skactiveml.pool.ContrastiveAL#
- class skactiveml.pool.ContrastiveAL(nearest_neighbors_dict=None, clf_embedding_flag_name=None, eps=1e-07, missing_label=nan, random_state=None)[source]#
Bases:
SingleAnnotatorPoolQueryStrategyContrastive Active Learning (ContrastiveAL)
This class implements the Contrastive Active Learning (ContrastiveAL) query strategy [1], which selects samples similar in the (classifier’s learned) feature space, while the classifier predicts maximally different class-membership probabilities.
- Parameters
- nearest_neighbors_dictdict, default=None
The parameters passed to the nearest neighboring algorithm sklearn.neighbors.NearestNeighbors.
- clf_embedding_flag_namestr or None, default=None
Name of the flag, which is passed to the predict_proba method for getting the (learned) sample representations. If clf_embedding_flag_name=None and predict_proba returns only one output, the input samples X are used. If predict_proba returns two outputs or clf_embedding_name is not None, (proba, embeddings) are expected as outputs.
- epsfloat > 0, default=1e-7
Minimum probability threshold to compute log-probabilities.
- 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
Margatina, Katerina, Giorgos Vernikos, Loïc Barrault, and Nikolaos Aletras. “Active Learning by Acquiring Contrastive Examples.” In EMNLP, pp. 650-663. 2021.
Methods
Get metadata routing of this object.
get_params([deep])Get parameters for this estimator.
query(X, y, clf[, fit_clf, sample_weight, ...])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, clf, fit_clf=True, sample_weight=None, candidates=None, batch_size=1, return_utilities=False)[source]#
Query the next samples to be labeled.
- 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).
- clfskactiveml.base.SkactivemlClassifier
Model implementing the methods fit and predict_proba.
- fit_clfbool, default=True
Defines whether the classifier should be fitted on X, y, and sample_weight.
- sample_weight: array-like of shape (n_samples,), default=None
Weights of training samples in X.
- candidatesNone or array-like of shape (n_candidates) with 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 a list of the indices of the samples in (X, y). If candidates is of shape (n_candidates, n_features), the candidate samples are directly given in candidates (not necessarily contained in X).
- 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 samples in X.
- utilitiesnumpy.ndarray of shape (batch_size, n_samples)
The utilities of samples for selecting each sample of the batch. Here, utilities refers to the Kullback-Leibler divergence between the sample’s own and its labeled nearest neighbors’ predicted class-membership probabilities. If candidates is None or of shape (n_candidates,), 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.
