PPP#

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

Personalized Prevalence Protocol (PPP) for targeted prevalence batch generation.

Generates batches with user-specified prevalence distributions, allowing for controlled evaluation on specific scenarios.

Parameters:

batch_sizeint or list of int: Batch sizes to generate.
prevalenceslist of floats or array-like: Custom target prevalences per class to generate evaluation batches.
repeatsint, optional (default=1): Number of evaluation repetitions with different batches.
random_stateint, optional: Random seed for reproducibility.

Examples

>>> protocol = PPP(batch_size=100, prevalences=[0.1, 0.9], 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.

PPP#

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