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<!DOCTYPE html>
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<li class="toctree-l1"><a class="reference internal" href="index.html">SKADA: SciKit Adaptation</a></li>
<li class="toctree-l1"><a class="reference internal" href="auto_examples/plot_how_to_use_skada.html">How to use SKADA</a></li>
<li class="toctree-l1"><a class="reference internal" href="quickstart.html">Users Guide</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">API and modules</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#module-skada">Main module <code class="xref py py-mod docutils literal notranslate"><span class="pre">skada</span></code></a><ul>
<li class="toctree-l3"><a class="reference internal" href="#sample-reweighting-da-methods">Sample reweighting DA methods</a><ul>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.DensityReweight.html">skada.DensityReweight</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.GaussianReweight.html">skada.GaussianReweight</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.DiscriminatorReweight.html">skada.DiscriminatorReweight</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.KLIEPReweight.html">skada.KLIEPReweight</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.NearestNeighborReweight.html">skada.NearestNeighborReweight</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.MMDTarSReweight.html">skada.MMDTarSReweight</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.KMMReweight.html">skada.KMMReweight</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.DensityReweightAdapter.html">skada.DensityReweightAdapter</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.GaussianReweightAdapter.html">skada.GaussianReweightAdapter</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.DiscriminatorReweightAdapter.html">skada.DiscriminatorReweightAdapter</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.KLIEPReweightAdapter.html">skada.KLIEPReweightAdapter</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.NearestNeighborReweightAdapter.html">skada.NearestNeighborReweightAdapter</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.MMDTarSReweightAdapter.html">skada.MMDTarSReweightAdapter</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.KMMReweightAdapter.html">skada.KMMReweightAdapter</a></li>
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<li class="toctree-l3"><a class="reference internal" href="#sample-mapping-and-alignment-da-methods">Sample mapping and alignment DA methods</a><ul>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.SubspaceAlignment.html">skada.SubspaceAlignment</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.TransferComponentAnalysis.html">skada.TransferComponentAnalysis</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.TransferJointMatching.html">skada.TransferJointMatching</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.TransferSubspaceLearning.html">skada.TransferSubspaceLearning</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.CORAL.html">skada.CORAL</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.OTMapping.html">skada.OTMapping</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.EntropicOTMapping.html">skada.EntropicOTMapping</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.ClassRegularizerOTMapping.html">skada.ClassRegularizerOTMapping</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.LinearOTMapping.html">skada.LinearOTMapping</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.MMDLSConSMapping.html">skada.MMDLSConSMapping</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.MultiLinearMongeAlignment.html">skada.MultiLinearMongeAlignment</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.SubspaceAlignmentAdapter.html">skada.SubspaceAlignmentAdapter</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.TransferComponentAnalysisAdapter.html">skada.TransferComponentAnalysisAdapter</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.TransferJointMatchingAdapter.html">skada.TransferJointMatchingAdapter</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.TransferSubspaceLearningAdapter.html">skada.TransferSubspaceLearningAdapter</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.CORALAdapter.html">skada.CORALAdapter</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.OTMappingAdapter.html">skada.OTMappingAdapter</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.EntropicOTMappingAdapter.html">skada.EntropicOTMappingAdapter</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.ClassRegularizerOTMappingAdapter.html">skada.ClassRegularizerOTMappingAdapter</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.LinearOTMappingAdapter.html">skada.LinearOTMappingAdapter</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.MMDLSConSMappingAdapter.html">skada.MMDLSConSMappingAdapter</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.MultiLinearMongeAlignmentAdapter.html">skada.MultiLinearMongeAlignmentAdapter</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="#other-da-methods">Other DA methods</a><ul>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.JDOTClassifier.html">skada.JDOTClassifier</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.JDOTRegressor.html">skada.JDOTRegressor</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.DASVMClassifier.html">skada.DASVMClassifier</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.OTLabelProp.html">skada.OTLabelProp</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.JCPOTLabelProp.html">skada.JCPOTLabelProp</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="#da-pipeline">DA pipeline</a><ul>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.make_da_pipeline.html">skada.make_da_pipeline</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.Shared.html">skada.Shared</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.PerDomain.html">skada.PerDomain</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.SelectSource.html">skada.SelectSource</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.SelectTarget.html">skada.SelectTarget</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.SelectSourceTarget.html">skada.SelectSourceTarget</a></li>
</ul>
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<li class="toctree-l3"><a class="reference internal" href="#utilities">Utilities</a><ul>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.source_target_split.html">skada.source_target_split</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.per_domain_split.html">skada.per_domain_split</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#module-skada.deep">Deep learning DA <code class="xref py py-mod docutils literal notranslate"><span class="pre">skada.deep</span></code>:</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#deep-learning-da-methods">Deep learning DA methods</a><ul>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.DeepCoral.html">skada.deep.DeepCoral</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.DeepJDOT.html">skada.deep.DeepJDOT</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.DAN.html">skada.deep.DAN</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.DANN.html">skada.deep.DANN</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.CDAN.html">skada.deep.CDAN</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.MCC.html">skada.deep.MCC</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.CAN.html">skada.deep.CAN</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.MDD.html">skada.deep.MDD</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.SPA.html">skada.deep.SPA</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="#skada-deep-learning-da-losses">SKADA deep learning DA losses</a><ul>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.DeepCoralLoss.html">skada.deep.DeepCoralLoss</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.DeepJDOTLoss.html">skada.deep.DeepJDOTLoss</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.DANLoss.html">skada.deep.DANLoss</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.DANNLoss.html">skada.deep.DANNLoss</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.CDANLoss.html">skada.deep.CDANLoss</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.MCCLoss.html">skada.deep.MCCLoss</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.CANLoss.html">skada.deep.CANLoss</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.MDDLoss.html">skada.deep.MDDLoss</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.SPALoss.html">skada.deep.SPALoss</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="#module-skada.deep.losses">Torch compatible DA losses in <code class="xref py py-mod docutils literal notranslate"><span class="pre">skada.deep.losses</span></code></a><ul>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.losses.dan_loss.html">skada.deep.losses.dan_loss</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.losses.deepcoral_loss.html">skada.deep.losses.deepcoral_loss</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.losses.deepjdot_loss.html">skada.deep.losses.deepjdot_loss</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.losses.mcc_loss.html">skada.deep.losses.mcc_loss</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.losses.cdd_loss.html">skada.deep.losses.cdd_loss</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.losses.gda_loss.html">skada.deep.losses.gda_loss</a></li>
<li class="toctree-l4"><a class="reference internal" href="gen_modules/skada.deep.losses.nap_loss.html">skada.deep.losses.nap_loss</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#module-skada.metrics">DA metrics <code class="xref py py-mod docutils literal notranslate"><span class="pre">skada.metrics</span></code></a><ul>
<li class="toctree-l3"><a class="reference internal" href="gen_modules/skada.metrics.SupervisedScorer.html">skada.metrics.SupervisedScorer</a></li>
<li class="toctree-l3"><a class="reference internal" href="gen_modules/skada.metrics.ImportanceWeightedScorer.html">skada.metrics.ImportanceWeightedScorer</a></li>
<li class="toctree-l3"><a class="reference internal" href="gen_modules/skada.metrics.PredictionEntropyScorer.html">skada.metrics.PredictionEntropyScorer</a></li>
<li class="toctree-l3"><a class="reference internal" href="gen_modules/skada.metrics.DeepEmbeddedValidation.html">skada.metrics.DeepEmbeddedValidation</a></li>
<li class="toctree-l3"><a class="reference internal" href="gen_modules/skada.metrics.SoftNeighborhoodDensity.html">skada.metrics.SoftNeighborhoodDensity</a></li>
<li class="toctree-l3"><a class="reference internal" href="gen_modules/skada.metrics.CircularValidation.html">skada.metrics.CircularValidation</a></li>
<li class="toctree-l3"><a class="reference internal" href="gen_modules/skada.metrics.MixValScorer.html">skada.metrics.MixValScorer</a></li>
<li class="toctree-l3"><a class="reference internal" href="gen_modules/skada.metrics.MaNoScorer.html">skada.metrics.MaNoScorer</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#module-skada.model_selection">Model Selection <code class="xref py py-mod docutils literal notranslate"><span class="pre">skada.model_selection</span></code></a><ul>
<li class="toctree-l3"><a class="reference internal" href="gen_modules/skada.model_selection.SourceTargetShuffleSplit.html">skada.model_selection.SourceTargetShuffleSplit</a></li>
<li class="toctree-l3"><a class="reference internal" href="gen_modules/skada.model_selection.DomainShuffleSplit.html">skada.model_selection.DomainShuffleSplit</a></li>
<li class="toctree-l3"><a class="reference internal" href="gen_modules/skada.model_selection.StratifiedDomainShuffleSplit.html">skada.model_selection.StratifiedDomainShuffleSplit</a></li>
<li class="toctree-l3"><a class="reference internal" href="gen_modules/skada.model_selection.LeaveOneDomainOut.html">skada.model_selection.LeaveOneDomainOut</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#module-skada.datasets">Datasets <code class="xref py py-mod docutils literal notranslate"><span class="pre">skada.datasets</span></code></a><ul>
<li class="toctree-l3"><a class="reference internal" href="gen_modules/skada.datasets.DomainAwareDataset.html">skada.datasets.DomainAwareDataset</a></li>
<li class="toctree-l3"><a class="reference internal" href="gen_modules/skada.datasets.make_shifted_blobs.html">skada.datasets.make_shifted_blobs</a></li>
<li class="toctree-l3"><a class="reference internal" href="gen_modules/skada.datasets.make_shifted_datasets.html">skada.datasets.make_shifted_datasets</a></li>
<li class="toctree-l3"><a class="reference internal" href="gen_modules/skada.datasets.make_dataset_from_moons_distribution.html">skada.datasets.make_dataset_from_moons_distribution</a></li>
<li class="toctree-l3"><a class="reference internal" href="gen_modules/skada.datasets.make_variable_frequency_dataset.html">skada.datasets.make_variable_frequency_dataset</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="#module-skada.utils">Utilities <code class="xref py py-mod docutils literal notranslate"><span class="pre">skada.utils</span></code></a><ul>
<li class="toctree-l3"><a class="reference internal" href="gen_modules/skada.utils.check_X_y_domain.html">skada.utils.check_X_y_domain</a></li>
<li class="toctree-l3"><a class="reference internal" href="gen_modules/skada.utils.extract_source_indices.html">skada.utils.extract_source_indices</a></li>
<li class="toctree-l3"><a class="reference internal" href="gen_modules/skada.utils.extract_domains_indices.html">skada.utils.extract_domains_indices</a></li>
<li class="toctree-l3"><a class="reference internal" href="gen_modules/skada.utils.source_target_merge.html">skada.utils.source_target_merge</a></li>
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<li class="toctree-l1"><a class="reference internal" href="auto_examples/index.html">Examples gallery</a></li>
<li class="toctree-l1"><a class="reference internal" href="releases.html">Release of SKADA</a></li>
<li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing to SKADA</a></li>
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<section id="api-and-modules">
<span id="sphx-glr-api-reference"></span><h1>API and modules<a class="headerlink" href="#api-and-modules" title="Link to this heading"></a></h1>
<section id="module-skada">
<span id="main-module-skada"></span><h2>Main module <a class="reference internal" href="#module-skada" title="skada"><code class="xref py py-mod docutils literal notranslate"><span class="pre">skada</span></code></a><a class="headerlink" href="#module-skada" title="Link to this heading"></a></h2>
<section id="sample-reweighting-da-methods">
<h3>Sample reweighting DA methods<a class="headerlink" href="#sample-reweighting-da-methods" title="Link to this heading"></a></h3>
<dl>
<dt>DAEstimators with adapters (Pipeline):</dt><dd><table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.DensityReweight.html#skada.DensityReweight" title="skada.DensityReweight"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DensityReweight</span></code></a>([base_estimator, ...])</p></td>
<td><p>Density re-weighting pipeline adapter and estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.GaussianReweight.html#skada.GaussianReweight" title="skada.GaussianReweight"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GaussianReweight</span></code></a>([base_estimator, reg])</p></td>
<td><p>Gaussian approximation re-weighting pipeline adapter and estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.DiscriminatorReweight.html#skada.DiscriminatorReweight" title="skada.DiscriminatorReweight"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DiscriminatorReweight</span></code></a>([base_estimator, ...])</p></td>
<td><p>Discriminator re-weighting pipeline adapter and estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.KLIEPReweight.html#skada.KLIEPReweight" title="skada.KLIEPReweight"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KLIEPReweight</span></code></a>([base_estimator, gamma, cv, ...])</p></td>
<td><p>KLIEPReweight pipeline adapter and estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.NearestNeighborReweight.html#skada.NearestNeighborReweight" title="skada.NearestNeighborReweight"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NearestNeighborReweight</span></code></a>([base_estimator, ...])</p></td>
<td><p>Density re-weighting pipeline adapter and estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.MMDTarSReweight.html#skada.MMDTarSReweight" title="skada.MMDTarSReweight"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MMDTarSReweight</span></code></a>([base_estimator, gamma, ...])</p></td>
<td><p>Target shift reweighting using MMD.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.KMMReweight.html#skada.KMMReweight" title="skada.KMMReweight"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KMMReweight</span></code></a>([base_estimator, kernel, gamma, ...])</p></td>
<td><p>KMMReweight pipeline adapter and estimator.</p></td>
</tr>
</tbody>
</table>
</dd>
<dt>Adapters:</dt><dd><table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.DensityReweightAdapter.html#skada.DensityReweightAdapter" title="skada.DensityReweightAdapter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DensityReweightAdapter</span></code></a>([weight_estimator])</p></td>
<td><p>Adapter based on re-weighting samples using density estimation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.GaussianReweightAdapter.html#skada.GaussianReweightAdapter" title="skada.GaussianReweightAdapter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GaussianReweightAdapter</span></code></a>([reg])</p></td>
<td><p>Gaussian approximation re-weighting method.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.DiscriminatorReweightAdapter.html#skada.DiscriminatorReweightAdapter" title="skada.DiscriminatorReweightAdapter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DiscriminatorReweightAdapter</span></code></a>([domain_classifier])</p></td>
<td><p>Gaussian approximation re-weighting method.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.KLIEPReweightAdapter.html#skada.KLIEPReweightAdapter" title="skada.KLIEPReweightAdapter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KLIEPReweightAdapter</span></code></a>(gamma[, cv, n_centers, ...])</p></td>
<td><p>Kullback-Leibler Importance Estimation Procedure (KLIEPReweight).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.NearestNeighborReweightAdapter.html#skada.NearestNeighborReweightAdapter" title="skada.NearestNeighborReweightAdapter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NearestNeighborReweightAdapter</span></code></a>([...])</p></td>
<td><p>Adapter based on re-weighting samples using a KNN,</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.MMDTarSReweightAdapter.html#skada.MMDTarSReweightAdapter" title="skada.MMDTarSReweightAdapter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MMDTarSReweightAdapter</span></code></a>(gamma[, reg, tol, ...])</p></td>
<td><p>Target shift reweighting using MMD.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.KMMReweightAdapter.html#skada.KMMReweightAdapter" title="skada.KMMReweightAdapter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KMMReweightAdapter</span></code></a>([kernel, gamma, degree, ...])</p></td>
<td><p>Kernel Mean Matching (KMMReweight).</p></td>
</tr>
</tbody>
</table>
</dd>
</dl>
</section>
<section id="sample-mapping-and-alignment-da-methods">
<h3>Sample mapping and alignment DA methods<a class="headerlink" href="#sample-mapping-and-alignment-da-methods" title="Link to this heading"></a></h3>
<dl>
<dt>DAEstimators with adapters (Pipeline):</dt><dd><table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.SubspaceAlignment.html#skada.SubspaceAlignment" title="skada.SubspaceAlignment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SubspaceAlignment</span></code></a>([base_estimator, ...])</p></td>
<td><p>Domain Adaptation Using Subspace Alignment.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.TransferComponentAnalysis.html#skada.TransferComponentAnalysis" title="skada.TransferComponentAnalysis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TransferComponentAnalysis</span></code></a>([base_estimator, ...])</p></td>
<td><p>Domain Adaptation Using Transfer Component Analysis.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.TransferJointMatching.html#skada.TransferJointMatching" title="skada.TransferJointMatching"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TransferJointMatching</span></code></a>([base_estimator, ...])</p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.TransferSubspaceLearning.html#skada.TransferSubspaceLearning" title="skada.TransferSubspaceLearning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TransferSubspaceLearning</span></code></a>([base_estimator, ...])</p></td>
<td><p>Domain Adaptation Using Transfer Subspace Learning.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.CORAL.html#skada.CORAL" title="skada.CORAL"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CORAL</span></code></a>([base_estimator, reg, assume_centered])</p></td>
<td><p>CORAL pipeline with adapter and estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.OTMapping.html#skada.OTMapping" title="skada.OTMapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OTMapping</span></code></a>([base_estimator, metric, norm, ...])</p></td>
<td><p>OTmapping pipeline with adapter and estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.EntropicOTMapping.html#skada.EntropicOTMapping" title="skada.EntropicOTMapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EntropicOTMapping</span></code></a>([base_estimator, metric, ...])</p></td>
<td><p>EntropicOTMapping pipeline with adapter and estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.ClassRegularizerOTMapping.html#skada.ClassRegularizerOTMapping" title="skada.ClassRegularizerOTMapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ClassRegularizerOTMapping</span></code></a>([base_estimator, ...])</p></td>
<td><p>ClassRegularizedOTMapping pipeline with adapter and estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.LinearOTMapping.html#skada.LinearOTMapping" title="skada.LinearOTMapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LinearOTMapping</span></code></a>([base_estimator, reg, bias])</p></td>
<td><p>Returns a the linear OT mapping method with adapter and estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.MMDLSConSMapping.html#skada.MMDLSConSMapping" title="skada.MMDLSConSMapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MMDLSConSMapping</span></code></a>([base_estimator, gamma, ...])</p></td>
<td><p>MMDLSConSMapping pipeline with adapter and estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.MultiLinearMongeAlignment.html#skada.MultiLinearMongeAlignment" title="skada.MultiLinearMongeAlignment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultiLinearMongeAlignment</span></code></a>([base_estimator, ...])</p></td>
<td><p>MultiLinearMongeAlignment pipeline with adapter and estimator.</p></td>
</tr>
</tbody>
</table>
</dd>
<dt>Adapters:</dt><dd><table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.SubspaceAlignmentAdapter.html#skada.SubspaceAlignmentAdapter" title="skada.SubspaceAlignmentAdapter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SubspaceAlignmentAdapter</span></code></a>([n_components, ...])</p></td>
<td><p>Domain Adaptation Using Subspace Alignment.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.TransferComponentAnalysisAdapter.html#skada.TransferComponentAnalysisAdapter" title="skada.TransferComponentAnalysisAdapter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TransferComponentAnalysisAdapter</span></code></a>([kernel, ...])</p></td>
<td><p>Transfer Component Analysis.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.TransferJointMatchingAdapter.html#skada.TransferJointMatchingAdapter" title="skada.TransferJointMatchingAdapter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TransferJointMatchingAdapter</span></code></a>([n_components, ...])</p></td>
<td><p>Domain Adaptation Using TJM: Transfer Joint Matching.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.TransferSubspaceLearningAdapter.html#skada.TransferSubspaceLearningAdapter" title="skada.TransferSubspaceLearningAdapter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TransferSubspaceLearningAdapter</span></code></a>([...])</p></td>
<td><p>Domain Adaptation Using TSL: Transfer Subspace Learning.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.CORALAdapter.html#skada.CORALAdapter" title="skada.CORALAdapter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CORALAdapter</span></code></a>([reg, assume_centered])</p></td>
<td><p>Estimator based on Correlation Alignment <a href="#id6"><span class="problematic" id="id1">[1]_</span></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.OTMappingAdapter.html#skada.OTMappingAdapter" title="skada.OTMappingAdapter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OTMappingAdapter</span></code></a>([metric, norm, max_iter])</p></td>
<td><p>Domain Adaptation Using Optimal Transport.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.EntropicOTMappingAdapter.html#skada.EntropicOTMappingAdapter" title="skada.EntropicOTMappingAdapter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EntropicOTMappingAdapter</span></code></a>([reg_e, metric, ...])</p></td>
<td><p>Domain Adaptation Using Optimal Transport.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.ClassRegularizerOTMappingAdapter.html#skada.ClassRegularizerOTMappingAdapter" title="skada.ClassRegularizerOTMappingAdapter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ClassRegularizerOTMappingAdapter</span></code></a>([reg_e, ...])</p></td>
<td><p>Domain Adaptation Using Optimal Transport.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.LinearOTMappingAdapter.html#skada.LinearOTMappingAdapter" title="skada.LinearOTMappingAdapter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LinearOTMappingAdapter</span></code></a>([reg, bias])</p></td>
<td><p>Domain Adaptation Using Optimal Transport.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.MMDLSConSMappingAdapter.html#skada.MMDLSConSMappingAdapter" title="skada.MMDLSConSMappingAdapter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MMDLSConSMappingAdapter</span></code></a>(gamma[, reg_k, ...])</p></td>
<td><p>Location-Scale mapping minimizing the MMD with a Gaussian kernel.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.MultiLinearMongeAlignmentAdapter.html#skada.MultiLinearMongeAlignmentAdapter" title="skada.MultiLinearMongeAlignmentAdapter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultiLinearMongeAlignmentAdapter</span></code></a>([reg, ...])</p></td>
<td><p>Aligns multiple domains using Gaussian Monge mapping to a barycenter.</p></td>
</tr>
</tbody>
</table>
</dd>
</dl>
</section>
<section id="other-da-methods">
<h3>Other DA methods<a class="headerlink" href="#other-da-methods" title="Link to this heading"></a></h3>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.JDOTClassifier.html#skada.JDOTClassifier" title="skada.JDOTClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">JDOTClassifier</span></code></a>([base_estimator, alpha, ...])</p></td>
<td><p>Joint Distribution Optimal Transport Classifier proposed in [10]</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.JDOTRegressor.html#skada.JDOTRegressor" title="skada.JDOTRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">JDOTRegressor</span></code></a>([base_estimator, alpha, ...])</p></td>
<td><p>Joint Distribution Optimal Transport Regressor proposed in [10]</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.DASVMClassifier.html#skada.DASVMClassifier" title="skada.DASVMClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DASVMClassifier</span></code></a>([base_estimator, k, ...])</p></td>
<td><p>DASVM Estimator:</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.OTLabelProp.html#skada.OTLabelProp" title="skada.OTLabelProp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OTLabelProp</span></code></a>([base_estimator, reg, metric, ...])</p></td>
<td><p>Label propagation using optimal transport plan.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.JCPOTLabelProp.html#skada.JCPOTLabelProp" title="skada.JCPOTLabelProp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">JCPOTLabelProp</span></code></a>([base_estimator, reg, ...])</p></td>
<td><p>JCPOT Label Propagation Adapter for multi source target shift</p></td>
</tr>
</tbody>
</table>
</section>
<section id="da-pipeline">
<h3>DA pipeline<a class="headerlink" href="#da-pipeline" title="Link to this heading"></a></h3>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.make_da_pipeline.html#skada.make_da_pipeline" title="skada.make_da_pipeline"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_da_pipeline</span></code></a>(*steps[, memory, verbose, ...])</p></td>
<td><p>Construct a <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="(in scikit-learn v1.7)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a> from the given estimators.</p></td>
</tr>
</tbody>
</table>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.Shared.html#skada.Shared" title="skada.Shared"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Shared</span></code></a>(base_estimator[, mask_target_labels])</p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.PerDomain.html#skada.PerDomain" title="skada.PerDomain"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PerDomain</span></code></a>(base_estimator[, mask_target_labels])</p></td>
<td><p></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.SelectSource.html#skada.SelectSource" title="skada.SelectSource"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SelectSource</span></code></a>(base_estimator[, ...])</p></td>
<td><p>Selects only source domains for fitting base estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.SelectTarget.html#skada.SelectTarget" title="skada.SelectTarget"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SelectTarget</span></code></a>(base_estimator[, ...])</p></td>
<td><p>Selects only target domains for fitting base estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.SelectSourceTarget.html#skada.SelectSourceTarget" title="skada.SelectSourceTarget"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SelectSourceTarget</span></code></a>(source_estimator[, ...])</p></td>
<td><p></p></td>
</tr>
</tbody>
</table>
</section>
<section id="utilities">
<h3>Utilities<a class="headerlink" href="#utilities" title="Link to this heading"></a></h3>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.source_target_split.html#skada.source_target_split" title="skada.source_target_split"><code class="xref py py-obj docutils literal notranslate"><span class="pre">source_target_split</span></code></a>(*arrays, sample_domain)</p></td>
<td><p>Split data into source and target domains</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.per_domain_split.html#skada.per_domain_split" title="skada.per_domain_split"><code class="xref py py-obj docutils literal notranslate"><span class="pre">per_domain_split</span></code></a>(*arrays, sample_domain)</p></td>
<td><p>Split data into multiple source and target domains</p></td>
</tr>
</tbody>
</table>
</section>
</section>
<section id="module-skada.deep">
<span id="deep-learning-da-skada-deep"></span><h2>Deep learning DA <a class="reference internal" href="#module-skada.deep" title="skada.deep"><code class="xref py py-mod docutils literal notranslate"><span class="pre">skada.deep</span></code></a>:<a class="headerlink" href="#module-skada.deep" title="Link to this heading"></a></h2>
<p>Some methods for deep domain adaptation.</p>
<section id="deep-learning-da-methods">
<h3>Deep learning DA methods<a class="headerlink" href="#deep-learning-da-methods" title="Link to this heading"></a></h3>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.deep.DeepCoral.html#skada.deep.DeepCoral" title="skada.deep.DeepCoral"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DeepCoral</span></code></a>(module, layer_name[, reg, ...])</p></td>
<td><p>DeepCORAL domain adaptation method.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.deep.DeepJDOT.html#skada.deep.DeepJDOT" title="skada.deep.DeepJDOT"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DeepJDOT</span></code></a>(module, layer_name[, reg_dist, ...])</p></td>
<td><p>DeepJDOT.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.deep.DAN.html#skada.deep.DAN" title="skada.deep.DAN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DAN</span></code></a>(module, layer_name[, reg, sigmas, ...])</p></td>
<td><p>DAN domain adaptation method.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.deep.DANN.html#skada.deep.DANN" title="skada.deep.DANN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DANN</span></code></a>(module, layer_name[, reg, ...])</p></td>
<td><p>Domain-Adversarial Training of Neural Networks (DANN).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.deep.CDAN.html#skada.deep.CDAN" title="skada.deep.CDAN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CDAN</span></code></a>(module, layer_name[, reg, ...])</p></td>
<td><p>Conditional Domain Adversarial Networks (CDAN).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.deep.MCC.html#skada.deep.MCC" title="skada.deep.MCC"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MCC</span></code></a>(module, layer_name[, reg, T, base_criterion])</p></td>
<td><p></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.deep.CAN.html#skada.deep.CAN" title="skada.deep.CAN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CAN</span></code></a>(module, layer_name[, reg, ...])</p></td>
<td><p>Contrastive Adaptation Network (CAN) domain adaptation method.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.deep.MDD.html#skada.deep.MDD" title="skada.deep.MDD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MDD</span></code></a>(module, layer_name[, reg, gamma, ...])</p></td>
<td><p>Margin Disparity Discrepancy (MDD).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.deep.SPA.html#skada.deep.SPA" title="skada.deep.SPA"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SPA</span></code></a>(module, layer_name[, reg_adv, reg_gsa, ...])</p></td>
<td><p>Domain Adaptation with SPA.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="skada-deep-learning-da-losses">
<h3>SKADA deep learning DA losses<a class="headerlink" href="#skada-deep-learning-da-losses" title="Link to this heading"></a></h3>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.deep.DeepCoralLoss.html#skada.deep.DeepCoralLoss" title="skada.deep.DeepCoralLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DeepCoralLoss</span></code></a>([assume_centered])</p></td>
<td><p>Loss DeepCORAL</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.deep.DeepJDOTLoss.html#skada.deep.DeepJDOTLoss" title="skada.deep.DeepJDOTLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DeepJDOTLoss</span></code></a>([reg_dist, reg_cl, ...])</p></td>
<td><p>Loss DeepJDOT.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.deep.DANLoss.html#skada.deep.DANLoss" title="skada.deep.DANLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DANLoss</span></code></a>([sigmas, eps])</p></td>
<td><p>Loss DAN</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.deep.DANNLoss.html#skada.deep.DANNLoss" title="skada.deep.DANNLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DANNLoss</span></code></a>([domain_criterion])</p></td>
<td><p>Loss DANN.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.deep.CDANLoss.html#skada.deep.CDANLoss" title="skada.deep.CDANLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CDANLoss</span></code></a>([domain_criterion])</p></td>
<td><p>Conditional Domain Adversarial Networks (CDAN) loss.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.deep.MCCLoss.html#skada.deep.MCCLoss" title="skada.deep.MCCLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MCCLoss</span></code></a>([T, eps])</p></td>
<td><p>Loss MCC.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.deep.CANLoss.html#skada.deep.CANLoss" title="skada.deep.CANLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CANLoss</span></code></a>([distance_threshold, ...])</p></td>
<td><p>Loss for Contrastive Adaptation Network (CAN)</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.deep.MDDLoss.html#skada.deep.MDDLoss" title="skada.deep.MDDLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MDDLoss</span></code></a>([gamma])</p></td>
<td><p>Loss MDD.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.deep.SPALoss.html#skada.deep.SPALoss" title="skada.deep.SPALoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SPALoss</span></code></a>(max_epochs[, domain_criterion, ...])</p></td>
<td><p>Loss SPA.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-skada.deep.losses">
<span id="torch-compatible-da-losses-in-skada-deep-losses"></span><h3>Torch compatible DA losses in <a class="reference internal" href="#module-skada.deep.losses" title="skada.deep.losses"><code class="xref py py-mod docutils literal notranslate"><span class="pre">skada.deep.losses</span></code></a><a class="headerlink" href="#module-skada.deep.losses" title="Link to this heading"></a></h3>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.deep.losses.dan_loss.html#skada.deep.losses.dan_loss" title="skada.deep.losses.dan_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dan_loss</span></code></a>(features_s, features_t[, sigmas, eps])</p></td>
<td><p>Define the mmd loss based on multi-kernel defined in <a class="reference internal" href="gen_modules/skada.deep.losses.dan_loss.html#r095e4befb364-14" id="id2"><span>[R095e4befb364-14]</span></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.deep.losses.deepcoral_loss.html#skada.deep.losses.deepcoral_loss" title="skada.deep.losses.deepcoral_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">deepcoral_loss</span></code></a>(features, features_target[, ...])</p></td>
<td><p>Estimate the Frobenius norm divide by 4*n**2</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.deep.losses.deepjdot_loss.html#skada.deep.losses.deepjdot_loss" title="skada.deep.losses.deepjdot_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">deepjdot_loss</span></code></a>(y_s, y_pred_t, features_s, ...)</p></td>
<td><p>Compute the OT loss for DeepJDOT method <a class="reference internal" href="gen_modules/skada.deep.losses.deepjdot_loss.html#ra0fd271667d9-13" id="id3"><span>[Ra0fd271667d9-13]</span></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.deep.losses.mcc_loss.html#skada.deep.losses.mcc_loss" title="skada.deep.losses.mcc_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mcc_loss</span></code></a>(y[, T, eps])</p></td>
<td><p>Estimate the Frobenius norm divide by 4*n**2</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.deep.losses.cdd_loss.html#skada.deep.losses.cdd_loss" title="skada.deep.losses.cdd_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cdd_loss</span></code></a>(y_s, features_s, features_t, ...[, ...])</p></td>
<td><p>Define the contrastive domain discrepancy loss based on <a class="reference internal" href="gen_modules/skada.deep.losses.cdd_loss.html#rfc1dd7997531-33" id="id4"><span>[Rfc1dd7997531-33]</span></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.deep.losses.gda_loss.html#skada.deep.losses.gda_loss" title="skada.deep.losses.gda_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gda_loss</span></code></a>(s, t[, metric, laplac])</p></td>
<td><p>Compute the GDA loss between two graphs.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.deep.losses.nap_loss.html#skada.deep.losses.nap_loss" title="skada.deep.losses.nap_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nap_loss</span></code></a>(features_t, y_pred_t, ...[, K])</p></td>
<td><p>Compute the NAP loss.</p></td>
</tr>
</tbody>
</table>
</section>
</section>
<section id="module-skada.metrics">
<span id="da-metrics-skada-metrics"></span><h2>DA metrics <a class="reference internal" href="#module-skada.metrics" title="skada.metrics"><code class="xref py py-mod docutils literal notranslate"><span class="pre">skada.metrics</span></code></a><a class="headerlink" href="#module-skada.metrics" title="Link to this heading"></a></h2>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.metrics.SupervisedScorer.html#skada.metrics.SupervisedScorer" title="skada.metrics.SupervisedScorer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SupervisedScorer</span></code></a>([scoring, greater_is_better])</p></td>
<td><p>Compute score on supervised dataset.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.metrics.ImportanceWeightedScorer.html#skada.metrics.ImportanceWeightedScorer" title="skada.metrics.ImportanceWeightedScorer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ImportanceWeightedScorer</span></code></a>([weight_estimator, ...])</p></td>
<td><p>Score based on source data using sample weight.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.metrics.PredictionEntropyScorer.html#skada.metrics.PredictionEntropyScorer" title="skada.metrics.PredictionEntropyScorer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PredictionEntropyScorer</span></code></a>([greater_is_better, ...])</p></td>
<td><p>Score based on the entropy of predictions on unsupervised dataset.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.metrics.DeepEmbeddedValidation.html#skada.metrics.DeepEmbeddedValidation" title="skada.metrics.DeepEmbeddedValidation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DeepEmbeddedValidation</span></code></a>([domain_classifier, ...])</p></td>
<td><p>Loss based on source data using features representation to weight samples.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.metrics.SoftNeighborhoodDensity.html#skada.metrics.SoftNeighborhoodDensity" title="skada.metrics.SoftNeighborhoodDensity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SoftNeighborhoodDensity</span></code></a>([T, greater_is_better])</p></td>
<td><p>Score based on the entropy of similarity between unsupervised dataset.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.metrics.CircularValidation.html#skada.metrics.CircularValidation" title="skada.metrics.CircularValidation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CircularValidation</span></code></a>([source_scorer, ...])</p></td>
<td><p>Score based on a circular validation strategy.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.metrics.MixValScorer.html#skada.metrics.MixValScorer" title="skada.metrics.MixValScorer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MixValScorer</span></code></a>([alpha, ice_type, scoring, ...])</p></td>
<td><p>MixVal scorer for unsupervised domain adaptation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.metrics.MaNoScorer.html#skada.metrics.MaNoScorer" title="skada.metrics.MaNoScorer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MaNoScorer</span></code></a>([p, threshold, greater_is_better])</p></td>
<td><p>MaNo scorer inspired by <a class="reference internal" href="gen_modules/skada.metrics.MaNoScorer.html#r34d6d6853b0e-37" id="id5"><span>[R34d6d6853b0e-37]</span></a>, an approach for unsupervised accuracy estimation.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-skada.model_selection">
<span id="model-selection-skada-model-selection"></span><h2>Model Selection <a class="reference internal" href="#module-skada.model_selection" title="skada.model_selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">skada.model_selection</span></code></a><a class="headerlink" href="#module-skada.model_selection" title="Link to this heading"></a></h2>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.model_selection.SourceTargetShuffleSplit.html#skada.model_selection.SourceTargetShuffleSplit" title="skada.model_selection.SourceTargetShuffleSplit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SourceTargetShuffleSplit</span></code></a>([n_splits, ...])</p></td>
<td><p>Source-Target-Shuffle-Split cross-validator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.model_selection.DomainShuffleSplit.html#skada.model_selection.DomainShuffleSplit" title="skada.model_selection.DomainShuffleSplit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DomainShuffleSplit</span></code></a>([n_splits, test_size, ...])</p></td>
<td><p>Domain-Shuffle-Split cross-validator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.model_selection.StratifiedDomainShuffleSplit.html#skada.model_selection.StratifiedDomainShuffleSplit" title="skada.model_selection.StratifiedDomainShuffleSplit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StratifiedDomainShuffleSplit</span></code></a>([n_splits, ...])</p></td>
<td><p>Stratified-Domain-Shuffle-Split cross-validator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.model_selection.LeaveOneDomainOut.html#skada.model_selection.LeaveOneDomainOut" title="skada.model_selection.LeaveOneDomainOut"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LeaveOneDomainOut</span></code></a>([max_n_splits, test_size, ...])</p></td>
<td><p>Leave-One-Domain-Out cross-validator.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-skada.datasets">
<span id="datasets-skada-datasets"></span><h2>Datasets <a class="reference internal" href="#module-skada.datasets" title="skada.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">skada.datasets</span></code></a><a class="headerlink" href="#module-skada.datasets" title="Link to this heading"></a></h2>
<p>Utilities to produce datasets for testing and benchmarking.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.datasets.DomainAwareDataset.html#skada.datasets.DomainAwareDataset" title="skada.datasets.DomainAwareDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DomainAwareDataset</span></code></a>([domains])</p></td>
<td><p>Container carrying all dataset domains.</p></td>
</tr>
</tbody>
</table>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.datasets.make_shifted_blobs.html#skada.datasets.make_shifted_blobs" title="skada.datasets.make_shifted_blobs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_shifted_blobs</span></code></a>([n_samples, n_features, ...])</p></td>
<td><p>Generate source and shift target isotropic Gaussian blobs .</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.datasets.make_shifted_datasets.html#skada.datasets.make_shifted_datasets" title="skada.datasets.make_shifted_datasets"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_shifted_datasets</span></code></a>([n_samples_source, ...])</p></td>
<td><p>Generate source and shift target.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.datasets.make_dataset_from_moons_distribution.html#skada.datasets.make_dataset_from_moons_distribution" title="skada.datasets.make_dataset_from_moons_distribution"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_dataset_from_moons_distribution</span></code></a>([...])</p></td>
<td><p>Make dataset from moons.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.datasets.make_variable_frequency_dataset.html#skada.datasets.make_variable_frequency_dataset" title="skada.datasets.make_variable_frequency_dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_variable_frequency_dataset</span></code></a>([...])</p></td>
<td><p>Make dataset with different peak frequency.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="module-skada.utils">
<span id="utilities-skada-utils"></span><h2>Utilities <a class="reference internal" href="#module-skada.utils" title="skada.utils"><code class="xref py py-mod docutils literal notranslate"><span class="pre">skada.utils</span></code></a><a class="headerlink" href="#module-skada.utils" title="Link to this heading"></a></h2>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.utils.check_X_y_domain.html#skada.utils.check_X_y_domain" title="skada.utils.check_X_y_domain"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_X_y_domain</span></code></a>(X, y[, sample_domain, ...])</p></td>
<td><p>Input validation for domain adaptation (DA) estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.utils.extract_source_indices.html#skada.utils.extract_source_indices" title="skada.utils.extract_source_indices"><code class="xref py py-obj docutils literal notranslate"><span class="pre">extract_source_indices</span></code></a>(sample_domain)</p></td>
<td><p>Extract the indices of the source samples.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="gen_modules/skada.utils.extract_domains_indices.html#skada.utils.extract_domains_indices" title="skada.utils.extract_domains_indices"><code class="xref py py-obj docutils literal notranslate"><span class="pre">extract_domains_indices</span></code></a>(sample_domain[, ...])</p></td>
<td><p>Extract the indices of the specific domain samples.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="gen_modules/skada.utils.source_target_merge.html#skada.utils.source_target_merge" title="skada.utils.source_target_merge"><code class="xref py py-obj docutils literal notranslate"><span class="pre">source_target_merge</span></code></a>(*arrays[, sample_domain])</p></td>
<td><p>Merge source and target domain data based on sample domain labels.</p></td>
</tr>
</tbody>
</table>
</section>
</section>
</div>
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