make_example_2d_numpy_collection¶

make_example_2d_numpy_collection(n_cases: int = 10, n_timepoints: int = 8, n_labels: int = 2, min_cases_per_label: int = 1, regression_target: bool = False, random_state: int | None = None, return_y: bool = True) → ndarray | tuple[ndarray, ndarray][source]¶

Randomly generate 2D numpy X and numpy y for testing.

Generates data in ‘numpy2D’ 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_timepointsint: The number of features/series length to generate.
n_labelsint: The number of unique labels to generate.
min_cases_per_labelint: The minimum number of samples per unique label.
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: If True, return the labels as well as the data.

Returns:

Xnp.ndarray: Randomly generated 2D data.
ynp.ndarray: Randomly generated labels if return_y is True.

Examples

>>> from aeon.testing.data_generation import make_example_2d_numpy_collection
>>> from aeon.utils.validation.collection import get_type
>>> data, labels = make_example_2d_numpy_collection(
...     n_cases=2,
...     n_timepoints=6,
...     n_labels=2,
...     random_state=0,
... )
>>> print(data)
[[0.         1.43037873 1.20552675 1.08976637 0.8473096  1.29178823]
 [2.         3.567092   3.85465104 1.53376608 3.16690015 2.11557968]]
>>> print(labels)
[0 1]
>>> get_type(data)
'numpy2D'