ft_bucketizer
Feature Transformation -- Bucketizer (Transformer)
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
- x
A
spark_connection
,ml_pipeline
, or atbl_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
: Whenx
is aspark_connection
, the function returns aml_transformer
, aml_estimator
, or one of their subclasses. The object contains a pointer to a SparkTransformer
orEstimator
object and can be used to composePipeline
objects.ml_pipeline
: Whenx
is aml_pipeline
, the function returns aml_pipeline
with the transformer or estimator appended to the pipeline.tbl_spark
: Whenx
is atbl_spark
, a transformer is constructed then immediately applied to the inputtbl_spark
, returning atbl_spark
See Also
See http://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()
Examples
# NOT RUN {
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)
# }
# NOT RUN {
# }