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<li class="toctree-l1 has-children"><a class="reference internal" href="../supervised_learning.html">1. Supervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="linear_model.html">1.1. Linear Models</a></li>
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<li class="toctree-l2"><a class="reference internal" href="neural_networks_supervised.html">1.17. Neural network models (supervised)</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../unsupervised_learning.html">2. Unsupervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="mixture.html">2.1. Gaussian mixture models</a></li>
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<li class="toctree-l2"><a class="reference internal" href="cross_validation.html">3.1. Cross-validation: evaluating estimator performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="grid_search.html">3.2. Tuning the hyper-parameters of an estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="classification_threshold.html">3.3. Tuning the decision threshold for class prediction</a></li>
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<li class="toctree-l2"><a class="reference internal" href="learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
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<li class="toctree-l2"><a class="reference internal" href="partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
<li class="toctree-l2"><a class="reference internal" href="permutation_importance.html">4.2. Permutation feature importance</a></li>
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<li class="toctree-l2"><a class="reference internal" href="compose.html">6.1. Pipelines and composite estimators</a></li>
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<li class="toctree-l2"><a class="reference internal" href="preprocessing.html">6.3. Preprocessing data</a></li>
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<li class="toctree-l2"><a class="reference internal" href="unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="random_projection.html">6.6. Random Projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="kernel_approximation.html">6.7. Kernel Approximation</a></li>
<li class="toctree-l2"><a class="reference internal" href="metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
<li class="toctree-l2"><a class="reference internal" href="preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../datasets/toy_dataset.html">7.1. Toy datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="../datasets/real_world.html">7.2. Real world datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="../datasets/sample_generators.html">7.3. Generated datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="../datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
<li class="toctree-l2"><a class="reference internal" href="../computing/computational_performance.html">8.2. Computational Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="../computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../model_persistence.html">9. Model persistence</a></li>
<li class="toctree-l1"><a class="reference internal" href="../common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
<li class="toctree-l1 current active has-children"><a class="reference internal" href="../dispatching.html">11. Dispatching</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
<li class="toctree-l2 current active"><a class="current reference internal" href="#">11.1. Array API support (experimental)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../machine_learning_map.html">12. Choosing the right estimator</a></li>
<li class="toctree-l1"><a class="reference internal" href="../presentations.html">13. External Resources, Videos and Talks</a></li>
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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="Link 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,
and the environment variable <code class="docutils literal notranslate"><span class="pre">SCIPY_ARRAY_API</span></code> must be set to <code class="docutils literal notranslate"><span class="pre">1</span></code> before importing
<code class="docutils literal notranslate"><span class="pre">scipy</span></code> and <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><style type="text/css">
span.prompt1:before {
content: "$ ";
}
</style><span class="prompt1"><span class="nb">export</span><span class="w"> </span><span class="nv">SCIPY_ARRAY_API</span><span class="o">=</span><span class="m">1</span></span>
</pre></div></div><p>Please note that this environment variable is intended for temporary use.
For more details, refer to SciPy’s <a class="reference external" href="https://docs.scipy.org/doc/scipy/dev/api-dev/array_api.html#using-array-api-standard-support">Array API documentation</a>.</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">array-api-strict</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>
<p>The following video provides an overview of the standard’s design principles
and how it facilitates interoperability between array libraries:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://www.youtube.com/watch?v=c_s8tr1AizA">Scikit-learn on GPUs with Array API</a>
by <a class="reference external" href="https://github.com/thomasjpfan">Thomas Fan</a> at PyData NYC 2023.</p></li>
</ul>
<section id="example-usage">
<h2><span class="section-number">11.1.1. </span>Example usage<a class="headerlink" href="#example-usage" title="Link 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="Link 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="support-for-array-api-compatible-inputs">
<span id="array-api-supported"></span><h2><span class="section-number">11.1.2. </span>Support for <code class="docutils literal notranslate"><span class="pre">Array</span> <span class="pre">API</span></code>-compatible inputs<a class="headerlink" href="#support-for-array-api-compatible-inputs" title="Link to this heading">#</a></h2>
<p>Estimators and other tools in scikit-learn that support Array API compatible inputs.</p>
<section id="estimators">
<h3><span class="section-number">11.1.2.1. </span>Estimators<a class="headerlink" href="#estimators" title="Link to this heading">#</a></h3>
<ul class="simple">
<li><p><a class="reference internal" href="generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA"><code class="xref py py-class docutils literal notranslate"><span class="pre">decomposition.PCA</span></code></a> (with <code class="docutils literal notranslate"><span class="pre">svd_solver="full"</span></code>,
<code class="docutils literal notranslate"><span class="pre">svd_solver="randomized"</span></code> and <code class="docutils literal notranslate"><span class="pre">power_iteration_normalizer="QR"</span></code>)</p></li>
<li><p><a class="reference internal" href="generated/sklearn.linear_model.Ridge.html#sklearn.linear_model.Ridge" title="sklearn.linear_model.Ridge"><code class="xref py py-class docutils literal notranslate"><span class="pre">linear_model.Ridge</span></code></a> (with <code class="docutils literal notranslate"><span class="pre">solver="svd"</span></code>)</p></li>
<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>
<li><p><a class="reference internal" href="generated/sklearn.preprocessing.KernelCenterer.html#sklearn.preprocessing.KernelCenterer" title="sklearn.preprocessing.KernelCenterer"><code class="xref py py-class docutils literal notranslate"><span class="pre">preprocessing.KernelCenterer</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder" title="sklearn.preprocessing.LabelEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">preprocessing.LabelEncoder</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.preprocessing.MaxAbsScaler.html#sklearn.preprocessing.MaxAbsScaler" title="sklearn.preprocessing.MaxAbsScaler"><code class="xref py py-class docutils literal notranslate"><span class="pre">preprocessing.MaxAbsScaler</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.preprocessing.MinMaxScaler.html#sklearn.preprocessing.MinMaxScaler" title="sklearn.preprocessing.MinMaxScaler"><code class="xref py py-class docutils literal notranslate"><span class="pre">preprocessing.MinMaxScaler</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.preprocessing.Normalizer.html#sklearn.preprocessing.Normalizer" title="sklearn.preprocessing.Normalizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">preprocessing.Normalizer</span></code></a></p></li>
</ul>
</section>
<section id="meta-estimators">
<h3><span class="section-number">11.1.2.2. </span>Meta-estimators<a class="headerlink" href="#meta-estimators" title="Link to this heading">#</a></h3>
<p>Meta-estimators that accept Array API inputs conditioned on the fact that the
base estimator also does:</p>
<ul class="simple">
<li><p><a class="reference internal" href="generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.GridSearchCV</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.model_selection.RandomizedSearchCV.html#sklearn.model_selection.RandomizedSearchCV" title="sklearn.model_selection.RandomizedSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.RandomizedSearchCV</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.model_selection.HalvingGridSearchCV.html#sklearn.model_selection.HalvingGridSearchCV" title="sklearn.model_selection.HalvingGridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.HalvingGridSearchCV</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.model_selection.HalvingRandomSearchCV.html#sklearn.model_selection.HalvingRandomSearchCV" title="sklearn.model_selection.HalvingRandomSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">model_selection.HalvingRandomSearchCV</span></code></a></p></li>
</ul>
</section>
<section id="metrics">
<h3><span class="section-number">11.1.2.3. </span>Metrics<a class="headerlink" href="#metrics" title="Link to this heading">#</a></h3>
<ul class="simple">
<li><p><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.cluster.entropy</span></code></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score" title="sklearn.metrics.accuracy_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.accuracy_score</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.d2_tweedie_score.html#sklearn.metrics.d2_tweedie_score" title="sklearn.metrics.d2_tweedie_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.d2_tweedie_score</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.f1_score.html#sklearn.metrics.f1_score" title="sklearn.metrics.f1_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.f1_score</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.max_error.html#sklearn.metrics.max_error" title="sklearn.metrics.max_error"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.max_error</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.mean_absolute_error.html#sklearn.metrics.mean_absolute_error" title="sklearn.metrics.mean_absolute_error"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.mean_absolute_error</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.mean_absolute_percentage_error.html#sklearn.metrics.mean_absolute_percentage_error" title="sklearn.metrics.mean_absolute_percentage_error"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.mean_absolute_percentage_error</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.mean_gamma_deviance.html#sklearn.metrics.mean_gamma_deviance" title="sklearn.metrics.mean_gamma_deviance"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.mean_gamma_deviance</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.mean_poisson_deviance.html#sklearn.metrics.mean_poisson_deviance" title="sklearn.metrics.mean_poisson_deviance"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.mean_poisson_deviance</span></code></a> (requires <a class="reference external" href="https://docs.scipy.org/doc/scipy/dev/api-dev/array_api.html#using-array-api-standard-support">enabling array API support for SciPy</a>)</p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error" title="sklearn.metrics.mean_squared_error"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.mean_squared_error</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.mean_squared_log_error.html#sklearn.metrics.mean_squared_log_error" title="sklearn.metrics.mean_squared_log_error"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.mean_squared_log_error</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.mean_tweedie_deviance.html#sklearn.metrics.mean_tweedie_deviance" title="sklearn.metrics.mean_tweedie_deviance"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.mean_tweedie_deviance</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.multilabel_confusion_matrix.html#sklearn.metrics.multilabel_confusion_matrix" title="sklearn.metrics.multilabel_confusion_matrix"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.multilabel_confusion_matrix</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.additive_chi2_kernel.html#sklearn.metrics.pairwise.additive_chi2_kernel" title="sklearn.metrics.pairwise.additive_chi2_kernel"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.pairwise.additive_chi2_kernel</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.chi2_kernel.html#sklearn.metrics.pairwise.chi2_kernel" title="sklearn.metrics.pairwise.chi2_kernel"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.pairwise.chi2_kernel</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.cosine_similarity.html#sklearn.metrics.pairwise.cosine_similarity" title="sklearn.metrics.pairwise.cosine_similarity"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.pairwise.cosine_similarity</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.cosine_distances.html#sklearn.metrics.pairwise.cosine_distances" title="sklearn.metrics.pairwise.cosine_distances"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.pairwise.cosine_distances</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.euclidean_distances.html#sklearn.metrics.pairwise.euclidean_distances" title="sklearn.metrics.pairwise.euclidean_distances"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.pairwise.euclidean_distances</span></code></a> (see <a class="reference internal" href="#device-support-for-float64"><span class="std std-ref">Note on device support for float64</span></a>)</p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.linear_kernel.html#sklearn.metrics.pairwise.linear_kernel" title="sklearn.metrics.pairwise.linear_kernel"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.pairwise.linear_kernel</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.paired_cosine_distances.html#sklearn.metrics.pairwise.paired_cosine_distances" title="sklearn.metrics.pairwise.paired_cosine_distances"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.pairwise.paired_cosine_distances</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.paired_euclidean_distances.html#sklearn.metrics.pairwise.paired_euclidean_distances" title="sklearn.metrics.pairwise.paired_euclidean_distances"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.pairwise.paired_euclidean_distances</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.polynomial_kernel.html#sklearn.metrics.pairwise.polynomial_kernel" title="sklearn.metrics.pairwise.polynomial_kernel"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.pairwise.polynomial_kernel</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.rbf_kernel.html#sklearn.metrics.pairwise.rbf_kernel" title="sklearn.metrics.pairwise.rbf_kernel"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.pairwise.rbf_kernel</span></code></a> (see <a class="reference internal" href="#device-support-for-float64"><span class="std std-ref">Note on device support for float64</span></a>)</p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.pairwise.sigmoid_kernel.html#sklearn.metrics.pairwise.sigmoid_kernel" title="sklearn.metrics.pairwise.sigmoid_kernel"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.pairwise.sigmoid_kernel</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.precision_recall_fscore_support.html#sklearn.metrics.precision_recall_fscore_support" title="sklearn.metrics.precision_recall_fscore_support"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.precision_recall_fscore_support</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.r2_score</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.root_mean_squared_error.html#sklearn.metrics.root_mean_squared_error" title="sklearn.metrics.root_mean_squared_error"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.root_mean_squared_error</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.root_mean_squared_log_error.html#sklearn.metrics.root_mean_squared_log_error" title="sklearn.metrics.root_mean_squared_log_error"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.root_mean_squared_log_error</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/sklearn.metrics.zero_one_loss.html#sklearn.metrics.zero_one_loss" title="sklearn.metrics.zero_one_loss"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.zero_one_loss</span></code></a></p></li>
</ul>
</section>
<section id="tools">
<h3><span class="section-number">11.1.2.4. </span>Tools<a class="headerlink" href="#tools" title="Link to this heading">#</a></h3>
<ul class="simple">
<li><p><a class="reference internal" href="generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split"><code class="xref py py-func docutils literal notranslate"><span class="pre">model_selection.train_test_split</span></code></a></p></li>
</ul>
<p>Coverage 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="type-of-return-values-and-fitted-attributes">
<h3><span class="section-number">11.1.2.5. </span>Type of return values and fitted attributes<a class="headerlink" href="#type-of-return-values-and-fitted-attributes" title="Link to this heading">#</a></h3>
<p>When calling functions or methods with Array API compatible inputs, the
convention is to return array values of the same array container type and
device as the input data.</p>
<p>Similarly, when an estimator is fitted with Array API compatible inputs, the
fitted attributes will be arrays from the same library as the input and stored
on the same device. The <code class="docutils literal notranslate"><span class="pre">predict</span></code> and <code class="docutils literal notranslate"><span class="pre">transform</span></code> method subsequently expect
inputs from the same array library and device as the data passed to the <code class="docutils literal notranslate"><span class="pre">fit</span></code>
method.</p>
<p>Note however that scoring functions that return scalar values return Python
scalars (typically a <code class="docutils literal notranslate"><span class="pre">float</span></code> instance) instead of an array scalar value.</p>
</section>
</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="Link 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. Therefore it’s important to run the tests with the
<code class="docutils literal notranslate"><span class="pre">-v</span></code> flag to see which checks are skipped:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span class="prompt1">pip<span class="w"> </span>install<span class="w"> </span>array-api-compat<span class="w"> </span><span class="c1"># and other libraries as needed</span></span>
<span class="prompt1">pytest<span class="w"> </span>-k<span class="w"> </span><span class="s2">"array_api"</span><span class="w"> </span>-v</span>
</pre></div></div><section id="note-on-mps-device-support">
<span id="mps-support"></span><h3><span class="section-number">11.1.3.1. </span>Note on MPS device support<a class="headerlink" href="#note-on-mps-device-support" title="Link to this heading">#</a></h3>
<p>On macOS, PyTorch can use the Metal Performance Shaders (MPS) to access
hardware accelerators (e.g. the internal GPU component of the M1 or M2 chips).
However, the MPS device support for PyTorch is incomplete at the time of
writing. See the following github issue for more details:</p>
<ul class="simple">
<li><p><a class="github reference external" href="https://github.com/pytorch/pytorch/issues/77764">pytorch/pytorch#77764</a></p></li>
</ul>
<p>To enable the MPS support in PyTorch, set the environment variable
<code class="docutils literal notranslate"><span class="pre">PYTORCH_ENABLE_MPS_FALLBACK=1</span></code> before running the tests:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span class="prompt1"><span class="nv">PYTORCH_ENABLE_MPS_FALLBACK</span><span class="o">=</span><span class="m">1</span><span class="w"> </span>pytest<span class="w"> </span>-k<span class="w"> </span><span class="s2">"array_api"</span><span class="w"> </span>-v</span>
</pre></div></div><p>At the time of writing all scikit-learn tests should pass, however, the
computational speed is not necessarily better than with the CPU device.</p>
</section>
<section id="note-on-device-support-for-float64">
<span id="device-support-for-float64"></span><h3><span class="section-number">11.1.3.2. </span>Note on device support for <code class="docutils literal notranslate"><span class="pre">float64</span></code><a class="headerlink" href="#note-on-device-support-for-float64" title="Link to this heading">#</a></h3>
<p>Certain operations within scikit-learn will automatically perform operations
on floating-point values with <code class="docutils literal notranslate"><span class="pre">float64</span></code> precision to prevent overflows and ensure
correctness (e.g., <a class="reference internal" href="generated/sklearn.metrics.pairwise.euclidean_distances.html#sklearn.metrics.pairwise.euclidean_distances" title="sklearn.metrics.pairwise.euclidean_distances"><code class="xref py py-func docutils literal notranslate"><span class="pre">metrics.pairwise.euclidean_distances</span></code></a>). However,
certain combinations of array namespaces and devices, such as <code class="docutils literal notranslate"><span class="pre">PyTorch</span> <span class="pre">on</span> <span class="pre">MPS</span></code>
(see <a class="reference internal" href="#mps-support"><span class="std std-ref">Note on MPS device support</span></a>) do not support the <code class="docutils literal notranslate"><span class="pre">float64</span></code> data type. In these cases,
scikit-learn will revert to using the <code class="docutils literal notranslate"><span class="pre">float32</span></code> data type instead. This can result in
different behavior (typically numerically unstable results) compared to not using array
API dispatching or using a device with <code class="docutils literal notranslate"><span class="pre">float64</span></code> support.</p>
</section>
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