updated plot res., added ca_housing ref

lshpaner · Sep 9, 2024 · 9c3316b · 9c3316b
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diff --git a/assets/2d_pdp_grid.svg b/assets/2d_pdp_grid.svg
diff --git a/docs/.doctrees/environment.pickle b/docs/.doctrees/environment.pickle
diff --git a/docs/.doctrees/references.doctree b/docs/.doctrees/references.doctree
diff --git a/docs/.doctrees/usage_guide.doctree b/docs/.doctrees/usage_guide.doctree
diff --git a/docs/_images/2d_pdp_grid.svg b/docs/_images/2d_pdp_grid.svg
diff --git a/docs/_sources/references.rst.txt b/docs/_sources/references.rst.txt
@@ -30,4 +30,4 @@ References
 
 3. Waskom, M. (2021). *Seaborn: Statistical Data Visualization*. *Journal of Open Source Software*, 6(60), 3021. `https://doi.org/10.21105/joss.03021 <https://doi.org/10.21105/joss.03021>`_.
 
-
+4. Pace, R. K., & Barry, R. (1997). *Sparse Spatial Autoregressions*. *Statistics & Probability Letters*, 33(3), 291-297. `https://doi.org/10.1016/S0167-7152(96)00140-X <https://doi.org/10.1016/S0167-7152(96)00140-X>`_.
diff --git a/docs/_sources/usage_guide.rst.txt b/docs/_sources/usage_guide.rst.txt
@@ -3341,7 +3341,7 @@ The ``plot_2d_pdp`` function generates 2D partial dependence plots for individua
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Consider a scenario where you have a machine learning model predicting median 
-house values in California. Suppose you want to understand how non-location 
+house values in California [4]_. Suppose you want to understand how non-location 
 features like the average number of occupants per household (``AveOccup``) and the 
 age of the house (``HouseAge``) jointly influence house values. A 2D partial 
 dependence plot allows you to visualize this relationship in two ways: either as 
@@ -3437,7 +3437,7 @@ the California housing dataset:
 
    <div class="no-click">
 
-.. image:: ../assets/2d_pdp_grid.png
+.. image:: ../assets/2d_pdp_grid.svg
    :alt: Scatter Plot Comparisons (Grouped)
    :align: center
    :width: 900px
@@ -3602,7 +3602,7 @@ The ``plot_3d_pdp`` function extends the concept of partial dependence to three
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Consider a scenario where you have a machine learning model predicting median 
-house values in California. Suppose you want to understand how non-location 
+house values in California [4]_. Suppose you want to understand how non-location 
 features like the average number of occupants per household (``AveOccup``) and the 
 age of the house (``HouseAge``) jointly influence house values. A 3D partial 
 dependence plot allows you to visualize this relationship in a more comprehensive 
@@ -3792,3 +3792,5 @@ By adjusting these parameters, you can customize the behavior and appearance of
 .. [2] Waskom, M. (2021). *Seaborn: Statistical Data Visualization*. *Journal of Open Source Software*, 6(60), 3021. `https://doi.org/10.21105/joss.03021 <https://doi.org/10.21105/joss.03021>`_.
 
 .. [3] Hunter, J. D. (2007). *Matplotlib: A 2D Graphics Environment*. *Computing in Science & Engineering*, 9(3), 90-95. `https://doi.org/10.1109/MCSE.2007.55 <https://doi.org/10.1109/MCSE.2007.55>`_.
+
+.. [4] Pace, R. K., & Barry, R. (1997). *Sparse Spatial Autoregressions*. *Statistics & Probability Letters*, 33(3), 291-297. `https://doi.org/10.1016/S0167-7152(96)00140-X <https://doi.org/10.1016/S0167-7152(96)00140-X>`_.
diff --git a/docs/index.html b/docs/index.html
@@ -209,9 +209,9 @@ <h1>Table of Contents<a class="headerlink" href="#table-of-contents" title="Link
 <li class="toctree-l3"><a class="reference internal" href="usage_guide.html#d-plots-ca-housing-example">2D Plots - CA Housing Example</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="usage_guide.html#id20">3D Partial Dependence Plots</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="usage_guide.html#id21">3D Partial Dependence Plots</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="usage_guide.html#plot_3d_pdp"><code class="docutils literal notranslate"><span class="pre">plot_3d_pdp()</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="usage_guide.html#id21">3D Plots - CA Housing Example</a></li>
+<li class="toctree-l3"><a class="reference internal" href="usage_guide.html#id22">3D Plots - CA Housing Example</a></li>
 <li class="toctree-l3"><a class="reference internal" href="usage_guide.html#static-plot">Static Plot</a></li>
 <li class="toctree-l3"><a class="reference internal" href="usage_guide.html#interactive-plot">Interactive Plot</a></li>
 </ul>

diff --git a/docs/objects.inv b/docs/objects.inv
diff --git a/docs/references.html b/docs/references.html
@@ -103,6 +103,7 @@ <h1>References<a class="headerlink" href="#id1" title="Link to this heading">
 <li><p>Hunter, J. D. (2007). <em>Matplotlib: A 2D Graphics Environment</em>. <em>Computing in Science &amp; Engineering</em>, 9(3), 90-95. <a class="reference external" href="https://doi.org/10.1109/MCSE.2007.55">https://doi.org/10.1109/MCSE.2007.55</a>.</p></li>
 <li><p>Kohavi, R. (1996). <em>Census Income</em>. UCI Machine Learning Repository. <a class="reference external" href="https://doi.org/10.24432/C5GP7S">https://doi.org/10.24432/C5GP7S</a>.</p></li>
 <li><p>Waskom, M. (2021). <em>Seaborn: Statistical Data Visualization</em>. <em>Journal of Open Source Software</em>, 6(60), 3021. <a class="reference external" href="https://doi.org/10.21105/joss.03021">https://doi.org/10.21105/joss.03021</a>.</p></li>
+<li><p>Pace, R. K., &amp; Barry, R. (1997). <em>Sparse Spatial Autoregressions</em>. <em>Statistics &amp; Probability Letters</em>, 33(3), 291-297. <a class="reference external" href="https://doi.org/10.1016/S0167-7152(96)00140-X">https://doi.org/10.1016/S0167-7152(96)00140-X</a>.</p></li>
 </ol>
 </section>
 

diff --git a/docs/searchindex.js b/docs/searchindex.js