APP#

class mlquantify.model_selection.APP(batch_size, n_prevalences, repeats=1, random_state=None, min_prev=0.0, max_prev=1.0)[source]#

Artificial Prevalence Protocol (APP) for exhaustive prevalent batch evaluation.

Generates batches with artificially imposed prevalences across all possible combinations within specified bounds. This allows comprehensive evaluation over a range of prevalence scenarios.

Parameters:

batch_sizeint or list of int: Size(s) of the evaluation batches.
n_prevalencesint: Number of artificial prevalence levels to sample per class dimension.
repeatsint, optional (default=1): Number of repetitions for each prevalence sampling.
random_stateint, optional: Random seed for reproducibility.
min_prevfloat, optional (default=0.0): Minimum possible prevalence for any class.
max_prevfloat, optional (default=1.0): Maximum possible prevalence for any class.

Notes

For multiclass problems, this protocol may have high computational complexity due to combinatorial explosion in prevalence combinations.

Examples

>>> protocol = APP(batch_size=[100, 200], n_prevalences=5, repeats=3, random_state=42)
>>> for idx in protocol.split(X, y):
...     # Train and evaluate
...     pass

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

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

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

save_quantifier(path: str | None = None) → None[source]#: Save the quantifier instance to a file.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

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.

APP#

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