sklearn.feature_extraction.text.HashingVectorizer

class sklearn.feature_extraction.text.HashingVectorizer(input='content', encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern='(?u)\b\w\w+\b', ngram_range=(1, 1), analyzer='word', n_features=1048576, binary=False, norm='l2', alternate_sign=True, non_negative=False, dtype=<class 'numpy.float64'>)

Convert a collection of text documents to a matrix of token occurrences

It turns a collection of text documents into a scipy.sparse matrix holding token occurrence counts (or binary occurrence information), possibly normalized as token frequencies if norm=’l1’ or projected on the euclidean unit sphere if norm=’l2’.

This text vectorizer implementation uses the hashing trick to find the token string name to feature integer index mapping.

This strategy has several advantages:

• it is very low memory scalable to large datasets as there is no need to store a vocabulary dictionary in memory
• it is fast to pickle and un-pickle as it holds no state besides the constructor parameters
• it can be used in a streaming (partial fit) or parallel pipeline as there is no state computed during fit.

There are also a couple of cons (vs using a CountVectorizer with an in-memory vocabulary):

• there is no way to compute the inverse transform (from feature indices to string feature names) which can be a problem when trying to introspect which features are most important to a model.
• there can be collisions: distinct tokens can be mapped to the same feature index. However in practice this is rarely an issue if n_features is large enough (e.g. 2 ** 18 for text classification problems).
• no IDF weighting as this would render the transformer stateful.

The hash function employed is the signed 32-bit version of Murmurhash3.

Read more in the User Guide.

Parameters:

input : string {‘filename’, ‘file’, ‘content’}

If ‘filename’, the sequence passed as an argument to fit is expected to be a list of filenames that need reading to fetch the raw content to analyze.

If ‘file’, the sequence items must have a ‘read’ method (file-like object) that is called to fetch the bytes in memory.

Otherwise the input is expected to be the sequence strings or bytes items are expected to be analyzed directly.

encoding : string, default=’utf-8’

If bytes or files are given to analyze, this encoding is used to decode.

decode_error : {‘strict’, ‘ignore’, ‘replace’}

Instruction on what to do if a byte sequence is given to analyze that contains characters not of the given encoding. By default, it is ‘strict’, meaning that a UnicodeDecodeError will be raised. Other values are ‘ignore’ and ‘replace’.

strip_accents : {‘ascii’, ‘unicode’, None}

Remove accents and perform other character normalization during the preprocessing step. ‘ascii’ is a fast method that only works on characters that have an direct ASCII mapping. ‘unicode’ is a slightly slower method that works on any characters. None (default) does nothing.

Both ‘ascii’ and ‘unicode’ use NFKD normalization from unicodedata.normalize.

lowercase : boolean, default=True

Convert all characters to lowercase before tokenizing.

preprocessor : callable or None (default)

Override the preprocessing (string transformation) stage while preserving the tokenizing and n-grams generation steps.

tokenizer : callable or None (default)

Override the string tokenization step while preserving the preprocessing and n-grams generation steps. Only applies if analyzer == 'word'.

stop_words : string {‘english’}, list, or None (default)

If ‘english’, a built-in stop word list for English is used. There are several known issues with ‘english’ and you should consider an alternative (see Using stop words).

If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. Only applies if analyzer == 'word'.

token_pattern : string

Regular expression denoting what constitutes a “token”, only used if analyzer == 'word'. The default regexp selects tokens of 2 or more alphanumeric characters (punctuation is completely ignored and always treated as a token separator).

ngram_range : tuple (min_n, max_n), default=(1, 1)

The lower and upper boundary of the range of n-values for different n-grams to be extracted. All values of n such that min_n <= n <= max_n will be used.

analyzer : string, {‘word’, ‘char’, ‘char_wb’} or callable

Whether the feature should be made of word or character n-grams. Option ‘char_wb’ creates character n-grams only from text inside word boundaries; n-grams at the edges of words are padded with space.

If a callable is passed it is used to extract the sequence of features out of the raw, unprocessed input.

n_features : integer, default=(2 ** 20)

The number of features (columns) in the output matrices. Small numbers of features are likely to cause hash collisions, but large numbers will cause larger coefficient dimensions in linear learners.

binary : boolean, default=False.

If True, all non zero counts are set to 1. This is useful for discrete probabilistic models that model binary events rather than integer counts.

norm : ‘l1’, ‘l2’ or None, optional

Norm used to normalize term vectors. None for no normalization.

alternate_sign : boolean, optional, default True

When True, an alternating sign is added to the features as to approximately conserve the inner product in the hashed space even for small n_features. This approach is similar to sparse random projection.

New in version 0.19.

non_negative : boolean, optional, default False

When True, an absolute value is applied to the features matrix prior to returning it. When used in conjunction with alternate_sign=True, this significantly reduces the inner product preservation property.

Deprecated since version 0.19: This option will be removed in 0.21.

dtype : type, optional

Type of the matrix returned by fit_transform() or transform().

See also

CountVectorizer, TfidfVectorizer

Examples

>>> from sklearn.feature_extraction.text import HashingVectorizer
>>> corpus = [
...     'This is the first document.',
...     'This document is the second document.',
...     'And this is the third one.',
...     'Is this the first document?',
... ]
>>> vectorizer = HashingVectorizer(n_features=2**4)
>>> X = vectorizer.fit_transform(corpus)
>>> print(X.shape)
(4, 16)

Methods

build_analyzer()	Return a callable that handles preprocessing and tokenization
build_preprocessor()	Return a function to preprocess the text before tokenization
build_tokenizer()	Return a function that splits a string into a sequence of tokens
decode(doc)	Decode the input into a string of unicode symbols
fit(X[, y])	Does nothing: this transformer is stateless.
fit_transform(X[, y])	Transform a sequence of documents to a document-term matrix.
get_params([deep])	Get parameters for this estimator.
get_stop_words()	Build or fetch the effective stop words list
partial_fit(X[, y])	Does nothing: this transformer is stateless.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Transform a sequence of documents to a document-term matrix.

__init__(input='content', encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern='(?u)\\b\\w\\w+\\b', ngram_range=(1, 1), analyzer='word', n_features=1048576, binary=False, norm='l2', alternate_sign=True, non_negative=False, dtype=<class 'numpy.float64'>)

Initialize self. See help(type(self)) for accurate signature.

build_analyzer()

Return a callable that handles preprocessing and tokenization

build_preprocessor()

Return a function to preprocess the text before tokenization

build_tokenizer()

Return a function that splits a string into a sequence of tokens

decode(doc)

Decode the input into a string of unicode symbols

The decoding strategy depends on the vectorizer parameters.

Parameters:

doc : string

The string to decode

fit(X, y=None)

Does nothing: this transformer is stateless.

Parameters:

X : array-like, shape [n_samples, n_features]

Training data.

fit_transform(X, y=None)

Transform a sequence of documents to a document-term matrix.

Parameters:

X : iterable over raw text documents, length = n_samples

Samples. Each sample must be a text document (either bytes or unicode strings, file name or file object depending on the constructor argument) which will be tokenized and hashed.

y : any

Ignored. This parameter exists only for compatibility with sklearn.pipeline.Pipeline.

Returns:

X : scipy.sparse matrix, shape = (n_samples, self.n_features)

Document-term matrix.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

get_stop_words()

Build or fetch the effective stop words list

partial_fit(X, y=None)

Does nothing: this transformer is stateless.

This method is just there to mark the fact that this transformer can work in a streaming setup.

Parameters:

X : array-like, shape [n_samples, n_features]

Training data.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:

self

transform(X)

Transform a sequence of documents to a document-term matrix.

Parameters:

X : iterable over raw text documents, length = n_samples

Samples. Each sample must be a text document (either bytes or unicode strings, file name or file object depending on the constructor argument) which will be tokenized and hashed.

Returns:

X : scipy.sparse matrix, shape = (n_samples, self.n_features)

Document-term matrix.

Examples using sklearn.feature_extraction.text.HashingVectorizer

Out-of-core classification of text documents

Clustering text documents using k-means

Classification of text documents using sparse features