Returns : X sparse matrix of (n_samples, n_features) This is equivalent to fit followed by transform, but more efficiently fit_transform ( raw_documents, y = None ) ¶ This parameter is not needed to compute tfidf. Parameters : raw_documents iterableĪn iterable which generates either str, unicode or file objects. Learn vocabulary and idf from training set. Parameters : doc bytes or strĪ string of unicode symbols. The decoding strategy depends on the vectorizer parameters. Returns : tokenizer: callableĪ function to split a string into a sequence of tokens. Return a function that splits a string into a sequence of tokens. Returns : preprocessor: callableĪ function to preprocess the text before tokenization. Return a function to preprocess the text before tokenization. Returns : analyzer: callableĪ function to handle preprocessing, tokenizationĪnd n-grams generation. The callable handles that handles preprocessing, tokenization, and Transform documents to document-term matrix. Return terms per document with nonzero entries in X. Get output feature names for transformation.īuild or fetch the effective stop words list. Learn vocabulary and idf, return document-term matrix.ĭEPRECATED: get_feature_names is deprecated in 1.0 and will be removed in 1.2. Return a function that splits a string into a sequence of tokens.ĭecode the input into a string of unicode symbols. > from sklearn.feature_extraction.text import TfidfVectorizer > corpus = > vectorizer = TfidfVectorizer () > X = vectorizer. Terms that were ignored because they either: Inverse document frequency vector, only defined if use_idf=True. True if a fixed vocabulary of term to indices mapping Attributes : vocabulary_ dictĪ mapping of terms to feature indices. sublinear_tf bool, default=FalseĪpply sublinear tf scaling, i.e. Smooth idf weights by adding one to document frequencies, as if anĮxtra document was seen containing every term in the collectionĮxactly once. ‘l1’: Sum of absolute values of vector elements is 1.Įnable inverse-document-frequency reweighting. Similarity between two vectors is their dot product when l2 norm has ‘l2’: Sum of squares of vector elements is 1. Parameters : input, default=’l2’Įach output row will have unit norm, either: TfidfVectorizer ( *, input='content', encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, analyzer='word', stop_words=None, token_pattern='(?u)\\b\\w\\w \\b', ngram_range=(1, 1), max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False, dtype=, norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False ) ¶Ĭonvert a collection of raw documents to a matrix of TF-IDF features.Įquivalent to CountVectorizer followed by Sklearn.feature_ ¶ class sklearn.feature_extraction.text.
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