functions

openml.runs.functions

delete_run

delete_run(run_id: int) -> bool

Delete run with id run_id from the OpenML server.

You can only delete runs which you uploaded.

PARAMETER	DESCRIPTION
run_id	OpenML id of the run TYPE: int

RETURNS	DESCRIPTION
bool	True if the deletion was successful. False otherwise.

Source code in openml/runs/functions.py

def delete_run(run_id: int) -> bool:
    """Delete run with id `run_id` from the OpenML server.

    You can only delete runs which you uploaded.

    Parameters
    ----------
    run_id : int
        OpenML id of the run

    Returns
    -------
    bool
        True if the deletion was successful. False otherwise.
    """
    return openml._backend.run.delete(run_id)

format_prediction

format_prediction(task: OpenMLSupervisedTask, repeat: int, fold: int, index: int, prediction: str | int | float, truth: str | int | float, sample: int | None = None, proba: dict[str, float] | None = None) -> list[str | int | float]

Format the predictions in the specific order as required for the run results.

PARAMETER	DESCRIPTION
task	Task for which to format the predictions. TYPE: OpenMLSupervisedTask
repeat	From which repeat this predictions is made. TYPE: int
fold	From which fold this prediction is made. TYPE: int
index	For which index this prediction is made. TYPE: int
prediction	The predicted class label or value. TYPE: str | int | float
truth	The true class label or value. TYPE: str | int | float
sample	From which sample set this prediction is made. Required only for LearningCurve tasks. TYPE: int | None DEFAULT: None
proba	For classification tasks only. A mapping from each class label to their predicted probability. The dictionary should contain an entry for each of the task.class_labels. E.g.: {"Iris-Setosa": 0.2, "Iris-Versicolor": 0.7, "Iris-Virginica": 0.1} TYPE: dict[str, float] | None DEFAULT: None

RETURNS	DESCRIPTION
A list with elements for the prediction results of a run.
The returned order of the elements is (if available):	[repeat, fold, sample, index, prediction, truth, *probabilities]
This order follows the R Client API.

Source code in openml/runs/functions.py

def format_prediction(  # noqa: PLR0913
    task: OpenMLSupervisedTask,
    repeat: int,
    fold: int,
    index: int,
    prediction: str | int | float,
    truth: str | int | float,
    sample: int | None = None,
    proba: dict[str, float] | None = None,
) -> list[str | int | float]:
    """Format the predictions in the specific order as required for the run results.

    Parameters
    ----------
    task: OpenMLSupervisedTask
        Task for which to format the predictions.
    repeat: int
        From which repeat this predictions is made.
    fold: int
        From which fold this prediction is made.
    index: int
        For which index this prediction is made.
    prediction: str, int or float
        The predicted class label or value.
    truth: str, int or float
        The true class label or value.
    sample: int, optional (default=None)
        From which sample set this prediction is made.
        Required only for LearningCurve tasks.
    proba: Dict[str, float], optional (default=None)
        For classification tasks only.
        A mapping from each class label to their predicted probability.
        The dictionary should contain an entry for each of the `task.class_labels`.
        E.g.: {"Iris-Setosa": 0.2, "Iris-Versicolor": 0.7, "Iris-Virginica": 0.1}

    Returns
    -------
    A list with elements for the prediction results of a run.

    The returned order of the elements is (if available):
        [repeat, fold, sample, index, prediction, truth, *probabilities]

    This order follows the R Client API.
    """
    if isinstance(task, OpenMLClassificationTask):
        if proba is None:
            raise ValueError("`proba` is required for classification task")
        if task.class_labels is None:
            raise ValueError("The classification task must have class labels set")
        if not set(task.class_labels) == set(proba):
            raise ValueError("Each class should have a predicted probability")
        if sample is None:
            if isinstance(task, OpenMLLearningCurveTask):
                raise ValueError("`sample` can not be none for LearningCurveTask")

            sample = 0
        probabilities = [proba[c] for c in task.class_labels]
        return [repeat, fold, sample, index, prediction, truth, *probabilities]

    if isinstance(task, OpenMLRegressionTask):
        return [repeat, fold, index, prediction, truth]

    raise NotImplementedError(
        f"Formatting for {type(task)} is not supported."
        f"Supported task types: OpenMLClassificationTask, OpenMLRegressionTask,"
        f"and OpenMLLearningCurveTask. "
        f"Please ensure your task is one of these types."
    )

get_run

get_run(run_id: int, ignore_cache: bool = False) -> OpenMLRun

Gets run corresponding to run_id.

PARAMETER	DESCRIPTION
run_id	TYPE: int
ignore_cache	Whether to ignore the cache. If true this will download and overwrite the run xml even if the requested run is already cached. TYPE: bool DEFAULT: False

RETURNS	DESCRIPTION
run	Run corresponding to ID, fetched from the server. TYPE: OpenMLRun

Source code in openml/runs/functions.py

@openml.utils.thread_safe_if_oslo_installed
def get_run(run_id: int, ignore_cache: bool = False) -> OpenMLRun:  # noqa: FBT002
    """Gets run corresponding to run_id.

    Parameters
    ----------
    run_id : int

    ignore_cache : bool
        Whether to ignore the cache. If ``true`` this will download and overwrite the run xml
        even if the requested run is already cached.

    Returns
    -------
    run : OpenMLRun
        Run corresponding to ID, fetched from the server.
    """
    return openml._backend.run.get(
        run_id,
        reset_cache=ignore_cache,
    )

get_run_trace

get_run_trace(run_id: int) -> OpenMLRunTrace

Get the optimization trace object for a given run id.

PARAMETER	DESCRIPTION
run_id	TYPE: int

RETURNS	DESCRIPTION
OpenMLTrace

Source code in openml/runs/functions.py

def get_run_trace(run_id: int) -> OpenMLRunTrace:
    """
    Get the optimization trace object for a given run id.

    Parameters
    ----------
    run_id : int

    Returns
    -------
    openml.runs.OpenMLTrace
    """
    trace_xml = openml._backend.run.download_text_file(f"run/trace/{run_id}")
    return OpenMLRunTrace.trace_from_xml(trace_xml)

get_runs

get_runs(run_ids: list[int]) -> list[OpenMLRun]

Gets all runs in run_ids list.

PARAMETER	DESCRIPTION
run_ids	TYPE: list of ints

RETURNS	DESCRIPTION
runs	List of runs corresponding to IDs, fetched from the server. TYPE: list of OpenMLRun

Source code in openml/runs/functions.py

def get_runs(run_ids: list[int]) -> list[OpenMLRun]:
    """Gets all runs in run_ids list.

    Parameters
    ----------
    run_ids : list of ints

    Returns
    -------
    runs : list of OpenMLRun
        List of runs corresponding to IDs, fetched from the server.
    """
    runs = []
    for run_id in run_ids:
        runs.append(get_run(run_id))
    return runs

initialize_model_from_run

initialize_model_from_run(run_id: int, *, strict_version: bool = True) -> Any

Initialized a model based on a run_id (i.e., using the exact same parameter settings)

PARAMETER	DESCRIPTION
run_id	The Openml run_id TYPE: int
strict_version	See flow_to_model strict_version. TYPE: bool DEFAULT: True

RETURNS	DESCRIPTION
model

Source code in openml/runs/functions.py

def initialize_model_from_run(run_id: int, *, strict_version: bool = True) -> Any:
    """
    Initialized a model based on a run_id (i.e., using the exact
    same parameter settings)

    Parameters
    ----------
    run_id : int
        The Openml run_id
    strict_version: bool (default=True)
        See `flow_to_model` strict_version.

    Returns
    -------
    model
    """
    run = get_run(run_id)
    # TODO(eddiebergman): I imagine this is None if it's not published,
    # might need to raise an explicit error for that
    if run.setup_id is None:
        raise ValueError(f"Run {run_id} has no associated setup_id. Cannot initialize model.")
    return initialize_model(setup_id=run.setup_id, strict_version=strict_version)

initialize_model_from_trace

initialize_model_from_trace(run_id: int, repeat: int, fold: int, iteration: int | None = None) -> Any

Initialize a model based on the parameters that were set by an optimization procedure (i.e., using the exact same parameter settings)

PARAMETER	DESCRIPTION
run_id	The Openml run_id. Should contain a trace file, otherwise a OpenMLServerException is raised TYPE: int
repeat	The repeat nr (column in trace file) TYPE: int
fold	The fold nr (column in trace file) TYPE: int
iteration	The iteration nr (column in trace file). If None, the best (selected) iteration will be searched (slow), according to the selection criteria implemented in OpenMLRunTrace.get_selected_iteration TYPE: int DEFAULT: None

RETURNS	DESCRIPTION
model

Source code in openml/runs/functions.py

def initialize_model_from_trace(
    run_id: int,
    repeat: int,
    fold: int,
    iteration: int | None = None,
) -> Any:
    """
    Initialize a model based on the parameters that were set
    by an optimization procedure (i.e., using the exact same
    parameter settings)

    Parameters
    ----------
    run_id : int
        The Openml run_id. Should contain a trace file,
        otherwise a OpenMLServerException is raised

    repeat : int
        The repeat nr (column in trace file)

    fold : int
        The fold nr (column in trace file)

    iteration : int
        The iteration nr (column in trace file). If None, the
        best (selected) iteration will be searched (slow),
        according to the selection criteria implemented in
        OpenMLRunTrace.get_selected_iteration

    Returns
    -------
    model
    """
    run = get_run(run_id)
    # TODO(eddiebergman): I imagine this is None if it's not published,
    # might need to raise an explicit error for that
    if run.flow_id is None:
        raise ValueError(f"Run {run_id} has no associated flow_id. Cannot initialize model.")

    flow = get_flow(run.flow_id)
    run_trace = get_run_trace(run_id)

    if iteration is None:
        iteration = run_trace.get_selected_iteration(repeat, fold)

    request = (repeat, fold, iteration)
    if request not in run_trace.trace_iterations:
        raise ValueError("Combination repeat, fold, iteration not available")
    current = run_trace.trace_iterations[(repeat, fold, iteration)]

    search_model = initialize_model_from_run(run_id)
    return flow.extension.instantiate_model_from_hpo_class(search_model, current)

list_runs

list_runs(offset: int | None = None, size: int | None = None, id: list | None = None, task: list[int] | None = None, setup: list | None = None, flow: list | None = None, uploader: list | None = None, tag: str | None = None, study: int | None = None, display_errors: bool = False, task_type: TaskType | int | None = None) -> DataFrame

List all runs matching all of the given filters. (Supports large amount of results)

PARAMETER	DESCRIPTION
offset	the number of runs to skip, starting from the first TYPE: int DEFAULT: None
size	the maximum number of runs to show TYPE: int DEFAULT: None
id	TYPE: list DEFAULT: None
task	TYPE: list DEFAULT: None
setup	TYPE: list | None DEFAULT: None
flow	TYPE: list DEFAULT: None
uploader	TYPE: list DEFAULT: None
tag	TYPE: str DEFAULT: None
study	TYPE: int DEFAULT: None
display_errors	Whether to list runs which have an error (for example a missing prediction file). TYPE: (bool, optional(default=None)) DEFAULT: False
task_type	TYPE: str DEFAULT: None

RETURNS	DESCRIPTION
dataframe

Source code in openml/runs/functions.py

def list_runs(  # noqa: PLR0913
    offset: int | None = None,
    size: int | None = None,
    id: list | None = None,  # noqa: A002
    task: list[int] | None = None,
    setup: list | None = None,
    flow: list | None = None,
    uploader: list | None = None,
    tag: str | None = None,
    study: int | None = None,
    display_errors: bool = False,  # noqa: FBT002
    task_type: TaskType | int | None = None,
) -> pd.DataFrame:
    """
    List all runs matching all of the given filters.
    (Supports large amount of results)

    Parameters
    ----------
    offset : int, optional
        the number of runs to skip, starting from the first
    size : int, optional
        the maximum number of runs to show

    id : list, optional

    task : list, optional

    setup: list, optional

    flow : list, optional

    uploader : list, optional

    tag : str, optional

    study : int, optional

    display_errors : bool, optional (default=None)
        Whether to list runs which have an error (for example a missing
        prediction file).

    task_type : str, optional

    Returns
    -------
    dataframe
    """
    if id is not None and (not isinstance(id, list)):
        raise TypeError("id must be of type list.")
    if task is not None and (not isinstance(task, list)):
        raise TypeError("task must be of type list.")
    if setup is not None and (not isinstance(setup, list)):
        raise TypeError("setup must be of type list.")
    if flow is not None and (not isinstance(flow, list)):
        raise TypeError("flow must be of type list.")
    if uploader is not None and (not isinstance(uploader, list)):
        raise TypeError("uploader must be of type list.")

    listing_call = partial(
        openml._backend.run.list,
        ids=id,
        task=task,
        setup=setup,
        flow=flow,
        uploader=uploader,
        tag=tag,
        study=study,
        display_errors=display_errors,
        task_type=task_type,
    )
    batches = openml.utils._list_all(listing_call, offset=offset, limit=size)
    if len(batches) == 0:
        return pd.DataFrame()

    return pd.concat(batches)

run_exists

run_exists(task_id: int, setup_id: int) -> set[int]

Checks whether a task/setup combination is already present on the server.

PARAMETER	DESCRIPTION
task_id	TYPE: int
setup_id	TYPE: int

RETURNS	DESCRIPTION
Set run ids for runs where flow setup_id was run on task_id. Empty	set if it wasn't run yet.

Source code in openml/runs/functions.py

def run_exists(task_id: int, setup_id: int) -> set[int]:
    """Checks whether a task/setup combination is already present on the
    server.

    Parameters
    ----------
    task_id : int

    setup_id : int

    Returns
    -------
        Set run ids for runs where flow setup_id was run on task_id. Empty
        set if it wasn't run yet.
    """
    if setup_id <= 0:
        # openml setups are in range 1-inf
        return set()

    try:
        result = list_runs(task=[task_id], setup=[setup_id])
        return set() if result.empty else set(result["run_id"])
    except OpenMLServerException as exception:
        # error code implies no results. The run does not exist yet
        if exception.code != ERROR_CODE:
            raise exception
        return set()

run_flow_on_task

run_flow_on_task(flow: OpenMLFlow, task: OpenMLTask, avoid_duplicate_runs: bool | None = None, flow_tags: list[str] | None = None, seed: int | None = None, add_local_measures: bool = True, upload_flow: bool = False, n_jobs: int | None = None) -> OpenMLRun

Run the model provided by the flow on the dataset defined by task.

Takes the flow and repeat information into account. The Flow may optionally be published.

PARAMETER	DESCRIPTION
flow	A flow wraps a machine learning model together with relevant information. The model has a function fit(X,Y) and predict(X), all supervised estimators of scikit learn follow this definition of a model. TYPE: OpenMLFlow
task	Task to perform. This may be an OpenMLFlow instead if the first argument is an OpenMLTask. TYPE: OpenMLTask
avoid_duplicate_runs	If True, the run will throw an error if the setup/task combination is already present on the server. This feature requires an internet connection. If not set, it will use the default from your openml configuration (False if unset). TYPE: (bool, optional(default=None)) DEFAULT: None
flow_tags	A list of tags that the flow should have at creation. TYPE: (List[str], optional(default=None)) DEFAULT: None
seed	Models that are not seeded will get this seed. TYPE: int | None DEFAULT: None
add_local_measures	Determines whether to calculate a set of evaluation measures locally, to later verify server behaviour. TYPE: (bool, optional(default=True)) DEFAULT: True
upload_flow	If True, upload the flow to OpenML if it does not exist yet. If False, do not upload the flow to OpenML. TYPE: bool(default=False) DEFAULT: False
n_jobs	The number of processes/threads to distribute the evaluation asynchronously. If None or 1, then the evaluation is treated as synchronous and processed sequentially. If -1, then the job uses as many cores available. TYPE: int(default=None) DEFAULT: None

RETURNS	DESCRIPTION
run	Result of the run. TYPE: OpenMLRun

Source code in openml/runs/functions.py

def run_flow_on_task(  # noqa: PLR0913
    flow: OpenMLFlow,
    task: OpenMLTask,
    avoid_duplicate_runs: bool | None = None,
    flow_tags: list[str] | None = None,
    seed: int | None = None,
    add_local_measures: bool = True,  # noqa: FBT002
    upload_flow: bool = False,  # noqa: FBT002
    n_jobs: int | None = None,
) -> OpenMLRun:
    """Run the model provided by the flow on the dataset defined by task.

    Takes the flow and repeat information into account.
    The Flow may optionally be published.

    Parameters
    ----------
    flow : OpenMLFlow
        A flow wraps a machine learning model together with relevant information.
        The model has a function fit(X,Y) and predict(X),
        all supervised estimators of scikit learn follow this definition of a model.
    task : OpenMLTask
        Task to perform. This may be an OpenMLFlow instead if the first argument is an OpenMLTask.
    avoid_duplicate_runs : bool, optional (default=None)
        If True, the run will throw an error if the setup/task combination is already present on
        the server. This feature requires an internet connection.
        If not set, it will use the default from your openml configuration (False if unset).
    flow_tags : List[str], optional (default=None)
        A list of tags that the flow should have at creation.
    seed: int, optional (default=None)
        Models that are not seeded will get this seed.
    add_local_measures : bool, optional (default=True)
        Determines whether to calculate a set of evaluation measures locally,
        to later verify server behaviour.
    upload_flow : bool (default=False)
        If True, upload the flow to OpenML if it does not exist yet.
        If False, do not upload the flow to OpenML.
    n_jobs : int (default=None)
        The number of processes/threads to distribute the evaluation asynchronously.
        If `None` or `1`, then the evaluation is treated as synchronous and processed sequentially.
        If `-1`, then the job uses as many cores available.

    Returns
    -------
    run : OpenMLRun
        Result of the run.
    """
    if avoid_duplicate_runs is None:
        avoid_duplicate_runs = openml.config.avoid_duplicate_runs

    # 1. Validate inputs
    flow, task = _validate_flow_and_task_inputs(flow, task, flow_tags)

    # 2. Prepare the model
    if flow.model is None:
        flow.model = flow.extension.flow_to_model(flow)
    flow.model = flow.extension.seed_model(flow.model, seed=seed)

    # 3. Sync with server and check for duplicates
    flow_id = _sync_flow_with_server(
        flow,
        task,
        upload_flow=upload_flow,
        avoid_duplicate_runs=avoid_duplicate_runs,
    )

    # 4. Prepare run environment
    tags, run_environment = _prepare_run_environment(flow)

    # 5. Check if model is already fitted
    if flow.extension.check_if_model_fitted(flow.model):
        warnings.warn(
            "The model is already fitted! This might cause inconsistency in comparison of results.",
            RuntimeWarning,
            stacklevel=2,
        )

    # 6. Execute the run (parallel processing happens here)
    data_content, trace, fold_evaluations, sample_evaluations = _run_task_get_arffcontent(
        model=flow.model,
        task=task,
        extension=flow.extension,
        add_local_measures=add_local_measures,
        n_jobs=n_jobs,
    )

    # 7. Create run from results
    run = _create_run_from_results(
        task=task,
        flow=flow,
        flow_id=flow_id,
        data_content=data_content,
        trace=trace,
        fold_evaluations=fold_evaluations,
        sample_evaluations=sample_evaluations,
        tags=tags,
        run_environment=run_environment,
        upload_flow=upload_flow,
        avoid_duplicate_runs=avoid_duplicate_runs,
    )

    # 8. Log completion message
    if flow_id:
        message = f"Executed Task {task.task_id} with Flow id:{run.flow_id}"
    else:
        message = f"Executed Task {task.task_id} on local Flow with name {flow.name}."
    openml.config.logger.info(message)

    return run

run_model_on_task

run_model_on_task(model: Any, task: int | str | OpenMLTask, avoid_duplicate_runs: bool | None = None, flow_tags: list[str] | None = None, seed: int | None = None, add_local_measures: bool = True, upload_flow: bool = False, return_flow: bool = False, n_jobs: int | None = None) -> OpenMLRun | tuple[OpenMLRun, OpenMLFlow]

Run the model on the dataset defined by the task.

PARAMETER	DESCRIPTION
model	A model which has a function fit(X,Y) and predict(X), all supervised estimators of scikit learn follow this definition of a model. TYPE: sklearn model
task	Task to perform or Task id. This may be a model instead if the first argument is an OpenMLTask. TYPE: OpenMLTask or int or str
avoid_duplicate_runs	If True, the run will throw an error if the setup/task combination is already present on the server. This feature requires an internet connection. If not set, it will use the default from your openml configuration (False if unset). TYPE: (bool, optional(default=None)) DEFAULT: None
flow_tags	A list of tags that the flow should have at creation. TYPE: (List[str], optional(default=None)) DEFAULT: None
seed	Models that are not seeded will get this seed. TYPE: int | None DEFAULT: None
add_local_measures	Determines whether to calculate a set of evaluation measures locally, to later verify server behaviour. TYPE: (bool, optional(default=True)) DEFAULT: True
upload_flow	If True, upload the flow to OpenML if it does not exist yet. If False, do not upload the flow to OpenML. TYPE: bool(default=False) DEFAULT: False
return_flow	If True, returns the OpenMLFlow generated from the model in addition to the OpenMLRun. TYPE: bool(default=False) DEFAULT: False
n_jobs	The number of processes/threads to distribute the evaluation asynchronously. If None or 1, then the evaluation is treated as synchronous and processed sequentially. If -1, then the job uses as many cores available. TYPE: int(default=None) DEFAULT: None

RETURNS	DESCRIPTION
run	Result of the run. TYPE: OpenMLRun
flow	Flow generated from the model. TYPE: OpenMLFlow (optional, only if `return_flow` is True).

Examples:

>>> import openml
>>> import openml_sklearn
>>> from sklearn.tree import DecisionTreeClassifier
>>> clf = DecisionTreeClassifier()
>>> task = openml.tasks.get_task(6)
>>> run = openml.runs.run_model_on_task(clf, task)

Source code in openml/runs/functions.py

def run_model_on_task(  # noqa: PLR0913
    model: Any,
    task: int | str | OpenMLTask,
    avoid_duplicate_runs: bool | None = None,
    flow_tags: list[str] | None = None,
    seed: int | None = None,
    add_local_measures: bool = True,  # noqa: FBT002
    upload_flow: bool = False,  # noqa: FBT002
    return_flow: bool = False,  # noqa: FBT002
    n_jobs: int | None = None,
) -> OpenMLRun | tuple[OpenMLRun, OpenMLFlow]:
    """Run the model on the dataset defined by the task.

    Parameters
    ----------
    model : sklearn model
        A model which has a function fit(X,Y) and predict(X),
        all supervised estimators of scikit learn follow this definition of a model.
    task : OpenMLTask or int or str
        Task to perform or Task id.
        This may be a model instead if the first argument is an OpenMLTask.
    avoid_duplicate_runs : bool, optional (default=None)
        If True, the run will throw an error if the setup/task combination is already present on
        the server. This feature requires an internet connection.
        If not set, it will use the default from your openml configuration (False if unset).
    flow_tags : List[str], optional (default=None)
        A list of tags that the flow should have at creation.
    seed: int, optional (default=None)
        Models that are not seeded will get this seed.
    add_local_measures : bool, optional (default=True)
        Determines whether to calculate a set of evaluation measures locally,
        to later verify server behaviour.
    upload_flow : bool (default=False)
        If True, upload the flow to OpenML if it does not exist yet.
        If False, do not upload the flow to OpenML.
    return_flow : bool (default=False)
        If True, returns the OpenMLFlow generated from the model in addition to the OpenMLRun.
    n_jobs : int (default=None)
        The number of processes/threads to distribute the evaluation asynchronously.
        If `None` or `1`, then the evaluation is treated as synchronous and processed sequentially.
        If `-1`, then the job uses as many cores available.

    Returns
    -------
    run : OpenMLRun
        Result of the run.
    flow : OpenMLFlow (optional, only if `return_flow` is True).
        Flow generated from the model.

    Examples
    --------
    >>> import openml
    >>> import openml_sklearn  # doctest: +SKIP
    >>> from sklearn.tree import DecisionTreeClassifier  # doctest: +SKIP
    >>> clf = DecisionTreeClassifier()  # doctest: +SKIP
    >>> task = openml.tasks.get_task(6)  # doctest: +SKIP
    >>> run = openml.runs.run_model_on_task(clf, task)  # doctest: +SKIP
    """
    if avoid_duplicate_runs is None:
        avoid_duplicate_runs = openml.config.avoid_duplicate_runs
    if avoid_duplicate_runs and not openml.config.apikey:
        warnings.warn(
            "avoid_duplicate_runs is set to True, but no API key is set. "
            "Please set your API key in the OpenML configuration file, see"
            "https://openml.github.io/openml-python/main/examples/20_basic/introduction_tutorial"
            ".html#authentication for more information on authentication.",
            RuntimeWarning,
            stacklevel=2,
        )

    # TODO: At some point in the future do not allow for arguments in old order (6-2018).
    # Flexibility currently still allowed due to code-snippet in OpenML100 paper (3-2019).
    # When removing this please also remove the method `is_estimator` from the extension
    # interface as it is only used here (MF, 3-2019)
    if isinstance(model, (int, str, OpenMLTask)):
        warnings.warn(
            "The old argument order (task, model) is deprecated and "
            "will not be supported in the future. Please use the "
            "order (model, task).",
            DeprecationWarning,
            stacklevel=2,
        )
        task, model = model, task

    extension = get_extension_by_model(model, raise_if_no_extension=True)
    if extension is None:
        # This should never happen and is only here to please mypy will be gone soon once the
        # whole function is removed
        raise TypeError(extension)

    flow = extension.model_to_flow(model)

    def get_task_and_type_conversion(_task: int | str | OpenMLTask) -> OpenMLTask:
        """Retrieve an OpenMLTask object from either an integer or string ID,
        or directly from an OpenMLTask object.

        Parameters
        ----------
        _task : Union[int, str, OpenMLTask]
            The task ID or the OpenMLTask object.

        Returns
        -------
        OpenMLTask
            The OpenMLTask object.
        """
        if isinstance(_task, (int, str)):
            return get_task(int(_task))  # type: ignore

        return _task

    task = get_task_and_type_conversion(task)

    run = run_flow_on_task(
        task=task,
        flow=flow,
        avoid_duplicate_runs=avoid_duplicate_runs,
        flow_tags=flow_tags,
        seed=seed,
        add_local_measures=add_local_measures,
        upload_flow=upload_flow,
        n_jobs=n_jobs,
    )
    if return_flow:
        return run, flow
    return run