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.
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_"),
...
)x |
A |
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. |
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
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()
## 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)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.