skactiveml.utils.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

Active Learning with Cost Embedding

Discriminative Active Learning

Discriminative Active Learning

Dropout Query (DropQuery)

Dropout Query (DropQuery)

Epistemic Uncertainty Sampling

Epistemic Uncertainty Sampling

Fast Active Learning by Contrastive UNcertainty (FALCUN)

Fast Active Learning by Contrastive UNcertainty (FALCUN)

Batch Density-Diversity-Distribution-Distance Sampling

Batch Density-Diversity-Distribution-Distance Sampling

Density-Diversity-Distribution-Distance Sampling

Density-Diversity-Distribution-Distance Sampling

Bayesian Active Learning by Disagreement (BALD)

Bayesian Active Learning by Disagreement (BALD)

Monte-Carlo EER with Log-Loss

Monte-Carlo EER with Log-Loss

Monte-Carlo EER with Misclassification-Loss

Monte-Carlo EER with Misclassification-Loss

Parallel Utility Estimation Wrapper

Parallel Utility Estimation Wrapper

Probability Coverage (ProbCover)

Probability Coverage (ProbCover)

Multi-class Probabilistic Active Learning

Multi-class Probabilistic Active Learning

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

Querying Informative and Representative Examples

Random Sampling

Random Sampling

Sub-sampling Wrapper

Sub-sampling Wrapper

Typical Clustering (TypiClust)

Typical Clustering (TypiClust)

Density-weighted Uncertainty Sampling

Density-weighted Uncertainty Sampling

Expected Average Precision

Expected Average Precision

Uncertainty Sampling with Entropy

Uncertainty Sampling with Entropy

Uncertainty Sampling with Least-Confidence

Uncertainty Sampling with Least-Confidence

Uncertainty Sampling with Margin

Uncertainty Sampling with Margin

Value of Information

Value of Information

Value of Information on Labeled Samples

Value of Information on Labeled Samples

Value of Information on Unlabeled Samples

Value of Information on Unlabeled Samples