merlin.systems.dag.ops.fil.PredictForest

class merlin.systems.dag.ops.fil.PredictForest(model, input_schema, *, backend='python', **fil_params)

Bases: InferenceOperator

Operator for running inference on Forest models.

This works for gradient-boosted decision trees (GBDTs) and Random forests (RF). While RF and GBDT algorithms differ in the way they train the models, they both produce a decision forest as their output.

Uses the Forest Inference Library (FIL) backend for inference.

__init__(model, input_schema, *, backend='python', **fil_params)

Instantiate a FIL inference operator.

Parameters:

• model (Forest Model Instance) – A forest model class. Supports XGBoost, LightGBM, and Scikit-Learn.

• input_schema (merlin.schema.Schema) – The schema representing the input columns expected by the model.

• backend (str) – The Triton backend to use to when running this operator.

• **fil_params – The parameters to pass to the FIL operator.

Methods

__init__(model, input_schema, *[, backend])	Instantiate a FIL inference 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_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
set_fil_model_name(fil_model_name)
transform(col_selector, transformable)	Transform the dataframe by applying this FIL 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
fil_model_name
is_subgraph
label
output_dtype
output_properties
output_tags
scalar_shape
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

Return the output schema representing the columns this operator returns.

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.

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.

property fil_model_name

set_fil_model_name(fil_model_name)

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

Transform the dataframe by applying this FIL 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

load_artifacts(artifact_path)