load_gunpoint#

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

Load the GunPoint time series classification problem and returns X and y.

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: 150 Train cases: 50 Test cases: 150 Number of classes: 2

This dataset involves one female actor and one male actor making a motion with their hand. The two classes are: Gun-Draw and Point: For Gun-Draw the actors have their hands by their sides. They draw a replicate gun from a hip-mounted holster, point it at a target for approximately one second, then return the gun to the holster, and their hands to their sides. For Point the actors have their gun by their sides. They point with their index fingers to a target for approximately one second, and then return their hands to their sides. For both classes, we tracked the centroid of the actor’s right hands in both X- and Y-axes, which appear to be highly correlated. The data in the archive is just the X-axis.

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

Examples

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