Crate sklears_semi_supervised 

Semi-supervised learning algorithms

This module provides semi-supervised learning algorithms that can utilize both labeled and unlabeled data for training.

Modules

autoregressive_models

Autoregressive models for generative semi-supervised learning

confident_learning

Confident Learning implementation

consistency_training

Consistency Training implementation

decision_boundary_semi_supervised

Decision Boundary Semi-Supervised Learning implementation

deep_belief_networks

Deep Belief Networks for semi-supervised learning

deep_gaussian_processes

Deep Gaussian Processes for semi-supervised learning

energy_based_models

Energy-based models for semi-supervised learning

entropy_active_learning

Entropy-based Active Learning implementation

entropy_regularization

Entropy Regularization implementation

flow_based_models

Flow-Based Models for Semi-Supervised Learning

ladder_networks

Ladder Networks for Deep Semi-Supervised Learning

maml

Model-Agnostic Meta-Learning (MAML) implementation

matching_networks

Matching Networks implementation

mean_teacher

Mean Teacher implementation

minimum_entropy_discrimination

Minimum Entropy Discrimination implementation

neural_ode

Neural Ordinary Differential Equations for Semi-Supervised Learning

parallel_graph

Parallel graph algorithms for semi-supervised learning

pi_model

Pi Model implementation

prototypical_networks

Prototypical Networks implementation

relation_networks

Relation Networks implementation

semi_supervised_gan

Semi-Supervised Generative Adversarial Networks (SS-GANs)

semi_supervised_vae

Semi-Supervised Variational Autoencoder implementation

simd_distances

SIMD-accelerated distance computations for semi-supervised learning

stacked_autoencoders

Stacked Autoencoders for semi-supervised learning

temporal_ensembling

Temporal Ensembling implementation

virtual_adversarial_training

Virtual Adversarial Training implementation

Structs

AdversarialAttack

Adversarial attack configuration

AdversarialGraphLearning

Adversarial graph learning for robust semi-supervised learning

AffineCouplingLayer

Affine coupling layer for normalizing flows

ApproximateKNN

Approximate k-NN graph construction using random projection and locality sensitive hashing

ApproximateSpectralClustering

Spectral clustering with approximate eigenvalue decomposition

AutoencoderLayer

Single Autoencoder layer for pre-training

AutoregressiveModel

Autoregressive neural network for semi-supervised learning

BanditBasedActiveLearning

Bandit-based Active Learning coordinator

BatchModeActiveLearning

Batch Mode Active Learning using uncertainty and diversity

BayesianActiveLearning

Bayesian Active Learning for Semi-Supervised Learning

BayesianActiveTrained

Trained state for BayesianActiveLearning

BreakdownPointAnalysis

Breakdown point analysis for robust graph learning methods

BreakdownResult

Result of breakdown point analysis

CoTraining

Co-Training classifier for semi-supervised learning with multiple views

CoTrainingTrained

Trained state for CoTraining

ConfidentLearning

Confident Learning for Semi-Supervised Learning

ConfidentLearningTrained

Trained state for ConfidentLearning

ConsistencyTraining

Consistency Training for Semi-Supervised Learning

ConsistencyTrainingTrained

Trained state for ConsistencyTraining

ContextualBandit

Contextual Bandit for active learning with context features

ContrastivePredictiveCoding

Contrastive Predictive Coding (CPC) for semi-supervised learning

ConvergenceTestConfig

Configuration for convergence testing

ConvergenceTestResult

Results of convergence testing

ConvergenceTester

Generic convergence tester for iterative algorithms

CoreSetApproach

Core-set approach for batch active learning

CrossModalContrastive

Cross-modal contrastive learning model

CrossModalContrastiveTrained

Trained state for Cross-Modal Contrastive Learning

DecisionBoundarySemiSupervised

Decision Boundary Semi-Supervised Learning

DecisionBoundarySemiSupervisedTrained

Trained state for DecisionBoundarySemiSupervised

DeepBeliefNetwork

Deep Belief Network for semi-supervised learning

DeepGaussianProcess

Deep Gaussian Process for semi-supervised learning

DemocraticCoLearning

Democratic Co-Learning classifier for semi-supervised learning

DemocraticCoLearningTrained

Trained state for DemocraticCoLearning

Discriminator

Discriminator network for Semi-Supervised GAN

DistributedGraphLearning

Distributed Graph Learning for Large-Scale Semi-Supervised Learning

DistributedGraphLearningTrained

Trained state for DistributedGraphLearning

DiverseMiniBatchSelection

Diverse Mini-batch Selection using k-means clustering

DiversityBasedSampling

Diversity-Based Sampling for active learning

DynamicGraphLearning

Dynamic graph learning for streaming and continuously evolving scenarios

EarthMoverDistance

Earth Mover’s Distance based Semi-Supervised Learning

EarthMoverTrained

Trained state for EarthMoverDistance

EncoderOutput

EnergyBasedModel

Energy-based model for semi-supervised learning

EnhancedSelfTraining

Enhanced Self-Training Classifier with multiple confidence measures

EnhancedSelfTrainingTrained

Trained state for EnhancedSelfTraining

EntropyActiveLearning

Entropy-based Active Learning for Semi-Supervised Learning

EntropyActiveLearningTrained

Trained state for EntropyActiveLearning

EntropyRegularization

Entropy Regularization for Semi-Supervised Learning

EntropyRegularizationTrained

Trained state for EntropyRegularization

EntropyRegularizedSemiSupervised

Entropy-based Regularization for Semi-Supervised Learning

EntropyRegularizedTrained

Trained state for EntropyRegularizedSemiSupervised

EpsilonGraphBuilder

Epsilon-ball graph builder

EpsilonGreedy

Epsilon-Greedy strategy for multi-armed bandit active learning

ExpectedModelChange

Expected Model Change for Active Learning

FittedContrastivePredictiveCoding

Fitted Contrastive Predictive Coding model

FittedDeepBeliefNetwork

Fitted Deep Belief Network model

FittedDeepGaussianProcess

FittedMomentumContrast

Fitted MomentumContrast model

FittedSimCLR

Fitted SimCLR model

FittedStackedAutoencoders

FittedSupervisedContrastiveLearning

Fitted Supervised Contrastive Learning model

GaussianProcessLayer

Single Gaussian Process layer in the deep architecture

GaussianProcessSemiSupervised

Gaussian Process Semi-Supervised Learning

GaussianProcessTrained

Trained state for GaussianProcessSemiSupervised

Generator

Generator network for Semi-Supervised GAN

GradientEmbeddingMethods

Gradient Embedding Methods for active learning

GraphPipeline

Composable graph pipeline

GraphStructureLearning

Graph Structure Learning for Semi-Supervised Learning

GraphStructureLearningTrained

Trained state for GraphStructureLearning

GraphUpdate

Represents a graph update operation

GromovWassersteinSemiSupervised

Gromov-Wasserstein Semi-Supervised Learning

GromovWassersteinTrained

Trained state for GromovWassersteinSemiSupervised

HarmonicFunctions

Harmonic Functions classifier

HarmonicFunctionsTrained

Trained state for HarmonicFunctions

HeterogeneousGraphLearning

Heterogeneous graph learning for mixed data types

HierarchicalBayesianSemiSupervised

Hierarchical Bayesian Semi-Supervised Learning

HierarchicalBayesianTrained

Trained state for HierarchicalBayesianSemiSupervised

HierarchicalGraph

Hierarchical graph structure

HierarchicalGraphConstruction

Hierarchical graph construction with multiple scales

HierarchyLevel

Single level in the hierarchy

InformationBottleneck

Information Bottleneck principle for semi-supervised learning

InformationBottleneckTrained

Trained state for InformationBottleneck

InformationDensity

Information Density for Active Learning

KLDivergenceOptimization

KL-Divergence Optimization for Semi-Supervised Learning

KLDivergenceTrained

Trained state for KLDivergenceOptimization

KNNGraphBuilder

K-Nearest Neighbors graph builder

LabelPropagation

Label Propagation classifier

LabelPropagationTrained

Trained state for LabelPropagation

LabelSpreading

Label Spreading classifier

LabelSpreadingTrained

Trained state for LabelSpreading

LadderNetworks

Ladder Networks for Deep Semi-Supervised Learning

LadderNetworksTrained

Trained state for LadderNetworks

LadderWeights

LandmarkGraphConstruction

Landmark-based graph construction for scalable semi-supervised learning

LandmarkGraphResult

Result of landmark graph construction

LandmarkLabelPropagation

Landmark-based label propagation for large-scale semi-supervised learning

LocalGlobalConsistency

Local and Global Consistency classifier

LocalGlobalConsistencyTrained

Trained state for LocalGlobalConsistency

MAML

Model-Agnostic Meta-Learning (MAML) for Few-Shot Learning

MAMLTrained

Trained state for MAML

ManifoldRegularization

Manifold Regularization classifier

ManifoldRegularizationTrained

Trained state for ManifoldRegularization

MatchingNetworks

Matching Networks for Few-Shot Learning

MatchingNetworksTrained

Trained state for MatchingNetworks

MeanTeacher

Mean Teacher for Semi-Supervised Learning

MeanTeacherTrained

Trained state for MeanTeacher

MinimumEntropyDiscrimination

Minimum Entropy Discrimination for Semi-Supervised Learning

MinimumEntropyDiscriminationTrained

Trained state for MinimumEntropyDiscrimination

MixtureDiscriminantAnalysis

Mixture Discriminant Analysis

MixtureDiscriminantAnalysisTrained

Trained state for MixtureDiscriminantAnalysis

MomentumContrast

Momentum Contrast (MoCo) adaptation for semi-supervised learning

MultiScaleSemiSupervised

Multi-scale semi-supervised learning using hierarchical graphs

MultiViewCoTraining

Multi-View Co-Training classifier for semi-supervised learning with multiple views

MultiViewCoTrainingTrained

Trained state for MultiViewCoTraining

MultiViewGraphLearning

Multi-view graph learning that constructs graphs from multiple data views

MutualInformationMaximization

Mutual Information Maximization for semi-supervised learning

MutualInformationTrained

Trained state for MutualInformationMaximization

NeuralODE

Neural ODE for semi-supervised learning

NeuralODELayer

Neural ODE layer for modeling continuous dynamics

NeuralODETrained

Trained state for Neural ODE

NoiseRobustPropagation

Noise-robust label propagation for semi-supervised learning

NormalizeTransform

Normalize graph transformation

NormalizingFlow

Normalizing flow model for semi-supervised learning

NormalizingFlowTrained

Trained state for Normalizing Flow

PiModel

Pi Model for Semi-Supervised Learning

PiModelTrained

Trained state for PiModel

ProjectionNetwork

Projection network for cross-modal contrastive learning

PrototypicalNetworks

Prototypical Networks for Few-Shot Learning

PrototypicalNetworksTrained

Trained state for PrototypicalNetworks

QueryByCommittee

Query by Committee for Active Learning

RelationNetworks

Relation Networks for Few-Shot Learning

RelationNetworksTrained

Trained state for RelationNetworks

RestrictedBoltzmannMachine

Restricted Boltzmann Machine (RBM) component

RobustGraphConstruction

Robust graph construction using M-estimators and outlier detection

RobustGraphLearning

Robust Graph Learning for Semi-Supervised Learning

RobustGraphLearningTrained

Trained state for RobustGraphLearning

SelfTrainingClassifier

Self-Training Classifier

SelfTrainingTrained

Trained state for SelfTrainingClassifier

SemiSupervisedGAN

Semi-Supervised GAN for semi-supervised learning

SemiSupervisedGANTrained

Trained state for Semi-Supervised GAN

SemiSupervisedGMM

Semi-supervised Gaussian Mixture Model

SemiSupervisedGMMTrained

Trained state for SemiSupervisedGMM

SemiSupervisedNaiveBayes

Semi-supervised Naive Bayes classifier

SemiSupervisedNaiveBayesTrained

Trained state for SemiSupervisedNaiveBayes

SemiSupervisedVAE

Semi-Supervised Variational Autoencoder

SemiSupervisedVAETrained

Trained state for SemiSupervisedVAE

SimCLR

SimCLR (A Simple Framework for Contrastive Learning) adaptation for semi-supervised learning

SparsifyTransform

Sparsify graph transformation

StackedAutoencoders

Stacked Autoencoders for semi-supervised learning

StreamingGraphLearning

Streaming Graph Learning for Dynamic Semi-Supervised Learning

StreamingGraphLearningTrained

Trained state for StreamingGraphLearning

SupervisedContrastiveLearning

Supervised Contrastive Learning for semi-supervised scenarios

SymmetrizeTransform

Symmetrize graph transformation

TemporalEnsembling

Temporal Ensembling for Semi-Supervised Learning

TemporalEnsemblingTrained

Trained state for TemporalEnsembling

TemporalGraphLearning

Temporal graph learning for time-evolving graphs

ThompsonSampling

Thompson Sampling strategy for multi-armed bandit active learning

TriTraining

Tri-Training classifier for semi-supervised learning

TriTrainingTrained

Trained state for TriTraining

UncertaintySampling

Uncertainty Sampling for Active Learning

UpperConfidenceBound

Upper Confidence Bound (UCB) strategy for multi-armed bandit active learning

VariationalBayesianSemiSupervised

Variational Bayesian Semi-Supervised Learning

VariationalBayesianTrained

Trained state for VariationalBayesianSemiSupervised

VirtualAdversarialTraining

Virtual Adversarial Training (VAT) for Semi-Supervised Learning

VirtualAdversarialTrainingTrained

Trained state for VirtualAdversarialTraining

WassersteinSemiSupervised

Wasserstein Distance Semi-Supervised Learning

WassersteinTrained

Trained state for WassersteinSemiSupervised

Enums

BanditError

BatchActiveLearningError

ContrastiveLearningError

DeepBeliefNetworkError

DeepGaussianProcessError

KernelType

Kernel function types for Gaussian Processes

StackedAutoencoderError

Traits

GraphBuilder

Trait for graph construction strategies

GraphTransform

Trait for graph transformations

Functions

adaptive_knn_graph

Adaptive neighborhood graph construction

adaptive_multi_scale_graph_construction

Adaptive multi-scale graph construction

diffusion_matrix

Compute diffusion matrix for diffusion maps

epsilon_graph

Construct epsilon-neighborhood graph

graph_laplacian

Construct graph Laplacian

knn_graph

Construct k-nearest neighbors graph

make_symmetric

Make graph symmetric

multi_scale_graph_construction

Multi-scale graph construction

multi_scale_spectral_clustering

Multi-scale spectral clustering

mutual_knn_graph

Construct mutual k-nearest neighbors graph

random_walk_laplacian

Construct random walk Laplacian

shared_nn_graph

Construct shared nearest neighbors graph

sparsify_graph

Graph sparsification using effective resistance sampling

spectral_clustering

Spectral clustering implementation for semi-supervised learning

spectral_embedding

Spectral embedding for dimensionality reduction

Type Aliases

CrossModalInput

Input for cross-modal learning: (modality1, modality2)