covid.ipynb

{
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  "metadata": {
    "colab": {
      "name": "covid",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1gC2jvi1r3AfZeLYpllbAoFGXbVDva5GJ",
      "authorship_tag": "ABX9TyMSY35MsmnvIIrX4S2n76tZ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rainmaker29/COVID19-Death-rate/blob/master/covid.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oT_8fpo40hNm",
        "colab_type": "text"
      },
      "source": [
        "# Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mRk2erViF131",
        "colab_type": "code",
        "outputId": "1e540abf-8c2c-4c4b-e2ea-601bf9845be2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "import torch\n",
        "\n",
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rc\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from pandas.plotting import register_matplotlib_converters\n",
        "from torch import nn, optim\n",
        "\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format='retina'\n",
        "\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
        "\n",
        "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#93D30C\", \"#8F00FF\"]\n",
        "\n",
        "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
        "\n",
        "rcParams['figure.figsize'] = 14, 10\n",
        "register_matplotlib_converters()\n",
        "\n",
        "RANDOM_SEED = 42\n",
        "np.random.seed(RANDOM_SEED)\n",
        "torch.manual_seed(RANDOM_SEED)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7f09f2939170>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ghg4ItUeGG_K",
        "colab_type": "code",
        "outputId": "d95c3496-ffe7-4390-ed72-75602dc0fbb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "# Download data\n",
        "!wget https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-05-03 13:35:25--  https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 69256 (68K) [text/plain]\n",
            "Saving to: ‘time_series_covid19_deaths_global.csv’\n",
            "\n",
            "\r          time_seri   0%[                    ]       0  --.-KB/s               \rtime_series_covid19 100%[===================>]  67.63K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2020-05-03 13:35:25 (4.58 MB/s) - ‘time_series_covid19_deaths_global.csv’ saved [69256/69256]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GMhdfKHO1KDD",
        "colab_type": "text"
      },
      "source": [
        "# PreProcessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K_5O98AGGnWA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('/content/drive/My Drive/covid/covid19_deaths.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C9naGPdHGxRv",
        "colab_type": "code",
        "outputId": "c3237a6f-1673-4356-9982-4233caf844a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>12</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "      <td>17</td>\n",
              "      <td>18</td>\n",
              "      <td>21</td>\n",
              "      <td>22</td>\n",
              "      <td>23</td>\n",
              "      <td>25</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>29</td>\n",
              "      <td>29</td>\n",
              "      <td>31</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>35</td>\n",
              "      <td>35</td>\n",
              "      <td>36</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Angola</td>\n",
              "      <td>-11.2027</td>\n",
              "      <td>17.8739</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 94 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "  Province/State Country/Region      Lat  ...  4/18/20  4/19/20  4/20/20\n",
              "0            NaN    Afghanistan  33.0000  ...       30       33       36\n",
              "1            NaN        Albania  41.1533  ...       26       26       26\n",
              "2            NaN        Algeria  28.0339  ...      367      375      384\n",
              "3            NaN        Andorra  42.5063  ...       35       36       37\n",
              "4            NaN         Angola -11.2027  ...        2        2        2\n",
              "\n",
              "[5 rows x 94 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j2dJecoqHCja",
        "colab_type": "code",
        "outputId": "bf4ae93f-c8e2-43d3-99e5-6023329d215e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        }
      },
      "source": [
        "# removing the first four variables and using only the dates\n",
        "df = df.iloc[:,4:]\n",
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>12</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "      <td>17</td>\n",
              "      <td>18</td>\n",
              "      <td>21</td>\n",
              "      <td>22</td>\n",
              "      <td>23</td>\n",
              "      <td>25</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>29</td>\n",
              "      <td>29</td>\n",
              "      <td>31</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>35</td>\n",
              "      <td>35</td>\n",
              "      <td>36</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</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",
              "      <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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</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",
              "      <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",
              "      <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",
              "      <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259</th>\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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>260</th>\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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>261</th>\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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>262</th>\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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>263</th>\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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>264 rows × 90 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     1/22/20  1/23/20  1/24/20  1/25/20  ...  4/17/20  4/18/20  4/19/20  4/20/20\n",
              "0          0        0        0        0  ...       30       30       33       36\n",
              "1          0        0        0        0  ...       26       26       26       26\n",
              "2          0        0        0        0  ...      364      367      375      384\n",
              "3          0        0        0        0  ...       35       35       36       37\n",
              "4          0        0        0        0  ...        2        2        2        2\n",
              "..       ...      ...      ...      ...  ...      ...      ...      ...      ...\n",
              "259        0        0        0        0  ...        0        0        0        0\n",
              "260        0        0        0        0  ...        0        0        0        0\n",
              "261        0        0        0        0  ...        0        0        0        0\n",
              "262        0        0        0        0  ...        0        0        0        0\n",
              "263        0        0        0        0  ...        0        0        0        0\n",
              "\n",
              "[264 rows x 90 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EuPRJ-ojHPvX",
        "colab_type": "code",
        "outputId": "dfcada07-8062-415a-98ca-34cc1da25abe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Checking for null values\n",
        "df.isnull().sum().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GligZVZBHxTR",
        "colab_type": "code",
        "outputId": "1aac4cb6-7ab8-4638-e078-3f122b36ea66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "# Extracting daily deaths count from cumulative daily deaths count\n",
        "daily_deaths = df.sum(axis=0)\n",
        "daily_deaths.index = pd.to_datetime(daily_deaths.index)\n",
        "daily_deaths.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2020-01-22    17\n",
              "2020-01-23    18\n",
              "2020-01-24    26\n",
              "2020-01-25    42\n",
              "2020-01-26    56\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WvLZNbuFH_Dp",
        "colab_type": "code",
        "outputId": "dd441fb0-523a-4986-fc37-c503f148b342",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        }
      },
      "source": [
        "plt.plot(daily_deaths)\n",
        "\n",
        "plt.title(\"Cumulative deaths\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Cumulative deaths')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 414,
              "height": 271
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_7p20O8dIEux",
        "colab_type": "code",
        "outputId": "b09f7a4a-e716-4a77-e757-ee63ce7f8068",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "daily_deaths = daily_deaths.diff().fillna(daily_deaths[0]).astype(np.int64)\n",
        "daily_deaths.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2020-01-22    17\n",
              "2020-01-23     1\n",
              "2020-01-24     8\n",
              "2020-01-25    16\n",
              "2020-01-26    14\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IqYqTjmaIRGp",
        "colab_type": "code",
        "outputId": "41e6285b-40d7-4066-d77f-82c5b2c0b3de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        }
      },
      "source": [
        "plt.plot(daily_deaths)\n",
        "plt.title(\"Daily deaths\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Daily deaths')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 406,
              "height": 271
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "REhlSao_IYIo",
        "colab_type": "code",
        "outputId": "f48cd5b4-6aef-421d-e6b4-9229c7d88cfa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "daily_deaths.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(90,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BZFtD1DW1PDe",
        "colab_type": "text"
      },
      "source": [
        "# Train-Test split and Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SnKK-qQJId7q",
        "colab_type": "code",
        "outputId": "50f48856-3e79-4648-d2f0-36ec86da5d26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_data_size = 10\n",
        "train_data = daily_deaths[:-test_data_size]\n",
        "test_data = daily_deaths[-test_data_size:]\n",
        "train_data.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(80,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_RSoFG8KIzPz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# We use scaling to speed up training process\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "scaler = scaler.fit(np.expand_dims(train_data, axis=1))\n",
        "\n",
        "train_data = scaler.transform(np.expand_dims(train_data, axis=1))\n",
        "\n",
        "test_data = scaler.transform(np.expand_dims(test_data, axis=1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k4FMKVBJI7ym",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Creates sequences of fixed lengths\n",
        "def create_sequences(data, seq_length):\n",
        "    xs = []\n",
        "    ys = []\n",
        "\n",
        "    for i in range(len(data)-seq_length-1):\n",
        "        x = data[i:(i+seq_length)]\n",
        "        y = data[i+seq_length]\n",
        "        xs.append(x)\n",
        "        ys.append(y)\n",
        "\n",
        "    return np.array(xs), np.array(ys)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6try8jtiI_mA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "seq_length = 5\n",
        "X_train, y_train = create_sequences(train_data, seq_length)\n",
        "X_test, y_test = create_sequences(test_data, seq_length)\n",
        "\n",
        "X_train = torch.from_numpy(X_train).float()\n",
        "y_train = torch.from_numpy(y_train).float()\n",
        "\n",
        "X_test = torch.from_numpy(X_test).float()\n",
        "y_test = torch.from_numpy(y_test).float()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N-260kFjJDe0",
        "colab_type": "code",
        "outputId": "c42246f0-6ea6-4a3d-85aa-60fa4c96406b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([74, 5, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jyk2j4qhJNee",
        "colab_type": "code",
        "outputId": "11205d09-c405-46c9-a6fc-6d8c70d2471f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        }
      },
      "source": [
        "X_train[:2]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[0.0022],\n",
              "         [0.0000],\n",
              "         [0.0009],\n",
              "         [0.0020],\n",
              "         [0.0018]],\n",
              "\n",
              "        [[0.0000],\n",
              "         [0.0009],\n",
              "         [0.0020],\n",
              "         [0.0018],\n",
              "         [0.0034]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iZ2pwTSJJQCq",
        "colab_type": "code",
        "outputId": "c0cf93e4-cfbc-41e7-d4b2-921ab071771d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([74, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kz9Fbc56JWan",
        "colab_type": "code",
        "outputId": "7b9a00c4-b674-4fd9-ace8-40545a59fdba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "y_train[:2]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.0034],\n",
              "        [0.0065]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uNSJ60jFJYqL",
        "colab_type": "code",
        "outputId": "d7536475-5a4e-4f6f-a663-626180e5dbeb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        }
      },
      "source": [
        "train_data[:10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00217067],\n",
              "       [0.        ],\n",
              "       [0.00094967],\n",
              "       [0.002035  ],\n",
              "       [0.00176367],\n",
              "       [0.00339167],\n",
              "       [0.00651201],\n",
              "       [0.00013567],\n",
              "       [0.00501967],\n",
              "       [0.00556234]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BDVVkMrt1jWe",
        "colab_type": "text"
      },
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_REFTY2BJa8O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class CoronaVirusPredictor(nn.Module):\n",
        "  def __init__(self,n_features,n_hidden,seq_len,n_layers=2):\n",
        "    super(CoronaVirusPredictor,self).__init__()\n",
        "\n",
        "    self.n_hidden = n_hidden\n",
        "    self.seq_len = seq_len\n",
        "    self.n_layers = n_layers\n",
        "    # A set of LSTM layers followed by a Dense/Linear layer that intakes the \n",
        "    # final time step from the final LSTM layer\n",
        "    self.lstm = nn.LSTM(\n",
        "        input_size=n_features,\n",
        "        hidden_size=n_hidden,\n",
        "        num_layers = n_layers,\n",
        "        dropout=0.5\n",
        "\n",
        "    )\n",
        "\n",
        "    self.linear = nn.Linear(in_features=n_hidden,out_features=1)\n",
        "\n",
        "  def reset_hidden_state(self):\n",
        "    self.hidden = (\n",
        "        torch.zeros(self.n_layers,self.seq_len,self.n_hidden),\n",
        "        torch.zeros(self.n_layers,self.seq_len,self.n_hidden)\n",
        "    )\n",
        "\n",
        "  def forward(self,sequences):\n",
        "    lstm_out,self.hidden = self.lstm(\n",
        "        sequences.view(len(sequences),self.seq_len,-1),\n",
        "        self.hidden\n",
        "    )\n",
        "\n",
        "    last_time_step = lstm_out.view(self.seq_len,len(sequences),self.n_hidden)[-1]\n",
        "\n",
        "    y_pred = self.linear(last_time_step)\n",
        "    return y_pred\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O7tD-W7p14CT",
        "colab_type": "text"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fgi9HwaDgPdc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_model(model,epochs,train_data,train_labels,test_data=None,test_labels=None):\n",
        "  \n",
        "  loss_fn = torch.nn.MSELoss(reduction='sum')\n",
        "  optimiser = torch.optim.Adam(model.parameters(),lr=1e-3)\n",
        "  epochs=epochs\n",
        "\n",
        "  train_hist = np.zeros(epochs)\n",
        "  test_hist = np.zeros(epochs)\n",
        "\n",
        "  for t in range(epochs):\n",
        "    # reset hidden state since we're using stateless LSTMs\n",
        "    model.reset_hidden_state()\n",
        "\n",
        "    y_pred = model(train_data)\n",
        "\n",
        "    loss = loss_fn(y_pred.float(),train_labels)\n",
        "\n",
        "\n",
        "    if test_data is not None:\n",
        "      #turn off gradient for test/validation prediction\n",
        "      with torch.no_grad():\n",
        "        \n",
        "        y_test_pred = model(test_data)\n",
        "        test_loss = loss_fn(y_test_pred.float(),test_labels)\n",
        "      test_hist[t] = test_loss.item()\n",
        "\n",
        "      if t%10==0:\n",
        "        print(f'Epoch {t} train loss : {loss.item()} test loss : {test_loss.item()}')\n",
        "\n",
        "    elif t%10==0:\n",
        "      print(f'Epoch {t} train loss {loss.item()}')\n",
        "\n",
        "\n",
        "    train_hist[t] = loss.item()\n",
        "\n",
        "    optimiser.zero_grad()\n",
        "\n",
        "    loss.backward()\n",
        "\n",
        "    optimiser.step()\n",
        "\n",
        "  return model.eval(),train_hist,test_hist\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UJfQ894ljGCp",
        "colab_type": "code",
        "outputId": "b66ad491-143a-4500-b751-25fb3ac1bea1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "model = CoronaVirusPredictor(\n",
        "    n_features = 1,\n",
        "    n_hidden=120,\n",
        "    seq_len=seq_length,\n",
        "    n_layers=3\n",
        "    )\n",
        "\n",
        "model, train_hist, test_hist = train_model(\n",
        "  model = model,\n",
        "  epochs=120,\n",
        "  train_data = X_train,\n",
        "  train_labels = y_train,\n",
        "  test_data  = X_test,\n",
        "  test_labels = y_test\n",
        ")\n",
        "\n",
        "# Best hyperparameters observed as of now\n",
        "    # n_features = 1,\n",
        "    # n_hidden=120,\n",
        "    # seq_len=seq_length,\n",
        "    # n_layers=3"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0 train loss : 6.8852105140686035 test loss : 4.354400634765625\n",
            "Epoch 10 train loss : 5.432178974151611 test loss : 3.386497974395752\n",
            "Epoch 20 train loss : 4.120081901550293 test loss : 2.0075178146362305\n",
            "Epoch 30 train loss : 2.431189775466919 test loss : 2.5409536361694336\n",
            "Epoch 40 train loss : 1.3158632516860962 test loss : 1.4478495121002197\n",
            "Epoch 50 train loss : 1.7354581356048584 test loss : 3.8745899200439453\n",
            "Epoch 60 train loss : 1.2947664260864258 test loss : 1.2669076919555664\n",
            "Epoch 70 train loss : 1.2321836948394775 test loss : 0.5428574085235596\n",
            "Epoch 80 train loss : 1.0305477380752563 test loss : 0.434100866317749\n",
            "Epoch 90 train loss : 0.8283181190490723 test loss : 0.6154117584228516\n",
            "Epoch 100 train loss : 0.9718959927558899 test loss : 0.33286911249160767\n",
            "Epoch 110 train loss : 0.9035385847091675 test loss : 0.4442078769207001\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "orCYhR1cj3qb",
        "colab_type": "code",
        "outputId": "83c030c1-112a-483e-e341-0c188ef15a8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        }
      },
      "source": [
        "# Train vs Test loss\n",
        "plt.plot(train_hist, label=\"Training loss\")\n",
        "plt.plot(test_hist, label=\"Test loss\")\n",
        "plt.ylim((0, 5))\n",
        "plt.legend();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 367,
              "height": 259
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rEC5gddfm6SN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Reusing the predicted values as input for next days\n",
        "# as our model predicts only for the next single day\n",
        "\n",
        "with torch.no_grad():\n",
        "  test_seq = X_test[:1]\n",
        "  preds = []\n",
        "  for _ in range(len(X_test)):\n",
        "    y_test_pred = model(test_seq)\n",
        "    pred = torch.flatten(y_test_pred).item()\n",
        "    preds.append(pred)\n",
        "    new_seq = test_seq.numpy().flatten()\n",
        "    new_seq = np.append(new_seq, [pred])\n",
        "    new_seq = new_seq[1:]\n",
        "    test_seq = torch.as_tensor(new_seq).view(1, seq_length, 1).float()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "62PoAuAmnDzk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "true_deaths = scaler.inverse_transform(\n",
        "    np.expand_dims(y_test.flatten().numpy(), axis=0)\n",
        ").flatten()\n",
        "\n",
        "predicted_deaths = scaler.inverse_transform(\n",
        "  np.expand_dims(preds, axis=0)\n",
        ").flatten()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O2ybeobCnJpj",
        "colab_type": "code",
        "outputId": "3761cefb-bdcb-48bc-fac6-28a4aa0e2715",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        }
      },
      "source": [
        "plt.plot(\n",
        "  daily_deaths.index[:len(train_data)],\n",
        "  scaler.inverse_transform(train_data).flatten(),\n",
        "  label='Historical Daily Deaths'\n",
        ")\n",
        "\n",
        "plt.plot(\n",
        "  daily_deaths.index[len(train_data):len(train_data) + len(true_deaths)],\n",
        "  true_deaths,\n",
        "  label='Real Daily Deaths'\n",
        ")\n",
        "\n",
        "plt.plot(\n",
        "  daily_deaths.index[len(train_data):len(train_data) + len(true_deaths)],\n",
        "  predicted_deaths,\n",
        "  label='Predicted Daily Deaths'\n",
        ")\n",
        "\n",
        "plt.legend();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 427,
              "height": 254
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zVhL6tCkzP1Y",
        "colab_type": "code",
        "outputId": "e072a2fd-9957-49af-d5e8-1dee36583b00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Repeating the same preprocessing but for entire data\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "scaler = scaler.fit(np.expand_dims(daily_deaths, axis=1))\n",
        "\n",
        "all_data = scaler.transform(np.expand_dims(daily_deaths, axis=1))\n",
        "\n",
        "all_data.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(90, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aPQXAmihzcGr",
        "colab_type": "code",
        "outputId": "7b38be4e-e300-4aa3-c856-4812a456d71f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "X_all, y_all = create_sequences(all_data, seq_length)\n",
        "\n",
        "X_all = torch.from_numpy(X_all).float()\n",
        "y_all = torch.from_numpy(y_all).float()\n",
        "\n",
        "model = CoronaVirusPredictor(\n",
        "  n_features=1,\n",
        "  n_hidden=120,\n",
        "  seq_len=seq_length,\n",
        "  n_layers=3\n",
        ")\n",
        "model, train_hist, _ = train_model(model=model,epochs=120,train_data=X_all, train_labels=y_all)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0 train loss 10.735997200012207\n",
            "Epoch 10 train loss 6.061316967010498\n",
            "Epoch 20 train loss 6.005399703979492\n",
            "Epoch 30 train loss 5.066762924194336\n",
            "Epoch 40 train loss 2.592839241027832\n",
            "Epoch 50 train loss 1.339086651802063\n",
            "Epoch 60 train loss 1.131709098815918\n",
            "Epoch 70 train loss 1.1012182235717773\n",
            "Epoch 80 train loss 0.8985336422920227\n",
            "Epoch 90 train loss 0.9701874256134033\n",
            "Epoch 100 train loss 0.812328577041626\n",
            "Epoch 110 train loss 0.9884158372879028\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eDJtr6T5zrZF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "DAYS_TO_PREDICT = 12\n",
        "\n",
        "with torch.no_grad():\n",
        "  test_seq = X_all[:1]\n",
        "  preds = []\n",
        "  for _ in range(DAYS_TO_PREDICT):\n",
        "    y_test_pred = model(test_seq)\n",
        "    pred = torch.flatten(y_test_pred).item()\n",
        "    preds.append(pred)\n",
        "    new_seq = test_seq.numpy().flatten()\n",
        "    new_seq = np.append(new_seq, [pred])\n",
        "    new_seq = new_seq[1:]\n",
        "    test_seq = torch.as_tensor(new_seq).view(1, seq_length, 1).float()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xOasheggz9Cf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predicted_deaths = scaler.inverse_transform(\n",
        "  np.expand_dims(preds, axis=0)\n",
        ").flatten()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G4zikmMo0Adi",
        "colab_type": "code",
        "outputId": "ce6ccc0c-8500-43fe-d0bb-a13c512061de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "daily_deaths.index[-1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2020-04-20 00:00:00')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8XtGifX0FJD",
        "colab_type": "code",
        "outputId": "c5369793-ada9-467b-f6c1-df86bd78c604",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        }
      },
      "source": [
        "predicted_index = pd.date_range(\n",
        "  start=daily_deaths.index[-1],\n",
        "  periods=DAYS_TO_PREDICT + 1,\n",
        "  closed='right'\n",
        ")\n",
        "\n",
        "predicted_deaths = pd.Series(\n",
        "  data=predicted_deaths,\n",
        "  index=predicted_index\n",
        ")\n",
        "\n",
        "plt.plot(predicted_deaths, label='Predicted Daily Deaths')\n",
        "plt.legend();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 392,
              "height": 254
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XGt8s9Bi0OLI",
        "colab_type": "code",
        "outputId": "d69b230f-e4ce-41c7-b184-da8cb681238c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        }
      },
      "source": [
        "plt.plot(daily_deaths, label='Historical Daily Death')\n",
        "plt.plot(predicted_deaths, label='Predicted Daily Deaths')\n",
        "plt.legend();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 254
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fX57FOJv0V_6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}