skactiveml.pool.TypiClust#
- class skactiveml.pool.TypiClust(missing_label=nan, random_state=None, cluster_algo=<class 'sklearn.cluster._kmeans.KMeans'>, cluster_algo_dict=None, n_cluster_param_name='n_clusters', k=5)[source]#
Bases:
SingleAnnotatorPoolQueryStrategyTypical Clustering (TypiClust)
This class implements the Typical Clustering (TypiClust) query strategy [1], which considers both diversity and typicality (representativeness) of the samples.
- Parameters
- 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.
- cluster_algoClusterMixin.__class__, default=KMeans
The cluster algorithm to be used.
- cluster_algo_dictdict, optional (default=None)
The parameters passed to the clustering algorithm cluster_algo, excluding the parameter for the number of clusters.
- n_cluster_param_namestring, default=”n_clusters”
The name of the parameter for the number of clusters.
- kint, default=5
The number for k-nearest-neighbors for the computation of typicality.
References
- 1
G. Hacohen, A. Dekel, and D. Weinshall. Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets. In Int. Conf. Mach. Learn., pages 8175–8195, 2022.
Methods
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
MetadataRequestencapsulating 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)[source]#
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.
