Convert a collection of text documents to a matrix of token counts
This implementation produces a sparse representation of the counts using scipy.sparse.csr_matrix.
If you do not provide an a-priori dictionary and you do not use an analyzer that does some kind of feature selection then the number of features will be equal to the vocabulary size found by analyzing the data.
Read more in the :ref:`User Guide <text_feature_extraction>`.
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 a sequence of items that can be of type string or byte.
encoding : string, 'utf-8' by default. 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 :func:`unicodedata.normalize`.
lowercase : boolean, True by default 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. Only applies if ``analyzer is not callable``.
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 :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 : string Regular expression denoting what constitutes a 'token', only used if ``analyzer == 'word'``. The default regexp select 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 word n-grams or char n-grams to be extracted. All values of n such 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 : string, {'word', 'char', 'char_wb'} or callable Whether the feature should be made of word n-gram 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. 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 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. Indices in the mapping should not be repeated and should not have any gap between 0 and the largest index. 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. dtype : type, optional Type of the matrix returned by fit_transform() or transform(). Attributes ---------- vocabulary_ : dict A mapping of terms to feature indices. fixed_vocabulary_: boolean True if a fixed vocabulary of term to indices mapping is provided by the user 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. Examples -------- >>> from sklearn.feature_extraction.text import CountVectorizer >>> corpus = [ ... 'This is the first document.', ... 'This document is the second document.', ... 'And this is the third one.', ... 'Is this the first document?', ... ] >>> vectorizer = CountVectorizer() >>> X = vectorizer.fit_transform(corpus) >>> print(vectorizer.get_feature_names()) ['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this'] >>> print(X.toarray()) [[0 1 1 1 0 0 1 0 1] [0 2 0 1 0 1 1 0 1] [1 0 0 1 1 0 1 1 1] [0 1 1 1 0 0 1 0 1]] >>> vectorizer2 = CountVectorizer(analyzer='word', ngram_range=(2, 2)) >>> X2 = vectorizer2.fit_transform(corpus) >>> print(vectorizer2.get_feature_names()) ['and this', 'document is', 'first document', 'is the', 'is this', 'second document', 'the first', 'the second', 'the third', 'third one', 'this document', 'this is', 'this the'] >>> print(X2.toarray()) [[0 0 1 1 0 0 1 0 0 0 0 1 0] [0 1 0 1 0 1 0 1 0 0 1 0 0] [1 0 0 1 0 0 0 0 1 1 0 1 0] [0 0 1 0 1 0 1 0 0 0 0 0 1]] See Also -------- HashingVectorizer, TfidfVectorizer 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.