document new example/dataset

docs/_sources/datasets/index.rst.txt

@@ -42,6 +42,13 @@ Google Scholar
    :noindex:
 
 
+Academic interests
+---------------------------------
+
+.. autofunction:: pymde.datasets.google_scholar_interests
+   :noindex:
+
+
 scRNA transcriptomes from COVID-19 patients
 -------------------------------------------
 

docs/_sources/examples/index.rst.txt

@@ -65,6 +65,15 @@ much longer than a minute, but it will take longer to compute on a CPU.
 
 - `Google Scholar notebook <https://github.com/cvxgrp/pymde/blob/main/examples/google_scholar.ipynb>`_ 
 
+Word Embedding of Academic Interests
+------------------------------------
+The academic interests notebook shows how to make basic word embeddings.
+In this notebook the words being embedded are the 5000 most popular academic
+interests on Google Scholar.
+
+- `Word embedding notebook <https://github.com/cvxgrp/pymde/blob/main/examples/word_embedding.ipynb>`_ 
+
+
 Population Genetics
 -------------------
 The population genetics notebook embeds genomic data of individuals thought
@@ -101,7 +110,7 @@ Drawing Graphs
 --------------
 PyMDE can be used to layout graphs in the Cartesian plane in an aesthetically
 pleasing way. Compared to many other graph layout libraries, PyMDE can scale
-to higher datasets. And, of course, PyMDE lets you design custom layouts, by
+to much larger datasets. PyMDE also lets you design custom layouts, by
 choosing your own distortion functions and constraints.
 
 This notebook shows various ways of drawing graphs with PyMDE. It also

docs/datasets/index.html

@@ -103,6 +103,7 @@
 <li class="toctree-l2"><a class="reference internal" href="#mnist">MNIST</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#fashion-mnist">Fashion MNIST</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#google-scholar">Google Scholar</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#academic-interests">Academic interests</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#scrna-transcriptomes-from-covid-19-patients">scRNA transcriptomes from COVID-19 patients</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#population-genetics">Population genetics</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#us-counties">US counties</a></li>
@@ -259,6 +260,25 @@ <h2>Google Scholar<a class="headerlink" href="#google-scholar" title="Permalink
 </ul>
 </dd></dl>
 
+</div>
+<div class="section" id="academic-interests">
+<h2>Academic interests<a class="headerlink" href="#academic-interests" title="Permalink to this headline">¶</a></h2>
+<dl class="py function">
+<dt>
+<code class="sig-prename descclassname">pymde.datasets.</code><code class="sig-name descname">google_scholar_interests</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">root</span><span class="o">=</span><span class="default_value">'./'</span></em>, <em class="sig-param"><span class="n">download</span><span class="o">=</span><span class="default_value">True</span></em><span class="sig-paren">)</span> &#x2192; <a class="reference internal" href="../api/index.html#pymde.datasets.Dataset" title="pymde.datasets.Dataset">pymde.datasets.Dataset</a></dt>
+<dd><p>Google Scholar academic interests</p>
+<p>This dataset contains a cooccurrence matrix of the 5000 most popular
+academic interests listed by authors on Google Scholar</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">data</span></code>: a cooccurrence matrix, counting the number of times two
+two interests appeared among a single author’s listed interests,
+with <code class="docutils literal notranslate"><span class="pre">data[i,</span> <span class="pre">j]</span></code> giving the cooccurrence count of <code class="docutils literal notranslate"><span class="pre">interests[i]</span></code> and
+<code class="docutils literal notranslate"><span class="pre">interests[j]</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">attributes</span></code>: one key, <code class="docutils literal notranslate"><span class="pre">interests</span></code>, where <code class="docutils literal notranslate"><span class="pre">interests[i]</span></code> is the
+interest corresponding to the ith row/column of <code class="docutils literal notranslate"><span class="pre">data</span></code>.</p></li>
+</ul>
+</dd></dl>
+
 </div>
 <div class="section" id="scrna-transcriptomes-from-covid-19-patients">
 <h2>scRNA transcriptomes from COVID-19 patients<a class="headerlink" href="#scrna-transcriptomes-from-covid-19-patients" title="Permalink to this headline">¶</a></h2>

docs/examples/index.html

@@ -105,6 +105,7 @@
 <li class="toctree-l2"><a class="reference internal" href="#fashion-mnist">Fashion MNIST</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#single-cell-genomics">Single-Cell Genomics</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#google-scholar">Google Scholar</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#word-embedding-of-academic-interests">Word Embedding of Academic Interests</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#population-genetics">Population Genetics</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#us-counties">US Counties</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#dissimilar-edges-and-negative-weights">Dissimilar Edges and Negative Weights</a></li>
@@ -237,6 +238,15 @@
 <li><p><a class="reference external" href="https://github.com/cvxgrp/pymde/blob/main/examples/google_scholar.ipynb">Google Scholar notebook</a></p></li>
 </ul>
 </div>
+<div class="section" id="word-embedding-of-academic-interests">
+<h2>Word Embedding of Academic Interests<a class="headerlink" href="#word-embedding-of-academic-interests" title="Permalink to this headline">¶</a></h2>
+<p>The academic interests notebook shows how to make basic word embeddings.
+In this notebook the words being embedded are the 5000 most popular academic
+interests on Google Scholar.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/cvxgrp/pymde/blob/main/examples/word_embedding.ipynb">Word embedding notebook</a></p></li>
+</ul>
+</div>
 <div class="section" id="population-genetics">
 <h2>Population Genetics<a class="headerlink" href="#population-genetics" title="Permalink to this headline">¶</a></h2>
 <p>The population genetics notebook embeds genomic data of individuals thought
@@ -273,7 +283,7 @@ <h2>Dissimilar Edges and Negative Weights<a class="headerlink" href="#dissimilar
 <span id="example-graphs"></span><h2>Drawing Graphs<a class="headerlink" href="#drawing-graphs" title="Permalink to this headline">¶</a></h2>
 <p>PyMDE can be used to layout graphs in the Cartesian plane in an aesthetically
 pleasing way. Compared to many other graph layout libraries, PyMDE can scale
-to higher datasets. And, of course, PyMDE lets you design custom layouts, by
+to much larger datasets. PyMDE also lets you design custom layouts, by
 choosing your own distortion functions and constraints.</p>
 <p>This notebook shows various ways of drawing graphs with PyMDE. It also
 introduces the <code class="docutils literal notranslate"><span class="pre">pymde.Graph</span></code> class.</p>

docs_src/source/datasets/index.rst

@@ -42,6 +42,13 @@ Google Scholar
    :noindex:
 
 
+Academic interests
+---------------------------------
+
+.. autofunction:: pymde.datasets.google_scholar_interests
+   :noindex:
+
+
 scRNA transcriptomes from COVID-19 patients
 -------------------------------------------
 

docs_src/source/examples/index.rst

@@ -65,6 +65,15 @@ much longer than a minute, but it will take longer to compute on a CPU.
 
 - `Google Scholar notebook <https://github.com/cvxgrp/pymde/blob/main/examples/google_scholar.ipynb>`_ 
 
+Word Embedding of Academic Interests
+------------------------------------
+The academic interests notebook shows how to make basic word embeddings.
+In this notebook the words being embedded are the 5000 most popular academic
+interests on Google Scholar.
+
+- `Word embedding notebook <https://github.com/cvxgrp/pymde/blob/main/examples/word_embedding.ipynb>`_ 
+
+
 Population Genetics
 -------------------
 The population genetics notebook embeds genomic data of individuals thought
@@ -101,7 +110,7 @@ Drawing Graphs
 --------------
 PyMDE can be used to layout graphs in the Cartesian plane in an aesthetically
 pleasing way. Compared to many other graph layout libraries, PyMDE can scale
-to higher datasets. And, of course, PyMDE lets you design custom layouts, by
+to much larger datasets. PyMDE also lets you design custom layouts, by
 choosing your own distortion functions and constraints.
 
 This notebook shows various ways of drawing graphs with PyMDE. It also

pymde/datasets.py

@@ -514,9 +514,9 @@ def google_scholar_interests(root="./", download=True) -> Dataset:
 
     - ``data``: a cooccurrence matrix, counting the number of times two
       two interests appeared among a single author's listed interests,
-      with data[i, j] giving the cooccurrence count of interests[i] and
-      interests[j]
-    - ``attributes``: one key, ``interests``, where interests[i] is the
+      with ``data[i, j]`` giving the cooccurrence count of ``interests[i]`` and
+      ``interests[j]``
+    - ``attributes``: one key, ``interests``, where ``interests[i]`` is the
       interest corresponding to the ith row/column of ``data``.
     """
     root = os.path.expanduser(root)