8.7.2.1. sklearn.feature_extraction.text.CountVectorizer

class sklearn.feature_extraction.text.CountVectorizer(input='content', charset='utf-8', charset_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern=u'\b\w\w+\b', min_n=1, max_n=1, analyzer='word', max_df=1.0, max_features=None, vocabulary=None, binary=False, dtype=<type 'long'>)

Convert a collection of raw 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 analysing the data. The default analyzer does simple stop word filtering for English.

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 ‘read’ method (file-like object) it 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.

charset: string, ‘utf-8’ by default. :

If bytes or files are given to analyze, this charset is used to decode.

charset_error: {‘strict’, ‘ignore’, ‘replace’} :

Instruction on what to do if a byte sequence is given to analyze that contains characters not of the given charset. 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’} or callable :

Whether the feature should be made of word or character n-grams.

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.

min_n: integer :

The lower boundary of the range of n-values for different n-grams to be extracted.

max_n: integer :

The 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 a string, it is passed to _check_stop_list and the appropriate stop list is returned is currently the only supported string value.

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.

token_pattern: string :

Regular expression denoting what constitutes a “token”, only used if tokenize == ‘word’. The default regexp select tokens of 2 or more letters characters (punctuation is completely ignored and always treated as a token separator).

max_df : float in range [0.0, 1.0], optional, 1.0 by default

When building the vocabulary ignore terms that have a term frequency strictly higher than the given threshold (corpus specific stop words).

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.

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().

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 split a string in 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 the count vectors
get_feature_names()	Array mapping from feature integer indicex to feature name
get_params([deep])	Get parameters for the 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 the estimator.
transform(raw_documents)	Extract token counts out of raw text documents using the vocabulary fitted with fit or the one provided in the constructor.

__init__(input='content', charset='utf-8', charset_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern=u'\b\w\w+\b', min_n=1, max_n=1, analyzer='word', max_df=1.0, max_features=None, vocabulary=None, binary=False, dtype=<type 'long'>)

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 split a string in 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 the count vectors

This is more efficient than calling fit followed by transform.

Parameters :

raw_documents: iterable :

an iterable which yields either str, unicode or file objects

Returns :

vectors: array, [n_samples, n_features] :

get_feature_names()

Array mapping from feature integer indicex to feature name

get_params(deep=True)

Get parameters for the estimator

Parameters :

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

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 the 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)

Extract token counts out of raw text documents using the vocabulary fitted with fit or the one provided in the constructor.

Parameters :

raw_documents: iterable :

an iterable which yields either str, unicode or file objects

Returns :

vectors: sparse matrix, [n_samples, n_features] :