Updates docs

Author: ankurankan

_images/examples_Creating_a_Linear_Gaussian_Bayesian_Network_3_0.png

@@ -46,6 +46,7 @@ <h1>All modules for which code is available</h1>
 <li><a href="pgmpy/estimators/StructureScore.html">pgmpy.estimators.StructureScore</a></li>
 <li><a href="pgmpy/estimators/TreeSearch.html">pgmpy.estimators.TreeSearch</a></li>
 <li><a href="pgmpy/estimators/expert.html">pgmpy.estimators.expert</a></li>
+<li><a href="pgmpy/factors/continuous/LinearGaussianCPD.html">pgmpy.factors.continuous.LinearGaussianCPD</a></li>
 <li><a href="pgmpy/factors/continuous/discretize.html">pgmpy.factors.continuous.discretize</a></li>
 <li><a href="pgmpy/factors/discrete/CPD.html">pgmpy.factors.discrete.CPD</a></li>
 <li><a href="pgmpy/factors/discrete/DiscreteFactor.html">pgmpy.factors.discrete.DiscreteFactor</a></li>
@@ -63,10 +64,10 @@ <h1>All modules for which code is available</h1>
 <li><a href="pgmpy/models/DynamicBayesianNetwork.html">pgmpy.models.DynamicBayesianNetwork</a></li>
 <li><a href="pgmpy/models/FactorGraph.html">pgmpy.models.FactorGraph</a></li>
 <li><a href="pgmpy/models/JunctionTree.html">pgmpy.models.JunctionTree</a></li>
+<li><a href="pgmpy/models/LinearGaussianBayesianNetwork.html">pgmpy.models.LinearGaussianBayesianNetwork</a></li>
 <li><a href="pgmpy/models/MarkovChain.html">pgmpy.models.MarkovChain</a></li>
 <li><a href="pgmpy/models/MarkovNetwork.html">pgmpy.models.MarkovNetwork</a></li>
 <li><a href="pgmpy/models/NaiveBayes.html">pgmpy.models.NaiveBayes</a></li>
-<li><a href="pgmpy/models/NoisyOrModel.html">pgmpy.models.NoisyOrModel</a></li>
 <li><a href="pgmpy/models/SEM.html">pgmpy.models.SEM</a></li>
 <li><a href="pgmpy/readwrite/BIF.html">pgmpy.readwrite.BIF</a></li>
 <li><a href="pgmpy/readwrite/PomdpX.html">pgmpy.readwrite.PomdpX</a></li>
@@ -80,7 +81,7 @@ <h1>All modules for which code is available</h1>
 
         </div>
       </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
+      <div class="sphinxsidebar" role="navigation" aria-label="Main">
         <div class="sphinxsidebarwrapper">
 <p class="logo">
   <a href="../index.html">
@@ -156,7 +157,7 @@ <h3 id="searchlabel">Quick search</h3>
       &#169;2023, Ankur Ankan.
 
       |
-      Powered by <a href="https://www.sphinx-doc.org/">Sphinx 7.3.7</a>
+      Powered by <a href="https://www.sphinx-doc.org/">Sphinx 7.4.7</a>
       &amp; <a href="https://alabaster.readthedocs.io">Alabaster 0.7.16</a>
 
     </div>

_images/examples_Creating_a_Linear_Gaussian_Bayesian_Network_6_0.png

@@ -39,6 +39,7 @@ <h1>Source code for pgmpy.estimators.BayesianEstimator</h1><div class="highlight
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
 
+<span class="kn">from</span> <span class="nn">pgmpy.base</span> <span class="kn">import</span> <span class="n">DAG</span>
 <span class="kn">from</span> <span class="nn">pgmpy.estimators</span> <span class="kn">import</span> <span class="n">ParameterEstimator</span>
 <span class="kn">from</span> <span class="nn">pgmpy.factors.discrete</span> <span class="kn">import</span> <span class="n">TabularCPD</span>
 <span class="kn">from</span> <span class="nn">pgmpy.global_vars</span> <span class="kn">import</span> <span class="n">logger</span>
@@ -54,15 +55,20 @@ <h1>Source code for pgmpy.estimators.BayesianEstimator</h1><div class="highlight
 <span class="sd">    &quot;&quot;&quot;</span>
 
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">BayesianNetwork</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="p">(</span><span class="n">DAG</span><span class="p">,</span> <span class="n">BayesianNetwork</span><span class="p">)):</span>
             <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span>
-                <span class="s2">&quot;Bayesian Parameter Estimation is only implemented for BayesianNetwork&quot;</span>
-            <span class="p">)</span>
-        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">latents</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="sa">f</span><span class="s2">&quot;Bayesian Parameter Estimation works only on models with all observed variables. Found latent variables: </span><span class="si">{</span><span class="n">model</span><span class="o">.</span><span class="n">latents</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="s2">&quot;Bayesian Parameter Estimation is only implemented for DAG or BayesianNetwork&quot;</span>
             <span class="p">)</span>
 
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="p">(</span><span class="n">DAG</span><span class="p">,</span> <span class="n">BayesianNetwork</span><span class="p">)):</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">latents</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;Bayesian Parameter Estimation works only on models with all observed variables. Found latent variables: </span><span class="si">{</span><span class="n">model</span><span class="o">.</span><span class="n">latents</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">DAG</span><span class="p">):</span>
+                <span class="n">model</span> <span class="o">=</span> <span class="n">BayesianNetwork</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">edges</span><span class="p">())</span>
+
         <span class="nb">super</span><span class="p">(</span><span class="n">BayesianEstimator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 
 <div class="viewcode-block" id="BayesianEstimator.get_parameters">

_images/examples_Creating_a_Linear_Gaussian_Bayesian_Network_9_0.png

@@ -38,7 +38,7 @@ <h1>Source code for pgmpy.estimators.ExhaustiveSearch</h1><div class="highlight"
 <span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
 
 <span class="kn">from</span> <span class="nn">pgmpy.base</span> <span class="kn">import</span> <span class="n">DAG</span>
-<span class="kn">from</span> <span class="nn">pgmpy.estimators</span> <span class="kn">import</span> <span class="n">K2Score</span><span class="p">,</span> <span class="n">ScoreCache</span><span class="p">,</span> <span class="n">StructureEstimator</span>
+<span class="kn">from</span> <span class="nn">pgmpy.estimators</span> <span class="kn">import</span> <span class="n">K2</span><span class="p">,</span> <span class="n">ScoreCache</span><span class="p">,</span> <span class="n">StructureEstimator</span>
 <span class="kn">from</span> <span class="nn">pgmpy.global_vars</span> <span class="kn">import</span> <span class="n">logger</span>
 <span class="kn">from</span> <span class="nn">pgmpy.utils.mathext</span> <span class="kn">import</span> <span class="n">powerset</span>
 
@@ -54,11 +54,11 @@ <h1>Source code for pgmpy.estimators.ExhaustiveSearch</h1><div class="highlight"
 <span class="sd">    ----------</span>
 <span class="sd">    data: pandas DataFrame object</span>
 <span class="sd">        dataframe object where each column represents one variable.</span>
-<span class="sd">        (If some values in the data are missing the data cells should be set to `numpy.nan`.</span>
-<span class="sd">        Note that pandas converts each column containing `numpy.nan`s to dtype `float`.)</span>
+<span class="sd">        (If some values in the data are missing the data cells should be set to `numpy.NaN`.</span>
+<span class="sd">        Note that pandas converts each column containing `numpy.NaN`s to dtype `float`.)</span>
 
-<span class="sd">    scoring_method: Instance of a `StructureScore`-subclass (`K2Score` is used as default)</span>
-<span class="sd">        An instance of `K2Score`, `BDeuScore`, `BicScore` or &#39;AICScore&#39;.</span>
+<span class="sd">    scoring_method: Instance of a `StructureScore`-subclass (`K2` is used as default)</span>
+<span class="sd">        An instance of `K2`, `BDeu`, `BIC` or &#39;AIC&#39;.</span>
 <span class="sd">        This score is optimized during structure estimation by the `estimate`-method.</span>
 
 <span class="sd">    state_names: dict (optional)</span>
@@ -79,7 +79,7 @@ <h1>Source code for pgmpy.estimators.ExhaustiveSearch</h1><div class="highlight"
             <span class="k">else</span><span class="p">:</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">scoring_method</span> <span class="o">=</span> <span class="n">scoring_method</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">scoring_method</span> <span class="o">=</span> <span class="n">ScoreCache</span><span class="o">.</span><span class="n">ScoreCache</span><span class="p">(</span><span class="n">K2Score</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">),</span> <span class="n">data</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">scoring_method</span> <span class="o">=</span> <span class="n">ScoreCache</span><span class="o">.</span><span class="n">ScoreCache</span><span class="p">(</span><span class="n">K2</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">),</span> <span class="n">data</span><span class="p">)</span>
 
         <span class="nb">super</span><span class="p">(</span><span class="n">ExhaustiveSearch</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 
@@ -157,11 +157,11 @@ <h1>Source code for pgmpy.estimators.ExhaustiveSearch</h1><div class="highlight"
 <span class="sd">        --------</span>
 <span class="sd">        &gt;&gt;&gt; import pandas as pd</span>
 <span class="sd">        &gt;&gt;&gt; import numpy as np</span>
-<span class="sd">        &gt;&gt;&gt; from pgmpy.estimators import ExhaustiveSearch, K2Score</span>
+<span class="sd">        &gt;&gt;&gt; from pgmpy.estimators import ExhaustiveSearch, K2</span>
 <span class="sd">        &gt;&gt;&gt; # create random data sample with 3 variables, where B and C are identical:</span>
 <span class="sd">        &gt;&gt;&gt; data = pd.DataFrame(np.random.randint(0, 5, size=(5000, 2)), columns=list(&#39;AB&#39;))</span>
 <span class="sd">        &gt;&gt;&gt; data[&#39;C&#39;] = data[&#39;B&#39;]</span>
-<span class="sd">        &gt;&gt;&gt; searcher = ExhaustiveSearch(data, scoring_method=K2Score(data))</span>
+<span class="sd">        &gt;&gt;&gt; searcher = ExhaustiveSearch(data, scoring_method=K2(data))</span>
 <span class="sd">        &gt;&gt;&gt; for score, model in searcher.all_scores():</span>
 <span class="sd">        ...   print(&quot;{0}\t{1}&quot;.format(score, model.edges()))</span>
 <span class="sd">        -24234.44977974726      [(&#39;A&#39;, &#39;B&#39;), (&#39;A&#39;, &#39;C&#39;)]</span>