NPP

class mlquantify.model_selection.NPP(batch_size, n_samples=1, repeats=1, random_state=None)

Natural Prevalence Protocol (NPP) that samples data without imposing prevalence constraints.

This protocol simply samples batches randomly with replacement, ignoring prevalence distributions.

Parameters:

batch_sizeint or list of int

Size(s) of the evaluation batches.

n_samplesint, optional (default=1)

Number of distinct batch samples per batch size.

repeatsint, optional (default=1)

Number of repetitions for each batch sample.

random_stateint, optional

Random seed for reproducibility.

Examples

>>> protocol = NPP(batch_size=100, random_state=42)
>>> for idx in protocol.split(X, y):
...     # Train and evaluate
...     pass

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.