openml.extensions.Extension

class openml.extensions.Extension

Defines the interface to connect machine learning libraries to OpenML-Python.

See openml.extension.sklearn.extension for an implementation to bootstrap from.

abstract classmethod can_handle_flow(flow: OpenMLFlow) bool

Check whether a given flow can be handled by this extension.

This is typically done by parsing the external_version field.

Parameters:
flowOpenMLFlow
Returns:
bool
abstract classmethod can_handle_model(model: Any) bool

Check whether a model flow can be handled by this extension.

This is typically done by checking the type of the model, or the package it belongs to.

Parameters:
modelAny
Returns:
bool
abstract check_if_model_fitted(model: Any) bool

Returns True/False denoting if the model has already been fitted/trained.

Parameters:
modelAny
Returns:
bool
abstract create_setup_string(model: Any) str

Create a string which can be used to reinstantiate the given model.

Parameters:
modelAny
Returns:
str
abstract flow_to_model(flow: OpenMLFlow, initialize_with_defaults: bool = False, strict_version: bool = True) Any

Instantiate a model from the flow representation.

Parameters:
flowOpenMLFlow
initialize_with_defaultsbool, optional (default=False)

If this flag is set, the hyperparameter values of flows will be ignored and a flow with its defaults is returned.

strict_versionbool, default=True

Whether to fail if version requirements are not fulfilled.

Returns:
Any
abstract get_version_information() List[str]

List versions of libraries required by the flow.

Returns:
List
abstract instantiate_model_from_hpo_class(model: Any, trace_iteration: OpenMLTraceIteration) Any

Instantiate a base model which can be searched over by the hyperparameter optimization model.

Parameters:
modelAny

A hyperparameter optimization model which defines the model to be instantiated.

trace_iterationOpenMLTraceIteration

Describing the hyperparameter settings to instantiate.

Returns:
Any
abstract is_estimator(model: Any) bool

Check whether the given model is an estimator for the given extension.

This function is only required for backwards compatibility and will be removed in the near future.

Parameters:
modelAny
Returns:
bool
abstract model_to_flow(model: Any) OpenMLFlow

Transform a model to a flow for uploading it to OpenML.

Parameters:
modelAny
Returns:
OpenMLFlow
abstract obtain_parameter_values(flow: OpenMLFlow, model: Any = None) List[Dict[str, Any]]

Extracts all parameter settings required for the flow from the model.

If no explicit model is provided, the parameters will be extracted from flow.model instead.

Parameters:
flowOpenMLFlow

OpenMLFlow object (containing flow ids, i.e., it has to be downloaded from the server)

model: Any, optional (default=None)

The model from which to obtain the parameter values. Must match the flow signature. If None, use the model specified in OpenMLFlow.model.

Returns:
list

A list of dicts, where each dict has the following entries: - oml:name : str: The OpenML parameter name - oml:value : mixed: A representation of the parameter value - oml:component : int: flow id to which the parameter belongs

abstract seed_model(model: Any, seed: int | None) Any

Set the seed of all the unseeded components of a model and return the seeded model.

Required so that all seed information can be uploaded to OpenML for reproducible results.

Parameters:
modelAny

The model to be seeded

seedint
Returns:
model