{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img width=\"800px\" src=\"../fidle/img/header.svg\"></img>\n",
"\n",
"# <!-- TITLE --> [K3IMDB1] - Sentiment analysis with hot-one encoding\n",
"<!-- DESC --> A basic example of sentiment analysis with sparse encoding, using a dataset from Internet Movie Database (IMDB), using Keras 3 on PyTorch\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 os\n",
"os.environ['KERAS_BACKEND'] = 'torch'\n",
"\n",
"import keras\n",
"import keras.datasets.imdb as imdb\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import fidle\n",
"\n",
"# Init Fidle environment\n",
"run_id, run_dir, datasets_dir = fidle.init('K3IMDB1')"
]
},
{
"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",
"`fit_verbosity` is the verbosity during training : 0 = silent, 1 = progress bar, 2 = one line per epoch"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"vocab_size = 5000\n",
"hide_most_frequently = 0\n",
"\n",
"epochs = 10\n",
"batch_size = 512\n",
"fit_verbosity = 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Override parameters (batch mode) - Just forget this cell"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fidle.override('vocab_size', 'hide_most_frequently', 'batch_size', 'epochs', 'fit_verbosity')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 2 - Understanding hot-one encoding\n",
"#### We have a **sentence** and a **dictionary** :"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sentence = \"I've never seen a movie like this before\"\n",
"\n",
"dictionary = {\"a\":0, \"before\":1, \"fantastic\":2, \"i've\":3, \"is\":4, \"like\":5, \"movie\":6, \"never\":7, \"seen\":8, \"this\":9}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### We encode our sentence as a **numerical vector** :"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sentence_words = sentence.lower().split()\n",
"\n",
"sentence_vect = [ dictionary[w] for w in sentence_words ]\n",
"\n",
"print('Words sentence are : ', sentence_words)\n",
"print('Our vectorized sentence is : ', sentence_vect)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Next, we **one-hot** encode our vectorized sentence as a tensor :"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ---- We get a (sentence length x vector size) matrix of zeros\n",
"#\n",
"onehot = np.zeros( (10,8) )\n",
"\n",
"# ---- We set some 1 for each word\n",
"#\n",
"for i,w in enumerate(sentence_vect):\n",
" onehot[w,i]=1\n",
"\n",
"# --- Show it\n",
"#\n",
"print('In a basic way :\\n\\n', onehot, '\\n\\nWith a pandas wiew :\\n')\n",
"data={ f'{sentence_words[i]:.^10}':onehot[:,i] for i,w in enumerate(sentence_vect) }\n",
"df=pd.DataFrame(data)\n",
"df.index=dictionary.keys()\n",
"# --- Pandas Warning \n",
"# \n",
"df.style.format('{:1.0f}').highlight_max(axis=0).set_properties(**{'text-align': 'center'})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3 - 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",
"### 3.1 - Data structure : \n",
"The dataset is composed of 2 parts: \n",
"\n",
" - **reviews**, this will be our **x**\n",
" - **opinions** (positive/negative), this will be our **y**\n",
"\n",
"There are also a **dictionary**, because words are indexed in reviews\n",
"\n",
"```\n",
"<dataset> = (<reviews>, <opinions>)\n",
"\n",
"with : <reviews> = [ <review1>, <review2>, ... ]\n",
" <opinions> = [ <rate1>, <rate2>, ... ] where <ratei> = integer\n",
"\n",
"where : <reviewi> = [ <w1>, <w2>, ...] <wi> are the index (int) of the word in the dictionary\n",
" <ratei> = int 0 for negative opinion, 1 for positive\n",
"\n",
"\n",
"<dictionary> = [ <word1>:<w1>, <word2>:<w2>, ... ]\n",
"\n",
"with : <wordi> = word\n",
" <wi> = int\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.2 - Load dataset\n",
"For simplicity, we will use a pre-formatted dataset - See [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb/load_data) \n",
"However, Keras offers some usefull tools for formatting textual data - See [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text) \n",
"\n",
"By default : \n",
" - Start of a sequence will be marked with : 1\n",
" - Out of vocabulary word will be : 2\n",
" - First index will be : 3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ----- Retrieve x,y\n",
"#\n",
"start_char = 1 # Start of a sequence (padding is 0)\n",
"oov_char = 2 # Out-of-vocabulary\n",
"index_from = 3 # First word id\n",
"\n",
"(x_train, y_train), (x_test, y_test) = imdb.load_data( num_words = vocab_size, \n",
" skip_top = hide_most_frequently,\n",
" start_char = start_char, \n",
" oov_char = oov_char, \n",
" index_from = index_from)\n",
"\n",
"# ---- About\n",
"#\n",
"print(\"Max(x_train,x_test) : \", fidle.utils.rmax([x_train,x_test]) )\n",
"print(\"Min(x_train,x_test) : \", fidle.utils.rmin([x_train,x_test]) )\n",
"print(\"Len(x_train) : \", len(x_train))\n",
"print(\"Len(x_test) : \", len(x_test))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 4 - About our dataset\n",
"When we loaded the dataset, we asked for using \\<start\\> as 1, \\<unknown word\\> as 2 \n",
"So, we shifted the dataset by 3 with the parameter index_from=3\n",
"\n",
"### 4.1 - Sentences encoding"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('\\nReview example (x_train[12]) :\\n\\n',x_train[12])\n",
"print('\\nOpinions (y_train) :\\n\\n',y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.2 - Load dictionary"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ---- Retrieve dictionary {word:index}, and encode it in ascii\n",
"#\n",
"word_index = imdb.get_word_index()\n",
"\n",
"# ---- Shift the dictionary from <index_from>\n",
"#\n",
"word_index = {w:(i+index_from) for w,i in word_index.items()}\n",
"\n",
"# ---- Add <pad>, <start> and <unknown> tags\n",
"#\n",
"word_index.update( {'<pad>':0, '<start>':1, '<unknown>':2, '<undef>':3,} )\n",
"\n",
"# ---- Create a reverse dictionary : {index:word}\n",
"#\n",
"index_word = {index:word for word,index in word_index.items()} \n",
"\n",
"# ---- About dictionary\n",
"#\n",
"print('\\nDictionary size : ', len(word_index))\n",
"print('\\nSmall extract :\\n')\n",
"for k in range(440,455):print(f' {k:2d} : {index_word[k]}' )\n",
"\n",
"# ---- Add 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": [
"### 4.3 - Have a look, for human"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fidle.utils.subtitle('Review example :')\n",
"print(x_train[12])\n",
"fidle.utils.subtitle('After translation :')\n",
"print(dataset2text(x_train[12]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.4 - Few statistics"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sizes=[len(i) for i in x_train]\n",
"plt.figure(figsize=(12,4))\n",
"plt.hist(sizes, bins=400)\n",
"plt.gca().set(title='Distribution of reviews by size - [{:5.2f}, {:5.2f}]'.format(min(sizes),max(sizes)), \n",
" xlabel='Size', ylabel='Density', xlim=[0,1500])\n",
"fidle.scrawler.save_fig('01-stats-sizes')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"unk=[ 100*(s.count(oov_char)/len(s)) for s in x_train]\n",
"plt.figure(figsize=(12,4))\n",
"plt.hist(unk, bins=100)\n",
"plt.gca().set(title='Percent of unknown words - [{:5.2f}, {:5.2f}]'.format(min(unk),max(unk)), \n",
" xlabel='# unknown', ylabel='Density', xlim=[0,30])\n",
"fidle.scrawler.save_fig('02-stats-unknown')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 5 - Basic approach with \"one-hot\" vector encoding\n",
"Basic approach. \n",
"\n",
"Each sentence is encoded with a **vector** of length equal to the **size of the dictionary**. \n",
"\n",
"Each sentence will therefore be encoded with a simple vector. \n",
"The value of each component is 0 if the word is not present in the sentence or 1 if the word is present.\n",
"\n",
"For a sentence s=[3,4,7] and a dictionary of 10 words... \n",
"We wil have a vector v=[0,0,0,1,1,0,0,1,0,0,0]\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.1 - Our one-hot encoder function"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def one_hot_encoder(x, vector_size=10000):\n",
"\n",
" # ---- Set all to 0\n",
" #\n",
" x_encoded = np.zeros((len(x), vector_size))\n",
" \n",
" # ---- For each sentence\n",
" #\n",
" for i,sentence in enumerate(x):\n",
" for word in sentence:\n",
" x_encoded[i, word] = 1.\n",
"\n",
" return x_encoded"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.2 - Encoding.."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x_train = one_hot_encoder(x_train, vector_size=vocab_size)\n",
"x_test = one_hot_encoder(x_test, vector_size=vocab_size)\n",
"\n",
"print(\"To have a look, x_train[12] became :\", x_train[12] )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 6 - Build a nice model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = keras.Sequential(name='My IMDB classifier')\n",
"\n",
"model.add(keras.layers.Input( shape=(vocab_size,) ))\n",
"model.add(keras.layers.Dense( 32, activation='relu'))\n",
"model.add(keras.layers.Dense( 32, activation='relu'))\n",
"model.add(keras.layers.Dense( 1, activation='sigmoid'))\n",
" \n",
"model.compile(optimizer = 'rmsprop',\n",
" loss = 'binary_crossentropy',\n",
" metrics = ['accuracy'])\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 7 - Train the model\n",
"### 7.1 - 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.keras'\n",
"\n",
"savemodel_callback = keras.callbacks.ModelCheckpoint( filepath=save_dir, monitor='val_accuracy', mode='max', save_best_only=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 7.2 - 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 = fit_verbosity,\n",
" callbacks = [savemodel_callback])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 8 - Evaluate\n",
"### 8.1 - Training history"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fidle.scrawler.history(history, save_as='02-history')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 8.2 - 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.keras')\n",
"\n",
"# ---- Evaluate\n",
"score = model.evaluate(x_test, y_test, verbose=0)\n",
"\n",
"print('\\n\\nModel evaluation :\\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",
"fidle.scrawler.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",
"fidle.scrawler.confusion_matrix_txt(y_test,y_pred,labels=range(2))\n",
"fidle.scrawler.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": [
"fidle.end()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"<img width=\"80px\" src=\"../fidle/img/logo-paysage.svg\"></img>"
]
}
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