ft_ngram: Feature Transformation - NGram (Transformer)

In rstudio/sparklyr: R Interface to Apache Spark

Feature Transformation – NGram (Transformer)

Description

A feature transformer that converts the input array of strings into an array of n-grams. Null values in the input array are ignored. It returns an array of n-grams where each n-gram is represented by a space-separated string of words.

Usage

ft_ngram(
  x,
  input_col = NULL,
  output_col = NULL,
  n = 2,
  uid = random_string("ngram_"),
  ...
)

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.
n	Minimum n-gram length, greater than or equal to 1. Default: 2, bigram features
uid	A character string used to uniquely identify the feature transformer.
...	Optional arguments; currently unused.

Details

When the input is empty, an empty array is returned. When the input array length is less than n (number of elements per n-gram), no n-grams are returned.

Value

The object returned depends on the class of x. If it is a spark_connection, the function returns a ml_estimator or a ml_estimator object. If it is a ml_pipeline, it will return a pipeline with the transformer or estimator appended to it. If a tbl_spark, it will return a tbl_spark with the transformation applied to it.

See Also

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_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()

rstudio/sparklyr documentation built on March 29, 2024, 3:30 p.m.