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# `sklearn.feature_extraction.text`.TfidfVectorizer

class sklearn.feature_extraction.text.TfidfVectorizer(*, input='content', encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, analyzer='word', stop_words=None, token_pattern='(?u)\\b\\w\\w+\\b', ngram_range=(1, 1), max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False, dtype=<class 'numpy.float64'>, norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False)

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

  Equivalent to `CountVectorizer` followed by `TfidfTransformer`.

  For an example of usage, see Classification of text documents using sparse features.

  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 characters. 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'`.

    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.

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

      If a string, it is passed to _check_stop_list and the appropriate stop list is returned. ‘english’ is currently the only supported string value. 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'`.

      If None, no stop words will be used. In this case, setting `max_df` to a higher value, such as in the range (0.7, 1.0), can automatically detect and filter stop words based on intra corpus document frequency of terms.

    token_pattern str, 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.

    max_df float or int, default=1.0

      When building the vocabulary ignore terms that have a document frequency strictly higher than the given threshold (corpus-specific stop words). If float in range [0.0, 1.0], the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None.

    min_df float or int, default=1

      When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold. This value is also called cut-off in the literature. If float in range of [0.0, 1.0], the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None.

    max_features int, default=None

      If not None, build a vocabulary that only consider the top `max_features` ordered by term frequency across the corpus. Otherwise, all features are used.

      This parameter is ignored if vocabulary is not None.

    vocabulary Mapping or iterable, default=None

      Either a Mapping (e.g., a dict) where keys are terms and values are indices in the feature matrix, or an iterable over terms. If not given, a vocabulary is determined from the input documents.

    binary bool, default=False

      If True, all non-zero term counts are set to 1. This does not mean outputs will have only 0/1 values, only that the tf term in tf-idf is binary. (Set `binary` to True, `use_idf` to False and `norm` to None to get 0/1 outputs).

    dtype dtype, default=float64

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

    norm{‘l1’, ‘l2’} or None, default=’l2’

      Each output row will have unit norm, either:

      - ‘l2’: Sum of squares of vector elements is 1. The cosine similarity between two vectors is their dot product when l2 norm has been applied.

      - ‘l1’: Sum of absolute values of vector elements is 1. See `normalize`.

      - None: No normalization.

    use_idf bool, default=True

      Enable inverse-document-frequency reweighting. If False, idf(t) = 1.

    smooth_idf bool, default=True

      Smooth idf weights by adding one to document frequencies, as if an extra document was seen containing every term in the collection exactly once. Prevents zero divisions.

    sublinear_tf bool, default=False

      Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).

  Attributes:
    vocabulary_dict

      A mapping of terms to feature indices.

    fixed_vocabulary_bool

      True if a fixed vocabulary of term to indices mapping is provided by the user.

    `idf_`array of shape (n_features,)

      Inverse document frequency vector, only defined if `use_idf=True`.

    stop_words_set

      Terms that were ignored because they either:

      > - occurred in too many documents (`max_df`)

      > - occurred in too few documents (`min_df`)

      > - were cut off by feature selection (`max_features`).

      This is only available if no vocabulary was given.

  See also

  `CountVectorizer`

    Transforms text into a sparse matrix of n-gram counts.

  `TfidfTransformer`

    Performs the TF-IDF transformation from a provided matrix of counts.

  Notes

  The `stop_words_` attribute can get large and increase the model size when pickling. This attribute is provided only for introspection and can be safely removed using delattr or set to None before pickling.

  Examples

      >>> from sklearn.feature_extraction.text import TfidfVectorizer
      >>> corpus = [
      ...     'This is the first document.',
      ...     'This document is the second document.',
      ...     'And this is the third one.',
      ...     'Is this the first document?',
      ... ]
      >>> vectorizer = TfidfVectorizer()
      >>> X = vectorizer.fit_transform(corpus)
      >>> vectorizer.get_feature_names_out()
      array(['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third',
             'this'], ...)
      >>> print(X.shape)
      (4, 9)

  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`(raw_documents[, y]) | Learn vocabulary and idf from training set.
  `fit_transform`(raw_documents[, y]) | Learn vocabulary and idf, return document-term matrix.
  `get_feature_names_out`([input_features]) | Get output feature names for transformation.
  `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.
  `inverse_transform`(X) | Return terms per document with nonzero entries in X.
  `set_fit_request`(*[, raw_documents]) | Request metadata passed to the `fit` method.
  `set_params`(**params) | Set the parameters of this estimator.
  `set_transform_request`(*[, raw_documents]) | Request metadata passed to the `transform` method.
  `transform`(raw_documents) | Transform documents to 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(raw_documents, y=None)

    Learn vocabulary and idf from training set.

    Parameters:
      raw_documents iterable

        An iterable which generates either str, unicode or file objects.

      y None

        This parameter is not needed to compute tfidf.

    Returns:
      self object

        Fitted vectorizer.

  fit_transform(raw_documents, y=None)

    Learn vocabulary and idf, return document-term matrix.

    This is equivalent to fit followed by transform, but more efficiently implemented.

    Parameters:
      raw_documents iterable

        An iterable which generates either str, unicode or file objects.

      y None

        This parameter is ignored.

    Returns:
      X sparse matrix of (n_samples, n_features)

        Tf-idf-weighted document-term matrix.

  get_feature_names_out(input_features=None)

    Get output feature names for transformation.

    Parameters:
      input_features array-like of str or None, default=None

        Not used, present here for API consistency by convention.

    Returns:
      feature_names_out ndarray of str objects

        Transformed feature names.

  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.

  property idf_

    Inverse document frequency vector, only defined if `use_idf=True`.

    Returns:
      ndarray of shape (n_features,)
  inverse_transform(X)

    Return terms per document with nonzero entries in X.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        Document-term matrix.

    Returns:
      X_inv list of arrays of shape (n_samples,)

        List of arrays of terms.

  set_fit_request(*, raw_documents: bool | None | str = '$UNCHANGED$') → TfidfVectorizer

    Request metadata passed to the `fit` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `fit` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `fit`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      raw_documents str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `raw_documents` parameter in `fit`.

    Returns:
      self object

        The updated object.

  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.

  set_transform_request(*, raw_documents: bool | None | str = '$UNCHANGED$') → TfidfVectorizer

    Request metadata passed to the `transform` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `transform` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `transform`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      raw_documents str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `raw_documents` parameter in `transform`.

    Returns:
      self object

        The updated object.

  transform(raw_documents)

    Transform documents to document-term matrix.

    Uses the vocabulary and document frequencies (df) learned by fit (or fit_transform).

    Parameters:
      raw_documents iterable

        An iterable which generates either str, unicode or file objects.

    Returns:
      X sparse matrix of (n_samples, n_features)

        Tf-idf-weighted document-term matrix.

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

Biclustering documents with the Spectral Co-clustering algorithm

Biclustering documents with the Spectral Co-clustering algorithm

Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation

Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation

Sample pipeline for text feature extraction and evaluation

Sample pipeline for text feature extraction and evaluation

Column Transformer with Heterogeneous Data Sources

Column Transformer with Heterogeneous Data Sources

Classification of text documents using sparse features

Classification of text documents using sparse features

Clustering text documents using k-means

Clustering text documents using k-means

FeatureHasher and DictVectorizer Comparison

FeatureHasher and DictVectorizer Comparison