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<!DOCTYPE html>
<html class="writer-html5" lang="en" >
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<title>adapt.instance_based.IWN — 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>
</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>
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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="#">IWN</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.instance_based.BalancedWeighting.html">BalancedWeighting</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.instance_based.TrAdaBoost.html">TrAdaBoost</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.instance_based.TrAdaBoostR2.html">TrAdaBoostR2</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.instance_based.TwoStageTrAdaBoostR2.html">TwoStageTrAdaBoostR2</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.instance_based.WANN.html">WANN</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../contents.html#adapt-parameter-based">Parameter-based</a><ul>
<li class="toctree-l2"><a class="reference internal" href="adapt.parameter_based.LinInt.html">LinInt</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.parameter_based.RegularTransferLR.html">RegularTransferLR</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.parameter_based.RegularTransferLC.html">RegularTransferLC</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.parameter_based.RegularTransferNN.html">RegularTransferNN</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.parameter_based.RegularTransferGP.html">RegularTransferGP</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.parameter_based.FineTuning.html">FineTuning</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.parameter_based.TransferTreeClassifier.html">TransferTreeClassifier</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.parameter_based.TransferForestClassifier.html">TransferForestClassifier</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.parameter_based.TransferTreeSelector.html">TransferTreeSelector</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.parameter_based.TransferForestSelector.html">TransferForestSelector</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../contents.html#adapt-metrics">Metrics</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="adapt.metrics.cov_distance.html">cov_distance</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.metrics.neg_j_score.html">neg_j_score</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.metrics.linear_discrepancy.html">linear_discrepancy</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.metrics.normalized_linear_discrepancy.html">normalized_linear_discrepancy</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.metrics.frechet_distance.html">frechet_distance</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.metrics.normalized_frechet_distance.html">normalized_frechet_distance</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.metrics.domain_classifier.html">domain_classifier</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.metrics.reverse_validation.html">reverse_validation</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../contents.html#adapt-utils">Utility Functions</a><ul>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.UpdateLambda.html">UpdateLambda</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.accuracy.html">accuracy</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.check_arrays.html">check_arrays</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.check_estimator.html">check_estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.check_network.html">check_network</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.get_default_encoder.html">get_default_encoder</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.get_default_task.html">get_default_task</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.get_default_discriminator.html">get_default_discriminator</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.GradientHandler.html">GradientHandler</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.make_classification_da.html">make_classification_da</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.make_regression_da.html">make_regression_da</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.check_sample_weight.html">check_sample_weight</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.set_random_seed.html">set_random_seed</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.check_fitted_estimator.html">check_fitted_estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="adapt.utils.check_fitted_network.html">check_fitted_network</a></li>
</ul>
</li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Synthetic Examples</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../examples/Classification.html">Classification</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/Classification.html#Experimental-Setup">Experimental Setup</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Classification.html#Src-Only">Src Only</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Classification.html#DANN">DANN</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Classification.html#Instance-Based">Instance Based</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../examples/Two_moons.html">Two Moons</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#Setup">Setup</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#Network">Network</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#Source-Only">Source Only</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#DANN">DANN</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#ADDA">ADDA</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#DeepCORAL">DeepCORAL</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#CORAL">CORAL</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#MCD">MCD</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#MDD">MDD</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#WDGRL">WDGRL</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Two_moons.html#CDAN">CDAN</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../examples/Rotation.html">Rotation</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/Rotation.html#Setup">Setup</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Rotation.html#Source-Only">Source Only</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Rotation.html#CORAL">CORAL</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Rotation.html#DANN">DANN</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Rotation.html#ADDA">ADDA</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../examples/Regression.html">Toy Regression</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/Regression.html#Experimental-Setup">Experimental Setup</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Regression.html#TGT-Only">TGT Only</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Regression.html#Src-Only">Src Only</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Regression.html#All">All</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Regression.html#CORAL">CORAL</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Regression.html#TrAdaBoostR2">TrAdaBoostR2</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Regression.html#RegularTransferNN">RegularTransferNN</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../examples/sample_bias.html">Sample Bias 1D</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/sample_bias.html#Setup">Setup</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/sample_bias.html#KMM">KMM</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/sample_bias.html#KLIEP">KLIEP</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../examples/sample_bias_2d.html">Sample Bias 2D</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/sample_bias_2d.html#Setup">Setup</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/sample_bias_2d.html#Estimator">Estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/sample_bias_2d.html#Source-Only">Source Only</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/sample_bias_2d.html#KMM">KMM</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/sample_bias_2d.html#KLIEP">KLIEP</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../examples/Multi_fidelity.html">Multi-Fidelity</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/Multi_fidelity.html#Setup">Setup</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Multi_fidelity.html#Network">Network</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Multi_fidelity.html#Low-fidelity-only">Low fidelity only</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Multi_fidelity.html#High-fidelity-only">High fidelity only</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Multi_fidelity.html#RegularTransferNN">RegularTransferNN</a></li>
</ul>
</li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Real Examples</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../examples/Sample_bias_example.html">Sample Bias</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/Sample_bias_example.html#Sample-Bias-on-the-diabetes-dataset">Diabetes Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Sample_bias_example.html#Applying-Transfer-Learning-for-correcting-sample-bias">Sample bias correction</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Sample_bias_example.html#Hidden-bias-and-features-importance-estimation">Features importance</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../examples/Flowers_example.html">Fine-Tuning</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/Flowers_example.html#Training-a-model-from-scratch">Train from Scratch</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Flowers_example.html#Model-based-Transfer">Model-based Transfer</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../examples/Office_example.html">Deep Domain Adaptation</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/Office_example.html#Dataset-Preprocessing">Dataset Preprocessing</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Office_example.html#Fit-without-adaptation">Fit without adaptation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Office_example.html#Fit-with-adaptation">Fit with adaptation</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../examples/tradaboost_experiments.html">TrAdaBoost Experiments</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/tradaboost_experiments.html#Mushrooms">Mushrooms</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/tradaboost_experiments.html#20-NewsGroup">20-NewsGroup</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../examples/Heart_Failure.html">Heart Failure</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#Setup">Setup</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#Network">Network</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#Source-Only">Source Only</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#DANN">DANN</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#ADDA">ADDA</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#DeepCORAL">DeepCORAL</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#CORAL">CORAL</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#MCD">MCD</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#MDD">MDD</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#WDGRL">WDGRL</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/Heart_Failure.html#CDAN">CDAN</a></li>
</ul>
</li>
</ul>
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<section id="adapt-instance-based-iwn">
<h1><a class="reference internal" href="../contents.html#adapt-instance-based"><span class="std std-ref">adapt.instance_based</span></a>.IWN<a class="headerlink" href="#adapt-instance-based-iwn" title="Permalink to this headline"></a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="adapt.instance_based.IWN">
<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">IWN</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">weighter</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">pretrain</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">sigma_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_sigma</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">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">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/_iwn.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.IWN" title="Permalink to this definition"></a></dt>
<dd><p>IWN : Importance Weighting Network</p>
<p>IWN is an instance-based method for unsupervised domain adaptation.</p>
<p>The goal of IWN is to reweight the source instances in order to
minimize the Maximum Mean Discreancy (MMD) between the reweighted
source and the target distributions.</p>
<p>IWN uses a weighting network to parameterized the weights of the
source instances. The MMD is computed with gaussian kernels
parameterized by the bandwidth <span class="math notranslate nohighlight">\(\sigma\)</span>. The <span class="math notranslate nohighlight">\(\sigma\)</span>
parameter is updated during the IWN optimization in order to
maximize the discriminative power of the MMD.</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>weighter</strong><span class="classifier">tensorflow Model (default=None)</span></dt><dd><p>Encoder netwok. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, a two layers network with 10
neurons per layer and ReLU activation is used as
weighter network.</p>
</dd>
<dt><strong>Xt</strong><span class="classifier">numpy array (default=None)</span></dt><dd><p>Target input data.</p>
</dd>
<dt><strong>pretrain</strong><span class="classifier">bool (default=True)</span></dt><dd><p>Weither to perform pretraining of the <code class="docutils literal notranslate"><span class="pre">weighter</span></code>
network or not. If True, the <code class="docutils literal notranslate"><span class="pre">weighter</span></code> is
pretrained in order to predict 1 for each source data.</p>
</dd>
<dt><strong>sigma_init</strong><span class="classifier">float (default=.1)</span></dt><dd><p>Initialization for the kernel bandwidth</p>
</dd>
<dt><strong>update_sigma</strong><span class="classifier">bool (default=True)</span></dt><dd><p>Weither to update the kernel bandwidth or not.
If <cite>False</cite>, the bandwidth stay equal to <cite>sigma_init</cite>.</p>
</dd>
<dt><strong>copy</strong><span class="classifier">boolean (default=True)</span></dt><dd><p>Whether to make a copy of <code class="docutils literal notranslate"><span class="pre">estimator</span></code> or not.</p>
</dd>
<dt><strong>verbose</strong><span class="classifier">int (default=1)</span></dt><dd><p>Verbosity level.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int (default=None)</span></dt><dd><p>Seed of random generator.</p>
</dd>
<dt><strong>params</strong><span class="classifier">key, value arguments</span></dt><dd><p>Arguments given at the different level of the adapt object.
It can be, for instance, compile or fit parameters of the
estimator or kernel parameters etc…
Accepted parameters can be found by calling the method
<code class="docutils literal notranslate"><span class="pre">_get_legal_params(params)</span></code>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Yields</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>optimizer</strong><span class="classifier">str or instance of tf.keras.optimizers (default=”rmsprop”)</span></dt><dd><p>Optimizer for the task. It should be an
instance of tf.keras.optimizers as:
<code class="docutils literal notranslate"><span class="pre">tf.keras.optimizers.SGD(0.001)</span></code> or
<code class="docutils literal notranslate"><span class="pre">tf.keras.optimizers.Adam(lr=0.001,</span> <span class="pre">beta_1=0.5)</span></code>.
A string can also be given as <code class="docutils literal notranslate"><span class="pre">"adam"</span></code>.
Default optimizer is <code class="docutils literal notranslate"><span class="pre">rmsprop</span></code>.</p>
</dd>
<dt><strong>loss</strong><span class="classifier">str or instance of tf.keras.losses (default=”mse”)</span></dt><dd><p>Loss for the task. It should be an
instance of tf.keras.losses as:
<code class="docutils literal notranslate"><span class="pre">tf.keras.losses.MeanSquaredError()</span></code> or
<code class="docutils literal notranslate"><span class="pre">tf.keras.losses.CategoricalCrossentropy()</span></code>.
A string can also be given as <code class="docutils literal notranslate"><span class="pre">"mse"</span></code> or
<code class="docutils literal notranslate"><span class="pre">categorical_crossentropy</span></code>.
Default loss is <code class="docutils literal notranslate"><span class="pre">mse</span></code>.</p>
</dd>
<dt><strong>metrics</strong><span class="classifier">list of str or list of tf.keras.metrics.Metric instance</span></dt><dd><p>List of metrics to be evaluated by the model during training
and testing. Typically you will use <code class="docutils literal notranslate"><span class="pre">metrics=['accuracy']</span></code>.</p>
</dd>
<dt><strong>optimizer_enc</strong><span class="classifier">str or instance of tf.keras.optimizers</span></dt><dd><p>If the Adapt Model has an <code class="docutils literal notranslate"><span class="pre">encoder</span></code> attribute,
a specific optimizer for the <code class="docutils literal notranslate"><span class="pre">encoder</span></code> network can
be given. Typically, this parameter can be used to
give a smaller learning rate to the encoder.
If not specified, <code class="docutils literal notranslate"><span class="pre">optimizer_enc=optimizer</span></code>.</p>
</dd>
<dt><strong>optimizer_disc</strong><span class="classifier">str or instance of tf.keras.optimizers</span></dt><dd><p>If the Adapt Model has a <code class="docutils literal notranslate"><span class="pre">discriminator</span></code> attribute,
a specific optimizer for the <code class="docutils literal notranslate"><span class="pre">discriminator</span></code> network can
be given. If not specified, <code class="docutils literal notranslate"><span class="pre">optimizer_disc=optimizer</span></code>.</p>
</dd>
<dt><strong>kwargs</strong><span class="classifier">key, value arguments</span></dt><dd><p>Any arguments of the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method from the Tensorflow
Model can be given, as <code class="docutils literal notranslate"><span class="pre">epochs</span></code> and <code class="docutils literal notranslate"><span class="pre">batch_size</span></code>.
Specific arguments from <code class="docutils literal notranslate"><span class="pre">optimizer</span></code> can also be given
as <code class="docutils literal notranslate"><span class="pre">learning_rate</span></code> or <code class="docutils literal notranslate"><span class="pre">beta_1</span></code> for <code class="docutils literal notranslate"><span class="pre">Adam</span></code>.
This allows to perform <code class="docutils literal notranslate"><span class="pre">GridSearchCV</span></code> from scikit-learn
on these arguments.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="adapt.instance_based.KMM.html#adapt.instance_based.KMM" title="adapt.instance_based.KMM"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KMM</span></code></a></dt><dd></dd>
<dt><a class="reference internal" href="adapt.instance_based.WANN.html#adapt.instance_based.WANN" title="adapt.instance_based.WANN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">WANN</span></code></a></dt><dd></dd>
</dl>
</div>
<p class="rubric">References</p>
<dl class="citation">
<dt class="label" id="r213e75091555-1"><span class="brackets">1</span></dt>
<dd><p><a class="reference external" href="https://arxiv.org/pdf/2209.04215.pdf">[1]</a> A. de Mathelin, F. Deheeger, M. Mougeot and N. Vayatis “Fast and Accurate Importance Weighting for Correcting Sample Bias” In ECML-PKDD, 2022.</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">IWN</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">IWN</span><span class="p">(</span><span class="n">RidgeClassifier</span><span class="p">(</span><span class="mf">0.</span><span class="p">),</span> <span class="n">Xt</span><span class="o">=</span><span class="n">Xt</span><span class="p">,</span> <span class="n">sigma_init</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">pretrain</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">pretrain__epochs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">pretrain__verbose</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="n">epochs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</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">0.78</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Attributes</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>weighter_</strong><span class="classifier">tensorflow Model</span></dt><dd><p>weighting network.</p>
</dd>
<dt><strong>history_</strong><span class="classifier">dict</span></dt><dd><p>history of the losses and metrics across the epochs.</p>
</dd>
<dt><strong>sigma_</strong><span class="classifier">tf.Variable</span></dt><dd><p>fitted kernel bandwidth.</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.IWN.__init__" title="adapt.instance_based.IWN.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([estimator, weighter, Xt, yt, ...])</p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#adapt.instance_based.IWN.compile" title="adapt.instance_based.IWN.compile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compile</span></code></a>([optimizer, loss, metrics, ...])</p></td>
<td><p>Configures the model for training.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#adapt.instance_based.IWN.fit" title="adapt.instance_based.IWN.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(X[, y, Xt, yt, domains, ...])</p></td>
<td><p>Fit Model.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#adapt.instance_based.IWN.fit_estimator" title="adapt.instance_based.IWN.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-odd"><td><p><a class="reference internal" href="#adapt.instance_based.IWN.fit_weights" title="adapt.instance_based.IWN.fit_weights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_weights</span></code></a>(Xs, Xt, **fit_params)</p></td>
<td><p>Fit importance weighting.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#adapt.instance_based.IWN.get_params" title="adapt.instance_based.IWN.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>([deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#adapt.instance_based.IWN.load_weights" title="adapt.instance_based.IWN.load_weights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_weights</span></code></a>(filepath[, skip_mismatch, ...])</p></td>
<td><p>Loads all layer weights from a saved files.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#adapt.instance_based.IWN.predict" title="adapt.instance_based.IWN.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</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#adapt.instance_based.IWN.predict_disc" title="adapt.instance_based.IWN.predict_disc"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_disc</span></code></a>(X)</p></td>
<td><p>Return predictions of the discriminator on the encoded features.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#adapt.instance_based.IWN.predict_task" title="adapt.instance_based.IWN.predict_task"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_task</span></code></a>(X)</p></td>
<td><p>Return predictions of the task on the encoded features.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#adapt.instance_based.IWN.predict_weights" title="adapt.instance_based.IWN.predict_weights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_weights</span></code></a>(X)</p></td>
<td><p>Return the predictions of weighting network</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#adapt.instance_based.IWN.pretrain_step" title="adapt.instance_based.IWN.pretrain_step"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pretrain_step</span></code></a>(data)</p></td>
<td><p></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#adapt.instance_based.IWN.save_weights" title="adapt.instance_based.IWN.save_weights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_weights</span></code></a>(filepath[, overwrite, ...])</p></td>
<td><p>Saves all layer weights.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#adapt.instance_based.IWN.score" title="adapt.instance_based.IWN.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(X, y[, sample_weight, domain])</p></td>
<td><p>Return the estimator score.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#adapt.instance_based.IWN.set_params" title="adapt.instance_based.IWN.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.IWN.transform" title="adapt.instance_based.IWN.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(X)</p></td>
<td><p>Return the encoded features of X.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#adapt.instance_based.IWN.unsupervised_score" title="adapt.instance_based.IWN.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.IWN.__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">weighter</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">pretrain</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">sigma_init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_sigma</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">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">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/_iwn.__init__.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.IWN.__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.IWN.compile">
<span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</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">loss</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">metrics</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">loss_weights</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">weighted_metrics</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">run_eagerly</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">steps_per_execution</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">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_iwn.compile.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.IWN.compile" title="Permalink to this definition"></a></dt>
<dd><p>Configures the model for training.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>optimizer: str or `tf.keras.optimizer` instance</strong></dt><dd><p>Optimizer</p>
</dd>
<dt><strong>loss: str or `tf.keras.losses.Loss` instance</strong></dt><dd><p>Loss function. A loss function is any callable
with the signature <cite>loss = fn(y_true, y_pred)</cite>,
where <cite>y_true</cite> are the ground truth values, and
<cite>y_pred</cite> are the model’s predictions.
<cite>y_true</cite> should have shape
<cite>(batch_size, d0, .. dN)</cite> (except in the case of
sparse loss functions such as
sparse categorical crossentropy which expects integer arrays of shape
<cite>(batch_size, d0, .. dN-1)</cite>).
<cite>y_pred</cite> should have shape <cite>(batch_size, d0, .. dN)</cite>.
The loss function should return a float tensor.
If a custom <cite>Loss</cite> instance is
used and reduction is set to <cite>None</cite>, return value has shape
<cite>(batch_size, d0, .. dN-1)</cite> i.e. per-sample or per-timestep loss
values; otherwise, it is a scalar. If the model has multiple outputs,
you can use a different loss on each output by passing a dictionary
or a list of losses. The loss value that will be minimized by the
model will then be the sum of all individual losses, unless
<cite>loss_weights</cite> is specified.</p>
</dd>
<dt><strong>metrics: list of str or list of `tf.keras.metrics.Metric` instance</strong></dt><dd><p>List of metrics to be evaluated by the model during training
and testing. Typically you will use <cite>metrics=[‘accuracy’]</cite>. A
function is any callable with the signature <cite>result = fn(y_true,
y_pred)</cite>. To specify different metrics for different outputs of a
multi-output model, you could also pass a dictionary, such as
<cite>metrics={‘output_a’: ‘accuracy’, ‘output_b’: [‘accuracy’, ‘mse’]}</cite>.
You can also pass a list to specify a metric or a list of metrics
for each output, such as <cite>metrics=[[‘accuracy’], [‘accuracy’, ‘mse’]]</cite>
or <cite>metrics=[‘accuracy’, [‘accuracy’, ‘mse’]]</cite>. When you pass the
strings ‘accuracy’ or ‘acc’, we convert this to one of
<cite>tf.keras.metrics.BinaryAccuracy</cite>,
<cite>tf.keras.metrics.CategoricalAccuracy</cite>,
<cite>tf.keras.metrics.SparseCategoricalAccuracy</cite> based on the loss
function used and the model output shape. We do a similar
conversion for the strings ‘crossentropy’ and ‘ce’ as well.</p>
</dd>
<dt><strong>loss_weights: List or dict of floats</strong></dt><dd><p>Scalars to weight the loss contributions of different model
outputs. The loss value that will be minimized by the model will then
be the <em>weighted sum</em> of all individual losses, weighted by the
<cite>loss_weights</cite> coefficients.
If a list, it is expected to have a 1:1 mapping to the model’s
outputs. If a dict, it is expected to map output names (strings)
to scalar coefficients.</p>
</dd>
<dt><strong>weighted_metrics: list of metrics</strong></dt><dd><p>List of metrics to be evaluated and weighted by
<cite>sample_weight</cite> or <cite>class_weight</cite> during training and testing.</p>
</dd>
<dt><strong>run_eagerly: bool (default=False)</strong></dt><dd><p>If <cite>True</cite>, this <cite>Model</cite>’s logic will not be wrapped
in a <cite>tf.function</cite>. Recommended to leave
this as <cite>None</cite> unless your <cite>Model</cite> cannot be run inside a
<cite>tf.function</cite>. <cite>run_eagerly=True</cite> is not supported when using
<cite>tf.distribute.experimental.ParameterServerStrategy</cite>.</p>
</dd>
<dt><strong>steps_per_execution: int (default=1)</strong></dt><dd><p>The number of batches to run during each
<cite>tf.function</cite> call. Running multiple batches
inside a single <cite>tf.function</cite> call can greatly improve performance
on TPUs or small models with a large Python overhead.
At most, one full epoch will be run each
execution. If a number larger than the size of the epoch is passed,
the execution will be truncated to the size of the epoch.
Note that if <cite>steps_per_execution</cite> is set to <cite>N</cite>,
<cite>Callback.on_batch_begin</cite> and <cite>Callback.on_batch_end</cite> methods
will only be called every <cite>N</cite> batches
(i.e. before/after each <cite>tf.function</cite> execution).</p>
</dd>
<dt><strong>**kwargs: key, value arguments</strong></dt><dd><p>Arguments supported for backwards compatibility only.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>None: None</dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.IWN.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><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">domains</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_params_estimator</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="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/_iwn.fit.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.IWN.fit" title="Permalink to this definition"></a></dt>
<dd><p>Fit Model. Note that <code class="docutils literal notranslate"><span class="pre">fit</span></code> does not reset
the model but extend the training.</p>
<p>Notice also that the compile method will be called
if the model has not been compiled yet.</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 or Tensor</span></dt><dd><p>Source input data.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array or Tensor (default=None)</span></dt><dd><p>Source output data.</p>
</dd>
<dt><strong>Xt</strong><span class="classifier">array (default=None)</span></dt><dd><p>Target input data. If None, the <cite>Xt</cite> argument
given in <cite>init</cite> is used.</p>
</dd>
<dt><strong>yt</strong><span class="classifier">array (default=None)</span></dt><dd><p>Target input data. Only needed for supervised
and semi-supervised Adapt model.
If None, the <cite>yt</cite> argument given in <cite>init</cite> is used.</p>
</dd>
<dt><strong>domains</strong><span class="classifier">array (default=None)</span></dt><dd><p>Vector giving the domain for each source
data. Can be used for multisource purpose.</p>
</dd>
<dt><strong>fit_params</strong><span class="classifier">key, value arguments</span></dt><dd><p>Arguments given to the fit method of the model
(epochs, batch_size, callbacks…).</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.IWN.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/_iwn.fit_estimator.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.IWN.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.IWN.fit_weights">
<span class="sig-name descname"><span class="pre">fit_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Xs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Xt</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">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/_iwn.fit_weights.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.IWN.fit_weights" title="Permalink to this definition"></a></dt>
<dd><p>Fit importance weighting.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>Xs</strong><span class="classifier">array</span></dt><dd><p>Input source data.</p>
</dd>
<dt><strong>Xt</strong><span class="classifier">array</span></dt><dd><p>Input target data.</p>
</dd>
<dt><strong>fit_params</strong><span class="classifier">key, value arguments</span></dt><dd><p>Arguments given to the fit method of the model
(epochs, batch_size, callbacks…).</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>weights_</strong><span class="classifier">sample weights</span></dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.IWN.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/_iwn.get_params.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.IWN.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.IWN.load_weights">
<span class="sig-name descname"><span class="pre">load_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filepath</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_mismatch</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">by_name</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">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_iwn.load_weights.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.IWN.load_weights" title="Permalink to this definition"></a></dt>
<dd><p>Loads all layer weights from a saved files.</p>
<p>The saved file could be a SavedModel file, a <cite>.keras</cite> file (v3 saving
format), or a file created via <cite>model.save_weights()</cite>.</p>
<p>By default, weights are loaded based on the network’s
topology. This means the architecture should be the same as when the
weights were saved. Note that layers that don’t have weights are not
taken into account in the topological ordering, so adding or removing
layers is fine as long as they don’t have weights.</p>
<p><strong>Partial weight loading</strong></p>
<p>If you have modified your model, for instance by adding a new layer
(with weights) or by changing the shape of the weights of a layer,
you can choose to ignore errors and continue loading
by setting <cite>skip_mismatch=True</cite>. In this case any layer with
mismatching weights will be skipped. A warning will be displayed
for each skipped layer.</p>
<p><strong>Weight loading by name</strong></p>
<p>If your weights are saved as a <cite>.h5</cite> file created
via <cite>model.save_weights()</cite>, you can use the argument <cite>by_name=True</cite>.</p>
<p>In this case, weights are loaded into layers only if they share
the same name. This is useful for fine-tuning or transfer-learning
models where some of the layers have changed.</p>
<p>Note that only topological loading (<cite>by_name=False</cite>) is supported when
loading weights from the <cite>.keras</cite> v3 format or from the TensorFlow
SavedModel format.</p>
<dl class="simple">
<dt>Args:</dt><dd><dl class="simple">
<dt>filepath: String, path to the weights file to load. For weight files</dt><dd><p>in TensorFlow format, this is the file prefix (the same as was
passed to <cite>save_weights()</cite>). This can also be a path to a
SavedModel or a <cite>.keras</cite> file (v3 saving format) saved
via <cite>model.save()</cite>.</p>
</dd>
<dt>skip_mismatch: Boolean, whether to skip loading of layers where</dt><dd><p>there is a mismatch in the number of weights, or a mismatch in
the shape of the weights.</p>
</dd>
<dt>by_name: Boolean, whether to load weights by name or by topological</dt><dd><p>order. Only topological loading is supported for weight files in
the <cite>.keras</cite> v3 format or in the TensorFlow SavedModel format.</p>
</dd>
<dt>options: Optional <cite>tf.train.CheckpointOptions</cite> object that specifies</dt><dd><p>options for loading weights (only valid for a SavedModel file).</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.IWN.predict">
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">domain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">predict_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_iwn.predict.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.IWN.predict" title="Permalink to this definition"></a></dt>
<dd><p>Return estimator predictions</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>Not used. For compatibility with <cite>adapt</cite> objects</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y_pred</strong><span class="classifier">array</span></dt><dd><p>prediction of the Adapt Model.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.IWN.predict_disc">
<span class="sig-name descname"><span class="pre">predict_disc</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/_iwn.predict_disc.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.IWN.predict_disc" title="Permalink to this definition"></a></dt>
<dd><p>Return predictions of the discriminator on the encoded features.</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_disc</strong><span class="classifier">array</span></dt><dd><p>predictions of discriminator network</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.IWN.predict_task">
<span class="sig-name descname"><span class="pre">predict_task</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/_iwn.predict_task.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.IWN.predict_task" title="Permalink to this definition"></a></dt>
<dd><p>Return predictions of the task on the encoded features.</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_task</strong><span class="classifier">array</span></dt><dd><p>predictions of task network</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.IWN.predict_weights">
<span class="sig-name descname"><span class="pre">predict_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_iwn.predict_weights.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.IWN.predict_weights" title="Permalink to this definition"></a></dt>
<dd><p>Return the predictions of weighting network</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X: array</strong></dt><dd><p>input data</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>array:</dt><dd><p>weights</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.IWN.pretrain_step">
<span class="sig-name descname"><span class="pre">pretrain_step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_iwn.pretrain_step.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.IWN.pretrain_step" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.IWN.save_weights">
<span class="sig-name descname"><span class="pre">save_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filepath</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</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">save_format</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">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_iwn.save_weights.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.IWN.save_weights" title="Permalink to this definition"></a></dt>
<dd><p>Saves all layer weights.</p>
<p>Either saves in HDF5 or in TensorFlow format based on the <cite>save_format</cite>
argument.</p>
<dl class="simple">
<dt>When saving in HDF5 format, the weight file has:</dt><dd><ul class="simple">
<li><dl class="simple">
<dt><cite>layer_names</cite> (attribute), a list of strings</dt><dd><p>(ordered names of model layers).</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>For every layer, a <cite>group</cite> named <cite>layer.name</cite></dt><dd><ul>
<li><dl class="simple">
<dt>For every such layer group, a group attribute <cite>weight_names</cite>,</dt><dd><p>a list of strings
(ordered names of weights tensor of the layer).</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>For every weight in the layer, a dataset</dt><dd><p>storing the weight value, named after the weight tensor.</p>
</dd>
</dl>
</li>
</ul>
</dd>
</dl>
</li>
</ul>
</dd>
</dl>
<p>When saving in TensorFlow format, all objects referenced by the network
are saved in the same format as <cite>tf.train.Checkpoint</cite>, including any
<cite>Layer</cite> instances or <cite>Optimizer</cite> instances assigned to object
attributes. For networks constructed from inputs and outputs using
<cite>tf.keras.Model(inputs, outputs)</cite>, <cite>Layer</cite> instances used by the network
are tracked/saved automatically. For user-defined classes which inherit
from <cite>tf.keras.Model</cite>, <cite>Layer</cite> instances must be assigned to object
attributes, typically in the constructor. See the documentation of
<cite>tf.train.Checkpoint</cite> and <cite>tf.keras.Model</cite> for details.</p>
<p>While the formats are the same, do not mix <cite>save_weights</cite> and
<cite>tf.train.Checkpoint</cite>. Checkpoints saved by <cite>Model.save_weights</cite> should
be loaded using <cite>Model.load_weights</cite>. Checkpoints saved using
<cite>tf.train.Checkpoint.save</cite> should be restored using the corresponding
<cite>tf.train.Checkpoint.restore</cite>. Prefer <cite>tf.train.Checkpoint</cite> over
<cite>save_weights</cite> for training checkpoints.</p>
<p>The TensorFlow format matches objects and variables by starting at a
root object, <cite>self</cite> for <cite>save_weights</cite>, and greedily matching attribute
names. For <cite>Model.save</cite> this is the <cite>Model</cite>, and for <cite>Checkpoint.save</cite>
this is the <cite>Checkpoint</cite> even if the <cite>Checkpoint</cite> has a model attached.
This means saving a <cite>tf.keras.Model</cite> using <cite>save_weights</cite> and loading
into a <cite>tf.train.Checkpoint</cite> with a <cite>Model</cite> attached (or vice versa)
will not match the <cite>Model</cite>’s variables. See the
[guide to training checkpoints](
<a class="reference external" href="https://www.tensorflow.org/guide/checkpoint">https://www.tensorflow.org/guide/checkpoint</a>) for details on
the TensorFlow format.</p>
<dl class="simple">
<dt>Args:</dt><dd><dl class="simple">
<dt>filepath: String or PathLike, path to the file to save the weights</dt><dd><p>to. When saving in TensorFlow format, this is the prefix used
for checkpoint files (multiple files are generated). Note that
the ‘.h5’ suffix causes weights to be saved in HDF5 format.</p>
</dd>
<dt>overwrite: Whether to silently overwrite any existing file at the</dt><dd><p>target location, or provide the user with a manual prompt.</p>
</dd>
<dt>save_format: Either ‘tf’ or ‘h5’. A <cite>filepath</cite> ending in ‘.h5’ or</dt><dd><p>‘.keras’ will default to HDF5 if <cite>save_format</cite> is <cite>None</cite>.
Otherwise, <cite>None</cite> becomes ‘tf’. Defaults to <cite>None</cite>.</p>
</dd>
<dt>options: Optional <cite>tf.train.CheckpointOptions</cite> object that specifies</dt><dd><p>options for saving weights.</p>
</dd>
</dl>
</dd>
<dt>Raises:</dt><dd><dl class="simple">
<dt>ImportError: If <cite>h5py</cite> is not available when attempting to save in</dt><dd><p>HDF5 format.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.IWN.score">
<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">domain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/antoinedemathelin/adapt/tree/master/adapt/instance_based/_iwn.score.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.IWN.score" title="Permalink to this definition"></a></dt>
<dd><p>Return the estimator score.</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>Not used.</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.IWN.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/_iwn.set_params.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.IWN.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.IWN.transform">
<span class="sig-name descname"><span class="pre">transform</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/_iwn.transform.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.IWN.transform" title="Permalink to this definition"></a></dt>
<dd><p>Return the encoded features of 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>X_enc</strong><span class="classifier">array</span></dt><dd><p>predictions of encoder network</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="adapt.instance_based.IWN.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/_iwn.unsupervised_score.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#adapt.instance_based.IWN.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>
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<p>© Copyright 2020, ADAPT team, Michelin and Centre Borelli, ENS Paris-Saclay.</p>
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