docs: update dev site for d8714f5f903a3ed0d8ec613f1bc51499bb077174

Author: github-actions[bot]

dev/_modules/pgmpy/models/LinearGaussianBayesianNetwork.html

@@ -962,107 +962,12 @@ <h1>Source code for pgmpy.models.LinearGaussianBayesianNetwork</h1><div class="h
 <span class="sd">    &gt;&gt;&gt; pred = model.predict(df_missing)</span>
 <span class="sd">    &gt;&gt;&gt; list(pred.columns)</span>
 <span class="sd">    ['x3']</span>
-<span class="sd">    &gt;&gt;&gt; print(pred.values)</span>
-<span class="sd">    [[ 8.01138228]</span>
-<span class="sd">     [13.61181367]</span>
-<span class="sd">     [ 8.70432782]</span>
-<span class="sd">     [ 3.71719153]</span>
-<span class="sd">     [ 8.1509597 ]</span>
-<span class="sd">     [ 6.24976516]</span>
-<span class="sd">     [12.2121776 ]</span>
-<span class="sd">     [ 6.01448446]</span>
-<span class="sd">     [ 5.49139518]</span>
-<span class="sd">     [ 9.23748708]</span>
-<span class="sd">     [17.92545478]</span>
-<span class="sd">     [ 3.24653756]</span>
-<span class="sd">     [ 8.78452503]</span>
-<span class="sd">     [10.3678509 ]</span>
-<span class="sd">     [ 5.33405765]</span>
-<span class="sd">     [ 9.09319649]</span>
-<span class="sd">     [10.66717573]</span>
-<span class="sd">     [10.9290793 ]</span>
-<span class="sd">     [ 6.48827753]</span>
-<span class="sd">     [12.7339279 ]</span>
-<span class="sd">     [ 0.79803275]</span>
-<span class="sd">     [ 9.69425692]</span>
-<span class="sd">     [ 5.27994359]</span>
-<span class="sd">     [ 8.80268511]</span>
-<span class="sd">     [ 4.31081468]</span>
-<span class="sd">     [10.76081874]</span>
-<span class="sd">     [10.05810137]</span>
-<span class="sd">     [ 5.93859429]</span>
-<span class="sd">     [ 4.10420816]</span>
-<span class="sd">     [ 7.74976272]</span>
-<span class="sd">     [11.67397411]</span>
-<span class="sd">     [ 9.63141961]</span>
-<span class="sd">     [ 1.72775337]</span>
-<span class="sd">     [ 2.2725024 ]</span>
-<span class="sd">     [ 8.44578257]</span>
-<span class="sd">     [ 7.602702  ]</span>
-<span class="sd">     [10.53853647]</span>
-<span class="sd">     [11.31860773]</span>
-<span class="sd">     [ 8.00975022]</span>
-<span class="sd">     [ 9.22702521]</span>
-<span class="sd">     [ 3.64868722]</span>
-<span class="sd">     [13.67114269]</span>
-<span class="sd">     [15.01854326]</span>
-<span class="sd">     [ 6.37691191]</span>
-<span class="sd">     [13.14971548]</span>
-<span class="sd">     [ 2.75588544]</span>
-<span class="sd">     [16.93490848]</span>
-<span class="sd">     [ 2.97009486]</span>
-<span class="sd">     [ 5.64759205]</span>
-<span class="sd">     [ 7.74788815]</span>
-<span class="sd">     [ 9.86681496]</span>
-<span class="sd">     [ 3.40585598]</span>
-<span class="sd">     [ 9.89093876]</span>
-<span class="sd">     [ 4.08221225]</span>
-<span class="sd">     [15.617452  ]</span>
-<span class="sd">     [ 4.14029637]</span>
-<span class="sd">     [ 8.59698685]</span>
-<span class="sd">     [11.89439088]</span>
-<span class="sd">     [ 0.44433568]</span>
-<span class="sd">     [ 8.42879464]</span>
-<span class="sd">     [14.45268215]</span>
-<span class="sd">     [10.62681186]</span>
-<span class="sd">     [10.76349781]</span>
-<span class="sd">     [16.0269725 ]</span>
-<span class="sd">     [ 8.83836337]</span>
-<span class="sd">     [ 5.30435055]</span>
-<span class="sd">     [ 7.63843465]</span>
-<span class="sd">     [13.18359343]</span>
-<span class="sd">     [ 0.92282836]</span>
-<span class="sd">     [ 3.35438779]</span>
-<span class="sd">     [11.61943098]</span>
-<span class="sd">     [ 4.52648267]</span>
-<span class="sd">     [11.18074558]</span>
-<span class="sd">     [ 4.86137485]</span>
-<span class="sd">     [ 8.49295864]</span>
-<span class="sd">     [ 7.07209154]</span>
-<span class="sd">     [ 6.85461911]</span>
-<span class="sd">     [ 3.96748462]</span>
-<span class="sd">     [ 8.3311032 ]</span>
-<span class="sd">     [ 8.04499479]</span>
-<span class="sd">     [ 7.27919516]</span>
-<span class="sd">     [ 4.77660469]</span>
-<span class="sd">     [-0.33549712]</span>
-<span class="sd">     [ 2.65815359]</span>
-<span class="sd">     [15.58173105]</span>
-<span class="sd">     [12.24334129]</span>
-<span class="sd">     [ 7.60858529]</span>
-<span class="sd">     [ 8.0673818 ]</span>
-<span class="sd">     [10.30962944]</span>
-<span class="sd">     [ 9.73931168]</span>
-<span class="sd">     [ 5.46107107]</span>
-<span class="sd">     [16.95243925]</span>
-<span class="sd">     [ 2.80408287]</span>
-<span class="sd">     [12.23910532]</span>
-<span class="sd">     [14.03289339]</span>
-<span class="sd">     [ 6.26117488]</span>
-<span class="sd">     [ 7.37468791]</span>
-<span class="sd">     [13.3850798 ]</span>
-<span class="sd">     [ 6.83845881]</span>
-<span class="sd">     [ 5.59547155]]</span>
+<span class="sd">    &gt;&gt;&gt; print(pred.values[:5])</span>
+<span class="sd">    [[11.44002506]</span>
+<span class="sd">     [13.55810512]</span>
+<span class="sd">     [ 9.17133321]</span>
+<span class="sd">     [ 6.65304283]</span>
+<span class="sd">     [ 4.12695995]]</span>
 <span class="sd">    """</span>
 
     <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span>
@@ -1577,10 +1482,10 @@ <h1>Source code for pgmpy.models.LinearGaussianBayesianNetwork</h1><div class="h
 <span class="sd">        Simple forward sampling</span>
 
 <span class="sd">        &gt;&gt;&gt; model.simulate(n_samples=3, seed=42) # doctest: +NORMALIZE_WHITESPACE</span>
-<span class="sd">                 x1        x2        x3</span>
-<span class="sd">        0 -3.307168 -4.270673  9.688070</span>
-<span class="sd">        1 -7.195367 -9.833986  9.493212</span>
-<span class="sd">        2 -0.324284 -4.959026  8.758940</span>
+<span class="sd">                 x1         x2         x3</span>
+<span class="sd">        0  2.218868  -8.050502  14.301856</span>
+<span class="sd">        1  4.762259 -10.423011  10.516473</span>
+<span class="sd">        2  1.511362  -5.509290   9.458886</span>
 
 <span class="sd">        Sampling with intervention (do)</span>
 
@@ -1593,10 +1498,10 @@ <h1>Source code for pgmpy.models.LinearGaussianBayesianNetwork</h1><div class="h
 <span class="sd">        Sampling with evidence</span>
 
 <span class="sd">        &gt;&gt;&gt; model.simulate(n_samples=3, seed=42, evidence={"x1": 2.0}) # doctest: +NORMALIZE_WHITESPACE</span>
-<span class="sd">            x1        x2         x3</span>
-<span class="sd">        0  2.0 -6.753790   8.242987</span>
-<span class="sd">        1  2.0 -5.284287  12.763190</span>
-<span class="sd">        2  2.0  1.133549  -3.023892</span>
+<span class="sd">            x1         x2         x3</span>
+<span class="sd">        0  2.0  -2.781132   3.661179</span>
+<span class="sd">        1  2.0  -0.998195   7.819889</span>
+<span class="sd">        2  2.0 -11.804141  11.897602</span>
 
 <span class="sd">        Sampling with both intervention and evidence</span>
 
@@ -1699,7 +1604,7 @@ <h1>Source code for pgmpy.models.LinearGaussianBayesianNetwork</h1><div class="h
         <span class="c1"># Step 4: Sample according to evidence</span>
         <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">evidence</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
             <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
-                <span class="n">rng</span><span class="o">.</span><span class="n">multivariate_normal</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="n">mean</span><span class="p">,</span> <span class="n">cov</span><span class="o">=</span><span class="n">cov</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">n_samples</span><span class="p">),</span>
+                <span class="n">rng</span><span class="o">.</span><span class="n">multivariate_normal</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="n">mean</span><span class="p">,</span> <span class="n">cov</span><span class="o">=</span><span class="n">cov</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">"cholesky"</span><span class="p">),</span>
                 <span class="n">columns</span><span class="o">=</span><span class="n">variables</span><span class="p">,</span>
             <span class="p">)</span>
 
@@ -1712,7 +1617,9 @@ <h1>Source code for pgmpy.models.LinearGaussianBayesianNetwork</h1><div class="h
             <span class="n">mean_cond</span> <span class="o">=</span> <span class="n">mean_cond</span><span class="p">[:,</span> <span class="n">sorted_indices</span><span class="p">]</span>
             <span class="n">cov_cond</span> <span class="o">=</span> <span class="n">cov_cond</span><span class="p">[</span><span class="n">sorted_indices</span><span class="p">][:,</span> <span class="n">sorted_indices</span><span class="p">]</span>
 
-            <span class="n">samples_missing</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">multivariate_normal</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="n">mean_cond</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">cov</span><span class="o">=</span><span class="n">cov_cond</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">n_samples</span><span class="p">)</span>
+            <span class="n">samples_missing</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">multivariate_normal</span><span class="p">(</span>
+                <span class="n">mean</span><span class="o">=</span><span class="n">mean_cond</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">cov</span><span class="o">=</span><span class="n">cov_cond</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">"cholesky"</span>
+            <span class="p">)</span>
             <span class="n">df_missing</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">samples_missing</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">missing_vars</span><span class="p">)</span>
 
             <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="n">n_samples</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="n">variables</span><span class="p">)</span>
@@ -1881,11 +1788,11 @@ <h1>Source code for pgmpy.models.LinearGaussianBayesianNetwork</h1><div class="h
 <span class="sd">        &gt;&gt;&gt; df = df.drop(columns=["folK"])</span>
 <span class="sd">        &gt;&gt;&gt; model.predict(df) # doctest: +NORMALIZE_WHITESPACE</span>
 <span class="sd">               folK</span>
-<span class="sd">        0  0.903384</span>
-<span class="sd">        1  0.576122</span>
-<span class="sd">        2  1.331394</span>
-<span class="sd">        3  0.027018</span>
-<span class="sd">        4  1.731904</span>
+<span class="sd">        0  1.198400</span>
+<span class="sd">        1  1.778992</span>
+<span class="sd">        2  1.605741</span>
+<span class="sd">        3  1.312239</span>
+<span class="sd">        4  1.885455</span>
 <span class="sd">        """</span>
         <span class="c1"># Step 0: Check the inputs</span>
         <span class="n">missing_vars</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nodes</span><span class="p">())</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
@@ -1944,11 +1851,11 @@ <h1>Source code for pgmpy.models.LinearGaussianBayesianNetwork</h1><div class="h
 <span class="sd">        &gt;&gt;&gt; df = df.drop(columns=["folK"])</span>
 <span class="sd">        &gt;&gt;&gt; model.predict(df) # doctest: +NORMALIZE_WHITESPACE</span>
 <span class="sd">               folK</span>
-<span class="sd">        0  0.903384</span>
-<span class="sd">        1  0.576122</span>
-<span class="sd">        2  1.331394</span>
-<span class="sd">        3  0.027018</span>
-<span class="sd">        4  1.731904</span>
+<span class="sd">        0  1.198400</span>
+<span class="sd">        1  1.778992</span>
+<span class="sd">        2  1.605741</span>
+<span class="sd">        3  1.312239</span>
+<span class="sd">        4  1.885455</span>
 <span class="sd">        """</span>
         <span class="n">missing_vars</span><span class="p">,</span> <span class="n">mu_cond</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict_probability</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">mu_cond</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">missing_vars</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">)</span></div>

dev/_modules/pgmpy/prediction/DoubleMLRegressor.html

@@ -988,7 +988,7 @@ <h1>Source code for pgmpy.prediction.DoubleMLRegressor</h1><div class="highlight
 <span class="sd">    &gt;&gt;&gt; lgbn = DAG.from_dagitty(</span>
 <span class="sd">    ...     "dag { X -&gt; T [beta=0.2] X -&gt; Y [beta=0.3] T -&gt; Y [beta=0.4] }"</span>
 <span class="sd">    ... )</span>
-<span class="sd">    &gt;&gt;&gt; data = lgbn.simulate(n_samples=1000, seed=42)</span>
+<span class="sd">    &gt;&gt;&gt; data = lgbn.simulate(n_samples=10000, seed=42)</span>
 <span class="sd">    &gt;&gt;&gt; X = data.loc[:, ["X", "T"]]</span>
 <span class="sd">    &gt;&gt;&gt; y = data["Y"]</span>
 
@@ -1015,7 +1015,7 @@ <h1>Source code for pgmpy.prediction.DoubleMLRegressor</h1><div class="highlight
 <span class="sd">    &gt;&gt;&gt; dml.n_folds_</span>
 <span class="sd">    3</span>
 <span class="sd">    &gt;&gt;&gt; dml.n_samples_</span>
-<span class="sd">    1000</span>
+<span class="sd">    10000</span>
 
 <span class="sd">    Notes</span>
 <span class="sd">    -----</span>

dev/api/generated/causal_inference/pgmpy.prediction.DoubleMLRegressor.DoubleMLRegressor.html

@@ -1030,7 +1030,7 @@ <h1>DoubleMLRegressor<a class="headerlink" href="#doublemlregressor" title="Link
 <span class="gp">&gt;&gt;&gt; </span><span class="n">lgbn</span> <span class="o">=</span> <span class="n">DAG</span><span class="o">.</span><span class="n">from_dagitty</span><span class="p">(</span>
 <span class="gp">... </span>    <span class="s2">"dag { X -&gt; T [beta=0.2] X -&gt; Y [beta=0.3] T -&gt; Y [beta=0.4] }"</span>
 <span class="gp">... </span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">lgbn</span><span class="o">.</span><span class="n">simulate</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">lgbn</span><span class="o">.</span><span class="n">simulate</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s2">"X"</span><span class="p">,</span> <span class="s2">"T"</span><span class="p">]]</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s2">"Y"</span><span class="p">]</span>
 </pre></div>
@@ -1060,7 +1060,7 @@ <h1>DoubleMLRegressor<a class="headerlink" href="#doublemlregressor" title="Link
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">dml</span><span class="o">.</span><span class="n">n_folds_</span>
 <span class="go">3</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">dml</span><span class="o">.</span><span class="n">n_samples_</span>
-<span class="go">1000</span>
+<span class="go">10000</span>
 </pre></div>
 </div>
 <dl class="py method">