ML 7.2HD.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ucimlrepo in /opt/anaconda3/lib/python3.11/site-packages (0.0.6)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install ucimlrepo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "from sklearn.metrics import mean_absolute_error, root_mean_squared_error\n",
    "from sklearn.ensemble import StackingRegressor\n",
    "from tabulate import tabulate\n",
    "from sklearn.pipeline import Pipeline\n",
    "from keras.models import Model, Sequential\n",
    "from keras.layers import Dense, Input\n",
    "from keras.optimizers import Adam\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'uci_id': 849, 'name': 'Power Consumption of Tetouan City', 'repository_url': 'https://archive.ics.uci.edu/dataset/849/power+consumption+of+tetouan+city', 'data_url': 'https://archive.ics.uci.edu/static/public/849/data.csv', 'abstract': 'This dataset is related to power consumption of three different distribution networks of Tetouan city which is located in north Morocco.', 'area': 'Social Science', 'tasks': ['Regression'], 'characteristics': ['Multivariate', 'Time-Series'], 'num_instances': 52417, 'num_features': 6, 'feature_types': ['Real', 'Integer'], 'demographics': [], 'target_col': ['Zone 1 Power Consumption', 'Zone 2  Power Consumption', 'Zone 3  Power Consumption'], 'index_col': None, 'has_missing_values': 'no', 'missing_values_symbol': None, 'year_of_dataset_creation': 2018, 'last_updated': 'Fri Mar 08 2024', 'dataset_doi': '10.24432/C5B034', 'creators': ['Abdulwahed Salam', 'Abdelaaziz El Hibaoui'], 'intro_paper': {'title': 'Comparison of Machine Learning Algorithms for the Power Consumption Prediction : - Case Study of Tetouan city –', 'authors': 'A. Salam, A. E. Hibaoui', 'published_in': 'International Renewable and Sustainable Energy Conference', 'year': 2018, 'url': 'https://www.semanticscholar.org/paper/177512e766fe5d8624a1b3e93abd11082ac37b3f', 'doi': None}, 'additional_info': {'summary': None, 'purpose': None, 'funded_by': None, 'instances_represent': None, 'recommended_data_splits': None, 'sensitive_data': None, 'preprocessing_description': None, 'variable_info': None, 'citation': 'Salam, A., & El Hibaoui, A. (2018, December). Comparison of Machine Learning Algorithms for the Power Consumption Prediction:-Case Study of Tetouan city“. In 2018 6th International Renewable and Sustainable Energy Conference (IRSEC) (pp. 1-5). IEEE.'}}\n",
      "                        name     role        type demographic  \\\n",
      "0                   DateTime  Feature        Date        None   \n",
      "1                Temperature  Feature  Continuous        None   \n",
      "2                   Humidity  Feature  Continuous        None   \n",
      "3                 Wind Speed  Feature  Continuous        None   \n",
      "4      general diffuse flows  Feature  Continuous        None   \n",
      "5              diffuse flows  Feature  Continuous        None   \n",
      "6   Zone 1 Power Consumption   Target  Continuous        None   \n",
      "7  Zone 2  Power Consumption   Target  Continuous        None   \n",
      "8  Zone 3  Power Consumption   Target  Continuous        None   \n",
      "\n",
      "                                   description units missing_values  \n",
      "0                             Each ten minutes  None             no  \n",
      "1          Weather Temperature of Tetouan city  None             no  \n",
      "2             Weather Humidity of Tetouan city  None             no  \n",
      "3                   Wind speed of Tetouan city  None             no  \n",
      "4                        general diffuse flows  None             no  \n",
      "5                                diffuse flows  None             no  \n",
      "6  power consumption of zone 1 of Tetouan city  None             no  \n",
      "7  power consumption of zone 2 of Tetouan city  None             no  \n",
      "8  power consumption of zone 3 of Tetouan city  None             no  \n"
     ]
    }
   ],
   "source": [
    "from ucimlrepo import fetch_ucirepo \n",
    "  \n",
    "# fetch dataset \n",
    "power_consumption_of_tetouan_city = fetch_ucirepo(id=849) \n",
    "  \n",
    "# data (as pandas dataframes) \n",
    "X = power_consumption_of_tetouan_city.data.features \n",
    "y = power_consumption_of_tetouan_city.data.targets \n",
    "  \n",
    "# metadata \n",
    "print(power_consumption_of_tetouan_city.metadata) \n",
    "  \n",
    "# variable information \n",
    "print(power_consumption_of_tetouan_city.variables) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Preprocessing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/zt/tl4cjgsd5rzfpyy9y2vkgbyr0000gn/T/ipykernel_1672/4132265938.py:2: 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",
      "  X['DateTime'] = pd.to_datetime(X['DateTime'])\n"
     ]
    }
   ],
   "source": [
    "#Convert the date_time column to a datetime datatype\n",
    "X['DateTime'] = pd.to_datetime(X['DateTime'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examinze the different networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))\n",
    "plt.stackplot(X['DateTime'], y.T, labels=y.columns)\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel('Datetime')\n",
    "plt.ylabel('Power Consumption')\n",
    "plt.title('Power Consumption')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Base on Fig 1 of paper, we can distinguish 3 different distribution network:\n",
    "\n",
    "- Zone 1 = Boussafou (the lowest power consumption)\n",
    "\n",
    "- Zone 2 = Smir\n",
    "\n",
    "- Zone 3 = Quods (highest peak)\n",
    "\n",
    "But the y value in the plot is much higher than the y value in the paper. Not sure do we use the same value with paper or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Temperature', 'Humidity', 'Wind Speed', 'general diffuse flows',\n",
      "       'diffuse flows', 'Month', 'Day', 'Hour', 'Minute', 'Weekdays',\n",
      "       'Quarter', 'DayOfYear', 'WeekOfYear'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/zt/tl4cjgsd5rzfpyy9y2vkgbyr0000gn/T/ipykernel_1672/909330328.py:2: 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",
      "  X.loc[:, 'Month'] = X['DateTime'].dt.month\n",
      "/var/folders/zt/tl4cjgsd5rzfpyy9y2vkgbyr0000gn/T/ipykernel_1672/909330328.py:3: 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",
      "  X.loc[:, 'Day'] = X['DateTime'].dt.day\n"
     ]
    }
   ],
   "source": [
    "#Extract the quater, month, weekdays, day, day of year, week of year, hour, and minute from the date_time column\n",
    "X.loc[:, 'Month'] = X['DateTime'].dt.month\n",
    "X.loc[:, 'Day'] = X['DateTime'].dt.day\n",
    "X.loc[:, 'Hour'] = X['DateTime'].dt.hour\n",
    "X.loc[:, 'Minute'] = X['DateTime'].dt.minute\n",
    "X.loc[:, 'Weekdays'] = X['DateTime'].dt.weekday\n",
    "X.loc[:, 'Quarter'] = X['DateTime'].dt.quarter\n",
    "X.loc[:, 'DayOfYear'] = X['DateTime'].dt.dayofyear\n",
    "X.loc[:, 'WeekOfYear'] = X['DateTime'].dt.isocalendar().week\n",
    "\n",
    "# Drop the date_time column\n",
    "X = X.drop('DateTime', axis = 1)\n",
    "\n",
    "# Print X columns\n",
    "print(X.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Scale the data using the MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "X_scaler = scaler.fit_transform(X)\n",
    "y_scaler = scaler.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(random_state=0)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(random_state=0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check feature importance using Random forest\n",
    "forest = RandomForestRegressor(random_state=0)\n",
    "forest.fit(X_scaler, y_scaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = forest.feature_importances_\n",
    "\n",
    "#plotting the feature importances\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(X.columns, importances)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hour is still the most importance feature but the 2nd highest importance feature from my calculation is Day of year, which is different than paper (Temparature). I think it is because of the randomness of the bootstrapping of the samples used when building trees in random forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Each algorithm is trained by using 75% of the data and 25% for testing\n",
    "X_train_scaler, X_test_scaler, y_train_scaler, y_test_scaler = train_test_split(X_scaler, y_scaler, test_size = 0.25, random_state = 0)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Train model**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply the best parameter for 5 models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10 minutes comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random Forest Regressor\n",
    "#The best values which are gotten by the grid search method were 30, 7, None, 2 and 1 for the number of trees and the number of features, max depth of the tree, min samples split and min sample leaf parameters respectively.\n",
    "rf = RandomForestRegressor(n_estimators=30, max_features=7, min_samples_split=2, max_depth=None, min_samples_leaf=1, random_state=0)\n",
    "multi_rf = MultiOutputRegressor(rf)\n",
    "multi_rf.fit(X_train, y_train)\n",
    "y_train_pred_rf = multi_rf.predict(X_train)\n",
    "y_test_pred_rf = multi_rf.predict(X_test)\n",
    "\n",
    "#Decision Tree Regressor\n",
    "#The depth of the tree, the minimum number of samples required to split an internal node, the minimum number of samples required to be at a leaf node and the number of features to consider when looking for the best split. The best result of these parameters which were gotten by grid search were None, 10, 10 and 9 for the parameters above respectively\n",
    "dt = DecisionTreeRegressor(max_depth=None, min_samples_split=10, min_samples_leaf=10, max_features=9, random_state=0)\n",
    "multi_dt = MultiOutputRegressor(dt)\n",
    "multi_dt.fit(X_train, y_train)\n",
    "y_train_pred_dt = multi_dt.predict(X_train)\n",
    "y_test_pred_dt = multi_dt.predict(X_test)\n",
    "\n",
    "#Support Vector Regressor\n",
    "#cost equals to 10 and when gamma equals to 0.01, support vector machine for regression (SVR) with radial basis function kernel.\n",
    "svrclf = SVR(C=10, gamma=0.01, kernel=\"rbf\")\n",
    "multi_svr = MultiOutputRegressor(svrclf)\n",
    "multi_svr.fit(X_train_scaler, y_train)\n",
    "y_train_pred_svr = multi_svr.predict(X_train_scaler)\n",
    "y_test_pred_svr = multi_svr.predict(X_test_scaler)\n",
    "\n",
    "#Feedforward Neural Network\n",
    "#One hidden layer with 10 neurons is selected by grid search, SELU activation is also chosen and the Adam is implemented as the preferable optimizer. We manually set the number of epochs to 100. The initial learning rate is seted to 0.001. The initialization of training is Glorot uniform initialization[26]. We used no dropout and 0.9 momentum.\n",
    "inputs = Input(shape=(X_train_scaler.shape[1],))\n",
    "hidden = Dense(10, activation='selu', kernel_initializer='glorot_uniform')(inputs)  # Hidden layer with 10 neurons and SELU activation\n",
    "output1 = Dense(1)(hidden)  # Output layer for target 1\n",
    "output2 = Dense(1)(hidden)  # Output layer for target 2\n",
    "output3 = Dense(1)(hidden)  # Output layer for target 3\n",
    "ffnn = Model(inputs=inputs, outputs=[output1, output2, output3])\n",
    "\n",
    "ffnn.compile(loss='mean_squared_error', optimizer=Adam(learning_rate=0.001, beta_1=0.9))\n",
    "ffnn.fit(X_train_scaler, [y_train.iloc[:, 0], y_train.iloc[:, 1], y_train.iloc[:, 2]], epochs=100, verbose=0)\n",
    "\n",
    "y_train_pred_ffnn_m = np.hstack(ffnn.predict(X_train_scaler))\n",
    "y_test_pred_ffnn_m = np.hstack(ffnn.predict(X_test_scaler))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For comparison purposes, we compared the median of 9 implementation in all models of dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_train_agg = np.median(y_train_pred_rf, axis=1)\n",
    "rf_test_agg = np.median(y_test_pred_rf, axis=1)\n",
    "dt_train_agg = np.median(y_train_pred_dt, axis=1)\n",
    "dt_test_agg = np.median(y_test_pred_dt, axis=1)\n",
    "svr_train_agg = np.median(y_train_pred_svr, axis=1)\n",
    "svr_test_agg = np.median(y_test_pred_svr, axis=1)\n",
    "ffnn_train_agg = np.median(y_train_pred_ffnn_m, axis=1)\n",
    "ffnn_test_agg = np.median(y_test_pred_ffnn_m, axis=1)\n",
    "y_train_agg = np.median(y_train, axis=1)\n",
    "y_test_agg = np.median(y_test, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**RSME AND MAE COMPARISON OF ALGORITHMS IN 3 DISTRIBUTIONS FOR 10 MINUTES POWER CONSUMPTION**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate RSME adn MAE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RSME calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#RF\n",
    "rsme_train_rf_bous = root_mean_squared_error(y_train.iloc[:, 0], y_train_pred_rf[:, 0])\n",
    "rsme_train_rf_smir = root_mean_squared_error(y_train.iloc[:, 1], y_train_pred_rf[:, 1])\n",
    "rsme_train_rf_quads = root_mean_squared_error(y_train.iloc[:, 2], y_train_pred_rf[:, 2])\n",
    "rsme_train_rf_agg = root_mean_squared_error(y_train_agg, rf_train_agg)\n",
    "\n",
    "rsme_test_rf_bous = root_mean_squared_error(y_test.iloc[:, 0], y_test_pred_rf[:, 0])\n",
    "rsme_test_rf_smir = root_mean_squared_error(y_test.iloc[:, 1], y_test_pred_rf[:, 1])\n",
    "rsme_test_rf_quads = root_mean_squared_error(y_test.iloc[:, 2], y_test_pred_dt[:, 2])\n",
    "rsme_test_rf_agg = root_mean_squared_error(y_test_agg, rf_test_agg)\n",
    "\n",
    "#DT\n",
    "rsme_train_dt_bous = root_mean_squared_error(y_train.iloc[:, 0], y_train_pred_dt[:, 0])\n",
    "rsme_train_dt_smir = root_mean_squared_error(y_train.iloc[:, 1], y_train_pred_dt[:, 1])\n",
    "rsme_train_dt_quads = root_mean_squared_error(y_train.iloc[:, 2], y_train_pred_dt[:, 2])\n",
    "rsme_train_dt_agg = root_mean_squared_error(y_train_agg, dt_train_agg)\n",
    "\n",
    "rsme_test_dt_bous = root_mean_squared_error(y_test.iloc[:, 0], y_test_pred_dt[:, 0])\n",
    "rsme_test_dt_smir = root_mean_squared_error(y_test.iloc[:, 1], y_test_pred_dt[:, 1])\n",
    "rsme_test_dt_quads = root_mean_squared_error(y_test.iloc[:, 2], y_test_pred_dt[:, 2])\n",
    "rsme_test_dt_agg = root_mean_squared_error(y_test_agg, dt_test_agg)\n",
    "\n",
    "#SVR\n",
    "rsme_train_svr_bous = root_mean_squared_error(y_train.iloc[:, 0], y_train_pred_svr[:, 0])\n",
    "rsme_train_svr_smir = root_mean_squared_error(y_train.iloc[:, 1], y_train_pred_svr[:, 1])\n",
    "rsme_train_svr_quads = root_mean_squared_error(y_train.iloc[:, 2], y_train_pred_svr[:, 2])\n",
    "rsme_train_svr_agg = root_mean_squared_error(y_train_agg, svr_train_agg)\n",
    "\n",
    "rsme_test_svr_bous = root_mean_squared_error(y_test.iloc[:, 0], y_test_pred_svr[:, 0])\n",
    "rsme_test_svr_smir = root_mean_squared_error(y_test.iloc[:, 1], y_test_pred_svr[:, 1])\n",
    "rsme_test_svr_quads = root_mean_squared_error(y_test.iloc[:, 2], y_test_pred_svr[:, 2])\n",
    "rsme_test_svr_agg = root_mean_squared_error(y_test_agg, svr_test_agg)\n",
    "\n",
    "#FFNN\n",
    "rsme_train_ffnn_bous = root_mean_squared_error(y_train.iloc[:, 0], y_train_pred_ffnn_m[:, 0])\n",
    "rsme_train_ffnn_smir = root_mean_squared_error(y_train.iloc[:, 1], y_train_pred_ffnn_m[:, 1])\n",
    "rsme_train_ffnn_quads = root_mean_squared_error(y_train.iloc[:, 2], y_train_pred_ffnn_m[:, 2])\n",
    "rsme_train_ffnn_agg = root_mean_squared_error(y_train_agg, ffnn_train_agg)\n",
    "\n",
    "rsme_test_ffnn_bous = root_mean_squared_error(y_test.iloc[:, 0], y_test_pred_ffnn_m[:, 0])\n",
    "rsme_test_ffnn_smir = root_mean_squared_error(y_test.iloc[:, 1], y_test_pred_ffnn_m[:, 1])\n",
    "rsme_test_ffnn_quads = root_mean_squared_error(y_test.iloc[:, 2], y_test_pred_ffnn_m[:, 2])\n",
    "rsme_test_ffnn_agg = root_mean_squared_error(y_test_agg, ffnn_test_agg)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MAE calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#RF\n",
    "mae_train_rf_bous = mean_absolute_error(y_train.iloc[:, 0], y_train_pred_rf[:, 0])\n",
    "mae_train_rf_smir = mean_absolute_error(y_train.iloc[:, 1], y_train_pred_rf[:, 1])\n",
    "mae_train_rf_quads = mean_absolute_error(y_train.iloc[:, 2], y_train_pred_rf[:, 2])\n",
    "mae_train_rf_agg = mean_absolute_error(y_train_agg, rf_train_agg)\n",
    "\n",
    "mae_test_rf_bous = mean_absolute_error(y_test.iloc[:, 0], y_test_pred_rf[:, 0])\n",
    "mae_test_rf_smir = mean_absolute_error(y_test.iloc[:, 1], y_test_pred_rf[:, 1])\n",
    "mae_test_rf_quads = mean_absolute_error(y_test.iloc[:, 2], y_test_pred_dt[:, 2])\n",
    "mae_test_rf_agg = mean_absolute_error(y_test_agg, rf_test_agg)\n",
    "\n",
    "#DT\n",
    "mae_train_dt_bous = mean_absolute_error(y_train.iloc[:, 0], y_train_pred_dt[:, 0])\n",
    "mae_train_dt_smir = mean_absolute_error(y_train.iloc[:, 1], y_train_pred_dt[:, 1])\n",
    "mae_train_dt_quads = mean_absolute_error(y_train.iloc[:, 2], y_train_pred_dt[:, 2])\n",
    "mae_train_dt_agg = mean_absolute_error(y_train_agg, dt_train_agg)\n",
    "\n",
    "mae_test_dt_bous = mean_absolute_error(y_test.iloc[:, 0], y_test_pred_dt[:, 0])\n",
    "mae_test_dt_smir = mean_absolute_error(y_test.iloc[:, 1], y_test_pred_dt[:, 1])\n",
    "mae_test_dt_quads = mean_absolute_error(y_test.iloc[:, 2], y_test_pred_dt[:, 2])\n",
    "mae_test_dt_agg = mean_absolute_error(y_test_agg, dt_test_agg)\n",
    "\n",
    "#SVR\n",
    "mae_train_svr_bous = mean_absolute_error(y_train.iloc[:, 0], y_train_pred_svr[:, 0])\n",
    "mae_train_svr_smir = mean_absolute_error(y_train.iloc[:, 1], y_train_pred_svr[:, 1])\n",
    "mae_train_svr_quads = mean_absolute_error(y_train.iloc[:, 2], y_train_pred_svr[:, 2])\n",
    "mae_train_svr_agg = mean_absolute_error(y_train_agg, svr_train_agg)\n",
    "\n",
    "mae_test_svr_bous = mean_absolute_error(y_test.iloc[:, 0], y_test_pred_svr[:, 0])\n",
    "mae_test_svr_smir = mean_absolute_error(y_test.iloc[:, 1], y_test_pred_svr[:, 1])\n",
    "mae_test_svr_quads = mean_absolute_error(y_test.iloc[:, 2], y_test_pred_svr[:, 2])\n",
    "mae_test_svr_agg = mean_absolute_error(y_test_agg, svr_test_agg)\n",
    "\n",
    "#FFNN\n",
    "mae_train_ffnn_bous = mean_absolute_error(y_train.iloc[:, 0], y_train_pred_ffnn_m[:, 0])\n",
    "mae_train_ffnn_smir = mean_absolute_error(y_train.iloc[:, 1], y_train_pred_ffnn_m[:, 1])\n",
    "mae_train_ffnn_quads = mean_absolute_error(y_train.iloc[:, 2], y_train_pred_ffnn_m[:, 2])\n",
    "mae_train_ffnn_agg = mean_absolute_error(y_train_agg, ffnn_train_agg)\n",
    "\n",
    "mae_test_ffnn_bous = mean_absolute_error(y_test.iloc[:, 0], y_test_pred_ffnn_m[:, 0])\n",
    "mae_test_ffnn_smir = mean_absolute_error(y_test.iloc[:, 1], y_test_pred_ffnn_m[:, 1])\n",
    "mae_test_ffnn_quads = mean_absolute_error(y_test.iloc[:, 2], y_test_pred_ffnn_m[:, 2])\n",
    "mae_test_ffnn_agg = mean_absolute_error(y_test_agg, ffnn_test_agg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------------------------+-------------------+-------------------+--------------------+-------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+-------------------+--------------------+--------------------+\n",
      "|           Model            | RSME Train Quads  |  RSME Test Quads  |  MAE Train Quads   |  MAE Test Quads   |  RSME Train Smir   |   RSME Test Smir   |   MAE Train Smir   |   MAE Test Smir    |  RSME Train Bous   |   RSME Test Bous   |   MAE Train Bous   |   MAE Test Bous    |   RSME Train Agg   |   RSME Test Agg   |   MAE Train Agg    |    MAE Test Agg    |\n",
      "+----------------------------+-------------------+-------------------+--------------------+-------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+-------------------+--------------------+--------------------+\n",
      "|       Random Forest        | 178.9585672847674 | 686.482089278667  | 113.6221126529482  | 452.3205273613677 | 195.55618249266715 | 485.21665795503907 | 129.13849941012438 | 323.26650610046465 | 285.36083839735227 |  709.604222368844  | 183.38069185796536 | 459.94954891404666 | 201.46428602075616 | 486.6710649556692 | 131.31974682995042 | 324.78876437468375 |\n",
      "|       Decision Tree        | 547.9046497833621 | 686.482089278667  | 360.4660978091446  | 452.3205273613677 | 671.2359129287523  | 855.2385200724704  | 444.04724880142743 | 560.3637192921988  | 896.8888624744225  | 1177.2812873226599 | 594.4935668107131  | 769.2781726264195  | 645.2761459322194  | 814.0532653079777 | 431.8663255630174  | 541.8075959078989  |\n",
      "|       Support Vector       | 6478.94843947602  | 6557.93512338024  | 4726.272719395413  | 4750.841738780069 |  4731.79746727407  | 4727.614193134834  | 3857.7826328019783 | 3845.1068795634724 | 6727.263839565509  | 6755.1783526805075 | 5552.218588331956  | 5556.693840596893  | 5380.836189162014  | 5445.78884237533  | 4195.150269145181  | 4207.583328665168  |\n",
      "| Feedforward Neural Network | 5222.766010619486 | 5304.888151353026 | 4087.6849532199453 | 4128.744876624394 | 3839.3650649017272 | 3833.4501944593476 | 3014.608912315112  | 3019.5771360678136 | 4388.061060697851  | 4385.176619327935  | 3442.8763261775025 | 3440.634476192384  | 4236.532177979269  | 4300.631932408349 | 3213.101255033525  | 3251.899617752499  |\n",
      "+----------------------------+-------------------+-------------------+--------------------+-------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+-------------------+--------------------+--------------------+\n"
     ]
    }
   ],
   "source": [
    "print(tabulate([['Random Forest', rsme_train_rf_quads, rsme_test_rf_quads, mae_train_rf_quads, mae_test_rf_quads, rsme_train_rf_smir, rsme_test_rf_smir, mae_train_rf_smir, mae_test_rf_smir, rsme_train_rf_bous, rsme_test_rf_bous, mae_train_rf_bous, mae_test_rf_bous, rsme_train_rf_agg, rsme_test_rf_agg, mae_train_rf_agg, mae_test_rf_agg],\n",
    "                ['Decision Tree', rsme_train_dt_quads, rsme_test_dt_quads, mae_train_dt_quads, mae_test_dt_quads, rsme_train_dt_smir, rsme_test_dt_smir, mae_train_dt_smir, mae_test_dt_smir, rsme_train_dt_bous, rsme_test_dt_bous, mae_train_dt_bous, mae_test_dt_bous, rsme_train_dt_agg, rsme_test_dt_agg, mae_train_dt_agg, mae_test_dt_agg],\n",
    "                ['Support Vector', rsme_train_svr_quads, rsme_test_svr_quads, mae_train_svr_quads, mae_test_svr_quads, rsme_train_svr_smir, rsme_test_svr_smir, mae_train_svr_smir, mae_test_svr_smir, rsme_train_svr_bous, rsme_test_svr_bous, mae_train_svr_bous, mae_test_svr_bous, rsme_train_svr_agg, rsme_test_svr_agg, mae_train_svr_agg, mae_test_svr_agg],\n",
    "                ['Feedforward Neural Network', rsme_train_ffnn_quads, rsme_test_ffnn_quads, mae_train_ffnn_quads, mae_test_ffnn_quads, rsme_train_ffnn_smir, rsme_test_ffnn_smir, mae_train_ffnn_smir, mae_test_ffnn_smir, rsme_train_ffnn_bous, rsme_test_ffnn_bous, mae_train_ffnn_bous, mae_test_ffnn_bous, rsme_train_ffnn_agg, rsme_test_ffnn_agg, mae_train_ffnn_agg, mae_test_ffnn_agg]], \n",
    "                headers=['Model', 'RSME Train Quads', 'RSME Test Quads', 'MAE Train Quads', 'MAE Test Quads', 'RSME Train Smir', 'RSME Test Smir', 'MAE Train Smir', 'MAE Test Smir', 'RSME Train Bous', 'RSME Test Bous', 'MAE Train Bous', 'MAE Test Bous', 'RSME Train Agg', 'RSME Test Agg', 'MAE Train Agg', 'MAE Test Agg'], tablefmt='pretty'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the two performance criterions, it is shown that random forest model achieved the best results for the four examinations. Also, it is noticed that feedforward neural network achieved a close result to the random forest in Quads distribution network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hourly comparison**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the hourly prediction, we reduced the number of parameters that became ineffective such as minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_h_scaler = np.delete(X_train_scaler, 8, axis=1)\n",
    "X_test_h_scaler = np.delete(X_test_scaler, 8, axis=1)\n",
    "\n",
    "X_train_h = X_train.drop('Minute', axis = 1)\n",
    "X_test_h = X_test.drop('Minute', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(39312, 12) (13104, 12)\n",
      "(39312, 12) (13104, 12)\n"
     ]
    }
   ],
   "source": [
    "print(X_train_h.shape, X_test_h.shape)\n",
    "print(X_train_h_scaler.shape, X_test_h_scaler.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change with optimized hyper parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#Quads Distribution - Zone 3 of y\n",
    "#Random Forest Regressor \n",
    "#Num of features = 3, min samples split = 2, Num of Trees = 50, max depth of the tree = None, min sample leaf = 1\n",
    "rf_quads = RandomForestRegressor(max_features=3, min_samples_split=2, n_estimators=50,  max_depth=None, min_samples_leaf=1, random_state=1)\n",
    "rf_quads.fit(X_train_h, y_train.iloc[:, 2])\n",
    "y_pred_train_rf_quads = rf_quads.predict(X_train_h)\n",
    "y_pred_test_rf_quads = rf_quads.predict(X_test_h)\n",
    "\n",
    "#Decision Tree Regressor\n",
    "#Num of features = 5, min samples split = 2, max depth of the tree = None, min sample leaf = 10\n",
    "dt_quads = DecisionTreeRegressor(max_features=5, min_samples_split=2, max_depth=None, min_samples_leaf=10, random_state=1)\n",
    "dt_quads.fit(X_train_h, y_train.iloc[:, 2])\n",
    "y_pred_train_dt_quads = dt_quads.predict(X_train_h)\n",
    "y_pred_test_dt_quads = dt_quads.predict(X_test_h)\n",
    "\n",
    "#Support Vector Regressor\n",
    "#C= 10, gamma= 0.01\n",
    "svr_quads = SVR(C=10, gamma=0.01, kernel=\"rbf\")\n",
    "svr_quads.fit(X_train_h_scaler, y_train.iloc[:, 2])\n",
    "y_pred_train_svr_quads = svr_quads.predict(X_train_h_scaler)\n",
    "y_pred_test_svr_quads = svr_quads.predict(X_test_h_scaler)\n",
    "\n",
    "#Feedforward Neural Network\n",
    "#Activation = ReLU, optimizer = SGD, batch size = 100, layers=one, neurons=25, number of epochs = 100, learning rate = 0.001\n",
    "mlp_quads = MLPRegressor(activation='relu', solver='sgd', batch_size=100, hidden_layer_sizes=(25,), max_iter=100, learning_rate_init=0.001, random_state=1) \n",
    "mlp_quads.fit(X_train_h_scaler, y_train.iloc[:, 2])\n",
    "y_pred_train_mlp_quads = mlp_quads.predict(X_train_h_scaler)\n",
    "y_pred_test_mlp_quads = mlp_quads.predict(X_test_h_scaler)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Smir Distribution - Zone 2 of y\n",
    "#Random Forest Regressor \n",
    "#Num of features = 7, min samples split = 3, Num of Trees = 10, max depth of the tree = None, min sample leaf = 1\n",
    "rf_smir = RandomForestRegressor(max_features=7, min_samples_split=3, n_estimators=10,  max_depth=None, min_samples_leaf=1, random_state=1)\n",
    "rf_smir.fit(X_train_h, y_train.iloc[:, 1])\n",
    "y_pred_train_rf_smir = rf_smir.predict(X_train_h)\n",
    "y_pred_test_rf_smir = rf_smir.predict(X_test_h)\n",
    "\n",
    "#Decision Tree Regressor\n",
    "#Num of features = 7, min samples split = 3, max depth of the tree = None, min sample leaf = 10\n",
    "dt_smir = DecisionTreeRegressor(max_features=7, min_samples_split=3, max_depth=None, min_samples_leaf=10, random_state=1)\n",
    "dt_smir.fit(X_train_h, y_train.iloc[:, 1])\n",
    "y_pred_train_dt_smir = dt_smir.predict(X_train_h)\n",
    "y_pred_test_dt_smir = dt_smir.predict(X_test_h)\n",
    "\n",
    "#Support Vector Regressor\n",
    "#'C': 1, 'gamma': 0.01\n",
    "svr_smir = SVR(C=1, gamma=0.01, kernel=\"rbf\")\n",
    "svr_smir.fit(X_train_h_scaler, y_train.iloc[:, 1])\n",
    "y_pred_train_svr_smir = svr_smir.predict(X_train_h_scaler)\n",
    "y_pred_test_svr_smir = svr_smir.predict(X_test_h_scaler)\n",
    "\n",
    "#Feedforward Neural Network\n",
    "#Activation = SELU, optimizer = Adam, batch size = 350, layers=one, neurons=4, number of epochs = 100, learning rate = 0.001\n",
    "model_smir = Sequential()\n",
    "model_smir.add(Input(shape=(X_train_h_scaler.shape[1],)))\n",
    "model_smir.add(Dense(4, activation='selu')) # Add the single hidden layer with 4 neurons and SELU activation\n",
    "model_smir.add(Dense(1)) \n",
    "optimizer = Adam(learning_rate=0.001) #Define the optimizer\n",
    "model_smir.compile(optimizer=optimizer, loss='mse')  # Use mean squared error as the loss function\n",
    "model_smir.fit(X_train_h_scaler, y_train.iloc[:, 1], batch_size=350, epochs=100)\n",
    "\n",
    "y_pred_train_ffnn_smir = np.hstack(model_smir.predict(X_train_h_scaler))\n",
    "y_pred_test_ffnn_smir = np.hstack(model_smir.predict(X_test_h_scaler))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Boussafou Distribution - Zone 1 of y\n",
    "#Random Forest Regressor \n",
    "#Num of features = 7, min samples split = 3, Num of Trees = 10, max depth of the tree = None, min sample leaf = 10\n",
    "rf_bous = RandomForestRegressor(max_features=7, min_samples_split=3, n_estimators=10,  max_depth=None, min_samples_leaf=10, random_state=1)\n",
    "rf_bous.fit(X_train_h, y_train.iloc[:, 0])\n",
    "y_pred_train_rf_bous = rf_bous.predict(X_train_h)\n",
    "y_pred_test_rf_bous = rf_bous.predict(X_test_h)\n",
    "\n",
    "#Decision Tree Regressor\n",
    "#Num of features = 9, min samples split = 2, max depth of the tree = None, min sample leaf = 10\n",
    "dt_bous = DecisionTreeRegressor(max_features=9, min_samples_split=2, max_depth=None, min_samples_leaf=10, random_state=1)\n",
    "dt_bous.fit(X_train_h, y_train.iloc[:, 0])\n",
    "y_pred_train_dt_bous = dt_bous.predict(X_train_h)\n",
    "y_pred_test_dt_bous = dt_bous.predict(X_test_h)\n",
    "\n",
    "#Support Vector Regressor\n",
    "#C: 1000, 'gamma': 0.01\n",
    "svr_bous = SVR(C=1000, gamma=0.01, kernel=\"rbf\")\n",
    "svr_bous.fit(X_train_h_scaler, y_train.iloc[:, 0])\n",
    "y_pred_train_svr_bous = svr_bous.predict(X_train_h_scaler)\n",
    "y_pred_test_svr_bous = svr_bous.predict(X_test_h_scaler)\n",
    "\n",
    "#Feedforward Neural Network\n",
    "#Activation = SELU, optimizer = Adam, batch size = 250, layers=one, neurons=8, number of epochs = 100, learning rate = 0.001\n",
    "model_bous = Sequential()\n",
    "model_bous.add(Input(shape=(X_train_h_scaler.shape[1],)))\n",
    "model_bous.add(Dense(8, activation='selu')) # Add the single hidden layer with 8 neurons and SELU activation\n",
    "model_bous.add(Dense(1)) \n",
    "optimizer = Adam(learning_rate=0.001) #Define the optimizer\n",
    "model_bous.compile(optimizer=optimizer, loss='mse')  # Use mean squared error as the loss function\n",
    "model_bous.fit(X_train_h_scaler, y_train.iloc[:, 0], batch_size=250, epochs=100)\n",
    "\n",
    "y_pred_train_ffnn_bous = np.hstack(model_bous.predict(X_train_h_scaler))\n",
    "y_pred_test_ffnn_bous = np.hstack(model_bous.predict(X_test_h_scaler))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random Forest Regressor \n",
    "#Num of features = 5, min samples split = 2, Num of Trees = 100, max depth of the tree =None, min sample leaf = 1\n",
    "rf_agg = RandomForestRegressor(max_features=5, min_samples_split=2, n_estimators=100,  max_depth=None, min_samples_leaf=1, random_state=1)\n",
    "rf_agg.fit(X_train_h, y_train_agg)\n",
    "y_pred_train_rf_agg = rf_agg.predict(X_train_h)\n",
    "y_pred_test_rf_agg = rf_agg.predict(X_test_h)\n",
    "\n",
    "#Decision Tree Regressor\n",
    "#Num of features = 9, min samples split = 3, max depth of the tree = None, min sample leaf = 3\n",
    "dt_agg = DecisionTreeRegressor(max_features=9, min_samples_split=3, max_depth=None, min_samples_leaf=3, random_state=1)\n",
    "dt_agg.fit(X_train_h, y_train_agg)\n",
    "y_pred_train_dt_agg = dt_agg.predict(X_train_h)\n",
    "y_pred_test_dt_agg = dt_agg.predict(X_test_h)\n",
    "\n",
    "#Support Vector Regressor\n",
    "#C= 1, gamma= 0.01\n",
    "svr_agg = SVR(C=1, gamma=0.01, kernel=\"rbf\")\n",
    "svr_agg.fit(X_train_h_scaler, y_train_agg)\n",
    "y_pred_train_svr_agg = svr_agg.predict(X_train_h_scaler)\n",
    "y_pred_test_svr_agg = svr_agg.predict(X_test_h_scaler)\n",
    "\n",
    "#Feedforward Neural Network\n",
    "#Activation = SELU, optimizer = Adam, batch size = 250, layers=one, neurons=4, number of epochs = 100, learning rate = 0.001\n",
    "model_agg = Sequential()\n",
    "model_agg.add(Dense(4, activation='selu', input_shape=(X_train_h_scaler.shape[1],))) # Add the single hidden layer with 8 neurons and SELU activation\n",
    "model_agg.add(Dense(1)) \n",
    "optimizer = Adam(learning_rate=0.001) #Define the optimizer\n",
    "model_agg.compile(optimizer=optimizer, loss='mse')  # Use mean squared error as the loss function\n",
    "model_agg.fit(X_train_h_scaler, y_train_agg, batch_size=250, epochs=100)\n",
    "\n",
    "y_pred_train_ffnn_agg = model_agg.predict(X_train_h_scaler)\n",
    "y_pred_test_ffnn_agg = model_agg.predict(X_test_h_scaler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RSME AND MAE COMPARISON OF ALGORITHMS IN 3 DISTRIBUTION NETWORKS FOR THE ONE HOURLY POWER CONSUMPTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate RSME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#RSME calculation \n",
    "# Quads\n",
    "rsme_train_h_rf_quads = root_mean_squared_error(y_train.iloc[:, 2], y_pred_train_rf_quads)\n",
    "rsme_train_h_dt_quads = root_mean_squared_error(y_train.iloc[:, 2], y_pred_train_dt_quads)\n",
    "rsme_train_h_svr_quads = root_mean_squared_error(y_train.iloc[:, 2], y_pred_train_svr_quads)\n",
    "rsme_train_h_mlp_quads = root_mean_squared_error(y_train.iloc[:, 2], y_pred_train_mlp_quads)\n",
    "rsme_test_h_rf_quads = root_mean_squared_error(y_test.iloc[:, 2], y_pred_test_rf_quads)\n",
    "rsme_test_h_dt_quads = root_mean_squared_error(y_test.iloc[:, 2], y_pred_test_dt_quads)\n",
    "rsme_test_h_svr_quads = root_mean_squared_error(y_test.iloc[:, 2], y_pred_test_svr_quads)\n",
    "rsme_test_h_mlp_quads = root_mean_squared_error(y_test.iloc[:, 2], y_pred_test_mlp_quads)\n",
    "\n",
    "# Smir\n",
    "rsme_train_h_rf_smir = root_mean_squared_error(y_train.iloc[:, 1], y_pred_train_rf_smir)\n",
    "rsme_train_h_dt_smir = root_mean_squared_error(y_train.iloc[:, 1], y_pred_train_dt_smir)\n",
    "rsme_train_h_svr_smir = root_mean_squared_error(y_train.iloc[:, 1], y_pred_train_svr_smir)\n",
    "rsme_train_h_ffnn_smir = root_mean_squared_error(y_train.iloc[:, 1], y_pred_train_ffnn_smir)\n",
    "rsme_test_h_rf_smir = root_mean_squared_error(y_test.iloc[:, 1], y_pred_test_rf_smir)\n",
    "rsme_test_h_dt_smir = root_mean_squared_error(y_test.iloc[:, 1], y_pred_test_dt_smir)\n",
    "rsme_test_h_svr_smir = root_mean_squared_error(y_test.iloc[:, 1], y_pred_test_svr_smir)\n",
    "rsme_test_h_ffnn_smir = root_mean_squared_error(y_test.iloc[:, 1], y_pred_test_ffnn_smir)\n",
    "\n",
    "# Boussafou\n",
    "rsme_train_h_rf_bous = root_mean_squared_error(y_train.iloc[:, 0], y_pred_train_rf_bous)\n",
    "rsme_train_h_dt_bous = root_mean_squared_error(y_train.iloc[:, 0], y_pred_train_dt_bous)\n",
    "rsme_train_h_svr_bous = root_mean_squared_error(y_train.iloc[:, 0], y_pred_train_svr_bous)\n",
    "rsme_train_h_ffnn_bous = root_mean_squared_error(y_train.iloc[:, 0], y_pred_train_ffnn_bous)\n",
    "rsme_test_h_rf_bous = root_mean_squared_error(y_test.iloc[:, 0], y_pred_test_rf_bous)\n",
    "rsme_test_h_dt_bous = root_mean_squared_error(y_test.iloc[:, 0], y_pred_test_dt_bous)\n",
    "rsme_test_h_svr_bous = root_mean_squared_error(y_test.iloc[:, 0], y_pred_test_svr_bous)\n",
    "rsme_test_h_ffnn_bous = root_mean_squared_error(y_test.iloc[:, 0], y_pred_test_ffnn_bous)\n",
    "\n",
    "# Aggregated\n",
    "rsme_train_h_rf_agg = root_mean_squared_error(y_train_agg, y_pred_train_rf_agg)\n",
    "rsme_train_h_dt_agg = root_mean_squared_error(y_train_agg, y_pred_train_dt_agg)\n",
    "rsme_train_h_svr_agg = root_mean_squared_error(y_train_agg, y_pred_train_svr_agg)\n",
    "rsme_train_h_ffnn_agg = root_mean_squared_error(y_train_agg, y_pred_train_ffnn_agg)\n",
    "rsme_test_h_rf_agg = root_mean_squared_error(y_test_agg, y_pred_test_rf_agg)\n",
    "rsme_test_h_dt_agg = root_mean_squared_error(y_test_agg, y_pred_test_dt_agg)\n",
    "rsme_test_h_svr_agg = root_mean_squared_error(y_test_agg, y_pred_test_svr_agg)\n",
    "rsme_test_h_ffnn_agg = root_mean_squared_error(y_test_agg, y_pred_test_ffnn_agg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MAE calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Quads\n",
    "mae_train_h_rf_quads = mean_absolute_error(y_train.iloc[:, 2], y_pred_train_rf_quads)\n",
    "mae_train_h_dt_quads = mean_absolute_error(y_train.iloc[:, 2], y_pred_train_dt_quads)\n",
    "mae_train_h_svr_quads = mean_absolute_error(y_train.iloc[:, 2], y_pred_train_svr_quads)\n",
    "mae_train_h_mlp_quads = mean_absolute_error(y_train.iloc[:, 2], y_pred_train_mlp_quads)\n",
    "mae_test_h_rf_quads = mean_absolute_error(y_test.iloc[:, 2], y_pred_test_rf_quads)\n",
    "mae_test_h_dt_quads = mean_absolute_error(y_test.iloc[:, 2], y_pred_test_dt_quads)\n",
    "mae_test_h_svr_quads = mean_absolute_error(y_test.iloc[:, 2], y_pred_test_svr_quads)\n",
    "mae_test_h_mlp_quads = mean_absolute_error(y_test.iloc[:, 2], y_pred_test_mlp_quads)\n",
    "\n",
    "# Smir\n",
    "mae_train_h_rf_smir = mean_absolute_error(y_train.iloc[:, 1], y_pred_train_rf_smir)\n",
    "mae_train_h_dt_smir = mean_absolute_error(y_train.iloc[:, 1], y_pred_train_dt_smir)\n",
    "mae_train_h_svr_smir = mean_absolute_error(y_train.iloc[:, 1], y_pred_train_svr_smir)\n",
    "mae_train_h_ffnn_smir = mean_absolute_error(y_train.iloc[:, 1], y_pred_train_ffnn_smir)\n",
    "mae_test_h_rf_smir = mean_absolute_error(y_test.iloc[:, 1], y_pred_test_rf_smir)\n",
    "mae_test_h_dt_smir = mean_absolute_error(y_test.iloc[:, 1], y_pred_test_dt_smir)\n",
    "mae_test_h_svr_smir = mean_absolute_error(y_test.iloc[:, 1], y_pred_test_svr_smir)\n",
    "mae_test_h_ffnn_smir = mean_absolute_error(y_test.iloc[:, 1], y_pred_test_ffnn_smir)\n",
    "\n",
    "# Boussafou\n",
    "mae_train_h_rf_bous = mean_absolute_error(y_train.iloc[:, 0], y_pred_train_rf_bous)\n",
    "mae_train_h_dt_bous = mean_absolute_error(y_train.iloc[:, 0], y_pred_train_dt_bous)\n",
    "mae_train_h_svr_bous = mean_absolute_error(y_train.iloc[:, 0], y_pred_train_svr_bous)\n",
    "mae_train_h_ffnn_bous = mean_absolute_error(y_train.iloc[:, 0], y_pred_train_ffnn_bous)\n",
    "mae_test_h_rf_bous = mean_absolute_error(y_test.iloc[:, 0], y_pred_test_rf_bous)\n",
    "mae_test_h_dt_bous = mean_absolute_error(y_test.iloc[:, 0], y_pred_test_dt_bous)\n",
    "mae_test_h_svr_bous = mean_absolute_error(y_test.iloc[:, 0], y_pred_test_svr_bous)\n",
    "mae_test_h_ffnn_bous = mean_absolute_error(y_test.iloc[:, 0], y_pred_test_ffnn_bous)\n",
    "\n",
    "# Aggregated\n",
    "mae_train_h_rf_agg = mean_absolute_error(y_train_agg, y_pred_train_rf_agg)\n",
    "mae_train_h_dt_agg = mean_absolute_error(y_train_agg, y_pred_train_dt_agg)\n",
    "mae_train_h_svr_agg = mean_absolute_error(y_train_agg, y_pred_train_svr_agg)\n",
    "mae_train_h_ffnn_agg = mean_absolute_error(y_train_agg, y_pred_train_ffnn_agg)\n",
    "mae_test_h_rf_agg = mean_absolute_error(y_test_agg, y_pred_test_rf_agg)\n",
    "mae_test_h_dt_agg = mean_absolute_error(y_test_agg, y_pred_test_dt_agg)\n",
    "mae_test_h_svr_agg = mean_absolute_error(y_test_agg, y_pred_test_svr_agg)\n",
    "mae_test_h_ffnn_agg = mean_absolute_error(y_test_agg, y_pred_test_ffnn_agg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table comparison "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------------------------+--------------------+-------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+-------------------+--------------------+--------------------+--------------------+--------------------+\n",
      "|           Model            |  RSME Train Quads  |  RSME Test Quads  |  MAE Train Quads   |   MAE Test Quads   |  RSME Train Smir   |   RSME Test Smir   |   MAE Train Smir   |   MAE Test Smir    |  RSME Train Bous   |   RSME Test Bous   |   MAE Train Bous   |   MAE Test Bous   |   RSME Train Agg   |   RSME Test Agg    |   MAE Train Agg    |    MAE Test Agg    |\n",
      "+----------------------------+--------------------+-------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+-------------------+--------------------+--------------------+--------------------+--------------------+\n",
      "|       Random Forest        | 244.00486967895137 | 605.5359054871135 |  156.344237350448  | 393.36069535039536 | 295.9112787047658  | 628.0464145399883  | 187.15773968958504 | 419.6900427113045  |  974.934934101985  | 1150.6779711948252 | 678.5089897715418  | 799.0532298238762 | 232.66778314854866 | 606.5305797988995  | 154.28693563746359 | 403.9911383688477  |\n",
      "|       Decision Tree        |  873.458820434156  | 1102.190858349175 | 511.58071321461426 | 632.7950128906225  |  715.539295961742  | 920.2329253042063  | 486.51967795391533 | 620.5804816395089  | 1000.3433910623274 | 1281.640348243489  | 674.7827625920484  | 859.1729339790105 | 394.8072184742285  | 870.0952867783005  | 244.82783135169075 | 543.0860800212148  |\n",
      "|       Support Vector       | 6478.045544287502  | 6557.012277727565 | 4725.724544478922  | 4750.249675473675  | 5150.676518846015  |  5140.59126848773  | 4205.483368858242  | 4183.904623788592  | 4331.254233414726  | 4330.061599851943  | 3396.5795632366394 | 3400.448238699657 | 5710.017280943892  | 5767.0553818027975 |  4527.76048138337  |  4536.63112111324  |\n",
      "| Feedforward Neural Network | 6599.414927857257  | 6689.712875063674 | 5162.298325718714  | 5201.142698773384  | 19183.244128769507 | 19180.076314589336 | 18531.46756666982  | 18528.276715045606 | 20668.111906736685 | 20678.40997445282  | 19382.54204587218  | 19361.64549713911 | 20238.258978158796 | 20260.381218080827 | 19469.74724802976  | 19473.521786479214 |\n",
      "+----------------------------+--------------------+-------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+-------------------+--------------------+--------------------+--------------------+--------------------+\n"
     ]
    }
   ],
   "source": [
    "print(tabulate([\n",
    "    ['Random Forest', rsme_train_h_rf_quads, rsme_test_h_rf_quads, mae_train_h_rf_quads, mae_test_h_rf_quads, rsme_train_h_rf_smir, rsme_test_h_rf_smir, mae_train_h_rf_smir, mae_test_h_rf_smir, rsme_train_h_rf_bous, rsme_test_h_rf_bous, mae_train_h_rf_bous, mae_test_h_rf_bous, rsme_train_h_rf_agg, rsme_test_h_rf_agg, mae_train_h_rf_agg, mae_test_h_rf_agg],\n",
    "    ['Decision Tree', rsme_train_h_dt_quads, rsme_test_h_dt_quads, mae_train_h_dt_quads, mae_test_h_dt_quads, rsme_train_h_dt_smir, rsme_test_h_dt_smir, mae_train_h_dt_smir, mae_test_h_dt_smir, rsme_train_h_dt_bous, rsme_test_h_dt_bous, mae_train_h_dt_bous, mae_test_h_dt_bous, rsme_train_h_dt_agg, rsme_test_h_dt_agg, mae_train_h_dt_agg, mae_test_h_dt_agg],\n",
    "    ['Support Vector', rsme_train_h_svr_quads, rsme_test_h_svr_quads, mae_train_h_svr_quads, mae_test_h_svr_quads, rsme_train_h_svr_smir, rsme_test_h_svr_smir, mae_train_h_svr_smir, mae_test_h_svr_smir, rsme_train_h_svr_bous, rsme_test_h_svr_bous, mae_train_h_svr_bous, mae_test_h_svr_bous, rsme_train_h_svr_agg, rsme_test_h_svr_agg, mae_train_h_svr_agg, mae_test_h_svr_agg],\n",
    "    ['Feedforward Neural Network', rsme_train_h_mlp_quads, rsme_test_h_mlp_quads, mae_train_h_mlp_quads, mae_test_h_mlp_quads, rsme_train_h_ffnn_smir, rsme_test_h_ffnn_smir, mae_train_h_ffnn_smir, mae_test_h_ffnn_smir, rsme_train_h_ffnn_bous, rsme_test_h_ffnn_bous, mae_train_h_ffnn_bous, mae_test_h_ffnn_bous, rsme_train_h_ffnn_agg, rsme_test_h_ffnn_agg, mae_train_h_ffnn_agg, mae_test_h_ffnn_agg]],\n",
    "headers=['Model', 'RSME Train Quads', 'RSME Test Quads', 'MAE Train Quads', 'MAE Test Quads', 'RSME Train Smir', 'RSME Test Smir', 'MAE Train Smir', 'MAE Test Smir', 'RSME Train Bous', 'RSME Test Bous', 'MAE Train Bous', 'MAE Test Bous', 'RSME Train Agg', 'RSME Test Agg', 'MAE Train Agg', 'MAE Test Agg'], tablefmt='pretty'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results present that also the random forest still performs the best achievement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**B/ Creating own method**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we already have predictions from multiple machine learning models on the same dataset with different models, to further develop, I employ Stacked Generalization so that it can combining all previous predictions and give the better prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**RSME AND MAE COMPARISON OF ALGORITHMS IN 3 DISTRIBUTIONS FOR 10 MINUTES POWER CONSUMPTION**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10 minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[29265.13274 17424.43992 19253.16923]\n",
      " [26784.93003 17535.12397 13249.76507]\n",
      " [27442.83186 14317.67152 14291.15424]\n",
      " ...\n",
      " [43905.82057 25856.53495 25333.54839]\n",
      " [44678.75135 24360.37152 27275.63636]\n",
      " [39527.2808  24900.6237  24430.34483]]\n"
     ]
    }
   ],
   "source": [
    "#Define base models, use defaut parameter\n",
    "level0 = [\n",
    "    ('rf', RandomForestRegressor(n_estimators=30, max_features=7, min_samples_split=2, max_depth=None, min_samples_leaf=1, random_state=0)),\n",
    "    ('svr', SVR(C=10, gamma=0.01, kernel=\"rbf\")),\n",
    "    ('mlp', MLPRegressor(activation='relu', solver='sgd', batch_size=100, hidden_layer_sizes=(25,), max_iter=100, learning_rate_init=0.001, random_state=1))\n",
    "]\n",
    "#Define meta learner model\n",
    "level1 = DecisionTreeRegressor(random_state=0)\n",
    "#Define the stacking ensemble\n",
    "stack = StackingRegressor(estimators=level0, final_estimator=level1, cv=10)\n",
    "multi_stack = MultiOutputRegressor(stack)\n",
    "\n",
    "#Fit the model on the training data\n",
    "fit = multi_stack.fit(X_train_scaler, y_train)\n",
    "\n",
    "y_pred_stack = fit.predict(X_test_scaler)\n",
    "\n",
    "print(y_pred_stack)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**RSME AND MAE COMPARISON OF ALGORITHMS IN 3 DISTRIBUTIONS FOR 1 HOUR POWER CONSUMPTION**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[29265.13274 17424.43992 19253.16923]\n",
      " [26784.93003 17535.12397 13249.76507]\n",
      " [27442.83186 14317.67152 14291.15424]\n",
      " ...\n",
      " [43905.82057 25856.53495 25333.54839]\n",
      " [44678.75135 24360.37152 27275.63636]\n",
      " [39527.2808  24900.6237  24430.34483]]\n"
     ]
    }
   ],
   "source": [
    "#Define base models, use defaut parameter\n",
    "level0 = [\n",
    "    ('rf', RandomForestRegressor(n_estimators=30, max_features=7, min_samples_split=2, max_depth=None, min_samples_leaf=1, random_state=0)),\n",
    "    ('svr', SVR(C=10, gamma=0.01, kernel=\"rbf\")),\n",
    "    ('mlp', MLPRegressor(activation='relu', solver='sgd', batch_size=100, hidden_layer_sizes=(25,), max_iter=100, learning_rate_init=0.001, random_state=1))\n",
    "]\n",
    "#Define meta learner model\n",
    "level1 = DecisionTreeRegressor(random_state=0)\n",
    "#Define the stacking ensemble\n",
    "stack = StackingRegressor(estimators=level0, final_estimator=level1, cv=10)\n",
    "multi_stack = MultiOutputRegressor(stack)\n",
    "\n",
    "#Fit the model on the training data\n",
    "fit = multi_stack.fit(X_train_h_scaler, y_train)\n",
    "\n",
    "y_pred_h_stack = fit.predict(X_test_h_scaler)\n",
    "\n",
    "print(y_pred_stack)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the RSME and MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 10 minutes\n",
    "rsme_test_bous_stack = root_mean_squared_error(y_test.iloc[:, 0], y_pred_stack[:, 0])\n",
    "rsme_test_smir_stack = root_mean_squared_error(y_test.iloc[:, 1], y_pred_stack[:, 1])\n",
    "rsme_test_squads_stack = root_mean_squared_error(y_test.iloc[:, 2], y_pred_stack[:, 2])\n",
    "\n",
    "mae_test_bous_stack = mean_absolute_error(y_test.iloc[:, 0], y_pred_stack[:, 0])\n",
    "mae_test_smir_stack = mean_absolute_error(y_test.iloc[:, 1], y_pred_stack[:, 1])\n",
    "mae_test_squads_stack = mean_absolute_error(y_test.iloc[:, 2], y_pred_stack[:, 2])\n",
    "\n",
    "# 1 hour\n",
    "rsme_test_h_bous_stack = root_mean_squared_error(y_test.iloc[:, 0], y_pred_h_stack[:, 0])\n",
    "rsme_test_h_smir_stack = root_mean_squared_error(y_test.iloc[:, 1], y_pred_h_stack[:, 1])\n",
    "rsme_test_h_squads_stack = root_mean_squared_error(y_test.iloc[:, 2], y_pred_h_stack[:, 2])\n",
    "\n",
    "mae_test_h_bouse_stack = mean_absolute_error(y_test.iloc[:, 0], y_pred_h_stack[:, 0])\n",
    "mae_test_h_smir_stack = mean_absolute_error(y_test.iloc[:, 1], y_pred_h_stack[:, 1])\n",
    "mae_test_h_squads_stack = mean_absolute_error(y_test.iloc[:, 2], y_pred_h_stack[:, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RSME & MAE table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10 minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+--------------+-------------+-------------+------------+-------------+------------+\n",
      "| Algorithm   |   RSME Quads |   MAE Quads |   RSME Smir |   MAE Smir |   RSME Bous |   MAE Bous |\n",
      "|-------------+--------------+-------------+-------------+------------+-------------+------------|\n",
      "| Stacking    |      685.395 |     467.455 |     742.016 |    523.436 |    1065.4   |    747.052 |\n",
      "| RF          |      686.482 |     452.321 |     485.217 |    323.267 |     709.604 |    459.95  |\n",
      "| DT          |      686.482 |     452.321 |     855.239 |    560.364 |    1177.28  |    769.278 |\n",
      "| SVR         |     6557.94  |    4750.84  |    4727.61  |   3845.11  |    6755.18  |   5556.69  |\n",
      "| FFNN        |     4385.18  |    3440.63  |    3833.45  |   3019.58  |    4385.18  |   3440.63  |\n",
      "+-------------+--------------+-------------+-------------+------------+-------------+------------+\n"
     ]
    }
   ],
   "source": [
    "# Data for the table\n",
    "data = {\n",
    "    \"Algorithm\": [\"Stacking\", \"RF\", \"DT\", \"SVR\", \"FFNN\"],\n",
    "    'RSME Quads': [rsme_test_squads_stack, rsme_test_rf_quads, rsme_test_dt_quads, rsme_test_svr_quads, rsme_test_ffnn_bous],\n",
    "    'MAE Quads': [mae_test_squads_stack, mae_test_rf_quads, mae_test_dt_quads, mae_test_svr_quads, mae_test_ffnn_bous],\n",
    "    'RSME Smir': [rsme_test_smir_stack, rsme_test_rf_smir, rsme_test_dt_smir, rsme_test_svr_smir, rsme_test_ffnn_smir],\n",
    "    'MAE Smir': [mae_test_smir_stack, mae_test_rf_smir, mae_test_dt_smir, mae_test_svr_smir, mae_test_ffnn_smir],\n",
    "    'RSME Bous': [rsme_test_bous_stack, rsme_test_rf_bous, rsme_test_dt_bous, rsme_test_svr_bous, rsme_test_ffnn_bous],\n",
    "    'MAE Bous': [mae_test_bous_stack, mae_test_rf_bous, mae_test_dt_bous, mae_test_svr_bous, mae_test_ffnn_bous],\n",
    "}\n",
    "\n",
    "# Print the table using tabulate for a nicer format\n",
    "print(tabulate(data, headers='keys', tablefmt='psql'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1 hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+--------------+-------------+-------------+------------+-------------+------------+\n",
      "| Algorithm   |   RSME Quads |   MAE Quads |   RSME Smir |   MAE Smir |   RSME Bous |   MAE Bous |\n",
      "|-------------+--------------+-------------+-------------+------------+-------------+------------|\n",
      "| Stacking    |      889.173 |     595.783 |     871.51  |    622.171 |     1331.53 |    937.255 |\n",
      "| RF          |      605.536 |     393.361 |     628.046 |    419.69  |     1150.68 |    799.053 |\n",
      "| DT          |     1102.19  |     632.795 |     920.233 |    620.58  |     1281.64 |    859.173 |\n",
      "| SVR         |     6557.01  |    4750.25  |    5140.59  |   4183.9   |     4330.06 |   3400.45  |\n",
      "| FFNN        |    20678.4   |   19361.6   |   19180.1   |  18528.3   |    20678.4  |  19361.6   |\n",
      "+-------------+--------------+-------------+-------------+------------+-------------+------------+\n"
     ]
    }
   ],
   "source": [
    "# Data for the table\n",
    "data1 = {\n",
    "    \"Algorithm\": [\"Stacking\", \"RF\", \"DT\", \"SVR\", \"FFNN\"],\n",
    "    'RSME Quads': [rsme_test_h_squads_stack, rsme_test_h_rf_quads, rsme_test_h_dt_quads, rsme_test_h_svr_quads, rsme_test_h_ffnn_bous],\n",
    "    'MAE Quads': [mae_test_h_squads_stack, mae_test_h_rf_quads, mae_test_h_dt_quads, mae_test_h_svr_quads, mae_test_h_ffnn_bous],\n",
    "    'RSME Smir': [rsme_test_h_smir_stack, rsme_test_h_rf_smir, rsme_test_h_dt_smir, rsme_test_h_svr_smir, rsme_test_h_ffnn_smir],\n",
    "    'MAE Smir': [mae_test_h_smir_stack, mae_test_h_rf_smir, mae_test_h_dt_smir, mae_test_h_svr_smir, mae_test_h_ffnn_smir],\n",
    "    'RSME Bous': [rsme_test_h_bous_stack, rsme_test_h_rf_bous, rsme_test_h_dt_bous, rsme_test_h_svr_bous, rsme_test_h_ffnn_bous],\n",
    "    'MAE Bous': [mae_test_h_bouse_stack, mae_test_h_rf_bous, mae_test_h_dt_bous, mae_test_h_svr_bous, mae_test_h_ffnn_bous],\n",
    "}\n",
    "\n",
    "# Print the table using tabulate for a nicer format\n",
    "print(tabulate(data1, headers='keys', tablefmt='psql'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the best hyperparameter for Quads network and feature engineering, stacking performed quite well, mostly it returns lowest error and can consider the best method among all."
   ]
  }
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