load_acsf1#

load_acsf1(split=None, return_X_y=True, return_type='numpy3d')[source]#

Load the ACSF1 univariate dataset on power consumption of typical appliances.

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, 1460) (if return_type=”numpy3d”) or shape (n_cases, 1460) (return_type=”numpy2d”), where n_cases where n_cases is either 100 (split = “train” or split=”test”) or 200.

y: np.ndarray

1D array of length 100 or 200 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: 1460 Train cases: 100 Test cases: 100 Number of classes: 10 Details: http://www.timeseriesclassification.com/description.php?Dataset=ACSF1

Examples

>>> from aeon.datasets import load_acsf1
>>> X, y = load_acsf1()