Word Count Vectorizer#
The Word Count Vectorizer builds a vocabulary from the training samples and transforms text blobs into fixed length 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 sample.
Interfaces: Transformer, Stateful
Data Type Compatibility: Categorical
Parameters#
| # | Param | Default | Type | Description |
|---|---|---|---|---|
| 1 | max vocabulary | PHP_INT_MAX | int | The maximum number of words to encode into each document vector. |
| 2 | min document frequency | 1 | int | The minimum number of documents a word must appear in to be added to the vocabulary. |
| 3 | tokenizer | Word | object | The tokenizer that extracts individual words from samples of text. |
Additional Methods#
Return the fitted vocabulary i.e. the words that will be vectorized:
public vocabulary() : array
Return the size of the vocabulary:
public size() : int
Example#
use Rubix\ML\Transformers\WordCountVectorizer;
use Rubix\ML\Other\Tokenizers\SkipGram;
$transformer = new WordCountVectorizer(10000, 3, new SkipGram());