skactiveml.pool.GreedySamplingX#
- class skactiveml.pool.GreedySamplingX(metric=None, metric_dict=None, missing_label=nan, random_state=None)[source]#
Bases:
SingleAnnotatorPoolQueryStrategyGreedy Sampling in the Feature Space (GSx)
This class implements the query strategy Greedy Sampling in the Feature Space (GSx) [1] that tries to select those samples that increase the diversity of the feature space the most.
- Parameters
- metricstr, default=”euclidean”
Metric used for calculating the distances of the samples in the feature space. It must be a valid argument for sklearn.metrics.pairwise_distances argument metric.
- metric_dictdict, default=None
Any further parameters are passed directly to the pairwise_distances function.
- missing_labelscalar or string or np.nan or None, default=np.nan
Value to represent a missing label.
- random_stateint or np.random.RandomState, default=None
Random state for candidate selection.
References
- 1
D. Wu, C.-T. Lin, and J. Huang. Active Learning for Regression using Greedy Sampling. Inf. Sci., 474:90–105, 2019.
Methods
Get metadata routing of this object.
get_params([deep])Get parameters for this estimator.
query(X, y[, candidates, batch_size, ...])Query the next samples to be labeled.
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]#
Query the next samples to be labeled.
- 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, ) of type int, 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).
If candidates is of shape (n_candidates, *), candidates is considered as the candidate 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.
If candidates is None or of shape (n_candidates,), the indexing refers to the samples in X.
If candidates is of shape (n_candidates, n_features), the indexing refers to the samples in candidates.
- utilitiesnumpy.ndarray of shape (batch_size, n_samples)
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.
If candidates is None, the indexing refers to the samples in X.
If candidates is of shape (n_candidates,) and of type int, utilities refers to the samples in X.
If candidates is of shape (n_candidates, *), utilities refers to the indexing in candidates.
- 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.
