sktime.datasets.base.load_gunpoint

sktime.datasets.base.load_gunpoint(split=None, return_X_y=False)[source]

Loads the GunPoint time series classification problem and returns X and y :param split: Whether to load the train or test partition of the problem. By

default it loads both.

Parameters

return_X_y (bool, optional (default=False)) – If True, returns (features, target) separately instead of a single dataframe with columns for features and the target.

Returns

  • X (pandas 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

  • Details

  • ——-

  • 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