nvtabular.ops.Bucketize

class nvtabular.ops.Bucketize(boundaries)

Bases: Operator

This operation transforms continuous features into categorical features with bins based on the provided bin boundaries.

Example usage:

#
cont_names = ['cont1', 'cont2']
boundaries = {
    'cont1': [-50, 0, 50],
    'cont2': [0, 25, 50, 75, 100]
}
bucketize_op = cont_names >> ops.Bucketize(boundaries)
processor = nvt.Workflow(bucketize_op)

Parameters:

boundaries (int, dict or callable) – Defines how to transform the continuous values into bins

__init__(boundaries)

Methods

__init__(boundaries)
column_mapping(col_selector)	Compute which output columns depend on which input columns
compute_column_schema(col_name, input_schema)
compute_input_schema(root_schema, ...)	Given the schemas coming from upstream sources and a column selector for the input columns, returns a set of schemas for the input columns this operator will use
compute_output_schema(input_schema, col_selector)	Given a set of schemas and a column selector for the input columns, returns a set of schemas for the transformed columns this operator will produce
compute_selector(input_schema, selector[, ...])	Provides a hook method for sub-classes to override to implement custom column selection logic.
create_node(selector)
export(path, input_schema, output_schema, ...)	Export the class object as a config and all related files to the user defined path.
inference_initialize(col_selector, model_config)	Configures this operator for use in inference.
load_artifacts([artifact_path])	Load artifacts from disk required for operator function.
output_column_names(col_selector)	Given a set of columns names returns the names of the transformed columns this operator will produce
save_artifacts([artifact_path])	Save artifacts required to be reload operator state from disk
transform(col_selector, df)	Transform the dataframe by applying this operator to the set of input columns
validate_schemas(parents_schema, ...[, ...])	Provides a hook method that sub-classes can override to implement schema validation logic.

Attributes

dependencies	Defines an optional list of column dependencies for this operator.
dynamic_dtypes
export_name	Provides a clear common english identifier for this operator.
is_subgraph
label
output_dtype
output_properties
output_tags
supported_formats
supports	Returns what kind of data representation this operator supports

transform(col_selector: ColumnSelector, df: DataFrame) → DataFrame

Transform the dataframe by applying this operator to the set of input columns

Parameters:

• columns (list of str or list of list of str) – The columns to apply this operator to

• df (Dataframe) – A pandas or cudf dataframe that this operator will work on

Returns:

Returns a transformed dataframe for this operator

Return type:

DataFrame

property output_tags

property output_dtype