UPP#

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

Uniform Prevalence Protocol (UPP) for evaluation. An extension of the APP that generates artificial prevalence distributions uniformly across all classes utilizing the kraemer sampling method.

Parameters:

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

Attributes:

n_prevalencesint: The number of artificial prevalences to generate.
repeatsint: The number of times to repeat the evaluation with different random seeds.
random_stateint: The random seed for reproducibility.

Examples

>>> protocol = UPP(batch_size=100, n_prevalences=5, 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.

UPP#

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