Convert a collection of raw documents to a matrix of TF-IDF features.
Equivalent to :class:`CountVectorizer` followed by :class:`TfidfTransformer`.
Read more in the :ref:`User Guide <text_feature_extraction>`.
Parameters ---------- input : str '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 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', None (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 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 :func:`unicodedata.normalize`.
lowercase : bool (default=True) Convert all characters to lowercase before tokenizing.
preprocessor : callable or None (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 or None (default=None) Override the string tokenization step while preserving the preprocessing and n-grams generation steps. Only applies if ``analyzer == 'word'``.
analyzer : str, '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.
.. versionchanged:: 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 : str 'english', list, or None (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 :ref:`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. max_df can be set to a value in the range 0.7, 1.0) to automatically detect and filter stop words based on intra corpus document frequency of terms. token_pattern : str 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. 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 in range [0.0, 1.0] 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, the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None. min_df : float in range [0.0, 1.0] 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, the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None. max_features : int or None (default=None) If not None, build a vocabulary that only consider the top max_features ordered by term frequency across the corpus. This parameter is ignored if vocabulary is not None. vocabulary : Mapping or iterable, optional (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 idf and normalization to False to get 0/1 outputs). dtype : type, optional (default=float64) Type of the matrix returned by fit_transform() or transform(). norm : 'l1', 'l2' or None, optional (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 :func:`preprocessing.normalize`. use_idf : bool (default=True) Enable inverse-document-frequency reweighting. 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, shape (n_features) The inverse document frequency (IDF) vector; only defined if ``use_idf`` is 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) >>> print(vectorizer.get_feature_names()) ['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this'] >>> print(X.shape) (4, 9)