nvtabular.ops.Operator

class nvtabular.ops.Operator

Bases: object

Base class for all operator classes.

__init__()

Methods

__init__()
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.
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, transformable)	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

compute_selector(input_schema: Schema, selector: ColumnSelector, parents_selector: Optional[ColumnSelector] = None, dependencies_selector: Optional[ColumnSelector] = None) → ColumnSelector

Provides a hook method for sub-classes to override to implement custom column selection logic.

Parameters:

• input_schema (Schema) – Schemas of the columns to apply this operator to

• selector (ColumnSelector) – Column selector to apply to the input schema

• parents_selector (ColumnSelector) – Combined selectors of the upstream parents feeding into this operator

• dependencies_selector (ColumnSelector) – Combined selectors of the upstream dependencies feeding into this operator

Returns:

Revised column selector to apply to the input schema

Return type:

ColumnSelector

compute_input_schema(root_schema: Schema, parents_schema: Schema, deps_schema: Schema, selector: ColumnSelector) → 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

Parameters:

• root_schema (Schema) – Base schema of the dataset before running any operators.

• parents_schema (Schema) – The combined schemas of the upstream parents feeding into this operator

• deps_schema (Schema) – The combined schemas of the upstream dependencies feeding into this operator

• col_selector (ColumnSelector) – The column selector to apply to the input schema

Returns:

The schemas of the columns used by this operator

Return type:

Schema

compute_output_schema(input_schema: Schema, col_selector: ColumnSelector, prev_output_schema: Optional[Schema] = None) → Schema

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

Parameters:

• input_schema (Schema) – The schemas of the columns to apply this operator to

• col_selector (ColumnSelector) – The column selector to apply to the input schema

Returns:

The schemas of the columns produced by this operator

Return type:

Schema

validate_schemas(parents_schema: Schema, deps_schema: Schema, input_schema: Schema, output_schema: Schema, strict_dtypes: bool = False)

Provides a hook method that sub-classes can override to implement schema validation logic.

Sub-class implementations should raise an exception if the schemas are not valid for the operations they implement.

Parameters:

• parents_schema (Schema) – The combined schemas of the upstream parents feeding into this operator

• deps_schema (Schema) – The combined schemas of the upstream dependencies feeding into this operator

• input_schema (Schema) – The schemas of the columns to apply this operator to

• output_schema (Schema) – The schemas of the columns produced by this operator

• strict_dtypes (Boolean, optional) – Enables strict checking for column dtype matching if True, by default False

transform(col_selector: ColumnSelector, transformable: Transformable) → Transformable

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)

Compute which output columns depend on which input columns

Parameters:

col_selector (ColumnSelector) – A selector containing a list of column names

Returns:

Mapping from output column names to list of the input columns they rely on

Return type:

Dict[str, List[str]]

load_artifacts(artifact_path: Optional[PathLike] = None)

Load artifacts from disk required for operator function.

Parameters:

artifact_path (str) – The path where artifacts are loaded from

save_artifacts(artifact_path: Optional[PathLike] = None) → None

Save artifacts required to be reload operator state from disk

Parameters:

artifact_path (str) – The path where artifacts are to be saved

compute_column_schema(col_name, input_schema)

property dynamic_dtypes

property is_subgraph

output_column_names(col_selector: ColumnSelector) → ColumnSelector

Given a set of columns names returns the names of the transformed columns this operator will produce

Parameters:

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

Returns:

The names of columns produced by this operator

Return type:

list of str, or list of list of str

property dependencies: List[Union[str, Any]]

Defines an optional list of column dependencies for this operator. This lets you consume columns that aren’t part of the main transformation workflow.

Returns:

Extra dependencies of this operator. Defaults to None

Return type:

str, list of str or ColumnSelector, optional

property output_dtype

property output_tags

property output_properties

property label: str

create_node(selector)

property supports: Supports

Returns what kind of data representation this operator supports

property supported_formats: DataFormats

property export_name

Provides a clear common english identifier for this operator.

Returns:

Name of the current class as spelled in module.

Return type:

String

export(path: str, input_schema: Schema, output_schema: Schema, **kwargs)

Export the class object as a config and all related files to the user defined path.

Parameters:

• path (str) – Artifact export path

• input_schema (Schema) – A schema with information about the inputs to this operator.

• output_schema (Schema) – A schema with information about the outputs of this operator.

• params (dict, optional) – Parameters dictionary of key, value pairs stored in exported config, by default None.

• node_id (int, optional) – The placement of the node in the graph (starts at 1), by default None.

• version (int, optional) – The version of the operator, by default 1.

Returns:

model_config – The config for the exported operator.

Return type:

dict