9.16.2.6. sklearn.feature_extraction.text.Vectorizer

Vectorizer(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'), max_df=1.0, max_features=None, norm='l2', use_idf=True, smooth_idf=True)

Convert a collection of raw documents to a matrix

Equivalent to CountVectorizer followed by TfidfTransformer.

Methods

fit
fit_transform
inverse_transform
set_params
transform

Vectorizer.fit(raw_documents)

Learn a conversion law from documents to array data

Vectorizer.fit_transform(raw_documents)

Learn the representation and return the vectors.

Parameters :

raw_documents: iterable :

an iterable which yields either str, unicode or file objects

Returns :

vectors: array, [n_samples, n_features] :

Vectorizer.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.

Vectorizer.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 :

Vectorizer.transform(raw_documents, copy=True)

Transform raw text documents to tf–idf vectors

Parameters :

raw_documents: iterable :

an iterable which yields either str, unicode or file objects

Returns :

vectors: sparse matrix, [n_samples, n_features] :