Update docs

Author: github-actions[bot]

docs/contents.html

@@ -83,12 +83,13 @@
 <li class="toctree-l2"><a class="reference internal" href="generated/adapt.parameter_based.RegularTransferLC.html">RegularTransferLC</a></li>
 <li class="toctree-l2"><a class="reference internal" href="generated/adapt.parameter_based.RegularTransferNN.html">RegularTransferNN</a></li>
 <li class="toctree-l2"><a class="reference internal" href="generated/adapt.parameter_based.FineTuning.html">FineTuning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/adapt.parameter_based.TransferTreeClassifier.html">TransferTreeClassifier</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="#adapt-metrics">Metrics</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="generated/adapt.metrics.make_uda_scorer.html">make_uda_scorer</a></li>
 <li class="toctree-l2"><a class="reference internal" href="generated/adapt.metrics.cov_distance.html">cov_distance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/adapt.metrics.j_score.html">j_score</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/adapt.metrics.neg_j_score.html">neg_j_score</a></li>
 <li class="toctree-l2"><a class="reference internal" href="generated/adapt.metrics.linear_discrepancy.html">linear_discrepancy</a></li>
 <li class="toctree-l2"><a class="reference internal" href="generated/adapt.metrics.normalized_linear_discrepancy.html">normalized_linear_discrepancy</a></li>
 <li class="toctree-l2"><a class="reference internal" href="generated/adapt.metrics.frechet_distance.html">frechet_distance</a></li>
@@ -98,6 +99,7 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="#adapt-utils">Utility Functions</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="generated/adapt.utils.UpdateLambda.html">UpdateLambda</a></li>
 <li class="toctree-l2"><a class="reference internal" href="generated/adapt.utils.accuracy.html">accuracy</a></li>
 <li class="toctree-l2"><a class="reference internal" href="generated/adapt.utils.check_arrays.html">check_arrays</a></li>
 <li class="toctree-l2"><a class="reference internal" href="generated/adapt.utils.check_estimator.html">check_estimator</a></li>
@@ -110,6 +112,8 @@
 <li class="toctree-l2"><a class="reference internal" href="generated/adapt.utils.make_regression_da.html">make_regression_da</a></li>
 <li class="toctree-l2"><a class="reference internal" href="generated/adapt.utils.check_sample_weight.html">check_sample_weight</a></li>
 <li class="toctree-l2"><a class="reference internal" href="generated/adapt.utils.set_random_seed.html">set_random_seed</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/adapt.utils.check_fitted_estimator.html">check_fitted_estimator</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/adapt.utils.check_fitted_network.html">check_fitted_network</a></li>
 </ul>
 </li>
 </ul>
@@ -372,6 +376,9 @@ <h1>ADAPT<a class="headerlink" href="#adapt" title="Permalink to this headline">
 <tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.parameter_based.FineTuning.html#adapt.parameter_based.FineTuning" title="adapt.parameter_based.FineTuning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameter_based.FineTuning</span></code></a>([encoder, task, ...])</p></td>
 <td><p>FineTuning : finetunes pretrained networks on target data.</p></td>
 </tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.parameter_based.TransferTreeClassifier.html#adapt.parameter_based.TransferTreeClassifier" title="adapt.parameter_based.TransferTreeClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameter_based.TransferTreeClassifier</span></code></a>([...])</p></td>
+<td><p>TBA</p></td>
+</tr>
 </tbody>
 </table>
 </section>
@@ -390,7 +397,7 @@ <h1>ADAPT<a class="headerlink" href="#adapt" title="Permalink to this headline">
 <tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.metrics.cov_distance.html#adapt.metrics.cov_distance" title="adapt.metrics.cov_distance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.cov_distance</span></code></a>(Xs, Xt)</p></td>
 <td><p>Compute the mean absolute difference between the covariance matrixes of Xs and Xt</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.metrics.j_score.html#adapt.metrics.j_score" title="adapt.metrics.j_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.j_score</span></code></a>(Xs, Xt[, max_centers, sigma])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.metrics.neg_j_score.html#adapt.metrics.neg_j_score" title="adapt.metrics.neg_j_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.neg_j_score</span></code></a>(Xs, Xt[, max_centers, sigma])</p></td>
 <td><p>Compute the negative J-score between Xs and Xt.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.metrics.linear_discrepancy.html#adapt.metrics.linear_discrepancy" title="adapt.metrics.linear_discrepancy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">metrics.linear_discrepancy</span></code></a>(Xs, Xt[, ...])</p></td>
@@ -423,42 +430,51 @@ <h1>ADAPT<a class="headerlink" href="#adapt" title="Permalink to this headline">
 <col style="width: 90%" />
 </colgroup>
 <tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.utils.accuracy.html#adapt.utils.accuracy" title="adapt.utils.accuracy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.accuracy</span></code></a>(y_true, y_pred)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.utils.UpdateLambda.html#adapt.utils.UpdateLambda" title="adapt.utils.UpdateLambda"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.UpdateLambda</span></code></a>([lambda_init, ...])</p></td>
+<td><p>Update Lambda trade-off</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.utils.accuracy.html#adapt.utils.accuracy" title="adapt.utils.accuracy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.accuracy</span></code></a>(y_true, y_pred)</p></td>
 <td><p>Custom accuracy function which can handle probas vector in both binary and multi classification</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.utils.check_arrays.html#adapt.utils.check_arrays" title="adapt.utils.check_arrays"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.check_arrays</span></code></a>(X, y)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.utils.check_arrays.html#adapt.utils.check_arrays" title="adapt.utils.check_arrays"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.check_arrays</span></code></a>(X, y)</p></td>
 <td><p>Check arrays and reshape 1D array in 2D array of shape (-1, 1).</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.utils.check_estimator.html#adapt.utils.check_estimator" title="adapt.utils.check_estimator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.check_estimator</span></code></a>([estimator, copy, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.utils.check_estimator.html#adapt.utils.check_estimator" title="adapt.utils.check_estimator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.check_estimator</span></code></a>([estimator, copy, ...])</p></td>
 <td><p>Check estimator.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.utils.check_network.html#adapt.utils.check_network" title="adapt.utils.check_network"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.check_network</span></code></a>(network[, copy, name, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.utils.check_network.html#adapt.utils.check_network" title="adapt.utils.check_network"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.check_network</span></code></a>(network[, copy, name, ...])</p></td>
 <td><p>Check if the given network is a tensorflow Model.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.utils.get_default_encoder.html#adapt.utils.get_default_encoder" title="adapt.utils.get_default_encoder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.get_default_encoder</span></code></a>([name])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.utils.get_default_encoder.html#adapt.utils.get_default_encoder" title="adapt.utils.get_default_encoder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.get_default_encoder</span></code></a>([name])</p></td>
 <td><p>Return a tensorflow Model of one layer with 10 neurons and a relu activation.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.utils.get_default_task.html#adapt.utils.get_default_task" title="adapt.utils.get_default_task"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.get_default_task</span></code></a>([activation, name])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.utils.get_default_task.html#adapt.utils.get_default_task" title="adapt.utils.get_default_task"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.get_default_task</span></code></a>([activation, name])</p></td>
 <td><p>Return a tensorflow Model of two hidden layers with 10 neurons each and relu activations.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.utils.get_default_discriminator.html#adapt.utils.get_default_discriminator" title="adapt.utils.get_default_discriminator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.get_default_discriminator</span></code></a>([name])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.utils.get_default_discriminator.html#adapt.utils.get_default_discriminator" title="adapt.utils.get_default_discriminator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.get_default_discriminator</span></code></a>([name])</p></td>
 <td><p>Return a tensorflow Model of two hidden layers with 10 neurons each and relu activations.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.utils.GradientHandler.html#adapt.utils.GradientHandler" title="adapt.utils.GradientHandler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.GradientHandler</span></code></a>(*args, **kwargs)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.utils.GradientHandler.html#adapt.utils.GradientHandler" title="adapt.utils.GradientHandler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.GradientHandler</span></code></a>(*args, **kwargs)</p></td>
 <td><p>Multiply gradients with a scalar during backpropagation.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.utils.make_classification_da.html#adapt.utils.make_classification_da" title="adapt.utils.make_classification_da"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.make_classification_da</span></code></a>([n_samples, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.utils.make_classification_da.html#adapt.utils.make_classification_da" title="adapt.utils.make_classification_da"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.make_classification_da</span></code></a>([n_samples, ...])</p></td>
 <td><p>Generate a classification dataset for DA.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.utils.make_regression_da.html#adapt.utils.make_regression_da" title="adapt.utils.make_regression_da"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.make_regression_da</span></code></a>([n_samples, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.utils.make_regression_da.html#adapt.utils.make_regression_da" title="adapt.utils.make_regression_da"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.make_regression_da</span></code></a>([n_samples, ...])</p></td>
 <td><p>Generate a regression dataset for DA.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.utils.check_sample_weight.html#adapt.utils.check_sample_weight" title="adapt.utils.check_sample_weight"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.check_sample_weight</span></code></a>(sample_weight, X)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.utils.check_sample_weight.html#adapt.utils.check_sample_weight" title="adapt.utils.check_sample_weight"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.check_sample_weight</span></code></a>(sample_weight, X)</p></td>
 <td><p>Check sample weights.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.utils.set_random_seed.html#adapt.utils.set_random_seed" title="adapt.utils.set_random_seed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.set_random_seed</span></code></a>(random_state)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.utils.set_random_seed.html#adapt.utils.set_random_seed" title="adapt.utils.set_random_seed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.set_random_seed</span></code></a>(random_state)</p></td>
 <td><p>Set random seed for numpy and  Tensorflow</p></td>
 </tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.utils.check_fitted_estimator.html#adapt.utils.check_fitted_estimator" title="adapt.utils.check_fitted_estimator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.check_fitted_estimator</span></code></a>(estimator)</p></td>
+<td><p>Check Fitted Estimator</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.utils.check_fitted_network.html#adapt.utils.check_fitted_network" title="adapt.utils.check_fitted_network"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utils.check_fitted_network</span></code></a>(estimator)</p></td>
+<td><p>Check Fitted Network</p></td>
+</tr>
 </tbody>
 </table>
 </section>

docs/examples/Classification.html

@@ -83,12 +83,13 @@
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.parameter_based.RegularTransferLC.html">RegularTransferLC</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.parameter_based.RegularTransferNN.html">RegularTransferNN</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.parameter_based.FineTuning.html">FineTuning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/adapt.parameter_based.TransferTreeClassifier.html">TransferTreeClassifier</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="../generated/adapt.metrics.make_uda_scorer.html">make_uda_scorer</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.metrics.cov_distance.html">cov_distance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../generated/adapt.metrics.j_score.html">j_score</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/adapt.metrics.neg_j_score.html">neg_j_score</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.metrics.linear_discrepancy.html">linear_discrepancy</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.metrics.normalized_linear_discrepancy.html">normalized_linear_discrepancy</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.metrics.frechet_distance.html">frechet_distance</a></li>
@@ -98,6 +99,7 @@
 </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="../generated/adapt.utils.UpdateLambda.html">UpdateLambda</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.utils.accuracy.html">accuracy</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.utils.check_arrays.html">check_arrays</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.utils.check_estimator.html">check_estimator</a></li>
@@ -110,6 +112,8 @@
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.utils.make_regression_da.html">make_regression_da</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.utils.check_sample_weight.html">check_sample_weight</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../generated/adapt.utils.set_random_seed.html">set_random_seed</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/adapt.utils.check_fitted_estimator.html">check_fitted_estimator</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../generated/adapt.utils.check_fitted_network.html">check_fitted_network</a></li>
 </ul>
 </li>
 </ul>
@@ -473,8 +477,8 @@
 </style>
 <section id="Classification">
 <h1>Classification<a class="headerlink" href="#Classification" title="Permalink to this headline"></a></h1>
-<div class="btn btn-notebook" role="button"><p><img alt="f6e3e691151b483e929e61b99dbcee1c" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1ANQUix9Y6V4RXu-vAaCFGmU979d5m4bO?usp=sharing">Run in Google Colab</a></p>
-</div><div class="btn btn-notebook" role="button"><p><img alt="812bc9d98efe46aeb33893afab590964" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Classification.ipynb">View on GitHub</a></p>
+<div class="btn btn-notebook" role="button"><p><img alt="a966f76b77f24009aa977763ca649bb9" src="../_images/colab_logo_32px.png" /> <a class="reference external" href="https://colab.research.google.com/drive/1ANQUix9Y6V4RXu-vAaCFGmU979d5m4bO?usp=sharing">Run in Google Colab</a></p>
+</div><div class="btn btn-notebook" role="button"><p><img alt="4335e175433742a2ad920bb842514876" src="../_images/github_logo_32px.png" /> <a class="reference external" href="https://github.com/adapt-python/notebooks/blob/d0364973c642ea4880756cef4e9f2ee8bb5e8495/Classification.ipynb">View on GitHub</a></p>
 </div><p>You will find here the application of DA methods from the ADAPT package on a simple two dimensional DA classification problem.</p>
 <p>First we import packages needed in the following. We will use <code class="docutils literal notranslate"><span class="pre">matplotlib</span> <span class="pre">Animation</span></code> tools in order to get a visual understanding of the mselected methods:</p>
 <div class="nbinput nblast docutils container">