randomforest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing libraries\n",
    "---------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pathlib\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "from functools import reduce"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading Files\n",
    "---------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding CABRa attributes csv files\n",
    "path_files = pathlib.Path('CABRa_attributes_csv/')\n",
    "cabra_attributes_files = path_files.rglob('*.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CABRa_attributes_csv\\CABra_dataset _Additional_attributes.csv\n",
      "CABRa_attributes_csv\\CABra_dataset _Climate_attributes.csv\n",
      "CABRa_attributes_csv\\CABra_dataset_Groundwater_attributes.csv\n",
      "CABRa_attributes_csv\\CABra_dataset_Hydrologic_disturbance_attributes.csv\n",
      "CABRa_attributes_csv\\CABra_dataset_Land_cover_attributes.csv\n",
      "CABRa_attributes_csv\\CABra_dataset_Streamflow_attributes.csv\n",
      "CABRa_attributes_csv\\cabra_general_attributes.csv\n",
      "CABRa_attributes_csv\\cabra_Geology_attributes.csv\n",
      "CABRa_attributes_csv\\CABra_soil_attributes.csv\n",
      "CABRa_attributes_csv\\CABra_topography_attributes.csv\n"
     ]
    }
   ],
   "source": [
    "# Reading files\n",
    "dfs = []\n",
    "for file in cabra_attributes_files:\n",
    "    print(file)\n",
    "    df = pd.read_csv(file, engine='python', delimiter=';')\n",
    "    dfs.append(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Dataset and calculating variables\n",
    "-----------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merging DataFrames\n",
    "df_merged = reduce(lambda  left,right: pd.merge(left, right, on=['CABra ID', 'ANA ID'], how='outer'), dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CABra ID</th>\n",
       "      <th>ANA ID</th>\n",
       "      <th>longitude_centroid</th>\n",
       "      <th>latitude_centroid</th>\n",
       "      <th>dist_coast</th>\n",
       "      <th>clim_p</th>\n",
       "      <th>clim_tmin</th>\n",
       "      <th>clim_tmax</th>\n",
       "      <th>clim_rh</th>\n",
       "      <th>clim_wind</th>\n",
       "      <th>...</th>\n",
       "      <th>soil_carbon</th>\n",
       "      <th>soil_bulk</th>\n",
       "      <th>soil_depth</th>\n",
       "      <th>catch_area</th>\n",
       "      <th>elev_mean</th>\n",
       "      <th>elev_min</th>\n",
       "      <th>elev_max</th>\n",
       "      <th>elev_gauge</th>\n",
       "      <th>catch_slope</th>\n",
       "      <th>catch_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>13880000</td>\n",
       "      <td>-68.028</td>\n",
       "      <td>-8.989</td>\n",
       "      <td>1407.55</td>\n",
       "      <td>5.64</td>\n",
       "      <td>21.61</td>\n",
       "      <td>30.48</td>\n",
       "      <td>83.17</td>\n",
       "      <td>0.82</td>\n",
       "      <td>...</td>\n",
       "      <td>34.439</td>\n",
       "      <td>25.456</td>\n",
       "      <td>1.335</td>\n",
       "      <td>243.410.279</td>\n",
       "      <td>178.97</td>\n",
       "      <td>44.65</td>\n",
       "      <td>564.03</td>\n",
       "      <td>44.65</td>\n",
       "      <td>3.14</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>14110000</td>\n",
       "      <td>-66.695</td>\n",
       "      <td>2.016</td>\n",
       "      <td>1009.09</td>\n",
       "      <td>8.88</td>\n",
       "      <td>21.83</td>\n",
       "      <td>28.60</td>\n",
       "      <td>86.41</td>\n",
       "      <td>0.42</td>\n",
       "      <td>...</td>\n",
       "      <td>21.926</td>\n",
       "      <td>17.609</td>\n",
       "      <td>1.256</td>\n",
       "      <td>76.898.617</td>\n",
       "      <td>222.97</td>\n",
       "      <td>80.40</td>\n",
       "      <td>2892.44</td>\n",
       "      <td>80.40</td>\n",
       "      <td>4.72</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>14230000</td>\n",
       "      <td>-68.936</td>\n",
       "      <td>1.573</td>\n",
       "      <td>1009.70</td>\n",
       "      <td>8.52</td>\n",
       "      <td>22.18</td>\n",
       "      <td>29.67</td>\n",
       "      <td>85.71</td>\n",
       "      <td>0.60</td>\n",
       "      <td>...</td>\n",
       "      <td>23.241</td>\n",
       "      <td>20.530</td>\n",
       "      <td>1.219</td>\n",
       "      <td>24.018.785</td>\n",
       "      <td>151.47</td>\n",
       "      <td>80.86</td>\n",
       "      <td>952.20</td>\n",
       "      <td>80.86</td>\n",
       "      <td>2.81</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>14250000</td>\n",
       "      <td>-67.305</td>\n",
       "      <td>1.734</td>\n",
       "      <td>1095.42</td>\n",
       "      <td>8.86</td>\n",
       "      <td>22.00</td>\n",
       "      <td>29.13</td>\n",
       "      <td>86.26</td>\n",
       "      <td>0.49</td>\n",
       "      <td>...</td>\n",
       "      <td>22.725</td>\n",
       "      <td>18.612</td>\n",
       "      <td>1.235</td>\n",
       "      <td>127.540.913</td>\n",
       "      <td>184.31</td>\n",
       "      <td>74.32</td>\n",
       "      <td>2892.44</td>\n",
       "      <td>74.32</td>\n",
       "      <td>3.71</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>14260000</td>\n",
       "      <td>-70.894</td>\n",
       "      <td>1.201</td>\n",
       "      <td>964.52</td>\n",
       "      <td>7.79</td>\n",
       "      <td>21.91</td>\n",
       "      <td>28.80</td>\n",
       "      <td>85.44</td>\n",
       "      <td>0.67</td>\n",
       "      <td>...</td>\n",
       "      <td>22.066</td>\n",
       "      <td>20.859</td>\n",
       "      <td>1.248</td>\n",
       "      <td>41.762.638</td>\n",
       "      <td>206.08</td>\n",
       "      <td>85.09</td>\n",
       "      <td>615.36</td>\n",
       "      <td>85.09</td>\n",
       "      <td>2.30</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 90 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CABra ID    ANA ID  longitude_centroid  latitude_centroid  dist_coast  \\\n",
       "0         1  13880000             -68.028             -8.989     1407.55   \n",
       "1         2  14110000             -66.695              2.016     1009.09   \n",
       "2         3  14230000             -68.936              1.573     1009.70   \n",
       "3         4  14250000             -67.305              1.734     1095.42   \n",
       "4         5  14260000             -70.894              1.201      964.52   \n",
       "\n",
       "   clim_p  clim_tmin  clim_tmax  clim_rh  clim_wind  ...  soil_carbon  \\\n",
       "0    5.64      21.61      30.48    83.17       0.82  ...       34.439   \n",
       "1    8.88      21.83      28.60    86.41       0.42  ...       21.926   \n",
       "2    8.52      22.18      29.67    85.71       0.60  ...       23.241   \n",
       "3    8.86      22.00      29.13    86.26       0.49  ...       22.725   \n",
       "4    7.79      21.91      28.80    85.44       0.67  ...       22.066   \n",
       "\n",
       "   soil_bulk  soil_depth   catch_area  elev_mean  elev_min elev_max  \\\n",
       "0     25.456       1.335  243.410.279     178.97     44.65   564.03   \n",
       "1     17.609       1.256   76.898.617     222.97     80.40  2892.44   \n",
       "2     20.530       1.219   24.018.785     151.47     80.86   952.20   \n",
       "3     18.612       1.235  127.540.913     184.31     74.32  2892.44   \n",
       "4     20.859       1.248   41.762.638     206.08     85.09   615.36   \n",
       "\n",
       "  elev_gauge  catch_slope  catch_order  \n",
       "0      44.65         3.14            7  \n",
       "1      80.40         4.72            6  \n",
       "2      80.86         2.81            4  \n",
       "3      74.32         3.71            6  \n",
       "4      85.09         2.30            5  \n",
       "\n",
       "[5 rows x 90 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CABra ID', 'ANA ID', 'longitude_centroid', 'latitude_centroid',\n",
       "       'dist_coast', 'clim_p', 'clim_tmin', 'clim_tmax', 'clim_rh',\n",
       "       'clim_wind', 'clim_srad', 'clim_et', 'clim_pet', 'aridity_index',\n",
       "       'p_seasonality', 'clim_quality', 'aquif_name', 'aquif_type',\n",
       "       'catch_wtd', 'catch_hand', 'hand_class', 'dist_urban', 'cover_urban_x',\n",
       "       'cover_crops_x', 'res_number', 'res_area', 'res_volume',\n",
       "       'res_regulation', 'water_demand', 'hdisturb_index', 'cover_main',\n",
       "       'cover_bare', 'cover_forest', 'cover_crops_y', 'cover_grass',\n",
       "       'cover_moss', 'cover_shrub', 'cover_urban_y', 'cover_snow',\n",
       "       'cover_waterp', 'cover_waters', 'ndvi_djf', 'ndvi_mam', 'ndvi_jja',\n",
       "       'ndvi_son', 'q_mean', 'q_1', 'q_5', 'q_95', 'q_99', 'q_lf', 'q_ld',\n",
       "       'q_hf', 'q_hd', 'q_hfd', 'q_zero', 'q_cv', 'q_lcv', 'q_hcv',\n",
       "       'q_elasticity', 'fdc_slope', 'baseflow_index', 'runoff_coef',\n",
       "       'longitude', 'latitude', 'gauge_hreg', 'gauge_biome', 'gauge_state',\n",
       "       'missing_data', 'series_length', 'quality_index', 'catch_lith',\n",
       "       'sub_porosity', 'sub_permeability', 'sub_hconduc', 'soil_type',\n",
       "       'soil_textclass', 'soil_sand', 'soil_silt', 'soil_clay', 'soil_carbon',\n",
       "       'soil_bulk', 'soil_depth', 'catch_area', 'elev_mean', 'elev_min',\n",
       "       'elev_max', 'elev_gauge', 'catch_slope', 'catch_order'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All CABRa attributes files\n",
    "df_merged.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\envs\\Teste_ML\\lib\\site-packages\\pandas\\core\\series.py:726: RuntimeWarning: invalid value encountered in log10\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Calculating ECI (Liu et al. (2020))\n",
    "# Aparently there is a Warning\n",
    "df_merged['ECI'] = np.log10(df_merged['q_mean']/(df_merged['clim_p']-df_merged['clim_et']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ECI</th>\n",
       "      <th>q_mean</th>\n",
       "      <th>clim_p</th>\n",
       "      <th>clim_et</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.64</td>\n",
       "      <td>1.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ECI  q_mean  clim_p  clim_et\n",
       "686  NaN    0.03    1.64     1.66"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There is one bad ID\n",
    "# log(0) => Erro\n",
    "df_merged.loc[df_merged['ECI'].isnull(),['ECI', 'q_mean', 'clim_p', 'clim_et']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating the ratio Effective Area / Topographic Area (Liu et al. (2020))\n",
    "df_merged['Aeff/Atopo'] = df_merged['q_mean']/(df_merged['clim_p']-df_merged['clim_et'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aeff/Atopo</th>\n",
       "      <th>ECI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>735.000000</td>\n",
       "      <td>734.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.033062</td>\n",
       "      <td>-0.024864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.472764</td>\n",
       "      <td>0.200450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.500000</td>\n",
       "      <td>-0.989005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.812509</td>\n",
       "      <td>-0.089254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.006250</td>\n",
       "      <td>0.002825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.196199</td>\n",
       "      <td>0.077825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.781955</td>\n",
       "      <td>0.831355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Aeff/Atopo         ECI\n",
       "count  735.000000  734.000000\n",
       "mean     1.033062   -0.024864\n",
       "std      0.472764    0.200450\n",
       "min     -1.500000   -0.989005\n",
       "25%      0.812509   -0.089254\n",
       "50%      1.006250    0.002825\n",
       "75%      1.196199    0.077825\n",
       "max      6.781955    0.831355"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking ECI and Ratio\n",
    "df_merged[['Aeff/Atopo','ECI']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating new column called 'substantial' and grouping into 3 groups\n",
    "# The ratio is according to Liu et al. (2020)\n",
    "max_ratio = 2\n",
    "min_ratio = 0.5\n",
    "\n",
    "df_merged.loc[df_merged['Aeff/Atopo']>=max_ratio, 'substantial'] = 'Gaining'\n",
    "df_merged.loc[(df_merged['Aeff/Atopo']<max_ratio)&\n",
    "              (df_merged['Aeff/Atopo']>1), 'substantial'] = 'na'\n",
    "df_merged.loc[(df_merged['Aeff/Atopo']>min_ratio)&\n",
    "              (df_merged['Aeff/Atopo']<=1), 'substantial'] = 'na'\n",
    "df_merged.loc[df_merged['Aeff/Atopo']<=min_ratio, 'substantial'] = 'Losing'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Intervals using ECI values\n",
    "max_ratio_eci = 0.113943\n",
    "min_ratio_eci = -0.1549\n",
    "df_merged.loc[df_merged['ECI']>= max_ratio_eci,'substantial_eci'] = 'Gaining'\n",
    "df_merged.loc[(df_merged['ECI']<max_ratio_eci)&\n",
    "              (df_merged['ECI']>0), 'substantial_eci'] = 'Small gain'\n",
    "df_merged.loc[(df_merged['ECI']>min_ratio_eci)&\n",
    "              (df_merged['ECI']<=0), 'substantial_eci'] = 'Small loss'\n",
    "df_merged.loc[df_merged['ECI']<=min_ratio_eci, 'substantial_eci'] = 'Losing'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The column 'catch_area' is problematic, because it was a object type.\n",
    "# Changed to 'int'\n",
    "df_merged['catch_area'] = df_merged['catch_area'].str.replace('.','')\n",
    "df_merged['catch_area'] = df_merged['catch_area'].astype('int64')\n",
    "df_merged['catch_area'] = df_merged['catch_area']/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label encoding function for non-numerical data\n",
    "def label_enconding(df, variable):\n",
    "    le = preprocessing.LabelEncoder()\n",
    "    df[f'le_{variable}'] = le.fit_transform(df[f'{variable}'])\n",
    "    for i, c in zip(le.fit_transform(le.classes_), le.classes_):\n",
    "#     print(le.fit_transform(le.classes_), le.classes_)\n",
    "        print(i, c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0                 Pampa\n",
      "1                Amazon\n",
      "2               Cerrado\n",
      "3              Caatinga\n",
      "4              Pantanal\n",
      "5       Atlantic forest\n"
     ]
    }
   ],
   "source": [
    "label_enconding(df=df_merged, variable='gauge_biome')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0             Clay\n",
      "1             Loam\n",
      "2        Clay loam\n",
      "3       Sandy clay\n",
      "4       Sandy loam\n",
      "5  Sandy clay loam\n"
     ]
    }
   ],
   "source": [
    "label_enconding(df=df_merged, variable='soil_textclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One hot encoding function for non-numerical data\n",
    "def onehot_enconding(df, variable):\n",
    "    oe = preprocessing.OneHotEncoder()\n",
    "    fit_oe = oe.fit_transform(df_merged[[variable]]).toarray()\n",
    "    oe_list = [f'{variable}_{categorie}' for categorie in oe.categories_[0]]\n",
    "    df[oe_list] = fit_oe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "onehot_enconding(df=df_merged, variable='gauge_biome')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "onehot_enconding(df=df_merged, variable='soil_textclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of relevant variables (PCA analysis)\n",
    "variables = ['aridity_index', \n",
    "             'catch_slope',\n",
    "             'elev_mean', \n",
    "             'res_area',\n",
    "             'catch_hand',\n",
    "             'catch_wtd',\n",
    "             'sub_permeability',\n",
    "             'sub_hconduc',\n",
    "             'hdisturb_index', \n",
    "             'sub_porosity',\n",
    "             'q_elasticity',\n",
    "             'p_seasonality']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Analysis\n",
    "----------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We need to drop NaN values before performing Random Forest\n",
    "df_merged.dropna(subset=variables+['ECI'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aridity_index</th>\n",
       "      <th>catch_slope</th>\n",
       "      <th>elev_mean</th>\n",
       "      <th>res_area</th>\n",
       "      <th>catch_hand</th>\n",
       "      <th>catch_wtd</th>\n",
       "      <th>sub_permeability</th>\n",
       "      <th>sub_hconduc</th>\n",
       "      <th>hdisturb_index</th>\n",
       "      <th>sub_porosity</th>\n",
       "      <th>q_elasticity</th>\n",
       "      <th>p_seasonality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.89</td>\n",
       "      <td>3.14</td>\n",
       "      <td>178.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.104</td>\n",
       "      <td>-10.446</td>\n",
       "      <td>-14.306</td>\n",
       "      <td>-7.306</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.192</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.56</td>\n",
       "      <td>4.72</td>\n",
       "      <td>222.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.006</td>\n",
       "      <td>-8.306</td>\n",
       "      <td>-13.883</td>\n",
       "      <td>-6.883</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.97</td>\n",
       "      <td>-0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.56</td>\n",
       "      <td>2.81</td>\n",
       "      <td>151.47</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.199</td>\n",
       "      <td>-2.846</td>\n",
       "      <td>-13.413</td>\n",
       "      <td>-6.413</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.070</td>\n",
       "      <td>1.08</td>\n",
       "      <td>-0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.55</td>\n",
       "      <td>3.71</td>\n",
       "      <td>184.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.372</td>\n",
       "      <td>-6.016</td>\n",
       "      <td>-13.679</td>\n",
       "      <td>-6.679</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.89</td>\n",
       "      <td>-0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.63</td>\n",
       "      <td>2.30</td>\n",
       "      <td>206.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.239</td>\n",
       "      <td>-4.131</td>\n",
       "      <td>-14.073</td>\n",
       "      <td>-7.073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.55</td>\n",
       "      <td>-0.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   aridity_index  catch_slope  elev_mean  res_area  catch_hand  catch_wtd  \\\n",
       "0           0.89         3.14     178.97       0.0      18.104    -10.446   \n",
       "1           0.56         4.72     222.97       0.0      38.006     -8.306   \n",
       "2           0.56         2.81     151.47       0.0      16.199     -2.846   \n",
       "3           0.55         3.71     184.31       0.0      28.372     -6.016   \n",
       "4           0.63         2.30     206.08       0.0      18.239     -4.131   \n",
       "\n",
       "   sub_permeability  sub_hconduc  hdisturb_index  sub_porosity  q_elasticity  \\\n",
       "0           -14.306       -7.306             0.0         0.192          0.36   \n",
       "1           -13.883       -6.883             0.0         0.055          0.97   \n",
       "2           -13.413       -6.413             0.0         0.070          1.08   \n",
       "3           -13.679       -6.679             0.0         0.064          0.89   \n",
       "4           -14.073       -7.073             0.0         0.129          0.55   \n",
       "\n",
       "   p_seasonality  \n",
       "0           0.46  \n",
       "1          -0.28  \n",
       "2          -0.27  \n",
       "3          -0.26  \n",
       "4          -0.27  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged[variables].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Without encoding variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=False, random_state=2, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest Classifier\n",
    "# X: list of variables\n",
    "# y: 'substantial' column\n",
    "rfc = RandomForestClassifier(random_state=2)\n",
    "rfc.fit(X=df_merged[variables], y=df_merged['substantial_eci'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
       "                      random_state=2, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr = RandomForestRegressor(random_state=2)\n",
    "rfr.fit(X=df_merged[variables], y=df_merged['ECI'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aridity_index\t: RFC: 0.156  |\t RFR: 0.388\n",
      "catch_slope\t: RFC: 0.111  |\t RFR: 0.147\n",
      "elev_mean\t: RFC: 0.088  |\t RFR: 0.073\n",
      "res_area\t: RFC: 0.061  |\t RFR: 0.026\n",
      "catch_hand\t: RFC: 0.104  |\t RFR: 0.066\n",
      "catch_wtd\t: RFC: 0.091  |\t RFR: 0.074\n",
      "sub_permeability\t: RFC: 0.056  |\t RFR: 0.016\n",
      "sub_hconduc\t: RFC: 0.055  |\t RFR: 0.016\n",
      "hdisturb_index\t: RFC: 0.070  |\t RFR: 0.045\n",
      "sub_porosity\t: RFC: 0.060  |\t RFR: 0.034\n",
      "q_elasticity\t: RFC: 0.072  |\t RFR: 0.035\n",
      "p_seasonality\t: RFC: 0.076  |\t RFR: 0.080\n"
     ]
    }
   ],
   "source": [
    "# Checking zipped results\n",
    "for i, c,r in zip(variables, rfc.feature_importances_,rfr.feature_importances_):\n",
    "    print(f'{i}\\t: RFC: {c:.3f}  |\\t RFR: {r:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### With label encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
       "                      random_state=2, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr2 = RandomForestRegressor(random_state=2)\n",
    "rfr2.fit(X=df_merged[variables+['le_gauge_biome','le_soil_textclass']], y=df_merged['ECI'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=False, random_state=2, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_02 = RandomForestClassifier(random_state=2)\n",
    "rfc_02.fit(X=df_merged[variables+['le_gauge_biome', 'le_soil_textclass']], y=df_merged['substantial_eci'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aridity_index\t: RFC: 0.134|\t RFR: 0.384\n",
      "catch_slope\t: RFC: 0.100|\t RFR: 0.143\n",
      "elev_mean\t: RFC: 0.081|\t RFR: 0.071\n",
      "res_area\t: RFC: 0.061|\t RFR: 0.024\n",
      "catch_hand\t: RFC: 0.099|\t RFR: 0.065\n",
      "catch_wtd\t: RFC: 0.087|\t RFR: 0.070\n",
      "sub_permeability\t: RFC: 0.052|\t RFR: 0.015\n",
      "sub_hconduc\t: RFC: 0.054|\t RFR: 0.017\n",
      "hdisturb_index\t: RFC: 0.069|\t RFR: 0.045\n",
      "sub_porosity\t: RFC: 0.057|\t RFR: 0.033\n",
      "q_elasticity\t: RFC: 0.068|\t RFR: 0.034\n",
      "p_seasonality\t: RFC: 0.077|\t RFR: 0.077\n",
      "le_gauge_biome\t: RFC: 0.037|\t RFR: 0.011\n",
      "le_soil_textclass\t: RFC: 0.024|\t RFR: 0.009\n"
     ]
    }
   ],
   "source": [
    "# Checking zipped results\n",
    "for i, c, r in zip(variables+['le_gauge_biome','le_soil_textclass'], rfc_02.feature_importances_, rfr2.feature_importances_):\n",
    "    print(f'{i}\\t: RFC: {c:.3f}|\\t RFR: {r:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### With one hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "oe_biome_list = ['gauge_biome_                Pampa', 'gauge_biome_               Amazon', 'gauge_biome_              Cerrado', 'gauge_biome_             Caatinga', 'gauge_biome_             Pantanal', 'gauge_biome_      Atlantic forest']\n",
    "oe_soil_list = ['soil_textclass_            Clay', 'soil_textclass_            Loam', 'soil_textclass_       Clay loam', 'soil_textclass_      Sandy clay', 'soil_textclass_      Sandy loam', 'soil_textclass_ Sandy clay loam']\n",
    "\n",
    "oe_list = oe_biome_list #+ oe_soil_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=False, random_state=2, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_03 = RandomForestClassifier(random_state=2)\n",
    "rfc_03.fit(X=df_merged[variables+oe_list], y=df_merged['substantial'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
       "                      random_state=2, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr3 = RandomForestRegressor(random_state=2)\n",
    "rfr3.fit(X=df_merged[variables+oe_list], y=df_merged['ECI'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aridity_index\t: RFC: 0.190 |\t RFR: 0.382\n",
      "catch_slope\t: RFC: 0.087 |\t RFR: 0.144\n",
      "elev_mean\t: RFC: 0.085 |\t RFR: 0.072\n",
      "res_area\t: RFC: 0.040 |\t RFR: 0.025\n",
      "catch_hand\t: RFC: 0.064 |\t RFR: 0.064\n",
      "catch_wtd\t: RFC: 0.094 |\t RFR: 0.071\n",
      "sub_permeability\t: RFC: 0.050 |\t RFR: 0.018\n",
      "sub_hconduc\t: RFC: 0.044 |\t RFR: 0.016\n",
      "hdisturb_index\t: RFC: 0.036 |\t RFR: 0.045\n",
      "sub_porosity\t: RFC: 0.052 |\t RFR: 0.033\n",
      "q_elasticity\t: RFC: 0.098 |\t RFR: 0.035\n",
      "p_seasonality\t: RFC: 0.093 |\t RFR: 0.081\n",
      "gauge_biome_                Pampa\t: RFC: 0.001 |\t RFR: 0.000\n",
      "gauge_biome_               Amazon\t: RFC: 0.007 |\t RFR: 0.003\n",
      "gauge_biome_              Cerrado\t: RFC: 0.005 |\t RFR: 0.001\n",
      "gauge_biome_             Caatinga\t: RFC: 0.043 |\t RFR: 0.004\n",
      "gauge_biome_             Pantanal\t: RFC: 0.006 |\t RFR: 0.000\n",
      "gauge_biome_      Atlantic forest\t: RFC: 0.006 |\t RFR: 0.007\n"
     ]
    }
   ],
   "source": [
    "# Checking zipped results\n",
    "for i, c, r in zip(variables+oe_list, rfc_03.feature_importances_, rfr3.feature_importances_):\n",
    "    print(f'{i}\\t: RFC: {c:.3f} |\\t RFR: {r:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10-fold Cross validation\n",
    "---------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We need to drop NaN values before performing Random Forest\n",
    "df_merged.dropna(subset=variables+['ECI'], inplace=True)\n",
    "df_merged.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cv = StratifiedKFold(n_splits=15, random_state=2, shuffle=True)\n",
    "group_kfold = KFold(n_splits=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_importance = pd.DataFrame()\n",
    "for i,(a,b) in enumerate(group_kfold.split(X=df_merged[variables+oe_list], y=df_merged['substantial_eci'])):\n",
    "    rfc = RandomForestClassifier(n_estimators=100)\n",
    "    rfc.fit(X=df_merged.loc[a, variables+oe_list], y=df_merged.loc[a, 'substantial_eci'])\n",
    "#     print(rfc.feature_importances_)\n",
    "    df_importance.loc[i, variables+oe_list] = rfc.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d3bdd57288>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_importance.plot(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_importance2 = pd.DataFrame()\n",
    "for i,(a,b) in enumerate(group_kfold.split(X=df_merged[variables+oe_list], y=df_merged['ECI'])):\n",
    "    rfc = RandomForestRegressor(n_estimators=100)\n",
    "    rfc.fit(X=df_merged.loc[a, variables+oe_list], y=df_merged.loc[a, 'ECI'])\n",
    "#     print(rfc.feature_importances_)\n",
    "    \n",
    "    df_importance2.loc[i, variables+oe_list] = rfc.feature_importances_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d3bdd24908>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_importance2.plot(figsize=(12,8))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Teste_ML",
   "language": "python",
   "name": "teste_ml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}