Get output feature names for transformation.īuild or fetch the effective stop words list. Learn the vocabulary dictionary and return document-term matrix. Learn a vocabulary dictionary of all tokens in the raw documents. Return a function that splits a string into a sequence of tokens.ĭecode the input into a string of unicode symbols. Return a function to preprocess the text before tokenization. toarray ()) ] > vectorizer2 = CountVectorizer ( analyzer = 'word', ngram_range = ( 2, 2 )) > X2 = vectorizer2. > from sklearn.feature_extraction.text import CountVectorizer > corpus = > vectorizer = CountVectorizer () > X = vectorizer. Terms that were ignored because they either: True if a fixed vocabulary of term to indices mapping Attributes : vocabulary_ dictĪ mapping of terms to feature indices. Type of the matrix returned by fit_transform() or transform(). Probabilistic models that model binary events rather than integer If True, all non zero counts are set to 1. In the mapping should not be repeated and should not have any gapīetween 0 and the largest index. Given, a vocabulary is determined from the input documents. Indices in the feature matrix, or an iterable over terms. vocabulary Mapping or iterable, default=NoneĮither a Mapping (e.g., a dict) where keys are terms and values are This parameter is ignored if vocabulary is not None. Max_features ordered by term frequency across the corpus. If not None, build a vocabulary that only consider the top min_df float in range or int, default=1įrequency strictly lower than the given threshold. If float, the parameter represents a proportion of documents, integer When building the vocabulary ignore terms that have a documentįrequency strictly higher than the given threshold (corpus-specific max_df float in range or int, default=1.0 Since v0.21, if input is filename or file, the data isįirst read from the file and then passed to the given callableĪnalyzer. If a callable is passed it is used to extract the sequence of features Word boundaries n-grams at the edges of words are padded with space. Option ‘char_wb’ creates character n-grams only from text inside Whether the feature should be made of word n-gram or character Parameters : input or callable, default=’word’ That does some kind of feature selection then the number of features willīe equal to the vocabulary size found by analyzing the data. If you do not provide an a-priori dictionary and you do not use an analyzer This implementation produces a sparse representation of the counts using CountVectorizer ( *, input='content', encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern='(?u)\\b\\w\\w \\b', ngram_range=(1, 1), analyzer='word', max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False, dtype= ) ¶Ĭonvert a collection of text documents to a matrix of token counts. Sklearn.feature_ ¶ class sklearn.feature_extraction.text.
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