make_example_2d_dataframe_collection¶

make_example_2d_dataframe_collection(n_cases: int = 10, n_timepoints: int = 8, n_labels: int = 2, regression_target: bool = False, random_state: int | None = None, return_y: bool = True) → DataFrame | tuple[DataFrame, ndarray][source]¶

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

Generates data in ‘pd-wide’ 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.
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:

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

Examples

>>> from aeon.testing.data_generation import make_example_2d_dataframe_collection
>>> from aeon.utils.validation.collection import get_type
>>> data, labels = make_example_2d_dataframe_collection(
...     n_cases=2,
...     n_timepoints=6,
...     n_labels=2,
...     random_state=0,
... )
>>> print(data)
     0         1         2         3        4         5
0  0.0  1.430379  1.205527  1.089766  0.84731  1.291788
1  2.0  3.567092  3.854651  1.533766  3.16690  2.115580
>>> print(labels)
[0 1]
>>> get_type(data)
'pd-wide'