APP

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

Artificial Prevalence Protocol (APP).

Generates evaluation batches with artificially imposed prevalences sampled on a regular grid over the probability simplex within [min_prev, max_prev]. Covers all combinations of prevalence levels for comprehensive evaluation.

Parameters:

batch_sizeint or list of int

Size(s) of the evaluation batches.

n_prevalencesint

Number of prevalence grid points per class dimension.

repeatsint, default=1

Number of repetitions for each prevalence combination.

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.

Attributes:

n_combinationsint

Total number of batches generated.

Notes

For multiclass problems the grid grows combinatorially; prefer UPP for large class counts.

References

References

[1]

Forman, G. (2008). Quantifying Counts and Costs via Classification. Data Mining and Knowledge Discovery, 17(2), 164–206.

[2]

Sebastiani, F., et al. (2020). A Critical Reassessment of the Evaluation of Machine Learning Approaches for Quantification. ArXiv preprint.

Examples

>>> from mlquantify.model_selection import APP
>>> import numpy as np
>>> X, y = np.random.randn(200, 5), np.random.randint(0, 2, 200)
>>> proto = APP(batch_size=50, n_prevalences=5, random_state=0)
>>> batches = list(proto.split(X, y))
>>> len(batches)
6

get_metadata_routing()

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()

Get the number of combinations for the current protocol.

get_params(deep=True)

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

Save the quantifier instance to a file.

set_params(**params)

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)

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.