sktime.datasets.base.load_osuleaf

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

Loads 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=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 (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