merlin.systems.dag.ops.workflow.TransformWorkflow#

class merlin.systems.dag.ops.workflow.TransformWorkflow(workflow=None, sparse_max: Optional[dict] = None, max_batch_size: Optional[int] = None, label_columns: Optional[List[str]] = None, cats: Optional[List[str]] = None, conts: Optional[List[str]] = None)[source]#

This operator takes a workflow and turns it into a ensemble operator so that we can execute feature engineering during ensemble on tritonserver.

__init__(workflow=None, sparse_max: Optional[dict] = None, max_batch_size: Optional[int] = None, label_columns: Optional[List[str]] = None, cats: Optional[List[str]] = None, conts: Optional[List[str]] = None)[source]#

Creates a Transform Workflow operator for a target workflow.

Parameters:

workflow (Nvtabular.Workflow) – The workflow to transform data in ensemble.
sparse_max (dict, optional) – Dictionary representing key(name)/val(max value) pairs of max sparsity, by default None
max_batch_size (int, optional) – Maximum batch size, by default None
label_columns (List[str], optional) – List of strings identifying the label columns, by default None
cats (List[str], optional) – List of strings identifying categorical columns, by default None
conts (List[str], optional) – List of string identifying continuous columns, by default None

Methods

`__init__`([workflow, sparse_max, ...])	Creates a Transform Workflow operator for a target workflow.
`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)	Returns output schema of operator
`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
`set_nvt_model_name`(nvt_model_name)
`transform`(col_selector, transformable)	Run nvtabular workflow transformations.
`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`
`nvt_model_name`
`output_dtype`
`output_properties`
`output_tags`
`supported_formats`
`supports`	Returns what kind of data representation this operator supports

compute_output_schema(input_schema: Schema, col_selector: ColumnSelector, prev_output_schema: Optional[Schema] = None) → Schema[source]#: Returns output schema of operator

transform(col_selector: ColumnSelector, transformable: Transformable) → Transformable[source]#

Run nvtabular workflow transformations.

Parameters:

Returns:

workflow transform

Return type:

Transformable

merlin.systems.dag.ops.workflow.TransformWorkflow