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<!DOCTYPE html>
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<title>adapt.parameter_based.TransferForestClassifier — adapt 0.1.0 documentation</title>
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<p class="caption" role="heading"><span class="caption-text">Installation</span></p>
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<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>
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<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>
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<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>
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<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="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="#">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>
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<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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<li><span class="xref std std-ref">adapt.parameter_based</span>.TransferForestClassifier</li>
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<section id="adapt-parameter-based-transferforestclassifier">
<h1><a class="reference internal" href="../contents.html#adapt-parameter-based"><span class="std std-ref">adapt.parameter_based</span></a>.TransferForestClassifier<a class="headerlink" href="#adapt-parameter-based-transferforestclassifier" title="Permalink to this headline"></a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="adapt.parameter_based.TransferForestClassifier">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">adapt.parameter_based.</span></span><span class="sig-name descname"><span class="pre">TransferForestClassifier</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">algo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</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/parameter_based/_transferforestclassifier.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.parameter_based.TransferForestClassifier" title="Permalink to this definition"></a></dt>
<dd><p>TransferForestClassifier: Modify a source Random Forest on a target dataset.</p>
<p>Random forest classifier structure for model-based transfer algorithms.</p>
<p>This includes several algorithms : leaves relabeling according to target data, SER and STRUT algorithms
and various variants for target class imbalance situations.</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 RandomForestClassifier (default=None)</span></dt><dd><p>Source random forest classifier.</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>algo</strong><span class="classifier">str or callable (default=””)</span></dt><dd><p>Leaves relabeling if “” or “relab”.
“ser” and “strut” for SER and STRUT algorithms</p>
</dd>
<dt><strong>bootstrap</strong><span class="classifier">boolean (default=True).</span></dt><dd></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>
</dl>
</dd>
</dl>
<p class="rubric">References</p>
<dl class="citation">
<dt class="label" id="r0525b000ade1-1"><span class="brackets">1</span></dt>
<dd><p><a class="reference external" href="https://ieeexplore.ieee.org/document/7592407">[1]</a> Segev, Noam and Harel, Maayan Mannor, Shie and Crammer, Koby and El-Yaniv, Ran “Learn on Source, Refine on Target: A Model Transfer Learning Framework with Random Forests”. In IEEE TPAMI, 2017.</p>
</dd>
<dt class="label" id="r0525b000ade1-2"><span class="brackets">2</span></dt>
<dd><p><a class="reference external" href="https://ieeexplore.ieee.org/document/8995296">[2]</a> Minvielle, Ludovic and Atiq, Mounir Peignier, Sergio and Mougeot, Mathilde “Transfer Learning on Decision Tree with Class Imbalance”. In IEEE ICTAI, 2019.</p>
</dd>
</dl>
<dl class="field-list simple">
<dt class="field-odd">Attributes</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>estimator_</strong><span class="classifier">sklearn RandomForestClassifier</span></dt><dd><p>Transferred random forest classifier using target data.</p>
</dd>
<dt><strong>rf_size</strong><span class="classifier">int.</span></dt><dd></dd>
<dt><strong>estimators_</strong><span class="classifier">numpy array of TransferTreeClassifier.</span></dt><dd></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.parameter_based.TransferForestClassifier.__init__" title="adapt.parameter_based.TransferForestClassifier.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([estimator, Xt, yt, algo, ...])</p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#adapt.parameter_based.TransferForestClassifier.fit" title="adapt.parameter_based.TransferForestClassifier.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>([Xt, yt])</p></td>
<td><p>Fit TransferTreeClassifier.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#adapt.parameter_based.TransferForestClassifier.fit_estimator" title="adapt.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier.get_metadata_routing" title="adapt.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier.get_params" title="adapt.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier.predict" title="adapt.parameter_based.TransferForestClassifier.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-odd"><td><p><a class="reference internal" href="#adapt.parameter_based.TransferForestClassifier.predict_estimator" title="adapt.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier.score" title="adapt.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier.set_fit_request" title="adapt.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier.set_params" title="adapt.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier.set_predict_request" title="adapt.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier.set_score_request" title="adapt.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier.unsupervised_score" title="adapt.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier.__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">algo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</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/parameter_based/_transferforestclassifier.__init__.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.parameter_based.TransferForestClassifier.__init__" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.parameter_based.TransferForestClassifier.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">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="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/parameter_based/_transferforestclassifier.fit.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.parameter_based.TransferForestClassifier.fit" title="Permalink to this definition"></a></dt>
<dd><p>Fit TransferTreeClassifier.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<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>fit_params</strong><span class="classifier">key, value arguments</span></dt><dd><p>Arguments for 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.parameter_based.TransferForestClassifier.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/parameter_based/_transferforestclassifier.fit_estimator.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier.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/parameter_based/_transferforestclassifier.get_metadata_routing.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier.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/parameter_based/_transferforestclassifier.get_params.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier.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/parameter_based/_transferforestclassifier.predict.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier.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/parameter_based/_transferforestclassifier.predict_estimator.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier.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/parameter_based/_transferforestclassifier.score.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier" title="adapt.parameter_based._transfer_tree.TransferForestClassifier"><span class="pre">adapt.parameter_based._transfer_tree.TransferForestClassifier</span></a></span></span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/parameter_based/_transferforestclassifier.set_fit_request.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier.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/parameter_based/_transferforestclassifier.set_params.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier" title="adapt.parameter_based._transfer_tree.TransferForestClassifier"><span class="pre">adapt.parameter_based._transfer_tree.TransferForestClassifier</span></a></span></span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/parameter_based/_transferforestclassifier.set_predict_request.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier" title="adapt.parameter_based._transfer_tree.TransferForestClassifier"><span class="pre">adapt.parameter_based._transfer_tree.TransferForestClassifier</span></a></span></span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/parameter_based/_transferforestclassifier.set_score_request.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.parameter_based.TransferForestClassifier.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.parameter_based.TransferForestClassifier.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/parameter_based/_transferforestclassifier.unsupervised_score.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.parameter_based.TransferForestClassifier.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></section>
</div>
</div>
<footer>
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