API and modules

Main module skada

Sample reweighting DA methods

DAEstimators with adapters (Pipeline):

DensityReweight([base_estimator, ...])	Density re-weighting pipeline adapter and estimator.
GaussianReweight([base_estimator, reg])	Gaussian approximation re-weighting pipeline adapter and estimator.
DiscriminatorReweight([base_estimator, ...])	Discriminator re-weighting pipeline adapter and estimator.
KLIEPReweight([base_estimator, gamma, cv, ...])	KLIEPReweight pipeline adapter and estimator.
NearestNeighborReweight([base_estimator, ...])	Density re-weighting pipeline adapter and estimator.
MMDTarSReweight([base_estimator, gamma, ...])	Target shift reweighting using MMD.
KMMReweight([base_estimator, kernel, gamma, ...])	KMMReweight pipeline adapter and estimator.

Adapters:

DensityReweightAdapter([weight_estimator])	Adapter based on re-weighting samples using density estimation.
GaussianReweightAdapter([reg])	Gaussian approximation re-weighting method.
DiscriminatorReweightAdapter([domain_classifier])	Gaussian approximation re-weighting method.
KLIEPReweightAdapter(gamma[, cv, n_centers, ...])	Kullback-Leibler Importance Estimation Procedure (KLIEPReweight).
NearestNeighborReweightAdapter([...])	Adapter based on re-weighting samples using a KNN,
MMDTarSReweightAdapter(gamma[, reg, tol, ...])	Target shift reweighting using MMD.
KMMReweightAdapter([kernel, gamma, degree, ...])	Kernel Mean Matching (KMMReweight).

Sample mapping and alignment DA methods

DAEstimators with adapters (Pipeline):

SubspaceAlignment([base_estimator, ...])	Domain Adaptation Using Subspace Alignment.
TransferComponentAnalysis([base_estimator, ...])	Domain Adaptation Using Transfer Component Analysis.
TransferJointMatching([base_estimator, ...])
CORAL([base_estimator, reg, assume_centered])	CORAL pipeline with adapter and estimator.
OTMapping([base_estimator, metric, norm, ...])	OTmapping pipeline with adapter and estimator.
EntropicOTMapping([base_estimator, metric, ...])	EntropicOTMapping pipeline with adapter and estimator.
ClassRegularizerOTMapping([base_estimator, ...])	ClassRegularizedOTMapping pipeline with adapter and estimator.
LinearOTMapping([base_estimator, reg, bias])	Returns a the linear OT mapping method with adapter and estimator.
MMDLSConSMapping([base_estimator, gamma, ...])	MMDLSConSMapping pipeline with adapter and estimator.

Adapters:

SubspaceAlignmentAdapter([n_components, ...])	Domain Adaptation Using Subspace Alignment.
TransferComponentAnalysisAdapter([kernel, ...])	Transfer Component Analysis.
TransferJointMatchingAdapter([n_components, ...])	Domain Adaptation Using TJM: Transfer Joint Matching.
TransferSubspaceLearning([base_estimator, ...])	Domain Adaptation Using Transfer Subspace Learning.
CORALAdapter([reg, assume_centered])	Estimator based on Correlation Alignment [1]_.
OTMappingAdapter([metric, norm, max_iter])	Domain Adaptation Using Optimal Transport.
EntropicOTMappingAdapter([reg_e, metric, ...])	Domain Adaptation Using Optimal Transport.
ClassRegularizerOTMappingAdapter([reg_e, ...])	Domain Adaptation Using Optimal Transport.
LinearOTMappingAdapter([reg, bias])	Domain Adaptation Using Optimal Transport.
MMDLSConSMappingAdapter(gamma[, reg_k, ...])	Location-Scale mapping minimizing the MMD with a Gaussian kernel.

Other DA methods

JDOTRegressor([base_estimator, alpha, ...])	Joint Distribution Optimal Transport Regressor proposed in [10]
DASVMClassifier([base_estimator, k, ...])	DASVM Estimator:

DA pipeline

make_da_pipeline(*steps[, memory, verbose, ...])

Construct a Pipeline from the given estimators.

Shared(base_estimator, **kwargs)
PerDomain(base_estimator, **kwargs)
SelectSource(base_estimator, **kwargs)	Selects only source domains for fitting base estimator.
SelectTarget(base_estimator, **kwargs)	Selects only target domains for fitting base estimator.

Utilities

source_target_split(*arrays, sample_domain)	Split data into source and target domains
per_domain_split(*arrays, sample_domain)	Split data into multiple source and target domains

Deep learning DA skada.deep:

Some methods for deep domain adaptation.

Deep learning DA methods

DeepCoral(module, layer_name[, reg, ...])	DeepCORAL domain adaptation method.
DeepJDOT(module, layer_name[, reg_dist, ...])	DeepJDOT.
DANN(module, layer_name[, reg, ...])	Domain-Adversarial Training of Neural Networks (DANN).
CDAN(module, layer_name[, reg, ...])	Conditional Domain Adversarial Networks (CDAN).
MCC(module, layer_name[, reg, T, base_criterion])
CAN(module, layer_name[, reg, ...])	Contrastive Adaptation Network (CAN) domain adaptation method.
MDD(module, layer_name[, reg, gamma, ...])	Margin Disparity Discrepancy (MDD).

SKADA deep learning DA losses

DeepCoralLoss([assume_centered])	Loss DeepCORAL
DeepJDOTLoss([reg_dist, reg_cl, ...])	Loss DeepJDOT.
DANLoss([sigmas, eps])	Loss DAN
CDANLoss([domain_criterion])	Conditional Domain Adversarial Networks (CDAN) loss.
MCCLoss([T, eps])	Loss MCC.
CANLoss([distance_threshold, ...])	Loss for Contrastive Adaptation Network (CAN)
MDDLoss([gamma])	Loss MDD.

Torch compatible DA losses in skada.deep.losses

dan_loss(features_s, features_t[, sigmas, eps])	Define the mmd loss based on multi-kernel defined in [R095e4befb364-14].
deepcoral_loss(features, features_target[, ...])	Estimate the Frobenius norm divide by 4*n**2
deepjdot_loss(y_s, y_pred_t, features_s, ...)	Compute the OT loss for DeepJDOT method [Ra0fd271667d9-13].
mcc_loss(y[, T, eps])	Estimate the Frobenius norm divide by 4*n**2
cdd_loss(y_s, features_s, features_t, ...[, ...])	Define the contrastive domain discrepancy loss based on [Rfc1dd7997531-33].

DA metrics skada.metrics

SupervisedScorer([scoring, greater_is_better])	Compute score on supervised dataset.
ImportanceWeightedScorer([weight_estimator, ...])	Score based on source data using sample weight.
PredictionEntropyScorer([greater_is_better, ...])	Score based on the entropy of predictions on unsupervised dataset.
DeepEmbeddedValidation([domain_classifier, ...])	Loss based on source data using features representation to weight samples.
SoftNeighborhoodDensity([T, greater_is_better])	Score based on the entropy of similarity between unsupervised dataset.
CircularValidation([source_scorer, ...])	Score based on a circular validation strategy.
MixValScorer([alpha, ice_type, scoring, ...])	MixVal scorer for unsupervised domain adaptation.

Model Selection skada.model_selection

SourceTargetShuffleSplit([n_splits, ...])	Source-Target-Shuffle-Split cross-validator.
DomainShuffleSplit([n_splits, test_size, ...])	Domain-Shuffle-Split cross-validator.
StratifiedDomainShuffleSplit([n_splits, ...])	Stratified-Domain-Shuffle-Split cross-validator.
LeaveOneDomainOut([max_n_splits, test_size, ...])	Leave-One-Domain-Out cross-validator.

Datasets skada.datasets

Utilities to produce datasets for testing and benchmarking.

make_shifted_blobs([n_samples, n_features, ...])	Generate source and shift target isotropic Gaussian blobs .
make_shifted_datasets([n_samples_source, ...])	Generate source and shift target.
make_dataset_from_moons_distribution([...])	Make dataset from moons.
make_variable_frequency_dataset([...])	Make dataset with different peak frequency.

Utilities skada.utils

check_X_y_domain(X, y[, sample_domain, ...])	Input validation for domain adaptation (DA) estimator.
extract_source_indices(sample_domain)	Extract the indices of the source samples.
extract_domains_indices(sample_domain[, ...])	Extract the indices of the specific domain samples.
source_target_merge(*arrays[, sample_domain])	Merge source and target domain data based on sample domain labels.