merlin.systems.dag.ops.implicit.PredictImplicit

class merlin.systems.dag.ops.implicit.PredictImplicit(model, num_to_recommend: int = 10, **kwargs)

Bases: PipelineableInferenceOperator

Operator for running inference on Implicit models..

__init__(model, num_to_recommend: int = 10, **kwargs)

Instantiate an Implicit prediction operator.

Parameters:

• model (An Implicit Model instance) –

• num_to_recommend (int) – the number of items to return

Methods

__init__(model[, num_to_recommend])	Instantiate an Implicit prediction operator.
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, ...)	Return the input schema representing the input columns this operator expects to use.
compute_output_schema(input_schema, col_selector)	Return the output schema representing the columns this operator returns.
compute_selector(input_schema, selector[, ...])	Provides a hook method for sub-classes to override to implement custom column selection logic.
create_node(selector)	_summary_
export(path, input_schema, output_schema[, ...])	Export the class and related files to the path specified.
from_config(config, **kwargs)	Instantiate the class from a dictionary representation.
from_model_registry(registry, **kwargs)	Loads the InferenceOperator from the provided ModelRegistry.
from_path(path, **kwargs)	Loads the InferenceOperator from the path where it was exported after training.
load_artifacts(artifact_path)
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.
exportable_backends
is_subgraph
label
output_dtype
output_properties
output_tags
scalar_shape
supported_formats
supports	Returns what kind of data representation this operator supports

load_artifacts(artifact_path: str)

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

Return the input schema representing the input columns this operator expects to use.

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

Return the output schema representing the columns this operator returns.

property exportable_backends

export(path: str, input_schema: Schema, output_schema: Schema, params: Optional[dict] = None, node_id: Optional[int] = None, version: int = 1, backend: str = 'ensemble')

Export the class and related files to the path specified.

classmethod from_config(config: dict, **kwargs) → PredictImplicit

Instantiate the class from a dictionary representation.

Expected config structure: {

“input_dict”: str # JSON dict with input names and schemas “params”: str # JSON dict with params saved at export

}

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

Transform the dataframe by applying this operator to the set of input columns.

Parameters:

df (DictArray) – A pandas or cudf dataframe that this operator will work on

Returns:

Returns a transformed dataframe for this operator

Return type:

DictArray