DengAI_test_code1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Integrate Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# get input dataset\n",
    "features_train = pd.read_csv('data/dengue_features_train.csv')\n",
    "labels_train = pd.read_csv('data/dengue_labels_train.csv')\n",
    "features_test = pd.read_csv('data/dengue_features_test.csv')\n",
    "\n",
    "# Normalize the week_start_date feature value\n",
    "features_train['week_start_date'] = pd.to_datetime(features_train['week_start_date'])\n",
    "features_test['week_start_date'] = pd.to_datetime(features_test['week_start_date'])\n",
    "\n",
    "# Divide training data into two parts w.r.t 'city'\n",
    "train_sj = features_train.loc[features_train['city'] == 'sj']\n",
    "train_iq = features_train.loc[features_train['city'] == 'iq']\n",
    "label_sj = labels_train.loc[labels_train['city'] == 'sj']\n",
    "label_iq = labels_train.loc[labels_train['city'] == 'iq']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocess the Training Data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection \n",
    "\n",
    "Select features from the features in the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "KEYS = ['city', 'year', 'weekofyear']\n",
    "\n",
    "ALL_FEATURES= ['city', 'year', 'weekofyear', 'week_start_date', 'ndvi_ne', 'ndvi_nw', \n",
    "                      'ndvi_se', 'ndvi_sw', 'precipitation_amt_mm', 'reanalysis_air_temp_k',\n",
    "                      'reanalysis_avg_temp_k', 'reanalysis_dew_point_temp_k',\n",
    "                      'reanalysis_max_air_temp_k', 'reanalysis_min_air_temp_k',\n",
    "                      'reanalysis_precip_amt_kg_per_m2',\n",
    "                      'reanalysis_relative_humidity_percent', 'reanalysis_sat_precip_amt_mm',\n",
    "                      'reanalysis_specific_humidity_g_per_kg', 'reanalysis_tdtr_k',\n",
    "                      'station_avg_temp_c', 'station_diur_temp_rng_c', 'station_max_temp_c',\n",
    "                      'station_min_temp_c', 'station_precip_mm']\n",
    "\n",
    "COMMON_TRAINING_FEATURES = ['reanalysis_dew_point_temp_k', 'reanalysis_precip_amt_kg_per_m2', \n",
    "                   'reanalysis_specific_humidity_g_per_kg', 'station_avg_temp_c',  'station_max_temp_c', \n",
    "                   'station_min_temp_c']\n",
    "\n",
    "SJ_FEATURES = ['reanalysis_dew_point_temp_k', 'reanalysis_precip_amt_kg_per_m2', 'reanalysis_specific_humidity_g_per_kg',\n",
    "               'station_avg_temp_c',  'station_max_temp_c', 'station_min_temp_c']\n",
    "\n",
    "IQ_FEATURES = ['reanalysis_dew_point_temp_k', 'reanalysis_specific_humidity_g_per_kg',\n",
    "               'station_avg_temp_c', 'station_min_temp_c']\n",
    "\n",
    "NEW_FEATURES = ['recent_mean_dew_point', 'recent_mean_spec_humid', 'recent_sum_precip']\n",
    "\n",
    "TIME_SERIES_FEATURES = ['week_start_date']\n",
    "\n",
    "DROP_FEATURES = list(set(ALL_FEATURES)-set(COMMON_TRAINING_FEATURES)-set(KEYS)-set(TIME_SERIES_FEATURES))\n",
    "\n",
    "DROP_SJ_FEATURES = list(set(ALL_FEATURES)-set(SJ_FEATURES)-set(KEYS)-set(TIME_SERIES_FEATURES))\n",
    "DROP_IQ_FEATURES = list(set(ALL_FEATURES)-set(IQ_FEATURES)-set(KEYS)-set(TIME_SERIES_FEATURES))\n",
    "\n",
    "# Specific features for the cities\n",
    "FEATURES_SJ = COMMON_TRAINING_FEATURES\n",
    "FEATURES_IQ = COMMON_TRAINING_FEATURES "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop DROP_FEATURES from a given data set\n",
    "def drop_unnecessary_features(df,drop_features=DROP_FEATURES):\n",
    "    df.drop(drop_features, axis=1, inplace=True)\n",
    "    df.drop(TIME_SERIES_FEATURES, axis=1, inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filing Null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "\n",
    "imputer = Imputer(strategy='mean')\n",
    "\n",
    "def fill_null_values_with_mean(df,features_list):\n",
    "    imputer.fit(df[features_list])\n",
    "    df[features_list] = imputer.transform(df[features_list])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py: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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n",
      "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:543: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py: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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n",
      "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:543: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "train_sj = fill_null_values_with_mean(train_sj,COMMON_TRAINING_FEATURES)\n",
    "train_iq = fill_null_values_with_mean(train_iq,COMMON_TRAINING_FEATURES)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add Time Series features\n",
    "\n",
    "def add_time_series_features(df, window):\n",
    "    df.set_index('week_start_date', inplace=True)\n",
    "\n",
    "    roll_df = df.rolling(window=window, min_periods=1)\n",
    "    df['recent_mean_dew_point'] = roll_df.reanalysis_dew_point_temp_k.mean()\n",
    "    df['recent_mean_spec_humid'] = roll_df.reanalysis_specific_humidity_g_per_kg.mean()\n",
    "    df['recent_sum_precip'] = roll_df.reanalysis_precip_amt_kg_per_m2.sum()\n",
    "    \n",
    "    df.reset_index(inplace=True)    \n",
    "    return df\n",
    "\n",
    "# normalize data\n",
    "def normalize(feature):\n",
    "    return (feature - feature.mean()) / feature.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py: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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "train_sj = add_time_series_features(train_sj, window=10)\n",
    "train_iq = add_time_series_features(train_iq, window=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop some unnecessory Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:3940: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    }
   ],
   "source": [
    "train_sj = drop_unnecessary_features(train_sj)\n",
    "train_iq = drop_unnecessary_features(train_iq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:3391: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[k1] = value[k2]\n"
     ]
    }
   ],
   "source": [
    "FEATURES_TO_NRMLZE = COMMON_TRAINING_FEATURES + NEW_FEATURES\n",
    "\n",
    "train_sj[FEATURES_TO_NRMLZE] = train_sj[FEATURES_TO_NRMLZE].apply(normalize, axis=0)\n",
    "train_iq[FEATURES_TO_NRMLZE] = train_iq[FEATURES_TO_NRMLZE].apply(normalize, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spliting the training data set into train and test data sets\n",
    "\n",
    "Here we divide the tarining data set into two parts: for training and for testing.\n",
    "**X_cross_sj** is the testing data set extracted from the training data of city 'sj'\n",
    "**y_cross_sj** is the testing label values extracted from the training data of city 'sj'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# train_sj.set_index('index', inplace=True)\n",
    "# train_iq.set_index('index', inplace=True)\n",
    "\n",
    "y_sj = labels_train.loc[labels_train['city'] == 'sj',:]\n",
    "y_iq = labels_train.loc[labels_train['city'] == 'iq',:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train_sj: (748, 12)\n",
      "y_train_sj: (748, 4)\n",
      "X_cross_sj: (188, 12)\n",
      "y_cross_sj: (188, 4)\n"
     ]
    }
   ],
   "source": [
    "X_train_sj, X_cross_sj, y_train_sj, y_cross_sj = train_test_split(train_sj, \n",
    "                                                                  y_sj,\n",
    "                                                                  test_size=0.2,\n",
    "                                                                  stratify=train_sj.weekofyear)\n",
    "\n",
    "print(f'X_train_sj: {X_train_sj.shape}')\n",
    "print(f'y_train_sj: {y_train_sj.shape}')\n",
    "print(f'X_cross_sj: {X_cross_sj.shape}')\n",
    "print(f'y_cross_sj: {y_cross_sj.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train_iq: (416, 12)\n",
      "y_train_iq: (416, 4)\n",
      "X_cross_iq: (104, 12)\n",
      "y_cross_iq: (104, 4)\n"
     ]
    }
   ],
   "source": [
    "X_train_iq, X_cross_iq, y_train_iq, y_cross_iq = train_test_split(train_iq, \n",
    "                                                                  y_iq, \n",
    "                                                                  test_size=0.2,\n",
    "                                                                  stratify=train_iq.weekofyear)\n",
    "\n",
    "print(f'X_train_iq: {X_train_iq.shape}')\n",
    "print(f'y_train_iq: {y_train_iq.shape}')\n",
    "print(f'X_cross_iq: {X_cross_iq.shape}')\n",
    "print(f'y_cross_iq: {y_cross_iq.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare for Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_unnecessary_columns(df,features):\n",
    "    return df[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train_iq: (416, 9)\n",
      "y_train_iq: (416, 1)\n",
      "X_cross_iq: (104, 9)\n",
      "y_cross_iq: (104, 1)\n"
     ]
    }
   ],
   "source": [
    "X_FEATURES = COMMON_TRAINING_FEATURES + NEW_FEATURES + ['weekofyear']\n",
    "Y_FEATURES = ['total_cases']\n",
    "\n",
    "X_train_sj = drop_unnecessary_columns(X_train_sj,X_FEATURES)\n",
    "X_train_iq = drop_unnecessary_columns(X_train_iq,X_FEATURES)\n",
    "X_cross_sj = drop_unnecessary_columns(X_cross_sj,X_FEATURES)\n",
    "X_cross_iq = drop_unnecessary_columns(X_cross_iq,X_FEATURES)\n",
    "\n",
    "y_train_iq = drop_unnecessary_columns(y_train_iq,Y_FEATURES)\n",
    "y_cross_iq = drop_unnecessary_columns(y_cross_iq,Y_FEATURES)\n",
    "\n",
    "X_train_sj.drop(['station_min_temp_c'], axis=1, inplace=True)\n",
    "X_train_iq.drop(['station_min_temp_c'], axis=1, inplace=True)\n",
    "X_cross_sj.drop(['station_min_temp_c'], axis=1, inplace=True)\n",
    "X_cross_iq.drop(['station_min_temp_c'], axis=1, inplace=True)\n",
    "\n",
    "print(f'X_train_iq: {X_train_iq.shape}')\n",
    "print(f'y_train_iq: {y_train_iq.shape}')\n",
    "print(f'X_cross_iq: {X_cross_iq.shape}')\n",
    "print(f'y_cross_iq: {y_cross_iq.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the Model with Data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "# find the acurancy of the model with the mean absolute value\n",
    "def cross_validate_out_of_sample(reg, X_train, y_train, X_cross, y_cross):\n",
    "    reg.fit(X_train, y_train)\n",
    "    y_pred = reg.predict(X_cross)\n",
    "    return mean_absolute_error(y_true=y_cross, y_pred=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grid search to get the best score hyper parameters\n",
    "def grid_search_cross_val(reg, X, y, param_grid, scoring='neg_mean_absolute_error'):\n",
    "    grid = GridSearchCV(reg, param_grid=param_grid, scoring=scoring)\n",
    "    grid.fit(X, y)\n",
    "    print(\"Best score: {}\".format(np.abs(grid.best_score_)))\n",
    "    print(\"Best params: {}\".format(grid.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Check the best hyper param values for GradientBoostingRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# reg = GradientBoostingRegressor(random_state=67)\n",
    "\n",
    "# param_grid = [\n",
    "#     {'learning_rate': [0.1, 0.3, 1.0, 3.0], 'n_estimators': [10, 30, 100, 300, 500], \n",
    "#      'max_depth': [3, 5, 7, 9]}\n",
    "# ]\n",
    "\n",
    "# grid_search_cross_val(reg, X_train_sj, y_train_sj.total_cases, param_grid)\n",
    "# grid_search_cross_val(reg, X_train_iq, y_train_iq.total_cases, param_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Check the best hyper param values for RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# reg = RandomForestRegressor(random_state=67)\n",
    "\n",
    "# param_grid = [\n",
    "#     {\n",
    "#       'n_estimators': [10, 30, 100, 300, 500], \n",
    "#       'max_depth': [3, 5, 7, None]\n",
    "#     } \n",
    "# ]\n",
    "\n",
    "# grid_search_cross_val(reg, X_train_sj, y_train_sj.total_cases, param_grid)\n",
    "# grid_search_cross_val(reg, X_train_iq, y_train_iq.total_cases, param_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reanalysis_dew_point_temp_k</th>\n",
       "      <th>reanalysis_precip_amt_kg_per_m2</th>\n",
       "      <th>reanalysis_specific_humidity_g_per_kg</th>\n",
       "      <th>station_avg_temp_c</th>\n",
       "      <th>station_max_temp_c</th>\n",
       "      <th>recent_mean_dew_point</th>\n",
       "      <th>recent_mean_spec_humid</th>\n",
       "      <th>recent_sum_precip</th>\n",
       "      <th>weekofyear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>-0.032469</td>\n",
       "      <td>-0.385884</td>\n",
       "      <td>-0.059791</td>\n",
       "      <td>9.783443e-01</td>\n",
       "      <td>0.149514</td>\n",
       "      <td>-0.665149</td>\n",
       "      <td>-0.690029</td>\n",
       "      <td>-1.014849</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>-0.567770</td>\n",
       "      <td>-0.604480</td>\n",
       "      <td>-0.661895</td>\n",
       "      <td>3.999434e-15</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.721338</td>\n",
       "      <td>-0.791119</td>\n",
       "      <td>-0.377973</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>-1.399560</td>\n",
       "      <td>-0.967210</td>\n",
       "      <td>-1.441556</td>\n",
       "      <td>-3.725443e-01</td>\n",
       "      <td>-0.462451</td>\n",
       "      <td>-1.068470</td>\n",
       "      <td>-1.049054</td>\n",
       "      <td>-0.939731</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>0.369259</td>\n",
       "      <td>-0.222786</td>\n",
       "      <td>0.316152</td>\n",
       "      <td>-1.473962e-01</td>\n",
       "      <td>-0.385955</td>\n",
       "      <td>0.231450</td>\n",
       "      <td>0.246222</td>\n",
       "      <td>1.074152</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0.317651</td>\n",
       "      <td>2.579037</td>\n",
       "      <td>0.291354</td>\n",
       "      <td>-9.354145e-01</td>\n",
       "      <td>-1.533390</td>\n",
       "      <td>0.230234</td>\n",
       "      <td>0.192160</td>\n",
       "      <td>0.460591</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     reanalysis_dew_point_temp_k  reanalysis_precip_amt_kg_per_m2  \\\n",
       "378                    -0.032469                        -0.385884   \n",
       "418                    -0.567770                        -0.604480   \n",
       "312                    -1.399560                        -0.967210   \n",
       "353                     0.369259                        -0.222786   \n",
       "140                     0.317651                         2.579037   \n",
       "\n",
       "     reanalysis_specific_humidity_g_per_kg  station_avg_temp_c  \\\n",
       "378                              -0.059791        9.783443e-01   \n",
       "418                              -0.661895        3.999434e-15   \n",
       "312                              -1.441556       -3.725443e-01   \n",
       "353                               0.316152       -1.473962e-01   \n",
       "140                               0.291354       -9.354145e-01   \n",
       "\n",
       "     station_max_temp_c  recent_mean_dew_point  recent_mean_spec_humid  \\\n",
       "378            0.149514              -0.665149               -0.690029   \n",
       "418            0.000000              -0.721338               -0.791119   \n",
       "312           -0.462451              -1.068470               -1.049054   \n",
       "353           -0.385955               0.231450                0.246222   \n",
       "140           -1.533390               0.230234                0.192160   \n",
       "\n",
       "     recent_sum_precip  weekofyear  \n",
       "378          -1.014849          41  \n",
       "418          -0.377973          29  \n",
       "312          -0.939731          26  \n",
       "353           1.074152          16  \n",
       "140           0.460591          11  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_iq.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.520732184233115"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model with SJ data set\n",
    "reg_sj = GradientBoostingRegressor(learning_rate=0.1, max_depth=5, n_estimators=30, random_state=67)\n",
    "cross_validate_out_of_sample(reg_sj, X_train_sj, y_train_sj.total_cases, X_cross_sj, y_cross_sj.total_cases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.701118809817512"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model with IQ data set\n",
    "reg_iq = GradientBoostingRegressor(learning_rate=0.1, max_depth=5, n_estimators=30, random_state=67)\n",
    "cross_validate_out_of_sample(reg_iq, X_train_iq, y_train_iq.total_cases, X_cross_iq, y_cross_iq.total_cases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Submission the results\n",
    "def submission(pred):\n",
    "    submission = pd.read_csv(\"data/submission_format.csv\", index_col=[0, 1, 2])\n",
    "    submission['total_cases'] = pred['total_cases']\n",
    "    submission.to_csv(\"./submissions/sub_DengAI.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocess the Test Data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py: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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n",
      "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:543: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py: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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n",
      "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:543: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n",
      "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:3391: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[k1] = value[k2]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_sj: (936, 9)\n",
      "train_iq: (520, 9)\n",
      "X_test_sj: (260, 9)\n",
      "X_test_iq: (156, 9)\n"
     ]
    }
   ],
   "source": [
    "train_sj = drop_unnecessary_columns(train_sj,X_FEATURES)\n",
    "train_iq = drop_unnecessary_columns(train_iq,X_FEATURES)\n",
    "\n",
    "# Impute the Values\n",
    "X_test_sj = features_test.loc[features_test['city']=='sj',:]\n",
    "X_test_iq = features_test.loc[features_test['city']=='iq',:]\n",
    "\n",
    "predict_sj = X_test_sj[KEYS].copy()\n",
    "predict_iq = X_test_iq[KEYS].copy()\n",
    "\n",
    "# FIll values\n",
    "X_test_sj = fill_null_values_with_mean(X_test_sj,COMMON_TRAINING_FEATURES)\n",
    "X_test_iq = fill_null_values_with_mean(X_test_iq,COMMON_TRAINING_FEATURES)\n",
    "\n",
    "\n",
    "# Normalization\n",
    "X_test_sj = add_time_series_features(X_test_sj, window=10)\n",
    "X_test_iq = add_time_series_features(X_test_iq, window=10)\n",
    "\n",
    "X_test_sj[FEATURES_TO_NRMLZE] = X_test_sj[FEATURES_TO_NRMLZE].apply(normalize, axis=0)\n",
    "X_test_iq[FEATURES_TO_NRMLZE] = X_test_iq[FEATURES_TO_NRMLZE].apply(normalize, axis=0)\n",
    "\n",
    "# Drop columns \n",
    "X_test_sj = drop_unnecessary_columns(X_test_sj,X_FEATURES)\n",
    "X_test_iq = drop_unnecessary_columns(X_test_iq,X_FEATURES)\n",
    "\n",
    "train_sj.drop(['station_min_temp_c'], axis=1, inplace=True)\n",
    "train_iq.drop(['station_min_temp_c'], axis=1, inplace=True)\n",
    "X_test_sj.drop(['station_min_temp_c'], axis=1, inplace=True)\n",
    "X_test_iq.drop(['station_min_temp_c'], axis=1, inplace=True)\n",
    "\n",
    "print(f'train_sj: {train_sj.shape}')\n",
    "print(f'train_iq: {train_iq.shape}')\n",
    "print(f'X_test_sj: {X_test_sj.shape}')\n",
    "print(f'X_test_iq: {X_test_iq.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "                          learning_rate=0.1, loss='ls', max_depth=5,\n",
       "                          max_features=None, max_leaf_nodes=None,\n",
       "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                          min_samples_leaf=1, min_samples_split=2,\n",
       "                          min_weight_fraction_leaf=0.0, n_estimators=30,\n",
       "                          n_iter_no_change=None, presort='auto',\n",
       "                          random_state=67, subsample=1.0, tol=0.0001,\n",
       "                          validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train models with full dataset\n",
    "reg_sj = GradientBoostingRegressor(learning_rate=0.1, max_depth=5, n_estimators=30, random_state=67)\n",
    "reg_sj.fit(train_sj, label_sj.total_cases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "                          learning_rate=0.1, loss='ls', max_depth=5,\n",
       "                          max_features=None, max_leaf_nodes=None,\n",
       "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                          min_samples_leaf=1, min_samples_split=2,\n",
       "                          min_weight_fraction_leaf=0.0, n_estimators=500,\n",
       "                          n_iter_no_change=None, presort='auto',\n",
       "                          random_state=67, subsample=1.0, tol=0.0001,\n",
       "                          validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_iq = GradientBoostingRegressor(learning_rate=0.1, max_depth=5, n_estimators=500, random_state=67)\n",
    "reg_iq.fit(train_iq, label_iq.total_cases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>year</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>total_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  city  year  weekofyear  total_cases\n",
       "0   sj  2008          18           11\n",
       "1   sj  2008          19            9\n",
       "2   sj  2008          20           11\n",
       "3   sj  2008          21           11\n",
       "4   sj  2008          22           11"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict SJ\n",
    "y_sj_pred = reg_sj.predict(X_test_sj)\n",
    "predict_sj['total_cases'] = y_sj_pred.round().astype(int)\n",
    "predict_sj.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>year</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>total_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>iq</td>\n",
       "      <td>2010</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    city  year  weekofyear  total_cases\n",
       "260   iq  2010          26            4\n",
       "261   iq  2010          27            5\n",
       "262   iq  2010          28            1\n",
       "263   iq  2010          29            5\n",
       "264   iq  2010          30            5"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict IQ\n",
    "y_iq_pred = reg_iq.predict(X_test_iq)\n",
    "predict_iq['total_cases'] = y_iq_pred.round().astype(int)\n",
    "predict_iq.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>year</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>total_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>iq</td>\n",
       "      <td>2011</td>\n",
       "      <td>24</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>iq</td>\n",
       "      <td>2011</td>\n",
       "      <td>32</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>iq</td>\n",
       "      <td>2012</td>\n",
       "      <td>32</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>iq</td>\n",
       "      <td>2012</td>\n",
       "      <td>33</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>iq</td>\n",
       "      <td>2012</td>\n",
       "      <td>34</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    city  year  weekofyear  total_cases\n",
       "310   iq  2011          24           -1\n",
       "318   iq  2011          32           -1\n",
       "370   iq  2012          32           -1\n",
       "371   iq  2012          33           -1\n",
       "372   iq  2012          34           -1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_df = pd.concat([predict_sj, predict_iq], axis=0)\n",
    "predict_df[predict_df.total_cases < 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>year</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>total_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [city, year, weekofyear, total_cases]\n",
       "Index: []"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_df.loc[predict_df.total_cases < 0, 'total_cases'] = 0\n",
    "predict_df[predict_df.total_cases < 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>year</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>total_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  city  year  weekofyear  total_cases\n",
       "0   sj  2008          18           11\n",
       "1   sj  2008          19            9\n",
       "2   sj  2008          20           11\n",
       "3   sj  2008          21           11\n",
       "4   sj  2008          22           11"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      year  weekofyear  total_cases\n",
      "city                               \n",
      "sj    2008          18           11\n",
      "sj    2008          19            9\n",
      "sj    2008          20           11\n",
      "sj    2008          21           11\n",
      "sj    2008          22           11\n",
      "sj    2008          23           12\n",
      "sj    2008          24           12\n",
      "sj    2008          25           14\n",
      "sj    2008          26           20\n",
      "sj    2008          27           13\n",
      "sj    2008          28           13\n",
      "sj    2008          29           17\n",
      "sj    2008          30           21\n",
      "sj    2008          31           34\n",
      "sj    2008          32           30\n",
      "sj    2008          33           25\n",
      "sj    2008          34           33\n",
      "sj    2008          35           37\n",
      "sj    2008          36           38\n",
      "sj    2008          37           42\n",
      "sj    2008          38           46\n",
      "sj    2008          39           42\n",
      "sj    2008          40           43\n",
      "sj    2008          41           42\n",
      "sj    2008          42           55\n",
      "sj    2008          43           37\n",
      "sj    2008          44           39\n",
      "sj    2008          45           41\n",
      "sj    2008          46           39\n",
      "sj    2008          47           39\n",
      "...    ...         ...          ...\n",
      "iq    2012          48           13\n",
      "iq    2012          49           46\n",
      "iq    2012          50           14\n",
      "iq    2012          51           13\n",
      "iq    2013           1           43\n",
      "iq    2013           2            6\n",
      "iq    2013           3            1\n",
      "iq    2013           4            8\n",
      "iq    2013           5           10\n",
      "iq    2013           6            9\n",
      "iq    2013           7           13\n",
      "iq    2013           8           13\n",
      "iq    2013           9           21\n",
      "iq    2013          10           13\n",
      "iq    2013          11            9\n",
      "iq    2013          12            8\n",
      "iq    2013          13            6\n",
      "iq    2013          14            8\n",
      "iq    2013          15            7\n",
      "iq    2013          16            7\n",
      "iq    2013          17            4\n",
      "iq    2013          18            2\n",
      "iq    2013          19            6\n",
      "iq    2013          20            5\n",
      "iq    2013          21            4\n",
      "iq    2013          22            4\n",
      "iq    2013          23            4\n",
      "iq    2013          24            3\n",
      "iq    2013          25            2\n",
      "iq    2013          26            1\n",
      "\n",
      "[416 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "submission = pd.read_csv(\"./data/submission_format.csv\")\n",
    "\n",
    "submission['total_cases'] = predict_df['total_cases']\n",
    "submission.set_index('city', inplace=True)\n",
    "print(submission)\n",
    "submission.to_csv(\"./submissions/sub_DengAI.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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