sklearn.feature_extraction.text.CountVectorizer¶
- class sklearn.feature_extraction.text.CountVectorizer(input=u'content', encoding=u'utf-8', charset=None, decode_error=u'strict', charset_error=None, strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern=u'(?u)\b\w\w+\b', ngram_range=(1, 1), analyzer=u'word', max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False, dtype=<type 'numpy.int64'>)¶
Convert a collection of text documents to a matrix of token counts
This implementation produces a sparse representation of the counts using scipy.sparse.coo_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.
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 the sequence strings or bytes items are expected to be analyzed directly.
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 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.
analyzer : string, {‘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.
If a callable is passed it is used to extract the sequence of features out of the raw, unprocessed input.
preprocessor : callable or None (default)
Override the preprocessing (string transformation) stage while preserving the tokenizing and n-grams generation steps.
tokenizer : callable or None (default)
Override the string tokenization step while preserving the preprocessing and n-grams generation steps.
ngram_range : tuple (min_n, max_n)
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.
stop_words : string {‘english’}, list, or None (default)
If ‘english’, a built-in stop word list for English is used.
If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens.
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.
lowercase : boolean, True by default
Convert all characters to lowercase before tokenizing.
token_pattern : string
Regular expression denoting what constitutes a “token”, only used if tokenize == ‘word’. The default regexp select tokens of 2 or more alphanumeric characters (punctuation is completely ignored and always treated as a token separator).
max_df : float in range [0.0, 1.0] or int, optional, 1.0 by default
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, optional, 1 by default
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 : optional, None by default
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, False by default.
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.
`stop_words_` : set
Terms that were ignored because they occurred in either too many (max_df) or in too few (min_df) documents. This is only available if no vocabulary was given.
See also
Methods
build_analyzer() Return a callable that handles preprocessing and tokenization 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 a vocabulary dictionary of all tokens in the raw documents. fit_transform(raw_documents[, y]) Learn the vocabulary dictionary and return term-document matrix. get_feature_names() Array mapping from feature integer indices to feature name 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_params(**params) Set the parameters of this estimator. transform(raw_documents) Transform documents to document-term matrix. - __init__(input=u'content', encoding=u'utf-8', charset=None, decode_error=u'strict', charset_error=None, strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern=u'(?u)\b\w\w+\b', ngram_range=(1, 1), analyzer=u'word', max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False, dtype=<type 'numpy.int64'>)¶
- build_analyzer()¶
Return a callable that handles preprocessing and tokenization
- 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
The decoding strategy depends on the vectorizer parameters.
- fit(raw_documents, y=None)¶
Learn a vocabulary dictionary of all tokens in the raw documents.
Parameters: raw_documents : iterable
An iterable which yields either str, unicode or file objects.
Returns: self :
- fit_transform(raw_documents, y=None)¶
Learn the vocabulary dictionary and return term-document matrix.
This is equivalent to fit followed by transform, but more efficiently implemented.
Parameters: raw_documents : iterable
An iterable which yields either str, unicode or file objects.
Returns: X : array, [n_samples, n_features]
Document-term matrix.
- fixed_vocabulary¶
DEPRECATED: The fixed_vocabulary attribute is deprecated and will be removed in 0.18. Please use fixed_vocabulary_ instead.
- get_feature_names()¶
Array mapping from feature integer indices to feature name
- get_params(deep=True)¶
Get parameters for this estimator.
Parameters: deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.
- get_stop_words()¶
Build or fetch the effective stop words list
- inverse_transform(X)¶
Return terms per document with nonzero entries in X.
Parameters: X : {array, sparse matrix}, shape = [n_samples, n_features]
Returns: X_inv : list of arrays, len = n_samples
List of arrays of terms.
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns: self :
- transform(raw_documents)¶
Transform documents to document-term matrix.
Extract token counts out of raw text documents using the vocabulary fitted with fit or the one provided to the constructor.
Parameters: raw_documents : iterable
An iterable which yields either str, unicode or file objects.
Returns: X : sparse matrix, [n_samples, n_features]
Document-term matrix.
