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Circle Ci committed Jun 30, 2024
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6 changes: 3 additions & 3 deletions dev/_sources/auto_examples/plot_pc_alg.rst.txt
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@@ -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, 1807.89it/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, 1577.84it/s]
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@@ -165,7 +165,7 @@ are conditionally independent.
w [1, 2, 0]
dtype: object

<graphviz.graphs.Digraph object at 0x7fb385f6ef10>
<graphviz.graphs.Digraph object at 0x7fd2e1d3f220>



@@ -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 2.291 seconds)
**Total running time of the script:** ( 0 minutes 2.579 seconds)


.. _sphx_glr_download_auto_examples_plot_pc_alg.py:
4 changes: 2 additions & 2 deletions dev/_sources/auto_examples/plot_psifci_alg.rst.txt
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@@ -272,7 +272,7 @@ Figure 8 in :footcite:`Jaber2020causal`.

.. code-block:: none
There are 151 edges in the resulting PAG
There are 159 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 26.954 seconds)
**Total running time of the script:** ( 1 minutes 37.699 seconds)


.. _sphx_glr_download_auto_examples_plot_psifci_alg.py:
4 changes: 2 additions & 2 deletions dev/_sources/auto_examples/plot_score_alg.rst.txt
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@@ -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, 1986.88it/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, 1902.18it/s]
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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 0.464 seconds)
**Total running time of the script:** ( 0 minutes 0.460 seconds)


.. _sphx_glr_download_auto_examples_plot_score_alg.py:
4 changes: 2 additions & 2 deletions dev/_sources/auto_examples/prior_know_score.rst.txt
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@@ -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, 1897.23it/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, 1875.60it/s]
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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 0.769 seconds)
**Total running time of the script:** ( 0 minutes 0.787 seconds)


.. _sphx_glr_download_auto_examples_prior_know_score.py:
6 changes: 3 additions & 3 deletions dev/auto_examples/plot_pc_alg.html
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@@ -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, 1807.89it/s]
Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1577.84it/s]
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@@ -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 0x7fb385f6ef10&gt;
&lt;graphviz.graphs.Digraph object at 0x7fd2e1d3f220&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 2.291 seconds)</p>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 2.579 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>
4 changes: 2 additions & 2 deletions dev/auto_examples/plot_psifci_alg.html
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@@ -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 151 edges in the resulting PAG
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>There are 159 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 26.954 seconds)</p>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes 37.699 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>
4 changes: 2 additions & 2 deletions dev/auto_examples/plot_score_alg.html
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@@ -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, 1986.88it/s]
Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1902.18it/s]
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@@ -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 0.464 seconds)</p>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.460 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>
4 changes: 2 additions & 2 deletions dev/auto_examples/prior_know_score.html
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@@ -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, 1897.23it/s]
Fitting causal mechanism of node w: 100%|##########| 4/4 [00:00&lt;00:00, 1875.60it/s]
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@@ -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>
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<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>
2 changes: 1 addition & 1 deletion dev/generated/dodiscover.ContextBuilder.html
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@@ -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 v3.0.0.dev0+1163.ga89f208535)"><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>
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</dd>
</dl>
</dd>
2 changes: 1 addition & 1 deletion dev/generated/dodiscover.InterventionalContextBuilder.html
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@@ -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
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<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 v3.0.0.dev0+1163.ga89f208535)"><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 v3.0.0.dev0+1164.g23e592f51b)"><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>
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