Text Count Vectorizer

Convert a collection of text documents to a matrix of token counts

Documentation

Attributes

stop_words_

Terms that were ignored because they either:

• occurred in too many documents (max_df)

• occurred in too few documents (min_df)

• were cut off by feature selection (max_features).

This is only available if no vocabulary was given.

vocabulary_

A mapping of terms to feature indices.

Definition

Output ports

model model

Model

Configuration

Analyzer (analyzer)

Whether the feature should be made of word n-gram or character n-grams. Option ‘char_wb’ creates character n-grams only from text inside word boundaries; n-grams at the edges of words are padded with space.

If a callable is passed it is used to extract the sequence of features out of the raw, unprocessed input.

Changed in version 0.21.

Since v0.21, if input is filename or file, the data is first read from the file and then passed to the given callable analyzer.

Binary (binary)

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.

Decoding error behavior (decode_error)

Instruction on what to do if a byte sequence is given to analyze that contains characters not of the given encoding. By default, it is ‘strict’, meaning that a UnicodeDecodeError will be raised. Other values are ‘ignore’ and ‘replace’.

Encoding (encoding)

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

Lowercase (lowercase)

Convert all characters to lowercase before tokenizing.

Maximum document frequency (max_df)

When building the vocabulary ignore terms that have a document frequency strictly higher than the given threshold (corpus-specific stop words). If float, the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None.

Maximum features (max_features)

If not None, build a vocabulary that only consider the top max_features ordered by term frequency across the corpus. Otherwise, all features are used.

This parameter is ignored if vocabulary is not None.

Minimum document frequency (min_df)

When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold. This value is also called cut-off in the literature. If float, the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None.

N-gram range (ngram_range)

The lower and upper boundary of the range of n-values for different word n-grams or char n-grams to be extracted. All values of n such such that min_n <= n <= max_n will be used. For example an ngram_range of (1, 1) means only unigrams, (1, 2) means unigrams and bigrams, and (2, 2) means only bigrams. Only applies if analyzer is not callable.

Stop words (stop_words)

If ‘english’, a built-in stop word list for English is used. There are several known issues with ‘english’ and you should consider an alternative (see stop_words).

If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. Only applies if analyzer == 'word'.

If None, no stop words will be used. In this case, setting max_df to a higher value, such as in the range (0.7, 1.0), can automatically detect and filter stop words based on intra corpus document frequency of terms.

Strip accents (strip_accents)

Remove accents and perform other character normalization during the preprocessing step. ‘ascii’ is a fast method that only works on characters that have a direct ASCII mapping. ‘unicode’ is a slightly slower method that works on any characters. None (default) does nothing.

Both ‘ascii’ and ‘unicode’ use NFKD normalization from unicodedata.normalize().

Implementation

class node_text.CountVectorizer