0_Passenger_Prediction_LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Long Short Term Memory with Keras​ (LSTM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"99\"></a>\n",
    "## Implementing Long Short Term Memory with Keras\n",
    "* [Loading and Visualizing Data](#41)\n",
    "* [Preprocessing Data](#42)\n",
    "* [Create LSTM Model](#43)\n",
    "* [Predictions and Visualising LSTM Model](#44)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"41\"></a>\n",
    "### Loading and Visualizing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>#Passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1949-06</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1949-07</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1949-08</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1949-09</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1949-10</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1949-11</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1949-12</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1950-01</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1950-02</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1950-03</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1950-04</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1950-05</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1950-06</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1950-07</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1950-08</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1950-09</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1950-10</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1950-11</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1950-12</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1951-01</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1951-02</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1951-03</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1951-04</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1951-05</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1951-06</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>1958-02</td>\n",
       "      <td>318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>1958-03</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>1958-04</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>1958-05</td>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>1958-06</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>1958-07</td>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>1958-08</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>1958-09</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>1958-10</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>1958-11</td>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>1958-12</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>1959-01</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>1959-02</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1959-03</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>1959-04</td>\n",
       "      <td>396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>1959-05</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1959-06</td>\n",
       "      <td>472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1959-07</td>\n",
       "      <td>548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1959-08</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1959-09</td>\n",
       "      <td>463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1959-10</td>\n",
       "      <td>407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>1959-11</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>1959-12</td>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>1960-01</td>\n",
       "      <td>417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>1960-02</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>1960-03</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>1960-04</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>1960-05</td>\n",
       "      <td>472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>1960-06</td>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>1960-07</td>\n",
       "      <td>622</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>139 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Month  #Passengers\n",
       "0    1949-01          112\n",
       "1    1949-02          118\n",
       "2    1949-03          132\n",
       "3    1949-04          129\n",
       "4    1949-05          121\n",
       "5    1949-06          135\n",
       "6    1949-07          148\n",
       "7    1949-08          148\n",
       "8    1949-09          136\n",
       "9    1949-10          119\n",
       "10   1949-11          104\n",
       "11   1949-12          118\n",
       "12   1950-01          115\n",
       "13   1950-02          126\n",
       "14   1950-03          141\n",
       "15   1950-04          135\n",
       "16   1950-05          125\n",
       "17   1950-06          149\n",
       "18   1950-07          170\n",
       "19   1950-08          170\n",
       "20   1950-09          158\n",
       "21   1950-10          133\n",
       "22   1950-11          114\n",
       "23   1950-12          140\n",
       "24   1951-01          145\n",
       "25   1951-02          150\n",
       "26   1951-03          178\n",
       "27   1951-04          163\n",
       "28   1951-05          172\n",
       "29   1951-06          178\n",
       "..       ...          ...\n",
       "109  1958-02          318\n",
       "110  1958-03          362\n",
       "111  1958-04          348\n",
       "112  1958-05          363\n",
       "113  1958-06          435\n",
       "114  1958-07          491\n",
       "115  1958-08          505\n",
       "116  1958-09          404\n",
       "117  1958-10          359\n",
       "118  1958-11          310\n",
       "119  1958-12          337\n",
       "120  1959-01          360\n",
       "121  1959-02          342\n",
       "122  1959-03          406\n",
       "123  1959-04          396\n",
       "124  1959-05          420\n",
       "125  1959-06          472\n",
       "126  1959-07          548\n",
       "127  1959-08          559\n",
       "128  1959-09          463\n",
       "129  1959-10          407\n",
       "130  1959-11          362\n",
       "131  1959-12          405\n",
       "132  1960-01          417\n",
       "133  1960-02          391\n",
       "134  1960-03          419\n",
       "135  1960-04          461\n",
       "136  1960-05          472\n",
       "137  1960-06          535\n",
       "138  1960-07          622\n",
       "\n",
       "[139 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('international-airline-passengers.csv',skipfooter=5)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = data.iloc[:,1].values\n",
    "plt.plot(dataset)\n",
    "plt.xlabel(\"time\")\n",
    "plt.ylabel(\"Number of Passenger\")\n",
    "plt.title(\"international airline passenger\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"42\"></a>\n",
    "### Preprocessing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(139, 1)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reshape\n",
    "dataset = dataset.reshape(-1,1)\n",
    "dataset = dataset.astype(\"float32\")\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling \n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "dataset = scaler.fit_transform(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(dataset, look_back):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset)-look_back-1):\n",
    "        a = dataset[i:(i+look_back), 0]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i + look_back, 0])\n",
    "    return numpy.array(dataX), numpy.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_data(X, y, test_size):\n",
    "    pos = int(round(len(X) * (1-test_size)))\n",
    "    X_train, y_train = X[:pos], y[:pos]\n",
    "    X_test, y_test = X[pos:], y[pos:]\n",
    "    return X_train, y_train, X_test, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X:(137, 1),y:(137,)\n",
      "X_train:(92, 1),y_train:(92,),X_test:(45, 1),y_test(45,)\n"
     ]
    }
   ],
   "source": [
    "look_back = 1\n",
    "X, y = create_dataset(dataset, look_back)\n",
    "print(\"X:{},y:{}\".format(X.shape, y.shape))\n",
    "X_train, y_train, X_test, y_test = split_data(X, y, 0.33)\n",
    "print(\"X_train:{},y_train:{},X_test:{},y_test{}\".format(X_train.shape, y_train.shape, X_test.shape, y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:(92, 1),y_train:(92, 1),X_test:(45, 1),y_test(45, 1)\n"
     ]
    }
   ],
   "source": [
    "#reshape\n",
    "y = y.reshape(-1,1)\n",
    "y_train=y_train.reshape(-1,1)\n",
    "y_test=y_test.reshape(-1,1)\n",
    "print(\"X_train:{},y_train:{},X_test:{},y_test{}\".format(X_train.shape, y_train.shape, X_test.shape, y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshape input to be [samples, time steps, features]\n",
    "\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"43\"></a>\n",
    "### Create LSTM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "92/92 [==============================] - 1s 13ms/step - loss: 0.0491\n",
      "Epoch 2/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0264\n",
      "Epoch 3/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0189\n",
      "Epoch 4/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0166\n",
      "Epoch 5/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0157\n",
      "Epoch 6/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0149\n",
      "Epoch 7/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0140\n",
      "Epoch 8/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0132\n",
      "Epoch 9/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0123\n",
      "Epoch 10/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0115\n",
      "Epoch 11/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0106\n",
      "Epoch 12/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0097\n",
      "Epoch 13/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0088\n",
      "Epoch 14/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0078\n",
      "Epoch 15/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0069\n",
      "Epoch 16/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0060\n",
      "Epoch 17/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0052\n",
      "Epoch 18/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0043\n",
      "Epoch 19/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0037\n",
      "Epoch 20/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0031\n",
      "Epoch 21/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0026\n",
      "Epoch 22/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0023\n",
      "Epoch 23/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0021\n",
      "Epoch 24/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0020\n",
      "Epoch 25/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0019\n",
      "Epoch 26/50\n",
      "92/92 [==============================] - 0s 2ms/step - loss: 0.0020\n",
      "Epoch 27/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0019\n",
      "Epoch 28/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0020\n",
      "Epoch 29/50\n",
      "92/92 [==============================] - 0s 2ms/step - loss: 0.0019\n",
      "Epoch 30/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0019\n",
      "Epoch 31/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0019\n",
      "Epoch 32/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0019\n",
      "Epoch 33/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0019\n",
      "Epoch 34/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0020\n",
      "Epoch 35/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0019\n",
      "Epoch 36/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0019\n",
      "Epoch 37/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0019\n",
      "Epoch 38/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0019\n",
      "Epoch 39/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0019\n",
      "Epoch 40/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0019\n",
      "Epoch 41/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0020\n",
      "Epoch 42/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0019\n",
      "Epoch 43/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0019\n",
      "Epoch 44/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0019\n",
      "Epoch 45/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0019\n",
      "Epoch 46/50\n",
      "92/92 [==============================] - 0s 2ms/step - loss: 0.0020\n",
      "Epoch 47/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0019\n",
      "Epoch 48/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0019\n",
      "Epoch 49/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0019\n",
      "Epoch 50/50\n",
      "92/92 [==============================] - 0s 3ms/step - loss: 0.0019\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1a2f7e6b00>"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model\n",
    "model = Sequential()\n",
    "model.add(LSTM(4, input_shape=(1, look_back)))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "model.fit(X_train, y_train, epochs=50, batch_size=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"44\"></a>\n",
    "### Predictions and Visualising LSTM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(92, 1, 1)\n",
      "(92, 1)\n",
      "(45, 1, 1)\n",
      "(45, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train = numpy.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))\n",
    "X_test = numpy.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)\n",
    "trainPredict = model.predict(X_train)\n",
    "testPredict = model.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainPredict = scaler.inverse_transform(trainPredict)\n",
    "trainY = scaler.inverse_transform(y_train)\n",
    "testPredict = scaler.inverse_transform(testPredict)\n",
    "testY = scaler.inverse_transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 92)\n",
      "(92, 1)\n",
      "Train Score: 17.90 RMSE\n",
      "Test Score: 43.60 RMSE\n"
     ]
    }
   ],
   "source": [
    "trainY=trainY.reshape(1,-1)\n",
    "testY = testY.reshape(1,-1)\n",
    "# calculate root mean squared error\n",
    "trainSctrainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))\n",
    "print('Train Score: %.2f RMSE' % (trainScore))\n",
    "testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))\n",
    "print('Test Score: %.2f RMSE' % (testScore))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainPredictPlot = numpy.empty_like(dataset)\n",
    "trainPredictPlot[:, :] = numpy.nan\n",
    "trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict\n",
    "# shift test predictions for plotting\n",
    "testPredictPlot = numpy.empty_like(dataset)\n",
    "testPredictPlot[:, :] = numpy.nan\n",
    "testPredictPlot[len(trainPredict)+(look_back):len(dataset)-1, :] = testPredict\n",
    "# plot baseline and predictions\n",
    "plt.plot(scaler.inverse_transform(dataset))\n",
    "plt.plot(trainPredictPlot)\n",
    "plt.plot(testPredictPlot)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "test1=numpy.reshape(test1,(len(test1),1,1))\n",
    "y_pred=model.predict(test1)\n",
    "y_pred=scaler.fit_transform(y_pred)\n",
    "test=scaler.fit_transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.24131274]],\n",
       "\n",
       "       [[0.1911197 ]],\n",
       "\n",
       "       [[0.24131274]],\n",
       "\n",
       "       [[0.26640925]],\n",
       "\n",
       "       [[0.24903473]],\n",
       "\n",
       "       [[0.31467178]],\n",
       "\n",
       "       [[0.3185328 ]],\n",
       "\n",
       "       [[0.32046333]],\n",
       "\n",
       "       [[0.4073359 ]],\n",
       "\n",
       "       [[0.5019305 ]],\n",
       "\n",
       "       [[0.46911195]],\n",
       "\n",
       "       [[0.40154442]],\n",
       "\n",
       "       [[0.32818535]],\n",
       "\n",
       "       [[0.25675675]],\n",
       "\n",
       "       [[0.3359073 ]],\n",
       "\n",
       "       [[0.34749034]],\n",
       "\n",
       "       [[0.33397684]],\n",
       "\n",
       "       [[0.41119692]],\n",
       "\n",
       "       [[0.4034749 ]],\n",
       "\n",
       "       [[0.4131274 ]],\n",
       "\n",
       "       [[0.52123547]],\n",
       "\n",
       "       [[0.5965251 ]],\n",
       "\n",
       "       [[0.58108103]],\n",
       "\n",
       "       [[0.484556  ]],\n",
       "\n",
       "       [[0.3899614 ]],\n",
       "\n",
       "       [[0.3223938 ]],\n",
       "\n",
       "       [[0.3899614 ]],\n",
       "\n",
       "       [[0.4073359 ]],\n",
       "\n",
       "       [[0.3803089 ]],\n",
       "\n",
       "       [[0.48648646]],\n",
       "\n",
       "       [[0.47104248]],\n",
       "\n",
       "       [[0.484556  ]],\n",
       "\n",
       "       [[0.6138996 ]],\n",
       "\n",
       "       [[0.6969112 ]],\n",
       "\n",
       "       [[0.70077217]],\n",
       "\n",
       "       [[0.57915056]],\n",
       "\n",
       "       [[0.46911195]],\n",
       "\n",
       "       [[0.38803086]],\n",
       "\n",
       "       [[0.44787642]],\n",
       "\n",
       "       [[0.45559844]],\n",
       "\n",
       "       [[0.4131274 ]],\n",
       "\n",
       "       [[0.4980695 ]],\n",
       "\n",
       "       [[0.47104248]],\n",
       "\n",
       "       [[0.49999997]],\n",
       "\n",
       "       [[0.6389961 ]],\n",
       "\n",
       "       [[0.7471043 ]],\n",
       "\n",
       "       [[0.7741313 ]],\n",
       "\n",
       "       [[0.57915056]],\n",
       "\n",
       "       [[0.492278  ]],\n",
       "\n",
       "       [[0.3976834 ]],\n",
       "\n",
       "       [[0.44980696]],\n",
       "\n",
       "       [[0.49420848]],\n",
       "\n",
       "       [[0.45945945]],\n",
       "\n",
       "       [[0.5830116 ]],\n",
       "\n",
       "       [[0.5637065 ]],\n",
       "\n",
       "       [[0.61003864]],\n",
       "\n",
       "       [[0.71042466]],\n",
       "\n",
       "       [[0.8571429 ]],\n",
       "\n",
       "       [[0.8783784 ]],\n",
       "\n",
       "       [[0.69305015]],\n",
       "\n",
       "       [[0.5849421 ]],\n",
       "\n",
       "       [[0.4980695 ]],\n",
       "\n",
       "       [[0.58108103]],\n",
       "\n",
       "       [[0.6042471 ]],\n",
       "\n",
       "       [[0.554054  ]],\n",
       "\n",
       "       [[0.60810804]],\n",
       "\n",
       "       [[0.6891892 ]],\n",
       "\n",
       "       [[0.71042466]],\n",
       "\n",
       "       [[0.8320464 ]],\n",
       "\n",
       "       [[1.        ]]], dtype=float32)"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_pred,color=\"red\",linewidth=2)\n",
    "plt.plot(test,color=\"black\",linewidth=1)\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}