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<!DOCTYPE html>
<html class="writer-html5" lang="en" >
<head>
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<title>adapt.instance_based.KLIEP — adapt 0.1.0 documentation</title>
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<p class="caption" role="heading"><span class="caption-text">Installation</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../install.html">Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="https://github.com/adapt-python/adapt">Github</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../map.html">Choosing the right algorithm</a></li>
<li class="toctree-l1"><a class="reference internal" href="../examples/Quick_start.html">Quick-Start</a></li>
<li class="toctree-l1"><a class="reference internal" href="../examples/Developer_Guide.html">Developer Guide</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">API Documentation</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../contents.html#adapt-feature-based">Feature-based</a><ul>
<li class="toctree-l2"><a class="reference internal" href="adapt.feature_based.PRED.html">PRED</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.feature_based.FA.html">FA</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.feature_based.CORAL.html">CORAL</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.feature_based.SA.html">SA</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.feature_based.TCA.html">TCA</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.feature_based.fMMD.html">fMMD</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.feature_based.DeepCORAL.html">DeepCORAL</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.feature_based.DANN.html">DANN</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.feature_based.ADDA.html">ADDA</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.feature_based.WDGRL.html">WDGRL</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.feature_based.CDAN.html">CDAN</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.feature_based.MCD.html">MCD</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.feature_based.MDD.html">MDD</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.feature_based.CCSA.html">CCSA</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../contents.html#adapt-instance-based">Instance-based</a><ul>
<li class="toctree-l2"><a class="reference internal" href="adapt.instance_based.LDM.html">LDM</a></li>
<li class="toctree-l2"><a class="reference internal" href="#">KLIEP</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.instance_based.KMM.html">KMM</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.instance_based.ULSIF.html">ULSIF</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.instance_based.RULSIF.html">RULSIF</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.instance_based.NearestNeighborsWeighting.html">NearestNeighborsWeighting</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.instance_based.IWC.html">IWC</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.instance_based.IWN.html">IWN</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.instance_based.BalancedWeighting.html">BalancedWeighting</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.instance_based.TrAdaBoost.html">TrAdaBoost</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.instance_based.TrAdaBoostR2.html">TrAdaBoostR2</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.instance_based.TwoStageTrAdaBoostR2.html">TwoStageTrAdaBoostR2</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.instance_based.WANN.html">WANN</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../contents.html#adapt-parameter-based">Parameter-based</a><ul>
<li class="toctree-l2"><a class="reference internal" href="adapt.parameter_based.LinInt.html">LinInt</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.parameter_based.RegularTransferLR.html">RegularTransferLR</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.parameter_based.RegularTransferLC.html">RegularTransferLC</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.parameter_based.RegularTransferNN.html">RegularTransferNN</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.parameter_based.RegularTransferGP.html">RegularTransferGP</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.parameter_based.FineTuning.html">FineTuning</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.parameter_based.TransferTreeClassifier.html">TransferTreeClassifier</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.parameter_based.TransferForestClassifier.html">TransferForestClassifier</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.parameter_based.TransferTreeSelector.html">TransferTreeSelector</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.parameter_based.TransferForestSelector.html">TransferForestSelector</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../contents.html#adapt-metrics">Metrics</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="adapt.metrics.cov_distance.html">cov_distance</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.metrics.neg_j_score.html">neg_j_score</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.metrics.linear_discrepancy.html">linear_discrepancy</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.metrics.normalized_linear_discrepancy.html">normalized_linear_discrepancy</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.metrics.frechet_distance.html">frechet_distance</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.metrics.normalized_frechet_distance.html">normalized_frechet_distance</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.metrics.domain_classifier.html">domain_classifier</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.metrics.reverse_validation.html">reverse_validation</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../contents.html#adapt-utils">Utility Functions</a><ul>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.UpdateLambda.html">UpdateLambda</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.accuracy.html">accuracy</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.check_arrays.html">check_arrays</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.check_estimator.html">check_estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.check_network.html">check_network</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.get_default_encoder.html">get_default_encoder</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.get_default_task.html">get_default_task</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.get_default_discriminator.html">get_default_discriminator</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.GradientHandler.html">GradientHandler</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.make_classification_da.html">make_classification_da</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.make_regression_da.html">make_regression_da</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.check_sample_weight.html">check_sample_weight</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.set_random_seed.html">set_random_seed</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.check_fitted_estimator.html">check_fitted_estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.check_fitted_network.html">check_fitted_network</a></li>
</ul>
</li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Synthetic Examples</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../examples/Classification.html">Classification</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/Classification.html#Experimental-Setup">Experimental Setup</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Classification.html#Src-Only">Src Only</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Classification.html#DANN">DANN</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Classification.html#Instance-Based">Instance Based</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../examples/Two_moons.html">Two Moons</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#Setup">Setup</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#Network">Network</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#Source-Only">Source Only</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#DANN">DANN</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#ADDA">ADDA</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#DeepCORAL">DeepCORAL</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#CORAL">CORAL</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#MCD">MCD</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#MDD">MDD</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#WDGRL">WDGRL</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#CDAN">CDAN</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../examples/Rotation.html">Rotation</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/Rotation.html#Setup">Setup</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Rotation.html#Source-Only">Source Only</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Rotation.html#CORAL">CORAL</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Rotation.html#DANN">DANN</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Rotation.html#ADDA">ADDA</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../examples/Regression.html">Toy Regression</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/Regression.html#Experimental-Setup">Experimental Setup</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Regression.html#TGT-Only">TGT Only</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Regression.html#Src-Only">Src Only</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Regression.html#All">All</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Regression.html#CORAL">CORAL</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Regression.html#TrAdaBoostR2">TrAdaBoostR2</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Regression.html#RegularTransferNN">RegularTransferNN</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../examples/sample_bias.html">Sample Bias 1D</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/sample_bias.html#Setup">Setup</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/sample_bias.html#KMM">KMM</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/sample_bias.html#KLIEP">KLIEP</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../examples/sample_bias_2d.html">Sample Bias 2D</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/sample_bias_2d.html#Setup">Setup</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/sample_bias_2d.html#Estimator">Estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/sample_bias_2d.html#Source-Only">Source Only</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/sample_bias_2d.html#KMM">KMM</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/sample_bias_2d.html#KLIEP">KLIEP</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../examples/Multi_fidelity.html">Multi-Fidelity</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/Multi_fidelity.html#Setup">Setup</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Multi_fidelity.html#Network">Network</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Multi_fidelity.html#Low-fidelity-only">Low fidelity only</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Multi_fidelity.html#High-fidelity-only">High fidelity only</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Multi_fidelity.html#RegularTransferNN">RegularTransferNN</a></li>
</ul>
</li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Real Examples</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../examples/Sample_bias_example.html">Sample Bias</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/Sample_bias_example.html#Sample-Bias-on-the-diabetes-dataset">Diabetes Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Sample_bias_example.html#Applying-Transfer-Learning-for-correcting-sample-bias">Sample bias correction</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Sample_bias_example.html#Hidden-bias-and-features-importance-estimation">Features importance</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../examples/Flowers_example.html">Fine-Tuning</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/Flowers_example.html#Training-a-model-from-scratch">Train from Scratch</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Flowers_example.html#Model-based-Transfer">Model-based Transfer</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../examples/Office_example.html">Deep Domain Adaptation</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/Office_example.html#Dataset-Preprocessing">Dataset Preprocessing</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Office_example.html#Fit-without-adaptation">Fit without adaptation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Office_example.html#Fit-with-adaptation">Fit with adaptation</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../examples/tradaboost_experiments.html">TrAdaBoost Experiments</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/tradaboost_experiments.html#Mushrooms">Mushrooms</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/tradaboost_experiments.html#20-NewsGroup">20-NewsGroup</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../examples/Heart_Failure.html">Heart Failure</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#Setup">Setup</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#Network">Network</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#Source-Only">Source Only</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#DANN">DANN</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#ADDA">ADDA</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#DeepCORAL">DeepCORAL</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#CORAL">CORAL</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#MCD">MCD</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#MDD">MDD</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#WDGRL">WDGRL</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#CDAN">CDAN</a></li>
</ul>
</li>
</ul>
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<section id="adapt-instance-based-kliep">
<h1><a class="reference internal" href="../contents.html#adapt-instance-based"><span class="std std-ref">adapt.instance_based</span></a>.KLIEP<a class="headerlink" href="#adapt-instance-based-kliep" title="Permalink to this headline"></a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="adapt.instance_based.KLIEP">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">adapt.instance_based.</span></span><span class="sig-name descname"><span class="pre">KLIEP</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xt</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rbf'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigmas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_centers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'FW'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.001,</span> <span class="pre">0.01,</span> <span class="pre">0.1,</span> <span class="pre">1.0,</span> <span class="pre">10.0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_kliep.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.KLIEP" title="Permalink to this definition"></a></dt>
<dd><p>KLIEP: Kullback–Leibler Importance Estimation Procedure</p>
<p>KLIEP is an instance-based method for domain adaptation.</p>
<p>The purpose of the algorithm is to correct the difference between
input distributions of source and target domains. This is done by
finding a source instances <strong>reweighting</strong> which minimizes the
<strong>Kullback-Leibler divergence</strong> between source and target distributions.</p>
<p>The source instance weights are given by the following formula:</p>
<div class="math notranslate nohighlight">
\[w(x) = \sum_{x_i \in X_T} \alpha_i K(x, x_i)\]</div>
<p>Where:</p>
<ul class="simple">
<li><p><span class="math notranslate nohighlight">\(x, x_i\)</span> are input instances.</p></li>
<li><p><span class="math notranslate nohighlight">\(X_T\)</span> is the target input data.</p></li>
<li><p><span class="math notranslate nohighlight">\(\alpha_i\)</span> are the basis functions coefficients.</p></li>
<li><p><span class="math notranslate nohighlight">\(K(x, x_i) = \text{exp}(-\gamma ||x - x_i||^2)\)</span>
for instance if <code class="docutils literal notranslate"><span class="pre">kernel="rbf"</span></code>.</p></li>
</ul>
<p>KLIEP algorithm consists in finding the optimal <span class="math notranslate nohighlight">\(\alpha_i\)</span> according to
the following optimization problem:</p>
<div class="math notranslate nohighlight">
\[\max_{\alpha_i } \sum_{x_j \in X_T} \log(
\sum_{x_i \in X_T} \alpha_i K(x_j, x_i))\]</div>
<p>Subject to:</p>
<div class="math notranslate nohighlight">
\[\sum_{x_j \in X_S} \sum_{x_i \in X_T} \alpha_i K(x_j, x_i)) = n_S\]</div>
<p>Where:</p>
<ul class="simple">
<li><p><span class="math notranslate nohighlight">\(X_S\)</span> is the source input data of size <span class="math notranslate nohighlight">\(n_S\)</span>.</p></li>
</ul>
<p>The above OP is solved through gradient ascent algorithm.</p>
<p>Furthemore a LCV procedure can be added to select the appropriate
parameters of the kernel function <span class="math notranslate nohighlight">\(K\)</span> (typically, the paramter
<span class="math notranslate nohighlight">\(\gamma\)</span> of the Gaussian kernel). The parameter is then selected using
cross-validation on the <span class="math notranslate nohighlight">\(J\)</span> score defined as follows:
<span class="math notranslate nohighlight">\(J = \frac{1}{|\mathcal{X}|} \sum_{x \in \mathcal{X}} \text{log}(w(x))\)</span></p>
<p>Finally, an estimator is fitted using the reweighted labeled source instances.</p>
<p>KLIEP method has been originally introduced for <strong>unsupervised</strong>
DA but it could be widen to <strong>supervised</strong> by simply adding labeled
target data to the training set.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>estimator</strong><span class="classifier">sklearn estimator or tensorflow Model (default=None)</span></dt><dd><p>Estimator used to learn the task.
If estimator is <code class="docutils literal notranslate"><span class="pre">None</span></code>, a <code class="docutils literal notranslate"><span class="pre">LinearRegression</span></code>
instance is used as estimator.</p>
</dd>
<dt><strong>Xt</strong><span class="classifier">numpy array (default=None)</span></dt><dd><p>Target input data.</p>
</dd>
<dt><strong>kernel</strong><span class="classifier">str (default=”rbf”)</span></dt><dd><p>Kernel metric.
Possible values: [‘additive_chi2’, ‘chi2’,
‘linear’, ‘poly’, ‘polynomial’, ‘rbf’,
‘laplacian’, ‘sigmoid’, ‘cosine’]</p>
</dd>
<dt><strong>sigmas</strong><span class="classifier">float or list of float (default=None)</span></dt><dd><p>Deprecated, please use the <code class="docutils literal notranslate"><span class="pre">gamma</span></code> parameter
instead. (See below).</p>
</dd>
<dt><strong>cv</strong><span class="classifier">int (default=5)</span></dt><dd><p>Cross-validation split parameter.
Used only if sigmas has more than one value.</p>
</dd>
<dt><strong>max_centers</strong><span class="classifier">int (default=100)</span></dt><dd><p>Maximal number of target instances use to
compute kernels.</p>
</dd>
<dt><strong>algo</strong><span class="classifier">str (default=”FW”)</span></dt><dd><p>Optimization algorithm.
Possible values: [‘original’, ‘PG’, ‘FW’]</p>
<ul class="simple">
<li><p>‘original’ follows the algorithm of [1]. Useful to reproduce the paper’s experiences.</p></li>
<li><p>‘PG’ is a improved version of ‘original’. A convex projection into the constraints set is used.</p></li>
<li><p>‘FW’ [2] uses the Frank-Wolfe algorithm to solve the above OP.</p></li>
</ul>
<p>In general, ‘FW’ is more efficient than ‘original’ or ‘PG’.
In some cases, ‘PG’ converges faster than ‘FW’ with a good choice of learning rate.</p>
</dd>
<dt><strong>lr</strong><span class="classifier">float or list of float (default=np.logspace(-3,1,5))</span></dt><dd><p>Learning rate of the gradient ascent.
Used only if algo different to ‘FW’</p>
</dd>
<dt><strong>tol</strong><span class="classifier">float (default=1e-6)</span></dt><dd><p>Optimization threshold.</p>
</dd>
<dt><strong>max_iter</strong><span class="classifier">int (default=2000)</span></dt><dd><p>Maximal iteration of the optimization algorithm.</p>
</dd>
<dt><strong>copy</strong><span class="classifier">boolean (default=True)</span></dt><dd><p>Whether to make a copy of <code class="docutils literal notranslate"><span class="pre">estimator</span></code> or not.</p>
</dd>
<dt><strong>verbose</strong><span class="classifier">int (default=1)</span></dt><dd><p>Verbosity level.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int (default=None)</span></dt><dd><p>Seed of random generator.</p>
</dd>
<dt><strong>params</strong><span class="classifier">key, value arguments</span></dt><dd><p>Arguments given at the different level of the adapt object.
It can be, for instance, compile or fit parameters of the
estimator or kernel parameters etc…
Accepted parameters can be found by calling the method
<code class="docutils literal notranslate"><span class="pre">_get_legal_params(params)</span></code>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Yields</dt>
<dd class="field-even"><dl>
<dt><strong>gamma</strong><span class="classifier">float or list of float</span></dt><dd><p>Kernel parameter <code class="docutils literal notranslate"><span class="pre">gamma</span></code>.</p>
<ul>
<li><p>For kernel = chi2:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">k</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="o">=</span> <span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">gamma</span> <span class="n">Sum</span> <span class="p">[(</span><span class="n">x</span> <span class="o">-</span> <span class="n">y</span><span class="p">)</span><span class="o">^</span><span class="mi">2</span> <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">)])</span>
</pre></div>
</div>
</li>
<li><p>For kernel = poly or polynomial:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">K</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span><span class="n">gamma</span> <span class="o"><</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">></span> <span class="o">+</span> <span class="n">coef0</span><span class="p">)</span><span class="o">^</span><span class="n">degree</span>
</pre></div>
</div>
</li>
<li><p>For kernel = rbf:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">K</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="o">=</span> <span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">gamma</span> <span class="o">||</span><span class="n">x</span><span class="o">-</span><span class="n">y</span><span class="o">||^</span><span class="mi">2</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p>For kernel = laplacian:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">K</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="o">=</span> <span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">gamma</span> <span class="o">||</span><span class="n">x</span><span class="o">-</span><span class="n">y</span><span class="o">||</span><span class="n">_1</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p>For kernel = sigmoid:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">K</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span> <span class="o">=</span> <span class="n">tanh</span><span class="p">(</span><span class="n">gamma</span> <span class="o"><</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">></span> <span class="o">+</span> <span class="n">coef0</span><span class="p">)</span>
</pre></div>
</div>
</li>
</ul>
<p>If a list is given, the LCV process is performed to
select the best parameter <code class="docutils literal notranslate"><span class="pre">gamma</span></code>.</p>
</dd>
<dt><strong>coef0</strong><span class="classifier">floaf or list of float</span></dt><dd><p>Kernel parameter <code class="docutils literal notranslate"><span class="pre">coef0</span></code>.
Used for ploynomial and sigmoid kernels.
See <code class="docutils literal notranslate"><span class="pre">gamma</span></code> parameter above for the
kernel formulas.
If a list is given, the LCV process is performed to
select the best parameter <code class="docutils literal notranslate"><span class="pre">coef0</span></code>.</p>
</dd>
<dt><strong>degree</strong><span class="classifier">int or list of int</span></dt><dd><p>Degree parameter for the polynomial
kernel. (see formula in the <code class="docutils literal notranslate"><span class="pre">gamma</span></code>
parameter description).
If a list is given, the LCV process is performed to
select the best parameter <code class="docutils literal notranslate"><span class="pre">degree</span></code>.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="adapt.instance_based.KMM.html#adapt.instance_based.KMM" title="adapt.instance_based.KMM"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KMM</span></code></a></dt><dd></dd>
</dl>
</div>
<p class="rubric">References</p>
<dl class="citation">
<dt class="label" id="r37425ff8ed62-1"><span class="brackets">1</span></dt>
<dd><p><a class="reference external" href="https://papers.nips.cc/paper/3248-direct-importance-estimation-with-model-selection-and-its-application-to-covariate-shift-adaptation.pdf">[1]</a> M. Sugiyama, S. Nakajima, H. Kashima, P. von Bünau and M. Kawanabe. “Direct importance estimation with model selection and its application to covariateshift adaptation”. In NIPS 2007</p>
</dd>
<dt class="label" id="r37425ff8ed62-2"><span class="brackets">2</span></dt>
<dd><p><a class="reference external" href="https://webdocs.cs.ualberta.ca/~dale/papers/ijcai15.pdf">[2]</a> J. Wen, R. Greiner and D. Schuurmans. “Correcting Covariate Shift with the Frank-Wolfe Algorithm”. In IJCAI 2015</p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">RidgeClassifier</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">adapt.utils</span> <span class="kn">import</span> <span class="n">make_classification_da</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">adapt.instance_based</span> <span class="kn">import</span> <span class="n">KLIEP</span>
<span class="gp">>>> </span><span class="n">Xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">Xt</span><span class="p">,</span> <span class="n">yt</span> <span class="o">=</span> <span class="n">make_classification_da</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">model</span> <span class="o">=</span> <span class="n">KLIEP</span><span class="p">(</span><span class="n">RidgeClassifier</span><span class="p">(),</span> <span class="n">Xt</span><span class="o">=</span><span class="n">Xt</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="s2">"rbf"</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">Xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">)</span>
<span class="go">Fit weights...</span>
<span class="go">Cross Validation process...</span>
<span class="go">Parameter {'gamma': 0.1} -- J-score = 0.013 (0.003)</span>
<span class="go">Parameter {'gamma': 1.0} -- J-score = 0.120 (0.026)</span>
<span class="go">Fit Estimator...</span>
<span class="gp">>>> </span><span class="n">model</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">Xt</span><span class="p">,</span> <span class="n">yt</span><span class="p">)</span>
<span class="go">0.85</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Attributes</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>weights_</strong><span class="classifier">numpy array</span></dt><dd><p>Training instance weights.</p>
</dd>
<dt><strong>best_params_</strong><span class="classifier">float</span></dt><dd><p>Best kernel params combination
deduced from the LCV procedure.</p>
</dd>
<dt><strong>alphas_</strong><span class="classifier">numpy array</span></dt><dd><p>Basis functions coefficients.</p>
</dd>
<dt><strong>centers_</strong><span class="classifier">numpy array</span></dt><dd><p>Center points for kernels.</p>
</dd>
<dt><strong>j_scores_</strong><span class="classifier">dict</span></dt><dd><p>dict of J scores with the
kernel params combination as
keys and the J scores as values.</p>
</dd>
<dt><strong>estimator_</strong><span class="classifier">object</span></dt><dd><p>Fitted estimator.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#adapt.instance_based.KLIEP.__init__" title="adapt.instance_based.KLIEP.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([estimator, Xt, kernel, sigmas, ...])</p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#adapt.instance_based.KLIEP.fit" title="adapt.instance_based.KLIEP.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(X, y[, Xt, yt, domains])</p></td>
<td><p>Fit Adapt Model.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#adapt.instance_based.KLIEP.fit_estimator" title="adapt.instance_based.KLIEP.fit_estimator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_estimator</span></code></a>(X, y[, sample_weight, ...])</p></td>
<td><p>Fit estimator on X, y.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#adapt.instance_based.KLIEP.fit_weights" title="adapt.instance_based.KLIEP.fit_weights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_weights</span></code></a>(Xs, Xt, **kwargs)</p></td>
<td><p>Fit importance weighting.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#adapt.instance_based.KLIEP.get_metadata_routing" title="adapt.instance_based.KLIEP.get_metadata_routing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a>()</p></td>
<td><p>Get metadata routing of this object.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#adapt.instance_based.KLIEP.get_params" title="adapt.instance_based.KLIEP.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>([deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#adapt.instance_based.KLIEP.predict" title="adapt.instance_based.KLIEP.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(X[, domain])</p></td>
<td><p>Return estimator predictions after adaptation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#adapt.instance_based.KLIEP.predict_estimator" title="adapt.instance_based.KLIEP.predict_estimator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_estimator</span></code></a>(X, **predict_params)</p></td>
<td><p>Return estimator predictions for X.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#adapt.instance_based.KLIEP.predict_weights" title="adapt.instance_based.KLIEP.predict_weights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_weights</span></code></a>([X])</p></td>
<td><p>Return fitted source weights</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#adapt.instance_based.KLIEP.score" title="adapt.instance_based.KLIEP.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(X, y[, sample_weight, domain])</p></td>
<td><p>Return the estimator score.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#adapt.instance_based.KLIEP.set_fit_request" title="adapt.instance_based.KLIEP.set_fit_request"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_fit_request</span></code></a>(*[, domains])</p></td>
<td><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#adapt.instance_based.KLIEP.set_params" title="adapt.instance_based.KLIEP.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(**params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#adapt.instance_based.KLIEP.set_predict_request" title="adapt.instance_based.KLIEP.set_predict_request"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_predict_request</span></code></a>(*[, domain])</p></td>
<td><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#adapt.instance_based.KLIEP.set_score_request" title="adapt.instance_based.KLIEP.set_score_request"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_score_request</span></code></a>(*[, domain, sample_weight])</p></td>
<td><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#adapt.instance_based.KLIEP.unsupervised_score" title="adapt.instance_based.KLIEP.unsupervised_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unsupervised_score</span></code></a>(Xs, Xt)</p></td>
<td><p>Return unsupervised score.</p></td>
</tr>
</tbody>
</table>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.KLIEP.__init__">
<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xt</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rbf'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigmas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_centers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'FW'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.001,</span> <span class="pre">0.01,</span> <span class="pre">0.1,</span> <span class="pre">1.0,</span> <span class="pre">10.0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_kliep.__init__.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.KLIEP.__init__" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.KLIEP.fit">
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xt</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">yt</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">domains</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_kliep.fit.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.KLIEP.fit" title="Permalink to this definition"></a></dt>
<dd><p>Fit Adapt Model.</p>
<p>For feature-based models, the transformation of the
input features <code class="docutils literal notranslate"><span class="pre">Xs</span></code> and <code class="docutils literal notranslate"><span class="pre">Xt</span></code> is first fitted. In a second
stage, the <code class="docutils literal notranslate"><span class="pre">estimator_</span></code> is fitted on the transformed features.</p>
<p>For instance-based models, source importance weights are
first learned based on <code class="docutils literal notranslate"><span class="pre">Xs,</span> <span class="pre">ys</span></code> and <code class="docutils literal notranslate"><span class="pre">Xt</span></code>. In a second
stage, the <code class="docutils literal notranslate"><span class="pre">estimator_</span></code> is fitted on <code class="docutils literal notranslate"><span class="pre">Xs,</span> <span class="pre">ys</span></code> with the learned
importance weights.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">numpy array</span></dt><dd><p>Source input data.</p>
</dd>
<dt><strong>y</strong><span class="classifier">numpy array</span></dt><dd><p>Source output data.</p>
</dd>
<dt><strong>Xt</strong><span class="classifier">array (default=None)</span></dt><dd><p>Target input data. If None, the <cite>Xt</cite> argument
given in <cite>init</cite> is used.</p>
</dd>
<dt><strong>yt</strong><span class="classifier">array (default=None)</span></dt><dd><p>Target input data. Only needed for supervised
and semi-supervised Adapt model.
If None, the <cite>yt</cite> argument given in <cite>init</cite> is used.</p>
</dd>
<dt><strong>domains</strong><span class="classifier">array (default=None)</span></dt><dd><p>Vector giving the domain for each source
data. Can be used for multisource purpose.</p>
</dd>
<dt><strong>fit_params</strong><span class="classifier">key, value arguments</span></dt><dd><p>Arguments given to the fit method of
the estimator.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">returns an instance of self</span></dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.KLIEP.fit_estimator">
<span class="sig-name descname"><span class="pre">fit_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_kliep.fit_estimator.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.KLIEP.fit_estimator" title="Permalink to this definition"></a></dt>
<dd><p>Fit estimator on X, y.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array</span></dt><dd><p>Input data.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array</span></dt><dd><p>Output data.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array</span></dt><dd><p>Importance weighting.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int (default=None)</span></dt><dd><p>Seed of the random generator</p>
</dd>
<dt><strong>warm_start</strong><span class="classifier">bool (default=True)</span></dt><dd><p>If True, continue to fit <code class="docutils literal notranslate"><span class="pre">estimator_</span></code>,
else, a new estimator is fitted based on
a copy of <code class="docutils literal notranslate"><span class="pre">estimator</span></code>. (Be sure to set
<code class="docutils literal notranslate"><span class="pre">copy=True</span></code> to use <code class="docutils literal notranslate"><span class="pre">warm_start=False</span></code>)</p>
</dd>
<dt><strong>fit_params</strong><span class="classifier">key, value arguments</span></dt><dd><p>Arguments given to the fit method of
the estimator and to the compile method
for tensorflow estimator.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>estimator_</strong><span class="classifier">fitted estimator</span></dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.KLIEP.fit_weights">
<span class="sig-name descname"><span class="pre">fit_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Xs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xt</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_kliep.fit_weights.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.KLIEP.fit_weights" title="Permalink to this definition"></a></dt>
<dd><p>Fit importance weighting.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>Xs</strong><span class="classifier">array</span></dt><dd><p>Input source data.</p>
</dd>
<dt><strong>Xt</strong><span class="classifier">array</span></dt><dd><p>Input target data.</p>
</dd>
<dt><strong>kwargs</strong><span class="classifier">key, value argument</span></dt><dd><p>Not used, present here for adapt consistency.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>weights_</strong><span class="classifier">sample weights</span></dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.KLIEP.get_metadata_routing">
<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_kliep.get_metadata_routing.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.KLIEP.get_metadata_routing" title="Permalink to this definition"></a></dt>
<dd><p>Get metadata routing of this object.</p>
<p>Please check <span class="xref std std-ref">User Guide</span> on how the routing
mechanism works.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>routing</strong><span class="classifier">MetadataRequest</span></dt><dd><p>A <code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRequest</span></code> encapsulating
routing information.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.KLIEP.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_kliep.get_params.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.KLIEP.get_params" title="Permalink to this definition"></a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>Not used, here for scikit-learn compatibility.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">dict</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.KLIEP.predict">
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">domain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">predict_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_kliep.predict.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.KLIEP.predict" title="Permalink to this definition"></a></dt>
<dd><p>Return estimator predictions after
adaptation.</p>
<p>For feature-based method (object which implements
a <code class="docutils literal notranslate"><span class="pre">transform</span></code> method), the input feature <code class="docutils literal notranslate"><span class="pre">X</span></code>
are first transformed. Then the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method
of the fitted estimator <code class="docutils literal notranslate"><span class="pre">estimator_</span></code> is applied
on the transformed <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array</span></dt><dd><p>input data</p>
</dd>
<dt><strong>domain</strong><span class="classifier">str (default=None)</span></dt><dd><p>For antisymetric feature-based method,
different transformation of the input X
are applied for different domains. The domain
should then be specified between “src” and “tgt”.
If <code class="docutils literal notranslate"><span class="pre">None</span></code> the default transformation is the
target one.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y_pred</strong><span class="classifier">array</span></dt><dd><p>prediction of the Adapt Model.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.KLIEP.predict_estimator">
<span class="sig-name descname"><span class="pre">predict_estimator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">predict_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_kliep.predict_estimator.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.KLIEP.predict_estimator" title="Permalink to this definition"></a></dt>
<dd><p>Return estimator predictions for X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array</span></dt><dd><p>input data</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y_pred</strong><span class="classifier">array</span></dt><dd><p>prediction of estimator.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.KLIEP.predict_weights">
<span class="sig-name descname"><span class="pre">predict_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_kliep.predict_weights.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.KLIEP.predict_weights" title="Permalink to this definition"></a></dt>
<dd><p>Return fitted source weights</p>
<p>If <code class="docutils literal notranslate"><span class="pre">None</span></code>, the fitted source weights are returned.
Else, sample weights are computing using the fitted
<code class="docutils literal notranslate"><span class="pre">alphas_</span></code> and the chosen <code class="docutils literal notranslate"><span class="pre">centers_</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array (default=None)</span></dt><dd><p>Input data.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>weights_</strong><span class="classifier">sample weights</span></dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.KLIEP.score">
<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">domain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_kliep.score.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.KLIEP.score" title="Permalink to this definition"></a></dt>
<dd><p>Return the estimator score.</p>
<p>If the object has a <code class="docutils literal notranslate"><span class="pre">transform</span></code> method, the
estimator is applied on the transformed
features X. For antisymetric transformation,
a parameter domain can be set to specified
between source and target transformation.</p>
<p>Call <cite>score</cite> on sklearn estimator and
<cite>evaluate</cite> on tensorflow Model.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array</span></dt><dd><p>input data</p>
</dd>
<dt><strong>y</strong><span class="classifier">array</span></dt><dd><p>output data</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array (default=None)</span></dt><dd><p>Sample weights</p>
</dd>
<dt><strong>domain</strong><span class="classifier">str (default=None)</span></dt><dd><p>This parameter specifies for antisymetric
feature-based method which transformation
will be applied between “source” and “target”.
If <code class="docutils literal notranslate"><span class="pre">None</span></code> the transformation by default is
the target one.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">float</span></dt><dd><p>estimator score.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.KLIEP.set_fit_request">
<span class="sig-name descname"><span class="pre">set_fit_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">domains</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">bool</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">None</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#adapt.instance_based.KLIEP" title="adapt.instance_based._kliep.KLIEP"><span class="pre">adapt.instance_based._kliep.KLIEP</span></a></span></span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_kliep.set_fit_request.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.KLIEP.set_fit_request" title="Permalink to this definition"></a></dt>
<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method.</p>
<p>Note that this method is only relevant if
<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
Please see <span class="xref std std-ref">User Guide</span> on how the routing
mechanism works.</p>
<p>The options for each parameter are:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
</ul>
<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
existing request. This allows you to change the request for some
parameters and not others.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.3.</span></p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>domains</strong><span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">domains</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>The updated object.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.KLIEP.set_params">
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_kliep.set_params.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.KLIEP.set_params" title="Permalink to this definition"></a></dt>
<dd><p>Set the parameters of this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.KLIEP.set_predict_request">
<span class="sig-name descname"><span class="pre">set_predict_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">domain</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">bool</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">None</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#adapt.instance_based.KLIEP" title="adapt.instance_based._kliep.KLIEP"><span class="pre">adapt.instance_based._kliep.KLIEP</span></a></span></span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_kliep.set_predict_request.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.KLIEP.set_predict_request" title="Permalink to this definition"></a></dt>
<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method.</p>
<p>Note that this method is only relevant if
<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
Please see <span class="xref std std-ref">User Guide</span> on how the routing
mechanism works.</p>
<p>The options for each parameter are:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">predict</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
</ul>
<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
existing request. This allows you to change the request for some
parameters and not others.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.3.</span></p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>domain</strong><span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">domain</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>The updated object.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.KLIEP.set_score_request">
<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">domain</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">bool</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">None</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">bool</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">None</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#adapt.instance_based.KLIEP" title="adapt.instance_based._kliep.KLIEP"><span class="pre">adapt.instance_based._kliep.KLIEP</span></a></span></span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_kliep.set_score_request.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.KLIEP.set_score_request" title="Permalink to this definition"></a></dt>
<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
<p>Note that this method is only relevant if
<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
Please see <span class="xref std std-ref">User Guide</span> on how the routing
mechanism works.</p>
<p>The options for each parameter are:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">score</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
</ul>
<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
existing request. This allows you to change the request for some
parameters and not others.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.3.</span></p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This method is only relevant if this estimator is used as a
sub-estimator of a meta-estimator, e.g. used inside a
<code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>. Otherwise it has no effect.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>domain</strong><span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">domain</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>The updated object.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.KLIEP.unsupervised_score">
<span class="sig-name descname"><span class="pre">unsupervised_score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Xs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xt</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_kliep.unsupervised_score.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.KLIEP.unsupervised_score" title="Permalink to this definition"></a></dt>
<dd><p>Return unsupervised score.</p>
<p>The normalized discrepancy distance is computed
between the reweighted/transformed source input
data and the target input data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>Xs</strong><span class="classifier">array</span></dt><dd><p>Source input data.</p>
</dd>
<dt><strong>Xt</strong><span class="classifier">array</span></dt><dd><p>Source input data.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">float</span></dt><dd><p>Unsupervised score.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
</dd></dl>
<h2> Examples </h2><div class="toctree-wrapper compound">
</div>
<div class="sphx-glr-thumbcontainer">
<div class="figure align-center">
<img alt="thumbnail" src="../_images/examples_sample_bias_6_0.png" />
<p class="caption">
<span class="caption-text">
<a class="reference internal" href="../examples/sample_bias.html">
<span class="std std-ref">Sample Bias 1D</span>
</a>
</span>
</p>
</div>
</div>
<div class="sphx-glr-thumbcontainer">
<div class="figure align-center">
<img alt="thumbnail" src="../_images/examples_sample_bias_2d_14_0.png" />
<p class="caption">
<span class="caption-text">
<a class="reference internal" href="../examples/sample_bias_2d.html">
<span class="std std-ref">Sample Bias 2D</span>
</a>
</span>
</p>
</div>
</div>
<div class="sphx-glr-thumbcontainer">
<div class="figure align-center">
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<span class="std std-ref">Correcting Sample Bias with Transfer Leanring</span>