text_classification/imdb_benchmark.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IMDB Movie Reviews Benchmark\n",
    "The IMDB movie review benchmark is commonly used for assessing text and sentiment classification. Keras data does contain a version of it, but not the full text (just indices based on word frequency). In this notebook we'll:\n",
    "* download and store the IMDB data set locally\n",
    "* preprocess and use the data\n",
    "* develop and assess our text classification code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "import logging \n",
    "from glob import glob \n",
    "from collections import Counter\n",
    "\n",
    "from IPython.display import SVG\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "from keras.utils import get_file\n",
    "import numpy as np  \n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add our parent path, to get to our common shared library code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import inspect\n",
    "from pathlib import Path \n",
    "currentdir = Path.cwd()\n",
    "# currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\n",
    "parentdir = os.path.dirname(currentdir)\n",
    "sys.path.insert(0,parentdir) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlyoucanuse.text_classification_modeler import TextClassificationModeler\n",
    "from mlyoucanuse.embeddings import (load_embeddings, \n",
    "                                    create_embeddings_matrix, \n",
    "                                    get_embeddings_layer, \n",
    "                                    get_embeddings_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fix random, for reproducible tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The below is necessary for starting Numpy generated random numbers\n",
    "# in a well-defined initial state.\n",
    "np.random.seed(42)\n",
    "\n",
    "# The below is necessary for starting core Python generated random numbers\n",
    "# in a well-defined state.\n",
    "random.seed(12345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "LOG = logging.getLogger('pos_tagger_training')\n",
    "LOG.addHandler(logging.NullHandler())\n",
    "logging.basicConfig(level=logging.INFO)\n",
    "tf.logging.set_verbosity(tf.logging.ERROR) # silence a deprecation warning; todo revisit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fetch the IMDB data set and store locally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://ai.stanford.edu/~amaas/data/sentiment aclImdb_v1.tar.gz\n"
     ]
    }
   ],
   "source": [
    "file_cache =os.path.join(parentdir, 'data', 'aclImdb')\n",
    "if not os.path.exists(file_cache):\n",
    "    os.makedirs(file_cache )\n",
    "\n",
    "# https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\n",
    "imdb_archive= 'https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz'\n",
    "print(imdb_archive[:imdb_archive.rfind('/')], imdb_archive[imdb_archive.rfind('/')+1:])\n",
    "\n",
    "origin, filename = imdb_archive[:imdb_archive.rfind('/')], imdb_archive[imdb_archive.rfind('/')+1:]\n",
    "data_archive= get_file(fname=filename, origin=imdb_archive, cache_dir=file_cache, untar=False, extract=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12500, 12500, 12500, 12500)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb_files = os.path.join(parentdir , 'data/aclImdb/datasets/aclImdb')\n",
    "\n",
    "train_pos_files = [tmp for tmp in glob(os.path.join(imdb_files, 'train', 'pos', '*.txt'))]\n",
    "train_neg_files = [tmp for tmp in glob(os.path.join(imdb_files, 'train', 'neg', '*.txt'))]\n",
    "test_pos_files = [tmp for tmp in glob(os.path.join(imdb_files, 'test', 'pos', '*.txt'))]\n",
    "test_neg_files =  [tmp for tmp in glob(os.path.join(imdb_files, 'test', 'neg', '*.txt'))]\n",
    "len(train_neg_files), len(train_pos_files), len(test_neg_files), len(test_pos_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read(filename):\n",
    "    with open(filename, 'rt') as freader:\n",
    "        return freader.read()\n",
    "\n",
    "train_pos = [read(textfile) for textfile in train_pos_files ]   \n",
    "train_neg = [read(textfile) for textfile in train_neg_files ]    \n",
    " \n",
    "test_pos = [read(textfile) for textfile in test_pos_files ]   \n",
    "test_neg = [read(textfile) for textfile in test_neg_files ]    \n",
    "\n",
    "\n",
    "tokenizer = Tokenizer()\n",
    "# The Keras tokenizer can be fit incrementally\n",
    "tokenizer.fit_on_texts(train_pos)\n",
    "tokenizer.fit_on_texts(train_neg)\n",
    "tokenizer.fit_on_texts(test_pos)\n",
    "tokenizer.fit_on_texts(test_neg)\n",
    "\n",
    "sequences = tokenizer.texts_to_sequences(train_pos +   train_neg +  test_pos +  test_neg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "text_lengths = [len(tmp.split())   for tmp in train_pos +   train_neg +  test_pos +  test_neg]\n",
    "\n",
    "top_label, side_label, bottom_label = ( 'Text Lengths', 'Number of texts',\n",
    "                                       'Text Lengths of IMDB review corpus')\n",
    "fig = plt.figure(figsize=(7,7))\n",
    "plt.xlabel(top_label)\n",
    "plt.ylabel(side_label)\n",
    "plt.title(bottom_label)\n",
    "n, bins, patches = plt.hist(text_lengths, bins=20, density=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_SEQUENCE_LEN = 2500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LEN)\n",
    "\n",
    "y= np.array([1] * len(train_pos_files) \n",
    "           + [0] * len (train_neg_files)\n",
    "           + [1] * len(test_pos_files)\n",
    "           + [0] * len(test_neg_files))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=12345)\n",
    "X_test, X_validation, y_test, y_validation = train_test_split(X_test, y_test, test_size=0.5, random_state=12345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "EMBEDDING_SIZE = 50 # 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:mlyoucanuse.embeddings:Downloading, please wait.\n",
      "INFO:mlyoucanuse.embeddings:Done downloading\n",
      "INFO:mlyoucanuse.embeddings:Found 400000 word vectors.\n"
     ]
    }
   ],
   "source": [
    "embed_index = get_embeddings_index( 'glove', parentdir=parentdir, embedding_dimensions = EMBEDDING_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary size: 124,252\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:mlyoucanuse.embeddings:Embeddings matrix shape: (124253, 50)\n"
     ]
    }
   ],
   "source": [
    "print(f'Vocabulary size: {len(tokenizer.word_index):,}')\n",
    "embeddings_matrix = create_embeddings_matrix(embed_index, \n",
    "                                            vocabulary=tokenizer.word_index,\n",
    "                                            embeddings_dimensions=EMBEDDING_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings_layer= get_embeddings_layer(embeddings_matrix,\n",
    "                                       'embedding_static',\n",
    "                                        max_len=MAX_SEQUENCE_LEN,\n",
    "                                        trainable=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_cls = TextClassificationModeler(max_sequence_len=MAX_SEQUENCE_LEN)\n",
    "model = text_cls.build_model(embeddings_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "main_input (InputLayer)         (None, 2500)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_static (Embedding)    (None, 2500, 50)     6212650     main_input[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "Conv_static_2 (Conv1D)          (None, 2499, 100)    10100       embedding_static[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "Conv_static_3 (Conv1D)          (None, 2498, 100)    15100       embedding_static[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "Conv_static_4 (Conv1D)          (None, 2497, 100)    20100       embedding_static[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "Conv_static_5 (Conv1D)          (None, 2496, 100)    25100       embedding_static[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "Dropout_static_2 (Dropout)      (None, 2499, 100)    0           Conv_static_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "Dropout_static_3 (Dropout)      (None, 2498, 100)    0           Conv_static_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "Dropout_static_4 (Dropout)      (None, 2497, 100)    0           Conv_static_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "Dropout_static_5 (Dropout)      (None, 2496, 100)    0           Conv_static_5[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "GlobalMaxPool_static_2 (GlobalM (None, 100)          0           Dropout_static_2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "GlobalMaxPool_static_3 (GlobalM (None, 100)          0           Dropout_static_3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "GlobalMaxPool_static_4 (GlobalM (None, 100)          0           Dropout_static_4[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "GlobalMaxPool_static_5 (GlobalM (None, 100)          0           Dropout_static_5[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "Concatenate_4 (Concatenate)     (None, 400)          0           GlobalMaxPool_static_2[0][0]     \n",
      "                                                                 GlobalMaxPool_static_3[0][0]     \n",
      "                                                                 GlobalMaxPool_static_4[0][0]     \n",
      "                                                                 GlobalMaxPool_static_5[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "output (Dense)                  (None, 1)            401         Concatenate_4[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 6,283,451\n",
      "Trainable params: 70,801\n",
      "Non-trainable params: 6,212,650\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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     "text": [
      "Train on 40000 samples, validate on 5000 samples\n",
      "Epoch 1/6\n",
      "40000/40000 [==============================] - 1813s 45ms/step - loss: 0.4742 - acc: 0.7686 - val_loss: 0.3963 - val_acc: 0.8492\n",
      "Epoch 2/6\n",
      "40000/40000 [==============================] - 1803s 45ms/step - loss: 0.3424 - acc: 0.8489 - val_loss: 0.3708 - val_acc: 0.8490\n",
      "Epoch 3/6\n",
      "40000/40000 [==============================] - 1800s 45ms/step - loss: 0.3060 - acc: 0.8693 - val_loss: 0.3574 - val_acc: 0.8494\n",
      "Epoch 4/6\n",
      "40000/40000 [==============================] - 1828s 46ms/step - loss: 0.2792 - acc: 0.8815 - val_loss: 0.3295 - val_acc: 0.8678\n",
      "Epoch 5/6\n",
      "40000/40000 [==============================] - 1824s 46ms/step - loss: 0.2561 - acc: 0.8930 - val_loss: 0.3131 - val_acc: 0.8790\n",
      "Epoch 6/6\n",
      "40000/40000 [==============================] - 1843s 46ms/step - loss: 0.2380 - acc: 0.9036 - val_loss: 0.3064 - val_acc: 0.8800\n"
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   "source": [
    "history = model.fit(X_train, y_train,\n",
    "                               validation_data=(X_validation, y_validation),\n",
    "                               epochs=6,\n",
    "                               batch_size=64)"
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    "def plot_history(history):\n",
    "    acc = history.history['acc']\n",
    "    val_acc = history.history['val_acc']\n",
    "    loss = history.history['loss']\n",
    "    val_loss = history.history['val_loss']\n",
    "    x = range(1, len(acc) + 1)\n",
    "    fig = plt.figure(figsize=(12, 5))\n",
    "    ax = fig.gca()\n",
    "    ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(x, acc, 'b', label='Training acc')\n",
    "    plt.plot(x, val_acc, 'r', label='Validation acc')\n",
    "    plt.title('Training and validation accuracy')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.legend()\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(x, loss, 'b', label='Training loss')\n",
    "    plt.plot(x, val_loss, 'r', label='Validation loss')\n",
    "    plt.title('Training and validation loss')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.legend() "
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   "execution_count": 25,
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     "text": [
      "Accuracy: 89.14\n"
     ]
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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = model.evaluate(X_test, y_test, verbose=0)\n",
    "print(f'Accuracy: {scores[1]*100:.2f}')\n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}