{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/covid19-global-forecasting-week-2/test.csv\n",
      "/kaggle/input/covid19-global-forecasting-week-2/train.csv\n",
      "/kaggle/input/covid19-global-forecasting-week-2/submission.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-2/train.csv')\n",
    "df_submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-2/submission.csv')\n",
    "df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-2/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.rename(columns={'Country_Region':'Country'}, inplace=True)\n",
    "df_test.rename(columns={'Country_Region':'Country'}, inplace=True)\n",
    "\n",
    "df_train.rename(columns={'Province_State':'State'}, inplace=True)\n",
    "df_test.rename(columns={'Province_State':'State'}, inplace=True)\n",
    "\n",
    "df_train['Date'] = pd.to_datetime(df_train['Date'], infer_datetime_format=True)\n",
    "df_test['Date'] = pd.to_datetime(df_test['Date'], infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "1    0.0\n",
       "2    0.0\n",
       "3    0.0\n",
       "4    0.0\n",
       "Name: Fatalities, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1_Train = df_train.iloc[:, -2]\n",
    "y1_Train.head()\n",
    "\n",
    "y2_Train = df_train.iloc[:, -1]\n",
    "y2_Train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "EMPTY_VAL = \"EMPTY_VAL\"\n",
    "\n",
    "def fillState(state, country):\n",
    "    if state == EMPTY_VAL: return country\n",
    "    return state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Id</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "      <th>ConfirmedCases</th>\n",
       "      <th>Fatalities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>124</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>126</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id        State      Country  Date  ConfirmedCases  Fatalities\n",
       "0   1  Afghanistan  Afghanistan   122             0.0         0.0\n",
       "1   2  Afghanistan  Afghanistan   123             0.0         0.0\n",
       "2   3  Afghanistan  Afghanistan   124             0.0         0.0\n",
       "3   4  Afghanistan  Afghanistan   125             0.0         0.0\n",
       "4   5  Afghanistan  Afghanistan   126             0.0         0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#X_Train = df_train.loc[:, ['State', 'Country', 'Date']]\n",
    "X_Train = df_train.copy()\n",
    "\n",
    "X_Train['State'].fillna(EMPTY_VAL, inplace=True)\n",
    "X_Train['State'] = X_Train.loc[:, ['State', 'Country']].apply(lambda x : fillState(x['State'], x['Country']), axis=1)\n",
    "\n",
    "X_Train.loc[:, 'Date'] = X_Train.Date.dt.strftime(\"%m%d\")\n",
    "X_Train[\"Date\"]  = X_Train[\"Date\"].astype(int)\n",
    "\n",
    "X_Train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>ForecastId</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ForecastId        State      Country  Date\n",
       "0           1  Afghanistan  Afghanistan   319\n",
       "1           2  Afghanistan  Afghanistan   320\n",
       "2           3  Afghanistan  Afghanistan   321\n",
       "3           4  Afghanistan  Afghanistan   322\n",
       "4           5  Afghanistan  Afghanistan   323"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#X_Test = df_test.loc[:, ['State', 'Country', 'Date']]\n",
    "X_Test = df_test.copy()\n",
    "\n",
    "X_Test['State'].fillna(EMPTY_VAL, inplace=True)\n",
    "X_Test['State'] = X_Test.loc[:, ['State', 'Country']].apply(lambda x : fillState(x['State'], x['Country']), axis=1)\n",
    "\n",
    "X_Test.loc[:, 'Date'] = X_Test.Date.dt.strftime(\"%m%d\")\n",
    "X_Test[\"Date\"]  = X_Test[\"Date\"].astype(int)\n",
    "\n",
    "X_Test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Id</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "      <th>ConfirmedCases</th>\n",
       "      <th>Fatalities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>126</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  State  Country  Date  ConfirmedCases  Fatalities\n",
       "0   1      0        0   122             0.0         0.0\n",
       "1   2      0        0   123             0.0         0.0\n",
       "2   3      0        0   124             0.0         0.0\n",
       "3   4      0        0   125             0.0         0.0\n",
       "4   5      0        0   126             0.0         0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "le = preprocessing.LabelEncoder()\n",
    "\n",
    "X_Train.Country = le.fit_transform(X_Train.Country)\n",
    "X_Train['State'] = le.fit_transform(X_Train['State'])\n",
    "\n",
    "X_Train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>ForecastId</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ForecastId  State  Country  Date\n",
       "0           1      0        0   319\n",
       "1           2      0        0   320\n",
       "2           3      0        0   321\n",
       "3           4      0        0   322\n",
       "4           5      0        0   323"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_Test.Country = le.fit_transform(X_Test.Country)\n",
    "X_Test['State'] = le.fit_transform(X_Test['State'])\n",
    "\n",
    "X_Test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV \n",
    "import time \n",
    "param_grid = {'n_estimators': [1000]}\n",
    "\n",
    "def gridSearchCV(model, X_Train, y_Train, param_grid, cv=10, scoring='neg_mean_squared_error'): \n",
    "    start = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor\n",
    "\n",
    "model = XGBRegressor()\n",
    "\n",
    "model1 = gridSearchCV(model, X_Train, y1_Train, param_grid, 10, 'neg_mean_squared_error') \n",
    "model2 = gridSearchCV(model, X_Train, y2_Train, param_grid, 10, 'neg_mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "le = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor\n",
    "\n",
    "countries = X_Train.Country.unique()\n",
    "\n",
    "#models_C = {}\n",
    "#models_F = {}\n",
    "\n",
    "df_out = pd.DataFrame({'ForecastId': [], 'ConfirmedCases': [], 'Fatalities': []})\n",
    "\n",
    "for country in countries:\n",
    "    states = X_Train.loc[X_Train.Country == country, :].State.unique()\n",
    "    #print(country, states)\n",
    "    # check whether string is nan or not\n",
    "    for state in states:\n",
    "        X_Train_CS = X_Train.loc[(X_Train.Country == country) & (X_Train.State == state), ['State', 'Country', 'Date', 'ConfirmedCases', 'Fatalities']]\n",
    "        \n",
    "        y1_Train_CS = X_Train_CS.loc[:, 'ConfirmedCases']\n",
    "        y2_Train_CS = X_Train_CS.loc[:, 'Fatalities']\n",
    "        \n",
    "        X_Train_CS = X_Train_CS.loc[:, ['State', 'Country', 'Date']]\n",
    "        \n",
    "        X_Train_CS.Country = le.fit_transform(X_Train_CS.Country)\n",
    "        X_Train_CS['State'] = le.fit_transform(X_Train_CS['State'])\n",
    "        \n",
    "        X_Test_CS = X_Test.loc[(X_Test.Country == country) & (X_Test.State == state), ['State', 'Country', 'Date', 'ForecastId']]\n",
    "        \n",
    "        X_Test_CS_Id = X_Test_CS.loc[:, 'ForecastId']\n",
    "        X_Test_CS = X_Test_CS.loc[:, ['State', 'Country', 'Date']]\n",
    "        \n",
    "        X_Test_CS.Country = le.fit_transform(X_Test_CS.Country)\n",
    "        X_Test_CS['State'] = le.fit_transform(X_Test_CS['State'])\n",
    "        \n",
    "        #models_C[country] = gridSearchCV(model, X_Train_CS, y1_Train_CS, param_grid, 10, 'neg_mean_squared_error')\n",
    "        #models_F[country] = gridSearchCV(model, X_Train_CS, y2_Train_CS, param_grid, 10, 'neg_mean_squared_error')\n",
    "        \n",
    "        model1 = XGBRegressor(n_estimators=1000)\n",
    "        model1.fit(X_Train_CS, y1_Train_CS)\n",
    "        y1_pred = model1.predict(X_Test_CS)\n",
    "        \n",
    "        model2 = XGBRegressor(n_estimators=1000)\n",
    "        model2.fit(X_Train_CS, y2_Train_CS)\n",
    "        y2_pred = model2.predict(X_Test_CS)\n",
    "        \n",
    "        df = pd.DataFrame({'ForecastId': X_Test_CS_Id, 'ConfirmedCases': y1_pred, 'Fatalities': y2_pred})\n",
    "        df_out = pd.concat([df_out, df], axis=0)\n",
    "    # Done for state loop\n",
    "# Done for country Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>ForecastId</th>\n",
       "      <th>ConfirmedCases</th>\n",
       "      <th>Fatalities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12637</th>\n",
       "      <td>12638</td>\n",
       "      <td>6.998692</td>\n",
       "      <td>0.999658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12638</th>\n",
       "      <td>12639</td>\n",
       "      <td>6.998692</td>\n",
       "      <td>0.999658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12639</th>\n",
       "      <td>12640</td>\n",
       "      <td>6.998692</td>\n",
       "      <td>0.999658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12640</th>\n",
       "      <td>12641</td>\n",
       "      <td>6.998692</td>\n",
       "      <td>0.999658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12641</th>\n",
       "      <td>12642</td>\n",
       "      <td>6.998692</td>\n",
       "      <td>0.999658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ForecastId  ConfirmedCases  Fatalities\n",
       "12637       12638        6.998692    0.999658\n",
       "12638       12639        6.998692    0.999658\n",
       "12639       12640        6.998692    0.999658\n",
       "12640       12641        6.998692    0.999658\n",
       "12641       12642        6.998692    0.999658"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_out.ForecastId = df_out.ForecastId.astype('int')\n",
    "df_out.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_out.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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