- load_acsf1(split=None, return_X_y=True, return_type=None)#
Load dataset on power consumption of typical appliances.
- split: None or one of “TRAIN”, “TEST”, optional (default=None)
Whether to load the train or test instances of the problem. By default it loads both train and test instances (in a single container).
- 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.
- return_type: valid Panel mtype str or None, optional (default=None=”nested_univ”)
Memory data format specification to return X in, None = “nested_univ” type. str can be any supported sktime Panel mtype,
for list of mtypes, see datatypes.MTYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb
- commonly used specifications:
“nested_univ: nested pd.DataFrame, pd.Series in cells “numpy3D”/”numpy3d”/”np3D”: 3D np.ndarray (instance, variable, time index) “numpy2d”/”np2d”/”numpyflat”: 2D np.ndarray (instance, time index) “pd-multiindex”: pd.DataFrame with 2-level (instance, time) MultiIndex
Exception is raised if the data cannot be stored in the requested type.
- X: sktime data container, following mtype specification return_type
The time series data for the problem, with n instances
- y: 1D numpy array of length n, only returned if return_X_y if True
The class labels for each time series instance in X If return_X_y is False, y is appended to X instead.
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
>>> from sktime.datasets import load_acsf1 >>> X, y = load_acsf1()