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reference/spotpython/utils/effects/index.html

@@ -4975,7 +4975,10 @@ <h2 id="spotpython.utils.effects.plot_all_partial_dependence" class="doc doc-hea
 
             <details class="quote">
               <summary>Source code in <code>spotpython/utils/effects.py</code></summary>
-              <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">306</span>
+              <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">303</span>
+<span class="normal">304</span>
+<span class="normal">305</span>
+<span class="normal">306</span>
 <span class="normal">307</span>
 <span class="normal">308</span>
 <span class="normal">309</span>
@@ -5040,10 +5043,7 @@ <h2 id="spotpython.utils.effects.plot_all_partial_dependence" class="doc doc-hea
 <span class="normal">368</span>
 <span class="normal">369</span>
 <span class="normal">370</span>
-<span class="normal">371</span>
-<span class="normal">372</span>
-<span class="normal">373</span>
-<span class="normal">374</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">plot_all_partial_dependence</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">df_target</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="s2">&quot;GradientBoostingRegressor&quot;</span><span class="p">,</span> <span class="n">nrows</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<span class="normal">371</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">plot_all_partial_dependence</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">df_target</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="s2">&quot;GradientBoostingRegressor&quot;</span><span class="p">,</span> <span class="n">nrows</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Generates Partial Dependence Plots (PDPs) for every feature in a DataFrame against a target variable,</span>
 <span class="sd">    arranged in a grid.</span>
@@ -5416,8 +5416,7 @@ <h2 id="spotpython.utils.effects.screening_plot" class="doc doc-heading">
             </td>
             <td>
               <div class="doc-md-description">
-                <p>The screening plan matrix, typically structured
-within a [0,1]^k box.</p>
+                <p>The screening plan matrix, typically structured within a [0,1]^k box.</p>
               </div>
             </td>
             <td>
@@ -5431,8 +5430,7 @@ <h2 id="spotpython.utils.effects.screening_plot" class="doc doc-heading">
             </td>
             <td>
               <div class="doc-md-description">
-                <p>The objective function to evaluate at each
-design point in the screening plan.</p>
+                <p>The objective function to evaluate at each design point in the screening plan.</p>
               </div>
             </td>
             <td>
@@ -5474,8 +5472,7 @@ <h2 id="spotpython.utils.effects.screening_plot" class="doc doc-heading">
             </td>
             <td>
               <div class="doc-md-description">
-                <p>A list of variable names corresponding to
-the design variables.</p>
+                <p>A list of variable names corresponding to the design variables.</p>
               </div>
             </td>
             <td>
@@ -5489,9 +5486,8 @@ <h2 id="spotpython.utils.effects.screening_plot" class="doc doc-heading">
             </td>
             <td>
               <div class="doc-md-description">
-                <p>A 2xk matrix where the first row contains
-lower bounds and the second row contains upper bounds for
-each variable.</p>
+                <p>A 2xk matrix where the first row contains lower bounds and
+the second row contains upper bounds for each variable.</p>
               </div>
             </td>
             <td>
@@ -5501,10 +5497,11 @@ <h2 id="spotpython.utils.effects.screening_plot" class="doc doc-heading">
           <tr class="doc-section-item">
             <td><code>show</code></td>
             <td>
+                  <code>bool</code>
             </td>
             <td>
               <div class="doc-md-description">
-                <p>(bool): If True, the plot is displayed. Defaults to True.</p>
+                <p>If True, the plot is displayed. Defaults to True.</p>
               </div>
             </td>
             <td>
@@ -5519,31 +5516,18 @@ <h2 id="spotpython.utils.effects.screening_plot" class="doc doc-heading">
     <table>
       <thead>
         <tr>
-          <th>Type</th>
+<th>Name</th>          <th>Type</th>
           <th>Description</th>
         </tr>
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td>
-            </td>
-            <td>
-              <div class="doc-md-description">
-                <p>pd.DataFrame: A DataFrame containing three columns:
-- &lsquo;varname&rsquo;: The name of each variable.
-- &lsquo;mean&rsquo;: The mean of the elementary effects for each variable.
-- &lsquo;sd&rsquo;: The standard deviation of the elementary effects for
-each variable.</p>
-              </div>
-            </td>
-          </tr>
-          <tr class="doc-section-item">
-            <td>
+<td><code>None</code></td>            <td>
+                  <code>None</code>
             </td>
             <td>
               <div class="doc-md-description">
-                <p>or None: If print is set to False, a plot of the results is
-generated instead of returning a DataFrame.</p>
+                <p>The function generates a plot of the results.</p>
               </div>
             </td>
           </tr>
@@ -5652,38 +5636,32 @@ <h2 id="spotpython.utils.effects.screening_plot" class="doc doc-heading">
 <span class="normal">297</span>
 <span class="normal">298</span>
 <span class="normal">299</span>
-<span class="normal">300</span>
-<span class="normal">301</span>
-<span class="normal">302</span>
-<span class="normal">303</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">screening_plot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">fun</span><span class="p">,</span> <span class="n">xi</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">bounds</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="normal">300</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">screening_plot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">fun</span><span class="p">,</span> <span class="n">xi</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">bounds</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Generates a plot with elementary effect screening metrics.</span>
 
 <span class="sd">    This function calculates the mean and standard deviation of the</span>
 <span class="sd">    elementary effects for a given set of design variables and plots</span>
 <span class="sd">    the results.</span>
 
 <span class="sd">    Args:</span>
-<span class="sd">        X (np.ndarray): The screening plan matrix, typically structured</span>
-<span class="sd">            within a [0,1]^k box.</span>
-<span class="sd">        fun (object): The objective function to evaluate at each</span>
-<span class="sd">            design point in the screening plan.</span>
-<span class="sd">        xi (float): The elementary effect step length factor.</span>
-<span class="sd">        p (int): Number of discrete levels along each dimension.</span>
-<span class="sd">        labels (list of str): A list of variable names corresponding to</span>
-<span class="sd">            the design variables.</span>
-<span class="sd">        bounds (np.ndarray): A 2xk matrix where the first row contains</span>
-<span class="sd">            lower bounds and the second row contains upper bounds for</span>
-<span class="sd">            each variable.</span>
-<span class="sd">        show: (bool): If True, the plot is displayed. Defaults to True.</span>
+<span class="sd">        X (np.ndarray):</span>
+<span class="sd">            The screening plan matrix, typically structured within a [0,1]^k box.</span>
+<span class="sd">        fun (object):</span>
+<span class="sd">            The objective function to evaluate at each design point in the screening plan.</span>
+<span class="sd">        xi (float):</span>
+<span class="sd">            The elementary effect step length factor.</span>
+<span class="sd">        p (int):</span>
+<span class="sd">            Number of discrete levels along each dimension.</span>
+<span class="sd">        labels (list of str):</span>
+<span class="sd">            A list of variable names corresponding to the design variables.</span>
+<span class="sd">        bounds (np.ndarray):</span>
+<span class="sd">            A 2xk matrix where the first row contains lower bounds and</span>
+<span class="sd">            the second row contains upper bounds for each variable.</span>
+<span class="sd">        show (bool):</span>
+<span class="sd">            If True, the plot is displayed. Defaults to True.</span>
 
 <span class="sd">    Returns:</span>
-<span class="sd">        pd.DataFrame: A DataFrame containing three columns:</span>
-<span class="sd">            - &#39;varname&#39;: The name of each variable.</span>
-<span class="sd">            - &#39;mean&#39;: The mean of the elementary effects for each variable.</span>
-<span class="sd">            - &#39;sd&#39;: The standard deviation of the elementary effects for</span>
-<span class="sd">            each variable.</span>
-<span class="sd">        or None: If print is set to False, a plot of the results is</span>
-<span class="sd">            generated instead of returning a DataFrame.</span>
+<span class="sd">        None: The function generates a plot of the results.</span>
 
 <span class="sd">    Examples:</span>
 <span class="sd">        &gt;&gt;&gt; import numpy as np</span>