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It refines MD-generated trajectories with customizable refinement.">
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<article id="content">
<header>
<h1 class="title">Module <code>MDRefine.MDRefinement</code></h1>
</header>
<section id="section-intro">
<p>Main tool: <code><a title="MDRefine.MDRefinement.MDRefinement" href="#MDRefine.MDRefinement.MDRefinement">MDRefinement()</a></code>.
It refines MD-generated trajectories with customizable refinement.</p>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="MDRefine.MDRefinement.MDRefinement"><code class="name flex">
<span>def <span class="ident">MDRefinement</span></span>(<span>infos: dict, *, regularization: dict = None, stride: int = 1, starting_alpha: float = inf, starting_beta: float = inf, starting_gamma: float = inf, random_states=5, which_set: str = 'validation', gtol: float = 0.5, ftol: float = 0.05, results_folder_name: str = 'results', n_parallel_jobs: int = None)</span>
</code></dt>
<dd>
<div class="desc"><p>This is the main tool of the package: it loads data, searches for the optimal hyperparameters and minimizes the loss function on the whole data set
by using the opimized hyperparameters. The output variables are then saved in a folder; they include <code>input</code> values, <code>min_lambdas</code> (optimal lambda coefficients for Ensemble Refinement, when performed),
<code>result</code>, <code>hyper_search</code> (steps in the search for optimal hyperparameters) (<code>.csv</code> files) and the <code>.npy</code> arrays with the new weights determined in the refinement.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>infos</code></strong> :&ensp;<code>dict</code></dt>
<dd>A dictionary of information used to load data with <code>load_data</code> (see in the Examples directory).</dd>
<dt><strong><code>regularization</code></strong> :&ensp;<code>dict</code></dt>
<dd>A dictionary which can include two keys: <code>force_field_reg</code> and <code>forward_model_reg</code>, to specify the regularizations to the force-field correction and the forward model, respectively;
the first key is either a string (among <code>plain l2</code>, <code>constraint 1</code>, <code>constraint 2</code>, <code>KL divergence</code>) or a user-defined
function which takes as input <code>pars_ff</code> and returns the regularization term to be multiplied by the hyperparameter <code>beta</code>;
the second key is a user-defined function which takes as input <code>pars_fm</code> and <code>forward_coeffs_0</code> (current and refined forward-model coefficients) and
returns the regularization term to be multiplied by the hyperparameter <code>gamma</code>.</dd>
<dt><strong><code>stride</code></strong> :&ensp;<code>int</code></dt>
<dd>The stride of the frames used to load data employed in search for optimal hyperparameters
(in order to reduce the computational cost, at the price of a lower representativeness of the ensembles).</dd>
<dt><strong><code>starting_alpha</code></strong>, <strong><code>starting_beta</code></strong>, <strong><code>starting_gamma</code></strong> :&ensp;<code>floats</code></dt>
<dd>Starting values of the hyperparameters (<code>np.inf</code> by default, namely no refinement in that direction).</dd>
<dt><strong><code>random_states</code></strong> :&ensp;<code>int</code> or <code>list</code> of <code>integers</code></dt>
<dd>Random states (i.e., seeds) used to split the data set in cross validation (if integer, then <code>random_states = np.arange(random_states)</code>.</dd>
<dt><strong><code>which_set</code></strong> :&ensp;<code>str</code></dt>
<dd>String chosen among <code>'training'</code>, <code>'validation'</code> or <code>'test'</code>, which specifies how to determine optimal hyperparameters:
if minimizing the (average) chi2 on the training set for <code>'training'</code>, on training observables and test frames for <code>'validation'</code>,
on test observables for <code>'test'</code>.</dd>
<dt><strong><code>gtol</code></strong> :&ensp;<code>float</code></dt>
<dd>Tolerance <code>gtol</code> (on the gradient) of scipy.optimize.minimize (0.5 by default).</dd>
<dt><strong><code>ftol</code></strong> :&ensp;<code>float</code></dt>
<dd>Tolerance <code>ftol</code> of scipy.optimize.minimize (0.05 by default).</dd>
<dt><strong><code>results_folder_name</code></strong> :&ensp;<code>str</code></dt>
<dd>String for the prefix of the folder where to save results; the complete folder name is <code>results_folder_name + '_' + time</code> where <code>time</code> is the current time
when the algorithm has finished, in order to uniquely identify the folder with the results.</dd>
<dt><strong><code>n_parallel_jobs</code></strong> :&ensp;<code>int</code></dt>
<dd>How many jobs are run in parallel (<code>None</code> by default).</dd>
</dl></div>
</dd>
<dt id="MDRefine.MDRefinement.save_txt"><code class="name flex">
<span>def <span class="ident">save_txt</span></span>(<span>input_values, Result, coeff_names, folder_name='Result')</span>
</code></dt>
<dd>
<div class="desc"><p>This is an internal tool of <code><a title="MDRefine.MDRefinement.MDRefinement" href="#MDRefine.MDRefinement.MDRefinement">MDRefinement()</a></code> used to save <code>input_values</code> and output <code>Result</code> as <code>csv</code> and <code>npy</code> files in a folder whose name is
<code>folder_name + '_' + date</code> where date is the current time when the computation ended (it uses <code>date_time</code>
to generate unique file name, on the assumption of a single folder name at given time).</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>input_values</code></strong> :&ensp;<code>dict</code></dt>
<dd>Dictionary with input values of the refinement, such as stride, starting values of the hyperparameters, random_states, which_set, tolerances (see <code><a title="MDRefine.MDRefinement.MDRefinement" href="#MDRefine.MDRefinement.MDRefinement">MDRefinement()</a></code>).</dd>
<dt><strong><code>Result</code></strong> :&ensp;<code>class instance</code></dt>
<dd>Class instance with the results of <code>minimizer</code> and the search for the optimal hyperparameters.</dd>
<dt><strong><code>coeff_names</code></strong> :&ensp;<code>list</code></dt>
<dd>List with the names of the coefficients (force-field and forward-model corrections).</dd>
<dt><strong><code>folder_name</code></strong> :&ensp;<code>str</code></dt>
<dd>String for the prefix of the folder name (by default, <code>'Result'</code>).</dd>
</dl></div>
</dd>
<dt id="MDRefine.MDRefinement.unwrap_2dict"><code class="name flex">
<span>def <span class="ident">unwrap_2dict</span></span>(<span>my_2dict)</span>
</code></dt>
<dd>
<div class="desc"><p>Tool to unwrap a 2-layer dictionary <code>my_2dict</code> into list of values and list of keys.</p></div>
</dd>
</dl>
</section>
<section>
</section>
</article>
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<div class="toc">
<ul></ul>
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<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="MDRefine" href="index.html">MDRefine</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="MDRefine.MDRefinement.MDRefinement" href="#MDRefine.MDRefinement.MDRefinement">MDRefinement</a></code></li>
<li><code><a title="MDRefine.MDRefinement.save_txt" href="#MDRefine.MDRefinement.save_txt">save_txt</a></code></li>
<li><code><a title="MDRefine.MDRefinement.unwrap_2dict" href="#MDRefine.MDRefinement.unwrap_2dict">unwrap_2dict</a></code></li>
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