ActivelearningBeijing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done uploading repositories\n"
     ]
    }
   ],
   "source": [
    "################################# Header files ########################################################\n",
    "from two_TrAdaBoostR2 import TwoStageTrAdaBoostR2\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",
    "from statistics import mean\n",
    "\n",
    "pd.options.display.max_columns = None\n",
    "\n",
    "print(\"Done uploading repositories\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>1023.2</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>1023.5</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>5.6</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>1024.5</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1025.2</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>1025.6</td>\n",
       "      <td>-19.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3.7</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>1026.5</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>1027.4</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1028.3</td>\n",
       "      <td>-19.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>4.1</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1028.5</td>\n",
       "      <td>-19.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2.6</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1028.2</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1028.2</td>\n",
       "      <td>-20.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3.7</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1027.3</td>\n",
       "      <td>-19.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>5.1</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1026.2</td>\n",
       "      <td>-19.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1025.9</td>\n",
       "      <td>-19.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1025.7</td>\n",
       "      <td>-18.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>9.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1025.6</td>\n",
       "      <td>-18.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1026.3</td>\n",
       "      <td>-18.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>11.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1027.4</td>\n",
       "      <td>-18.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1028.3</td>\n",
       "      <td>-18.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  month  day  hour  PM2.5  PM10   SO2   NO2     CO    O3  TEMP  \\\n",
       "0   2013      3    1     0    4.0   4.0   4.0   7.0  300.0  77.0  -0.7   \n",
       "1   2013      3    1     1    8.0   8.0   4.0   7.0  300.0  77.0  -1.1   \n",
       "2   2013      3    1     2    7.0   7.0   5.0  10.0  300.0  73.0  -1.1   \n",
       "3   2013      3    1     3    6.0   6.0  11.0  11.0  300.0  72.0  -1.4   \n",
       "4   2013      3    1     4    3.0   3.0  12.0  12.0  300.0  72.0  -2.0   \n",
       "5   2013      3    1     5    5.0   5.0  18.0  18.0  400.0  66.0  -2.2   \n",
       "6   2013      3    1     6    3.0   3.0  18.0  32.0  500.0  50.0  -2.6   \n",
       "7   2013      3    1     7    3.0   6.0  19.0  41.0  500.0  43.0  -1.6   \n",
       "8   2013      3    1     8    3.0   6.0  16.0  43.0  500.0  45.0   0.1   \n",
       "9   2013      3    1     9    3.0   8.0  12.0  28.0  400.0  59.0   1.2   \n",
       "10  2013      3    1    10    3.0   6.0   9.0  12.0  400.0  72.0   1.9   \n",
       "11  2013      3    1    11    3.0   6.0   9.0  14.0  400.0  71.0   2.9   \n",
       "12  2013      3    1    12    3.0   6.0   7.0  13.0  300.0  74.0   3.9   \n",
       "13  2013      3    1    13    3.0   6.0   7.0  12.0  400.0  76.0   5.3   \n",
       "14  2013      3    1    14    6.0   9.0   7.0  11.0  400.0  77.0   6.0   \n",
       "15  2013      3    1    15    8.0  15.0   7.0  14.0  400.0  76.0   6.2   \n",
       "16  2013      3    1    16    9.0  19.0   9.0  13.0  400.0  76.0   5.9   \n",
       "17  2013      3    1    17   10.0  23.0  11.0  15.0  400.0  74.0   4.3   \n",
       "18  2013      3    1    18   11.0  20.0   8.0  20.0  500.0  70.0   3.1   \n",
       "19  2013      3    1    19    8.0  14.0  12.0  30.0  500.0  60.0   2.3   \n",
       "\n",
       "      PRES  DEWP  RAIN   wd  WSPM       station  \n",
       "0   1023.0 -18.8   0.0  NNW   4.4  Aotizhongxin  \n",
       "1   1023.2 -18.2   0.0    N   4.7  Aotizhongxin  \n",
       "2   1023.5 -18.2   0.0  NNW   5.6  Aotizhongxin  \n",
       "3   1024.5 -19.4   0.0   NW   3.1  Aotizhongxin  \n",
       "4   1025.2 -19.5   0.0    N   2.0  Aotizhongxin  \n",
       "5   1025.6 -19.6   0.0    N   3.7  Aotizhongxin  \n",
       "6   1026.5 -19.1   0.0  NNE   2.5  Aotizhongxin  \n",
       "7   1027.4 -19.1   0.0  NNW   3.8  Aotizhongxin  \n",
       "8   1028.3 -19.2   0.0  NNW   4.1  Aotizhongxin  \n",
       "9   1028.5 -19.3   0.0    N   2.6  Aotizhongxin  \n",
       "10  1028.2 -19.4   0.0  NNW   3.6  Aotizhongxin  \n",
       "11  1028.2 -20.5   0.0    N   3.7  Aotizhongxin  \n",
       "12  1027.3 -19.7   0.0  NNW   5.1  Aotizhongxin  \n",
       "13  1026.2 -19.3   0.0   NW   4.3  Aotizhongxin  \n",
       "14  1025.9 -19.6   0.0   NW   4.4  Aotizhongxin  \n",
       "15  1025.7 -18.6   0.0  NNE   2.8  Aotizhongxin  \n",
       "16  1025.6 -18.1   0.0  NNW   3.9  Aotizhongxin  \n",
       "17  1026.3 -18.7   0.0  NNE   2.8  Aotizhongxin  \n",
       "18  1027.4 -18.4   0.0  NNE   2.1  Aotizhongxin  \n",
       "19  1028.3 -18.4   0.0    N   2.8  Aotizhongxin  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "################# UCI Beijing dataset (2013 - 2017) #####################################################################\n",
    "############### Spacio-temporal dataset. (multi-year and multi-terrain)\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",
    "\n",
    "## O3: Dependent, Predictors: SO2, NO2, year, month, CO\n",
    "## PM25: Dependent, Predictors:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": {
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       "</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",
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       "      <td>10.0</td>\n",
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       "      <td>Aotizhongxin</td>\n",
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       "    <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",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
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       "      <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",
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       "      <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",
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       "    <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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Seperate the dataset into predictors and target variable.\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",
    "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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "############# Replacing missing values ##############################################################\n",
    "beijing_predictors_df = beijing_predictors_df.dropna(axis=0) # , subset=['O3'])\n",
    "\n",
    "###### percentage of missing values in each column #####################################3\n",
    "round(beijing_predictors_df.isna().sum()/len(beijing_predictors_df.index), 2)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd364747370>"
      ]
     },
     "execution_count": 54,
     "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",
    "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": 55,
   "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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Get correlation between O3 and other predictors\n",
    "# corrMatrix_beijing = beijing_aqi_df.corr()\n",
    "corrMatrix_beijing = beijing_predictors_df.corr()\n",
    "corrMatrix_beijing[\"O3\"].sort_values(ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
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       "      year  month   SO2    NO2      CO  TEMP    PRES  DEWP  RAIN    wd  WSPM  \\\n",
       "7345  2014      1  25.0   91.0  1100.0  -2.6  1006.9 -12.1   0.0  14.0   0.2   \n",
       "7346  2014      1  41.0  103.0  1800.0  -3.0  1006.9 -11.3   0.0  14.0   0.6   \n",
       "7347  2014      1  43.0  101.0  2100.0  -3.3  1006.4 -11.1   0.0   8.0   0.6   \n",
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       "7350  2014      1  45.0   91.0  1900.0  -2.6  1007.2 -10.9   0.0   4.0   1.0   \n",
       "7351  2014      1  24.0   69.0   900.0  -2.9  1007.9 -10.2   0.0   7.0   1.1   \n",
       "7352  2014      1  18.0   52.0   700.0   0.0  1008.8  -8.5   0.0   3.0   1.5   \n",
       "7353  2014      1  25.0   87.0  1200.0   9.0  1009.6 -11.6   0.0  10.0   1.9   \n",
       "7354  2014      1  27.0   63.0  1200.0  10.3  1009.4 -11.7   0.0  10.0   1.7   \n",
       "\n",
       "        O3  \n",
       "7345   1.0  \n",
       "7346   1.0  \n",
       "7347   1.0  \n",
       "7348   1.0  \n",
       "7349   1.0  \n",
       "7350   1.0  \n",
       "7351   4.0  \n",
       "7352  17.0  \n",
       "7353   1.0  \n",
       "7354  16.0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "\n",
    "## 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",
    "beijing_predictors_df_source.shape\n",
    "beijing_predictors_df_target.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-57-a047f75d3bc1>: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-57-a047f75d3bc1>: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"
     ]
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-58-778fb603e673>:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train['O3'] = y_train\n",
      "<ipython-input-58-778fb603e673>:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_test['O3'] = y_test\n"
     ]
    },
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       "      <td>-0.078282</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.646044</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12748</th>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.633920</td>\n",
       "      <td>-0.066627</td>\n",
       "      <td>-0.638267</td>\n",
       "      <td>0.299359</td>\n",
       "      <td>-0.771120</td>\n",
       "      <td>1.078999</td>\n",
       "      <td>-0.078282</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-0.495658</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19809</th>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.374656</td>\n",
       "      <td>0.639901</td>\n",
       "      <td>-0.468915</td>\n",
       "      <td>0.785241</td>\n",
       "      <td>-0.869022</td>\n",
       "      <td>0.689325</td>\n",
       "      <td>-0.078282</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-0.087907</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>197 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  month       SO2       NO2        CO      TEMP      PRES  \\\n",
       "12120  2014      7 -0.633920 -0.123149  0.293173  1.235786 -1.182308   \n",
       "24343  2015     12  0.403135  1.148601  3.002817 -1.237794  0.942163   \n",
       "12664  2014      8 -0.633920 -0.942721 -0.638267  1.889518 -1.936152   \n",
       "19949  2015      6 -0.590710 -0.631849 -0.214885  0.396536 -1.495594   \n",
       "10881  2014      5  0.100661  2.250784 -0.384238  1.518481 -1.192098   \n",
       "...     ...    ...       ...       ...       ...       ...       ...   \n",
       "14943  2014     11 -0.504288 -0.631849 -0.722944 -0.080512  0.511395   \n",
       "17596  2015      3 -0.633920 -1.394899 -0.807620 -1.820852  0.775730   \n",
       "7515   2014      1 -0.288235 -0.999243 -0.722944 -1.405644  1.793909   \n",
       "12748  2014      8 -0.633920 -0.066627 -0.638267  0.299359 -0.771120   \n",
       "19809  2015      6 -0.374656  0.639901 -0.468915  0.785241 -0.869022   \n",
       "\n",
       "           DEWP      RAIN    wd      WSPM     O3  \n",
       "12120  1.573586 -0.078282  13.0 -1.392710  129.0  \n",
       "24343 -0.224911 -0.078282   5.0 -0.251008    9.0  \n",
       "12664  0.606894 -0.078282  14.0  1.787746  209.0  \n",
       "19949  1.064012 -0.078282  15.0 -0.821859   60.0  \n",
       "10881  0.082332 -0.078282   8.0  0.646044   17.0  \n",
       "...         ...       ...   ...       ...    ...  \n",
       "14943 -1.079198 -0.078282  14.0 -0.169457   60.0  \n",
       "17596 -1.881028 -0.078282  14.0 -0.577208   64.0  \n",
       "7515  -1.828572 -0.078282   5.0  0.646044   53.0  \n",
       "12748  1.078999 -0.078282  15.0 -0.495658    5.0  \n",
       "19809  0.689325 -0.078282  15.0 -0.087907   49.0  \n",
       "\n",
       "[197 rows x 12 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###Splitting the dataset into train and test set ########\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_source['O3'] = y_source\n",
    "X_train['O3'] = y_train\n",
    "X_test['O3'] = y_test\n",
    "\n",
    "X_train.shape, X_test.shape, X_source.shape, y_train.shape\n",
    "X_test\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Row selected:  165\n",
      "Min. distance:  197.83732724268395\n",
      "Matrix shape:  (197, 12)\n",
      "Row selected:  175\n",
      "Min. distance:  210.66800360031093\n",
      "Matrix shape:  (196, 12)\n",
      "Row selected:  110\n",
      "Min. distance:  191.86603309082236\n",
      "Matrix shape:  (195, 12)\n",
      "Row selected:  112\n",
      "Min. distance:  146.28902827357595\n",
      "Matrix shape:  (194, 12)\n",
      "Row selected:  69\n",
      "Min. distance:  171.26843142953635\n",
      "Matrix shape:  (193, 12)\n",
      "Row selected:  22\n",
      "Min. distance:  310.57851110985115\n",
      "Matrix shape:  (192, 12)\n",
      "Row selected:  52\n",
      "Min. distance:  216.56505138531477\n",
      "Matrix shape:  (191, 12)\n",
      "Row selected:  35\n",
      "Min. distance:  111.1667161894014\n",
      "Matrix shape:  (190, 12)\n",
      "Row selected:  149\n",
      "Min. distance:  219.14780726634962\n",
      "Matrix shape:  (189, 12)\n",
      "Row selected:  10\n",
      "Min. distance:  175.89249390388852\n",
      "Matrix shape:  (188, 12)\n",
      "Row selected:  2\n",
      "Min. distance:  124.06255739258464\n",
      "Matrix shape:  (187, 12)\n",
      "Row selected:  102\n",
      "Min. distance:  198.36002381993347\n",
      "Matrix shape:  (186, 12)\n",
      "Row selected:  37\n",
      "Min. distance:  137.24265665403266\n",
      "Matrix shape:  (185, 12)\n",
      "Row selected:  106\n",
      "Min. distance:  96.55596414283619\n",
      "Matrix shape:  (184, 12)\n",
      "Row selected:  53\n",
      "Min. distance:  123.49825405605047\n",
      "Matrix shape:  (183, 12)\n",
      "Row selected:  35\n",
      "Min. distance:  127.03349983548236\n",
      "Matrix shape:  (182, 12)\n",
      "Row selected:  60\n",
      "Min. distance:  167.01547229950455\n",
      "Matrix shape:  (181, 12)\n",
      "Row selected:  137\n",
      "Min. distance:  168.52096624687115\n",
      "Matrix shape:  (180, 12)\n",
      "Row selected:  10\n",
      "Min. distance:  138.99346381537057\n",
      "Matrix shape:  (179, 12)\n",
      "Row selected:  33\n",
      "Min. distance:  252.91767564541317\n",
      "Matrix shape:  (178, 12)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(20, 12)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "######################################## Seeding Technique ###################################################\n",
    "kmeans = KMeans(n_clusters = 20, random_state=0).fit(X_source)\n",
    "\n",
    "Alternate_df = X_train.copy()\n",
    "idxlist = []\n",
    "Alternate_df_np = Alternate_df.to_numpy()\n",
    "\n",
    "new_df_list = []\n",
    "\n",
    "\n",
    "for rowkm in kmeans.cluster_centers_:\n",
    "    mindist = -99\n",
    "    rowidx = 0\n",
    "    idx = 0\n",
    "    for row in Alternate_df_np:\n",
    "        dst = distance.euclidean(row, rowkm)\n",
    "\n",
    "        if(dst >= mindist):\n",
    "            mindist = dst\n",
    "            rowidx = idx\n",
    "            rowval = row\n",
    "\n",
    "        idx = idx + 1\n",
    "\n",
    "    print(\"Row selected: \", rowidx) #Alternate_df.loc[rowidx,:]\\\n",
    "    print(\"Min. distance: \", mindist)\n",
    "    print(\"Matrix shape: \", Alternate_df_np.shape)\n",
    "    new_df_list.append(rowval)\n",
    "    Alternate_df_np = np.delete(Alternate_df_np, rowidx, 0)\n",
    "    idxlist.append(rowidx)\n",
    "\n",
    "\n",
    "new_df = pd.DataFrame(np.vstack(new_df_list))\n",
    "#new_df\n",
    "new_df.shape\n",
    "#new_df.dtypes\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Row selected:  99614\n",
      "Min. distance:  264.14640633546503\n",
      "Matrix shape:  (99615, 12)\n",
      "Row selected:  24683\n",
      "Min. distance:  274.20512395255156\n",
      "Matrix shape:  (99614, 12)\n",
      "Row selected:  96540\n",
      "Min. distance:  282.2112704198866\n",
      "Matrix shape:  (99613, 12)\n",
      "Row selected:  96539\n",
      "Min. distance:  297.1417052177414\n",
      "Matrix shape:  (99612, 12)\n",
      "Row selected:  20957\n",
      "Min. distance:  280.0347257417264\n",
      "Matrix shape:  (99611, 12)\n",
      "Row selected:  96126\n",
      "Min. distance:  269.5605416696099\n",
      "Matrix shape:  (99610, 12)\n",
      "Row selected:  20953\n",
      "Min. distance:  280.0087948575396\n",
      "Matrix shape:  (99609, 12)\n",
      "Row selected:  96124\n",
      "Min. distance:  291.08463227969264\n",
      "Matrix shape:  (99608, 12)\n",
      "Row selected:  96123\n",
      "Min. distance:  271.26945189262426\n",
      "Matrix shape:  (99607, 12)\n",
      "Row selected:  20952\n",
      "Min. distance:  287.04412822940407\n",
      "Matrix shape:  (99606, 12)\n",
      "Row selected:  20129\n",
      "Min. distance:  291.2752360570831\n",
      "Matrix shape:  (99605, 12)\n",
      "Row selected:  0\n",
      "Min. distance:  -99\n",
      "Matrix shape:  (99604, 12)\n",
      "Row selected:  0\n",
      "Min. distance:  -99\n",
      "Matrix shape:  (99603, 12)\n",
      "Row selected:  95960\n",
      "Min. distance:  277.1281547904255\n",
      "Matrix shape:  (99602, 12)\n",
      "Row selected:  0\n",
      "Min. distance:  -99\n",
      "Matrix shape:  (99601, 12)\n",
      "Row selected:  95958\n",
      "Min. distance:  255.07705306458425\n",
      "Matrix shape:  (99600, 12)\n",
      "Row selected:  95889\n",
      "Min. distance:  270.06949332442093\n",
      "Matrix shape:  (99599, 12)\n",
      "Row selected:  0\n",
      "Min. distance:  -99\n",
      "Matrix shape:  (99598, 12)\n",
      "Row selected:  0\n",
      "Min. distance:  -99\n",
      "Matrix shape:  (99597, 12)\n",
      "Row selected:  95886\n",
      "Min. distance:  271.37104151396994\n",
      "Matrix shape:  (99596, 12)\n",
      "Shape before : (99615, 12)\n",
      "Shape after : (99466, 12)\n"
     ]
    }
   ],
   "source": [
    "##################################################### Phase 2: Seeding ################################################\n",
    "\n",
    "alt_source_df = X_source.copy()\n",
    "idxlist2 = []\n",
    "alt_source_df_np = alt_source_df.to_numpy()\n",
    "\n",
    "final_df_list = []\n",
    "\n",
    "for row_nm in new_df_list:\n",
    "    min_dist = -99\n",
    "    row_idx = 0\n",
    "    idx_val = 0\n",
    "    for row_alt in alt_source_df_np:\n",
    "        dst = distance.euclidean(row_alt, row_nm)\n",
    "        if(dst >= mindist):\n",
    "            min_dist = dst\n",
    "            row_idx = idx_val\n",
    "            row_val = row_alt\n",
    "\n",
    "        idx_val = idx_val + 1\n",
    "\n",
    "    print(\"Row selected: \", row_idx) #Alternate_df.loc[rowidx,:]\\\n",
    "    print(\"Min. distance: \", min_dist)\n",
    "    print(\"Matrix shape: \", alt_source_df_np.shape)\n",
    "    final_df_list.append(row_val)\n",
    "    alt_source_df_np = np.delete(alt_source_df_np, row_idx, 0)\n",
    "    idxlist2.append(row_idx)\n",
    "\n",
    "\n",
    "final_df = pd.DataFrame(np.vstack(final_df_list), columns= X_source.columns)\n",
    "final_df\n",
    "final_df.shape\n",
    "\n",
    "print(\"Shape before :\",X_source.shape)\n",
    "X_source = pd.concat([X_source, final_df])\n",
    "X_source = X_source.drop_duplicates(keep=False)\n",
    "print(\"Shape after :\",X_source.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = pd.concat([X_train, final_df], ignore_index=True)\n",
    "\n",
    "X_train.to_csv('ActiveSampling/B_O3_seeded_train.csv',index=False)\n",
    "X_source.to_csv('ActiveSampling/B_O3_seeded_source.csv',index=False)\n",
    "\n",
    "# X_train.to_csv('ActiveSampling/UCI_NO2_activesampling_train.csv',index=False)\n",
    "# X_source.to_csv('ActiveSampling/UCI_NO2_activesampling_source.csv',index=False)\n",
    "# X_test.to_csv('ActiveSampling/UCI_NO2_activesampling_test.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done !!!!!\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>O3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</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>1</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</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>2</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</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>3</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</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>4</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</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>11995</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-0.592630</td>\n",
       "      <td>-0.287787</td>\n",
       "      <td>-0.400425</td>\n",
       "      <td>0.546432</td>\n",
       "      <td>-0.768355</td>\n",
       "      <td>1.174477</td>\n",
       "      <td>-0.084862</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-1.107047</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11996</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-0.592630</td>\n",
       "      <td>0.488564</td>\n",
       "      <td>-0.400425</td>\n",
       "      <td>0.546432</td>\n",
       "      <td>-0.786990</td>\n",
       "      <td>1.209597</td>\n",
       "      <td>-0.084862</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-0.699815</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11997</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-0.592630</td>\n",
       "      <td>0.279546</td>\n",
       "      <td>-0.400425</td>\n",
       "      <td>0.538027</td>\n",
       "      <td>-0.796307</td>\n",
       "      <td>1.188525</td>\n",
       "      <td>-0.084862</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.455476</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11998</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-0.592630</td>\n",
       "      <td>0.249686</td>\n",
       "      <td>-0.400425</td>\n",
       "      <td>0.529622</td>\n",
       "      <td>-0.777672</td>\n",
       "      <td>1.181501</td>\n",
       "      <td>-0.084862</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-0.781261</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11999</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-0.592630</td>\n",
       "      <td>0.309406</td>\n",
       "      <td>-0.310343</td>\n",
       "      <td>0.538027</td>\n",
       "      <td>-0.740402</td>\n",
       "      <td>1.188525</td>\n",
       "      <td>-0.084862</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-0.944154</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         year  month       SO2       NO2        CO      TEMP      PRES  \\\n",
       "0      2016.0    1.0  2.772768  2.280143  3.743354 -1.336244  1.225578   \n",
       "1      2016.0    1.0  4.193714  2.160704  3.833436 -1.420293  1.160356   \n",
       "2      2016.0    1.0  4.044141  2.041265  2.842532 -1.521150  1.151039   \n",
       "3      2016.0    1.0  3.445848  1.921827  2.662368 -1.428697  1.095134   \n",
       "4      2016.0    1.0  2.697982  1.802388  2.211957 -1.554769  1.020595   \n",
       "...       ...    ...       ...       ...       ...       ...       ...   \n",
       "11995  2016.0    6.0 -0.592630 -0.287787 -0.400425  0.546432 -0.768355   \n",
       "11996  2016.0    6.0 -0.592630  0.488564 -0.400425  0.546432 -0.786990   \n",
       "11997  2016.0    6.0 -0.592630  0.279546 -0.400425  0.538027 -0.796307   \n",
       "11998  2016.0    6.0 -0.592630  0.249686 -0.400425  0.529622 -0.777672   \n",
       "11999  2016.0    6.0 -0.592630  0.309406 -0.310343  0.538027 -0.740402   \n",
       "\n",
       "           DEWP      RAIN    wd      WSPM    O3  \n",
       "0     -0.707968 -0.084862   9.0 -0.618369  13.0  \n",
       "1     -0.644751 -0.084862   5.0 -0.699815  16.0  \n",
       "2     -0.644751 -0.084862  15.0 -0.862708  12.0  \n",
       "3     -0.672847 -0.084862  15.0 -0.292584   9.0  \n",
       "4     -0.672847 -0.084862   2.0 -0.699815   7.0  \n",
       "...         ...       ...   ...       ...   ...  \n",
       "11995  1.174477 -0.084862  12.0 -1.107047  37.0  \n",
       "11996  1.209597 -0.084862  10.0 -0.699815  25.0  \n",
       "11997  1.188525 -0.084862   1.0 -0.455476  22.0  \n",
       "11998  1.181501 -0.084862   4.0 -0.781261  14.0  \n",
       "11999  1.188525 -0.084862  13.0 -0.944154  26.0  \n",
       "\n",
       "[12000 rows x 12 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = pd.read_csv('ActiveSampling/B_O3_seeded_train.csv')\n",
    "X_source = pd.read_csv('ActiveSampling/B_O3_seeded_source.csv')\n",
    "\n",
    "X_source['ManDis'] = 0.0\n",
    "\n",
    "train_df_mean = []\n",
    "prow = X_train.mean()\n",
    "train_df_mean = [prow.year, prow.month, prow.SO2, prow.NO2, prow.CO, prow.TEMP, prow.PRES, prow.DEWP, prow.wd, prow.WSPM, prow.O3]\n",
    "\n",
    "rowidx = 0\n",
    "for row in X_source.itertuples():\n",
    "    row_list =[row.year, row.month, row.SO2, row.NO2, row.CO, row.TEMP, row.PRES, row.DEWP, row.wd, row.WSPM, row.O3]\n",
    "\n",
    "    man_dis = 0\n",
    "    for i in range(0, len(row_list)):\n",
    "        tempval = train_df_mean[i] - row_list[i]\n",
    "        man_dis = man_dis + abs(tempval)\n",
    "    \n",
    "    X_source.loc[rowidx,\"ManDis\"] = man_dis\n",
    "    # print(Source_df.loc[rowidx,\"ManDis\"])\n",
    "    rowidx = rowidx + 1\n",
    "\n",
    "\n",
    "X_source_rest = X_source.sort_values('ManDis')\n",
    "X_source = X_source.head(12000)\n",
    "X_source_rest = X_source.iloc[12000:]\n",
    "X_source_rest = X_source_rest.drop(['ManDis'], axis =1)\n",
    "X_source = X_source.drop(['ManDis'], axis =1)\n",
    "\n",
    "X_train.to_csv('ActiveSampling/Beijing_O3_activesampling_train.csv',index=False)\n",
    "X_source.to_csv('ActiveSampling/Beijing_O3_activesampling_source.csv',index=False)\n",
    "X_test.to_csv('ActiveSampling/Beijing_O3_activesampling_test.csv',index=False)\n",
    "\n",
    "print(\"Done !!!!!\")\n",
    "X_source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['My', 'name', 'is', 'Kelly']\n"
     ]
    }
   ],
   "source": [
    "list1 = [\"M\", \"na\", \"i\", \"Ke\"] \n",
    "list2 = [\"y\", \"me\", \"s\", \"lly\"]\n",
    "list3 = []\n",
    "for i, j in zip(list1, list2):\n",
    "    list3.append(i + j)\n",
    "print(list3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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