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2.4.3. Working with text data

The goal of this section 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 Downloading the data and loading it from Python

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.

To download the dataset, go to $TUTORIAL_HOME/data/twenty_newsgroups and run the script.

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',
...               '', '']

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', '', '', 'soc.religion.christian']

The files themselves are loaded in memory in the data attribute. For reference the filenames are also available:

>>> len(
>>> len(twenty_train.filenames)

Let’s print the first lines of the first loaded file:

>>> print "\n".join([0].split("\n")[:2])
From: (Michael Collier)
Subject: Converting images to HP LaserJet III?

>>> print twenty_train.target_names[[0]]

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:

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[:10]:
...     print twenty_train.target_names[t]

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:

  1. 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).
  2. for each document #i, count the number of occurrences of each word w and store it in X[i, j] as the value of feature #j where j is the index of word w 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

scikit-learn offers a provides basic tools to process text using the Bag of Words representation.

To build such a representation we will proceed as follows:

  • tokenize strings and give an integer id for each possible token, for instance by using whitespaces and punctuation as token separators.
  • count the occurrences of tokens in each document.
  • normalize and weighting with diminishing importance tokens that occur in the majority of samples / documents.

In order to do the first two steps, scikit-learn provides the :class:sklearn.feature_extraction.text.CountVectorizer class:

>>> from sklearn.feature_extraction.text import CountVectorizer

This class exposes many utility functions, in particular the analyzer function used for tokenizing the text:

>>> analyze = CountVectorizer().build_analyzer()
>>> text = "A WONDERFUL test phrase!"
>>> analyze(text)
[u'wonderful', u'test', u'phrase']

Note that punctuation and single letter words have automatically been removed.

It is further possible to configure CountVectorizer to extract n-grams instead of single words:

>>> CountVectorizer(min_n=1, max_n=2).build_analyzer()(text)
[u'wonderful', u'test', u'phrase', u'wonderful test', u'test phrase']

The analyzer is used internally by CountVectorizer to build a dictionary of features and transform documents to feature vectors:

>>> count_vect = CountVectorizer(stop_words='english')
>>> X_train_counts = count_vect.fit_transform(
>>> X_train_counts.shape
(2257, 35481)

Once fitted, the vectorizer has built a dictionary of feature indices:

>>> count_vect.vocabulary_.get(u'algorithm')

The index value of a word in the vocabulary is linked to its frequency in the whole training corpus. From occurrence counts to normalized 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, 35481)

>>> tfidf_transformer = TfidfTransformer()
>>> X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
>>> X_train_tfidf.shape
(2257, 35481) Training a classifier

Now that we have our feature, 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,

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' => 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:

>>> _ =, 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 =
>>> predicted = text_clf.predict(docs_test)
>>> np.mean(predicted ==            

I.e., we achieved 89.4% 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)),
... ])
>>> _ =,
>>> predicted = text_clf.predict(docs_test)
>>> np.mean(predicted ==            

scikit-learn further provides utilities for more detailed performance analysis of the results:

>>> from sklearn import metrics
>>> print metrics.classification_report(
..., predicted,
...     target_names=twenty_test.target_names)
                          precision    recall  f1-score   support
             alt.atheism       0.93      0.82      0.87       319
        0.88      0.98      0.93       389
              0.95      0.89      0.92       396
  soc.religion.christian       0.90      0.95      0.92       398
             avg / total       0.92      0.91      0.91      1502

>>> metrics.confusion_matrix(, predicted)
array([[261,  10,  12,  36],
       [  5, 380,   2,   2],
       [  7,  33, 352,   4],
       [  7,   9,   4, 378]])

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.