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 ++++++++++++++
.../.$GridManager_diagrams.drawio.dtmp | 371 +++++++
docs/diagrams/Class overview.drawio | 35 +-
19 files changed, 4431 insertions(+), 896 deletions(-)
create mode 100644 docs/build/doctrees/_autosummary/bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.doctree
create mode 100644 docs/build/doctrees/_autosummary/bayesvalidrox.bayes_inference.rejection_sampler.doctree
create mode 100644 docs/build/doctrees/_autosummary/bayesvalidrox.surrogate_models.meta_model.MetaModel.doctree
create mode 100644 docs/build/doctrees/_autosummary/bayesvalidrox.surrogate_models.meta_model.doctree
create mode 100644 docs/build/doctrees/_autosummary/bayesvalidrox.surrogate_models.meta_model.transform_y.doctree
create mode 100644 docs/build/html/_autosummary/bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.html
create mode 100644 docs/build/html/_autosummary/bayesvalidrox.bayes_inference.rejection_sampler.html
create mode 100644 docs/build/html/_autosummary/bayesvalidrox.surrogate_models.meta_model.MetaModel.html
create mode 100644 docs/build/html/_autosummary/bayesvalidrox.surrogate_models.meta_model.html
create mode 100644 docs/build/html/_autosummary/bayesvalidrox.surrogate_models.meta_model.transform_y.html
create mode 100644 docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.rst.txt
create mode 100644 docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.rejection_sampler.rst.txt
create mode 100644 docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.meta_model.MetaModel.rst.txt
create mode 100644 docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.meta_model.rst.txt
create mode 100644 docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.meta_model.transform_y.rst.txt
create mode 100644 docs/diagrams/.$GridManager_diagrams.drawio.bkp
create mode 100644 docs/diagrams/.$GridManager_diagrams.drawio.dtmp
diff --git a/docs/build/doctrees/_autosummary/bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.doctree b/docs/build/doctrees/_autosummary/bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler.doctree
new file mode 100644
index 0000000000000000000000000000000000000000..2b1d08e16957a0c30b25d339e695ae009a4d3759
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+ <section id="bayesvalidrox-bayes-inference-rejection-sampler-rejectionsampler">
+<h1>bayesvalidrox.bayes_inference.rejection_sampler.RejectionSampler<a class="headerlink" href="#bayesvalidrox-bayes-inference-rejection-sampler-rejectionsampler" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object 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>
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+ <ul>
+<li><a class="reference internal" 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>
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+<span id="bayesvalidrox-bayes-inference-rejection-sampler"></span><h1>bayesvalidrox.bayes_inference.rejection_sampler<a class="headerlink" href="#module-bayesvalidrox.bayes_inference.rejection_sampler" title="Link to this heading">¶</a></h1>
+<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>
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+ <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>
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+ <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>
+
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+ <section id="module-bayesvalidrox.surrogate_models.meta_model">
+<span id="bayesvalidrox-surrogate-models-meta-model"></span><h1>bayesvalidrox.surrogate_models.meta_model<a class="headerlink" href="#module-bayesvalidrox.surrogate_models.meta_model" title="Link to this heading">¶</a></h1>
+<p>Implementation of metamodel as either PC, aPC or GPE</p>
+<p class="rubric">Functions</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.transform_y.html#bayesvalidrox.surrogate_models.meta_model.transform_y" title="bayesvalidrox.surrogate_models.meta_model.transform_y"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform_y</span></code></a>(self, y[, b_i, 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>
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+<li class="toctree-l4 has-children"><a class="reference internal" href="bayesvalidrox.surrogate_models.gaussian_process_sklearn.html">bayesvalidrox.surrogate_models.gaussian_process_sklearn</a><input class="toctree-checkbox" id="toctree-checkbox-23" name="toctree-checkbox-23" role="switch" type="checkbox"/><label for="toctree-checkbox-23"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.gaussian_process_sklearn</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+<li class="toctree-l5"><a class="reference internal" href="bayesvalidrox.surrogate_models.gaussian_process_sklearn.MySklGPE.html">bayesvalidrox.surrogate_models.gaussian_process_sklearn.MySklGPE</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="bayesvalidrox.surrogate_models.glexindex.html">bayesvalidrox.surrogate_models.glexindex</a><input class="toctree-checkbox" id="toctree-checkbox-24" name="toctree-checkbox-24" role="switch" type="checkbox"/><label for="toctree-checkbox-24"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.glexindex</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="bayesvalidrox.surrogate_models.glexindex.cross_truncate.html">bayesvalidrox.surrogate_models.glexindex.cross_truncate</a></li>
+<li class="toctree-l5"><a class="reference internal" href="bayesvalidrox.surrogate_models.glexindex.glexindex.html">bayesvalidrox.surrogate_models.glexindex.glexindex</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="bayesvalidrox.surrogate_models.input_space.html">bayesvalidrox.surrogate_models.input_space</a><input class="toctree-checkbox" id="toctree-checkbox-25" name="toctree-checkbox-25" role="switch" type="checkbox"/><label for="toctree-checkbox-25"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.input_space</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="bayesvalidrox.surrogate_models.inputs.html">bayesvalidrox.surrogate_models.inputs</a><input class="toctree-checkbox" id="toctree-checkbox-26" name="toctree-checkbox-26" role="switch" type="checkbox"/><label for="toctree-checkbox-26"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.inputs</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+<li class="toctree-l5"><a class="reference internal" href="bayesvalidrox.surrogate_models.inputs.Marginal.html">bayesvalidrox.surrogate_models.inputs.Marginal</a></li>
+</ul>
+</li>
+<li class="toctree-l4 current has-children"><a class="reference internal" href="bayesvalidrox.surrogate_models.meta_model.html">bayesvalidrox.surrogate_models.meta_model</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-27" name="toctree-checkbox-27" role="switch" type="checkbox"/><label for="toctree-checkbox-27"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.meta_model</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="current">
+<li class="toctree-l5 current current-page"><a class="current reference internal" href="#">bayesvalidrox.surrogate_models.meta_model.transform_y</a></li>
+<li class="toctree-l5"><a class="reference internal" href="bayesvalidrox.surrogate_models.meta_model.MetaModel.html">bayesvalidrox.surrogate_models.meta_model.MetaModel</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a><input class="toctree-checkbox" id="toctree-checkbox-28" name="toctree-checkbox-28" role="switch" type="checkbox"/><label for="toctree-checkbox-28"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr</a></li>
+<li class="toctree-l5"><a class="reference internal" href="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="bayesvalidrox.surrogate_models.pce_gpr.html">bayesvalidrox.surrogate_models.pce_gpr</a><input class="toctree-checkbox" id="toctree-checkbox-29" name="toctree-checkbox-29" role="switch" type="checkbox"/><label for="toctree-checkbox-29"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.pce_gpr</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="bayesvalidrox.surrogate_models.pce_gpr.PCEGPR.html">bayesvalidrox.surrogate_models.pce_gpr.PCEGPR</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="bayesvalidrox.surrogate_models.polynomial_chaos.html">bayesvalidrox.surrogate_models.polynomial_chaos</a><input class="toctree-checkbox" id="toctree-checkbox-30" name="toctree-checkbox-30" role="switch" type="checkbox"/><label for="toctree-checkbox-30"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.polynomial_chaos</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="bayesvalidrox.surrogate_models.polynomial_chaos.PCE.html">bayesvalidrox.surrogate_models.polynomial_chaos.PCE</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_ard.html">bayesvalidrox.surrogate_models.reg_fast_ard</a><input class="toctree-checkbox" id="toctree-checkbox-31" name="toctree-checkbox-31" role="switch" type="checkbox"/><label for="toctree-checkbox-31"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.reg_fast_ard</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+<li class="toctree-l5"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html">bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_laplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace</a><input class="toctree-checkbox" id="toctree-checkbox-32" name="toctree-checkbox-32" role="switch" type="checkbox"/><label for="toctree-checkbox-32"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.reg_fast_laplace</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace</a></li>
+</ul>
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+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="bayesvalidrox.surrogate_models.supplementary.html">bayesvalidrox.surrogate_models.supplementary</a><input class="toctree-checkbox" id="toctree-checkbox-34" name="toctree-checkbox-34" role="switch" type="checkbox"/><label for="toctree-checkbox-34"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.supplementary</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="bayesvalidrox.surrogate_models.supplementary.check_ranges.html">bayesvalidrox.surrogate_models.supplementary.check_ranges</a></li>
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+<li class="toctree-l5"><a class="reference internal" href="bayesvalidrox.surrogate_models.supplementary.subdomain.html">bayesvalidrox.surrogate_models.supplementary.subdomain</a></li>
+</ul>
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+ <article role="main" id="furo-main-content">
+ <section id="bayesvalidrox-surrogate-models-meta-model-transform-y">
+<h1>bayesvalidrox.surrogate_models.meta_model.transform_y<a class="headerlink" href="#bayesvalidrox-surrogate-models-meta-model-transform-y" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" 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">
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+ <div class="left-details">
+ <div class="copyright">
+ Copyright © 2023, Farid Mohammadi, Rebecca Kohlhaas
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+
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+ <span class="toc-title">
+ On this page
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+ <div class="toc-tree-container">
+ <div class="toc-tree">
+ <ul>
+<li><a class="reference internal" href="#">bayesvalidrox.surrogate_models.meta_model.transform_y</a><ul>
+<li><a class="reference internal" 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>
+
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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/.$Class overview.drawio.bkp
@@ -1,962 +1,191 @@
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diff --git a/docs/diagrams/.$GridManager_diagrams.drawio.bkp b/docs/diagrams/.$GridManager_diagrams.drawio.bkp
new file mode 100644
index 000000000..0c9dfa9d2
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diff --git a/docs/diagrams/Class overview.drawio b/docs/diagrams/Class overview.drawio
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--- a/docs/diagrams/Class overview.drawio
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--
GitLab