Dengue Random Forest Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "1968f25a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from scipy.integrate import cumtrapz\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.metrics import make_scorer, mean_absolute_error, mean_squared_error, r2_score\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, learning_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "07cc9f6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(r\"C:\\dengueredo\")\n",
    "#Load preprocessed dataframes\n",
    "sj_train_features_processed = pd.read_csv('sj_training_data_imputed.csv')\n",
    "iq_train_features_processed = pd.read_csv('iq_training_data_imputed.csv')\n",
    "\n",
    "sj_train_labels_processed = pd.read_csv('sj_train_labels.csv')\n",
    "iq_train_labels_processed = pd.read_csv('iq_train_labels.csv')\n",
    "\n",
    "sj_test_features_processed = pd.read_csv('sj_testing_data.csv')\n",
    "iq_test_features_processed = pd.read_csv('iq_testing_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "9195ca2f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>ndvi_ne</th>\n",
       "      <th>ndvi_nw</th>\n",
       "      <th>ndvi_se</th>\n",
       "      <th>ndvi_sw</th>\n",
       "      <th>precipitation_amt_mm</th>\n",
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       "      <th>reanalysis_avg_temp_k</th>\n",
       "      <th>reanalysis_dew_point_temp_k</th>\n",
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       "      <th>station_diur_temp_rng_c</th>\n",
       "      <th>station_max_temp_c</th>\n",
       "      <th>station_min_temp_c</th>\n",
       "      <th>station_precip_mm</th>\n",
       "      <th>season</th>\n",
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       "      <th>0</th>\n",
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       "      <td>12.42</td>\n",
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       "      <td>26.65</td>\n",
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       "      <td>25.442857</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>29.4</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
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       "      <td>0.169900</td>\n",
       "      <td>0.142175</td>\n",
       "      <td>0.162357</td>\n",
       "      <td>0.155486</td>\n",
       "      <td>22.82</td>\n",
       "      <td>25.061429</td>\n",
       "      <td>25.292857</td>\n",
       "      <td>20.801429</td>\n",
       "      <td>27.75</td>\n",
       "      <td>23.25</td>\n",
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       "      <td>22.82</td>\n",
       "      <td>15.372857</td>\n",
       "      <td>-270.778571</td>\n",
       "      <td>26.714286</td>\n",
       "      <td>6.371429</td>\n",
       "      <td>31.7</td>\n",
       "      <td>22.2</td>\n",
       "      <td>8.6</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.032250</td>\n",
       "      <td>0.172967</td>\n",
       "      <td>0.157200</td>\n",
       "      <td>0.170843</td>\n",
       "      <td>34.54</td>\n",
       "      <td>25.631429</td>\n",
       "      <td>25.728571</td>\n",
       "      <td>22.284286</td>\n",
       "      <td>27.35</td>\n",
       "      <td>24.15</td>\n",
       "      <td>...</td>\n",
       "      <td>34.54</td>\n",
       "      <td>16.848571</td>\n",
       "      <td>-270.850000</td>\n",
       "      <td>26.714286</td>\n",
       "      <td>6.485714</td>\n",
       "      <td>32.2</td>\n",
       "      <td>22.8</td>\n",
       "      <td>41.4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.128633</td>\n",
       "      <td>0.245067</td>\n",
       "      <td>0.227557</td>\n",
       "      <td>0.235886</td>\n",
       "      <td>15.36</td>\n",
       "      <td>25.837143</td>\n",
       "      <td>26.078571</td>\n",
       "      <td>22.160000</td>\n",
       "      <td>28.25</td>\n",
       "      <td>23.85</td>\n",
       "      <td>...</td>\n",
       "      <td>15.36</td>\n",
       "      <td>16.672857</td>\n",
       "      <td>-270.721429</td>\n",
       "      <td>27.471429</td>\n",
       "      <td>6.771429</td>\n",
       "      <td>33.3</td>\n",
       "      <td>23.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0.262200</td>\n",
       "      <td>0.251200</td>\n",
       "      <td>0.247340</td>\n",
       "      <td>7.52</td>\n",
       "      <td>26.368571</td>\n",
       "      <td>26.514286</td>\n",
       "      <td>22.671429</td>\n",
       "      <td>28.75</td>\n",
       "      <td>24.35</td>\n",
       "      <td>...</td>\n",
       "      <td>7.52</td>\n",
       "      <td>17.210000</td>\n",
       "      <td>-270.135714</td>\n",
       "      <td>28.942857</td>\n",
       "      <td>9.371429</td>\n",
       "      <td>35.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>5.8</td>\n",
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       "      <td>18.945714</td>\n",
       "      <td>26.55</td>\n",
       "      <td>21.25</td>\n",
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       "      <td>-270.264286</td>\n",
       "      <td>25.042857</td>\n",
       "      <td>5.785714</td>\n",
       "      <td>30.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
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       "      <td>25.078571</td>\n",
       "      <td>20.085714</td>\n",
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       "      <td>23.35</td>\n",
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       "      <td>-270.435714</td>\n",
       "      <td>26.242857</td>\n",
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       "      <td>30.6</td>\n",
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       "      <td>16.96</td>\n",
       "      <td>24.310000</td>\n",
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       "      <td>19.582857</td>\n",
       "      <td>26.25</td>\n",
       "      <td>22.65</td>\n",
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       "      <td>5.714286</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>25.522857</td>\n",
       "      <td>25.542857</td>\n",
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       "      <td>22.75</td>\n",
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       "      <td>31.7</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
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      ],
      "text/plain": [
       "      ndvi_ne   ndvi_nw   ndvi_se   ndvi_sw  precipitation_amt_mm  \\\n",
       "0    0.122600  0.103725  0.198483  0.177617                 12.42   \n",
       "1    0.169900  0.142175  0.162357  0.155486                 22.82   \n",
       "2    0.032250  0.172967  0.157200  0.170843                 34.54   \n",
       "3    0.128633  0.245067  0.227557  0.235886                 15.36   \n",
       "4    0.196200  0.262200  0.251200  0.247340                  7.52   \n",
       "..        ...       ...       ...       ...                   ...   \n",
       "931  0.077850 -0.039900  0.310471  0.296243                 27.19   \n",
       "932 -0.038000 -0.016833  0.119371  0.066386                  3.82   \n",
       "933 -0.155200 -0.052750  0.137757  0.141214                 16.96   \n",
       "934  0.001800 -0.031558  0.203900  0.209843                  0.00   \n",
       "935 -0.037000 -0.010367  0.077314  0.090586                  0.00   \n",
       "\n",
       "     reanalysis_air_temp_k  reanalysis_avg_temp_k  \\\n",
       "0                24.422857              24.592857   \n",
       "1                25.061429              25.292857   \n",
       "2                25.631429              25.728571   \n",
       "3                25.837143              26.078571   \n",
       "4                26.368571              26.514286   \n",
       "..                     ...                    ...   \n",
       "931              23.808571              23.807143   \n",
       "932              24.931429              25.078571   \n",
       "933              24.310000              24.414286   \n",
       "934              24.480000              24.628571   \n",
       "935              25.522857              25.542857   \n",
       "\n",
       "     reanalysis_dew_point_temp_k  reanalysis_max_air_temp_k  \\\n",
       "0                      19.264286                      26.65   \n",
       "1                      20.801429                      27.75   \n",
       "2                      22.284286                      27.35   \n",
       "3                      22.160000                      28.25   \n",
       "4                      22.671429                      28.75   \n",
       "..                           ...                        ...   \n",
       "931                    18.945714                      26.55   \n",
       "932                    20.085714                      26.65   \n",
       "933                    19.582857                      26.25   \n",
       "934                    19.124286                      26.55   \n",
       "935                    21.130000                      27.75   \n",
       "\n",
       "     reanalysis_min_air_temp_k  ...  reanalysis_sat_precip_amt_mm  \\\n",
       "0                        22.75  ...                         12.42   \n",
       "1                        23.25  ...                         22.82   \n",
       "2                        24.15  ...                         34.54   \n",
       "3                        23.85  ...                         15.36   \n",
       "4                        24.35  ...                          7.52   \n",
       "..                         ...  ...                           ...   \n",
       "931                      21.25  ...                         27.19   \n",
       "932                      23.35  ...                          3.82   \n",
       "933                      22.65  ...                         16.96   \n",
       "934                      22.75  ...                          0.00   \n",
       "935                      22.75  ...                          0.00   \n",
       "\n",
       "     reanalysis_specific_humidity_g_per_kg  reanalysis_tdtr_k  \\\n",
       "0                                14.012857        -270.521429   \n",
       "1                                15.372857        -270.778571   \n",
       "2                                16.848571        -270.850000   \n",
       "3                                16.672857        -270.721429   \n",
       "4                                17.210000        -270.135714   \n",
       "..                                     ...                ...   \n",
       "931                              13.644286        -270.264286   \n",
       "932                              14.662857        -270.435714   \n",
       "933                              14.184286        -270.964286   \n",
       "934                              13.858571        -270.364286   \n",
       "935                              15.671429        -269.192857   \n",
       "\n",
       "     station_avg_temp_c  station_diur_temp_rng_c  station_max_temp_c  \\\n",
       "0             25.442857                 6.900000                29.4   \n",
       "1             26.714286                 6.371429                31.7   \n",
       "2             26.714286                 6.485714                32.2   \n",
       "3             27.471429                 6.771429                33.3   \n",
       "4             28.942857                 9.371429                35.0   \n",
       "..                  ...                      ...                 ...   \n",
       "931           25.042857                 5.785714                30.0   \n",
       "932           26.242857                 6.814286                30.6   \n",
       "933           25.000000                 5.714286                29.4   \n",
       "934           25.314286                 6.242857                29.4   \n",
       "935           27.042857                 7.514286                31.7   \n",
       "\n",
       "     station_min_temp_c  station_precip_mm  season  month  \n",
       "0                  20.0               16.0       2      4  \n",
       "1                  22.2                8.6       2      5  \n",
       "2                  22.8               41.4       2      5  \n",
       "3                  23.3                4.0       2      5  \n",
       "4                  23.9                5.8       2      5  \n",
       "..                  ...                ...     ...    ...  \n",
       "931                21.1                1.8       1      3  \n",
       "932                22.2                0.5       2      4  \n",
       "933                21.7               30.7       2      4  \n",
       "934                21.7               11.2       2      4  \n",
       "935                23.3                0.3       2      4  \n",
       "\n",
       "[936 rows x 22 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verify preprocessed dataframe loaded correctly\n",
    "sj_train_features_processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "459bdcc3",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>ndvi_ne</th>\n",
       "      <th>ndvi_nw</th>\n",
       "      <th>ndvi_se</th>\n",
       "      <th>ndvi_sw</th>\n",
       "      <th>precipitation_amt_mm</th>\n",
       "      <th>reanalysis_air_temp_k</th>\n",
       "      <th>reanalysis_avg_temp_k</th>\n",
       "      <th>reanalysis_dew_point_temp_k</th>\n",
       "      <th>reanalysis_max_air_temp_k</th>\n",
       "      <th>reanalysis_min_air_temp_k</th>\n",
       "      <th>...</th>\n",
       "      <th>reanalysis_sat_precip_amt_mm</th>\n",
       "      <th>reanalysis_specific_humidity_g_per_kg</th>\n",
       "      <th>reanalysis_tdtr_k</th>\n",
       "      <th>station_avg_temp_c</th>\n",
       "      <th>station_diur_temp_rng_c</th>\n",
       "      <th>station_max_temp_c</th>\n",
       "      <th>station_min_temp_c</th>\n",
       "      <th>station_precip_mm</th>\n",
       "      <th>season</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.01890</td>\n",
       "      <td>-0.018900</td>\n",
       "      <td>0.102729</td>\n",
       "      <td>0.091200</td>\n",
       "      <td>78.60</td>\n",
       "      <td>25.342857</td>\n",
       "      <td>25.400000</td>\n",
       "      <td>21.377143</td>\n",
       "      <td>27.95</td>\n",
       "      <td>23.25</td>\n",
       "      <td>...</td>\n",
       "      <td>78.60</td>\n",
       "      <td>15.918571</td>\n",
       "      <td>-270.021429</td>\n",
       "      <td>26.528571</td>\n",
       "      <td>7.057143</td>\n",
       "      <td>33.3</td>\n",
       "      <td>21.7</td>\n",
       "      <td>75.2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.01800</td>\n",
       "      <td>-0.012400</td>\n",
       "      <td>0.082043</td>\n",
       "      <td>0.072314</td>\n",
       "      <td>12.56</td>\n",
       "      <td>25.325714</td>\n",
       "      <td>25.407143</td>\n",
       "      <td>21.245714</td>\n",
       "      <td>27.65</td>\n",
       "      <td>23.55</td>\n",
       "      <td>...</td>\n",
       "      <td>12.56</td>\n",
       "      <td>15.791429</td>\n",
       "      <td>-270.578571</td>\n",
       "      <td>26.071429</td>\n",
       "      <td>5.557143</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>34.3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.00150</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.151083</td>\n",
       "      <td>0.091529</td>\n",
       "      <td>3.66</td>\n",
       "      <td>26.305714</td>\n",
       "      <td>26.207143</td>\n",
       "      <td>22.158571</td>\n",
       "      <td>29.05</td>\n",
       "      <td>23.25</td>\n",
       "      <td>...</td>\n",
       "      <td>3.66</td>\n",
       "      <td>16.674286</td>\n",
       "      <td>-268.721429</td>\n",
       "      <td>27.928571</td>\n",
       "      <td>7.785714</td>\n",
       "      <td>32.8</td>\n",
       "      <td>22.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.019867</td>\n",
       "      <td>0.124329</td>\n",
       "      <td>0.125686</td>\n",
       "      <td>0.00</td>\n",
       "      <td>26.540000</td>\n",
       "      <td>26.578571</td>\n",
       "      <td>21.252857</td>\n",
       "      <td>29.85</td>\n",
       "      <td>23.75</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.775714</td>\n",
       "      <td>-268.807143</td>\n",
       "      <td>28.057143</td>\n",
       "      <td>6.271429</td>\n",
       "      <td>33.3</td>\n",
       "      <td>24.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.05680</td>\n",
       "      <td>0.039833</td>\n",
       "      <td>0.062267</td>\n",
       "      <td>0.075914</td>\n",
       "      <td>0.76</td>\n",
       "      <td>26.630000</td>\n",
       "      <td>26.521429</td>\n",
       "      <td>21.610000</td>\n",
       "      <td>29.15</td>\n",
       "      <td>24.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.76</td>\n",
       "      <td>16.137143</td>\n",
       "      <td>-269.607143</td>\n",
       "      <td>27.614286</td>\n",
       "      <td>7.085714</td>\n",
       "      <td>33.3</td>\n",
       "      <td>23.3</td>\n",
       "      <td>84.1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</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",
       "      <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>255</th>\n",
       "      <td>-0.08740</td>\n",
       "      <td>-0.016183</td>\n",
       "      <td>0.156343</td>\n",
       "      <td>0.105186</td>\n",
       "      <td>30.34</td>\n",
       "      <td>25.520000</td>\n",
       "      <td>25.735714</td>\n",
       "      <td>21.525714</td>\n",
       "      <td>28.15</td>\n",
       "      <td>23.25</td>\n",
       "      <td>...</td>\n",
       "      <td>30.34</td>\n",
       "      <td>15.985714</td>\n",
       "      <td>-269.835714</td>\n",
       "      <td>27.542857</td>\n",
       "      <td>7.942857</td>\n",
       "      <td>33.9</td>\n",
       "      <td>22.8</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>-0.20325</td>\n",
       "      <td>-0.077833</td>\n",
       "      <td>0.204171</td>\n",
       "      <td>0.178914</td>\n",
       "      <td>6.55</td>\n",
       "      <td>24.885714</td>\n",
       "      <td>25.007143</td>\n",
       "      <td>21.478571</td>\n",
       "      <td>28.35</td>\n",
       "      <td>22.75</td>\n",
       "      <td>...</td>\n",
       "      <td>6.55</td>\n",
       "      <td>15.881429</td>\n",
       "      <td>-270.321429</td>\n",
       "      <td>26.642857</td>\n",
       "      <td>6.642857</td>\n",
       "      <td>33.3</td>\n",
       "      <td>22.8</td>\n",
       "      <td>17.6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>-0.11760</td>\n",
       "      <td>-0.008200</td>\n",
       "      <td>0.192700</td>\n",
       "      <td>0.170429</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.907143</td>\n",
       "      <td>26.178571</td>\n",
       "      <td>21.798571</td>\n",
       "      <td>28.35</td>\n",
       "      <td>24.35</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.212857</td>\n",
       "      <td>-269.978571</td>\n",
       "      <td>27.914286</td>\n",
       "      <td>8.114286</td>\n",
       "      <td>32.8</td>\n",
       "      <td>23.3</td>\n",
       "      <td>9.4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>0.08275</td>\n",
       "      <td>0.031200</td>\n",
       "      <td>0.135014</td>\n",
       "      <td>0.074857</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.762857</td>\n",
       "      <td>25.914286</td>\n",
       "      <td>21.528571</td>\n",
       "      <td>27.75</td>\n",
       "      <td>24.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.965714</td>\n",
       "      <td>-270.107143</td>\n",
       "      <td>27.728571</td>\n",
       "      <td>6.942857</td>\n",
       "      <td>31.7</td>\n",
       "      <td>23.9</td>\n",
       "      <td>22.9</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>-0.08730</td>\n",
       "      <td>-0.048667</td>\n",
       "      <td>0.129814</td>\n",
       "      <td>0.117671</td>\n",
       "      <td>45.47</td>\n",
       "      <td>24.917143</td>\n",
       "      <td>24.892857</td>\n",
       "      <td>20.982857</td>\n",
       "      <td>27.05</td>\n",
       "      <td>23.45</td>\n",
       "      <td>...</td>\n",
       "      <td>45.47</td>\n",
       "      <td>15.451429</td>\n",
       "      <td>-270.807143</td>\n",
       "      <td>26.442857</td>\n",
       "      <td>6.742857</td>\n",
       "      <td>31.1</td>\n",
       "      <td>21.7</td>\n",
       "      <td>47.5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>260 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ndvi_ne   ndvi_nw   ndvi_se   ndvi_sw  precipitation_amt_mm  \\\n",
       "0   -0.01890 -0.018900  0.102729  0.091200                 78.60   \n",
       "1   -0.01800 -0.012400  0.082043  0.072314                 12.56   \n",
       "2   -0.00150       NaN  0.151083  0.091529                  3.66   \n",
       "3        NaN -0.019867  0.124329  0.125686                  0.00   \n",
       "4    0.05680  0.039833  0.062267  0.075914                  0.76   \n",
       "..       ...       ...       ...       ...                   ...   \n",
       "255 -0.08740 -0.016183  0.156343  0.105186                 30.34   \n",
       "256 -0.20325 -0.077833  0.204171  0.178914                  6.55   \n",
       "257 -0.11760 -0.008200  0.192700  0.170429                  0.00   \n",
       "258  0.08275  0.031200  0.135014  0.074857                  0.00   \n",
       "259 -0.08730 -0.048667  0.129814  0.117671                 45.47   \n",
       "\n",
       "     reanalysis_air_temp_k  reanalysis_avg_temp_k  \\\n",
       "0                25.342857              25.400000   \n",
       "1                25.325714              25.407143   \n",
       "2                26.305714              26.207143   \n",
       "3                26.540000              26.578571   \n",
       "4                26.630000              26.521429   \n",
       "..                     ...                    ...   \n",
       "255              25.520000              25.735714   \n",
       "256              24.885714              25.007143   \n",
       "257              25.907143              26.178571   \n",
       "258              25.762857              25.914286   \n",
       "259              24.917143              24.892857   \n",
       "\n",
       "     reanalysis_dew_point_temp_k  reanalysis_max_air_temp_k  \\\n",
       "0                      21.377143                      27.95   \n",
       "1                      21.245714                      27.65   \n",
       "2                      22.158571                      29.05   \n",
       "3                      21.252857                      29.85   \n",
       "4                      21.610000                      29.15   \n",
       "..                           ...                        ...   \n",
       "255                    21.525714                      28.15   \n",
       "256                    21.478571                      28.35   \n",
       "257                    21.798571                      28.35   \n",
       "258                    21.528571                      27.75   \n",
       "259                    20.982857                      27.05   \n",
       "\n",
       "     reanalysis_min_air_temp_k  ...  reanalysis_sat_precip_amt_mm  \\\n",
       "0                        23.25  ...                         78.60   \n",
       "1                        23.55  ...                         12.56   \n",
       "2                        23.25  ...                          3.66   \n",
       "3                        23.75  ...                          0.00   \n",
       "4                        24.15  ...                          0.76   \n",
       "..                         ...  ...                           ...   \n",
       "255                      23.25  ...                         30.34   \n",
       "256                      22.75  ...                          6.55   \n",
       "257                      24.35  ...                          0.00   \n",
       "258                      24.15  ...                          0.00   \n",
       "259                      23.45  ...                         45.47   \n",
       "\n",
       "     reanalysis_specific_humidity_g_per_kg  reanalysis_tdtr_k  \\\n",
       "0                                15.918571        -270.021429   \n",
       "1                                15.791429        -270.578571   \n",
       "2                                16.674286        -268.721429   \n",
       "3                                15.775714        -268.807143   \n",
       "4                                16.137143        -269.607143   \n",
       "..                                     ...                ...   \n",
       "255                              15.985714        -269.835714   \n",
       "256                              15.881429        -270.321429   \n",
       "257                              16.212857        -269.978571   \n",
       "258                              15.965714        -270.107143   \n",
       "259                              15.451429        -270.807143   \n",
       "\n",
       "     station_avg_temp_c  station_diur_temp_rng_c  station_max_temp_c  \\\n",
       "0             26.528571                 7.057143                33.3   \n",
       "1             26.071429                 5.557143                30.0   \n",
       "2             27.928571                 7.785714                32.8   \n",
       "3             28.057143                 6.271429                33.3   \n",
       "4             27.614286                 7.085714                33.3   \n",
       "..                  ...                      ...                 ...   \n",
       "255           27.542857                 7.942857                33.9   \n",
       "256           26.642857                 6.642857                33.3   \n",
       "257           27.914286                 8.114286                32.8   \n",
       "258           27.728571                 6.942857                31.7   \n",
       "259           26.442857                 6.742857                31.1   \n",
       "\n",
       "     station_min_temp_c  station_precip_mm  season  month  \n",
       "0                  21.7               75.2       2      4  \n",
       "1                  22.2               34.3       2      5  \n",
       "2                  22.8                3.0       2      5  \n",
       "3                  24.4                0.3       2      5  \n",
       "4                  23.3               84.1       2      5  \n",
       "..                  ...                ...     ...    ...  \n",
       "255                22.8                3.5       1      3  \n",
       "256                22.8               17.6       2      4  \n",
       "257                23.3                9.4       2      4  \n",
       "258                23.9               22.9       2      4  \n",
       "259                21.7               47.5       2      4  \n",
       "\n",
       "[260 rows x 22 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sj_test_features_processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cf5b93a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train-test-splitting for both cities for model optimization, using preprocessed features and labels\n",
    "sj_train_X, sj_test_X, sj_train_y, sj_test_y = train_test_split(sj_train_features_processed, \n",
    "                                                                sj_train_labels_processed, \n",
    "                                                                test_size=0.2,\n",
    "                                                               random_state=56)\n",
    "\n",
    "iq_train_X, iq_test_X, iq_train_y, iq_test_y = train_test_split(iq_train_features_processed, \n",
    "                                                                iq_train_labels_processed, \n",
    "                                                                test_size=0.2,\n",
    "                                                               random_state=58)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ad49e211",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Method to perform 10-fold RFECV and log the mean scores, feature importances, and the selected features\n",
    "def perform_rfecv(X, y, estimator, cv=10, scoring='neg_mean_absolute_error', verbose=2):\n",
    "    rfecv = RFECV(estimator=estimator, step=1, cv=cv, scoring=scoring, verbose=verbose)\n",
    "    rfecv.fit(X, y.values.ravel())\n",
    "\n",
    "    cv_results = rfecv.cv_results_\n",
    "    mean_scores = cv_results['mean_test_score']\n",
    "\n",
    "    print(\"\\nMean score for each number of features:\")\n",
    "    for count, score in enumerate(mean_scores, start=1):\n",
    "        print(f\"Number of features: {count}, Mean CV Score: {score}\")\n",
    "    \n",
    "    feature_importances = rfecv.estimator_.feature_importances_\n",
    "    print(\"Features selected:\", rfecv.support_)\n",
    "    print(\"Feature ranking:\", rfecv.ranking_)\n",
    "\n",
    "    selected_features = X.columns[rfecv.support_]\n",
    "    importances = {feature: importance for feature, importance in zip(selected_features, feature_importances)}\n",
    "\n",
    "    print(\"\\nFeature importances:\")\n",
    "    for feature, importance in sorted(importances.items(), key=lambda x: x[1], reverse=True):\n",
    "        print(f\"{feature}: {importance}\")\n",
    "\n",
    "    return rfecv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "16cb096e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RFECV for SJ:\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "\n",
      "Mean score for each number of features:\n",
      "Number of features: 1, Mean CV Score: -30.650315765193767\n",
      "Number of features: 2, Mean CV Score: -27.482499999999998\n",
      "Number of features: 3, Mean CV Score: -25.64318882882883\n",
      "Number of features: 4, Mean CV Score: -25.476007747747747\n",
      "Number of features: 5, Mean CV Score: -24.24574666666667\n",
      "Number of features: 6, Mean CV Score: -23.656783243243243\n",
      "Number of features: 7, Mean CV Score: -23.250054594594594\n",
      "Number of features: 8, Mean CV Score: -23.80296486486487\n",
      "Number of features: 9, Mean CV Score: -23.583746306306306\n",
      "Number of features: 10, Mean CV Score: -23.43369081081081\n",
      "Number of features: 11, Mean CV Score: -23.533325585585587\n",
      "Number of features: 12, Mean CV Score: -23.17656594594595\n",
      "Number of features: 13, Mean CV Score: -22.990706846846845\n",
      "Number of features: 14, Mean CV Score: -22.83340648648649\n",
      "Number of features: 15, Mean CV Score: -23.00664072072072\n",
      "Number of features: 16, Mean CV Score: -23.262218198198198\n",
      "Number of features: 17, Mean CV Score: -22.785019459459456\n",
      "Number of features: 18, Mean CV Score: -22.943069729729736\n",
      "Number of features: 19, Mean CV Score: -22.959004144144146\n",
      "Number of features: 20, Mean CV Score: -22.96492882882883\n",
      "Number of features: 21, Mean CV Score: -22.947305945945946\n",
      "Number of features: 22, Mean CV Score: -23.202543423423425\n",
      "Features selected: [ True  True  True  True False  True  True  True  True  True  True  True\n",
      " False  True  True  True False  True False  True False  True]\n",
      "Feature ranking: [1 1 1 1 5 1 1 1 1 1 1 1 4 1 1 1 2 1 6 1 3 1]\n",
      "\n",
      "Feature importances:\n",
      "month: 0.14233281126599925\n",
      "station_max_temp_c: 0.12385958897617597\n",
      "reanalysis_dew_point_temp_k: 0.105095688087695\n",
      "reanalysis_specific_humidity_g_per_kg: 0.0778603388866283\n",
      "ndvi_sw: 0.07070025681356662\n",
      "station_precip_mm: 0.06070454607314931\n",
      "reanalysis_precip_amt_kg_per_m2: 0.060086875372859755\n",
      "reanalysis_tdtr_k: 0.04188190557492176\n",
      "ndvi_se: 0.04160734609588347\n",
      "reanalysis_max_air_temp_k: 0.03988621270937201\n",
      "ndvi_nw: 0.03912693647964329\n",
      "reanalysis_avg_temp_k: 0.036795186391298494\n",
      "station_avg_temp_c: 0.03533430400866842\n",
      "ndvi_ne: 0.03503556515119032\n",
      "reanalysis_air_temp_k: 0.033834495099199995\n",
      "reanalysis_min_air_temp_k: 0.028679636307885465\n",
      "reanalysis_relative_humidity_percent: 0.027178306705862652\n",
      "\n",
      "RFECV for IQ:\n",
      "Fitting estimator with 22 features.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n",
      "Fitting estimator with 22 features.\n",
      "\n",
      "Mean score for each number of features:\n",
      "Number of features: 1, Mean CV Score: -8.048105642663568\n",
      "Number of features: 2, Mean CV Score: -7.433990127758419\n",
      "Number of features: 3, Mean CV Score: -7.476371080139373\n",
      "Number of features: 4, Mean CV Score: -7.550491289198608\n",
      "Number of features: 5, Mean CV Score: -7.553623693379791\n",
      "Number of features: 6, Mean CV Score: -7.488443670150987\n",
      "Number of features: 7, Mean CV Score: -7.317450638792103\n",
      "Number of features: 8, Mean CV Score: -7.284466898954706\n",
      "Number of features: 9, Mean CV Score: -7.221567944250872\n",
      "Number of features: 10, Mean CV Score: -7.249162020905923\n",
      "Number of features: 11, Mean CV Score: -7.282491869918699\n",
      "Number of features: 12, Mean CV Score: -7.109353658536587\n",
      "Number of features: 13, Mean CV Score: -7.262749128919862\n",
      "Number of features: 14, Mean CV Score: -7.21518524970964\n",
      "Number of features: 15, Mean CV Score: -7.2752961672473875\n",
      "Number of features: 16, Mean CV Score: -7.290797328687573\n",
      "Number of features: 17, Mean CV Score: -7.211012775842043\n",
      "Number of features: 18, Mean CV Score: -7.144907084785134\n",
      "Number of features: 19, Mean CV Score: -7.2876376306620205\n",
      "Number of features: 20, Mean CV Score: -7.2391167247386745\n",
      "Number of features: 21, Mean CV Score: -7.060004645760744\n",
      "Number of features: 22, Mean CV Score: -7.183737514518003\n",
      "Features selected: [ True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True False  True]\n",
      "Feature ranking: [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1]\n",
      "\n",
      "Feature importances:\n",
      "reanalysis_specific_humidity_g_per_kg: 0.08435715902982643\n",
      "month: 0.0831281263245399\n",
      "reanalysis_precip_amt_kg_per_m2: 0.07636437334356498\n",
      "station_precip_mm: 0.06811396369425711\n",
      "ndvi_sw: 0.05983799088451515\n",
      "station_max_temp_c: 0.054984762670447614\n",
      "station_avg_temp_c: 0.05343038048655116\n",
      "ndvi_se: 0.05293311417652809\n",
      "station_diur_temp_rng_c: 0.05251253444417131\n",
      "ndvi_nw: 0.045651109686015066\n",
      "reanalysis_tdtr_k: 0.0433108250507158\n",
      "station_min_temp_c: 0.040836979785953416\n",
      "reanalysis_max_air_temp_k: 0.04083109908478216\n",
      "reanalysis_dew_point_temp_k: 0.03637027194563254\n",
      "reanalysis_min_air_temp_k: 0.03632824870941395\n",
      "ndvi_ne: 0.03348307529584479\n",
      "reanalysis_avg_temp_k: 0.032314964007452\n",
      "reanalysis_relative_humidity_percent: 0.031871509139771825\n",
      "reanalysis_sat_precip_amt_mm: 0.025741222380373196\n",
      "reanalysis_air_temp_k: 0.02485502018793742\n",
      "precipitation_amt_mm: 0.022743269671706003\n"
     ]
    }
   ],
   "source": [
    "#Define the model to be used and then perform RFECV for each city\n",
    "model = RandomForestRegressor()\n",
    "\n",
    "print(\"RFECV for San Juan:\")\n",
    "sj_rfecv = perform_rfecv(sj_train_X, sj_train_y, model)\n",
    "\n",
    "print(\"\\nRFECV for Iquitos:\")\n",
    "iq_rfecv = perform_rfecv(iq_train_X, iq_train_y, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "65ff0b5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function that returns a feature-selected dataframe based on RFECV results or user choice\n",
    "def feature_selector(features, city):\n",
    "    if city == 'sj':\n",
    "        relevant_features_sj = ['reanalysis_specific_humidity_g_per_kg',  \n",
    "                                'station_max_temp_c', 'month', \n",
    "                                'reanalysis_dew_point_temp_k']\n",
    "        return features[relevant_features_sj]\n",
    "    \n",
    "    elif city == 'iq':\n",
    "        relevant_features_iq = ['reanalysis_specific_humidity_g_per_kg', 'ndvi_se', \n",
    "                                'station_min_temp_c', 'reanalysis_dew_point_temp_k', \n",
    "                                'station_max_temp_c', 'month', 'reanalysis_tdtr_k']\n",
    "        return features[relevant_features_iq]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e3a12c26",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Select only the most important features as determined by RFECV or user choice\n",
    "sj_train_relevant_features = feature_selector(sj_train_X, 'sj')\n",
    "iq_train_relevant_features = feature_selector(iq_train_X, 'iq')\n",
    "\n",
    "sj_test_relevant_features = feature_selector(sj_test_X, 'sj')\n",
    "iq_test_relevant_features = feature_selector(iq_test_X, 'iq')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6208d6ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Grid search, model creation, and evaluation function\n",
    "def train_and_evaluate(X_train, y_train, X_test, y_test, model_type, param_grid):\n",
    "    grid_search = GridSearchCV(model_type, param_grid, verbose=2, cv=10, \n",
    "                               scoring='neg_mean_squared_error', n_jobs=-1)\n",
    "    grid_search.fit(X_train, y_train.values.ravel())\n",
    "    \n",
    "    best_params = grid_search.best_params_\n",
    "    model = model_type.set_params(**best_params)\n",
    "    model.fit(X_train, y_train.values.ravel())\n",
    "    \n",
    "    predicted = model.predict(X_test)\n",
    "    mae = mean_absolute_error(y_test, predicted)\n",
    "    mse = mean_absolute_error(y_test, predicted) ** 0.5\n",
    "    \n",
    "    return model, mae, mse, best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "151463df",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Method to plot learning curve loss as a function of training samples\n",
    "def plot_learning_curve(model, X, y, title='Learning Curve'):\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        model, X, y, cv=10, scoring='neg_mean_squared_error', n_jobs=-1,\n",
    "        train_sizes=np.linspace(0.1, 1.0, 5))\n",
    "    \n",
    "    train_scores_mean = -train_scores.mean(axis=1)\n",
    "    test_scores_std = test_scores.std(axis=1)\n",
    "    test_scores_mean = -test_scores.mean(axis=1)\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score (MSE)\")\n",
    "    plt.grid()\n",
    "\n",
    "    plt.fill_between(train_sizes, train_scores_mean - train_scores.std(axis=1),\n",
    "                     train_scores_mean + train_scores.std(axis=1), alpha=0.1, color=\"r\")\n",
    "    \n",
    "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "    \n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\", label=\"Cross-validation score\")\n",
    "\n",
    "    plt.legend(loc=\"best\")\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e1941a54",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random Forest Regression Model parameters\n",
    "rfr_params = {\n",
    "    'max_depth': [2, 5, 10, 20, 40],\n",
    "    'n_estimators': [5, 20, 100],\n",
    "    'max_features': [5, 9, 13, 19],\n",
    "    'min_samples_leaf': [3, 4, 5],\n",
    "    'min_samples_split': [2, 4, 8, 10]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "afb75af5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 720 candidates, totalling 7200 fits\n",
      "Fitting 10 folds for each of 720 candidates, totalling 7200 fits\n",
      "\n",
      "San Juan: MAE = 25.314893739901915, MSE = 5.031390835534635\n",
      "Iquitos: MAE = 5.197817900104018, MSE = 2.2798723429402834\n",
      "\n",
      "San Juan Best Params:  {'max_depth': 5, 'max_features': 13, 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 5}\n",
      "Iquitos Best Params:  {'max_depth': 20, 'max_features': 5, 'min_samples_leaf': 4, 'min_samples_split': 8, 'n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "#Train and evaluate models for both cities, \n",
    "#returning the RFR model with the best score and its parameters\n",
    "sj_rfr_model, sj_mae, sj_mse, sj_best_params = train_and_evaluate(sj_train_relevant_features, \n",
    "                                                                  sj_train_y, \n",
    "                                                  sj_test_relevant_features, sj_test_y, \n",
    "                                                  RandomForestRegressor(), rfr_params)\n",
    "\n",
    "iq_rfr_model, iq_mae, iq_mse, iq_best_params = train_and_evaluate(iq_train_relevant_features, \n",
    "                                                                  iq_train_y, \n",
    "                                                  iq_test_relevant_features, iq_test_y, \n",
    "                                                  RandomForestRegressor(), rfr_params)\n",
    "# Print model performance\n",
    "print(f\"\\nSan Juan: MAE = {sj_mae}, MSE = {sj_mse}\")\n",
    "print(f\"Iquitos: MAE = {iq_mae}, MSE = {iq_mse}\")\n",
    "\n",
    "print(f\"\\nSan Juan Best Params: \", sj_best_params)\n",
    "print(f\"Iquitos Best Params: \", iq_best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ad1ca6df",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curve(RandomForestRegressor(**sj_best_params), \n",
    "                    sj_train_relevant_features, sj_train_y, 'Learning Curve for San Juan').show()\n",
    "\n",
    "plot_learning_curve(RandomForestRegressor(**iq_best_params), \n",
    "                    iq_train_relevant_features, iq_train_y, 'Learning Curve for Iquitos').show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4f7e7bb4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\BOMBSH~1\\AppData\\Local\\Temp/ipykernel_60964/1622687738.py:10: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  sj_full_model = sj_rfr_model.fit(sj_full_X, sj_full_y)\n",
      "C:\\Users\\BOMBSH~1\\AppData\\Local\\Temp/ipykernel_60964/1622687738.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  iq_full_model = iq_rfr_model.fit(iq_full_X, iq_full_y)\n",
      "C:\\Users\\Bombshell\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:6392: 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 self._update_inplace(result)\n",
      "C:\\Users\\Bombshell\\anaconda3\\lib\\site-packages\\sklearn\\base.py:432: UserWarning: X has feature names, but RandomForestRegressor was fitted without feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\Bombshell\\anaconda3\\lib\\site-packages\\sklearn\\base.py:432: UserWarning: X has feature names, but RandomForestRegressor was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#Combine training features and labels, as well as testing features and labels \n",
    "#to create full features and labels for submission prediction\n",
    "sj_full_X = np.concatenate([sj_train_relevant_features, sj_test_relevant_features])\n",
    "sj_full_y = np.concatenate([sj_train_y, sj_test_y])\n",
    "\n",
    "iq_full_X = np.concatenate([iq_train_relevant_features, iq_test_relevant_features])\n",
    "iq_full_y = np.concatenate([iq_train_y, iq_test_y])\n",
    "\n",
    "#Fit models on full features and labels\n",
    "sj_full_model = sj_rfr_model.fit(sj_full_X, sj_full_y)\n",
    "iq_full_model = iq_rfr_model.fit(iq_full_X, iq_full_y)\n",
    "\n",
    "#Select test columns and fill their NAs (using the mean) for final prediction,\n",
    "#otherwise the required submission format will not match\n",
    "sj_sub_prediction_features = feature_selector(sj_test_features_processed, 'sj')\n",
    "sj_sub_prediction_features.fillna(sj_sub_prediction_features.mean(),inplace=True)\n",
    "\n",
    "iq_sub_prediction_features = feature_selector(iq_test_features_processed, 'iq')\n",
    "iq_sub_prediction_features.fillna(iq_sub_prediction_features.mean(),inplace=True)\n",
    "\n",
    "#Generate submission predictions\n",
    "sj_predicted_full_model_RF = sj_full_model.predict(sj_sub_prediction_features).astype(int)\n",
    "iq_predicted_full_model_RF = iq_full_model.predict(iq_sub_prediction_features).astype(int)\n",
    "\n",
    "#Read in submission format and fill in predictions by concatenating per-city results\n",
    "submission = pd.read_csv('submission_format.csv', index_col=[0, 1, 2])\n",
    "submission.reset_index()\n",
    "\n",
    "submission.total_cases = np.concatenate([sj_predicted_full_model_RF, iq_predicted_full_model_RF])\n",
    "submission.to_csv('DengueSimplified5.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "53c1c60b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load predictions for plotting\n",
    "predicted_data = pd.read_csv('DengueSimplified5.csv')\n",
    "\n",
    "sj_predicted_cases = predicted_data.loc[predicted_data['city'] =='sj']\n",
    "iq_predicted_cases = predicted_data.loc[predicted_data['city'] =='iq']\n",
    "\n",
    "#Create date column for x-axis plotting of total cases against years\n",
    "sj_predicted_cases['date'] = pd.to_datetime(sj_predicted_cases['year'].astype(str) + \n",
    "                                            '-' + \n",
    "                                            (sj_predicted_cases['weekofyear']-1).astype(str) + \n",
    "                                            '-0', format='%Y-%U-%w')\n",
    "\n",
    "iq_predicted_cases['date'] = pd.to_datetime(iq_predicted_cases['year'].astype(str) + \n",
    "                                            '-' + \n",
    "                                            (iq_predicted_cases['weekofyear']-1).astype(str) + \n",
    "                                            '-0', format='%Y-%U-%w')\n",
    "\n",
    "sj_predicted_cases.set_index('date', inplace=True)\n",
    "iq_predicted_cases.set_index('date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d2ae3077",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot predicted total_cases for both cities\n",
    "plt.figure(figsize=(17, 12))\n",
    "\n",
    "#Plot for San Juan\n",
    "plt.subplot(2, 1, 1)\n",
    "sj_predicted_cases.total_cases.plot(lw=0.8)\n",
    "plt.title('San Juan Predicted Dengue Cases')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Total Cases')\n",
    "\n",
    "#Plot for Iquitos\n",
    "plt.subplot(2, 1, 2)\n",
    "iq_predicted_cases.total_cases.plot(lw=0.8, color='r')\n",
    "plt.title('Iquitos Predicted Dengue Cases')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Total Cases')\n",
    "\n",
    "plt.subplots_adjust(hspace=5.0)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1f941220",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load labels for projection plotting\n",
    "train_w_preds_appended = pd.read_csv('dengue_labels_train.csv')\n",
    "\n",
    "sj_train_w_preds_appended = train_w_preds_appended.loc[train_w_preds_appended['city'] =='sj']\n",
    "iq_train_w_preds_appended = train_w_preds_appended.loc[train_w_preds_appended['city'] =='iq']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a2f54890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Method to plot all training data with concatenated predictions\n",
    "def plot_training_and_predictions(train_df, pred_df, city_name):\n",
    "    #Ensure 'date' is not set as index\n",
    "    train_df = train_df.reset_index(drop=True)\n",
    "    pred_df = pred_df.reset_index(drop=True)\n",
    "\n",
    "    train_df['date'] = pd.to_datetime(train_df['year'].astype(str) + \n",
    "                                      train_df['weekofyear'].astype(str) +\n",
    "                                      '1', format='%Y%W%w')\n",
    "    \n",
    "    pred_df['date'] = pd.to_datetime(pred_df['year'].astype(str) + \n",
    "                                     pred_df['weekofyear'].astype(str) + \n",
    "                                     '1', format='%Y%W%w')\n",
    "\n",
    "    train_df_sorted = train_df.sort_values('date')\n",
    "    pred_df_sorted = pred_df.sort_values('date')\n",
    "\n",
    "    plt.figure(figsize=(15, 5))\n",
    "    plt.plot(train_df_sorted['date'], train_df_sorted['total_cases'], \n",
    "             label='Training Data', color='#1f77b4')\n",
    "\n",
    "    plt.plot(pred_df_sorted['date'], pred_df_sorted['total_cases'], \n",
    "             label='Predictions', color='orange')\n",
    "\n",
    "    plt.xlabel('Date')\n",
    "    plt.ylabel('Total Cases')\n",
    "    plt.title(f'{city_name} Data with Predictions')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "plot_training_and_predictions(sj_train_w_preds_appended, sj_predicted_cases, 'San Juan')\n",
    "plot_training_and_predictions(iq_train_w_preds_appended, iq_predicted_cases, 'Iquitos')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a45d5a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Based on the submission result of 24.86 MAE, and the observation that the \n",
    "# evaluation MAE for San Juan (25.31) was much higher than Iquitos' (5.19), it is likely that\n",
    "# the San Juan model contributes more heavily to the prediction error. This could be due to San Juan's \n",
    "# predictions pessimistic outlook on future cases of dengue. Perhaps performing additive outlier removal \n",
    "# on the target before training could yield more realistic results."
   ]
  }
 ],
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