.. _text_data_tutorial:

======================
Working With Text Data
======================

The goal of this guide is to explore some of the main ``scikit-learn``
tools on a single practical task: analyzing 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 must first install
*scikit-learn* and all of its required dependencies.

Please refer to the :ref:`installation instructions <installation-instructions>`
page for more information and for system-specific instructions.

The source of this tutorial can be found within your scikit-learn folder::

    scikit-learn/doc/tutorial/text_analytics/

The source can also be found `on Github
<https://github.com/scikit-learn/scikit-learn/tree/main/doc/tutorial/text_analytics>`_.

The tutorial folder should contain the following sub-folders:

  * ``*.rst files`` - the source of the tutorial document written with sphinx

  * ``data`` - folder to put the datasets used during the tutorial

  * ``skeletons`` - sample incomplete scripts for the exercises

  * ``solutions`` - 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:

.. prompt:: bash $

  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:

.. prompt:: bash $

  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
<http://people.csail.mit.edu/jrennie/20Newsgroups/>`_:

  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 website and use the :func:`sklearn.datasets.load_files`
function by pointing it to the ``20news-bydate-train`` sub-folder 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 might have noticed that the samples were shuffled randomly when we called
``fetch_20newsgroups(..., shuffle=True, random_state=42)``: this is useful if
you wish to select only a subset of 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.

.. currentmodule:: sklearn.feature_extraction.text


Bags of words
~~~~~~~~~~~~~

The most intuitive way to do so is to use a 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 than 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 few thousand 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 all included
in :class:`CountVectorizer`, which builds a dictionary of features and
transforms 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)

:class:`CountVectorizer` supports counts of N-grams of words or consecutive
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.

.. note:

  The method ``count_vect.fit_transform`` performs two actions:
  it learns the vocabulary and transforms the documents into count vectors.
  It's possible to separate these steps by calling
  ``count_vect.fit(twenty_train.data)`` followed by
  ``X_train_counts = count_vect.transform(twenty_train.data)``,
  but doing so would tokenize and vectorize each text file twice.


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".

.. _`tf–idf`: https://en.wikipedia.org/wiki/Tf-idf


Both **tf** and **tf–idf** can be computed as follows using
:class:`TfidfTransformer`::

  >>> 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 :ref:`naïve Bayes <naive_bayes>`
classifier, which
provides a nice baseline for this task. ``scikit-learn`` includes several
variants of this classifier, and 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 :class:`~sklearn.pipeline.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 will use them to perform grid search for suitable hyperparameters below.
We can now train the model with a single command::

  >>> text_clf.fit(twenty_train.data, twenty_train.target)
  Pipeline(...)


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)
  0.8348...

We achieved 83.5% accuracy. Let's see if we can do better with a
linear :ref:`support vector machine (SVM) <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 simply 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, random_state=42,
  ...                           max_iter=5, tol=None)),
  ... ])

  >>> text_clf.fit(twenty_train.data, twenty_train.target)
  Pipeline(...)
  >>> predicted = text_clf.predict(docs_test)
  >>> np.mean(predicted == twenty_test.target)
  0.9101...

We achieved 91.3% accuracy using the SVM. ``scikit-learn`` provides further
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
  <BLANKLINE>
             alt.atheism       0.95      0.80      0.87       319
           comp.graphics       0.87      0.98      0.92       389
                 sci.med       0.94      0.89      0.91       396
  soc.religion.christian       0.90      0.95      0.93       398
  <BLANKLINE>
                accuracy                           0.91      1502
               macro avg       0.91      0.91      0.91      1502
            weighted avg       0.91      0.91      0.91      1502
  <BLANKLINE>

  >>> metrics.confusion_matrix(twenty_test.target, predicted)
  array([[256,  11,  16,  36],
         [  4, 380,   3,   2],
         [  5,  35, 353,   3],
         [  5,  11,   4, 378]])

As expected the confusion matrix shows that posts from the newsgroups
on atheism and Christianity are more often confused for one another than
with computer graphics.

.. note:

  SGD stands for Stochastic Gradient Descent. This is a simple
  optimization algorithms that is known to be scalable when the dataset
  has many samples.

  By setting ``loss="hinge"`` and ``penalty="l2"`` we are configuring
  the classifier model to tune its parameters for the linear Support
  Vector Machine cost function.

  Alternatively we could have used ``sklearn.svm.LinearSVC`` (Linear
  Support Vector Machine Classifier) that provides an alternative
  optimizer for the same cost function based on the liblinear_ C++
  library.

.. _liblinear: https://www.csie.ntu.edu.tw/~cjlin/liblinear/


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.model_selection 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 use them all::

  >>> gs_clf = GridSearchCV(text_clf, parameters, cv=5, 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'])[0]]
  'soc.religion.christian'

The object's ``best_score_`` and ``best_params_`` attributes store the best
mean score and the parameters setting corresponding to that score::

  >>> gs_clf.best_score_
  0.9...
  >>> for param_name in sorted(parameters.keys()):
  ...     print("%s: %r" % (param_name, gs_clf.best_params_[param_name]))
  ...
  clf__alpha: 0.001
  tfidf__use_idf: True
  vect__ngram_range: (1, 1)

A more detailed summary of the search is available at ``gs_clf.cv_results_``.

The ``cv_results_`` parameter can be easily imported into pandas as a
``DataFrame`` for further inspection.

.. note:

  A ``GridSearchCV`` object also stores the best classifier that it trained
  as its ``best_estimator_`` attribute. In this case, that isn't much use as
  we trained on a small, 400-document subset of our full training set.


Exercises
~~~~~~~~~

To do the exercises, copy the content of the 'skeletons' folder as
a new folder named 'workspace':

.. prompt:: bash $

  cp -r skeletons workspace


You can then edit the content of the workspace without fear of losing
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 accuracy 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`` and ``token normalisation`` under
  :class:`CountVectorizer`.

* If you don't have labels, try using
  :ref:`Clustering <sphx_glr_auto_examples_text_plot_document_clustering.py>`
  on your problem.

* If you have multiple labels per document, e.g categories, have a look
  at the :ref:`Multiclass and multilabel section <multiclass>`.

* Try using :ref:`Truncated SVD <LSA>` for
  `latent semantic analysis <https://en.wikipedia.org/wiki/Latent_semantic_analysis>`_.

* Have a look at using
  :ref:`Out-of-core Classification
  <sphx_glr_auto_examples_applications_plot_out_of_core_classification.py>` to
  learn from data that would not fit into the computer main memory.

* Have a look at the :ref:`Hashing Vectorizer <hashing_vectorizer>`
  as a memory efficient alternative to :class:`CountVectorizer`.
