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# `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, 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.

  For an efficiency comparision of the different feature extractors, see FeatureHasher and DictVectorizer Comparison.

  Read more in the User Guide.

  Parameters:
    input{‘filename’, ‘file’, ‘content’}, default=’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.

      - If `'content'`, the input is expected to be a sequence of items that can be of type string or byte.

    encoding str, default=’utf-8’

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

    decode_error{‘strict’, ‘ignore’, ‘replace’}, default=’strict’

      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’} or callable, default=None

      Remove accents and perform other character normalization during the preprocessing step. ‘ascii’ is a fast method that only works on characters that have a direct ASCII mapping. ‘unicode’ is a slightly slower method that works on any character. None (default) means no character normalization is performed.

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

    lowercase bool, default=True

      Convert all characters to lowercase before tokenizing.

    preprocessor callable, default=None

      Override the preprocessing (string transformation) stage while preserving the tokenizing and n-grams generation steps. Only applies if `analyzer` is not callable.

    tokenizer callable, default=None

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

    stop_words{‘english’}, list, default=None

      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 str or None, default=r”(?u)\b\w\w+\b”

      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).

      If there is a capturing group in token_pattern then the captured group content, not the entire match, becomes the token. At most one capturing group is permitted.

    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. For example an `ngram_range` of `(1, 1)` means only unigrams, `(1, 2)` means unigrams and bigrams, and `(2, 2)` means only bigrams. Only applies if `analyzer` is not callable.

    analyzer{‘word’, ‘char’, ‘char_wb’} or callable, default=’word’

      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.

      Changed in version 0.21: Since v0.21, if `input` is `'filename'` or `'file'`, the data is first read from the file and then passed to the given callable analyzer.

    n_features int, 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 bool, 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’}, default=’l2’

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

    alternate_sign bool, 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.

    dtype type, default=np.float64

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

  See also

  `CountVectorizer`

    Convert a collection of text documents to a matrix of token counts.

  `TfidfVectorizer`

    Convert a collection of raw documents to a matrix of TF-IDF features.

  Notes

  This estimator is stateless and does not need to be fitted. However, we recommend to call `fit_transform` instead of `transform`, as parameter validation is only performed in `fit`.

  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 to process input data.
  `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]) | Only validates estimator's parameters.
  `fit_transform`(X[, y]) | Transform a sequence of documents to a document-term matrix.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `get_stop_words`() | Build or fetch the effective stop words list.
  `partial_fit`(X[, y]) | Only validates estimator's parameters.
  `set_output`(*[, transform]) | Set output container.
  `set_params`(**params) | Set the parameters of this estimator.
  `transform`(X) | Transform a sequence of documents to a document-term matrix.

  build_analyzer()

    Return a callable to process input data.

    The callable handles preprocessing, tokenization, and n-grams generation.

    Returns:
      analyzer: callable

        A function to handle preprocessing, tokenization and n-grams generation.

  build_preprocessor()

    Return a function to preprocess the text before tokenization.

    Returns:
      preprocessor: callable

        A function to preprocess the text before tokenization.

  build_tokenizer()

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

    Returns:
      tokenizer: callable

        A function to split 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 bytes or str

        The string to decode.

    Returns:
      doc: str

        A string of unicode symbols.

  fit(X, y=None)

    Only validates estimator’s parameters.

    This method allows to: (i) validate the estimator’s parameters and (ii) be consistent with the scikit-learn transformer API.

    Parameters:
      X ndarray of shape [n_samples, n_features]

        Training data.

      y Ignored

        Not used, present for API consistency by convention.

    Returns:
      self object

        HashingVectorizer instance.

  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 sparse matrix of shape (n_samples, n_features)

        Document-term matrix.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

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

    Returns:
      params dict

        Parameter names mapped to their values.

  get_stop_words()

    Build or fetch the effective stop words list.

    Returns:
      stop_words: list or None

        A list of stop words.

  partial_fit(X, y=None)

    Only validates estimator’s parameters.

    This method allows to: (i) validate the estimator’s parameters and (ii) be consistent with the scikit-learn transformer API.

    Parameters:
      X ndarray of shape [n_samples, n_features]

        Training data.

      y Ignored

        Not used, present for API consistency by convention.

    Returns:
      self object

        HashingVectorizer instance.

  set_output(*, transform=None)

    Set output container.

    See Introducing the set_output API for an example on how to use the API.

    Parameters:
      transform{“default”, “pandas”}, default=None

        Configure output of `transform` and `fit_transform`.

        - `"default"`: Default output format of a transformer

        - `"pandas"`: DataFrame output

        - `None`: Transform configuration is unchanged

    Returns:
      self estimator instance

        Estimator instance.

  set_params(**params)

    Set the parameters of this estimator.

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

    Parameters:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

  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 sparse matrix of shape (n_samples, n_features)

        Document-term matrix.

## Examples using `sklearn.feature_extraction.text.HashingVectorizer`

Out-of-core classification of text documents

Out-of-core classification of text documents

Clustering text documents using k-means

Clustering text documents using k-means

FeatureHasher and DictVectorizer Comparison

FeatureHasher and DictVectorizer Comparison