NPP#

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

Natural Prevalence Protocol (NPP).

Samples evaluation batches uniformly at random from the dataset, preserving the natural class distribution without imposing any prevalence constraints.

Parameters:

batch_sizeint or list of int: Size(s) of the evaluation batches.
n_samplesint, default=1: Number of distinct batch samples per batch size.
repeatsint, default=1: Number of repetitions for each sample.
random_stateint or None, default=None: Random seed for reproducibility.

Attributes:

n_combinationsint: Total number of batches generated.

Examples

>>> from mlquantify.model_selection import NPP
>>> import numpy as np
>>> X, y = np.random.randn(200, 5), np.random.randint(0, 2, 200)
>>> proto = NPP(batch_size=50, n_samples=3, random_state=0)
>>> batches = list(proto.split(X, y))
>>> len(batches)
3

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.