functions

openml.tasks.functions

create_task

create_task(task_type: TaskType, dataset_id: int, estimation_procedure_id: int, target_name: str | None = None, evaluation_measure: str | None = None, **kwargs: Any) -> OpenMLClassificationTask | OpenMLRegressionTask | OpenMLLearningCurveTask | OpenMLClusteringTask

Create a task based on different given attributes.

Builds a task object with the function arguments as attributes. The type of the task object built is determined from the task type id. More information on how the arguments (task attributes), relate to the different possible tasks can be found in the individual task objects at the openml.tasks.task module.

PARAMETER	DESCRIPTION
task_type	Id of the task type. TYPE: TaskType
dataset_id	The id of the dataset for the task. TYPE: int
target_name	The name of the feature used as a target. At the moment, only optional for the clustering tasks. TYPE: str DEFAULT: None
estimation_procedure_id	The id of the estimation procedure. TYPE: int
evaluation_measure	The name of the evaluation measure. TYPE: str DEFAULT: None
kwargs	Other task attributes that are not mandatory for task upload. TYPE: dict DEFAULT: {}

RETURNS	DESCRIPTION
(OpenMLClassificationTask, OpenMLRegressionTask)
(OpenMLLearningCurveTask, OpenMLClusteringTask)

Source code in openml/tasks/functions.py

def create_task(
    task_type: TaskType,
    dataset_id: int,
    estimation_procedure_id: int,
    target_name: str | None = None,
    evaluation_measure: str | None = None,
    **kwargs: Any,
) -> (
    OpenMLClassificationTask | OpenMLRegressionTask | OpenMLLearningCurveTask | OpenMLClusteringTask
):
    """Create a task based on different given attributes.

    Builds a task object with the function arguments as
    attributes. The type of the task object built is
    determined from the task type id.
    More information on how the arguments (task attributes),
    relate to the different possible tasks can be found in
    the individual task objects at the openml.tasks.task
    module.

    Parameters
    ----------
    task_type : TaskType
        Id of the task type.
    dataset_id : int
        The id of the dataset for the task.
    target_name : str, optional
        The name of the feature used as a target.
        At the moment, only optional for the clustering tasks.
    estimation_procedure_id : int
        The id of the estimation procedure.
    evaluation_measure : str, optional
        The name of the evaluation measure.
    kwargs : dict, optional
        Other task attributes that are not mandatory
        for task upload.

    Returns
    -------
    OpenMLClassificationTask, OpenMLRegressionTask,
    OpenMLLearningCurveTask, OpenMLClusteringTask
    """
    if task_type == TaskType.CLUSTERING:
        task_cls = OpenMLClusteringTask
    elif task_type == TaskType.LEARNING_CURVE:
        task_cls = OpenMLLearningCurveTask  # type: ignore
    elif task_type == TaskType.SUPERVISED_CLASSIFICATION:
        task_cls = OpenMLClassificationTask  # type: ignore
    elif task_type == TaskType.SUPERVISED_REGRESSION:
        task_cls = OpenMLRegressionTask  # type: ignore
    else:
        raise NotImplementedError(
            f"Task type ID {task_type:d} is not supported. "
            f"Supported task type IDs: {TaskType.SUPERVISED_CLASSIFICATION.value},"
            f"{TaskType.SUPERVISED_REGRESSION.value}, "
            f"{TaskType.CLUSTERING.value}, {TaskType.LEARNING_CURVE.value}. "
            f"Please refer to the TaskType enum for valid task type identifiers."
        )

    return task_cls(
        task_id=None,
        task_type_id=task_type,
        task_type="None",  # TODO: refactor to get task type string from ID.
        data_set_id=dataset_id,
        target_name=target_name,  # type: ignore
        estimation_procedure_id=estimation_procedure_id,
        evaluation_measure=evaluation_measure,
        **kwargs,
    )

delete_task

delete_task(task_id: int) -> bool

Delete task with id task_id from the OpenML server.

You can only delete tasks which you created and have no runs associated with them.

PARAMETER	DESCRIPTION
task_id	OpenML id of the task TYPE: int

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

Source code in openml/tasks/functions.py

def delete_task(task_id: int) -> bool:
    """Delete task with id `task_id` from the OpenML server.

    You can only delete tasks which you created and have
    no runs associated with them.

    Parameters
    ----------
    task_id : int
        OpenML id of the task

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

get_task

get_task(task_id: int, download_splits: bool = False, **get_dataset_kwargs: Any) -> OpenMLTask

Download OpenML task for a given task ID.

Downloads the task representation.

Use the download_splits parameter to control whether the splits are downloaded. Moreover, you may pass additional parameter (args or kwargs) that are passed to :meth:openml.datasets.get_dataset.

PARAMETER	DESCRIPTION
task_id	The OpenML task id of the task to download. TYPE: int
download_splits	Whether to download the splits as well. TYPE: bool DEFAULT: False
get_dataset_kwargs	Args and kwargs can be used pass optional parameters to :meth:openml.datasets.get_dataset. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
task	TYPE: OpenMLTask

Examples:

>>> import openml
>>> task = openml.tasks.get_task(1)
>>> task = openml.tasks.get_task(1, download_splits=True)

Source code in openml/tasks/functions.py

@openml.utils.thread_safe_if_oslo_installed
def get_task(
    task_id: int,
    download_splits: bool = False,  # noqa: FBT002
    **get_dataset_kwargs: Any,
) -> OpenMLTask:
    """Download OpenML task for a given task ID.

    Downloads the task representation.

    Use the `download_splits` parameter to control whether the splits are downloaded.
    Moreover, you may pass additional parameter (args or kwargs) that are passed to
    :meth:`openml.datasets.get_dataset`.

    Parameters
    ----------
    task_id : int
        The OpenML task id of the task to download.
    download_splits: bool (default=False)
        Whether to download the splits as well.
    get_dataset_kwargs :
        Args and kwargs can be used pass optional parameters to :meth:`openml.datasets.get_dataset`.

    Returns
    -------
    task: OpenMLTask

    Examples
    --------
    >>> import openml
    >>> task = openml.tasks.get_task(1)  # doctest: +SKIP
    >>> task = openml.tasks.get_task(1, download_splits=True)  # doctest: +SKIP
    """
    if not isinstance(task_id, int):
        raise TypeError(f"Task id should be integer, is {type(task_id)}")

    task = openml._backend.task.get(task_id)
    dataset = get_dataset(task.dataset_id, **get_dataset_kwargs)

    if (
        isinstance(task, (OpenMLClassificationTask, OpenMLLearningCurveTask))
        and task.target_name is not None
    ):
        task.class_labels = dataset.retrieve_class_labels(task.target_name)

    if download_splits and isinstance(task, OpenMLSupervisedTask):
        task.download_split()

    return task

get_tasks

get_tasks(task_ids: list[int], download_data: bool | None = None, download_qualities: bool | None = None) -> list[OpenMLTask]

Download tasks.

This function iterates :meth:openml.task.get.

PARAMETER	DESCRIPTION
task_ids	A list of task ids to download. TYPE: List[int]
download_data	Option to trigger download of data along with the meta data. TYPE: bool(default=True) DEFAULT: None
download_qualities	Option to download 'qualities' meta-data in addition to the minimal dataset description. TYPE: bool(default=True) DEFAULT: None

RETURNS	DESCRIPTION
list

Source code in openml/tasks/functions.py

def get_tasks(
    task_ids: list[int],
    download_data: bool | None = None,
    download_qualities: bool | None = None,
) -> list[OpenMLTask]:
    """Download tasks.

    This function iterates :meth:`openml.task.get`.

    Parameters
    ----------
    task_ids : List[int]
        A list of task ids to download.
    download_data : bool (default = True)
        Option to trigger download of data along with the meta data.
    download_qualities : bool (default=True)
        Option to download 'qualities' meta-data in addition to the minimal dataset description.

    Returns
    -------
    list
    """
    if download_data is None:
        warnings.warn(
            "`download_data` will default to False starting in 0.16. "
            "Please set `download_data` explicitly to suppress this warning.",
            stacklevel=1,
        )
        download_data = True

    if download_qualities is None:
        warnings.warn(
            "`download_qualities` will default to False starting in 0.16. "
            "Please set `download_qualities` explicitly to suppress this warning.",
            stacklevel=1,
        )
        download_qualities = True

    tasks = []
    for task_id in task_ids:
        tasks.append(
            get_task(
                task_id,
                download_data=download_data,
                download_qualities=download_qualities,
            )
        )
    return tasks

list_tasks

list_tasks(task_type: TaskType | None = None, offset: int | None = None, size: int | None = None, tag: str | None = None, data_tag: str | None = None, status: str | None = None, data_name: str | None = None, data_id: int | None = None, number_instances: int | None = None, number_features: int | None = None, number_classes: int | None = None, number_missing_values: int | None = None) -> DataFrame

Return a number of tasks having the given tag and task_type

PARAMETER	DESCRIPTION
Filter
it
type
offset	the number of tasks to skip, starting from the first TYPE: int DEFAULT: None
task_type	Refers to the type of task. TYPE: TaskType DEFAULT: None
size	the maximum number of tasks to show TYPE: int DEFAULT: None
tag	the tag to include TYPE: str DEFAULT: None
data_tag	the tag of the dataset TYPE: str DEFAULT: None
data_id	TYPE: int DEFAULT: None
status	TYPE: str DEFAULT: None
data_name	TYPE: str DEFAULT: None
number_instances	TYPE: int DEFAULT: None
number_features	TYPE: int DEFAULT: None
number_classes	TYPE: int DEFAULT: None
number_missing_values	TYPE: int DEFAULT: None

RETURNS	DESCRIPTION
dataframe	All tasks having the given task_type and the give tag. Every task is represented by a row in the data frame containing the following information as columns: task id, dataset id, task_type and status. If qualities are calculated for the associated dataset, some of these are also returned.

Source code in openml/tasks/functions.py

def list_tasks(  # noqa: PLR0913,PLR0917
    task_type: TaskType | None = None,
    offset: int | None = None,
    size: int | None = None,
    tag: str | None = None,
    data_tag: str | None = None,
    status: str | None = None,
    data_name: str | None = None,
    data_id: int | None = None,
    number_instances: int | None = None,
    number_features: int | None = None,
    number_classes: int | None = None,
    number_missing_values: int | None = None,
) -> pd.DataFrame:
    """
    Return a number of tasks having the given tag and task_type

    Parameters
    ----------
    Filter task_type is separated from the other filters because
    it is used as task_type in the task description, but it is named
    type when used as a filter in list tasks call.
    offset : int, optional
        the number of tasks to skip, starting from the first
    task_type : TaskType, optional
        Refers to the type of task.
    size : int, optional
        the maximum number of tasks to show
    tag : str, optional
        the tag to include
    data_tag : str, optional
        the tag of the dataset
    data_id : int, optional
    status : str, optional
    data_name : str, optional
    number_instances : int, optional
    number_features : int, optional
    number_classes : int, optional
    number_missing_values : int, optional

    Returns
    -------
    dataframe
        All tasks having the given task_type and the give tag. Every task is
        represented by a row in the data frame containing the following information
        as columns: task id, dataset id, task_type and status. If qualities are
        calculated for the associated dataset, some of these are also returned.
    """
    listing_call = partial(
        openml._backend.task.list,
        task_type=task_type,
        estimation_procedures=_get_estimation_procedure_list(),
        tag=tag,
        data_tag=data_tag,
        status=status,
        data_id=data_id,
        data_name=data_name,
        number_instances=number_instances,
        number_features=number_features,
        number_classes=number_classes,
        number_missing_values=number_missing_values,
    )
    batches = openml.utils._list_all(listing_call, offset=offset, limit=size)
    if len(batches) == 0:
        return pd.DataFrame()

    return pd.concat(batches)