load_pig_central_venous_pressure#

pyts.datasets.load_pig_central_venous_pressure(return_X_y: bool = False) → Bunch | tuple[NDArray[float64], NDArray[float64], NDArray[Any], NDArray[Any]][source]#

Load and return the PigCVP dataset.

In the test set, a class is represented by four examples, the second and third 2000 data points of the before time series and the second and third 2000 data points of the after time series. Data created by Mathieu Guillame-Bert et al. Data edited by Shaghayegh Gharghabi and Eamonn Keogh.

Training samples	104
Test samples	208
Timestamps	2000
Classes	52

Parameters:

return_X_ybool (default = False): If True, return (data_train, data_test, target_train, target_test) instead of a Bunch object.

Returns:

dataBunch

Dictionary-like object, with attributes:

data_trainarray of floats: The time series in the training set.
data_testarray of floats: The time series in the test set.
target_trainarray of integers: The classification labels in the training set.
target_testarray of integers: The classification labels in the test set.
DESCRstr: The full description of the dataset.
urlstr: The url of the dataset.

(data_train, data_test, target_train, target_test)tuple if return_X_y is True

References

[1]

M. Guillame-Bert and A. Dubrawski, “Classification of Time Sequences using Graphs of Temporal Constraints”. Journal of Machine Learning Research, 2017.

[2]

UCR archive entry for the PigCVP dataset

Examples

>>> from pyts.datasets import load_pig_central_venous_pressure
>>> bunch = load_pig_central_venous_pressure()
>>> bunch.data_train.shape
(104, 2000)
>>> X_train, X_test, y_train, y_test = load_pig_central_venous_pressure(
...    return_X_y=True)
>>> X_train.shape
(104, 2000)