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