.. _20newsgroups_dataset:

The 20 newsgroups text dataset
------------------------------

The 20 newsgroups dataset comprises around 18000 newsgroups posts on
20 topics split in two subsets: one for training (or development)
and the other one for testing (or for performance evaluation). The split
between the train and test set is based upon a messages posted before
and after a specific date.

This module contains two loaders. The first one,
:func:`sklearn.datasets.fetch_20newsgroups`,
returns a list of the raw texts that can be fed to text feature
extractors such as :class:`~sklearn.feature_extraction.text.CountVectorizer`
with custom parameters so as to extract feature vectors.
The second one, :func:`sklearn.datasets.fetch_20newsgroups_vectorized`,
returns ready-to-use features, i.e., it is not necessary to use a feature
extractor.

**Data Set Characteristics:**

    =================   ==========
    Classes                     20
    Samples total            18846
    Dimensionality               1
    Features                  text
    =================   ==========

Usage
~~~~~

The :func:`sklearn.datasets.fetch_20newsgroups` function is a data
fetching / caching functions that downloads the data archive from
the original `20 newsgroups website`_, extracts the archive contents
in the ``~/scikit_learn_data/20news_home`` folder and calls the
:func:`sklearn.datasets.load_files` on either the training or
testing set folder, or both of them::

  >>> from sklearn.datasets import fetch_20newsgroups
  >>> newsgroups_train = fetch_20newsgroups(subset='train')

  >>> from pprint import pprint
  >>> pprint(list(newsgroups_train.target_names))
  ['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']

The real data lies in the ``filenames`` and ``target`` attributes. The target
attribute is the integer index of the category::

  >>> newsgroups_train.filenames.shape
  (11314,)
  >>> newsgroups_train.target.shape
  (11314,)
  >>> newsgroups_train.target[:10]
  array([ 7,  4,  4,  1, 14, 16, 13,  3,  2,  4])

It is possible to load only a sub-selection of the categories by passing the
list of the categories to load to the
:func:`sklearn.datasets.fetch_20newsgroups` function::

  >>> cats = ['alt.atheism', 'sci.space']
  >>> newsgroups_train = fetch_20newsgroups(subset='train', categories=cats)

  >>> list(newsgroups_train.target_names)
  ['alt.atheism', 'sci.space']
  >>> newsgroups_train.filenames.shape
  (1073,)
  >>> newsgroups_train.target.shape
  (1073,)
  >>> newsgroups_train.target[:10]
  array([0, 1, 1, 1, 0, 1, 1, 0, 0, 0])

Converting text to vectors
~~~~~~~~~~~~~~~~~~~~~~~~~~

In order to feed predictive or clustering models with the text data,
one first need to turn the text into vectors of numerical values suitable
for statistical analysis. This can be achieved with the utilities of the
``sklearn.feature_extraction.text`` as demonstrated in the following
example that extract `TF-IDF`_ vectors of unigram tokens
from a subset of 20news::

  >>> from sklearn.feature_extraction.text import TfidfVectorizer
  >>> categories = ['alt.atheism', 'talk.religion.misc',
  ...               'comp.graphics', 'sci.space']
  >>> newsgroups_train = fetch_20newsgroups(subset='train',
  ...                                       categories=categories)
  >>> vectorizer = TfidfVectorizer()
  >>> vectors = vectorizer.fit_transform(newsgroups_train.data)
  >>> vectors.shape
  (2034, 34118)

The extracted TF-IDF vectors are very sparse, with an average of 159 non-zero
components by sample in a more than 30000-dimensional space
(less than .5% non-zero features)::

  >>> vectors.nnz / float(vectors.shape[0])
  159.01327...

:func:`sklearn.datasets.fetch_20newsgroups_vectorized` is a function which
returns ready-to-use token counts features instead of file names.

.. _`20 newsgroups website`: http://people.csail.mit.edu/jrennie/20Newsgroups/
.. _`TF-IDF`: https://en.wikipedia.org/wiki/Tf-idf


Filtering text for more realistic training
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

It is easy for a classifier to overfit on particular things that appear in the
20 Newsgroups data, such as newsgroup headers. Many classifiers achieve very
high F-scores, but their results would not generalize to other documents that
aren't from this window of time.

For example, let's look at the results of a multinomial Naive Bayes classifier,
which is fast to train and achieves a decent F-score::

  >>> from sklearn.naive_bayes import MultinomialNB
  >>> from sklearn import metrics
  >>> newsgroups_test = fetch_20newsgroups(subset='test',
  ...                                      categories=categories)
  >>> vectors_test = vectorizer.transform(newsgroups_test.data)
  >>> clf = MultinomialNB(alpha=.01)
  >>> clf.fit(vectors, newsgroups_train.target)
  MultinomialNB(alpha=0.01, class_prior=None, fit_prior=True)

  >>> pred = clf.predict(vectors_test)
  >>> metrics.f1_score(newsgroups_test.target, pred, average='macro')
  0.88213...

(The example :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py` shuffles
the training and test data, instead of segmenting by time, and in that case
multinomial Naive Bayes gets a much higher F-score of 0.88. Are you suspicious
yet of what's going on inside this classifier?)

Let's take a look at what the most informative features are:

  >>> import numpy as np
  >>> def show_top10(classifier, vectorizer, categories):
  ...     feature_names = vectorizer.get_feature_names_out()
  ...     for i, category in enumerate(categories):
  ...         top10 = np.argsort(classifier.coef_[i])[-10:]
  ...         print("%s: %s" % (category, " ".join(feature_names[top10])))
  ...
  >>> show_top10(clf, vectorizer, newsgroups_train.target_names)
  alt.atheism: edu it and in you that is of to the
  comp.graphics: edu in graphics it is for and of to the
  sci.space: edu it that is in and space to of the
  talk.religion.misc: not it you in is that and to of the


You can now see many things that these features have overfit to:

- Almost every group is distinguished by whether headers such as
  ``NNTP-Posting-Host:`` and ``Distribution:`` appear more or less often.
- Another significant feature involves whether the sender is affiliated with
  a university, as indicated either by their headers or their signature.
- The word "article" is a significant feature, based on how often people quote
  previous posts like this: "In article [article ID], [name] <[e-mail address]>
  wrote:"
- Other features match the names and e-mail addresses of particular people who
  were posting at the time.

With such an abundance of clues that distinguish newsgroups, the classifiers
barely have to identify topics from text at all, and they all perform at the
same high level.

For this reason, the functions that load 20 Newsgroups data provide a
parameter called **remove**, telling it what kinds of information to strip out
of each file. **remove** should be a tuple containing any subset of
``('headers', 'footers', 'quotes')``, telling it to remove headers, signature
blocks, and quotation blocks respectively.

  >>> newsgroups_test = fetch_20newsgroups(subset='test',
  ...                                      remove=('headers', 'footers', 'quotes'),
  ...                                      categories=categories)
  >>> vectors_test = vectorizer.transform(newsgroups_test.data)
  >>> pred = clf.predict(vectors_test)
  >>> metrics.f1_score(pred, newsgroups_test.target, average='macro')
  0.77310...

This classifier lost over a lot of its F-score, just because we removed
metadata that has little to do with topic classification.
It loses even more if we also strip this metadata from the training data:

  >>> newsgroups_train = fetch_20newsgroups(subset='train',
  ...                                       remove=('headers', 'footers', 'quotes'),
  ...                                       categories=categories)
  >>> vectors = vectorizer.fit_transform(newsgroups_train.data)
  >>> clf = MultinomialNB(alpha=.01)
  >>> clf.fit(vectors, newsgroups_train.target)
  MultinomialNB(alpha=0.01, class_prior=None, fit_prior=True)

  >>> vectors_test = vectorizer.transform(newsgroups_test.data)
  >>> pred = clf.predict(vectors_test)
  >>> metrics.f1_score(newsgroups_test.target, pred, average='macro')
  0.76995...

Some other classifiers cope better with this harder version of the task. Try the
:ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`
example with and without the `remove` option to compare the results.

.. topic:: Data Considerations

  The Cleveland Indians is a major league baseball team based in Cleveland,
  Ohio, USA. In December 2020, it was reported that "After several months of
  discussion sparked by the death of George Floyd and a national reckoning over
  race and colonialism, the Cleveland Indians have decided to change their
  name." Team owner Paul Dolan "did make it clear that the team will not make
  its informal nickname -- the Tribe -- its new team name." "It's not going to
  be a half-step away from the Indians," Dolan said."We will not have a Native
  American-themed name."

  https://www.mlb.com/news/cleveland-indians-team-name-change

.. topic:: Recommendation

  - When evaluating text classifiers on the 20 Newsgroups data, you
    should strip newsgroup-related metadata. In scikit-learn, you can do this
    by setting ``remove=('headers', 'footers', 'quotes')``. The F-score will be
    lower because it is more realistic.
  - This text dataset contains data which may be inappropriate for certain NLP
    applications. An example is listed in the "Data Considerations" section
    above. The challenge with using current text datasets in NLP for tasks such
    as sentence completion, clustering, and other applications is that text
    that is culturally biased and inflammatory will propagate biases. This
    should be taken into consideration when using the dataset, reviewing the
    output, and the bias should be documented.

.. topic:: Examples

   * :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`

   * :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`

   * :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`

   * :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`
