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inference

Algorithm to evaluate a model on remote data.

Classes​

ModelInference​

class ModelInference(    *,    model: INFERRABLE_MODEL_TR,    class_outputs: Optional[list[str]] = None,    postprocessors: Optional[list[dict[str, Any]]] = None,    pretrained_file: Optional[Union[str, os.PathLike]] = None,    project_id: Optional[str] = None,):

Algorithm for running inference on a model and returning the predictions.

danger

This algorithm could potentially return the data unfiltered so should only be used when the other party is trusted.

Arguments

  • class_outputs: A list of strings corresponding to prediction outputs. If provided, the model will return a dataframe of results with the class outputs list elements as columns. Defaults to None.
  • model: The model to infer on remote data.
  • postprocessors: Post-processing configuration, which can be either a preset string or a list of postprocessor configuration dicts.
  • pretrained_file: A file path or a string containing a pre-trained model. Defaults to None.

Attributes

  • class_name: The name of the algorithm class.
  • class_outputs: A list of strings corresponding to prediction outputs. If provided, the model will return a dataframe of results with the class outputs list elements as columns. Defaults to None.
  • fields_dict: A dictionary mapping all attributes that will be serialized in the class to their marshmallow field type. (e.g. fields_dict = {"class_name": fields.Str()}).
  • model: The model to infer on remote data.
  • nested_fields: A dictionary mapping all nested attributes to a registry that contains class names mapped to the respective classes. (e.g. nested_fields = {"datastructure": datastructure.registry})
  • postprocessors: A list of postprocessor configuration dicts. Defaults to None.
  • pretrained_file: A file path or a string containing a pre-trained model. Defaults to None.

Ancestors​

  • bitfount.federated.algorithms.model_algorithms.base._BaseModelAlgorithmFactory
  • BaseAlgorithmFactory
  • abc.ABC
  • bitfount.federated.roles._RolesMixIn
  • bitfount.types._BaseSerializableObjectMixIn
  • typing.Generic

Variables​

Methods​

create​

def create(self, role: Union[str, Role], **kwargs: Any) ‑> Any:

Create an instance representing the role specified.

modeller​

def modeller(    self, *, context: ProtocolContext, **kwargs: Any,) ‑> bitfount.federated.algorithms.model_algorithms.inference._ModellerSide:

Returns the modeller side of the ModelInference algorithm.

worker​

def worker(    self, *, hub: BitfountHub, context: ProtocolContext, **kwargs: Any,) ‑> bitfount.federated.algorithms.model_algorithms.inference._WorkerSide:

Returns the worker side of the ModelInference algorithm.

Arguments

  • hub: BitfountHub object to use for communication with the hub.
  • context: Optional. Run-time protocol context details for running. May contain URLs for downloading models directly rather than from the hub.
  • ****kwargs**: Additional keyword arguments to pass to the worker side.

Returns Worker side of the ModelInference algorithm.