UPP#

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

Uniform Prevalence Protocol (UPP).

Similar to APP, but samples prevalences uniformly over the probability simplex rather than on a regular grid, avoiding bias towards the simplex corners. Supports Kraemer or uniform simplex sampling.

Parameters:

batch_sizeint or list of int: Batch size(s) for evaluation.
n_prevalencesint: Number of prevalence points to sample.
repeatsint, default=1: Number of repetitions for each prevalence point.
random_stateint or None, default=None: Random seed for reproducibility.
min_prevfloat, default=0.0: Minimum class prevalence.
max_prevfloat, default=1.0: Maximum class prevalence.
algorithm{‘kraemer’, ‘uniform’}, default=’kraemer’: Simplex sampling algorithm. 'kraemer' uses the Kraemer method; 'uniform' uses uniform Dirichlet sampling.

Attributes:

n_combinationsint: Total number of batches generated.

Examples

>>> from mlquantify.model_selection import UPP
>>> import numpy as np
>>> X, y = np.random.randn(200, 5), np.random.randint(0, 2, 200)
>>> proto = UPP(batch_size=50, n_prevalences=5, random_state=0)
>>> batches = list(proto.split(X, y))
>>> len(batches)
5

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.