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py-why · Sep 26, 2023 · a66d9f4 · a66d9f4
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diff --git a/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip b/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
diff --git a/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
diff --git a/dev/_images/sphx_glr_plot_psifci_alg_002.png b/dev/_images/sphx_glr_plot_psifci_alg_002.png
diff --git a/dev/_images/sphx_glr_plot_psifci_alg_003.png b/dev/_images/sphx_glr_plot_psifci_alg_003.png
diff --git a/dev/_sources/auto_examples/plot_pc_alg.rst.txt b/dev/_sources/auto_examples/plot_pc_alg.rst.txt
@@ -150,7 +150,7 @@ are conditionally independent.
 
  .. code-block:: none
 
-    Fitting causal models:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node x:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node y:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node z:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node w:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00<00:00, 1656.68it/s]
+    Fitting causal models:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node x:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node y:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node z:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node w:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00<00:00, 1676.21it/s]
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@@ -165,7 +165,7 @@ are conditionally independent.
     w    [1, 2, 0]
     dtype: object
 
-    <graphviz.graphs.Digraph object at 0x7f992c17b040>
+    <graphviz.graphs.Digraph object at 0x7fb9cfb86400>
 
 
 
@@ -330,7 +330,7 @@ always a possibility that the CI test makes a mistake.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  2.311 seconds)
+   **Total running time of the script:** ( 0 minutes  2.740 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_pc_alg.py:

diff --git a/dev/_sources/auto_examples/plot_psifci_alg.rst.txt b/dev/_sources/auto_examples/plot_psifci_alg.rst.txt
@@ -272,7 +272,7 @@ Figure 8 in :footcite:`Jaber2020causal`.
 
  .. code-block:: none
 
-    There are 158 edges in the resulting PAG
+    There are 153 edges in the resulting PAG
 
 
 
@@ -347,7 +347,7 @@ Visualize the graph without the F-nodes
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  44.752 seconds)
+   **Total running time of the script:** ( 1 minutes  44.720 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_psifci_alg.py:

diff --git a/dev/_sources/auto_examples/plot_score_alg.rst.txt b/dev/_sources/auto_examples/plot_score_alg.rst.txt
@@ -187,7 +187,7 @@ This will then induce a causal graph, which we can visualize.
 
  .. code-block:: none
 
-    Fitting causal models:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node x:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node y:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node z:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node w:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00<00:00, 1809.25it/s]
+    Fitting causal models:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node x:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node y:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node z:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node w:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00<00:00, 1312.16it/s]
        x  ...  w
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@@ -333,7 +333,7 @@ step, corresponding to the causal graph inferred by SCORE.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.538 seconds)
+   **Total running time of the script:** ( 0 minutes  0.670 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_score_alg.py:

diff --git a/dev/_sources/auto_examples/prior_know_score.rst.txt b/dev/_sources/auto_examples/prior_know_score.rst.txt
@@ -137,7 +137,7 @@ This will then induce a causal graph, which we can visualize.
 
  .. code-block:: none
 
-    Fitting causal models:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node x:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node y:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node z:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node w:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00<00:00, 1936.88it/s]
+    Fitting causal models:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node x:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node y:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node z:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node w:   0%|          | 0/4 [00:00<?, ?it/s]    Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00<00:00, 1551.43it/s]
        x  ...  w
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@@ -273,7 +273,7 @@ For a detailed example on order-based discovery approaches, see this
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.870 seconds)
+   **Total running time of the script:** ( 0 minutes  1.075 seconds)
 
 
 .. _sphx_glr_download_auto_examples_prior_know_score.py:

diff --git a/dev/auto_examples/plot_pc_alg.html b/dev/auto_examples/plot_pc_alg.html
@@ -379,7 +379,7 @@ <h2>Simulate some data<a class="headerlink" href="#simulate-some-data" title="Pe
 Fitting causal mechanism of node y:   0%|          | 0/4 [00:00&lt;?, ?it/s]
 Fitting causal mechanism of node z:   0%|          | 0/4 [00:00&lt;?, ?it/s]
 Fitting causal mechanism of node w:   0%|          | 0/4 [00:00&lt;?, ?it/s]
-Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1656.68it/s]
+Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1676.21it/s]
    x  ...  w
 0  0  ...  1
 1  1  ...  2
@@ -394,7 +394,7 @@ <h2>Simulate some data<a class="headerlink" href="#simulate-some-data" title="Pe
 w    [1, 2, 0]
 dtype: object
 
-&lt;graphviz.graphs.Digraph object at 0x7f992c17b040&gt;
+&lt;graphviz.graphs.Digraph object at 0x7fb9cfb86400&gt;
 </pre></div>
 </div>
 </section>
@@ -488,7 +488,7 @@ <h2>Run structure learning algorithm<a class="headerlink" href="#run-structure-l
 <img src="../_images/sphx_glr_plot_pc_alg_002.png" srcset="../_images/sphx_glr_plot_pc_alg_002.png" alt="plot pc alg" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&#39;ci_cpdag.png&#39;
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  2.311 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  2.740 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-pc-alg-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/f1c5e835df0a73798ede9531ab050d9e/plot_pc_alg.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_pc_alg.py</span></code></a></p>

diff --git a/dev/auto_examples/plot_psifci_alg.html b/dev/auto_examples/plot_psifci_alg.html
@@ -438,7 +438,7 @@ <h3>Analyze the results<a class="headerlink" href="#analyze-the-results" title="
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;There are </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><a href="https://www.pywhy.org/pywhy-graphs/dev/generated/pywhy_graphs.networkx.MixedEdgeGraph.html#pywhy_graphs.networkx.MixedEdgeGraph.to_undirected" title="pywhy_graphs.networkx.MixedEdgeGraph.to_undirected" class="sphx-glr-backref-module-pywhy_graphs-networkx sphx-glr-backref-type-py-method"><span class="n">est_pag</span><span class="o">.</span><span class="n">to_undirected</span></a><span class="p">()</span><span class="o">.</span><span class="n">edges</span><span class="p">)</span><span class="si">}</span><span class="s2"> edges in the resulting PAG&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>There are 158 edges in the resulting PAG
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>There are 153 edges in the resulting PAG
 </pre></div>
 </div>
 <p>Visualize the full graph including the F-node</p>
@@ -467,7 +467,7 @@ <h3>Analyze the results<a class="headerlink" href="#analyze-the-results" title="
 <img src="../_images/sphx_glr_plot_psifci_alg_003.png" srcset="../_images/sphx_glr_plot_psifci_alg_003.png" alt="plot psifci alg" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&#39;psi_pag.png&#39;
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  44.752 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  44.720 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-psifci-alg-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/75f223a7812556342f0f4b79148d944b/plot_psifci_alg.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_psifci_alg.py</span></code></a></p>

diff --git a/dev/auto_examples/plot_score_alg.html b/dev/auto_examples/plot_score_alg.html
@@ -411,7 +411,7 @@ <h2>Simulate some data<a class="headerlink" href="#simulate-some-data" title="Pe
 Fitting causal mechanism of node y:   0%|          | 0/4 [00:00&lt;?, ?it/s]
 Fitting causal mechanism of node z:   0%|          | 0/4 [00:00&lt;?, ?it/s]
 Fitting causal mechanism of node w:   0%|          | 0/4 [00:00&lt;?, ?it/s]
-Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1809.25it/s]
+Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1312.16it/s]
    x  ...  w
 0  0  ...  1
 1  1  ...  2
@@ -511,7 +511,7 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headin
 One is the fully connected graph associated to the inferred topological order
 <code class="docutils literal notranslate"><span class="pre">[z,</span> <span class="pre">x,</span> <span class="pre">y,</span> <span class="pre">w]</span></code> of the graph nodes. The other is the sparser graph after the pruning
 step, corresponding to the causal graph inferred by SCORE.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.538 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.670 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-score-alg-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/7a0af3d327be5615defba776eff15f30/plot_score_alg.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_score_alg.py</span></code></a></p>

diff --git a/dev/auto_examples/prior_know_score.html b/dev/auto_examples/prior_know_score.html
@@ -372,7 +372,7 @@ <h2>Simulate some data<a class="headerlink" href="#simulate-some-data" title="Pe
 Fitting causal mechanism of node y:   0%|          | 0/4 [00:00&lt;?, ?it/s]
 Fitting causal mechanism of node z:   0%|          | 0/4 [00:00&lt;?, ?it/s]
 Fitting causal mechanism of node w:   0%|          | 0/4 [00:00&lt;?, ?it/s]
-Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1936.88it/s]
+Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1551.43it/s]
    x  ...  w
 0  0  ...  1
 1  1  ...  2
@@ -450,7 +450,7 @@ <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this headin
 This example can be generalized to the case of <code class="docutils literal notranslate"><span class="pre">NoGAM</span></code>, <code class="docutils literal notranslate"><span class="pre">DAS</span></code>, and <code class="docutils literal notranslate"><span class="pre">CAM</span></code> methods.
 For a detailed example on order-based discovery approaches, see this
 <a class="reference internal" href="plot_score_alg.html#ex-score-algorithm"><span class="std std-ref">tutorial</span></a>.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.870 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  1.075 seconds)</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-prior-know-score-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/54fa84cb158e29731e39a59414e303a3/prior_know_score.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">prior_know_score.py</span></code></a></p>

diff --git a/dev/generated/dodiscover.ContextBuilder.html b/dev/generated/dodiscover.ContextBuilder.html
@@ -801,7 +801,7 @@ <h1>dodiscover.ContextBuilder<a class="headerlink" href="#dodiscover-contextbuil
 nor <code class="docutils literal notranslate"><span class="pre">variables</span></code> is set, then it is presumed that <code class="docutils literal notranslate"><span class="pre">variables</span></code> consists
 of the columns of <code class="docutils literal notranslate"><span class="pre">data</span></code> and <code class="docutils literal notranslate"><span class="pre">latents</span></code> is the empty set.</p>
 </dd>
-<dt><strong>data</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Optional</span></code>[<a class="reference external" href="https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.0dev0+276.gd01669f5cd)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pd.DataFrame</span></code></a>]</span></dt><dd><p>the data to use for variable inference.</p>
+<dt><strong>data</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Optional</span></code>[<a class="reference external" href="https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.0dev0+283.g89bd5695df)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pd.DataFrame</span></code></a>]</span></dt><dd><p>the data to use for variable inference.</p>
 </dd>
 </dl>
 </dd>

diff --git a/dev/generated/dodiscover.InterventionalContextBuilder.html b/dev/generated/dodiscover.InterventionalContextBuilder.html
@@ -884,7 +884,7 @@ <h1>dodiscover.InterventionalContextBuilder<a class="headerlink" href="#dodiscov
 nor <code class="docutils literal notranslate"><span class="pre">variables</span></code> is set, then it is presumed that <code class="docutils literal notranslate"><span class="pre">variables</span></code> consists
 of the columns of <code class="docutils literal notranslate"><span class="pre">data</span></code> and <code class="docutils literal notranslate"><span class="pre">latents</span></code> is the empty set.</p>
 </dd>
-<dt><strong>data</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Optional</span></code>[<a class="reference external" href="https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.0dev0+276.gd01669f5cd)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pd.DataFrame</span></code></a>]</span></dt><dd><p>the data to use for variable inference.</p>
+<dt><strong>data</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Optional</span></code>[<a class="reference external" href="https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.0dev0+283.g89bd5695df)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pd.DataFrame</span></code></a>]</span></dt><dd><p>the data to use for variable inference.</p>
 </dd>
 </dl>
 </dd>