Predict_with_LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\bhavna\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "D:\\Users\\bhavna\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "D:\\Users\\bhavna\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "D:\\Users\\bhavna\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "D:\\Users\\bhavna\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "D:\\Users\\bhavna\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "D:\\Users\\bhavna\\Anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "D:\\Users\\bhavna\\Anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "D:\\Users\\bhavna\\Anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "D:\\Users\\bhavna\\Anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "D:\\Users\\bhavna\\Anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "D:\\Users\\bhavna\\Anaconda3\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "%matplotlib inline"
   ]
  },
  {
   "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>Id</th>\n",
       "      <th>Province_State</th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>Date</th>\n",
       "      <th>ConfirmedCases</th>\n",
       "      <th>Fatalities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10703</th>\n",
       "      <td>14874</td>\n",
       "      <td>NaN</td>\n",
       "      <td>India</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10704</th>\n",
       "      <td>14875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>India</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10705</th>\n",
       "      <td>14876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>India</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10706</th>\n",
       "      <td>14877</td>\n",
       "      <td>NaN</td>\n",
       "      <td>India</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10707</th>\n",
       "      <td>14878</td>\n",
       "      <td>NaN</td>\n",
       "      <td>India</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id Province_State Country_Region        Date  ConfirmedCases  \\\n",
       "10703  14874            NaN          India  2020-01-22             0.0   \n",
       "10704  14875            NaN          India  2020-01-23             0.0   \n",
       "10705  14876            NaN          India  2020-01-24             0.0   \n",
       "10706  14877            NaN          India  2020-01-25             0.0   \n",
       "10707  14878            NaN          India  2020-01-26             0.0   \n",
       "\n",
       "       Fatalities  \n",
       "10703         0.0  \n",
       "10704         0.0  \n",
       "10705         0.0  \n",
       "10706         0.0  \n",
       "10707         0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"train.csv\")\n",
    "df=df[df[\"Country_Region\"]==\"India\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of days in the dataset: 77\n"
     ]
    }
   ],
   "source": [
    "data_to_use = df['ConfirmedCases'].values\n",
    "data_to_use_f = df['Fatalities'].values\n",
    "print('Total number of days in the dataset: {}'.format(len(data_to_use)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#normalising the dataset\n",
    "scaler = StandardScaler()\n",
    "scaled_dataset = scaler.fit_transform(data_to_use.reshape(-1, 1))\n",
    "scaled_dataset_f = scaler.fit_transform(data_to_use_f.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7), frameon=False, facecolor='brown', edgecolor='blue')\n",
    "plt.title('Graph for Confirmed Cases')\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('Scaled value of Confirmed Cases')\n",
    "plt.plot(scaled_dataset, label='confirmed-cases-data')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7), frameon=False, facecolor='brown', edgecolor='blue')\n",
    "plt.title('Graph for Fatalities')\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('Scaled value of Fatalities')\n",
    "plt.plot(scaled_dataset_f, label='Fatalities-data')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def window_data(data, window_size):\n",
    "    X = []\n",
    "    y = []\n",
    "    \n",
    "    i = 0\n",
    "    while (i + window_size) <= len(data) - 1:\n",
    "        X.append(data[i:i+window_size])\n",
    "        y.append(data[i+window_size])\n",
    "        \n",
    "        i += 1\n",
    "    assert len(X) ==  len(y)\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = window_data(scaled_dataset, 7)\n",
    "X_f, y_f = window_data(scaled_dataset_f, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train size: (60, 7, 1)\n",
      "y_train size: (60, 1)\n",
      "X_test size: (10, 7, 1)\n",
      "y_test size: (10, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train  = np.array(X[:60])\n",
    "y_train = np.array(y[:60])\n",
    "\n",
    "X_test = np.array(X[60:])\n",
    "y_test = np.array(y[60:])\n",
    "\n",
    "\n",
    "X_train_f  = np.array(X_f[:60])\n",
    "y_train_f = np.array(y_f[:60])\n",
    "\n",
    "X_test_f = np.array(X_f[60:])\n",
    "y_test_f = np.array(y_f[60:])\n",
    "\n",
    "print(\"X_train size: {}\".format(X_train.shape))\n",
    "print(\"y_train size: {}\".format(y_train.shape))\n",
    "print(\"X_test size: {}\".format(X_test.shape))\n",
    "print(\"y_test size: {}\".format(y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 10\n",
    "batch_size = 7\n",
    "def LSTM_cell(hidden_layer_size, batch_size,number_of_layers, dropout=True, dropout_rate=0.8):\n",
    "    \n",
    "    layer = tf.contrib.rnn.BasicLSTMCell(hidden_layer_size)\n",
    "    \n",
    "    if dropout:\n",
    "        layer = tf.contrib.rnn.DropoutWrapper(layer, output_keep_prob=dropout_rate)\n",
    "        \n",
    "    cell = tf.contrib.rnn.MultiRNNCell([layer]*number_of_layers)\n",
    "    \n",
    "    init_state = cell.zero_state(batch_size, tf.float32)\n",
    "    \n",
    "    return cell, init_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def output_layer(lstm_output, in_size, out_size):\n",
    "    \n",
    "    x = lstm_output[:, -1, :]\n",
    "    print(x)\n",
    "    weights = tf.Variable(tf.truncated_normal([in_size, out_size], stddev=0.05), name='output_layer_weights')\n",
    "    bias = tf.Variable(tf.zeros([out_size]), name='output_layer_bias')\n",
    "    \n",
    "    output = tf.matmul(x, weights) + bias\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def opt_loss(logits, targets, learning_rate, grad_clip_margin):\n",
    "    \n",
    "    losses = []\n",
    "    for i in range(targets.get_shape()[0]):\n",
    "        losses.append([(tf.pow(logits[i] - targets[i], 2))])\n",
    "        \n",
    "    loss = tf.reduce_sum(losses)/(2*batch_size)\n",
    "    \n",
    "    #Cliping the gradient loss\n",
    "    gradients = tf.gradients(loss, tf.trainable_variables())\n",
    "    clipper_, _ = tf.clip_by_global_norm(gradients, grad_clip_margin)\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "    train_optimizer = optimizer.apply_gradients(zip(gradients, tf.trainable_variables()))\n",
    "    return loss, train_optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PredictionRNN(object):\n",
    "    \n",
    "    def __init__(self, learning_rate=0.001, batch_size=7, hidden_layer_size=512, number_of_layers=1, \n",
    "                 dropout=True, dropout_rate=0.8, number_of_classes=1, gradient_clip_margin=4, window_size=7):\n",
    "    \n",
    "        self.inputs = tf.placeholder(tf.float32, [batch_size, window_size, 1], name='input_data')\n",
    "        self.targets = tf.placeholder(tf.float32, [batch_size, 1], name='targets')\n",
    "\n",
    "        cell, init_state = LSTM_cell(hidden_layer_size, batch_size, number_of_layers, dropout, dropout_rate)\n",
    "\n",
    "        outputs, states = tf.nn.dynamic_rnn(cell, self.inputs, initial_state=init_state)\n",
    "\n",
    "        self.logits = output_layer(outputs, hidden_layer_size, number_of_classes)\n",
    "\n",
    "        self.loss, self.opt = opt_loss(self.logits, self.targets, learning_rate, gradient_clip_margin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:\n",
      "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "  * https://github.com/tensorflow/io (for I/O related ops)\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n",
      "WARNING:tensorflow:From <ipython-input-11-0da7a3a02951>:5: BasicLSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by that in Tensorflow 2.0.\n",
      "WARNING:tensorflow:From <ipython-input-11-0da7a3a02951>:10: MultiRNNCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This class is equivalent as tf.keras.layers.StackedRNNCells, and will be replaced by that in Tensorflow 2.0.\n",
      "WARNING:tensorflow:From <ipython-input-14-c82968a51ae4>:11: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
      "WARNING:tensorflow:Entity <bound method MultiRNNCell.call of <tensorflow.python.ops.rnn_cell_impl.MultiRNNCell object at 0x000001C810DC3FC8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method MultiRNNCell.call of <tensorflow.python.ops.rnn_cell_impl.MultiRNNCell object at 0x000001C810DC3FC8>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING: Entity <bound method MultiRNNCell.call of <tensorflow.python.ops.rnn_cell_impl.MultiRNNCell object at 0x000001C810DC3FC8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method MultiRNNCell.call of <tensorflow.python.ops.rnn_cell_impl.MultiRNNCell object at 0x000001C810DC3FC8>>: AttributeError: module 'gast' has no attribute 'Num'\n",
      "WARNING:tensorflow:From D:\\Users\\bhavna\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "WARNING:tensorflow:From D:\\Users\\bhavna\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\rnn_cell_impl.py:738: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "WARNING:tensorflow:Entity <bound method BasicLSTMCell.call of <tensorflow.python.ops.rnn_cell_impl.BasicLSTMCell object at 0x000001C80FCF3AC8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method BasicLSTMCell.call of <tensorflow.python.ops.rnn_cell_impl.BasicLSTMCell object at 0x000001C80FCF3AC8>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method BasicLSTMCell.call of <tensorflow.python.ops.rnn_cell_impl.BasicLSTMCell object at 0x000001C80FCF3AC8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method BasicLSTMCell.call of <tensorflow.python.ops.rnn_cell_impl.BasicLSTMCell object at 0x000001C80FCF3AC8>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "Tensor(\"strided_slice:0\", shape=(7, 512), dtype=float32)\n",
      "WARNING:tensorflow:From D:\\Users\\bhavna\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_grad.py:1205: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model = PredictionRNN()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "session =  tf.Session()\n",
    "session.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/10  Current loss: 0.03172873705625534\n",
      "Epoch 2/10  Current loss: 0.006769789382815361\n",
      "Epoch 4/10  Current loss: 0.004746404942125082\n",
      "Epoch 6/10  Current loss: 0.0035713566467165947\n",
      "Epoch 8/10  Current loss: 0.003460346022620797\n"
     ]
    }
   ],
   "source": [
    "for i in range(epochs):\n",
    "    traind_scores = []\n",
    "    ii = 0\n",
    "    epoch_loss = []\n",
    "    while(ii + batch_size) <= len(X_train):\n",
    "        X_batch = X_train[ii:ii+batch_size]\n",
    "        y_batch = y_train[ii:ii+batch_size]\n",
    "        \n",
    "        o, c, _ = session.run([model.logits, model.loss, model.opt], feed_dict={model.inputs:X_batch, model.targets:y_batch})\n",
    "        \n",
    "        epoch_loss.append(c)\n",
    "        traind_scores.append(o)\n",
    "        ii += batch_size\n",
    "    if (i % 2) == 0:\n",
    "        print('Epoch {}/{}'.format(i, epochs), ' Current loss: {}'.format(np.mean(epoch_loss)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sup =[]\n",
    "for i in range(len(traind_scores)):\n",
    "    for j in range(len(traind_scores[i])):\n",
    "        sup.append(traind_scores[i][j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "tests = []\n",
    "i = 0\n",
    "while i+batch_size <= len(X_test):\n",
    "    \n",
    "    o = session.run([model.logits], feed_dict={model.inputs:X_test[i:i+batch_size]})\n",
    "    i += batch_size\n",
    "    tests.append(o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "tests_new = []\n",
    "for i in range(len(tests)):\n",
    "    for j in range(len(tests[i][0])):\n",
    "        tests_new.append(tests[i][0][j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_results = []\n",
    "for i in range(70):\n",
    "    if i >= 63:\n",
    "        test_results.append(tests_new[i-63])\n",
    "    else:\n",
    "        test_results.append(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 7))\n",
    "plt.plot(scaled_dataset, label='Original data',color=\"grey\")\n",
    "plt.plot(sup, label='Training data',color=\"blue\")\n",
    "plt.plot(test_results, label='Testing data',color=\"red\")\n",
    "plt.title(\"graph for confirmed cases \")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/10  Current loss: 0.001688475487753749\n",
      "Epoch 2/10  Current loss: 0.0013316352851688862\n",
      "Epoch 4/10  Current loss: 0.0014849796425551176\n",
      "Epoch 6/10  Current loss: 0.0011607491178438067\n",
      "Epoch 8/10  Current loss: 0.0007573539041914046\n"
     ]
    }
   ],
   "source": [
    "for i in range(epochs):\n",
    "    traind_scores = []\n",
    "    ii = 0\n",
    "    epoch_loss = []\n",
    "    while(ii + batch_size) <= len(X_train):\n",
    "        X_batch = X_train_f[ii:ii+batch_size]\n",
    "        y_batch = y_train_f[ii:ii+batch_size]\n",
    "        \n",
    "        o, c, _ = session.run([model.logits, model.loss, model.opt], feed_dict={model.inputs:X_batch, model.targets:y_batch})\n",
    "        \n",
    "        epoch_loss.append(c)\n",
    "        traind_scores.append(o)\n",
    "        ii += batch_size\n",
    "    if (i % 2) == 0:\n",
    "        print('Epoch {}/{}'.format(i, epochs), ' Current loss: {}'.format(np.mean(epoch_loss)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "sup =[]\n",
    "for i in range(len(traind_scores)):\n",
    "    for j in range(len(traind_scores[i])):\n",
    "        sup.append(traind_scores[i][j])\n",
    "\n",
    "tests = []\n",
    "i = 0\n",
    "while i+batch_size <= len(X_test_f):\n",
    "    \n",
    "    o = session.run([model.logits], feed_dict={model.inputs:X_test_f[i:i+batch_size]})\n",
    "    i += batch_size\n",
    "    tests.append(o)\n",
    "\n",
    "tests_new = []\n",
    "for i in range(len(tests)):\n",
    "    for j in range(len(tests[i][0])):\n",
    "        tests_new.append(tests[i][0][j])\n",
    "        \n",
    "test_results = []\n",
    "for i in range(70):\n",
    "    if i >= 63:\n",
    "        test_results.append(tests_new[i-63])\n",
    "    else:\n",
    "        test_results.append(None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 7))\n",
    "plt.plot(scaled_dataset, label='Original data',color=\"grey\")\n",
    "plt.plot(sup, label='Training data',color=\"blue\")\n",
    "plt.plot(test_results, label='Testing data',color=\"red\")\n",
    "plt.title(\"graph for Fatalities\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}