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()