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Rescale each feature individually to range [-1, 1] by dividing through the largest maximum absolute value in each feature. It does not shift/center the data, and thus does not destroy any sparsity.

Usage

ft_max_abs_scaler(
  x,
  input_col = NULL,
  output_col = NULL,
  uid = random_string("max_abs_scaler_"),
  ...
)

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.

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. 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.

Details

In the case where x is a tbl_spark, the estimator fits against x to obtain a transformer, returning a tbl_spark.

Examples

if (FALSE) { # \dontrun{
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)

features <- c("Sepal_Length", "Sepal_Width", "Petal_Length", "Petal_Width")

iris_tbl %>%
  ft_vector_assembler(
    input_col = features,
    output_col = "features_temp"
  ) %>%
  ft_max_abs_scaler(
    input_col = "features_temp",
    output_col = "features"
  )
} # }