nvtabular.ops.HashedCross

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

Bases: nvtabular.ops.operator.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]])[source]

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)
`inference_initialize`(col_selector, model_config)	Configures this operator for use in inference.
`output_column_names`(col_selector)	Given a set of columns names returns the names of the transformed columns this operator will produce
`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`
`label`
`output_dtype`
`output_properties`
`output_tags`
`supports`	Returns what kind of data representation this operator supports

transform(col_selector: merlin.dag.selector.ColumnSelector, df: pandas.core.frame.DataFrame) → pandas.core.frame.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

column_mapping(col_selector)[source]

property output_dtype