Feature Transformation – CountVectorizer (Estimator)




Extracts a vocabulary from document collections.


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



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.


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.


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.

See Also

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

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()