02-Keras-embedding.ipynb

{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
    "\n",
    "# <!-- TITLE --> [IMDB2] - Sentiment analysis with text embedding\n",
    "<!-- DESC --> A very classical example of word embedding with a dataset from Internet Movie Database (IMDB)\n",
    "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
    "\n",
    "## Objectives :\n",
    " - The objective is to guess whether film reviews are **positive or negative** based on the analysis of the text. \n",
    " - Understand the management of **textual data** and **sentiment analysis**\n",
    "\n",
    "Original dataset can be find **[there](http://ai.stanford.edu/~amaas/data/sentiment/)**  \n",
    "Note that [IMDb.com](https://imdb.com) offers several easy-to-use [datasets](https://www.imdb.com/interfaces/)  \n",
    "For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)\n",
    "\n",
    "## What we're going to do :\n",
    "\n",
    " - Retrieve data\n",
    " - Preparing the data\n",
    " - Build a model\n",
    " - Train the model\n",
    " - Evaluate the result\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1 - Import and init\n",
    "### 1.1 - Python stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "import tensorflow.keras.datasets.imdb as imdb\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "import os,sys,h5py,json\n",
    "from importlib import reload\n",
    "\n",
    "sys.path.append('..')\n",
    "import fidle.pwk as pwk\n",
    "\n",
    "run_dir = './run/IMDB2'\n",
    "datasets_dir = pwk.init('IMDB2', run_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 - Parameters\n",
    "The words in the vocabulary are classified from the most frequent to the rarest.  \n",
    "`vocab_size` is the number of words we will remember in our vocabulary (the other words will be considered as unknown).  \n",
    "`hide_most_frequently` is the number of ignored words, among the most common ones  \n",
    "`review_len` is the review length  \n",
    "`dense_vector_size` is the size of the generated dense vectors  \n",
    "`output_dir` is where we will go to save our dictionaries. (./data is a good choice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab_size           = 10000\n",
    "hide_most_frequently = 0\n",
    "\n",
    "review_len           = 256\n",
    "dense_vector_size    = 16\n",
    "\n",
    "epochs               = 30\n",
    "batch_size           = 512\n",
    "\n",
    "output_dir           = './data'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Override parameters (batch mode) - Just forget this cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pwk.override('vocab_size', 'hide_most_frequently', 'review_len', 'dense_vector_size')\n",
    "pwk.override('batch_size', 'epochs', 'output_dir')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2 - Retrieve data\n",
    "\n",
    "IMDb dataset can bet get directly from Keras - see [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)  \n",
    "Note : Due to their nature, textual data can be somewhat complex.\n",
    "\n",
    "For more details about the management of this dataset, see notebook [IMDB1](01-One-hot-encoding.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 - Get dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = imdb.load_data( num_words=vocab_size, skip_top=hide_most_frequently, seed= 42,)\n",
    "\n",
    "y_train = np.asarray(y_train).astype('float32')\n",
    "y_test  = np.asarray(y_test ).astype('float32')\n",
    "\n",
    "# ---- About\n",
    "#\n",
    "print(\"Max(x_train,x_test)  : \", pwk.rmax([x_train,x_test]) )\n",
    "print(\"Min(x_train,x_test)  : \", pwk.rmin([x_train,x_test]) )\n",
    "print(\"x_train : {}  y_train : {}\".format(x_train.shape, y_train.shape))\n",
    "print(\"x_test  : {}  y_test  : {}\".format(x_test.shape,  y_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 - Load dictionary\n",
    "Not essential, but nice if you want to take a closer look at our reviews ;-)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---- Retrieve dictionary {word:index}, and encode it in ascii\n",
    "#      Shift the dictionary from +3\n",
    "#      Add <pad>, <start> and <unknown> tags\n",
    "#      Create a reverse dictionary : {index:word}\n",
    "#\n",
    "word_index = imdb.get_word_index()\n",
    "word_index = {w:(i+3) for w,i in word_index.items()}\n",
    "word_index.update( {'<pad>':0, '<start>':1, '<unknown>':2, '<undef>':3,} )\n",
    "index_word = {index:word for word,index in word_index.items()} \n",
    "\n",
    "# ---- A nice function to transpose :\n",
    "#\n",
    "def dataset2text(review):\n",
    "    return ' '.join([index_word.get(i, '?') for i in review])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3 - Preprocess the data (padding)\n",
    "In order to be processed by an NN, all entries must have the **same length.**  \n",
    "We chose a review length of **review_len**  \n",
    "We will therefore complete them with a padding (of \\<pad\\>\\)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = keras.preprocessing.sequence.pad_sequences(x_train,\n",
    "                                                     value   = 0,\n",
    "                                                     padding = 'post',\n",
    "                                                     maxlen  = review_len)\n",
    "\n",
    "x_test  = keras.preprocessing.sequence.pad_sequences(x_test,\n",
    "                                                     value   = 0 ,\n",
    "                                                     padding = 'post',\n",
    "                                                     maxlen  = review_len)\n",
    "\n",
    "pwk.subtitle('After padding :')\n",
    "print(x_train[12])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Save dataset and dictionary (For future use but not mandatory)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---- Write dataset in a h5 file, could be usefull\n",
    "#\n",
    "pwk.mkdir(output_dir)\n",
    "\n",
    "with h5py.File(f'{output_dir}/dataset_imdb.h5', 'w') as f:\n",
    "    f.create_dataset(\"x_train\",    data=x_train)\n",
    "    f.create_dataset(\"y_train\",    data=y_train)\n",
    "    f.create_dataset(\"x_test\",     data=x_test)\n",
    "    f.create_dataset(\"y_test\",     data=y_test)\n",
    "\n",
    "with open(f'{output_dir}/word_index.json', 'w') as fp:\n",
    "    json.dump(word_index, fp)\n",
    "\n",
    "with open(f'{output_dir}/index_word.json', 'w') as fp:\n",
    "    json.dump(index_word, fp)\n",
    "\n",
    "print('Saved.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4 - Build the model\n",
    "\n",
    "More documentation about this model functions :\n",
    " - [Embedding](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding)\n",
    " - [GlobalAveragePooling1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling1D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model(vocab_size=10000, dense_vector_size=32, review_len=256):\n",
    "    \n",
    "    model = keras.Sequential()\n",
    "    model.add(keras.layers.Input( shape=(review_len,) ))\n",
    "    model.add(keras.layers.Embedding(input_dim    = vocab_size, \n",
    "                                     output_dim   = dense_vector_size, \n",
    "                                     input_length = review_len))\n",
    "    model.add(keras.layers.GlobalAveragePooling1D())\n",
    "    model.add(keras.layers.Dense(dense_vector_size, activation='relu'))\n",
    "    model.add(keras.layers.Dense(1,                 activation='sigmoid'))\n",
    "\n",
    "    model.compile(optimizer = 'adam',\n",
    "                  loss      = 'binary_crossentropy',\n",
    "                  metrics   = ['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5 - Train the model\n",
    "### 5.1 - Get it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = get_model(vocab_size, dense_vector_size, review_len)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 - Add callback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(f'{run_dir}/models',   mode=0o750, exist_ok=True)\n",
    "save_dir = f'{run_dir}/models/best_model.h5'\n",
    "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 - Train it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "history = model.fit(x_train,\n",
    "                    y_train,\n",
    "                    epochs          = epochs,\n",
    "                    batch_size      = batch_size,\n",
    "                    validation_data = (x_test, y_test),\n",
    "                    verbose         = 1,\n",
    "                    callbacks       = [savemodel_callback])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6 - Evaluate\n",
    "### 6.1 - Training history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pwk.plot_history(history, save_as='02-history')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2 - Reload and evaluate best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.load_model(f'{run_dir}/models/best_model.h5')\n",
    "\n",
    "# ---- Evaluate\n",
    "score  = model.evaluate(x_test, y_test, verbose=0)\n",
    "\n",
    "print('x_test / loss      : {:5.4f}'.format(score[0]))\n",
    "print('x_test / accuracy  : {:5.4f}'.format(score[1]))\n",
    "\n",
    "values=[score[1], 1-score[1]]\n",
    "pwk.plot_donut(values,[\"Accuracy\",\"Errors\"], title=\"#### Accuracy donut is :\", save_as='03-donut')\n",
    "\n",
    "# ---- Confusion matrix\n",
    "\n",
    "y_sigmoid = model.predict(x_test)\n",
    "\n",
    "y_pred = y_sigmoid.copy()\n",
    "y_pred[ y_sigmoid< 0.5 ] = 0\n",
    "y_pred[ y_sigmoid>=0.5 ] = 1    \n",
    "\n",
    "pwk.display_confusion_matrix(y_test,y_pred,labels=range(2))\n",
    "pwk.plot_confusion_matrix(y_test,y_pred,range(2), figsize=(8, 8),normalize=False, save_as='04-confusion-matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pwk.end()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
   ]
  }
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