nvtabular.ops.HashedCross

class nvtabular.ops.HashedCross(num_buckets: Union[int, Dict[str, int]])

Bases: Operator

This ops creates hashed cross columns by first combining categorical features and hashing the combined feature, then reducing modulo the number of buckets.

Example usage:

# Define parameters
cat_names = [["name-string", "id"]]
num_buckets = 10

# Use HashedCross operator to define NVTabular workflow
hashed_cross = cat_names >> ops.HashedCross(num_buckets)
processor = nvtabular.Workflow(hashed_cross)

Parameters:

num_buckets (int or dict) – Column-wise modulo to apply after hash function. Note that this means that the corresponding value will be the categorical cardinality of the transformed categorical feature. That value will be used as the number of “hash buckets” for every output feature.

__init__(num_buckets: Union[int, Dict[str, int]])

Methods

__init__(num_buckets)
column_mapping(col_selector)
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.
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:

• col_selector (ColumnSelector) – The columns to apply this operator to

• transformable (Transformable) – A pandas or cudf dataframe that this operator will work on

Returns:

Returns a transformed dataframe or dictarray for this operator

Return type:

Transformable

column_mapping(col_selector)

property output_dtype