mirror of https://github.com/gsi-upm/sitc
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416 lines
12 KiB
Plaintext
416 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, © 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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"# Text Classification"
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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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"* [Classifier](#Classifier)"
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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 how the vector models we have presented previously can be used for applying machine learning techniques, such as classification.\n",
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"\n",
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"The main objectives of this session are:\n",
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"* Understand how to apply machine learning techniques on textual sources\n",
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"* Learn the facilities provided by scikit-learn"
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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": 1,
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"metadata": {},
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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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"['alt.atheism', 'comp.graphics', 'comp.os.ms-windows.misc', 'comp.sys.ibm.pc.hardware', 'comp.sys.mac.hardware', 'comp.windows.x', 'misc.forsale', 'rec.autos', 'rec.motorcycles', 'rec.sport.baseball', 'rec.sport.hockey', 'sci.crypt', 'sci.electronics', 'sci.med', 'sci.space', 'soc.religion.christian', 'talk.politics.guns', 'talk.politics.mideast', 'talk.politics.misc', 'talk.religion.misc']\n"
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]
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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 remove metadata to avoid bias in the classification\n",
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"newsgroups_train = fetch_20newsgroups(subset='train', remove=('headers', 'footers', 'quotes'))\n",
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"newsgroups_test = fetch_20newsgroups(subset='test', remove=('headers', 'footers', 'quotes'))\n",
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"\n",
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"# print categories\n",
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"print(list(newsgroups_train.target_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": 2,
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"metadata": {},
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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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"20\n"
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]
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}
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],
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"source": [
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"#Number of categories\n",
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"print(len(newsgroups_train.target_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": 3,
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"metadata": {},
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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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"Category id 4 comp.sys.mac.hardware\n",
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"Doc A fair number of brave souls who upgraded their SI clock oscillator have\n",
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"shared their experiences for this poll. Please send a brief message detailing\n",
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"your experiences with the procedure. Top speed attained, CPU rated speed,\n",
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"add on cards and adapters, heat sinks, hour of usage per day, floppy disk\n",
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"functionality with 800 and 1.4 m floppies are especially requested.\n",
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"\n",
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"I will be summarizing in the next two days, so please add to the network\n",
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"knowledge base if you have done the clock upgrade and haven't answered this\n",
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"poll. Thanks.\n"
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]
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}
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],
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"source": [
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"# Show a document\n",
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"docid = 1\n",
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"doc = newsgroups_train.data[docid]\n",
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"cat = newsgroups_train.target[docid]\n",
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"\n",
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"print(\"Category id \" + str(cat) + \" \" + newsgroups_train.target_names[cat])\n",
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"print(\"Doc \" + doc)"
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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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"outputs": [
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{
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"data": {
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"text/plain": [
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"(11314,)"
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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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"#Number of files\n",
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"newsgroups_train.filenames.shape"
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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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"outputs": [
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{
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"data": {
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"text/plain": [
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"(11314, 101322)"
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]
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},
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"execution_count": 5,
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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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"# 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')\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": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"66.802987449178"
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]
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},
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"execution_count": 6,
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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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"# The tf-idf vectors are very sparse with an average of 66 non zero components in 101.323 dimensions (.06%)\n",
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"vectors_train.nnz / float(vectors_train.shape[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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"# Classifier"
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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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"Once we have vectors, we can create classifiers (or other machine learning algorithms such as clustering) as we saw previously in the notebooks of machine learning with scikit-learn."
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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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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.695453607190013"
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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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"from sklearn.naive_bayes import MultinomialNB\n",
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"\n",
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"from sklearn import metrics\n",
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"\n",
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"\n",
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"# We learn the vocabulary (fit) with the train dataset and transform into vectors (fit_transform)\n",
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"# Nevertheless, we only transform the test dataset into vectors (transform, not fit_transform)\n",
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"\n",
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"model = MultinomialNB(alpha=.01)\n",
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"model.fit(vectors_train, newsgroups_train.target)\n",
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"\n",
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"vectors_test = vectorizer.transform(newsgroups_test.data)\n",
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"pred = model.predict(vectors_test)\n",
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"\n",
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"metrics.f1_score(newsgroups_test.target, pred, average='weighted')\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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"We are getting F1 of 0.69 for 20 categories this could be improved (optimization, preprocessing, etc.)"
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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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"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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"alt.atheism: islam atheists say just religion atheism think don people god\n",
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"comp.graphics: looking format 3d know program file files thanks image graphics\n",
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"comp.os.ms-windows.misc: card problem thanks driver drivers use files dos file windows\n",
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"comp.sys.ibm.pc.hardware: monitor disk thanks pc ide controller bus card scsi drive\n",
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"comp.sys.mac.hardware: know monitor does quadra simms thanks problem drive apple mac\n",
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"comp.windows.x: using windows x11r5 use application thanks widget server motif window\n",
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"misc.forsale: asking email sell price condition new shipping offer 00 sale\n",
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"rec.autos: don ford new good dealer just engine like cars car\n",
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"rec.motorcycles: don just helmet riding like motorcycle ride bikes dod bike\n",
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"rec.sport.baseball: braves players pitching hit runs games game baseball team year\n",
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"rec.sport.hockey: league year nhl games season players play hockey team game\n",
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"sci.crypt: people use escrow nsa keys government chip clipper encryption key\n",
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"sci.electronics: don thanks voltage used know does like circuit power use\n",
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"sci.med: skepticism cadre dsl banks chastity n3jxp pitt gordon geb msg\n",
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"sci.space: just lunar earth shuttle like moon launch orbit nasa space\n",
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"soc.religion.christian: believe faith christian christ bible people christians church jesus god\n",
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"talk.politics.guns: just law firearms government fbi don weapons people guns gun\n",
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"talk.politics.mideast: said arabs arab turkish people armenians armenian jews israeli israel\n",
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"talk.politics.misc: know state clinton president just think tax don government people\n",
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"talk.religion.misc: think don koresh objective christians bible people christian jesus god\n"
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]
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}
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],
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"source": [
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"# We can review the top features per topic in Bayes (attribute feature_log_prob_)\n",
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"import numpy as np\n",
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"\n",
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"def show_top10(classifier, vectorizer, categories):\n",
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" feature_names = np.asarray(vectorizer.get_feature_names_out())\n",
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" for i, category in enumerate(categories):\n",
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" top10 = np.argsort(classifier.feature_log_prob_[i, :])[-10:]\n",
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" print(\"%s: %s\" % (category, \" \".join(feature_names[top10])))\n",
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"\n",
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" \n",
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"show_top10(model, vectorizer, newsgroups_train.target_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": 10,
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"metadata": {},
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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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"[ 2 15]\n",
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"['comp.os.ms-windows.misc', 'soc.religion.christian']\n"
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]
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}
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],
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"source": [
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"# We try the classifier in two new docs\n",
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"\n",
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"new_docs = ['This is a survey of PC computers', 'God is love']\n",
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"new_vectors = vectorizer.transform(new_docs)\n",
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"\n",
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"pred_docs = model.predict(new_vectors)\n",
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"print(pred_docs)\n",
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"print([newsgroups_train.target_names[i] for i in pred_docs])"
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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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"* [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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"© 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 (ipykernel)",
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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": "3.10.10"
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},
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"latex_envs": {
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"LaTeX_envs_menu_present": true,
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"autocomplete": true,
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"bibliofile": "biblio.bib",
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"cite_by": "apalike",
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"current_citInitial": 1,
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"eqLabelWithNumbers": true,
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"eqNumInitial": 1,
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"hotkeys": {
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"equation": "Ctrl-E",
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"itemize": "Ctrl-I"
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"labels_anchors": false,
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"latex_user_defs": false,
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"report_style_numbering": false,
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"user_envs_cfg": false
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"nbformat": 4,
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"nbformat_minor": 1
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