PPP#

class mlquantify.evaluation.protocol.PPP(batch_size, prevalences, repeats=1, random_state=None)[source]#

Personalized Prevalence Protocol (PPP) for evaluation. This protocol generates artificial prevalence distributions personalized for each class.

Parameters:

batch_sizeint or list of int: The size of the batches to be used in the evaluation.
prevalenceslist of float: The list of artificial prevalences to generate for each class.
repeatsint: The number of times to repeat the evaluation with different random seeds.
random_stateint, optional: The random seed for reproducibility.

Attributes:

prevalenceslist of float: The list of artificial prevalences to generate for each class.
repeatsint: The number of times to repeat the evaluation with different random seeds.
random_stateint: The random seed for reproducibility.

Examples

>>> protocol = PPP(batch_size=100, prevalences=[0.1, 0.9], repeats=3, random_state=42)
>>> for train_idx, test_idx in protocol.split(X, y):
...     # Train and evaluate model
...     pass

get_n_combinations() → int[source]#: Get the number of combinations for the current protocol.

split(X: ndarray, y: ndarray)[source]#

Split the data into samples for evaluation.

Parameters:

Xnp.ndarray: The input features.
ynp.ndarray: The target labels.

Yields:

Generator[np.ndarray, np.ndarray]: A generator that yields the indices for each split.

PPP#

This Page