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 | str | None | np.nan, optional (default=np.nan)

Symbol to represent a missing label.

Returns

lbld_indicesnumpy.ndarray, 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

Core Set

Core Set

Random Sampling

Random Sampling

Active Learning with Cost Embedding

Active Learning with Cost Embedding

Clustering Uncertainty-weighted Embeddings (CLUE)

Clustering Uncertainty-weighted Embeddings (CLUE)

Contrastive Active Learning (CAL)

Contrastive Active Learning (CAL)

Batch Active Learning by Diverse Gradient Embedding (BADGE)

Batch Active Learning by Diverse Gradient Embedding (BADGE)

Typical Clustering (TypiClust)

Typical Clustering (TypiClust)

Uncertainty Sampling with Entropy

Uncertainty Sampling with Entropy

Epistemic Uncertainty Sampling

Epistemic Uncertainty Sampling

Querying Informative and Representative Examples

Querying Informative and Representative Examples

Uncertainty Sampling with Margin

Uncertainty Sampling with Margin

Uncertainty Sampling with Least-Confidence

Uncertainty Sampling with Least-Confidence

Probability Coverage (ProbCover)

Probability Coverage (ProbCover)

Expected Average Precision

Expected Average Precision

Density-Diversity-Distribution-Distance Sampling

Density-Diversity-Distribution-Distance Sampling

Monte-Carlo EER with Log-Loss

Monte-Carlo EER with Log-Loss

Batch Density-Diversity-Distribution-Distance Sampling

Batch Density-Diversity-Distribution-Distance Sampling

Monte-Carlo EER with Misclassification-Loss

Monte-Carlo EER with Misclassification-Loss

Sub-sampling Wrapper

Sub-sampling Wrapper

Discriminative Active Learning

Discriminative Active Learning

Parallel Utility Estimation Wrapper

Parallel Utility Estimation Wrapper

Multi-class Probabilistic Active Learning

Multi-class Probabilistic Active Learning

Query-by-Committee (QBC) with Vote Entropy

Query-by-Committee (QBC) with Vote Entropy

Query-by-Committee (QBC) with Variation Ratios

Query-by-Committee (QBC) with Variation Ratios

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

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

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

Batch Bayesian Active Learning by Disagreement (BatchBALD)

Batch Bayesian Active Learning by Disagreement (BatchBALD)

Bayesian Active Learning by Disagreement (BALD)

Bayesian Active Learning by Disagreement (BALD)

Density-weighted Uncertainty Sampling

Density-weighted Uncertainty Sampling

Greedy Sampling on the Feature Space (GSx)

Greedy Sampling on the Feature Space (GSx)

Greedy Sampling on the Target Space (GSy)

Greedy Sampling on the Target Space (GSy)

Improved Greedy Sampling (GSi)

Improved Greedy Sampling (GSi)

Expected Model Variance Reduction

Expected Model Variance Reduction

Expected Model Change

Expected Model Change

Regression Tree Based Active Learning (RT-AL) with Random Selection

Regression Tree Based Active Learning (RT-AL) with Random Selection

Regression Tree Based Active Learning (RT-AL) with Diversity Selection

Regression Tree Based Active Learning (RT-AL) with Diversity Selection

Regression Tree Based Active Learning (RT-AL) with Representativity Selection

Regression Tree Based Active Learning (RT-AL) with Representativity Selection

Query-by-Committee (QBC) with Empirical Variance

Query-by-Committee (QBC) with Empirical Variance

Expected Model Output Change

Expected Model Output Change

Regression based Kullback Leibler Divergence Maximization

Regression based Kullback Leibler Divergence Maximization