9.16.2.4. sklearn.feature_extraction.text.CountVectorizer¶
- CountVectorizer(analyzer=WordNGramAnalyzer(charset='utf-8', max_n=1, min_n=1,
- preprocessor=RomanPreprocessor(),
- stop_words=set(['all', 'six', 'less', 'being', 'indeed', 'over', 'move', 'anyway', 'four', 'not', 'own', 'through', 'yourselves', 'fify', 'where', 'mill', 'only', 'find', 'before', 'one', 'whose', 'system', 'how', 'somewhere', 'with', 'thick', 'show', 'had', 'enough', 'should', 'to', 'must', 'whom',...'amoungst', 'yours', 'their', 'rather', 'without', 'so', 'five', 'the', 'first', 'whereas', 'once']),
- token_pattern='\b\w\w+\b'), vocabulary=None, max_df=1.0, max_features=None, 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 : analyzer: WordNGramAnalyzer or CharNGramAnalyzer, optional :
vocabulary: dict or iterable, optional :
Either a dictionary where keys are tokens and values are indices in the matrix, or an iterable over terms (in which case the indices are determined by the iteration order as per enumerate).
This is useful in order to fix the vocabulary in advance.
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.
dtype: type, optional :
Type of the matrix returned by fit_transform() or transform().
Methods
fit fit_transform inverse_transform set_params transform - CountVectorizer.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 :
- CountVectorizer.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] :
- CountVectorizer.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.
- CountVectorizer.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 :
- CountVectorizer.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] :