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) for uniform sampling of artificial prevalences.

Similar to APP, but uses uniform prevalence distribution generation methods such as Kraemer or uniform simplex sampling to generate batches with uniformly sampled class prevalences.

Parameters:

batch_sizeint or list of int: Batch size(s) for evaluation.
n_prevalencesint: Number of prevalence samples per class.
repeatsint: Number of evaluation repeats with different samples.
random_stateint, optional: Random seed for reproducibility.
min_prevfloat, optional (default=0.0): Minimum prevalence limit.
max_prevfloat, optional (default=1.0): Maximum prevalence limit.
algorithm{‘kraemer’, ‘uniform’}, optional (default=’kraemer’): Sampling algorithm used to generate artificial prevalences.

Examples

>>> protocol = UPP(batch_size=100, n_prevalences=5, 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.

UPP#

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