Colombia-mu.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Week_Number</th>\n",
       "      <th>State</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>amazonas</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>antioquia</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>arauca</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>atlantico</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>bogota</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1135</th>\n",
       "      <td>49</td>\n",
       "      <td>sucre</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136</th>\n",
       "      <td>49</td>\n",
       "      <td>tolima</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1137</th>\n",
       "      <td>49</td>\n",
       "      <td>valledelcauca</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>49</td>\n",
       "      <td>vaupes</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>49</td>\n",
       "      <td>vichada</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1140 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Week_Number          State  Cases\n",
       "0               1       amazonas    0.0\n",
       "1               1      antioquia   22.0\n",
       "2               1         arauca    0.0\n",
       "3               1      atlantico    4.0\n",
       "4               1         bogota    0.0\n",
       "...           ...            ...    ...\n",
       "1135           49          sucre    4.0\n",
       "1136           49         tolima    0.0\n",
       "1137           49  valledelcauca    5.0\n",
       "1138           49         vaupes    0.0\n",
       "1139           49        vichada    0.0\n",
       "\n",
       "[1140 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mu_df= pd.read_csv('data/colombia/Covid19_mu_preprocessed.csv')\n",
    "mu_df.iloc[:, 1] = pd.to_datetime(mu_df.iloc[:, 1])\n",
    "mu_df_m = mu_df.melt(id_vars=['Row_Num', 'Date'], \n",
    "              value_vars=['amazonas','antioquia','arauca','atlantico','bogota','bolivar','boyaca','caldas','caqueta','casanare','cesar','choco','cordoba','cundinamarca','guaviare ','huila','laguajira','magdalena','meta','narino','nortedesantander','quindio','risaralda','sanandres','santander','sucre','tolima','valledelcauca','vaupes','vichada'],\n",
    "              var_name='State', value_name='Cases')\n",
    "mu_df_m[mu_df_m['State'] == 'antioquia']\n",
    "mu_df_m['Week_Number'] = mu_df_m['Date'].dt.isocalendar().week\n",
    "co_mu_cases_df = mu_df_m.groupby(['Week_Number','State']).sum('Cases').drop('Row_Num', axis=1).reset_index()\n",
    "antioquia_mu_cases_df = co_mu_cases_df[co_mu_cases_df['State'] == 'antioquia'] \n",
    "plt.plot(antioquia_mu_cases_df['Week_Number'], antioquia_mu_cases_df['Cases'])\n",
    "co_mu_cases_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Week_Number</th>\n",
       "      <th>State</th>\n",
       "      <th>Cases</th>\n",
       "      <th>One_Week_Prior_Cases</th>\n",
       "      <th>Two_Week_Prior_Cases</th>\n",
       "      <th>Three_Week_Prior_Cases</th>\n",
       "      <th>State_Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>amazonas</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>antioquia</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>arauca</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>atlantico</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>bogota</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>105</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1135</th>\n",
       "      <td>49</td>\n",
       "      <td>sucre</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136</th>\n",
       "      <td>49</td>\n",
       "      <td>tolima</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1137</th>\n",
       "      <td>49</td>\n",
       "      <td>valledelcauca</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>49</td>\n",
       "      <td>vaupes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>49</td>\n",
       "      <td>vichada</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1140 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Week_Number          State  Cases  One_Week_Prior_Cases  \\\n",
       "0               1       amazonas    0.0                   0.0   \n",
       "1               1      antioquia   22.0                   0.0   \n",
       "2               1         arauca    0.0                   0.0   \n",
       "3               1      atlantico    4.0                   0.0   \n",
       "4               1         bogota    0.0                   0.0   \n",
       "...           ...            ...    ...                   ...   \n",
       "1135           49          sucre    4.0                   0.0   \n",
       "1136           49         tolima    0.0                   2.0   \n",
       "1137           49  valledelcauca    5.0                   1.0   \n",
       "1138           49         vaupes    0.0                   0.0   \n",
       "1139           49        vichada    0.0                   0.0   \n",
       "\n",
       "      Two_Week_Prior_Cases  Three_Week_Prior_Cases  State_Code  \n",
       "0                      0.0                     0.0         101  \n",
       "1                      0.0                     0.0         102  \n",
       "2                      0.0                     0.0         103  \n",
       "3                      0.0                     0.0         104  \n",
       "4                      0.0                     0.0         105  \n",
       "...                    ...                     ...         ...  \n",
       "1135                   0.0                     0.0         126  \n",
       "1136                   0.0                     0.0         127  \n",
       "1137                   0.0                     0.0         128  \n",
       "1138                   0.0                     0.0         129  \n",
       "1139                   0.0                     0.0         130  \n",
       "\n",
       "[1140 rows x 7 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co_mu_cases_df_past_week_df = co_mu_cases_df.copy() \n",
    "co_mu_cases_df_past_week_df[\"Week_Number\"] = co_mu_cases_df[\"Week_Number\"] + 1\n",
    "co_mu_cases_df_past_week_df.rename(columns = {'Cases':'One_Week_Prior_Cases'}, inplace = True)\n",
    "\n",
    "co_mu_cases_df_two_weeks_prior_df = co_mu_cases_df.copy() \n",
    "co_mu_cases_df_two_weeks_prior_df[\"Week_Number\"] = co_mu_cases_df[\"Week_Number\"] + 2\n",
    "co_mu_cases_df_two_weeks_prior_df.rename(columns = {'Cases':'Two_Week_Prior_Cases'}, inplace = True)\n",
    "\n",
    "co_mu_cases_df_three_weeks_prior_df = co_mu_cases_df.copy() \n",
    "co_mu_cases_df_three_weeks_prior_df[\"Week_Number\"] = co_mu_cases_df[\"Week_Number\"] + 3\n",
    "co_mu_cases_df_three_weeks_prior_df.rename(columns = {'Cases':'Three_Week_Prior_Cases'}, inplace = True)\n",
    "\n",
    "co_mu_cases_merged_df = co_mu_cases_df.merge(co_mu_cases_df_past_week_df[['One_Week_Prior_Cases',  'State', 'Week_Number']], how='left', left_on=['State', 'Week_Number'], right_on=['State', 'Week_Number'])\n",
    "co_mu_cases_merged_df = co_mu_cases_merged_df.merge(co_mu_cases_df_two_weeks_prior_df[['Two_Week_Prior_Cases',  'State', 'Week_Number']], how='left', left_on=['State', 'Week_Number'], right_on=['State', 'Week_Number'])\n",
    "co_mu_cases_merged_df = co_mu_cases_merged_df.merge(co_mu_cases_df_three_weeks_prior_df[['Three_Week_Prior_Cases',  'State', 'Week_Number']], how='left', left_on=['State', 'Week_Number'], right_on=['State', 'Week_Number'])\n",
    "co_mu_cases_merged_df = co_mu_cases_merged_df.fillna(0)\n",
    "states = {'amazonas':101,\n",
    "'antioquia':102,\n",
    "'arauca': 103,\n",
    "'atlantico':104,\n",
    "'bogota':105,\n",
    "'bolivar':106,\n",
    "'boyaca':107,\n",
    "'caldas':108,\n",
    "'caqueta':109,\n",
    "'casanare':110,\n",
    "'cesar':111,\n",
    "'choco':112,\n",
    "'cordoba':113,\n",
    "'cundinamarca':114,\n",
    "'guaviare ':115,\n",
    "'huila':116,\n",
    "'laguajira':117,\n",
    "'magdalena':118,\n",
    "'meta':119,\n",
    "'narino':120,\n",
    "'nortedesantander':121,\n",
    "'quindio':122,\n",
    "'risaralda':123,\n",
    "'sanandres':124,\n",
    "'santander':125,\n",
    "'sucre':126,\n",
    "'tolima':127,\n",
    "'valledelcauca':128,\n",
    "'vaupes':129,\n",
    "'vichada':130}\n",
    "co_mu_cases_merged_df['State_Code'] = co_mu_cases_merged_df['State'].map(states)\n",
    "co_mu_cases_merged_df\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: 28 : 32  predict: 32 : 34\n",
      "Fitting 2 folds for each of 48 candidates, totalling 96 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/snathikudi/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_split.py:670: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=2.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    5.4s\n",
      "[Parallel(n_jobs=4)]: Done  96 out of  96 | elapsed:   11.1s finished\n",
      "/Users/snathikudi/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_split.py:670: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=2.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13991518891850718\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.01, 'max_depth': 2, 'min_child_weight': 2, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.8}\n",
      "0    32\n",
      "1    33\n",
      "dtype: int64\n",
      "-307.64772739658235\n",
      "train: 30 : 34  predict: 34 : 36\n",
      "Fitting 2 folds for each of 48 candidates, totalling 96 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done  76 tasks      | elapsed:    7.3s\n",
      "[Parallel(n_jobs=4)]: Done  96 out of  96 | elapsed:    9.3s finished\n",
      "/Users/snathikudi/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_split.py:670: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=2.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0008935956540994661\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.01, 'max_depth': 2, 'min_child_weight': 2, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.8}\n",
      "0    34\n",
      "1    35\n",
      "dtype: int64\n",
      "-0.046802628983554806\n",
      "train: 32 : 36  predict: 36 : 38\n",
      "Fitting 2 folds for each of 48 candidates, totalling 96 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done  76 tasks      | elapsed:    7.2s\n",
      "[Parallel(n_jobs=4)]: Done  96 out of  96 | elapsed:    9.3s finished\n",
      "/Users/snathikudi/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_split.py:670: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=2.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n",
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10182056902438502\n",
      "{'colsample_bytree': 0.8, 'learning_rate': 0.01, 'max_depth': 1, 'min_child_weight': 2, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.8}\n",
      "0    36\n",
      "1    37\n",
      "dtype: int64\n",
      "-0.0857608968652448\n",
      "train: 34 : 38  predict: 38 : 40\n",
      "Fitting 2 folds for each of 48 candidates, totalling 96 fits\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import MultiTaskLassoCV\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "from sklearn.metrics import r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import warnings\n",
    "import matplotlib\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "import xgboost as xgb\n",
    "from datetime import datetime\n",
    "from sklearn.model_selection import GridSearchCV,TimeSeriesSplit\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "\n",
    "\n",
    "min_week=co_mu_cases_merged_df['Week_Number'].min()\n",
    "max_week=co_mu_cases_merged_df['Week_Number'].max()\n",
    "\n",
    "\n",
    "num_weeks=int((max_week-min_week))\n",
    "\n",
    "train_start_week = 28\n",
    "train_end_week = train_start_week + 4\n",
    "\n",
    "predict_start_week = train_end_week\n",
    "predict_end_week = predict_start_week + 2\n",
    "\n",
    "predicted_df_all_days = pd.DataFrame(columns=['Week_Number','State_code','Predicted_Weekly_Cases', 'Actual_Weekly_Cases'])\n",
    "\n",
    "for week_num in range(num_weeks):\n",
    "    if (predict_end_week > 42):\n",
    "        break\n",
    "    \n",
    "    print(\"train:\", train_start_week ,\":\", train_end_week, \n",
    "        \" predict:\" , predict_start_week, \":\",  predict_end_week)\n",
    "    train_week = pd.Series(range(train_start_week,train_end_week))\n",
    "    #print (\"training week series :\" , train_week.array)\n",
    "    X_train = co_mu_cases_merged_df[['Week_Number', 'State_Code', 'One_Week_Prior_Cases', 'Two_Week_Prior_Cases','Three_Week_Prior_Cases']].copy()\n",
    "    X_train_week = X_train.loc[(X_train['Week_Number'].isin(train_week))]\n",
    "    X_train_week =X_train_week.drop(['Week_Number'], axis=1)\n",
    "    Weekly_Cases_Deaths = co_mu_cases_merged_df.loc[(co_mu_cases_merged_df['Week_Number'].isin(train_week))]['Cases']  \n",
    "    predicted_df = pd.DataFrame(columns=['Week_Number','State_code','Predicted_Weekly_Cases', 'Actual_Weekly_Cases'])\n",
    "    if len(Weekly_Cases_Deaths) > 0:\n",
    "       \n",
    "        X_train = X_train_week\n",
    "        Y_train = Weekly_Cases_Deaths\n",
    "        \n",
    "        #reg = LassoCV(cv=5, random_state=0).fit(X_train, Y_train)\n",
    "        xgbReg = xgb.XGBRegressor()\n",
    "\n",
    "        parameters = {\n",
    "              'objective':['reg:squarederror'],\n",
    "              'learning_rate': [0.01, 0.05, 0.08], #so called `eta` value\n",
    "              'max_depth': [1,2,3,4],\n",
    "              'min_child_weight': [1,2],\n",
    "              'subsample': [0.8],\n",
    "              'colsample_bytree' : [0.8],\n",
    "              'n_estimators': [ 100,200]}\n",
    "        tscv = StratifiedKFold(n_splits=2, shuffle=False, random_state = None)\n",
    "        xgb_grid = GridSearchCV(xgbReg,\n",
    "                        parameters,\n",
    "                        cv = tscv,\n",
    "                        n_jobs = 4,\n",
    "                        scoring = 'r2',\n",
    "                        verbose=True)\n",
    "\n",
    "        xgb_grid.fit(X_train,Y_train)\n",
    "\n",
    "\n",
    "        print(xgb_grid.best_score_)\n",
    "        print(xgb_grid.best_params_)\n",
    "        results = xgb_grid.cv_results_\n",
    "        predict_week = pd.Series(range(predict_start_week, predict_end_week))\n",
    "        #print (\"predicting next week series :\" , predict_week.array)\n",
    "        print(predict_week)\n",
    "        X_predict = co_mu_cases_merged_df[['Week_Number', 'State_Code', 'One_Week_Prior_Cases', 'Two_Week_Prior_Cases','Three_Week_Prior_Cases']].copy()\n",
    "        X_predict_week = X_predict.loc[(X_predict['Week_Number'].isin(predict_week))] \n",
    "        X_predict_week =X_predict_week.drop(['Week_Number'], axis=1)\n",
    "        predict_week_df = co_mu_cases_merged_df.loc[(co_mu_cases_merged_df['Week_Number'].isin(predict_week))]\n",
    "        Y_actual_predict_week = predict_week_df['Cases']\n",
    "        #best_reg.fit(X_tune_week, Y_actual_tune_week)\n",
    "        Y_predict_week = xgb_grid.best_estimator_.predict(X_predict_week)\n",
    "        \n",
    "\n",
    "        predict_score = r2_score(Y_actual_predict_week, Y_predict_week)\n",
    "        print(predict_score)\n",
    "\n",
    "        predicted_df[\"State_Code\"] = predict_week_df['State_Code']\n",
    "        predicted_df[\"Week_Number\"] = predict_week_df['Week_Number']\n",
    "        \n",
    "        predicted_df[\"Predicted_Weekly_Cases\"] = Y_predict_week\n",
    "        predicted_df[\"Actual_Weekly_Cases\"] = Y_actual_predict_week\n",
    "        #predicted_df.to_csv(\"data/predictions/predicted_us_deaths_\" +  forecast_day + \".csv\")                     \n",
    "        predicted_df_all_days = pd.concat([predicted_df_all_days, predicted_df])\n",
    "        \n",
    "        \n",
    "    train_start_week = train_start_week + 2\n",
    "    train_end_week = train_end_week + 2\n",
    "    predict_start_week = train_end_week\n",
    "    predict_end_week = predict_start_week + 2\n",
    "predicted_df_all_days.to_csv(\"predicted_all_weeks_mu.csv\", index=False)  \n",
    "   \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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