sparklyr (version 1.8.4)

ft_regex_tokenizer: Feature Transformation -- RegexTokenizer (Transformer)

Description

A regex based tokenizer that extracts tokens either by using the provided regex pattern to split the text (default) or repeatedly matching the regex (if gaps is false). Optional parameters also allow filtering tokens using a minimal length. It returns an array of strings that can be empty.

Usage

ft_regex_tokenizer(
  x,
  input_col = NULL,
  output_col = NULL,
  gaps = TRUE,
  min_token_length = 1,
  pattern = "\\s+",
  to_lower_case = TRUE,
  uid = random_string("regex_tokenizer_"),
  ...
)

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

Arguments

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.

gaps

Indicates whether regex splits on gaps (TRUE) or matches tokens (FALSE).

min_token_length

Minimum token length, greater than or equal to 0.

pattern

The regular expression pattern to be used.

to_lower_case

Indicates whether to convert all characters to lowercase before tokenizing.

uid

A character string used to uniquely identify the feature transformer.

...

Optional arguments; currently unused.

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_count_vectorizer(), 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_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()