Covid-19.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h2>Covid 2019</h2></center>\n",
    "<a href=\"http://theconversation.com/how-to-model-a-pandemic-134187\" style=\"color:black;font-size:15px;\" ><strong>SIR model explained</strong>></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FutureWarning: pandas.core.index is deprecated and will be removed in a future version.  The public classes are available in the top-level namespace.\n",
      "UserWarning: The Dask Engine for Modin is experimental.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "# import pandas as pd\n",
    "import modin.pandas as pd   ############## To make pandas faster, requires dask, distributed packages\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib.dates import DateFormatter, WeekdayLocator,\\\n",
    "    DayLocator, MONDAY, YearLocator, MonthLocator\n",
    "from datetime import datetime as dt\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import WhiteKernel, ExpSineSquared\n",
    "\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "# %matplotlib notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Functions for Bayesian Regression\n",
    "def getPolynomialDataMatrix(x, degree):\n",
    "    Xtilde = np.ones(x.shape)\n",
    "    for i in range(1,degree + 1):\n",
    "        Xtilde = np.column_stack((Xtilde, x ** i))\n",
    "    return Xtilde\n",
    "\n",
    "def getBetaForPolynomialFit(x, y, degree):\n",
    "    Xtilde = getPolynomialDataMatrix(x, degree)\n",
    "    XX = Xtilde.transpose().dot(Xtilde)\n",
    "    regularizer = np.eye(XX.shape[0]) * 10 **-6\n",
    "    betaTilde = np.linalg.solve(XX + regularizer, Xtilde.transpose().dot(y))\n",
    "    return betaTilde\n",
    "\n",
    "def getBayesianBetaForPolynomialFit(x, y, degree, sigma, regLambda):  # Gaussian Bayes rule 2\n",
    "    # get polynomi\n",
    "    Xtilde = getPolynomialDataMatrix(x, degree)\n",
    "    XX = Xtilde.transpose().dot(Xtilde)\n",
    "    regularizer = np.eye(XX.shape[0]) * regLambda * sigma\n",
    "    betaTilde = np.linalg.solve(XX + regularizer, Xtilde.transpose().dot(y))\n",
    "    sigmaBeta = sigma * np.linalg.inv(XX + regularizer)\n",
    "    return betaTilde, sigmaBeta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Cumulative Cases</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Cumulative Deaths UK</th>\n",
       "      <th>Cumulative Deaths England</th>\n",
       "      <th>Cumulative Deaths  Northern Ireland</th>\n",
       "      <th>Cumulative Deaths  Scotland</th>\n",
       "      <th>Cumulative Deaths  Wales</th>\n",
       "      <th>Cumulative Recovered Patients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31/01/2020</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/02/2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02/02/2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03/02/2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>04/02/2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Cases  Cumulative Cases  Deaths  Cumulative Deaths UK  \\\n",
       "0  31/01/2020    2.0               2.0       0                     0   \n",
       "1  01/02/2020    0.0               2.0       0                     0   \n",
       "2  02/02/2020    0.0               2.0       0                     0   \n",
       "3  03/02/2020    0.0               2.0       0                     0   \n",
       "4  04/02/2020    0.0               2.0       0                     0   \n",
       "\n",
       "   Cumulative Deaths England  Cumulative Deaths  Northern Ireland  \\\n",
       "0                        NaN                                  NaN   \n",
       "1                        NaN                                  NaN   \n",
       "2                        NaN                                  NaN   \n",
       "3                        NaN                                  NaN   \n",
       "4                        NaN                                  NaN   \n",
       "\n",
       "   Cumulative Deaths  Scotland  Cumulative Deaths  Wales  \\\n",
       "0                          NaN                       NaN   \n",
       "1                          NaN                       NaN   \n",
       "2                          NaN                       NaN   \n",
       "3                          NaN                       NaN   \n",
       "4                          NaN                       NaN   \n",
       "\n",
       "   Cumulative Recovered Patients  \n",
       "0                            NaN  \n",
       "1                            NaN  \n",
       "2                            NaN  \n",
       "3                            NaN  \n",
       "4                            NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./Historic COVID-19 Dashboard Data.csv')\n",
    "# data = pd.read_csv('/home/ash/Downloads/Historic COVID-19 Dashboard Data.csv',\n",
    "#                     usecols=['Date', 'Cases', 'Cumulative Cases'], )\n",
    "\n",
    "df.head()\n",
    "# data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2020-01-31    2.0\n",
       "2020-02-01    2.0\n",
       "2020-02-02    2.0\n",
       "2020-02-03    2.0\n",
       "2020-02-04    2.0\n",
       "Name: Cumulative Cases, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'] = pd.to_datetime(df['Date'], infer_datetime_format=True)\n",
    "df.set_index('Date', inplace=True)\n",
    "\n",
    "uni_data = df['Cumulative Cases'].dropna()\n",
    "uni_data.index = df.index.dropna()\n",
    "uni_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotData(df, symbol, axisLocator='Year'):\n",
    "    fig, ax = plt.subplots()\n",
    "    mondays = WeekdayLocator(MONDAY)        # major ticks on the mondays\n",
    "    alldays = DayLocator()              # minor ticks on the days\n",
    "    weekFormatter = DateFormatter('%b %d')  # e.g., Jan 12\n",
    "    dayFormatter = DateFormatter('%d')      # e.g., 12\n",
    "    \n",
    "    fig.subplots_adjust(bottom=0.2)\n",
    "    \n",
    "    if axisLocator == 'month':\n",
    "        ax.xaxis.set_major_locator(mondays)\n",
    "        ax.xaxis.set_major_formatter(weekFormatter)\n",
    "        \n",
    "    ax.plot(df[symbol])\n",
    "    ax.scatter(df.index, df[symbol])\n",
    "    ax.xaxis_date()\n",
    "    ax.autoscale_view()\n",
    "    plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')\n",
    "    return ax\n",
    "#     plt.show()\n",
    "    \n",
    "    # ax.xaxis.set_minor_formatter(dayFormatter)\n",
    "    # ax.xaxis.set_minor_locator(alldays)\n",
    "    # ax.xaxis.set_major_locator(YearLocator())\n",
    "    # ax.xaxis.set_major_locator(MonthLocator((1,2,3)))\n",
    "\n",
    "    # ax.xaxis.set_major_formatter(DateFormatter(\"\\n%m\"))\n",
    "    # ax.xaxis.set_minor_formatter(DateFormatter(\"%b\"))\n",
    "\n",
    "    # df[symbol].plot(ax=ax)\n",
    "    \n",
    "    # ax.plot(df['Cumulative Cases'])\n",
    "    # ax.set_xticks(df.index)\n",
    "    # ax.set_xticks(df['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fddf85c2ef0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotData(df, 'Cumulative Cases', axisLocator='month')  # axisLocator is either Year or month which comes on X axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2020-01-31', '2020-02-01', '2020-02-02', '2020-02-03',\n",
       "               '2020-02-04', '2020-02-05', '2020-02-06', '2020-02-07',\n",
       "               '2020-02-08', '2020-02-09', '2020-02-10', '2020-02-11',\n",
       "               '2020-02-12', '2020-02-13', '2020-02-14', '2020-02-15',\n",
       "               '2020-02-16', '2020-02-17', '2020-02-18', '2020-02-19',\n",
       "               '2020-02-20', '2020-02-21', '2020-02-22', '2020-02-23',\n",
       "               '2020-02-24', '2020-02-25', '2020-02-26', '2020-02-27',\n",
       "               '2020-02-28', '2020-02-29', '2020-03-01', '2020-03-02',\n",
       "               '2020-03-03', '2020-03-04', '2020-03-05', '2020-03-06',\n",
       "               '2020-03-07', '2020-03-08', '2020-03-09', '2020-03-10',\n",
       "               '2020-03-11', '2020-03-12', '2020-03-13', '2020-03-14',\n",
       "               '2020-03-15', '2020-03-16', '2020-03-17', '2020-03-18',\n",
       "               '2020-03-19', '2020-03-20', '2020-03-21', '2020-03-22',\n",
       "               '2020-03-23', '2020-03-24', '2020-03-25', '2020-03-26',\n",
       "               '2020-03-27', '2020-03-28',        'NaT'],\n",
       "              dtype='datetime64[ns]', name='Date', freq=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# date_index.map(dt.fromordinal)\n",
    "# [2020-01-31].map(dt.toordinal)\n",
    "# df['Cumulative Cases']\n",
    "uni_data.index.dropna()\n",
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdda74aa198>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_date, end_date = '2020-02-05', '2020-03-26'\n",
    "dates = pd.date_range(start_date, end_date)  # date range as index\n",
    "date_index = dates.map(dt.toordinal)\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "full_dates = uni_data.index.map(dt.toordinal)\n",
    "full_dates = full_dates.to_numpy().reshape(-1, 1)\n",
    "full_dates = scaler.fit_transform(full_dates)\n",
    "\n",
    "X_tran = date_index.to_numpy().reshape(-1, 1)\n",
    "y_train = df.loc[start_date:end_date, ['Cumulative Cases']]\n",
    "\n",
    "# from sklearn import preprocessing\n",
    "X_tran = scaler.fit_transform(X_tran)\n",
    "# y_train = scaler.fit_transform(y_train)\n",
    "\n",
    "\n",
    "plt.subplot(221)\n",
    "plt.plot(X_tran, y_train)\n",
    "plt.subplot(222)\n",
    "plt.plot(full_dates)\n",
    "plt.subplot(223)\n",
    "plt.plot(X_tran)\n",
    "\n",
    "\n",
    "# X_tet = X_test.to_numpy().reshape(-1, 1)\n",
    "# x_train = df.loc[start_date:end_date, ['Cumulative Cases']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train, X_test, y_train, y_test = train_test_split(date_index, df['Cumulative Cases'],\n",
    "#                                                     test_size=0.8, random_state=3)\n",
    "# X_tran = X_train.to_numpy().reshape(-1, 1)\n",
    "# X_tet = X_test.to_numpy().reshape(-1, 1)\n",
    "# X_train.map(dt.fromordinal)\n",
    "# X_tran"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bayesian Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "degree, sigma, regLambda = 4, 25, 10**-8\n",
    "\n",
    "beta, sigmaBeta = getBayesianBetaForPolynomialFit(X_tran,y_train, degree, sigma, regLambda)\n",
    "\n",
    "# compute predictive mean\n",
    "x_test = full_dates\n",
    "X_test = getPolynomialDataMatrix(x_test, degree)\n",
    "y_pred = X_test.dot(beta)\n",
    "\n",
    "# Compute predictive variance for each data point\n",
    "y_sigma = np.zeros((X_test.shape[0]))\n",
    "for i in range(X_test.shape[0]):\n",
    "    y_sigma[i] = sigma + X_test[i,:].dot(sigmaBeta.dot(X_test[i,:].T))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fddbc10da90>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc10dc18>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc10dd68>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc10deb8>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc12c048>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc12c198>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc12c2e8>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc12c438>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc12c588>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc12c6d8>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc12c828>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc12c978>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc12cac8>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc12cc18>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc12cd68>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc12ceb8>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc133048>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc133198>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc1332e8>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc133438>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc133588>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc1336d8>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc133828>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc133978>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc133ac8>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc133c18>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc133d68>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc133eb8>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc03d048>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc03d198>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc03d2e8>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc03d438>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc03d588>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc03d6d8>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc03d828>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc03d978>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc03dac8>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc03dc18>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc03dd68>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc03deb8>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc042048>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc042198>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc0422e8>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc042438>,\n",
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       " <matplotlib.lines.Line2D at 0x7fddbc0426d8>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc042828>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc042978>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc042ac8>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc042c18>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc042d68>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc042eb8>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc049048>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc049198>,\n",
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       " <matplotlib.lines.Line2D at 0x7fddbc049438>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc049588>,\n",
       " <matplotlib.lines.Line2D at 0x7fddbc0496d8>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_test, y_pred)\n",
    "# ax.plot(x_test,y_pred, 'r')\n",
    "plt.plot(x_test, y_pred + 2 * np.sqrt(y_sigma), 'r--')\n",
    "plt.plot(x_test, y_pred - 2 * np.sqrt(y_sigma), 'r--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5000\n",
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    {
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     "text": [
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    {
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Epoch 4860/5000\n",
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      "Epoch 4861/5000\n",
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      "Epoch 4862/5000\n",
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      "Epoch 4908/5000\n",
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      "Epoch 4909/5000\n",
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      "Epoch 4937/5000\n",
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      "Epoch 4938/5000\n",
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      "Epoch 4939/5000\n",
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      "Epoch 4956/5000\n",
      "40/40 [==============================] - 0s 2ms/step - loss: 556.0853\n",
      "Epoch 4957/5000\n",
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      "Epoch 4960/5000\n",
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      "Epoch 4964/5000\n",
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      "Epoch 4966/5000\n",
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      "Epoch 4967/5000\n",
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      "Epoch 4968/5000\n",
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      "Epoch 4969/5000\n",
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      "Epoch 4970/5000\n",
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      "Epoch 4971/5000\n",
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      "Epoch 4972/5000\n",
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      "Epoch 4973/5000\n",
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      "Epoch 4974/5000\n",
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      "Epoch 4975/5000\n",
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      "Epoch 4976/5000\n",
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      "Epoch 4977/5000\n",
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      "Epoch 4978/5000\n",
      "40/40 [==============================] - 0s 2ms/step - loss: 535.8148\n",
      "Epoch 4979/5000\n",
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      "Epoch 4980/5000\n",
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      "Epoch 4981/5000\n",
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      "Epoch 4982/5000\n",
      "40/40 [==============================] - 0s 2ms/step - loss: 761.2037\n",
      "Epoch 4983/5000\n",
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      "Epoch 4984/5000\n",
      "40/40 [==============================] - 0s 2ms/step - loss: 453.3044\n",
      "Epoch 4985/5000\n",
      "40/40 [==============================] - 0s 2ms/step - loss: 452.4329\n",
      "Epoch 4986/5000\n",
      "40/40 [==============================] - 0s 2ms/step - loss: 452.3889\n",
      "Epoch 4987/5000\n",
      "40/40 [==============================] - 0s 2ms/step - loss: 452.3729\n",
      "Epoch 4988/5000\n",
      "40/40 [==============================] - 0s 2ms/step - loss: 452.3647\n",
      "Epoch 4989/5000\n",
      "40/40 [==============================] - 0s 2ms/step - loss: 452.3610\n",
      "Epoch 4990/5000\n",
      "40/40 [==============================] - 0s 2ms/step - loss: 452.3583\n",
      "Epoch 4991/5000\n",
      "40/40 [==============================] - 0s 2ms/step - loss: 452.3557\n",
      "Epoch 4992/5000\n",
      "40/40 [==============================] - 0s 2ms/step - loss: 452.3545\n",
      "Epoch 4993/5000\n",
      "40/40 [==============================] - 0s 2ms/step - loss: 452.3537\n",
      "Epoch 4994/5000\n",
      "40/40 [==============================] - 0s 2ms/step - loss: 452.3525\n",
      "Epoch 4995/5000\n",
      "40/40 [==============================] - 0s 2ms/step - loss: 613.5150\n",
      "Epoch 4996/5000\n",
      "40/40 [==============================] - 0s 2ms/step - loss: 496.7949\n",
      "Epoch 4997/5000\n",
      "40/40 [==============================] - 0s 2ms/step - loss: 453.0626\n",
      "Epoch 4998/5000\n",
      "40/40 [==============================] - 0s 2ms/step - loss: 452.3816\n",
      "Epoch 4999/5000\n",
      "40/40 [==============================] - 0s 2ms/step - loss: 452.3581\n",
      "Epoch 5000/5000\n",
      "40/40 [==============================] - 0s 2ms/step - loss: 452.3510\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fdda633bda0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf  # library for machine learning\n",
    "model = tf.keras.Sequential([\n",
    "                            tf.keras.layers.Dense(input_shape=[1], units=100, activation='relu'),\n",
    "                            tf.keras.layers.Dense(units=100, activation='relu'),\n",
    "                            tf.keras.layers.Dense(units=100, activation='relu'),\n",
    "                            tf.keras.layers.Dense(units=100, activation='relu'),\n",
    "                            tf.keras.layers.Dense(units=1)\n",
    "                            ])\n",
    "# model.compile(optimizer='sgd', loss='mean_squared_error')\n",
    "model.compile(optimizer='Adam', loss='mean_squared_error')\n",
    "# model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy')\n",
    "model.fit(X_tran, np.ravel(y_train), epochs=500, steps_per_epoch=40,\n",
    "                      verbose=1)  # Find out the parameters\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdda63b9828>]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xTest = full_dates\n",
    "# xTest\n",
    "prediction = model.predict(xTest)  # \n",
    "# print(prediction)\n",
    "# ax = plotData(df, 'Cumulative Cases', axisLocator='month') \n",
    "plt.plot(xTest, prediction)\n",
    "plt.plot(xTest, df['Cumulative Cases'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv3.6",
   "language": "python",
   "name": "venv3.6"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}