openml.tasks.OpenMLLearningCurveTask

class openml.tasks.OpenMLLearningCurveTask(task_type_id: TaskType, task_type: str, data_set_id: int, target_name: str, estimation_procedure_id: int = 13, estimation_procedure_type: str | None = None, estimation_parameters: dict[str, str] | None = None, data_splits_url: str | None = None, task_id: int | None = None, evaluation_measure: str | None = None, class_labels: list[str] | None = None, cost_matrix: np.ndarray | None = None)

OpenML Learning Curve object.

Parameters:

task_type_idTaskType

ID of the Learning Curve task.

task_typestr

Name of the Learning Curve task.

data_set_idint

ID of the dataset that this task is associated with.

target_namestr

Name of the target feature in the dataset.

estimation_procedure_idint, default=None

ID of the estimation procedure to use for evaluating models.

estimation_procedure_typestr, default=None

Type of the estimation procedure.

estimation_parametersdict, default=None

Additional parameters for the estimation procedure.

data_splits_urlstr, default=None

URL of the file containing the data splits for Learning Curve task.

task_idUnion[int, None]

ID of the Learning Curve task.

evaluation_measurestr, default=None

Name of the evaluation measure to use for evaluating models.

class_labelslist of str, default=None

Class labels for Learning Curve tasks.

cost_matrixnumpy array, default=None

Cost matrix for Learning Curve tasks.

download_split() → OpenMLSplit

Download the OpenML split for a given task.

property estimation_parameters: dict[str, str] | None

Return the estimation parameters for the task.

get_X_and_y(dataset_format: Literal['dataframe', 'array'] = 'array') → tuple[np.ndarray | pd.DataFrame | scipy.sparse.spmatrix, np.ndarray | pd.Series | pd.DataFrame | None]

Get data associated with the current task.

Parameters:

dataset_formatstr

Data structure of the returned data. See openml.datasets.OpenMLDataset.get_data() for possible options.

Returns:

tuple - X and y

get_dataset(**kwargs: Any) → OpenMLDataset

Download dataset associated with task.

Accepts the same keyword arguments as the openml.datasets.get_dataset.

get_split_dimensions() → tuple[int, int, int]

Get the (repeats, folds, samples) of the split for a given task.

get_train_test_split_indices(fold: int = 0, repeat: int = 0, sample: int = 0) → tuple[np.ndarray, np.ndarray]

Get the indices of the train and test splits for a given task.

property id: int | None

Return the OpenML ID of this task.

open_in_browser() → None

Opens the OpenML web page corresponding to this object in your default browser.

property openml_url: str | None

The URL of the object on the server, if it was uploaded, else None.

publish() → OpenMLBase

Publish the object on the OpenML server.

push_tag(tag: str) → None

Annotates this entity with a tag on the server.

Parameters:

tagstr

Tag to attach to the flow.

remove_tag(tag: str) → None

Removes a tag from this entity on the server.

Parameters:

tagstr

Tag to attach to the flow.

classmethod url_for_id(id_: int) → str

Return the OpenML URL for the object of the class entity with the given id.