load_acsf1#

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

Load dataset on power consumption of typical appliances.

Parameters
split: None or str{“train”, “test”}, optional (default=None)

Whether to load the train or test partition of the problem. By default it loads both.

return_X_y: bool, optional (default=True)

If True, returns (features, target) separately instead of a single dataframe with columns for features and the target.

Returns
X: pd.DataFrame with m rows and c columns

The time series data for the problem with m cases and c dimensions

y: numpy array

The class labels for each case in X

Notes

Dimensionality: univariate Series length: 1460 Train cases: 100 Test cases: 100 Number of classes: 10

The dataset contains the power consumption of typical appliances. The recordings are characterized by long idle periods and some high bursts of energy consumption when the appliance is active. The classes correspond to 10 categories of home appliances; mobile phones (via chargers), coffee machines, computer stations (including monitor), fridges and freezers, Hi-Fi systems (CD players), lamp (CFL), laptops (via chargers), microwave ovens, printers, and televisions (LCD or LED).”

Dataset details: http://www.timeseriesclassification.com/description.php?Dataset =ACSF1

Examples

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