BaseProtocol

class mlquantify.model_selection.BaseProtocol(batch_size, random_state=None, **kwargs)

Base class for evaluation protocols.

Parameters:

batch_sizeint or list of int

The size of the batches to be used in the evaluation.

random_stateint, optional

The random seed for reproducibility.

Attributes:

n_combinationsint

Raises:

ValueError

If the batch size is not a positive integer or list of positive integers.

Notes

This class serves as a base class for different evaluation protocols, each with its own strategy for splitting the data into batches.

Examples

>>> class MyCustomProtocol(Protocol):
...     def _iter_indices(self, X: np.ndarray, y: np.ndarray):
...         for batch_size in self.batch_size:
...             yield np.random.choice(X.shape[0], batch_size, replace=True)
...
>>> protocol = MyCustomProtocol(batch_size=100, random_state=42)
>>> for idx in protocol.split(X, y):
...     # Train and evaluate model
...     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.