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<!DOCTYPE html>
<html class="writer-html5" lang="en" >
<head>
<meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
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<title>adapt.instance_based.TrAdaBoost — 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="adapt.instance_based.KLIEP.html">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="#">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>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../contents.html#adapt-metrics">Metrics</a><ul>
<li class="toctree-l2"><a class="reference internal" href="adapt.metrics.make_uda_scorer.html">make_uda_scorer</a></li>
<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>
</div>
</div>
</nav>
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<section id="adapt-instance-based-tradaboost">
<h1><a class="reference internal" href="../contents.html#adapt-instance-based"><span class="std std-ref">adapt.instance_based</span></a>.TrAdaBoost<a class="headerlink" href="#adapt-instance-based-tradaboost" title="Permalink to this headline"></a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="adapt.instance_based.TrAdaBoost">
<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">TrAdaBoost</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">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">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</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">1.0</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/_tradaboost.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.TrAdaBoost" title="Permalink to this definition"></a></dt>
<dd><p>Transfer AdaBoost for Classification</p>
<p>TrAdaBoost algorithm is a <strong>supervised</strong> instances-based domain
adaptation method suited for <strong>classification</strong> tasks.</p>
<p>The method is based on a “<strong>reverse boosting</strong>” principle where the
weights of source instances poorly predicted decrease at each
boosting iteration whereas the ones of target instances increase.</p>
<p>The algorithm performs the following steps:</p>
<ul>
<li><p><strong>1.</strong> Normalize weights: <span class="math notranslate nohighlight">\(\sum w_S + \sum w_T = 1\)</span>.</p></li>
<li><p><strong>2.</strong> Fit an estimator <span class="math notranslate nohighlight">\(f\)</span> on source and target labeled data
<span class="math notranslate nohighlight">\((X_S, y_S), (X_T, y_T)\)</span> with the respective importances
weights: <span class="math notranslate nohighlight">\(w_S, w_T\)</span>.</p></li>
<li><p><strong>3.</strong> Compute error vectors of training instances:</p>
<ul class="simple">
<li><p><span class="math notranslate nohighlight">\(\epsilon_S = L_{01}(f(X_S), y_S)\)</span>.</p></li>
<li><p><span class="math notranslate nohighlight">\(\epsilon_T = L_{01}(f(X_T), y_T)\)</span>.</p></li>
</ul>
</li>
<li><p><strong>4.</strong> Compute total weighted error of target instances:
<span class="math notranslate nohighlight">\(E_T = \frac{1}{n_T} w_T^T \epsilon_T\)</span>.</p></li>
<li><p><strong>5.</strong> Update source and target weights:</p>
<blockquote>
<div><ul class="simple">
<li><p><span class="math notranslate nohighlight">\(w_S = w_S \beta^{\epsilon_S}\)</span>.</p></li>
<li><p><span class="math notranslate nohighlight">\(w_T = w_T \beta_T^{-\epsilon_T}\)</span>.</p></li>
</ul>
</div></blockquote>
<p>Where:</p>
<ul class="simple">
<li><p><span class="math notranslate nohighlight">\(\beta = 1 \setminus (1 + \sqrt{2 \text{ln} n_S \setminus N})\)</span>.</p></li>
<li><p><span class="math notranslate nohighlight">\(\beta_T = E_T \setminus (1 - E_T)\)</span>.</p></li>
</ul>
</li>
<li><p><strong>6.</strong> Return to step <strong>1</strong> and loop until the number <span class="math notranslate nohighlight">\(N\)</span>
of boosting iteration is reached.</p></li>
</ul>
<p>The prediction are then given by the vote of the <span class="math notranslate nohighlight">\(N \setminus 2\)</span>
last computed estimators weighted by their respective parameter
<span class="math notranslate nohighlight">\(\beta_T\)</span>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<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>yt</strong><span class="classifier">numpy array (default=None)</span></dt><dd><p>Target output data.</p>
</dd>
<dt><strong>n_estimators</strong><span class="classifier">int (default=10)</span></dt><dd><p>Number of boosting iteration.</p>
</dd>
<dt><strong>lr</strong><span class="classifier">float (default=1.)</span></dt><dd><p>Learning rate. For higher <code class="docutils literal notranslate"><span class="pre">lr</span></code>, the sample
weights are updating faster.</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>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="adapt.instance_based.TrAdaBoostR2.html#adapt.instance_based.TrAdaBoostR2" title="adapt.instance_based.TrAdaBoostR2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TrAdaBoostR2</span></code></a>, <a class="reference internal" href="adapt.instance_based.TwoStageTrAdaBoostR2.html#adapt.instance_based.TwoStageTrAdaBoostR2" title="adapt.instance_based.TwoStageTrAdaBoostR2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TwoStageTrAdaBoostR2</span></code></a></dt><dd></dd>
</dl>
</div>
<p class="rubric">References</p>
<dl class="citation">
<dt class="label" id="r70ec62e3f07c-1"><span class="brackets">1</span></dt>
<dd><p><a class="reference external" href="http://www.cs.ust.hk/~qyang/Docs/2007/tradaboost.pdf">[1]</a> Dai W., Yang Q., Xue G., and Yu Y. “Boosting for transfer learning”. In ICML, 2007.</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">TrAdaBoost</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">TrAdaBoost</span><span class="p">(</span><span class="n">RidgeClassifier</span><span class="p">(),</span> <span class="n">n_estimators</span><span class="o">=</span><span class="mi">10</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="mi">10</span><span class="p">],</span> <span class="n">yt</span><span class="o">=</span><span class="n">yt</span><span class="p">[:</span><span class="mi">10</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">Iteration 0 - Error: 0.2550</span>
<span class="go">Iteration 1 - Error: 0.2820</span>
<span class="go">Iteration 2 - Error: 0.3011</span>
<span class="go">Iteration 3 - Error: 0.3087</span>
<span class="go">Iteration 4 - Error: 0.3046</span>
<span class="go">Iteration 5 - Error: 0.2933</span>
<span class="go">Iteration 6 - Error: 0.2819</span>
<span class="go">Iteration 7 - Error: 0.2747</span>
<span class="go">Iteration 8 - Error: 0.2712</span>
<span class="go">Iteration 9 - Error: 0.2698</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">0.66</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Attributes</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>estimators_</strong><span class="classifier">list of object</span></dt><dd><p>List of fitted estimators</p>
</dd>
<dt><strong>estimator_errors_</strong><span class="classifier">1D array of float</span></dt><dd><p>Array of weighted estimator errors computed on
labeled target data.</p>
</dd>
<dt><strong>estimator_weights_</strong><span class="classifier">1D array of float</span></dt><dd><p>Array of estimator importance weights.</p>
</dd>
<dt><strong>sample_weights_src_</strong><span class="classifier">list of numpy arrays</span></dt><dd><p>List of source sample weight for each iteration.</p>
</dd>
<dt><strong>sample_weights_tgt_</strong><span class="classifier">list of numpy arrays</span></dt><dd><p>List of target sample weight for each iteration.</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.TrAdaBoost.__init__" title="adapt.instance_based.TrAdaBoost.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([estimator, Xt, yt, n_estimators, ...])</p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#adapt.instance_based.TrAdaBoost.fit" title="adapt.instance_based.TrAdaBoost.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(X, y[, Xt, yt, sample_weight_src, ...])</p></td>
<td><p>Fit TrAdaBoost</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#adapt.instance_based.TrAdaBoost.fit_estimator" title="adapt.instance_based.TrAdaBoost.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.TrAdaBoost.get_metadata_routing" title="adapt.instance_based.TrAdaBoost.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-odd"><td><p><a class="reference internal" href="#adapt.instance_based.TrAdaBoost.get_params" title="adapt.instance_based.TrAdaBoost.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-even"><td><p><a class="reference internal" href="#adapt.instance_based.TrAdaBoost.predict" title="adapt.instance_based.TrAdaBoost.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(X)</p></td>
<td><p>Return weighted vote of estimators.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#adapt.instance_based.TrAdaBoost.predict_estimator" title="adapt.instance_based.TrAdaBoost.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-even"><td><p><a class="reference internal" href="#adapt.instance_based.TrAdaBoost.predict_weights" title="adapt.instance_based.TrAdaBoost.predict_weights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_weights</span></code></a>([domain])</p></td>
<td><p>Return sample weights.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#adapt.instance_based.TrAdaBoost.score" title="adapt.instance_based.TrAdaBoost.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(X, y)</p></td>
<td><p>Return the TrAdaboost score on X, y.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#adapt.instance_based.TrAdaBoost.set_fit_request" title="adapt.instance_based.TrAdaBoost.set_fit_request"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_fit_request</span></code></a>(*[, sample_weight_src, ...])</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-odd"><td><p><a class="reference internal" href="#adapt.instance_based.TrAdaBoost.set_params" title="adapt.instance_based.TrAdaBoost.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-even"><td><p><a class="reference internal" href="#adapt.instance_based.TrAdaBoost.set_predict_request" title="adapt.instance_based.TrAdaBoost.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-odd"><td><p><a class="reference internal" href="#adapt.instance_based.TrAdaBoost.set_score_request" title="adapt.instance_based.TrAdaBoost.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-even"><td><p><a class="reference internal" href="#adapt.instance_based.TrAdaBoost.unsupervised_score" title="adapt.instance_based.TrAdaBoost.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.TrAdaBoost.__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">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">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</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">1.0</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/_tradaboost.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.TrAdaBoost.__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.TrAdaBoost.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">sample_weight_src</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">sample_weight_tgt</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/_tradaboost.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.TrAdaBoost.fit" title="Permalink to this definition"></a></dt>
<dd><p>Fit TrAdaBoost</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. If None, the <cite>Xt</cite> argument
given in <cite>init</cite> is used.</p>
</dd>
<dt><strong>sample_weight_src</strong><span class="classifier">numpy array, (default=None)</span></dt><dd><p>Initial sample weight of source data</p>
</dd>
<dt><strong>sample_weight_tgt</strong><span class="classifier">numpy array, (default=None)</span></dt><dd><p>Initial sample weight of target data</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.TrAdaBoost.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/_tradaboost.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.TrAdaBoost.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.TrAdaBoost.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/_tradaboost.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.TrAdaBoost.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.TrAdaBoost.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/_tradaboost.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.TrAdaBoost.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.TrAdaBoost.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><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_tradaboost.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.TrAdaBoost.predict" title="Permalink to this definition"></a></dt>
<dd><p>Return weighted vote of estimators.</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>Vote results.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.TrAdaBoost.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/_tradaboost.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.TrAdaBoost.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.TrAdaBoost.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">domain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'src'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_tradaboost.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.TrAdaBoost.predict_weights" title="Permalink to this definition"></a></dt>
<dd><p>Return sample weights.</p>
<p>Return the final importance weighting.</p>
<p>You can secify between “source” and “target” weights
with the domain parameter.</p>
<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 (default=”tgt”)</span></dt><dd><p>Choose between <code class="docutils literal notranslate"><span class="pre">"source",</span> <span class="pre">"src"</span></code> and
<code class="docutils literal notranslate"><span class="pre">"target",</span> <span class="pre">"tgt"</span></code>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>weights</strong><span class="classifier">source 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.TrAdaBoost.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><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_tradaboost.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.TrAdaBoost.score" title="Permalink to this definition"></a></dt>
<dd><p>Return the TrAdaboost score 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>
</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.TrAdaBoost.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">sample_weight_src</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_tgt</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.TrAdaBoost" title="adapt.instance_based._tradaboost.TrAdaBoost"><span class="pre">adapt.instance_based._tradaboost.TrAdaBoost</span></a></span></span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_tradaboost.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.TrAdaBoost.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>sample_weight_src</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_src</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
</dd>
<dt><strong>sample_weight_tgt</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_tgt</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.TrAdaBoost.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/_tradaboost.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.TrAdaBoost.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.TrAdaBoost.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.TrAdaBoost" title="adapt.instance_based._tradaboost.TrAdaBoost"><span class="pre">adapt.instance_based._tradaboost.TrAdaBoost</span></a></span></span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_tradaboost.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.TrAdaBoost.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.TrAdaBoost.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.TrAdaBoost" title="adapt.instance_based._tradaboost.TrAdaBoost"><span class="pre">adapt.instance_based._tradaboost.TrAdaBoost</span></a></span></span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_tradaboost.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.TrAdaBoost.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.TrAdaBoost.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/_tradaboost.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.TrAdaBoost.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="../_static/no_image.png" />
<p class="caption">
<span class="caption-text">
<a class="reference internal" href="../examples/tradaboost_experiments.html">
<span class="std std-ref">Reproduction of the TrAdaBoost experiments</span>
</a>
</span>
</p>
</div>
</div>
<div class="sphx-glr-clear"></div></section>
</div>
</div>
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