'p_micro':0.00,\n", - " 'transit':7.00,\n", - " 'walk':5.00,\n", - " 'ebike':0.00}\n", - " mode_travel_times = {}\n", - " for mode in pd.unique(data[mode_col]):\n", - "\n", - " # Linear model for duration based on distance for trips belonging to each mode\n", - " mode_data = data[data[mode_col]==mode]\n", - " regr = linear_model.LinearRegression()\n", - " regr.fit(np.log(mode_data[dist_col].values.reshape(-1,1)), np.log(mode_data[duration_col].values.reshape(-1,1)))\n", - " \n", - " # Make prediction for ALL trips, reverse the log transform to get mins\n", - " mode_duration_pred = regr.predict(np.log(data[dist_col].values.reshape(-1,1)))\n", - " mode_travel_times['tt_'+mode] = np.exp(mode_duration_pred) + wait_times_init[mode]\n", - "\n", - " # Apply for each mode existing in the dataframe\n", - " for mode in mode_travel_times:\n", - " data[mode] = mode_travel_times[mode]\n", - "\n", - " return regr, data\n", - "\n", - "# Calculate all mode travel times and add to dataframe\n", - "regr, data = add_all_mode_tt(data,'Mode_confirm','duration','distance_miles')\n", - "\n", - "# Calculate vehicle costs\n", - "cost_factors_init = {'car':0.00,\n", - " 's_car':0.00,\n", - " 'ridehail':5.00,\n", - " 's_micro':1.00,\n", - " 'p_micro':0.00,\n", - " 'transit':3.50,\n", - " 'ebike':0.00,\n", - " 'walk':0.00}\n", - "cost_factors = {'car':0.62,\n", - " 's_car':0.31,\n", - " 'ridehail':0.80,\n", - " 's_micro':0.90,\n", - " 'p_micro':0.00,\n", - " 'transit':0.00,\n", - " 'ebike':0.11,\n", - " 'walk':0.00}\n", - "\n", - "def add_all_mode_cost(data, cost_factors, dist_col):\n", - " for factor in cost_factors:\n", - " data['cost_'+factor] = cost_factors_init[factor] + (cost_factors[factor] * data[dist_col])\n", - " return data\n", - "\n", - "# Calculate all mode travel costs and add to dataframe\n", - "add_all_mode_cost(data, cost_factors, 'distance_miles')\n", - "\n", - "# Labels for modes in the availability survey\n", - "availability_codes = {'Public transportation (bus, subway, light rail, etc.)':'transit',\n", - " 'Get a ride from a friend or family member':'s_car',\n", - " 'Rental car (including Zipcar/ Car2Go)':'car',\n", - " 'Taxi (regular taxi, Uber, Lyft, etc)':'ridehail',\n", - " 'Bicycle':'p_micro',\n", - " 'Shared bicycle or scooter':'s_micro',\n", - " 'Walk/roll':'walk',\n", - " 'Skateboard':'p_micro',\n", - " 'ebike':'ebike',\n", - " 'None':'none'}\n", - "\n", - "def add_mode_availability(data, availability_codes, availability_col, choice_col, replaced_col, is_sp):\n", - " mode_list = np.unique(list(availability_codes.values())[:-1])\n", - " choice_list = data[choice_col].values\n", - " replaced_list = data[replaced_col].values\n", - " for mode in mode_list:\n", - " mode_avail = []\n", - " for i, available in enumerate(data[availability_col].values):\n", - " available_modes = [availability_codes[x] for x in available.split(';')]\n", - " # For SP: Replacement/stated available should be 1, chosen should be 0\n", - " if is_sp:\n", - " if mode==choice_list[i]:\n", - " mode_check = False\n", - " else:\n", - " mode_check = mode==replaced_list[i] or mode in available_modes\n", - " # For RP: Chosen/replacement/stated available should be 1\n", - " else:\n", - " mode_check = mode==choice_list[i] or mode==replaced_list[i] or mode in available_modes\n", - " # Keep binary list of which trips the mode was available for\n", - " if mode_check:\n", - " mode_avail.append(1)\n", - " else:\n", - " mode_avail.append(0)\n", - " # For each mode add a column with binary availability\n", - " data['av_'+mode] = mode_avail\n", - " return data\n", - "\n", - "# Add mode availability according to survey responses\n", - "data = add_mode_availability(data, availability_codes, 'available_modes', 'Mode_confirm', 'Replaced_mode', is_sp=False)\n", - "\n", - "# # Add mode availability as all available\n", - "mode_list = ['car','s_car','ridehail','transit','p_micro','s_micro','walk','ebike','no_travel']\n", - "# for mode in mode_list:\n", - "# data[f\"av_{mode}\"] = 1\n", - "\n", - "# Handle all variables that are ordinal; otherwise they may not end up in correct order\n", - "# Make sure that all mode variables align after being converted to numeric variables\n", - "data.Mode_confirm = pd.Categorical(data.Mode_confirm, ordered=True, categories=mode_list)\n", - "data.Replaced_mode = pd.Categorical(data.Replaced_mode, ordered=True, categories=mode_list)\n", - "data['Mode_confirm_num'] = data.Mode_confirm.cat.codes\n", - "data['Replaced_mode_num'] = data.Replaced_mode.cat.codes\n", - "data = data[data['Mode_confirm_num']!=data['Replaced_mode_num']]\n", - "\n", - "## Save cleaned data to be used in modeling\n", - "data.to_csv(\"processed_replacement_modeling_data.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Checks" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Data stats before cleaning\n", - "print(f\"Trips: {len(expanded_ct)}\")\n", - "print(f\"Users: {len(np.unique(expanded_ct.user_id))}\")\n", - "print(f\"Trips per user: {len(expanded_ct) / len(pd.unique(expanded_ct.user_id))}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Data stats after cleaning + columns available\n", - "print(f\"Trips: {len(data)}\")\n", - "print(f\"Users: {len(np.unique(data.user_id))}\")\n", - "print(f\"Trips per user: {len(data) / len(pd.unique(data.user_id))}\\n\")\n", - "print(f\"Columns: \\n{data.columns.values}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Check for NAs\n", - "data.isna().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check # obs for each class\n", - "print(f\"Mode_confirm:\\n{pd.value_counts(data.Mode_confirm)}\\n\")\n", - "print(f\"Replaced_mode:\\n{pd.value_counts(data.Replaced_mode)}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/viz_scripts/replacement_models.py b/viz_scripts/replacement_models.py deleted file mode 100644 index 5e303dc..0000000 --- a/viz_scripts/replacement_models.py +++ /dev/null @@ -1,122 +0,0 @@ -import numpy as np -import pandas as pd -import sklearn.metrics -from sklearn.preprocessing import MinMaxScaler -from sklearn.neighbors import KNeighborsClassifier -from sklearn.ensemble import RandomForestClassifier -from sklearn.ensemble import GradientBoostingClassifier -from sklearn.svm import SVC - -def gbdt(data, choice_col, feature_list, kf): - # Save metrics from each run - accuracy = [] - f1 = [] - confusion = [] -# # Scale numeric variables -# scaler = MinMaxScaler() -# data_scaled = scaler.fit_transform(data[feature_list]) - data_scaled = data[feature_list].values - - for train_indices, test_indices in kf.split(data.values): - X_train, X_test = data_scaled[train_indices], data_scaled[test_indices] - y_train, y_test = data[choice_col].values[train_indices], data[choice_col].values[test_indices] - - # Train random forest on training set - model = GradientBoostingClassifier(n_estimators=50) - model.fit(X_train, y_train) - - # Predict for test set - y_pred = model.predict(X_test) - - # Track metrics - accuracy.append(sklearn.metrics.accuracy_score(y_test, y_pred)) - f1.append(sklearn.metrics.f1_score(y_test, y_pred, average='weighted')) - confusion.append(sklearn.metrics.confusion_matrix(y_test, y_pred, labels=[0,1,2,3,4,5,6,7], normalize='pred')) - - # Collect all model scores for comparison at the end - return model, accuracy, f1, confusion - -def rf(data, choice_col, feature_list, kf): - # Save metrics from each run - accuracy = [] - f1 = [] - confusion = [] -# # Scale numeric variables -# scaler = MinMaxScaler() -# data_scaled = scaler.fit_transform(data[feature_list]) - data_scaled = data[feature_list].values - - for train_indices, test_indices in kf.split(data.values): - X_train, X_test = data_scaled[train_indices], data_scaled[test_indices] - y_train, y_test = data[choice_col].values[train_indices], data[choice_col].values[test_indices] - - # Train random forest on training set - model = RandomForestClassifier(n_estimators=50) - model.fit(X_train, y_train) - - # Predict for test set - y_pred = model.predict(X_test) - - # Track metrics - accuracy.append(sklearn.metrics.accuracy_score(y_test, y_pred)) - f1.append(sklearn.metrics.f1_score(y_test, y_pred, average='weighted')) - confusion.append(sklearn.metrics.confusion_matrix(y_test, y_pred, labels=[0,1,2,3,4,5,6,7], normalize='pred')) - - # Collect all model scores for comparison at the end - return model, accuracy, f1, confusion - -def svm(data, choice_col, feature_list, kf): - # Save metrics from each run - accuracy = [] - f1 = [] - confusion = [] - # Scale numeric variables - scaler = MinMaxScaler() - data_scaled = scaler.fit_transform(data[feature_list]) - - for train_indices, test_indices in kf.split(data.values): - X_train, X_test = data_scaled[train_indices], data_scaled[test_indices] - y_train, y_test = data[choice_col].values[train_indices], data[choice_col].values[test_indices] - - # Train random forest on training set - model = SVC() - model.fit(X_train, y_train) - - # Predict for test set - y_pred = model.predict(X_test) - - # Track metrics - accuracy.append(sklearn.metrics.accuracy_score(y_test, y_pred)) - f1.append(sklearn.metrics.f1_score(y_test, y_pred, average='weighted')) - confusion.append(sklearn.metrics.confusion_matrix(y_test, y_pred, labels=[0,1,2,3,4,5,6,7], normalize='pred')) - - # Collect all model scores for comparison at the end - return model, accuracy, f1, confusion - -def knn(data, choice_col, feature_list, kf): - # Save metrics from each run - accuracy = [] - f1 = [] - confusion = [] - # Scale numeric variables - scaler = MinMaxScaler() - data_scaled = scaler.fit_transform(data[feature_list]) - - for train_indices, test_indices in kf.split(data.values): - X_train, X_test = data_scaled[train_indices], data_scaled[test_indices] - y_train, y_test = data[choice_col].values[train_indices], data[choice_col].values[test_indices] - - # Train random forest on training set - model = KNeighborsClassifier(n_neighbors=len(pd.unique(y_train))) - model.fit(X_train, y_train) - - # Predict for test set - y_pred = model.predict(X_test) - - # Track metrics - accuracy.append(sklearn.metrics.accuracy_score(y_test, y_pred)) - f1.append(sklearn.metrics.f1_score(y_test, y_pred, average='weighted')) - confusion.append(sklearn.metrics.confusion_matrix(y_test, y_pred, labels=[0,1,2,3,4,5,6,7], normalize='pred')) - - # Collect all model scores for comparison at the end - return model, accuracy, f1, confusion \ No newline at end of file diff --git a/viz_scripts/rm_src/LSTM.ipynb b/viz_scripts/rm_src/LSTM.ipynb deleted file mode 100644 index fcaca73..0000000 --- a/viz_scripts/rm_src/LSTM.ipynb +++ /dev/null @@ -1,2774 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "5f2cdb77", - "metadata": {}, - "outputs": [], - "source": [ - "import seaborn as sns\n", - "from abc import ABC, abstractmethod\n", - "from typing import List\n", - "import ast" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "0ebc3879", - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import random\n", - "import torch.nn as nn\n", - "import torch.nn.functional as F\n", - "import torch.optim as optim\n", - "import numpy as np\n", - "import pandas as pd\n", - "from torch.utils.data import Dataset, DataLoader\n", - "from enum import Enum\n", - "import matplotlib.pyplot as plt\n", - "from torch.nn.utils.rnn import pad_sequence\n", - "from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.linear_model import LinearRegression\n", - "from sklearn.metrics import r2_score\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a2ace37f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Global experiment flags and variables.\n", - "SEED = 19348\n", - "TARGETS = ['p_micro', 'no_trip', 's_car', 'transit', 'car', 's_micro', 'ridehail', 'walk', 'unknown']\n", - "\n", - "# Set the Numpy seed too.\n", - "random.seed(SEED)\n", - "np.random.seed(SEED)\n", - "torch.manual_seed(SEED)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "9addd580", - "metadata": {}, - "outputs": [], - "source": [ - "# lstm = nn.LSTM(12, 64, batch_first=True, bidirectional=True, bias=False, num_layers=3)\n", - "# inp = torch.FloatTensor(8, 3, 12).fill_(0.)\n", - "# out, _ = lstm(inp)\n", - "\n", - "# print(out.size())" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "889bd770", - "metadata": {}, - "outputs": [], - "source": [ - "class SPLIT_TYPE(Enum):\n", - " INTRA_USER = 0\n", - " INTER_USER = 1\n", - " TARGET = 2\n", - " MIXED = 3\n", - " \n", - "\n", - "class SPLIT(Enum):\n", - " TRAIN = 0\n", - " TEST = 1\n", - "\n", - "\n", - "def get_train_test_splits(data: pd.DataFrame, how=SPLIT_TYPE, test_ratio=0.2, shuffle=True):\n", - "\n", - " n_users = list(data.user_id.unique())\n", - " n = data.shape[0]\n", - " \n", - " if shuffle:\n", - " data = data.sample(data.shape[0], random_state=SEED).reset_index(drop=True, inplace=False)\n", - " \n", - " # There are certain users with only one observation. What do we do with those?\n", - " # As per the mobilitynet modeling pipeline, we randomly assign them to either the\n", - " # training or test set.\n", - "\n", - " value_counts = data.user_id.value_counts()\n", - " single_count_ids = value_counts[value_counts == 1].index\n", - "\n", - " data_filtered = data.loc[~data.user_id.isin(single_count_ids), :].reset_index(drop=True)\n", - " data_single_counts = data.loc[data.user_id.isin(single_count_ids), :].reset_index(drop=True)\n", - "\n", - " X_tr, X_te = train_test_split(\n", - " data_filtered, test_size=test_ratio, shuffle=shuffle, stratify=data_filtered.user_id,\n", - " random_state=SEED\n", - " )\n", - "\n", - " data_single_counts['assigned'] = np.random.choice(['train', 'test'], len(data_single_counts))\n", - " X_tr_merged = pd.concat(\n", - " [X_tr, data_single_counts.loc[data_single_counts.assigned == 'train', :].drop(\n", - " columns=['assigned'], inplace=False\n", - " )],\n", - " ignore_index=True, axis=0\n", - " )\n", - "\n", - " X_te_merged = pd.concat(\n", - " [X_te, data_single_counts.loc[data_single_counts.assigned == 'test', :].drop(\n", - " columns=['assigned'], inplace=False\n", - " )],\n", - " ignore_index=True, axis=0\n", - " )\n", - "\n", - " return X_tr_merged, X_te_merged" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "6cfa847a", - "metadata": {}, - "outputs": [], - "source": [ - "# RM_weather contains more samples because data is not processed.\n", - "# _Fix contains lesser samples, but is cleaned using heuristics.\n", - "\n", - "data = pd.read_csv('../data/FULL_preprocessed_data_RM_weather.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "8fa73ebc", - "metadata": {}, - "outputs": [], - "source": [ - "class DataPipeline(ABC):\n", - " \n", - " def __init__(self, df: pd.DataFrame):\n", - " self.df = df\n", - " \n", - " # Establish hooks.\n", - " @abstractmethod\n", - " def remove_negative_sections(self):\n", - " raise NotImplementedError()\n", - " \n", - " @abstractmethod\n", - " def get_mode_outliers(self):\n", - " raise NotImplementedError()\n", - " \n", - " @abstractmethod\n", - " def drop_outliers(self, outliers: List):\n", - " raise NotImplementedError()\n", - " \n", - " @abstractmethod\n", - " def compute_mph_feature(self):\n", - " raise NotImplementedError()\n", - " \n", - " def run_pipeline(self):\n", - " self.remove_negative_sections()\n", - " outlier_ix = self.get_mode_outliers()\n", - " self.drop_outliers(list(outlier_ix))\n", - " return self.compute_mph_feature()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "d8e129c8", - "metadata": {}, - "outputs": [], - "source": [ - "class SectionDataPipeline(DataPipeline):\n", - " def __init__(self, df: pd.DataFrame):\n", - " super().__init__(df)\n", - " \n", - " def remove_negative_sections(self):\n", - " f_rows = list()\n", - " for _, row in self.df[['section_durations', 'section_distances', 'section_modes']].iterrows():\n", - "\n", - " dist = np.array(ast.literal_eval(row['section_distances']))\n", - " dur = np.array(ast.literal_eval(row['section_durations']))\n", - " modes = np.array(ast.literal_eval(row['section_modes']))\n", - "\n", - " assert len(dist) == len(dur) == len(modes)\n", - "\n", - " mask = np.logical_and(dist > 0, dur > 0)\n", - "\n", - " f_dist, f_dur, f_modes = dist[mask], dur[mask], modes[mask]\n", - "\n", - " assert len(f_dist) == len(f_dur) == len(f_modes)\n", - "\n", - " f_rows.append({\n", - " # scale to miles.\n", - " 'distances': f_dist * 0.00062,\n", - " # Scale to hours.\n", - " 'durations': f_dur / 3600.,\n", - " 'modes': f_modes\n", - " })\n", - "\n", - " parsed = pd.DataFrame(f_rows, index=data.index)\n", - "\n", - " self.df.drop(columns=['section_durations', 'section_distances', 'section_modes'], inplace=True)\n", - " self.df = pd.concat([self.df, parsed], axis=1)\n", - " \n", - " def get_mode_outliers(self):\n", - " \n", - " def compute_outliers(mode: str):\n", - " x = self.df[self.df.modes.str.contains(mode, regex=False)]\n", - " outlier_ix = []\n", - " dist, dur = np.array([]), np.array([])\n", - "\n", - " # First, iterate to compute the 99th percentile for the mode.\n", - " for row_ix, row in x.iterrows():\n", - " ix = np.where(row['modes'] == mode)[0]\n", - " dist = np.append(dist, row['distances'][ix])\n", - " dur = np.append(dur, row['durations'][ix])\n", - "\n", - " dist_99p = np.percentile(dist, 99)\n", - " dur_99p = np.percentile(dur, 99)\n", - "\n", - " print(f\"99th Percentile for {mode}:\\n\\tdistance = {dist_99p}\\n\\tduration = {dur_99p}\")\n", - "\n", - " # Iterate again.\n", - " for row_ix, row in x.iterrows():\n", - " ix = np.where(row['modes'] == mode)[0]\n", - " row_distances = row['distances'][ix]\n", - " row_durations = row['durations'][ix]\n", - "\n", - " if np.any(np.logical_or(row_distances > dist_99p, row_durations > dur_99p)):\n", - " outlier_ix.append(row_ix)\n", - "\n", - " print(\"Number of candidate rows for deletion: \", len(outlier_ix))\n", - "\n", - " return outlier_ix\n", - " \n", - " all_bad_ix = set()\n", - " for mode in ['walking', 'bicycling', 'train', 'bus', 'car']:\n", - " bad_mode_ix = compute_outliers(mode)\n", - " all_bad_ix = all_bad_ix.union(set(bad_mode_ix))\n", - " print(50*'=')\n", - " \n", - " print(f\"After analysis, {len(all_bad_ix)} total unique rows will be dropped.\")\n", - " \n", - " return all_bad_ix\n", - " \n", - " def drop_outliers(self, outliers: List):\n", - " self.df.drop(index=outliers, inplace=True)\n", - " \n", - " def compute_mph_feature(self):\n", - " self.df['mph'] = self.df['distances']/self.df['durations']\n", - " return self.df" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "38af3aad", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "99th Percentile for walking:\n", - "\tdistance = 4.305297486707881\n", - "\tduration = 2.9548623706234824\n", - "Number of candidate rows for deletion: 765\n", - "==================================================\n", - "99th Percentile for bicycling:\n", - "\tdistance = 8.82048257219703\n", - "\tduration = 0.9153121545202207\n", - "Number of candidate rows for deletion: 228\n", - "==================================================\n", - "99th Percentile for train:\n", - "\tdistance = 16.629460194644828\n", - "\tduration = 0.7250753228180938\n", - "Number of candidate rows for deletion: 2\n", - "==================================================\n", - "99th Percentile for bus:\n", - "\tdistance = 7.551944729816364\n", - "\tduration = 0.7316025587360053\n", - "Number of candidate rows for deletion: 12\n", - "==================================================\n", - "99th Percentile for car:\n", - "\tdistance = 40.548478579524726\n", - "\tduration = 1.2145738347887987\n", - "Number of candidate rows for deletion: 721\n", - "==================================================\n", - "After analysis, 1718 total unique rows will be dropped.\n" - ] - } - ], - "source": [ - "pipeline = SectionDataPipeline(data)\n", - "cleaned = pipeline.run_pipeline()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "81151c12", - "metadata": {}, - "outputs": [], - "source": [ - "def preprocess(df: pd.DataFrame):\n", - " \n", - " # Offset by -1 for torch.\n", - " df.chosen -= 1\n", - " \n", - "# df['start_fmt_time'] = pd.to_datetime(df['start_fmt_time'], utc=True)\n", - "# df['start_fmt_time'] = df['start_fmt_time'].dt.tz_convert('America/Denver')\n", - " \n", - " df.rename(\n", - " columns={'start_local_dt_weekday': 'start:DOW', 'end_local_dt_weekday': 'end:DOW'},\n", - " inplace=True\n", - " )\n", - "\n", - " df.n_working_residents = df.n_working_residents.apply(lambda x: 0 if x < 0 else x)\n", - "\n", - " # Fix some age preprocessing issues.\n", - " df.age = df.age.apply(lambda x: x if x < 100 else 2024-x)\n", - " \n", - " return df" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "0d4d6b76", - "metadata": {}, - "outputs": [], - "source": [ - "def get_duration_estimate(df: pd.DataFrame, dset: SPLIT, model_dict: dict):\n", - " \n", - " X_features = ['section_distance_argmax', 'age']\n", - " \n", - " if 'mph' in df.columns:\n", - " X_features += ['mph']\n", - " \n", - " if dset == SPLIT.TRAIN and model_dict is None:\n", - " model_dict = dict()\n", - " \n", - " if dset == SPLIT.TEST and model_dict is None:\n", - " raise AttributeError(\"Expected model dict for testing.\")\n", - " \n", - " if dset == SPLIT.TRAIN:\n", - " for section_mode in df.section_mode_argmax.unique():\n", - " section_data = df.loc[df.section_mode_argmax == section_mode, :]\n", - " if section_mode not in model_dict:\n", - " model_dict[section_mode] = dict()\n", - "\n", - " model = LinearRegression(fit_intercept=True)\n", - "\n", - " X = section_data[\n", - " X_features\n", - " ]\n", - " Y = section_data[['section_duration_argmax']]\n", - "\n", - " model.fit(X, Y.values.ravel())\n", - "\n", - " r2 = r2_score(y_pred=model.predict(X), y_true=Y.values.ravel())\n", - " print(f\"Train R2 for {section_mode}: {r2}\")\n", - "\n", - " model_dict[section_mode]['model'] = model\n", - " \n", - " elif dset == SPLIT.TEST:\n", - " for section_mode in df.section_mode_argmax.unique():\n", - " section_data = df.loc[df.section_mode_argmax == section_mode, :]\n", - " X = section_data[\n", - " X_features\n", - " ]\n", - " \n", - " Y = section_data[['section_duration_argmax']]\n", - " \n", - " y_pred = model_dict[section_mode]['model'].predict(X)\n", - " r2 = r2_score(y_pred=y_pred, y_true=Y.values.ravel())\n", - " print(f\"Test R2 for {section_mode}: {r2}\")\n", - " \n", - " # Create the new columns for the duration.\n", - " df[TARGETS] = 0\n", - " df['temp'] = 0\n", - " \n", - " for section in df.section_mode_argmax.unique():\n", - " X_section = df.loc[df.section_mode_argmax == section, X_features]\n", - " \n", - " # broadcast to all columns.\n", - " df.loc[df.section_mode_argmax == section, 'temp'] = model_dict[section]['model'].predict(X_section)\n", - " \n", - " for c in TARGETS:\n", - " df[c] = df['av_' + c] * df['temp']\n", - " \n", - " df.drop(columns=['temp'], inplace=True)\n", - " \n", - " df.rename(columns=dict([(x, 'tt_'+x) for x in TARGETS]), inplace=True)\n", - " \n", - " # return model_dict, result_df\n", - " return model_dict, df" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "7b34ced0", - "metadata": {}, - "outputs": [], - "source": [ - "def drop_columns(df: pd.DataFrame):\n", - " u_time_features = [\n", - " 'start:year', 'start:month', 'start:day',\n", - " 'start:hour', 'end_fmt_time', 'end:year',\n", - " 'end:month', 'end:day', 'end:hour', 'end:n_days_in_month', \n", - " 'start:sin_DOM', 'start:sin_MOY', 'start:cos_MOY', 'start:cos_DOM',\n", - " 'end:sin_DOM', 'end:sin_MOY', 'end:cos_DOM', 'end:cos_MOY', 'start:n_days_in_month',\n", - " 'start_local_dt_weekday', 'end_local_dt_weekday', 'start_fmt_time'\n", - " ]\n", - "\n", - " u_user_features = [\n", - " '_id', 'original_user_id', 'gender', 'birth_year', 'user_id'\n", - " ]\n", - "\n", - " u_trip_features = [\n", - " 'cleaned_trip', 'Mode_confirm', 'available_modes', 'duration', 'start_loc',\n", - " 'end_loc', 'section_locations_argmax', 'section_coordinates_argmax',\n", - " 'start_lat', 'start_lng', 'end_lat', 'end_lng', 'section_duration_argmax',\n", - " 'section_distance_argmax', 'section_mode_argmax'\n", - " ]\n", - "\n", - " # Drop section_mode_argmax and available_modes.\n", - " return df.drop(\n", - " columns=u_time_features + u_user_features + u_trip_features, \n", - " inplace=False\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "904fa4dc", - "metadata": {}, - "outputs": [], - "source": [ - "train_df, test_df = get_train_test_splits(data=cleaned, how=SPLIT_TYPE.INTRA_USER, shuffle=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "da30bcdc", - "metadata": {}, - "outputs": [], - "source": [ - "train_df, test_df = preprocess(drop_columns(train_df)), preprocess(drop_columns(test_df))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "78683828", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(train_df.columns == test_df.columns).all()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "cbb81799", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.preprocessing import StandardScaler\n", - "\n", - "def normalize_data(\n", - " df: pd.DataFrame, split: SPLIT, section_scalers: dict = None, scaler: StandardScaler = None\n", - "):\n", - " # We start by normalizing the temporal features first. This has to be done carefully.\n", - " \n", - " if split == SPLIT.TRAIN:\n", - " \n", - " mode_dict = dict()\n", - "\n", - " for _, row in df[['modes', 'durations', 'distances', 'mph']].iterrows():\n", - "\n", - " for (mode, duration, distance, mph) in zip(\n", - " row['modes'], row['durations'], row['distances'], row['mph']\n", - " ):\n", - " if mode not in mode_dict:\n", - " mode_dict[mode] = {\n", - " 'duration': np.array([duration]), \n", - " 'distance': np.array([distance]),\n", - " 'mph': np.array([mph])\n", - " }\n", - " else:\n", - " mode_dict[mode] = {\n", - " 'duration': np.append(mode_dict[mode]['duration'], duration),\n", - " 'distance': np.append(mode_dict[mode]['distance'], distance),\n", - " 'mph': np.append(mode_dict[mode]['mph'], mph)\n", - " }\n", - "\n", - " section_scalers = dict()\n", - "\n", - " for mode in mode_dict.keys():\n", - " # Fit, but don't transform. Instead, get the mean and scale.\n", - " section_scalers[mode] = {'duration': dict(), 'distance': dict(), 'mph': dict()}\n", - " \n", - " section_scalers[mode]['duration']['mean'] = mode_dict[mode]['duration'].mean()\n", - " section_scalers[mode]['duration']['std'] = mode_dict[mode]['duration'].std()\n", - " \n", - " section_scalers[mode]['distance']['mean'] = mode_dict[mode]['distance'].mean()\n", - " section_scalers[mode]['distance']['std'] = mode_dict[mode]['distance'].std()\n", - " \n", - " section_scalers[mode]['mph']['mean'] = mode_dict[mode]['mph'].mean()\n", - " section_scalers[mode]['mph']['std'] = mode_dict[mode]['mph'].std()\n", - " \n", - " # Normalize the temporal features.\n", - " rows = []\n", - " for _, row in df.iterrows():\n", - " row_dict = row.to_dict()\n", - " for ix, mode in enumerate(row_dict['modes']):\n", - " row_dict['durations'][ix] = (\n", - " row_dict['durations'][ix] - section_scalers[mode]['duration']['mean']\n", - " )/section_scalers[mode]['duration']['std']\n", - " \n", - " row_dict['distances'][ix] = (\n", - " row_dict['distances'][ix] - section_scalers[mode]['distance']['mean']\n", - " )/section_scalers[mode]['distance']['std']\n", - " \n", - " row_dict['mph'][ix] = (\n", - " row_dict['mph'][ix] - section_scalers[mode]['mph']['mean']\n", - " )/section_scalers[mode]['mph']['std']\n", - " \n", - " rows.append(row_dict)\n", - " \n", - " df = pd.DataFrame(rows)\n", - " \n", - " # -----------------------------------------\n", - " # Now, we start with the regular features.\n", - " # -----------------------------------------\n", - " \n", - " required_features = [\n", - " 'income_category', 'n_motor_vehicles', 'n_residence_members', 'n_residents_u18',\n", - " 'n_residents_with_license', 'distance_miles', 'age', 'n_working_residents', \n", - " 'start:sin_HOD', 'start:cos_HOD', 'end:sin_HOD', 'end:cos_HOD', \n", - " 'temperature_2m (°F)', 'relative_humidity_2m (%)', 'dew_point_2m (°F)', \n", - " 'rain (inch)', 'snowfall (inch)', 'cloud_cover (%)', 'wind_speed_10m (mp/h)',\n", - " 'wind_gusts_10m (mp/h)', 'cost_p_micro', 'cost_no_trip', 'cost_s_car', \n", - " 'cost_transit', 'cost_car', 'cost_s_micro', 'cost_ridehail', 'cost_walk', 'cost_unknown',\n", - " ]\n", - " \n", - " if split == SPLIT.TRAIN:\n", - " scaler = StandardScaler()\n", - " \n", - " t_df = pd.DataFrame(\n", - " scaler.fit_transform(df[required_features]),\n", - " columns=required_features,\n", - " index=df.index\n", - " )\n", - " else:\n", - " t_df = pd.DataFrame(\n", - " scaler.transform(df[required_features]),\n", - " columns=required_features,\n", - " index=df.index\n", - " )\n", - " \n", - " df.drop(columns=required_features, inplace=True)\n", - " \n", - " return pd.concat([df, t_df], axis=1), section_scalers, scaler" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "9b2ab162", - "metadata": {}, - "outputs": [], - "source": [ - "train_df, section_scaler, scaler = normalize_data(train_df, SPLIT.TRAIN)\n", - "test_df, _, _ = normalize_data(test_df, SPLIT.TEST, section_scaler, scaler)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "df774dd9", - "metadata": {}, - "outputs": [], - "source": [ - "train_df.reset_index(drop=True, inplace=True)\n", - "test_df.reset_index(drop=True, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "5ce6bafe", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Final features for training:\n", - "['is_student', 'is_overnight_trip', 'is_male', 'mph', 'chosen', 'av_no_trip', 'av_s_car', 'av_p_micro', 'av_walk', 'av_s_micro', 'av_transit', 'av_ridehail', 'av_car', 'av_unknown', 'distances', 'durations', 'modes', 'income_category', 'n_motor_vehicles', 'n_residence_members', 'n_residents_u18', 'n_residents_with_license', 'distance_miles', 'age', 'n_working_residents', 'start:sin_HOD', 'start:cos_HOD', 'end:sin_HOD', 'end:cos_HOD', 'temperature_2m (°F)', 'relative_humidity_2m (%)', 'dew_point_2m (°F)', 'rain (inch)', 'snowfall (inch)', 'cloud_cover (%)', 'wind_speed_10m (mp/h)', 'wind_gusts_10m (mp/h)', 'cost_p_micro', 'cost_no_trip', 'cost_s_car', 'cost_transit', 'cost_car', 'cost_s_micro', 'cost_ridehail', 'cost_walk', 'cost_unknown']\n" - ] - }, - { - "data": { - "text/html": [ - "
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is_studentis_overnight_tripis_malemphchosenav_no_tripav_s_carav_p_microav_walkav_s_micro...wind_gusts_10m (mp/h)cost_p_microcost_no_tripcost_s_carcost_transitcost_carcost_s_microcost_ridehailcost_walkcost_unknown
000.01[-0.6226942218711282]800010...-1.1181550.00.0-0.614415-0.286713-0.550431-0.431234-0.5924650.00.0
100.01[-0.32572174462565845]801100...-0.5987570.00.00.102862-0.5290270.167956-0.4312340.1285160.00.0
200.01[1.2722796444295434]101110...0.0465550.00.0-0.409306-0.304397-0.550431-0.431234-0.3862960.00.0
300.01[1.785796573896156]200100...1.3057020.00.0-0.614415-0.529027-0.550431-0.431234-0.5924650.00.0
410.00[-0.12709085963426053, -0.8967034453081592]101110...-0.4571040.00.0-0.177887-0.529027-0.113227-0.431234-0.1536820.00.0
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5 rows × 46 columns

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" - ], - "text/plain": [ - " is_student is_overnight_trip is_male \\\n", - "0 0 0.0 1 \n", - "1 0 0.0 1 \n", - "2 0 0.0 1 \n", - "3 0 0.0 1 \n", - "4 1 0.0 0 \n", - "\n", - " mph chosen av_no_trip av_s_car \\\n", - "0 [-0.6226942218711282] 8 0 0 \n", - "1 [-0.32572174462565845] 8 0 1 \n", - "2 [1.2722796444295434] 1 0 1 \n", - "3 [1.785796573896156] 2 0 0 \n", - "4 [-0.12709085963426053, -0.8967034453081592] 1 0 1 \n", - "\n", - " av_p_micro av_walk av_s_micro ... wind_gusts_10m (mp/h) cost_p_micro \\\n", - "0 0 1 0 ... -1.118155 0.0 \n", - "1 1 0 0 ... -0.598757 0.0 \n", - "2 1 1 0 ... 0.046555 0.0 \n", - "3 1 0 0 ... 1.305702 0.0 \n", - "4 1 1 0 ... -0.457104 0.0 \n", - "\n", - " cost_no_trip cost_s_car cost_transit cost_car cost_s_micro \\\n", - "0 0.0 -0.614415 -0.286713 -0.550431 -0.431234 \n", - "1 0.0 0.102862 -0.529027 0.167956 -0.431234 \n", - "2 0.0 -0.409306 -0.304397 -0.550431 -0.431234 \n", - "3 0.0 -0.614415 -0.529027 -0.550431 -0.431234 \n", - "4 0.0 -0.177887 -0.529027 -0.113227 -0.431234 \n", - "\n", - " cost_ridehail cost_walk cost_unknown \n", - "0 -0.592465 0.0 0.0 \n", - "1 0.128516 0.0 0.0 \n", - "2 -0.386296 0.0 0.0 \n", - "3 -0.592465 0.0 0.0 \n", - "4 -0.153682 0.0 0.0 \n", - "\n", - "[5 rows x 46 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(f\"Final features for training:\\n{list(train_df.columns)}\")\n", - "display(train_df.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "97a8d6b2", - "metadata": {}, - "outputs": [], - "source": [ - "class CustomDataset(Dataset):\n", - " def __init__(self, df: pd.DataFrame):\n", - " self.seq_df = df[['modes', 'durations', 'distances', 'mph']]\n", - " self.chosen = df['chosen']\n", - " self.features = df.drop(columns=[\n", - " 'modes', 'durations', 'distances', 'mph', 'chosen', 'distance_miles'\n", - " ])\n", - " \n", - " # Start from 1 so that 0 becomes padding.\n", - " self.modes_dict = {\n", - " m: ix+1 for (ix, m) in enumerate(['walking', 'car', 'train', 'bus', 'bicycling', 'no_sensed'])\n", - " }\n", - " \n", - " def __len__(self):\n", - " return self.seq_df.shape[0]\n", - " \n", - " def __getitem__(self, ix):\n", - " \n", - " t_modes = list()\n", - " t_metrics = list()\n", - " \n", - " chosen = self.chosen[ix]\n", - " seq = self.seq_df.loc[ix, :]\n", - " \n", - " # OHE the target.\n", - " y = F.one_hot(torch.tensor(chosen), num_classes=9)\n", - " \n", - " # Parse the sequence.\n", - " for seq_ix in range(len(seq['modes'])):\n", - " mode_ix = self.modes_dict[seq['modes'][seq_ix]]\n", - " \n", - " t_modes.append(torch.tensor(mode_ix))\n", - " \n", - " # distances, durations.\n", - " seq_vector = torch.cat(\n", - " [\n", - " torch.tensor([seq['durations'][seq_ix]]), \n", - " torch.tensor([seq['distances'][seq_ix]]), \n", - " torch.tensor([seq['mph'][seq_ix]])\n", - " ], dim=-1\n", - " )\n", - " \n", - " t_metrics.append(seq_vector)\n", - " \n", - " # Flat feature vector.\n", - " features = torch.tensor(self.features.loc[ix, :].values)\n", - " \n", - " return [torch.stack(t_modes).long(), torch.stack(t_metrics), features], y" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "02b78758", - "metadata": {}, - "outputs": [], - "source": [ - "train_dset = CustomDataset(train_df)\n", - "test_dset = CustomDataset(test_df)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "627b6fa4", - "metadata": {}, - "outputs": [], - "source": [ - "def collate(batch):\n", - " X, y = zip(*batch)\n", - " \n", - " seq_modes = [x[0] for x in X]\n", - " seq_metrics = [x[1] for x in X]\n", - " features = [x[-1] for x in X]\n", - "\n", - " padded_seq = pad_sequence([s for s in seq_modes], batch_first=True)\n", - " padded_metrics = pad_sequence([m for m in seq_metrics], batch_first=True)\n", - " lengths = [len(seq) for seq in seq_modes]\n", - " stacked_features = torch.stack(features)\n", - "\n", - " return (padded_seq, padded_metrics, stacked_features), torch.stack(y), lengths" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "5ca34681", - "metadata": {}, - "outputs": [], - "source": [ - "train_loader = DataLoader(train_dset, batch_size=16, collate_fn=collate, shuffle=True, drop_last=False)\n", - "test_loader = DataLoader(test_dset, batch_size=8, collate_fn=collate, shuffle=False, drop_last=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "31ca5ab1", - "metadata": {}, - "outputs": [], - "source": [ - "(modes, metrics, features), sY1, lX = next(iter(train_loader))" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "9eb5a93a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(torch.Size([16, 6, 3]), torch.Size([16, 6]))" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "metrics.size(), modes.size()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "a0abf380", - "metadata": {}, - "outputs": [], - "source": [ - "# Set to 0 for no dropout.\n", - "DROPOUT = 0." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "48871ea4", - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "\n", - "def new_gelu(x):\n", - " \"\"\"\n", - " Taken from OpenAI GPT-2 implementation.\n", - " \"\"\"\n", - " return 0.5 * x * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))\n", - "\n", - "\n", - "class DilatedBlock(nn.Module):\n", - " def __init__(self, n_c):\n", - " super(DilatedBlock, self).__init__()\n", - " \n", - " self.block = nn.Sequential(\n", - " nn.Linear(n_c, 4*n_c, bias=False),\n", - " nn.ELU(),\n", - " nn.Linear(4*n_c, n_c, bias=False),\n", - " nn.Dropout(DROPOUT)\n", - " )\n", - " \n", - " def forward(self, x):\n", - " return self.block(x)\n", - "\n", - " \n", - "class SelfAttention(nn.Module):\n", - " def __init__(self, n_features, head_size):\n", - " super(SelfAttention, self).__init__()\n", - " # in: (B, F, 64)\n", - " self.k = nn.Linear(n_features, head_size, bias=False)\n", - " self.q = nn.Linear(n_features, head_size, bias=False)\n", - " self.v = nn.Linear(n_features, head_size, bias=False)\n", - " self.dpt = nn.Dropout(DROPOUT)\n", - " self.sqrt_d = torch.sqrt(torch.tensor(head_size))\n", - " \n", - " def forward(self, x):\n", - " k = self.k(x)\n", - " q = self.q(x)\n", - " v = self.v(x)\n", - " \n", - " # Q.K.t\n", - " dot = torch.bmm(q, k.permute(0, 2, 1))\n", - " \n", - " # normalize dot product.\n", - " dot /= self.sqrt_d\n", - " \n", - " # softmax over -1 dim.\n", - " softmax = self.dpt(torch.softmax(dot, dim=-1))\n", - " \n", - " # dot with values. (B, F, F) * (B, F, x) = (B, F, x)\n", - " return torch.bmm(softmax, v)\n", - " \n", - "\n", - "class MultiHeadAttention(nn.Module):\n", - " def __init__(self, n_heads, n_dim):\n", - " super(MultiHeadAttention, self).__init__()\n", - " \n", - " # 64 dims, 4 heads => 16 dims per head.\n", - " head_size = n_dim//n_heads\n", - " self.heads = nn.ModuleList([SelfAttention(n_dim, head_size) for _ in range(n_heads)])\n", - " self.proj = nn.Linear(n_dim, n_dim, bias=False)\n", - " \n", - " def forward(self, x):\n", - " # x is (B, seq, n_dim)\n", - " cat = torch.cat([head(x) for head in self.heads], dim=-1)\n", - " return self.proj(cat)\n", - "\n", - "\n", - "class Block(nn.Module):\n", - " def __init__(self, n_c):\n", - " super(Block, self).__init__()\n", - " \n", - " self.sa = MultiHeadAttention(n_heads=4, n_dim=n_c)\n", - " self.dilated = DilatedBlock(n_c)\n", - " self.ln1 = nn.LayerNorm(n_c)\n", - " self.ln2 = nn.LayerNorm(n_c)\n", - " \n", - " \n", - " def forward(self, x):\n", - " x = x + self.sa(self.ln1(x))\n", - " x = x + self.dilated(self.ln2(x))\n", - " return x\n", - " \n", - "\n", - "class LSTMLayer(nn.Module):\n", - " def __init__(\n", - " self, input_size: int, hidden_size: int, \n", - " output_size: int, n_lstm_layers: int = 1\n", - " ):\n", - " super(LSTMLayer, self).__init__()\n", - " \n", - " self.hidden_size = hidden_size\n", - " self.embedding = nn.Embedding(7, 4, padding_idx=0)\n", - " self.dpt = nn.Dropout(DROPOUT)\n", - " \n", - " self.lstm = nn.LSTM(\n", - " input_size=input_size + 4,\n", - " hidden_size=hidden_size,\n", - " bias=False,\n", - " bidirectional=True,\n", - " batch_first=True,\n", - " num_layers=n_lstm_layers\n", - " )\n", - " \n", - " def forward(self, modes, x, lengths):\n", - " mode_emb = self.embedding(modes)\n", - " x = torch.cat([x, mode_emb], dim=-1)\n", - " \n", - " packed = pack_padded_sequence(x, lengths, batch_first=True, enforce_sorted=False)\n", - " out, _ = self.lstm(packed)\n", - " unpacked, _ = pad_packed_sequence(out, batch_first=True)\n", - " \n", - " return self.dpt(unpacked)\n", - "\n", - "\n", - "class Model(nn.Module):\n", - " def __init__(\n", - " self, input_size: int, hidden_size: int, output_size: int, \n", - " n_features: int, n_lstm_layers: int = 1, **kwargs\n", - " ):\n", - " super(Model, self).__init__()\n", - " \n", - " block1_ip_dim = hidden_size*2\n", - " block2_ip_dim = (hidden_size*2) + n_features\n", - " \n", - " self.lstm = LSTMLayer(\n", - " input_size, hidden_size, \n", - " output_size, n_lstm_layers\n", - " )\n", - " \n", - " self.block_l1 = nn.ModuleList([Block(block1_ip_dim) for _ in range(kwargs['l1_blocks'])])\n", - " self.block_l2 = nn.ModuleList([Block(block2_ip_dim) for _ in range(kwargs['l2_blocks'])])\n", - " self.final_proj = nn.Linear(block2_ip_dim, output_size, bias=True)\n", - " \n", - " def forward(self, modes, x, features, lengths):\n", - " \n", - " b = x.size(0)\n", - " \n", - " # Out = (B, seq, hidden*2)\n", - " lstm_out = self.lstm(modes, x, lengths)\n", - " \n", - " # Pass the raw output through the blocks.\n", - " for module in self.block_l1:\n", - " lstm_out = module(lstm_out)\n", - " \n", - " features_rshp = features.unsqueeze(1).expand(b, lstm_out.size(1), -1)\n", - " \n", - " # Out = (B, seq, n+40)\n", - " cat = torch.cat([lstm_out, features_rshp], dim=-1)\n", - " \n", - " for module in self.block_l2:\n", - " cat = module(cat)\n", - " \n", - " # (8, 3, 104) -> (B, 104)\n", - " # flattened = cat.view(b, -1)\n", - " \n", - " # proj = self.runtime_ffw(flattened.size(-1), 64)(flattened)\n", - " proj = cat.mean(dim=1)\n", - " \n", - " return self.final_proj(proj)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "70b4d4ea", - "metadata": {}, - "outputs": [], - "source": [ - "import torch.nn.init as init\n", - "\n", - "def init_weights(module):\n", - " if isinstance(module, nn.Embedding):\n", - " module.weight.data.normal_(mean=0.0, std=1.0)\n", - " if module.padding_idx is not None:\n", - " module.weight.data[module.padding_idx].zero_()\n", - " elif isinstance(module, nn.LayerNorm):\n", - " module.bias.data.zero_()\n", - " module.weight.data.fill_(1.0)\n", - " elif isinstance(module, nn.BatchNorm1d):\n", - " init.normal_(m.weight.data, mean=1, std=0.02)\n", - " init.constant_(m.bias.data, 0)\n", - " elif isinstance(module, nn.Linear):\n", - " init.xavier_normal_(module.weight.data)\n", - " if module.bias is not None:\n", - " init.normal_(module.bias.data)\n", - " elif isinstance(module, nn.LSTM):\n", - " for param in module.parameters():\n", - " if len(param.shape) >= 2:\n", - " init.orthogonal_(param.data)\n", - " else:\n", - " init.normal_(param.data)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "282ecd1a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model(\n", - " (lstm): LSTMLayer(\n", - " (embedding): Embedding(7, 4, padding_idx=0)\n", - " (dpt): Dropout(p=0.0, inplace=False)\n", - " (lstm): LSTM(7, 16, bias=False, batch_first=True, bidirectional=True)\n", - " )\n", - " (block_l1): ModuleList(\n", - 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" (dpt): Dropout(p=0.0, inplace=False)\n", - " )\n", - " (2): SelfAttention(\n", - " (k): Linear(in_features=72, out_features=18, bias=False)\n", - " (q): Linear(in_features=72, out_features=18, bias=False)\n", - " (v): Linear(in_features=72, out_features=18, bias=False)\n", - " (dpt): Dropout(p=0.0, inplace=False)\n", - " )\n", - " (3): SelfAttention(\n", - " (k): Linear(in_features=72, out_features=18, bias=False)\n", - " (q): Linear(in_features=72, out_features=18, bias=False)\n", - " (v): Linear(in_features=72, out_features=18, bias=False)\n", - " (dpt): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " (proj): Linear(in_features=72, out_features=72, bias=False)\n", - " )\n", - " (dilated): DilatedBlock(\n", - " (block): Sequential(\n", - " (0): Linear(in_features=72, out_features=288, bias=False)\n", - " (1): ELU(alpha=1.0)\n", - " (2): Linear(in_features=288, out_features=72, bias=False)\n", - " (3): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " (ln1): LayerNorm((72,), eps=1e-05, elementwise_affine=True)\n", - " (ln2): LayerNorm((72,), eps=1e-05, elementwise_affine=True)\n", - " )\n", - " (3): Block(\n", - " (sa): MultiHeadAttention(\n", - " (heads): ModuleList(\n", - " (0): SelfAttention(\n", - " (k): Linear(in_features=72, out_features=18, bias=False)\n", - " (q): Linear(in_features=72, out_features=18, bias=False)\n", - " (v): Linear(in_features=72, out_features=18, bias=False)\n", - " (dpt): Dropout(p=0.0, inplace=False)\n", - " )\n", - " (1): SelfAttention(\n", - " (k): Linear(in_features=72, out_features=18, bias=False)\n", - " (q): Linear(in_features=72, out_features=18, bias=False)\n", - " (v): Linear(in_features=72, out_features=18, bias=False)\n", - " (dpt): Dropout(p=0.0, inplace=False)\n", - " )\n", - " (2): SelfAttention(\n", - " (k): Linear(in_features=72, out_features=18, bias=False)\n", - " (q): Linear(in_features=72, out_features=18, bias=False)\n", - " (v): Linear(in_features=72, out_features=18, bias=False)\n", - " (dpt): Dropout(p=0.0, inplace=False)\n", - " )\n", - " (3): SelfAttention(\n", - " (k): Linear(in_features=72, out_features=18, bias=False)\n", - " (q): Linear(in_features=72, out_features=18, bias=False)\n", - " (v): Linear(in_features=72, out_features=18, bias=False)\n", - " (dpt): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " (proj): Linear(in_features=72, out_features=72, bias=False)\n", - " )\n", - " (dilated): DilatedBlock(\n", - " (block): Sequential(\n", - " (0): Linear(in_features=72, out_features=288, bias=False)\n", - " (1): ELU(alpha=1.0)\n", - " (2): Linear(in_features=288, out_features=72, bias=False)\n", - " (3): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " (ln1): LayerNorm((72,), eps=1e-05, elementwise_affine=True)\n", - " (ln2): LayerNorm((72,), eps=1e-05, elementwise_affine=True)\n", - " )\n", - " (4): Block(\n", - " (sa): MultiHeadAttention(\n", - " (heads): ModuleList(\n", - " (0): SelfAttention(\n", - " (k): Linear(in_features=72, out_features=18, bias=False)\n", - " (q): Linear(in_features=72, out_features=18, bias=False)\n", - " (v): Linear(in_features=72, out_features=18, bias=False)\n", - " (dpt): Dropout(p=0.0, inplace=False)\n", - " )\n", - " (1): SelfAttention(\n", - " (k): Linear(in_features=72, out_features=18, bias=False)\n", - " (q): Linear(in_features=72, out_features=18, bias=False)\n", - " (v): Linear(in_features=72, out_features=18, bias=False)\n", - " (dpt): Dropout(p=0.0, inplace=False)\n", - " )\n", - " (2): SelfAttention(\n", - " (k): Linear(in_features=72, out_features=18, bias=False)\n", - " (q): Linear(in_features=72, out_features=18, bias=False)\n", - " (v): Linear(in_features=72, out_features=18, bias=False)\n", - " (dpt): Dropout(p=0.0, inplace=False)\n", - " )\n", - " (3): SelfAttention(\n", - " (k): Linear(in_features=72, out_features=18, bias=False)\n", - " (q): Linear(in_features=72, out_features=18, bias=False)\n", - " (v): Linear(in_features=72, out_features=18, bias=False)\n", - " (dpt): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " (proj): Linear(in_features=72, out_features=72, bias=False)\n", - " )\n", - " (dilated): DilatedBlock(\n", - " (block): Sequential(\n", - " (0): Linear(in_features=72, out_features=288, bias=False)\n", - " (1): ELU(alpha=1.0)\n", - " (2): Linear(in_features=288, out_features=72, bias=False)\n", - " (3): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " (ln1): LayerNorm((72,), eps=1e-05, elementwise_affine=True)\n", - " (ln2): LayerNorm((72,), eps=1e-05, elementwise_affine=True)\n", - " )\n", - " (5): Block(\n", - " (sa): MultiHeadAttention(\n", - " (heads): ModuleList(\n", - " (0): SelfAttention(\n", - " (k): Linear(in_features=72, out_features=18, bias=False)\n", - " (q): Linear(in_features=72, out_features=18, bias=False)\n", - " (v): Linear(in_features=72, out_features=18, bias=False)\n", - " (dpt): Dropout(p=0.0, inplace=False)\n", - " )\n", - " (1): SelfAttention(\n", - " (k): Linear(in_features=72, out_features=18, bias=False)\n", - " (q): Linear(in_features=72, out_features=18, bias=False)\n", - " (v): Linear(in_features=72, out_features=18, bias=False)\n", - " (dpt): Dropout(p=0.0, inplace=False)\n", - " )\n", - " (2): SelfAttention(\n", - " (k): Linear(in_features=72, out_features=18, bias=False)\n", - " (q): Linear(in_features=72, out_features=18, bias=False)\n", - " (v): Linear(in_features=72, out_features=18, bias=False)\n", - " (dpt): Dropout(p=0.0, inplace=False)\n", - " )\n", - " (3): SelfAttention(\n", - " (k): Linear(in_features=72, out_features=18, bias=False)\n", - " (q): Linear(in_features=72, out_features=18, bias=False)\n", - " (v): Linear(in_features=72, out_features=18, bias=False)\n", - " (dpt): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " (proj): Linear(in_features=72, out_features=72, bias=False)\n", - " )\n", - " (dilated): DilatedBlock(\n", - " (block): Sequential(\n", - " (0): Linear(in_features=72, out_features=288, bias=False)\n", - " (1): ELU(alpha=1.0)\n", - " (2): Linear(in_features=288, out_features=72, bias=False)\n", - " (3): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " (ln1): LayerNorm((72,), eps=1e-05, elementwise_affine=True)\n", - " (ln2): LayerNorm((72,), eps=1e-05, elementwise_affine=True)\n", - " )\n", - " )\n", - " (final_proj): Linear(in_features=72, out_features=9, bias=True)\n", - ")\n" - ] - } - ], - "source": [ - "model = Model(\n", - " n_lstm_layers=1,\n", - " input_size=3,\n", - " hidden_size=16, \n", - " output_size=9,\n", - " n_features=40,\n", - " l1_blocks=6,\n", - " l2_blocks=6\n", - ")\n", - "\n", - "model = model.apply(init_weights)\n", - "\n", - "print(model)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "20fec22b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "453101\n" - ] - } - ], - "source": [ - "print(sum(p.numel() for p in model.parameters()))" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "1ca4b65a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 1.31637209 0.45785206 0.97595891 2.59617829 0.35062587 72.2209596\n", - " 4.71122642 1.08826104 1.53996715]\n" - ] - } - ], - "source": [ - "weights = train_df.shape[0]/(np.bincount(train_df.chosen.values) * len(np.unique(train_df.chosen)))\n", - "\n", - "print(weights)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "e7a2017b", - "metadata": {}, - "outputs": [], - "source": [ - "INIT_LR = 7e-4\n", - "optimizer = optim.Adam(model.parameters(), lr=INIT_LR)\n", - "criterion = nn.CrossEntropyLoss(weight=torch.Tensor(weights))" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "c6f1f235", - "metadata": {}, - "outputs": [], - "source": [ - "# sX, sY, sL = next(iter(train_loader))\n", - "\n", - "# print(sX.size())" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "e53e4fd1", - "metadata": {}, - "outputs": [], - "source": [ - "def train(epoch, model, loader, opt, criterion):\n", - " \n", - " print(\"\\tBeginning training.\")\n", - " \n", - " model.train()\n", - " \n", - " print_every = len(loader)//5\n", - " \n", - " losses = []\n", - " \n", - " for ix, (X, y, lengths) in enumerate(loader):\n", - " \n", - " modes, metrics, features = X\n", - " y = y.float()\n", - "\n", - " opt.zero_grad()\n", - "\n", - " y_pred = model(modes, metrics.float(), features.float(), lengths)\n", - "\n", - " loss = criterion(y_pred.view(-1, 9), y.view(-1, 9))\n", - "\n", - " losses.append(loss.item())\n", - " \n", - " if ix and ix % print_every == 0:\n", - " print(f\"\\t-> Average loss: {np.nanmean(losses)}\")\n", - "\n", - " loss.backward()\n", - "\n", - " optimizer.step()\n", - "\n", - " print(50*'-')" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "3a33fefa", - "metadata": {}, - "outputs": [], - "source": [ - "def evaluate(epoch, model, loader, criterion):\n", - " \n", - " print(\"\\tBeginning evaluation.\")\n", - " \n", - " model.eval()\n", - " \n", - " print_every = len(loader)//5\n", - " \n", - " losses = []\n", - " \n", - " for ix, (X, y, lengths) in enumerate(loader):\n", - " \n", - " modes, metrics, features = X\n", - "\n", - " y_pred = model(modes, metrics.float(), features.float(), lengths)\n", - " y = y.float()\n", - " \n", - " loss = criterion(y_pred.view(-1, 9), y.view(-1, 9))\n", - "\n", - " losses.append(loss.item())\n", - " \n", - " if ix and ix % print_every == 0:\n", - " print(f\"\\t -> Average loss: {np.nanmean(losses)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "650a5240", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import f1_score\n", - "\n", - "\n", - "def evaluate_f1(model, tr_loader, te_loader):\n", - " tr_preds, te_preds = np.array([]), np.array([])\n", - " tr_gt, te_gt = np.array([]), np.array([])\n", - " \n", - " model.eval()\n", - " print(\"\\tEvaluating F1...\")\n", - " \n", - " with torch.no_grad():\n", - " for ix, (X, y, lengths) in enumerate(tr_loader):\n", - " \n", - " modes, metrics, features = X\n", - "\n", - " y_pred = model(modes, metrics.float(), features.float(), lengths).view(-1, 9)\n", - " y = y.float().view(-1, 9)\n", - "\n", - " preds = torch.argmax(F.softmax(y_pred, dim=-1), dim=-1).numpy().ravel()\n", - " true = torch.argmax(y.long(), dim=-1).numpy().ravel()\n", - "\n", - " tr_preds = np.append(tr_preds, preds)\n", - " tr_gt = np.append(tr_gt, true)\n", - " \n", - " tr_f1 = f1_score(y_true=tr_gt, y_pred=tr_preds, average='weighted')\n", - " print(f\"\\t -> Train F1: {tr_f1}\")\n", - " \n", - " for ix, (X, y, lengths) in enumerate(te_loader):\n", - " \n", - " modes, metrics, features = X\n", - "\n", - " y_pred = model(modes, metrics.float(), features.float(), lengths).view(-1, 9)\n", - " y = y.float().view(-1, 9)\n", - " \n", - " preds = torch.argmax(F.softmax(y_pred, dim=-1), dim=-1).numpy().ravel()\n", - " true = torch.argmax(y.long(), dim=-1).numpy().ravel()\n", - "\n", - " te_preds = np.append(te_preds, preds)\n", - " te_gt = np.append(te_gt, true)\n", - " \n", - " te_f1 = f1_score(y_true=te_gt, y_pred=te_preds, average='weighted')\n", - " print(f\"\\t -> Test F1: {te_f1}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "7191e78b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 - 5 :: 0.00070\n", - "5 - 10 :: 0.00067\n", - "10 - 15 :: 0.00063\n", - "15 - 20 :: 0.00060\n", - "20 - 25 :: 0.00057\n", - "25 - 30 :: 0.00054\n" - ] - } - ], - "source": [ - "# Other training hyperparameters.\n", - "num_epochs = 30\n", - "num_decays = 6\n", - "decay_at = num_epochs // num_decays\n", - "decay = 0.95\n", - "\n", - "# Just checking what LRs should be after decaying.\n", - "for power in range(num_decays):\n", - " print(f\"{decay_at * power} - {decay_at * (power + 1)} :: {INIT_LR * decay**power:.5f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fc4b72de", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1:\n", - "\tBeginning training.\n", - "\t-> Average loss: 3.4292205003553264\n", - "\t-> Average loss: 3.0203611136815667\n", - "\t-> Average loss: 2.9269992429190226\n", - "\t-> Average loss: 2.7440315224998524\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.9845308899546468\n", - "\t -> Average loss: 2.0912061837169675\n", - "\t -> Average loss: 2.0278757847845554\n", - "\t -> Average loss: 2.079901454872481\n", - "\t -> Average loss: 2.119112247215273\n", - "\tEvaluating F1...\n", - "\t -> Train F1: 0.17357571210719355\n", - "\t -> Test F1: 0.16929205554584628\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 2:\n", - "\tBeginning training.\n", - "\t-> Average loss: 2.2447872489024805\n", - "\t-> Average loss: 2.179984362536923\n", - "\t-> Average loss: 2.184303961117914\n", - "\t-> Average loss: 2.1579395363231675\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.8604771541483576\n", - "\t -> Average loss: 1.96936972224629\n", - "\t -> Average loss: 1.9072258116594\n", - "\t -> Average loss: 1.9617513231750632\n", - "\t -> Average loss: 1.9915817378616547\n", - "\tEvaluating F1...\n", - "\t -> Train F1: 0.2375672788266048\n", - "\t -> Test F1: 0.23161577435832603\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 3:\n", - "\tBeginning training.\n", - "\t-> Average loss: 2.009245126523785\n", - "\t-> Average loss: 1.9820919889074202\n", - "\t-> Average loss: 2.020936541432881\n", - "\t-> Average loss: 1.9902592548037359\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.948894374363915\n", - "\t -> Average loss: 2.0106319545865894\n", - "\t -> Average loss: 1.9851899344418475\n", - "\t -> Average loss: 2.007645890627648\n", - "\t -> Average loss: 1.9973548486545132\n", - "\tEvaluating F1...\n", - "\t -> Train F1: 0.178078853483658\n", - "\t -> Test F1: 0.17889559699341365\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 4:\n", - "\tBeginning training.\n", - "\t-> Average loss: 1.9483501227707836\n", - "\t-> Average loss: 1.954028883528493\n", - "\t-> Average loss: 1.9755866212591542\n", - "\t-> Average loss: 1.923766012835111\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.8338173299861353\n", - "\t -> Average loss: 1.917397732834716\n", - "\t -> Average loss: 1.8882776866875477\n", - "\t -> Average loss: 1.9411674299967547\n", - "\t -> Average loss: 1.9390272717393124\n", - "\tEvaluating F1...\n", - "\t -> Train F1: 0.2937676034584698\n", - "\t -> Test F1: 0.2797533840531726\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 5:\n", - "\tBeginning training.\n", - "\t-> Average loss: 1.912619205160514\n", - "\t-> Average loss: 1.9341210979918848\n", - "\t-> Average loss: 1.8943603618724927\n", - "\t-> Average loss: 1.8774088714056938\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.690006344844509\n", - "\t -> Average loss: 1.7453476386470395\n", - "\t -> Average loss: 1.7205723014229268\n", - "\t -> Average loss: 1.749604014185778\n", - "\t -> Average loss: 1.742094624483038\n", - "\tEvaluating F1...\n", - "\t -> Train F1: 0.19402127381450823\n", - "\t -> Test F1: 0.1897796133960193\n", - "Learning rate is now: 0.00067\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 6:\n", - "\tBeginning training.\n", - "\t-> Average loss: 1.6016220477040253\n", - "\t-> Average loss: 1.7033424217912552\n", - "\t-> Average loss: 1.6674059770536112\n", - "\t-> Average loss: 1.6761879756166485\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.5424185771515915\n", - "\t -> Average loss: 1.579921578193878\n", - "\t -> Average loss: 1.5411476174842065\n", - "\t -> Average loss: 1.5840133090508075\n", - "\t -> Average loss: 1.5650895305837256\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 7:\n", - "\tBeginning training.\n", - "\t-> Average loss: 1.6066383687810526\n", - "\t-> Average loss: 1.5486349471376795\n", - "\t-> Average loss: 1.5466778286405511\n", - "\t-> Average loss: 1.5999376366539295\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.6492426906551063\n", - "\t -> Average loss: 1.664988368999708\n", - "\t -> Average loss: 1.6178990316980366\n", - "\t -> Average loss: 1.6296465851319595\n", - "\t -> Average loss: 1.640547810166173\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 8:\n", - "\tBeginning training.\n", - "\t-> Average loss: 1.4707723498011434\n", - "\t-> Average loss: 1.5709665634680428\n", - "\t-> Average loss: 1.554789892461182\n", - "\t-> Average loss: 1.5148731287814903\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.3899180381324705\n", - "\t -> Average loss: 1.4627432638531799\n", - "\t -> Average loss: 1.4123834414101804\n", - "\t -> Average loss: 1.4201137627063722\n", - "\t -> Average loss: 1.4303626401260212\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 9:\n", - "\tBeginning training.\n", - "\t-> Average loss: 1.5543432552471508\n", - "\t-> Average loss: 1.4533692163074874\n", - "\t-> Average loss: 1.3998552637881245\n", - "\t-> Average loss: 1.4387366909199102\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.3969969056005584\n", - "\t -> Average loss: 1.4605997060562348\n", - "\t -> Average loss: 1.4105439760458114\n", - "\t -> Average loss: 1.4442899707441483\n", - "\t -> Average loss: 1.4501932569907734\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 10:\n", - "\tBeginning training.\n", - "\t-> Average loss: 1.4883403652789873\n", - "\t-> Average loss: 1.4671129221103143\n", - "\t-> Average loss: 1.4545180360540984\n", - "\t-> Average loss: 1.4436850206850294\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.508042848642978\n", - "\t -> Average loss: 1.502184015554148\n", - "\t -> Average loss: 1.44931043248012\n", - "\t -> Average loss: 1.4784588873428093\n", - "\t -> Average loss: 1.4841084074967386\n", - "\tEvaluating F1...\n", - "\t -> Train F1: 0.48333374111246125\n", - "\t -> Test F1: 0.4722792652308931\n", - "Learning rate is now: 0.00063\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 11:\n", - "\tBeginning training.\n", - "\t-> Average loss: 1.4074798150185766\n", - "\t-> Average loss: 1.3590504143056463\n", - "\t-> Average loss: 1.3730208680991913\n", - "\t-> Average loss: 1.4093443684042937\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.65324043395133\n", - "\t -> Average loss: 1.6848366409748585\n", - "\t -> Average loss: 1.639395641301995\n", - "\t -> Average loss: 1.626926458699458\n", - "\t -> Average loss: 1.6171495253501786\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 12:\n", - "\tBeginning training.\n", - "\t-> Average loss: 1.31716444697127\n", - "\t-> Average loss: 1.2871220497340277\n", - "\t-> Average loss: 1.2746168654707137\n", - "\t-> Average loss: 1.2706415657187125\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.2732290915270756\n", - "\t -> Average loss: 1.3285720576564748\n", - "\t -> Average loss: 1.2918836618982144\n", - "\t -> Average loss: 1.2952009379613474\n", - "\t -> Average loss: 1.2965014228495078\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 13:\n", - "\tBeginning training.\n", - "\t-> Average loss: 1.2921466177235768\n", - "\t-> Average loss: 1.2243878973021864\n", - "\t-> Average loss: 1.2450620088472837\n", - "\t-> Average loss: 1.251400762683901\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.206730307313983\n", - "\t -> Average loss: 1.2312866071721056\n", - "\t -> Average loss: 1.208183620277959\n", - "\t -> Average loss: 1.2216688226973618\n", - "\t -> Average loss: 1.2299620342197903\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 14:\n", - "\tBeginning training.\n", - "\t-> Average loss: 1.1780336717736788\n", - "\t-> Average loss: 1.2387724560844906\n", - "\t-> Average loss: 1.2171782795114152\n", - "\t-> Average loss: 1.2286130927401546\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.1736671675683399\n", - "\t -> Average loss: 1.214920432480065\n", - "\t -> Average loss: 1.1988129002083816\n", - "\t -> Average loss: 1.1989111086031705\n", - "\t -> Average loss: 1.2194756852068667\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 15:\n", - "\tBeginning training.\n", - "\t-> Average loss: 1.2168475217576133\n", - "\t-> Average loss: 1.1433030896716647\n", - "\t-> Average loss: 1.1906079676311259\n", - "\t-> Average loss: 1.2125332475604398\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.4089278240776595\n", - "\t -> Average loss: 1.445234133480312\n", - "\t -> Average loss: 1.4113176648240926\n", - "\t -> Average loss: 1.4362174157624148\n", - "\t -> Average loss: 1.435766333294876\n", - "\tEvaluating F1...\n", - "\t -> Train F1: 0.5093168030147552\n", - "\t -> Test F1: 0.5026682286547539\n", - "Learning rate is now: 0.00060\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 16:\n", - "\tBeginning training.\n", - "\t-> Average loss: 1.1724162185824782\n", - "\t-> Average loss: 1.1224480470021565\n", - "\t-> Average loss: 1.1285216524044377\n", - "\t-> Average loss: 1.1660368838476076\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.210316137894572\n", - "\t -> Average loss: 1.246378187157891\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\t -> Average loss: 1.2420701925267479\n", - "\t -> Average loss: 1.2695545183091919\n", - "\t -> Average loss: 1.273952293582052\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 17:\n", - "\tBeginning training.\n", - "\t-> Average loss: 1.0339172488734043\n", - "\t-> Average loss: 1.0751683378473684\n", - "\t-> Average loss: 1.1208586298338965\n", - "\t-> Average loss: 1.1269230102115642\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.248624034475681\n", - "\t -> Average loss: 1.2845101706423125\n", - "\t -> Average loss: 1.2307171229161877\n", - "\t -> Average loss: 1.2223140466163174\n", - "\t -> Average loss: 1.2198367260838676\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 18:\n", - "\tBeginning training.\n", - "\t-> Average loss: 1.0498548433933843\n", - "\t-> Average loss: 1.0665208559345316\n", - "\t-> Average loss: 1.0762758616007386\n", - "\t-> Average loss: 1.099983853102772\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.3901189185221101\n", - "\t -> Average loss: 1.4441073030024976\n", - "\t -> Average loss: 1.373450560356254\n", - "\t -> Average loss: 1.3496260701087575\n", - "\t -> Average loss: 1.3835973867276747\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 19:\n", - "\tBeginning training.\n", - "\t-> Average loss: 1.146373841807496\n", - "\t-> Average loss: 1.085291807392025\n", - "\t-> Average loss: 1.079380213429688\n", - "\t-> Average loss: 1.1145779568127758\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.263674235169115\n", - "\t -> Average loss: 1.2977842691269788\n", - "\t -> Average loss: 1.243837918641407\n", - "\t -> Average loss: 1.2629515259071111\n", - "\t -> Average loss: 1.2418897094670163\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 20:\n", - "\tBeginning training.\n", - "\t-> Average loss: 0.9654322663665483\n", - "\t-> Average loss: 1.0184031338499278\n", - "\t-> Average loss: 1.0715286856706412\n", - "\t-> Average loss: 1.0910986138052643\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.1675123429165206\n", - "\t -> Average loss: 1.2705587090520591\n", - "\t -> Average loss: 1.200293446571302\n", - "\t -> Average loss: 1.2205115221362584\n", - "\t -> Average loss: 1.2177007432530602\n", - "\tEvaluating F1...\n", - "\t -> Train F1: 0.6190054946992625\n", - "\t -> Test F1: 0.6097765604266691\n", - "Learning rate is now: 0.00057\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 21:\n", - "\tBeginning training.\n", - "\t-> Average loss: 1.0445893098855152\n", - "\t-> Average loss: 1.1112236296230893\n", - "\t-> Average loss: 1.0962648112726123\n", - "\t-> Average loss: 1.089011266353621\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.0674441020165741\n", - "\t -> Average loss: 1.1406326175361245\n", - "\t -> Average loss: 1.1207181137305364\n", - "\t -> Average loss: 1.1347275835346557\n", - "\t -> Average loss: 1.1342128750775322\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 22:\n", - "\tBeginning training.\n", - "\t-> Average loss: 1.0676555513877775\n", - "\t-> Average loss: 1.0773798781600055\n", - "\t-> Average loss: 1.0710756811421827\n", - "\t-> Average loss: 1.0416493509739522\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.1352787101068975\n", - "\t -> Average loss: 1.2286776423037469\n", - "\t -> Average loss: 1.1571767105262225\n", - "\t -> Average loss: 1.1650525711899125\n", - "\t -> Average loss: 1.1702847076456864\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 23:\n", - "\tBeginning training.\n", - "\t-> Average loss: 0.9855309208053783\n", - "\t-> Average loss: 1.0090662437060927\n", - "\t-> Average loss: 1.0291784378419606\n", - "\t-> Average loss: 1.0479850026693265\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.190445274031362\n", - "\t -> Average loss: 1.2569891696834898\n", - "\t -> Average loss: 1.1954538017761573\n", - "\t -> Average loss: 1.2319692372545819\n", - "\t -> Average loss: 1.2384895061925971\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 24:\n", - "\tBeginning training.\n", - "\t-> Average loss: 1.035038524541442\n", - "\t-> Average loss: 1.007654058233497\n", - "\t-> Average loss: 1.0265389064957022\n", - "\t-> Average loss: 1.0250597899920955\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.27851615721287\n", - "\t -> Average loss: 1.3390878153847647\n", - "\t -> Average loss: 1.315815781799159\n", - "\t -> Average loss: 1.2937761211286816\n", - "\t -> Average loss: 1.2826113693858314\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 25:\n", - "\tBeginning training.\n", - "\t-> Average loss: 0.993596878173298\n", - "\t-> Average loss: 1.0079565033431157\n", - "\t-> Average loss: 1.0364318410870301\n", - "\t-> Average loss: 1.032162597538999\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.2048218791378278\n", - "\t -> Average loss: 1.2166278918723126\n", - "\t -> Average loss: 1.1783969860607342\n", - "\t -> Average loss: 1.1786176204535408\n", - "\t -> Average loss: 1.1798632480913287\n", - "\tEvaluating F1...\n", - "\t -> Train F1: 0.6030226790226174\n", - "\t -> Test F1: 0.5861836286317499\n", - "Learning rate is now: 0.00054\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 26:\n", - "\tBeginning training.\n", - "\t-> Average loss: 0.9192066853189601\n", - "\t-> Average loss: 1.0098440380341351\n", - "\t-> Average loss: 1.006183010403619\n", - "\t-> Average loss: 1.0023870221366835\n", - "--------------------------------------------------\n", - "\tBeginning evaluation.\n", - "\t -> Average loss: 1.1226611448316601\n", - "\t -> Average loss: 1.1899717054792218\n", - "\t -> Average loss: 1.124479945067928\n", - "\t -> Average loss: 1.1427228787120112\n", - "\t -> Average loss: 1.1324994321057338\n", - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "Epoch 27:\n", - "\tBeginning training.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ERROR:root:Internal Python error in the inspect module.\n", - "Below is the traceback from this internal error.\n", - "\n", - "ERROR:root:Internal Python error in the inspect module.\n", - "Below is the traceback from this internal error.\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Traceback (most recent call last):\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/654011310.py\", line 5, in \n", - " train(epoch_ix, model, train_loader, optimizer, criterion)\n", - " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/2806942638.py\", line 18, in train\n", - " y_pred = model(modes, metrics.float(), features.float(), lengths)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", - " return forward_call(*input, **kwargs)\n", - " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 143, in forward\n", - " lstm_out = module(lstm_out)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", - " return forward_call(*input, **kwargs)\n", - " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 79, in forward\n", - " x = x + self.sa(self.ln1(x))\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", - " return forward_call(*input, **kwargs)\n", - " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 64, in forward\n", - " cat = torch.cat([head(x) for head in self.heads], dim=-1)\n", - " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 64, in \n", - " cat = torch.cat([head(x) for head in self.heads], dim=-1)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", - " return forward_call(*input, **kwargs)\n", - " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 37, in forward\n", - " q = self.q(x)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", - " return forward_call(*input, **kwargs)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/linear.py\", line 114, in forward\n", - " return F.linear(input, self.weight, self.bias)\n", - "KeyboardInterrupt\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n", - " stb = value._render_traceback_()\n", - "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1101, in get_records\n", - " return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 248, in wrapped\n", - " return f(*args, **kwargs)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 281, in _fixed_getinnerframes\n", - " records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 1502, in getinnerframes\n", - " frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 1460, in getframeinfo\n", - " filename = getsourcefile(frame) or getfile(frame)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 696, in getsourcefile\n", - " if getattr(getmodule(object, filename), '__loader__', None) is not None:\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 742, in getmodule\n", - " os.path.realpath(f)] = module.__name__\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 395, in realpath\n", - " path, ok = _joinrealpath(filename[:0], filename, {})\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 428, in _joinrealpath\n", - " newpath = join(path, name)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 81, in join\n", - " sep = _get_sep(a)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 42, in _get_sep\n", - " if isinstance(path, bytes):\n", - "KeyboardInterrupt\n", - "Traceback (most recent call last):\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/654011310.py\", line 5, in \n", - " train(epoch_ix, model, train_loader, optimizer, criterion)\n", - " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/2806942638.py\", line 18, in train\n", - " y_pred = model(modes, metrics.float(), features.float(), lengths)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", - " return forward_call(*input, **kwargs)\n", - " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 143, in forward\n", - " lstm_out = module(lstm_out)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", - " return forward_call(*input, **kwargs)\n", - " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 79, in forward\n", - " x = x + self.sa(self.ln1(x))\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", - " return forward_call(*input, **kwargs)\n", - " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 64, in forward\n", - " cat = torch.cat([head(x) for head in self.heads], dim=-1)\n", - " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 64, in \n", - " cat = torch.cat([head(x) for head in self.heads], dim=-1)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", - " return forward_call(*input, **kwargs)\n", - " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 37, in forward\n", - " q = self.q(x)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", - " return forward_call(*input, **kwargs)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/linear.py\", line 114, in forward\n", - " return F.linear(input, self.weight, self.bias)\n", - "KeyboardInterrupt\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n", - " stb = value._render_traceback_()\n", - "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3473, in run_ast_nodes\n", - " if (await self.run_code(code, result, async_=asy)):\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3575, in run_code\n", - " self.showtraceback(running_compiled_code=True)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2102, in showtraceback\n", - " value, tb, tb_offset=tb_offset)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1368, in structured_traceback\n", - " self, etype, value, tb, tb_offset, number_of_lines_of_context)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1268, in structured_traceback\n", - " self, etype, value, tb, tb_offset, number_of_lines_of_context\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1125, in structured_traceback\n", - " tb_offset)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1082, in format_exception_as_a_whole\n", - " last_unique, recursion_repeat = find_recursion(orig_etype, evalue, records)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 382, in find_recursion\n", - " return len(records), 0\n", - "TypeError: object of type 'NoneType' has no len()\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n", - " stb = value._render_traceback_()\n", - "AttributeError: 'TypeError' object has no attribute '_render_traceback_'\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1101, in get_records\n", - " return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 248, in wrapped\n", - " return f(*args, **kwargs)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 281, in _fixed_getinnerframes\n", - " records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 1502, in getinnerframes\n", - " frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 1460, in getframeinfo\n", - " filename = getsourcefile(frame) or getfile(frame)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 696, in getsourcefile\n", - " if getattr(getmodule(object, filename), '__loader__', None) is not None:\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 742, in getmodule\n", - " os.path.realpath(f)] = module.__name__\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 395, in realpath\n", - " path, ok = _joinrealpath(filename[:0], filename, {})\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 429, in _joinrealpath\n", - " if not islink(newpath):\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 171, in islink\n", - " st = os.lstat(path)\n", - "KeyboardInterrupt\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ERROR:root:Internal Python error in the inspect module.\n", - "Below is the traceback from this internal error.\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Traceback (most recent call last):\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n", - " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/654011310.py\", line 5, in \n", - " train(epoch_ix, model, train_loader, optimizer, criterion)\n", - " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/2806942638.py\", line 18, in train\n", - " y_pred = model(modes, metrics.float(), features.float(), lengths)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", - " return forward_call(*input, **kwargs)\n", - " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 143, in forward\n", - " lstm_out = module(lstm_out)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", - " return forward_call(*input, **kwargs)\n", - " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 79, in forward\n", - " x = x + self.sa(self.ln1(x))\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", - " return forward_call(*input, **kwargs)\n", - " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 64, in forward\n", - " cat = torch.cat([head(x) for head in self.heads], dim=-1)\n", - " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 64, in \n", - " cat = torch.cat([head(x) for head in self.heads], dim=-1)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", - " return forward_call(*input, **kwargs)\n", - " File \"/var/folders/4x/l9lw50rn7qvf79m01f21x70mlpd6gh/T/ipykernel_13888/515180498.py\", line 37, in forward\n", - " q = self.q(x)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/module.py\", line 1194, in _call_impl\n", - " return forward_call(*input, **kwargs)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/torch/nn/modules/linear.py\", line 114, in forward\n", - " return F.linear(input, self.weight, self.bias)\n", - "KeyboardInterrupt\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n", - " stb = value._render_traceback_()\n", - "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3473, in run_ast_nodes\n", - " if (await self.run_code(code, result, async_=asy)):\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3575, in run_code\n", - " self.showtraceback(running_compiled_code=True)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2102, in showtraceback\n", - " value, tb, tb_offset=tb_offset)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1368, in structured_traceback\n", - " self, etype, value, tb, tb_offset, number_of_lines_of_context)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1268, in structured_traceback\n", - " self, etype, value, tb, tb_offset, number_of_lines_of_context\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1125, in structured_traceback\n", - " tb_offset)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1082, in format_exception_as_a_whole\n", - " last_unique, recursion_repeat = find_recursion(orig_etype, evalue, records)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 382, in find_recursion\n", - " return len(records), 0\n", - "TypeError: object of type 'NoneType' has no len()\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n", - " stb = value._render_traceback_()\n", - "AttributeError: 'TypeError' object has no attribute '_render_traceback_'\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3030, in _run_cell\n", - " return runner(coro)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/async_helpers.py\", line 78, in _pseudo_sync_runner\n", - " coro.send(None)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3258, in run_cell_async\n", - " interactivity=interactivity, compiler=compiler, result=result)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 3492, in run_ast_nodes\n", - " self.showtraceback()\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2102, in showtraceback\n", - " value, tb, tb_offset=tb_offset)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1368, in structured_traceback\n", - " self, etype, value, tb, tb_offset, number_of_lines_of_context)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1268, in structured_traceback\n", - " self, etype, value, tb, tb_offset, number_of_lines_of_context\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1143, in structured_traceback\n", - " chained_exceptions_tb_offset)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1082, in format_exception_as_a_whole\n", - " last_unique, recursion_repeat = find_recursion(orig_etype, evalue, records)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 382, in find_recursion\n", - " return len(records), 0\n", - "TypeError: object of type 'NoneType' has no len()\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n", - " stb = value._render_traceback_()\n", - "AttributeError: 'TypeError' object has no attribute '_render_traceback_'\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 1101, in get_records\n", - " return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 248, in wrapped\n", - " return f(*args, **kwargs)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/site-packages/IPython/core/ultratb.py\", line 281, in _fixed_getinnerframes\n", - " records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 1502, in getinnerframes\n", - " frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 1460, in getframeinfo\n", - " filename = getsourcefile(frame) or getfile(frame)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 696, in getsourcefile\n", - " if getattr(getmodule(object, filename), '__loader__', None) is not None:\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/inspect.py\", line 742, in getmodule\n", - " os.path.realpath(f)] = module.__name__\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 395, in realpath\n", - " path, ok = _joinrealpath(filename[:0], filename, {})\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 428, in _joinrealpath\n", - " newpath = join(path, name)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 81, in join\n", - " sep = _get_sep(a)\n", - " File \"/Users/rkulhall/miniconda3/envs/pytorch/lib/python3.7/posixpath.py\", line 42, in _get_sep\n", - " if isinstance(path, bytes):\n", - "KeyboardInterrupt\n" - ] - } - ], - "source": [ - "# We'd like to start at a loss of at most -ln(1/9) ~ 2.19\n", - "\n", - "for epoch_ix in range(1, num_epochs+1):\n", - " print(f\"Epoch {epoch_ix}:\")\n", - " train(epoch_ix, model, train_loader, optimizer, criterion)\n", - " evaluate(epoch_ix, model, test_loader, criterion)\n", - " \n", - " if epoch_ix < 5 or epoch_ix % 5 == 0:\n", - " # Evaluate every 5 epochs.\n", - " evaluate_f1(model, train_loader, test_loader)\n", - " \n", - " if epoch_ix % decay_at == 0:\n", - " optimizer.param_groups[0]['lr'] *= decay\n", - " print(f\"Learning rate is now: {optimizer.param_groups[0]['lr']:.5f}\")\n", - " \n", - " print(40*'~')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8bcc396c", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "a7d53498", - "metadata": {}, - "source": [ - "## Benchmarking\n", - "\n", - "```\n", - "\n", - "model = Model(\n", - " n_lstm_layers=1,\n", - " input_size=3,\n", - " hidden_size=16, \n", - " output_size=9,\n", - " n_features=40,\n", - " l1_blocks=3,\n", - " l2_blocks=3\n", - ")\n", - "\n", - "emb_dim = 16\n", - "\n", - "Best stats:\n", - "-> Train F1: 0.6327794050521978\n", - "-> Test F1: 0.6208816430930885\n", - "```\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "46a8dc7d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "pytorch", - "language": "python", - "name": "pytorch" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/viz_scripts/rm_src/baseline_modeling0.ipynb b/viz_scripts/rm_src/baseline_modeling0.ipynb deleted file mode 100644 index 043572e..0000000 --- a/viz_scripts/rm_src/baseline_modeling0.ipynb +++ /dev/null @@ -1,1136 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### All experiments are logged in Notion [here](https://www.notion.so/Replacement-mode-modeling-257c2f460377498d921e6b167f465945)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from enum import Enum\n", - "import random\n", - "\n", - "# Math and graphing.\n", - "import pandas as pd\n", - "import numpy as np\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# sklearn imports.\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.linear_model import LinearRegression\n", - "from sklearn.metrics import f1_score, r2_score, ConfusionMatrixDisplay\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Global experiment flags and variables.\n", - "SEED = 19348\n", - "TARGETS = ['p_micro', 'no_trip', 's_car', 'transit', 'car', 's_micro', 'ridehail', 'walk', 'unknown']\n", - "\n", - "# Set the Numpy seed too.\n", - "random.seed(SEED)\n", - "np.random.seed(SEED)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "class SPLIT_TYPE(Enum):\n", - " # 5 trips for user1, 4 trips in train, 1 in test\n", - " INTRA_USER = 0\n", - " # 5 users, 4 users in train, 1 user in test\n", - " INTER_USER = 1\n", - " # stratifying using target (chosen)\n", - " # [5000 samples (c=1), 8000 samples (c=2)]\n", - " # [4000 samples c=1, 3200 samples c=2 in train]\n", - " TARGET = 2\n", - " \n", - " MIXED = 3\n", - " \n", - "\n", - "class SPLIT(Enum):\n", - " TRAIN = 0\n", - " TEST = 1\n", - "\n", - "def get_splits(count_df: pd.DataFrame, n:int, test_size=0.2):\n", - " maxsize = int(n * test_size)\n", - "\n", - " max_threshold = int(maxsize * 1.05)\n", - " min_threshold = int(maxsize * 0.95)\n", - "\n", - " print(f\"{min_threshold}, {max_threshold}\")\n", - " \n", - " # Allow a 10% tolerance\n", - " def _dp(ix, curr_size, ids, cache):\n", - " \n", - " if ix >= count_df.shape[0]:\n", - " return []\n", - "\n", - " key = ix\n", - "\n", - " if key in cache:\n", - " return cache[key]\n", - "\n", - " if curr_size > max_threshold:\n", - " return []\n", - "\n", - " if min_threshold <= curr_size <= max_threshold:\n", - " return ids\n", - "\n", - " # two options - either pick the current id or skip it.\n", - " branch_a = _dp(ix, curr_size+count_df.loc[ix, 'count'], ids+[count_df.loc[ix, 'index']], cache)\n", - " branch_b = _dp(ix+1, curr_size, ids, cache)\n", - " \n", - " curr_max = []\n", - " if branch_a and len(branch_a) > 0:\n", - " curr_max = branch_a\n", - " \n", - " if branch_b and len(branch_b) > len(branch_a):\n", - " curr_max = branch_b\n", - " \n", - " cache[key] = curr_max\n", - " return cache[key]\n", - " \n", - " return _dp(0, 0, ids=list(), cache=dict())\n", - "\n", - "\n", - "def get_train_test_splits(data: pd.DataFrame, how=SPLIT_TYPE, test_ratio=0.2, shuffle=True):\n", - "\n", - " n_users = list(data.user_id.unique())\n", - " n = data.shape[0]\n", - " \n", - " if shuffle:\n", - " data = data.sample(data.shape[0], random_state=SEED).reset_index(drop=True, inplace=False)\n", - "\n", - " if how == SPLIT_TYPE.INTER_USER:\n", - " # Make the split, ensuring that a user in one fold is not leaked into the other fold.\n", - " # Basic idea: we want to start with the users with the highest instances and place \n", - " # alternating users in each set.\n", - " counts = data.user_id.value_counts().reset_index(drop=False, inplace=False, name='count')\n", - "\n", - " # Now, start with the user_id at the top, and keep adding to either split.\n", - " # This can be achieved using a simple DP program.\n", - " test_ids = get_splits(counts, data.shape[0])\n", - " test_data = data.loc[data.user_id.isin(test_ids), :]\n", - " train_index = data.index.difference(test_data.index)\n", - " train_data = data.loc[data.user_id.isin(train_index), :]\n", - " \n", - " return train_data, test_data\n", - " \n", - " elif how == SPLIT_TYPE.INTRA_USER:\n", - " \n", - " # There are certain users with only one observation. What do we do with those?\n", - " # As per the mobilitynet modeling pipeline, we randomly assign them to either the\n", - " # training or test set.\n", - " \n", - " value_counts = data.user_id.value_counts()\n", - " single_count_ids = value_counts[value_counts == 1].index\n", - " \n", - " data_filtered = data.loc[~data.user_id.isin(single_count_ids), :].reset_index(drop=True)\n", - " data_single_counts = data.loc[data.user_id.isin(single_count_ids), :].reset_index(drop=True)\n", - " \n", - " X_tr, X_te = train_test_split(\n", - " data_filtered, test_size=test_ratio, shuffle=shuffle, stratify=data_filtered.user_id,\n", - " random_state=SEED\n", - " )\n", - " \n", - " data_single_counts['assigned'] = np.random.choice(['train', 'test'], len(data_single_counts))\n", - " X_tr_merged = pd.concat(\n", - " [X_tr, data_single_counts.loc[data_single_counts.assigned == 'train', :].drop(\n", - " columns=['assigned'], inplace=False\n", - " )],\n", - " ignore_index=True, axis=0\n", - " )\n", - " \n", - " X_te_merged = pd.concat(\n", - " [X_te, data_single_counts.loc[data_single_counts.assigned == 'test', :].drop(\n", - " columns=['assigned'], inplace=False\n", - " )],\n", - " ignore_index=True, axis=0\n", - " )\n", - " \n", - " return X_tr_merged, X_te_merged\n", - " \n", - " elif how == SPLIT_TYPE.TARGET:\n", - " \n", - " X_tr, X_te = train_test_split(\n", - " data, test_size=test_ratio, shuffle=shuffle, stratify=data.chosen,\n", - " random_state=SEED\n", - " )\n", - " \n", - " return X_tr, X_te\n", - " \n", - " raise NotImplementedError(\"Unknown split type\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Modeling" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Read the data.\n", - "# data = pd.read_csv('../data/FULL_preprocessed_data_RM_weather.csv')\n", - "# data = pd.read_csv('../data/ReplacedMode_Fix.csv')\n", - "data = pd.read_csv('../data/ReplacedMode_Fix_02072024.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "data.drop_duplicates(inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_hist(df, features=None):\n", - " if not features:\n", - " # All features.\n", - " features = df.columns.tolist()\n", - " \n", - " n_features = len(features)\n", - " \n", - " ncols = 6\n", - " nrows = n_features//ncols if n_features%ncols == 0 else (n_features//ncols) + 1\n", - " \n", - " fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(10, 10))\n", - " for ix, ax in enumerate(axes.flatten()):\n", - " \n", - " if ix > n_features:\n", - " break\n", - " \n", - " df[features[ix]].hist(ax=ax)\n", - " ax.set(title=features[ix])\n", - " \n", - " plt.tight_layout()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# First, we map the user IDs to ints.\n", - "\n", - "USERS = list(data.user_id.unique())\n", - "\n", - "USER_MAP = {\n", - " u: i+1 for (i, u) in enumerate(USERS)\n", - "}\n", - "\n", - "data['user_id'] = data['user_id'].apply(lambda x: USER_MAP[x])\n", - "\n", - "# data.rename(\n", - "# columns={'start_local_dt_weekday': 'start:DOW', 'end_local_dt_weekday': 'end:DOW'},\n", - "# inplace=True\n", - "# )\n", - "\n", - "# Drop the samples with chosen == no trip or chosen == unknown\n", - "# data.drop(index=data.loc[data.chosen.isin([2, 9])].index, inplace=True)\n", - "\n", - "# data.n_working_residents = data.n_working_residents.apply(lambda x: 0 if x < 0 else x)\n", - "\n", - "# Fix some age preprocessing issues.\n", - "# data.age = data.age.apply(lambda x: x if x < 100 else 2024-x)\n", - "\n", - "# Collapse 'train' and 'bus' into 'transit'\n", - "# data.loc[\n", - "# data.section_mode_argmax.isin(['train', 'bus']), 'section_mode_argmax'\n", - "# ] = 'transit'" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# display(data.section_mode_argmax.value_counts())" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# transit = data.loc[data.section_mode_argmax == 'transit', :].copy()\n", - "# transit['section_duration_argmax'] /= 60.\n", - "\n", - "# transit['mph'] = transit['section_distance_argmax']/transit['section_duration_argmax']\n", - "\n", - "# display(transit[['section_duration_argmax', 'section_distance_argmax', 'mph']].describe())" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# import plotly.express as px\n", - "\n", - "# sp = data.loc[data.section_mode_argmax.isin(['car', 'transit', 'walking']), :]\n", - "# fig = px.line(sp, y='section_distance_argmax', color='section_mode_argmax')\n", - "# fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Close the figure above.\n", - "# plt.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def get_duration_estimate(df: pd.DataFrame, dset: SPLIT, model_dict: dict):\n", - " \n", - " X_features = ['section_distance_argmax', 'age']\n", - " \n", - " if 'mph' in df.columns:\n", - " X_features += ['mph']\n", - " \n", - " if dset == SPLIT.TRAIN and model_dict is None:\n", - " model_dict = dict()\n", - " \n", - " if dset == SPLIT.TEST and model_dict is None:\n", - " raise AttributeError(\"Expected model dict for testing.\")\n", - " \n", - " if dset == SPLIT.TRAIN:\n", - " for section_mode in df.section_mode_argmax.unique():\n", - " section_data = df.loc[df.section_mode_argmax == section_mode, :]\n", - " if section_mode not in model_dict:\n", - " model_dict[section_mode] = dict()\n", - "\n", - " model = LinearRegression(fit_intercept=True)\n", - "\n", - " X = section_data[\n", - " X_features\n", - " ]\n", - " Y = section_data[['section_duration_argmax']]\n", - "\n", - " model.fit(X, Y.values.ravel())\n", - "\n", - " r2 = r2_score(y_pred=model.predict(X), y_true=Y.values.ravel())\n", - " print(f\"Train R2 for {section_mode}: {r2}\")\n", - "\n", - " model_dict[section_mode]['model'] = model\n", - " \n", - " elif dset == SPLIT.TEST:\n", - " for section_mode in df.section_mode_argmax.unique():\n", - " section_data = df.loc[df.section_mode_argmax == section_mode, :]\n", - " X = section_data[\n", - " X_features\n", - " ]\n", - " Y = section_data[['section_duration_argmax']]\n", - " \n", - " y_pred = model_dict[section_mode]['model'].predict(X)\n", - " r2 = r2_score(y_pred=y_pred, y_true=Y.values.ravel())\n", - " print(f\"Test R2 for {section_mode}: {r2}\")\n", - " \n", - " # Create the new columns for the duration.\n", - " new_columns = ['p_micro','no_trip','s_car','transit','car','s_micro','ridehail','walk','unknown']\n", - " df[new_columns] = 0\n", - " df['temp'] = 0\n", - " \n", - " for section in df.section_mode_argmax.unique():\n", - " X_section = df.loc[df.section_mode_argmax == section, X_features]\n", - " \n", - " # broadcast to all columns.\n", - " df.loc[df.section_mode_argmax == section, 'temp'] = model_dict[section]['model'].predict(X_section)\n", - " \n", - " for c in new_columns:\n", - " df[c] = df['av_' + c] * df['temp']\n", - " \n", - " df.drop(columns=['temp'], inplace=True)\n", - " \n", - " df.rename(columns=dict([(x, 'tt_'+x) for x in new_columns]), inplace=True)\n", - " \n", - " # return model_dict, result_df\n", - " return model_dict, df" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Now, we split the data (either inter-user or intra-user split)\n", - "\n", - "# train_data, test_data = get_train_test_splits(data=data, how=SPLIT_TYPE.INTER_USER, shuffle=True)\n", - "\n", - "train_data, test_data = get_train_test_splits(data=data, how=SPLIT_TYPE.INTRA_USER, shuffle=True)\n", - "\n", - "# train_data, test_data = get_train_test_splits(data=data, how=SPLIT_TYPE.TARGET, shuffle=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index(['user_id', '_id', 'original_user_id', 'cleaned_trip', 'Mode_confirm',\n", - " 'start_fmt_time', 'start:year', 'start:month', 'start:day',\n", - " 'start:hour', 'start:DOW', 'end_fmt_time', 'end:year', 'end:month',\n", - " 'end:day', 'end:hour', 'end:DOW', 'available_modes', 'birth_year',\n", - " 'income_category', 'n_motor_vehicles', 'n_residence_members',\n", - " 'n_residents_u18', 'gender', 'is_student', 'n_residents_with_license',\n", - " 'duration', 'distance_miles', 'start_loc', 'end_loc', 'section_modes',\n", - " 'section_distances', 'start:n_days_in_month', 'end:n_days_in_month',\n", - " 'age', 'is_overnight_trip', 'n_working_residents', 'is_male',\n", - " 'start_lat', 'start_lng', 'end_lat', 'end_lng', 'start:sin_HOD',\n", - " 'start:sin_DOM', 'start:sin_MOY', 'start:cos_HOD', 'start:cos_DOM',\n", - " 'start:cos_MOY', 'end:sin_HOD', 'end:sin_DOM', 'end:sin_MOY',\n", - " 'end:cos_HOD', 'end:cos_DOM', 'end:cos_MOY', 'section_durations',\n", - " 'section_locations_argmax', 'temperature_2m (°F)',\n", - " 'relative_humidity_2m (%)', 'dew_point_2m (°F)', 'rain (inch)',\n", - " 'snowfall (inch)', 'cloud_cover (%)', 'wind_speed_10m (mp/h)',\n", - " 'wind_gusts_10m (mp/h)', 'section_distance_argmax',\n", - " 'section_duration_argmax', 'section_mode_argmax',\n", - " 'section_coordinates_argmax', 'mph', 'chosen', 'av_car', 'av_s_car',\n", - " 'av_no_trip', 'av_walk', 'av_transit', 'av_s_micro', 'av_p_micro',\n", - " 'av_ridehail', 'av_unknown', 'cost_p_micro', 'cost_no_trip',\n", - " 'cost_s_car', 'cost_transit', 'cost_car', 'cost_s_micro',\n", - " 'cost_ridehail', 'cost_walk', 'cost_unknown'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "print(train_data.columns)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train R2 for car: 0.9107819633844028\n", - "Train R2 for bicycling: 0.9381499933467025\n", - "Train R2 for walking: 0.7840120837242898\n", - "Train R2 for no_sensed: 0.838164213315293\n", - "Train R2 for transit: 0.9167190695089265\n", - "----------\n", - "Test R2 for car: 0.9115083509175145\n", - "Test R2 for walking: 0.7836715824022498\n", - "Test R2 for no_sensed: 0.8457638150514823\n", - "Test R2 for bicycling: 0.94215202813422\n", - "Test R2 for transit: 0.9130004787209818\n" - ] - } - ], - "source": [ - "params, train_data = get_duration_estimate(train_data, SPLIT.TRAIN, None)\n", - "print(10 * \"-\")\n", - "_, test_data = get_duration_estimate(test_data, SPLIT.TEST, params)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((34064, 97), (8517, 97))" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_data.shape, test_data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Some helper functions that will help ease redundancy in the code.\n", - "\n", - "def drop_columns(df: pd.DataFrame):\n", - " u_time_features = [\n", - " 'start_fmt_time', 'start:year', 'start:month', 'start:day',\n", - " 'start:hour', 'end_fmt_time', 'end:year',\n", - " 'end:month', 'end:day', 'end:hour', 'end:n_days_in_month', \n", - " 'start:sin_DOM', 'start:sin_MOY', 'start:cos_MOY', 'start:cos_DOM',\n", - " 'end:sin_DOM', 'end:sin_MOY', 'end:cos_DOM', 'end:cos_MOY', 'start:n_days_in_month'\n", - " ]\n", - "\n", - " u_user_features = [\n", - " '_id', 'original_user_id', 'gender', 'birth_year',\n", - "# 'user_id', \n", - " ]\n", - " \n", - " u_trip_features = [\n", - " 'cleaned_trip', 'Mode_confirm', 'available_modes', 'duration', 'start_loc',\n", - " 'end_loc', 'section_modes', 'section_distances', 'section_durations',\n", - " 'section_locations_argmax', 'section_mode_argmax', 'section_coordinates_argmax',\n", - "# 'start_lat', 'start_lng', 'end_lat', 'end_lng'\n", - " ]\n", - " \n", - " # Drop section_mode_argmax and available_modes.\n", - " return df.drop(\n", - " columns=u_time_features + u_user_features + u_trip_features, \n", - " inplace=False\n", - " )\n", - "\n", - "\n", - "def scale_values(df: pd.DataFrame, split: SPLIT, scalers=None):\n", - " # Scale costs using StandardScaler.\n", - " costs = df[[c for c in df.columns if 'cost_' in c]].copy()\n", - " times = df[[c for c in df.columns if 'tt_' in c or 'duration' in c]].copy()\n", - " distances = df[[c for c in df.columns if 'distance' in c]]\n", - " \n", - " print(\n", - " \"Cost columns to be scaled: \", costs.columns,\"\\nTime columns to be scaled: \", times.columns, \\\n", - " \"\\nDistance columns to be scaled: \", distances.columns\n", - " )\n", - " \n", - " if split == SPLIT.TRAIN and scalers is None:\n", - " cost_scaler = StandardScaler()\n", - " tt_scaler = StandardScaler()\n", - " dist_scaler = StandardScaler()\n", - " \n", - " cost_scaled = pd.DataFrame(\n", - " cost_scaler.fit_transform(costs), \n", - " columns=costs.columns, \n", - " index=costs.index\n", - " )\n", - " \n", - " tt_scaled = pd.DataFrame(\n", - " tt_scaler.fit_transform(times),\n", - " columns=times.columns,\n", - " index=times.index\n", - " )\n", - " \n", - " dist_scaled = pd.DataFrame(\n", - " dist_scaler.fit_transform(distances),\n", - " columns=distances.columns,\n", - " index=distances.index\n", - " )\n", - " \n", - " elif split == SPLIT.TEST and scalers is not None:\n", - " \n", - " cost_scaler, tt_scaler, dist_scaler = scalers\n", - " \n", - " cost_scaled = pd.DataFrame(\n", - " cost_scaler.transform(costs), \n", - " columns=costs.columns, \n", - " index=costs.index\n", - " )\n", - " \n", - " tt_scaled = pd.DataFrame(\n", - " tt_scaler.transform(times), \n", - " columns=times.columns, \n", - " index=times.index\n", - " )\n", - " \n", - " dist_scaled = pd.DataFrame(\n", - " dist_scaler.transform(distances),\n", - " columns=distances.columns,\n", - " index=distances.index\n", - " )\n", - " \n", - " else:\n", - " raise NotImplementedError(\"Unknown split\")\n", - " \n", - " # Drop the original columns.\n", - " df.drop(\n", - " columns=costs.columns.tolist() + times.columns.tolist() + distances.columns.tolist(), \n", - " inplace=True\n", - " )\n", - " \n", - " df = df.merge(right=cost_scaled, left_index=True, right_index=True)\n", - " df = df.merge(right=tt_scaled, left_index=True, right_index=True)\n", - " df = df.merge(right=dist_scaled, left_index=True, right_index=True)\n", - " \n", - " return df, (cost_scaler, tt_scaler, dist_scaler)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# First, drop columns.\n", - "\n", - "train_data = drop_columns(train_data)\n", - "\n", - "# Scale cost.\n", - "# train_data, scalers = scale_values(train_data, SPLIT.TRAIN, None)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "test_data = drop_columns(test_data)\n", - "\n", - "# Scale cost.\n", - "# test_data, _ = scale_values(test_data, SPLIT.TEST, scalers)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['user_id', 'start:DOW', 'end:DOW', 'income_category',\n", - " 'n_motor_vehicles', 'n_residence_members', 'n_residents_u18',\n", - " 'is_student', 'n_residents_with_license', 'distance_miles', 'age',\n", - " 'is_overnight_trip', 'n_working_residents', 'is_male', 'start_lat',\n", - " 'start_lng', 'end_lat', 'end_lng', 'start:sin_HOD', 'start:cos_HOD',\n", - " 'end:sin_HOD', 'end:cos_HOD', 'temperature_2m (°F)',\n", - " 'relative_humidity_2m (%)', 'dew_point_2m (°F)', 'rain (inch)',\n", - " 'snowfall (inch)', 'cloud_cover (%)', 'wind_speed_10m (mp/h)',\n", - " 'wind_gusts_10m (mp/h)', 'section_distance_argmax',\n", - " 'section_duration_argmax', 'mph', 'chosen', 'av_car', 'av_s_car',\n", - " 'av_no_trip', 'av_walk', 'av_transit', 'av_s_micro', 'av_p_micro',\n", - " 'av_ridehail', 'av_unknown', 'cost_p_micro', 'cost_no_trip',\n", - " 'cost_s_car', 'cost_transit', 'cost_car', 'cost_s_micro',\n", - " 'cost_ridehail', 'cost_walk', 'cost_unknown', 'tt_p_micro',\n", - " 'tt_no_trip', 'tt_s_car', 'tt_transit', 'tt_car', 'tt_s_micro',\n", - " 'tt_ridehail', 'tt_walk', 'tt_unknown'],\n", - " dtype='object')" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_data.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "9" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(train_data.chosen.unique())" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import classification_report\n", - "from sklearn.model_selection import GridSearchCV, StratifiedKFold\n", - "from pprint import pprint\n", - "from sklearn.inspection import permutation_importance\n", - "from time import perf_counter" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Random Forest classifier" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "CV = True" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 3 folds for each of 12 candidates, totalling 36 fits\n" - ] - } - ], - "source": [ - "from sklearn.ensemble import RandomForestClassifier\n", - "\n", - "# exp question - compute sample weights using user_id.\n", - "\n", - "rf_train = train_data.drop(columns=['chosen', 'start_lat', 'start_lng', 'end_lat', 'end_lng', 'user_id'])\n", - "rf_test = test_data.drop(columns=['chosen', 'start_lat', 'start_lng', 'end_lat', 'end_lng', 'user_id'])\n", - "\n", - "if CV:\n", - "\n", - " model = RandomForestClassifier(random_state=SEED)\n", - "\n", - " # We want to build bootstrapped trees that would not always use all the features.\n", - "\n", - " param_set2 = {\n", - " 'n_estimators': [150, 200, 250],\n", - " 'min_samples_split': [2, 3],\n", - " 'class_weight': ['balanced_subsample'],\n", - " 'max_features': [None, 'sqrt'],\n", - " 'bootstrap': [True]\n", - " }\n", - "\n", - " cv_set2 = StratifiedKFold(n_splits=3, shuffle=True, random_state=SEED)\n", - "\n", - " clf_set2 = GridSearchCV(model, param_set2, cv=cv_set2, n_jobs=-1, scoring='f1_weighted', verbose=1)\n", - "\n", - " start = perf_counter()\n", - "\n", - " clf_set2.fit(\n", - " rf_train,\n", - " train_data.chosen.values.ravel()\n", - " )\n", - "\n", - " time_req = (perf_counter() - start)/60.\n", - "\n", - " best_model = clf_set2.best_estimator_\n", - "else:\n", - " best_model = RandomForestClassifier(\n", - " n_estimators=200,\n", - " max_depth=None,\n", - " min_samples_split=3,\n", - " bootstrap=True,\n", - " class_weight='balanced_subsample',\n", - " random_state=SEED,\n", - " n_jobs=-1\n", - " ).fit(rf_train, train_data.chosen.values.ravel())" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# tr_f1_set1 = f1_score(\n", - "# y_true=train_data.chosen.values,\n", - "# y_pred=model_set1.predict(rf_train),\n", - "# average='weighted'\n", - "# )\n", - "\n", - "tr_f1_set2 = f1_score(\n", - " y_true=train_data.chosen.values,\n", - " y_pred=best_model.predict(rf_train),\n", - " average='weighted'\n", - ")\n", - "\n", - "# print(tr_f1)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# te_f1_set1 = f1_score(\n", - "# y_true=test_data.chosen.values,s\n", - "# y_pred=model_set1.predict(rf_test),\n", - "# average='weighted'\n", - "# )\n", - "\n", - "te_f1_set2 = f1_score(\n", - " y_true=test_data.chosen.values,\n", - " y_pred=best_model.predict(rf_test),\n", - " average='weighted'\n", - ")\n", - "\n", - "# print(te_f1)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[BOOTSTRAPPED] | Train F1: 1.0, Test F1: 0.7344136324607913\n" - ] - } - ], - "source": [ - "# print(f\"[NON BOOTSTRAPPED] | Train F1: {tr_f1_set1}, Test F1: {te_f1_set1}\")\n", - "print(f\"[BOOTSTRAPPED] | Train F1: {tr_f1_set2}, Test F1: {te_f1_set2}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[('age', 0.13052971325233453),\n", - " ('income_category', 0.05563529282438796),\n", - " ('n_motor_vehicles', 0.05117069898186447),\n", - " ('mph', 0.04901096852140811),\n", - " ('dew_point_2m (°F)', 0.046445523590839706),\n", - " ('temperature_2m (°F)', 0.04332851959366878),\n", - " ('n_residents_u18', 0.04079428459138862),\n", - " ('cost_transit', 0.03599739479849181),\n", - " ('distance_miles', 0.03410054518532979),\n", - " ('wind_gusts_10m (mp/h)', 0.02664916084517161),\n", - " ('relative_humidity_2m (%)', 0.026557460924608728),\n", - " ('wind_speed_10m (mp/h)', 0.02621237413168378),\n", - " ('n_residence_members', 0.024503799663918274),\n", - " ('section_duration_argmax', 0.024244750804804545),\n", - " ('n_working_residents', 0.023773443113737733),\n", - " ('n_residents_with_license', 0.023628029234229537),\n", - " ('cloud_cover (%)', 0.02246590194480327),\n", - " ('cost_s_micro', 0.022458268111427003),\n", - " ('tt_p_micro', 0.02038772534963909),\n", - " ('start:cos_HOD', 0.019229222189042564),\n", - " ('is_male', 0.01922412856640586),\n", - " ('end:cos_HOD', 0.019203897735717918),\n", - " ('section_distance_argmax', 0.015253896310661041),\n", - " ('end:sin_HOD', 0.015124047093651035),\n", - " ('tt_walk', 0.015027814935913562),\n", - " ('start:DOW', 0.014649650753683777),\n", - " ('start:sin_HOD', 0.014574215098017478),\n", - " ('cost_s_car', 0.013824194676480045),\n", - " ('end:DOW', 0.013763191399800697),\n", - " ('cost_car', 0.013558725784127607),\n", - " ('cost_ridehail', 0.013029878623553506),\n", - " ('tt_transit', 0.012674148135131848),\n", - " ('is_student', 0.010245832739145545),\n", - " ('tt_s_car', 0.009950842169405066),\n", - " ('tt_car', 0.00979054895155533),\n", - " ('tt_s_micro', 0.008712803885105263),\n", - " ('tt_ridehail', 0.008579952310351583),\n", - " ('av_p_micro', 0.005419336568222103),\n", - " ('av_walk', 0.003300488646146801),\n", - " ('av_transit', 0.0028638611907256797),\n", - " ('rain (inch)', 0.0026761074332931224),\n", - " ('av_car', 0.002642247634705974),\n", - " ('av_s_car', 0.0018072513622939949),\n", - " ('snowfall (inch)', 0.0016959335576669646),\n", - " ('tt_unknown', 0.001566922069109617),\n", - " ('av_ridehail', 0.0014388235245583704),\n", - " ('av_s_micro', 0.0009841196788956074),\n", - " ('tt_no_trip', 0.0007220255139425819),\n", - " ('av_unknown', 0.0004000980804160885),\n", - " ('av_no_trip', 0.00017193791853587658),\n", - " ('is_overnight_trip', 0.0),\n", - " ('cost_p_micro', 0.0),\n", - " ('cost_no_trip', 0.0),\n", - " ('cost_walk', 0.0),\n", - " ('cost_unknown', 0.0)]\n" - ] - } - ], - "source": [ - "# Feature importances - gini entropy\n", - "\n", - "pprint(\n", - " sorted(\n", - " zip(\n", - " best_model.feature_names_in_, \n", - " best_model.feature_importances_\n", - " ), \n", - " key=lambda x: x[-1], reverse=True\n", - " )\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'permutation_importance' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m importance \u001b[38;5;241m=\u001b[39m \u001b[43mpermutation_importance\u001b[49m(\n\u001b[1;32m 2\u001b[0m best_model,\n\u001b[1;32m 3\u001b[0m rf_test,\n\u001b[1;32m 4\u001b[0m test_data\u001b[38;5;241m.\u001b[39mchosen\u001b[38;5;241m.\u001b[39mvalues,\n\u001b[1;32m 5\u001b[0m n_repeats\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m,\n\u001b[1;32m 6\u001b[0m random_state\u001b[38;5;241m=\u001b[39mSEED,\n\u001b[1;32m 7\u001b[0m n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 8\u001b[0m scoring\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mf1_weighted\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 9\u001b[0m )\n", - "\u001b[0;31mNameError\u001b[0m: name 'permutation_importance' is not defined" - ] - } - ], - "source": [ - "importance = permutation_importance(\n", - " best_model,\n", - " rf_test,\n", - " test_data.chosen.values,\n", - " n_repeats=5,\n", - " random_state=SEED,\n", - " n_jobs=-1,\n", - " scoring='f1_weighted'\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pd.DataFrame(\n", - " {\n", - " 'feature names': test_data.columns.delete(\n", - " test_data.columns.isin(['chosen'])\n", - " ),\n", - " 'imp_mean': importance.importances_mean, \n", - " 'imp_std': importance.importances_std\n", - " }\n", - ").sort_values(by=['imp_mean'], axis='rows', ascending=False).head(20)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fig, ax = plt.subplots(nrows=1, ncols=2)\n", - "y_pred = best_model.predict(rf_test)\n", - "pred_df = pd.DataFrame(\n", - " {\n", - " 'y_pred': y_pred.ravel(),\n", - " 'y_true': test_data.chosen.values.ravel()\n", - " }\n", - ")\n", - "\n", - "# pred_df.y_pred.hist(ax=ax[0])\n", - "# pred_df.y_true.hist(ax=ax[1])\n", - "\n", - "# ax[0].set(\n", - "# xlabel=\"Label\",\n", - "# ylabel=\"Count\",\n", - "# title=\"Prediction\"\n", - "# )\n", - "\n", - "# ax[1].set(\n", - "# xlabel=\"Label\",\n", - "# ylabel=\"Count\",\n", - "# title=\"GT\"\n", - "# )\n", - "\n", - "# plt.tight_layout()\n", - "# plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(7, 7))\n", - "cm = ConfusionMatrixDisplay.from_estimator(\n", - " best_model,\n", - " X=rf_test,\n", - " y=test_data[['chosen']],\n", - " ax=ax\n", - ")\n", - "# ax.set_xticklabels(TARGETS, rotation=45)\n", - "# ax.set_yticklabels(TARGETS)\n", - "fig.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(classification_report(y_true=pred_df.y_true, y_pred=pred_df.y_pred))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## XGBoost" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# from sklearn.utils.class_weight import compute_sample_weight\n", - "\n", - "# sample_weights = compute_sample_weight(class_weight='balanced', y=train_data.user_id.values.ravel())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from xgboost import XGBClassifier\n", - "\n", - "y_train = train_data.chosen.values.ravel() - 1\n", - "y_test = test_data.chosen.values.ravel()\n", - "\n", - "# weights = compute_class_weight(class_weight='balanced', classes=np.unique(y_pred), y_pred)\n", - "\n", - "xgm = XGBClassifier(\n", - " n_estimators=250,\n", - " max_depth=None,\n", - " tree_method='hist',\n", - " objective='multi:softmax',\n", - " num_class=9\n", - ").fit(rf_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "preds = xgm.predict(rf_test) + 1\n", - "\n", - "print(classification_report(y_true=y_test, y_pred=preds))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# import pickle\n", - "\n", - "# # RF_RM.pkl = 0.8625 on test.\n", - "# # RF_RM_1.pkl = 0.77 on test.\n", - "# with open('../models/RF_RM_1.pkl', 'wb') as f:\n", - "# f.write(pickle.dumps(model))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## TODO:\n", - "\n", - "\n", - "- Explain why location might not be a good feature to add (plot start and end on map and explain how model might just overfit to the raw coordinates)\n", - "- Merge `unknown` and `no_trip` into one category and validate against models trained on (a) separate labels (b) dropped labels\n", - "- Explore more of the abnormal `walking` trips" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "interpreter": { - "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" - }, - "kernelspec": { - "display_name": "emission", - "language": "python", - "name": "emission" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/viz_scripts/rm_src/biogeme_modeling.ipynb b/viz_scripts/rm_src/biogeme_modeling.ipynb deleted file mode 100644 index 035d597..0000000 --- a/viz_scripts/rm_src/biogeme_modeling.ipynb +++ /dev/null @@ -1,526 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import biogeme.biogeme as bio\n", - "import biogeme.database as db\n", - "from biogeme import models\n", - "from biogeme.expressions import Beta, DefineVariable\n", - "from biogeme.expressions import Variable\n", - "import numpy as np\n", - "\n", - "from sklearn.preprocessing import MinMaxScaler" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = pd.read_csv('../data/preprocessed_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "USER_MAP = {\n", - " u: i+1 for (i, u) in enumerate(data.user_id.unique())\n", - "}\n", - "\n", - "# Drop section_mode_argmax and available_modes.\n", - "data.drop(columns=[\n", - " 'section_mode_argmax', 'available_modes', 'section_duration_argmax', 'section_distance_argmax'\n", - " ], inplace=True)\n", - "\n", - "data['user_id'] = data['user_id'].apply(lambda x: USER_MAP[x])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Convert from min -> hrs\n", - "data[[c for c in data.columns if 'tt_' in c]] /= 60.\n", - "\n", - "# Scale costs using StandardScaler.\n", - "costs = data[[c for c in data.columns if 'cost_' in c]].copy()\n", - "\n", - "scaler = MinMaxScaler()\n", - "cost_scaled = pd.DataFrame(scaler.fit_transform(costs), columns=['scaled_' + c for c in costs.columns], index=costs.index)\n", - "data = data.merge(right=cost_scaled, left_index=True, right_index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# display(data[[c for c in data.columns if 'tt_' in c]].describe())\n", - "# display(data[[c for c in data.columns if 'cost_' in c]].describe())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "for c in data.columns:\n", - " if 'av_' in c:\n", - " print(f\"Feature {c} availability: {data.loc[:, c].sum()}/{data.shape[0]}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# data.drop(columns=['tt_unknown','av_unknown'], inplace=True)\n", - "# data.drop(index=data.loc[(data.chosen == 9)].index, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "database = db.Database('emission', data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Variable definitions.\n", - "\n", - "# ID.\n", - "USER_ID = Variable('user_id')\n", - "\n", - "# Availability.\n", - "AV_P_MICRO = Variable('av_p_micro')\n", - "AV_NO_TRIP = Variable('av_no_trip')\n", - "AV_S_CAR = Variable('av_s_car')\n", - "AV_TRANSIT = Variable('av_transit')\n", - "AV_CAR = Variable('av_car')\n", - "AV_S_MICRO = Variable('av_s_micro')\n", - "AV_RIDEHAIL = Variable('av_ridehail')\n", - "AV_WALK = Variable('av_walk')\n", - "AV_UNKNOWN = Variable('av_unknown')\n", - "\n", - "# Time.\n", - "TT_P_MICRO = Variable('tt_p_micro')\n", - "TT_NO_TRIP = Variable('tt_no_trip')\n", - "TT_S_CAR = Variable('tt_s_car')\n", - "TT_TRANSIT = Variable('tt_transit')\n", - "TT_CAR = Variable('tt_car')\n", - "TT_S_MICRO = Variable('tt_s_micro')\n", - "TT_RIDEHAIL = Variable('tt_ridehail')\n", - "TT_WALK = Variable('tt_walk')\n", - "TT_UNKNOWN = Variable('tt_unknown')\n", - "\n", - "# Cost.\n", - "CO_P_MICRO = Variable('scaled_cost_p_micro')\n", - "CO_NO_TRIP = Variable('scaled_cost_no_trip')\n", - "CO_S_CAR = Variable('scaled_cost_s_car')\n", - "CO_TRANSIT = Variable('scaled_cost_transit')\n", - "CO_CAR = Variable('scaled_cost_car')\n", - "CO_S_MICRO = Variable('scaled_cost_s_micro')\n", - "CO_RIDEHAIL = Variable('scaled_cost_ridehail')\n", - "CO_WALK = Variable('scaled_cost_walk')\n", - "CO_UNKNOWN = Variable('scaled_cost_unknown')\n", - "\n", - "# Choice.\n", - "CHOICE = Variable('chosen')\n", - "\n", - "# Exclusion. Choice must not be equal to or either 2 or 9.\n", - "EXCLUDE = (CHOICE == 2) + (CHOICE == 9) > 0\n", - "\n", - "database.remove(EXCLUDE)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Parameters\n", - "\n", - "# Create a coefficient from time and set the initial value to 0. There is no lower bound for this coefficient,\n", - "# but we would like to constrain the ceil to 0 since we want these coefficients to denote the utiltiy function.\n", - "# Finally, we want this parameter to be updated by the optimization algorithm.\n", - "B_TIME = Beta('B_TIME', 0, None, 0, 0)\n", - "B_COST = Beta('B_COST', 0, None, None, 0)\n", - "\n", - "# Alternative-Specific Constants.\n", - "ASC_P_MICRO = Beta('ASC_P_MICRO', 0, None, None, 0)\n", - "ASC_NO_TRIP = Beta('ASC_NO_TRIP', 0, None, None, 0)\n", - "ASC_S_CAR = Beta('ASC_S_CAR', 0, None, None, 0)\n", - "ASC_TRANSIT = Beta('ASC_TRANSIT', 0, None, None, 0)\n", - "ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)\n", - "ASC_S_MICRO = Beta('ASC_S_MICRO', 0, None, None, 0)\n", - "ASC_RIDEHAIL = Beta('ASC_RIDEHAIL', 0, None, None, 0)\n", - "ASC_WALK = Beta('ASC_WALK', 0, None, None, 0)\n", - "ASC_UNKNOWN = Beta('ASC_UNKNOWN', 0, None, None, 0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the utility functions.\n", - "# number of alternatives = number of UFs.\n", - "\n", - "# Not multiplying by cost.\n", - "V_P_MICRO = (\n", - " ASC_P_MICRO +\n", - " B_TIME * TT_P_MICRO\n", - " + B_COST * CO_P_MICRO\n", - ")\n", - "\n", - "V_NO_TRIP = (\n", - " ASC_NO_TRIP +\n", - " B_TIME * TT_NO_TRIP +\n", - " B_COST * CO_NO_TRIP\n", - ")\n", - "\n", - "V_S_CAR = (\n", - " ASC_S_CAR +\n", - " B_TIME * TT_S_CAR +\n", - " B_COST * CO_S_CAR\n", - ")\n", - "\n", - "V_TRANSIT = (\n", - " ASC_TRANSIT +\n", - " B_TIME * TT_TRANSIT +\n", - " B_COST * CO_TRANSIT\n", - ")\n", - "\n", - "V_CAR = (\n", - " ASC_CAR +\n", - " B_TIME * TT_CAR +\n", - " B_COST * CO_CAR\n", - ")\n", - "\n", - "V_S_MICRO = (\n", - " ASC_S_MICRO +\n", - " B_TIME * TT_S_MICRO +\n", - " B_COST * CO_S_MICRO\n", - ")\n", - "\n", - "V_RIDEHAIL = (\n", - " ASC_RIDEHAIL +\n", - " B_TIME * TT_RIDEHAIL +\n", - " B_COST * CO_RIDEHAIL\n", - ")\n", - "\n", - "# Cost is 0, so no point multiplying.\n", - "V_WALK = (\n", - " ASC_WALK +\n", - " B_TIME * TT_WALK\n", - " + B_COST * CO_WALK\n", - ")\n", - "\n", - "V_UNKNOWN = (\n", - " ASC_UNKNOWN +\n", - " B_TIME * TT_UNKNOWN +\n", - " B_COST * CO_UNKNOWN\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Map alterative to utility functions.\n", - "V = {\n", - " 1: V_P_MICRO, \n", - " 2: V_NO_TRIP,\n", - " 3: V_S_CAR, \n", - " 4: V_TRANSIT,\n", - " 5: V_CAR, \n", - " 6: V_S_MICRO,\n", - " 7: V_RIDEHAIL, \n", - " 8: V_WALK, \n", - " 9: V_UNKNOWN\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Availability.\n", - "# We have already masked-off costs and times for the instances where a particular mode is not available to the user.\n", - "# So here, we can specify the availability as 1 for every mode, saying that every other mode should be included when computing\n", - "# the logprob for a certain mode.\n", - "\n", - "av = {\n", - " 1: AV_P_MICRO,\n", - " 2: AV_NO_TRIP,\n", - " 3: AV_S_CAR,\n", - " 4: AV_TRANSIT,\n", - " 5: AV_CAR,\n", - " 6: AV_S_MICRO,\n", - " 7: AV_RIDEHAIL,\n", - " 8: AV_WALK,\n", - " 9: AV_UNKNOWN\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "logprob = models.loglogit(V, av, CHOICE)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = bio.BIOGEME(database, logprob)\n", - "model.modelName = 'linearModelCostsModel'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results = model.estimate()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(results.short_summary())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results.getEstimatedParameters()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_metrics(model: bio.BIOGEME, database: db.Database, V: dict):\n", - " \"\"\"\n", - " Given a test dataframe and the trained model, generate precision, recall, F1.\n", - " \"\"\"\n", - "\n", - " predictions = list()\n", - "\n", - " for row_ix in range(data.shape[0]):\n", - " preds = dict()\n", - " for alt_id, utility in V.items():\n", - " preds[alt_id] = utility.getValue_c(database)\n", - " \n", - " predictions.append(preds)\n", - " return pd.DataFrame(predictions)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results.getBetaValues()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def get_utility_df(results, data):\n", - "\n", - " ''' \n", - " 1: V_P_MICRO, \n", - " 2: V_NO_TRIP,\n", - " 3: V_S_CAR, \n", - " 4: V_TRANSIT,\n", - " 5: V_CAR, \n", - " 6: V_S_MICRO,\n", - " 7: V_RIDEHAIL, \n", - " 8: V_WALK, \n", - " # 9: V_UNKNOWN\n", - " # '''\n", - "\n", - " def compute_utilities(betas, row: pd.Series):\n", - " data = row.to_dict()\n", - "\n", - " utility_p_micro = betas['ASC_P_MICRO'] + (betas['B_TIME'] * data['tt_p_micro'])\n", - " utility_no_trip = betas['ASC_NO_TRIP'] + (betas['B_TIME'] * data['tt_no_trip']) + (betas['B_COST'] * data['scaled_cost_no_trip'])\n", - " utility_s_car = betas['ASC_S_CAR'] + (betas['B_COST'] * data['scaled_cost_s_car']) + (betas['B_TIME'] * data['tt_s_car'])\n", - " utility_transit = betas['ASC_TRANSIT'] + (betas['B_COST'] * data['scaled_cost_transit']) + (betas['B_TIME'] * data['tt_transit'])\n", - " utility_car = betas['ASC_CAR'] + (betas['B_COST'] * data['scaled_cost_car'] + (betas['B_TIME'] * data['tt_car']))\n", - " utility_s_micro = betas['ASC_S_MICRO'] + (betas['B_COST'] * data['scaled_cost_s_micro']) + (betas['B_TIME'] * data['tt_s_micro'])\n", - " utility_ridehail = betas['ASC_RIDEHAIL'] + (betas['B_COST'] * data['scaled_cost_ridehail']) + (betas['B_TIME'] * data['tt_ridehail'])\n", - " utility_walk = betas['ASC_WALK'] + (betas['B_TIME'] * data['tt_walk'])\n", - " utility_unknown = betas['ASC_UNKNOWN'] + (betas['B_TIME'] * data['tt_unknown']) + (betas['B_COST'] * data['scaled_cost_unknown'])\n", - "\n", - " return {\n", - " 'utility_p_micro': utility_p_micro, 'utility_no_trip': utility_no_trip,\n", - " 'utility_s_car': utility_s_car, 'utility_transit': utility_transit,\n", - " 'utility_car': utility_car, 'utility_s_micro': utility_s_micro,\n", - " 'utility_ridehail': utility_ridehail, 'utility_walk': utility_walk, \n", - " 'utility_unknown': utility_unknown, \n", - " }\n", - " \n", - " betas = results.getBetaValues()\n", - "\n", - " u_data = data.apply(lambda x: compute_utilities(betas, x), axis=1).tolist()\n", - " return pd.DataFrame(u_data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "u_df = get_utility_df(results, database.fullData)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "u_np = u_df.values\n", - "choice_df = np.exp(u_np)/np.sum(np.exp(u_np), axis=1, keepdims=True)\n", - "\n", - "choice_df = pd.DataFrame(choice_df, columns=u_df.columns)\n", - "display(choice_df.head())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import f1_score\n", - "\n", - "y_pred = np.argmax(choice_df.values, axis=1) + 1\n", - "\n", - "print(np.unique(y_pred))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pd.Series(y_pred).hist()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# labels are 1-9\n", - "y_true = data.chosen\n", - "\n", - "pd.Series(y_true).hist()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "score = f1_score(y_true, y_pred, average='weighted')\n", - "print(score)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "interpreter": { - "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" - }, - "kernelspec": { - "display_name": "Python 3.9.16 ('emission')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/viz_scripts/rm_src/biogeme_modeling_train_test.ipynb b/viz_scripts/rm_src/biogeme_modeling_train_test.ipynb deleted file mode 100644 index 00ed265..0000000 --- a/viz_scripts/rm_src/biogeme_modeling_train_test.ipynb +++ /dev/null @@ -1,1099 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from enum import Enum\n", - "from sklearn.model_selection import train_test_split" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class SPLIT_TYPE(Enum):\n", - " INTRA_USER = 0\n", - " INTER_USER = 1\n", - " \n", - "\n", - "class SPLIT(Enum):\n", - " TRAIN = 0\n", - " TEST = 1\n", - "\n", - "\n", - "def get_splits(count_df: pd.DataFrame, n:int, test_size=0.2):\n", - " maxsize = int(n * test_size)\n", - "\n", - " max_threshold = int(maxsize * 1.05)\n", - " min_threshold = int(maxsize * 0.95)\n", - "\n", - " print(f\"{min_threshold=}, {max_threshold=}\")\n", - " \n", - " # Allow a 10% tolerance\n", - " def _dp(ix, curr_size, ids, cache):\n", - " \n", - " if ix >= count_df.shape[0]:\n", - " return []\n", - "\n", - " key = ix\n", - "\n", - " if key in cache:\n", - " return cache[key]\n", - "\n", - " if curr_size > max_threshold:\n", - " return []\n", - "\n", - " if min_threshold <= curr_size <= max_threshold:\n", - " return ids\n", - "\n", - " # two options - either pick the current id or skip it.\n", - " branch_a = _dp(ix, curr_size+count_df.loc[ix, 'count'], ids+[count_df.loc[ix, 'index']], cache)\n", - " branch_b = _dp(ix+1, curr_size, ids, cache)\n", - " \n", - " curr_max = []\n", - " if branch_a and len(branch_a) > 0:\n", - " curr_max = branch_a\n", - " \n", - " if branch_b and len(branch_b) > len(branch_a):\n", - " curr_max = branch_b\n", - " \n", - " cache[key] = curr_max\n", - " return cache[key]\n", - " \n", - " return _dp(0, 0, ids=list(), cache=dict())\n", - "\n", - "\n", - "def get_train_test_splits(data: pd.DataFrame, how=SPLIT_TYPE, test_ratio=0.2, shuffle=True):\n", - "\n", - " n_users = list(data.user_id.unique())\n", - " n = data.shape[0]\n", - " \n", - " if shuffle:\n", - " data = data.sample(data.shape[0]).reset_index(drop=True, inplace=False)\n", - "\n", - " if how == SPLIT_TYPE.INTER_USER:\n", - " # Make the split, ensuring that a user in one fold is not leaked into the other fold.\n", - " # Basic idea: we want to start with the users with the highest instances and place alternating users in each set.\n", - " counts = data.user_id.value_counts().reset_index(drop=False, inplace=False, name='count')\n", - "\n", - " # Now, start with the user_id at the top, and keep adding to either split.\n", - " # This can be achieved using a simple DP program.\n", - " test_ids = get_splits(counts, data.shape[0])\n", - " test_data = data.loc[data.user_id.isin(test_ids), :]\n", - " train_index = data.index.difference(test_data.index)\n", - " train_data = data.loc[data.user_id.isin(train_index), :]\n", - " \n", - " return train_data, test_data\n", - " \n", - " elif how == SPLIT_TYPE.INTRA_USER:\n", - " \n", - " # There are certain users with only one observation. What do we do with those?\n", - " # As per the mobilitynet modeling pipeline, we randomly assign them to either the\n", - " # training or test set.\n", - " \n", - " value_counts = data.user_id.value_counts()\n", - " single_count_ids = value_counts[value_counts == 1].index\n", - " \n", - " data_filtered = data.loc[~data.user_id.isin(single_count_ids), :].reset_index(drop=True)\n", - " data_single_counts = data.loc[data.user_id.isin(single_count_ids), :].reset_index(drop=True)\n", - " \n", - " X_tr, X_te = train_test_split(\n", - " data_filtered, test_size=test_ratio, shuffle=shuffle, stratify=data_filtered.user_id\n", - " )\n", - " \n", - " data_single_counts['assigned'] = np.random.choice(['train', 'test'], len(data_single_counts))\n", - " X_tr_merged = pd.concat(\n", - " [X_tr, data_single_counts.loc[data_single_counts.assigned == 'train', :].drop(\n", - " columns=['assigned'], inplace=False\n", - " )],\n", - " ignore_index=True, axis=0\n", - " )\n", - " \n", - " X_te_merged = pd.concat(\n", - " [X_te, data_single_counts.loc[data_single_counts.assigned == 'test', :].drop(\n", - " columns=['assigned'], inplace=False\n", - " )],\n", - " ignore_index=True, axis=0\n", - " )\n", - " \n", - " return X_tr_merged, X_te_merged\n", - " \n", - " raise NotImplementedError(\"Unknown split type\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Modeling" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import biogeme.biogeme as bio\n", - "import biogeme.database as db\n", - "from biogeme import models\n", - "from biogeme.expressions import Beta, DefineVariable\n", - "from biogeme.expressions import Variable\n", - "import numpy as np\n", - "\n", - "from sklearn.preprocessing import MinMaxScaler" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Read the data.\n", - "data = pd.read_csv('../data/preprocessed_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# First, we map the user IDs to ints.\n", - "\n", - "USER_MAP = {\n", - " u: i+1 for (i, u) in enumerate(data.user_id.unique())\n", - "}\n", - "\n", - "data['user_id'] = data['user_id'].apply(lambda x: USER_MAP[x])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Now, we split the data (either inter-user or intra-user split)\n", - "\n", - "# 0.98 with this approach.????\n", - "# train_data, test_data = get_train_test_splits(data=data, how=SPLIT_TYPE.INTER_USER, shuffle=True)\n", - "\n", - "# 0.965 with this approach.???\n", - "train_data, test_data = get_train_test_splits(data=data, how=SPLIT_TYPE.INTRA_USER, shuffle=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Some helper functions that will help ease redundancy in the code.\n", - "\n", - "def drop_columns(df: pd.DataFrame):\n", - " # Drop section_mode_argmax and available_modes.\n", - " return df.drop(columns=[\n", - " 'section_mode_argmax', 'available_modes', 'section_duration_argmax', 'section_distance_argmax'\n", - " ], inplace=False)\n", - "\n", - "\n", - "def scale_time(df: pd.DataFrame):\n", - " # Convert from min -> hrs\n", - " df[[c for c in df.columns if 'tt_' in c]] /= 60.\n", - " return df\n", - "\n", - "\n", - "def scale_cost(df: pd.DataFrame, split: SPLIT, scaler=None):\n", - " # Scale costs using MinMaxScaler.\n", - " costs = df[[c for c in df.columns if 'cost_' in c]].copy()\n", - " \n", - " if split == SPLIT.TRAIN and scaler is None:\n", - " scaler = MinMaxScaler()\n", - " cost_scaled = pd.DataFrame(\n", - " scaler.fit_transform(costs), \n", - " columns=['scaled_' + c for c in costs.columns], \n", - " index=costs.index\n", - " )\n", - " \n", - " elif split == SPLIT.TEST and scaler is not None:\n", - " cost_scaled = pd.DataFrame(\n", - " scaler.transform(costs), \n", - " columns=['scaled_' + c for c in costs.columns], \n", - " index=costs.index\n", - " )\n", - " \n", - " else:\n", - " raise NotImplementedError(\"Unknown split\")\n", - " \n", - " df = df.merge(right=cost_scaled, left_index=True, right_index=True)\n", - " \n", - " return df, scaler\n", - "\n", - "\n", - "def get_database(df: pd.DataFrame, split: SPLIT):\n", - " return db.Database(split.name + '_db', df)\n", - "\n", - "\n", - "def get_variables():\n", - " USER_ID = Variable('user_id')\n", - "\n", - " # Availability.\n", - " AV_P_MICRO = Variable('av_p_micro')\n", - " AV_NO_TRIP = Variable('av_no_trip')\n", - " AV_S_CAR = Variable('av_s_car')\n", - " AV_TRANSIT = Variable('av_transit')\n", - " AV_CAR = Variable('av_car')\n", - " AV_S_MICRO = Variable('av_s_micro')\n", - " AV_RIDEHAIL = Variable('av_ridehail')\n", - " AV_WALK = Variable('av_walk')\n", - " AV_UNKNOWN = Variable('av_unknown')\n", - "\n", - " # Time.\n", - " TT_P_MICRO = Variable('tt_p_micro')\n", - " TT_NO_TRIP = Variable('tt_no_trip')\n", - " TT_S_CAR = Variable('tt_s_car')\n", - " TT_TRANSIT = Variable('tt_transit')\n", - " TT_CAR = Variable('tt_car')\n", - " TT_S_MICRO = Variable('tt_s_micro')\n", - " TT_RIDEHAIL = Variable('tt_ridehail')\n", - " TT_WALK = Variable('tt_walk')\n", - " TT_UNKNOWN = Variable('tt_unknown')\n", - "\n", - " # Cost.\n", - " CO_P_MICRO = Variable('scaled_cost_p_micro')\n", - " CO_NO_TRIP = Variable('scaled_cost_no_trip')\n", - " CO_S_CAR = Variable('scaled_cost_s_car')\n", - " CO_TRANSIT = Variable('scaled_cost_transit')\n", - " CO_CAR = Variable('scaled_cost_car')\n", - " CO_S_MICRO = Variable('scaled_cost_s_micro')\n", - " CO_RIDEHAIL = Variable('scaled_cost_ridehail')\n", - " CO_WALK = Variable('scaled_cost_walk')\n", - " CO_UNKNOWN = Variable('scaled_cost_unknown')\n", - "\n", - " # Choice.\n", - " CHOICE = Variable('chosen')\n", - " \n", - " # return the filtered locals() dictionary.\n", - " return {k:v for k,v in locals().items() if not k.startswith('_')}\n", - "\n", - "\n", - "def exclude_from_db(v_dict: dict, db: db.Database):\n", - " EXCLUDE = (v_dict['CHOICE'] == 2) + (v_dict['CHOICE'] == 9) > 0\n", - " db.remove(EXCLUDE)\n", - "\n", - "\n", - "def get_params():\n", - " B_TIME = Beta('B_TIME', 0, None, 0, 0)\n", - " B_COST = Beta('B_COST', 0, None, None, 0)\n", - "\n", - " # Alternative-Specific Constants.\n", - " ASC_P_MICRO = Beta('ASC_P_MICRO', 0, None, None, 0)\n", - " ASC_NO_TRIP = Beta('ASC_NO_TRIP', 0, None, None, 0)\n", - " ASC_S_CAR = Beta('ASC_S_CAR', 0, None, None, 0)\n", - " ASC_TRANSIT = Beta('ASC_TRANSIT', 0, None, None, 0)\n", - " ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)\n", - " ASC_S_MICRO = Beta('ASC_S_MICRO', 0, None, None, 0)\n", - " ASC_RIDEHAIL = Beta('ASC_RIDEHAIL', 0, None, None, 0)\n", - " ASC_WALK = Beta('ASC_WALK', 0, None, None, 0)\n", - " ASC_UNKNOWN = Beta('ASC_UNKNOWN', 0, None, None, 0)\n", - " \n", - " # Return filtered locals dict.\n", - " return {k:v for k,v in locals().items() if not k.startswith('_')}\n", - "\n", - "\n", - "def get_utility_functions(v: dict):\n", - " V_P_MICRO = (\n", - " v['ASC_P_MICRO'] +\n", - " v['B_TIME'] * v['TT_P_MICRO']\n", - " + v['B_COST'] * v['CO_P_MICRO']\n", - " )\n", - "\n", - " V_NO_TRIP = (\n", - " v['ASC_NO_TRIP'] +\n", - " v['B_TIME'] * v['TT_NO_TRIP'] +\n", - " v['B_COST'] * v['CO_NO_TRIP']\n", - " )\n", - "\n", - " V_S_CAR = (\n", - " v['ASC_S_CAR'] +\n", - " v['B_TIME'] * v['TT_S_CAR'] +\n", - " v['B_COST'] * v['CO_S_CAR']\n", - " )\n", - "\n", - " V_TRANSIT = (\n", - " v['ASC_TRANSIT'] +\n", - " v['B_TIME'] * v['TT_TRANSIT'] +\n", - " v['B_COST'] * v['CO_TRANSIT']\n", - " )\n", - "\n", - " V_CAR = (\n", - " v['ASC_CAR'] +\n", - " v['B_TIME'] * v['TT_CAR'] +\n", - " v['B_COST'] * v['CO_CAR']\n", - " )\n", - "\n", - " V_S_MICRO = (\n", - " v['ASC_S_MICRO'] +\n", - " v['B_TIME'] * v['TT_S_MICRO'] +\n", - " v['B_COST'] * v['CO_S_MICRO']\n", - " )\n", - "\n", - " V_RIDEHAIL = (\n", - " v['ASC_RIDEHAIL'] +\n", - " v['B_TIME'] * v['TT_RIDEHAIL'] +\n", - " v['B_COST'] * v['CO_RIDEHAIL']\n", - " )\n", - "\n", - " V_WALK = (\n", - " v['ASC_WALK'] +\n", - " v['B_TIME'] * v['TT_WALK']\n", - " + v['B_COST'] * v['CO_WALK']\n", - " )\n", - "\n", - " V_UNKNOWN = (\n", - " v['ASC_UNKNOWN'] +\n", - " v['B_TIME'] * v['TT_UNKNOWN'] +\n", - " v['B_COST'] * v['CO_UNKNOWN']\n", - " )\n", - " \n", - " # Remember to exclude the input argument.\n", - " return {k:v for k,v in locals().items() if not k.startswith('_') and k != 'v'}\n", - "\n", - "\n", - "def get_utility_mapping(var: dict):\n", - " # Map alterative to utility functions.\n", - " return {\n", - " 1: var['V_P_MICRO'], \n", - " 2: var['V_NO_TRIP'],\n", - " 3: var['V_S_CAR'], \n", - " 4: var['V_TRANSIT'],\n", - " 5: var['V_CAR'], \n", - " 6: var['V_S_MICRO'],\n", - " 7: var['V_RIDEHAIL'], \n", - " 8: var['V_WALK'], \n", - " 9: var['V_UNKNOWN']\n", - " }\n", - "\n", - "\n", - "def get_availability_mapping(var: dict):\n", - " return {\n", - " 1: var['AV_P_MICRO'],\n", - " 2: var['AV_NO_TRIP'],\n", - " 3: var['AV_S_CAR'],\n", - " 4: var['AV_TRANSIT'],\n", - " 5: var['AV_CAR'],\n", - " 6: var['AV_S_MICRO'],\n", - " 7: var['AV_RIDEHAIL'],\n", - " 8: var['AV_WALK'],\n", - " 9: var['AV_UNKNOWN']\n", - " }" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# First, drop columns.\n", - "\n", - "train_data = drop_columns(train_data)\n", - "\n", - "# Next, scale time.\n", - "train_data = scale_time(train_data)\n", - "\n", - "# Scale cost.\n", - "train_data, scaler = scale_cost(train_data, SPLIT.TRAIN, None)\n", - "\n", - "# get dbs.\n", - "train_db = get_database(train_data, SPLIT.TRAIN)\n", - "\n", - "# get vars.\n", - "train_vars = get_variables()\n", - "\n", - "# exclude wrong points.\n", - "exclude_from_db(train_vars, train_db)\n", - "\n", - "train_params = get_params()\n", - "train_vars.update(train_params)\n", - "\n", - "train_V = get_utility_functions(train_vars)\n", - "train_vars.update(train_V)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "V = get_utility_mapping(train_vars)\n", - "av = get_availability_mapping(train_vars)\n", - "train_logprob = models.loglogit(V, av, train_vars['CHOICE'])\n", - "\n", - "model = bio.BIOGEME(train_db, train_logprob)\n", - "model.modelName = 'linearModelCostsModel-intraUser'" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rkulhall/miniconda3/envs/emission/lib/python3.9/site-packages/biogeme_optimization/simple_bounds.py:213: RuntimeWarning: invalid value encountered in scalar divide\n", - " rho = num / denom\n" - ] - } - ], - "source": [ - "train_results = model.estimate()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Results for model linearModelCostsModel-intraUser\n", - "Nbr of parameters:\t\t11\n", - "Sample size:\t\t\t57579\n", - "Excluded data:\t\t\t2118\n", - "Final log likelihood:\t\t-3.102707e-05\n", - "Akaike Information Criterion:\t22.00006\n", - "Bayesian Information Criterion:\t120.5701\n", - "\n" - ] - } - ], - "source": [ - "print(train_results.short_summary())" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ValueRob. Std errRob. t-testRob. p-value
ASC_CAR42.6844941.797693e+3082.374404e-3071.0
ASC_NO_TRIP-260.4133749.457320e+00-2.753564e+010.0
ASC_P_MICRO33.0861801.797693e+3081.840480e-3071.0
ASC_RIDEHAIL34.0023201.797693e+3081.891442e-3071.0
ASC_S_CAR40.6975321.797693e+3082.263875e-3071.0
ASC_S_MICRO18.9711891.797693e+3081.055307e-3071.0
ASC_TRANSIT35.8428131.797693e+3081.993823e-3071.0
ASC_UNKNOWN0.0000001.845808e-130.000000e+001.0
ASC_WALK55.1288451.797693e+3083.066644e-3071.0
B_COST-2272.7710031.214323e+02-1.871636e+010.0
B_TIME-145.5888655.280683e-01-2.757008e+020.0
\n", - "
" - ], - "text/plain": [ - " Value Rob. Std err Rob. t-test Rob. p-value\n", - "ASC_CAR 42.684494 1.797693e+308 2.374404e-307 1.0\n", - "ASC_NO_TRIP -260.413374 9.457320e+00 -2.753564e+01 0.0\n", - "ASC_P_MICRO 33.086180 1.797693e+308 1.840480e-307 1.0\n", - "ASC_RIDEHAIL 34.002320 1.797693e+308 1.891442e-307 1.0\n", - "ASC_S_CAR 40.697532 1.797693e+308 2.263875e-307 1.0\n", - "ASC_S_MICRO 18.971189 1.797693e+308 1.055307e-307 1.0\n", - "ASC_TRANSIT 35.842813 1.797693e+308 1.993823e-307 1.0\n", - "ASC_UNKNOWN 0.000000 1.845808e-13 0.000000e+00 1.0\n", - "ASC_WALK 55.128845 1.797693e+308 3.066644e-307 1.0\n", - "B_COST -2272.771003 1.214323e+02 -1.871636e+01 0.0\n", - "B_TIME -145.588865 5.280683e-01 -2.757008e+02 0.0" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(train_results.getEstimatedParameters())" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "def get_utility_df(results, data):\n", - "\n", - " def compute_utilities(betas, row: pd.Series):\n", - " data = row.to_dict()\n", - "\n", - " utility_p_micro = betas['ASC_P_MICRO'] + (betas['B_TIME'] * data['tt_p_micro'])\n", - " utility_no_trip = betas['ASC_NO_TRIP'] + (betas['B_TIME'] * data['tt_no_trip']) + (betas['B_COST'] * data['scaled_cost_no_trip'])\n", - " utility_s_car = betas['ASC_S_CAR'] + (betas['B_COST'] * data['scaled_cost_s_car']) + (betas['B_TIME'] * data['tt_s_car'])\n", - " utility_transit = betas['ASC_TRANSIT'] + (betas['B_COST'] * data['scaled_cost_transit']) + (betas['B_TIME'] * data['tt_transit'])\n", - " utility_car = betas['ASC_CAR'] + (betas['B_COST'] * data['scaled_cost_car'] + (betas['B_TIME'] * data['tt_car']))\n", - " utility_s_micro = betas['ASC_S_MICRO'] + (betas['B_COST'] * data['scaled_cost_s_micro']) + (betas['B_TIME'] * data['tt_s_micro'])\n", - " utility_ridehail = betas['ASC_RIDEHAIL'] + (betas['B_COST'] * data['scaled_cost_ridehail']) + (betas['B_TIME'] * data['tt_ridehail'])\n", - " utility_walk = betas['ASC_WALK'] + (betas['B_TIME'] * data['tt_walk'])\n", - " utility_unknown = betas['ASC_UNKNOWN'] + (betas['B_TIME'] * data['tt_unknown']) + (betas['B_COST'] * data['scaled_cost_unknown'])\n", - "\n", - " return {\n", - " 'utility_p_micro': utility_p_micro, 'utility_no_trip': utility_no_trip,\n", - " 'utility_s_car': utility_s_car, 'utility_transit': utility_transit,\n", - " 'utility_car': utility_car, 'utility_s_micro': utility_s_micro,\n", - " 'utility_ridehail': utility_ridehail, 'utility_walk': utility_walk, \n", - " 'utility_unknown': utility_unknown, \n", - " }\n", - " \n", - " betas = results.getBetaValues()\n", - "\n", - " u_data = data.apply(lambda x: compute_utilities(betas, x), axis=1).tolist()\n", - " return pd.DataFrame(u_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "test_data = drop_columns(test_data)\n", - "\n", - "# Next, scale time.\n", - "test_data = scale_time(test_data)\n", - "\n", - "# Scale cost.\n", - "test_data, _ = scale_cost(test_data, SPLIT.TEST, scaler)\n", - "\n", - "# get dbs.\n", - "test_db = get_database(test_data, SPLIT.TEST)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "test_utilities = get_utility_df(train_results, test_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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utility_p_microutility_no_triputility_s_carutility_transitutility_carutility_s_microutility_ridehailutility_walkutility_unknown
0-18.241189-260.41337417.827530-13.045518-6.745978-45.912237-15.428152-157.474693-47.457612
115.548732-260.41337411.62647013.06722113.613432-0.5584744.93125853.723791-26.574742
214.974088-260.413374-31.381507-42.094303-29.394545-96.365456-38.076719-325.992645-70.688496
3-21.417490-260.41337415.997066-15.500152-8.659791-50.175555-17.341965-171.714529-49.420631
4-54.925702-260.413374-3.313322-41.395187-28.849463-95.151201-37.531637-321.936932-70.129400
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" - ], - "text/plain": [ - " utility_p_micro utility_no_trip utility_s_car utility_transit \\\n", - "0 -18.241189 -260.413374 17.827530 -13.045518 \n", - "1 15.548732 -260.413374 11.626470 13.067221 \n", - "2 14.974088 -260.413374 -31.381507 -42.094303 \n", - "3 -21.417490 -260.413374 15.997066 -15.500152 \n", - "4 -54.925702 -260.413374 -3.313322 -41.395187 \n", - "\n", - " utility_car utility_s_micro utility_ridehail utility_walk \\\n", - "0 -6.745978 -45.912237 -15.428152 -157.474693 \n", - "1 13.613432 -0.558474 4.931258 53.723791 \n", - "2 -29.394545 -96.365456 -38.076719 -325.992645 \n", - "3 -8.659791 -50.175555 -17.341965 -171.714529 \n", - "4 -28.849463 -95.151201 -37.531637 -321.936932 \n", - "\n", - " utility_unknown \n", - "0 -47.457612 \n", - "1 -26.574742 \n", - "2 -70.688496 \n", - "3 -49.420631 \n", - "4 -70.129400 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(test_utilities.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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utility_p_microutility_no_triputility_s_carutility_transitutility_carutility_s_microutility_ridehailutility_walkutility_unknown
02.165480e-161.450476e-1211.000000e+003.908458e-142.127458e-112.080515e-283.607785e-157.365664e-774.436322e-29
12.635013e-173.732181e-1375.216339e-192.203312e-183.804454e-182.663865e-246.451670e-221.000000e+001.339030e-35
21.000000e+002.516197e-1207.379397e-211.642527e-255.382046e-204.424358e-499.126982e-248.318201e-1496.269204e-38
35.637221e-179.046302e-1211.000000e+002.093784e-141.957325e-111.826401e-293.319272e-153.005315e-823.885574e-29
43.846168e-232.202248e-1121.000000e+002.892386e-178.124469e-121.304116e-401.377764e-154.202665e-1399.597242e-30
\n", - "
" - ], - "text/plain": [ - " utility_p_micro utility_no_trip utility_s_car utility_transit \\\n", - "0 2.165480e-16 1.450476e-121 1.000000e+00 3.908458e-14 \n", - "1 2.635013e-17 3.732181e-137 5.216339e-19 2.203312e-18 \n", - "2 1.000000e+00 2.516197e-120 7.379397e-21 1.642527e-25 \n", - "3 5.637221e-17 9.046302e-121 1.000000e+00 2.093784e-14 \n", - "4 3.846168e-23 2.202248e-112 1.000000e+00 2.892386e-17 \n", - "\n", - " utility_car utility_s_micro utility_ridehail utility_walk \\\n", - "0 2.127458e-11 2.080515e-28 3.607785e-15 7.365664e-77 \n", - "1 3.804454e-18 2.663865e-24 6.451670e-22 1.000000e+00 \n", - "2 5.382046e-20 4.424358e-49 9.126982e-24 8.318201e-149 \n", - "3 1.957325e-11 1.826401e-29 3.319272e-15 3.005315e-82 \n", - "4 8.124469e-12 1.304116e-40 1.377764e-15 4.202665e-139 \n", - "\n", - " utility_unknown \n", - "0 4.436322e-29 \n", - "1 1.339030e-35 \n", - "2 6.269204e-38 \n", - "3 3.885574e-29 \n", - "4 9.597242e-30 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "u_np = test_utilities.values\n", - "choice_df = np.exp(u_np)/np.sum(np.exp(u_np), axis=1, keepdims=True)\n", - "\n", - "choice_df = pd.DataFrame(choice_df, columns=test_utilities.columns)\n", - "display(choice_df.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1 2 3 4 5 8 9]\n" - ] - } - ], - "source": [ - "from sklearn.metrics import f1_score\n", - "\n", - "y_pred = np.argmax(choice_df.values, axis=1) + 1\n", - "\n", - "print(np.unique(y_pred))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.9692918926841162\n" - ] - } - ], - "source": [ - "y_true = test_data.chosen\n", - "score = f1_score(y_true, y_pred, average='weighted')\n", - "\n", - "print(score)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 5))\n", - "\n", - "sns.histplot(y_pred, ax=ax[0])\n", - "sns.histplot(y_true, ax=ax[1])\n", - "\n", - "labels = [\n", - " 'p_micro', \n", - " 'no_trip',\n", - " 's_car', \n", - " 'transit',\n", - " 'car', \n", - " 's_micro',\n", - " 'ridehail', \n", - " 'walk', \n", - " 'unknown'\n", - "]\n", - "\n", - "ax[0].set(\n", - " title='predicted label distribution',\n", - " xlabel='Labels',\n", - " xticks=range(1, 10),\n", - " xticklabels=labels\n", - ")\n", - "\n", - "ax[1].set(\n", - " title='true label distribution',\n", - " xlabel='Labels',\n", - " xticks=range(1, 10),\n", - " xticklabels=labels\n", - ")\n", - "\n", - "ax[0].set_xticklabels(ax[0].get_xticklabels(), rotation=45)\n", - "ax[1].set_xticklabels(ax[0].get_xticklabels(), rotation=45)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "interpreter": { - "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" - }, - "kernelspec": { - "display_name": "emission", - "language": "python", - "name": "emission" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/viz_scripts/rm_src/cost_time_avl_preprocessing.ipynb b/viz_scripts/rm_src/cost_time_avl_preprocessing.ipynb deleted file mode 100644 index 9f2bb05..0000000 --- a/viz_scripts/rm_src/cost_time_avl_preprocessing.ipynb +++ /dev/null @@ -1,1789 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import ast\n", - "from traceback import print_exception\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Data loading. Refer to the mnl_exploration.ipynb file for details on how user-level features are generated.\n", - "\n", - "df = pd.read_csv('../data/final_modeling_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape: (74631, 57)\n" - ] - } - ], - "source": [ - "print(\"Shape: \", df.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "df.drop_duplicates(inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Add weather info" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Downloaded using OpenMeteo API.\n", - "weather_df = pd.read_csv('../data/denver_weather_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "weather_df.time = pd.to_datetime(weather_df.time, utc=True).dt.tz_convert('America/Denver')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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temperature_2m (°F)relative_humidity_2m (%)dew_point_2m (°F)rain (inch)snowfall (inch)cloud_cover (%)wind_speed_10m (mp/h)wind_gusts_10m (mp/h)
count64728.00000064728.00000064728.00000064728.00000064728.00000064728.00000064728.00000064728.000000
mean49.36332552.40535829.1052540.0011580.00495829.3645415.53808411.855783
std21.50211223.37017915.5786720.0086320.03559033.1812153.1671995.973471
min-22.4000005.000000-27.8000000.0000000.0000000.0000000.0000001.100000
25%32.50000033.00000018.0000000.0000000.0000000.0000003.5000007.600000
50%49.20000053.00000028.9000000.0000000.00000018.0000004.90000010.500000
75%65.60000071.00000041.5000000.0000000.00000046.0000006.80000015.000000
max100.900000100.00000068.8000000.4490001.378000100.00000045.90000077.600000
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" - ], - "text/plain": [ - " temperature_2m (°F) relative_humidity_2m (%) dew_point_2m (°F) \\\n", - "count 64728.000000 64728.000000 64728.000000 \n", - "mean 49.363325 52.405358 29.105254 \n", - "std 21.502112 23.370179 15.578672 \n", - "min -22.400000 5.000000 -27.800000 \n", - "25% 32.500000 33.000000 18.000000 \n", - "50% 49.200000 53.000000 28.900000 \n", - "75% 65.600000 71.000000 41.500000 \n", - "max 100.900000 100.000000 68.800000 \n", - "\n", - " rain (inch) snowfall (inch) cloud_cover (%) wind_speed_10m (mp/h) \\\n", - "count 64728.000000 64728.000000 64728.000000 64728.000000 \n", - "mean 0.001158 0.004958 29.364541 5.538084 \n", - "std 0.008632 0.035590 33.181215 3.167199 \n", - "min 0.000000 0.000000 0.000000 0.000000 \n", - "25% 0.000000 0.000000 0.000000 3.500000 \n", - "50% 0.000000 0.000000 18.000000 4.900000 \n", - "75% 0.000000 0.000000 46.000000 6.800000 \n", - "max 0.449000 1.378000 100.000000 45.900000 \n", - "\n", - " wind_gusts_10m (mp/h) \n", - "count 64728.000000 \n", - "mean 11.855783 \n", - "std 5.973471 \n", - "min 1.100000 \n", - "25% 7.600000 \n", - "50% 10.500000 \n", - "75% 15.000000 \n", - "max 77.600000 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "weather_df.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# First, convert to UTC. Then, parse to America/Denver.\n", - "df['start_fmt_time'] = pd.to_datetime(\n", - " df['start_fmt_time'], utc=True\n", - ").dt.tz_convert('America/Denver')\n", - "\n", - "original_shape = df.shape[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Round start time to nearest hour.\n", - "df['start_time_rounded'] = df.start_fmt_time.dt.round(\n", - " 'H', ambiguous='infer', nonexistent='shift_backward'\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# True intersection\n", - "df = df.merge(right=weather_df, left_on='start_time_rounded', right_on='time', how='inner')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Old shape: 74631. new shape: 74622\n" - ] - } - ], - "source": [ - "print(f\"Old shape: {original_shape}. new shape: {df.shape[0]}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "df.drop(columns=['start_time_rounded', 'time'], inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating the dummy variables for the available modes" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# We want this to be ordinal because 2 > 1 implies that it has higher associated value.\n", - "\n", - "income_ordinal_mapping = {\n", - " np.nan: 0,\n", - " 'Prefer not to say': 0,\n", - " 'Less than $24,999': 1,\n", - " '$25,000-$49,999': 2,\n", - " '$50,000-$99,999': 3,\n", - " '$100,000 -$149,999': 4,\n", - " '$150,000-$199,999': 5\n", - "}\n", - "\n", - "df.income_category = df.income_category.apply(lambda x: income_ordinal_mapping[x])" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# | [d1, d2, d3] | [t1, t2, t3] | [m1, m2, m3] |\n", - "\n", - "\n", - "def compute_argmax(df: pd.DataFrame):\n", - " # Create the two columns.\n", - " df[['section_distance_argmax', 'section_duration_argmax', 'section_mode_argmax', 'section_coordinates_argmax']] = None\n", - " rows = list()\n", - "\n", - " for ix, row in df.iterrows():\n", - " row_dict = row.to_dict()\n", - " \n", - " try:\n", - " parsed_distances = ast.literal_eval(row_dict['section_distances'])\n", - " parsed_durations = ast.literal_eval(row_dict['section_durations'])\n", - " parsed_modes = ast.literal_eval(row_dict['section_modes'])\n", - "\n", - " argmax_ix = np.argmax(parsed_distances)\n", - " \n", - " # Pick the argmax and scale to miles. (meters -> miles)\n", - " row_dict['section_distance_argmax'] = parsed_distances[argmax_ix] * 0.0006213712\n", - " \n", - " # Pick the argmax and scale to minutes.\n", - " row_dict['section_duration_argmax'] = parsed_durations[argmax_ix] / 60\n", - " \n", - " # Pick the argmax mode.\n", - " row_dict['section_mode_argmax'] = parsed_modes[argmax_ix]\n", - " \n", - " row_dict['mark'] = False\n", - "\n", - " except Exception as e:\n", - " row_dict['mark'] = True\n", - "\n", - " rows.append(row_dict)\n", - "\n", - " return pd.DataFrame(rows)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Let's plot the mode-wise durations as a function of distance.\n", - "df = compute_argmax(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "df = df.loc[~df.mark, :].reset_index(drop=True, inplace=False).drop(columns=['mark'], inplace=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Drop instances where duration/distance is unusable.\n", - "df.drop(\n", - " index=df.loc[(df.section_distance_argmax <= 0) | (df.section_duration_argmax <= 0), :].index,\n", - " inplace=False\n", - ").reset_index(drop=True, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# bus, train, bicycling, walking, car\n", - "# split-apply-combine\n", - "def drop_outliers(df: pd.DataFrame) -> pd.DataFrame:\n", - " def filter_by_percentiles(group):\n", - " distance_low = group['section_distance_argmax'].quantile(0.1)\n", - " distance_high = group['section_distance_argmax'].quantile(0.9)\n", - " duration_low = group['section_duration_argmax'].quantile(0.1)\n", - " duration_high = group['section_duration_argmax'].quantile(0.9)\n", - " \n", - " l1_filter = group[\n", - " (group['section_distance_argmax'] >= distance_low) &\n", - " (group['section_distance_argmax'] <= distance_high)\n", - " ].reset_index(drop=True)\n", - " \n", - " l2_filter = l1_filter[\n", - " (l1_filter['section_duration_argmax'] >= duration_low) &\n", - " (l1_filter['section_duration_argmax'] <= duration_high)\n", - " ].reset_index(drop=True)\n", - " \n", - " return l2_filter\n", - " \n", - " return df.groupby('section_mode_argmax').apply(filter_by_percentiles).reset_index(drop=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "filtered_df = drop_outliers(df)\n", - "\n", - "# Ideal speed. distance/time.\n", - "filtered_df['mph'] = (\n", - " (filtered_df['section_distance_argmax'] * 60.)/filtered_df['section_duration_argmax']\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "def filter_mph(df: pd.DataFrame) -> pd.DataFrame:\n", - " \n", - " MPH_THRESHOLDS = {\n", - " # https://www.sciencedirect.com/science/article/pii/S2210670718304682\n", - " 'bicycling': 15.,\n", - " # https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806575/\n", - " 'walking': 2.93\n", - " }\n", - " \n", - " def custom_filter(group):\n", - " # Drop data specified in the dict manually.\n", - " if group.name in MPH_THRESHOLDS.keys():\n", - " f_df = group[group['mph'] <= MPH_THRESHOLDS[group.name]]\n", - " else:\n", - " mph_low = group['mph'].quantile(0.1)\n", - " mph_high = group['mph'].quantile(0.9)\n", - "\n", - " f_df = group[(group['mph'] >= mph_low) & (group['mph'] <= mph_high)]\n", - " \n", - " return f_df\n", - " \n", - " return df.groupby('section_mode_argmax').apply(custom_filter).reset_index(drop=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "filtered_df = filter_mph(filtered_df)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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section_distance_argmaxsection_duration_argmax
countmeanstdmin25%50%75%maxcountmeanstdmin25%50%75%max
section_mode_argmax
bicycling6894.02.1394110.8631500.8163011.5082451.9541512.5704084.8630866894.013.6059434.9737915.8833339.91527912.60526816.44149428.536488
bus262.01.4152400.5975870.3728750.9224741.4553521.9141972.790900262.08.8709383.4957832.9631776.5598038.24097410.53729019.882077
car22816.03.9718982.4654580.9025742.1413053.3318985.10122914.18317622816.013.6251996.4578265.6133498.62463511.84566416.99331134.438154
no_sensed1537.02.5943222.2860700.1759341.0195841.9249043.36957913.1542171537.015.28034811.2996531.0450787.12964912.03516720.78817652.847837
train40.011.0875402.2841916.35232510.18673010.38422312.41472215.74303740.026.4695555.94026318.18095321.97713524.72284429.04570040.568931
walking11032.00.4786250.3742180.1195070.2202220.3473970.5986982.25137111032.020.02442913.3724015.2842339.43475615.74349526.81028561.655195
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" - ], - "text/plain": [ - " section_distance_argmax \\\n", - " count mean std min \n", - "section_mode_argmax \n", - "bicycling 6894.0 2.139411 0.863150 0.816301 \n", - "bus 262.0 1.415240 0.597587 0.372875 \n", - "car 22816.0 3.971898 2.465458 0.902574 \n", - "no_sensed 1537.0 2.594322 2.286070 0.175934 \n", - "train 40.0 11.087540 2.284191 6.352325 \n", - "walking 11032.0 0.478625 0.374218 0.119507 \n", - "\n", - " \\\n", - " 25% 50% 75% max \n", - "section_mode_argmax \n", - "bicycling 1.508245 1.954151 2.570408 4.863086 \n", - "bus 0.922474 1.455352 1.914197 2.790900 \n", - "car 2.141305 3.331898 5.101229 14.183176 \n", - "no_sensed 1.019584 1.924904 3.369579 13.154217 \n", - "train 10.186730 10.384223 12.414722 15.743037 \n", - "walking 0.220222 0.347397 0.598698 2.251371 \n", - "\n", - " section_duration_argmax \\\n", - " count mean std min \n", - "section_mode_argmax \n", - "bicycling 6894.0 13.605943 4.973791 5.883333 \n", - "bus 262.0 8.870938 3.495783 2.963177 \n", - "car 22816.0 13.625199 6.457826 5.613349 \n", - "no_sensed 1537.0 15.280348 11.299653 1.045078 \n", - "train 40.0 26.469555 5.940263 18.180953 \n", - "walking 11032.0 20.024429 13.372401 5.284233 \n", - "\n", - " \n", - " 25% 50% 75% max \n", - "section_mode_argmax \n", - "bicycling 9.915279 12.605268 16.441494 28.536488 \n", - "bus 6.559803 8.240974 10.537290 19.882077 \n", - "car 8.624635 11.845664 16.993311 34.438154 \n", - "no_sensed 7.129649 12.035167 20.788176 52.847837 \n", - "train 21.977135 24.722844 29.045700 40.568931 \n", - "walking 9.434756 15.743495 26.810285 61.655195 " - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "filtered_df.groupby('section_mode_argmax')[['section_distance_argmax', 'section_duration_argmax']].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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mph
countmeanstdmin25%50%75%max
section_mode_argmax
bicycling6894.09.6205812.3537762.8175807.9284989.65686611.28416714.998472
bus262.09.8418723.1534654.2331957.2158559.81802412.48795315.579753
car22816.017.1542125.4561968.51005512.61861016.42148521.20360829.536059
no_sensed1537.010.3879264.5759564.6081776.5969609.28998013.20603122.796647
train40.025.4152283.46861117.51393823.23404825.79593427.93265730.865389
walking11032.01.6053260.7474900.1315920.9830491.5913852.2474522.929953
\n", - "
" - ], - "text/plain": [ - " mph \\\n", - " count mean std min 25% \n", - "section_mode_argmax \n", - "bicycling 6894.0 9.620581 2.353776 2.817580 7.928498 \n", - "bus 262.0 9.841872 3.153465 4.233195 7.215855 \n", - "car 22816.0 17.154212 5.456196 8.510055 12.618610 \n", - "no_sensed 1537.0 10.387926 4.575956 4.608177 6.596960 \n", - "train 40.0 25.415228 3.468611 17.513938 23.234048 \n", - "walking 11032.0 1.605326 0.747490 0.131592 0.983049 \n", - "\n", - " \n", - " 50% 75% max \n", - "section_mode_argmax \n", - "bicycling 9.656866 11.284167 14.998472 \n", - "bus 9.818024 12.487953 15.579753 \n", - "car 16.421485 21.203608 29.536059 \n", - "no_sensed 9.289980 13.206031 22.796647 \n", - "train 25.795934 27.932657 30.865389 \n", - "walking 1.591385 2.247452 2.929953 " - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "filtered_df.groupby('section_mode_argmax')[['mph']].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dropped 32041 rows.\n" - ] - } - ], - "source": [ - "print(f\"Dropped {df.shape[0] - filtered_df.shape[0]} rows.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'p_micro': 1, 'no_trip': 2, 's_car': 3, 'transit': 4, 'car': 5, 's_micro': 6, 'ridehail': 7, 'walk': 8, 'unknown': 9}\n" - ] - } - ], - "source": [ - "## Define the mapping strategy\n", - "\n", - "\"\"\"\n", - "p_micro: Personal micromobility\n", - "s_micro: Shared micromobility\n", - "s_car: Shared car\n", - "car: Car/rental car\n", - "transit: train + bus\n", - "no_trip: No vehicle / None\n", - "ridehail: Uber/Lyft\n", - "\"\"\"\n", - "\n", - "available = {\n", - " 'Bicycle': 'p_micro',\n", - " 'Do not have vehicle': 'no_trip',\n", - " 'Get a ride from a friend or family member': 's_car',\n", - " 'None': 'no_trip',\n", - " 'Public transportation (bus, subway, light rail, etc.)': 'transit',\n", - " 'Rental car (including Zipcar/ Car2Go)': 'car',\n", - " 'Shared bicycle or scooter': 's_micro',\n", - " 'Skateboard': 'p_micro',\n", - " 'Taxi (regular taxi, Uber, Lyft, etc)': 'ridehail',\n", - " 'Walk/roll': 'walk'\n", - "}\n", - "\n", - "# Map each sensed mode to the binary indicators.\n", - "section_mode_mapping = {\n", - " 'bicycling': ['p_micro', 's_micro'],\n", - " 'bus': ['transit'],\n", - " 'car': ['s_car', 'car', 'ridehail'],\n", - " 'no_sensed': ['unknown'],\n", - " 'train': ['transit'],\n", - " 'walking': ['walk']\n", - "}\n", - "\n", - "# For target - mode_confirm\n", - "# target_mapping = {\n", - "# 'Regular Bike': 'p_micro',\n", - "# 'Walk': 'walk',\n", - "# 'Gas Car, with others': 's_car',\n", - "# 'Gas Car, drove alone': 'car',\n", - "# 'Bikeshare': 's_micro',\n", - "# 'Other': 'unknown',\n", - "# 'Bus': 'transit',\n", - "# 'Not a Trip': 'no_trip',\n", - "# 'E-bike': 'p_micro',\n", - "# 'Train': 'transit',\n", - "# 'Taxi/Uber/Lyft': 'ridehail',\n", - "# 'Free Shuttle': 'transit',\n", - "# 'Scooter share': 's_micro',\n", - "# 'Skate board': 'p_micro'\n", - "# }\n", - "\n", - "# For target - replaced_mode\n", - "target_mapping = {\n", - " 'Regular Bike': 'p_micro', \n", - " 'Walk': 'walk', \n", - " 'No Travel': 'no_trip', \n", - " 'Other': 'unknown',\n", - " 'Gas Car, with others': 's_car',\n", - " 'Gas Car, drove alone': 'car',\n", - " 'Train': 'transit',\n", - " 'Scooter share': 's_micro', \n", - " 'Taxi/Uber/Lyft': 'ridehail', \n", - " 'Free Shuttle': 'transit', \n", - " 'Skate board': 'p_micro',\n", - " 'Bikeshare': 's_micro',\n", - " 'Bus': 'transit',\n", - " 'Not a Trip': 'no_trip',\n", - " 'E-bike': 'p_micro'\n", - "}\n", - "\n", - "# # These values were obtained using a simple linear regression model. Specifics can be found in time_distance_estimation.ipynb\n", - "# duration_dict = {\n", - "# 'walk': {\n", - "# 'intercept': 14.09698152519682,\n", - "# 'coef': 19.153802211061134\n", - "# },\n", - "# 'p_micro': {\n", - "# 'intercept': 4.753590860550791,\n", - "# 'coef': 4.2723965916811935\n", - "# },\n", - "# 's_micro': {\n", - "# 'intercept': 4.753590860550791,\n", - "# 'coef': 4.2723965916811935\n", - "# },\n", - "# 's_car': {\n", - "# 'intercept': 10.515751350707365, \n", - "# 'coef': 1.112111276629702\n", - "# },\n", - "# 'car': {\n", - "# 'intercept': 10.515751350707365, \n", - "# 'coef': 1.112111276629702\n", - "# },\n", - "# 'ridehail': {\n", - "# 'intercept': 10.515751350707365, \n", - "# 'coef': 1.112111276629702\n", - "# },\n", - "# 'transit': {\n", - "# 'intercept': 7.5000351741492395,\n", - "# 'coef': 1.839562017986485\n", - "# },\n", - "# 'unknown': {\n", - "# 'intercept': 9.423026094931313,\n", - "# 'coef': 2.640429444466706\n", - "# },\n", - "# 'no_trip': {\n", - "# 'intercept': 0.,\n", - "# 'coef': 0.\n", - "# }\n", - "# }\n", - "\n", - "ordinal_mapping = {'p_micro': 1, 'no_trip': 2, 's_car': 3, 'transit': 4, 'car': 5, 's_micro': 6, 'ridehail': 7, 'walk': 8, 'unknown': 9}\n", - "reverse_ordinal_mapping = {v:k for k,v in ordinal_mapping.items()}\n", - "\n", - "print(ordinal_mapping)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_available_features(\n", - " df: pd.DataFrame, av: dict, sm: dict, target_mapper: dict\n", - "):\n", - " \"\"\"\n", - " This method generates the wide-form dummy features for available modes.\n", - " The current implementation uses (section_mode_argmax OR available_modes) to indicate mode availability.\n", - " \"\"\"\n", - "\n", - " # Reset indices.\n", - " df = df.reset_index(drop=True, inplace=False)\n", - "\n", - " columns = list(set(av.values())) + ['unknown']\n", - "\n", - " # Create the columns along with the 'chosen' column.\n", - " df[columns] = 0\n", - "\n", - " row_dicts = list()\n", - "\n", - " for i, row in df.iterrows():\n", - " row_dict = row.to_dict()\n", - "\n", - " # Access the available modes. Split on ; and strip all the elements.\n", - " available_modes = [x.strip() for x in str(row_dict['available_modes']).split(';')]\n", - " \n", - " # Set all the available modes associated with the chosen modes too.\n", - " # Toggle this flag off if you don't want the target to be considered as an available mode.\n", - " # row_dict[row_dict['chosen']] = 1\n", - " \n", - " row_dict['chosen'] = target_mapper[row_dict['chosen']]\n", - " \n", - " # Update the available modes.\n", - " for available_mode in available_modes:\n", - " row_dict[av[available_mode]] = 1\n", - " \n", - " # Now, look at the section_mode_argmax as a proxy for the confirmed mode.\n", - " chosen_modes = sm[row_dict['section_mode_argmax']]\n", - "\n", - " # Determine the length of the mapping.\n", - " n = len(chosen_modes)\n", - "\n", - " # # Go ahead and modify the current dict with the first value.\n", - " # row_dict['chosen'] = ordinal_mapper[chosen_modes[0]]\n", - " # row_dict['primary_mode'] = 1\n", - "\n", - " # Also update the available modes.\n", - " for chosen_mode in chosen_modes:\n", - " row_dict[chosen_mode] = 1\n", - "\n", - " # # Append the first row to the list.\n", - " row_dicts.append(row_dict)\n", - " \n", - " constructed = pd.DataFrame(row_dicts)\n", - " constructed.rename(columns=dict([(c, 'av_'+c) for c in target_mapper.keys()]), inplace=True)\n", - "\n", - " return constructed" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```language=python\n", - "{'p_micro': 1, 'no_trip': 2, 's_car': 3, 'transit': 4, 'car': 5, 's_micro': 6, 'ridehail': 7, 'walk': 8, 'unknown': 9}\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_target_variable(df: pd.DataFrame, mapper: dict):\n", - " df['chosen'] = df['Replaced_mode'].apply(lambda x: mapper[x])\n", - " df.drop(columns=['Replaced_mode'], inplace=True)\n", - " return df\n", - "\n", - "filtered_df = generate_target_variable(filtered_df, target_mapping)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate wide-format availability indicators..\n", - "av_df = generate_available_features(filtered_df, available, section_mode_mapping, ordinal_mapping)\n", - "\n", - "# Collapse train and bus into 'transit'.\n", - "av_df.loc[av_df.section_mode_argmax.isin(['bus', 'train']), 'section_mode_argmax'] = 'transit'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "av_df.section_mode_argmax.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " user_id _id \\\n", - "0 6373dfb8cb9b47e88e8f76adcfadde20 611fdd7cbc8ce4a9e3911adb \n", - "1 6373dfb8cb9b47e88e8f76adcfadde20 611fdd7cbc8ce4a9e3911add \n", - "2 6373dfb8cb9b47e88e8f76adcfadde20 611fdd7ebc8ce4a9e3911af4 \n", - "3 6373dfb8cb9b47e88e8f76adcfadde20 611fdd7fbc8ce4a9e3911af9 \n", - "4 6373dfb8cb9b47e88e8f76adcfadde20 611fdd7fbc8ce4a9e3911afb \n", - "\n", - " original_user_id cleaned_trip \\\n", - "0 6373dfb8-cb9b-47e8-8e8f-76adcfadde20 6082e48127dcf1f393b70ea8 \n", - "1 6373dfb8-cb9b-47e8-8e8f-76adcfadde20 60830ead2b9762e085f774f2 \n", - "2 6373dfb8-cb9b-47e8-8e8f-76adcfadde20 608b21db654c4a5ab2fa4364 \n", - "3 6373dfb8-cb9b-47e8-8e8f-76adcfadde20 608c1f002f548f5db71d16f9 \n", - "4 6373dfb8-cb9b-47e8-8e8f-76adcfadde20 608c8f7fb2d3b88f6947766b \n", - "\n", - " Mode_confirm start_fmt_time start:year start:month \\\n", - "0 Regular Bike 2021-04-22 18:48:48.365291-06:00 2021.0 4.0 \n", - "1 Regular Bike 2021-04-23 10:45:48.219950-06:00 2021.0 4.0 \n", - "2 Other 2021-04-29 14:05:50.979267-06:00 2021.0 4.0 \n", - "3 Other 2021-04-30 08:34:49.162054-06:00 2021.0 4.0 \n", - "4 Regular Bike 2021-04-30 11:58:07.194775-06:00 2021.0 4.0 \n", - "\n", - " start:day start:hour ... chosen av_s_car av_walk av_car av_ridehail \\\n", - "0 22.0 18.0 ... 1 0 0 0 0 \n", - "1 23.0 10.0 ... 1 0 0 0 0 \n", - "2 29.0 14.0 ... 9 0 0 0 0 \n", - "3 30.0 8.0 ... 9 0 0 0 0 \n", - "4 30.0 11.0 ... 1 0 0 0 0 \n", - "\n", - " av_p_micro av_no_trip av_s_micro av_transit av_unknown \n", - "0 1 0 1 0 0 \n", - "1 1 0 1 0 0 \n", - "2 1 0 1 0 0 \n", - "3 1 0 1 0 0 \n", - "4 1 0 1 0 0 \n", - "\n", - "[5 rows x 79 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(av_df.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# def compute_alt_durations(df: pd.DataFrame, reverse_mapper: dict, duration_mapper: dict):\n", - "# '''\n", - "# We re-iterate over the generated available mode df and populate the modes.\n", - "# We are only concerned with the rows that have primary_mode = 0, which indicates that they are alternate mode rows.\n", - "# '''\n", - "\n", - "# column_names = list(reverse_mapper.values())\n", - "\n", - "# # Start with a 0 cost for everyone.\n", - "# df[column_names] = 0\n", - "\n", - "# rows = list()\n", - "# for ix, row in df.iterrows():\n", - " \n", - "# # Convert to a dict\n", - "# row_dict = row.to_dict()\n", - "\n", - "# for mode in column_names:\n", - "# if mode == reverse_mapper[row['chosen']]:\n", - "# row_dict[mode] = row_dict['section_distance_argmax']\n", - "# continue\n", - " \n", - "# mode_params = duration_mapper[mode]\n", - "# # use availability as a mask.\n", - "# row_dict[mode] = mode_params['intercept'] + (mode_params['coef'] * row_dict['section_distance_argmax'])\n", - " \n", - "# rows.append(row_dict)\n", - " \n", - "# return pd.DataFrame(rows).rename(columns=dict([(c, 'tt_'+c) for c in column_names]), inplace=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "# av_time_df = compute_alt_durations(av_df, reverse_ordinal_mapping, duration_dict)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cost estimation\n", - "\n", - "```\n", - "{'p_micro': 1, 'no_trip': 2, 's_car': 3, 'transit': 4, 'car': 5, 's_micro': 6, 'ridehail': 7, 'walk': 8, 'unknown': 9}\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# All values are taken from VTPI.\n", - "# https://www.vtpi.org/tca/tca0501.pdf\n", - "mode_cost_per_mile = {\n", - " # bicycle/skateboard\n", - " 'p_micro': 0.,\n", - " 'no_trip': 0.,\n", - " # Shared car is half the cost of regular car, which is $0.6/mile.\n", - " 's_car': 0.3,\n", - " # Rental car.\n", - " 'car': 0.6,\n", - " # Average of bus and train taken.\n", - " 'transit': 0.6,\n", - " # Shared bicyle or scooter - values taken from https://nacto.org/shared-micromobility-2020-2021/ and \n", - " # https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/how-sharing-the-road-is-likely-to-transform-american-mobility\n", - " 's_micro': 0.3,\n", - " # uber/taxi/lyft\n", - " 'ridehail': 2.,\n", - " 'walk': 0.,\n", - " 'unknown': 0.\n", - "}\n", - "\n", - "# All 0 for now.\n", - "mode_init_cost = {\n", - " 'p_micro': 0.,\n", - " 'no_trip': 0.,\n", - " # Shared car is half the cost of regular car, which is $0.6/mile.\n", - " 's_car': 0.,\n", - " # Rental car.\n", - " 'car': 0.,\n", - " # Average of bus and train taken.\n", - " 'transit': 0.,\n", - " # $1 unlocking cost.\n", - " 's_micro': 1.,\n", - " # uber/taxi/lyft\n", - " 'ridehail': 0.,\n", - " 'walk': 0.,\n", - " 'unknown': 0.\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "def compute_cost_estimates(df: pd.DataFrame, cost_mapper: dict, init_cost_mapper: dict, ordinal_mapper: dict):\n", - " \n", - " # Create some extra colums.\n", - " columns = list(ordinal_mapper.keys())\n", - "\n", - " # Initialize the columns to 0.\n", - " df[columns] = 0.\n", - "\n", - " rows = list()\n", - "\n", - " # Iterate over every row.\n", - " for _, row in df.iterrows():\n", - " # Check which flags are active.\n", - " row_dict = row.to_dict()\n", - "\n", - " # Access the section_distance_argmax attribute for the distance. Note that this is now in miles.\n", - " distance = row_dict['section_distance_argmax']\n", - " \n", - " # Mask using availability.\n", - " for lookup in columns:\n", - " row_dict[lookup] = row_dict['av_' + lookup] * (init_cost_mapper[lookup] + (cost_mapper[lookup] * distance))\n", - "\n", - " rows.append(row_dict)\n", - "\n", - " new_df = pd.DataFrame(rows)\n", - " new_df.rename(columns=dict([(c, 'cost_'+c) for c in ordinal_mapper.keys()]), inplace=True)\n", - "\n", - " return new_df" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "cost_df = compute_cost_estimates(\n", - " av_df, cost_mapper=mode_cost_per_mile, \n", - " init_cost_mapper=mode_init_cost, \n", - " ordinal_mapper=ordinal_mapping\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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cost_p_microcost_no_tripcost_s_carcost_transitcost_carcost_s_microcost_ridehailcost_walkcost_unknown
count42581.042581.042581.00000042581.00000042581.00000042581.00000042581.00000042581.042581.0
mean0.00.00.7306771.0990121.2922300.4135214.6961480.00.0
std0.00.00.7667831.4103881.6028410.7745875.1796420.00.0
min0.00.00.0000000.0000000.0000000.0000000.0000000.00.0
25%0.00.00.0640500.0000000.0000000.0000000.0000000.00.0
50%0.00.00.5569000.6609250.8623170.0000003.5109220.00.0
75%0.00.01.0790301.6329992.1067100.0000007.1414600.00.0
max0.00.04.2549539.4458228.5099055.25376428.3663510.00.0
\n", - "
" - ], - "text/plain": [ - " cost_p_micro cost_no_trip cost_s_car cost_transit cost_car \\\n", - "count 42581.0 42581.0 42581.000000 42581.000000 42581.000000 \n", - "mean 0.0 0.0 0.730677 1.099012 1.292230 \n", - "std 0.0 0.0 0.766783 1.410388 1.602841 \n", - "min 0.0 0.0 0.000000 0.000000 0.000000 \n", - "25% 0.0 0.0 0.064050 0.000000 0.000000 \n", - "50% 0.0 0.0 0.556900 0.660925 0.862317 \n", - "75% 0.0 0.0 1.079030 1.632999 2.106710 \n", - "max 0.0 0.0 4.254953 9.445822 8.509905 \n", - "\n", - " cost_s_micro cost_ridehail cost_walk cost_unknown \n", - "count 42581.000000 42581.000000 42581.0 42581.0 \n", - "mean 0.413521 4.696148 0.0 0.0 \n", - "std 0.774587 5.179642 0.0 0.0 \n", - "min 0.000000 0.000000 0.0 0.0 \n", - "25% 0.000000 0.000000 0.0 0.0 \n", - "50% 0.000000 3.510922 0.0 0.0 \n", - "75% 0.000000 7.141460 0.0 0.0 \n", - "max 5.253764 28.366351 0.0 0.0 " - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cost_df[[c for c in cost_df.columns if 'cost_' in c]].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cost_df.is_male = cost_df.apply(lambda x: 1 if x.gender == 'Man' else 0, axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "student_status = {\n", - " 'Not a student': 0,\n", - " 'Yes - Full Time College/University': 1,\n", - " 'Yes - Vocation/Technical/Trade School': 1,\n", - " 'Yes - Part-Time College/University': 1,\n", - " 'Fire Fighter 2 Training': 0,\n", - " 'Taking prerequisites missing for grad program ': 1,\n", - " 'Work': 0,\n", - " 'Graduate': 1,\n", - " 'Work at csu': 0,\n", - " 'Custodian': 0, \n", - " 'taking classes toward early childhood licensure': 1,\n", - " 'Prefer not to say': 0\n", - "}\n", - "\n", - "\n", - "cost_df['is_student'] = cost_df['is_student'].apply(lambda x: student_status[x])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cost_df['age'] = cost_df['age'].apply(lambda x: x if x < 100 else 2024 - x)\n", - "cost_df['n_working_residents'] = cost_df['n_working_residents'].apply(lambda x: 0 if x < 0 else x)\n", - "cost_df.rename(\n", - " columns={'start_local_dt_weekday': 'start:DOW', 'end_local_dt_weekday': 'end:DOW'},\n", - " inplace=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# cost_df.to_csv('../data/FULL_preprocessed_data_RM_weather.csv', index=False)\n", - "cost_df.to_csv('../data/ReplacedMode_Fix_02072024.csv', index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "interpreter": { - "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" - }, - "kernelspec": { - "display_name": "emission", - "language": "python", - "name": "emission" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/viz_scripts/rm_src/mnl_exploration.ipynb b/viz_scripts/rm_src/mnl_exploration.ipynb deleted file mode 100644 index 9ca40bb..0000000 --- a/viz_scripts/rm_src/mnl_exploration.ipynb +++ /dev/null @@ -1,935 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "import pickle\n", - "import importlib\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "\n", - "from pathlib import Path\n", - "from uuid import UUID\n", - "from collections import defaultdict\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "emission_path = Path(os.getcwd()).parent.parent / 'my_emission_server' / 'e-mission-server'\n", - "sys.path.append(str(emission_path))\n", - "\n", - "# Also add the home (viz_scripts) to the path\n", - "sys.path.append('../viz_scripts')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "import scaffolding\n", - "import emission.core.get_database as edb" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "importlib.reload(scaffolding)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def r(df: pd.DataFrame) -> pd.DataFrame:\n", - " return df.reset_index(drop=True, inplace=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "with open('../viz_scripts/auxiliary_files/dic_re.pkl', 'rb') as f:\n", - " dic_re = pickle.loads(f.read())\n", - "\n", - "with open('../viz_scripts/auxiliary_files/dic_pur.pkl', 'rb') as f:\n", - " dic_pur = pickle.loads(f.read())\n", - "\n", - "# convert a dictionary to a defaultdict\n", - "dic_re = defaultdict(lambda: 'Other', dic_re)\n", - "dic_pur = defaultdict(lambda: 'Other', dic_pur)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found new program stage, creating new list\n", - "Found new program 4c, creating new list\n", - "Found new program cc, creating new list\n", - "Found new program fc, creating new list\n", - "Found new program pc, creating new list\n", - "Found new program sc, creating new list\n", - "Found new program vail, creating new list\n", - "Found new program prepilot, creating new list\n" - ] - } - ], - "source": [ - "# Split UUIDs by program\n", - "program_uuid_map = {}\n", - "for ue in edb.get_uuid_db().find():\n", - " uuid = str(ue['uuid'])\n", - " # uuid = str(ue['uuid'])\n", - " program = ue['user_email'].split(\"_\")[0]\n", - " if program in program_uuid_map.keys():\n", - " program_uuid_map[program].append(uuid)\n", - " else:\n", - " print(f\"Found new program {program}, creating new list\")\n", - " program_uuid_map[program] = []\n", - " program_uuid_map[program].append(uuid)\n", - "\n", - "uuid_program_list = []\n", - "for ue in edb.get_uuid_db().find():\n", - " # uuid = str(ue['uuid'].as_uuid(3))\n", - " uuid = str(ue['uuid'])\n", - " program = ue['user_email'].split(\"_\")[0]\n", - " uuid_program_list.append({\"program\": program, \"opcode\": ue[\"user_email\"], \"user_id\": uuid})" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "uuid_program_df = pd.DataFrame(uuid_program_list)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded all confirmed trips of length 241123\n", - "After filtering, found 241123 participant trips \n", - "After filtering, found 92446 labeled trips\n", - "Found Index(['mode_confirm', 'purpose_confirm', 'replaced_mode'], dtype='object') columns of length 3\n", - "After expanding, columns went from 41 -> 44\n", - "_prepilot\n", - "Based on 92395 confirmed trips from 235 users\n", - "of 241123 total trips from 261 users (38.32%)\n" - ] - } - ], - "source": [ - "# %%capture\n", - "\n", - "# for program in uuid_program_df.program.unique():\n", - "expanded_ct, file_suffix, quality_text, debug_df = scaffolding.load_viz_notebook_data(None,\n", - " None,\n", - " 'prepilot',\n", - " 'program',\n", - " dic_re,\n", - " dic_pur=dic_pur)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Join to the program df to get each user's program\n", - "expanded_ct['original_user_id'] = expanded_ct['user_id'].copy()\n", - "expanded_ct['user_id'] = expanded_ct['user_id'].apply(lambda x: str(x))\n", - "expanded_ct = expanded_ct.merge(uuid_program_df, on='user_id')\n", - "expanded_ct['user_id'] = expanded_ct['user_id'].apply(lambda x: str(x).replace(\"-\", \"\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['source', 'end_ts', 'end_fmt_time', 'end_loc', 'raw_trip', 'start_ts',\n", - " 'start_fmt_time', 'start_loc', 'duration', 'distance', 'start_place',\n", - " 'end_place', 'cleaned_trip', 'inferred_labels', 'inferred_trip',\n", - " 'expectation', 'confidence_threshold', 'expected_trip', 'user_input',\n", - " 'section_modes', 'section_distances', 'start_local_dt_year',\n", - " 'start_local_dt_month', 'start_local_dt_day', 'start_local_dt_hour',\n", - " 'start_local_dt_minute', 'start_local_dt_second',\n", - " 'start_local_dt_weekday', 'start_local_dt_timezone',\n", - " 'end_local_dt_year', 'end_local_dt_month', 'end_local_dt_day',\n", - " 'end_local_dt_hour', 'end_local_dt_minute', 'end_local_dt_second',\n", - " 'end_local_dt_weekday', 'end_local_dt_timezone', '_id', 'user_id',\n", - " 'metadata_write_ts', 'additions', 'mode_confirm', 'purpose_confirm',\n", - " 'replaced_mode', 'distance_miles', 'Mode_confirm', 'Replaced_mode',\n", - " 'Trip_purpose', 'original_user_id', 'program', 'opcode'],\n", - " dtype='object')" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "expanded_ct.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Rename the target column.\n", - "expanded_ct.drop(columns=['replaced_mode'], axis='rows', inplace=True)\n", - "expanded_ct['Replaced_mode'] = expanded_ct['Replaced_mode'].fillna('Unlabeled')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# We only wish to focus on Denver data for now.\n", - "\n", - "denver_data = r(expanded_ct.loc[\n", - " (expanded_ct.start_local_dt_timezone == \"America/Denver\") & (expanded_ct.end_local_dt_timezone == \"America/Denver\"), \n", - " :])" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "denver_data['start_fmt_time'] = pd.to_datetime(\n", - " denver_data['start_fmt_time'], utc=True\n", - ").dt.tz_convert('America/Denver')" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2020-09-22 17:13:55.883513-06:00 2022-12-30 23:33:27.147785-07:00\n" - ] - } - ], - "source": [ - "print(denver_data.start_fmt_time.min(), denver_data.start_fmt_time.max())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Parse the datetime to Denver time.\n", - "# denver_data['start_fmt_time'] = pd.to_datetime(denver_data['start_fmt_time'], utc=True).dt.tz_convert('America/Denver')\n", - "\n", - "# Re-compute all the start variables.\n", - "denver_data['start_local_dt_year'] = denver_data['start_fmt_time'].dt.year\n", - "denver_data['start_local_dt_month'] = denver_data['start_fmt_time'].dt.month\n", - "denver_data['start_local_dt_day'] = denver_data['start_fmt_time'].dt.day\n", - "denver_data['start_local_dt_hour'] = denver_data['start_fmt_time'].dt.hour\n", - "denver_data['start_local_dt_weekday'] = denver_data['start_fmt_time'].dt.weekday\n", - "\n", - "## Do the same with the end time.\n", - "denver_data['end_fmt_time'] = pd.to_datetime(denver_data['end_fmt_time'], utc=True).dt.tz_convert('America/Denver')\n", - "\n", - "# Re-compute all the end variables.\n", - "denver_data['end_local_dt_year'] = denver_data['end_fmt_time'].dt.year\n", - "denver_data['end_local_dt_month'] = denver_data['end_fmt_time'].dt.month\n", - "denver_data['end_local_dt_day'] = denver_data['end_fmt_time'].dt.day\n", - "denver_data['end_local_dt_hour'] = denver_data['end_fmt_time'].dt.hour\n", - "denver_data['end_local_dt_weekday'] = denver_data['end_fmt_time'].dt.weekday" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Read the Demographic data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Read the Denver dedmographic info\n", - "survey_data = pd.read_csv('../viz_scripts/Can Do Colorado eBike Program - en.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# column renaming here!\n", - "\n", - "survey_data.rename(\n", - " {\n", - " \"Unique User ID (auto-filled, do not edit)\": \"user_id\",\n", - " \"In which year were you born?\": \"birth_year\",\n", - " \"What is your gender?\": \"gender\",\n", - " \"Do you have a valid driver's license?\": \"has_drivers_license\",\n", - " \"Are you a student?\": \"is_student\",\n", - " \"What is the highest grade or degree that you have completed?\": \"highest_education\",\n", - " \"Do you work for either pay or profit?\": \"is_paid\",\n", - " \"Do you have more than one job?\": \"has_multiple_jobs\",\n", - " \"Do you work full-time or part-time at your primary job?\": \"primary_job_type\",\n", - " \"Which best describes your primary job?\": \"primary_job_description\",\n", - " \"How did you usually get to your primary job last week? \": \"primary_job_commute_mode\",\n", - " \"Thinking about your daily commute to work last week, how many minutes did it usually take to get from home to the primary job/work place?\": \"primary_job_commute_time\",\n", - " \"At your primary job, do you have the ability to set or change your own start time?\": \"is_primary_job_flexible\",\n", - " \"Do you have the option of working from home or an alternate location instead of going into your primary work place?\": \"primary_job_can_wfh\",\n", - " \"How many days per week do you usually work from home or an alternate location?\": \"wfh_days\",\n", - " \"Do you own or rent your place of residence?\": \"residence_ownership_type\",\n", - " \"What is your home type?\": \"residence_type\",\n", - " \"Please identify which category represents your total household income, before taxes, for last year.\": \"income_category\",\n", - " \"Including yourself, how many people live in your home?\": \"n_residence_members\",\n", - " \"How many children under age 18 live in your home?\": \"n_residents_u18\",\n", - " \"Including yourself, how many people have a driver's license in your household?\": \"n_residents_with_license\",\n", - " \"How many motor vehicles are owned, leased, or available for regular use by the people who currently live in your household?\": \"n_motor_vehicles\",\n", - " \"If you were unable to use your household vehicle(s), which of the following options would be available to you to get you from place to place?\": \"available_modes\",\n", - " \"Do you have a medical condition that makes it difficult to travel outside of the home?\": \"has_medical_condition\",\n", - " \"How long have you had this condition?\": \"medical_condition_duration\"\n", - " },\n", - " axis='columns',\n", - " inplace=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Now, if we have duplicate users, we'd like to retain the last chronological entry.\n", - "survey_data = survey_data.loc[~((survey_data.user_id.isna())|(survey_data.user_id == \"\")), :]\n", - "\n", - "# timezonoe-aware parsing:\n", - "survey_data['Timestamp'] = survey_data['Timestamp'].str.replace('PDT|PST', '', regex=True)\n", - "survey_data['Timestamp'] = pd.to_datetime(survey_data['Timestamp']).dt.tz_localize('America/Denver')\n", - "\n", - "# Sort by user_id and time, then drop everything but the last entry.\n", - "survey_data.sort_values(by=['user_id', 'Timestamp'], ascending=True, inplace=True, axis='rows')\n", - "survey_data.drop_duplicates(['user_id'], keep='last', inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Merge the trip data with the survey data.\n", - "\n", - "merged_data = denver_data.merge(\n", - " survey_data, left_on='user_id', right_on='user_id'\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Let's start choosing features for modeling.\n", - "\n", - "base_time_features = ['fmt_time', 'local_dt_year', 'local_dt_month', 'local_dt_day', 'local_dt_hour', 'local_dt_weekday']\n", - "time_features = ['start_' + x for x in base_time_features] + ['end_' + x for x in base_time_features]\n", - "\n", - "demographic_features = ['available_modes',\n", - " 'birth_year', 'income_category', 'n_motor_vehicles', 'n_residence_members', 'n_residents_u18', 'gender', \n", - " 'is_student', 'n_residents_with_license']\n", - "\n", - "sensed_features = ['duration', 'distance_miles', 'cleaned_trip', 'start_loc', 'end_loc', 'section_modes', 'section_distances']\n", - "\n", - "modeling_data = merged_data[['user_id', '_id', 'original_user_id', 'cleaned_trip', 'Replaced_mode', 'Mode_confirm'] + time_features + demographic_features + sensed_features].copy()\n", - "\n", - "# Rename columns in-place.\n", - "modeling_data.rename(columns={\n", - " 'start_local_dt_year': 'start:year', 'start_local_dt_month': 'start:month', 'start_local_dt_day': 'start:day', 'start_local_dt_hour': 'start:hour',\n", - " 'end_local_dt_year': 'end:year', 'end_local_dt_month': 'end:month', 'end_local_dt_day': 'end:day', 'end_local_dt_hour': 'end:hour'\n", - " }, inplace=True)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "modeling_data.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from calendar import monthrange\n", - "\n", - "# Find day of month: use monthrange with (mm, yyyy) args and find how many days that month had (leap years are supported).\n", - "def get_num_days_in_month(yyyy, mm):\n", - " return monthrange(yyyy, mm)[1]\n", - "\n", - "def is_overnight_trip(start_date, end_date):\n", - " return int((end_date - start_date).days > 0)\n", - "\n", - "# get the number of days for the start and end times.\n", - "modeling_data['start:n_days_in_month'] = modeling_data.apply(lambda x: get_num_days_in_month(x['start:year'], x['start:month']), axis=1)\n", - "modeling_data['end:n_days_in_month'] = modeling_data.apply(lambda x: get_num_days_in_month(x['end:year'], x['end:month']), axis=1)\n", - "\n", - "# age = current year - year of birth\n", - "modeling_data['age'] = 2023 - modeling_data['birth_year']\n", - "\n", - "# overnight trips may be more likely taken by car.\n", - "modeling_data['is_overnight_trip'] = modeling_data.apply(lambda x: is_overnight_trip(x.start_fmt_time, x.end_fmt_time), axis=1)\n", - "\n", - "# Number of working individuals in the household = number of individuals in the house - number of children.\n", - "modeling_data['n_working_residents'] = (modeling_data['n_residence_members'] - modeling_data['n_residents_u18']).astype(int)\n", - "\n", - "# Create a binary indicator.\n", - "modeling_data['is_male'] = modeling_data.gender.apply(lambda x: 1 if x==\"Male\" else 0)\n", - "\n", - "# Bin the number of vehicles owned.\n", - "# Drop the observations with (Prefer not to say)\n", - "modeling_data = modeling_data.loc[~modeling_data['n_motor_vehicles'].isin(['Prefer not to say / Prefiero no decir.']), :]\n", - "modeling_data.loc[modeling_data['n_motor_vehicles'].isin(['4+']), 'n_motor_vehicles'] = 4\n", - "modeling_data['n_motor_vehicles'] = modeling_data['n_motor_vehicles'].astype(int)\n", - "\n", - "# Convert the total duration of the trip into minutes.\n", - "modeling_data[['duration']] = modeling_data[['duration']]/60\n", - "\n", - "# Extract start and end latitudes and longitudes.\n", - "modeling_data['start_lat'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][1])\n", - "modeling_data['start_lng'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][0])\n", - "\n", - "modeling_data['end_lat'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][1])\n", - "modeling_data['end_lng'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from calendar import monthrange\n", - "\n", - "# Find day of month: use monthrange with (mm, yyyy) args and find how many days that month had (leap years are supported).\n", - "def get_num_days_in_month(yyyy, mm):\n", - " return monthrange(yyyy, mm)[1]\n", - "\n", - "def is_overnight_trip(start_date, end_date):\n", - " return int((end_date - start_date).days > 0)\n", - "\n", - "# get the number of days for the start and end times.\n", - "modeling_data['start:n_days_in_month'] = modeling_data.apply(lambda x: get_num_days_in_month(x['start:year'], x['start:month']), axis=1)\n", - "modeling_data['end:n_days_in_month'] = modeling_data.apply(lambda x: get_num_days_in_month(x['end:year'], x['end:month']), axis=1)\n", - "\n", - "# age = current year - year of birth\n", - "modeling_data['age'] = 2023 - modeling_data['birth_year']\n", - "\n", - "# overnight trips may be more likely taken by car.\n", - "modeling_data['is_overnight_trip'] = modeling_data.apply(lambda x: is_overnight_trip(x.start_fmt_time, x.end_fmt_time), axis=1)\n", - "\n", - "# Number of working individuals in the household = number of individuals in the house - number of children.\n", - "modeling_data['n_working_residents'] = (modeling_data['n_residence_members'] - modeling_data['n_residents_u18']).astype(int)\n", - "\n", - "# Create a binary indicator.\n", - "modeling_data['is_male'] = modeling_data.gender.apply(lambda x: 1 if x==\"Male\" else 0)\n", - "\n", - "# Bin the number of vehicles owned.\n", - "# Drop the observations with (Prefer not to say)\n", - "modeling_data = modeling_data.loc[~modeling_data['n_motor_vehicles'].isin(['Prefer not to say / Prefiero no decir.']), :]\n", - "modeling_data.loc[modeling_data['n_motor_vehicles'].isin(['4+']), 'n_motor_vehicles'] = 4\n", - "modeling_data['n_motor_vehicles'] = modeling_data['n_motor_vehicles'].astype(int)\n", - "\n", - "# Convert the total duration of the trip into minutes.\n", - "modeling_data[['duration']] = modeling_data[['duration']]/60\n", - "\n", - "# Extract start and end latitudes and longitudes.\n", - "modeling_data['start_lat'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][1])\n", - "modeling_data['start_lng'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][0])\n", - "\n", - "modeling_data['end_lat'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][1])\n", - "modeling_data['end_lng'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Time-related feature engineeering:\n", - "'''\n", - "HOD: hour of day\n", - "DOM: day of month\n", - "MOY: month of year\n", - "'''\n", - "\n", - "def get_HOD(hour, how='sin'):\n", - " if how == 'sin':\n", - " return np.sin(2 * np.pi * (hour/24))\n", - " return np.cos(2 * np.pi * (hour/24))\n", - "\n", - "def get_DOM(day, n_days, how='sin'):\n", - " if how == 'sin':\n", - " return np.sin(2 * np.pi * (day/n_days))\n", - " return np.cos(2 * np.pi * (day/n_days))\n", - "\n", - "def get_MOY(month, how='sin'):\n", - " if how == 'sin':\n", - " return np.sin(2 * np.pi * (month/12))\n", - " return np.cos(2 * np.pi * (month/12))\n", - "\n", - "# Start - sin\n", - "modeling_data['start:sin_HOD'] = modeling_data.apply(lambda x: get_HOD(x['start:hour']), axis=1)\n", - "modeling_data['start:sin_DOM'] = modeling_data.apply(lambda x: get_DOM(x['start:day'], x['start:n_days_in_month']), axis=1)\n", - "modeling_data['start:sin_MOY'] = modeling_data.apply(lambda x: get_MOY(x['start:year']), axis=1)\n", - "\n", - "# Start - cos\n", - "modeling_data['start:cos_HOD'] = modeling_data.apply(lambda x: get_HOD(x['start:hour'], how='cos'), axis=1)\n", - "modeling_data['start:cos_DOM'] = modeling_data.apply(lambda x: get_DOM(x['start:day'], x['start:n_days_in_month'], how='cos'), axis=1)\n", - "modeling_data['start:cos_MOY'] = modeling_data.apply(lambda x: get_MOY(x['start:year'], how='cos'), axis=1)\n", - "\n", - "# End - sin\n", - "modeling_data['end:sin_HOD'] = modeling_data.apply(lambda x: get_HOD(x['end:hour']), axis=1)\n", - "modeling_data['end:sin_DOM'] = modeling_data.apply(lambda x: get_DOM(x['end:day'], x['end:n_days_in_month']), axis=1)\n", - "modeling_data['end:sin_MOY'] = modeling_data.apply(lambda x: get_MOY(x['end:year']), axis=1)\n", - "\n", - "# End - cos\n", - "modeling_data['end:cos_HOD'] = modeling_data.apply(lambda x: get_HOD(x['end:hour'], how='cos'), axis=1)\n", - "modeling_data['end:cos_DOM'] = modeling_data.apply(lambda x: get_DOM(x['end:day'], x['end:n_days_in_month'], how='cos'), axis=1)\n", - "modeling_data['end:cos_MOY'] = modeling_data.apply(lambda x: get_MOY(x['end:year'], how='cos'), axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "modeling_data.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, for every trip, we have the corresponding section mode that covered the longest distance for the trip.\n", - "\n", - "Using this as well as the `available_modes` column:\n", - "\n", - "```language=python\n", - "\n", - " # unique available modes:\n", - " {'Bicycle',\n", - " 'Do not have vehicle ',\n", - " 'Get a ride from a friend or family member',\n", - " 'None',\n", - " 'Public transportation (bus, subway, light rail, etc.)',\n", - " 'Rental car (including Zipcar/ Car2Go)',\n", - " 'Shared bicycle or scooter',\n", - " 'Skateboard',\n", - " 'Taxi (regular taxi, Uber, Lyft, etc)',\n", - " 'Walk/roll'}\n", - "\n", - " # unique section modes:\n", - " {'bicycling', 'bus', 'car', 'no_sensed', 'train', 'walking'}\n", - "\n", - " \n", - "```\n", - "\n", - "What mapping can we establish here? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def remove_air_or_hsr(df):\n", - "\n", - " df['mark'] = 0\n", - "\n", - " for ix, row in df.iterrows():\n", - " sections = row['section_modes']\n", - " if 'air_or_hsr' in sections:\n", - " df.loc[ix, 'mark'] = 1\n", - " \n", - " df = r(df.loc[df.mark == 0, :])\n", - " df.drop(columns=['mark'], inplace=True)\n", - "\n", - " return df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "modeling_data = remove_air_or_hsr(modeling_data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "modeling_data.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "importlib.reload(scaffolding)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results = list()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "modeling_data = modeling_data.loc[:,~modeling_data.columns.duplicated()].copy()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# | [a, b, c] | start_time | end_time |\n", - "# -> | [a, b, c] | [s1, s2, s3] |" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# print(modeling_data.shape[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# I manually split the modeling data into chunks of 10000 points at one time.\n", - "# This significantly expedited the processing time. Each chunk takes ~26 minutes to finish.\n", - "# split = modeling_data.loc[70000:, :]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# display(split[['original_user_id', 'cleaned_trip']].head())\n", - "# print(split.shape[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# from time import perf_counter\n", - "\n", - "# now = perf_counter()\n", - "# result = scaffolding.get_section_durations(split)\n", - "# end = perf_counter() - now\n", - "\n", - "# print(f\"Took {end/60} minutes to complete\")\n", - "# results.append(result)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# print(len(results))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# final_df = pd.concat(results, axis=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# print(modeling_data.shape[0], final_df.shape[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# modeling_data.to_csv('../data/modeling_data.csv', index=False)\n", - "# final_df.to_csv('../data/modeling_w_duration.csv', index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# from time import perf_counter\n", - "# importlib.reload(scaffolding)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_csv('../data/modeling_w_duration.csv')\n", - "df.drop_duplicates(inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# df.shape, modeling_data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dummy = modeling_data.iloc[50000:, :]\n", - "\n", - "print(dummy.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# now = perf_counter()\n", - "df_modded = scaffolding.get_section_coordinates(dummy)\n", - "# end = perf_counter() - now\n", - "\n", - "# print(f\"Fetched sections in {end/60} minutes\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def verify(locations):\n", - " return locations is not None and len(locations) > 0\n", - "\n", - "\n", - "bools = df_modded.apply(lambda x: verify(x.section_locations_argmax), axis=1)\n", - "print(bools.all())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results.append(df_modded)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(len(results))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "final_results = pd.concat(results, axis=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "final_df = pd.concat([df, final_results['section_locations_argmax']], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "final_df[['section_modes', 'section_distances', 'section_durations', 'section_locations_argmax']].head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "final_df.to_csv('../data/final_modeling_data.csv', index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "final_df.section_locations_argmax[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "interpreter": { - "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" - }, - "kernelspec": { - "display_name": "emission", - "language": "python", - "name": "emission" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/viz_scripts/rm_src/time_distance_estimation.ipynb b/viz_scripts/rm_src/time_distance_estimation.ipynb deleted file mode 100644 index 7591030..0000000 --- a/viz_scripts/rm_src/time_distance_estimation.ipynb +++ /dev/null @@ -1,235 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Data loading. Refer to the user_data_preprocessing.ipynb file for details on how user-level features are generated.\n", - "\n", - "df = pd.read_csv('../data/modeling_w_duration.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Drop duplicate rows (if any)\n", - "df.drop_duplicates(inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def compute_argmax(df: pd.DataFrame):\n", - " # Create the two columns.\n", - " df[['section_distance_argmax', 'section_duration_argmax', 'section_mode_argmax']] = None\n", - " rows = list()\n", - "\n", - " for ix, row in df.iterrows():\n", - " parsed_distances = eval(row['section_distances'])\n", - " parsed_durations = eval(row['section_durations'])\n", - " parsed_modes = eval(row['section_modes'])\n", - "\n", - " argmax_ix = np.argmax(parsed_distances)\n", - "\n", - " row['section_distance_argmax'] = parsed_distances[argmax_ix]\n", - " row['section_duration_argmax'] = parsed_durations[argmax_ix]\n", - " row['section_mode_argmax'] = parsed_modes[argmax_ix]\n", - "\n", - " rows.append(row.to_dict())\n", - " \n", - " return pd.DataFrame(rows)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Let's plot the mode-wise durations as a function of distance.\n", - "df_modded = compute_argmax(df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_modded.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "analysis = df_modded[['section_mode_argmax', 'section_duration_argmax', 'section_distance_argmax']].copy()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "analysis.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.stats import pearsonr\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import r2_score\n", - "from sklearn.linear_model import LinearRegression\n", - "from sklearn.preprocessing import StandardScaler\n", - "\n", - "analysis.loc[(analysis.section_mode_argmax == 'bus') | (analysis.section_mode_argmax == 'train'), ['section_mode_argmax']] = 'public_transport'\n", - "\n", - "for mode in analysis.section_mode_argmax.unique():\n", - " mode_df = analysis.loc[(analysis.section_mode_argmax == mode) \n", - " & (analysis.section_duration_argmax >=0) \n", - " & ((analysis.section_distance_argmax >=0)), ['section_duration_argmax', 'section_distance_argmax']]\n", - "\n", - " X = mode_df[['section_distance_argmax']].values * 0.00062\n", - " Y = mode_df[['section_duration_argmax']].values / 60\n", - "\n", - " X_tr, X_te, Y_tr, Y_te = train_test_split(X, Y, test_size=0.2, random_state=42, shuffle=True)\n", - "\n", - " # scaler = StandardScaler()\n", - "\n", - " # X_tr = scaler.fit_transform(X_tr)\n", - " # X_te = scaler.transform(X_te)\n", - "\n", - " # regr = LinearRegression()\n", - " regr = LinearRegression(fit_intercept=True)\n", - " regr.fit(X_tr.reshape(-1,1), Y_tr.reshape(-1,1))\n", - "\n", - " y_tr_pred = regr.predict(X_tr)\n", - " y_te_pred = regr.predict(X_te)\n", - "\n", - " train_r2 = r2_score(y_true=Y_tr.flatten(), y_pred=y_tr_pred.flatten())\n", - " test_r2 = r2_score(y_true=Y_te.flatten(), y_pred=y_te_pred.flatten())\n", - "\n", - " print(mode, train_r2, test_r2)\n", - " print('intercept: ', regr.intercept_[0], 'coeff: ', regr.coef_[0][0])\n", - "\n", - "\n", - "'''\n", - "Previous results:\n", - "\n", - "walking 0.3843977052858275 0.3749466865077252\n", - "bicycling 0.7396768569714562 0.735986721086616\n", - "car 0.5839819702140339 0.5918942114399524\n", - "no_sensed 0.8045590529878717 0.8059202285373765\n", - "public_transport 0.44880904441364 0.6020723455289356\n", - "'''" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.stats import pearsonr\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.metrics import r2_score\n", - "\n", - "\n", - "# Combine bus and train into 'public transport'\n", - "analysis.loc[(analysis.section_mode_argmax == 'bus') | (analysis.section_mode_argmax == 'train'), ['section_mode_argmax']] = 'public_transport'\n", - "\n", - "for mode in analysis.section_mode_argmax.unique():\n", - "\n", - " mode_df = analysis.loc[\n", - " (analysis.section_mode_argmax == mode) & (analysis.section_duration_argmax >= 0) & (analysis.section_distance_argmax >= 0), \n", - " ['section_duration_argmax', 'section_distance_argmax']\n", - " ]\n", - " \n", - "\n", - " X = mode_df[['section_distance_argmax']].values\n", - " Y = mode_df[['section_duration_argmax']].values\n", - "\n", - " scaler = StandardScaler()\n", - "\n", - " X_tr, X_te, Y_tr, Y_te = train_test_split(X, Y, test_size=0.2, random_state=42, shuffle=True)\n", - "\n", - " X_tr = X_tr.flatten()\n", - " X_te = X_te.flatten()\n", - " Y_tr, Y_te = Y_tr.flatten(), Y_te.flatten()\n", - "\n", - " fit = np.polyfit(X_tr, Y_tr.flatten(), 1)\n", - " # Using the fit, predict tr and te.\n", - " y_tr_pred = np.poly1d(fit)(X_tr)\n", - " y_te_pred = np.poly1d(fit)(X_te)\n", - "\n", - " train_r2 = r2_score(y_true=Y_tr, y_pred=y_tr_pred)\n", - " test_r2 = r2_score(y_true=Y_te, y_pred=y_te_pred)\n", - "\n", - " fig, ax = plt.subplots()\n", - " # Plot the testing samples.\n", - " sns.scatterplot(x=X_te, y=Y_te, ax=ax)\n", - " # Plot the line.\n", - " # line = mode_model.intercept_ + (mode_model.coef_[0] * X_te.flatten())\n", - " sns.lineplot(x=X_te, y=y_te_pred, ax=ax)\n", - " plt.show()\n", - "\n", - " print(mode, train_r2, test_r2)" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "ab0c6e94c9422d07d42069ec9e3bb23090f5e156fc0e23cc25ca45a62375bf53" - }, - "kernelspec": { - "display_name": "Python 3.9.16 ('emission')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/viz_scripts/rm_src/user_data_preprocessing.ipynb b/viz_scripts/rm_src/user_data_preprocessing.ipynb deleted file mode 100644 index b8a756c..0000000 --- a/viz_scripts/rm_src/user_data_preprocessing.ipynb +++ /dev/null @@ -1,586 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "import pickle\n", - "import importlib\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "\n", - "from pathlib import Path\n", - "from uuid import UUID\n", - "from collections import defaultdict\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "emission_path = Path(os.getcwd()).parent.parent / 'my_emission_server' / 'e-mission-server'\n", - "sys.path.append(str(emission_path))\n", - "\n", - "# Also add the home (viz_scripts) to the path\n", - "sys.path.append('../viz_scripts')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import scaffolding\n", - "import emission.core.get_database as edb\n", - "import emission.core.wrapper.entry as ecwe\n", - "import emission.storage.decorations.analysis_timeseries_queries as esda\n", - "import emission.storage.decorations.trip_queries as esdt\n", - "import emission.storage.decorations.timeline as esdl\n", - "import emission.storage.timeseries.abstract_timeseries as esta\n", - "import emission.storage.timeseries.timequery as estt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "importlib.reload(scaffolding)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def r(df: pd.DataFrame) -> pd.DataFrame:\n", - " return df.reset_index(drop=True, inplace=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# (Please run the mapping_dictionaries.ipynb notebook before the next cell block is run)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with open('../viz_scripts/auxiliary_files/dic_re.pkl', 'rb') as f:\n", - " dic_re = pickle.loads(f.read())\n", - "\n", - "with open('../viz_scripts/auxiliary_files/dic_pur.pkl', 'rb') as f:\n", - " dic_pur = pickle.loads(f.read())\n", - "\n", - "# convert a dictionary to a defaultdict\n", - "dic_re = defaultdict(lambda: 'Other', dic_re)\n", - "dic_pur = defaultdict(lambda: 'Other', dic_pur)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Split UUIDs by program\n", - "program_uuid_map = {}\n", - "for ue in edb.get_uuid_db().find():\n", - " uuid = str(ue['uuid'])\n", - " # uuid = str(ue['uuid'])\n", - " program = ue['user_email'].split(\"_\")[0]\n", - " if program in program_uuid_map.keys():\n", - " program_uuid_map[program].append(uuid)\n", - " else:\n", - " print(f\"Found new program {program}, creating new list\")\n", - " program_uuid_map[program] = []\n", - " program_uuid_map[program].append(uuid)\n", - "\n", - "uuid_program_list = []\n", - "for ue in edb.get_uuid_db().find():\n", - " # uuid = str(ue['uuid'].as_uuid(3))\n", - " uuid = str(ue['uuid'])\n", - " program = ue['user_email'].split(\"_\")[0]\n", - " uuid_program_list.append({\"program\": program, \"opcode\": ue[\"user_email\"], \"user_id\": uuid})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "uuid_program_df = pd.DataFrame.from_dict(uuid_program_list)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# %%capture\n", - "\n", - "# for program in uuid_program_df.program.unique():\n", - "expanded_ct, file_suffix, quality_text, debug_df = scaffolding.load_viz_notebook_data(None,\n", - " None,\n", - " 'prepilot',\n", - " 'program',\n", - " dic_re,\n", - " dic_pur=dic_pur)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Join to the program df to get each user's program\n", - "# Keep a copy of the user_id column for querying section durations.\n", - "expanded_ct['original_user_id'] = expanded_ct['user_id'].copy()\n", - "expanded_ct['user_id'] = expanded_ct['user_id'].apply(lambda x: str(x))\n", - "expanded_ct = expanded_ct.merge(uuid_program_df, on='user_id')\n", - "expanded_ct['user_id'] = expanded_ct['user_id'].apply(lambda x: str(x).replace(\"-\", \"\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Rename the target column.\n", - "expanded_ct.drop(columns=['replaced_mode'], axis='rows', inplace=True)\n", - "expanded_ct['Replaced_mode'] = expanded_ct['Replaced_mode'].fillna('Unlabeled')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# We only wish to focus on Denver data for now.\n", - "\n", - "denver_data = r(expanded_ct.loc[\n", - " (expanded_ct.start_local_dt_timezone == \"America/Denver\") & (expanded_ct.end_local_dt_timezone == \"America/Denver\"), \n", - " :])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Parse the datetime to Denver time.\n", - "denver_data['start_fmt_time'] = pd.to_datetime(denver_data['start_fmt_time'], utc=True).dt.tz_convert('America/Denver')\n", - "\n", - "# Re-compute all the start variables.\n", - "denver_data['start_local_dt_year'] = denver_data['start_fmt_time'].dt.year\n", - "denver_data['start_local_dt_month'] = denver_data['start_fmt_time'].dt.month\n", - "denver_data['start_local_dt_day'] = denver_data['start_fmt_time'].dt.day\n", - "denver_data['start_local_dt_hour'] = denver_data['start_fmt_time'].dt.hour\n", - "denver_data['start_local_dt_weekday'] = denver_data['start_fmt_time'].dt.weekday\n", - "\n", - "## Do the same with the end time.\n", - "denver_data['end_fmt_time'] = pd.to_datetime(denver_data['end_fmt_time'], utc=True).dt.tz_convert('America/Denver')\n", - "\n", - "# Re-compute all the end variables.\n", - "denver_data['end_local_dt_year'] = denver_data['end_fmt_time'].dt.year\n", - "denver_data['end_local_dt_month'] = denver_data['end_fmt_time'].dt.month\n", - "denver_data['end_local_dt_day'] = denver_data['end_fmt_time'].dt.day\n", - "denver_data['end_local_dt_hour'] = denver_data['end_fmt_time'].dt.hour\n", - "denver_data['end_local_dt_weekday'] = denver_data['end_fmt_time'].dt.weekday" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Read the Demographic data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Read the Denver dedmographic info\n", - "survey_data = pd.read_csv('../viz_scripts/Can Do Colorado eBike Program - en.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# column renaming here!\n", - "\n", - "survey_data.rename(\n", - " {\n", - " \"Unique User ID (auto-filled, do not edit)\": \"user_id\",\n", - " \"In which year were you born?\": \"birth_year\",\n", - " \"What is your gender?\": \"gender\",\n", - " \"Do you have a valid driver's license?\": \"has_drivers_license\",\n", - " \"Are you a student?\": \"is_student\",\n", - " \"What is the highest grade or degree that you have completed?\": \"highest_education\",\n", - " \"Do you work for either pay or profit?\": \"is_paid\",\n", - " \"Do you have more than one job?\": \"has_multiple_jobs\",\n", - " \"Do you work full-time or part-time at your primary job?\": \"primary_job_type\",\n", - " \"Which best describes your primary job?\": \"primary_job_description\",\n", - " \"How did you usually get to your primary job last week? \": \"primary_job_commute_mode\",\n", - " \"Thinking about your daily commute to work last week, how many minutes did it usually take to get from home to the primary job/work place?\": \"primary_job_commute_time\",\n", - " \"At your primary job, do you have the ability to set or change your own start time?\": \"is_primary_job_flexible\",\n", - " \"Do you have the option of working from home or an alternate location instead of going into your primary work place?\": \"primary_job_can_wfh\",\n", - " \"How many days per week do you usually work from home or an alternate location?\": \"wfh_days\",\n", - " \"Do you own or rent your place of residence?\": \"residence_ownership_type\",\n", - " \"What is your home type?\": \"residence_type\",\n", - " \"Please identify which category represents your total household income, before taxes, for last year.\": \"income_category\",\n", - " \"Including yourself, how many people live in your home?\": \"n_residence_members\",\n", - " \"How many children under age 18 live in your home?\": \"n_residents_u18\",\n", - " \"Including yourself, how many people have a driver's license in your household?\": \"n_residents_with_license\",\n", - " \"How many motor vehicles are owned, leased, or available for regular use by the people who currently live in your household?\": \"n_motor_vehicles\",\n", - " \"If you were unable to use your household vehicle(s), which of the following options would be available to you to get you from place to place?\": \"available_modes\",\n", - " \"Do you have a medical condition that makes it difficult to travel outside of the home?\": \"has_medical_condition\",\n", - " \"How long have you had this condition?\": \"medical_condition_duration\"\n", - " },\n", - " axis='columns',\n", - " inplace=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Now, if we have duplicate users, we'd like to retain the last chronological entry.\n", - "survey_data = survey_data.loc[~((survey_data.user_id.isna())|(survey_data.user_id == \"\")), :]\n", - "\n", - "# timezonoe-aware parsing:\n", - "survey_data['Timestamp'] = survey_data['Timestamp'].str.replace('PDT|PST', '', regex=True)\n", - "survey_data['Timestamp'] = pd.to_datetime(survey_data['Timestamp']).dt.tz_localize('America/Denver')\n", - "\n", - "# Sort by user_id and time, then drop everything but the last entry.\n", - "survey_data.sort_values(by=['user_id', 'Timestamp'], ascending=True, inplace=True, axis='rows')\n", - "survey_data.drop_duplicates(['user_id'], keep='last', inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Merge the trip data with the survey data.\n", - "\n", - "merged_data = denver_data.merge(\n", - " survey_data, left_on='user_id', right_on='user_id'\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Let's start choosing features for modeling.\n", - "\n", - "base_time_features = ['fmt_time', 'local_dt_year', 'local_dt_month', 'local_dt_day', 'local_dt_hour', 'local_dt_weekday']\n", - "time_features = ['start_' + x for x in base_time_features] + ['end_' + x for x in base_time_features]\n", - "\n", - "demographic_features = ['available_modes',\n", - " 'birth_year', 'income_category', 'n_motor_vehicles', 'n_residence_members', 'n_residents_u18', 'gender', \n", - " 'is_student', 'n_residents_with_license']\n", - "\n", - "sensed_features = ['duration', 'distance_miles', 'cleaned_trip', 'start_loc', 'end_loc', 'section_modes', 'section_distances']\n", - "\n", - "modeling_data = merged_data[['user_id', '_id', 'original_user_id', 'cleaned_trip', 'Replaced_mode', 'Mode_confirm'] + time_features + demographic_features + sensed_features].copy()\n", - "\n", - "# Rename columns in-place.\n", - "modeling_data.rename(columns={\n", - " 'start_local_dt_year': 'start:year', 'start_local_dt_month': 'start:month', 'start_local_dt_day': 'start:day', 'start_local_dt_hour': 'start:hour',\n", - " 'end_local_dt_year': 'end:year', 'end_local_dt_month': 'end:month', 'end_local_dt_day': 'end:day', 'end_local_dt_hour': 'end:hour'\n", - " }, inplace=True)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from calendar import monthrange\n", - "\n", - "# Find day of month: use monthrange with (mm, yyyy) args and find how many days that month had (leap years are supported).\n", - "def get_num_days_in_month(yyyy, mm):\n", - " return monthrange(yyyy, mm)[1]\n", - "\n", - "def is_overnight_trip(start_date, end_date):\n", - " return int((end_date - start_date).days > 0)\n", - "\n", - "# get the number of days for the start and end times.\n", - "modeling_data['start:n_days_in_month'] = modeling_data.apply(lambda x: get_num_days_in_month(x['start:year'], x['start:month']), axis=1)\n", - "modeling_data['end:n_days_in_month'] = modeling_data.apply(lambda x: get_num_days_in_month(x['end:year'], x['end:month']), axis=1)\n", - "\n", - "# age = current year - year of birth\n", - "modeling_data['age'] = 2023 - modeling_data['birth_year']\n", - "\n", - "# overnight trips may be more likely taken by car.\n", - "modeling_data['is_overnight_trip'] = modeling_data.apply(lambda x: is_overnight_trip(x.start_fmt_time, x.end_fmt_time), axis=1)\n", - "\n", - "# Number of working individuals in the household = number of individuals in the house - number of children.\n", - "modeling_data['n_working_residents'] = (modeling_data['n_residence_members'] - modeling_data['n_residents_u18']).astype(int)\n", - "\n", - "# Create a binary indicator.\n", - "modeling_data['is_male'] = modeling_data.gender.apply(lambda x: 1 if x==\"Male\" else 0)\n", - "\n", - "# Bin the number of vehicles owned.\n", - "# Drop the observations with (Prefer not to say)\n", - "modeling_data = modeling_data.loc[~modeling_data['n_motor_vehicles'].isin(['Prefer not to say / Prefiero no decir.']), :]\n", - "modeling_data.loc[modeling_data['n_motor_vehicles'].isin(['4+']), 'n_motor_vehicles'] = 4\n", - "modeling_data['n_motor_vehicles'] = modeling_data['n_motor_vehicles'].astype(int)\n", - "\n", - "# Convert the total duration of the trip into minutes.\n", - "modeling_data[['duration']] = modeling_data[['duration']]/60\n", - "\n", - "# Extract start and end latitudes and longitudes.\n", - "modeling_data['start_lat'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][1])\n", - "modeling_data['start_lng'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][0])\n", - "\n", - "modeling_data['end_lat'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][1])\n", - "modeling_data['end_lng'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from calendar import monthrange\n", - "\n", - "# Find day of month: use monthrange with (mm, yyyy) args and find how many days that month had (leap years are supported).\n", - "def get_num_days_in_month(yyyy, mm):\n", - " return monthrange(yyyy, mm)[1]\n", - "\n", - "def is_overnight_trip(start_date, end_date):\n", - " return int((end_date - start_date).days > 0)\n", - "\n", - "# get the number of days for the start and end times.\n", - "modeling_data['start:n_days_in_month'] = modeling_data.apply(lambda x: get_num_days_in_month(x['start:year'], x['start:month']), axis=1)\n", - "modeling_data['end:n_days_in_month'] = modeling_data.apply(lambda x: get_num_days_in_month(x['end:year'], x['end:month']), axis=1)\n", - "\n", - "# age = current year - year of birth\n", - "modeling_data['age'] = 2023 - modeling_data['birth_year']\n", - "\n", - "# overnight trips may be more likely taken by car.\n", - "modeling_data['is_overnight_trip'] = modeling_data.apply(lambda x: is_overnight_trip(x.start_fmt_time, x.end_fmt_time), axis=1)\n", - "\n", - "# Number of working individuals in the household = number of individuals in the house - number of children.\n", - "modeling_data['n_working_residents'] = (modeling_data['n_residence_members'] - modeling_data['n_residents_u18']).astype(int)\n", - "\n", - "# Create a binary indicator.\n", - "modeling_data['is_male'] = modeling_data.gender.apply(lambda x: 1 if x==\"Male\" else 0)\n", - "\n", - "# Bin the number of vehicles owned.\n", - "# Drop the observations with (Prefer not to say)\n", - "modeling_data = modeling_data.loc[~modeling_data['n_motor_vehicles'].isin(['Prefer not to say / Prefiero no decir.']), :]\n", - "modeling_data.loc[modeling_data['n_motor_vehicles'].isin(['4+']), 'n_motor_vehicles'] = 4\n", - "modeling_data['n_motor_vehicles'] = modeling_data['n_motor_vehicles'].astype(int)\n", - "\n", - "# Convert the total duration of the trip into minutes.\n", - "modeling_data[['duration']] = modeling_data[['duration']]/60\n", - "\n", - "# Extract start and end latitudes and longitudes.\n", - "modeling_data['start_lat'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][1])\n", - "modeling_data['start_lng'] = modeling_data['start_loc'].apply(lambda x: x['coordinates'][0])\n", - "\n", - "modeling_data['end_lat'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][1])\n", - "modeling_data['end_lng'] = modeling_data['end_loc'].apply(lambda x: x['coordinates'][0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Time-related feature engineeering:\n", - "'''\n", - "HOD: hour of day\n", - "DOM: day of month\n", - "MOY: month of year\n", - "'''\n", - "\n", - "def get_HOD(hour, how='sin'):\n", - " if how == 'sin':\n", - " return np.sin(2 * np.pi * (hour/24))\n", - " return np.cos(2 * np.pi * (hour/24))\n", - "\n", - "def get_DOM(day, n_days, how='sin'):\n", - " if how == 'sin':\n", - " return np.sin(2 * np.pi * (day/n_days))\n", - " return np.cos(2 * np.pi * (day/n_days))\n", - "\n", - "def get_MOY(month, how='sin'):\n", - " if how == 'sin':\n", - " return np.sin(2 * np.pi * (month/12))\n", - " return np.cos(2 * np.pi * (month/12))\n", - "\n", - "# Start - sin\n", - "modeling_data['start:sin_HOD'] = modeling_data.apply(lambda x: get_HOD(x['start:hour']), axis=1)\n", - "modeling_data['start:sin_DOM'] = modeling_data.apply(lambda x: get_DOM(x['start:day'], x['start:n_days_in_month']), axis=1)\n", - "modeling_data['start:sin_MOY'] = modeling_data.apply(lambda x: get_MOY(x['start:year']), axis=1)\n", - "\n", - "# Start - cos\n", - "modeling_data['start:cos_HOD'] = modeling_data.apply(lambda x: get_HOD(x['start:hour'], how='cos'), axis=1)\n", - "modeling_data['start:cos_DOM'] = modeling_data.apply(lambda x: get_DOM(x['start:day'], x['start:n_days_in_month'], how='cos'), axis=1)\n", - "modeling_data['start:cos_MOY'] = modeling_data.apply(lambda x: get_MOY(x['start:year'], how='cos'), axis=1)\n", - "\n", - "# End - sin\n", - "modeling_data['end:sin_HOD'] = modeling_data.apply(lambda x: get_HOD(x['end:hour']), axis=1)\n", - "modeling_data['end:sin_DOM'] = modeling_data.apply(lambda x: get_DOM(x['end:day'], x['end:n_days_in_month']), axis=1)\n", - "modeling_data['end:sin_MOY'] = modeling_data.apply(lambda x: get_MOY(x['end:year']), axis=1)\n", - "\n", - "# End - cos\n", - "modeling_data['end:cos_HOD'] = modeling_data.apply(lambda x: get_HOD(x['end:hour'], how='cos'), axis=1)\n", - "modeling_data['end:cos_DOM'] = modeling_data.apply(lambda x: get_DOM(x['end:day'], x['end:n_days_in_month'], how='cos'), axis=1)\n", - "modeling_data['end:cos_MOY'] = modeling_data.apply(lambda x: get_MOY(x['end:year'], how='cos'), axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, for every trip, we have the corresponding section mode that covered the longest distance for the trip.\n", - "\n", - "Using this as well as the `available_modes` column:\n", - "\n", - "```language=python\n", - "\n", - " # unique available modes:\n", - " {'Bicycle',\n", - " 'Do not have vehicle ',\n", - " 'Get a ride from a friend or family member',\n", - " 'None',\n", - " 'Public transportation (bus, subway, light rail, etc.)',\n", - " 'Rental car (including Zipcar/ Car2Go)',\n", - " 'Shared bicycle or scooter',\n", - " 'Skateboard',\n", - " 'Taxi (regular taxi, Uber, Lyft, etc)',\n", - " 'Walk/roll'}\n", - "\n", - " # unique section modes:\n", - " {'bicycling', 'bus', 'car', 'no_sensed', 'train', 'walking'}\n", - "\n", - " \n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "Remove air trips.\n", - "\"\"\"\n", - "def remove_air_or_hsr(df):\n", - "\n", - " df['mark'] = 0\n", - "\n", - " for ix, row in df.iterrows():\n", - " sections = row['section_modes']\n", - " if 'air_or_hsr' in sections:\n", - " df.loc[ix, 'mark'] = 1\n", - " \n", - " df = r(df.loc[df.mark == 0, :])\n", - " df.drop(columns=['mark'], inplace=True)\n", - "\n", - " return df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "modeling_data = remove_air_or_hsr(modeling_data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get section durations for trips.\n", - "modeling_data = scaffolding.get_section_durations(modeling_data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Save the dataframe.\n", - "# final_df.to_csv('../data/modeling_w_duration.csv', index=False)" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "88f8afe72b095c7f825e3358bd97ba362c33037cbe77fe6effd118f9a11a2d38" - }, - "kernelspec": { - "display_name": "Python 3.8.18 ('mnl')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.18" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/viz_scripts/run_from_host/generate_plots.sh b/viz_scripts/run_from_host/generate_plots.sh deleted file mode 100755 index 789fd1d..0000000 --- a/viz_scripts/run_from_host/generate_plots.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/bash -docker exec em-public-dashboard_notebook-server_1 /bin/bash -c "/usr/src/app/saved-notebooks/docker/generate_plots.sh $*" diff --git a/viz_scripts/run_from_host/update_mappings.sh b/viz_scripts/run_from_host/update_mappings.sh deleted file mode 100755 index a2be9fc..0000000 --- a/viz_scripts/run_from_host/update_mappings.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/bash -docker exec em-public-dashboard_notebook-server_1 /bin/bash -c "/usr/src/app/saved-notebooks/docker/update_mappings.sh $*" diff --git a/viz_scripts/run_replacement_models.ipynb b/viz_scripts/run_replacement_models.ipynb deleted file mode 100644 index 364040c..0000000 --- a/viz_scripts/run_replacement_models.ipynb +++ /dev/null @@ -1,454 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import sklearn\n", - "from sklearn.model_selection import KFold\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "from plots import *\n", - "import replacement_models as rm\n", - "import scaffolding\n", - "\n", - "# For reloading modules from Jupyter\n", - "import importlib\n", - "importlib.reload(rm)\n", - "\n", - "sns.set_style(\"whitegrid\")\n", - "sns.set()\n", - "%matplotlib inline\n", - "\n", - "SAVE_DIR = '/plots/'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = pd.read_csv('./processed_replacement_modeling_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "choice_col = 'Replaced_mode_num'\n", - "feature_list = ['Mode_confirm_num',\n", - " 'hhinc_$25,000-$49,999',\n", - " 'hhinc_$50,000-$99,999',\n", - " 'hhinc_Less than $24,999',\n", - " 'purp_Home',\n", - " 'purp_commute',\n", - " 'purp_discretionary',\n", - " 'purp_pudo',\n", - " 'purp_recreation',\n", - " 'purp_transit_transfer',\n", - " 'AGE',\n", - " 'VEH',\n", - " 'HHSIZE',\n", - " 'is_male',\n", - " 'sin_time',\n", - " 'cos_time',\n", - " 'sin_month',\n", - " 'cos_month',\n", - " 'is_weekend',\n", - " 'duration',\n", - " 'distance_miles',\n", - " 'av_car',\n", - " 'av_s_car',\n", - " 'av_ebike',\n", - " 'av_p_micro',\n", - " 'av_s_micro',\n", - " 'av_ridehail',\n", - " 'av_transit',\n", - " 'av_walk']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set up K-fold cross validation\n", - "kf = KFold(n_splits=3)\n", - "\n", - "# Collect all scores to show at end of modeling\n", - "score_results = {}\n", - "score_results_holdout = {}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set up holdout users\n", - "all_users = pd.unique(data['user_id'])\n", - "holdout_users = np.random.choice(all_users, 10)\n", - "holdout_data = data[data['user_id'].isin(holdout_users)]\n", - "non_holdout_data = data[~data['user_id'].isin(holdout_users)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Random Forest" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test on All Users" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Train and test model\n", - "rf, accuracy, f1, confusion = rm.rf(data, choice_col, feature_list, kf)\n", - "\n", - "# Save scores for model comparison\n", - "score_results['rf'] = (np.mean(accuracy), np.mean(f1))\n", - "print(f\"Accuracy: {np.mean(accuracy)}\")\n", - "print(f\"F1: {np.mean(f1)}\")\n", - "\n", - "# Average and plot the confusion matrices\n", - "confusion_mean = np.mean(np.array(confusion), axis=0)\n", - "fig, ax = plt.subplots(figsize=(6,6))\n", - "sns.heatmap(confusion_mean, annot=True, fmt='.1%', cmap='YlGnBu', linewidths=.5, cbar=False).set(title='Random Forest Confusion Matrix', xlabel='Predicted', ylabel='Actual')\n", - "plt.subplots_adjust(bottom=0.25)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test on Holdout Users" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Train and test model\n", - "rf, accuracy, f1, confusion = rm.rf(non_holdout_data, choice_col, feature_list, kf)\n", - "\n", - "holdout_true = holdout_data[choice_col].values\n", - "holdout_pred = rf.predict(holdout_data[feature_list].values)\n", - "\n", - "accuracy = sklearn.metrics.accuracy_score(holdout_true, holdout_pred)\n", - "f1 = sklearn.metrics.f1_score(holdout_true, holdout_pred, average='weighted')\n", - "score_results_holdout['rf'] = (np.mean(accuracy), np.mean(f1))\n", - "\n", - "print(f\"Holdout Accuracy: {accuracy}\")\n", - "print(f\"Holdout F1: {f1}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# GBDT" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test on All Users" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Train and test model\n", - "rf, accuracy, f1, confusion = rm.gbdt(data, choice_col, feature_list, kf)\n", - "\n", - "# Save scores for model comparison\n", - "score_results['gbdt'] = (np.mean(accuracy), np.mean(f1))\n", - "print(f\"Accuracy: {np.mean(accuracy)}\")\n", - "print(f\"F1: {np.mean(f1)}\")\n", - "\n", - "# Average and plot the confusion matrices\n", - "confusion_mean = np.mean(np.array(confusion), axis=0)\n", - "fig, ax = plt.subplots(figsize=(6,6))\n", - "sns.heatmap(confusion_mean, annot=True, fmt='.1%', cmap='YlGnBu', linewidths=.5, cbar=False).set(title='GBDT Confusion Matrix', xlabel='Predicted', ylabel='Actual')\n", - "plt.subplots_adjust(bottom=0.25)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test on Holdout Users" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Train and test model\n", - "gbdt, accuracy, f1, confusion = rm.gbdt(non_holdout_data, choice_col, feature_list, kf)\n", - "\n", - "holdout_true = holdout_data[choice_col].values\n", - "holdout_pred = gbdt.predict(holdout_data[feature_list].values)\n", - "\n", - "accuracy = sklearn.metrics.accuracy_score(holdout_true, holdout_pred)\n", - "f1 = sklearn.metrics.f1_score(holdout_true, holdout_pred, average='weighted')\n", - "score_results_holdout['gbdt'] = (np.mean(accuracy), np.mean(f1))\n", - "\n", - "print(f\"Holdout Accuracy: {accuracy}\")\n", - "print(f\"Holdout F1: {f1}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SVM" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test on All Users" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Train and test model\n", - "rf, accuracy, f1, confusion = rm.svm(data, choice_col, feature_list, kf)\n", - "\n", - "# Save scores for model comparison\n", - "score_results['svm'] = (np.mean(accuracy), np.mean(f1))\n", - "print(f\"Accuracy: {np.mean(accuracy)}\")\n", - "print(f\"F1: {np.mean(f1)}\")\n", - "\n", - "# Average and plot the confusion matrices\n", - "confusion_mean = np.mean(np.array(confusion), axis=0)\n", - "fig, ax = plt.subplots(figsize=(6,6))\n", - "sns.heatmap(confusion_mean, annot=True, fmt='.1%', cmap='YlGnBu', linewidths=.5, cbar=False).set(title='SVM Confusion Matrix', xlabel='Predicted', ylabel='Actual')\n", - "plt.subplots_adjust(bottom=0.25)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test on Holdout Users" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Train and test model\n", - "svm, accuracy, f1, confusion = rm.svm(non_holdout_data, choice_col, feature_list, kf)\n", - "\n", - "holdout_true = holdout_data[choice_col].values\n", - "holdout_pred = svm.predict(holdout_data[feature_list].values)\n", - "\n", - "accuracy = sklearn.metrics.accuracy_score(holdout_true, holdout_pred)\n", - "f1 = sklearn.metrics.f1_score(holdout_true, holdout_pred, average='weighted')\n", - "score_results_holdout['svm'] = (np.mean(accuracy), np.mean(f1))\n", - "\n", - "print(f\"Holdout Accuracy: {accuracy}\")\n", - "print(f\"Holdout F1: {f1}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# K Nearest Neighbors" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test on All Users" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Train and test model\n", - "knn, accuracy, f1, confusion = rm.knn(data, choice_col, feature_list, kf)\n", - "\n", - "# Save scores for model comparison\n", - "score_results['knn'] = (np.mean(accuracy), np.mean(f1))\n", - "print(f\"Accuracy: {np.mean(accuracy)}\")\n", - "print(f\"F1: {np.mean(f1)}\")\n", - "\n", - "# Average and plot the confusion matrices\n", - "confusion_mean = np.mean(np.array(confusion), axis=0)\n", - "fig, ax = plt.subplots(figsize=(6,6))\n", - "sns.heatmap(confusion_mean, annot=True, fmt='.1%', cmap='YlGnBu', linewidths=.5, cbar=False).set(title='KNN Confusion Matrix', xlabel='Predicted', ylabel='Actual')\n", - "plt.subplots_adjust(bottom=0.25)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test on Holdout Users" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Train and test model\n", - "knn, accuracy, f1, confusion = rm.knn(non_holdout_data, choice_col, feature_list, kf)\n", - "\n", - "holdout_true = holdout_data[choice_col].values\n", - "holdout_pred = knn.predict(holdout_data[feature_list].values)\n", - "\n", - "accuracy = sklearn.metrics.accuracy_score(holdout_true, holdout_pred)\n", - "f1 = sklearn.metrics.f1_score(holdout_true, holdout_pred, average='weighted')\n", - "score_results_holdout['knn'] = (np.mean(accuracy), np.mean(f1))\n", - "\n", - "print(f\"Holdout Accuracy: {accuracy}\")\n", - "print(f\"Holdout F1: {f1}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Model Comparison" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Overall results\n", - "accuracy_all = pd.DataFrame(pd.DataFrame(score_results).iloc[0,:]).reset_index()\n", - "accuracy_all.columns = ['Model','Score']\n", - "accuracy_all['Type'] = 'All'\n", - "f1_all = pd.DataFrame(pd.DataFrame(score_results).iloc[1,:]).reset_index()\n", - "f1_all.columns = ['Model','Score']\n", - "f1_all['Type'] = 'All'\n", - "score_results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Overall results\n", - "accuracy_holdout = pd.DataFrame(pd.DataFrame(score_results_holdout).iloc[0,:]).reset_index()\n", - "accuracy_holdout.columns = ['Model','Score']\n", - "accuracy_holdout['Type'] = 'Holdout'\n", - "f1_holdout = pd.DataFrame(pd.DataFrame(score_results_holdout).iloc[1,:]).reset_index()\n", - "f1_holdout.columns = ['Model','Score']\n", - "f1_holdout['Type'] = 'Holdout'\n", - "score_results_holdout" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "accuracy = pd.concat([accuracy_all, accuracy_holdout])\n", - "f1 = pd.concat([f1_all, f1_holdout])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "sns.barplot(data=accuracy, x='Model', y='Score', hue='Type').set(title='Accuracy')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sns.barplot(data=f1, x='Model', y='Score', hue='Type').set(title='F1')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/viz_scripts/scaffolding.py b/viz_scripts/scaffolding.py index 4dcccb9..40e0517 100644 --- a/viz_scripts/scaffolding.py +++ b/viz_scripts/scaffolding.py @@ -10,6 +10,7 @@ import emission.core.wrapper.localdate as ecwl import emission.storage.decorations.trip_queries as esdt from pandarallel import pandarallel +from multiprocessing import cpu_count # Module for pretty-printing outputs (e.g. head) to help users @@ -145,35 +146,12 @@ def get_durations(user_id, trip_id): return [] confirmed_trips['section_durations'] = confirmed_trips.parallel_apply( - lambda x: get_durations(x.original_user_id, x.cleaned_trip), axis=1 + lambda x: get_durations(x.user_id, x.cleaned_trip), axis=1 ) return confirmed_trips -def get_section_coordinates(confirmed_trips: pd.DataFrame): - # Initialize pandarallel - pandarallel.initialize(progress_bar=False) - - key = 'analysis/inferred_section' - - def get_coordinates(user_id, trip_id, distances): - sections = esdt.get_sections_for_trip(key = key, - user_id = user_id, trip_id = trip_id) - - if sections and len(sections) > 0 and len(distances) == len(sections): - argmax = np.argmax(distances) - section = sections[argmax] - return section.data.start_loc['coordinates'], section.data.end_loc['coordinates'] - - return [] - - confirmed_trips['section_locations_argmax'] = confirmed_trips.parallel_apply( - lambda x: get_coordinates(x.original_user_id, x.cleaned_trip, x.section_distances), axis=1 - ) - - return confirmed_trips - # CASE 2 of https://github.com/e-mission/em-public-dashboard/issues/69#issuecomment-1256835867 unique_users = lambda df: len(df.user_id.unique()) if "user_id" in df.columns else 0 @@ -185,6 +163,9 @@ def load_viz_notebook_data(year, month, program, study_type, dic_re, dic_pur=Non dic_* = label mappings; if dic_pur is included it will be used to recode trip purpose Pipeline to load and process the data before use in visualization notebooks. + + year = None, month = None, program='prepilot', study_type='program', dict(), dict() + """ # Access database tq = get_time_query(year, month) diff --git a/viz_scripts/tests/TestGetMonthList.py b/viz_scripts/tests/TestGetMonthList.py deleted file mode 100644 index a9690e4..0000000 --- a/viz_scripts/tests/TestGetMonthList.py +++ /dev/null @@ -1,39 +0,0 @@ -import arrow -import unittest - -class TestGetMonthList(unittest.TestCase): - def test_same_month(self): - start_date = arrow.get(2020, 5, 1) - end_date = arrow.get(2020, 5, 1) - month_range = list(arrow.Arrow.range('month', start_date, end_date)) - self.assertEqual(len(month_range), 1) - self.assertEqual([m.year for m in month_range], [2020]) - self.assertEqual([m.month for m in month_range], [5]) - - def test_same_year(self): - start_date = arrow.get(2020, 5, 1) - end_date = arrow.get(2020, 10, 1) - month_range = list(arrow.Arrow.range('month', start_date, end_date)) - self.assertEqual([m.year for m in month_range], [2020] * 6) - self.assertEqual([m.month for m in month_range], list(range(5,11))) - - def test_less_than_twelve_months_spans_two_years(self): - start_date = arrow.get(2020, 7, 1) - end_date = arrow.get(2021, 5, 1) - month_range = list(arrow.Arrow.range('month', start_date, end_date)) - self.assertEqual([m.year for m in month_range[:6]], [2020] * 6) - self.assertEqual([m.year for m in month_range[6:]], [2021] * 5) - self.assertEqual([m.month for m in month_range[:6]], list(range(7, 13))) - self.assertEqual([m.month for m in month_range[6:]], list(range(1, 6))) - - def test_more_than_twelve_months_spans_two_years(self): - start_date = arrow.get(2020, 7, 1) - end_date = arrow.get(2021, 9, 1) - month_range = list(arrow.Arrow.range('month', start_date, end_date)) - self.assertEqual([m.year for m in month_range[:6]], [2020] * 6) - self.assertEqual([m.year for m in month_range[6:]], [2021] * 9) - self.assertEqual([m.month for m in month_range[:6]], list(range(7, 13))) - self.assertEqual([m.month for m in month_range[6:]], list(range(1, 10))) - -if __name__ == '__main__': - unittest.main() diff --git a/viz_scripts/variation_across_individuals.ipynb b/viz_scripts/variation_across_individuals.ipynb deleted file mode 100644 index 2e043f5..0000000 --- a/viz_scripts/variation_across_individuals.ipynb +++ /dev/null @@ -1,263 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "32b25c19", - "metadata": {}, - "outputs": [], - "source": [ - "# This is still exploratory analysis, so it is not converted to work with the cold start changes\n", - "# similar to the other notebooks" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "lyric-montgomery", - "metadata": {}, - "outputs": [], - "source": [ - "year = None\n", - "month = None\n", - "program = None" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "mobile-certificate", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "from collections import defaultdict\n", - "\n", - "sns.set_style(\"whitegrid\")\n", - "sns.set()\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "existing-tanzania", - "metadata": {}, - "outputs": [], - "source": [ - "import scaffolding \n", - "from plots import *" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "unique-topic", - "metadata": {}, - "outputs": [], - "source": [ - "# Loading mapping dictionaries from mapping_dictionaries notebook\n", - "%store -r dic_ei\n", - "%store -r dic_re\n", - "%store -r dic_pur\n", - "\n", - "# convert a dictionary to a defaultdict\n", - "dic_pur = defaultdict(lambda: 'Other',dic_pur)\n", - "dic_re = defaultdict(lambda: 'Other',dic_re)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "legitimate-ethics", - "metadata": {}, - "outputs": [], - "source": [ - "tq = scaffolding.get_time_query(year, month)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "contrary-poverty", - "metadata": {}, - "outputs": [], - "source": [ - "participant_ct_df = scaffolding.load_all_participant_trips(program, tq)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "international-retention", - "metadata": {}, - "outputs": [], - "source": [ - "labeled_ct = scaffolding.filter_labeled_trips(participant_ct_df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dynamic-allowance", - "metadata": {}, - "outputs": [], - "source": [ - "expanded_ct = scaffolding.expand_userinputs(labeled_ct)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "micro-gathering", - "metadata": {}, - "outputs": [], - "source": [ - "expanded_ct.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "sweet-muscle", - "metadata": {}, - "outputs": [], - "source": [ - "expanded_ct = scaffolding.data_quality_check(expanded_ct)\n", - "expanded_ct.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "compact-reader", - "metadata": {}, - "outputs": [], - "source": [ - "## Mapping new labels with dictionaries\n", - "expanded_ct['Trip_purpose']= expanded_ct['purpose_confirm'].map(dic_pur)\n", - "expanded_ct['Mode_confirm']= expanded_ct['mode_confirm'].map(dic_re)\n", - "expanded_ct['Replaced_mode']= expanded_ct['replaced_mode'].map(dic_re)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "studied-oxide", - "metadata": {}, - "outputs": [], - "source": [ - "assert len(expanded_ct[(expanded_ct['Mode_confirm'] == 'Pilot ebike') & (expanded_ct[\"Replaced_mode\"] == \"Pilot ebike\")]) == 0" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "auburn-driving", - "metadata": {}, - "outputs": [], - "source": [ - "# Energy Impact Calculation\n", - "scaffolding.unit_conversions(expanded_ct)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "mental-compact", - "metadata": {}, - "outputs": [], - "source": [ - "# group by user\n", - "all_trip_user_count = participant_ct_df.groupby(\"user_id\")[\"user_id\"].agg([\"count\"])\n", - "all_trip_user_count" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "international-morrison", - "metadata": {}, - "outputs": [], - "source": [ - "# group by user\n", - "labeled_trip_user_count = expanded_ct.groupby(\"user_id\")[\"user_id\"].agg([\"count\"])\n", - "labeled_trip_user_count" - ] - }, - { - "cell_type": "markdown", - "id": "junior-scholar", - "metadata": {}, - "source": [ - "## eBike only" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "sticky-fiber", - "metadata": {}, - "outputs": [], - "source": [ - "data_eb = expanded_ct.query(\"Mode_confirm == 'Pilot ebike'\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "representative-scale", - "metadata": {}, - "outputs": [], - "source": [ - "eb_user_count = data_eb.groupby(\"user_id\")[\"user_id\"].count()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "liked-silver", - "metadata": {}, - "outputs": [], - "source": [ - "compare_df = pd.concat([eb_user_count, labeled_trip_user_count, all_trip_user_count], axis=1)\n", - "compare_df.columns = [\"ebike_trips\", \"labeled_trips\", \"all_trips\"]\n", - "compare_df[\"labeled Trip Pct\"] = (compare_df.labeled_trips * 100) / compare_df.all_trips\n", - "compare_df[\"ebike Trip Pct\"] = (compare_df.ebike_trips * 100) / compare_df.labeled_trips\n", - "compare_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "comic-audience", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}