Feature Transformation – Binarizer (Transformer)

R/ml_feature_binarizer.R

ft_binarizer

Description

Apply thresholding to a column, such that values less than or equal to the threshold are assigned the value 0.0, and values greater than the threshold are assigned the value 1.0. Column output is numeric for compatibility with other modeling functions.

Usage

 
ft_binarizer( 
  x, 
  input_col, 
  output_col, 
  threshold = 0, 
  uid = random_string("binarizer_"), 
  ... 
)

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.
threshold	Threshold used to binarize continuous features.
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_binarizer( 
    input_col = "Sepal_Length", 
    output_col = "Sepal_Length_bin", 
    threshold = 5 
  ) %>% 
  select(Sepal_Length, Sepal_Length_bin, Species) 
#> # Source: spark<?> [?? x 3]
#>    Sepal_Length Sepal_Length_bin Species
#>           <dbl>            <dbl> <chr>  
#>  1          5.1                1 setosa 
#>  2          4.9                0 setosa 
#>  3          4.7                0 setosa 
#>  4          4.6                0 setosa 
#>  5          5                  0 setosa 
#>  6          5.4                1 setosa 
#>  7          4.6                0 setosa 
#>  8          5                  0 setosa 
#>  9          4.4                0 setosa 
#> 10          4.9                0 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_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_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()