mirror of https://github.com/gsi-upm/sitc
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
664 lines
18 KiB
Plaintext
664 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"![](images/EscUpmPolit_p.gif \"UPM\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Course Notes for Learning Intelligent Systems"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Semantic Models"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Table of Contents\n",
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"* [Objectives](#Objectives)\n",
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"* [Corpus](#Corpus)\n",
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"* [Converting Scikit-learn to gensim](#Converting-Scikit-learn-to-gensim)\n",
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"* [Latent Dirichlet Allocation (LDA)](#Latent-Dirichlet-Allocation-%28LDA%29)\n",
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"* [Latent Semantic Indexing (LSI)](#Latent-Semantic-Indexing-%28LSI%29)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Objectives"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In this session we provide a quick overview of the semantic models presented during the classes. In this case, we will use a real corpus so that we can extract meaningful patterns.\n",
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"\n",
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"The main objectives of this session are:\n",
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"* Understand the models and their differences\n",
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"* Learn to use some of the most popular NLP libraries"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Corpus"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We are going to use on of the corpus that come prepackaged with Scikit-learn: the [20 newsgroup datase](http://qwone.com/~jason/20Newsgroups/). The 20 newsgroup dataset contains 20k documents that belong to 20 topics.\n",
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"\n",
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"We inspect now the corpus using the facilities from Scikit-learn, as explain in [scikit-learn](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html#newsgroups)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(2034, 2807)"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from sklearn.datasets import fetch_20newsgroups\n",
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"\n",
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"# We filter only some categories, otherwise we have 20 categories\n",
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"categories = ['alt.atheism', 'talk.religion.misc', 'comp.graphics', 'sci.space']\n",
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"# We remove metadata to avoid bias in the classification\n",
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"newsgroups_train = fetch_20newsgroups(subset='train', \n",
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" remove=('headers', 'footers', 'quotes'), \n",
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" categories=categories)\n",
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"newsgroups_test = fetch_20newsgroups(subset='test', remove=('headers', 'footers', 'quotes'),\n",
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" categories=categories)\n",
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"\n",
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"\n",
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"# Obtain a vector\n",
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"\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"\n",
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"vectorizer = TfidfVectorizer(analyzer='word', stop_words='english', min_df=10)\n",
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"\n",
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"vectors_train = vectorizer.fit_transform(newsgroups_train.data)\n",
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"vectors_train.shape"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Converting Scikit-learn to gensim"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Although scikit-learn provides an LDA implementation, it is more popular the package *gensim*, which also provides an LSI implementation, as well as other functionalities. Fortunately, scikit-learn sparse matrices can be used in Gensim using the function *matutils.Sparse2Corpus()*. Anyway, if you are using intensively LDA,it can be convenient to create the corpus with their functions.\n",
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"\n",
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"You should install first *gensim*. Run 'conda install -c anaconda gensim=0.12.4' in a terminal."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"from gensim import matutils\n",
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"\n",
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"vocab = vectorizer.get_feature_names()\n",
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"\n",
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"dictionary = dict([(i, s) for i, s in enumerate(vectorizer.get_feature_names())])\n",
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"corpus_tfidf = matutils.Sparse2Corpus(vectors_train)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Latent Dirichlet Allocation (LDA)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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|
"source": [
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"Although scikit-learn provides an LDA implementation, it is more popular the package *gensim*, which also provides an LSI implementation, as well as other functionalities. Fortunately, scikit-learn sparse matrices can be used in Gensim using the function *matutils.Sparse2Corpus()*."
|
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"from gensim.models.ldamodel import LdaModel\n",
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"\n",
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"# It takes a long time\n",
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"\n",
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"# train the lda model, choosing number of topics equal to 4\n",
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"lda = LdaModel(corpus_tfidf, num_topics=4, passes=20, id2word=dictionary)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[(0,\n",
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" '0.004*objects + 0.004*obtained + 0.003*comets + 0.003*manhattan + 0.003*member + 0.003*beginning + 0.003*center + 0.003*groups + 0.003*aware + 0.003*increased'),\n",
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" (1,\n",
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" '0.003*activity + 0.002*objects + 0.002*professional + 0.002*eyes + 0.002*manhattan + 0.002*pressure + 0.002*netters + 0.002*chosen + 0.002*attempted + 0.002*medical'),\n",
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" (2,\n",
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" '0.003*mechanism + 0.003*led + 0.003*platform + 0.003*frank + 0.003*mormons + 0.003*aeronautics + 0.002*concepts + 0.002*header + 0.002*forces + 0.002*profit'),\n",
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" (3,\n",
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" '0.005*diameter + 0.005*having + 0.004*complex + 0.004*conclusions + 0.004*activity + 0.004*looking + 0.004*action + 0.004*inflatable + 0.004*defined + 0.004*association')]"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# check the topics\n",
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"lda.print_topics(4)"
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]
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},
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{
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"cell_type": "markdown",
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|
"metadata": {},
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"source": [
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"Since there are some problems for translating the corpus from Scikit-Learn to LSI, we are now going to create 'natively' the corpus with Gensim."
|
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# import the gensim.corpora module to generate dictionary\n",
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"from gensim import corpora\n",
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"\n",
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"from nltk import word_tokenize\n",
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"from nltk.corpus import stopwords\n",
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"from nltk import RegexpTokenizer\n",
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"\n",
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"import string\n",
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"\n",
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"def preprocess(words):\n",
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" tokenizer = RegexpTokenizer('[A-Z]\\w+')\n",
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" tokens = [w.lower() for w in tokenizer.tokenize(words)]\n",
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" stoplist = stopwords.words('english')\n",
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" tokens_stop = [w for w in tokens if w not in stoplist]\n",
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" punctuation = set(string.punctuation)\n",
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" tokens_clean = [w for w in tokens_stop if w not in punctuation]\n",
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" return tokens_clean\n",
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"\n",
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"#words = preprocess(newsgroups_train.data)\n",
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"#dictionary = corpora.Dictionary(newsgroups_train.data)\n",
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"\n",
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"texts = [preprocess(document) for document in newsgroups_train.data]\n",
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"\n",
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"dictionary = corpora.Dictionary(texts)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Dictionary(10913 unique tokens: ['whose', 'used', 'hoc', 'transfinite', 'newtek']...)\n"
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]
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}
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],
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"source": [
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"# You can save the dictionary\n",
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"dictionary.save('newsgroup.dict')\n",
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"\n",
|
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"print(dictionary)"
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]
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},
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{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
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|
"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
|
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"# Generate a list of docs, where each doc is a list of words\n",
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"\n",
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"docs = [preprocess(doc) for doc in newsgroups_train.data]"
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]
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},
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|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# import the gensim.corpora module to generate dictionary\n",
|
|
"from gensim import corpora\n",
|
|
"\n",
|
|
"dictionary = corpora.Dictionary(docs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
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|
"outputs": [],
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"source": [
|
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"# You can optionally save the dictionary \n",
|
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"\n",
|
|
"dictionary.save('newsgroups.dict')\n",
|
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"lda = LdaModel.load('newsgroups.lda')"
|
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]
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},
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|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Dictionary(10913 unique tokens: ['whose', 'used', 'hoc', 'transfinite', 'newtek']...)\n"
|
|
]
|
|
}
|
|
],
|
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"source": [
|
|
"# We can print the dictionary, it is a mappying of id and tokens\n",
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"\n",
|
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"print(dictionary)"
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|
]
|
|
},
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|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
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"outputs": [],
|
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"source": [
|
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"# construct the corpus representing each document as a bag-of-words (bow) vector\n",
|
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"corpus = [dictionary.doc2bow(doc) for doc in docs]"
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]
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},
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{
|
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"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {
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"collapsed": false
|
|
},
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"outputs": [],
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"source": [
|
|
"from gensim.models import TfidfModel\n",
|
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"\n",
|
|
"# calculate tfidf\n",
|
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"tfidf_model = TfidfModel(corpus)\n",
|
|
"corpus_tfidf = tfidf_model[corpus]"
|
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]
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},
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{
|
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"cell_type": "code",
|
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"execution_count": 16,
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|
"metadata": {
|
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"collapsed": false
|
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},
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"outputs": [
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{
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"name": "stdout",
|
|
"output_type": "stream",
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|
"text": [
|
|
"[(0, 0.1598114653031772), (1, 0.10438175896914427), (2, 0.5700978153855775), (3, 0.24093628445650234), (4, 0.722808853369507), (5, 0.24093628445650234)]\n"
|
|
]
|
|
}
|
|
],
|
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"source": [
|
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"#print tf-idf of first document\n",
|
|
"print(corpus_tfidf[0])"
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]
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},
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{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from gensim.models.ldamodel import LdaModel\n",
|
|
"\n",
|
|
"# train the lda model, choosing number of topics equal to 4, it takes a long time\n",
|
|
"\n",
|
|
"lda_model = LdaModel(corpus_tfidf, num_topics=4, passes=20, id2word=dictionary)"
|
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]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
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"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[(0,\n",
|
|
" '0.010*targa + 0.007*ns + 0.006*thanks + 0.006*davidian + 0.006*ssrt + 0.006*yayayay + 0.005*craig + 0.005*bull + 0.005*gerald + 0.005*sorry'),\n",
|
|
" (1,\n",
|
|
" '0.011*god + 0.010*mary + 0.008*baptist + 0.008*islam + 0.006*zoroastrians + 0.006*joseph + 0.006*lucky + 0.006*khomeini + 0.006*samaritan + 0.005*crusades'),\n",
|
|
" (2,\n",
|
|
" '0.007*koresh + 0.007*moon + 0.007*western + 0.006*plane + 0.006*jeff + 0.006*unix + 0.005*bible + 0.005*also + 0.005*basically + 0.005*bob'),\n",
|
|
" (3,\n",
|
|
" '0.011*whatever + 0.008*joy + 0.007*happy + 0.006*virtual + 0.006*reality + 0.004*really + 0.003*samuel___ + 0.003*oh + 0.003*virtually + 0.003*toaster')]"
|
|
]
|
|
},
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# check the topics\n",
|
|
"lda_model.print_topics(4)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[(0, 0.085176135689180726), (1, 0.6919655173835938), (2, 0.1377903468164027), (3, 0.0850680001108228)]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# check the lsa vector for the first document\n",
|
|
"corpus_lda = lda_model[corpus_tfidf]\n",
|
|
"print(corpus_lda[0])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[('lord', 1), ('god', 2)]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"#predict topics of a new doc\n",
|
|
"new_doc = \"God is love and God is the Lord\"\n",
|
|
"#transform into BOW space\n",
|
|
"bow_vector = dictionary.doc2bow(preprocess(new_doc))\n",
|
|
"print([(dictionary[id], count) for id, count in bow_vector])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[(0, 0.062509420435514051), (1, 0.81246608790618835), (2, 0.062508281488992554), (3, 0.062516210169305114)]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"#transform into LDA space\n",
|
|
"lda_vector = lda_model[bow_vector]\n",
|
|
"print(lda_vector)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"0.011*god + 0.010*mary + 0.008*baptist + 0.008*islam + 0.006*zoroastrians + 0.006*joseph + 0.006*lucky + 0.006*khomeini + 0.006*samaritan + 0.005*crusades\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# print the document's single most prominent LDA topic\n",
|
|
"print(lda_model.print_topic(max(lda_vector, key=lambda item: item[1])[0]))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[(0, 0.10392179866025079), (1, 0.68822094221870811), (2, 0.10391916429993264), (3, 0.10393809482110833)]\n",
|
|
"0.011*god + 0.010*mary + 0.008*baptist + 0.008*islam + 0.006*zoroastrians + 0.006*joseph + 0.006*lucky + 0.006*khomeini + 0.006*samaritan + 0.005*crusades\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"lda_vector_tfidf = lda_model[tfidf_model[bow_vector]]\n",
|
|
"print(lda_vector_tfidf)\n",
|
|
"# print the document's single most prominent LDA topic\n",
|
|
"print(lda_model.print_topic(max(lda_vector_tfidf, key=lambda item: item[1])[0]))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Latent Semantic Indexing (LSI)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from gensim.models.lsimodel import LsiModel\n",
|
|
"\n",
|
|
"#It takes a long time\n",
|
|
"\n",
|
|
"# train the lsi model, choosing number of topics equal to 20\n",
|
|
"\n",
|
|
"\n",
|
|
"lsi_model = LsiModel(corpus_tfidf, num_topics=4, id2word=dictionary)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[(0,\n",
|
|
" '0.769*\"god\" + 0.346*\"jesus\" + 0.235*\"bible\" + 0.204*\"christian\" + 0.149*\"christians\" + 0.107*\"christ\" + 0.090*\"well\" + 0.085*\"koresh\" + 0.081*\"kent\" + 0.080*\"christianity\"'),\n",
|
|
" (1,\n",
|
|
" '-0.863*\"thanks\" + -0.255*\"please\" + -0.159*\"hello\" + -0.153*\"hi\" + 0.123*\"god\" + -0.112*\"sorry\" + -0.087*\"could\" + -0.074*\"windows\" + -0.067*\"jpeg\" + -0.063*\"vga\"'),\n",
|
|
" (2,\n",
|
|
" '0.780*\"well\" + -0.229*\"god\" + 0.165*\"yes\" + -0.153*\"thanks\" + 0.133*\"ico\" + 0.133*\"tek\" + 0.130*\"bronx\" + 0.130*\"beauchaine\" + 0.130*\"queens\" + 0.129*\"manhattan\"'),\n",
|
|
" (3,\n",
|
|
" '0.340*\"well\" + -0.335*\"ico\" + -0.334*\"tek\" + -0.328*\"beauchaine\" + -0.328*\"bronx\" + -0.328*\"queens\" + -0.326*\"manhattan\" + -0.305*\"bob\" + -0.305*\"com\" + -0.072*\"god\"')]"
|
|
]
|
|
},
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# check the topics\n",
|
|
"lsi_model.print_topics(4)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[(0, 0.1598114653031772), (1, 0.10438175896914427), (2, 0.5700978153855775), (3, 0.24093628445650234), (4, 0.722808853369507), (5, 0.24093628445650234)]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# check the lsi vector for the first document\n",
|
|
"print(corpus_tfidf[0])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# References"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"* [NLTK Book. Natural Language Processing with Python. Steven Bird, Ewan Klein, and Edward Loper. O'Reilly Media, 2009 ](http://www.nltk.org/book_1ed/)\n",
|
|
"* [NLTK Essentials, Nitin Hardeniya, Packt Publishing, 2015](http://proquest.safaribooksonline.com/search?q=NLTK%20Essentials)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Licence"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
|
|
"\n",
|
|
"© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
|
|
]
|
|
}
|
|
],
|
|
"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": "3.5.1"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|