load_osuleaf#

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

Load the OSULeaf 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: 427 Train cases: 200 Test cases: 242 Number of classes: 6

The OSULeaf data set consist of one dimensional outlines of leaves. The series were obtained by color image segmentation and boundary extraction (in the anti-clockwise direction) from digitized leaf images of six classes: Acer Circinatum, Acer Glabrum, Acer Macrophyllum, Acer Negundo, Quercus Garryanaand Quercus Kelloggii for the MSc thesis “Content-Based Image Retrieval: Plant Species Identification” by A Grandhi.

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

Examples

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