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.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/text/plot_hashing_vs_dict_vectorizer.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_text_plot_hashing_vs_dict_vectorizer.py>`
        to download the full example code or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py:


===========================================
FeatureHasher and DictVectorizer Comparison
===========================================

In this example we illustrate text vectorization, which is the process of
representing non-numerical input data (such as dictionaries or text documents)
as vectors of real numbers.

We first compare :func:`~sklearn.feature_extraction.FeatureHasher` and
:func:`~sklearn.feature_extraction.DictVectorizer` by using both methods to
vectorize text documents that are preprocessed (tokenized) with the help of a
custom Python function.

Later we introduce and analyze the text-specific vectorizers
:func:`~sklearn.feature_extraction.text.HashingVectorizer`,
:func:`~sklearn.feature_extraction.text.CountVectorizer` and
:func:`~sklearn.feature_extraction.text.TfidfVectorizer` that handle both the
tokenization and the assembling of the feature matrix within a single class.

The objective of the example is to demonstrate the usage of text vectorization
API and to compare their processing time. See the example scripts
:ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`
and :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py` for actual
learning on text documents.

.. GENERATED FROM PYTHON SOURCE LINES 28-34

.. code-block:: default


    # Author: Lars Buitinck
    #         Olivier Grisel <olivier.grisel@ensta.org>
    #         Arturo Amor <david-arturo.amor-quiroz@inria.fr>
    # License: BSD 3 clause








.. GENERATED FROM PYTHON SOURCE LINES 35-42

Load Data
---------

We load data from :ref:`20newsgroups_dataset`, which comprises around
18000 newsgroups posts on 20 topics split in two subsets: one for training and
one for testing. For the sake of simplicity and reducing the computational
cost, we select a subset of 7 topics and use the training set only.

.. GENERATED FROM PYTHON SOURCE LINES 42-60

.. code-block:: default


    from sklearn.datasets import fetch_20newsgroups

    categories = [
        "alt.atheism",
        "comp.graphics",
        "comp.sys.ibm.pc.hardware",
        "misc.forsale",
        "rec.autos",
        "sci.space",
        "talk.religion.misc",
    ]

    print("Loading 20 newsgroups training data")
    raw_data, _ = fetch_20newsgroups(subset="train", categories=categories, return_X_y=True)
    data_size_mb = sum(len(s.encode("utf-8")) for s in raw_data) / 1e6
    print(f"{len(raw_data)} documents - {data_size_mb:.3f}MB")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Loading 20 newsgroups training data
    3803 documents - 6.245MB




.. GENERATED FROM PYTHON SOURCE LINES 61-69

Define preprocessing functions
------------------------------

A token may be a word, part of a word or anything comprised between spaces or
symbols in a string. Here we define a function that extracts the tokens using
a simple regular expression (regex) that matches Unicode word characters. This
includes most characters that can be part of a word in any language, as well
as numbers and the underscore:

.. GENERATED FROM PYTHON SOURCE LINES 69-85

.. code-block:: default


    import re


    def tokenize(doc):
        """Extract tokens from doc.

        This uses a simple regex that matches word characters to break strings
        into tokens. For a more principled approach, see CountVectorizer or
        TfidfVectorizer.
        """
        return (tok.lower() for tok in re.findall(r"\w+", doc))


    list(tokenize("This is a simple example, isn't it?"))





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    ['this', 'is', 'a', 'simple', 'example', 'isn', 't', 'it']



.. GENERATED FROM PYTHON SOURCE LINES 86-89

We define an additional function that counts the (frequency of) occurrence of
each token in a given document. It returns a frequency dictionary to be used
by the vectorizers.

.. GENERATED FROM PYTHON SOURCE LINES 89-104

.. code-block:: default


    from collections import defaultdict


    def token_freqs(doc):
        """Extract a dict mapping tokens from doc to their occurrences."""

        freq = defaultdict(int)
        for tok in tokenize(doc):
            freq[tok] += 1
        return freq


    token_freqs("That is one example, but this is another one")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    defaultdict(<class 'int'>, {'that': 1, 'is': 2, 'one': 2, 'example': 1, 'but': 1, 'this': 1, 'another': 1})



.. GENERATED FROM PYTHON SOURCE LINES 105-111

Observe in particular that the repeated token `"is"` is counted twice for
instance.

Breaking a text document into word tokens, potentially losing the order
information between the words in a sentence is often called a `Bag of Words
representation <https://en.wikipedia.org/wiki/Bag-of-words_model>`_.

.. GENERATED FROM PYTHON SOURCE LINES 113-119

DictVectorizer
--------------

First we benchmark the :func:`~sklearn.feature_extraction.DictVectorizer`,
then we compare it to :func:`~sklearn.feature_extraction.FeatureHasher` as
both of them receive dictionaries as input.

.. GENERATED FROM PYTHON SOURCE LINES 119-137

.. code-block:: default


    from time import time

    from sklearn.feature_extraction import DictVectorizer

    dict_count_vectorizers = defaultdict(list)

    t0 = time()
    vectorizer = DictVectorizer()
    vectorizer.fit_transform(token_freqs(d) for d in raw_data)
    duration = time() - t0
    dict_count_vectorizers["vectorizer"].append(
        vectorizer.__class__.__name__ + "\non freq dicts"
    )
    dict_count_vectorizers["speed"].append(data_size_mb / duration)
    print(f"done in {duration:.3f} s at {data_size_mb / duration:.1f} MB/s")
    print(f"Found {len(vectorizer.get_feature_names_out())} unique terms")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    done in 0.670 s at 9.3 MB/s
    Found 47928 unique terms




.. GENERATED FROM PYTHON SOURCE LINES 138-141

The actual mapping from text token to column index is explicitly stored in
the `.vocabulary_` attribute which is a potentially very large Python
dictionary:

.. GENERATED FROM PYTHON SOURCE LINES 141-143

.. code-block:: default

    type(vectorizer.vocabulary_)








.. GENERATED FROM PYTHON SOURCE LINES 144-146

.. code-block:: default

    len(vectorizer.vocabulary_)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    47928



.. GENERATED FROM PYTHON SOURCE LINES 147-149

.. code-block:: default

    vectorizer.vocabulary_["example"]





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    19145



.. GENERATED FROM PYTHON SOURCE LINES 150-166

FeatureHasher
-------------

Dictionaries take up a large amount of storage space and grow in size as the
training set grows. Instead of growing the vectors along with a dictionary,
feature hashing builds a vector of pre-defined length by applying a hash
function `h` to the features (e.g., tokens), then using the hash values
directly as feature indices and updating the resulting vector at those
indices. When the feature space is not large enough, hashing functions tend to
map distinct values to the same hash code (hash collisions). As a result, it
is impossible to determine what object generated any particular hash code.

Because of the above it is impossible to recover the original tokens from the
feature matrix and the best approach to estimate the number of unique terms in
the original dictionary is to count the number of active columns in the
encoded feature matrix. For such a purpose we define the following function:

.. GENERATED FROM PYTHON SOURCE LINES 166-179

.. code-block:: default


    import numpy as np


    def n_nonzero_columns(X):
        """Number of columns with at least one non-zero value in a CSR matrix.

        This is useful to count the number of features columns that are effectively
        active when using the FeatureHasher.
        """
        return len(np.unique(X.nonzero()[1]))









.. GENERATED FROM PYTHON SOURCE LINES 180-185

The default number of features for the
:func:`~sklearn.feature_extraction.FeatureHasher` is 2**20. Here we set
`n_features = 2**18` to illustrate hash collisions.

**FeatureHasher on frequency dictionaries**

.. GENERATED FROM PYTHON SOURCE LINES 185-199

.. code-block:: default


    from sklearn.feature_extraction import FeatureHasher

    t0 = time()
    hasher = FeatureHasher(n_features=2**18)
    X = hasher.transform(token_freqs(d) for d in raw_data)
    duration = time() - t0
    dict_count_vectorizers["vectorizer"].append(
        hasher.__class__.__name__ + "\non freq dicts"
    )
    dict_count_vectorizers["speed"].append(data_size_mb / duration)
    print(f"done in {duration:.3f} s at {data_size_mb / duration:.1f} MB/s")
    print(f"Found {n_nonzero_columns(X)} unique tokens")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    done in 0.432 s at 14.5 MB/s
    Found 43873 unique tokens




.. GENERATED FROM PYTHON SOURCE LINES 200-210

The number of unique tokens when using the
:func:`~sklearn.feature_extraction.FeatureHasher` is lower than those obtained
using the :func:`~sklearn.feature_extraction.DictVectorizer`. This is due to
hash collisions.

The number of collisions can be reduced by increasing the feature space.
Notice that the speed of the vectorizer does not change significantly when
setting a large number of features, though it causes larger coefficient
dimensions and then requires more memory usage to store them, even if a
majority of them is inactive.

.. GENERATED FROM PYTHON SOURCE LINES 210-219

.. code-block:: default


    t0 = time()
    hasher = FeatureHasher(n_features=2**22)
    X = hasher.transform(token_freqs(d) for d in raw_data)
    duration = time() - t0

    print(f"done in {duration:.3f} s at {data_size_mb / duration:.1f} MB/s")
    print(f"Found {n_nonzero_columns(X)} unique tokens")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    done in 0.427 s at 14.6 MB/s
    Found 47668 unique tokens




.. GENERATED FROM PYTHON SOURCE LINES 220-229

We confirm that the number of unique tokens gets closer to the number of
unique terms found by the :func:`~sklearn.feature_extraction.DictVectorizer`.

**FeatureHasher on raw tokens**

Alternatively, one can set `input_type="string"` in the
:func:`~sklearn.feature_extraction.FeatureHasher` to vectorize the strings
output directly from the customized `tokenize` function. This is equivalent to
passing a dictionary with an implied frequency of 1 for each feature name.

.. GENERATED FROM PYTHON SOURCE LINES 229-241

.. code-block:: default


    t0 = time()
    hasher = FeatureHasher(n_features=2**18, input_type="string")
    X = hasher.transform(tokenize(d) for d in raw_data)
    duration = time() - t0
    dict_count_vectorizers["vectorizer"].append(
        hasher.__class__.__name__ + "\non raw tokens"
    )
    dict_count_vectorizers["speed"].append(data_size_mb / duration)
    print(f"done in {duration:.3f} s at {data_size_mb / duration:.1f} MB/s")
    print(f"Found {n_nonzero_columns(X)} unique tokens")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    done in 0.408 s at 15.3 MB/s
    Found 43873 unique tokens




.. GENERATED FROM PYTHON SOURCE LINES 242-243

We now plot the speed of the above methods for vectorizing.

.. GENERATED FROM PYTHON SOURCE LINES 243-255

.. code-block:: default


    import matplotlib.pyplot as plt

    fig, ax = plt.subplots(figsize=(12, 6))

    y_pos = np.arange(len(dict_count_vectorizers["vectorizer"]))
    ax.barh(y_pos, dict_count_vectorizers["speed"], align="center")
    ax.set_yticks(y_pos)
    ax.set_yticklabels(dict_count_vectorizers["vectorizer"])
    ax.invert_yaxis()
    _ = ax.set_xlabel("speed (MB/s)")




.. image-sg:: /auto_examples/text/images/sphx_glr_plot_hashing_vs_dict_vectorizer_001.png
   :alt: plot hashing vs dict vectorizer
   :srcset: /auto_examples/text/images/sphx_glr_plot_hashing_vs_dict_vectorizer_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 256-279

In both cases :func:`~sklearn.feature_extraction.FeatureHasher` is
approximately twice as fast as
:func:`~sklearn.feature_extraction.DictVectorizer`. This is handy when dealing
with large amounts of data, with the downside of losing the invertibility of
the transformation, which in turn makes the interpretation of a model a more
complex task.

The `FeatureHeasher` with `input_type="string"` is slightly faster than the
variant that works on frequency dict because it does not count repeated
tokens: each token is implicitly counted once, even if it was repeated.
Depending on the downstream machine learning task, it can be a limitation or
not.

Comparison with special purpose text vectorizers
------------------------------------------------

:func:`~sklearn.feature_extraction.text.CountVectorizer` accepts raw data as
it internally implements tokenization and occurrence counting. It is similar
to the :func:`~sklearn.feature_extraction.DictVectorizer` when used along with
the customized function `token_freqs` as done in the previous section. The
difference being that :func:`~sklearn.feature_extraction.text.CountVectorizer`
is more flexible. In particular it accepts various regex patterns through the
`token_pattern` parameter.

.. GENERATED FROM PYTHON SOURCE LINES 279-291

.. code-block:: default


    from sklearn.feature_extraction.text import CountVectorizer

    t0 = time()
    vectorizer = CountVectorizer()
    vectorizer.fit_transform(raw_data)
    duration = time() - t0
    dict_count_vectorizers["vectorizer"].append(vectorizer.__class__.__name__)
    dict_count_vectorizers["speed"].append(data_size_mb / duration)
    print(f"done in {duration:.3f} s at {data_size_mb / duration:.1f} MB/s")
    print(f"Found {len(vectorizer.get_feature_names_out())} unique terms")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    done in 0.429 s at 14.6 MB/s
    Found 47885 unique terms




.. GENERATED FROM PYTHON SOURCE LINES 292-306

We see that using the :func:`~sklearn.feature_extraction.text.CountVectorizer`
implementation is approximately twice as fast as using the
:func:`~sklearn.feature_extraction.DictVectorizer` along with the simple
function we defined for mapping the tokens. The reason is that
:func:`~sklearn.feature_extraction.text.CountVectorizer` is optimized by
reusing a compiled regular expression for the full training set instead of
creating one per document as done in our naive tokenize function.

Now we make a similar experiment with the
:func:`~sklearn.feature_extraction.text.HashingVectorizer`, which is
equivalent to combining the “hashing trick” implemented by the
:func:`~sklearn.feature_extraction.FeatureHasher` class and the text
preprocessing and tokenization of the
:func:`~sklearn.feature_extraction.text.CountVectorizer`.

.. GENERATED FROM PYTHON SOURCE LINES 306-317

.. code-block:: default


    from sklearn.feature_extraction.text import HashingVectorizer

    t0 = time()
    vectorizer = HashingVectorizer(n_features=2**18)
    vectorizer.fit_transform(raw_data)
    duration = time() - t0
    dict_count_vectorizers["vectorizer"].append(vectorizer.__class__.__name__)
    dict_count_vectorizers["speed"].append(data_size_mb / duration)
    print(f"done in {duration:.3f} s at {data_size_mb / duration:.1f} MB/s")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    done in 0.366 s at 17.1 MB/s




.. GENERATED FROM PYTHON SOURCE LINES 318-341

We can observe that this is the fastest text tokenization strategy so far,
assuming that the downstream machine learning task can tolerate a few
collisions.

TfidfVectorizer
---------------

In a large text corpus, some words appear with higher frequency (e.g. “the”,
“a”, “is” in English) and do not carry meaningful information about the actual
contents of a document. If we were to feed the word count data directly to a
classifier, those very common terms would shadow the frequencies of rarer yet
more informative terms. In order to re-weight the count features into floating
point values suitable for usage by a classifier it is very common to use the
tf–idf transform as implemented by the
:func:`~sklearn.feature_extraction.text.TfidfTransformer`. TF stands for
"term-frequency" while "tf–idf" means term-frequency times inverse
document-frequency.

We now benchmark the :func:`~sklearn.feature_extraction.text.TfidfVectorizer`,
which is equivalent to combining the tokenization and occurrence counting of
the :func:`~sklearn.feature_extraction.text.CountVectorizer` along with the
normalizing and weighting from a
:func:`~sklearn.feature_extraction.text.TfidfTransformer`.

.. GENERATED FROM PYTHON SOURCE LINES 341-353

.. code-block:: default


    from sklearn.feature_extraction.text import TfidfVectorizer

    t0 = time()
    vectorizer = TfidfVectorizer()
    vectorizer.fit_transform(raw_data)
    duration = time() - t0
    dict_count_vectorizers["vectorizer"].append(vectorizer.__class__.__name__)
    dict_count_vectorizers["speed"].append(data_size_mb / duration)
    print(f"done in {duration:.3f} s at {data_size_mb / duration:.1f} MB/s")
    print(f"Found {len(vectorizer.get_feature_names_out())} unique terms")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    done in 0.443 s at 14.1 MB/s
    Found 47885 unique terms




.. GENERATED FROM PYTHON SOURCE LINES 354-358

Summary
-------
Let's conclude this notebook by summarizing all the recorded processing speeds
in a single plot:

.. GENERATED FROM PYTHON SOURCE LINES 358-368

.. code-block:: default


    fig, ax = plt.subplots(figsize=(12, 6))

    y_pos = np.arange(len(dict_count_vectorizers["vectorizer"]))
    ax.barh(y_pos, dict_count_vectorizers["speed"], align="center")
    ax.set_yticks(y_pos)
    ax.set_yticklabels(dict_count_vectorizers["vectorizer"])
    ax.invert_yaxis()
    _ = ax.set_xlabel("speed (MB/s)")




.. image-sg:: /auto_examples/text/images/sphx_glr_plot_hashing_vs_dict_vectorizer_002.png
   :alt: plot hashing vs dict vectorizer
   :srcset: /auto_examples/text/images/sphx_glr_plot_hashing_vs_dict_vectorizer_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 369-386

Notice from the plot that
:func:`~sklearn.feature_extraction.text.TfidfVectorizer` is slightly slower
than :func:`~sklearn.feature_extraction.text.CountVectorizer` because of the
extra operation induced by the
:func:`~sklearn.feature_extraction.text.TfidfTransformer`.

Also notice that, by setting the number of features `n_features = 2**18`, the
:func:`~sklearn.feature_extraction.text.HashingVectorizer` performs better
than the :func:`~sklearn.feature_extraction.text.CountVectorizer` at the
expense of inversibility of the transformation due to hash collisions.

We highlight that :func:`~sklearn.feature_extraction.text.CountVectorizer` and
:func:`~sklearn.feature_extraction.text.HashingVectorizer` perform better than
their equivalent :func:`~sklearn.feature_extraction.DictVectorizer` and
:func:`~sklearn.feature_extraction.FeatureHasher` on manually tokenized
documents since the internal tokenization step of the former vectorizers
compiles a regular expression once and then reuses it for all the documents.


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 3.546 seconds)


.. _sphx_glr_download_auto_examples_text_plot_hashing_vs_dict_vectorizer.py:

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