cothec / neural4coref

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   "source": [
    "from keras.models import Model\n",
    "from keras.layers import Input, Dense, Dropout, Activation, BatchNormalization\n",
    "from keras.optimizers import SGD, Adam\n",
    "import numpy as np\n",
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "editable": true
   },
   "source": [
    "# Preparing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "filename = 'input_data.csv'\n",
    "raw_data = open(filename, 'rt')\n",
    "data = np.loadtxt(raw_data, delimiter= '\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "print data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Our dataset consists of ~466K examples (pairs of mentions), each example described by 1126 features. Labels say whether a pair belongs to the same cluster (1) or not (0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
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   "outputs": [],
   "source": [
    "size_of_dataset = 466852\n",
    "number_of_features = 1126\n",
    "\n",
    "X = data[:,0:1126]\n",
    "Y = data[:,1126] #last column consists of labels\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# 10-fold cross validation of the neural network model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "seed = 1\n",
    "kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)\n",
    "cvscores = []\n",
    "precision_scores = []\n",
    "recall_scores = []\n",
    "f1_scores = []\n",
    "\n",
    "for train, test in kfold.split(X, Y):\n",
    "\n",
    "    inputs = Input(shape=(number_of_features,))\n",
    "    output_from_1st_layer = Dense(1000, activation='relu')(inputs)\n",
    "    output_from_1st_layer = Dropout(0.5)(output_from_1st_layer)\n",
    "    output_from_1st_layer = BatchNormalization()(output_from_1st_layer)\n",
    "    output_from_2nd_layer = Dense(500, activation='relu')(output_from_1st_layer)\n",
    "    output_from_2nd_layer = Dropout(0.5)(output_from_2nd_layer)\n",
    "    output_from_2nd_layer = BatchNormalization()(output_from_2nd_layer)\n",
    "    output = Dense(1, activation='sigmoid')(output_from_2nd_layer)\n",
    "\n",
    "    model = Model(inputs, output)\n",
    "    model.compile(optimizer='Adam',loss='binary_crossentropy',metrics=['accuracy'])\n",
    "    model.fit(X[train], Y[train], batch_size=256, nb_epoch=25)\n",
    "   \n",
    "    # evaluate the model\n",
    "    scores = model.evaluate(X[test], Y[test])\n",
    "    print(\"%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))\n",
    "    cvscores.append(scores[1] * 100)\n",
    "\n",
    "    #calculate other metrics: precision, recall, f1\n",
    "    predictions = model.predict(X[test])\n",
    "    true_positives = 0.0\n",
    "    false_positives = 0.0\n",
    "    true_negatives = 0.0\n",
    "    false_negatives = 0.0\n",
    "\n",
    "    for i in range(len(X[test])):\n",
    "        if (predictions[i]<0.5 and Y[test][i]==0): true_negatives += 1     \n",
    "        if (predictions[i]<0.5 and Y[test][i]==1): false_negatives += 1\n",
    "        if (predictions[i]>=0.5 and Y[test][i]==1): true_positives += 1\n",
    "        if (predictions[i]>=0.5 and Y[test][i]==0): false_positives += 1  \n",
    "    \n",
    "    precision = true_positives/(true_positives+false_positives)\n",
    "    recall = true_positives/(true_positives+false_negatives)\n",
    "    f1 = 2*(precision*recall)/(precision+recall)\n",
    "\n",
    "    precision_scores.append(precision)\n",
    "    recall_scores.append(recall)\n",
    "    f1_scores.append(f1)\n",
    "\n",
    "    print ('Precision: ' + repr(precision))\n",
    "    print ('Recall: ' + repr(recall))\n",
    "    print ('F1: ' + repr(f1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "print(\"%.2f%% (+/- %.2f%%)\" % (np.mean(cvscores), np.std(cvscores)))\n",
    "print(\"%.2f%% (+/- %.2f%%)\" % (np.mean(precision_scores), np.std(precision_scores)))\n",
    "print(\"%.2f%% (+/- %.2f%%)\" % (np.mean(recall_scores), np.std(recall_scores)))\n",
    "print(\"%.2f%% (+/- %.2f%%)\" % (np.mean(f1_scores), np.std(f1_scores)))"
   ]
  }
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