DM_Regression_and_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import math\n",
    "import pickle\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### EPIDEMIC dataset #####\n",
    "## Cases and Testing\n",
    "cases_malaysia = pickle.load(open('pickle_files/cases_malaysia.pkl', 'rb'))\n",
    "cases_state = pickle.load(open('pickle_files/cases_state.pkl', 'rb')) \n",
    "\n",
    "tests_malaysia = pickle.load(open('pickle_files/tests_malaysia.pkl', 'rb')) \n",
    "tests_state = pickle.load(open('pickle_files/tests_state.pkl', 'rb')) \n",
    "\n",
    "## Deaths\n",
    "deaths_malaysia = pickle.load(open('pickle_files/deaths_malaysia.pkl', 'rb'))\n",
    "deaths_state = pickle.load(open('pickle_files/deaths_state.pkl', 'rb')) \n",
    "\n",
    "## Static Data\n",
    "population = pd.read_csv('dataset/static/population_moh.csv')\n",
    "\n",
    "##### VACCINATION & REGISTRATION dataset #####\n",
    "## Adverse Events Following Immunization (AEFI)\n",
    "# aefi = pickle.load(open('pickle_files/aefi.pkl', 'rb'))\n",
    "# aefi_serious = pickle.load(open('pickle_files/aefi_serious.pkl', 'rb'))\n",
    "\n",
    "## Vaccination\n",
    "vax_malaysia = pickle.load(open('pickle_files/vax_malaysia.pkl', 'rb'))\n",
    "vax_state = pickle.load(open('pickle_files/vax_state.pkl', 'rb'))\n",
    "\n",
    "## Registration\n",
    "# vaxreg_malaysia = pickle.load(open('pickle_files/vaxreg_malaysia.pkl', 'rb'))\n",
    "# vaxreg_state = pickle.load(open('pickle_files/vaxreg_state.pkl', 'rb'))\n",
    "\n",
    "##### MYSEJAHTERA dataset #####\n",
    "checkin_malaysia = pickle.load(open('pickle_files/checkin_malaysia.pkl', 'rb'))\n",
    "checkin_malaysia_time = pickle.load(open('pickle_files/checkin_malaysia_time.pkl', 'rb'))\n",
    "checkin_state = pickle.load(open('pickle_files/checkin_state.pkl', 'rb'))\n",
    "trace_malaysia = pickle.load(open('pickle_files/trace_malaysia.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Todo\n",
    "\n",
    "1. Identify data range and merge data (data preprocessing)\n",
    "2. Find a way to group previous data (window sliding if needed)\n",
    "3. Identify key dates of MCO and label it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cases_malaysia\n",
    "2020-01-25 to 2021-10-05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>cases_new</th>\n",
       "      <th>cases_import</th>\n",
       "      <th>cases_recovered</th>\n",
       "      <th>cases_active</th>\n",
       "      <th>cases_cluster</th>\n",
       "      <th>cases_pvax</th>\n",
       "      <th>cases_fvax</th>\n",
       "      <th>cases_child</th>\n",
       "      <th>cases_adolescent</th>\n",
       "      <th>cases_adult</th>\n",
       "      <th>cases_elderly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  cases_new  cases_import  cases_recovered  cases_active  \\\n",
       "0  2020-01-25          4             4                0             4   \n",
       "\n",
       "   cases_cluster  cases_pvax  cases_fvax  cases_child  cases_adolescent  \\\n",
       "0              0           0           0            0                 0   \n",
       "\n",
       "   cases_adult  cases_elderly  \n",
       "0            1              0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cases_malaysia.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tests_malaysia\n",
    "2020-01-24 to 2021-10-02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>rtk-ag</th>\n",
       "      <th>pcr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "         date  rtk-ag  pcr\n",
       "0  2020-01-24       0    2"
      ]
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     "execution_count": 4,
     "metadata": {},
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   "source": [
    "tests_malaysia.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### deaths_malaysia\n",
    "2020-03-17 to 2021-10-05\n",
    "<br>\n",
    "[First patient to die from Covid19 occured on 03-17](https://www.channelnewsasia.com/asia/malaysia-first-covid-19-death-kuching-sarawak-pastor-church-772771)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>deaths_new</th>\n",
       "      <th>deaths_bid</th>\n",
       "      <th>deaths_new_dod</th>\n",
       "      <th>deaths_bid_dod</th>\n",
       "      <th>deaths_pvax</th>\n",
       "      <th>deaths_fvax</th>\n",
       "      <th>deaths_tat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "         date  deaths_new  deaths_bid  deaths_new_dod  deaths_bid_dod  \\\n",
       "0  2020-03-17           2           0               2               0   \n",
       "\n",
       "   deaths_pvax  deaths_fvax  deaths_tat  \n",
       "0            0            0           0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deaths_malaysia.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### vax_malaysia\n",
    "2021-02-24 to 2021-10-05\n",
    "<br>\n",
    "[First vaccination started at Feb 24](https://www.straitstimes.com/asia/se-asia/pm-muhyiddin-receives-first-covid-19-vaccine-as-malaysia-kicks-off-mass-inoculation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>daily_partial</th>\n",
       "      <th>daily_full</th>\n",
       "      <th>daily</th>\n",
       "      <th>daily_partial_child</th>\n",
       "      <th>daily_full_child</th>\n",
       "      <th>cumul_partial</th>\n",
       "      <th>cumul_full</th>\n",
       "      <th>cumul</th>\n",
       "      <th>cumul_partial_child</th>\n",
       "      <th>cumul_full_child</th>\n",
       "      <th>pfizer1</th>\n",
       "      <th>pfizer2</th>\n",
       "      <th>sinovac1</th>\n",
       "      <th>sinovac2</th>\n",
       "      <th>astra1</th>\n",
       "      <th>astra2</th>\n",
       "      <th>cansino</th>\n",
       "      <th>pending</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-02-24</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        date  daily_partial  daily_full  daily  daily_partial_child  \\\n",
       "0 2021-02-24             60           0     60                    0   \n",
       "\n",
       "   daily_full_child  cumul_partial  cumul_full  cumul  cumul_partial_child  \\\n",
       "0                 0             60           0     60                    0   \n",
       "\n",
       "   cumul_full_child  pfizer1  pfizer2  sinovac1  sinovac2  astra1  astra2  \\\n",
       "0                 0       60        0         0         0       0       0   \n",
       "\n",
       "   cansino  pending  \n",
       "0        0        0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vax_malaysia.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### checkin_malaysia\n",
    "2020-12-01 to 2021-10-05\n",
    "<br>\n",
    "Unable to track the reason why checkins data starts at 2020-12-01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>checkins</th>\n",
       "      <th>unique_ind</th>\n",
       "      <th>unique_loc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>17492265</td>\n",
       "      <td>6614474</td>\n",
       "      <td>630363</td>\n",
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      ],
      "text/plain": [
       "        date  checkins  unique_ind  unique_loc\n",
       "0 2020-12-01  17492265     6614474      630363"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkin_malaysia.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>checkins</th>\n",
       "      <th>unique_ind</th>\n",
       "      <th>unique_loc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>2021-10-05</td>\n",
       "      <td>28991261</td>\n",
       "      <td>10785507</td>\n",
       "      <td>887285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          date  checkins  unique_ind  unique_loc\n",
       "308 2021-10-05  28991261    10785507      887285"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkin_malaysia.tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### trace_malaysia\n",
    "2020-04-20 to 2021-10-05\n",
    "<br>\n",
    "Unable to track the reason why trace data starts at 2021-04-20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>casual_contacts</th>\n",
       "      <th>hide_large</th>\n",
       "      <th>hide_small</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-04-20</td>\n",
       "      <td>18550</td>\n",
       "      <td>70.0</td>\n",
       "      <td>628.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  casual_contacts  hide_large  hide_small\n",
       "0 2021-04-20            18550        70.0       628.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace_malaysia.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>casual_contacts</th>\n",
       "      <th>hide_large</th>\n",
       "      <th>hide_small</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>2021-10-05</td>\n",
       "      <td>37312</td>\n",
       "      <td>39.0</td>\n",
       "      <td>841.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          date  casual_contacts  hide_large  hide_small\n",
       "167 2021-10-05            37312        39.0       841.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace_malaysia.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "malaysia_population = population[population['state']=='Malaysia']['pop'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input\n",
    "# - df\n",
    "# - string of attribute name\n",
    "# - sliding window, default is 7\n",
    "# - shiftNum for shifting the column, default 0 means no shifting.\n",
    "#\n",
    "# output\n",
    "# df with 2 more attributes\n",
    "# - normalized dimension\n",
    "# - normalized mov avg dimension\n",
    "\n",
    "def calculateMovAvg(df, attribute_name, window_number=7, shiftNum=0):\n",
    "    temp = df.copy()\n",
    "    min_max_range = temp[attribute_name].max() - temp[attribute_name].min()\n",
    "    new_attribute_name =  attribute_name + \"_norm\"\n",
    "    new_attribute_name_ma =  attribute_name + \"_norm_ma\" + str(window_number) + \"_shift\" + str(shiftNum)\n",
    "    temp[new_attribute_name] = (temp[attribute_name] - temp[attribute_name].min()) / min_max_range# * 100\n",
    "    temp[new_attribute_name_ma] = temp[new_attribute_name].rolling(window=window_number).mean().shift(shiftNum)\n",
    "    return temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-14-5f8b1e97b035>:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_4['cumul_partial'] = df_4['cumul_partial'] / malaysia_population\n",
      "<ipython-input-14-5f8b1e97b035>:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_4['cumul_full'] = df_4['cumul_full'] / malaysia_population\n"
     ]
    }
   ],
   "source": [
    "# cases\n",
    "df_1 = cases_malaysia[['date','cases_new']]\n",
    "df_1 = calculateMovAvg(df_1, 'cases_new', 5, 0) # mov avg of last 7 days, including current day\n",
    "df_1 = calculateMovAvg(df_1, 'cases_new', 7, 0) # mov avg of last 7 days, including current day\n",
    "df_1 = calculateMovAvg(df_1, 'cases_new', 7, -1) # mov avg of last 7 days, excluding current day, training set label\n",
    "\n",
    "# tests\n",
    "df_2 = tests_malaysia[['date','rtk-ag','pcr']]\n",
    "df_2 = calculateMovAvg(df_2, 'rtk-ag')\n",
    "df_2 = calculateMovAvg(df_2, 'pcr')\n",
    "\n",
    "# deaths\n",
    "df_3 = deaths_malaysia[['date','deaths_new']]\n",
    "df_3 = calculateMovAvg(df_3, 'deaths_new')\n",
    "\n",
    "# vaccinations\n",
    "df_4 = vax_malaysia[['date','cumul_partial','cumul_full']]\n",
    "df_4['cumul_partial'] = df_4['cumul_partial'] / malaysia_population\n",
    "df_4['cumul_full'] = df_4['cumul_full'] / malaysia_population\n",
    "\n",
    "# checkins\n",
    "# checkin_malaysia\n",
    "\n",
    "# Trace\n",
    "# trace_malaysia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-15-cd430f9d9112>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_4['date'] = df_4['date'].astype('datetime64[ns]')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>cases_new</th>\n",
       "      <th>cases_new_norm</th>\n",
       "      <th>cases_new_norm_ma5_shift0</th>\n",
       "      <th>cases_new_norm_ma7_shift0</th>\n",
       "      <th>cases_new_norm_ma7_shift-1</th>\n",
       "      <th>rtk-ag</th>\n",
       "      <th>pcr</th>\n",
       "      <th>rtk-ag_norm</th>\n",
       "      <th>rtk-ag_norm_ma7_shift0</th>\n",
       "      <th>pcr_norm</th>\n",
       "      <th>pcr_norm_ma7_shift0</th>\n",
       "      <th>deaths_new</th>\n",
       "      <th>deaths_new_norm</th>\n",
       "      <th>deaths_new_norm_ma7_shift0</th>\n",
       "      <th>cumul_partial</th>\n",
       "      <th>cumul_full</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000163</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000565</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000764</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  cases_new  cases_new_norm  cases_new_norm_ma5_shift0  \\\n",
       "0 2020-01-25          4        0.000163                        NaN   \n",
       "1 2020-01-26          0        0.000000                        NaN   \n",
       "2 2020-01-27          0        0.000000                        NaN   \n",
       "3 2020-01-28          0        0.000000                        NaN   \n",
       "4 2020-01-29          3        0.000122                   0.000057   \n",
       "\n",
       "   cases_new_norm_ma7_shift0  cases_new_norm_ma7_shift-1  rtk-ag   pcr  \\\n",
       "0                        NaN                         NaN     0.0   5.0   \n",
       "1                        NaN                         NaN     0.0  14.0   \n",
       "2                        NaN                         NaN     0.0  24.0   \n",
       "3                        NaN                         NaN     0.0  53.0   \n",
       "4                        NaN                         NaN     0.0  71.0   \n",
       "\n",
       "   rtk-ag_norm  rtk-ag_norm_ma7_shift0  pcr_norm  pcr_norm_ma7_shift0  \\\n",
       "0          0.0                     NaN  0.000033                  NaN   \n",
       "1          0.0                     NaN  0.000133                  NaN   \n",
       "2          0.0                     NaN  0.000244                  NaN   \n",
       "3          0.0                     NaN  0.000565                  NaN   \n",
       "4          0.0                     NaN  0.000764                  NaN   \n",
       "\n",
       "   deaths_new  deaths_new_norm  deaths_new_norm_ma7_shift0  cumul_partial  \\\n",
       "0         NaN              NaN                         NaN            NaN   \n",
       "1         NaN              NaN                         NaN            NaN   \n",
       "2         NaN              NaN                         NaN            NaN   \n",
       "3         NaN              NaN                         NaN            NaN   \n",
       "4         NaN              NaN                         NaN            NaN   \n",
       "\n",
       "   cumul_full  \n",
       "0         NaN  \n",
       "1         NaN  \n",
       "2         NaN  \n",
       "3         NaN  \n",
       "4         NaN  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preprocess data\n",
    "df_1['date'] = df_1['date'].astype('datetime64[ns]')\n",
    "df_2['date'] = df_2['date'].astype('datetime64[ns]')\n",
    "df_3['date'] = df_3['date'].astype('datetime64[ns]')\n",
    "df_4['date'] = df_4['date'].astype('datetime64[ns]')\n",
    "\n",
    "# merge df_1 cases + df_2 test\n",
    "df_merged = pd.merge(df_1, df_2, on='date', how='left', indicator=True)\n",
    "df_merged.drop(['_merge'], axis=1, inplace=True)\n",
    "\n",
    "# merge df_merged + df_3 deaths\n",
    "df_merged = pd.merge(df_merged, df_3, on='date', how='left', indicator=True)\n",
    "df_merged.drop(['_merge'], axis=1, inplace=True)\n",
    "\n",
    "# merge df_merged + df_4 vaccinations\n",
    "df_merged = pd.merge(df_merged, df_4, on='date', how='left', indicator=True)\n",
    "df_merged.drop(['_merge'], axis=1, inplace=True)\n",
    "\n",
    "df_merged.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add new dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-02-14 00:00:00\n",
      "2020\n",
      "02\n",
      "06\n",
      "045\n",
      "Friday\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "import datetime \n",
    "\n",
    "# get a random date from any df with 'date'\n",
    "x = df_merged.iloc[20,0]\n",
    "\n",
    "# print out results\n",
    "print(x)\n",
    "print(x.strftime(\"%Y\")) # Year, full version\n",
    "print(x.strftime(\"%m\")) # Month as a number 01-12\n",
    "print(x.strftime(\"%U\")) # Week number of year, Sunday as the first day of week, 00-53\n",
    "print(x.strftime(\"%j\")) # Day number of year 001-366\n",
    "print(x.strftime(\"%A\")) # Weekday, full version\n",
    "print(x.strftime(\"%w\")) # Weekday as a number 0-6, 0 is Sunday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime \n",
    "\n",
    "# Year, full version\n",
    "def strftime_year(date):\n",
    "    return date.strftime(\"%Y\")\n",
    "\n",
    "# Month as a number 01-12\n",
    "def strftime_monthNumber(date):\n",
    "    return date.strftime(\"%m\")\n",
    "\n",
    "# Week number of year, Sunday as the first day of week, 00-53\n",
    "def strftime_weekNumber(date):\n",
    "    return date.strftime(\"%W\")\n",
    "\n",
    "# Day number of year 001-366\n",
    "def strftime_dayNumber(date):\n",
    "    return date.strftime(\"%j\")\n",
    "\n",
    "#Weekday as a number 0-6, 0 is Sunday\n",
    "def strftime_weekdayNumber(date):\n",
    "    return date.strftime(\"%w\")\n",
    "\n",
    "# Weekday, full version\n",
    "def strftime_weekdayName(date):\n",
    "    return date.strftime(\"%A\")\n",
    "\n",
    "# shortcut for strftime a dataframe\n",
    "def strftime_all(df):\n",
    "    df_dt = df.copy()\n",
    "    df_dt['year'] = df['date'].apply(strftime_year)\n",
    "    df_dt['month_number'] = df_dt['date'].apply(strftime_monthNumber)\n",
    "    df_dt['week_number'] = df_dt['date'].apply(strftime_weekNumber)\n",
    "    df_dt['day_number'] = df_dt['date'].apply(strftime_dayNumber)\n",
    "    df_dt['weekday_number'] = df_dt['date'].apply(strftime_weekdayNumber)\n",
    "    df_dt['weekday_name'] = df_dt['date'].apply(strftime_weekdayName)\n",
    "    return df_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>date</th>\n",
       "      <th>cases_new</th>\n",
       "      <th>cases_new_norm</th>\n",
       "      <th>cases_new_norm_ma5_shift0</th>\n",
       "      <th>cases_new_norm_ma7_shift0</th>\n",
       "      <th>cases_new_norm_ma7_shift-1</th>\n",
       "      <th>rtk-ag</th>\n",
       "      <th>pcr</th>\n",
       "      <th>rtk-ag_norm</th>\n",
       "      <th>rtk-ag_norm_ma7_shift0</th>\n",
       "      <th>...</th>\n",
       "      <th>deaths_new_norm</th>\n",
       "      <th>deaths_new_norm_ma7_shift0</th>\n",
       "      <th>cumul_partial</th>\n",
       "      <th>cumul_full</th>\n",
       "      <th>year</th>\n",
       "      <th>month_number</th>\n",
       "      <th>week_number</th>\n",
       "      <th>day_number</th>\n",
       "      <th>weekday_number</th>\n",
       "      <th>weekday_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000163</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>01</td>\n",
       "      <td>03</td>\n",
       "      <td>025</td>\n",
       "      <td>6</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>01</td>\n",
       "      <td>03</td>\n",
       "      <td>026</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>01</td>\n",
       "      <td>04</td>\n",
       "      <td>027</td>\n",
       "      <td>1</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>01</td>\n",
       "      <td>04</td>\n",
       "      <td>028</td>\n",
       "      <td>2</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>01</td>\n",
       "      <td>04</td>\n",
       "      <td>029</td>\n",
       "      <td>3</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>11889</td>\n",
       "      <td>0.483312</td>\n",
       "      <td>0.482532</td>\n",
       "      <td>0.501484</td>\n",
       "      <td>0.484154</td>\n",
       "      <td>100825.0</td>\n",
       "      <td>37382.0</td>\n",
       "      <td>0.790010</td>\n",
       "      <td>0.729699</td>\n",
       "      <td>...</td>\n",
       "      <td>0.204392</td>\n",
       "      <td>0.368002</td>\n",
       "      <td>0.724042</td>\n",
       "      <td>0.624349</td>\n",
       "      <td>2021</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>274</td>\n",
       "      <td>5</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>2021-10-02</td>\n",
       "      <td>10915</td>\n",
       "      <td>0.443717</td>\n",
       "      <td>0.482174</td>\n",
       "      <td>0.484154</td>\n",
       "      <td>0.460704</td>\n",
       "      <td>67827.0</td>\n",
       "      <td>34369.0</td>\n",
       "      <td>0.531455</td>\n",
       "      <td>0.724302</td>\n",
       "      <td>...</td>\n",
       "      <td>0.184122</td>\n",
       "      <td>0.339286</td>\n",
       "      <td>0.726839</td>\n",
       "      <td>0.628080</td>\n",
       "      <td>2021</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>275</td>\n",
       "      <td>6</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>2021-10-03</td>\n",
       "      <td>9066</td>\n",
       "      <td>0.368552</td>\n",
       "      <td>0.463751</td>\n",
       "      <td>0.460704</td>\n",
       "      <td>0.443955</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.199324</td>\n",
       "      <td>0.300676</td>\n",
       "      <td>0.729800</td>\n",
       "      <td>0.631465</td>\n",
       "      <td>2021</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>276</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>2021-10-04</td>\n",
       "      <td>8075</td>\n",
       "      <td>0.328265</td>\n",
       "      <td>0.428310</td>\n",
       "      <td>0.443955</td>\n",
       "      <td>0.429350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.128378</td>\n",
       "      <td>0.256757</td>\n",
       "      <td>0.733713</td>\n",
       "      <td>0.634385</td>\n",
       "      <td>2021</td>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "      <td>277</td>\n",
       "      <td>1</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>2021-10-05</td>\n",
       "      <td>8817</td>\n",
       "      <td>0.358429</td>\n",
       "      <td>0.396455</td>\n",
       "      <td>0.429350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.197635</td>\n",
       "      <td>0.227075</td>\n",
       "      <td>0.737510</td>\n",
       "      <td>0.637505</td>\n",
       "      <td>2021</td>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "      <td>278</td>\n",
       "      <td>2</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>620 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  cases_new  cases_new_norm  cases_new_norm_ma5_shift0  \\\n",
       "0   2020-01-25          4        0.000163                        NaN   \n",
       "1   2020-01-26          0        0.000000                        NaN   \n",
       "2   2020-01-27          0        0.000000                        NaN   \n",
       "3   2020-01-28          0        0.000000                        NaN   \n",
       "4   2020-01-29          3        0.000122                   0.000057   \n",
       "..         ...        ...             ...                        ...   \n",
       "615 2021-10-01      11889        0.483312                   0.482532   \n",
       "616 2021-10-02      10915        0.443717                   0.482174   \n",
       "617 2021-10-03       9066        0.368552                   0.463751   \n",
       "618 2021-10-04       8075        0.328265                   0.428310   \n",
       "619 2021-10-05       8817        0.358429                   0.396455   \n",
       "\n",
       "     cases_new_norm_ma7_shift0  cases_new_norm_ma7_shift-1    rtk-ag      pcr  \\\n",
       "0                          NaN                         NaN       0.0      5.0   \n",
       "1                          NaN                         NaN       0.0     14.0   \n",
       "2                          NaN                         NaN       0.0     24.0   \n",
       "3                          NaN                         NaN       0.0     53.0   \n",
       "4                          NaN                         NaN       0.0     71.0   \n",
       "..                         ...                         ...       ...      ...   \n",
       "615                   0.501484                    0.484154  100825.0  37382.0   \n",
       "616                   0.484154                    0.460704   67827.0  34369.0   \n",
       "617                   0.460704                    0.443955       NaN      NaN   \n",
       "618                   0.443955                    0.429350       NaN      NaN   \n",
       "619                   0.429350                         NaN       NaN      NaN   \n",
       "\n",
       "     rtk-ag_norm  rtk-ag_norm_ma7_shift0  ...  deaths_new_norm  \\\n",
       "0       0.000000                     NaN  ...              NaN   \n",
       "1       0.000000                     NaN  ...              NaN   \n",
       "2       0.000000                     NaN  ...              NaN   \n",
       "3       0.000000                     NaN  ...              NaN   \n",
       "4       0.000000                     NaN  ...              NaN   \n",
       "..           ...                     ...  ...              ...   \n",
       "615     0.790010                0.729699  ...         0.204392   \n",
       "616     0.531455                0.724302  ...         0.184122   \n",
       "617          NaN                     NaN  ...         0.199324   \n",
       "618          NaN                     NaN  ...         0.128378   \n",
       "619          NaN                     NaN  ...         0.197635   \n",
       "\n",
       "     deaths_new_norm_ma7_shift0  cumul_partial  cumul_full  year  \\\n",
       "0                           NaN            NaN         NaN  2020   \n",
       "1                           NaN            NaN         NaN  2020   \n",
       "2                           NaN            NaN         NaN  2020   \n",
       "3                           NaN            NaN         NaN  2020   \n",
       "4                           NaN            NaN         NaN  2020   \n",
       "..                          ...            ...         ...   ...   \n",
       "615                    0.368002       0.724042    0.624349  2021   \n",
       "616                    0.339286       0.726839    0.628080  2021   \n",
       "617                    0.300676       0.729800    0.631465  2021   \n",
       "618                    0.256757       0.733713    0.634385  2021   \n",
       "619                    0.227075       0.737510    0.637505  2021   \n",
       "\n",
       "     month_number  week_number day_number weekday_number weekday_name  \n",
       "0              01           03        025              6     Saturday  \n",
       "1              01           03        026              0       Sunday  \n",
       "2              01           04        027              1       Monday  \n",
       "3              01           04        028              2      Tuesday  \n",
       "4              01           04        029              3    Wednesday  \n",
       "..            ...          ...        ...            ...          ...  \n",
       "615            10           39        274              5       Friday  \n",
       "616            10           39        275              6     Saturday  \n",
       "617            10           39        276              0       Sunday  \n",
       "618            10           40        277              1       Monday  \n",
       "619            10           40        278              2      Tuesday  \n",
       "\n",
       "[620 rows x 23 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged['date'] = df_merged['date'].astype('datetime64[ns]')\n",
    "df_merged = strftime_all(df_merged)\n",
    "df_merged\n",
    "\n",
    "# only week 03 in 2020 and week 40 in 2021 is incomplete"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>cases_new_norm</th>\n",
       "      <th>cases_new_norm_ma5_shift0</th>\n",
       "      <th>cases_new_norm_ma7_shift0</th>\n",
       "      <th>ma7_next_day</th>\n",
       "      <th>rtk-ag_norm</th>\n",
       "      <th>rtk-ag_norm_ma7_shift0</th>\n",
       "      <th>pcr_norm</th>\n",
       "      <th>pcr_norm_ma7_shift0</th>\n",
       "      <th>deaths_new_norm</th>\n",
       "      <th>deaths_new_norm_ma7_shift0</th>\n",
       "      <th>cumul_partial</th>\n",
       "      <th>cumul_full</th>\n",
       "      <th>weekday_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>0.000419</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000709</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000432</td>\n",
       "      <td>0.000528</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000543</td>\n",
       "      <td>0.000525</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>0.460669</td>\n",
       "      <td>0.519111</td>\n",
       "      <td>0.537722</td>\n",
       "      <td>0.522878</td>\n",
       "      <td>0.823546</td>\n",
       "      <td>0.754924</td>\n",
       "      <td>0.521514</td>\n",
       "      <td>0.482681</td>\n",
       "      <td>0.405405</td>\n",
       "      <td>0.448118</td>\n",
       "      <td>0.709733</td>\n",
       "      <td>0.610753</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>0.505468</td>\n",
       "      <td>0.501874</td>\n",
       "      <td>0.522878</td>\n",
       "      <td>0.516961</td>\n",
       "      <td>0.779424</td>\n",
       "      <td>0.723413</td>\n",
       "      <td>0.511457</td>\n",
       "      <td>0.478717</td>\n",
       "      <td>0.351351</td>\n",
       "      <td>0.380792</td>\n",
       "      <td>0.715608</td>\n",
       "      <td>0.615287</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>0.517704</td>\n",
       "      <td>0.492410</td>\n",
       "      <td>0.516961</td>\n",
       "      <td>0.501484</td>\n",
       "      <td>0.757477</td>\n",
       "      <td>0.724502</td>\n",
       "      <td>0.477124</td>\n",
       "      <td>0.471257</td>\n",
       "      <td>0.324324</td>\n",
       "      <td>0.399131</td>\n",
       "      <td>0.720847</td>\n",
       "      <td>0.620239</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>0.483312</td>\n",
       "      <td>0.482532</td>\n",
       "      <td>0.501484</td>\n",
       "      <td>0.484154</td>\n",
       "      <td>0.790010</td>\n",
       "      <td>0.729699</td>\n",
       "      <td>0.413995</td>\n",
       "      <td>0.458131</td>\n",
       "      <td>0.204392</td>\n",
       "      <td>0.368002</td>\n",
       "      <td>0.724042</td>\n",
       "      <td>0.624349</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>0.443717</td>\n",
       "      <td>0.482174</td>\n",
       "      <td>0.484154</td>\n",
       "      <td>0.460704</td>\n",
       "      <td>0.531455</td>\n",
       "      <td>0.724302</td>\n",
       "      <td>0.380625</td>\n",
       "      <td>0.448478</td>\n",
       "      <td>0.184122</td>\n",
       "      <td>0.339286</td>\n",
       "      <td>0.726839</td>\n",
       "      <td>0.628080</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>611 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cases_new_norm  cases_new_norm_ma5_shift0  cases_new_norm_ma7_shift0  \\\n",
       "6          0.000000                   0.000033                   0.000046   \n",
       "7          0.000000                   0.000033                   0.000023   \n",
       "8          0.000000                   0.000033                   0.000023   \n",
       "9          0.000000                   0.000008                   0.000023   \n",
       "10         0.000081                   0.000016                   0.000035   \n",
       "..              ...                        ...                        ...   \n",
       "612        0.460669                   0.519111                   0.537722   \n",
       "613        0.505468                   0.501874                   0.522878   \n",
       "614        0.517704                   0.492410                   0.516961   \n",
       "615        0.483312                   0.482532                   0.501484   \n",
       "616        0.443717                   0.482174                   0.484154   \n",
       "\n",
       "     ma7_next_day  rtk-ag_norm  rtk-ag_norm_ma7_shift0  pcr_norm  \\\n",
       "6        0.000023     0.000000                0.000000  0.000498   \n",
       "7        0.000023     0.000000                0.000000  0.000288   \n",
       "8        0.000023     0.000000                0.000000  0.000709   \n",
       "9        0.000035     0.000000                0.000000  0.000432   \n",
       "10       0.000029     0.000000                0.000000  0.000543   \n",
       "..            ...          ...                     ...       ...   \n",
       "612      0.522878     0.823546                0.754924  0.521514   \n",
       "613      0.516961     0.779424                0.723413  0.511457   \n",
       "614      0.501484     0.757477                0.724502  0.477124   \n",
       "615      0.484154     0.790010                0.729699  0.413995   \n",
       "616      0.460704     0.531455                0.724302  0.380625   \n",
       "\n",
       "     pcr_norm_ma7_shift0  deaths_new_norm  deaths_new_norm_ma7_shift0  \\\n",
       "6               0.000383         0.000000                    0.000000   \n",
       "7               0.000419         0.000000                    0.000000   \n",
       "8               0.000502         0.000000                    0.000000   \n",
       "9               0.000528         0.000000                    0.000000   \n",
       "10              0.000525         0.000000                    0.000000   \n",
       "..                   ...              ...                         ...   \n",
       "612             0.482681         0.405405                    0.448118   \n",
       "613             0.478717         0.351351                    0.380792   \n",
       "614             0.471257         0.324324                    0.399131   \n",
       "615             0.458131         0.204392                    0.368002   \n",
       "616             0.448478         0.184122                    0.339286   \n",
       "\n",
       "     cumul_partial  cumul_full weekday_number  \n",
       "6         0.000000    0.000000              5  \n",
       "7         0.000000    0.000000              6  \n",
       "8         0.000000    0.000000              0  \n",
       "9         0.000000    0.000000              1  \n",
       "10        0.000000    0.000000              2  \n",
       "..             ...         ...            ...  \n",
       "612       0.709733    0.610753              2  \n",
       "613       0.715608    0.615287              3  \n",
       "614       0.720847    0.620239              4  \n",
       "615       0.724042    0.624349              5  \n",
       "616       0.726839    0.628080              6  \n",
       "\n",
       "[611 rows x 13 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean data\n",
    "\n",
    "# drop last 3 rows because test was not recorded at that time period\n",
    "df_merged_cleaned = df_merged.drop(df_merged.tail(3).index)\n",
    "\n",
    "# drop first 6 rows because mov avg is not yet calculated \n",
    "df_merged_cleaned.drop(df_merged.head(6).index,inplace=True)\n",
    "\n",
    "# fill NaN value with 0\n",
    "df_merged_cleaned.fillna(0, inplace=True)\n",
    "\n",
    "# drop unused columns\n",
    "df_merged_cleaned.drop(['date','cases_new','rtk-ag','pcr','deaths_new','year','month_number','week_number','day_number','weekday_name'], axis=1, inplace=True)\n",
    "df_merged_cleaned.rename(columns={\"cases_new_norm_ma7_shift-1\": \"ma7_next_day\"}, inplace=True)\n",
    "\n",
    "df_merged_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check whether all columns is being normalized\n",
    "\n",
    "# for i in range(1,12):\n",
    "#     print((df_merged_cleaned.iloc[:,i]>1).any())\n",
    "    \n",
    "# for i in range(1,12):\n",
    "#     print((df_merged_cleaned.iloc[:,i]<0).any())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\anaconda3\\lib\\site-packages\\seaborn\\matrix.py:1215: UserWarning: ``square=True`` ignored in clustermap\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Malaysia')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation\n",
    "corr_msia = df_merged_cleaned.corr()\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "g = sns.clustermap(corr_msia, square=True,annot=True,cmap=\"YlGnBu\")\n",
    "\n",
    "# highlight specific parts\n",
    "from matplotlib.patches import Rectangle\n",
    "ax = g.ax_heatmap\n",
    "ax.add_patch(Rectangle((0, 9), 12, 1, fill=False, edgecolor='red', lw=3))\n",
    "\n",
    "plt.title('Malaysia')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_theme()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df_merged_cleaned, x='ma7_next_day')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Most important configuration for binning\n",
    "bin_num = 5\n",
    "group_names = ['1','2','3','4','5']\n",
    "# group_names = ['1','2','3','4','5','6','7','8','9','10']\n",
    "df_merged_binned = df_merged_cleaned.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distance Bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.17730337, 0.35460675, 0.53191012, 0.7092135 ,\n",
       "       0.88651687])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins_distance = np.linspace(min(df_merged_cleaned['ma7_next_day']),\\\n",
    "                            max(df_merged_cleaned['ma7_next_day']),\\\n",
    "                            bin_num+1) # for 4 bins, you need 5 edges\n",
    "bins_distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged_binned['ma7_next_day_bin_distance'] = pd.cut(df_merged_binned['ma7_next_day'], \\\n",
    "                                                    bins_distance, \\\n",
    "                                                    labels=group_names, \\\n",
    "                                                    include_lowest=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([466.,  62.,  17.,  21.,  45.]),\n",
       " array([0. , 0.8, 1.6, 2.4, 3.2, 4. ]),\n",
       " <BarContainer object of 5 artists>)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_merged_binned['ma7_next_day_bin_distance'], bins=bin_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Frequency Bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged_binned['ma7_next_day_bin_qcut'], qcutRange = pd.qcut(df_merged_binned['ma7_next_day'],\\\n",
    "                                                    q=bin_num, \\\n",
    "                                                    precision=5, \\\n",
    "                                                    labels=group_names,\\\n",
    "                                                    retbins=True) # return range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00000000e+00 7.43351936e-04 7.03280621e-03 6.82257699e-02\n",
      " 2.27274047e-01 8.86516874e-01]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([123., 122., 122., 122., 122.]),\n",
       " array([0. , 0.8, 1.6, 2.4, 3.2, 4. ]),\n",
       " <BarContainer object of 5 artists>)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(qcutRange)\n",
    "plt.hist(df_merged_binned['ma7_next_day_bin_qcut'], bins=bin_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Sampling Bin](https://towardsdatascience.com/data-preprocessing-with-python-pandas-part-5-binning-c5bd5fd1b950)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import binned_statistic\n",
    "\n",
    "x_data = np.arange(0, len(df_merged_binned))\n",
    "y_data = df_merged_binned['ma7_next_day']\n",
    "x_bins,bin_edges, misc = binned_statistic(y_data,x_data, statistic=\"median\", bins=bin_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Thus we convert the bin edges to an IntervalIndex\n",
    "bin_intervals = pd.IntervalIndex.from_arrays(bin_edges[:-1], bin_edges[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_to_median(x, bin_intervals):\n",
    "    for interval in bin_intervals:\n",
    "        if x in interval:\n",
    "            return interval.mid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged_binned['ma7_next_day_sampled'] = df_merged_binned['ma7_next_day']\\\n",
    "                                            .apply(lambda x: set_to_median(x, bin_intervals))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_merged_binned['ma7_next_day'], label='original')\n",
    "plt.plot(df_merged_binned['ma7_next_day_sampled'], color='red', label='sampled')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.08865169, 0.26595506, 0.44325844, 0.62056181, 0.79786519])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_bins = (bin_edges[:-1]+bin_edges[1:])/2\n",
    "y_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(x_data,y_data)\n",
    "# plt.xlabel(\"X\"); \n",
    "# plt.ylabel(\"Y\")\n",
    "# plt.scatter(x_bins, y_bins,  color= 'red',linewidth=5)\n",
    "# plt.title('Mean')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_data,y_data)\n",
    "plt.xlabel(\"X\"); \n",
    "plt.ylabel(\"Y\")\n",
    "plt.scatter(x_bins, y_bins,  color= 'red',linewidth=5)\n",
    "plt.title('Median')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install jenkspy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jenkspy\n",
    "breaks = jenkspy.jenks_breaks(df_merged_binned['ma7_next_day'], nb_class=bin_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged_binned['ma7_next_day_bin_cut_break'] = pd.cut(df_merged_binned['ma7_next_day'],\\\n",
    "                                                         bins=breaks,\\\n",
    "                                                         labels=group_names,\\\n",
    "                                                         include_lowest=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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njiTJ7/eru7tbr732mkZHR1VSUiJJqq2tVVtbm1asWJG+6me48YlJWVZ+0vOfZm6mjI5FdP/ew0yXAUwbcUN++/btjxzfuXNHlmXFjj0ej4aGhh47b1mWhoaGHBc0e/bzjufMVLNysuV/vzPTZTxTxz+u0XMZenOajm+KmUbPnElHv+KG/H+LRqNyuVyxY9u25XK5vve8UyMjYUWjtuN5vJhmjlDo/jO/pmXlZ+S60xk9cybZfmVluZ64OHb86ZqCggKFQqHYcSgUksfjeez88PCwPB6P06cHAKSQ45B//fXXdf36dd24cUOTk5Pq6upSWVmZCgsLlZubq97eXklSZ2enysrKUl4wACBxjrdrcnNz1dLSog0bNmhsbEzl5eWqqqqSJLW2tqq5uVnhcFjFxcWqr69PecEAgMQlHPKff/557OfS0lIdO3bssTHz58/XkSNHUlMZAOCp8RuvAGAwQh4ADEbIA4DBCHkAMBghDwAGI+QBwGCEPAAYjJAHAIMR8gBgMEIeAAxGyAOAwQh5ADAYIQ8ABiPkAcBghDwAGIyQBwCDEfIAYDBCHgAMRsgDgMEc/yHv/23lypX66quv5HZ/+zR/+MMf9ODBA/3pT3/S2NiYfv3rX2vjxo0pKRSQpPGJSVlWfkaunanrjo5FdP/ew4xcG9Nf0iFv27aCwaC++OKLWMiPjo6qqqpKn3zyiV555RWtWbNGZ8+eVXl5ecoKxsw2Kydb/vc7M13GM3X84xrdz3QRz1j+/83Tc7lPtQaddsYnJtPyvEl38csvv5QkrVq1Sl9//bV++9vf6sc//rHmzp2rOXPmSJL8fr+6u7sJeQCOPJfrnpFv5umQdMjfu3dPpaWl+uCDDzQxMaH6+nqtXr1almXFxng8Hg0NDTl63tmzn0+2JMBYmdoqelrTte5MSUe/kg75hQsXauHChbHjZcuWqa2tTW+88UbsnG3bcrlcjp53ZCSsaNR2XA8vJpgsFJp+GzaWlZ903TP133My/crKcj1xcZz0p2suXbqk8+fPx45t21ZhYaFCoVDsXCgUksfjSfYSAICnlHTI379/Xzt37tTY2JjC4bA6Ojr0+9//XtevX9eNGzc0OTmprq4ulZWVpbJeAIADSW/XvPXWW+rr69PSpUsVjUa1YsUKLVy4UC0tLdqwYYPGxsZUXl6uqqqqVNYLAHDgqT6j9N577+m999575FxpaamOHTv2NE8LAEgRfuMVAAxGyAOAwQh5ADAYIQ8ABiPkAcBghDwAGIyQBwCDzazv8gSmoUx+h/7Tmq51m4SQB6a4mfod+kgNtmsAwGCEPAAYjJAHAIMR8gBgMEIeAAxGyAOAwQh5ADAYIQ8ABiPkAcBghDwAGCwtIX/8+HEtWbJEXq9Xn376aTouAQBIQMq/u2ZoaEi7du3SP/7xD82aNUvLly/Xz372M7322mupvhQAII6Uh3xPT48WL16sF154QZJUWVmp7u5urV+/PqH5WVmupK/teTEv6bnTFfc8M3DPM0My+Rdvjsu2bTvZgr7LX/7yF33zzTfauHGjJOmzzz7T5cuX9cc//jGVlwEAJCDle/LRaFQu1/+8s9i2/cgxAODZSXnIFxQUKBQKxY5DoZA8Hk+qLwMASEDKQ/7nP/+5zp8/r6+++koPHz7UP//5T5WVlaX6MgCABKT8P15ffvllbdy4UfX19ZqYmNCyZcv0k5/8JNWXAQAkIOX/8QoAmDr4jVcAMBghDwAGI+QBwGCEPAAYzIiQD4fDqq6u1uDgYKZLmRb27Nkjn88nn8+nnTt3ZrqcKW/37t1asmSJfD6fDhw4kOlypo0///nP2rx5c6bLmPJWrlwpn8+nmpoa1dTUqK+vL6XPn/KPUD5rfX19am5uVjAYzHQp00JPT4/OnTunjo4OuVwurV69WqdPn1ZFRUWmS5uSLl68qAsXLujYsWOKRCJasmSJysvL9eqrr2a6tCnt/Pnz6ujo0C9/+ctMlzKl2batYDCoL774Qm53euJ42q/k29vbtW3bNn6rNkGWZWnz5s2aNWuWcnJyVFRUpFu3bmW6rClr0aJFOnjwoNxut0ZGRjQ5Oakf/OAHmS5rSvv666+1a9curV27NtOlTHlffvmlJGnVqlX6zW9+o0OHDqX8GtN+Jb99+/ZMlzCtzJs3L/ZzMBjUyZMn9be//S2DFU19OTk5amtr01//+ldVVVXp5ZdfznRJU9rWrVu1ceNG3b59O9OlTHn37t1TaWmpPvjgA01MTKi+vl4/+tGP9Itf/CJl15j2K3kk5+rVq1q1apU2bdqkH/7wh5kuZ8praGjQ+fPndfv2bbW3t2e6nCnrs88+0yuvvKLS0tJMlzItLFy4UDt37lR+fr5eeuklLVu2TGfPnk3pNab9Sh7O9fb2qqGhQYFAQD6fL9PlTGnXrl3T+Pi4FixYoLy8PHm9Xl25ciXTZU1ZJ06cUCgUUk1Nje7evatvvvlGO3bsUCAQyHRpU9KlS5c0MTERe1O0bTvle/Os5GeY27dva926dWptbSXgEzA4OKjm5maNj49rfHxcZ86c0RtvvJHpsqasAwcOqKurS52dnWpoaNCvfvUrAv4J7t+/r507d2psbEzhcFgdHR0p/xAEK/kZZv/+/RobG1NLS0vs3PLly1VXV5fBqqau8vJyXb58WUuXLlV2dra8Xi9vjkiZt956S319fVq6dKmi0ahWrFihhQsXpvQafEEZABiM7RoAMBghDwAGI+QBwGCEPAAYjJAHAIMR8gBgMEIeAAxGyAOAwf4fM/cFtxzrwr0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_merged_binned['ma7_next_day_bin_cut_break'], bins=bin_num)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>cases_new_norm</th>\n",
       "      <th>cases_new_norm_ma5_shift0</th>\n",
       "      <th>cases_new_norm_ma7_shift0</th>\n",
       "      <th>ma7_next_day</th>\n",
       "      <th>rtk-ag_norm</th>\n",
       "      <th>rtk-ag_norm_ma7_shift0</th>\n",
       "      <th>pcr_norm</th>\n",
       "      <th>pcr_norm_ma7_shift0</th>\n",
       "      <th>deaths_new_norm</th>\n",
       "      <th>deaths_new_norm_ma7_shift0</th>\n",
       "      <th>cumul_partial</th>\n",
       "      <th>cumul_full</th>\n",
       "      <th>weekday_number</th>\n",
       "      <th>ma7_next_day_bin_distance</th>\n",
       "      <th>ma7_next_day_bin_qcut</th>\n",
       "      <th>ma7_next_day_sampled</th>\n",
       "      <th>ma7_next_day_bin_cut_break</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.088652</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>0.000419</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.088652</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000709</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.088652</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000432</td>\n",
       "      <td>0.000528</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.088652</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000543</td>\n",
       "      <td>0.000525</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.088652</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>0.460669</td>\n",
       "      <td>0.519111</td>\n",
       "      <td>0.537722</td>\n",
       "      <td>0.522878</td>\n",
       "      <td>0.823546</td>\n",
       "      <td>0.754924</td>\n",
       "      <td>0.521514</td>\n",
       "      <td>0.482681</td>\n",
       "      <td>0.405405</td>\n",
       "      <td>0.448118</td>\n",
       "      <td>0.709733</td>\n",
       "      <td>0.610753</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0.443258</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>0.505468</td>\n",
       "      <td>0.501874</td>\n",
       "      <td>0.522878</td>\n",
       "      <td>0.516961</td>\n",
       "      <td>0.779424</td>\n",
       "      <td>0.723413</td>\n",
       "      <td>0.511457</td>\n",
       "      <td>0.478717</td>\n",
       "      <td>0.351351</td>\n",
       "      <td>0.380792</td>\n",
       "      <td>0.715608</td>\n",
       "      <td>0.615287</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0.443258</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>0.517704</td>\n",
       "      <td>0.492410</td>\n",
       "      <td>0.516961</td>\n",
       "      <td>0.501484</td>\n",
       "      <td>0.757477</td>\n",
       "      <td>0.724502</td>\n",
       "      <td>0.477124</td>\n",
       "      <td>0.471257</td>\n",
       "      <td>0.324324</td>\n",
       "      <td>0.399131</td>\n",
       "      <td>0.720847</td>\n",
       "      <td>0.620239</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0.443258</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>0.483312</td>\n",
       "      <td>0.482532</td>\n",
       "      <td>0.501484</td>\n",
       "      <td>0.484154</td>\n",
       "      <td>0.790010</td>\n",
       "      <td>0.729699</td>\n",
       "      <td>0.413995</td>\n",
       "      <td>0.458131</td>\n",
       "      <td>0.204392</td>\n",
       "      <td>0.368002</td>\n",
       "      <td>0.724042</td>\n",
       "      <td>0.624349</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0.443258</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>0.443717</td>\n",
       "      <td>0.482174</td>\n",
       "      <td>0.484154</td>\n",
       "      <td>0.460704</td>\n",
       "      <td>0.531455</td>\n",
       "      <td>0.724302</td>\n",
       "      <td>0.380625</td>\n",
       "      <td>0.448478</td>\n",
       "      <td>0.184122</td>\n",
       "      <td>0.339286</td>\n",
       "      <td>0.726839</td>\n",
       "      <td>0.628080</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0.443258</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>611 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cases_new_norm  cases_new_norm_ma5_shift0  cases_new_norm_ma7_shift0  \\\n",
       "6          0.000000                   0.000033                   0.000046   \n",
       "7          0.000000                   0.000033                   0.000023   \n",
       "8          0.000000                   0.000033                   0.000023   \n",
       "9          0.000000                   0.000008                   0.000023   \n",
       "10         0.000081                   0.000016                   0.000035   \n",
       "..              ...                        ...                        ...   \n",
       "612        0.460669                   0.519111                   0.537722   \n",
       "613        0.505468                   0.501874                   0.522878   \n",
       "614        0.517704                   0.492410                   0.516961   \n",
       "615        0.483312                   0.482532                   0.501484   \n",
       "616        0.443717                   0.482174                   0.484154   \n",
       "\n",
       "     ma7_next_day  rtk-ag_norm  rtk-ag_norm_ma7_shift0  pcr_norm  \\\n",
       "6        0.000023     0.000000                0.000000  0.000498   \n",
       "7        0.000023     0.000000                0.000000  0.000288   \n",
       "8        0.000023     0.000000                0.000000  0.000709   \n",
       "9        0.000035     0.000000                0.000000  0.000432   \n",
       "10       0.000029     0.000000                0.000000  0.000543   \n",
       "..            ...          ...                     ...       ...   \n",
       "612      0.522878     0.823546                0.754924  0.521514   \n",
       "613      0.516961     0.779424                0.723413  0.511457   \n",
       "614      0.501484     0.757477                0.724502  0.477124   \n",
       "615      0.484154     0.790010                0.729699  0.413995   \n",
       "616      0.460704     0.531455                0.724302  0.380625   \n",
       "\n",
       "     pcr_norm_ma7_shift0  deaths_new_norm  deaths_new_norm_ma7_shift0  \\\n",
       "6               0.000383         0.000000                    0.000000   \n",
       "7               0.000419         0.000000                    0.000000   \n",
       "8               0.000502         0.000000                    0.000000   \n",
       "9               0.000528         0.000000                    0.000000   \n",
       "10              0.000525         0.000000                    0.000000   \n",
       "..                   ...              ...                         ...   \n",
       "612             0.482681         0.405405                    0.448118   \n",
       "613             0.478717         0.351351                    0.380792   \n",
       "614             0.471257         0.324324                    0.399131   \n",
       "615             0.458131         0.204392                    0.368002   \n",
       "616             0.448478         0.184122                    0.339286   \n",
       "\n",
       "     cumul_partial  cumul_full weekday_number ma7_next_day_bin_distance  \\\n",
       "6         0.000000    0.000000              5                         1   \n",
       "7         0.000000    0.000000              6                         1   \n",
       "8         0.000000    0.000000              0                         1   \n",
       "9         0.000000    0.000000              1                         1   \n",
       "10        0.000000    0.000000              2                         1   \n",
       "..             ...         ...            ...                       ...   \n",
       "612       0.709733    0.610753              2                         3   \n",
       "613       0.715608    0.615287              3                         3   \n",
       "614       0.720847    0.620239              4                         3   \n",
       "615       0.724042    0.624349              5                         3   \n",
       "616       0.726839    0.628080              6                         3   \n",
       "\n",
       "    ma7_next_day_bin_qcut  ma7_next_day_sampled ma7_next_day_bin_cut_break  \n",
       "6                       1              0.088652                          1  \n",
       "7                       1              0.088652                          1  \n",
       "8                       1              0.088652                          1  \n",
       "9                       1              0.088652                          1  \n",
       "10                      1              0.088652                          1  \n",
       "..                    ...                   ...                        ...  \n",
       "612                     5              0.443258                          4  \n",
       "613                     5              0.443258                          4  \n",
       "614                     5              0.443258                          4  \n",
       "615                     5              0.443258                          4  \n",
       "616                     5              0.443258                          4  \n",
       "\n",
       "[611 rows x 17 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged_binned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Mining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import roc_curve, roc_auc_score, auc\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_predict = df_merged_binned.copy()\n",
    "\n",
    "# y = df_predict[['ma7_next_day_bin_cut_break']]\n",
    "y = df_predict[['ma7_next_day_bin_qcut']]\n",
    "\n",
    "\n",
    "X = df_predict.loc[:, df_predict.columns.difference(['ma7_next_day',\\\n",
    "                                                     'ma7_next_day_bin_qcut',\\\n",
    "                                                     'ma7_next_day_bin_cut_break',\\\n",
    "                                                     'ma7_next_day_sampled',\n",
    "                                                     'ma7_next_day_bin_distance',\n",
    "                                                     'ma7_next_day_bin_qcut',\n",
    "                                                     'weekday_number'])]                            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cases_new_norm</th>\n",
       "      <th>cases_new_norm_ma5_shift0</th>\n",
       "      <th>cases_new_norm_ma7_shift0</th>\n",
       "      <th>cumul_full</th>\n",
       "      <th>cumul_partial</th>\n",
       "      <th>deaths_new_norm</th>\n",
       "      <th>deaths_new_norm_ma7_shift0</th>\n",
       "      <th>pcr_norm</th>\n",
       "      <th>pcr_norm_ma7_shift0</th>\n",
       "      <th>rtk-ag_norm</th>\n",
       "      <th>rtk-ag_norm_ma7_shift0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.002317</td>\n",
       "      <td>0.002114</td>\n",
       "      <td>0.002317</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003378</td>\n",
       "      <td>0.001931</td>\n",
       "      <td>0.133347</td>\n",
       "      <td>0.143011</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>0.472296</td>\n",
       "      <td>0.404317</td>\n",
       "      <td>0.393611</td>\n",
       "      <td>0.124771</td>\n",
       "      <td>0.272402</td>\n",
       "      <td>0.199324</td>\n",
       "      <td>0.177365</td>\n",
       "      <td>0.776013</td>\n",
       "      <td>0.676449</td>\n",
       "      <td>0.560823</td>\n",
       "      <td>0.436532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>0.110452</td>\n",
       "      <td>0.106671</td>\n",
       "      <td>0.101009</td>\n",
       "      <td>0.015417</td>\n",
       "      <td>0.024398</td>\n",
       "      <td>0.018581</td>\n",
       "      <td>0.013514</td>\n",
       "      <td>0.542291</td>\n",
       "      <td>0.515305</td>\n",
       "      <td>0.183397</td>\n",
       "      <td>0.214941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>0.000285</td>\n",
       "      <td>0.000732</td>\n",
       "      <td>0.000674</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058190</td>\n",
       "      <td>0.053185</td>\n",
       "      <td>0.019745</td>\n",
       "      <td>0.016322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>0.029757</td>\n",
       "      <td>0.034140</td>\n",
       "      <td>0.031459</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011824</td>\n",
       "      <td>0.008205</td>\n",
       "      <td>0.154644</td>\n",
       "      <td>0.193009</td>\n",
       "      <td>0.041434</td>\n",
       "      <td>0.035555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>0.042847</td>\n",
       "      <td>0.034985</td>\n",
       "      <td>0.033410</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003378</td>\n",
       "      <td>0.006998</td>\n",
       "      <td>0.326112</td>\n",
       "      <td>0.273193</td>\n",
       "      <td>0.052451</td>\n",
       "      <td>0.041920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001174</td>\n",
       "      <td>0.001144</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>0.136022</td>\n",
       "      <td>0.149843</td>\n",
       "      <td>0.147317</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018581</td>\n",
       "      <td>0.018581</td>\n",
       "      <td>0.450898</td>\n",
       "      <td>0.575734</td>\n",
       "      <td>0.206253</td>\n",
       "      <td>0.285964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>0.000447</td>\n",
       "      <td>0.000577</td>\n",
       "      <td>0.000540</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.049363</td>\n",
       "      <td>0.056248</td>\n",
       "      <td>0.017653</td>\n",
       "      <td>0.016135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>0.880849</td>\n",
       "      <td>0.799732</td>\n",
       "      <td>0.804382</td>\n",
       "      <td>0.304666</td>\n",
       "      <td>0.512867</td>\n",
       "      <td>0.537162</td>\n",
       "      <td>0.403475</td>\n",
       "      <td>0.972168</td>\n",
       "      <td>0.815109</td>\n",
       "      <td>0.732286</td>\n",
       "      <td>0.627155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>488 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cases_new_norm  cases_new_norm_ma5_shift0  cases_new_norm_ma7_shift0  \\\n",
       "96         0.002317                   0.002114                   0.002317   \n",
       "536        0.472296                   0.404317                   0.393611   \n",
       "455        0.110452                   0.106671                   0.101009   \n",
       "206        0.000285                   0.000732                   0.000674   \n",
       "270        0.029757                   0.034140                   0.031459   \n",
       "..              ...                        ...                        ...   \n",
       "283        0.042847                   0.034985                   0.033410   \n",
       "15         0.000041                   0.000057                   0.000052   \n",
       "365        0.136022                   0.149843                   0.147317   \n",
       "198        0.000447                   0.000577                   0.000540   \n",
       "565        0.880849                   0.799732                   0.804382   \n",
       "\n",
       "     cumul_full  cumul_partial  deaths_new_norm  deaths_new_norm_ma7_shift0  \\\n",
       "96     0.000000       0.000000         0.003378                    0.001931   \n",
       "536    0.124771       0.272402         0.199324                    0.177365   \n",
       "455    0.015417       0.024398         0.018581                    0.013514   \n",
       "206    0.000000       0.000000         0.000000                    0.000000   \n",
       "270    0.000000       0.000000         0.011824                    0.008205   \n",
       "..          ...            ...              ...                         ...   \n",
       "283    0.000000       0.000000         0.003378                    0.006998   \n",
       "15     0.000000       0.000000         0.000000                    0.000000   \n",
       "365    0.000000       0.000000         0.018581                    0.018581   \n",
       "198    0.000000       0.000000         0.000000                    0.000000   \n",
       "565    0.304666       0.512867         0.537162                    0.403475   \n",
       "\n",
       "     pcr_norm  pcr_norm_ma7_shift0  rtk-ag_norm  rtk-ag_norm_ma7_shift0  \n",
       "96   0.133347             0.143011     0.000000                0.000000  \n",
       "536  0.776013             0.676449     0.560823                0.436532  \n",
       "455  0.542291             0.515305     0.183397                0.214941  \n",
       "206  0.058190             0.053185     0.019745                0.016322  \n",
       "270  0.154644             0.193009     0.041434                0.035555  \n",
       "..        ...                  ...          ...                     ...  \n",
       "283  0.326112             0.273193     0.052451                0.041920  \n",
       "15   0.001174             0.001144     0.000000                0.000000  \n",
       "365  0.450898             0.575734     0.206253                0.285964  \n",
       "198  0.049363             0.056248     0.017653                0.016135  \n",
       "565  0.972168             0.815109     0.732286                0.627155  \n",
       "\n",
       "[488 rows x 11 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle.dump(X_train, open('pickle_files/classification_X_train.pkl', 'wb'))\n",
    "# pickle.dump(y_train, open('pickle_files/classification_y_train.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import shap\n",
    "\n",
    "# checking feature importance using Decision Tree and Random Forest\n",
    "def feature_importance(X, y):\n",
    "    \n",
    "    # Decision Tree\n",
    "    model = DecisionTreeRegressor(criterion='mse', splitter='random')\n",
    "    model.fit(X, y)\n",
    "    importance = model.feature_importances_\n",
    "    \n",
    "    print(f'Decision Tree Regression Feature Importance\\n')\n",
    "    for i, col in enumerate(X.columns):\n",
    "        print(col, importance[i])\n",
    "\n",
    "    explainer = shap.TreeExplainer(model)\n",
    "    shap_values = explainer.shap_values(X)\n",
    "    shap.summary_plot(shap_values, X)\n",
    "    shap.summary_plot(shap_values, X, plot_type=\"bar\")\n",
    "    \n",
    "    \n",
    "    # Random Forest\n",
    "    model = RandomForestRegressor(max_depth=7, random_state=0)\n",
    "    model.fit(X, np.ravel(y))\n",
    "    importance = model.feature_importances_\n",
    "\n",
    "    print(f'Random Forest Regression Feature Importance\\n')\n",
    "    for i, col in enumerate(X.columns):\n",
    "        print(col, importance[i])\n",
    "\n",
    "    explainer = shap.TreeExplainer(model)\n",
    "    shap_values = explainer.shap_values(X)\n",
    "    shap.summary_plot(shap_values, X)\n",
    "    shap.summary_plot(shap_values, X, plot_type='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Regression Feature Importance\n",
      "\n",
      "cases_new_norm 0.0035884304737110262\n",
      "cases_new_norm_ma5_shift0 0.7220440146186207\n",
      "cases_new_norm_ma7_shift0 0.09399725010591174\n",
      "cumul_full 0.0\n",
      "cumul_partial 0.10301333671301183\n",
      "deaths_new_norm 0.0\n",
      "deaths_new_norm_ma7_shift0 0.011834268383092384\n",
      "pcr_norm 0.0014865752808728878\n",
      "pcr_norm_ma7_shift0 0.05758480227553416\n",
      "rtk-ag_norm 0.0015183339346006126\n",
      "rtk-ag_norm_ma7_shift0 0.004932988214644836\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x424.8 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x424.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Regression Feature Importance\n",
      "\n",
      "cases_new_norm 0.1041029621766123\n",
      "cases_new_norm_ma5_shift0 0.4750466255934858\n",
      "cases_new_norm_ma7_shift0 0.30425261165481077\n",
      "cumul_full 0.0001178228532847983\n",
      "cumul_partial 0.00012054372391627718\n",
      "deaths_new_norm 0.00020587567215438172\n",
      "deaths_new_norm_ma7_shift0 0.00033603139876229015\n",
      "pcr_norm 0.0003471784807689928\n",
      "pcr_norm_ma7_shift0 0.0037963708342590875\n",
      "rtk-ag_norm 0.007780133372256789\n",
      "rtk-ag_norm_ma7_shift0 0.10389384423968852\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x424.8 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x424.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regression Models\n",
    "\n",
    "Regression models that will be used:\n",
    "1. Linear Regression\n",
    "2. Decision Tree Regressor\n",
    "3. Random Forest Regressor\n",
    "4. Support Vector Regressor\n",
    "\n",
    "Evaluation matrics that will be used:\n",
    "1. R Square\n",
    "2. Mean Absolute Error(MAE)\n",
    "3. Root Mean Square Error(RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_l = LinearRegression()\n",
    "\n",
    "reg_l = reg_l.fit(X_train, np.ravel(y_train))\n",
    "\n",
    "y_pred = reg_l.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Square                    : 0.89606\n",
      "Mean Absolute Error(MAE)    : 0.40600\n",
      "Root Mean Square Error(RMSE): 0.47124\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n"
     ]
    }
   ],
   "source": [
    "print(\"R Square                    : {:.5f}\".format(r2_score(y_test, y_pred)) )\n",
    "print(\"Mean Absolute Error(MAE)    : {:.5f}\".format(mean_absolute_error(y_test, y_pred)) )\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(\"Root Mean Square Error(RMSE): {:.5f}\".format(math.sqrt(mse)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_dt = DecisionTreeRegressor(criterion='mse', splitter='random')\n",
    "\n",
    "reg_dt = reg_dt.fit(X_train, y_train)\n",
    "\n",
    "y_pred = reg_dt.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Square                    : 0.98478\n",
      "Mean Absolute Error(MAE)    : 0.03252\n",
      "Root Mean Square Error(RMSE): 0.18033\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n"
     ]
    }
   ],
   "source": [
    "print(\"R Square                    : {:.5f}\".format(r2_score(y_test, y_pred)) )\n",
    "print(\"Mean Absolute Error(MAE)    : {:.5f}\".format(mean_absolute_error(y_test, y_pred)) )\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(\"Root Mean Square Error(RMSE): {:.5f}\".format(math.sqrt(mse)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_rf = RandomForestRegressor(max_depth=7, random_state=0)\n",
    "\n",
    "reg_rf = reg_rf.fit(X_train, np.ravel(y_train))\n",
    "\n",
    "y_pred = reg_rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Square                    : 0.99123\n",
      "Mean Absolute Error(MAE)    : 0.03008\n",
      "Root Mean Square Error(RMSE): 0.13691\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n"
     ]
    }
   ],
   "source": [
    "print(\"R Square                    : {:.5f}\".format(r2_score(y_test, y_pred)) )\n",
    "print(\"Mean Absolute Error(MAE)    : {:.5f}\".format(mean_absolute_error(y_test, y_pred)) )\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(\"Root Mean Square Error(RMSE): {:.5f}\".format(math.sqrt(mse)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_sv = SVR(kernel='linear')\n",
    "\n",
    "reg_sv = reg_sv.fit(X_train, np.ravel(y_train))\n",
    "\n",
    "y_pred = reg_sv.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Square                    : 0.88914\n",
      "Mean Absolute Error(MAE)    : 0.40035\n",
      "Root Mean Square Error(RMSE): 0.48668\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n",
      "Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.\n"
     ]
    }
   ],
   "source": [
    "print(\"R Square                    : {:.5f}\".format(r2_score(y_test, y_pred)) )\n",
    "print(\"Mean Absolute Error(MAE)    : {:.5f}\".format(mean_absolute_error(y_test, y_pred)) )\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(\"Root Mean Square Error(RMSE): {:.5f}\".format(math.sqrt(mse)) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "|                               | Linear Regression | Decision Tree | Random Forest | SVR       |\n",
    "| ----------------------------- | ----------------- | ------------- | ------------- | --------- |\n",
    "| R Square                      | 0.89642           | 0.98097       | 0.93570       | 0.88917   |\n",
    "| Mean Absolute Error (MAE)     | 0.40572           | 0.04065       | 0.03211       | 0.39836   |\n",
    "| Root Mean Square Error (RMSE) | 0.47043           | 0.20162       | 0.14114       | 0.48661   |\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classification Models\n",
    "\n",
    "Classification models that will be used:\n",
    "1. K-Nearest Neighbors Classifier\n",
    "2. Naive Bayes Classifier\n",
    "3. Decision Tree Classifier\n",
    "4. Random Forest Classifier\n",
    "\n",
    "Performance evaluation metrics that will be used:\n",
    "1. Confusion Matrix\n",
    "2. Precision, Recall, F1-score\n",
    "3. ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.96721\n",
      "Accuracy on test set:     0.93496\n"
     ]
    }
   ],
   "source": [
    "clf_KNN = KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "clf_KNN = clf_KNN.fit(X_train, np.ravel(y_train))\n",
    "\n",
    "y_pred = clf_KNN.predict(X_test)\n",
    "\n",
    "print(\"Accuracy on training set: {:.5f}\".format(clf_KNN.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set:     {:.5f}\".format(clf_KNN.score(X_test, y_test))) # accuracy score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save all y_pred in a dict\n",
    "model_y_pred = {}\n",
    "model_y_pred['KNN'] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.95492\n",
      "Accuracy on test set:     0.94309\n"
     ]
    }
   ],
   "source": [
    "clf_NB = GaussianNB()\n",
    "\n",
    "clf_NB = clf_NB.fit(X_train, np.ravel(y_train))\n",
    "\n",
    "y_pred = clf_NB.predict(X_test)\n",
    "\n",
    "print(\"Accuracy on training set: {:.5f}\".format(clf_NB.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set:     {:.5f}\".format(clf_NB.score(X_test, y_test)))\n",
    "\n",
    "model_y_pred['Naive Bayes'] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 1.00000\n",
      "Accuracy on test set:     0.98374\n"
     ]
    }
   ],
   "source": [
    "clf_DT = DecisionTreeClassifier(criterion='entropy', max_depth=7, splitter='best')\n",
    "\n",
    "clf_DT = clf_DT.fit(X_train, y_train)\n",
    "\n",
    "y_pred = clf_DT.predict(X_test)\n",
    "\n",
    "print(\"Accuracy on training set: {:.5f}\".format(clf_DT.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set:     {:.5f}\".format(clf_DT.score(X_test, y_test)))\n",
    "\n",
    "model_y_pred['Decision Tree'] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 1.00000\n",
      "Accuracy on test set:     0.97561\n"
     ]
    }
   ],
   "source": [
    "clf_RF = RandomForestClassifier(max_depth=10, random_state=0)\n",
    "\n",
    "clf_RF = clf_RF.fit(X_train, np.ravel(y_train))\n",
    "\n",
    "y_pred = clf_RF.predict(X_test)\n",
    "\n",
    "print(\"Accuracy on training set: {:.5f}\".format(clf_RF.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set:     {:.5f}\".format(clf_RF.score(X_test, y_test)))\n",
    "\n",
    "model_y_pred['Random Forest'] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.82377\n",
      "Accuracy on test set:     0.75610\n"
     ]
    }
   ],
   "source": [
    "clf_SV = SVC(kernel='linear', probability=True)\n",
    "\n",
    "clf_SV = clf_SV.fit(X_train, np.ravel(y_train))\n",
    "\n",
    "y_pred = clf_SV.predict(X_test)\n",
    "\n",
    "print(\"Accuracy on training set: {:.5f}\".format(clf_SV.score(X_train, y_train)))\n",
    "print(\"Accuracy on test set:     {:.5f}\".format(clf_SV.score(X_test, y_test)))\n",
    "\n",
    "model_y_pred['SVC'] = y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|                        | KNN       | GaussianNB  | Decision Tree | Random Forest | SVC      |\n",
    "| ---------------------- | ----------| ------------| ------------- | ------------- | -------- |\n",
    "| Test Set Accuracy      | 0.94309   | 0.95122     | 0.95935       | 0.95935       | 0.78049  |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2.6 Classification Model Evaluation\n",
    "\n",
    "To evaluate the performance of each classifier, we plot the confusion matrix for each of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_confusion_matrix(y_pred, name=\"\"):\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    \n",
    "    ax = sns.heatmap(cm,annot=True, cmap='Greens')\n",
    "    ax.set_xlabel('Predicted Label')\n",
    "    ax.set_ylabel('True Label')\n",
    "    ax.set_title(f\"{name} Confusion Matrix\")\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 4))\n",
    "plt.subplot(1, 3, 1)\n",
    "get_confusion_matrix(model_y_pred.get(\"KNN\"), name=\"KNN\")\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "get_confusion_matrix(model_y_pred.get(\"Naive Bayes\"), name=\"Naive Bayes\")\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "get_confusion_matrix(model_y_pred.get(\"Decision Tree\"), name=\"Decision Tree\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(20, 4))\n",
    "plt.subplot(1, 3, 1)\n",
    "get_confusion_matrix(model_y_pred.get(\"Random Forest\"), name=\"Random Forest\")\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "get_confusion_matrix(model_y_pred.get(\"SVC\"), name=\"SVC\")\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.axis(\"off\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_classification_report(y_pred, name=\"\"):\n",
    "    # calculate precision, recall, f1 for each class\n",
    "    precision = precision_score(y_test, y_pred, average=None)\n",
    "    recall = recall_score(y_test, y_pred, average=None)\n",
    "    f1 = f1_score(y_test, y_pred, average=None)\n",
    "    \n",
    "    report = np.flipud(np.rot90([precision, recall, f1]))\n",
    "    ax = sns.heatmap(report,annot=True, cmap='RdBu', vmin=0, vmax=1)\n",
    "    ax.set_xlabel('Metrics')\n",
    "    ax.set_ylabel('Classes')\n",
    "    ax.set_xticklabels(['Precision', 'Recall', 'F1-score'])\n",
    "    ax.set_title(f\"{name} Classification Report\")\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 4))\n",
    "plt.subplot(1, 3, 1)\n",
    "get_classification_report(model_y_pred.get(\"KNN\"), name=\"KNN\")\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "get_classification_report(model_y_pred.get(\"Naive Bayes\"), name=\"Naive Bayes\")\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "get_classification_report(model_y_pred.get(\"Decision Tree\"), name=\"Decision Tree\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(20, 4))\n",
    "plt.subplot(1, 3, 1)\n",
    "get_classification_report(model_y_pred.get(\"Random Forest\"), name=\"Random Forest\")\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "get_classification_report(model_y_pred.get(\"SVC\"), name=\"SVC\")\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.axis(\"off\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Found input variables with inconsistent numbers of samples: [123, 3]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-100-4bcd688d6797>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproba\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m     \u001b[0mfpr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtpr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mthresholds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproba\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpos_label\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfpr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtpr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;31m# extra_args > 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_ranking.py\u001b[0m in \u001b[0;36mroc_curve\u001b[1;34m(y_true, y_score, pos_label, sample_weight, drop_intermediate)\u001b[0m\n\u001b[0;32m    911\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    912\u001b[0m     \"\"\"\n\u001b[1;32m--> 913\u001b[1;33m     fps, tps, thresholds = _binary_clf_curve(\n\u001b[0m\u001b[0;32m    914\u001b[0m         y_true, y_score, pos_label=pos_label, sample_weight=sample_weight)\n\u001b[0;32m    915\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_ranking.py\u001b[0m in \u001b[0;36m_binary_clf_curve\u001b[1;34m(y_true, y_score, pos_label, sample_weight)\u001b[0m\n\u001b[0;32m    691\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"{0} format is not supported\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    692\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 693\u001b[1;33m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_score\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    694\u001b[0m     \u001b[0my_true\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolumn_or_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    695\u001b[0m     \u001b[0my_score\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolumn_or_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_score\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[1;34m(*arrays)\u001b[0m\n\u001b[0;32m    260\u001b[0m     \u001b[0muniques\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m         raise ValueError(\"Found input variables with inconsistent numbers of\"\n\u001b[0m\u001b[0;32m    263\u001b[0m                          \" samples: %r\" % [int(l) for l in lengths])\n\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [123, 3]"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 4\n",
    "prob_KNN = clf_KNN.predict_proba(X_test)[:,n]\n",
    "prob_NB = clf_NB.predict_proba(X_test)[:,n]\n",
    "prob_DT = clf_DT.predict_proba(X_test)[:,n]\n",
    "prob_RF = clf_RF.predict_proba(X_test)[:,n]\n",
    "prob_SV = clf_SV.predict_proba(X_test)[:,n]\n",
    "\n",
    "proba = [prob_KNN, prob_NB, prob_DT, prob_RF, prob_SV]\n",
    "color = ['orange', 'blue', 'purple', 'red', 'lime']\n",
    "label = ['KNN', 'Naive Bayes', 'Decision Tree', 'Random Forest', 'SVC']\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "for i in range(len(proba)):\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, proba[i], pos_label=1)\n",
    "    plt.plot(fpr, tpr, color=color[i], label=label[i]) \n",
    "    \n",
    "plt.plot([0, 1], [0, 1], color='green', linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cases_new_norm</th>\n",
       "      <th>cases_new_norm_ma5_shift0</th>\n",
       "      <th>cases_new_norm_ma7_shift0</th>\n",
       "      <th>cumul_full</th>\n",
       "      <th>cumul_partial</th>\n",
       "      <th>deaths_new_norm</th>\n",
       "      <th>deaths_new_norm_ma7_shift0</th>\n",
       "      <th>pcr_norm</th>\n",
       "      <th>pcr_norm_ma7_shift0</th>\n",
       "      <th>rtk-ag_norm</th>\n",
       "      <th>rtk-ag_norm_ma7_shift0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>0.849506</td>\n",
       "      <td>0.857035</td>\n",
       "      <td>0.886517</td>\n",
       "      <td>0.463551</td>\n",
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       "      <td>0.435440</td>\n",
       "      <td>0.581362</td>\n",
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       "      <th>58</th>\n",
       "      <td>0.008618</td>\n",
       "      <td>0.005919</td>\n",
       "      <td>0.005604</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008446</td>\n",
       "      <td>0.003861</td>\n",
       "      <td>0.042330</td>\n",
       "      <td>0.038669</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>0.509289</td>\n",
       "      <td>0.495801</td>\n",
       "      <td>0.456813</td>\n",
       "      <td>0.137715</td>\n",
       "      <td>0.298702</td>\n",
       "      <td>0.233108</td>\n",
       "      <td>0.192809</td>\n",
       "      <td>0.838256</td>\n",
       "      <td>0.747390</td>\n",
       "      <td>0.467009</td>\n",
       "      <td>0.506437</td>\n",
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       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>0.171308</td>\n",
       "      <td>0.203740</td>\n",
       "      <td>0.187720</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.016892</td>\n",
       "      <td>0.019546</td>\n",
       "      <td>0.255363</td>\n",
       "      <td>0.459937</td>\n",
       "      <td>0.240870</td>\n",
       "      <td>0.249882</td>\n",
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       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>0.009553</td>\n",
       "      <td>0.006895</td>\n",
       "      <td>0.006568</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008446</td>\n",
       "      <td>0.005792</td>\n",
       "      <td>0.042895</td>\n",
       "      <td>0.041062</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>0.062157</td>\n",
       "      <td>0.074060</td>\n",
       "      <td>0.072064</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.005137</td>\n",
       "      <td>0.013514</td>\n",
       "      <td>0.010135</td>\n",
       "      <td>0.259140</td>\n",
       "      <td>0.318731</td>\n",
       "      <td>0.232368</td>\n",
       "      <td>0.228695</td>\n",
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       "      <th>443</th>\n",
       "      <td>0.053539</td>\n",
       "      <td>0.062645</td>\n",
       "      <td>0.058911</td>\n",
       "      <td>0.012672</td>\n",
       "      <td>0.018814</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.009170</td>\n",
       "      <td>0.319113</td>\n",
       "      <td>0.353353</td>\n",
       "      <td>0.222558</td>\n",
       "      <td>0.187536</td>\n",
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       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>0.070694</td>\n",
       "      <td>0.061198</td>\n",
       "      <td>0.057476</td>\n",
       "      <td>0.012246</td>\n",
       "      <td>0.018039</td>\n",
       "      <td>0.013514</td>\n",
       "      <td>0.009894</td>\n",
       "      <td>0.259051</td>\n",
       "      <td>0.345023</td>\n",
       "      <td>0.124717</td>\n",
       "      <td>0.186414</td>\n",
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       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>0.000163</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.047181</td>\n",
       "      <td>0.040751</td>\n",
       "      <td>0.014049</td>\n",
       "      <td>0.014074</td>\n",
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       "      <th>407</th>\n",
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       "      <td>0.073801</td>\n",
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       "      <td>0.005068</td>\n",
       "      <td>0.009411</td>\n",
       "      <td>0.239570</td>\n",
       "      <td>0.320334</td>\n",
       "      <td>0.151718</td>\n",
       "      <td>0.236587</td>\n",
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       "  </tbody>\n",
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       "<p>123 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cases_new_norm  cases_new_norm_ma5_shift0  cases_new_norm_ma7_shift0  \\\n",
       "584        0.849506                   0.857035                   0.886517   \n",
       "58         0.008618                   0.005919                   0.005604   \n",
       "539        0.509289                   0.495801                   0.456813   \n",
       "373        0.171308                   0.203740                   0.187720   \n",
       "61         0.009553                   0.006895                   0.006568   \n",
       "..              ...                        ...                        ...   \n",
       "408        0.062157                   0.074060                   0.072064   \n",
       "443        0.053539                   0.062645                   0.058911   \n",
       "442        0.070694                   0.061198                   0.057476   \n",
       "171        0.000163                   0.000374                   0.000319   \n",
       "407        0.068417                   0.075816                   0.073801   \n",
       "\n",
       "     cumul_full  cumul_partial  deaths_new_norm  deaths_new_norm_ma7_shift0  \\\n",
       "584    0.463551       0.607314         0.476351                    0.509411   \n",
       "58     0.000000       0.000000         0.008446                    0.003861   \n",
       "539    0.137715       0.298702         0.233108                    0.192809   \n",
       "373    0.000000       0.000000         0.016892                    0.019546   \n",
       "61     0.000000       0.000000         0.008446                    0.005792   \n",
       "..          ...            ...              ...                         ...   \n",
       "408    0.000002       0.005137         0.013514                    0.010135   \n",
       "443    0.012672       0.018814         0.006757                    0.009170   \n",
       "442    0.012246       0.018039         0.013514                    0.009894   \n",
       "171    0.000000       0.000000         0.000000                    0.000241   \n",
       "407    0.000001       0.004307         0.005068                    0.009411   \n",
       "\n",
       "     pcr_norm  pcr_norm_ma7_shift0  rtk-ag_norm  rtk-ag_norm_ma7_shift0  \n",
       "584  0.743407             0.761327     0.435440                0.581362  \n",
       "58   0.042330             0.038669     0.000000                0.000000  \n",
       "539  0.838256             0.747390     0.467009                0.506437  \n",
       "373  0.255363             0.459937     0.240870                0.249882  \n",
       "61   0.042895             0.041062     0.000000                0.000000  \n",
       "..        ...                  ...          ...                     ...  \n",
       "408  0.259140             0.318731     0.232368                0.228695  \n",
       "443  0.319113             0.353353     0.222558                0.187536  \n",
       "442  0.259051             0.345023     0.124717                0.186414  \n",
       "171  0.047181             0.040751     0.014049                0.014074  \n",
       "407  0.239570             0.320334     0.151718                0.236587  \n",
       "\n",
       "[123 rows x 11 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "# Counter(y_test)\n",
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array([1, 1, 2, 2])\n",
    "scores = np.array([0.1, 0.4, 0.35, 0.8])\n",
    "fpr, tpr, thresholds = roc_curve(y, scores, pos_label=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2d41c3de9a0>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fpr, tpr, color=color[i], label='test') \n",
    "    \n",
    "plt.plot([0, 1], [0, 1], color='green', linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2d41c7f0700>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure(figsize=(15,10))\n",
    "# plt.grid(True)\n",
    "# plt.plot(df_4['cumul_partial'],label='cumul_partial')\n",
    "# plt.plot(df_4['cumul_full'],label='cumul_full')\n",
    "# plt.legend(loc=2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}