LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM Recurrent Neural Network\n",
    "- train model on google stock prices from 2012 to 2016\n",
    "- predict on jan 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1258\n"
     ]
    },
    {
     "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/3/2012</td>\n",
       "      <td>325.25</td>\n",
       "      <td>332.83</td>\n",
       "      <td>324.97</td>\n",
       "      <td>663.59</td>\n",
       "      <td>7,380,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/4/2012</td>\n",
       "      <td>331.27</td>\n",
       "      <td>333.87</td>\n",
       "      <td>329.08</td>\n",
       "      <td>666.45</td>\n",
       "      <td>5,749,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/5/2012</td>\n",
       "      <td>329.83</td>\n",
       "      <td>330.75</td>\n",
       "      <td>326.89</td>\n",
       "      <td>657.21</td>\n",
       "      <td>6,590,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/6/2012</td>\n",
       "      <td>328.34</td>\n",
       "      <td>328.77</td>\n",
       "      <td>323.68</td>\n",
       "      <td>648.24</td>\n",
       "      <td>5,405,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/9/2012</td>\n",
       "      <td>322.04</td>\n",
       "      <td>322.29</td>\n",
       "      <td>309.46</td>\n",
       "      <td>620.76</td>\n",
       "      <td>11,688,800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date    Open    High     Low   Close      Volume\n",
       "0  1/3/2012  325.25  332.83  324.97  663.59   7,380,500\n",
       "1  1/4/2012  331.27  333.87  329.08  666.45   5,749,400\n",
       "2  1/5/2012  329.83  330.75  326.89  657.21   6,590,300\n",
       "3  1/6/2012  328.34  328.77  323.68  648.24   5,405,900\n",
       "4  1/9/2012  322.04  322.29  309.46  620.76  11,688,800"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv('Google_Stock_Price_Train.csv')\n",
    "print(train_df.shape[0])\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "       Date    Open    High     Low   Close     Volume\n",
      "0  1/3/2017  778.81  789.63  775.80  786.14  1,657,300\n",
      "1  1/4/2017  788.36  791.34  783.16  786.90  1,073,000\n",
      "2  1/5/2017  786.08  794.48  785.02  794.02  1,335,200\n",
      "3  1/6/2017  795.26  807.90  792.20  806.15  1,640,200\n",
      "4  1/9/2017  806.40  809.97  802.83  806.65  1,272,400\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[778.81],\n",
       "       [788.36],\n",
       "       [786.08],\n",
       "       [795.26],\n",
       "       [806.4 ]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv('Google_Stock_Price_Test.csv')\n",
    "test = test_df.iloc[:,1:2].values\n",
    "print(test_df.shape[0])\n",
    "print(test_df.head())\n",
    "test[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- only get the opening stock price\n",
    "- use reshape to convert to column vector\n",
    "- apply feature scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[325.25],\n",
       "       [331.27],\n",
       "       [329.83],\n",
       "       [328.34],\n",
       "       [322.04]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = (train_df.iloc[:, 1].values).reshape(-1,1)\n",
    "scaler = MinMaxScaler()\n",
    "scaled_train = scaler.fit_transform(train)\n",
    "train[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### create training set structure\n",
    "- 60 previous days to predict the 61st day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = []\n",
    "y_train = []\n",
    "\n",
    "for i in range(60, len(train)):\n",
    "    X_train.append(scaled_train[i-60:i,0])\n",
    "    y_train.append(scaled_train[i,0])\n",
    "\n",
    "X_train = np.array(X_train)\n",
    "y_train = np.array(y_train) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- reshape for a keras RNN https://keras.io/layers/recurrent/\n",
    "- https://medium.com/@shivajbd/understanding-input-and-output-shape-in-lstm-keras-c501ee95c65e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- get the previous data, reshape, scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "allOpen = pd.concat((train_df['Open'], test_df['Open']), axis=0) # vertical \n",
    "inputs  = allOpen[len(allOpen)-len(test_df)-60:].values\n",
    "inputs  = inputs.reshape(-1,1)\n",
    "inputs  = scaler.transform(inputs)\n",
    "\n",
    "X_test  = []\n",
    "\n",
    "for i in range(60, 80):\n",
    "    X_test.append(inputs[i-60:i,0])\n",
    "\n",
    "X_test = np.array(X_test)\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build the RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = Sequential()\n",
    "\n",
    "# first layer\n",
    "regressor.add(LSTM(units=64, return_sequences=True, input_shape=(X_train.shape[1], 1)))\n",
    "regressor.add(Dropout(.25))\n",
    "# second layer\n",
    "regressor.add(LSTM(units=64, return_sequences=True))\n",
    "regressor.add(Dropout(.25))\n",
    "# third layer\n",
    "regressor.add(LSTM(units=64, return_sequences=True))\n",
    "regressor.add(Dropout(.25))\n",
    "# fourth layer\n",
    "regressor.add(LSTM(units=64))\n",
    "regressor.add(Dropout(.25))\n",
    "# output layer\n",
    "regressor.add(Dense(units=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- compile the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor.compile(optimizer='adam', loss='mean_squared_error')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- fit the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/96\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0503\n",
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      "Epoch 6/96\n",
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      "Epoch 8/96\n",
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      "Epoch 9/96\n",
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      "1198/1198 [==============================] - 4s 3ms/step - loss: 0.0034\n",
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      "1198/1198 [==============================] - 3s 3ms/step - loss: 0.0036\n",
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      "Epoch 93/96\n",
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      "Epoch 94/96\n",
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      "Epoch 95/96\n",
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      "Epoch 96/96\n",
      "1198/1198 [==============================] - 4s 3ms/step - loss: 0.0013\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f087f29e358>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(X_train, y_train, epochs=96, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "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(test)\n",
    "plt.title(\"Google Stock Prices (Jan 2017)\")\n",
    "plt.xlabel(\"Financial Day\")\n",
    "plt.ylabel(\"Price (USD)\")\n",
    "plt.xticks([0,4,9,14,19], [\"1\", \"5\",\"10\",\"15\",\"20\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict the january stock prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_prices = regressor.predict(X_test)\n",
    "predicted_prices = scaler.inverse_transform(predicted_prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "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(predicted_prices, color='green', label='predicted')\n",
    "plt.plot(test, color='blue', label='actual')\n",
    "plt.title(\"Predicted Google Stock Prices (Jan 2017)\")\n",
    "plt.xlabel(\"Financial Day\")\n",
    "plt.ylabel(\"Price (USD)\")\n",
    "plt.xticks([0,4,9,14,19], [\"1\", \"5\",\"10\",\"15\",\"20\"])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = (test - predicted_prices) / (840 - 780)\n",
    "res = np.square(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f081570f8d0>]"
      ]
     },
     "execution_count": 87,
     "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(res/100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1198, 60, 1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}