Time_series_forecast.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1dcfb4bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import Sequential, layers, callbacks\n",
    "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, Bidirectional\n",
    "\n",
    "from pandas import read_csv\n",
    "from pandas import DataFrame\n",
    "from pandas import concat\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ae88eb8",
   "metadata": {},
   "source": [
    "# Read data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7602a8e4",
   "metadata": {},
   "source": [
    "## COVID Cases in Colombia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "139c5a6b",
   "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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>2022-04-29</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>2022-04-30</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>2022-05-01</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>791</th>\n",
       "      <td>2022-05-02</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>2022-05-03</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>793 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  Cases\n",
       "0   2020-03-02    1.0\n",
       "1   2020-03-03    1.0\n",
       "2   2020-03-04    0.0\n",
       "3   2020-03-05    0.0\n",
       "4   2020-03-06    1.0\n",
       "..         ...    ...\n",
       "788 2022-04-29  125.0\n",
       "789 2022-04-30   74.0\n",
       "790 2022-05-01   53.0\n",
       "791 2022-05-02   66.0\n",
       "792 2022-05-03    9.0\n",
       "\n",
       "[793 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_cases = pd.read_csv('Covid_Cases_Colombia/output_datasets/daily_covid_cases_in_Colombia.csv')\n",
    "covid_cases['epiweek'] = pd.to_datetime(covid_cases['epiweek'])\n",
    "covid_cases = covid_cases.rename(columns={'epiweek':'date'})\n",
    "covid_cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d4e89605",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 8))\n",
    "plt.plot(covid_cases.date, covid_cases.Cases, color ='blue')\n",
    "plt.xlabel('Date') \n",
    "plt.ylabel('Covid Cases')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1128e3b",
   "metadata": {},
   "source": [
    "## COVID Mobility in Colombia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6cd6d04e",
   "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>retail_and_recreation_percent_change_from_baseline</th>\n",
       "      <th>grocery_and_pharmacy_percent_change_from_baseline</th>\n",
       "      <th>parks_percent_change_from_baseline</th>\n",
       "      <th>transit_stations_percent_change_from_baseline</th>\n",
       "      <th>workplaces_percent_change_from_baseline</th>\n",
       "      <th>residential_percent_change_from_baseline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>-6.569536</td>\n",
       "      <td>-2.630252</td>\n",
       "      <td>-15.682848</td>\n",
       "      <td>-14.753086</td>\n",
       "      <td>5.266272</td>\n",
       "      <td>1.261682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>-5.907801</td>\n",
       "      <td>1.018519</td>\n",
       "      <td>-14.738854</td>\n",
       "      <td>-17.493827</td>\n",
       "      <td>-0.298507</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>-4.377953</td>\n",
       "      <td>1.136364</td>\n",
       "      <td>-7.422642</td>\n",
       "      <td>2.897436</td>\n",
       "      <td>15.296296</td>\n",
       "      <td>-1.288000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>-4.661417</td>\n",
       "      <td>-1.623853</td>\n",
       "      <td>-7.338403</td>\n",
       "      <td>1.773333</td>\n",
       "      <td>16.084656</td>\n",
       "      <td>-1.650794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>-5.909091</td>\n",
       "      <td>-4.467890</td>\n",
       "      <td>-8.819853</td>\n",
       "      <td>0.435897</td>\n",
       "      <td>13.041451</td>\n",
       "      <td>-1.905512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>2022-05-09</td>\n",
       "      <td>16.883333</td>\n",
       "      <td>46.515152</td>\n",
       "      <td>11.991701</td>\n",
       "      <td>30.457143</td>\n",
       "      <td>48.916996</td>\n",
       "      <td>4.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>2022-05-10</td>\n",
       "      <td>20.853448</td>\n",
       "      <td>51.385417</td>\n",
       "      <td>17.454167</td>\n",
       "      <td>29.544118</td>\n",
       "      <td>57.861660</td>\n",
       "      <td>3.358621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>2022-05-11</td>\n",
       "      <td>19.154472</td>\n",
       "      <td>45.561224</td>\n",
       "      <td>16.274590</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>56.860558</td>\n",
       "      <td>3.326531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>2022-05-12</td>\n",
       "      <td>24.480000</td>\n",
       "      <td>48.031250</td>\n",
       "      <td>22.159184</td>\n",
       "      <td>30.742857</td>\n",
       "      <td>55.767717</td>\n",
       "      <td>2.609589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>2022-05-13</td>\n",
       "      <td>22.130769</td>\n",
       "      <td>44.989796</td>\n",
       "      <td>15.973384</td>\n",
       "      <td>28.565789</td>\n",
       "      <td>58.471774</td>\n",
       "      <td>3.283688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>819 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  retail_and_recreation_percent_change_from_baseline  \\\n",
       "0   2020-02-15                                          -6.569536    \n",
       "1   2020-02-16                                          -5.907801    \n",
       "2   2020-02-17                                          -4.377953    \n",
       "3   2020-02-18                                          -4.661417    \n",
       "4   2020-02-19                                          -5.909091    \n",
       "..         ...                                                ...    \n",
       "814 2022-05-09                                          16.883333    \n",
       "815 2022-05-10                                          20.853448    \n",
       "816 2022-05-11                                          19.154472    \n",
       "817 2022-05-12                                          24.480000    \n",
       "818 2022-05-13                                          22.130769    \n",
       "\n",
       "     grocery_and_pharmacy_percent_change_from_baseline  \\\n",
       "0                                            -2.630252   \n",
       "1                                             1.018519   \n",
       "2                                             1.136364   \n",
       "3                                            -1.623853   \n",
       "4                                            -4.467890   \n",
       "..                                                 ...   \n",
       "814                                          46.515152   \n",
       "815                                          51.385417   \n",
       "816                                          45.561224   \n",
       "817                                          48.031250   \n",
       "818                                          44.989796   \n",
       "\n",
       "     parks_percent_change_from_baseline  \\\n",
       "0                            -15.682848   \n",
       "1                            -14.738854   \n",
       "2                             -7.422642   \n",
       "3                             -7.338403   \n",
       "4                             -8.819853   \n",
       "..                                  ...   \n",
       "814                           11.991701   \n",
       "815                           17.454167   \n",
       "816                           16.274590   \n",
       "817                           22.159184   \n",
       "818                           15.973384   \n",
       "\n",
       "     transit_stations_percent_change_from_baseline  \\\n",
       "0                                       -14.753086   \n",
       "1                                       -17.493827   \n",
       "2                                         2.897436   \n",
       "3                                         1.773333   \n",
       "4                                         0.435897   \n",
       "..                                             ...   \n",
       "814                                      30.457143   \n",
       "815                                      29.544118   \n",
       "816                                      27.800000   \n",
       "817                                      30.742857   \n",
       "818                                      28.565789   \n",
       "\n",
       "     workplaces_percent_change_from_baseline  \\\n",
       "0                                   5.266272   \n",
       "1                                  -0.298507   \n",
       "2                                  15.296296   \n",
       "3                                  16.084656   \n",
       "4                                  13.041451   \n",
       "..                                       ...   \n",
       "814                                48.916996   \n",
       "815                                57.861660   \n",
       "816                                56.860558   \n",
       "817                                55.767717   \n",
       "818                                58.471774   \n",
       "\n",
       "     residential_percent_change_from_baseline  \n",
       "0                                    1.261682  \n",
       "1                                    2.500000  \n",
       "2                                   -1.288000  \n",
       "3                                   -1.650794  \n",
       "4                                   -1.905512  \n",
       "..                                        ...  \n",
       "814                                  4.833333  \n",
       "815                                  3.358621  \n",
       "816                                  3.326531  \n",
       "817                                  2.609589  \n",
       "818                                  3.283688  \n",
       "\n",
       "[819 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colombia_mobility = pd.read_csv('Google_Mobility_Data/Google_Mobility/Mobility_Colombia/mobility_colombia.csv')\n",
    "colombia_mobility['date'] = pd.to_datetime(colombia_mobility['date'])\n",
    "colombia_mobility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "da9e33f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                                                  datetime64[ns]\n",
       "retail_and_recreation_percent_change_from_baseline           float64\n",
       "grocery_and_pharmacy_percent_change_from_baseline            float64\n",
       "parks_percent_change_from_baseline                           float64\n",
       "transit_stations_percent_change_from_baseline                float64\n",
       "workplaces_percent_change_from_baseline                      float64\n",
       "residential_percent_change_from_baseline                     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colombia_mobility.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "00eb10ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14ffb2e80>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Covid cases in time\n",
    "plt.figure(figsize = (12, 8))\n",
    "plt.plot(colombia_mobility.date, colombia_mobility['retail_and_recreation_percent_change_from_baseline'], color ='blue', label='retail_and_recreation_percent_change_from_baseline')\n",
    "plt.plot(colombia_mobility.date, colombia_mobility['grocery_and_pharmacy_percent_change_from_baseline'], color ='red', label='grocery_and_pharmacy_percent_change_from_baseline')\n",
    "plt.plot(colombia_mobility.date, colombia_mobility['workplaces_percent_change_from_baseline'], color ='green', label='workplaces_percent_change_from_baseline')\n",
    "plt.plot(colombia_mobility.date, colombia_mobility['residential_percent_change_from_baseline'], color ='black', label='residential_percent_change_from_baseline')\n",
    "plt.xlabel('date of mobility score in Colombia') \n",
    "plt.ylabel('Mobility score')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebbfd1ff",
   "metadata": {},
   "source": [
    "## Merge Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "532d28b6",
   "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>retail_and_recreation_percent_change_from_baseline</th>\n",
       "      <th>grocery_and_pharmacy_percent_change_from_baseline</th>\n",
       "      <th>parks_percent_change_from_baseline</th>\n",
       "      <th>transit_stations_percent_change_from_baseline</th>\n",
       "      <th>workplaces_percent_change_from_baseline</th>\n",
       "      <th>residential_percent_change_from_baseline</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-02</th>\n",
       "      <td>-2.362205</td>\n",
       "      <td>6.216216</td>\n",
       "      <td>-7.699248</td>\n",
       "      <td>1.615385</td>\n",
       "      <td>17.797872</td>\n",
       "      <td>-1.825397</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>-3.480000</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>-8.198529</td>\n",
       "      <td>-0.320513</td>\n",
       "      <td>18.291005</td>\n",
       "      <td>-1.881890</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-04</th>\n",
       "      <td>-3.189394</td>\n",
       "      <td>1.490741</td>\n",
       "      <td>-6.159259</td>\n",
       "      <td>-1.531646</td>\n",
       "      <td>16.036082</td>\n",
       "      <td>-2.007692</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-05</th>\n",
       "      <td>-3.046154</td>\n",
       "      <td>1.570093</td>\n",
       "      <td>-5.978182</td>\n",
       "      <td>-0.435897</td>\n",
       "      <td>15.796875</td>\n",
       "      <td>-1.938462</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-06</th>\n",
       "      <td>-3.978102</td>\n",
       "      <td>-0.607143</td>\n",
       "      <td>-9.683502</td>\n",
       "      <td>0.132530</td>\n",
       "      <td>22.229508</td>\n",
       "      <td>-2.256000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-29</th>\n",
       "      <td>28.734375</td>\n",
       "      <td>54.346939</td>\n",
       "      <td>22.787645</td>\n",
       "      <td>27.844156</td>\n",
       "      <td>64.234568</td>\n",
       "      <td>2.295775</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-30</th>\n",
       "      <td>32.681159</td>\n",
       "      <td>60.704762</td>\n",
       "      <td>29.571942</td>\n",
       "      <td>39.256757</td>\n",
       "      <td>49.647321</td>\n",
       "      <td>1.646154</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-01</th>\n",
       "      <td>33.223022</td>\n",
       "      <td>63.901099</td>\n",
       "      <td>32.505226</td>\n",
       "      <td>36.794521</td>\n",
       "      <td>37.321244</td>\n",
       "      <td>2.532110</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-02</th>\n",
       "      <td>28.404959</td>\n",
       "      <td>65.510417</td>\n",
       "      <td>27.982906</td>\n",
       "      <td>38.142857</td>\n",
       "      <td>58.008230</td>\n",
       "      <td>2.826389</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-03</th>\n",
       "      <td>26.239316</td>\n",
       "      <td>60.610526</td>\n",
       "      <td>26.995726</td>\n",
       "      <td>28.623188</td>\n",
       "      <td>60.027778</td>\n",
       "      <td>2.650685</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>793 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            retail_and_recreation_percent_change_from_baseline  \\\n",
       "date                                                             \n",
       "2020-03-02                                          -2.362205    \n",
       "2020-03-03                                          -3.480000    \n",
       "2020-03-04                                          -3.189394    \n",
       "2020-03-05                                          -3.046154    \n",
       "2020-03-06                                          -3.978102    \n",
       "...                                                       ...    \n",
       "2022-04-29                                          28.734375    \n",
       "2022-04-30                                          32.681159    \n",
       "2022-05-01                                          33.223022    \n",
       "2022-05-02                                          28.404959    \n",
       "2022-05-03                                          26.239316    \n",
       "\n",
       "            grocery_and_pharmacy_percent_change_from_baseline  \\\n",
       "date                                                            \n",
       "2020-03-02                                           6.216216   \n",
       "2020-03-03                                           2.555556   \n",
       "2020-03-04                                           1.490741   \n",
       "2020-03-05                                           1.570093   \n",
       "2020-03-06                                          -0.607143   \n",
       "...                                                       ...   \n",
       "2022-04-29                                          54.346939   \n",
       "2022-04-30                                          60.704762   \n",
       "2022-05-01                                          63.901099   \n",
       "2022-05-02                                          65.510417   \n",
       "2022-05-03                                          60.610526   \n",
       "\n",
       "            parks_percent_change_from_baseline  \\\n",
       "date                                             \n",
       "2020-03-02                           -7.699248   \n",
       "2020-03-03                           -8.198529   \n",
       "2020-03-04                           -6.159259   \n",
       "2020-03-05                           -5.978182   \n",
       "2020-03-06                           -9.683502   \n",
       "...                                        ...   \n",
       "2022-04-29                           22.787645   \n",
       "2022-04-30                           29.571942   \n",
       "2022-05-01                           32.505226   \n",
       "2022-05-02                           27.982906   \n",
       "2022-05-03                           26.995726   \n",
       "\n",
       "            transit_stations_percent_change_from_baseline  \\\n",
       "date                                                        \n",
       "2020-03-02                                       1.615385   \n",
       "2020-03-03                                      -0.320513   \n",
       "2020-03-04                                      -1.531646   \n",
       "2020-03-05                                      -0.435897   \n",
       "2020-03-06                                       0.132530   \n",
       "...                                                   ...   \n",
       "2022-04-29                                      27.844156   \n",
       "2022-04-30                                      39.256757   \n",
       "2022-05-01                                      36.794521   \n",
       "2022-05-02                                      38.142857   \n",
       "2022-05-03                                      28.623188   \n",
       "\n",
       "            workplaces_percent_change_from_baseline  \\\n",
       "date                                                  \n",
       "2020-03-02                                17.797872   \n",
       "2020-03-03                                18.291005   \n",
       "2020-03-04                                16.036082   \n",
       "2020-03-05                                15.796875   \n",
       "2020-03-06                                22.229508   \n",
       "...                                             ...   \n",
       "2022-04-29                                64.234568   \n",
       "2022-04-30                                49.647321   \n",
       "2022-05-01                                37.321244   \n",
       "2022-05-02                                58.008230   \n",
       "2022-05-03                                60.027778   \n",
       "\n",
       "            residential_percent_change_from_baseline  Cases  \n",
       "date                                                         \n",
       "2020-03-02                                 -1.825397    1.0  \n",
       "2020-03-03                                 -1.881890    1.0  \n",
       "2020-03-04                                 -2.007692    0.0  \n",
       "2020-03-05                                 -1.938462    0.0  \n",
       "2020-03-06                                 -2.256000    1.0  \n",
       "...                                              ...    ...  \n",
       "2022-04-29                                  2.295775  125.0  \n",
       "2022-04-30                                  1.646154   74.0  \n",
       "2022-05-01                                  2.532110   53.0  \n",
       "2022-05-02                                  2.826389   66.0  \n",
       "2022-05-03                                  2.650685    9.0  \n",
       "\n",
       "[793 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colombia_df = colombia_mobility.merge(covid_cases, how='inner', on=\"date\")\n",
    "colombia_df.set_index('date', inplace=True)\n",
    "colombia_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbfcaa7e",
   "metadata": {},
   "source": [
    "# Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd69f989",
   "metadata": {},
   "source": [
    "## Prepare data to supervised learning time series\n",
    "\n",
    "we will use:\n",
    "\n",
    "* data: is the dataframe in our case (Covid Cases and Google Mobile Data)\n",
    "* n_in: is the number of lag weeks in the past (length of window)\n",
    "\n",
    "The heart of this \"series_to_supervised\" function is the <a href=\"https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.shift.html\">shift</a> fuction of pandas\n",
    "\n",
    "This function gets as input the number of periods(in this case the number of days represented as rows up or down in the dataframe) to move the columns of a dataframe. E.g.\n",
    "\n",
    "If we have df.shift(1) all the rows of column cases_medellin will move one row down\n",
    "If we have df.shift(-1) all the rows of column cases_medellin will move one row up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "647e120c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare data for lstm\n",
    "\n",
    "# convert series to supervised learning\n",
    "def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n",
    "    n_vars = 1 if type(data) is list else data.shape[1]\n",
    "    df = DataFrame(data)\n",
    "    cols, names = list(), list()\n",
    "    # input sequence (t-n, ... t-1)\n",
    "    for i in range(n_in, 0, -1):\n",
    "        cols.append(df.shift(i))\n",
    "        names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # forecast sequence (t, t+1, ... t+n)\n",
    "    for i in range(0, n_out):\n",
    "        cols.append(df.shift(-i))\n",
    "        if i == 0:\n",
    "            names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n",
    "        else:\n",
    "            names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # put it all together\n",
    "    agg = concat(cols, axis=1)\n",
    "    agg.columns = names\n",
    "    # drop rows with NaN values\n",
    "    if dropnan:\n",
    "        agg.dropna(inplace=True)\n",
    "    return agg\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc2a64e6",
   "metadata": {},
   "source": [
    "## normalize features\n",
    "As we are working with a Neural Network the data values ​​must be normalized to help backpropagation algorithm So we will use the <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html\">MinMaxScaler</a> from sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4c85f750",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0, 1)) # Scaler between 0 and 1\n",
    "scaled = scaler.fit_transform(colombia_df) # As we can see data set has 3 Columns (This shape is also important for inverse scaler as we will see in future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "960d7397",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>var1(t-10)</th>\n",
       "      <th>var2(t-10)</th>\n",
       "      <th>var3(t-10)</th>\n",
       "      <th>var4(t-10)</th>\n",
       "      <th>var5(t-10)</th>\n",
       "      <th>var6(t-10)</th>\n",
       "      <th>var7(t-10)</th>\n",
       "      <th>var1(t-9)</th>\n",
       "      <th>var2(t-9)</th>\n",
       "      <th>var3(t-9)</th>\n",
       "      <th>...</th>\n",
       "      <th>var5(t-1)</th>\n",
       "      <th>var6(t-1)</th>\n",
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       "      <th>var1(t)</th>\n",
       "      <th>var2(t)</th>\n",
       "      <th>var3(t)</th>\n",
       "      <th>var4(t)</th>\n",
       "      <th>var5(t)</th>\n",
       "      <th>var6(t)</th>\n",
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       "      <th>10</th>\n",
       "      <td>0.545480</td>\n",
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       "      <td>0.464994</td>\n",
       "      <td>0.612165</td>\n",
       "      <td>0.668078</td>\n",
       "      <td>0.033621</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.538404</td>\n",
       "      <td>0.401380</td>\n",
       "      <td>0.461680</td>\n",
       "      <td>...</td>\n",
       "      <td>0.646631</td>\n",
       "      <td>0.046529</td>\n",
       "      <td>0.000181</td>\n",
       "      <td>0.507397</td>\n",
       "      <td>0.369347</td>\n",
       "      <td>0.444165</td>\n",
       "      <td>0.562113</td>\n",
       "      <td>0.641020</td>\n",
       "      <td>0.054580</td>\n",
       "      <td>0.000248</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.538404</td>\n",
       "      <td>0.401380</td>\n",
       "      <td>0.461680</td>\n",
       "      <td>0.598666</td>\n",
       "      <td>0.671563</td>\n",
       "      <td>0.032300</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.540244</td>\n",
       "      <td>0.395992</td>\n",
       "      <td>0.475214</td>\n",
       "      <td>...</td>\n",
       "      <td>0.641020</td>\n",
       "      <td>0.054580</td>\n",
       "      <td>0.000248</td>\n",
       "      <td>0.497543</td>\n",
       "      <td>0.372097</td>\n",
       "      <td>0.419578</td>\n",
       "      <td>0.546732</td>\n",
       "      <td>0.681284</td>\n",
       "      <td>0.052353</td>\n",
       "      <td>0.000294</td>\n",
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       "      <th>12</th>\n",
       "      <td>0.540244</td>\n",
       "      <td>0.395992</td>\n",
       "      <td>0.475214</td>\n",
       "      <td>0.590221</td>\n",
       "      <td>0.655628</td>\n",
       "      <td>0.029359</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.541150</td>\n",
       "      <td>0.396394</td>\n",
       "      <td>0.476416</td>\n",
       "      <td>...</td>\n",
       "      <td>0.681284</td>\n",
       "      <td>0.052353</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0.508076</td>\n",
       "      <td>0.399589</td>\n",
       "      <td>0.422857</td>\n",
       "      <td>0.498297</td>\n",
       "      <td>0.606373</td>\n",
       "      <td>0.099259</td>\n",
       "      <td>0.000452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.541150</td>\n",
       "      <td>0.396394</td>\n",
       "      <td>0.476416</td>\n",
       "      <td>0.597861</td>\n",
       "      <td>0.653938</td>\n",
       "      <td>0.030978</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.535252</td>\n",
       "      <td>0.385378</td>\n",
       "      <td>0.451824</td>\n",
       "      <td>...</td>\n",
       "      <td>0.606373</td>\n",
       "      <td>0.099259</td>\n",
       "      <td>0.000452</td>\n",
       "      <td>0.470481</td>\n",
       "      <td>0.414450</td>\n",
       "      <td>0.406772</td>\n",
       "      <td>0.456210</td>\n",
       "      <td>0.547662</td>\n",
       "      <td>0.158989</td>\n",
       "      <td>0.000429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.535252</td>\n",
       "      <td>0.385378</td>\n",
       "      <td>0.451824</td>\n",
       "      <td>0.601825</td>\n",
       "      <td>0.699395</td>\n",
       "      <td>0.023552</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.547266</td>\n",
       "      <td>0.399539</td>\n",
       "      <td>0.451571</td>\n",
       "      <td>...</td>\n",
       "      <td>0.547662</td>\n",
       "      <td>0.158989</td>\n",
       "      <td>0.000429</td>\n",
       "      <td>0.466248</td>\n",
       "      <td>0.432898</td>\n",
       "      <td>0.400329</td>\n",
       "      <td>0.499571</td>\n",
       "      <td>0.540779</td>\n",
       "      <td>0.180065</td>\n",
       "      <td>0.000655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>0.666544</td>\n",
       "      <td>0.623898</td>\n",
       "      <td>0.632282</td>\n",
       "      <td>0.834751</td>\n",
       "      <td>0.926369</td>\n",
       "      <td>0.157428</td>\n",
       "      <td>0.005307</td>\n",
       "      <td>0.688895</td>\n",
       "      <td>0.628698</td>\n",
       "      <td>0.641560</td>\n",
       "      <td>...</td>\n",
       "      <td>0.910477</td>\n",
       "      <td>0.183487</td>\n",
       "      <td>0.003703</td>\n",
       "      <td>0.742306</td>\n",
       "      <td>0.663417</td>\n",
       "      <td>0.667335</td>\n",
       "      <td>0.795060</td>\n",
       "      <td>0.996226</td>\n",
       "      <td>0.129993</td>\n",
       "      <td>0.002823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>0.688895</td>\n",
       "      <td>0.628698</td>\n",
       "      <td>0.641560</td>\n",
       "      <td>0.814641</td>\n",
       "      <td>0.924765</td>\n",
       "      <td>0.156299</td>\n",
       "      <td>0.005081</td>\n",
       "      <td>0.704017</td>\n",
       "      <td>0.626245</td>\n",
       "      <td>0.644719</td>\n",
       "      <td>...</td>\n",
       "      <td>0.996226</td>\n",
       "      <td>0.129993</td>\n",
       "      <td>0.002823</td>\n",
       "      <td>0.767287</td>\n",
       "      <td>0.695584</td>\n",
       "      <td>0.712362</td>\n",
       "      <td>0.874641</td>\n",
       "      <td>0.893145</td>\n",
       "      <td>0.114802</td>\n",
       "      <td>0.001671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>0.704017</td>\n",
       "      <td>0.626245</td>\n",
       "      <td>0.644719</td>\n",
       "      <td>0.798338</td>\n",
       "      <td>0.925820</td>\n",
       "      <td>0.156299</td>\n",
       "      <td>0.005216</td>\n",
       "      <td>0.682640</td>\n",
       "      <td>0.606658</td>\n",
       "      <td>0.607491</td>\n",
       "      <td>...</td>\n",
       "      <td>0.893145</td>\n",
       "      <td>0.114802</td>\n",
       "      <td>0.001671</td>\n",
       "      <td>0.770717</td>\n",
       "      <td>0.711756</td>\n",
       "      <td>0.731830</td>\n",
       "      <td>0.857472</td>\n",
       "      <td>0.806041</td>\n",
       "      <td>0.135520</td>\n",
       "      <td>0.001197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>791</th>\n",
       "      <td>0.682640</td>\n",
       "      <td>0.606658</td>\n",
       "      <td>0.607491</td>\n",
       "      <td>0.784697</td>\n",
       "      <td>0.989313</td>\n",
       "      <td>0.155749</td>\n",
       "      <td>0.004923</td>\n",
       "      <td>0.688378</td>\n",
       "      <td>0.614121</td>\n",
       "      <td>0.642387</td>\n",
       "      <td>...</td>\n",
       "      <td>0.806041</td>\n",
       "      <td>0.135520</td>\n",
       "      <td>0.001197</td>\n",
       "      <td>0.740221</td>\n",
       "      <td>0.719898</td>\n",
       "      <td>0.701816</td>\n",
       "      <td>0.866874</td>\n",
       "      <td>0.952227</td>\n",
       "      <td>0.142402</td>\n",
       "      <td>0.001490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>0.688378</td>\n",
       "      <td>0.614121</td>\n",
       "      <td>0.642387</td>\n",
       "      <td>0.823003</td>\n",
       "      <td>0.887976</td>\n",
       "      <td>0.154683</td>\n",
       "      <td>0.002506</td>\n",
       "      <td>0.655820</td>\n",
       "      <td>0.589214</td>\n",
       "      <td>0.621396</td>\n",
       "      <td>...</td>\n",
       "      <td>0.952227</td>\n",
       "      <td>0.142402</td>\n",
       "      <td>0.001490</td>\n",
       "      <td>0.726514</td>\n",
       "      <td>0.695107</td>\n",
       "      <td>0.695264</td>\n",
       "      <td>0.800492</td>\n",
       "      <td>0.966499</td>\n",
       "      <td>0.138293</td>\n",
       "      <td>0.000203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>783 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     var1(t-10)  var2(t-10)  var3(t-10)  var4(t-10)  var5(t-10)  var6(t-10)  \\\n",
       "10     0.545480    0.419901    0.464994    0.612165    0.668078    0.033621   \n",
       "11     0.538404    0.401380    0.461680    0.598666    0.671563    0.032300   \n",
       "12     0.540244    0.395992    0.475214    0.590221    0.655628    0.029359   \n",
       "13     0.541150    0.396394    0.476416    0.597861    0.653938    0.030978   \n",
       "14     0.535252    0.385378    0.451824    0.601825    0.699395    0.023552   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "788    0.666544    0.623898    0.632282    0.834751    0.926369    0.157428   \n",
       "789    0.688895    0.628698    0.641560    0.814641    0.924765    0.156299   \n",
       "790    0.704017    0.626245    0.644719    0.798338    0.925820    0.156299   \n",
       "791    0.682640    0.606658    0.607491    0.784697    0.989313    0.155749   \n",
       "792    0.688378    0.614121    0.642387    0.823003    0.887976    0.154683   \n",
       "\n",
       "     var7(t-10)  var1(t-9)  var2(t-9)  var3(t-9)  ...  var5(t-1)  var6(t-1)  \\\n",
       "10     0.000023   0.538404   0.401380   0.461680  ...   0.646631   0.046529   \n",
       "11     0.000023   0.540244   0.395992   0.475214  ...   0.641020   0.054580   \n",
       "12     0.000000   0.541150   0.396394   0.476416  ...   0.681284   0.052353   \n",
       "13     0.000000   0.535252   0.385378   0.451824  ...   0.606373   0.099259   \n",
       "14     0.000023   0.547266   0.399539   0.451571  ...   0.547662   0.158989   \n",
       "..          ...        ...        ...        ...  ...        ...        ...   \n",
       "788    0.005307   0.688895   0.628698   0.641560  ...   0.910477   0.183487   \n",
       "789    0.005081   0.704017   0.626245   0.644719  ...   0.996226   0.129993   \n",
       "790    0.005216   0.682640   0.606658   0.607491  ...   0.893145   0.114802   \n",
       "791    0.004923   0.688378   0.614121   0.642387  ...   0.806041   0.135520   \n",
       "792    0.002506   0.655820   0.589214   0.621396  ...   0.952227   0.142402   \n",
       "\n",
       "     var7(t-1)   var1(t)   var2(t)   var3(t)   var4(t)   var5(t)   var6(t)  \\\n",
       "10    0.000181  0.507397  0.369347  0.444165  0.562113  0.641020  0.054580   \n",
       "11    0.000248  0.497543  0.372097  0.419578  0.546732  0.681284  0.052353   \n",
       "12    0.000294  0.508076  0.399589  0.422857  0.498297  0.606373  0.099259   \n",
       "13    0.000452  0.470481  0.414450  0.406772  0.456210  0.547662  0.158989   \n",
       "14    0.000429  0.466248  0.432898  0.400329  0.499571  0.540779  0.180065   \n",
       "..         ...       ...       ...       ...       ...       ...       ...   \n",
       "788   0.003703  0.742306  0.663417  0.667335  0.795060  0.996226  0.129993   \n",
       "789   0.002823  0.767287  0.695584  0.712362  0.874641  0.893145  0.114802   \n",
       "790   0.001671  0.770717  0.711756  0.731830  0.857472  0.806041  0.135520   \n",
       "791   0.001197  0.740221  0.719898  0.701816  0.866874  0.952227  0.142402   \n",
       "792   0.001490  0.726514  0.695107  0.695264  0.800492  0.966499  0.138293   \n",
       "\n",
       "      var7(t)  \n",
       "10   0.000248  \n",
       "11   0.000294  \n",
       "12   0.000452  \n",
       "13   0.000429  \n",
       "14   0.000655  \n",
       "..        ...  \n",
       "788  0.002823  \n",
       "789  0.001671  \n",
       "790  0.001197  \n",
       "791  0.001490  \n",
       "792  0.000203  \n",
       "\n",
       "[783 rows x 77 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# length of window\n",
    "days = 10\n",
    "\n",
    "# frame as supervised learning\n",
    "data = series_to_supervised(scaled, n_in=days)\n",
    "DataFrame(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aff5c285",
   "metadata": {},
   "source": [
    "### Features Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "33526a58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(783, 70)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We define the number of features as 7 (Cases and 6 from google mobility)\n",
    "n_features = 7\n",
    "\n",
    "# The features to train the model will be all except the values of the actual week \n",
    "# We can't use the temperature and precipitation in week t because whe need to resample a a 3D Array\n",
    "features_set = DataFrame(data.values[:,:-n_features])\n",
    "# Convert pandas data frame to np.array to reshape as 3D Array\n",
    "features_set = features_set.to_numpy()\n",
    "features_set.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c023fdfc",
   "metadata": {},
   "source": [
    "### Labels Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fbee3ef0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(783, 1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will use Dengue cases in last week \n",
    "labels_set = DataFrame(data.values[:,-1])\n",
    "# Convert pandas data frame to np.array\n",
    "labels_set = labels_set.to_numpy()\n",
    "labels_set.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46bfc4d5",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a9317425",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We need a sequence so we can't split randomly\n",
    "# To divide into Train (90%) and test (10%) to do that we need to know the 90% of the total dataframe\n",
    "size = features_set.shape[0]\n",
    "split = int(size*(9/10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd35bc1e",
   "metadata": {},
   "source": [
    "### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "270d22bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will train with 1st 90% of data and test with last 10%\n",
    "train_X = features_set[:split] ##90% train\n",
    "train_y = labels_set[:split]  ##90% train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da76980b",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fc04be45",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X = features_set[split:] ##10% test\n",
    "test_y = labels_set[split:] ##10% test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "900628ce",
   "metadata": {},
   "source": [
    "### Reshape to tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "080ff259",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(704, 10, 7) (704, 1) (79, 10, 7) (79, 1)\n"
     ]
    }
   ],
   "source": [
    "# reshape input to be 3D [samples, timesteps, features]\n",
    "train_X = train_X.reshape((train_X.shape[0], days, n_features))\n",
    "test_X = test_X.reshape((test_X.shape[0], days, n_features))\n",
    "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb172f0a",
   "metadata": {},
   "source": [
    "# Model\n",
    "## LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "aee9c42d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-10 09:43:48.427201: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2022-07-10 09:43:48.427331: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    }
   ],
   "source": [
    "# Set Seed\n",
    "tf.random.set_seed(0)\n",
    "\n",
    "# design network\n",
    "model = Sequential()\n",
    "model.add(LSTM(120, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True))\n",
    "model.add(LSTM(240, input_shape=(train_X.shape[1], 120)))\n",
    "model.add(Dense(60))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mae', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3c102729",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 10, 120)           61440     \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 240)               346560    \n",
      "                                                                 \n",
      " dense (Dense)               (None, 60)                14460     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 61        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 422,521\n",
      "Trainable params: 422,521\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7f97551",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "76983f91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-10 09:43:48.819088: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2022-07-10 09:43:49.899322: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-07-10 09:43:50.083016: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-07-10 09:43:50.139457: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-07-10 09:43:50.207006: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-07-10 09:43:50.303595: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 - 2s - loss: 0.2057 - val_loss: 0.0652 - 2s/epoch - 217ms/step\n",
      "Epoch 2/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-10 09:43:50.831389: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-07-10 09:43:50.899036: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-07-10 09:43:50.926745: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 - 0s - loss: 0.1525 - val_loss: 0.1354 - 158ms/epoch - 16ms/step\n",
      "Epoch 3/50\n",
      "10/10 - 0s - loss: 0.1149 - val_loss: 0.0611 - 148ms/epoch - 15ms/step\n",
      "Epoch 4/50\n",
      "10/10 - 0s - loss: 0.0891 - val_loss: 0.0220 - 149ms/epoch - 15ms/step\n",
      "Epoch 5/50\n",
      "10/10 - 0s - loss: 0.1160 - val_loss: 0.0151 - 151ms/epoch - 15ms/step\n",
      "Epoch 6/50\n",
      "10/10 - 0s - loss: 0.0908 - val_loss: 0.1004 - 151ms/epoch - 15ms/step\n",
      "Epoch 7/50\n",
      "10/10 - 0s - loss: 0.0843 - val_loss: 0.0465 - 145ms/epoch - 14ms/step\n",
      "Epoch 8/50\n",
      "10/10 - 0s - loss: 0.0611 - val_loss: 0.0234 - 146ms/epoch - 15ms/step\n",
      "Epoch 9/50\n",
      "10/10 - 0s - loss: 0.0641 - val_loss: 0.0221 - 151ms/epoch - 15ms/step\n",
      "Epoch 10/50\n",
      "10/10 - 0s - loss: 0.0580 - val_loss: 0.0234 - 139ms/epoch - 14ms/step\n",
      "Epoch 11/50\n",
      "10/10 - 0s - loss: 0.0667 - val_loss: 0.0055 - 146ms/epoch - 15ms/step\n",
      "Epoch 12/50\n",
      "10/10 - 0s - loss: 0.0640 - val_loss: 0.0088 - 151ms/epoch - 15ms/step\n",
      "Epoch 13/50\n",
      "10/10 - 0s - loss: 0.0653 - val_loss: 0.0061 - 147ms/epoch - 15ms/step\n",
      "Epoch 14/50\n",
      "10/10 - 0s - loss: 0.0608 - val_loss: 0.0138 - 140ms/epoch - 14ms/step\n",
      "Epoch 15/50\n",
      "10/10 - 0s - loss: 0.0561 - val_loss: 0.0117 - 150ms/epoch - 15ms/step\n",
      "Epoch 16/50\n",
      "10/10 - 0s - loss: 0.0530 - val_loss: 0.0132 - 149ms/epoch - 15ms/step\n",
      "Epoch 17/50\n",
      "10/10 - 0s - loss: 0.0591 - val_loss: 0.0091 - 158ms/epoch - 16ms/step\n",
      "Epoch 18/50\n",
      "10/10 - 0s - loss: 0.0531 - val_loss: 0.0082 - 151ms/epoch - 15ms/step\n",
      "Epoch 19/50\n",
      "10/10 - 0s - loss: 0.0624 - val_loss: 0.0080 - 143ms/epoch - 14ms/step\n",
      "Epoch 20/50\n",
      "10/10 - 0s - loss: 0.0581 - val_loss: 0.0155 - 144ms/epoch - 14ms/step\n",
      "Epoch 21/50\n",
      "10/10 - 0s - loss: 0.0516 - val_loss: 0.0039 - 154ms/epoch - 15ms/step\n",
      "Epoch 22/50\n",
      "10/10 - 0s - loss: 0.0549 - val_loss: 0.0148 - 151ms/epoch - 15ms/step\n",
      "Epoch 23/50\n",
      "10/10 - 0s - loss: 0.0545 - val_loss: 0.0077 - 150ms/epoch - 15ms/step\n",
      "Epoch 24/50\n",
      "10/10 - 0s - loss: 0.0491 - val_loss: 0.0078 - 149ms/epoch - 15ms/step\n",
      "Epoch 25/50\n",
      "10/10 - 0s - loss: 0.0566 - val_loss: 0.0131 - 146ms/epoch - 15ms/step\n",
      "Epoch 26/50\n",
      "10/10 - 0s - loss: 0.0487 - val_loss: 0.0080 - 151ms/epoch - 15ms/step\n",
      "Epoch 27/50\n",
      "10/10 - 0s - loss: 0.0546 - val_loss: 0.0073 - 153ms/epoch - 15ms/step\n",
      "Epoch 28/50\n",
      "10/10 - 0s - loss: 0.0578 - val_loss: 0.0082 - 154ms/epoch - 15ms/step\n",
      "Epoch 29/50\n",
      "10/10 - 0s - loss: 0.0481 - val_loss: 0.0120 - 150ms/epoch - 15ms/step\n",
      "Epoch 30/50\n",
      "10/10 - 0s - loss: 0.0629 - val_loss: 0.0050 - 150ms/epoch - 15ms/step\n",
      "Epoch 31/50\n",
      "10/10 - 0s - loss: 0.0479 - val_loss: 0.0262 - 145ms/epoch - 15ms/step\n",
      "Epoch 32/50\n",
      "10/10 - 0s - loss: 0.0601 - val_loss: 0.0077 - 147ms/epoch - 15ms/step\n",
      "Epoch 33/50\n",
      "10/10 - 0s - loss: 0.0555 - val_loss: 0.0231 - 154ms/epoch - 15ms/step\n",
      "Epoch 34/50\n",
      "10/10 - 0s - loss: 0.0541 - val_loss: 0.0235 - 153ms/epoch - 15ms/step\n",
      "Epoch 35/50\n",
      "10/10 - 0s - loss: 0.0494 - val_loss: 0.0108 - 144ms/epoch - 14ms/step\n",
      "Epoch 36/50\n",
      "10/10 - 0s - loss: 0.0448 - val_loss: 0.0079 - 157ms/epoch - 16ms/step\n",
      "Epoch 37/50\n",
      "10/10 - 0s - loss: 0.0463 - val_loss: 0.0191 - 152ms/epoch - 15ms/step\n",
      "Epoch 38/50\n",
      "10/10 - 0s - loss: 0.0455 - val_loss: 0.0131 - 152ms/epoch - 15ms/step\n",
      "Epoch 39/50\n",
      "10/10 - 0s - loss: 0.0480 - val_loss: 0.0083 - 149ms/epoch - 15ms/step\n",
      "Epoch 40/50\n",
      "10/10 - 0s - loss: 0.0448 - val_loss: 0.0231 - 148ms/epoch - 15ms/step\n",
      "Epoch 41/50\n",
      "10/10 - 0s - loss: 0.0579 - val_loss: 0.0115 - 147ms/epoch - 15ms/step\n",
      "Epoch 42/50\n",
      "10/10 - 0s - loss: 0.0449 - val_loss: 0.0062 - 155ms/epoch - 16ms/step\n",
      "Epoch 43/50\n",
      "10/10 - 0s - loss: 0.0561 - val_loss: 0.0107 - 147ms/epoch - 15ms/step\n",
      "Epoch 44/50\n",
      "10/10 - 0s - loss: 0.0457 - val_loss: 0.0265 - 148ms/epoch - 15ms/step\n",
      "Epoch 45/50\n",
      "10/10 - 0s - loss: 0.0508 - val_loss: 0.0143 - 151ms/epoch - 15ms/step\n",
      "Epoch 46/50\n",
      "10/10 - 0s - loss: 0.0531 - val_loss: 0.0190 - 149ms/epoch - 15ms/step\n",
      "Epoch 47/50\n",
      "10/10 - 0s - loss: 0.0464 - val_loss: 0.0084 - 149ms/epoch - 15ms/step\n",
      "Epoch 48/50\n",
      "10/10 - 0s - loss: 0.0531 - val_loss: 0.0152 - 153ms/epoch - 15ms/step\n",
      "Epoch 49/50\n",
      "10/10 - 0s - loss: 0.0459 - val_loss: 0.0101 - 145ms/epoch - 15ms/step\n",
      "Epoch 50/50\n",
      "10/10 - 0s - loss: 0.0496 - val_loss: 0.0166 - 149ms/epoch - 15ms/step\n"
     ]
    }
   ],
   "source": [
    "# fit network\n",
    "history = model.fit(train_X, train_y, epochs=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "69b6c39f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "# plot history\n",
    "pyplot.plot(history.history['loss'], label='train')\n",
    "pyplot.plot(history.history['val_loss'], label='validation')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2a3096a",
   "metadata": {},
   "source": [
    "# Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "be443521",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-10 09:43:58.769444: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-07-10 09:43:58.830890: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-07-10 09:43:58.860690: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    }
   ],
   "source": [
    "from math import sqrt\n",
    "from numpy import concatenate\n",
    "\n",
    "# make a prediction\n",
    "yhat = model.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c9d39eba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(79, 1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "69a5ce66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert test data to 2D \n",
    "test_X = test_X.reshape((test_X.shape[0], days*n_features))\n",
    "\n",
    "# invert scaling for forecast\n",
    "# As we said Scaler needs 3 columns so we can take those columns from test data and take again the predictions\n",
    "# Concatenate last 2 columns of test data with predicted data (yhat)\n",
    "inv_yhat = concatenate((test_X[:, -(n_features-1):], yhat), axis=1)\n",
    "# Inverse Scaler\n",
    "inv_yhat = scaler.inverse_transform(inv_yhat)\n",
    "# Take predicted data scaled to original Dengue cases\n",
    "inv_yhat = inv_yhat[:,-1]\n",
    "\n",
    "# invert scaling for actual\n",
    "# Same process than for predicted data (yhat)\n",
    "test_y = test_y.reshape((len(test_y), 1))\n",
    "inv_y = concatenate((test_X[:, -(n_features-1):], test_y), axis=1)\n",
    "inv_y = scaler.inverse_transform(inv_y)\n",
    "inv_y = inv_y[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5e024c14",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_absolute_percentage_error(y_true, y_pred): \n",
    "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b3c8de78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MAPE: 459.604\n"
     ]
    }
   ],
   "source": [
    "# calculate MAPE\n",
    "mape = mean_absolute_percentage_error(inv_y, inv_yhat)\n",
    "print('Test MAPE: %.3f' % mape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1a54e242",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MAE: 733.482\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import  mean_absolute_error\n",
    "\n",
    "# calculate MAE\n",
    "mae = mean_absolute_error(inv_y, inv_yhat)\n",
    "print('Test MAE: %.3f' % mae)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc266f58",
   "metadata": {},
   "source": [
    "# Plot predicted vs actual dengue cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39ac3909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_predict = inv_yhat  ## predicted target  cases\n",
    "dataY_plot = inv_y  ##  real test-target cases\n",
    "\n",
    "data_predict = data_predict.reshape(len(data_predict), 1)\n",
    "dataY_plot = dataY_plot.reshape(len(dataY_plot), 1)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(dataY_plot, label = 'actual')\n",
    "plt.plot(data_predict, label = 'predicted')\n",
    "plt.legend(loc=\"upper left\")\n",
    "\n",
    "plt.suptitle('Time-Series Prediction')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b14837c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d05e246",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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