make_example_3d_numpy_list¶

make_example_3d_numpy_list(n_cases: int = 10, n_channels: int = 1, min_n_timepoints: int = 8, max_n_timepoints: int = 12, n_labels: int = 2, regression_target: bool = False, random_state: int | None = None, return_y: bool = True) → list[ndarray] | tuple[list[ndarray], ndarray][source]¶

Randomly generate 3D list of numpy X and numpy y for testing.

Generates data in ‘np-list’ format.

Will ensure there is at least one sample per label if a classification label is being returned (regression_target=False).

Parameters:

n_casesint: The number of samples to generate.
n_channelsint: The number of series channels to generate.
min_n_timepointsint: The minimum number of features/series length to generate for individual series.
max_n_timepointsint: The maximum number of features/series length to generate for individual series.
n_labelsint: The number of unique labels to generate.
regression_targetbool: If True, the target will be a scalar float, otherwise an int.
random_stateint or None: Seed for random number generation.
return_ybool, default = True: Return the y target variable.

Returns:

Xlist of np.ndarray: Randomly generated potentially unequal length 3D data.
ynp.ndarray: Randomly generated labels if return_y is True.

Examples

>>> from aeon.testing.data_generation import make_example_3d_numpy_list
>>> from aeon.utils.validation.collection import get_type
>>> data, labels = make_example_3d_numpy_list(
...     n_cases=2,
...     n_channels=2,
...     min_n_timepoints=4,
...     max_n_timepoints=6,
...     n_labels=2,
...     random_state=0,
... )
>>> print(data)  
[array([[0.        , 1.6885315 , 1.71589124, 1.69450348],
       [1.24712739, 0.76876341, 0.59506921, 0.11342595]]),
       array([[2.        , 3.16690015, 2.11557968, 2.27217824],
       [3.70238655, 0.28414423, 0.3485172 , 0.08087359]])]
>>> print(labels)
[0 1]
>>> get_type(data)
'np-list'