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 comparison of the different feature extractors, see FeatureHasher and DictVectorizer Comparison .

For an example of document clustering and comparison with HashingVectorizer , see Clustering text documents using k-means .

Read more in the User Guide .

• 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 .

>>> 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)
build_analyzer()[source]#
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()[source]#
Return a function to preprocess the text before tokenization.
Returns:
preprocessor: callable
A function to preprocess the text before tokenization.
build_tokenizer()[source]#
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)[source]#
Decode the input into a string of unicode symbols.
The decoding strategy depends on the vectorizer parameters.
Parameters:
docbytes or str
The string to decode.
Returns:
doc: str
A string of unicode symbols.
Parameters:
raw_documentsiterable
An iterable which generates either str, unicode or file objects.
yNone
This parameter is not needed to compute tfidf.
Returns:
selfobject
Fitted vectorizer.
fit_transform(raw_documents, y=None)[source]#
Learn vocabulary and idf, return document-term matrix.
This is equivalent to fit followed by transform, but more efficiently
implemented.
Parameters:
raw_documentsiterable
An iterable which generates either str, unicode or file objects.
yNone
This parameter is ignored.
Returns:
Xsparse matrix of (n_samples, n_features)
Tf-idf-weighted document-term matrix.
get_feature_names_out(input_features=None)[source]#
Get output feature names for transformation.
Parameters:
input_featuresarray-like of str or None, default=None
Not used, present here for API consistency by convention.
Returns:
feature_names_outndarray of str objects
Transformed feature names.
get_metadata_routing()[source]#
Get metadata routing of this object.
Please check User Guide on how the routing
mechanism works.
Returns:
routingMetadataRequest
A MetadataRequest encapsulating
routing information.
Parameters:
deepbool, default=True
If True, will return the parameters for this estimator and
contained subobjects that are estimators.
Returns:
paramsdict
Parameter names mapped to their values.
set_fit_request(*, raw_documents: bool | None | str = '$UNCHANGED$') → TfidfVectorizer[source]#
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.
Added in version 1.3.
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_documentsstr, True, False, or None,                     default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for raw_documents parameter in fit.
Returns:
selfobject
The updated object.
set_params(**params)[source]#
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:
**paramsdict
Estimator parameters.
Returns:
selfestimator instance
Estimator instance.
set_transform_request(*, raw_documents: bool | None | str = '$UNCHANGED$') → TfidfVectorizer[source]#
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.
Added in version 1.3.
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_documentsstr, True, False, or None,                     default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for raw_documents parameter in transform.
Returns:
selfobject
The updated object.
transform(raw_documents)[source]#
Transform documents to document-term matrix.
Uses the vocabulary and document frequencies (df) learned by fit (or
fit_transform).
Parameters:
raw_documentsiterable
An iterable which generates either str, unicode or file objects.
Returns:
Xsparse matrix of (n_samples, n_features)
Tf-idf-weighted document-term matrix.
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