COVID_timeseries.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled0.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMY1PIJNp8xi7EcEgurANIw",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aissak21/aissak21.github.io/blob/master/COVID_timeseries.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3J_t-RaS2exr",
        "colab_type": "text"
      },
      "source": [
        "**Load Packages**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82gJPBZctxZW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9f95d98a-0708-432e-a076-498ac2275f4d"
      },
      "source": [
        "import torch #Pytorch = DL library\n",
        "\n",
        "import os #Operating system = interact with os\n",
        "\n",
        "import numpy as np #Numpy = array-processing package.\n",
        "\n",
        "import pandas as pd # Pandas = data manipulation tool\n",
        "\n",
        "from tqdm import tqdm #Progress = make your loops show a progress meter\n",
        "\n",
        "import seaborn as sns #Seaborn =  library for making statistical graphics in Python\n",
        "\n",
        "from pylab import rcParams #Pylab = bulk import (matplotlib, numpy...)\n",
        "\n",
        "import matplotlib.pyplot as plt #Pyplot = a collection of functions that make matplotlib work like MATLAB.\n",
        "\n",
        "from matplotlib import rc #Matplotlib = visualization in Python\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler #Sklearn = ML in python: classification, regression, clustering...\n",
        "\n",
        "from pandas.plotting import register_matplotlib_converters\n",
        "\n",
        "from torch import nn, optim #From module, import a specific aspect\n",
        "\n",
        "import torch.nn.functional as F\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",
        "\n",
        "register_matplotlib_converters()\n",
        "\n",
        "RANDOM_SEED = 42\n",
        "np.random.seed(RANDOM_SEED)\n",
        "torch.manual_seed(RANDOM_SEED)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7fc8e5a75198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OA37je5V2aoD",
        "colab_type": "text"
      },
      "source": [
        "# Import Data as Pandas DF"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P8Vsp3Qw0KRL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "23e764d7-27d9-41cd-bf6b-f86851f647b0"
      },
      "source": [
        "url = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'\n",
        "df = pd.read_csv(url)\n",
        "df.head() #See the column headers"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Province/State</th>\n",
              "      <th>Country/Region</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long</th>\n",
              "      <th>1/22/20</th>\n",
              "      <th>1/23/20</th>\n",
              "      <th>1/24/20</th>\n",
              "      <th>1/25/20</th>\n",
              "      <th>1/26/20</th>\n",
              "      <th>1/27/20</th>\n",
              "      <th>1/28/20</th>\n",
              "      <th>1/29/20</th>\n",
              "      <th>1/30/20</th>\n",
              "      <th>1/31/20</th>\n",
              "      <th>2/1/20</th>\n",
              "      <th>2/2/20</th>\n",
              "      <th>2/3/20</th>\n",
              "      <th>2/4/20</th>\n",
              "      <th>2/5/20</th>\n",
              "      <th>2/6/20</th>\n",
              "      <th>2/7/20</th>\n",
              "      <th>2/8/20</th>\n",
              "      <th>2/9/20</th>\n",
              "      <th>2/10/20</th>\n",
              "      <th>2/11/20</th>\n",
              "      <th>2/12/20</th>\n",
              "      <th>2/13/20</th>\n",
              "      <th>2/14/20</th>\n",
              "      <th>2/15/20</th>\n",
              "      <th>2/16/20</th>\n",
              "      <th>2/17/20</th>\n",
              "      <th>2/18/20</th>\n",
              "      <th>2/19/20</th>\n",
              "      <th>2/20/20</th>\n",
              "      <th>2/21/20</th>\n",
              "      <th>2/22/20</th>\n",
              "      <th>2/23/20</th>\n",
              "      <th>2/24/20</th>\n",
              "      <th>2/25/20</th>\n",
              "      <th>2/26/20</th>\n",
              "      <th>...</th>\n",
              "      <th>8/8/20</th>\n",
              "      <th>8/9/20</th>\n",
              "      <th>8/10/20</th>\n",
              "      <th>8/11/20</th>\n",
              "      <th>8/12/20</th>\n",
              "      <th>8/13/20</th>\n",
              "      <th>8/14/20</th>\n",
              "      <th>8/15/20</th>\n",
              "      <th>8/16/20</th>\n",
              "      <th>8/17/20</th>\n",
              "      <th>8/18/20</th>\n",
              "      <th>8/19/20</th>\n",
              "      <th>8/20/20</th>\n",
              "      <th>8/21/20</th>\n",
              "      <th>8/22/20</th>\n",
              "      <th>8/23/20</th>\n",
              "      <th>8/24/20</th>\n",
              "      <th>8/25/20</th>\n",
              "      <th>8/26/20</th>\n",
              "      <th>8/27/20</th>\n",
              "      <th>8/28/20</th>\n",
              "      <th>8/29/20</th>\n",
              "      <th>8/30/20</th>\n",
              "      <th>8/31/20</th>\n",
              "      <th>9/1/20</th>\n",
              "      <th>9/2/20</th>\n",
              "      <th>9/3/20</th>\n",
              "      <th>9/4/20</th>\n",
              "      <th>9/5/20</th>\n",
              "      <th>9/6/20</th>\n",
              "      <th>9/7/20</th>\n",
              "      <th>9/8/20</th>\n",
              "      <th>9/9/20</th>\n",
              "      <th>9/10/20</th>\n",
              "      <th>9/11/20</th>\n",
              "      <th>9/12/20</th>\n",
              "      <th>9/13/20</th>\n",
              "      <th>9/14/20</th>\n",
              "      <th>9/15/20</th>\n",
              "      <th>9/16/20</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>33.93911</td>\n",
              "      <td>67.709953</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>37054</td>\n",
              "      <td>37054</td>\n",
              "      <td>37162</td>\n",
              "      <td>37269</td>\n",
              "      <td>37345</td>\n",
              "      <td>37424</td>\n",
              "      <td>37431</td>\n",
              "      <td>37551</td>\n",
              "      <td>37596</td>\n",
              "      <td>37599</td>\n",
              "      <td>37599</td>\n",
              "      <td>37599</td>\n",
              "      <td>37856</td>\n",
              "      <td>37894</td>\n",
              "      <td>37953</td>\n",
              "      <td>37999</td>\n",
              "      <td>38054</td>\n",
              "      <td>38070</td>\n",
              "      <td>38113</td>\n",
              "      <td>38129</td>\n",
              "      <td>38140</td>\n",
              "      <td>38143</td>\n",
              "      <td>38162</td>\n",
              "      <td>38165</td>\n",
              "      <td>38196</td>\n",
              "      <td>38243</td>\n",
              "      <td>38288</td>\n",
              "      <td>38304</td>\n",
              "      <td>38324</td>\n",
              "      <td>38398</td>\n",
              "      <td>38494</td>\n",
              "      <td>38520</td>\n",
              "      <td>38544</td>\n",
              "      <td>38572</td>\n",
              "      <td>38606</td>\n",
              "      <td>38641</td>\n",
              "      <td>38716</td>\n",
              "      <td>38772</td>\n",
              "      <td>38815</td>\n",
              "      <td>38855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Albania</td>\n",
              "      <td>41.15330</td>\n",
              "      <td>20.168300</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>6275</td>\n",
              "      <td>6411</td>\n",
              "      <td>6536</td>\n",
              "      <td>6676</td>\n",
              "      <td>6817</td>\n",
              "      <td>6971</td>\n",
              "      <td>7117</td>\n",
              "      <td>7260</td>\n",
              "      <td>7380</td>\n",
              "      <td>7499</td>\n",
              "      <td>7654</td>\n",
              "      <td>7812</td>\n",
              "      <td>7967</td>\n",
              "      <td>8119</td>\n",
              "      <td>8275</td>\n",
              "      <td>8427</td>\n",
              "      <td>8605</td>\n",
              "      <td>8759</td>\n",
              "      <td>8927</td>\n",
              "      <td>9083</td>\n",
              "      <td>9195</td>\n",
              "      <td>9279</td>\n",
              "      <td>9380</td>\n",
              "      <td>9513</td>\n",
              "      <td>9606</td>\n",
              "      <td>9728</td>\n",
              "      <td>9844</td>\n",
              "      <td>9967</td>\n",
              "      <td>10102</td>\n",
              "      <td>10255</td>\n",
              "      <td>10406</td>\n",
              "      <td>10553</td>\n",
              "      <td>10704</td>\n",
              "      <td>10860</td>\n",
              "      <td>11021</td>\n",
              "      <td>11185</td>\n",
              "      <td>11353</td>\n",
              "      <td>11520</td>\n",
              "      <td>11672</td>\n",
              "      <td>11816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>28.03390</td>\n",
              "      <td>1.659600</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>34693</td>\n",
              "      <td>35160</td>\n",
              "      <td>35712</td>\n",
              "      <td>36204</td>\n",
              "      <td>36699</td>\n",
              "      <td>37187</td>\n",
              "      <td>37664</td>\n",
              "      <td>38133</td>\n",
              "      <td>38583</td>\n",
              "      <td>39025</td>\n",
              "      <td>39444</td>\n",
              "      <td>39847</td>\n",
              "      <td>40258</td>\n",
              "      <td>40667</td>\n",
              "      <td>41068</td>\n",
              "      <td>41460</td>\n",
              "      <td>41858</td>\n",
              "      <td>42228</td>\n",
              "      <td>42619</td>\n",
              "      <td>43016</td>\n",
              "      <td>43403</td>\n",
              "      <td>43781</td>\n",
              "      <td>44146</td>\n",
              "      <td>44494</td>\n",
              "      <td>44833</td>\n",
              "      <td>45158</td>\n",
              "      <td>45469</td>\n",
              "      <td>45773</td>\n",
              "      <td>46071</td>\n",
              "      <td>46364</td>\n",
              "      <td>46653</td>\n",
              "      <td>46938</td>\n",
              "      <td>47216</td>\n",
              "      <td>47488</td>\n",
              "      <td>47752</td>\n",
              "      <td>48007</td>\n",
              "      <td>48254</td>\n",
              "      <td>48496</td>\n",
              "      <td>48734</td>\n",
              "      <td>48966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Andorra</td>\n",
              "      <td>42.50630</td>\n",
              "      <td>1.521800</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>955</td>\n",
              "      <td>955</td>\n",
              "      <td>963</td>\n",
              "      <td>963</td>\n",
              "      <td>977</td>\n",
              "      <td>981</td>\n",
              "      <td>989</td>\n",
              "      <td>989</td>\n",
              "      <td>989</td>\n",
              "      <td>1005</td>\n",
              "      <td>1005</td>\n",
              "      <td>1024</td>\n",
              "      <td>1024</td>\n",
              "      <td>1045</td>\n",
              "      <td>1045</td>\n",
              "      <td>1045</td>\n",
              "      <td>1060</td>\n",
              "      <td>1060</td>\n",
              "      <td>1098</td>\n",
              "      <td>1098</td>\n",
              "      <td>1124</td>\n",
              "      <td>1124</td>\n",
              "      <td>1124</td>\n",
              "      <td>1176</td>\n",
              "      <td>1184</td>\n",
              "      <td>1199</td>\n",
              "      <td>1199</td>\n",
              "      <td>1215</td>\n",
              "      <td>1215</td>\n",
              "      <td>1215</td>\n",
              "      <td>1261</td>\n",
              "      <td>1261</td>\n",
              "      <td>1301</td>\n",
              "      <td>1301</td>\n",
              "      <td>1344</td>\n",
              "      <td>1344</td>\n",
              "      <td>1344</td>\n",
              "      <td>1438</td>\n",
              "      <td>1438</td>\n",
              "      <td>1483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Angola</td>\n",
              "      <td>-11.20270</td>\n",
              "      <td>17.873900</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>1572</td>\n",
              "      <td>1672</td>\n",
              "      <td>1679</td>\n",
              "      <td>1735</td>\n",
              "      <td>1762</td>\n",
              "      <td>1815</td>\n",
              "      <td>1852</td>\n",
              "      <td>1879</td>\n",
              "      <td>1906</td>\n",
              "      <td>1935</td>\n",
              "      <td>1966</td>\n",
              "      <td>2015</td>\n",
              "      <td>2044</td>\n",
              "      <td>2068</td>\n",
              "      <td>2134</td>\n",
              "      <td>2171</td>\n",
              "      <td>2222</td>\n",
              "      <td>2283</td>\n",
              "      <td>2332</td>\n",
              "      <td>2415</td>\n",
              "      <td>2471</td>\n",
              "      <td>2551</td>\n",
              "      <td>2624</td>\n",
              "      <td>2654</td>\n",
              "      <td>2729</td>\n",
              "      <td>2777</td>\n",
              "      <td>2805</td>\n",
              "      <td>2876</td>\n",
              "      <td>2935</td>\n",
              "      <td>2965</td>\n",
              "      <td>2981</td>\n",
              "      <td>3033</td>\n",
              "      <td>3092</td>\n",
              "      <td>3217</td>\n",
              "      <td>3279</td>\n",
              "      <td>3335</td>\n",
              "      <td>3388</td>\n",
              "      <td>3439</td>\n",
              "      <td>3569</td>\n",
              "      <td>3675</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 243 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "  Province/State Country/Region       Lat  ...  9/14/20  9/15/20  9/16/20\n",
              "0            NaN    Afghanistan  33.93911  ...    38772    38815    38855\n",
              "1            NaN        Albania  41.15330  ...    11520    11672    11816\n",
              "2            NaN        Algeria  28.03390  ...    48496    48734    48966\n",
              "3            NaN        Andorra  42.50630  ...     1438     1438     1483\n",
              "4            NaN         Angola -11.20270  ...     3439     3569     3675\n",
              "\n",
              "[5 rows x 243 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P_NBFnon2T6e",
        "colab_type": "text"
      },
      "source": [
        "#Data Exploration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O1MNj_6k2IXX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "b7878ca8-9851-46ad-981d-9398f8eedadb"
      },
      "source": [
        "df.head() #See the column headers\n",
        "df = df.iloc[:,4:] #Drop first 4 columns\n",
        "df.head() #New df with no qualitative data\n",
        "df.isnull().sum() #Check for missing values"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1/22/20    0\n",
              "1/23/20    0\n",
              "1/24/20    0\n",
              "1/25/20    0\n",
              "1/26/20    0\n",
              "          ..\n",
              "9/12/20    0\n",
              "9/13/20    0\n",
              "9/14/20    0\n",
              "9/15/20    0\n",
              "9/16/20    0\n",
              "Length: 239, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TiPf8LME3Op3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "570912c9-b519-4887-db75-4bc7b0a1e908"
      },
      "source": [
        "daily_cases = df.sum(axis=0) #each column in the represents a daily case, sum the rows for all cases in a day\n",
        "daily_cases.head()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1/22/20     555\n",
              "1/23/20     654\n",
              "1/24/20     941\n",
              "1/25/20    1434\n",
              "1/26/20    2118\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LK1pZ5m-5qFi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "151b999f-e2b5-4cb1-daad-8e652c1afc22"
      },
      "source": [
        "daily_cases.index = pd.to_datetime(daily_cases.index) #Change date time formatting\n",
        "daily_cases.head()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2020-01-22     555\n",
              "2020-01-23     654\n",
              "2020-01-24     941\n",
              "2020-01-25    1434\n",
              "2020-01-26    2118\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H4QXpuXV55vt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "outputId": "4fe4e4b3-cc74-44f8-d966-32bb111888d4"
      },
      "source": [
        "plt.plot(daily_cases)\n",
        "plt.title('Cumulative Daily Cases');"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1008x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 826,
              "height": 597
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BXAH28uL6yVV",
        "colab_type": "text"
      },
      "source": [
        "Taking the difference to see how many cases are found per day"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cd3ZT9yB6WpY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "705bf6e6-c46f-496f-895f-f77f4675b9ce"
      },
      "source": [
        "daily_cases = daily_cases.diff().fillna(daily_cases[0]).astype(np.int64)\n",
        "daily_cases.head()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2020-01-22    555\n",
              "2020-01-23     99\n",
              "2020-01-24    287\n",
              "2020-01-25    493\n",
              "2020-01-26    684\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wwho5PEE7Nur",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "outputId": "a2079e2f-d2ee-4356-c49d-850daa07f2d5"
      },
      "source": [
        "plt.plot(daily_cases)\n",
        "plt.title('Daily Cases');"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1008x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 855,
              "height": 597
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pjx771Hg72sY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cee34f47-5bb6-4a8f-c337-d04d182f9e23"
      },
      "source": [
        "daily_cases.shape #239 days worth of data"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(239,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dE3Cy4uc8N9Z",
        "colab_type": "text"
      },
      "source": [
        "# Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VZ8RabgT8PkO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_data_size = 48\n",
        "train_data = daily_cases[:-test_data_size] #80% training\n",
        "test_data = daily_cases.iloc[-test_data_size:] #20% testing"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wnzTcZ-w87sY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "410c467d-7148-46ba-e05d-d90ac1c47ed1"
      },
      "source": [
        "train_data.shape"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(191,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sj9GENg_9ANY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "666b0825-aac1-41e5-92d7-e1e750d2c187"
      },
      "source": [
        "test_data.shape"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(48,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KkrCfPE-9VFs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scaler = MinMaxScaler() #Default scale from 0 - 1\n",
        "scaler = scaler.fit(np.expand_dims(train_data, axis=1)) #Create a scale, based on train data\n",
        "\n",
        "train_data = scaler.transform(np.expand_dims(train_data, axis=1)) #Apply to train date\n",
        "test_data = scaler.transform(np.expand_dims(test_data, axis=1)) #Apply to test date"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQ-Zcmr4-KGi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create sequences. \n",
        "#LSTM = sequence based models \n",
        "\n",
        "def sliding_windows(data, seq_length):\n",
        "  xs = []\n",
        "  ys = []\n",
        "\n",
        "  for i in range(len(data) - seq_length -1 ):\n",
        "    \n",
        "    # Next element in the data. Prediction\n",
        "    x = data[i:(i+seq_length)]\n",
        "    y = data[i+seq_length] \n",
        "\n",
        "    # Add to our array. Addition\n",
        "    xs.append(x)\n",
        "    ys.append(y)\n",
        "\n",
        "  return np.array(xs), np.array(ys)"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K4VlhxCz_xwG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "seq_length = 5 #Sequence of 5 days to create prediction\n",
        "\n",
        "x_train, y_train = sliding_windows(train_data, seq_length)\n",
        "x_test, y_test = sliding_windows(test_data, seq_length)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QB6jdtFFATfd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "81551f42-1d02-4aaa-c70e-85aefc06ddda"
      },
      "source": [
        "x_train.shape #Number of sequences, sequnce length, feature\n",
        "train_data[:15]"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00157751],\n",
              "       [0.        ],\n",
              "       [0.00065038],\n",
              "       [0.00136302],\n",
              "       [0.00202378],\n",
              "       [0.00245621],\n",
              "       [0.00882853],\n",
              "       [0.00169167],\n",
              "       [0.00681166],\n",
              "       [0.00551091],\n",
              "       [0.00696388],\n",
              "       [0.01608646],\n",
              "       [0.01038182],\n",
              "       [0.01353338],\n",
              "       [0.01261317]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "upDAdlEoAzqo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "6f59f294-18ad-4111-8476-d23924785b58"
      },
      "source": [
        "x_train[:3] \n",
        "#Last entry in 2nd sequence (x_train) = 1st entry in y_train\n",
        "#Last entry in 3rd sequence (x_train) = 2nd entry in y_train ..."
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0.00157751],\n",
              "        [0.        ],\n",
              "        [0.00065038],\n",
              "        [0.00136302],\n",
              "        [0.00202378]],\n",
              "\n",
              "       [[0.        ],\n",
              "        [0.00065038],\n",
              "        [0.00136302],\n",
              "        [0.00202378],\n",
              "        [0.00245621]],\n",
              "\n",
              "       [[0.00065038],\n",
              "        [0.00136302],\n",
              "        [0.00202378],\n",
              "        [0.00245621],\n",
              "        [0.00882853]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "itcqQcPAAWS8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e2d521d9-7e6f-4176-c779-2a4d52f83e1b"
      },
      "source": [
        "y_train.shape"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(185, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EZ5_6HtFBBrG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "7a456bf0-eeb5-466b-8225-4ddd65b193a5"
      },
      "source": [
        "y_train[:3]"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00245621],\n",
              "       [0.00882853],\n",
              "       [0.00169167]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8VwCgl9EAWpe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4e203bd2-edd0-406e-c930-f3394ab2ad40"
      },
      "source": [
        "x_test.shape"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42, 5, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c9fWI_ZkAXZT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "994d1cbc-50b3-4f4d-fd09-4dc28b005d32"
      },
      "source": [
        "y_test.shape"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MqdEB3WvCfz6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Create Prediction. LSTM Model\n",
        "x_train = torch.from_numpy(x_train).float()\n",
        "y_train = torch.from_numpy(y_train).float()\n",
        "\n",
        "\n",
        "x_test = torch.from_numpy(x_test).float()\n",
        "y_test = torch.from_numpy(y_test).float()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S1B2aXkfC4zT",
        "colab_type": "text"
      },
      "source": [
        "## Building a Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3X9JQvzlC6vg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class CoronaVirusPredictor(nn.Module):\n",
        "\n",
        "  #Constructor\n",
        "  def __init__(self, input_dim, hidden_dim, seq_length, num_layers=2): #2 layers: 1. LSTM, 2.Linear\n",
        "    super(CoronaVirusPredictor, self).__init__()\n",
        "\n",
        "    #Get variables into properties\n",
        "    self.input_dim = input_dim\n",
        "    self.hidden_dim = hidden_dim\n",
        "    self.seq_length = seq_length\n",
        "    self.num_layers = num_layers\n",
        "\n",
        "    # 1st layer = LSTM\n",
        "    self.lstm = nn.LSTM(\n",
        "      input_size = input_dim, \n",
        "      hidden_size = hidden_dim, #no of neurons\n",
        "      num_layers = num_layers, \n",
        "      dropout = 0.5 #eliminates post-processing\n",
        "    )\n",
        "\n",
        "    # 2nd layer = Linear\n",
        "    self.linear = nn.Linear(\n",
        "      in_features = hidden_dim, \n",
        "      out_features = 1\n",
        "    )\n",
        "\n",
        "  def reset_hidden_state(self): #define a method\n",
        "    self.hidden = (\n",
        "        torch.zeros(self.num_layers, self.seq_length, self.hidden_dim),\n",
        "        torch.zeros(self.num_layers, self.seq_length, self.hidden_dim)\n",
        "    )\n",
        "\n",
        "  def forward(self, input):\n",
        "    lstm_out, = self.lstm(input.view(len(input), self.seq_length, -1), self.hidden)\n",
        "    y_pred = self.linear(lstm_out.view(self.seq_len, len(input), self.hidden_dim)[-1]) \n",
        "\n",
        "    return y_pred"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ScVMaqbqHSlo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_model(model, train_data, train_labels, test_data=None, test_labels=None): #=None implies optional parameter\n",
        "  loss_fn = nn.MSELoss(reduction = 'sum') #loss is by MSE sum\n",
        "\n",
        "  optimiser = optim.Adam(model.parameters(),lr = 1e-3) #lr = learning rate, 1e-3 = 0.001\n",
        "\n",
        "  num_epochs = 60\n",
        "\n",
        "  train_hist = np.zeros(num_epochs)\n",
        "  test_hist = np.zeros(num_epochs)\n",
        "\n",
        "  for t in range(num_epochs):\n",
        "    model.reset_hidden_state()\n",
        "    y_pred = model(x_train)\n",
        "    loss = loss_fn(y_pred.float(), y_train)\n",
        "\n",
        "    if test_data is not None: #If we have test data\n",
        "      with torch.no_grad(): #No gradient calculations\n",
        "        y_test_pred = model(x_test) #calling model\n",
        "        test_loss = loss_fn(y_test_pred.float(), y_test) #calculating the loss\n",
        "      test_hist[t] = test_loss.item() #appending to history\n",
        "\n",
        "      #Respition t % 10 == 0 means every 10 epochs\n",
        "      if t % 10 == 0: #print train and test loss\n",
        "        print(f'Epoch {t} train loss: {loss.item()} test loss: {test_loss.item()}')\n",
        "    elif t % 10 == 0: #otherwise just train loss\n",
        "      print(f'Epoch {t} train loss: {loss.item()}')\n",
        "\n",
        "    train_hist[t] = loss.item() #Add true loss history\n",
        "\n",
        "    optimiser.zero_grad() #optimisers gradient\n",
        "\n",
        "    loss.backward()\n",
        "\n",
        "    optimiser.step()\n",
        "\n",
        "  return model.eval(), train_hist, test_hist"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T-tkJmVpK8Q-",
        "colab_type": "text"
      },
      "source": [
        "Create instance of model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cldKVQT8K5sv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "outputId": "17be08a4-af78-48dd-ce92-2df94ed749ad"
      },
      "source": [
        "model = CoronaVirusPredictor(1, 512, seq_length = seq_length, num_layers = 2)\n",
        "model, train_hist, test_hist = train_model(model, x_train, y_train, x_test, y_test)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-71-f5f2f172c77a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCoronaVirusPredictor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m512\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseq_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseq_length\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_layers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_hist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_hist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-70-383bcf1bf0a6>\u001b[0m in \u001b[0;36mtrain_model\u001b[0;34m(model, train_data, train_labels, test_data, test_labels)\u001b[0m\n\u001b[1;32m     11\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_hidden_state\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m     \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    720\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    721\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    723\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    724\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-69-7439ee569a58>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     33\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m     lstm_out, = self.lstm(\n\u001b[0;32m---> 35\u001b[0;31m         \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseq_length\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhidden\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m     )\n",
            "\u001b[0;31mTypeError\u001b[0m: view() takes at most 2 arguments (3 given)"
          ]
        }
      ]
    }
  ]
}