MaxHerding

class skactiveml.pool.MaxHerding(normalize_samples=True, metric='rbf', metric_dict=None, missing_label=nan, random_state=None)

Bases: SingleAnnotatorPoolQueryStrategy

This class implements the MaxHerding query strategy [1], which greedily selects batch_size unlabeled samples that most increase a smooth, kernel-based coverage objective in embedding space, accounting for the already labeled set. The objective promotes representativeness and diversity via kernel similarity.

Parameters:

normalize_samplesbool, default=True

Flag whether to normalize the samples to have unit length.

metricstr or callable, default=None

The metric must be None or a valid kernel as defined by the function sklearn.metrics.pairwise.pairwise_kernels.

metric_dictdict, default=None

Any further parameters that should be passed directly to the kernel function sklearn.metrics.pairwise.pairwise_kernels.

missing_labelscalar or string or np.nan or None, default=np.nan

Value to represent a missing label.

random_stateNone or int or np.random.RandomState, default=None

The random state to use.

References

[1]

Bae, Wonho, Junhyug Noh, and Danica J. Sutherland. “Generalized Coverage for More Robust Low-Budget Active Learning.” In Eur. Conf. Comput. Vis. 2024.

Methods

get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
query(X, y[, candidates, batch_size, ...])	Determines for which candidate samples labels are to be queried.
set_params(**params)	Set the parameters of this estimator.

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_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.

query(X, y, candidates=None, batch_size=1, return_utilities=False)

Determines for which candidate samples labels are to be queried.

Parameters:

Xarray-like of shape (n_samples, n_features)

Training data set, usually complete, i.e., including the labeled and unlabeled samples.

yarray-like of shape (n_samples,)

Labels of the training data set (possibly including unlabeled ones indicated by self.missing_label).

candidatesNone or array-like of shape (n_candidates,), dtype=int or array-like of shape (n_candidates, n_features), default=None

• If candidates is None, the unlabeled samples from (X,y) are considered as candidates.

• If candidates is of shape (n_candidates,) and of type int, candidates is considered as the indices of the samples in (X,y).

batch_sizeint, default=1

The number of samples to be selected in one AL cycle.

return_utilitiesbool, default=False

If True, also return the utilities based on the query strategy.

Returns:

query_indicesnumpy.ndarray of shape (batch_size,)

The query indices indicate for which candidate sample a label is to be queried, e.g., query_indices[0] indicates the first selected sample. The indexing refers to the samples in X.

utilitiesnumpy.ndarray of shape (batch_size, n_samples) or numpy.ndarray of shape (batch_size, n_candidates)

The utilities of samples after each selected sample of the batch, e.g., utilities[0] indicates the utilities used for selecting the first sample (with index query_indices[0]) of the batch. Utilities for labeled samples will be set to np.nan. The indexing refers to the samples in X.

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.

Examples using skactiveml.pool.MaxHerding

MaxHerding

MaxHerding