Conv-LSTM-Keras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import json, os, itertools\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os, time, datetime, pickle\n",
    "\n",
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "np.random.seed(1337)  # for reproducibility\n",
    "\n",
    "from keras.preprocessing import sequence\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation\n",
    "from keras.layers import Embedding\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Convolution1D, MaxPooling1D\n",
    "\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Data Preparation\n",
    "# ==================================================\n",
    "# Loads x/y from pickle, assumes pre-shuffled\n",
    "datapath = './mapped_data/'\n",
    "datafiles = [f for f in os.listdir(datapath) if '.pkl' in f]\n",
    "filename = datafiles[0]\n",
    "with open(datapath + filename, 'r') as f:\n",
    "    dataset = pickle.load(f)\n",
    "    x_shuffled = dataset['x']\n",
    "    y_shuffled = dataset['y']\n",
    "\n",
    "\n",
    "# Split train/test set\n",
    "split_sample_index = -1 * int(.1 * float(len(y_shuffled)))\n",
    "X_train, X_test = x_shuffled[:split_sample_index], x_shuffled[split_sample_index:]\n",
    "y_train, y_test = y_shuffled[:split_sample_index], y_shuffled[split_sample_index:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(540000, 71)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example input data and label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(71,)\n",
      "[   290    290   9294   5976    374 115081   6093 110179   2146 113007\n",
      " 110594    290   1978 113376 110693      9      9      9      9      9\n",
      "      9      9      9      9      9      9      9      9      9      9\n",
      "      9      9      9      9      9      9      9      9      9      9\n",
      "      9      9      9      9      9      9      9      9      9      9\n",
      "      9      9      9      9      9      9      9      9      9      9\n",
      "      9      9      9      9      9      9      9      9      9      9\n",
      "      9]\n",
      "[0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "#Example of input data\n",
    "example = x_shuffled[0]\n",
    "print(example.shape)\n",
    "print(example)\n",
    "print(y_shuffled[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train...\n",
      "Train on 540000 samples, validate on 60000 samples\n",
      "Epoch 1/1\n",
      "222390/540000 [===========>..................] - ETA: 2552s - loss: 0.0220 - acc: 0.9943"
     ]
    }
   ],
   "source": [
    "# Embedding\n",
    "max_features = np.max(x_shuffled)\n",
    "maxlen = max([len(t) for t in x_shuffled])\n",
    "embedding_size = 128\n",
    "\n",
    "# Convolution\n",
    "filter_length = 5\n",
    "nb_filter = 64\n",
    "pool_length = 4\n",
    "\n",
    "# LSTM\n",
    "lstm_output_size = 70\n",
    "\n",
    "# Training\n",
    "batch_size = 30\n",
    "nb_epoch = 1\n",
    "\n",
    "#For user in final fully connected layer\n",
    "classes = len(y_shuffled[0]) \n",
    "\n",
    "model = Sequential()\n",
    "model.add(Embedding(max_features, embedding_size, input_length=maxlen))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Convolution1D(nb_filter=nb_filter,\n",
    "                        filter_length=filter_length,\n",
    "                        border_mode='valid',\n",
    "                        activation='relu',\n",
    "                        subsample_length=1))\n",
    "model.add(MaxPooling1D(pool_length=pool_length))\n",
    "model.add(LSTM(lstm_output_size))\n",
    "model.add(Dense(classes))\n",
    "model.add(Activation('sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "print('Train...')\n",
    "model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch,\n",
    "          validation_data=(X_test, y_test))\n",
    "score, acc = model.evaluate(X_test, y_test, batch_size=batch_size)\n",
    "print('Test score:', score)\n",
    "print('Test accuracy:', acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000/60000 [==============================] - 23s    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.0032406840410122337, 0.99912301915486657]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save_weights('./misc/cnn_lstm_20170131.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Startup.ML Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('./misc/startup_test_set.pkl', 'r') as f:\n",
    "    dataset = pickle.load(f)\n",
    "    x_startup = dataset['x']\n",
    "    y_startup = dataset['y']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21000/21000 [==============================] - 8s     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.0078778330333242642, 0.99777551396687825]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_startup, y_startup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "preds = model.predict(x_startup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "act_label = np.argmax(y_startup, axis=1)\n",
    "pred_label = np.argmax(preds, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'actual': act_label, 'predicted':pred_label})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title, fontsize=18)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45, fontsize=14)\n",
    "    plt.yticks(tick_marks, classes, fontsize=14)\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\", fontsize=12)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label', fontsize=16)\n",
    "    plt.xlabel('Predicted label', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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5O0TrfqUcZddF7Nheja97Mm/hz1HJzgV/OOd+SjLvt7j/TnjOFhERERHJF4XZroCIiIhU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\nIiIiInlJHWEiIiIiIiIiIiI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X8sD95/H3+75np22XW3Vqn3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\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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3878101890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classnames =['bg', 'cs', 'da', 'de', 'el', 'en', 'es', 'et', 'fi', 'fr', 'hu', 'it',\n",
    "            'lt', 'lv', 'nl', 'pl', 'pt', 'ro', 'sk', 'sl', 'sv']\n",
    "\n",
    "cnf_matrix = confusion_matrix(act_label, pred_label)\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "fig = plt.figure(figsize=(30,15))\n",
    "plot_confusion_matrix(cnf_matrix, classes=classnames, normalize=True,\n",
    "                      title='Confusion matrix for Language Prediction')\n",
    "\n",
    "plt.show()\n",
    "fig.savefig('./misc/conf_matrix.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}