aspect_extraction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import nltk\n",
    "import pickle\n",
    "from nltk import PunktSentenceTokenizer\n",
    "import matplotlib.pyplot as plt\n",
    "# nltk.download('averaged_perceptron_tagger')\n",
    "# nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords\n",
    "import gensim\n",
    "import cPickle as cp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Load Google's pre-trained Word2Vec model.\n",
    "# print('start loading wordToVec..............')\n",
    "# wordToVec_model = gensim.models.KeyedVectors.load_word2vec_format('./GoogleNews-vectors-negative300.bin', binary=True)\n",
    "# print('finish loading wordToVec..............')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(wordToVec_model.most_similar(positive=['woman', 'king'], negative=['man']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(u'The', 'DT', 0), (u'duck', 'NN', 1), (u'confit', 'NN', 1), (u'is', 'VBZ', 0), (u'always', 'RB', 0), (u'amazing', 'JJ', 0), (u'and', 'CC', 0), (u'the', 'DT', 0), (u'foie', 'NN', 1), (u'gras', 'NNS', 1), (u'terrine', 'VBP', 1), (u'with', 'IN', 1), (u'figs', 'NNS', 1), (u'was', 'VBD', 0), (u'out', 'IN', 0), (u'of', 'IN', 0), (u'this', 'DT', 0), (u'world', 'NN', 0), (u'.', '.', 0)]\n"
     ]
    }
   ],
   "source": [
    "stop_words = set(stopwords.words('english'))\n",
    "stop_words.add('.')\n",
    "stop_words.add(',')\n",
    "\n",
    "tokenizer = PunktSentenceTokenizer()\n",
    "def pos_tag(sentence):\n",
    "    res_tokens = []\n",
    "    tokens_temp = tokenizer.tokenize(sentence)\n",
    "    tokens = []\n",
    "    for t in tokens_temp :\n",
    "        words = nltk.word_tokenize(t)\n",
    "        tagged = nltk.pos_tag(words)\n",
    "        tagged = [t for t in tagged if t[0]]\n",
    "        tokens += tagged\n",
    "    return tokens\n",
    "\n",
    "def getSentences(file_name) :\n",
    "    sentences = []\n",
    "    long_sentences = []\n",
    "    with open(file_name) as f :\n",
    "        reviews = json.load(f)['Reviews']['Review']\n",
    "        for d in reviews:\n",
    "            for s in d[\"sentences\"]['sentence']:\n",
    "                aspect_terms = []\n",
    "                if \"Opinions\" in s :\n",
    "                    opinions = s['Opinions']['Opinion']\n",
    "                    if isinstance(opinions, list) :\n",
    "                        for o in opinions :\n",
    "                            aspect_terms.append(o['@target'].split(\" \"))\n",
    "                    else :\n",
    "                        aspect_terms.append(opinions['@target'].split(\" \"))\n",
    "                        \n",
    "                    pos_tagged = pos_tag(s['text'])\n",
    "                    tagged = []\n",
    "                    for t in pos_tagged:\n",
    "                        hasAspect = False\n",
    "#                         if t[1] == 'NN' or t[1] == 'NNP' or t[1] == 'NNPS' or t[1] == 'NNS':\n",
    "                        for aspects in aspect_terms :\n",
    "                            if t[0] in aspects:\n",
    "                                tagged.append((t[0], t[1], 1))\n",
    "                                hasAspect = True\n",
    "                                break\n",
    "                        if not hasAspect :\n",
    "                            tagged.append((t[0], t[1], 0))\n",
    "                                \n",
    "                    sentences.append(tagged)\n",
    "                    long_sentences.append(s['text'])\n",
    "#                     print(tagged)\n",
    "#                     print(s)\n",
    "#                     print(aspect_terms)\n",
    "#                     print('')\n",
    "    return sentences, long_sentences\n",
    "\n",
    "train_sentences, long_sentences = getSentences('Data/ABSA-15_Restaurants_Train.json')\n",
    "\n",
    "with open('train_sentences.pkl', 'wb') as f:\n",
    "    pickle.dump(train_sentences, f)\n",
    "\n",
    "with open('train_sentences.pkl', 'rb') as f:\n",
    "    train_sentences = pickle.load(f)\n",
    "\n",
    "print(train_sentences[8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# nltk.download('tagsets')\n",
    "# nltk.help.upenn_tagset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(len(train_sentences))\n",
    "# temp_sentences = []\n",
    "# for s in train_sentences:\n",
    "#     suit = False\n",
    "#     for w in s:\n",
    "#         if w[2] == 1:\n",
    "#             suit = True\n",
    "#             break\n",
    "#     if suit : temp_sentences.append(s)\n",
    "\n",
    "# train_sentences = temp_sentences\n",
    "# print(len(train_sentences))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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.style.use(\"ggplot\")\n",
    "plt.hist([len(s) for s in train_sentences], bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18\n"
     ]
    }
   ],
   "source": [
    "maxx = 0\n",
    "for s in train_sentences:\n",
    "    c = 0\n",
    "    for w in s:\n",
    "        if w[2]: c += 1\n",
    "    if c > maxx: maxx = c\n",
    "\n",
    "print(maxx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2677"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_set = set()\n",
    "\n",
    "for s in train_sentences:\n",
    "    for t in s:\n",
    "        word_set.add(t[0])\n",
    "\n",
    "words = list(word_set)\n",
    "words.append(\"ENDPAD\")\n",
    "\n",
    "n_words = len(words); n_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "max_len = 50\n",
    "word2idx = {w: i+1 for i, w in enumerate(words)}\n",
    "\n",
    "# word2idx\n",
    "# words\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1664,  610, 1254,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0], dtype=int32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.preprocessing.sequence import pad_sequences\n",
    "def processSentence(s) :\n",
    "    tokens = pos_tag(s)\n",
    "    X = [word2idx[w[0]] if w[0] in word2idx else 0 for w in tokens]\n",
    "    X = pad_sequences(maxlen=max_len, sequences=[X], padding=\"post\", value=0)[0]\n",
    "    return X\n",
    "\n",
    "processSentence('Food was awsome')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.sequence import pad_sequences\n",
    "X = [[ word2idx[w[0]] if w[0] in word2idx else 0 for w in s ] for s in train_sentences]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pad_sequences(maxlen=max_len, sequences=X, padding=\"post\", value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1036,  178, 1292,  976,    4, 1261, 1070,  178, 1850, 1335, 1241,\n",
       "        311,  505,  929,  610, 1798,  311,  980,  505,  739, 1782, 1895,\n",
       "        400,  976, 1067, 1258,  188,  980,  194,  976, 1392, 1504,  432,\n",
       "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "          0,    0,    0,    0,    0,    0], dtype=int32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = [[w[2] for w in s] for s in train_sentences]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = pad_sequences(maxlen=max_len, sequences=y, padding=\"post\", value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1120, 50)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[1]\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'cds' in  word2idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50,)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.1, shuffle=False)\n",
    "\n",
    "# X_tr = X_tr.reshape(X_tr.shape[0], 1, X_tr.shape[1])\n",
    "\n",
    "# y_tr = y_tr.reshape(y_tr.shape[0], 1, y_tr.shape[1])\n",
    "\n",
    "X_tr[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import backend as K\n",
    "\n",
    "def recall(y_true, y_pred):\n",
    "    \"\"\"Recall metric.\n",
    "\n",
    "    Only computes a batch-wise average of recall.\n",
    "\n",
    "    Computes the recall, a metric for multi-label classification of\n",
    "    how many relevant items are selected.\n",
    "    \"\"\"\n",
    "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
    "    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
    "    recall = true_positives / (possible_positives + K.epsilon())\n",
    "    return recall\n",
    "\n",
    "\n",
    "def precision(y_true, y_pred):\n",
    "    \"\"\"Precision metric.\n",
    "\n",
    "    Only computes a batch-wise average of precision.\n",
    "\n",
    "    Computes the precision, a metric for multi-label classification of\n",
    "    how many selected items are relevant.\n",
    "    \"\"\"\n",
    "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
    "    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
    "    precision = true_positives / (predicted_positives + K.epsilon())\n",
    "    return precision\n",
    "\n",
    "def f1(y_true, y_pred):\n",
    "    prec = precision(y_true, y_pred)\n",
    "    rec = recall(y_true, y_pred)\n",
    "    return 2*((prec*rec)/(prec+rec+K.epsilon()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:17: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=50, activation=\"sigmoid\")`\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 907 samples, validate on 101 samples\n",
      "Epoch 1/30\n",
      "907/907 [==============================] - 5s 5ms/step - loss: 0.3206 - acc: 0.9488 - f1: 0.0195 - precision: 0.0518 - recall: 0.0401 - val_loss: 0.0942 - val_acc: 0.9636 - val_f1: 0.1743 - val_precision: 0.2158 - val_recall: 0.1472\n",
      "Epoch 2/30\n",
      "907/907 [==============================] - 1s 2ms/step - loss: 0.1059 - acc: 0.9682 - f1: 0.0396 - precision: 0.0760 - recall: 0.0307 - val_loss: 0.0885 - val_acc: 0.9741 - val_f1: 0.0000e+00 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n",
      "Epoch 3/30\n",
      "907/907 [==============================] - 1s 2ms/step - loss: 0.0995 - acc: 0.9695 - f1: 0.0000e+00 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_loss: 0.0854 - val_acc: 0.9741 - val_f1: 0.0000e+00 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n",
      "Epoch 4/30\n",
      "907/907 [==============================] - 2s 2ms/step - loss: 0.0950 - acc: 0.9695 - f1: 0.0000e+00 - precision: 0.0000e+00 - recall: 0.0000e+00 - val_loss: 0.0821 - val_acc: 0.9741 - val_f1: 0.0000e+00 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00\n",
      "Epoch 5/30\n",
      "907/907 [==============================] - 1s 2ms/step - loss: 0.0886 - acc: 0.9699 - f1: 0.0353 - precision: 0.5337 - recall: 0.0188 - val_loss: 0.0777 - val_acc: 0.9756 - val_f1: 0.1096 - val_precision: 0.9505 - val_recall: 0.0586\n",
      "Epoch 6/30\n",
      "907/907 [==============================] - 1s 1ms/step - loss: 0.0825 - acc: 0.9722 - f1: 0.2108 - precision: 0.7829 - recall: 0.1257 - val_loss: 0.0741 - val_acc: 0.9752 - val_f1: 0.1536 - val_precision: 0.6356 - val_recall: 0.0876\n",
      "Epoch 7/30\n",
      "907/907 [==============================] - 1s 2ms/step - loss: 0.0769 - acc: 0.9731 - f1: 0.2710 - precision: 0.7730 - recall: 0.1670 - val_loss: 0.0711 - val_acc: 0.9754 - val_f1: 0.1534 - val_precision: 0.6425 - val_recall: 0.0880\n",
      "Epoch 8/30\n",
      "907/907 [==============================] - 1s 1ms/step - loss: 0.0710 - acc: 0.9741 - f1: 0.3200 - precision: 0.7962 - recall: 0.2042 - val_loss: 0.0692 - val_acc: 0.9760 - val_f1: 0.2656 - val_precision: 0.6299 - val_recall: 0.1700\n",
      "Epoch 9/30\n",
      "907/907 [==============================] - 1s 1ms/step - loss: 0.0655 - acc: 0.9753 - f1: 0.3828 - precision: 0.7945 - recall: 0.2605 - val_loss: 0.0657 - val_acc: 0.9752 - val_f1: 0.2240 - val_precision: 0.5661 - val_recall: 0.1420\n",
      "Epoch 10/30\n",
      "907/907 [==============================] - 1s 1ms/step - loss: 0.0609 - acc: 0.9765 - f1: 0.4365 - precision: 0.8177 - recall: 0.3048 - val_loss: 0.0634 - val_acc: 0.9762 - val_f1: 0.3310 - val_precision: 0.6093 - val_recall: 0.2349\n",
      "Epoch 11/30\n",
      "907/907 [==============================] - 1s 2ms/step - loss: 0.0558 - acc: 0.9781 - f1: 0.4952 - precision: 0.8102 - recall: 0.3647 - val_loss: 0.0624 - val_acc: 0.9770 - val_f1: 0.3600 - val_precision: 0.6399 - val_recall: 0.2550\n",
      "Epoch 12/30\n",
      "907/907 [==============================] - 1s 2ms/step - loss: 0.0518 - acc: 0.9795 - f1: 0.5571 - precision: 0.8086 - recall: 0.4303 - val_loss: 0.0618 - val_acc: 0.9762 - val_f1: 0.3854 - val_precision: 0.5479 - val_recall: 0.2976\n",
      "Epoch 13/30\n",
      "907/907 [==============================] - 1s 2ms/step - loss: 0.0477 - acc: 0.9808 - f1: 0.6051 - precision: 0.8193 - recall: 0.4870 - val_loss: 0.0602 - val_acc: 0.9778 - val_f1: 0.3939 - val_precision: 0.6505 - val_recall: 0.2844\n",
      "Epoch 14/30\n",
      "907/907 [==============================] - 1s 2ms/step - loss: 0.0430 - acc: 0.9825 - f1: 0.6361 - precision: 0.8530 - recall: 0.5159 - val_loss: 0.0592 - val_acc: 0.9778 - val_f1: 0.4214 - val_precision: 0.6231 - val_recall: 0.3204\n",
      "Epoch 15/30\n",
      "907/907 [==============================] - 1s 2ms/step - loss: 0.0386 - acc: 0.9845 - f1: 0.6886 - precision: 0.8603 - recall: 0.5832 - val_loss: 0.0585 - val_acc: 0.9782 - val_f1: 0.4268 - val_precision: 0.6258 - val_recall: 0.3259\n",
      "Epoch 16/30\n",
      "907/907 [==============================] - 2s 2ms/step - loss: 0.0340 - acc: 0.9863 - f1: 0.7402 - precision: 0.8793 - recall: 0.6435 - val_loss: 0.0566 - val_acc: 0.9788 - val_f1: 0.4761 - val_precision: 0.6239 - val_recall: 0.3851\n",
      "Epoch 17/30\n",
      "907/907 [==============================] - 1s 2ms/step - loss: 0.0300 - acc: 0.9882 - f1: 0.7796 - precision: 0.8971 - recall: 0.6940 - val_loss: 0.0566 - val_acc: 0.9784 - val_f1: 0.4745 - val_precision: 0.6048 - val_recall: 0.3907\n",
      "Epoch 18/30\n",
      "907/907 [==============================] - 1s 1ms/step - loss: 0.0262 - acc: 0.9905 - f1: 0.8271 - precision: 0.9154 - recall: 0.7573 - val_loss: 0.0546 - val_acc: 0.9794 - val_f1: 0.4981 - val_precision: 0.6269 - val_recall: 0.4134\n",
      "Epoch 19/30\n",
      "907/907 [==============================] - 1s 1ms/step - loss: 0.0232 - acc: 0.9916 - f1: 0.8528 - precision: 0.9276 - recall: 0.7917 - val_loss: 0.0553 - val_acc: 0.9802 - val_f1: 0.5527 - val_precision: 0.6745 - val_recall: 0.4781\n",
      "Epoch 20/30\n",
      "907/907 [==============================] - 1s 1ms/step - loss: 0.0200 - acc: 0.9939 - f1: 0.8953 - precision: 0.9511 - recall: 0.8474 - val_loss: 0.0559 - val_acc: 0.9814 - val_f1: 0.5612 - val_precision: 0.7202 - val_recall: 0.4676\n",
      "Epoch 21/30\n",
      "907/907 [==============================] - 1s 2ms/step - loss: 0.0172 - acc: 0.9948 - f1: 0.9103 - precision: 0.9469 - recall: 0.8784 - val_loss: 0.0569 - val_acc: 0.9812 - val_f1: 0.5321 - val_precision: 0.6951 - val_recall: 0.4314\n",
      "Epoch 22/30\n",
      "907/907 [==============================] - 1s 2ms/step - loss: 0.0145 - acc: 0.9963 - f1: 0.9356 - precision: 0.9669 - recall: 0.9072 - val_loss: 0.0587 - val_acc: 0.9818 - val_f1: 0.5699 - val_precision: 0.7380 - val_recall: 0.4795\n",
      "Epoch 23/30\n",
      "907/907 [==============================] - 1s 1ms/step - loss: 0.0126 - acc: 0.9972 - f1: 0.9512 - precision: 0.9700 - recall: 0.9347 - val_loss: 0.0606 - val_acc: 0.9818 - val_f1: 0.5508 - val_precision: 0.7050 - val_recall: 0.4531\n",
      "Epoch 24/30\n",
      "907/907 [==============================] - 1s 1ms/step - loss: 0.0112 - acc: 0.9974 - f1: 0.9545 - precision: 0.9746 - recall: 0.9359 - val_loss: 0.0604 - val_acc: 0.9838 - val_f1: 0.6249 - val_precision: 0.7415 - val_recall: 0.5462\n",
      "Epoch 25/30\n",
      "907/907 [==============================] - 2s 2ms/step - loss: 0.0096 - acc: 0.9978 - f1: 0.9630 - precision: 0.9785 - recall: 0.9489 - val_loss: 0.0624 - val_acc: 0.9834 - val_f1: 0.6066 - val_precision: 0.7560 - val_recall: 0.5114\n",
      "Epoch 26/30\n",
      "907/907 [==============================] - 1s 1ms/step - loss: 0.0083 - acc: 0.9983 - f1: 0.9716 - precision: 0.9810 - recall: 0.9632 - val_loss: 0.0629 - val_acc: 0.9836 - val_f1: 0.6255 - val_precision: 0.7431 - val_recall: 0.5456\n",
      "Epoch 27/30\n",
      "907/907 [==============================] - 2s 2ms/step - loss: 0.0076 - acc: 0.9986 - f1: 0.9774 - precision: 0.9892 - recall: 0.9663 - val_loss: 0.0676 - val_acc: 0.9830 - val_f1: 0.6112 - val_precision: 0.7201 - val_recall: 0.5378\n",
      "Epoch 28/30\n",
      "907/907 [==============================] - 1s 2ms/step - loss: 0.0067 - acc: 0.9988 - f1: 0.9806 - precision: 0.9857 - recall: 0.9761 - val_loss: 0.0678 - val_acc: 0.9842 - val_f1: 0.6276 - val_precision: 0.7845 - val_recall: 0.5307\n",
      "Epoch 29/30\n",
      "907/907 [==============================] - 1s 1ms/step - loss: 0.0058 - acc: 0.9991 - f1: 0.9838 - precision: 0.9930 - recall: 0.9753 - val_loss: 0.0703 - val_acc: 0.9840 - val_f1: 0.6441 - val_precision: 0.7491 - val_recall: 0.5673\n",
      "Epoch 30/30\n",
      "907/907 [==============================] - 1s 2ms/step - loss: 0.0052 - acc: 0.9993 - f1: 0.9872 - precision: 0.9920 - recall: 0.9827 - val_loss: 0.0732 - val_acc: 0.9836 - val_f1: 0.6116 - val_precision: 0.7711 - val_recall: 0.5091\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from keras.models import Model, Input, Sequential\n",
    "from keras.layers import LSTM, Embedding, Dense, TimeDistributed, Dropout, Bidirectional, Flatten\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras_contrib.layers import CRF\n",
    "\n",
    "earlyStopper = EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=1, mode='auto')\n",
    "\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Embedding(input_dim=n_words, output_dim=max_len, input_length=max_len))\n",
    "model.add(Dropout(0.1))\n",
    "# model.add(Bidirectional(LSTM(units=max_len, return_sequences=True, recurrent_dropout=0.1)))\n",
    "# model.add(Bidirectional(LSTM(units=max_len, return_sequences=True, recurrent_dropout=0.1)))\n",
    "model.add(LSTM(units=max_len, return_sequences=True, recurrent_dropout=0.1))\n",
    "# model.add(LSTM(units=max_len, return_sequences=True, recurrent_dropout=0.1))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(activation='sigmoid', output_dim=max_len))\n",
    "\n",
    "\n",
    "# # model = Model(inputt, out)\n",
    "model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"accuracy\", f1, precision, recall])\n",
    "\n",
    "history = model.fit(X_tr, y_tr, batch_size=32, epochs=30, validation_split=0.1, verbose=1, callbacks=[])\n",
    "\n",
    "hist = pd.DataFrame(history.history)\n",
    "plt.figure(figsize=(12,12))\n",
    "# plt.plot(hist[\"acc\"] , label='acc')\n",
    "# plt.plot(hist[\"val_acc\"] , label='val_acc')\n",
    "\n",
    "# plt.plot(hist['loss'], label='loss')\n",
    "# plt.plot(hist['val_loss'], label='val_loss')\n",
    "plt.plot(hist['precision'], label='precision')\n",
    "plt.plot(hist['val_precision'], label='val_precision')\n",
    "plt.plot(hist['recall'], label='recall')\n",
    "plt.plot(hist['val_recall'], label='val_recall')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# hist\n",
    "\n",
    "# from keras.utils import plot_model\n",
    "# plot_model(model, to_file='model.png')\n",
    "\n",
    "# from IPython.display import SVG\n",
    "# from keras.utils.vis_utils import model_to_dot\n",
    "\n",
    "# SVG(model_to_dot(model).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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(hist['loss'], label='loss')\n",
    "plt.plot(hist['val_loss'], label='val_loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision : 0.7238805970149254\n",
      "Recall : 0.4597156398104265\n",
      "F1-score : 0.5623188405797102\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score, precision_score, f1_score\n",
    "\n",
    "threshold = 0.5\n",
    "\n",
    "pred_y_te = model.predict(X_te)\n",
    "# pred_y_te = np.round(pred_y_te)\n",
    "pred_y_te = np.array([ [0 if e < threshold else 1 for e in p] for p in pred_y_te ])\n",
    "\n",
    "pred_y_te[6]\n",
    "\n",
    "print('Precision : ' + str(precision_score(y_te, pred_y_te, average='micro')))\n",
    "print('Recall : ' + str(recall_score(y_te, pred_y_te, average='micro')))\n",
    "print('F1-score : ' + str(f1_score(y_te, pred_y_te, average='micro')))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.83746471e-04 9.99650478e-01 1.40084530e-05 6.17005398e-08\n",
      "  3.66039876e-06 5.69145886e-05 2.90648692e-04 5.13216928e-02\n",
      "  1.89365044e-01 4.02585324e-03 4.03267813e-06 1.95606049e-06\n",
      "  9.93725189e-05 1.35955677e-04 5.11401311e-07 5.27340882e-09\n",
      "  2.49367371e-08 1.08773600e-06 1.59375638e-10 4.42573364e-04\n",
      "  1.90132298e-04 5.90454256e-05 1.60749732e-05 1.88769911e-06\n",
      "  1.53535599e-04 1.70615564e-07 1.27257795e-08 1.92718671e-05\n",
      "  3.13719829e-06 5.31977412e-06 3.04567550e-07 1.10577831e-08\n",
      "  7.35357442e-10 1.08161274e-07 1.15550506e-06 1.08337506e-09\n",
      "  4.68966777e-09 5.48380186e-09 1.51830278e-07 5.99522210e-10\n",
      "  8.06059386e-10 2.52949506e-09 5.55131185e-09 7.79008591e-09\n",
      "  5.53633939e-09 6.30539176e-09 9.49136680e-09 6.27312335e-09\n",
      "  5.26136290e-09 4.01412503e-09]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ex = 'The food is very average...the crispy chicken is a bit too sweet, every thing they serve is too sweet here.'\n",
    "X_ex = processSentence(ex)\n",
    "\n",
    "\n",
    "X_ex.shape\n",
    "p = model.predict(np.array([X_ex]))\n",
    "\n",
    "print(p)\n",
    "\n",
    "# i = 45  #45 54 5 65 110 32  # 111 out\n",
    "# p = model.predict(np.array([X_te[i]]))\n",
    "# arg_p = np.argmax(p, axis=-1)\n",
    "# print(arg_p)\n",
    "\n",
    "words_p = [words[w - 1] for w in X_ex]\n",
    "\n",
    "\n",
    "# print(train_sentences[len(X_tr) + i])\n",
    "# print(long_sentences[len(X_tr) + i])\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_ylim([0,1])\n",
    "\n",
    "plt.plot(words_p, p[0])\n",
    "for xy in zip(words_p, p[0]):                                       \n",
    "    ax.annotate('%s' % xy[0], xy=xy, textcoords='data')\n",
    "\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "# p.max()\n",
    "# X_te[i][10]\n",
    "# print(words[X_te[i][p[0]]])\n",
    "\n",
    "# print(\"{:15} ({:5})\".format(\"Word\", \"True\"))\n",
    "\n",
    "# j = 0\n",
    "# for w in X_tr[i]:\n",
    "#     if j == p[0]:\n",
    "#         print(\"{:15} {:5}\").format(words[w], 1)\n",
    "#     else :\n",
    "#         print(\"{:15} {:5}\").format(words[w], 0)\n",
    "#     j += 1\n",
    "# for w, pred in zip(X_te[i], p[0]):\n",
    "#     print(\"{:15}: {}\".format(words[w], tags[pred]))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}