From cfd0dca3d63761447b9a83be920a7ab5dd826fd2 Mon Sep 17 00:00:00 2001 From: kohlhaasrebecca <rebecca.kohlhaas@outlook.com> Date: Tue, 4 Mar 2025 11:27:13 +0100 Subject: [PATCH] Update files and add diagram --- ...rejection_sampler.RejectionSampler.doctree | Bin 0 -> 65262 bytes ....bayes_inference.rejection_sampler.doctree | Bin 0 -> 6298 bytes ...rogate_models.meta_model.MetaModel.doctree | Bin 0 -> 97029 bytes ...lidrox.surrogate_models.meta_model.doctree | Bin 0 -> 8237 bytes ...gate_models.meta_model.transform_y.doctree | Bin 0 -> 11511 bytes ...ce.rejection_sampler.RejectionSampler.html | 688 ++++++++++++ ...rox.bayes_inference.rejection_sampler.html | 484 +++++++++ ...surrogate_models.meta_model.MetaModel.html | 819 +++++++++++++++ ...svalidrox.surrogate_models.meta_model.html | 493 +++++++++ ...rrogate_models.meta_model.transform_y.html | 526 ++++++++++ ...rejection_sampler.RejectionSampler.rst.txt | 29 + ....bayes_inference.rejection_sampler.rst.txt | 31 + ...rogate_models.meta_model.MetaModel.rst.txt | 33 + ...lidrox.surrogate_models.meta_model.rst.txt | 38 + ...gate_models.meta_model.transform_y.rst.txt | 6 + docs/diagrams/.$Class overview.drawio.bkp | 983 ++---------------- .../.$GridManager_diagrams.drawio.bkp | 791 ++++++++++++++ .../.$GridManager_diagrams.drawio.dtmp | 371 +++++++ docs/diagrams/Class overview.drawio | 35 +- 19 files changed, 4431 insertions(+), 896 deletions(-) create mode 100644 docs/build/doctrees/_autosummary/bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.doctree create mode 100644 docs/build/doctrees/_autosummary/bayesvalidrox.bayes_inference.rejection_sampler.doctree create mode 100644 docs/build/doctrees/_autosummary/bayesvalidrox.surrogate_models.meta_model.MetaModel.doctree create mode 100644 docs/build/doctrees/_autosummary/bayesvalidrox.surrogate_models.meta_model.doctree create mode 100644 docs/build/doctrees/_autosummary/bayesvalidrox.surrogate_models.meta_model.transform_y.doctree 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py" id="bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.bayes_inference.rejection_sampler.</span></span><span class="sig-name descname"><span class="pre">RejectionSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">engine</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">discrepancy</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">observation</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">out_names</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">selected_indices</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">use_emulator</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">out_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prior_samples</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="headerlink" href="#bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler" title="Link to this definition">¶</a></dt> +<dd><p>Bases: <a class="reference internal" href="bayesvalidrox.bayes_inference.post_sampler.PostSampler.html#bayesvalidrox.bayes_inference.post_sampler.PostSampler" title="bayesvalidrox.bayes_inference.post_sampler.PostSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">PostSampler</span></code></a></p> +<p>A class for generating posterior samples via rejection sampling.</p> +<section id="attributes"> +<h2>Attributes<a class="headerlink" href="#attributes" title="Link to this heading">¶</a></h2> +<dl class="simple"> +<dt>engine<span class="classifier">object, optional</span></dt><dd><p>Trained bvr.Engine object. The default is None.</p> +</dd> +<dt>discrepancy<span class="classifier">object, optional</span></dt><dd><p>Object of class bvr.Discrepancy. The default is None.</p> +</dd> +<dt>observation<span class="classifier">dict, optional</span></dt><dd><p>Measurement/observation to use as reference. The default is None.</p> +</dd> +<dt>out_names<span class="classifier">list, optional</span></dt><dd><p>The list of requested output keys to be used for the analysis. +The default is <cite>None</cite>. If None, all the defined outputs from the engine +are used.</p> +</dd> +<dt>selected_indices<span class="classifier">dict, optional</span></dt><dd><p>A dictionary with the selected indices of each model output. The +default is <cite>None</cite>. If <cite>None</cite>, all measurement points are used in the +analysis.</p> +</dd> +<dt>use_emulator<span class="classifier">bool</span></dt><dd><p>Set to True if the emulator/metamodel should be used in the analysis. +If False, the model is run.</p> +</dd> +<dt>out_dir<span class="classifier">string, optional</span></dt><dd><p>The output directory. The default is ‘’.</p> +</dd> +<dt>prior_samples<span class="classifier">np.nddarray, optional</span></dt><dd><p>Prior samples to be used in the rejection sampling.</p> +</dd> +</dl> +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.__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">engine</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">discrepancy</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">observation</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">out_names</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">selected_indices</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">use_emulator</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">out_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prior_samples</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="headerlink" href="#bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.__init__" title="Link to this definition">¶</a></dt> +<dd></dd></dl> + +<p class="rubric">Methods</p> +<div class="table-wrapper autosummary longtable docutils container"> +<table class="autosummary longtable docutils align-default"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.__init__" title="bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([engine, discrepancy, observation, ...])</p></td> +<td><p></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.calculate_loglik_logbme" title="bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.calculate_loglik_logbme"><code class="xref py py-obj docutils literal notranslate"><span class="pre">calculate_loglik_logbme</span></code></a>(model_evals[, ...])</p></td> +<td><p>Calculate log-likelihoods and logbme on the perturbed data.</p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.calculate_valid_metrics" title="bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.calculate_valid_metrics"><code class="xref py py-obj docutils literal notranslate"><span class="pre">calculate_valid_metrics</span></code></a>([exp_design, parallel])</p></td> +<td><p>Calculate metrics including logBME, infEntropy, KLD.</p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.normpdf" title="bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.normpdf"><code class="xref py py-obj docutils literal notranslate"><span class="pre">normpdf</span></code></a>(outputs[, std_outputs, rmse])</p></td> +<td><p>Calculates the likelihood of simulation outputs compared with observation data.</p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.run_sampler" title="bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.run_sampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">run_sampler</span></code></a>([outputs, std_outputs, ...])</p></td> +<td><p>Performs rejection sampling to update the prior distribution on the input parameters.</p></td> +</tr> +</tbody> +</table> +</div> +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.calculate_loglik_logbme"> +<span class="sig-name descname"><span class="pre">calculate_loglik_logbme</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_evals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">surr_error</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">std_outputs</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> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">ndarray</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">ndarray</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.calculate_loglik_logbme" title="Link to this definition">¶</a></dt> +<dd><p>Calculate log-likelihoods and logbme on the perturbed data. +This function assumes everything as Gaussian.</p> +<section id="parameters"> +<h3>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h3> +<dl class="simple"> +<dt>model_evals<span class="classifier">dict</span></dt><dd><p>Model or metamodel outputs as a dictionary.</p> +</dd> +<dt>surr_error<span class="classifier">dict, optional</span></dt><dd><p>A dictionary containing the root mean squared error as array of +shape (n_samples, n_measurement) for each model output. The default +is None.</p> +</dd> +<dt>std_outputs<span class="classifier">dict of 2d np arrays, optional</span></dt><dd><p>Standard deviation (uncertainty) associated to the output. +The default is None.</p> +</dd> +</dl> +</section> +<section id="returns"> +<h3>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h3> +<dl class="simple"> +<dt>log_likelihood<span class="classifier">np.ndarray</span></dt><dd><p>The calculated loglikelihoods. +Size: (n_samples, n_bootstrap_itr).</p> +</dd> +<dt>log_bme<span class="classifier">np.ndarray</span></dt><dd><p>The log bme. This also accounts for metamodel error, if +self.use_emulator is True. Size: (1,n_bootstrap_itr).</p> +</dd> +</dl> +</section> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.calculate_valid_metrics"> +<span class="sig-name descname"><span class="pre">calculate_valid_metrics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">exp_design</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">parallel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">ndarray</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">ndarray</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.calculate_valid_metrics" title="Link to this definition">¶</a></dt> +<dd><p>Calculate metrics including logBME, infEntropy, KLD. +Code is taken from previous Engine and SeqDesign.</p> +<section id="id1"> +<h3>Parameters<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h3> +<dl class="simple"> +<dt>exp_design<span class="classifier">obj, optional</span></dt><dd><p>Object of class bvr.ExpDesigns. +The default is None.</p> +</dd> +<dt>parallel<span class="classifier">bool, optional</span></dt><dd><p>Use multiprocessing in calculation if set to True. +The default is False.</p> +</dd> +</dl> +</section> +<section id="id2"> +<h3>Returns<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h3> +<dl class="simple"> +<dt>kld<span class="classifier">np.ndarray</span></dt><dd><p>KLD</p> +</dd> +<dt>inf_entropy<span class="classifier">np.ndarray</span></dt><dd><p>Information entropy</p> +</dd> +</dl> +</section> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.normpdf"> +<span class="sig-name descname"><span class="pre">normpdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">outputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std_outputs</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">rmse</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> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">ndarray</span></span></span><a class="headerlink" href="#bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.normpdf" title="Link to this definition">¶</a></dt> +<dd><p>Calculates the likelihood of simulation outputs compared with +observation data.</p> +<section id="id3"> +<h3>Parameters<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h3> +<dl class="simple"> +<dt>outputs<span class="classifier">dict</span></dt><dd><p>The metamodel outputs as an array of shape +(n_samples, n_measurement) for each model output.</p> +</dd> +<dt>std_outputs<span class="classifier">dict of 2d np arrays, optional</span></dt><dd><p>Standard deviation (uncertainty) associated to the output. +The default is None.</p> +</dd> +<dt>rmse<span class="classifier">dict, optional</span></dt><dd><p>A dictionary containing the root mean squared error as array of +shape (n_samples, n_measurement) for each model output. The default +is None.</p> +</dd> +</dl> +</section> +<section id="id4"> +<h3>Returns<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h3> +<dl class="simple"> +<dt>logLik<span class="classifier">np.ndarray</span></dt><dd><p>Log-likelihoods. Shape: (n_samples)</p> +</dd> +</dl> +</section> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.run_sampler"> +<span class="sig-name descname"><span class="pre">run_sampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">outputs</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">std_outputs</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">surr_error</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">consider_samplesize</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">recalculate_loglik</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">DataFrame</span></span></span><a class="headerlink" href="#bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.run_sampler" title="Link to this definition">¶</a></dt> +<dd><p>Performs rejection sampling to update the prior distribution on the +input parameters. +If the likelihood is not given to the object, it will be calculated using the +additional inputs in this function.</p> +<section id="id5"> +<h3>Parameters<a class="headerlink" href="#id5" title="Link to this heading">¶</a></h3> +<dl class="simple"> +<dt>outputs<span class="classifier">dict, optional</span></dt><dd><p>The metamodel outputs as an array of shape +(n_samples, n_measurement) for each model output. +The default is None.</p> +</dd> +<dt>std_outputs<span class="classifier">dict of 2d np arrays, optional</span></dt><dd><p>Standard deviation (uncertainty) associated to the output. +The default is None.</p> +</dd> +<dt>surr_error<span class="classifier">dict, optional</span></dt><dd><p>A dictionary containing the root mean squared error as array of +shape (n_samples, n_measurement) for each model output. The default +is None.</p> +</dd> +<dt>consider_samplesize<span class="classifier">bool, optional</span></dt><dd><p>If set to True will stop the sampler if the effective sample size is +to small and return None. The default is False</p> +</dd> +<dt>recalculate_loglik<span class="classifier">bool, optional</span></dt><dd><p>If set to True will recalculate the log_likelihood, even if it is +already given. The default is False.</p> +</dd> +</dl> +</section> +<section id="raises"> +<h3>Raises<a class="headerlink" href="#raises" title="Link to this heading">¶</a></h3> +<p>AttributeError</p> +</section> +<section id="id6"> +<h3>Returns<a class="headerlink" href="#id6" title="Link to this heading">¶</a></h3> +<dl class="simple"> +<dt>posterior<span class="classifier">pd.DataFrame</span></dt><dd><p>Posterior samples of the input parameters.</p> +</dd> +</dl> +</section> +</dd></dl> + +</section> +</dd></dl> + +</section> + + </article> + </div> + <footer> + + <div class="related-pages"> + <a class="next-page" href="bayesvalidrox.post_processing.html"> + <div class="page-info"> + <div class="context"> + <span>Next</span> + </div> + <div class="title">bayesvalidrox.post_processing</div> + </div> + <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg> + </a> + <a class="prev-page" href="bayesvalidrox.bayes_inference.rejection_sampler.html"> + <svg 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href="#">bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler</a><ul> +<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler"><code class="docutils literal notranslate"><span class="pre">RejectionSampler</span></code></a><ul> +<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.__init__"><code class="docutils literal notranslate"><span class="pre">RejectionSampler.__init__()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.calculate_loglik_logbme"><code class="docutils literal notranslate"><span class="pre">RejectionSampler.calculate_loglik_logbme()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.calculate_valid_metrics"><code class="docutils literal notranslate"><span class="pre">RejectionSampler.calculate_valid_metrics()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.normpdf"><code class="docutils literal notranslate"><span class="pre">RejectionSampler.normpdf()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.run_sampler"><code class="docutils literal notranslate"><span class="pre">RejectionSampler.run_sampler()</span></code></a></li> +</ul> +</li> +</ul> +</li> +</ul> + + </div> + </div> + </div> + + + </aside> + </div> +</div><script src="../_static/documentation_options.js?v=79f5c508"></script> + <script src="../_static/doctools.js?v=9bcbadda"></script> + <script src="../_static/sphinx_highlight.js?v=dc90522c"></script> + <script src="../_static/scripts/furo.js?v=5fa4622c"></script> + </body> +</html> \ No newline at end of file diff --git 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+<p>PostSampler for rejection sampling. +# TODO: rename the file to rejection_sampler.py?</p> +<p class="rubric">Classes</p> +<div class="table-wrapper autosummary longtable docutils container"> +<table class="autosummary longtable docutils align-default"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.html#bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler" title="bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RejectionSampler</span></code></a>([engine, discrepancy, ...])</p></td> +<td><p>A class for generating posterior samples via rejection sampling.</p></td> +</tr> +</tbody> +</table> +</div> +</section> + + </article> + </div> + <footer> + + <div class="related-pages"> + <a class="next-page" href="bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.html"> + <div 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class="content-icon-container"> + <div class="view-this-page"> + <a class="muted-link" href="../_sources/_autosummary/bayesvalidrox.surrogate_models.meta_model.MetaModel.rst.txt" title="View this page"> + <svg><use href="#svg-eye"></use></svg> + <span class="visually-hidden">View this page</span> + </a> +</div> +<div class="theme-toggle-container theme-toggle-content"> + <button class="theme-toggle"> + <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div> + <svg class="theme-icon-when-auto-light"><use href="#svg-sun-with-moon"></use></svg> + <svg class="theme-icon-when-auto-dark"><use href="#svg-moon-with-sun"></use></svg> + <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg> + <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg> + </button> + </div> + <label class="toc-overlay-icon toc-content-icon" for="__toc"> + <div class="visually-hidden">Toggle table of contents sidebar</div> + <i class="icon"><svg><use href="#svg-toc"></use></svg></i> + </label> + </div> + <article role="main" id="furo-main-content"> + <section id="bayesvalidrox-surrogate-models-meta-model-metamodel"> +<h1>bayesvalidrox.surrogate_models.meta_model.MetaModel<a class="headerlink" href="#bayesvalidrox-surrogate-models-meta-model-metamodel" title="Link to this heading">¶</a></h1> +<dl class="py class"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.meta_model.</span></span><span class="sig-name descname"><span class="pre">MetaModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">meta_model_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fast'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bootstrap_itrs</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">dim_red_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_gaussian</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">verbose</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">n_mc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'user'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel" title="Link to this definition">¶</a></dt> +<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> +<p>Meta (surrogate) model base class</p> +<p>This class describes the necessary functions and propoerties of a +surrogate model in bayesvalidrox. It accepts an input object (input_obj) +containing the specification of the distributions for uncertain parameters.</p> +<section id="attributes"> +<h2>Attributes<a class="headerlink" href="#attributes" title="Link to this heading">¶</a></h2> +<dl> +<dt>input_obj<span class="classifier">obj</span></dt><dd><p>Input object with the information on the model input parameters.</p> +</dd> +<dt>bootstrap_method<span class="classifier">str</span></dt><dd><p>Bootstraping method. Options are <cite>‘normal’</cite> and <cite>‘fast’</cite>. The default +is <cite>‘fast’</cite>. It means that in each iteration except the first one, only +the coefficent are recalculated with the ordinary least square method.</p> +</dd> +<dt>n_bootstrap_itrs<span class="classifier">int</span></dt><dd><p>Number of iterations for the bootstrap sampling. The default is <cite>1</cite>.</p> +</dd> +<dt>dim_red_method<span class="classifier">str</span></dt><dd><p>Dimensionality reduction method for the output space. The available +method is based on principal component analysis (PCA). The Default is +<cite>‘no’</cite>. There are two ways to select number of components: use +percentage of the explainable variance threshold (between 0 and 100) +(Option A) or direct prescription of components’ number (Option B):</p> +<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">MetaModelOpts</span> <span class="o">=</span> <span class="n">MetaModel</span><span class="p">()</span> +<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">dim_red_method</span> <span class="o">=</span> <span class="s1">'PCA'</span> +<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">var_pca_threshold</span> <span class="o">=</span> <span class="mf">99.999</span> <span class="c1"># Option A</span> +<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">n_pca_components</span> <span class="o">=</span> <span class="mi">12</span> <span class="c1"># Option B</span> +</pre></div> +</div> +</dd> +<dt>is_gaussian<span class="classifier">bool</span></dt><dd><p>Set to True if the surrogate model returns mean and stdev. +The default is <cite>False</cite>.</p> +</dd> +<dt>verbose<span class="classifier">bool</span></dt><dd><p>Prints summary of the regression results. Default is <cite>False</cite>.</p> +</dd> +<dt>n_mc<span class="classifier">int</span></dt><dd><p>Number of Monte Carlo samples used for the calculation of the moments. Standard is 1000.</p> +</dd> +</dl> +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel.__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">input_obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">meta_model_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fast'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bootstrap_itrs</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">dim_red_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_gaussian</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">verbose</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">n_mc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'user'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.__init__" title="Link to this definition">¶</a></dt> +<dd></dd></dl> + +<p class="rubric">Methods</p> +<div class="table-wrapper autosummary longtable docutils container"> +<table class="autosummary longtable docutils align-default"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.__init__" title="bayesvalidrox.surrogate_models.meta_model.MetaModel.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(input_obj[, meta_model_type, ...])</p></td> +<td><p></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.add_input_space" title="bayesvalidrox.surrogate_models.meta_model.MetaModel.add_input_space"><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_input_space</span></code></a>()</p></td> +<td><p>Instanciates experimental design object.</p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.build_metamodel" title="bayesvalidrox.surrogate_models.meta_model.MetaModel.build_metamodel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_metamodel</span></code></a>()</p></td> +<td><p>Builds the parts for the metamodel (polynomes,...) that are needed before fitting.</p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.calculate_moments" title="bayesvalidrox.surrogate_models.meta_model.MetaModel.calculate_moments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">calculate_moments</span></code></a>()</p></td> +<td><p>Computes the first two moments of the metamodel.</p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.check_is_gaussian" title="bayesvalidrox.surrogate_models.meta_model.MetaModel.check_is_gaussian"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_is_gaussian</span></code></a>()</p></td> +<td><p>Check if the current surrogate will return both a mean and stdev as the output of being evaluated.</p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.copy_meta_model_opts" title="bayesvalidrox.surrogate_models.meta_model.MetaModel.copy_meta_model_opts"><code class="xref py py-obj docutils literal notranslate"><span class="pre">copy_meta_model_opts</span></code></a>()</p></td> +<td><p>This method is a convinient function to copy the metamodel options.</p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.eval_metamodel" title="bayesvalidrox.surrogate_models.meta_model.MetaModel.eval_metamodel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval_metamodel</span></code></a>(samples)</p></td> +<td><p>Evaluates metamodel at the requested samples.</p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.fit" title="bayesvalidrox.surrogate_models.meta_model.MetaModel.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(X, y[, parallel, verbose])</p></td> +<td><p>Fits the surrogate to the given data (samples X, outputs y).</p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.pca_transformation" title="bayesvalidrox.surrogate_models.meta_model.MetaModel.pca_transformation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pca_transformation</span></code></a>(target, n_pca_components)</p></td> +<td><p>Transforms the targets (outputs) via Principal Component Analysis.</p></td> +</tr> +</tbody> +</table> +</div> +<dl class="py class"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">AutoVivification</span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification" title="Link to this definition">¶</a></dt> +<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></p> +<p>Implementation of perl’s AutoVivification feature.</p> +<p>Source: <a class="reference external" href="https://stackoverflow.com/a/651879/18082457">https://stackoverflow.com/a/651879/18082457</a></p> +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.clear"> +<span class="sig-name descname"><span class="pre">clear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None.</span>  <span class="pre">Remove</span> <span class="pre">all</span> <span class="pre">items</span> <span class="pre">from</span> <span class="pre">D.</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.clear" title="Link to this definition">¶</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.copy"> +<span class="sig-name descname"><span class="pre">copy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">a</span> <span class="pre">shallow</span> <span class="pre">copy</span> <span class="pre">of</span> <span class="pre">D</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.copy" title="Link to this definition">¶</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.fromkeys"> +<span class="sig-name descname"><span class="pre">fromkeys</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</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></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.fromkeys" title="Link to this definition">¶</a></dt> +<dd><p>Create a new dictionary with keys from iterable and values set to value.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.get"> +<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</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></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.get" title="Link to this definition">¶</a></dt> +<dd><p>Return the value for key if key is in the dictionary, else default.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.items"> +<span class="sig-name descname"><span class="pre">items</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">a</span> <span class="pre">set-like</span> <span class="pre">object</span> <span class="pre">providing</span> <span class="pre">a</span> <span class="pre">view</span> <span class="pre">on</span> <span class="pre">D's</span> <span class="pre">items</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.items" title="Link to this definition">¶</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.keys"> +<span class="sig-name descname"><span class="pre">keys</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">a</span> <span class="pre">set-like</span> <span class="pre">object</span> <span class="pre">providing</span> <span class="pre">a</span> <span class="pre">view</span> <span class="pre">on</span> <span class="pre">D's</span> <span class="pre">keys</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.keys" title="Link to this definition">¶</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.pop"> +<span class="sig-name descname"><span class="pre">pop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">k</span></span></em><span class="optional">[</span>, <em class="sig-param"><span class="n"><span class="pre">d</span></span></em><span class="optional">]</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">v,</span> <span class="pre">remove</span> <span class="pre">specified</span> <span class="pre">key</span> <span class="pre">and</span> <span class="pre">return</span> <span class="pre">the</span> <span class="pre">corresponding</span> <span class="pre">value.</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.pop" title="Link to this definition">¶</a></dt> +<dd><p>If the key is not found, return the default if given; otherwise, +raise a KeyError.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.popitem"> +<span class="sig-name descname"><span class="pre">popitem</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.popitem" title="Link to this definition">¶</a></dt> +<dd><p>Remove and return a (key, value) pair as a 2-tuple.</p> +<p>Pairs are returned in LIFO (last-in, first-out) order. +Raises KeyError if the dict is empty.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.setdefault"> +<span class="sig-name descname"><span class="pre">setdefault</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</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></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.setdefault" title="Link to this definition">¶</a></dt> +<dd><p>Insert key with a value of default if key is not in the dictionary.</p> +<p>Return the value for key if key is in the dictionary, else default.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.update"> +<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="optional">[</span><em class="sig-param"><span class="n"><span class="pre">E</span></span></em>, <span class="optional">]</span><em class="sig-param"><span class="n"><span class="pre">**F</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None.</span>  <span class="pre">Update</span> <span class="pre">D</span> <span class="pre">from</span> <span class="pre">dict/iterable</span> <span class="pre">E</span> <span class="pre">and</span> <span class="pre">F.</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.update" title="Link to this definition">¶</a></dt> +<dd><p>If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] +If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v +In either case, this is followed by: for k in F: D[k] = F[k]</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.values"> +<span class="sig-name descname"><span class="pre">values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">an</span> <span class="pre">object</span> <span class="pre">providing</span> <span class="pre">a</span> <span class="pre">view</span> <span class="pre">on</span> <span class="pre">D's</span> <span class="pre">values</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.values" title="Link to this definition">¶</a></dt> +<dd></dd></dl> + +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel.add_input_space"> +<span class="sig-name descname"><span class="pre">add_input_space</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.add_input_space" title="Link to this definition">¶</a></dt> +<dd><p>Instanciates experimental design object.</p> +<section id="returns"> +<h3>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h3> +<p>None.</p> +</section> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel.build_metamodel"> +<span class="sig-name descname"><span class="pre">build_metamodel</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.build_metamodel" title="Link to this definition">¶</a></dt> +<dd><p>Builds the parts for the metamodel (polynomes,…) that are needed +before fitting.</p> +<p>This function should be extended and called in/before training the +surrogate child classes!</p> +<section id="id1"> +<h3>Returns<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h3> +<p>None</p> +</section> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel.calculate_moments"> +<span class="sig-name descname"><span class="pre">calculate_moments</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.calculate_moments" title="Link to this definition">¶</a></dt> +<dd><p>Computes the first two moments of the metamodel.</p> +<section id="id2"> +<h3>Returns<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h3> +<dl class="simple"> +<dt>means: dict</dt><dd><p>The first moment (mean) of the surrogate.</p> +</dd> +<dt>stds: dict</dt><dd><p>The second moment (standard deviation) of the surrogate.</p> +</dd> +</dl> +</section> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel.check_is_gaussian"> +<span class="sig-name descname"><span class="pre">check_is_gaussian</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.check_is_gaussian" title="Link to this definition">¶</a></dt> +<dd><p>Check if the current surrogate will return both a mean and stdev as +the output of being evaluated.</p> +<p>This function should be extended and applied in the constructor of +all child classes!</p> +<section id="id3"> +<h3>Returns<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h3> +<dl class="simple"> +<dt>bool</dt><dd><p>True if stdev is also returned.</p> +</dd> +</dl> +</section> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel.copy_meta_model_opts"> +<span class="sig-name descname"><span class="pre">copy_meta_model_opts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.copy_meta_model_opts" title="Link to this definition">¶</a></dt> +<dd><p>This method is a convinient function to copy the metamodel options.</p> +<section id="id4"> +<h3>Returns<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h3> +<dl class="simple"> +<dt>metamod_copy<span class="classifier">object</span></dt><dd><p>The copied object.</p> +</dd> +</dl> +</section> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel.eval_metamodel"> +<span class="sig-name descname"><span class="pre">eval_metamodel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">samples</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.eval_metamodel" title="Link to this definition">¶</a></dt> +<dd><p>Evaluates metamodel at the requested samples. One can also generate +nsamples.</p> +<section id="parameters"> +<h3>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h3> +<dl class="simple"> +<dt>samples<span class="classifier">array of shape (n_samples, ndim), optional</span></dt><dd><p>Samples to evaluate metamodel at. The default is None.</p> +</dd> +</dl> +</section> +<section id="id5"> +<h3>Returns<a class="headerlink" href="#id5" title="Link to this heading">¶</a></h3> +<dl class="simple"> +<dt>mean_pred<span class="classifier">dict</span></dt><dd><p>Mean of the predictions.</p> +</dd> +<dt>std_pred<span class="classifier">dict</span></dt><dd><p>Standard deviatioon of the predictions. Return None if +self.is_gaussian == False</p> +</dd> +</dl> +</section> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel.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><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parallel</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">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.fit" title="Link to this definition">¶</a></dt> +<dd><p>Fits the surrogate to the given data (samples X, outputs y). +Note here that the samples X should be the transformed samples provided +by the experimental design if the transformation is used there.</p> +<section id="id6"> +<h3>Parameters<a class="headerlink" href="#id6" title="Link to this heading">¶</a></h3> +<dl class="simple"> +<dt>X<span class="classifier">2D list or np.array of shape (#samples, #dim)</span></dt><dd><p>The parameter value combinations that the model was evaluated at.</p> +</dd> +<dt>y<span class="classifier">dict of 2D lists or arrays of shape (#samples, #timesteps)</span></dt><dd><p>The respective model evaluations.</p> +</dd> +<dt>parallel<span class="classifier">bool</span></dt><dd><p>Set to True to run the training in parallel for various keys. +The default is False.</p> +</dd> +<dt>verbose<span class="classifier">bool</span></dt><dd><p>Set to True to obtain more information during runtime. +The default is False.</p> +</dd> +</dl> +</section> +<section id="id7"> +<h3>Returns<a class="headerlink" href="#id7" title="Link to this heading">¶</a></h3> +<p>None.</p> +</section> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.meta_model.MetaModel.pca_transformation"> +<span class="sig-name descname"><span class="pre">pca_transformation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_pca_components</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.pca_transformation" title="Link to this definition">¶</a></dt> +<dd><p>Transforms the targets (outputs) via Principal Component Analysis. +The number of features is set by <cite>self.n_pca_components</cite>. +If this is not given, <cite>self.var_pca_threshold</cite> is used as a threshold.</p> +<p>ToDo: Check the inputs needed for this class, there is an error when PCA is used. +ToDo: From the y_transformation() function, a dictionary is being sent +instead of an array for target.</p> +<section id="id8"> +<h3>Parameters<a class="headerlink" href="#id8" title="Link to this heading">¶</a></h3> +<dl class="simple"> +<dt>target<span class="classifier">array of shape (n_samples,)</span></dt><dd><p>Target values.</p> +</dd> +</dl> +</section> +<section id="id9"> +<h3>Returns<a class="headerlink" href="#id9" title="Link to this heading">¶</a></h3> +<dl class="simple"> +<dt>pca<span class="classifier">obj</span></dt><dd><p>Fitted sklearnPCA object.</p> +</dd> +<dt>OutputMatrix<span class="classifier">array of shape (n_samples,)</span></dt><dd><p>Transformed target values.</p> +</dd> +<dt>n_pca_components<span class="classifier">int</span></dt><dd><p>Number of selected principal components.</p> +</dd> +</dl> +</section> +</dd></dl> + +</section> +</dd></dl> + +</section> + + </article> + </div> + <footer> + + <div class="related-pages"> + <a class="next-page" href="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html"> + <div class="page-info"> + <div class="context"> + <span>Next</span> + </div> + <div class="title">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</div> + </div> + <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg> + </a> + <a class="prev-page" href="bayesvalidrox.surrogate_models.meta_model.transform_y.html"> + <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg> + <div class="page-info"> + <div class="context"> + <span>Previous</span> + </div> + + <div class="title">bayesvalidrox.surrogate_models.meta_model.transform_y</div> + + </div> + </a> + </div> + <div class="bottom-of-page"> + <div class="left-details"> + <div class="copyright"> + Copyright © 2023, Farid Mohammadi, Rebecca Kohlhaas + </div> + Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s + + <a href="https://github.com/pradyunsg/furo">Furo</a> + + </div> + <div class="right-details"> + + </div> + </div> + + </footer> + </div> + <aside class="toc-drawer"> + + + <div class="toc-sticky toc-scroll"> + <div class="toc-title-container"> + <span class="toc-title"> + On this page + </span> + </div> + <div class="toc-tree-container"> + <div class="toc-tree"> + <ul> +<li><a class="reference internal" href="#">bayesvalidrox.surrogate_models.meta_model.MetaModel</a><ul> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel"><code class="docutils literal notranslate"><span class="pre">MetaModel</span></code></a><ul> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.__init__"><code class="docutils literal notranslate"><span class="pre">MetaModel.__init__()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification"><code class="docutils literal notranslate"><span class="pre">MetaModel.AutoVivification</span></code></a><ul> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.clear"><code class="docutils literal notranslate"><span class="pre">MetaModel.AutoVivification.clear()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.copy"><code class="docutils literal notranslate"><span class="pre">MetaModel.AutoVivification.copy()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.fromkeys"><code class="docutils literal notranslate"><span class="pre">MetaModel.AutoVivification.fromkeys()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.get"><code class="docutils literal notranslate"><span class="pre">MetaModel.AutoVivification.get()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.items"><code class="docutils literal notranslate"><span class="pre">MetaModel.AutoVivification.items()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.keys"><code class="docutils literal notranslate"><span class="pre">MetaModel.AutoVivification.keys()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.pop"><code class="docutils literal notranslate"><span class="pre">MetaModel.AutoVivification.pop()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.popitem"><code class="docutils literal notranslate"><span class="pre">MetaModel.AutoVivification.popitem()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.setdefault"><code class="docutils literal notranslate"><span class="pre">MetaModel.AutoVivification.setdefault()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.update"><code class="docutils literal notranslate"><span class="pre">MetaModel.AutoVivification.update()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.AutoVivification.values"><code class="docutils literal notranslate"><span class="pre">MetaModel.AutoVivification.values()</span></code></a></li> +</ul> +</li> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.add_input_space"><code class="docutils literal notranslate"><span class="pre">MetaModel.add_input_space()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.build_metamodel"><code class="docutils literal notranslate"><span class="pre">MetaModel.build_metamodel()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.calculate_moments"><code class="docutils literal notranslate"><span class="pre">MetaModel.calculate_moments()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.check_is_gaussian"><code class="docutils literal notranslate"><span class="pre">MetaModel.check_is_gaussian()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.copy_meta_model_opts"><code class="docutils literal notranslate"><span class="pre">MetaModel.copy_meta_model_opts()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.eval_metamodel"><code class="docutils literal notranslate"><span class="pre">MetaModel.eval_metamodel()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.fit"><code class="docutils literal notranslate"><span class="pre">MetaModel.fit()</span></code></a></li> +<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.meta_model.MetaModel.pca_transformation"><code class="docutils literal notranslate"><span class="pre">MetaModel.pca_transformation()</span></code></a></li> +</ul> +</li> +</ul> +</li> +</ul> + + </div> + </div> + </div> + + + </aside> + </div> +</div><script src="../_static/documentation_options.js?v=79f5c508"></script> + <script src="../_static/doctools.js?v=9bcbadda"></script> + <script src="../_static/sphinx_highlight.js?v=dc90522c"></script> + <script src="../_static/scripts/furo.js?v=5fa4622c"></script> + </body> +</html> \ No newline at end of file diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.meta_model.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.meta_model.html new file mode 100644 index 000000000..2cca37ef4 --- /dev/null +++ b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.meta_model.html @@ -0,0 +1,493 @@ +<!doctype html> +<html class="no-js" lang="en" data-content_root="../"> + <head><meta charset="utf-8"/> + <meta name="viewport" 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trafo_type])</p></td> +<td><p>Apply chosen transformation to model outputs per key.</p></td> +</tr> +</tbody> +</table> +</div> +<p class="rubric">Classes</p> +<div class="table-wrapper autosummary longtable docutils container"> +<table class="autosummary longtable docutils align-default"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.meta_model.MetaModel.html#bayesvalidrox.surrogate_models.meta_model.MetaModel" title="bayesvalidrox.surrogate_models.meta_model.MetaModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MetaModel</span></code></a>(input_obj[, meta_model_type, ...])</p></td> +<td><p>Meta (surrogate) model base class</p></td> +</tr> +</tbody> +</table> +</div> +</section> + + </article> + </div> + <footer> + + <div class="related-pages"> + <a class="next-page" href="bayesvalidrox.surrogate_models.meta_model.transform_y.html"> + <div class="page-info"> + <div class="context"> + 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id="bayesvalidrox.surrogate_models.meta_model.transform_y"> +<span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.meta_model.</span></span><span class="sig-name descname"><span class="pre">transform_y</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</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">b_i</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trafo_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.meta_model.transform_y" title="Link to this definition">¶</a></dt> +<dd><p>Apply chosen transformation to model outputs per key. +Currently supports no transform, bootstrap and PCA.</p> +<section id="parameters"> +<h2>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2> +<dl> +<dt>self<span class="classifier">object</span></dt><dd><p>An object of class MetaModel that contains needed parameters.</p> +</dd> +<dt>y<span class="classifier">dict</span></dt><dd><p>Output to transform, should contain arrays for each key.</p> +</dd> +<dt>b_i<span class="classifier">int</span></dt><dd><p>Current bootstrap index. This is used in PCA-transformation to use +the same number of PCA components in each bootstrap iteration. +The default is 0.</p> +</dd> +<dt>trafo_type<span class="classifier">string</span></dt><dd><p>The type of transformation to apply. Currently supported are +‘bootstrap’: Bootstrap each key with self.b_indices +‘pca’: Principal Component Analysis. The transformation has to be</p> +<blockquote> +<div><p>available in self.</p> +</div></blockquote> +<p>‘’: No transformation</p> +</dd> +</dl> +</section> +<section id="returns"> +<h2>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h2> +<dl class="simple"> +<dt>y_transform<span class="classifier">dict</span></dt><dd><p>Transformed outputs.</p> +</dd> +</dl> +</section> +</dd></dl> + +</section> + + </article> + </div> + <footer> + + <div class="related-pages"> + <a class="next-page" href="bayesvalidrox.surrogate_models.meta_model.MetaModel.html"> + <div class="page-info"> + <div class="context"> + <span>Next</span> + </div> + <div class="title">bayesvalidrox.surrogate_models.meta_model.MetaModel</div> + </div> + <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg> + </a> + <a class="prev-page" href="bayesvalidrox.surrogate_models.meta_model.html"> + <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg> + <div 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href="#bayesvalidrox.surrogate_models.meta_model.transform_y"><code class="docutils literal notranslate"><span class="pre">transform_y()</span></code></a></li> +</ul> +</li> +</ul> + + </div> + </div> + </div> + + + </aside> + </div> +</div><script src="../_static/documentation_options.js?v=79f5c508"></script> + <script src="../_static/doctools.js?v=9bcbadda"></script> + <script src="../_static/sphinx_highlight.js?v=dc90522c"></script> + <script src="../_static/scripts/furo.js?v=5fa4622c"></script> + </body> +</html> \ No newline at end of file diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.rst.txt new file mode 100644 index 000000000..25b944078 --- /dev/null +++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.rst.txt @@ -0,0 +1,29 @@ +bayesvalidrox.bayes\_inference.rejection\_sampler.RejectionSampler +================================================================== + +.. currentmodule:: bayesvalidrox.bayes_inference.rejection_sampler + +.. autoclass:: RejectionSampler + :members: + :show-inheritance: + :inherited-members: + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~RejectionSampler.__init__ + ~RejectionSampler.calculate_loglik_logbme + ~RejectionSampler.calculate_valid_metrics + ~RejectionSampler.normpdf + ~RejectionSampler.run_sampler + + + + + + \ No newline at end of file diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.rejection_sampler.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.rejection_sampler.rst.txt new file mode 100644 index 000000000..57fa1a109 --- /dev/null +++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.rejection_sampler.rst.txt @@ -0,0 +1,31 @@ +bayesvalidrox.bayes\_inference.rejection\_sampler +================================================= + +.. automodule:: bayesvalidrox.bayes_inference.rejection_sampler + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom-class-template.rst + + RejectionSampler + + + + + + + + + diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.meta_model.MetaModel.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.meta_model.MetaModel.rst.txt new file mode 100644 index 000000000..719b2aa41 --- /dev/null +++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.meta_model.MetaModel.rst.txt @@ -0,0 +1,33 @@ +bayesvalidrox.surrogate\_models.meta\_model.MetaModel +===================================================== + +.. currentmodule:: bayesvalidrox.surrogate_models.meta_model + +.. autoclass:: MetaModel + :members: + :show-inheritance: + :inherited-members: + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~MetaModel.__init__ + ~MetaModel.add_input_space + ~MetaModel.build_metamodel + ~MetaModel.calculate_moments + ~MetaModel.check_is_gaussian + ~MetaModel.copy_meta_model_opts + ~MetaModel.eval_metamodel + ~MetaModel.fit + ~MetaModel.pca_transformation + + + + + + \ No newline at end of file diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.meta_model.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.meta_model.rst.txt new file mode 100644 index 000000000..a8dd02d50 --- /dev/null +++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.meta_model.rst.txt @@ -0,0 +1,38 @@ +bayesvalidrox.surrogate\_models.meta\_model +=========================================== + +.. automodule:: bayesvalidrox.surrogate_models.meta_model + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + + transform_y + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom-class-template.rst + + MetaModel + + + + + + + + + diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.meta_model.transform_y.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.meta_model.transform_y.rst.txt new file mode 100644 index 000000000..892c64401 --- /dev/null +++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.meta_model.transform_y.rst.txt @@ -0,0 +1,6 @@ +bayesvalidrox.surrogate\_models.meta\_model.transform\_y +======================================================== + +.. currentmodule:: bayesvalidrox.surrogate_models.meta_model + +.. autofunction:: transform_y \ No newline at end of file diff --git a/docs/diagrams/.$Class overview.drawio.bkp b/docs/diagrams/.$Class overview.drawio.bkp index d71ef7225..8336e807b 100644 --- a/docs/diagrams/.$Class overview.drawio.bkp +++ b/docs/diagrams/.$Class overview.drawio.bkp @@ -1,962 +1,191 @@ -<mxfile 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