Keras+Deep Learning Cookbook sentiment analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import dependencies\n",
    "import csv\n",
    "from numpy import array, asarray, zeros\n",
    "from keras.preprocessing.text import one_hot, Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, SpatialDropout1D, LSTM\n",
    "from keras.layers import Flatten\n",
    "from keras.layers.embeddings import Embedding\n",
    "import pandas as pd\n",
    "import re\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read the given imdb dataset.\n",
    "# Download the imdb data set from\n",
    "# https://data.world/popculture/imdb-5000-movie-dataset\n",
    "# or try to search over kaggle\n",
    "\n",
    "import pandas as pd\n",
    "file_encoding = 'cp1252'   \n",
    "pData=pd.read_csv('imdb_master.csv',encoding=file_encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X=pData[['review','label']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X.label.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X=X[X.label !='unsup']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Data Cleaning\n",
    "X['review'] = X['review'].apply(lambda x: x.lower())\n",
    "X['review'] = X['review'].apply((lambda x: re.sub('[^a-zA-z0-9\\s]', '', x)))\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# maximum number of words to be selected from the complete corpous.\n",
    "maxFeatures = 20000\n",
    "tokenizer = Tokenizer(num_words=maxFeatures, split=' ')\n",
    "tokenizer.fit_on_texts(X['review'].values)\n",
    "\n",
    "# loading\n",
    "import pickle\n",
    "\n",
    "with open('tokenizer.pickle', 'rb') as handle:\n",
    "    tokenizer1 = pickle.load(handle)\n",
    "\n",
    "\n",
    "# encodeDocuments = tokenizer1.texts_to_sequences(X['review'].values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# saving\n",
    "with open('tokenizer.pickle', 'wb') as handle:\n",
    "    pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(encodeDocuments[30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(tokenizer.document_count)    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "max_length = 200\n",
    "paddedDocuments = pad_sequences(encodeDocuments, maxlen=max_length, padding='post')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "paddedDocuments[30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# split data\n",
    "labels = []\n",
    "for i in X['label']:\n",
    "    if i == 'pos':\n",
    "        labels.append(1)\n",
    "    else:\n",
    "        labels.append(0)\n",
    "\n",
    "labels = array(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(len(labels))\n",
    "\n",
    "\n",
    "labels[25000:40000]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "# Globe word embedding\n",
    "#download from \n",
    "# https://nlp.stanford.edu/projects/glove/\n",
    "\n",
    "emb_dict = {}\n",
    "glove = open(os.path.join(\"\",'glove.6B.200d.txt'),encoding='utf-8')\n",
    "for line in glove:\n",
    "    values = line.split()\n",
    "    word = values[0]\n",
    "    vector = np.asarray(values[1:], dtype='float32')\n",
    "    emb_dict[word] = vector\n",
    "glove.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(paddedDocuments,labels, test_size = 0.30, random_state = 42)\n",
    "print(X_train.shape,Y_train.shape)\n",
    "print(X_test.shape,Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create coefficient matrix for training data\n",
    "trainingToEmbeddings = zeros((maxFeatures, 200))\n",
    "for word, i in tokenizer.word_index.items():\n",
    "    if i < 20000:\n",
    "        gloveVector = emb_dict.get(word)\n",
    "        if gloveVector is not None:\n",
    "            trainingToEmbeddings[i] = gloveVector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainingToEmbeddings[10]\n",
    "print(len(trainingToEmbeddings))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Embedding(maxFeatures, 200, weights=[trainingToEmbeddings], input_length=max_length, trainable=False))\n",
    "model.add(LSTM(200)) \n",
    "# model.add(Flatten())\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "model.fit(X_train, Y_train, epochs=5, batch_size=batch_size, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The output of the Embedding layer is a 2D vector with one embedding for each word in the input sequence of words (input document).\n",
    "\n",
    "####  If you wish to connect a Dense layer directly to an Embedding layer, you must first flatten the 2D output matrix to a 1D vector using the Flatten layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "model.save('my_model.h5')  # creates a HDF5 file 'my_model.h5'\n",
    "# del model  # deletes the existing model\n",
    "\n",
    "# returns a compiled model\n",
    "# identical to the previous one\n",
    "model = load_model('my_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loss, accuracy = model.evaluate(X_test, Y_test, verbose=0)\n",
    "print('Accuracy: %f' % (accuracy * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new=[['i hate this particular movie becauase of its poor direction and bad location , even the casting of the actors is not proper'],\n",
    "     ['movie was fucking awesome i love every single second'],\n",
    "     ['this movie is really awesome'],\n",
    "     ['i really liked the movie and had fun'],\n",
    "    [\"A powerful, funny, bitter and hopeful drama about love and friendship.\"],\n",
    "    [\"I think this film has enough going for it to deserve a recommendation, even as it inevitably fails to recapture the full measure of energy and wonder of the original. \"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "f=[]\n",
    "max_length = 200\n",
    "for i in new:\n",
    "    tokenizer1.fit_on_texts(i)\n",
    "    enc = tokenizer1.texts_to_sequences(i)\n",
    "    enc=pad_sequences(enc, maxlen=max_length, padding='post')\n",
    "    print(enc)\n",
    "    \n",
    "    f.append(enc)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m=model.predict(f[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.6582396]], dtype=float32)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sing=X_train[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in sing:\n",
    "    p=model.predict(np.array([i]))\n",
    "    print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n=np.array(X_train[10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Lets try out the saved model for prediction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "model1 = load_model('my_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p=model1.predict(f[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p1=model1.predict(f[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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