Feature Transformation – CountVectorizer (Estimator)

R/ml_feature_count_vectorizer.R

ft_count_vectorizer

Description

Extracts a vocabulary from document collections.

Usage

ft_count_vectorizer( 
  x, 
  input_col = NULL, 
  output_col = NULL, 
  binary = FALSE, 
  min_df = 1, 
  min_tf = 1, 
  vocab_size = 2^18, 
  uid = random_string("count_vectorizer_"), 
  ... 
) 

ml_vocabulary(model)

Arguments

Arguments	Description
x	A `spark_connection`, `ml_pipeline`, or a `tbl_spark`.
input_col	The name of the input column.
output_col	The name of the output column.
binary	Binary toggle to control the output vector values. If `TRUE`, all nonzero counts (after `min_tf` filter applied) are set to 1. This is useful for discrete probabilistic models that model binary events rather than integer counts. Default: `FALSE`
min_df	Specifies the minimum number of different documents a term must appear in to be included in the vocabulary. If this is an integer greater than or equal to 1, this specifies the number of documents the term must appear in; if this is a double in [0,1), then this specifies the fraction of documents. Default: 1.
min_tf	Filter to ignore rare words in a document. For each document, terms with frequency/count less than the given threshold are ignored. If this is an integer greater than or equal to 1, then this specifies a count (of times the term must appear in the document); if this is a double in [0,1), then this specifies a fraction (out of the document’s token count). Default: 1.
vocab_size	Build a vocabulary that only considers the top `vocab_size` terms ordered by term frequency across the corpus. Default: `2^18`.
uid	A character string used to uniquely identify the feature transformer.
…	Optional arguments; currently unused.
model	A `ml_count_vectorizer_model`.

Details

In the case where x is a tbl_spark, the estimator fits against x

to obtain a transformer, which is then immediately used to transform x, returning a tbl_spark.

Value

The object returned depends on the class of x.

spark_connection: When x is a spark_connection, the function returns a ml_transformer, a ml_estimator, or one of their subclasses. The object contains a pointer to a Spark Transformer or Estimator object and can be used to compose Pipeline objects.
ml_pipeline: When x is a ml_pipeline, the function returns a ml_pipeline with the transformer or estimator appended to the pipeline.
tbl_spark: When x is a tbl_spark, a transformer is constructed then immediately applied to the input tbl_spark, returning a tbl_spark

ml_vocabulary() returns a vector of vocabulary built.

Other feature transformers: ft_binarizer(), ft_bucketizer(), ft_chisq_selector(), ft_dct(), ft_elementwise_product(), ft_feature_hasher(), ft_hashing_tf(), ft_idf(), ft_imputer(), ft_index_to_string(), ft_interaction(), ft_lsh, ft_max_abs_scaler(), ft_min_max_scaler(), ft_ngram(), ft_normalizer(), ft_one_hot_encoder_estimator(), ft_one_hot_encoder(), ft_pca(), ft_polynomial_expansion(), ft_quantile_discretizer(), ft_r_formula(), ft_regex_tokenizer(), ft_robust_scaler(), ft_sql_transformer(), ft_standard_scaler(), ft_stop_words_remover(), ft_string_indexer(), ft_tokenizer(), ft_vector_assembler(), ft_vector_indexer(), ft_vector_slicer(), ft_word2vec()