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<section id="array-api-support-experimental">
<span id="array-api"></span><h1><span class="section-number">11.1. </span>Array API support (experimental)<a class="headerlink" href="#array-api-support-experimental" title="Permalink to this heading">¶</a></h1>
<p>The <a class="reference external" href="https://data-apis.org/array-api/latest/">Array API</a> specification defines
a standard API for all array manipulation libraries with a NumPy-like API.
Scikit-learn’s Array API support requires
<a class="reference external" href="https://github.com/data-apis/array-api-compat">array-api-compat</a> to be installed.</p>
<p>Some scikit-learn estimators that primarily rely on NumPy (as opposed to using
Cython) to implement the algorithmic logic of their <code class="docutils literal notranslate"><span class="pre">fit</span></code>, <code class="docutils literal notranslate"><span class="pre">predict</span></code> or
<code class="docutils literal notranslate"><span class="pre">transform</span></code> methods can be configured to accept any Array API compatible input
datastructures and automatically dispatch operations to the underlying namespace
instead of relying on NumPy.</p>
<p>At this stage, this support is <strong>considered experimental</strong> and must be enabled
explicitly as explained in the following.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Currently, only <code class="docutils literal notranslate"><span class="pre">cupy.array_api</span></code>, <code class="docutils literal notranslate"><span class="pre">numpy.array_api</span></code>, <code class="docutils literal notranslate"><span class="pre">cupy</span></code>, and <code class="docutils literal notranslate"><span class="pre">PyTorch</span></code>
are known to work with scikit-learn’s estimators.</p>
</div>
<section id="example-usage">
<h2><span class="section-number">11.1.1. </span>Example usage<a class="headerlink" href="#example-usage" title="Permalink to this heading">¶</a></h2>
<p>Here is an example code snippet to demonstrate how to use <a class="reference external" href="https://cupy.dev/">CuPy</a> to run
<a class="reference internal" href="generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis"><code class="xref py py-class docutils literal notranslate"><span class="pre">LinearDiscriminantAnalysis</span></code></a> on a GPU:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_classification</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">config_context</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.discriminant_analysis</span> <span class="kn">import</span> <span class="n">LinearDiscriminantAnalysis</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">cupy</span>
<span class="gp">>>> </span><span class="n">X_np</span><span class="p">,</span> <span class="n">y_np</span> <span class="o">=</span> <span class="n">make_classification</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X_cu</span> <span class="o">=</span> <span class="n">cupy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X_np</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y_cu</span> <span class="o">=</span> <span class="n">cupy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">y_np</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X_cu</span><span class="o">.</span><span class="n">device</span>
<span class="go"><CUDA Device 0></span>
<span class="gp">>>> </span><span class="k">with</span> <span class="n">config_context</span><span class="p">(</span><span class="n">array_api_dispatch</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">lda</span> <span class="o">=</span> <span class="n">LinearDiscriminantAnalysis</span><span class="p">()</span>
<span class="gp">... </span> <span class="n">X_trans</span> <span class="o">=</span> <span class="n">lda</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_cu</span><span class="p">,</span> <span class="n">y_cu</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X_trans</span><span class="o">.</span><span class="n">device</span>
<span class="go"><CUDA Device 0></span>
</pre></div>
</div>
<p>After the model is trained, fitted attributes that are arrays will also be
from the same Array API namespace as the training data. For example, if CuPy’s
Array API namespace was used for training, then fitted attributes will be on the
GPU. We provide a experimental <code class="docutils literal notranslate"><span class="pre">_estimator_with_converted_arrays</span></code> utility that
transfers an estimator attributes from Array API to a ndarray:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.utils._array_api</span> <span class="kn">import</span> <span class="n">_estimator_with_converted_arrays</span>
<span class="gp">>>> </span><span class="n">cupy_to_ndarray</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">array</span> <span class="p">:</span> <span class="n">array</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">lda_np</span> <span class="o">=</span> <span class="n">_estimator_with_converted_arrays</span><span class="p">(</span><span class="n">lda</span><span class="p">,</span> <span class="n">cupy_to_ndarray</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X_trans</span> <span class="o">=</span> <span class="n">lda_np</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_np</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">X_trans</span><span class="p">)</span>
<span class="go"><class 'numpy.ndarray'></span>
</pre></div>
</div>
<section id="pytorch-support">
<h3><span class="section-number">11.1.1.1. </span>PyTorch Support<a class="headerlink" href="#pytorch-support" title="Permalink to this heading">¶</a></h3>
<p>PyTorch Tensors are supported by setting <code class="docutils literal notranslate"><span class="pre">array_api_dispatch=True</span></code> and passing in
the tensors 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">torch</span>
<span class="gp">>>> </span><span class="n">X_torch</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X_np</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y_torch</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">y_np</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">with</span> <span class="n">config_context</span><span class="p">(</span><span class="n">array_api_dispatch</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">lda</span> <span class="o">=</span> <span class="n">LinearDiscriminantAnalysis</span><span class="p">()</span>
<span class="gp">... </span> <span class="n">X_trans</span> <span class="o">=</span> <span class="n">lda</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_torch</span><span class="p">,</span> <span class="n">y_torch</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">X_trans</span><span class="p">)</span>
<span class="go"><class 'torch.Tensor'></span>
<span class="gp">>>> </span><span class="n">X_trans</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">type</span>
<span class="go">'cuda'</span>
</pre></div>
</div>
</section>
</section>
<section id="estimators-with-support-for-array-api-compatible-inputs">
<span id="array-api-estimators"></span><h2><span class="section-number">11.1.2. </span>Estimators with support for <code class="docutils literal notranslate"><span class="pre">Array</span> <span class="pre">API</span></code>-compatible inputs<a class="headerlink" href="#estimators-with-support-for-array-api-compatible-inputs" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p><a class="reference internal" href="generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis" title="sklearn.discriminant_analysis.LinearDiscriminantAnalysis"><code class="xref py py-class docutils literal notranslate"><span class="pre">discriminant_analysis.LinearDiscriminantAnalysis</span></code></a> (with <code class="docutils literal notranslate"><span class="pre">solver="svd"</span></code>)</p></li>
</ul>
<p>Coverage for more estimators is expected to grow over time. Please follow the
dedicated <a class="reference external" href="https://github.com/scikit-learn/scikit-learn/issues/22352">meta-issue on GitHub</a> to track progress.</p>
</section>
<section id="common-estimator-checks">
<h2><span class="section-number">11.1.3. </span>Common estimator checks<a class="headerlink" href="#common-estimator-checks" title="Permalink to this heading">¶</a></h2>
<p>Add the <code class="docutils literal notranslate"><span class="pre">array_api_support</span></code> tag to an estimator’s set of tags to indicate that
it supports the Array API. This will enable dedicated checks as part of the
common tests to verify that the estimators result’s are the same when using
vanilla NumPy and Array API inputs.</p>
<p>To run these checks you need to install
<a class="reference external" href="https://github.com/data-apis/array-api-compat">array_api_compat</a> in your
test environment. To run the full set of checks you need to install both
<a class="reference external" href="https://pytorch.org/">PyTorch</a> and <a class="reference external" href="https://cupy.dev/">CuPy</a> and have
a GPU. Checks that can not be executed or have missing dependencies will be
automatically skipped.</p>
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