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

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

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: 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


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