labeled_indices

skactiveml.utils.labeled_indices(y, missing_label=nan)

Return an array of indices indicating present labels.

Parameters:

yarray-like, shape (n_samples,) or (n_samples, n_outputs)

Class labels to be checked w.r.t. to present labels.

missing_labelnumber or str or None or np.nan, default=np.nan

Value to represent a missing label.

Returns:

lbld_indicesnumpy.ndarray of shape (n_samples) or (n_samples, 2)

Index array of present labels. If y is a 2D-array, the indices have shape (n_samples, 2), otherwise it has the shape `(n_samples).

Examples using skactiveml.utils.labeled_indices

Batch Active Learning by Diverse Gradient Embedding (BADGE)

Batch Active Learning by Diverse Gradient Embedding (BADGE)

Batch Bayesian Active Learning by Disagreement (BatchBALD)

Batch Bayesian Active Learning by Disagreement (BatchBALD)

Clustering Uncertainty-weighted Embeddings (CLUE)

Clustering Uncertainty-weighted Embeddings (CLUE)

Contrastive Active Learning (CAL)

Contrastive Active Learning (CAL)

Core Set

Core Set

Active Learning with Cost Embedding (ALCE)

Active Learning with Cost Embedding (ALCE)

Discriminative Active Learning (DAL)

Discriminative Active Learning (DAL)

Dropout Query (DropQuery)

Dropout Query (DropQuery)

Epistemic Uncertainty Sampling (EpisUS)

Epistemic Uncertainty Sampling (EpisUS)

Fast Active Learning by Contrastive UNcertainty (FALCUN)

Fast Active Learning by Contrastive UNcertainty (FALCUN)

Batch Density-Diversity-Distribution-Distance Sampling (4DS)

Batch Density-Diversity-Distribution-Distance Sampling (4DS)

Density-Diversity-Distribution-Distance Sampling (4DS)

Density-Diversity-Distribution-Distance Sampling (4DS)

Bayesian Active Learning by Disagreement (BALD)

Bayesian Active Learning by Disagreement (BALD)

MaxHerding

MaxHerding

Monte-Carlo Expected Error Reduction (EER) with Log-Loss

Monte-Carlo Expected Error Reduction (EER) with Log-Loss

Monte-Carlo Expected Error Reduction (EER) with Misclassification-Loss

Monte-Carlo Expected Error Reduction (EER) with Misclassification-Loss

Parallel Utility Estimation Wrapper

Parallel Utility Estimation Wrapper

Probability Coverage (ProbCover)

Probability Coverage (ProbCover)

Multi-class Probabilistic Active Learning (McPAL)

Multi-class Probabilistic Active Learning (McPAL)

Query-by-Committee (QBC) with Kullback-Leibler Divergence

Query-by-Committee (QBC) with Kullback-Leibler Divergence

Query-by-Committee (QBC) with Variation Ratios

Query-by-Committee (QBC) with Variation Ratios

Query-by-Committee (QBC) with Vote Entropy

Query-by-Committee (QBC) with Vote Entropy

Querying Informative and Representative Examples (QUIRE)

Querying Informative and Representative Examples (QUIRE)

Random Sampling

Random Sampling

Sub-sampling Wrapper

Sub-sampling Wrapper

Typical Clustering (TypiClust)

Typical Clustering (TypiClust)

Density-weighted Uncertainty Sampling (DWUS)

Density-weighted Uncertainty Sampling (DWUS)

Uncertainty Sampling (US) with Entropy

Uncertainty Sampling (US) with Entropy

Uncertainty Sampling (US) with Least-Confidence

Uncertainty Sampling (US) with Least-Confidence

Uncertainty Sampling (US) with Margin

Uncertainty Sampling (US) with Margin

Uncertainty Sampling with Expected Average Precision (USAP)

Uncertainty Sampling with Expected Average Precision (USAP)

Value of Information (VOI)

Value of Information (VOI)

Value of Information (VOI) on Labeled Samples

Value of Information (VOI) on Labeled Samples

Value of Information (VOI) on Unlabeled Samples

Value of Information (VOI) on Unlabeled Samples