DeepLearning/notebooks/general/test_03_timeseries_prediction_RNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#!conda install --name root sk-learn -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#!conda install --name root pandas -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#!conda install --name root -c conda-forge tensorflow -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#!conda install --name root keras -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#!conda install --name root dask -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas\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",
    "\n",
    "# fix random seed for reproducibility\n",
    "numpy.random.seed(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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09o5xfl3RvK+3GkmgF8JGXcM+Y9ONueFoQ7mxzd+OPL1V4TLXDLyqMIuJYDhy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\ncBLohRBihZNAL4QQK9z/Dxb9B3vY4hpdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21b6fda2588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "dataset = pandas.read_csv('international-airline-passengers.csv', usecols=[1], engine='python', skipfooter=3)\n",
    "plt.plot(dataset)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataframe = pandas.read_csv('international-airline-passengers.csv', usecols=[1], engine='python', skipfooter=3)\n",
    "dataset = dataframe.values\n",
    "dataset = dataset.astype('float32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# normalize the dataset\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "dataset = scaler.fit_transform(dataset)\n",
    "\n",
    "# normalize the dataset\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "dataset = scaler.fit_transform(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training / Test data splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96 48\n",
      "96 48\n"
     ]
    }
   ],
   "source": [
    "# split into train and test sets\n",
    "train_size = int(len(dataset) * 0.67)\n",
    "test_size = len(dataset) - train_size\n",
    "train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]\n",
    "print(len(train), len(test))\n",
    "\n",
    "# split into train and test sets\n",
    "train_size = int(len(dataset) * 0.67)\n",
    "test_size = len(dataset) - train_size\n",
    "train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]\n",
    "print(len(train), len(test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create matrix considering 'n' previous timesteps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# convert an array of values into a dataset matrix\n",
    "def create_dataset(dataset, look_back=1):\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": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# reshape into X=t and Y=t+1\n",
    "look_back = 2\n",
    "trainX, trainY = create_dataset(train, look_back)\n",
    "testX, testY = create_dataset(test, look_back)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Reshaping the input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# reshape input to be [samples, time steps, features]\n",
    "trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) # it is like a volume now (z is the feature)\n",
    "testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build LSTM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      " - 1s - loss: 0.0222\n",
      "Epoch 2/100\n",
      " - 0s - loss: 0.0087\n",
      "Epoch 3/100\n",
      " - 0s - loss: 0.0073\n",
      "Epoch 4/100\n",
      " - 0s - loss: 0.0060\n",
      "Epoch 5/100\n",
      " - 0s - loss: 0.0051\n",
      "Epoch 6/100\n",
      " - 0s - loss: 0.0043\n",
      "Epoch 7/100\n",
      " - 0s - loss: 0.0038\n",
      "Epoch 8/100\n",
      " - 0s - loss: 0.0034\n",
      "Epoch 9/100\n",
      " - 0s - loss: 0.0031\n",
      "Epoch 10/100\n",
      " - 0s - loss: 0.0029\n",
      "Epoch 11/100\n",
      " - 0s - loss: 0.0028\n",
      "Epoch 12/100\n",
      " - 0s - loss: 0.0028\n",
      "Epoch 13/100\n",
      " - 0s - loss: 0.0027\n",
      "Epoch 14/100\n",
      " - 0s - loss: 0.0027\n",
      "Epoch 15/100\n",
      " - 0s - loss: 0.0027\n",
      "Epoch 16/100\n",
      " - 0s - loss: 0.0026\n",
      "Epoch 17/100\n",
      " - 0s - loss: 0.0027\n",
      "Epoch 18/100\n",
      " - 0s - loss: 0.0027\n",
      "Epoch 19/100\n",
      " - 0s - loss: 0.0026\n",
      "Epoch 20/100\n",
      " - 0s - loss: 0.0026\n",
      "Epoch 21/100\n",
      " - 0s - loss: 0.0026\n",
      "Epoch 22/100\n",
      " - 0s - loss: 0.0026\n",
      "Epoch 23/100\n",
      " - 0s - loss: 0.0025\n",
      "Epoch 24/100\n",
      " - 0s - loss: 0.0026\n",
      "Epoch 25/100\n",
      " - 0s - loss: 0.0025\n",
      "Epoch 26/100\n",
      " - 0s - loss: 0.0025\n",
      "Epoch 27/100\n",
      " - 0s - loss: 0.0025\n",
      "Epoch 28/100\n",
      " - 0s - loss: 0.0024\n",
      "Epoch 29/100\n",
      " - 0s - loss: 0.0027\n",
      "Epoch 30/100\n",
      " - 0s - loss: 0.0025\n",
      "Epoch 31/100\n",
      " - 0s - loss: 0.0027\n",
      "Epoch 32/100\n",
      " - 0s - loss: 0.0025\n",
      "Epoch 33/100\n",
      " - 0s - loss: 0.0025\n",
      "Epoch 34/100\n",
      " - 0s - loss: 0.0025\n",
      "Epoch 35/100\n",
      " - 0s - loss: 0.0024\n",
      "Epoch 36/100\n",
      " - 0s - loss: 0.0024\n",
      "Epoch 37/100\n",
      " - 0s - loss: 0.0024\n",
      "Epoch 38/100\n",
      " - 0s - loss: 0.0024\n",
      "Epoch 39/100\n",
      " - 0s - loss: 0.0024\n",
      "Epoch 40/100\n",
      " - 0s - loss: 0.0024\n",
      "Epoch 41/100\n",
      " - 0s - loss: 0.0023\n",
      "Epoch 42/100\n",
      " - 0s - loss: 0.0024\n",
      "Epoch 43/100\n",
      " - 0s - loss: 0.0024\n",
      "Epoch 44/100\n",
      " - 0s - loss: 0.0024\n",
      "Epoch 45/100\n",
      " - 0s - loss: 0.0023\n",
      "Epoch 46/100\n",
      " - 0s - loss: 0.0023\n",
      "Epoch 47/100\n",
      " - 0s - loss: 0.0023\n",
      "Epoch 48/100\n",
      " - 0s - loss: 0.0023\n",
      "Epoch 49/100\n",
      " - 0s - loss: 0.0023\n",
      "Epoch 50/100\n",
      " - 0s - loss: 0.0023\n",
      "Epoch 51/100\n",
      " - 0s - loss: 0.0023\n",
      "Epoch 52/100\n",
      " - 0s - loss: 0.0023\n",
      "Epoch 53/100\n",
      " - 0s - loss: 0.0022\n",
      "Epoch 54/100\n",
      " - 0s - loss: 0.0023\n",
      "Epoch 55/100\n",
      " - 0s - loss: 0.0022\n",
      "Epoch 56/100\n",
      " - 0s - loss: 0.0022\n",
      "Epoch 57/100\n",
      " - 0s - loss: 0.0022\n",
      "Epoch 58/100\n",
      " - 0s - loss: 0.0022\n",
      "Epoch 59/100\n",
      " - 0s - loss: 0.0022\n",
      "Epoch 60/100\n",
      " - 0s - loss: 0.0022\n",
      "Epoch 61/100\n",
      " - 0s - loss: 0.0022\n",
      "Epoch 62/100\n",
      " - 0s - loss: 0.0022\n",
      "Epoch 63/100\n",
      " - 0s - loss: 0.0022\n",
      "Epoch 64/100\n",
      " - 0s - loss: 0.0022\n",
      "Epoch 65/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 66/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 67/100\n",
      " - 0s - loss: 0.0023\n",
      "Epoch 68/100\n",
      " - 0s - loss: 0.0022\n",
      "Epoch 69/100\n",
      " - 0s - loss: 0.0022\n",
      "Epoch 70/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 71/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 72/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 73/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 74/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 75/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 76/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 77/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 78/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 79/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 80/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 81/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 82/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 83/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 84/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 85/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 86/100\n",
      " - 0s - loss: 0.0019\n",
      "Epoch 87/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 88/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 89/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 90/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 91/100\n",
      " - 0s - loss: 0.0021\n",
      "Epoch 92/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 93/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 94/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 95/100\n",
      " - 0s - loss: 0.0019\n",
      "Epoch 96/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 97/100\n",
      " - 0s - loss: 0.0019\n",
      "Epoch 98/100\n",
      " - 0s - loss: 0.0019\n",
      "Epoch 99/100\n",
      " - 0s - loss: 0.0020\n",
      "Epoch 100/100\n",
      " - 0s - loss: 0.0019\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x21b7347c358>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create and fit the LSTM network\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(trainX, trainY, epochs=100, batch_size=1, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.04 RMSE\n",
      "Test Score: 0.10 RMSE\n"
     ]
    }
   ],
   "source": [
    "# make predictions\n",
    "trainPredict = model.predict(trainX)\n",
    "testPredict = model.predict(testX)\n",
    "# invert predictions\n",
    "trainPredict = scaler.inverse_transform(trainPredict)\n",
    "trainY = scaler.inverse_transform([trainY])\n",
    "testPredict = scaler.inverse_transform(testPredict)\n",
    "testY = scaler.inverse_transform([testY])\n",
    "# calculate root mean squared error\n",
    "trainScore = 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": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5FFPxKdpdcxt8XNJ4CQA7vDuwGC1LDXXaocVdozkDGPDHVRpkoB+yCRW519rp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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21b767fd668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# shift train predictions for plotting\n",
    "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*2)+1: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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}