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