load_osuleaf#

load_osuleaf(split=None, return_X_y=True, return_type='numpy3d')[source]#

Load the OSULeaf univariate time series classification problem.

Parameters:

split: None or one of “TRAIN”, “TEST”, default=None: Whether to load the train or test instances of the problem. By default it loads both train and test instances into a single array.
return_X_y: bool, default=True: If True, returns (features, target) separately instead of as single data structure.
return_type: string, optional (default=”numpy3d”): Data structure to use for time series, should be either “numpy2d” or “numpy3d”.

Returns:

X: np.ndarray: shape (n_cases, 1, 427) (return_type=”numpy3d”) or shape (n_cases, 427) (return_type=”numpy2d”), where n_cases where n_cases is either 200 (split = “train”) 242 (split=”test”) or 442.
y: np.ndarray: 1D array of length 200, 242 or 542, only returned if return_X_y is True The class labels for each time series instance in X If return_X_y is False, y is appended to X instead.

Raises:

ValueError is raised if the data cannot be stored in the requested return_type.

Notes

Dimensionality: univariate Series length: 427 Train cases: 200 Test cases: 242 Number of classes: 6 Details: http://www.timeseriesclassification.com/description.php?Dataset=OSULeaf

Examples

>>> from aeon.datasets import load_osuleaf
>>> X, y = load_osuleaf()