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diff --git a/docs/build/doctrees/surrogate_description.doctree b/docs/build/doctrees/surrogate_description.doctree
index 0c4941c5424b03f3bdb35a35f68fb22aa3e7b881..81898fef1fbbe5d6a6b76b051892a12dc74302fc 100644
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diff --git a/docs/build/html/_sources/al_description.rst.txt b/docs/build/html/_sources/al_description.rst.txt
index 636fa7877bb9b02fd4512c11459647e3c9a57ef7..c1d5e99f2ec65aeaf8f0933096bd351ca0c6e465 100644
--- a/docs/build/html/_sources/al_description.rst.txt
+++ b/docs/build/html/_sources/al_description.rst.txt
@@ -1,8 +1,27 @@
Active learning: iteratively expanding the training set
*******************************************************
+Active learning (AL), also called sequential training, is the iterative choice of additional training samples after the initial training of a surrogate model.
+The new samples can be chosen in an explorative manner or by exploiting available data and properties of the surrogate.
-
+.. warning::
+ The active learning methods are currently being reworked.
+ This should not change the function call ``Engine.train_sequential()``, but will change the associated class structures.
.. image:: ../diagrams/active_learning_reduced.png
:width: 550
- :alt: UML diagram for the classes and functions used in active learning in BayesValidRox.
\ No newline at end of file
+ :alt: UML diagram for the classes and functions used in active learning in BayesValidRox.
+
+In BayesValidRox AL is realized by additional properties of the :any:`bayesvalidrox.surrogate_models.exp_designs.ExpDesigns` and :any:`bayesvalidrox.surrogate_models.engine.Engine` classes without any changes to the surrogate model.
+
+Exploration, exploitation and tradeoff
+======================================
+**Exploration** methods choose the new samples in a space-filling manner, while **exploitation methods** make use of available data or properties of the surrogate models, such as the estimated surrogate standard deviation.
+Exploration methods in BayesValidRox include random or latin-hypercube sampling, voronoi sampling, choice based on leave-one-out cross validation or dual-annealing.
+Exploitation can be set to Bayesian designs, such as Bayesian3 Active Learning, or variance-based designs.
+
+The tradeoff between exploration and exploitation is defined by **tradeoff-schemes**, such as an equal split, epsilon-decreaseing or adaptive schemes.
+
+
+Example
+=======
+We take the engine from :any:`surrogate_description` and change the settings to perform sequential training.
diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt
index dd9bb06fb0d2e92d10a644de1f03f8bd6e112265..b533a730c30cb41d03e9e2c20d0ef9683185f53d 100644
--- a/docs/build/html/_sources/index.rst.txt
+++ b/docs/build/html/_sources/index.rst.txt
@@ -18,7 +18,6 @@ Links
* PyPI: https://pypi.org/project/bayesvalidrox/
* Documentation:
-#TODO Add other links
Installation
------------
@@ -28,35 +27,37 @@ This package runs under Python 3.9 for versions <1.0.0 and 3.9+ from version 1.0
pip install bayesvalidrox
-#TODO Note other needed installations and tips
Quickstart
----------
#TODO A minimal example to get people started
-See also
---------
-#TODO Link other websites that are related to BVR
-
License
-------
#TODO Note the License under which BVR is released
+Contribution
+------------
+We would be happy for you to contribute to **BayesValidRox**.
+This can include e.g. reporting issues, proposing new features, working on features, or support with the documentation.
+If you want to contibute, check out our contribution_ guidelines.
+You can contact us on the gitlab_ page.
+
+.. _gitlab: https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox
+
+.. _contribution: https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/-/blob/docs/sphinx_new/CONTRIBUTING.md?ref_type=heads
+
Further contents
----------------
.. toctree::
- :maxdepth: 2
+ :maxdepth: 1
packagedescription
tutorial
examples
api
-
-Contact
--------
-#TODO Add options here
-
+
Indices and tables
==================
diff --git a/docs/build/html/_sources/surrogate_description.rst.txt b/docs/build/html/_sources/surrogate_description.rst.txt
index 6cd7bcd296ddd884cc3b07399f82b21d4f84d81e..61d89e6a6ff5bf1ffa27210ce1a077ac0634e8c9 100644
--- a/docs/build/html/_sources/surrogate_description.rst.txt
+++ b/docs/build/html/_sources/surrogate_description.rst.txt
@@ -2,16 +2,9 @@ Training surrogate models
*************************
Surrogate models, also called metamodels, are models that are built on evaluations of full models with the goal to capture the full behaviour, but reduce the cost of evaluations.
-In BayesValidRox two types of surrogate model are available, polynomial chaos expansion and Gaussian processes.
-The Polynomial Chaos Expansion (PCE) and its variant the arbitrary Polynomial Chaos Expansion (aPC) build polynomials from the given distributions of uncertain inputs.
-Gaussian processes (GP) give kernel-based representations of the model results.
-
The surrogate models are trained on datasets :math:`\mathcal{D}=(x_i, y_i)_{i=1,\dots,M)` that consist of :math:`M` samples of the uncertain parameters and the corresponding model outputs.
We call this dataset the training data, with training samples :math:`(x_i)_{i=1,\dots,M)`.
-
-MetaModel and Engine classes
-============================
BayesValidRox creates surrogate models as objects of the class :any:`bayesvalidrox.surrogate_models.surrogate_models.MetaModel`.
Training is performed by the class :any:`bayesvalidrox.surrogate_models.engine.Engine`.
@@ -21,6 +14,19 @@ Training is performed by the class :any:`bayesvalidrox.surrogate_models.engine.E
MetaModel options
=================
+In BayesValidRox two types of surrogate model are available, Polynomial Chaos Expansion (PCE) and Gaussian Processes (GP).
+The Polynomial Chaos Expansion (PCE) and its variant the arbitrary Polynomial Chaos Expansion (aPC) build polynomials from the given distributions of uncertain inputs.
+Gaussian processes (GP) give kernel-based representations of the model results.
+
+We provide a broad range of regression methods for useage with PCE-surrogates that can be set by the parameter ``MetaModel.pce_reg_method``.
+These include Ordinary Least Squares (``ols``), Bayesian Ridge Regression (``brr``), Least angle regression (``lars``), Bayesian ARD Regression (``ard``), Fast Bayesian ARD Regression (``fastard``), Variational Bayesian Learning (``vbl``) and Emperical Bayesian Learning (``ebl``).
+Depending on the chosen regression method, the surrogate outputs a mean approximation and an associated standard deviation.
+
+Dimensionality reduction can be performed on outputs with Principal Component Analysis (PCA).
+PCA is applied on the set of surrogates built for the ``x_values`` defined in the model.
+
+If bootstrapping is used, multiple surrogates will be created based on bootstrapped training data, and jointly evaluated.
+The final outputs will then be the mean and standard deviation of their approximations.
Training with the engine
diff --git a/docs/build/html/al_description.html b/docs/build/html/al_description.html
index aa3d9fc17c66f32af123c59bce1c89f079b2b990..d00aea23bc8449a6c2ef71e173813d4df09573bf 100644
--- a/docs/build/html/al_description.html
+++ b/docs/build/html/al_description.html
@@ -141,7 +141,7 @@
<svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
</button>
</div>
- <label class="toc-overlay-icon toc-header-icon no-toc" for="__toc">
+ <label class="toc-overlay-icon toc-header-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>
@@ -329,7 +329,7 @@
<svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
</button>
</div>
- <label class="toc-overlay-icon toc-content-icon no-toc" for="__toc">
+ <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>
@@ -337,7 +337,26 @@
<article role="main">
<section id="active-learning-iteratively-expanding-the-training-set">
<h1>Active learning: iteratively expanding the training set<a class="headerlink" href="#active-learning-iteratively-expanding-the-training-set" title="Link to this heading">¶</a></h1>
+<p>Active learning (AL), also called sequential training, is the iterative choice of additional training samples after the initial training of a surrogate model.
+The new samples can be chosen in an explorative manner or by exploiting available data and properties of the surrogate.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>The active learning methods are currently being reworked.
+This should not change the function call <code class="docutils literal notranslate"><span class="pre">Engine.train_sequential()</span></code>, but will change the associated class structures.</p>
+</div>
<a class="reference internal image-reference" href="_images/active_learning_reduced.png"><img alt="UML diagram for the classes and functions used in active learning in BayesValidRox." src="_images/active_learning_reduced.png" style="width: 550px;" /></a>
+<p>In BayesValidRox AL is realized by additional properties of the <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.exp_designs.ExpDesigns</span></code></a> and <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine" title="bayesvalidrox.surrogate_models.engine.Engine"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.engine.Engine</span></code></a> classes without any changes to the surrogate model.</p>
+<section id="exploration-exploitation-and-tradeoff">
+<h2>Exploration, exploitation and tradeoff<a class="headerlink" href="#exploration-exploitation-and-tradeoff" title="Link to this heading">¶</a></h2>
+<p><strong>Exploration</strong> methods choose the new samples in a space-filling manner, while <strong>exploitation methods</strong> make use of available data or properties of the surrogate models, such as the estimated surrogate standard deviation.
+Exploration methods in BayesValidRox include random or latin-hypercube sampling, voronoi sampling, choice based on leave-one-out cross validation or dual-annealing.
+Exploitation can be set to Bayesian designs, such as Bayesian3 Active Learning, or variance-based designs.</p>
+<p>The tradeoff between exploration and exploitation is defined by <strong>tradeoff-schemes</strong>, such as an equal split, epsilon-decreaseing or adaptive schemes.</p>
+</section>
+<section id="example">
+<h2>Example<a class="headerlink" href="#example" title="Link to this heading">¶</a></h2>
+<p>We take the engine from <a class="reference internal" href="surrogate_description.html"><span class="doc">Training surrogate models</span></a> and change the settings to perform sequential training.</p>
+</section>
</section>
</article>
@@ -383,9 +402,29 @@
</footer>
</div>
- <aside class="toc-drawer no-toc">
+ <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="#">Active learning: iteratively expanding the training set</a><ul>
+<li><a class="reference internal" href="#exploration-exploitation-and-tradeoff">Exploration, exploitation and tradeoff</a></li>
+<li><a class="reference internal" href="#example">Example</a></li>
+</ul>
+</li>
+</ul>
+
+ </div>
+ </div>
+ </div>
+
</aside>
</div>
diff --git a/docs/build/html/index.html b/docs/build/html/index.html
index 9fd0f8c02f314f19c4cf3f22c086cf6074b1b757..ac12863ce5183832f1a06829fdb056e17619fa1b 100644
--- a/docs/build/html/index.html
+++ b/docs/build/html/index.html
@@ -348,7 +348,6 @@ The functionality and options for the different classes is described more in-dep
<li><p>PyPI: <a class="reference external" href="https://pypi.org/project/bayesvalidrox/">https://pypi.org/project/bayesvalidrox/</a></p></li>
<li><p>Documentation:</p></li>
</ul>
-<p>#TODO Add other links</p>
</section>
<section id="installation">
<h2>Installation<a class="headerlink" href="#installation" title="Link to this heading">¶</a></h2>
@@ -356,62 +355,33 @@ The functionality and options for the different classes is described more in-dep
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>bayesvalidrox
</pre></div>
</div>
-<p>#TODO Note other needed installations and tips</p>
</section>
<section id="quickstart">
<h2>Quickstart<a class="headerlink" href="#quickstart" title="Link to this heading">¶</a></h2>
<p>#TODO A minimal example to get people started</p>
</section>
-<section id="see-also">
-<h2>See also<a class="headerlink" href="#see-also" title="Link to this heading">¶</a></h2>
-<p>#TODO Link other websites that are related to BVR</p>
-</section>
<section id="license">
<h2>License<a class="headerlink" href="#license" title="Link to this heading">¶</a></h2>
<p>#TODO Note the License under which BVR is released</p>
</section>
+<section id="contribution">
+<h2>Contribution<a class="headerlink" href="#contribution" title="Link to this heading">¶</a></h2>
+<p>We would be happy for you to contribute to <strong>BayesValidRox</strong>.
+This can include e.g. reporting issues, proposing new features, working on features, or support with the documentation.
+If you want to contibute, check out our <a class="reference external" href="https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/-/blob/docs/sphinx_new/CONTRIBUTING.md?ref_type=heads">contribution</a> guidelines.
+You can contact us on the <a class="reference external" href="https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox">gitlab</a> page.</p>
+</section>
<section id="further-contents">
<h2>Further contents<a class="headerlink" href="#further-contents" title="Link to this heading">¶</a></h2>
<div class="toctree-wrapper compound">
<ul>
-<li class="toctree-l1"><a class="reference internal" href="packagedescription.html">USER GUIDE</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="packagedescription.html#installation">Installation</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="tutorial.html">TUTORIAL</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#import-necessary-libraries">Import necessary libraries</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#define-the-model-with-pylinkforwardmodel">Define the model with PyLinkForwardModel</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#define-probabilistic-input-model">Define probabilistic input model</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#define-surrogate-meta-model">Define surrogate (meta) model</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#set-the-experimental-design">Set the experimental design</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#train-the-surrogate-with-an-engine">Train the surrogate with an engine</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#sequential-training">Sequential training</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#post-processing">Post-processing</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#bayesian-inference">Bayesian Inference</a></li>
-<li class="toctree-l2"><a class="reference internal" href="tutorial.html#define-the-data-uncertainty">Define the data uncertainty</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="examples.html">EXAMPLES</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="analyticalfunction.html">Analytical function</a></li>
-<li class="toctree-l2"><a class="reference internal" href="beam.html">Beam</a></li>
-<li class="toctree-l2"><a class="reference internal" href="borehole.html">Borehole</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ishigami.html">Ishigami</a></li>
-<li class="toctree-l2"><a class="reference internal" href="modelcomparison.html">Model comparison</a></li>
-<li class="toctree-l2"><a class="reference internal" href="ohaganfunction.html">OHagan-function</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pollution.html">Pollution</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="api.html">API</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="_autosummary/bayesvalidrox.html">bayesvalidrox</a></li>
-</ul>
-</li>
+<li class="toctree-l1"><a class="reference internal" href="packagedescription.html">USER GUIDE</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorial.html">TUTORIAL</a></li>
+<li class="toctree-l1"><a class="reference internal" href="examples.html">EXAMPLES</a></li>
+<li class="toctree-l1"><a class="reference internal" href="api.html">API</a></li>
</ul>
</div>
</section>
-<section id="contact">
-<h2>Contact<a class="headerlink" href="#contact" title="Link to this heading">¶</a></h2>
-<p>#TODO Add options here</p>
-</section>
</section>
<section id="indices-and-tables">
<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Link to this heading">¶</a></h1>
@@ -471,10 +441,9 @@ The functionality and options for the different classes is described more in-dep
<li><a class="reference internal" href="#links">Links</a></li>
<li><a class="reference internal" href="#installation">Installation</a></li>
<li><a class="reference internal" href="#quickstart">Quickstart</a></li>
-<li><a class="reference internal" href="#see-also">See also</a></li>
<li><a class="reference internal" href="#license">License</a></li>
+<li><a class="reference internal" href="#contribution">Contribution</a></li>
<li><a class="reference internal" href="#further-contents">Further contents</a></li>
-<li><a class="reference internal" href="#contact">Contact</a></li>
</ul>
</li>
<li><a class="reference internal" href="#indices-and-tables">Indices and tables</a></li>
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index fba7b51c0e7b132a77a494e891df606e07ea0c41..649219d8b553a969734cc8d31b10ccee2dd1bf14 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file
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design", "Example: ishigami", "Models", "Example: model comparison", "Example: OHagan-function", "USER GUIDE", "Example: pollution", "Postprocessing", "Training surrogate models", "TUTORIAL"], "titleterms": {"1": 75, "3": 75, "activ": 64, "adaptplot": [19, 20], "also": [], "an": 81, "analyt": 65, "api": 66, "apoly_construct": [21, 22], "argument": [29, 42], "assist": 71, "attribut": [3, 5, 7, 9, 13, 16, 25, 26, 39, 42, 47, 49, 50, 52, 55, 58, 60], "bay": [], "bayes_infer": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "bayes_linear": [23, 24, 25, 26, 27], "bayes_model_comparison": [4, 5], "bayesian": [67, 71, 81], "bayesianlinearregress": 24, "bayesinfer": 3, "bayesmodelcomparison": 5, "bayesvalidrox": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63], "beam": 68, "borehol": 69, "check_rang": 40, "choic": 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\ No newline at end of file
diff --git a/docs/build/html/surrogate_description.html b/docs/build/html/surrogate_description.html
index cbaf39a07c0daf3614e576bb3a4949c656b23ab0..a9af8695565ac3e9e094942bcccbdcf924f298be 100644
--- a/docs/build/html/surrogate_description.html
+++ b/docs/build/html/surrogate_description.html
@@ -338,19 +338,23 @@
<section id="training-surrogate-models">
<h1>Training surrogate models<a class="headerlink" href="#training-surrogate-models" title="Link to this heading">¶</a></h1>
<p>Surrogate models, also called metamodels, are models that are built on evaluations of full models with the goal to capture the full behaviour, but reduce the cost of evaluations.</p>
-<p>In BayesValidRox two types of surrogate model are available, polynomial chaos expansion and Gaussian processes.
-The Polynomial Chaos Expansion (PCE) and its variant the arbitrary Polynomial Chaos Expansion (aPC) build polynomials from the given distributions of uncertain inputs.
-Gaussian processes (GP) give kernel-based representations of the model results.</p>
<p>The surrogate models are trained on datasets <span class="math notranslate nohighlight">\(\mathcal{D}=(x_i, y_i)_{i=1,\dots,M)\)</span> that consist of <span class="math notranslate nohighlight">\(M\)</span> samples of the uncertain parameters and the corresponding model outputs.
We call this dataset the training data, with training samples <span class="math notranslate nohighlight">\((x_i)_{i=1,\dots,M)\)</span>.</p>
-<section id="metamodel-and-engine-classes">
-<h2>MetaModel and Engine classes<a class="headerlink" href="#metamodel-and-engine-classes" title="Link to this heading">¶</a></h2>
<p>BayesValidRox creates surrogate models as objects of the class <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel" title="bayesvalidrox.surrogate_models.surrogate_models.MetaModel"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.surrogate_models.MetaModel</span></code></a>.
Training is performed by the class <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine" title="bayesvalidrox.surrogate_models.engine.Engine"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.engine.Engine</span></code></a>.</p>
<a class="reference internal image-reference" href="_images/metamod_training_reduced.png"><img alt="UML diagram for metamodel-related classes in bayesvalidrox" src="_images/metamod_training_reduced.png" style="width: 800px;" /></a>
-</section>
<section id="metamodel-options">
<h2>MetaModel options<a class="headerlink" href="#metamodel-options" title="Link to this heading">¶</a></h2>
+<p>In BayesValidRox two types of surrogate model are available, Polynomial Chaos Expansion (PCE) and Gaussian Processes (GP).
+The Polynomial Chaos Expansion (PCE) and its variant the arbitrary Polynomial Chaos Expansion (aPC) build polynomials from the given distributions of uncertain inputs.
+Gaussian processes (GP) give kernel-based representations of the model results.</p>
+<p>We provide a broad range of regression methods for useage with PCE-surrogates that can be set by the parameter <code class="docutils literal notranslate"><span class="pre">MetaModel.pce_reg_method</span></code>.
+These include Ordinary Least Squares (<code class="docutils literal notranslate"><span class="pre">ols</span></code>), Bayesian Ridge Regression (<code class="docutils literal notranslate"><span class="pre">brr</span></code>), Least angle regression (<code class="docutils literal notranslate"><span class="pre">lars</span></code>), Bayesian ARD Regression (<code class="docutils literal notranslate"><span class="pre">ard</span></code>), Fast Bayesian ARD Regression (<code class="docutils literal notranslate"><span class="pre">fastard</span></code>), Variational Bayesian Learning (<code class="docutils literal notranslate"><span class="pre">vbl</span></code>) and Emperical Bayesian Learning (<code class="docutils literal notranslate"><span class="pre">ebl</span></code>).
+Depending on the chosen regression method, the surrogate outputs a mean approximation and an associated standard deviation.</p>
+<p>Dimensionality reduction can be performed on outputs with Principal Component Analysis (PCA).
+PCA is applied on the set of surrogates built for the <code class="docutils literal notranslate"><span class="pre">x_values</span></code> defined in the model.</p>
+<p>If bootstrapping is used, multiple surrogates will be created based on bootstrapped training data, and jointly evaluated.
+The final outputs will then be the mean and standard deviation of their approximations.</p>
</section>
<section id="training-with-the-engine">
<h2>Training with the engine<a class="headerlink" href="#training-with-the-engine" title="Link to this heading">¶</a></h2>
@@ -491,7 +495,6 @@ Evaluation via the surrogate model can make use of the sampling in the experimen
<div class="toc-tree">
<ul>
<li><a class="reference internal" href="#">Training surrogate models</a><ul>
-<li><a class="reference internal" href="#metamodel-and-engine-classes">MetaModel and Engine classes</a></li>
<li><a class="reference internal" href="#metamodel-options">MetaModel options</a></li>
<li><a class="reference internal" href="#training-with-the-engine">Training with the engine</a></li>
<li><a class="reference internal" href="#example">Example</a></li>
diff --git a/docs/source/al_description.rst b/docs/source/al_description.rst
index 636fa7877bb9b02fd4512c11459647e3c9a57ef7..c1d5e99f2ec65aeaf8f0933096bd351ca0c6e465 100644
--- a/docs/source/al_description.rst
+++ b/docs/source/al_description.rst
@@ -1,8 +1,27 @@
Active learning: iteratively expanding the training set
*******************************************************
+Active learning (AL), also called sequential training, is the iterative choice of additional training samples after the initial training of a surrogate model.
+The new samples can be chosen in an explorative manner or by exploiting available data and properties of the surrogate.
-
+.. warning::
+ The active learning methods are currently being reworked.
+ This should not change the function call ``Engine.train_sequential()``, but will change the associated class structures.
.. image:: ../diagrams/active_learning_reduced.png
:width: 550
- :alt: UML diagram for the classes and functions used in active learning in BayesValidRox.
\ No newline at end of file
+ :alt: UML diagram for the classes and functions used in active learning in BayesValidRox.
+
+In BayesValidRox AL is realized by additional properties of the :any:`bayesvalidrox.surrogate_models.exp_designs.ExpDesigns` and :any:`bayesvalidrox.surrogate_models.engine.Engine` classes without any changes to the surrogate model.
+
+Exploration, exploitation and tradeoff
+======================================
+**Exploration** methods choose the new samples in a space-filling manner, while **exploitation methods** make use of available data or properties of the surrogate models, such as the estimated surrogate standard deviation.
+Exploration methods in BayesValidRox include random or latin-hypercube sampling, voronoi sampling, choice based on leave-one-out cross validation or dual-annealing.
+Exploitation can be set to Bayesian designs, such as Bayesian3 Active Learning, or variance-based designs.
+
+The tradeoff between exploration and exploitation is defined by **tradeoff-schemes**, such as an equal split, epsilon-decreaseing or adaptive schemes.
+
+
+Example
+=======
+We take the engine from :any:`surrogate_description` and change the settings to perform sequential training.
diff --git a/docs/source/index.rst b/docs/source/index.rst
index dd9bb06fb0d2e92d10a644de1f03f8bd6e112265..b533a730c30cb41d03e9e2c20d0ef9683185f53d 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -18,7 +18,6 @@ Links
* PyPI: https://pypi.org/project/bayesvalidrox/
* Documentation:
-#TODO Add other links
Installation
------------
@@ -28,35 +27,37 @@ This package runs under Python 3.9 for versions <1.0.0 and 3.9+ from version 1.0
pip install bayesvalidrox
-#TODO Note other needed installations and tips
Quickstart
----------
#TODO A minimal example to get people started
-See also
---------
-#TODO Link other websites that are related to BVR
-
License
-------
#TODO Note the License under which BVR is released
+Contribution
+------------
+We would be happy for you to contribute to **BayesValidRox**.
+This can include e.g. reporting issues, proposing new features, working on features, or support with the documentation.
+If you want to contibute, check out our contribution_ guidelines.
+You can contact us on the gitlab_ page.
+
+.. _gitlab: https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox
+
+.. _contribution: https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/-/blob/docs/sphinx_new/CONTRIBUTING.md?ref_type=heads
+
Further contents
----------------
.. toctree::
- :maxdepth: 2
+ :maxdepth: 1
packagedescription
tutorial
examples
api
-
-Contact
--------
-#TODO Add options here
-
+
Indices and tables
==================
diff --git a/docs/source/surrogate_description.rst b/docs/source/surrogate_description.rst
index 6cd7bcd296ddd884cc3b07399f82b21d4f84d81e..61d89e6a6ff5bf1ffa27210ce1a077ac0634e8c9 100644
--- a/docs/source/surrogate_description.rst
+++ b/docs/source/surrogate_description.rst
@@ -2,16 +2,9 @@ Training surrogate models
*************************
Surrogate models, also called metamodels, are models that are built on evaluations of full models with the goal to capture the full behaviour, but reduce the cost of evaluations.
-In BayesValidRox two types of surrogate model are available, polynomial chaos expansion and Gaussian processes.
-The Polynomial Chaos Expansion (PCE) and its variant the arbitrary Polynomial Chaos Expansion (aPC) build polynomials from the given distributions of uncertain inputs.
-Gaussian processes (GP) give kernel-based representations of the model results.
-
The surrogate models are trained on datasets :math:`\mathcal{D}=(x_i, y_i)_{i=1,\dots,M)` that consist of :math:`M` samples of the uncertain parameters and the corresponding model outputs.
We call this dataset the training data, with training samples :math:`(x_i)_{i=1,\dots,M)`.
-
-MetaModel and Engine classes
-============================
BayesValidRox creates surrogate models as objects of the class :any:`bayesvalidrox.surrogate_models.surrogate_models.MetaModel`.
Training is performed by the class :any:`bayesvalidrox.surrogate_models.engine.Engine`.
@@ -21,6 +14,19 @@ Training is performed by the class :any:`bayesvalidrox.surrogate_models.engine.E
MetaModel options
=================
+In BayesValidRox two types of surrogate model are available, Polynomial Chaos Expansion (PCE) and Gaussian Processes (GP).
+The Polynomial Chaos Expansion (PCE) and its variant the arbitrary Polynomial Chaos Expansion (aPC) build polynomials from the given distributions of uncertain inputs.
+Gaussian processes (GP) give kernel-based representations of the model results.
+
+We provide a broad range of regression methods for useage with PCE-surrogates that can be set by the parameter ``MetaModel.pce_reg_method``.
+These include Ordinary Least Squares (``ols``), Bayesian Ridge Regression (``brr``), Least angle regression (``lars``), Bayesian ARD Regression (``ard``), Fast Bayesian ARD Regression (``fastard``), Variational Bayesian Learning (``vbl``) and Emperical Bayesian Learning (``ebl``).
+Depending on the chosen regression method, the surrogate outputs a mean approximation and an associated standard deviation.
+
+Dimensionality reduction can be performed on outputs with Principal Component Analysis (PCA).
+PCA is applied on the set of surrogates built for the ``x_values`` defined in the model.
+
+If bootstrapping is used, multiple surrogates will be created based on bootstrapped training data, and jointly evaluated.
+The final outputs will then be the mean and standard deviation of their approximations.
Training with the engine