mirror of
https://github.com/gsi-upm/sitc
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526 lines
13 KiB
Plaintext
526 lines
13 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": null,
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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 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": null,
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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": 3,
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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": null,
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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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"# 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": null,
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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": null,
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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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"# 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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{
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"cell_type": "code",
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"execution_count": null,
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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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{
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"cell_type": "code",
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"execution_count": null,
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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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"dictionary = corpora.Dictionary(docs)"
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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": null,
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"metadata": {
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"collapsed": true
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},
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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",
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"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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{
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"cell_type": "code",
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"execution_count": null,
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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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"# 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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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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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",
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"execution_count": null,
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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 import TfidfModel\n",
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"\n",
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"# calculate tfidf\n",
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"tfidf_model = TfidfModel(corpus)\n",
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"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": null,
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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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"#print tf-idf of first document\n",
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"print(corpus_tfidf[0])"
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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": null,
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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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"# train the lda model, choosing number of topics equal to 4, it takes a long time\n",
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"\n",
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"lda_model = 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": null,
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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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"# check the topics\n",
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"lda_model.print_topics(4)"
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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": null,
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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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"# check the lsa vector for the first document\n",
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"corpus_lda = lda_model[corpus_tfidf]\n",
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"print(corpus_lda[0])"
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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": null,
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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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"#predict topics of a new doc\n",
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"new_doc = \"God is love and God is the Lord\"\n",
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"#transform into BOW space\n",
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"bow_vector = dictionary.doc2bow(preprocess(new_doc))\n",
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"print([(dictionary[id], count) for id, count in bow_vector])"
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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": null,
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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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"#transform into LDA space\n",
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"lda_vector = lda_model[bow_vector]\n",
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"print(lda_vector)"
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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": null,
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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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"# print the document's single most prominent LDA topic\n",
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"print(lda_model.print_topic(max(lda_vector, key=lambda item: item[1])[0]))"
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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": null,
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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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"lda_vector_tfidf = lda_model[tfidf_model[bow_vector]]\n",
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"print(lda_vector_tfidf)\n",
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"# print the document's single most prominent LDA topic\n",
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"print(lda_model.print_topic(max(lda_vector_tfidf, key=lambda item: item[1])[0]))"
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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 Semantic Indexing (LSI)"
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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": null,
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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.lsimodel import LsiModel\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 lsi model, choosing number of topics equal to 20\n",
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"\n",
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"\n",
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"lsi_model = LsiModel(corpus_tfidf, num_topics=4, 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": null,
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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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"# check the topics\n",
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"lsi_model.print_topics(4)"
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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": null,
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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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"# check the lsi vector for the first document\n",
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"print(corpus_tfidf[0])"
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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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"# References"
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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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"* [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",
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"* [NLTK Essentials, Nitin Hardeniya, Packt Publishing, 2015](http://proquest.safaribooksonline.com/search?q=NLTK%20Essentials)"
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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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"## Licence"
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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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"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
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"\n",
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"© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
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|
]
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|
}
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|
],
|
|
"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": "3.5.2"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|