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extension_interface

openml.extensions.extension_interface #

Extension #

Bases: ABC

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

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

can_handle_flow abstractmethod 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#

flow : OpenMLFlow

Returns#

bool

Source code in openml/extensions/extension_interface.py 
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@classmethod
@abstractmethod
def can_handle_flow(cls, 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
    ----------
    flow : OpenMLFlow

    Returns
    -------
    bool
    """

can_handle_model abstractmethod 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#

model : Any

Returns#

bool

Source code in openml/extensions/extension_interface.py 
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@classmethod
@abstractmethod
def can_handle_model(cls, 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
    ----------
    model : Any

    Returns
    -------
    bool
    """

check_if_model_fitted abstractmethod #

check_if_model_fitted(model: Any) -> bool

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

Parameters#

model : Any

Returns#

bool

Source code in openml/extensions/extension_interface.py 
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@abstractmethod
def check_if_model_fitted(self, model: Any) -> bool:
    """Returns True/False denoting if the model has already been fitted/trained.

    Parameters
    ----------
    model : Any

    Returns
    -------
    bool
    """

create_setup_string abstractmethod #

create_setup_string(model: Any) -> str

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

Parameters#

model : Any

Returns#

str

Source code in openml/extensions/extension_interface.py 
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@abstractmethod
def create_setup_string(self, model: Any) -> str:
    """Create a string which can be used to reinstantiate the given model.

    Parameters
    ----------
    model : Any

    Returns
    -------
    str
    """

flow_to_model abstractmethod #

flow_to_model(flow: OpenMLFlow, initialize_with_defaults: bool = False, strict_version: bool = True) -> Any

Instantiate a model from the flow representation.

Parameters#

flow : OpenMLFlow

bool, optional (default=False)

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

bool, default=True

Whether to fail if version requirements are not fulfilled.

Returns#

Any

Source code in openml/extensions/extension_interface.py 
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@abstractmethod
def flow_to_model(
    self,
    flow: OpenMLFlow,
    initialize_with_defaults: bool = False,  # noqa: FBT001, FBT002
    strict_version: bool = True,  # noqa: FBT002, FBT001
) -> Any:
    """Instantiate a model from the flow representation.

    Parameters
    ----------
    flow : OpenMLFlow

    initialize_with_defaults : bool, 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_version : bool, default=True
        Whether to fail if version requirements are not fulfilled.

    Returns
    -------
    Any
    """

get_version_information abstractmethod #

get_version_information() -> list[str]

List versions of libraries required by the flow.

Returns#

List

Source code in openml/extensions/extension_interface.py 
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@abstractmethod
def get_version_information(self) -> list[str]:
    """List versions of libraries required by the flow.

    Returns
    -------
    List
    """

instantiate_model_from_hpo_class abstractmethod #

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#

model : Any A hyperparameter optimization model which defines the model to be instantiated. trace_iteration : OpenMLTraceIteration Describing the hyperparameter settings to instantiate.

Returns#

Any

Source code in openml/extensions/extension_interface.py 
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@abstractmethod
def instantiate_model_from_hpo_class(
    self,
    model: Any,
    trace_iteration: OpenMLTraceIteration,
) -> Any:
    """Instantiate a base model which can be searched over by the hyperparameter optimization
    model.

    Parameters
    ----------
    model : Any
        A hyperparameter optimization model which defines the model to be instantiated.
    trace_iteration : OpenMLTraceIteration
        Describing the hyperparameter settings to instantiate.

    Returns
    -------
    Any
    """

is_estimator abstractmethod #

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#

model : Any

Returns#

bool

Source code in openml/extensions/extension_interface.py 
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@abstractmethod
def is_estimator(self, 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
    ----------
    model : Any

    Returns
    -------
    bool
    """

model_to_flow abstractmethod #

model_to_flow(model: Any) -> OpenMLFlow

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

Parameters#

model : Any

Returns#

OpenMLFlow

Source code in openml/extensions/extension_interface.py 
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@abstractmethod
def model_to_flow(self, model: Any) -> OpenMLFlow:
    """Transform a model to a flow for uploading it to OpenML.

    Parameters
    ----------
    model : Any

    Returns
    -------
    OpenMLFlow
    """

obtain_parameter_values abstractmethod #

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#

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

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

Source code in openml/extensions/extension_interface.py 
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@abstractmethod
def obtain_parameter_values(
    self,
    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
    ----------
    flow : OpenMLFlow
        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
    """

seed_model abstractmethod #

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#

model : Any The model to be seeded seed : int

Returns#

model

Source code in openml/extensions/extension_interface.py 
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@abstractmethod
def seed_model(self, 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
    ----------
    model : Any
        The model to be seeded
    seed : int

    Returns
    -------
    model
    """