Feature Tranformation -- CountVectorizer

Extracts a vocabulary from document collections.

ft_count_vectorizer(x, input.col, output.col, min.df = NULL, min.tf = NULL,
  vocab.size = NULL, vocabulary.only = FALSE, ...)

Arguments

x

An object (usually a spark_tbl) coercable to a Spark DataFrame.

input.col

The name of the input column(s).

output.col

The name of the output column.

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

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).

vocab.size

Build a vocabulary that only considers the top vocab.size terms ordered by term frequency across the corpus.

vocabulary.only

Boolean; should the vocabulary only be returned?

...

Optional arguments; currently unused.

See also

See http://spark.apache.org/docs/latest/ml-features for more information on the set of transformations available for DataFrame columns in Spark.

Other feature transformation routines: ft_binarizer, ft_bucketizer, ft_discrete_cosine_transform, ft_elementwise_product, ft_index_to_string, ft_one_hot_encoder, ft_quantile_discretizer, ft_regex_tokenizer, ft_stop_words_remover, ft_string_indexer, ft_tokenizer, ft_vector_assembler, sdf_mutate