Feature Transformation – Bucketizer (Transformer)

R/ml_feature_bucketizer.R

ft_bucketizer

Description

Similar to R’s cut function, this transforms a numeric column into a discretized column, with breaks specified through the splits parameter.

Usage

 
ft_bucketizer( 
  x, 
  input_col = NULL, 
  output_col = NULL, 
  splits = NULL, 
  input_cols = NULL, 
  output_cols = NULL, 
  splits_array = NULL, 
  handle_invalid = "error", 
  uid = random_string("bucketizer_"), 
  ... 
)

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.
splits	A numeric vector of cutpoints, indicating the bucket boundaries.
input_cols	Names of input columns.
output_cols	Names of output columns.
splits_array	Parameter for specifying multiple splits parameters. Each element in this array can be used to map continuous features into buckets.
handle_invalid	(Spark 2.1.0+) Param for how to handle invalid entries. Options are ‘skip’ (filter out rows with invalid values), ‘error’ (throw an error), or ‘keep’ (keep invalid values in a special additional bucket). Default: “error”
uid	A character string used to uniquely identify the feature transformer.
…	Optional arguments; currently unused.

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

Examples

library(sparklyr)
 
library(dplyr) 
 
sc <- spark_connect(master = "local") 
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE) 
 
iris_tbl %>% 
  ft_bucketizer( 
    input_col = "Sepal_Length", 
    output_col = "Sepal_Length_bucket", 
    splits = c(0, 4.5, 5, 8) 
  ) %>% 
  select(Sepal_Length, Sepal_Length_bucket, Species) 
#> # Source: spark<?> [?? x 3]
#>    Sepal_Length Sepal_Length_bucket Species
#>           <dbl>               <dbl> <chr>  
#>  1          5.1                   2 setosa 
#>  2          4.9                   1 setosa 
#>  3          4.7                   1 setosa 
#>  4          4.6                   1 setosa 
#>  5          5                     2 setosa 
#>  6          5.4                   2 setosa 
#>  7          4.6                   1 setosa 
#>  8          5                     2 setosa 
#>  9          4.4                   0 setosa 
#> 10          4.9                   1 setosa 
#> # … with more rows

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_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_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()