nvtabular.ops.Bucketize#

class nvtabular.ops.Bucketize(boundaries)[source]#

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)[source]#

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[source]#

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#