bidirectional-lstm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from numpy import array\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Bidirectional\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import metrics \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('dataset1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>iso_code</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>total_deaths_per_million</th>\n",
       "      <th>new_deaths_per_million</th>\n",
       "      <th>total_tests</th>\n",
       "      <th>new_tests</th>\n",
       "      <th>total_tests_per_thousand</th>\n",
       "      <th>new_tests_per_thousand</th>\n",
       "      <th>tests_units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18.733</td>\n",
       "      <td>18.733</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</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>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.465</td>\n",
       "      <td>18.733</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</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>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112.395</td>\n",
       "      <td>74.930</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</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>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>159.227</td>\n",
       "      <td>46.831</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</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>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>177.959</td>\n",
       "      <td>18.733</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13075</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-04-21</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.682</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.000</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>13076</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-04-22</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.884</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.000</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>13077</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-04-23</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.884</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.000</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>13078</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-04-24</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.951</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.000</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>13079</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-04-25</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.951</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.269</td>\n",
       "      <td>0.067</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",
       "<p>13080 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      iso_code  location        date  total_cases  new_cases  total_deaths  \\\n",
       "0          ABW     Aruba  2020-03-13            2          2             0   \n",
       "1          ABW     Aruba  2020-03-20            4          2             0   \n",
       "2          ABW     Aruba  2020-03-24           12          8             0   \n",
       "3          ABW     Aruba  2020-03-25           17          5             0   \n",
       "4          ABW     Aruba  2020-03-26           19          2             0   \n",
       "...        ...       ...         ...          ...        ...           ...   \n",
       "13075      ZWE  Zimbabwe  2020-04-21           25          0             3   \n",
       "13076      ZWE  Zimbabwe  2020-04-22           28          3             3   \n",
       "13077      ZWE  Zimbabwe  2020-04-23           28          0             3   \n",
       "13078      ZWE  Zimbabwe  2020-04-24           29          1             3   \n",
       "13079      ZWE  Zimbabwe  2020-04-25           29          0             4   \n",
       "\n",
       "       new_deaths  total_cases_per_million  new_cases_per_million  \\\n",
       "0               0                   18.733                 18.733   \n",
       "1               0                   37.465                 18.733   \n",
       "2               0                  112.395                 74.930   \n",
       "3               0                  159.227                 46.831   \n",
       "4               0                  177.959                 18.733   \n",
       "...           ...                      ...                    ...   \n",
       "13075           0                    1.682                  0.000   \n",
       "13076           0                    1.884                  0.202   \n",
       "13077           0                    1.884                  0.000   \n",
       "13078           0                    1.951                  0.067   \n",
       "13079           1                    1.951                  0.000   \n",
       "\n",
       "       total_deaths_per_million  new_deaths_per_million  total_tests  \\\n",
       "0                         0.000                   0.000          NaN   \n",
       "1                         0.000                   0.000          NaN   \n",
       "2                         0.000                   0.000          NaN   \n",
       "3                         0.000                   0.000          NaN   \n",
       "4                         0.000                   0.000          NaN   \n",
       "...                         ...                     ...          ...   \n",
       "13075                     0.202                   0.000          NaN   \n",
       "13076                     0.202                   0.000          NaN   \n",
       "13077                     0.202                   0.000          NaN   \n",
       "13078                     0.202                   0.000          NaN   \n",
       "13079                     0.269                   0.067          NaN   \n",
       "\n",
       "       new_tests  total_tests_per_thousand  new_tests_per_thousand tests_units  \n",
       "0            NaN                       NaN                     NaN         NaN  \n",
       "1            NaN                       NaN                     NaN         NaN  \n",
       "2            NaN                       NaN                     NaN         NaN  \n",
       "3            NaN                       NaN                     NaN         NaN  \n",
       "4            NaN                       NaN                     NaN         NaN  \n",
       "...          ...                       ...                     ...         ...  \n",
       "13075        NaN                       NaN                     NaN         NaN  \n",
       "13076        NaN                       NaN                     NaN         NaN  \n",
       "13077        NaN                       NaN                     NaN         NaN  \n",
       "13078        NaN                       NaN                     NaN         NaN  \n",
       "13079        NaN                       NaN                     NaN         NaN  \n",
       "\n",
       "[13080 rows x 16 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['location','total_cases']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>location</th>\n",
       "      <th>total_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13075</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13076</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13077</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13078</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13079</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13080 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       location  total_cases\n",
       "0         Aruba            2\n",
       "1         Aruba            4\n",
       "2         Aruba           12\n",
       "3         Aruba           17\n",
       "4         Aruba           19\n",
       "...         ...          ...\n",
       "13075  Zimbabwe           25\n",
       "13076  Zimbabwe           28\n",
       "13077  Zimbabwe           28\n",
       "13078  Zimbabwe           29\n",
       "13079  Zimbabwe           29\n",
       "\n",
       "[13080 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "lis1 = dict()\n",
    "for rows in df.itertuples():\n",
    "    if rows.location not in lis1:\n",
    "        lis1[rows.location] = []\n",
    "    lis1[rows.location].append(rows.total_cases)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = []\n",
    "y = []\n",
    "for key in lis1.keys():\n",
    "    lis = lis1[key]\n",
    "    n = len(lis)\n",
    "    for i in range(0,n-3):\n",
    "        temp = lis[i:i+3]\n",
    "        X.append(temp)\n",
    "        y.append(lis[i+3])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12462\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[ 2,  4, 12]],\n",
       "\n",
       "       [[ 4, 12, 17]],\n",
       "\n",
       "       [[12, 17, 19]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[25, 25, 28]],\n",
       "\n",
       "       [[25, 28, 28]],\n",
       "\n",
       "       [[28, 28, 29]]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = array(X)\n",
    "X = X.reshape(len(X),1,3)\n",
    "print(len(y))\n",
    "X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def split_sequence(sequence, n_steps):\n",
    "#     X, y = list(), list()\n",
    "#     for i in range(len(sequence)): \n",
    "#         end_ix = i + n_steps\n",
    "#         if end_ix > len(sequence)-1:\n",
    "#             break\n",
    "#         seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "#         X.append(seq_x)\n",
    "#         y.append(seq_y)\n",
    "#     return array(X), array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# raw_seq = [48,63,71,81,91,102,112,126,146,171,198,256,334,403,497,571,657,730,883,1019,1139,1326,1635,2059,2545,3105,3684,4289,4778,5351,5916,6729,7600,8454,9212]\n",
    "# # choose a number of time steps\n",
    "# n_steps = 3\n",
    "# # split into samples\n",
    "# X, y = split_sequence(raw_seq, n_steps)\n",
    "# print('X=')\n",
    "# print(X)\n",
    "# print('y')\n",
    "# print(y)\n",
    "# n_features = 1\n",
    "# X = X.reshape((X.shape[0], X.shape[1], n_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 2,  4, 12]],\n",
       "\n",
       "       [[ 4, 12, 17]],\n",
       "\n",
       "       [[12, 17, 19]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[25, 25, 28]],\n",
       "\n",
       "       [[25, 28, 28]],\n",
       "\n",
       "       [[28, 28, 29]]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Bidirectional(LSTM(100, activation='relu'), input_shape=(1, 3)))\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer='adam', loss='mse')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
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    {
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     "text": [
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      "Epoch 265/300\n",
      " - 1s - loss: 1105814.7360\n",
      "Epoch 266/300\n",
      " - 1s - loss: 704599.2645\n",
      "Epoch 267/300\n",
      " - 1s - loss: 341102.3903\n",
      "Epoch 268/300\n",
      " - 1s - loss: 371934.7125\n",
      "Epoch 269/300\n",
      " - 1s - loss: 366247.8843\n",
      "Epoch 270/300\n",
      " - 1s - loss: 268108.1513\n",
      "Epoch 271/300\n",
      " - 1s - loss: 885507.6212\n",
      "Epoch 272/300\n",
      " - 1s - loss: 696465.8906\n",
      "Epoch 273/300\n",
      " - 1s - loss: 336436.6620\n",
      "Epoch 274/300\n",
      " - 1s - loss: 197049.9108\n",
      "Epoch 275/300\n",
      " - 1s - loss: 1682822.8216\n",
      "Epoch 276/300\n",
      " - 1s - loss: 203285.7148\n",
      "Epoch 277/300\n",
      " - 1s - loss: 297525.8623\n",
      "Epoch 278/300\n",
      " - 1s - loss: 498220.2037\n",
      "Epoch 279/300\n",
      " - 1s - loss: 362408.4999\n",
      "Epoch 280/300\n",
      " - 1s - loss: 518677.3471\n",
      "Epoch 281/300\n",
      " - 1s - loss: 444713.3167\n",
      "Epoch 282/300\n",
      " - 1s - loss: 1072766.1706\n",
      "Epoch 283/300\n",
      " - 1s - loss: 341711.1176\n",
      "Epoch 284/300\n",
      " - 1s - loss: 608486.4119\n",
      "Epoch 285/300\n",
      " - 1s - loss: 160589.5647\n",
      "Epoch 286/300\n",
      " - 1s - loss: 429189.4930\n",
      "Epoch 287/300\n",
      " - 1s - loss: 302735.8387\n",
      "Epoch 288/300\n",
      " - 1s - loss: 432769.8137\n",
      "Epoch 289/300\n",
      " - 1s - loss: 435774.4630\n",
      "Epoch 290/300\n",
      " - 1s - loss: 296882.1297\n",
      "Epoch 291/300\n",
      " - 1s - loss: 390766.7513\n",
      "Epoch 292/300\n",
      " - 1s - loss: 337458.2771\n",
      "Epoch 293/300\n",
      " - 1s - loss: 341144.7420\n",
      "Epoch 294/300\n",
      " - 1s - loss: 497766.1110\n",
      "Epoch 295/300\n",
      " - 1s - loss: 670365.1376\n",
      "Epoch 296/300\n",
      " - 1s - loss: 632838.1108\n",
      "Epoch 297/300\n",
      " - 1s - loss: 411504.8165\n",
      "Epoch 298/300\n",
      " - 1s - loss: 237222.5238\n",
      "Epoch 299/300\n",
      " - 1s - loss: 422919.7696\n",
      "Epoch 300/300\n",
      " - 1s - loss: 618426.5727\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x142eacb10>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y, epochs=300, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 1, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a=7600\n",
    "# b=8454\n",
    "# c=9212\n",
    "# for _ in range(10):\n",
    "#     x_input = array([a, b, c])\n",
    "  \n",
    "#     x_input = x_input.reshape((1, n_steps, n_features))\n",
    "#     yhat = model.predict(x_input, verbose=0)\n",
    "#     print(yhat)\n",
    "#     a=b\n",
    "#     b=c\n",
    "#     c=yhat\n",
    "# D=[]\n",
    "# n=35\n",
    "# scale=[]\n",
    "# for i in range(0,9500,500):\n",
    "#     scale.append(i)\n",
    "# for i in range(4,(n+1),1):\n",
    "#     D.append(i)\n",
    "\n",
    "#plt.figure(figsize=(18,10))\n",
    "#plt.xlabel(\"Cases\")\n",
    "#plt.ylabel(\"day\")\n",
    "#plt.plot(D,y)\n",
    "#plt.plot(D,yhatd)\n",
    "#plt.yticks(scale)\n",
    "import random\n",
    "X_test = []\n",
    "y_test = []\n",
    "indexes = random.sample(range(0,len(y)-1),100)\n",
    "for i in indexes:\n",
    "    X_test.append(X[i])\n",
    "    y_test.append(y[i])\n",
    "X_test = array(X_test)\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "bidirectional_1 (Bidirection (None, 200)               83200     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 201       \n",
      "=================================================================\n",
      "Total params: 83,401\n",
      "Trainable params: 83,401\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "k\n"
     ]
    }
   ],
   "source": [
    "yhatd=model.predict(X_test)\n",
    "print(model.summary())\n",
    "print(\"k\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE    386513.3969950157\n",
      "RMSE    621.7020162384996\n",
      "MAE    [112.43588789]\n"
     ]
    }
   ],
   "source": [
    "# print(yhatd)\n",
    "# print(y_test)\n",
    "# print('MSE=')\n",
    "from sklearn import metrics\n",
    "# print(metrics.mean_squared_error(yhatd,y_test))\n",
    "print (\"MSE   \",metrics.mean_squared_error(yhatd,y_test))\n",
    "print (\"RMSE   \",metrics.mean_squared_error(yhatd,y_test)**0.5)\n",
    "\n",
    "MAE = 0\n",
    "for i in range(0,len(y_test)):\n",
    "    MAE = MAE + (abs(y_test[i]-yhatd[i]))\n",
    "print('MAE   ',MAE/len(y_test))\n",
    "# Q=X[0:len(X)]\n",
    "# P=yhatd[0:len(yhatd)]\n",
    "# r=[]\n",
    "# for i in range(len(X)):\n",
    "# \tr.append(i)\n",
    "# sum=0\n",
    "# for i in range(len(r)):\n",
    "#   #print(abs(Q[i]-P[i]))\n",
    "#   sum=sum+abs(((yhatd[i]-y[i])/y[i]))\n",
    "#   #print(sum)\n",
    "\n",
    "#   #print(abs([i]-yhatd[i]))\n",
    "# #print('a')\n",
    "# print(sum,len(r))\n",
    "# print(\"MAPI\", (sum/len(r))*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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