mirror of
https://github.com/gsi-upm/sitc
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465 lines
12 KiB
Plaintext
465 lines
12 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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"# Vector Representation"
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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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"* [Tools](#Tools)\n",
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"* [Vector representation: Count vector](#Vector-representation:-Count-vector)\n",
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"* [Binary vectors](#Binary-vectors)\n",
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"* [Bigram vectors](#Bigram-vectors)\n",
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"* [Tf-idf vector representation](#Tf-idf-vector-representation)"
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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 notebook we are going to transform text into feature vectors, using several representations as presented in class.\n",
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"\n",
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"We are going to use the examples from the slides."
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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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"outputs": [],
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"source": [
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"doc1 = 'Summer is coming but Summer is short'\n",
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"doc2 = 'I like the Summer and I like the Winter'\n",
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"doc3 = 'I like sandwiches and I like the Winter'\n",
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"documents = [doc1, doc2, doc3]"
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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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"# Tools"
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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 different tools we have presented so far (NLTK, Scikit-Learn, TextBlob and CLiPS) provide overlapping functionalities for obtaining vector representations and apply machine learning algorithms.\n",
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"\n",
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"We are going to focus on the use of scikit-learn so that we can also use easily Pandas as we saw in the previous topic.\n",
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"\n",
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"Scikit-learn provides specific facililities for processing texts, as described in the [manual](http://scikit-learn.org/stable/modules/feature_extraction.html)."
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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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"# Vector representation: Count vector"
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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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"Scikit-learn provides two classes for binary vectors: [CountVectorizer](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html#sklearn.feature_extraction.text.CountVectorizer) and [HashingVectorizer](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.HashingVectorizer.html). The latter is more efficient but does not allow to understand which features are more important, so we use the first class. Nevertheless, they are compatible, so, they can be interchanged for production environments.\n",
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"\n",
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"The first step for vectorizing with scikit-learn is creating a CountVectorizer object and then we should call 'fit_transform' to fit the vocabulary."
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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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"outputs": [],
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"source": [
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"from sklearn.feature_extraction.text import CountVectorizer\n",
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"\n",
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"vectorizer = CountVectorizer(analyzer = \"word\", max_features = 5000) \n",
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"vectorizer"
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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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"As we can see, [CountVectorizer](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html#sklearn.feature_extraction.text.CountVectorizer) comes with many options. We can define many configuration options, such as the maximum or minimum frequency of a term (*min_fd*, *max_df*), maximum number of features (*max_features*), if we analyze words or characters (*analyzer*), or if the output is binary or not (*binary*). *CountVectorizer* also allows us to include if we want to preprocess the input (*preprocessor*) before tokenizing it (*tokenizer*) and exclude stop words (*stop_words*).\n",
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"\n",
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"We can use NLTK preprocessing and tokenizer functions to tune *CountVectorizer* using these parameters.\n",
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"\n",
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"We are going to see how the vectors look like."
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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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"outputs": [],
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"source": [
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"vectors = vectorizer.fit_transform(documents)\n",
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"vectors"
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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 see the vectors are stored as a sparse matrix of 3x6 dimensions.\n",
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"We can print the matrix as well as the feature names."
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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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"outputs": [],
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"source": [
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"print(vectors.toarray())\n",
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"print(vectorizer.get_feature_names())"
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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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"As you can see, the pronoun 'I' has been removed because of the default token_pattern. \n",
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"We can change this as follows."
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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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"outputs": [],
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"source": [
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"vectorizer = CountVectorizer(analyzer=\"word\", stop_words=None, token_pattern='(?u)\\\\b\\\\w+\\\\b') \n",
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"vectors = vectorizer.fit_transform(documents)\n",
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"vectorizer.get_feature_names()"
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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 can now filter the stop words (it will remove 'and', 'but', 'I', 'is' and 'the')."
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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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"outputs": [],
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"source": [
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"vectorizer = CountVectorizer(analyzer=\"word\", stop_words='english', token_pattern='(?u)\\\\b\\\\w+\\\\b') \n",
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"vectors = vectorizer.fit_transform(documents)\n",
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"vectorizer.get_feature_names()"
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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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"outputs": [],
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"source": [
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"#stop words in scikit-learn for English\n",
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"print(vectorizer.get_stop_words())"
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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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"outputs": [],
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"source": [
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"# Vectors\n",
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"f_array = vectors.toarray()\n",
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"f_array"
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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 can compute now the **distance** between vectors."
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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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"outputs": [],
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"source": [
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"from scipy.spatial.distance import cosine\n",
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"d12 = cosine(f_array[0], f_array[1])\n",
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"d13 = cosine(f_array[0], f_array[2])\n",
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"d23 = cosine(f_array[1], f_array[2])\n",
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"print(d12, d13, d23)"
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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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"# Binary vectors"
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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 can also get **binary vectors** as follows."
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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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"outputs": [],
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"source": [
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"vectorizer = CountVectorizer(analyzer=\"word\", stop_words='english', binary=True) \n",
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"vectors = vectorizer.fit_transform(documents)\n",
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"vectorizer.get_feature_names()"
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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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"outputs": [],
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"source": [
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"vectors.toarray()"
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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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"# Bigram vectors"
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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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"It is also easy to get bigram vectors."
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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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"outputs": [],
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"source": [
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"vectorizer = CountVectorizer(analyzer=\"word\", stop_words='english', ngram_range=[2,2]) \n",
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"vectors = vectorizer.fit_transform(documents)\n",
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"vectorizer.get_feature_names()"
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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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"outputs": [],
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"source": [
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"vectors.toarray()"
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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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"# Tf-idf vector representation"
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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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"Finally, we can also get a tf-idf vector representation using the class [TfidfVectorizer](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer) instead of CountVectorizer."
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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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"outputs": [],
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"source": [
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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')\n",
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"vectors = vectorizer.fit_transform(documents)\n",
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"vectorizer.get_feature_names()"
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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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"outputs": [],
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"source": [
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"vectors.toarray()"
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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 can now compute the similarity of a query and a set of documents as follows."
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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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"outputs": [],
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"source": [
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"train = [doc1, doc2, doc3]\n",
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"vectorizer = TfidfVectorizer(analyzer=\"word\", stop_words='english')\n",
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"\n",
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"# We learn the vocabulary (fit) and tranform the docs into vectors\n",
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"vectors = vectorizer.fit_transform(train)\n",
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"vectorizer.get_feature_names()"
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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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"outputs": [],
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"source": [
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"vectors.toarray()"
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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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"Scikit-learn provides a method to calculate the cosine similarity between one vector and a set of vectors. Based on this, we can rank the similarity. In this case, the ranking for the query is [d1, d2, d3]."
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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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"outputs": [],
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"source": [
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"from sklearn.metrics.pairwise import cosine_similarity\n",
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"\n",
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"query = ['winter short']\n",
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"\n",
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"# We transform the query into a vector of the learnt vocabulary\n",
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"vector_query = vectorizer.transform(query)\n",
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"\n",
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"# Here we calculate the distance of the query to the docs\n",
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"cosine_similarity(vector_query, vectors)"
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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 same result can be obtained with pairwise metrics (kernels in ML terminology) if we use the linear kernel."
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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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"outputs": [],
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"source": [
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"from sklearn.metrics.pairwise import linear_kernel\n",
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"cosine_similarity = linear_kernel(vector_query, vectors).flatten()\n",
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"cosine_similarity"
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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\n",
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"\n"
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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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"* [Scikit-learn](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html#converting-text-to-vectors) Scikit-learn Convert Text to Vectors"
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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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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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