TL_Boost_AQI_Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done uploading repositories\n"
     ]
    }
   ],
   "source": [
    "## Header files \n",
    "# from two_TrAdaBoostR2 import TwoStageTrAdaBoostR2 ##For STrAdaBoost.R2\n",
    "# from TwoStageTrAdaBoostR2 import TwoStageTrAdaBoostR2 ## For two-stage TrAdaBoost.R2\n",
    "\n",
    "import pandas as pd\n",
    "import sys\n",
    "import numpy as np\n",
    "from pandas import DataFrame\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "\n",
    "from keras.models import Sequential, load_model, Model\n",
    "from keras.layers import Input, Dense, Activation, Conv2D, Dropout, Flatten\n",
    "from keras import optimizers, utils, initializers, regularizers\n",
    "import keras.backend as K\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.preprocessing import StandardScaler #Importing the StandardScaler\n",
    "\n",
    "from itertools import combinations\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from scipy.stats.stats import pearsonr\n",
    "from math import sqrt\n",
    "\n",
    "#Geo plotting libraries\n",
    "#import geopandas as gdp\n",
    "#from matplotlib.colors import ListedColormap\n",
    "#import geoplot as glpt\n",
    "\n",
    "import xgboost as xgb\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn import linear_model\n",
    "from keras.models import Sequential\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "import matplotlib.lines as mlines\n",
    "import folium\n",
    "import glob\n",
    "\n",
    "import statistics\n",
    "from sklearn.cluster import KMeans\n",
    "from scipy.spatial import distance\n",
    "\n",
    "pd.options.display.max_columns = None\n",
    "\n",
    "from adapt.instance_based import (TrAdaBoost, TrAdaBoostR2, TwoStageTrAdaBoostR2)\n",
    "\n",
    "\n",
    "print(\"Done uploading repositories\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Time', 'CO_GT', 'PT08_S1_CO', 'NMHC_GT', 'C6H6_GT',\n",
       "       'PT08_S2_NMHC', 'NOx_GT', 'PT08_S3_NOx', 'NO2_GT', 'PT08_S4_NO2',\n",
       "       'PT08_S5_O3', 'T', 'RH', 'AH', 'Unnamed: 15', 'Unnamed: 16'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "############################## UCI Italian dataset #######################################################\n",
    "## 2 years and single terrain\n",
    "aqi_df = pd.read_csv('AQI_datasets/UCI_AQI/AirQualityUCI.csv', sep=',', delimiter=\";\", decimal=\",\", index_col = None, header=0)\n",
    "\n",
    "aqi_df.head(20)\n",
    "# aqi_df.shape\n",
    "aqi_df.columns\n",
    "\n",
    "## Predictors: T,Ah,Rh, NMHC_GT, NOx_GT, CO_GT, C6H6_GT ,Target: O3 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9357, 15)\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CO_GT</th>\n",
       "      <th>NMHC_GT</th>\n",
       "      <th>C6H6_GT</th>\n",
       "      <th>NOx_GT</th>\n",
       "      <th>PT08_S5_O3</th>\n",
       "      <th>T</th>\n",
       "      <th>RH</th>\n",
       "      <th>AH</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.6</td>\n",
       "      <td>150.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1268.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>48.9</td>\n",
       "      <td>0.7578</td>\n",
       "      <td>2004</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>103.0</td>\n",
       "      <td>972.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>47.7</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>2004</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.2</td>\n",
       "      <td>88.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>1074.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.7502</td>\n",
       "      <td>2004</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.2</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>172.0</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.7867</td>\n",
       "      <td>2004</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.6</td>\n",
       "      <td>51.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>131.0</td>\n",
       "      <td>1110.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>59.6</td>\n",
       "      <td>0.7888</td>\n",
       "      <td>2004</td>\n",
       "      <td>10</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9352</th>\n",
       "      <td>3.1</td>\n",
       "      <td>275.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>472.0</td>\n",
       "      <td>1729.0</td>\n",
       "      <td>21.9</td>\n",
       "      <td>29.3</td>\n",
       "      <td>0.7568</td>\n",
       "      <td>2005</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9353</th>\n",
       "      <td>2.4</td>\n",
       "      <td>275.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>353.0</td>\n",
       "      <td>1269.0</td>\n",
       "      <td>24.3</td>\n",
       "      <td>23.7</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>2005</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>9354</th>\n",
       "      <td>2.4</td>\n",
       "      <td>275.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>293.0</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>26.9</td>\n",
       "      <td>18.3</td>\n",
       "      <td>0.6406</td>\n",
       "      <td>2005</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>9355</th>\n",
       "      <td>2.1</td>\n",
       "      <td>275.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>235.0</td>\n",
       "      <td>770.0</td>\n",
       "      <td>28.3</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.5139</td>\n",
       "      <td>2005</td>\n",
       "      <td>4</td>\n",
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       "      <th>9356</th>\n",
       "      <td>2.2</td>\n",
       "      <td>275.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>265.0</td>\n",
       "      <td>816.0</td>\n",
       "      <td>28.5</td>\n",
       "      <td>13.1</td>\n",
       "      <td>0.5028</td>\n",
       "      <td>2005</td>\n",
       "      <td>4</td>\n",
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       "<p>9357 rows × 10 columns</p>\n",
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      ],
      "text/plain": [
       "      CO_GT  NMHC_GT  C6H6_GT  NOx_GT  PT08_S5_O3     T    RH      AH  Year  \\\n",
       "0       2.6    150.0     11.9   166.0      1268.0  13.6  48.9  0.7578  2004   \n",
       "1       2.0    112.0      9.4   103.0       972.0  13.3  47.7  0.7255  2004   \n",
       "2       2.2     88.0      9.0   131.0      1074.0  11.9  54.0  0.7502  2004   \n",
       "3       2.2     80.0      9.2   172.0      1203.0  11.0  60.0  0.7867  2004   \n",
       "4       1.6     51.0      6.5   131.0      1110.0  11.2  59.6  0.7888  2004   \n",
       "...     ...      ...      ...     ...         ...   ...   ...     ...   ...   \n",
       "9352    3.1    275.0     13.5   472.0      1729.0  21.9  29.3  0.7568  2005   \n",
       "9353    2.4    275.0     11.4   353.0      1269.0  24.3  23.7  0.7119  2005   \n",
       "9354    2.4    275.0     12.4   293.0      1092.0  26.9  18.3  0.6406  2005   \n",
       "9355    2.1    275.0      9.5   235.0       770.0  28.3  13.5  0.5139  2005   \n",
       "9356    2.2    275.0     11.9   265.0       816.0  28.5  13.1  0.5028  2005   \n",
       "\n",
       "      Month  \n",
       "0        10  \n",
       "1        10  \n",
       "2        10  \n",
       "3        10  \n",
       "4        10  \n",
       "...     ...  \n",
       "9352      4  \n",
       "9353      4  \n",
       "9354      4  \n",
       "9355      4  \n",
       "9356      4  \n",
       "\n",
       "[9357 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_outlier(col):\n",
    "    aqi_df[col] = aqi_df.groupby('Date')[col].transform(lambda x: x.fillna(x.mean()))\n",
    "\n",
    "\n",
    "#drop end rows with NaN values\n",
    "aqi_df.dropna(how = 'all', inplace = True)\n",
    "\n",
    "drop_unamed = ['Unnamed: 15', 'Unnamed: 16']\n",
    "aqi_df = aqi_df.drop(drop_unamed, axis = 1)\n",
    "aqi_df.columns\n",
    "print(aqi_df.shape)\n",
    "aqi_df\n",
    "\n",
    "#Observing data statistics\n",
    "aqi_df.describe()\n",
    "\n",
    "#Drop unwanted columns\n",
    "drop_uw = ['Time', 'PT08_S1_CO', 'PT08_S2_NMHC', 'PT08_S3_NOx', 'NO2_GT', 'PT08_S4_NO2']\n",
    "aqi_df = aqi_df.drop(drop_uw, axis = 1)\n",
    "aqi_df\n",
    "\n",
    "#Replace the -200 values seen in the dataset with \n",
    "aqi_df.replace(to_replace = -200, value = np.NaN, inplace = True)\n",
    "\n",
    "#Replace the NaN values with the column mean\n",
    "col_list = aqi_df.columns[1:]\n",
    "for i in col_list:\n",
    "    remove_outlier(i)\n",
    "\n",
    "aqi_df.fillna(method ='ffill', inplace= True)\n",
    "aqi_df.dropna(axis = 0)\n",
    "aqi_df\n",
    "\n",
    "#Convert 'Date' column to datetime and then seperate out year and month into different columns.\n",
    "aqi_df.Date = pd.to_datetime(aqi_df.Date)\n",
    "aqi_df['Year'] = aqi_df['Date'].dt.year\n",
    "aqi_df['Month'] = aqi_df['Date'].dt.month\n",
    "drop_date = ['Date']\n",
    "aqi_df = aqi_df.drop(drop_date, axis = 1)\n",
    "aqi_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "## Get the Spearman Correlation Concrete for the features\n",
    "aqi_df_corr = aqi_df.copy()\n",
    "target_uci_col = ['PT08_S5_O3']\n",
    "aqi_df_corr = aqi_df_corr.drop(target_uci_col, axis = 1)\n",
    "\n",
    "corr_aqi_df =  aqi_df_corr.corr(method = 'spearman')\n",
    "corr_aqi_df\n",
    "\n",
    "corr_aqi_df_lsum = np.tril(corr_aqi_df).sum() - np.trace(corr_aqi_df)\n",
    "aqi_df_elems = len(corr_aqi_df) * (len(corr_aqi_df) - 1) / 2\n",
    "corr_aqi_df_lavg = abs(corr_aqi_df_lsum / aqi_df_elems)\n",
    "corr_aqi_df_lavg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "####################### Plot all toxic gases (Not required) ########################################\n",
    "\n",
    "# aqi_df.drop(['T','RH','AH','Year', 'Month'], axis=1).resample('M').mean().plot(figsize = (20,8))\n",
    "# plt.legend(loc=1)\n",
    "# plt.xlabel('Month')\n",
    "# plt.ylabel('Toxic Gases in the Air')\n",
    "# plt.title(\"All Toxic Gases' Frequency by Month\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7110, 1) (7110, 9)\n",
      "(2247, 1) (2247, 9)\n"
     ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CO</th>\n",
       "      <th>NMHC</th>\n",
       "      <th>C6H6</th>\n",
       "      <th>NOx</th>\n",
       "      <th>Temp</th>\n",
       "      <th>RH</th>\n",
       "      <th>AH</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.134783</td>\n",
       "      <td>275.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>311.181818</td>\n",
       "      <td>8.2</td>\n",
       "      <td>40.1</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.600000</td>\n",
       "      <td>275.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>5.3</td>\n",
       "      <td>50.7</td>\n",
       "      <td>0.4564</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>275.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>5.9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.4689</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.700000</td>\n",
       "      <td>275.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>311.181818</td>\n",
       "      <td>4.9</td>\n",
       "      <td>53.9</td>\n",
       "      <td>0.4693</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.900000</td>\n",
       "      <td>275.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>4.3</td>\n",
       "      <td>55.3</td>\n",
       "      <td>0.4650</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
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       "      <th>2242</th>\n",
       "      <td>3.100000</td>\n",
       "      <td>275.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>472.000000</td>\n",
       "      <td>21.9</td>\n",
       "      <td>29.3</td>\n",
       "      <td>0.7568</td>\n",
       "      <td>2005</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2243</th>\n",
       "      <td>2.400000</td>\n",
       "      <td>275.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>353.000000</td>\n",
       "      <td>24.3</td>\n",
       "      <td>23.7</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>2005</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2244</th>\n",
       "      <td>2.400000</td>\n",
       "      <td>275.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>293.000000</td>\n",
       "      <td>26.9</td>\n",
       "      <td>18.3</td>\n",
       "      <td>0.6406</td>\n",
       "      <td>2005</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2245</th>\n",
       "      <td>2.100000</td>\n",
       "      <td>275.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>28.3</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.5139</td>\n",
       "      <td>2005</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2246</th>\n",
       "      <td>2.200000</td>\n",
       "      <td>275.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>265.000000</td>\n",
       "      <td>28.5</td>\n",
       "      <td>13.1</td>\n",
       "      <td>0.5028</td>\n",
       "      <td>2005</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2247 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            CO   NMHC  C6H6         NOx  Temp    RH      AH  Year  Month\n",
       "0     2.134783  275.0   4.2  311.181818   8.2  40.1  0.4375  2005      1\n",
       "1     1.600000  275.0   8.8  215.000000   5.3  50.7  0.4564  2005      1\n",
       "2     2.500000  275.0   7.5  300.000000   5.9  50.0  0.4689  2005      1\n",
       "3     2.700000  275.0   7.6  311.181818   4.9  53.9  0.4693  2005      1\n",
       "4     1.900000  275.0   5.6  253.000000   4.3  55.3  0.4650  2005      1\n",
       "...        ...    ...   ...         ...   ...   ...     ...   ...    ...\n",
       "2242  3.100000  275.0  13.5  472.000000  21.9  29.3  0.7568  2005      4\n",
       "2243  2.400000  275.0  11.4  353.000000  24.3  23.7  0.7119  2005      4\n",
       "2244  2.400000  275.0  12.4  293.000000  26.9  18.3  0.6406  2005      4\n",
       "2245  2.100000  275.0   9.5  235.000000  28.3  13.5  0.5139  2005      4\n",
       "2246  2.200000  275.0  11.9  265.000000  28.5  13.1  0.5028  2005      4\n",
       "\n",
       "[2247 rows x 9 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Split the dataset according to the year.\n",
    "aqi_df_2004 = aqi_df[aqi_df['Year'] == 2004]\n",
    "aqi_df_2005 = aqi_df[aqi_df['Year'] == 2005]\n",
    "\n",
    "aqi_df_2004 = aqi_df_2004.reset_index(drop=True)\n",
    "aqi_df_2005 = aqi_df_2005.reset_index(drop=True)\n",
    "\n",
    "\n",
    "#Divide the dataframe into target and the predictors.\n",
    "target_uci_col = ['PT08_S5_O3']\n",
    "aqi_df_2004_target = aqi_df_2004[target_uci_col]\n",
    "aqi_df_2004_target.columns = ['O3']\n",
    "\n",
    "aqi_df_2005_target = aqi_df_2005[target_uci_col]\n",
    "aqi_df_2005_target.columns = ['O3']\n",
    "\n",
    "### Source Dataset\n",
    "aqi_df_2004_predictors = aqi_df_2004.drop(target_uci_col, axis = 1)\n",
    "aqi_df_2004_predictors.columns = ['CO', 'NMHC', 'C6H6', 'NOx', 'Temp', 'RH', 'AH', 'Year', 'Month']\n",
    "\n",
    "### Target Dataset\n",
    "aqi_df_2005_predictors = aqi_df_2005.drop(target_uci_col, axis = 1)\n",
    "aqi_df_2005_predictors.columns = ['CO', 'NMHC', 'C6H6', 'NOx', 'Temp', 'RH', 'AH', 'Year', 'Month']\n",
    "\n",
    "print(aqi_df_2004_target.shape, aqi_df_2004_predictors.shape)\n",
    "print(aqi_df_2005_target.shape, aqi_df_2005_predictors.shape)\n",
    "\n",
    "aqi_df_2005_target \n",
    "aqi_df_2005_predictors\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>CO</th>\n",
       "      <th>NMHC</th>\n",
       "      <th>C6H6</th>\n",
       "      <th>NOx</th>\n",
       "      <th>Temp</th>\n",
       "      <th>RH</th>\n",
       "      <th>AH</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.357434</td>\n",
       "      <td>-1.403072</td>\n",
       "      <td>0.167727</td>\n",
       "      <td>-0.251434</td>\n",
       "      <td>-0.897718</td>\n",
       "      <td>0.030505</td>\n",
       "      <td>-1.028223</td>\n",
       "      <td>2004</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.073637</td>\n",
       "      <td>-1.872833</td>\n",
       "      <td>-0.163670</td>\n",
       "      <td>-0.580486</td>\n",
       "      <td>-0.935168</td>\n",
       "      <td>-0.038507</td>\n",
       "      <td>-1.115824</td>\n",
       "      <td>2004</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.070053</td>\n",
       "      <td>-2.169524</td>\n",
       "      <td>-0.216693</td>\n",
       "      <td>-0.434241</td>\n",
       "      <td>-1.109937</td>\n",
       "      <td>0.323803</td>\n",
       "      <td>-1.048835</td>\n",
       "      <td>2004</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.070053</td>\n",
       "      <td>-2.268421</td>\n",
       "      <td>-0.190181</td>\n",
       "      <td>-0.220095</td>\n",
       "      <td>-1.222288</td>\n",
       "      <td>0.668860</td>\n",
       "      <td>-0.949843</td>\n",
       "      <td>2004</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.361018</td>\n",
       "      <td>-2.626923</td>\n",
       "      <td>-0.548090</td>\n",
       "      <td>-0.434241</td>\n",
       "      <td>-1.197321</td>\n",
       "      <td>0.645856</td>\n",
       "      <td>-0.944148</td>\n",
       "      <td>2004</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7105</th>\n",
       "      <td>-0.648398</td>\n",
       "      <td>0.142195</td>\n",
       "      <td>-0.707160</td>\n",
       "      <td>-0.256657</td>\n",
       "      <td>-1.434508</td>\n",
       "      <td>-0.849390</td>\n",
       "      <td>-2.010003</td>\n",
       "      <td>2004</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7106</th>\n",
       "      <td>-0.648398</td>\n",
       "      <td>0.142195</td>\n",
       "      <td>-0.707160</td>\n",
       "      <td>-0.256657</td>\n",
       "      <td>-1.397057</td>\n",
       "      <td>-0.924153</td>\n",
       "      <td>-2.033598</td>\n",
       "      <td>2004</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7107</th>\n",
       "      <td>-0.648398</td>\n",
       "      <td>0.142195</td>\n",
       "      <td>-0.680649</td>\n",
       "      <td>-0.256657</td>\n",
       "      <td>-1.459475</td>\n",
       "      <td>-0.912651</td>\n",
       "      <td>-2.062075</td>\n",
       "      <td>2004</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7108</th>\n",
       "      <td>-0.648398</td>\n",
       "      <td>0.142195</td>\n",
       "      <td>-0.667393</td>\n",
       "      <td>-0.256657</td>\n",
       "      <td>-1.459475</td>\n",
       "      <td>-0.642356</td>\n",
       "      <td>-1.914537</td>\n",
       "      <td>2004</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7109</th>\n",
       "      <td>-0.648398</td>\n",
       "      <td>0.142195</td>\n",
       "      <td>-0.799951</td>\n",
       "      <td>-0.256657</td>\n",
       "      <td>-1.621760</td>\n",
       "      <td>-0.573345</td>\n",
       "      <td>-1.975559</td>\n",
       "      <td>2004</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7110 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            CO      NMHC      C6H6       NOx      Temp        RH        AH  \\\n",
       "0     0.357434 -1.403072  0.167727 -0.251434 -0.897718  0.030505 -1.028223   \n",
       "1    -0.073637 -1.872833 -0.163670 -0.580486 -0.935168 -0.038507 -1.115824   \n",
       "2     0.070053 -2.169524 -0.216693 -0.434241 -1.109937  0.323803 -1.048835   \n",
       "3     0.070053 -2.268421 -0.190181 -0.220095 -1.222288  0.668860 -0.949843   \n",
       "4    -0.361018 -2.626923 -0.548090 -0.434241 -1.197321  0.645856 -0.944148   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "7105 -0.648398  0.142195 -0.707160 -0.256657 -1.434508 -0.849390 -2.010003   \n",
       "7106 -0.648398  0.142195 -0.707160 -0.256657 -1.397057 -0.924153 -2.033598   \n",
       "7107 -0.648398  0.142195 -0.680649 -0.256657 -1.459475 -0.912651 -2.062075   \n",
       "7108 -0.648398  0.142195 -0.667393 -0.256657 -1.459475 -0.642356 -1.914537   \n",
       "7109 -0.648398  0.142195 -0.799951 -0.256657 -1.621760 -0.573345 -1.975559   \n",
       "\n",
       "      Year  Month  \n",
       "0     2004     10  \n",
       "1     2004     10  \n",
       "2     2004     10  \n",
       "3     2004     10  \n",
       "4     2004     10  \n",
       "...    ...    ...  \n",
       "7105  2004     12  \n",
       "7106  2004     12  \n",
       "7107  2004     12  \n",
       "7108  2004     12  \n",
       "7109  2004     12  \n",
       "\n",
       "[7110 rows x 9 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Standardize the dataset\n",
    "columns_uci = ['CO', 'NMHC', 'C6H6', 'NOx', 'Temp', 'RH', 'AH', 'Year', 'Month']\n",
    "cols_to_norm = ['CO', 'NMHC', 'C6H6', 'NOx', 'Temp', 'RH', 'AH']\n",
    "\n",
    "ss = StandardScaler()\n",
    "aqi_df_2004_predictors[cols_to_norm] = ss.fit_transform(aqi_df_2004_predictors[cols_to_norm])\n",
    "aqi_df_2005_predictors[cols_to_norm] = ss.fit_transform(aqi_df_2005_predictors[cols_to_norm])\n",
    "\n",
    "aqi_df_2004_predictors\n",
    "# aqi_df_2005_predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(89, 9)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Splitting dataset into train, test and source.\n",
    "X_2005_train, X_2005_test, y_2005_train, y_2005_test = train_test_split(aqi_df_2005_predictors, aqi_df_2005_target, test_size = 0.96, random_state = 1)\n",
    "\n",
    "X_2004 = aqi_df_2004_predictors\n",
    "y_2004 = aqi_df_2004_target\n",
    "\n",
    "X_2005_train.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoost\n",
      "RMSE of AdaboostR2: 183.5902863147077\n",
      "R^2 of AdaboostR2: 0.8351295795136586\n",
      "RMSE of AdaboostR2: 182.6919378065448\n",
      "R^2 of AdaboostR2: 0.8365418707786034\n",
      "RMSE of AdaboostR2: 183.2971506446896\n",
      "R^2 of AdaboostR2: 0.8351374464773499\n",
      "RMSE of AdaboostR2: 184.02558111397323\n",
      "R^2 of AdaboostR2: 0.8337207818629461\n",
      "RMSE of AdaboostR2: 181.74608159654906\n",
      "R^2 of AdaboostR2: 0.8373266218438412\n",
      "RMSE of AdaboostR2: 180.32427841651773\n",
      "R^2 of AdaboostR2: 0.84058274921385\n",
      "RMSE of AdaboostR2: 182.18338806485036\n",
      "R^2 of AdaboostR2: 0.8384355086349115\n",
      "RMSE of AdaboostR2: 185.2858246977906\n",
      "R^2 of AdaboostR2: 0.831995885989319\n",
      "RMSE of AdaboostR2: 180.40977831902464\n",
      "R^2 of AdaboostR2: 0.8395032203769454\n",
      "RMSE of AdaboostR2: 180.09778870754144\n",
      "R^2 of AdaboostR2: 0.8402613383086137\n",
      "mean RMSE of AdaboostR2: 182.36520956821892\n",
      "mean R^2 of AdaboostR2: 0.8368635003000039\n"
     ]
    }
   ],
   "source": [
    "########################################## AdaBoost #####################################################\n",
    "\n",
    "predictionlist = []\n",
    "r2scorelist = []\n",
    "rmselist = []\n",
    "\n",
    "print(\"AdaBoost\")\n",
    "\n",
    "for x in range(0, 10):\n",
    "\n",
    "    np_TF_train_X = X_2005_train.to_numpy()\n",
    "    np_TF_train_y = y_2005_train.to_numpy()\n",
    "\n",
    "    np_test_X = X_2005_test.to_numpy()\n",
    "    np_test_y = y_2005_test.to_numpy()\n",
    "\n",
    "    np_TF_train_y_list = np_TF_train_y.ravel()\n",
    "    np_test_y_list = np_test_y.ravel()\n",
    "\n",
    "    sample_size = [len(X_2004), len(X_2005_train)]\n",
    "    n_estimators = 100\n",
    "    steps = 30\n",
    "    fold = 10\n",
    "    random_state = np.random.RandomState(1)\n",
    "\n",
    "    regr_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=6), n_estimators = n_estimators)\n",
    "    \n",
    "    regr_2.fit(np_TF_train_X, np_TF_train_y_list)\n",
    "    y_pred2 = regr_2.predict(np_test_X)\n",
    "    predictionlist.append(y_pred2)\n",
    "    \n",
    "    rmse_adaboost = np.sqrt(mean_squared_error(np_test_y_list, y_pred2))\n",
    "    print(\"RMSE of AdaboostR2:\", rmse_adaboost)\n",
    "    rmselist.append(rmse_adaboost)\n",
    "    \n",
    "    r2_score_adaboost_values = pearsonr(np_test_y_list, y_pred2)\n",
    "    r2_score_adaboost = (r2_score_adaboost_values[0])**2\n",
    "    print(\"R^2 of AdaboostR2:\", r2_score_adaboost)\n",
    "    r2scorelist.append(r2_score_adaboost)\n",
    "\n",
    "\n",
    "predict_adaboost = np.mean(predictionlist, axis=0)\n",
    "list_orginal_adaboost = np_test_y_list\n",
    "\n",
    "print(\"mean RMSE of AdaboostR2:\", mean(rmselist))\n",
    "print(\"mean R^2 of AdaboostR2:\", mean(r2scorelist))\n",
    "\n",
    "# plt.xlabel('Predicted')\n",
    "# plt.ylabel('Actual')\n",
    "# plt.title('AdaBoost.R2 Predicted vs Actual')\n",
    "# # plt.savefig(\"AQI_datasets/UCI_AQI_Results/NOx/AdaBoostR2_Transfer.png\")\n",
    "# plt.show()\n",
    "\n",
    "with open('AQI_datasets/UCI_AQI_Results/O3/AdaBoostOut_R2_std.txt', 'w') as filehandle1:\n",
    "    for listitem in r2scorelist:\n",
    "        filehandle1.write('%s\\n' % listitem)\n",
    "\n",
    "with open('AQI_datasets/UCI_AQI_Results/O3/AdaBoostOut_RMSE_std.txt', 'w') as filehandle2:\n",
    "    for listitem in rmselist:\n",
    "        filehandle2.write('%s\\n' % listitem)\n",
    "\n",
    "with open('AQI_datasets/UCI_AQI_Results/O3/AdaBoostOut_prediction_std.txt', 'w') as filehandle3:\n",
    "    for listitem in predict_adaboost:\n",
    "        filehandle3.write('%s\\n' % listitem)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TrAdaBoost.R2\n",
      "RMSE of TrAdaboostR2: 171.6515413016132\n",
      "R^2 of TrAdaboostR2: 0.8533748798630838\n",
      "RMSE of TrAdaboostR2: 170.14551538005634\n",
      "R^2 of TrAdaboostR2: 0.8533157948351648\n",
      "RMSE of TrAdaboostR2: 170.69377563176536\n",
      "R^2 of TrAdaboostR2: 0.8534914143838899\n",
      "RMSE of TrAdaboostR2: 170.23152266767838\n",
      "R^2 of TrAdaboostR2: 0.8526018221355194\n",
      "RMSE of TrAdaboostR2: 172.05586024289434\n",
      "R^2 of TrAdaboostR2: 0.8510481166211975\n",
      "RMSE of TrAdaboostR2: 169.06705817239322\n",
      "R^2 of TrAdaboostR2: 0.8554409433731467\n",
      "RMSE of TrAdaboostR2: 173.20665423910825\n",
      "R^2 of TrAdaboostR2: 0.848584522950516\n",
      "RMSE of TrAdaboostR2: 172.54867998954228\n",
      "R^2 of TrAdaboostR2: 0.8490425791881079\n",
      "RMSE of TrAdaboostR2: 170.27823788650394\n",
      "R^2 of TrAdaboostR2: 0.8535451402656496\n",
      "RMSE of TrAdaboostR2: 170.2347615205619\n",
      "R^2 of TrAdaboostR2: 0.853871346908591\n",
      "mean RMSE of TrAdaboostR2: 171.01136070321172\n",
      "mean R^2 of TrAdaboostR2: 0.8524316560524867\n"
     ]
    }
   ],
   "source": [
    "########################### Prediction for TrAdaBoost.R2 ##########################################\n",
    "predictionlist = []\n",
    "r2scorelist = []\n",
    "rmselist = []\n",
    "\n",
    "print(\"TrAdaBoost.R2\")\n",
    "\n",
    "for x in range(0, 10):\n",
    "\n",
    "    TF_train_X = pd.concat([X_2004, X_2005_train], sort= False)\n",
    "    TF_train_y = pd.concat([y_2004, y_2005_train], sort= False)\n",
    "\n",
    "    np_TF_train_X = TF_train_X.to_numpy()\n",
    "    np_TF_train_y = TF_train_y.to_numpy()\n",
    "\n",
    "    np_test_X = X_2005_test.to_numpy()\n",
    "    np_test_y = y_2005_test.to_numpy()\n",
    "\n",
    "    np_TF_train_y_list = np_TF_train_y.ravel()\n",
    "    np_test_y_list = np_test_y.ravel()\n",
    "\n",
    "    sample_size = [len(X_2004), len(X_2005_train)]\n",
    "    n_estimators = 100\n",
    "    steps = 30\n",
    "    fold = 10\n",
    "    random_state = np.random.RandomState(1)\n",
    "\n",
    "    ################################################TrAdaBoost.R2############################################################################\n",
    "    regr_1 = TwoStageTrAdaBoostR2(DecisionTreeRegressor(max_depth=6), #xgb.XGBRegressor(objective ='reg:squarederror',learning_rate = 0.0001,max_depth = 6,n_estimators = 2000),\n",
    "                          n_estimators = n_estimators, sample_size = sample_size,\n",
    "                          steps = steps, fold = fold,\n",
    "                          random_state = random_state)\n",
    "\n",
    "    regr_1.fit(np_TF_train_X, np_TF_train_y_list)\n",
    "    y_pred1 = regr_1.predict(np_test_X)\n",
    "    predictionlist.append(y_pred1)\n",
    "\n",
    "    mse_twostageboost = np.sqrt(mean_squared_error(np_test_y_list, y_pred1))\n",
    "    print(\"RMSE of TrAdaboostR2:\", mse_twostageboost)\n",
    "    rmselist.append(mse_twostageboost)\n",
    "\n",
    "    r2_score_twostageboost_values = pearsonr(np_test_y_list, y_pred1)\n",
    "    r2_score_twostageboost = (r2_score_twostageboost_values[0])**2\n",
    "    print(\"R^2 of TrAdaboostR2:\", r2_score_twostageboost)\n",
    "    r2scorelist.append(r2_score_twostageboost)\n",
    "\n",
    "predict_tradaboost = np.mean(predictionlist, axis=0)\n",
    "list_orginal_tradaboost = np_test_y_list\n",
    "\n",
    "print(\"mean RMSE of TrAdaboostR2:\", mean(rmselist))\n",
    "print(\"mean R^2 of TrAdaboostR2:\", mean(r2scorelist))\n",
    "\n",
    "# plt.xlabel('Predicted')\n",
    "# plt.ylabel('Actual')\n",
    "# plt.title('TrAdaBoost.R2 Predicted vs Actual')\n",
    "# # plt.savefig(\"AQI_datasets/UCI_AQI_Results/NOx/AdaBoostR2_Transfer.png\")\n",
    "# plt.show()\n",
    "\n",
    "with open('AQI_datasets/UCI_AQI_Results/O3/TrAdaBoostOut_R2_std.txt', 'w') as filehandle1:\n",
    "    for listitem in r2scorelist:\n",
    "        filehandle1.write('%s\\n' % listitem)\n",
    "\n",
    "with open('AQI_datasets/UCI_AQI_Results/O3/TrAdaBoostOut_RMSE_std.txt', 'w') as filehandle2:\n",
    "    for listitem in rmselist:\n",
    "        filehandle2.write('%s\\n' % listitem)\n",
    "\n",
    "with open('AQI_datasets/UCI_AQI_Results/O3/TrAdaBoostOut_prediction_std.txt', 'w') as filehandle3:\n",
    "    for listitem in predict_tradaboost:\n",
    "        filehandle3.write('%s\\n' % listitem)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(98, 10)\n",
      "(2158, 10)\n",
      "(3600, 10)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>CO</th>\n",
       "      <th>NMHC</th>\n",
       "      <th>C6H6</th>\n",
       "      <th>NOx</th>\n",
       "      <th>Temp</th>\n",
       "      <th>RH</th>\n",
       "      <th>AH</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.122519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.118436</td>\n",
       "      <td>-1.272088</td>\n",
       "      <td>-0.692434</td>\n",
       "      <td>-0.572013</td>\n",
       "      <td>-0.954190</td>\n",
       "      <td>2005</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.122519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.118436</td>\n",
       "      <td>-1.080373</td>\n",
       "      <td>-1.301512</td>\n",
       "      <td>0.394126</td>\n",
       "      <td>-0.816529</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.019686</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.443230</td>\n",
       "      <td>0.601925</td>\n",
       "      <td>2.120074</td>\n",
       "      <td>-0.295094</td>\n",
       "      <td>2.295396</td>\n",
       "      <td>2005</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.576471</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.806695</td>\n",
       "      <td>1.258548</td>\n",
       "      <td>0.525722</td>\n",
       "      <td>-0.178173</td>\n",
       "      <td>0.337425</td>\n",
       "      <td>2005</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.900912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.761355</td>\n",
       "      <td>-0.672979</td>\n",
       "      <td>-0.423723</td>\n",
       "      <td>1.151037</td>\n",
       "      <td>0.369745</td>\n",
       "      <td>2005</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2153</th>\n",
       "      <td>-0.974781</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.947658</td>\n",
       "      <td>-0.931794</td>\n",
       "      <td>0.651121</td>\n",
       "      <td>-0.885854</td>\n",
       "      <td>-0.245937</td>\n",
       "      <td>2005</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2154</th>\n",
       "      <td>-0.900912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.994234</td>\n",
       "      <td>-1.142681</td>\n",
       "      <td>1.027316</td>\n",
       "      <td>-0.313555</td>\n",
       "      <td>0.734845</td>\n",
       "      <td>2005</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2155</th>\n",
       "      <td>2.718677</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.228634</td>\n",
       "      <td>2.662859</td>\n",
       "      <td>-0.352067</td>\n",
       "      <td>0.480278</td>\n",
       "      <td>-0.010118</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>-1.099435</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.196062</td>\n",
       "      <td>-1.329603</td>\n",
       "      <td>-1.265684</td>\n",
       "      <td>0.135668</td>\n",
       "      <td>-0.926259</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2157</th>\n",
       "      <td>1.093555</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.403038</td>\n",
       "      <td>0.851154</td>\n",
       "      <td>0.686949</td>\n",
       "      <td>-0.018175</td>\n",
       "      <td>0.668209</td>\n",
       "      <td>2005</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2158 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            CO  NMHC      C6H6       NOx      Temp        RH        AH  Year  \\\n",
       "0    -1.122519   0.0 -1.118436 -1.272088 -0.692434 -0.572013 -0.954190  2005   \n",
       "1    -1.122519   0.0 -1.118436 -1.080373 -1.301512  0.394126 -0.816529  2005   \n",
       "2     1.019686   0.0  1.443230  0.601925  2.120074 -0.295094  2.295396  2005   \n",
       "3     0.576471   0.0  0.806695  1.258548  0.525722 -0.178173  0.337425  2005   \n",
       "4    -0.900912   0.0 -0.761355 -0.672979 -0.423723  1.151037  0.369745  2005   \n",
       "...        ...   ...       ...       ...       ...       ...       ...   ...   \n",
       "2153 -0.974781   0.0 -0.947658 -0.931794  0.651121 -0.885854 -0.245937  2005   \n",
       "2154 -0.900912   0.0 -0.994234 -1.142681  1.027316 -0.313555  0.734845  2005   \n",
       "2155  2.718677   0.0  3.228634  2.662859 -0.352067  0.480278 -0.010118  2005   \n",
       "2156 -1.099435   0.0 -1.196062 -1.329603 -1.265684  0.135668 -0.926259  2005   \n",
       "2157  1.093555   0.0  0.403038  0.851154  0.686949 -0.018175  0.668209  2005   \n",
       "\n",
       "      Month  \n",
       "0         8  \n",
       "1         1  \n",
       "2         3  \n",
       "3        11  \n",
       "4         3  \n",
       "...     ...  \n",
       "2153      2  \n",
       "2154      3  \n",
       "2155      1  \n",
       "2156      1  \n",
       "2157      3  \n",
       "\n",
       "[2158 rows x 9 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_column = ['O3']\n",
    "\n",
    "X_train = pd.read_csv('ActiveSampling/UCI_O3_activesampling_train.csv')\n",
    "X_test = pd.read_csv('ActiveSampling/UCI_O3_activesampling_test.csv')\n",
    "X_source = pd.read_csv('ActiveSampling/UCI_O3_activesampling_source.csv')\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(X_source.shape)\n",
    "\n",
    "y_2004 = X_source[target_column]\n",
    "X_2004 = X_source.drop(target_column, axis = 1)\n",
    "\n",
    "y_2005_train = X_train[target_column]\n",
    "X_2005_train = X_train.drop(target_column, axis = 1)\n",
    "\n",
    "y_2005_test = X_test[target_column]\n",
    "X_2005_test = X_test.drop(target_column, axis = 1)\n",
    "\n",
    "X_2005_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STrAdaBoost.R2\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'STrAdaBoostR2' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-101-0bb30b99d08e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0;31m################################################STrAdaBoost.R2############################################################################\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m     regr_1 = STrAdaBoostR2(DecisionTreeRegressor(max_depth=6), #xgb.XGBRegressor(objective ='reg:squarederror',learning_rate = 0.0001,max_depth = 6,n_estimators = 2000),\n\u001b[0m\u001b[1;32m     30\u001b[0m                           \u001b[0mn_estimators\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mn_estimators\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m                           \u001b[0msteps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfold\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'STrAdaBoostR2' is not defined"
     ]
    }
   ],
   "source": [
    "################################# Prediction for STrAdaBoost ##################################################################\n",
    "predictionlist = []\n",
    "r2scorelist = []\n",
    "rmselist = []\n",
    "\n",
    "print(\"STrAdaBoost.R2\")\n",
    "\n",
    "for x in range(0, 10):\n",
    "\n",
    "    TF_train_X = pd.concat([X_2004, X_2005_train], sort= False)\n",
    "    TF_train_y = pd.concat([y_2004, y_2005_train], sort= False)\n",
    "\n",
    "    np_TF_train_X = TF_train_X.to_numpy()\n",
    "    np_TF_train_y = TF_train_y.to_numpy()\n",
    "\n",
    "    np_test_X = X_2005_test.to_numpy()\n",
    "    np_test_y = y_2005_test.to_numpy()\n",
    "\n",
    "    np_TF_train_y_list = np_TF_train_y.ravel()\n",
    "    np_test_y_list = np_test_y.ravel()\n",
    "\n",
    "    sample_size = [len(X_2004), len(X_2005_train)]\n",
    "    n_estimators = 100\n",
    "    steps = 30\n",
    "    fold = 10\n",
    "    random_state = np.random.RandomState(1)\n",
    "\n",
    "    ################################################STrAdaBoost.R2############################################################################\n",
    "    regr_1 = STrAdaBoostR2(DecisionTreeRegressor(max_depth=6), #xgb.XGBRegressor(objective ='reg:squarederror',learning_rate = 0.0001,max_depth = 6,n_estimators = 2000),\n",
    "                          n_estimators = n_estimators, sample_size = sample_size,\n",
    "                          steps = steps, fold = fold,\n",
    "                          random_state = random_state)\n",
    "\n",
    "    regr_1.fit(np_TF_train_X, np_TF_train_y_list)\n",
    "    y_pred1 = regr_1.predict(np_test_X)\n",
    "    predictionlist.append(y_pred1)\n",
    "\n",
    "    mse_twostageboost = np.sqrt(mean_squared_error(np_test_y_list, y_pred1))\n",
    "    print(\"RMSE of STrAdaboostR2:\", mse_twostageboost)\n",
    "    rmselist.append(mse_twostageboost)\n",
    "\n",
    "    r2_score_twostageboost_values = pearsonr(np_test_y_list, y_pred1)\n",
    "    r2_score_twostageboost = (r2_score_twostageboost_values[0])**2\n",
    "    print(\"R^2 of STrAdaboostR2:\", r2_score_twostageboost)\n",
    "    r2scorelist.append(r2_score_twostageboost)\n",
    "\n",
    "predict_stradaboost = np.mean(predictionlist, axis=0)\n",
    "list_orginal_stradaboost = np_test_y_list\n",
    "\n",
    "print(\"mean RMSE of STrAdaboostR2:\", mean(rmselist))\n",
    "print(\"mean R^2 of STrAdaboostR2:\", mean(r2scorelist))\n",
    "\n",
    "\n",
    "with open('AQI_datasets/UCI_AQI_Results/O3/STrAdaBoostOut_R2_2.txt', 'w') as filehandle1:\n",
    "    for listitem in r2scorelist:\n",
    "        filehandle1.write('%s\\n' % listitem)\n",
    "\n",
    "with open('AQI_datasets/UCI_AQI_Results/O3/STrAdaBoostOut_RMSE_2.txt', 'w') as filehandle2:\n",
    "    for listitem in rmselist:\n",
    "        filehandle2.write('%s\\n' % listitem)\n",
    "\n",
    "with open('AQI_datasets/UCI_AQI_Results/O3/STrAdaBoostOut_prediction_2.txt', 'w') as filehandle3:\n",
    "    for listitem in predict_stradaboost:\n",
    "        filehandle3.write('%s\\n' % listitem)\n",
    "\n",
    "\n",
    "\n",
    "plt.scatter(predict_adaboost, list_orginal_adaboost,  c =\"red\", alpha=0.6)\n",
    "plt.scatter(predict_tradaboost, list_orginal_tradaboost,  c =\"blue\", alpha=0.4)\n",
    "plt.scatter(predict_stradaboost, list_orginal_stradaboost,  c =\"green\", alpha=0.2)\n",
    "\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Predicted vs Actual')\n",
    "# plt.savefig(\"AQI_datasets/UCI_AQI_Results/NOx/AdaBoostR2_Transfer.png\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "############## How to superimpose scatter plots ########################\n",
    "## https://stackoverflow.com/questions/11190735/python-matplotlib-superimpose-scatter-plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Regression Transfer Learning\n",
      "-------------------------------------------\n",
      "RMSE of GBR: 169.83804568642555\n",
      "R^2 of GBR: 0.8580780955472149\n",
      "\n",
      "\n",
      "RMSE of GBR: [172.43722076085052, 171.0841442188501, 167.13597759478478, 168.9832296999789, 169.64656882081957, 168.76243894893932, 169.03423237950398, 170.05147081688312, 170.62779332810234, 170.61738029554294]\n",
      "R^2 of GBR: [0.8546475807470808, 0.8564545321441435, 0.861665218090882, 0.8585715731331532, 0.8586169771866579, 0.8594151292840027, 0.8589606436645931, 0.8586756229740192, 0.8574898869239121, 0.8562837913237045]\n",
      "-------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "######################### Gradient Boosting Regression Transfer Learning Italian #######################################\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "print(\"Gradient Boosting Regression Transfer Learning\")\n",
    "print(\"-------------------------------------------\")\n",
    "\n",
    "r2scorelist_GBRTL_italian = []\n",
    "rmselist_GBRTL_italian = []\n",
    "\n",
    "for x in range(0, 10):\n",
    "    \n",
    "    np_TF_train_X = X_2005_train.to_numpy()\n",
    "    np_TF_train_y = y_2005_train.to_numpy()\n",
    "\n",
    "    np_test_X = X_2005_test.to_numpy()\n",
    "    np_test_y = y_2005_test.to_numpy()\n",
    "\n",
    "    np_TF_train_y_list = np_TF_train_y.ravel()\n",
    "    np_test_y_list = np_test_y.ravel()\n",
    "\n",
    "\n",
    "    model_GBRTL_italian = GradientBoostingRegressor(learning_rate=0.01, max_depth=4, n_estimators=1000, subsample=0.5)\n",
    "    model_GBRTL_italian.fit(np_TF_train_X, np_TF_train_y_list)\n",
    "\n",
    "    y_pred_GBRTL_italian = model_GBRTL_italian.predict(np_test_X) ##Using dataframe instead of the numpy matrix\n",
    "\n",
    "    mse_GBRTL_italian = sqrt(mean_squared_error(np_test_y_list, y_pred_GBRTL_italian))\n",
    "    rmselist_GBRTL_italian.append(mse_GBRTL_italian)\n",
    "        \n",
    "    r2_score_GBRTL_italian = pearsonr(np_test_y_list, y_pred_GBRTL_italian)\n",
    "    r2_score_GBRTL_italian = (r2_score_GBRTL_italian[0])**2\n",
    "    r2scorelist_GBRTL_italian.append(r2_score_GBRTL_italian)\n",
    "\n",
    "print(\"RMSE of GBR:\", statistics.mean(rmselist_GBRTL_italian))\n",
    "print(\"R^2 of GBR:\", statistics.mean(r2scorelist_GBRTL_italian))\n",
    "print(\"\\n\")\n",
    "print(\"RMSE of GBR:\", rmselist_GBRTL_italian)\n",
    "print(\"R^2 of GBR:\", r2scorelist_GBRTL_italian)\n",
    "\n",
    "print(\"-------------------------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'month', 'day', 'hour', 'PM2.5', 'PM10', 'SO2', 'NO2', 'CO',\n",
       "       'O3', 'TEMP', 'PRES', 'DEWP', 'RAIN', 'wd', 'WSPM', 'station'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "################# UCI Beijing dataset (2013 - 2017) #####################################################################\n",
    "############### Spacio-temporal dataset. (multi-year and multi-terrain) #################################################\n",
    "\n",
    "# beijing_aqi_df = pd.read_csv('AQI_datasets/Beijing_AQI/PRSA_Data_20130301-20170228/PRSA_Data_Aotizhongxin_20130301-20170228.csv') \n",
    "# drop_index = ['No']\n",
    "# beijing_aqi_df = beijing_aqi_df.drop(drop_index, axis=1)\n",
    "# #print(beijing_aqi_df.head())\n",
    "# print(beijing_aqi_df.isnull().sum())\n",
    "\n",
    "path_beijing = r'AQI_datasets/Beijing_AQI/PRSA_Data_20130301-20170228/' ## Path for all the files\n",
    "allFiles = glob.glob(path_beijing + \"/*.csv\")\n",
    "beijing_aqi_df = pd.DataFrame()\n",
    "list_beijing = []\n",
    "for file_ in allFiles:\n",
    "    temp_df = pd.read_csv(file_, index_col = None, header=0)\n",
    "    list_beijing.append(temp_df)\n",
    "beijing_aqi_df = pd.concat(list_beijing)\n",
    "\n",
    "cols = ['No', 'year', 'month', 'day', 'hour', 'PM2.5', 'PM10', 'SO2', 'NO2', 'CO', 'O3', 'TEMP',\n",
    "       'PRES', 'DEWP', 'RAIN', 'wd', 'WSPM', 'station']\n",
    "\n",
    "beijing_aqi_df = beijing_aqi_df[cols]\n",
    "\n",
    "drop_index = ['No']\n",
    "beijing_aqi_df = beijing_aqi_df.drop(drop_index, axis=1)\n",
    "\n",
    "beijing_aqi_df = beijing_aqi_df.sort_values(['station', 'year'])\n",
    "\n",
    "# beijing_aqi_df.head(20)\n",
    "# beijing_aqi_df.year.value_counts()\n",
    "# beijing_aqi_df.wd.value_counts()\n",
    "# beijing_aqi_df.shape\n",
    "# beijing_aqi_df['station'].nunique()\n",
    "beijing_aqi_df.columns\n",
    "## O3: Dependent, Predictors: SO2, NO2, year, month, CO\n",
    "## PM25: Dependent, Predictors:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shrey/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py:4166: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().replace(\n"
     ]
    },
    {
     "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>wd</th>\n",
       "      <th>WSPM</th>\n",
       "      <th>station</th>\n",
       "      <th>O3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>1023.2</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>1023.5</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>1024.5</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1025.2</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35059</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>1013.5</td>\n",
       "      <td>-16.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Wanshouxigong</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35060</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>1013.6</td>\n",
       "      <td>-15.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>Wanshouxigong</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35061</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>1014.2</td>\n",
       "      <td>-13.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Wanshouxigong</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35062</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1014.4</td>\n",
       "      <td>-12.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Wanshouxigong</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35063</th>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>1014.1</td>\n",
       "      <td>-15.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Wanshouxigong</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>420768 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  month   SO2   NO2     CO  TEMP    PRES  DEWP  RAIN    wd  WSPM  \\\n",
       "0      2013      3   4.0   7.0  300.0  -0.7  1023.0 -18.8   0.0  10.0   4.4   \n",
       "1      2013      3   4.0   7.0  300.0  -1.1  1023.2 -18.2   0.0   1.0   4.7   \n",
       "2      2013      3   5.0  10.0  300.0  -1.1  1023.5 -18.2   0.0  10.0   5.6   \n",
       "3      2013      3  11.0  11.0  300.0  -1.4  1024.5 -19.4   0.0   6.0   3.1   \n",
       "4      2013      3  12.0  12.0  300.0  -2.0  1025.2 -19.5   0.0   1.0   2.0   \n",
       "...     ...    ...   ...   ...    ...   ...     ...   ...   ...   ...   ...   \n",
       "35059  2017      2   3.0  24.0  400.0  12.5  1013.5 -16.2   0.0   6.0   2.4   \n",
       "35060  2017      2   3.0  41.0  500.0  11.6  1013.6 -15.1   0.0  13.0   0.9   \n",
       "35061  2017      2   4.0  38.0  500.0  10.8  1014.2 -13.3   0.0   6.0   1.1   \n",
       "35062  2017      2   4.0  30.0  400.0  10.5  1014.4 -12.9   0.0  10.0   1.2   \n",
       "35063  2017      2   4.0  38.0  600.0   8.6  1014.1 -15.9   0.0   9.0   1.3   \n",
       "\n",
       "             station    O3  \n",
       "0       Aotizhongxin  77.0  \n",
       "1       Aotizhongxin  77.0  \n",
       "2       Aotizhongxin  73.0  \n",
       "3       Aotizhongxin  72.0  \n",
       "4       Aotizhongxin  72.0  \n",
       "...              ...   ...  \n",
       "35059  Wanshouxigong  72.0  \n",
       "35060  Wanshouxigong  50.0  \n",
       "35061  Wanshouxigong  54.0  \n",
       "35062  Wanshouxigong  59.0  \n",
       "35063  Wanshouxigong  49.0  \n",
       "\n",
       "[420768 rows x 13 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "######################### Seperate the dataset into predictors and target variable. ###################################\n",
    "\n",
    "predictors = ['year', 'month', 'SO2', 'NO2', 'CO', 'TEMP', 'PRES', 'DEWP', 'RAIN', 'wd', 'WSPM', 'station', 'O3']\n",
    "beijing_predictors_df = beijing_aqi_df[predictors]\n",
    "wd_codes = {'N':1, 'E': 2, 'W': 3, 'S': 4, 'NE': 5, 'NW': 6, 'SE': 7, 'SW': 8, 'NNE': 9, 'NNW': 10, 'SSE': 11, \n",
    "            'SSW': 12, 'WNW': 13, 'WSW': 14, 'ENE': 15, 'ESE': 16 }\n",
    "\n",
    "\n",
    "\n",
    "beijing_predictors_df.replace(wd_codes, inplace=True)\n",
    "beijing_predictors_df.wd.value_counts()\n",
    "beijing_predictors_df\n",
    "\n",
    "# target = ['O3']\n",
    "# beijing_target_df = beijing_aqi_df[target]\n",
    "# beijing_target_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year       0.0\n",
      "month      0.0\n",
      "SO2        2.0\n",
      "NO2        3.0\n",
      "CO         5.0\n",
      "TEMP       0.0\n",
      "PRES       0.0\n",
      "DEWP       0.0\n",
      "RAIN       0.0\n",
      "wd         0.0\n",
      "WSPM       0.0\n",
      "station    0.0\n",
      "O3         3.0\n",
      "dtype: float64\n",
      "year       0.0\n",
      "month      0.0\n",
      "SO2        0.0\n",
      "NO2        0.0\n",
      "CO         0.0\n",
      "TEMP       0.0\n",
      "PRES       0.0\n",
      "DEWP       0.0\n",
      "RAIN       0.0\n",
      "wd         0.0\n",
      "WSPM       0.0\n",
      "station    0.0\n",
      "O3         0.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "############# Replacing missing values ##############################################################\n",
    "# beijing_predictors_df['SO2'].fillna(beijing_predictors_df['SO2'].median(), inplace=True)\n",
    "# beijing_predictors_df['NO2'].fillna(beijing_predictors_df['NO2'].mean(), inplace=True)\n",
    "# beijing_predictors_df['CO'].fillna(beijing_predictors_df['CO'].median(), inplace=True)\n",
    "# beijing_predictors_df['O3'].fillna(beijing_predictors_df['O3'].median(), inplace=True)\n",
    "                                   \n",
    "# beijing_predictors_df = beijing_predictors_df.dropna(axis=0) # , subset=['O3'])\n",
    "\n",
    "###### percentage of missing values in each column #####################################3\n",
    "print(round(beijing_predictors_df.isna().sum()/len(beijing_predictors_df.index), 2)*100)\n",
    "\n",
    "beijing_predictors_df = beijing_predictors_df.dropna()\n",
    "\n",
    "print(round(beijing_predictors_df.isna().sum()/len(beijing_predictors_df.index), 2)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb7b10ca0a0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "################ plotting concentration of PM 2.5 values over the years ###################################\n",
    "\n",
    "df_plot_beijing = beijing_predictors_df[['O3','year','station']].groupby([\"year\"]).median().reset_index().sort_values(by = 'year',ascending=False)\n",
    "f,ax = plt.subplots(figsize = (15,5))\n",
    "sns.pointplot(x = 'year', y = 'O3', data = df_plot_beijing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "O3       1.000000\n",
       "TEMP     0.597431\n",
       "DEWP     0.313067\n",
       "WSPM     0.296701\n",
       "wd       0.056912\n",
       "RAIN     0.023375\n",
       "year    -0.021672\n",
       "month   -0.101012\n",
       "SO2     -0.166692\n",
       "CO      -0.314770\n",
       "PRES    -0.447751\n",
       "NO2     -0.476478\n",
       "Name: O3, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "################# Get correlation between O3 and other predictors. #####################################\n",
    "\n",
    "# corrMatrix_beijing = beijing_aqi_df.corr()\n",
    "corrMatrix_beijing = beijing_predictors_df.corr()\n",
    "corrMatrix_beijing[\"O3\"].sort_values(ascending=False)\n",
    "\n",
    "# sns.heatmap(corrMatrix_beijing, annot=True)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "################ Drop stations (dropping stations at this stage so more combination of splits can be made later) #########################\n",
    "drop_stations = ['station']\n",
    "beijing_predictors_df = beijing_predictors_df.drop(drop_stations, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.013171122206609615"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Get the Spearman Correlation Concrete for the features\n",
    "\n",
    "beijing_predictors_df_corr = beijing_predictors_df.copy()\n",
    "target_beijing_col = ['O3']\n",
    "beijing_predictors_df_corr = beijing_predictors_df_corr.drop(target_beijing_col, axis = 1)\n",
    "\n",
    "corr_beijing_predictors_df =  beijing_predictors_df_corr.corr(method = 'spearman')\n",
    "corr_beijing_predictors_df\n",
    "\n",
    "corr_beijing_predictors_df_lsum = np.tril(corr_beijing_predictors_df).sum() - np.trace(corr_beijing_predictors_df)\n",
    "beijing_predictors_df_elems = len(corr_beijing_predictors_df) * (len(corr_beijing_predictors_df) - 1) / 2\n",
    "corr_beijing_predictors_df_lavg = abs(corr_beijing_predictors_df_lsum / beijing_predictors_df_elems)\n",
    "corr_beijing_predictors_df_lavg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "################ Splitting the dataset by the year ########################################\n",
    "beijing_predictors_df.year.value_counts()\n",
    "beijing_predictors_df_target = beijing_predictors_df[beijing_predictors_df['year'].isin([2014, 2015])]\n",
    "beijing_predictors_df_source = beijing_predictors_df[beijing_predictors_df['year'].isin([2016])]\n",
    "\n",
    "print(beijing_predictors_df_source.shape)\n",
    "print(beijing_predictors_df_target.shape)\n",
    "# beijing_predictors_df_target.head(10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-68-feb43c26a1e9>:6: 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",
      "  beijing_predictors_df_target[cols_to_norm] = ss.fit_transform(beijing_predictors_df_target[cols_to_norm])\n",
      "/Users/shrey/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py:966: 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",
      "  self.obj[item] = s\n",
      "<ipython-input-68-feb43c26a1e9>:7: 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",
      "  beijing_predictors_df_source[cols_to_norm] = ss.fit_transform(beijing_predictors_df_source[cols_to_norm])\n",
      "/Users/shrey/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py:966: 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",
      "  self.obj[item] = s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>wd</th>\n",
       "      <th>WSPM</th>\n",
       "      <th>O3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24864</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2.772768</td>\n",
       "      <td>2.280143</td>\n",
       "      <td>3.743354</td>\n",
       "      <td>-1.336244</td>\n",
       "      <td>1.225578</td>\n",
       "      <td>-0.707968</td>\n",
       "      <td>-0.084862</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-0.618369</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24865</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4.193714</td>\n",
       "      <td>2.160704</td>\n",
       "      <td>3.833436</td>\n",
       "      <td>-1.420293</td>\n",
       "      <td>1.160356</td>\n",
       "      <td>-0.644751</td>\n",
       "      <td>-0.084862</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-0.699815</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24866</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4.044141</td>\n",
       "      <td>2.041265</td>\n",
       "      <td>2.842532</td>\n",
       "      <td>-1.521150</td>\n",
       "      <td>1.151039</td>\n",
       "      <td>-0.644751</td>\n",
       "      <td>-0.084862</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-0.862708</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24867</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3.445848</td>\n",
       "      <td>1.921827</td>\n",
       "      <td>2.662368</td>\n",
       "      <td>-1.428697</td>\n",
       "      <td>1.095134</td>\n",
       "      <td>-0.672847</td>\n",
       "      <td>-0.084862</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-0.292584</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24868</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2.697982</td>\n",
       "      <td>1.802388</td>\n",
       "      <td>2.211957</td>\n",
       "      <td>-1.554769</td>\n",
       "      <td>1.020595</td>\n",
       "      <td>-0.672847</td>\n",
       "      <td>-0.084862</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.699815</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33643</th>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>0.005663</td>\n",
       "      <td>3.175932</td>\n",
       "      <td>3.022697</td>\n",
       "      <td>-1.285816</td>\n",
       "      <td>0.992642</td>\n",
       "      <td>-0.560463</td>\n",
       "      <td>-0.084862</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-0.781261</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33644</th>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>0.155236</td>\n",
       "      <td>2.966914</td>\n",
       "      <td>2.662368</td>\n",
       "      <td>-1.336244</td>\n",
       "      <td>1.029912</td>\n",
       "      <td>-0.518318</td>\n",
       "      <td>-0.084862</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-0.944154</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33645</th>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>0.155236</td>\n",
       "      <td>2.817616</td>\n",
       "      <td>2.932615</td>\n",
       "      <td>-1.378269</td>\n",
       "      <td>1.001960</td>\n",
       "      <td>-0.504270</td>\n",
       "      <td>-0.084862</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.781261</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33646</th>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>0.155236</td>\n",
       "      <td>2.310002</td>\n",
       "      <td>3.473108</td>\n",
       "      <td>-1.378269</td>\n",
       "      <td>1.057864</td>\n",
       "      <td>-0.483198</td>\n",
       "      <td>-0.084862</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-1.432832</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33647</th>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.143910</td>\n",
       "      <td>1.921827</td>\n",
       "      <td>3.833436</td>\n",
       "      <td>-1.462317</td>\n",
       "      <td>1.048547</td>\n",
       "      <td>-0.532366</td>\n",
       "      <td>-0.084862</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-0.781261</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99615 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  month       SO2       NO2        CO      TEMP      PRES  \\\n",
       "24864  2016      1  2.772768  2.280143  3.743354 -1.336244  1.225578   \n",
       "24865  2016      1  4.193714  2.160704  3.833436 -1.420293  1.160356   \n",
       "24866  2016      1  4.044141  2.041265  2.842532 -1.521150  1.151039   \n",
       "24867  2016      1  3.445848  1.921827  2.662368 -1.428697  1.095134   \n",
       "24868  2016      1  2.697982  1.802388  2.211957 -1.554769  1.020595   \n",
       "...     ...    ...       ...       ...       ...       ...       ...   \n",
       "33643  2016     12  0.005663  3.175932  3.022697 -1.285816  0.992642   \n",
       "33644  2016     12  0.155236  2.966914  2.662368 -1.336244  1.029912   \n",
       "33645  2016     12  0.155236  2.817616  2.932615 -1.378269  1.001960   \n",
       "33646  2016     12  0.155236  2.310002  3.473108 -1.378269  1.057864   \n",
       "33647  2016     12 -0.143910  1.921827  3.833436 -1.462317  1.048547   \n",
       "\n",
       "           DEWP      RAIN    wd      WSPM    O3  \n",
       "24864 -0.707968 -0.084862   9.0 -0.618369  13.0  \n",
       "24865 -0.644751 -0.084862   5.0 -0.699815  16.0  \n",
       "24866 -0.644751 -0.084862  15.0 -0.862708  12.0  \n",
       "24867 -0.672847 -0.084862  15.0 -0.292584   9.0  \n",
       "24868 -0.672847 -0.084862   2.0 -0.699815   7.0  \n",
       "...         ...       ...   ...       ...   ...  \n",
       "33643 -0.560463 -0.084862  16.0 -0.781261   4.0  \n",
       "33644 -0.518318 -0.084862  15.0 -0.944154   4.0  \n",
       "33645 -0.504270 -0.084862   2.0 -0.781261   5.0  \n",
       "33646 -0.483198 -0.084862   8.0 -1.432832   8.0  \n",
       "33647 -0.532366 -0.084862  15.0 -0.781261   7.0  \n",
       "\n",
       "[99615 rows x 12 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "########################## Standardize the dataset. #########################################\n",
    "\n",
    "cols_to_norm = ['SO2', 'NO2', 'CO', 'TEMP', 'PRES', 'DEWP', 'RAIN', 'WSPM']\n",
    "\n",
    "ss = StandardScaler()\n",
    "beijing_predictors_df_target[cols_to_norm] = ss.fit_transform(beijing_predictors_df_target[cols_to_norm])\n",
    "beijing_predictors_df_source[cols_to_norm] = ss.fit_transform(beijing_predictors_df_source[cols_to_norm])\n",
    "\n",
    "beijing_predictors_df_source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((197, 11), (197331, 11), (99615, 11), (197, 1))"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##################### Splitting the dataset into train and test set ########################################\n",
    "\n",
    "target_beijing_col = ['O3']\n",
    "beijing_predictors_df_target_Y = beijing_predictors_df_target[target_beijing_col]\n",
    "beijing_predictors_df_target_X = beijing_predictors_df_target.drop(target_beijing_col, axis =1)\n",
    "\n",
    "beijing_predictors_df_source_Y = beijing_predictors_df_source[target_beijing_col]\n",
    "beijing_predictors_df_source_X = beijing_predictors_df_source.drop(target_beijing_col, axis =1)\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(beijing_predictors_df_target_X, beijing_predictors_df_target_Y, test_size = 0.999, random_state = 1)\n",
    "\n",
    "X_source = beijing_predictors_df_source_X\n",
    "y_source = beijing_predictors_df_source_Y\n",
    "\n",
    "X_train.shape, X_test.shape, X_source.shape, y_train.shape\n",
    "\n",
    "# X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoost\n",
      "RMSE of AdaboostR2: 32.532776731519704\n",
      "R^2 of AdaboostR2: 0.6877995743566426\n",
      "RMSE of AdaboostR2: 32.87056299620226\n",
      "R^2 of AdaboostR2: 0.6818912457359528\n",
      "RMSE of AdaboostR2: 33.209571666985205\n",
      "R^2 of AdaboostR2: 0.6749755848925322\n",
      "RMSE of AdaboostR2: 32.803891840326386\n",
      "R^2 of AdaboostR2: 0.6828800483317818\n",
      "RMSE of AdaboostR2: 32.905321680189594\n",
      "R^2 of AdaboostR2: 0.6808104853777054\n",
      "RMSE of AdaboostR2: 32.56279939323596\n",
      "R^2 of AdaboostR2: 0.6871231501325993\n",
      "RMSE of AdaboostR2: 32.62059511292233\n",
      "R^2 of AdaboostR2: 0.6862144738061179\n",
      "RMSE of AdaboostR2: 32.90140658387795\n",
      "R^2 of AdaboostR2: 0.6806879184812922\n",
      "RMSE of AdaboostR2: 33.22063579056246\n",
      "R^2 of AdaboostR2: 0.6752377685173588\n",
      "RMSE of AdaboostR2: 32.85005964211906\n",
      "R^2 of AdaboostR2: 0.6821784926637742\n",
      "mean RMSE of AdaboostR2: 32.84776214379409\n",
      "mean R^2 of AdaboostR2: 0.6819798742295757\n"
     ]
    }
   ],
   "source": [
    "#################################### (Demo) Prediction for AdaBoost #######################################################\n",
    "predictionlist = []\n",
    "r2scorelist = []\n",
    "rmselist = []\n",
    "\n",
    "print(\"AdaBoost\")\n",
    "\n",
    "for x in range(0, 10):\n",
    "\n",
    "    np_TF_train_X = X_train.to_numpy()\n",
    "    np_TF_train_y = y_train.to_numpy()\n",
    "\n",
    "    np_test_X = X_test.to_numpy()\n",
    "    np_test_y = y_test.to_numpy()\n",
    "\n",
    "    np_TF_train_y_list = np_TF_train_y.ravel()\n",
    "    np_test_y_list = np_test_y.ravel()\n",
    "\n",
    "    sample_size = [len(X_source), len(X_train)]\n",
    "    n_estimators = 100\n",
    "    steps = 30\n",
    "    fold = 10\n",
    "    random_state = np.random.RandomState(1)\n",
    "\n",
    "    regr_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=6), n_estimators = n_estimators)\n",
    "    \n",
    "    regr_2.fit(np_TF_train_X, np_TF_train_y_list)\n",
    "    y_pred2 = regr_2.predict(np_test_X)\n",
    "    predictionlist.append(y_pred2)\n",
    "    \n",
    "    rmse_adaboost = np.sqrt(mean_squared_error(np_test_y_list, y_pred2))\n",
    "    print(\"RMSE of AdaboostR2:\", rmse_adaboost)\n",
    "    rmselist.append(rmse_adaboost)\n",
    "    \n",
    "    r2_score_adaboost_values = pearsonr(np_test_y_list, y_pred2)\n",
    "    r2_score_adaboost = (r2_score_adaboost_values[0])**2\n",
    "    print(\"R^2 of AdaboostR2:\", r2_score_adaboost)\n",
    "    r2scorelist.append(r2_score_adaboost)\n",
    "\n",
    "\n",
    "predict_adaboost = np.mean(predictionlist, axis=0)\n",
    "list_orginal_adaboost = np_test_y_list\n",
    "\n",
    "print(\"mean RMSE of AdaboostR2:\", mean(rmselist))\n",
    "print(\"mean R^2 of AdaboostR2:\", mean(r2scorelist))\n",
    "\n",
    "# plt.xlabel('Predicted')\n",
    "# plt.ylabel('Actual')\n",
    "# plt.title('AdaBoost.R2 Predicted vs Actual')\n",
    "# # plt.savefig(\"AQI_datasets/UCI_AQI_Results/NOx/AdaBoostR2_Transfer.png\")\n",
    "# plt.show()\n",
    "\n",
    "with open('AQI_datasets/Beijing_AQI_Results/O3/AdaBoostOut_R2_std_999.txt', 'w') as filehandle1:\n",
    "    for listitem in r2scorelist:\n",
    "        filehandle1.write('%s\\n' % listitem)\n",
    "\n",
    "with open('AQI_datasets/Beijing_AQI_Results/O3/AdaBoostOut_RMSE_std_999.txt', 'w') as filehandle2:\n",
    "    for listitem in rmselist:\n",
    "        filehandle2.write('%s\\n' % listitem)\n",
    "\n",
    "with open('AQI_datasets/Beijing_AQI_Results/O3/AdaBoostOut_prediction_std_999.txt', 'w') as filehandle3:\n",
    "    for listitem in predict_adaboost:\n",
    "        filehandle3.write('%s\\n' % listitem)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoost\n",
      "RMSE of AdaboostR2: 32.6981135277245\n",
      "R^2 of AdaboostR2: 0.6850750051913875\n",
      "RMSE of AdaboostR2: 32.73791615469271\n",
      "R^2 of AdaboostR2: 0.6842065864376252\n",
      "RMSE of AdaboostR2: 32.99266348767973\n",
      "R^2 of AdaboostR2: 0.6790249312583293\n",
      "RMSE of AdaboostR2: 33.03118366747117\n",
      "R^2 of AdaboostR2: 0.6785077299503172\n",
      "RMSE of AdaboostR2: 32.86294436598774\n",
      "R^2 of AdaboostR2: 0.6817944793648458\n",
      "RMSE of AdaboostR2: 32.794193622188835\n",
      "R^2 of AdaboostR2: 0.6826820495079257\n",
      "RMSE of AdaboostR2: 32.688494311486124\n",
      "R^2 of AdaboostR2: 0.6847141152233336\n",
      "RMSE of AdaboostR2: 33.20180622738959\n",
      "R^2 of AdaboostR2: 0.6752643455245851\n",
      "RMSE of AdaboostR2: 32.72732238986638\n",
      "R^2 of AdaboostR2: 0.6841200970924285\n",
      "RMSE of AdaboostR2: 33.282930559146386\n",
      "R^2 of AdaboostR2: 0.6736955749942777\n",
      "mean RMSE of AdaboostR2: 32.901756831363315\n",
      "mean R^2 of AdaboostR2: 0.6809084914545056\n"
     ]
    }
   ],
   "source": [
    "#################################### Prediction for AdaBoost #######################################################\n",
    "predictionlist = []\n",
    "r2scorelist = []\n",
    "rmselist = []\n",
    "\n",
    "print(\"AdaBoost\")\n",
    "\n",
    "for x in range(0, 10):\n",
    "\n",
    "    np_TF_train_X = X_train.to_numpy()\n",
    "    np_TF_train_y = y_train.to_numpy()\n",
    "\n",
    "    np_test_X = X_test.to_numpy()\n",
    "    np_test_y = y_test.to_numpy()\n",
    "\n",
    "    np_TF_train_y_list = np_TF_train_y.ravel()\n",
    "    np_test_y_list = np_test_y.ravel()\n",
    "\n",
    "    sample_size = [len(X_source), len(X_train)]\n",
    "    n_estimators = 100\n",
    "    steps = 30\n",
    "    fold = 10\n",
    "    random_state = np.random.RandomState(1)\n",
    "\n",
    "    regr_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=6), n_estimators = n_estimators)\n",
    "    \n",
    "    regr_2.fit(np_TF_train_X, np_TF_train_y_list)\n",
    "    y_pred2 = regr_2.predict(np_test_X)\n",
    "    predictionlist.append(y_pred2)\n",
    "    \n",
    "    rmse_adaboost = np.sqrt(mean_squared_error(np_test_y_list, y_pred2))\n",
    "    print(\"RMSE of AdaboostR2:\", rmse_adaboost)\n",
    "    rmselist.append(rmse_adaboost)\n",
    "    \n",
    "    r2_score_adaboost_values = pearsonr(np_test_y_list, y_pred2)\n",
    "    r2_score_adaboost = (r2_score_adaboost_values[0])**2\n",
    "    print(\"R^2 of AdaboostR2:\", r2_score_adaboost)\n",
    "    r2scorelist.append(r2_score_adaboost)\n",
    "\n",
    "\n",
    "predict_adaboost = np.mean(predictionlist, axis=0)\n",
    "list_orginal_adaboost = np_test_y_list\n",
    "\n",
    "print(\"mean RMSE of AdaboostR2:\", mean(rmselist))\n",
    "print(\"mean R^2 of AdaboostR2:\", mean(r2scorelist))\n",
    "\n",
    "# plt.xlabel('Predicted')\n",
    "# plt.ylabel('Actual')\n",
    "# plt.title('AdaBoost.R2 Predicted vs Actual')\n",
    "# # plt.savefig(\"AQI_datasets/UCI_AQI_Results/NOx/AdaBoostR2_Transfer.png\")\n",
    "# plt.show()\n",
    "\n",
    "with open('AQI_datasets/Beijing_AQI_Results/O3/AdaBoostOut_R2_std_999.txt', 'w') as filehandle1:\n",
    "    for listitem in r2scorelist:\n",
    "        filehandle1.write('%s\\n' % listitem)\n",
    "\n",
    "with open('AQI_datasets/Beijing_AQI_Results/O3/AdaBoostOut_RMSE_std_999.txt', 'w') as filehandle2:\n",
    "    for listitem in rmselist:\n",
    "        filehandle2.write('%s\\n' % listitem)\n",
    "\n",
    "with open('AQI_datasets/Beijing_AQI_Results/O3/AdaBoostOut_prediction_std_999.txt', 'w') as filehandle3:\n",
    "    for listitem in predict_adaboost:\n",
    "        filehandle3.write('%s\\n' % listitem)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TrAdaBoost.R2\n",
      "RMSE of TrAdaboostR2: 31.782374170832192\n",
      "R^2 of TrAdaboostR2: 0.7055145810402719\n",
      "RMSE of TrAdaboostR2: 31.782368467121945\n",
      "R^2 of TrAdaboostR2: 0.7055153442700947\n",
      "RMSE of TrAdaboostR2: 31.781676892702276\n",
      "R^2 of TrAdaboostR2: 0.7055287480231154\n",
      "RMSE of TrAdaboostR2: 31.78192822435267\n",
      "R^2 of TrAdaboostR2: 0.705523263533137\n",
      "RMSE of TrAdaboostR2: 31.782338730096622\n",
      "R^2 of TrAdaboostR2: 0.7055162372419563\n",
      "RMSE of TrAdaboostR2: 31.782938609624683\n",
      "R^2 of TrAdaboostR2: 0.7055025365414932\n",
      "RMSE of TrAdaboostR2: 31.78338785552483\n",
      "R^2 of TrAdaboostR2: 0.7054927167992427\n",
      "RMSE of TrAdaboostR2: 31.783423923484538\n",
      "R^2 of TrAdaboostR2: 0.7054919421548778\n",
      "RMSE of TrAdaboostR2: 31.782923701688297\n",
      "R^2 of TrAdaboostR2: 0.7055028500690232\n",
      "RMSE of TrAdaboostR2: 31.782294679362202\n",
      "R^2 of TrAdaboostR2: 0.705515092997604\n",
      "mean RMSE of TrAdaboostR2: 31.782565525479026\n",
      "mean R^2 of TrAdaboostR2: 0.7055103312670816\n"
     ]
    }
   ],
   "source": [
    "########################### Prediction for TrAdaBoost.R2 ##########################################\n",
    "predictionlist = []\n",
    "r2scorelist = []\n",
    "rmselist = []\n",
    "\n",
    "print(\"TrAdaBoost.R2\")\n",
    "\n",
    "for x in range(0, 10):\n",
    "\n",
    "    TF_train_X = pd.concat([X_source, X_train], sort= False)\n",
    "    TF_train_y = pd.concat([y_source, y_train], sort= False)\n",
    "\n",
    "    np_TF_train_X = TF_train_X.to_numpy()\n",
    "    np_TF_train_y = TF_train_y.to_numpy()\n",
    "\n",
    "    np_test_X = X_test.to_numpy()\n",
    "    np_test_y = y_test.to_numpy()\n",
    "\n",
    "    np_TF_train_y_list = np_TF_train_y.ravel()\n",
    "    np_test_y_list = np_test_y.ravel()\n",
    "\n",
    "    sample_size = [len(X_source), len(X_train)]\n",
    "    n_estimators = 100\n",
    "    steps = 30\n",
    "    fold = 10\n",
    "    random_state = np.random.RandomState(1)\n",
    "\n",
    "    ################################################TrAdaBoost.R2############################################################################\n",
    "    regr_1 = TwoStageTrAdaBoostR2(DecisionTreeRegressor(max_depth=6), #xgb.XGBRegressor(objective ='reg:squarederror',learning_rate = 0.0001,max_depth = 6,n_estimators = 2000),\n",
    "                          n_estimators = n_estimators, sample_size = sample_size,\n",
    "                          steps = steps, fold = fold,\n",
    "                          random_state = random_state)\n",
    "\n",
    "    regr_1.fit(np_TF_train_X, np_TF_train_y_list)\n",
    "    y_pred1 = regr_1.predict(np_test_X)\n",
    "    predictionlist.append(y_pred1)\n",
    "\n",
    "    mse_twostageboost = np.sqrt(mean_squared_error(np_test_y_list, y_pred1))\n",
    "    print(\"RMSE of TrAdaboostR2:\", mse_twostageboost)\n",
    "    rmselist.append(mse_twostageboost)\n",
    "\n",
    "    r2_score_twostageboost_values = pearsonr(np_test_y_list, y_pred1)\n",
    "    r2_score_twostageboost = (r2_score_twostageboost_values[0])**2\n",
    "    print(\"R^2 of TrAdaboostR2:\", r2_score_twostageboost)\n",
    "    r2scorelist.append(r2_score_twostageboost)\n",
    "\n",
    "predict_tradaboost = np.mean(predictionlist, axis=0)\n",
    "list_orginal_tradaboost = np_test_y_list\n",
    "\n",
    "print(\"mean RMSE of TrAdaboostR2:\", mean(rmselist))\n",
    "print(\"mean R^2 of TrAdaboostR2:\", mean(r2scorelist))\n",
    "\n",
    "# plt.xlabel('Predicted')\n",
    "# plt.ylabel('Actual')\n",
    "# plt.title('TrAdaBoost.R2 Predicted vs Actual')\n",
    "# # plt.savefig(\"AQI_datasets/UCI_AQI_Results/NOx/AdaBoostR2_Transfer.png\")\n",
    "# plt.show()\n",
    "\n",
    "with open('AQI_datasets/Beijing_AQI_Results/O3/TrAdaBoostOut_R2_std_999.txt', 'w') as filehandle1:\n",
    "    for listitem in r2scorelist:\n",
    "        filehandle1.write('%s\\n' % listitem)\n",
    "\n",
    "with open('AQI_datasets/Beijing_AQI_Results/O3/TrAdaBoostOut_RMSE_std_999.txt', 'w') as filehandle2:\n",
    "    for listitem in rmselist:\n",
    "        filehandle2.write('%s\\n' % listitem)\n",
    "\n",
    "with open('AQI_datasets/Beijing_AQI_Results/O3/TrAdaBoostOut_prediction_std_999.txt', 'w') as filehandle3:\n",
    "    for listitem in predict_tradaboost:\n",
    "        filehandle3.write('%s\\n' % listitem)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(217, 12)\n",
      "(197331, 12)\n",
      "(12000, 12)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>wd</th>\n",
       "      <th>WSPM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>0.489557</td>\n",
       "      <td>1.374690</td>\n",
       "      <td>-0.045533</td>\n",
       "      <td>-1.220125</td>\n",
       "      <td>1.196708</td>\n",
       "      <td>-1.206591</td>\n",
       "      <td>-0.078282</td>\n",
       "      <td>8.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.504288</td>\n",
       "      <td>-1.253593</td>\n",
       "      <td>-0.807620</td>\n",
       "      <td>0.440707</td>\n",
       "      <td>0.286220</td>\n",
       "      <td>-0.509674</td>\n",
       "      <td>-0.078282</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.401394</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.245024</td>\n",
       "      <td>0.470334</td>\n",
       "      <td>1.309289</td>\n",
       "      <td>-1.555826</td>\n",
       "      <td>0.560346</td>\n",
       "      <td>-0.764461</td>\n",
       "      <td>-0.078282</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-0.006357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.633920</td>\n",
       "      <td>-0.744893</td>\n",
       "      <td>-0.384238</td>\n",
       "      <td>0.705733</td>\n",
       "      <td>-0.731959</td>\n",
       "      <td>0.981581</td>\n",
       "      <td>-0.078282</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.332558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "      <td>1.137716</td>\n",
       "      <td>1.374690</td>\n",
       "      <td>0.293173</td>\n",
       "      <td>-0.407378</td>\n",
       "      <td>1.686217</td>\n",
       "      <td>-0.944311</td>\n",
       "      <td>-0.078282</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-0.903409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197326</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.028971</td>\n",
       "      <td>0.668162</td>\n",
       "      <td>4.696345</td>\n",
       "      <td>-1.043441</td>\n",
       "      <td>0.903002</td>\n",
       "      <td>-0.157468</td>\n",
       "      <td>-0.078282</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.658758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197327</th>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.633920</td>\n",
       "      <td>-0.970982</td>\n",
       "      <td>-0.638267</td>\n",
       "      <td>0.794076</td>\n",
       "      <td>-0.810281</td>\n",
       "      <td>0.869175</td>\n",
       "      <td>-0.078282</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-0.414108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197328</th>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.158603</td>\n",
       "      <td>0.018157</td>\n",
       "      <td>-0.384238</td>\n",
       "      <td>1.147444</td>\n",
       "      <td>-0.516575</td>\n",
       "      <td>-0.015087</td>\n",
       "      <td>-0.078282</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.401394</td>\n",
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       "    <tr>\n",
       "      <th>197329</th>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.633920</td>\n",
       "      <td>-0.660110</td>\n",
       "      <td>-0.722944</td>\n",
       "      <td>0.484878</td>\n",
       "      <td>0.207899</td>\n",
       "      <td>0.599400</td>\n",
       "      <td>-0.078282</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.646044</td>\n",
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       "    <tr>\n",
       "      <th>197330</th>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.633920</td>\n",
       "      <td>-0.999243</td>\n",
       "      <td>-0.722944</td>\n",
       "      <td>1.059102</td>\n",
       "      <td>-0.173918</td>\n",
       "      <td>0.374588</td>\n",
       "      <td>-0.078282</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-0.169457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>197331 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        year  month       SO2       NO2        CO      TEMP      PRES  \\\n",
       "0       2015      2  0.489557  1.374690 -0.045533 -1.220125  1.196708   \n",
       "1       2015      4 -0.504288 -1.253593 -0.807620  0.440707  0.286220   \n",
       "2       2015     12 -0.245024  0.470334  1.309289 -1.555826  0.560346   \n",
       "3       2014      6 -0.633920 -0.744893 -0.384238  0.705733 -0.731959   \n",
       "4       2014     11  1.137716  1.374690  0.293173 -0.407378  1.686217   \n",
       "...      ...    ...       ...       ...       ...       ...       ...   \n",
       "197326  2015     12 -0.028971  0.668162  4.696345 -1.043441  0.903002   \n",
       "197327  2015      6 -0.633920 -0.970982 -0.638267  0.794076 -0.810281   \n",
       "197328  2014      4 -0.158603  0.018157 -0.384238  1.147444 -0.516575   \n",
       "197329  2015      9 -0.633920 -0.660110 -0.722944  0.484878  0.207899   \n",
       "197330  2015      9 -0.633920 -0.999243 -0.722944  1.059102 -0.173918   \n",
       "\n",
       "            DEWP      RAIN    wd      WSPM  \n",
       "0      -1.206591 -0.078282   8.0 -0.332558  \n",
       "1      -0.509674 -0.078282  13.0  0.401394  \n",
       "2      -0.764461 -0.078282  10.0 -0.006357  \n",
       "3       0.981581 -0.078282   2.0 -0.332558  \n",
       "4      -0.944311 -0.078282   8.0 -0.903409  \n",
       "...          ...       ...   ...       ...  \n",
       "197326 -0.157468 -0.078282   1.0 -0.658758  \n",
       "197327  0.869175 -0.078282   8.0 -0.414108  \n",
       "197328 -0.015087 -0.078282  12.0  0.401394  \n",
       "197329  0.599400 -0.078282   9.0  0.646044  \n",
       "197330  0.374588 -0.078282  15.0 -0.169457  \n",
       "\n",
       "[197331 rows x 11 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_column = ['O3']\n",
    "\n",
    "X_train = pd.read_csv('ActiveSampling/Beijing_O3_activesampling_train.csv')\n",
    "X_test = pd.read_csv('ActiveSampling/Beijing_O3_activesampling_test.csv')\n",
    "X_source = pd.read_csv('ActiveSampling/Beijing_O3_activesampling_source.csv')\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(X_source.shape)\n",
    "\n",
    "y_source = X_source[target_column]\n",
    "X_source = X_source.drop(target_column, axis = 1)\n",
    "\n",
    "y_train = X_train[target_column]\n",
    "X_train = X_train.drop(target_column, axis = 1)\n",
    "\n",
    "y_test = X_test[target_column]\n",
    "X_test = X_test.drop(target_column, axis = 1)\n",
    "\n",
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STrAdaBoost.R2\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "fit() missing 2 required positional arguments: 'src_index' and 'tgt_index'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-71-bc30a3078f48>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     32\u001b[0m                           random_state = random_state)\n\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m     \u001b[0mregr_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp_TF_train_X\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp_TF_train_y_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m     \u001b[0my_pred1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mregr_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp_test_X\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0mpredictionlist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: fit() missing 2 required positional arguments: 'src_index' and 'tgt_index'"
     ]
    }
   ],
   "source": [
    "################################# Prediction for STrAdaBoost ##################################################################\n",
    "predictionlist = []\n",
    "r2scorelist = []\n",
    "rmselist = []\n",
    "\n",
    "print(\"STrAdaBoost.R2\")\n",
    "\n",
    "for x in range(0, 10):\n",
    "\n",
    "    TF_train_X = pd.concat([X_source, X_train], sort= False)\n",
    "    TF_train_y = pd.concat([y_source, y_train], sort= False)\n",
    "\n",
    "    np_TF_train_X = TF_train_X.to_numpy()\n",
    "    np_TF_train_y = TF_train_y.to_numpy()\n",
    "\n",
    "    np_test_X = X_test.to_numpy()\n",
    "    np_test_y = y_test.to_numpy()\n",
    "\n",
    "    np_TF_train_y_list = np_TF_train_y.ravel()\n",
    "    np_test_y_list = np_test_y.ravel()\n",
    "\n",
    "    sample_size = [len(X_source), len(X_train)]\n",
    "    n_estimators = 100\n",
    "    steps = 30\n",
    "    fold = 10\n",
    "    random_state = np.random.RandomState(1)\n",
    "\n",
    "    ################################################TrAdaBoost.R2############################################################################\n",
    "    regr_1 = TwoStageTrAdaBoostR2(DecisionTreeRegressor(max_depth=6), #xgb.XGBRegressor(objective ='reg:squarederror',learning_rate = 0.0001,max_depth = 6,n_estimators = 2000),\n",
    "                          n_estimators = n_estimators, sample_size = sample_size,\n",
    "                          steps = steps, fold = fold,\n",
    "                          random_state = random_state)\n",
    "\n",
    "    regr_1.fit(np_TF_train_X, np_TF_train_y_list)\n",
    "    y_pred1 = regr_1.predict(np_test_X)\n",
    "    predictionlist.append(y_pred1)\n",
    "\n",
    "    mse_twostageboost = np.sqrt(mean_squared_error(np_test_y_list, y_pred1))\n",
    "    print(\"RMSE of STrAdaboostR2:\", mse_twostageboost)\n",
    "    rmselist.append(mse_twostageboost)\n",
    "\n",
    "    r2_score_twostageboost_values = pearsonr(np_test_y_list, y_pred1)\n",
    "    r2_score_twostageboost = (r2_score_twostageboost_values[0])**2\n",
    "    print(\"R^2 of STrAdaboostR2:\", r2_score_twostageboost)\n",
    "    r2scorelist.append(r2_score_twostageboost)\n",
    "\n",
    "predict_stradaboost = np.mean(predictionlist, axis=0)\n",
    "list_orginal_stradaboost = np_test_y_list\n",
    "\n",
    "with open('AQI_datasets/Beijing_AQI_Results/O3/STrAdaBoostOut_R2_999.txt', 'w') as filehandle1:\n",
    "    for listitem in r2scorelist:\n",
    "        filehandle1.write('%s\\n' % listitem)\n",
    "\n",
    "with open('AQI_datasets/Beijing_AQI_Results/O3/STrAdaBoostOut_RMSE_999.txt', 'w') as filehandle2:\n",
    "    for listitem in rmselist:\n",
    "        filehandle2.write('%s\\n' % listitem)\n",
    "\n",
    "with open('AQI_datasets/Beijing_AQI_Results/O3/STrAdaBoostOut_prediction_999.txt', 'w') as filehandle3:\n",
    "    for listitem in predict_stradaboost:\n",
    "        filehandle3.write('%s\\n' % listitem)\n",
    "\n",
    "print(\"mean RMSE of STrAdaboostR2:\", mean(rmselist))\n",
    "print(\"mean R^2 of STrAdaboostR2:\", mean(r2scorelist))\n",
    "\n",
    "plt.scatter(predict_adaboost, list_orginal_adaboost,  c =\"red\", alpha=0.6)\n",
    "plt.scatter(predict_tradaboost, list_orginal_tradaboost,  c =\"blue\", alpha=0.4)\n",
    "plt.scatter(predict_stradaboost, list_orginal_stradaboost,  c =\"green\", alpha=0.2)\n",
    "\n",
    "\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Predicted vs Actual')\n",
    "# plt.savefig(\"AQI_datasets/UCI_AQI_Results/NOx/AdaBoostR2_Transfer.png\")\n",
    "plt.show()\n",
    "\n",
    "############## How to superimpose scatter plots ########################\n",
    "## https://stackoverflow.com/questions/11190735/python-matplotlib-superimpose-scatter-plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Regression Transfer Learning\n",
      "-------------------------------------------\n",
      "RMSE of GBR: 30.62704331502563\n",
      "R^2 of GBR: 0.7235424307101544\n",
      "\n",
      "\n",
      "RMSE of GBR: [30.810700956310754, 30.7873418500275, 30.616971280249956, 30.61476165851817, 30.585603389027693, 30.584611670323117, 30.491811949769637, 30.626618109076993, 30.566268584561577, 30.585743702390925]\n",
      "R^2 of GBR: [0.7200252114892826, 0.720848762462318, 0.7237281909816046, 0.7239303330176193, 0.7241140472249447, 0.7243652028548561, 0.7261891801001833, 0.7234765800989651, 0.7245794270989913, 0.7241673717727796]\n",
      "-------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "######################### Gradient Boosting Regression Transfer Learning Beijing #######################################\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "print(\"Gradient Boosting Regression Transfer Learning\")\n",
    "print(\"-------------------------------------------\")\n",
    "\n",
    "r2scorelist_GBRTL_beijing = []\n",
    "rmselist_GBRTL_beijing = []\n",
    "\n",
    "for x in range(0, 10):\n",
    "    \n",
    "    np_TF_train_X = X_train.to_numpy()\n",
    "    np_TF_train_y = y_train.to_numpy()\n",
    "\n",
    "    np_test_X = X_test.to_numpy()\n",
    "    np_test_y = y_test.to_numpy()\n",
    "\n",
    "    np_TF_train_y_list = np_TF_train_y.ravel()\n",
    "    np_test_y_list = np_test_y.ravel()\n",
    "\n",
    "    model_GBRTL_beijing = GradientBoostingRegressor(learning_rate=0.01, max_depth=4, n_estimators=1000, subsample=0.5)\n",
    "    model_GBRTL_beijing.fit(np_TF_train_X, np_TF_train_y_list)\n",
    "\n",
    "    y_pred_GBRTL_beijing = model_GBRTL_beijing.predict(np_test_X) ##Using dataframe instead of the numpy matrix\n",
    "\n",
    "    mse_GBRTL_beijing = sqrt(mean_squared_error(np_test_y_list, y_pred_GBRTL_beijing))\n",
    "    rmselist_GBRTL_beijing.append(mse_GBRTL_beijing)\n",
    "        \n",
    "    r2_score_GBRTL_beijing = pearsonr(np_test_y_list, y_pred_GBRTL_beijing)\n",
    "    r2_score_GBRTL_beijing = (r2_score_GBRTL_beijing[0])**2\n",
    "    r2scorelist_GBRTL_beijing.append(r2_score_GBRTL_beijing)\n",
    "\n",
    "print(\"RMSE of GBR:\", statistics.mean(rmselist_GBRTL_beijing))\n",
    "print(\"R^2 of GBR:\", statistics.mean(r2scorelist_GBRTL_beijing))\n",
    "print(\"\\n\")\n",
    "print(\"RMSE of GBR:\", rmselist_GBRTL_beijing)\n",
    "print(\"R^2 of GBR:\", r2scorelist_GBRTL_beijing)\n",
    "\n",
    "print(\"-------------------------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Series' object has no attribute 'counts'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-114-187585326d58>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mbeijing_aqi_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbeijing_aqi_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrop_date_cols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mbeijing_aqi_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mbeijing_aqi_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcounts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5272\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5273\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5274\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5276\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'counts'"
     ]
    }
   ],
   "source": [
    "##### creating a date column ####################\n",
    "\n",
    "beijing_aqi_df['date'] = pd.to_datetime(beijing_aqi_df[['year', 'month', 'day']])\n",
    "\n",
    "drop_date_cols = ['year', 'month', 'day', 'hour']\n",
    "beijing_aqi_df = beijing_aqi_df.drop(drop_date_cols, axis=1)\n",
    "beijing_aqi_df.head(10)\n",
    "beijing_aqi_df.station.counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1470264, 16)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "######################## Madrid dataset ####################################################################\n",
    "### Multi-year temporal dataset (multi-year and single terrain)\n",
    "\n",
    "madrid_2011 = pd.read_csv(\"AQI_datasets/Madrid_AQI/madrid_2011.csv\")\n",
    "madrid_2012 = pd.read_csv(\"AQI_datasets/Madrid_AQI/madrid_2012.csv\") \n",
    "madrid_2013 = pd.read_csv(\"AQI_datasets/Madrid_AQI/madrid_2013.csv\")\n",
    "madrid_2014 = pd.read_csv(\"AQI_datasets/Madrid_AQI/madrid_2014.csv\")\n",
    "madrid_2015 = pd.read_csv(\"AQI_datasets/Madrid_AQI/madrid_2015.csv\")\n",
    "madrid_2016 = pd.read_csv(\"AQI_datasets/Madrid_AQI/madrid_2016.csv\")\n",
    "madrid_2017 = pd.read_csv(\"AQI_datasets/Madrid_AQI/madrid_2017.csv\")\n",
    "madrid_2018 = pd.read_csv(\"AQI_datasets/Madrid_AQI/madrid_2018.csv\")\n",
    "\n",
    "station = pd.read_csv('AQI_datasets/Madrid_AQI/madrid_stations.csv')\n",
    "madrid_18_train = pd.concat([madrid_2011,madrid_2012,madrid_2013,madrid_2014,madrid_2015,madrid_2016,madrid_2017], ignore_index=True, sort= True)\n",
    "                                  \n",
    "madrid_18_train.head()\n",
    "madrid_18_train.shape\n",
    "# # madrid_18_train.columns\n",
    "\n",
    "# madrid_18_train.station.value_counts()\n",
    "# madrid_18_train['station'].nunique() ## No. of stations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BEN</th>\n",
       "      <th>CH4</th>\n",
       "      <th>CO</th>\n",
       "      <th>EBE</th>\n",
       "      <th>NMHC</th>\n",
       "      <th>NO</th>\n",
       "      <th>NO_2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>O_3</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM25</th>\n",
       "      <th>SO_2</th>\n",
       "      <th>TCH</th>\n",
       "      <th>TOL</th>\n",
       "      <th>date</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <td>69624</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.5</td>\n",
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       "      <td>47.0</td>\n",
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       "      <td>2011-01-01 01:00:00</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-01-01 10:00:00</td>\n",
       "      <td>28079004</td>\n",
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      ],
      "text/plain": [
       "       BEN  CH4   CO  EBE  NMHC    NO  NO_2  NOx  O_3  PM10  PM25  SO_2  TCH  \\\n",
       "69624  NaN  NaN  0.5  NaN   NaN  47.0  36.0  NaN  NaN   NaN   NaN   6.0  NaN   \n",
       "69648  NaN  NaN  0.6  NaN   NaN  77.0  55.0  NaN  NaN   NaN   NaN   6.0  NaN   \n",
       "69672  NaN  NaN  0.4  NaN   NaN  18.0  53.0  NaN  NaN   NaN   NaN   5.0  NaN   \n",
       "69696  NaN  NaN  0.3  NaN   NaN  14.0  41.0  NaN  NaN   NaN   NaN   5.0  NaN   \n",
       "69720  NaN  NaN  0.3  NaN   NaN  13.0  33.0  NaN  NaN   NaN   NaN   5.0  NaN   \n",
       "69744  NaN  NaN  0.3  NaN   NaN  13.0  31.0  NaN  NaN   NaN   NaN   5.0  NaN   \n",
       "69768  NaN  NaN  0.3  NaN   NaN   8.0  22.0  NaN  NaN   NaN   NaN   4.0  NaN   \n",
       "69792  NaN  NaN  0.3  NaN   NaN  13.0  32.0  NaN  NaN   NaN   NaN   5.0  NaN   \n",
       "69816  NaN  NaN  0.5  NaN   NaN  79.0  41.0  NaN  NaN   NaN   NaN   6.0  NaN   \n",
       "69840  NaN  NaN  0.7  NaN   NaN  99.0  35.0  NaN  NaN   NaN   NaN   6.0  NaN   \n",
       "\n",
       "       TOL                 date   station  \n",
       "69624  NaN  2011-01-01 01:00:00  28079004  \n",
       "69648  NaN  2011-01-01 02:00:00  28079004  \n",
       "69672  NaN  2011-01-01 03:00:00  28079004  \n",
       "69696  NaN  2011-01-01 04:00:00  28079004  \n",
       "69720  NaN  2011-01-01 05:00:00  28079004  \n",
       "69744  NaN  2011-01-01 06:00:00  28079004  \n",
       "69768  NaN  2011-01-01 07:00:00  28079004  \n",
       "69792  NaN  2011-01-01 08:00:00  28079004  \n",
       "69816  NaN  2011-01-01 09:00:00  28079004  \n",
       "69840  NaN  2011-01-01 10:00:00  28079004  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_madrid = ['date', 'station', 'BEN', 'CH4', 'CO', 'EBE', 'MXY', 'NMHC', 'NO', 'NO_2', 'NOx', 'OXY', 'O_3', 'PM10', 'PM25', 'PXY', 'SO_2', 'TCH', 'TOL']\n",
    "madrid_18_train = madrid_18_train.sort_values(['station', 'date'])\n",
    "madrid_18_train.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7fa5b86d6810>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locations  = station[['lat', 'lon']]\n",
    "locationlist = locations.values.tolist()\n",
    "\n",
    "popup = station[['name']]\n",
    "\n",
    "import folium\n",
    "map_osm = folium.Map(location = [40.44, -3.69],\n",
    "                    # tiles='Stamen Toner',\n",
    "                     zoom_start = 11) \n",
    "\n",
    "for point in range(0, len(locationlist)):\n",
    "    folium.Marker(locationlist[point], popup = popup.iloc[point,0]).add_to(map_osm)\n",
    "    \n",
    "map_osm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bengaluru             2009\n",
       "Chennai               2009\n",
       "Lucknow               2009\n",
       "Mumbai                2009\n",
       "Ahmedabad             2009\n",
       "Delhi                 2009\n",
       "Hyderabad             2006\n",
       "Patna                 1858\n",
       "Gurugram              1679\n",
       "Visakhapatnam         1462\n",
       "Amritsar              1221\n",
       "Jorapokhar            1169\n",
       "Jaipur                1114\n",
       "Thiruvananthapuram    1112\n",
       "Amaravati              951\n",
       "Brajrajnagar           938\n",
       "Talcher                925\n",
       "Kolkata                814\n",
       "Guwahati               502\n",
       "Coimbatore             386\n",
       "Shillong               310\n",
       "Chandigarh             304\n",
       "Bhopal                 289\n",
       "Kochi                  162\n",
       "Ernakulam              162\n",
       "Aizawl                 113\n",
       "Name: City, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "########################## AQI India dataset (2015 - 2020) #############################################\n",
    "india_df = pd.read_csv('AQI_datasets/India_AQI/city_day.csv')\n",
    "india_df.head(10)\n",
    "india_df.shape\n",
    "# cities_count = india_df['City'].value_counts()\n",
    "# cities_count\n",
    "\n",
    "india_df['City'].value_counts()\n",
    "# india_df['City'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 16 columns.\n",
      "There are 14 columns that have missing values.\n"
     ]
    },
    {
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       "                        <th id=\"T_34343306_de0c_11ea_99d7_13051026cc46level0_row0\" class=\"row_heading level0 row0\" >Xylene</th>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row0_col0\" class=\"data row0 col0\" >18109</td>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row0_col1\" class=\"data row0 col1\" >61.3</td>\n",
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       "                        <th id=\"T_34343306_de0c_11ea_99d7_13051026cc46level0_row1\" class=\"row_heading level0 row1\" >PM10</th>\n",
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       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row1_col1\" class=\"data row1 col1\" >37.7</td>\n",
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       "                        <th id=\"T_34343306_de0c_11ea_99d7_13051026cc46level0_row2\" class=\"row_heading level0 row2\" >NH3</th>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row2_col0\" class=\"data row2 col0\" >10328</td>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row2_col1\" class=\"data row2 col1\" >35</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34343306_de0c_11ea_99d7_13051026cc46level0_row3\" class=\"row_heading level0 row3\" >Toluene</th>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row3_col0\" class=\"data row3 col0\" >8041</td>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row3_col1\" class=\"data row3 col1\" >27.2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34343306_de0c_11ea_99d7_13051026cc46level0_row4\" class=\"row_heading level0 row4\" >Benzene</th>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row4_col0\" class=\"data row4 col0\" >5623</td>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row4_col1\" class=\"data row4 col1\" >19</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34343306_de0c_11ea_99d7_13051026cc46level0_row5\" class=\"row_heading level0 row5\" >AQI</th>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row5_col0\" class=\"data row5 col0\" >4681</td>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row5_col1\" class=\"data row5 col1\" >15.9</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34343306_de0c_11ea_99d7_13051026cc46level0_row6\" class=\"row_heading level0 row6\" >AQI_Bucket</th>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row6_col0\" class=\"data row6 col0\" >4681</td>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row6_col1\" class=\"data row6 col1\" >15.9</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34343306_de0c_11ea_99d7_13051026cc46level0_row7\" class=\"row_heading level0 row7\" >PM2.5</th>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row7_col0\" class=\"data row7 col0\" >4598</td>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row7_col1\" class=\"data row7 col1\" >15.6</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34343306_de0c_11ea_99d7_13051026cc46level0_row8\" class=\"row_heading level0 row8\" >NOx</th>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row8_col0\" class=\"data row8 col0\" >4185</td>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row8_col1\" class=\"data row8 col1\" >14.2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34343306_de0c_11ea_99d7_13051026cc46level0_row9\" class=\"row_heading level0 row9\" >O3</th>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row9_col0\" class=\"data row9 col0\" >4022</td>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row9_col1\" class=\"data row9 col1\" >13.6</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34343306_de0c_11ea_99d7_13051026cc46level0_row10\" class=\"row_heading level0 row10\" >SO2</th>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row10_col0\" class=\"data row10 col0\" >3854</td>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row10_col1\" class=\"data row10 col1\" >13.1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34343306_de0c_11ea_99d7_13051026cc46level0_row11\" class=\"row_heading level0 row11\" >NO2</th>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row11_col0\" class=\"data row11 col0\" >3585</td>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row11_col1\" class=\"data row11 col1\" >12.1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34343306_de0c_11ea_99d7_13051026cc46level0_row12\" class=\"row_heading level0 row12\" >NO</th>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row12_col0\" class=\"data row12 col0\" >3582</td>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row12_col1\" class=\"data row12 col1\" >12.1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34343306_de0c_11ea_99d7_13051026cc46level0_row13\" class=\"row_heading level0 row13\" >CO</th>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row13_col0\" class=\"data row13 col0\" >2059</td>\n",
       "                        <td id=\"T_34343306_de0c_11ea_99d7_13051026cc46row13_col1\" class=\"data row13 col1\" >7</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fb2d27239d0>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "################### Counting the total no. of missing values ###########################\n",
    "\n",
    "# Missing values\n",
    "def missing_values_table(df):\n",
    "        # Total missing values\n",
    "        mis_val = df.isnull().sum()\n",
    "        \n",
    "        # Percentage of missing values\n",
    "        mis_val_percent = 100 * df.isnull().sum() / len(df)\n",
    "        \n",
    "        # Make a table with the results\n",
    "        mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)\n",
    "        \n",
    "        # Rename the columns\n",
    "        mis_val_table_ren_columns = mis_val_table.rename(\n",
    "        columns = {0 : 'Missing Values', 1 : '% of Total Values'})\n",
    "        \n",
    "        # Sort the table by percentage of missing descending\n",
    "        mis_val_table_ren_columns = mis_val_table_ren_columns[\n",
    "            mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(\n",
    "        '% of Total Values', ascending=False).round(1)\n",
    "        \n",
    "        # Print some summary information\n",
    "        print (\"Your selected dataframe has \" + str(df.shape[1]) + \" columns.\\n\"      \n",
    "            \"There are \" + str(mis_val_table_ren_columns.shape[0]) +\n",
    "              \" columns that have missing values.\")\n",
    "        \n",
    "        # Return the dataframe with missing information\n",
    "        return mis_val_table_ren_columns\n",
    "\n",
    "missing_values = missing_values_table(india_df)\n",
    "missing_values.style.background_gradient(cmap ='Reds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>City</th>\n",
       "      <th>Date</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>NH3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>Xylene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQI_Bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.92</td>\n",
       "      <td>18.22</td>\n",
       "      <td>17.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.92</td>\n",
       "      <td>27.64</td>\n",
       "      <td>133.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.97</td>\n",
       "      <td>15.69</td>\n",
       "      <td>16.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.97</td>\n",
       "      <td>24.55</td>\n",
       "      <td>34.06</td>\n",
       "      <td>3.68</td>\n",
       "      <td>5.50</td>\n",
       "      <td>3.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.40</td>\n",
       "      <td>19.30</td>\n",
       "      <td>29.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.40</td>\n",
       "      <td>29.07</td>\n",
       "      <td>30.70</td>\n",
       "      <td>6.80</td>\n",
       "      <td>16.40</td>\n",
       "      <td>2.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.48</td>\n",
       "      <td>17.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.59</td>\n",
       "      <td>36.08</td>\n",
       "      <td>4.43</td>\n",
       "      <td>10.14</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.10</td>\n",
       "      <td>21.42</td>\n",
       "      <td>37.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.10</td>\n",
       "      <td>39.33</td>\n",
       "      <td>39.31</td>\n",
       "      <td>7.01</td>\n",
       "      <td>18.89</td>\n",
       "      <td>2.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.41</td>\n",
       "      <td>38.48</td>\n",
       "      <td>81.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.41</td>\n",
       "      <td>45.76</td>\n",
       "      <td>46.51</td>\n",
       "      <td>5.42</td>\n",
       "      <td>10.83</td>\n",
       "      <td>1.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112.16</td>\n",
       "      <td>40.62</td>\n",
       "      <td>130.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112.16</td>\n",
       "      <td>32.28</td>\n",
       "      <td>33.47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.87</td>\n",
       "      <td>36.74</td>\n",
       "      <td>96.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.87</td>\n",
       "      <td>38.54</td>\n",
       "      <td>31.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.16</td>\n",
       "      <td>31.00</td>\n",
       "      <td>48.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.16</td>\n",
       "      <td>58.68</td>\n",
       "      <td>25.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.29</td>\n",
       "      <td>4.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        City       Date  PM2.5  PM10      NO    NO2     NOx  NH3      CO  \\\n",
       "0  Ahmedabad 2015-01-01    NaN   NaN    0.92  18.22   17.15  NaN    0.92   \n",
       "1  Ahmedabad 2015-01-02    NaN   NaN    0.97  15.69   16.46  NaN    0.97   \n",
       "2  Ahmedabad 2015-01-03    NaN   NaN   17.40  19.30   29.70  NaN   17.40   \n",
       "3  Ahmedabad 2015-01-04    NaN   NaN    1.70  18.48   17.97  NaN    1.70   \n",
       "4  Ahmedabad 2015-01-05    NaN   NaN   22.10  21.42   37.76  NaN   22.10   \n",
       "5  Ahmedabad 2015-01-06    NaN   NaN   45.41  38.48   81.50  NaN   45.41   \n",
       "6  Ahmedabad 2015-01-07    NaN   NaN  112.16  40.62  130.77  NaN  112.16   \n",
       "7  Ahmedabad 2015-01-08    NaN   NaN   80.87  36.74   96.75  NaN   80.87   \n",
       "8  Ahmedabad 2015-01-09    NaN   NaN   29.16  31.00   48.00  NaN   29.16   \n",
       "9  Ahmedabad 2015-01-10    NaN   NaN     NaN   7.04    0.00  NaN     NaN   \n",
       "\n",
       "     SO2      O3  Benzene  Toluene  Xylene  AQI AQI_Bucket  \n",
       "0  27.64  133.36     0.00     0.02    0.00  NaN        NaN  \n",
       "1  24.55   34.06     3.68     5.50    3.77  NaN        NaN  \n",
       "2  29.07   30.70     6.80    16.40    2.25  NaN        NaN  \n",
       "3  18.59   36.08     4.43    10.14    1.00  NaN        NaN  \n",
       "4  39.33   39.31     7.01    18.89    2.78  NaN        NaN  \n",
       "5  45.76   46.51     5.42    10.83    1.93  NaN        NaN  \n",
       "6  32.28   33.47     0.00     0.00    0.00  NaN        NaN  \n",
       "7  38.54   31.89     0.00     0.00    0.00  NaN        NaN  \n",
       "8  58.68   25.75     0.00     0.00    0.00  NaN        NaN  \n",
       "9   8.29    4.55     0.00     0.00    0.00  NaN        NaN  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "india_df['Date'] = pd.to_datetime(india_df['Date'])\n",
    "india_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1440 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "####################### Visualizing growth of pollutants over the years #############################\n",
    "india_df['BTX'] = india_df['Benzene'] + india_df['Toluene'] + india_df['Xylene']\n",
    "india_df.drop(['Benzene','Toluene','Xylene'],axis=1)\n",
    "\n",
    "pollutants = ['PM2.5','PM10','NO2', 'CO', 'SO2','O3', 'BTX']\n",
    "\n",
    "india_df.set_index('Date', inplace = True)\n",
    "axes = india_df[pollutants].plot(marker ='.', alpha = 0.5, linestyle ='None', figsize =(16, 20), subplots =True)\n",
    "for ax in axes:\n",
    "    \n",
    "    ax.set_xlabel('Years')\n",
    "    ax.set_ylabel('ug / m3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[[2, 3, 9], [1,5,0]] > [[2, 3, 9], [1,2,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the lazy brown dog\n",
      "-1\n",
      "5\n",
      "False\n",
      "THE LAZY BROWN DOG\n",
      "the lazy gray dog\n",
      "the lazy brown dog\n"
     ]
    }
   ],
   "source": [
    "text = 'the lazy brown dog'\n",
    "\n",
    "print(text)\n",
    "print(text.find('f'))\n",
    "print(text.index('a'))\n",
    "print(text.isdigit())\n",
    "print(text.upper())\n",
    "print(text.replace('brown', 'gray'))\n",
    "print(text.strip())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "def Find(text, character):\n",
    "    index = 0\n",
    "    for c in text:\n",
    "        if c == character:\n",
    "            return index\n",
    "        index += 1\n",
    "\n",
    "print(Find('banana', 'n')) #prints 2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "200\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Test functions for tradaboost module.\n",
    "\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "\n",
    "from adapt.instance_based import (TrAdaBoost,\n",
    "                                  TrAdaBoostR2,\n",
    "                                  TwoStageTrAdaBoostR2)\n",
    "\n",
    "np.random.seed(0)\n",
    "Xs = np.concatenate((np.random.randn(50)*0.1, np.random.randn(50)*0.1 + 1.,)).reshape(-1, 1)\n",
    "Xt = (np.random.randn(100) * 0.1).reshape(-1, 1)\n",
    "X = np.concatenate((Xs, Xt))\n",
    "y_reg = np.array([0.2 * x if x<0.5 else 10 for x in X.ravel()])\n",
    "y_classif = np.array([x<0 if x<0.5 else x<1 for x in X.ravel()]).astype(float)\n",
    "\n",
    "print(len(Xs))\n",
    "print(len(y_reg))\n",
    "\n",
    "def test_setup():\n",
    "    lr = LinearRegression(fit_intercept=False)\n",
    "    lr.fit(Xs, y_reg[:100])\n",
    "    assert np.abs(lr.coef_[0] - 10) < 1\n",
    "    \n",
    "    lr = LogisticRegression(penalty='none', solver='lbfgs')\n",
    "    lr.fit(Xs, y_classif[:100])\n",
    "    assert (lr.predict(Xt) == y_classif[100:]).sum() < 70\n",
    "\n",
    "\n",
    "def test_tradaboost_fit():\n",
    "    np.random.seed(0)\n",
    "    model = TrAdaBoost(LogisticRegression,\n",
    "                       n_estimators=20,\n",
    "                       penalty='none',\n",
    "                       solver='lbfgs')\n",
    "    model.fit(X, y_classif, range(100), range(100, 110))\n",
    "    assert len(model.sample_weights_src_[0]) == 100\n",
    "    assert (model.sample_weights_src_[0][:50].sum() ==\n",
    "            model.sample_weights_src_[0][50:].sum())\n",
    "    assert len(model.sample_weights_tgt_[-1]) == 10\n",
    "    assert model.sample_weights_tgt_[-1].sum() > 0.7\n",
    "    assert (model.predict(Xt) == y_classif[100:]).sum() > 90\n",
    "    \n",
    "    \n",
    "def test_tradaboostr2_fit():\n",
    "    np.random.seed(0)\n",
    "    model = TrAdaBoostR2(LinearRegression,\n",
    "                         n_estimators=20,\n",
    "                         fit_intercept=False)\n",
    "    model.fit(X, y_reg, range(100), range(100, 110))\n",
    "    assert np.abs(model.estimators_[-1].coef_[0] - 0.2) < 1\n",
    "    assert np.abs(model.sample_weights_src_[-1][:50].sum() / \n",
    "            model.sample_weights_src_[-1][50:].sum()) > 10\n",
    "    assert model.sample_weights_tgt_[-1].sum() > 0.7\n",
    "    assert np.abs(model.predict(Xt).ravel()\n",
    "                  - y_reg[100:]).sum() < 1\n",
    "\n",
    "\n",
    "def test_twostagetradaboostr2_fit():\n",
    "    np.random.seed(0)\n",
    "    model = TwoStageTrAdaBoostR2(LinearRegression,\n",
    "                         n_estimators=10,\n",
    "                         fit_intercept=False)\n",
    "    model.fit(X, y_reg, range(100), range(100, 110))\n",
    "    assert np.abs(model.estimators_[-1].estimators_[-1].coef_[0]\n",
    "           - 0.2) < 1\n",
    "    assert np.abs(model.sample_weights_src_[-1][:50].sum() / \n",
    "            model.sample_weights_src_[-1][50:].sum()) > 10\n",
    "    assert model.sample_weights_tgt_[-1].sum() > 0.7\n",
    "    assert np.abs(model.predict(Xt).ravel()\n",
    "                  - y_reg[100:]).sum() < 1"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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