Working With Text Data¶
The goal of this guide is to explore some of the main scikit-learn
tools on a single practical task: analysing a collection of text
documents (newsgroups posts) on twenty different topics.
In this section we will see how to:
- load the file contents and the categories
- extract feature vectors suitable for machine learning
- train a linear model to perform categorization
- use a grid search strategy to find a good configuration of both the feature extraction components and the classifier
Tutorial setup¶
To get started with this tutorial, you firstly must have the scikit-learn and all of its required dependencies installed.
Please refer to the installation instructions page for more information and for per-system instructions.
The source of this tutorial can be found within your scikit-learn folder:
scikit-learn/doc/tutorial/text_analytics/
The tutorial folder, should contain the following folders:
*.rst files
- the source of the tutorial document written with sphinxdata
- folder to put the datasets used during the tutorialskeletons
- sample incomplete scripts for the exercisessolutions
- solutions of the exercises
You can already copy the skeletons into a new folder somewhere
on your hard-drive named sklearn_tut_workspace
where you
will edit your own files for the exercises while keeping
the original skeletons intact:
% cp -r skeletons work_directory/sklearn_tut_workspace
Machine Learning algorithms need data. Go to each $TUTORIAL_HOME/data
sub-folder and run the fetch_data.py
script from there (after
having read them first).
For instance:
% cd $TUTORIAL_HOME/data/languages
% less fetch_data.py
% python fetch_data.py
Loading the 20 newsgroups dataset¶
The dataset is called “Twenty Newsgroups”. Here is the official description, quoted from the website:
The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups. To the best of our knowledge, it was originally collected by Ken Lang, probably for his paper “Newsweeder: Learning to filter netnews,” though he does not explicitly mention this collection. The 20 newsgroups collection has become a popular data set for experiments in text applications of machine learning techniques, such as text classification and text clustering.
In the following we will use the built-in dataset loader for 20 newsgroups
from scikit-learn. Alternatively, it is possible to download the dataset
manually from the web-site and use the sklearn.datasets.load_files
function by pointing it to the 20news-bydate-train
subfolder of the
uncompressed archive folder.
In order to get faster execution times for this first example we will work on a partial dataset with only 4 categories out of the 20 available in the dataset:
>>> categories = ['alt.atheism', 'soc.religion.christian',
... 'comp.graphics', 'sci.med']
We can now load the list of files matching those categories as follows:
>>> from sklearn.datasets import fetch_20newsgroups
>>> twenty_train = fetch_20newsgroups(subset='train',
... categories=categories, shuffle=True, random_state=42)
The returned dataset is a scikit-learn
“bunch”: a simple holder
object with fields that can be both accessed as python dict
keys or object
attributes for convenience, for instance the
target_names
holds the list of the requested category names:
>>> twenty_train.target_names
['alt.atheism', 'comp.graphics', 'sci.med', 'soc.religion.christian']
The files themselves are loaded in memory in the data
attribute. For
reference the filenames are also available:
>>> len(twenty_train.data)
2257
>>> len(twenty_train.filenames)
2257
Let’s print the first lines of the first loaded file:
>>> print("\n".join(twenty_train.data[0].split("\n")[:3]))
From: sd345@city.ac.uk (Michael Collier)
Subject: Converting images to HP LaserJet III?
Nntp-Posting-Host: hampton
>>> print(twenty_train.target_names[twenty_train.target[0]])
comp.graphics
Supervised learning algorithms will require a category label for each document in the training set. In this case the category is the name of the newsgroup which also happens to be the name of the folder holding the individual documents.
For speed and space efficiency reasons scikit-learn
loads the
target attribute as an array of integers that corresponds to the
index of the category name in the target_names
list. The category
integer id of each sample is stored in the target
attribute:
>>> twenty_train.target[:10]
array([1, 1, 3, 3, 3, 3, 3, 2, 2, 2])
It is possible to get back the category names as follows:
>>> for t in twenty_train.target[:10]:
... print(twenty_train.target_names[t])
...
comp.graphics
comp.graphics
soc.religion.christian
soc.religion.christian
soc.religion.christian
soc.religion.christian
soc.religion.christian
sci.med
sci.med
sci.med
You can notice that the samples have been shuffled randomly (with a fixed RNG seed): this is useful if you select only the first samples to quickly train a model and get a first idea of the results before re-training on the complete dataset later.
Extracting features from text files¶
In order to perform machine learning on text documents, we first need to turn the text content into numerical feature vectors.
Bags of words¶
The most intuitive way to do so is the bags of words representation:
- assign a fixed integer id to each word occurring in any document of the training set (for instance by building a dictionary from words to integer indices).
- for each document
#i
, count the number of occurrences of each wordw
and store it inX[i, j]
as the value of feature#j
wherej
is the index of wordw
in the dictionary
The bags of words representation implies that n_features
is
the number of distinct words in the corpus: this number is typically
larger that 100,000.
If n_samples == 10000
, storing X
as a numpy array of type
float32 would require 10000 x 100000 x 4 bytes = 4GB in RAM which
is barely manageable on today’s computers.
Fortunately, most values in X will be zeros since for a given document less than a couple thousands of distinct words will be used. For this reason we say that bags of words are typically high-dimensional sparse datasets. We can save a lot of memory by only storing the non-zero parts of the feature vectors in memory.
scipy.sparse
matrices are data structures that do exactly this,
and scikit-learn
has built-in support for these structures.
Tokenizing text with scikit-learn
¶
Text preprocessing, tokenizing and filtering of stopwords are included in a high level component that is able to build a dictionary of features and transform documents to feature vectors:
>>> from sklearn.feature_extraction.text import CountVectorizer
>>> count_vect = CountVectorizer()
>>> X_train_counts = count_vect.fit_transform(twenty_train.data)
>>> X_train_counts.shape
(2257, 35788)
CountVectorizer
supports counts of N-grams of words or consequective characters.
Once fitted, the vectorizer has built a dictionary of feature indices:
>>> count_vect.vocabulary_.get(u'algorithm')
4690
The index value of a word in the vocabulary is linked to its frequency in the whole training corpus.
From occurrences to frequencies¶
Occurrence count is a good start but there is an issue: longer documents will have higher average count values than shorter documents, even though they might talk about the same topics.
To avoid these potential discrepancies it suffices to divide the
number of occurrences of each word in a document by the total number
of words in the document: these new features are called tf
for Term
Frequencies.
Another refinement on top of tf is to downscale weights for words that occur in many documents in the corpus and are therefore less informative than those that occur only in a smaller portion of the corpus.
This downscaling is called tf–idf for “Term Frequency times Inverse Document Frequency”.
Both tf and tf–idf can be computed as follows:
>>> from sklearn.feature_extraction.text import TfidfTransformer
>>> tf_transformer = TfidfTransformer(use_idf=False).fit(X_train_counts)
>>> X_train_tf = tf_transformer.transform(X_train_counts)
>>> X_train_tf.shape
(2257, 35788)
In the above example-code, we firstly use the fit(..)
method to fit our
estimator to the data and secondly the transform(..)
method to transform
our count-matrix to a tf-idf representation.
These two steps can be combined to achieve the same end result faster
by skipping redundant processing. This is done through using the
fit_transform(..)
method as shown below, and as mentioned in the note
in the previous section:
>>> tfidf_transformer = TfidfTransformer()
>>> X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
>>> X_train_tfidf.shape
(2257, 35788)
Training a classifier¶
Now that we have our features, we can train a classifier to try to predict
the category of a post. Let’s start with a naïve Bayes
classifier, which
provides a nice baseline for this task. scikit-learn
includes several
variants of this classifier; the one most suitable for word counts is the
multinomial variant:
>>> from sklearn.naive_bayes import MultinomialNB
>>> clf = MultinomialNB().fit(X_train_tfidf, twenty_train.target)
To try to predict the outcome on a new document we need to extract
the features using almost the same feature extracting chain as before.
The difference is that we call transform
instead of fit_transform
on the transformers, since they have already been fit to the training set:
>>> docs_new = ['God is love', 'OpenGL on the GPU is fast']
>>> X_new_counts = count_vect.transform(docs_new)
>>> X_new_tfidf = tfidf_transformer.transform(X_new_counts)
>>> predicted = clf.predict(X_new_tfidf)
>>> for doc, category in zip(docs_new, predicted):
... print('%r => %s' % (doc, twenty_train.target_names[category]))
...
'God is love' => soc.religion.christian
'OpenGL on the GPU is fast' => comp.graphics
Building a pipeline¶
In order to make the vectorizer => transformer => classifier easier
to work with, scikit-learn
provides a Pipeline
class that behaves
like a compound classifier:
>>> from sklearn.pipeline import Pipeline
>>> text_clf = Pipeline([('vect', CountVectorizer()),
... ('tfidf', TfidfTransformer()),
... ('clf', MultinomialNB()),
... ])
The names vect
, tfidf
and clf
(classifier) are arbitrary.
We shall see their use in the section on grid search, below.
We can now train the model with a single command:
>>> text_clf = text_clf.fit(twenty_train.data, twenty_train.target)
Evaluation of the performance on the test set¶
Evaluating the predictive accuracy of the model is equally easy:
>>> import numpy as np
>>> twenty_test = fetch_20newsgroups(subset='test',
... categories=categories, shuffle=True, random_state=42)
>>> docs_test = twenty_test.data
>>> predicted = text_clf.predict(docs_test)
>>> np.mean(predicted == twenty_test.target)
0.834...
I.e., we achieved 83.4% accuracy. Let’s see if we can do better with a linear support vector machine (SVM), which is widely regarded as one of the best text classification algorithms (although it’s also a bit slower than naïve Bayes). We can change the learner by just plugging a different classifier object into our pipeline:
>>> from sklearn.linear_model import SGDClassifier
>>> text_clf = Pipeline([('vect', CountVectorizer()),
... ('tfidf', TfidfTransformer()),
... ('clf', SGDClassifier(loss='hinge', penalty='l2',
... alpha=1e-3, n_iter=5, random_state=42)),
... ])
>>> _ = text_clf.fit(twenty_train.data, twenty_train.target)
>>> predicted = text_clf.predict(docs_test)
>>> np.mean(predicted == twenty_test.target)
0.912...
scikit-learn
further provides utilities for more detailed performance
analysis of the results:
>>> from sklearn import metrics
>>> print(metrics.classification_report(twenty_test.target, predicted,
... target_names=twenty_test.target_names))
...
precision recall f1-score support
alt.atheism 0.95 0.81 0.87 319
comp.graphics 0.88 0.97 0.92 389
sci.med 0.94 0.90 0.92 396
soc.religion.christian 0.90 0.95 0.93 398
avg / total 0.92 0.91 0.91 1502
>>> metrics.confusion_matrix(twenty_test.target, predicted)
array([[258, 11, 15, 35],
[ 4, 379, 3, 3],
[ 5, 33, 355, 3],
[ 5, 10, 4, 379]])
As expected the confusion matrix shows that posts from the newsgroups on atheism and christian are more often confused for one another than with computer graphics.
Parameter tuning using grid search¶
We’ve already encountered some parameters such as use_idf
in the
TfidfTransformer
. Classifiers tend to have many parameters as well;
e.g., MultinomialNB
includes a smoothing parameter alpha
and
SGDClassifier
has a penalty parameter alpha
and configurable loss
and penalty terms in the objective function (see the module documentation,
or use the Python help
function, to get a description of these).
Instead of tweaking the parameters of the various components of the chain, it is possible to run an exhaustive search of the best parameters on a grid of possible values. We try out all classifiers on either words or bigrams, with or without idf, and with a penalty parameter of either 0.01 or 0.001 for the linear SVM:
>>> from sklearn.grid_search import GridSearchCV
>>> parameters = {'vect__ngram_range': [(1, 1), (1, 2)],
... 'tfidf__use_idf': (True, False),
... 'clf__alpha': (1e-2, 1e-3),
... }
Obviously, such an exhaustive search can be expensive. If we have multiple
CPU cores at our disposal, we can tell the grid searcher to try these eight
parameter combinations in parallel with the n_jobs
parameter. If we give
this parameter a value of -1
, grid search will detect how many cores
are installed and uses them all:
>>> gs_clf = GridSearchCV(text_clf, parameters, n_jobs=-1)
The grid search instance behaves like a normal scikit-learn
model. Let’s perform the search on a smaller subset of the training data
to speed up the computation:
>>> gs_clf = gs_clf.fit(twenty_train.data[:400], twenty_train.target[:400])
The result of calling fit
on a GridSearchCV
object is a classifier
that we can use to predict
:
>>> twenty_train.target_names[gs_clf.predict(['God is love'])]
'soc.religion.christian'
but otherwise, it’s a pretty large and clumsy object. We can, however, get the
optimal parameters out by inspecting the object’s grid_scores_
attribute,
which is a list of parameters/score pairs. To get the best scoring attributes,
we can do:
>>> best_parameters, score, _ = max(gs_clf.grid_scores_, key=lambda x: x[1])
>>> for param_name in sorted(parameters.keys()):
... print("%s: %r" % (param_name, best_parameters[param_name]))
...
clf__alpha: 0.001
tfidf__use_idf: True
vect__ngram_range: (1, 1)
>>> score
0.900...
Exercises¶
To do the exercises, copy the content of the ‘skeletons’ folder as a new folder named ‘workspace’:
% cp -r skeletons workspace
You can then edit the content of the workspace without fear of loosing the original exercise instructions.
Then fire an ipython shell and run the work-in-progress script with:
[1] %run workspace/exercise_XX_script.py arg1 arg2 arg3
If an exception is triggered, use %debug
to fire-up a post
mortem ipdb session.
Refine the implementation and iterate until the exercise is solved.
For each exercise, the skeleton file provides all the necessary import statements, boilerplate code to load the data and sample code to evaluate the predictive accurracy of the model.
Exercise 1: Language identification¶
- Write a text classification pipeline using a custom preprocessor and
CharNGramAnalyzer
using data from Wikipedia articles as training set. - Evaluate the performance on some held out test set.
ipython command line:
%run workspace/exercise_01_language_train_model.py data/languages/paragraphs/
Exercise 2: Sentiment Analysis on movie reviews¶
- Write a text classification pipeline to classify movie reviews as either positive or negative.
- Find a good set of parameters using grid search.
- Evaluate the performance on a held out test set.
ipython command line:
%run workspace/exercise_02_sentiment.py data/movie_reviews/txt_sentoken/
Exercise 3: CLI text classification utility¶
Using the results of the previous exercises and the cPickle
module of the standard library, write a command line utility that
detects the language of some text provided on stdin
and estimate
the polarity (positive or negative) if the text is written in
English.
Bonus point if the utility is able to give a confidence level for its predictions.
Where to from here¶
Here are a few suggestions to help further your scikit-learn intuition upon the completion of this tutorial:
- Try playing around with the
analyzer
andtoken normalisation
underCountVectorizer
- If you don’t have labels, try using Clustering on your problem.
- If you have multiple labels per document, e.g categories, have a look at the Multiclass and multilabel section
- Try using Truncated SVD for latent semantic analysis.
- Have a look at using Out-of-core Classification to learn from data that would not fit into the computer main memory.
- Have a look at the Hashing Vectorizer
as a memory efficient alternative to
CountVectorizer
.