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<section id="nxep-4-adopting-numpy-random-generator-as-default-random-interface">
<span id="nxep4"></span><h1>NXEP 4 — Adopting <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> as default random interface<a class="headerlink" href="#nxep-4-adopting-numpy-random-generator-as-default-random-interface" title="Permalink to this heading">#</a></h1>
<dl class="field-list simple">
<dt class="field-odd">Author<span class="colon">:</span></dt>
<dd class="field-odd"><p>Ross Barnowski (<a class="reference external" href="mailto:rossbar%40berkeley.edu">rossbar<span>@</span>berkeley<span>.</span>edu</a>)</p>
</dd>
<dt class="field-even">Status<span class="colon">:</span></dt>
<dd class="field-even"><p>Draft</p>
</dd>
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Standards Track</p>
</dd>
<dt class="field-even">Created<span class="colon">:</span></dt>
<dd class="field-even"><p>2022-02-24</p>
</dd>
</dl>
<section id="abstract">
<h2>Abstract<a class="headerlink" href="#abstract" title="Permalink to this heading">#</a></h2>
<p>Pseudo-random numbers play an important role in many graph and network analysis
algorithms in NetworkX.
NetworkX provides a <a class="reference internal" href="../../reference/randomness.html#randomness"><span class="std std-ref">standard interface to random number generators</span></a>
that includes support for <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a> and the Python built-in <a class="reference external" href="https://docs.python.org/3/library/random.html#module-random" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random</span></code></a> module.
<a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a> is used extensively within NetworkX and in several cases is the
preferred package for random number generation.
NumPy introduced a new interface in the <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a> package in NumPy version
1.17.
According to <span class="xref std std-doc">NEP19</span>, the new interface based on
<a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a>
is recommended over the legacy <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> as the former has
<a class="reference external" href="https://www.pcg-random.org/index.html">better statistical properties</a>,
<span class="xref std std-doc">more features</span>,
and <span class="xref std std-doc">improved performance</span>.
This NXEP proposes a strategy for adopting <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> as the
<strong>default</strong> interface for random number generation within NetworkX.</p>
</section>
<section id="motivation-and-scope">
<h2>Motivation and Scope<a class="headerlink" href="#motivation-and-scope" title="Permalink to this heading">#</a></h2>
<p>The primary motivation for adopting <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> as the default
random number generation engine in NetworkX is to allow users to benefit from
the improvements in <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a>, including:
- Advances in statistical quality of modern pRNG’s
- Improved performance
- Additional features</p>
<p>The <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> API is very similar to the <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a>
API, so users can benefit from these improvements without any additional changes
<a class="footnote-reference brackets" href="#f1" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> to their existing NetworkX code.</p>
<p>In principle this change would impact NetworkX users that use any of the
functions decorated by <a class="reference internal" href="../../reference/generated/networkx.utils.decorators.np_random_state.html#networkx.utils.decorators.np_random_state" title="networkx.utils.decorators.np_random_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">np_random_state</span></code></a>
or <a class="reference internal" href="../../reference/generated/networkx.utils.decorators.py_random_state.html#networkx.utils.decorators.py_random_state" title="networkx.utils.decorators.py_random_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">py_random_state</span></code></a> (when the <code class="docutils literal notranslate"><span class="pre">random_state</span></code> argument
involves <code class="docutils literal notranslate"><span class="pre">numpy</span></code>).
See the next section for details.</p>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="f1" role="note">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
<p>See note about the compatibility layer in the <a class="reference internal" href="#implementation"><span class="std std-ref">Implementation section</span></a></p>
</aside>
</aside>
</section>
<section id="usage-and-impact">
<h2>Usage and Impact<a class="headerlink" href="#usage-and-impact" title="Permalink to this heading">#</a></h2>
<p>In NetworkX, random number generators are typically created via a decorator:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">networkx.utils</span> <span class="kn">import</span> <span class="n">np_random_state</span>
<span class="nd">@np_random_state</span><span class="p">(</span><span class="s2">"seed"</span><span class="p">)</span> <span class="c1"># Or could be the arg position, i.e. 0</span>
<span class="k">def</span> <span class="nf">foo</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">return</span> <span class="n">seed</span>
</pre></div>
</div>
<p>The decorator is responsible for mapping various different inputs into an
instance of a random number generator within the function.
Currently, the random number generator instance that is returned is a
<a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> object:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">foo</span><span class="p">(</span><span class="kc">None</span><span class="p">))</span>
<span class="go">numpy.random.mtrand.RandomState</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">foo</span><span class="p">(</span><span class="mi">12345</span><span class="p">))</span>
<span class="go">numpy.random.mtrand.RandomState</span>
</pre></div>
</div>
<p>The only way to get a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> instance from the random state
decorators is to pass the instance in directly:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">()</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">foo</span><span class="p">(</span><span class="n">rng</span><span class="p">))</span>
<span class="go">numpy.random._generator.Generator</span>
</pre></div>
</div>
<p>This NXEP proposes to change the behavior so that when e.g. and integer or
<a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a> is given for the <code class="docutils literal notranslate"><span class="pre">seed</span></code> parameter, a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> instance
is returned instead, i.e.:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">foo</span><span class="p">(</span><span class="kc">None</span><span class="p">))</span>
<span class="go">numpy.random._generator.Generator</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">foo</span><span class="p">(</span><span class="mi">12345</span><span class="p">))</span>
<span class="go">numpy.random._generator.Generator</span>
</pre></div>
</div>
<p><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> instances can still be used as <code class="docutils literal notranslate"><span class="pre">seed</span></code>, but they
must be explicitly passed in:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">rs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">12345</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">foo</span><span class="p">(</span><span class="n">rs</span><span class="p">))</span>
<span class="go">numpy.random.mtrand.RandomState</span>
</pre></div>
</div>
</section>
<section id="backward-compatibility">
<h2>Backward compatibility<a class="headerlink" href="#backward-compatibility" title="Permalink to this heading">#</a></h2>
<p>There are three main concerns:</p>
<ol class="arabic simple">
<li><p>The <code class="docutils literal notranslate"><span class="pre">Generator</span></code> interface is not stream-compatible with <code class="docutils literal notranslate"><span class="pre">RandomState</span></code>,
thus the results of the <code class="docutils literal notranslate"><span class="pre">Generator</span></code> methods will not be exactly the same
as the corresponding <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> methods.</p></li>
<li><p>There are a few slight differences in method names and availability between
the <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> and <code class="docutils literal notranslate"><span class="pre">Generator</span></code> APIs.</p></li>
<li><p>There is no global <code class="docutils literal notranslate"><span class="pre">Generator</span></code> instance internal to <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a> as is
the case for <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a>.</p></li>
</ol>
<p>The <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> interface breaks the stream-compatibility
guarantee that <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> upheld of exact reproducibility of
values.
Switching the default random number generator from <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> to
<code class="docutils literal notranslate"><span class="pre">Generator</span></code> would mean functions decorated with <code class="docutils literal notranslate"><span class="pre">np_random_state</span></code> would
produce different results when a value <em>other than an instantiated rng</em> is used
as the seed.
For example, let’s take the following function:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@np_random_state</span><span class="p">(</span><span class="s2">"seed"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">bar</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""Return an array of `num` uniform random numbers."""</span>
<span class="k">return</span> <span class="n">seed</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="n">num</span><span class="p">)</span>
</pre></div>
</div>
<p>With the current implementation of <code class="docutils literal notranslate"><span class="pre">np_random_state</span></code>, a user can pass in an
integer value to <code class="docutils literal notranslate"><span class="pre">seed</span></code> which will be used to seed a new <code class="docutils literal notranslate"><span class="pre">RandomState</span></code>
instance.
Using the same seed value guarantees the output is always exactly reproducible:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">bar</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">12345</span><span class="p">)</span>
<span class="go">array([0.92961609, 0.31637555, 0.18391881, 0.20456028, 0.56772503,</span>
<span class="go"> 0.5955447 , 0.96451452, 0.6531771 , 0.74890664, 0.65356987])</span>
<span class="gp">>>> </span><span class="n">bar</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">12345</span><span class="p">)</span>
<span class="go">array([0.92961609, 0.31637555, 0.18391881, 0.20456028, 0.56772503,</span>
<span class="go"> 0.5955447 , 0.96451452, 0.6531771 , 0.74890664, 0.65356987])</span>
</pre></div>
</div>
<p>However, after changing the default rng returned by <code class="docutils literal notranslate"><span class="pre">np_random_state</span></code> to
a <code class="docutils literal notranslate"><span class="pre">Generator</span></code> instance, the values produced by the decorated <code class="docutils literal notranslate"><span class="pre">bar</span></code> function
for integer seeds would no longer be identical:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">bar</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">12345</span><span class="p">)</span>
<span class="go">array([0.22733602, 0.31675834, 0.79736546, 0.67625467, 0.39110955,</span>
<span class="go"> 0.33281393, 0.59830875, 0.18673419, 0.67275604, 0.94180287])</span>
</pre></div>
</div>
<p>In order to recover exact reproducibility of the original results, a seeded
<code class="docutils literal notranslate"><span class="pre">RandomState</span></code> instance would need to be explicitly created and passed in
via <code class="docutils literal notranslate"><span class="pre">seed</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">12345</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">bar</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">rng</span><span class="p">)</span>
<span class="go">array([0.92961609, 0.31637555, 0.18391881, 0.20456028, 0.56772503,</span>
<span class="go"> 0.5955447 , 0.96451452, 0.6531771 , 0.74890664, 0.65356987])</span>
</pre></div>
</div>
<p>Because the streams would no longer be compatible, it is proposed in this NXEP
that switching the default random number generator only be considered for a
major release, e.g. the transition from NetworkX 2.X to NetworkX 3.0.</p>
<p>The second point is only a concern for users who are using
<a class="reference internal" href="../../reference/generated/networkx.utils.misc.create_random_state.html#networkx.utils.misc.create_random_state" title="networkx.utils.misc.create_random_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_random_state</span></code></a> and the corresponding decorator
<a class="reference internal" href="../../reference/generated/networkx.utils.decorators.np_random_state.html#networkx.utils.decorators.np_random_state" title="networkx.utils.decorators.np_random_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">np_random_state</span></code></a> in their own libraries.
For example, the <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.RandomState.randint.html#numpy.random.RandomState.randint" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState.randint</span></code></a> method has been replaced
by <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.integers.html#numpy.random.Generator.integers" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator.integers</span></code></a>.
Thus any code that uses <code class="xref py py-obj docutils literal notranslate"><span class="pre">create_random_state</span></code> or <code class="xref py py-obj docutils literal notranslate"><span class="pre">create_py_random_state</span></code> and
relies on the <code class="docutils literal notranslate"><span class="pre">randint</span></code> method of the returned rng would result in an
<a class="reference external" href="https://docs.python.org/3/library/exceptions.html#AttributeError" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AttributeError</span></code></a>.
This can be addressed with a compatiblity class similar to the
<code class="xref py py-obj docutils literal notranslate"><span class="pre">networkx.utils.misc.PythonRandomInterface</span></code> class, which provides a compatibility
layer between <a class="reference external" href="https://docs.python.org/3/library/random.html#module-random" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random</span></code></a> and <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a>.</p>
<p><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_random_state</span></code> currently returns the global <code class="docutils literal notranslate"><span class="pre">numpy.random.mtrand._rand</span></code>
<code class="xref py py-obj docutils literal notranslate"><span class="pre">RandomState</span></code> instance when the input is <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a> or the <code class="docutils literal notranslate"><span class="pre">numpy.random</span></code> module.
By switching to <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a>, this will no longer be possible as
there is no global, internal <code class="xref py py-obj docutils literal notranslate"><span class="pre">Generator</span></code> instance in the <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a> module.
This should have no effect on users, as <code class="docutils literal notranslate"><span class="pre">seed=None</span></code> currently does not
guarantee reproducible results.</p>
</section>
<section id="detailed-description">
<h2>Detailed description<a class="headerlink" href="#detailed-description" title="Permalink to this heading">#</a></h2>
<p>This NXEP proposes to change the default random number generator produced by
the <a class="reference internal" href="../../reference/generated/networkx.utils.misc.create_random_state.html#networkx.utils.misc.create_random_state" title="networkx.utils.misc.create_random_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_random_state</span></code></a> function (and the related
decorator <a class="reference internal" href="../../reference/generated/networkx.utils.decorators.np_random_state.html#networkx.utils.decorators.np_random_state" title="networkx.utils.decorators.np_random_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">np_random_state</span></code></a>) from a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a>
instance to a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> instance when the input to the
function is either an integer or <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a>.</p>
</section>
<section id="related-work">
<h2>Related Work<a class="headerlink" href="#related-work" title="Permalink to this heading">#</a></h2>
<p>Scikit-learn has a similar pattern for imposing determinism on functions that
depend on randomness.
For example, many functions in <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> have a <code class="docutils literal notranslate"><span class="pre">random_state</span></code> argument
that functions similarly to how <code class="docutils literal notranslate"><span class="pre">seed</span></code> behaves in many NetworkX function
signatures.
One difference between <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> and <code class="docutils literal notranslate"><span class="pre">networkx</span></code> is that scikit-learn
<strong>only</strong> supports <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> via the <code class="docutils literal notranslate"><span class="pre">random_state</span></code> keyword argument,
whereas NetworkX implicitly supports both the built-in <a class="reference external" href="https://docs.python.org/3/library/random.html#module-random" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random</span></code></a> module, as well
as both the numpy <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> and <code class="docutils literal notranslate"><span class="pre">Generator</span></code> instances (depending on
the type of <code class="docutils literal notranslate"><span class="pre">seed</span></code>).
This is reflected in the name of the keyword argument as <code class="docutils literal notranslate"><span class="pre">random_state</span></code>
(used by scikit-learn) is les ambiguous than <code class="docutils literal notranslate"><span class="pre">seed</span></code> (used by NetworkX).</p>
<p>There are multiple relevant discussions in the scikit-learn community about
potential approaches to supporting the new NumPy random interface:</p>
<ul class="simple">
<li><p><a class="reference external" href="sklearn16988">scikit-learn/scikit-learn#16988</a> covers strategies and concerns
related to enabling users to use the <code class="docutils literal notranslate"><span class="pre">Generator</span></code>-based random number generators.</p></li>
<li><p><a class="reference external" href="sklearn14042">scikit-learn/scikit-learn#14042</a> is a higher-level discussion
that includes additional information about the design considerations and constraints
related to scikit-learn’s <code class="docutils literal notranslate"><span class="pre">random_state</span></code>.</p></li>
<li><p>There is also a releated <a class="reference external" href="slep011">SLEP</a>.</p></li>
</ul>
</section>
<section id="implementation">
<span id="id2"></span><h2>Implementation<a class="headerlink" href="#implementation" title="Permalink to this heading">#</a></h2>
<p>The implementation itself is quite simple. The logic that determines how
inputs are mapped to random number generators is encapsulated in the
<a class="reference internal" href="../../reference/generated/networkx.utils.misc.create_random_state.html#networkx.utils.misc.create_random_state" title="networkx.utils.misc.create_random_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_random_state</span></code></a> function (and the related
<a class="reference internal" href="../../reference/generated/networkx.utils.misc.create_py_random_state.html#networkx.utils.misc.create_py_random_state" title="networkx.utils.misc.create_py_random_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_py_random_state</span></code></a>).
Currently (i.e. NetworkX <= 2.X), this function maps inputs like <code class="docutils literal notranslate"><span class="pre">None</span></code>,
<code class="docutils literal notranslate"><span class="pre">numpy.random</span></code>, and integers to <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> instances:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">create_random_state</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">if</span> <span class="n">random_state</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">random_state</span> <span class="ow">is</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="p">:</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">mtrand</span><span class="o">.</span><span class="n">_rand</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">random_state</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">):</span>
<span class="k">return</span> <span class="n">random_state</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">random_state</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">random_state</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">Generator</span><span class="p">):</span>
<span class="k">return</span> <span class="n">random_state</span>
<span class="n">msg</span> <span class="o">=</span> <span class="p">(</span>
<span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">random_state</span><span class="si">}</span><span class="s2"> cannot be used to create a numpy.random.RandomState or</span><span class="se">\n</span><span class="s2">"</span>
<span class="s2">"numpy.random.Generator instance"</span>
<span class="p">)</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
</pre></div>
</div>
<p>This NXEP proposes to modify the function to produce <code class="docutils literal notranslate"><span class="pre">Generator</span></code> instances
for these inputs. An example implementation might look something like:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">create_random_state</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">if</span> <span class="n">random_state</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">random_state</span> <span class="ow">is</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="p">:</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">()</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">random_state</span><span class="p">,</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">Generator</span><span class="p">)):</span>
<span class="k">return</span> <span class="n">random_state</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">random_state</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
<span class="n">msg</span> <span class="o">=</span> <span class="p">(</span>
<span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">random_state</span><span class="si">}</span><span class="s2"> cannot be used to create a numpy.random.RandomState or</span><span class="se">\n</span><span class="s2">"</span>
<span class="s2">"numpy.random.Generator instance"</span>
<span class="p">)</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
</pre></div>
</div>
<p>The above captures the essential change in logic, though implementation details
may differ.
Most of the work related implementing this change will be associated with
improved/reorganized tests; including adding tests rng-stream reproducibility.</p>
</section>
<section id="alternatives">
<h2>Alternatives<a class="headerlink" href="#alternatives" title="Permalink to this heading">#</a></h2>
<p>The status quo, i.e. using <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> by default, is a completely
acceptable alternative.
<code class="docutils literal notranslate"><span class="pre">RandomState</span></code> is not deprecated, and is expected to maintain its stream-compatibility
guarantee in perpetuity.</p>
<p>Another possible alternative would be to provide a package-level toggle that
users could use to switch the behavior the <code class="docutils literal notranslate"><span class="pre">seed</span></code> kwarg for all functions
decorated by <code class="docutils literal notranslate"><span class="pre">np_random_state</span></code> or <code class="docutils literal notranslate"><span class="pre">py_random_state</span></code>.
To illustrate (ignoring implementation details):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">networkx.utils.misc</span> <span class="kn">import</span> <span class="n">create_random_state</span>
<span class="go"># NetworkX 2.X behavior: RandomState by default</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">create_random_state</span><span class="p">(</span><span class="mi">12345</span><span class="p">))</span>
<span class="go">numpy.random.mtrand.RandomState</span>
<span class="go"># Change random backend by setting pkg attr</span>
<span class="gp">>>> </span><span class="n">nx</span><span class="o">.</span><span class="n">_random_backend</span> <span class="o">=</span> <span class="s2">"Generator"</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">create_random_state</span><span class="p">(</span><span class="mi">12345</span><span class="p">))</span>
<span class="go">numpy.random._generator.Generator</span>
</pre></div>
</div>
</section>
<section id="discussion">
<h2>Discussion<a class="headerlink" href="#discussion" title="Permalink to this heading">#</a></h2>
<p>This section may just be a bullet list including links to any discussions
regarding the NXEP:</p>
<ul class="simple">
<li><p>This includes links to mailing list threads or relevant GitHub issues.</p></li>
</ul>
</section>
</section>
</article>
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