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Word Count Vectorizer#

The Word Count Vectorizer builds a vocabulary from the training samples and transforms text blobs into fixed length sparse feature vectors. Each feature column represents a word or token from the vocabulary and the value denotes the number of times that word appears in a given document.

Interfaces: Transformer, Stateful, Persistable

Data Type Compatibility: Categorical


# Name Default Type Description
1 maxVocabularySize PHP_INT_MAX int The maximum number of unique tokens to embed into each document vector.
2 minDocumentCount 1 float The minimum number of documents a word must appear in to be added to the vocabulary.
3 maxDocumentRatio 0.8 float The maximum ratio of documents a word can appear in to be added to the vocabulary.
4 tokenizer Word Tokenizer The tokenizer used to extract features from blobs of text.


use Rubix\ML\Transformers\WordCountVectorizer;
use Rubix\ML\Tokenizers\NGram;

$transformer = new WordCountVectorizer(10000, 5, 0.5, new NGram(1, 2));

Additional Methods#

Return an array of words that comprise each of the vocabularies:

public vocabularies() : array