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<section id="callbacks">
<span id="callbacks-user"></span><h1><span class="section-number">7. </span>Callbacks<a class="headerlink" href="#callbacks" title="Link to this heading">#</a></h1>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The callback API is experimental, and is not yet implemented for all estimators.
Please refer to the <a class="reference internal" href="#callback-compatible-estimators"><span class="std std-ref">list of callback-compatible estimators</span></a> for more information. It may change without the
usual deprecation cycle.</p>
</div>
<p>This guide demonstrates how to use scikit-learn’s <a class="reference internal" href="glossary.html#term-callbacks"><span class="xref std std-term">callbacks</span></a> on compatible
<a class="reference internal" href="glossary.html#term-estimators"><span class="xref std std-term">estimators</span></a>. For information about how to implement the callback API, you can
refer to the following sections of the developer’s guide:</p>
<ul class="simple">
<li><p>the <a class="reference internal" href="developers/callback_support.html#callbacks-support"><span class="std std-ref">Implementing callback support in estimators</span></a> section for
making estimators compatible with callbacks.</p></li>
<li><p>the <a class="reference internal" href="developers/developing_callbacks.html#developing-callbacks"><span class="std std-ref">Developing callbacks</span></a> section for how to implement a
new callback.</p></li>
</ul>
<p>In scikit-learn, callbacks are objects from the <a class="reference internal" href="api/sklearn.callback.html#module-sklearn.callback" title="sklearn.callback"><code class="xref py py-mod docutils literal notranslate"><span class="pre">callback</span></code></a> module that can
be registered on an estimator to insert custom logic like monitoring progress or
metrics, without modifying the underlying learning algorithm. The registered callbacks
are invoked at specific steps of the <a class="reference internal" href="glossary.html#term-fitting"><span class="xref std std-term">fitting</span></a> process.</p>
<section id="registering-callbacks">
<h2><span class="section-number">7.1. </span>Registering callbacks<a class="headerlink" href="#registering-callbacks" title="Link to this heading">#</a></h2>
<p>Estimators that support callbacks expose a <a class="reference internal" href="modules/generated/sklearn.callback.CallbackSupportMixin.html#sklearn.callback.CallbackSupportMixin.set_callbacks" title="sklearn.callback.CallbackSupportMixin.set_callbacks"><code class="xref py py-meth docutils literal notranslate"><span class="pre">set_callbacks</span></code></a>
method to register callbacks on them. The following example shows how to register a
<a class="reference internal" href="modules/generated/sklearn.callback.ProgressBar.html#sklearn.callback.ProgressBar" title="sklearn.callback.ProgressBar"><code class="xref py py-class docutils literal notranslate"><span class="pre">ProgressBar</span></code></a> callback on a <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegression</span></code></a>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.callback</span><span class="w"> </span><span class="kn">import</span> <span class="n">ProgressBar</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="gp">>>> </span><span class="n">progress_bar</span> <span class="o">=</span> <span class="n">ProgressBar</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">logreg</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">max_iter</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">logreg</span><span class="o">.</span><span class="n">set_callbacks</span><span class="p">(</span><span class="n">progress_bar</span><span class="p">)</span>
<span class="go">LogisticRegression(max_iter=200)</span>
</pre></div>
</div>
<p>Now that the progress bar is registered on the estimator, calling its <code class="docutils literal notranslate"><span class="pre">fit</span></code> method will
display a progress bar:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_iris</span>
<span class="gp">>>> </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">load_iris</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">logreg</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="go">LogisticRegression - fit ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00</span>
<span class="go">LogisticRegression(max_iter=200)</span>
</pre></div>
</div>
<p>Multiple callbacks can be registered on the same estimator, for example a
<a class="reference internal" href="modules/generated/sklearn.callback.ScoringMonitor.html#sklearn.callback.ScoringMonitor" title="sklearn.callback.ScoringMonitor"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScoringMonitor</span></code></a> callback can be registered in addition to the
<a class="reference internal" href="modules/generated/sklearn.callback.ProgressBar.html#sklearn.callback.ProgressBar" title="sklearn.callback.ProgressBar"><code class="xref py py-class docutils literal notranslate"><span class="pre">ProgressBar</span></code></a>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.callback</span><span class="w"> </span><span class="kn">import</span> <span class="n">ScoringMonitor</span>
<span class="gp">>>> </span><span class="n">scoring_monitor</span> <span class="o">=</span> <span class="n">ScoringMonitor</span><span class="p">(</span><span class="n">scoring</span><span class="o">=</span><span class="s2">"accuracy"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">logreg</span><span class="o">.</span><span class="n">set_callbacks</span><span class="p">(</span><span class="n">progress_bar</span><span class="p">,</span> <span class="n">scoring_monitor</span><span class="p">)</span>
<span class="go">LogisticRegression(max_iter=200)</span>
</pre></div>
</div>
</section>
<section id="callback-invocation">
<h2><span class="section-number">7.2. </span>Callback invocation<a class="headerlink" href="#callback-invocation" title="Link to this heading">#</a></h2>
<p>During <code class="docutils literal notranslate"><span class="pre">fit</span></code>, the callbacks are invoked at the start and end of each <a class="reference internal" href="glossary.html#term-fit-task"><span class="xref std std-term">task</span></a>, where tasks are arbitrary units of work defined by the estimator. Usually, tasks
correspond to iterations of the estimator’s learning algorithm, but they can also
correspond to more abstract operations like fitting an estimator, steps of a pipeline,
cross-validation folds, etc. Within <code class="docutils literal notranslate"><span class="pre">fit</span></code>, tasks are divided into subtasks, which can
themselves be divided and so on, giving them a natural <a class="reference internal" href="developers/callback_support.html#example-task-tree"><span class="std std-ref">tree structure</span></a> where fitting the estimator is the root task.</p>
<p>This tree structure will usually be reflected in a callback’s generated objects and
tasks will be identified by their name, id, and a reference to their parent task.
For tasks that have a natural ordering, like the iterations of a learning algorithm, the
ids are consecutive integers starting from 0. Some callbacks may provide additional
contextual information about the tasks. Here’s an example of the logs of the
<a class="reference internal" href="modules/generated/sklearn.callback.ScoringMonitor.html#sklearn.callback.ScoringMonitor" title="sklearn.callback.ScoringMonitor"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScoringMonitor</span></code></a>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">logreg</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="go">LogisticRegression - fit ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00</span>
<span class="go">LogisticRegression(max_iter=200)</span>
<span class="gp">>>> </span><span class="n">scoring_monitor</span><span class="o">.</span><span class="n">get_logs</span><span class="p">()</span><span class="o">.</span><span class="n">data_as_pandas</span><span class="p">[[</span><span class="s2">"task_name"</span><span class="p">,</span> <span class="s2">"task_id"</span><span class="p">,</span> <span class="s2">"accuracy"</span><span class="p">]]</span>
<span class="go"> task_name task_id accuracy</span>
<span class="go">0 fit 0 0.973...</span>
<span class="go">1 lbfgs-iter 0 0.333...</span>
<span class="go">2 lbfgs-iter 1 0.333...</span>
<span class="go">3 lbfgs-iter 2 0.666...</span>
<span class="go">4 lbfgs-iter 3 0.926...</span>
<span class="go">...</span>
</pre></div>
</div>
</section>
<section id="usage-with-meta-estimators">
<h2><span class="section-number">7.3. </span>Usage with meta-estimators<a class="headerlink" href="#usage-with-meta-estimators" title="Link to this heading">#</a></h2>
<p>When using callbacks in estimator compositions, involving estimators and
<a class="reference internal" href="glossary.html#term-meta-estimators"><span class="xref std std-term">meta-estimators</span></a>, we distinguish two types of callbacks: regular and
auto-propagated. They serve different purposes and are meant to be registered on
different estimators or meta-estimators in the composition.</p>
<section id="regular-callbacks">
<h3><span class="section-number">7.3.1. </span>Regular callbacks<a class="headerlink" href="#regular-callbacks" title="Link to this heading">#</a></h3>
<p>Regular callbacks are meant to be invoked within the <code class="docutils literal notranslate"><span class="pre">fit</span></code> of a given estimator. Their
goal is usually to track the learning process of that estimator.
<a class="reference internal" href="modules/generated/sklearn.callback.ScoringMonitor.html#sklearn.callback.ScoringMonitor" title="sklearn.callback.ScoringMonitor"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScoringMonitor</span></code></a>, for example, records the scores at each iteration of a model.
A regular callback can be registered on an estimator at any level of a composition. If a
regular callback is registered on an estimator that is <a class="reference internal" href="glossary.html#term-cloned"><span class="xref std std-term">cloned</span></a> by a
meta-estimator, possibly multiple times, that callback will be invoked in each one of
the <code class="docutils literal notranslate"><span class="pre">fit</span></code> executions of the clones.</p>
<p>For example, when tuning the hyperparameters of a
<a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegression</span></code></a> using a
<a class="reference internal" href="modules/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">GridSearchCV</span></code></a>, a <a class="reference internal" href="modules/generated/sklearn.callback.ScoringMonitor.html#sklearn.callback.ScoringMonitor" title="sklearn.callback.ScoringMonitor"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScoringMonitor</span></code></a> can be
registered on the <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegression</span></code></a> to monitor the
scores of the logistic regression model for each parameter combination and each fold of
the grid search:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">GridSearchCV</span>
<span class="gp">>>> </span><span class="n">scoring_monitor</span> <span class="o">=</span> <span class="n">ScoringMonitor</span><span class="p">(</span><span class="n">scoring</span><span class="o">=</span><span class="s2">"accuracy"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">logreg</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">max_iter</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span><span class="o">.</span><span class="n">set_callbacks</span><span class="p">(</span><span class="n">scoring_monitor</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">grid_search</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">logreg</span><span class="p">,</span> <span class="p">{</span><span class="s2">"C"</span><span class="p">:</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]})</span>
<span class="gp">>>> </span><span class="n">grid_search</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="go">GridSearchCV(estimator=LogisticRegression(max_iter=200),</span>
<span class="go"> param_grid={'C': [10, 1, 0.1]})</span>
<span class="gp">>>> </span><span class="n">log</span> <span class="o">=</span> <span class="n">scoring_monitor</span><span class="o">.</span><span class="n">get_logs</span><span class="p">()</span><span class="o">.</span><span class="n">data_as_pandas</span>
<span class="gp">>>> </span><span class="c1"># show the scores at the end of each fit of the search</span>
<span class="gp">>>> </span><span class="n">log</span><span class="p">[</span><span class="n">log</span><span class="p">[</span><span class="s2">"parent_task_id_path"</span><span class="p">]</span> <span class="o">==</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">)][[</span><span class="s2">"task_name"</span><span class="p">,</span> <span class="s2">"task_id"</span><span class="p">,</span> <span class="s2">"accuracy"</span><span class="p">]]</span>
<span class="go"> task_name task_id accuracy</span>
<span class="go">1 fit 0 0.975...</span>
<span class="go">2 fit 1 0.975...</span>
<span class="go">3 fit 2 0.991...</span>
<span class="go">4 fit 3 0.991...</span>
<span class="go">5 fit 4 0.975...</span>
<span class="go">6 fit 5 0.966...</span>
<span class="go">7 fit 6 0.966...</span>
<span class="go">8 fit 7 0.983...</span>
<span class="go">9 fit 8 0.983...</span>
<span class="go">10 fit 9 0.975...</span>
<span class="go">11 fit 10 0.958...</span>
<span class="go">12 fit 11 0.958...</span>
<span class="go">13 fit 12 0.958...</span>
<span class="go">14 fit 13 0.958...</span>
<span class="go">15 fit 14 0.941...</span>
</pre></div>
</div>
<p>See <a class="reference internal" href="auto_examples/callbacks/plot_scoring_monitor.html#sphx-glr-auto-examples-callbacks-plot-scoring-monitor-py"><span class="std std-ref">Analysis of the convergence of penalized logistic regression models</span></a> for an
example of how to use and plot the scores from the logs.</p>
</section>
<section id="auto-propagated-callbacks">
<h3><span class="section-number">7.3.2. </span>Auto-propagated callbacks<a class="headerlink" href="#auto-propagated-callbacks" title="Link to this heading">#</a></h3>
<p><a class="reference internal" href="glossary.html#term-auto-propagated"><span class="xref std std-term">Auto-propagated</span></a> callbacks are meant to be invoked within the
<code class="docutils literal notranslate"><span class="pre">fit</span></code> of all (meta-)estimators in an estimator composition. Their goal is usually to
report more general information about the status at each step of the composition.
<a class="reference internal" href="modules/generated/sklearn.callback.ProgressBar.html#sklearn.callback.ProgressBar" title="sklearn.callback.ProgressBar"><code class="xref py py-class docutils literal notranslate"><span class="pre">ProgressBar</span></code></a> for instance displays nested progress bars for the
meta-estimators, their sub-estimators and so on. When registered on a meta-estimator, an
auto-propagated callback will automatically be registered on all its sub-estimators that
support callbacks.</p>
<details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="registration-restrictions">
<summary class="sd-summary-title sd-card-header">
<span class="sd-summary-text">registration restrictions<a class="headerlink" href="#registration-restrictions" title="Link to this dropdown">#</a></span><span class="sd-summary-state-marker sd-summary-chevron-right"><svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-right" viewBox="0 0 24 24" aria-hidden="true"><path d="M8.72 18.78a.75.75 0 0 1 0-1.06L14.44 12 8.72 6.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018l6.25 6.25a.75.75 0 0 1 0 1.06l-6.25 6.25a.75.75 0 0 1-1.06 0Z"></path></svg></span></summary><div class="sd-summary-content sd-card-body docutils">
<p class="sd-card-text">Auto-propagated callbacks are designed to be registered on the top-level
meta-estimator of an estimator composition. If some of its sub-estimators already
have auto-propagated callbacks registered on them, an error will be raised.</p>
<p class="sd-card-text">If the top-level meta-estimator doesn’t itself support callbacks, then its
sub-estimators are allowed to have auto-propagated callbacks registered on them.
However, be aware that this is not optimal and that callbacks might not be able to
deliver their full capabilities.</p>
</div>
</details><p>Let’s add progress bars to the example of the previous section, tuning the
hyperparameters of a <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegression</span></code></a>. The
<a class="reference internal" href="modules/generated/sklearn.callback.ProgressBar.html#sklearn.callback.ProgressBar" title="sklearn.callback.ProgressBar"><code class="xref py py-class docutils literal notranslate"><span class="pre">ProgressBar</span></code></a> callback needs to be registered on the grid search:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">grid_search</span><span class="o">.</span><span class="n">set_callbacks</span><span class="p">(</span><span class="n">ProgressBar</span><span class="p">())</span>
<span class="go">GridSearchCV(estimator=LogisticRegression(max_iter=200),</span>
<span class="go"> param_grid={'C': [10, 1, 0.1]})</span>
<span class="gp">>>> </span><span class="n">grid_search</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="go">GridSearchCV - fit ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00</span>
<span class="go"> GridSearchCV - search #0 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00</span>
<span class="go"> GridSearchCV - candidate-split-evaluation | LogisticRegression - fit #0 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00</span>
<span class="go"> GridSearchCV - candidate-split-evaluation | LogisticRegression - fit #3 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00</span>
<span class="go"> GridSearchCV - candidate-split-evaluation | LogisticRegression - fit #6 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00</span>
<span class="go"> GridSearchCV - candidate-split-evaluation | LogisticRegression - fit #9 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00</span>
<span class="go"> GridSearchCV - candidate-split-evaluation | LogisticRegression - fit #12 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00</span>
<span class="go"> GridSearchCV - candidate-split-evaluation | LogisticRegression - fit #1 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00</span>
<span class="go"> GridSearchCV - candidate-split-evaluation | LogisticRegression - fit #4 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00</span>
<span class="go"> GridSearchCV - candidate-split-evaluation | LogisticRegression - fit #7 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00</span>
<span class="go"> GridSearchCV - candidate-split-evaluation | LogisticRegression - fit #10 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00</span>
<span class="go"> GridSearchCV - candidate-split-evaluation | LogisticRegression - fit #13 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00</span>
<span class="go"> GridSearchCV - candidate-split-evaluation | LogisticRegression - fit #2 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00</span>
<span class="go"> GridSearchCV - candidate-split-evaluation | LogisticRegression - fit #5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00</span>
<span class="go"> GridSearchCV - candidate-split-evaluation | LogisticRegression - fit #8 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00</span>
<span class="go"> GridSearchCV - candidate-split-evaluation | LogisticRegression - fit #11 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00</span>
<span class="go"> GridSearchCV - candidate-split-evaluation | LogisticRegression - fit #14 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00</span>
<span class="go"> GridSearchCV - refit-with-best-params | LogisticRegression - fit #1 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00</span>
<span class="go">GridSearchCV(estimator=LogisticRegression(max_iter=200),</span>
<span class="go"> param_grid={'C': [10, 1, 0.1]})</span>
</pre></div>
</div>
<details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="control-the-propagation-depth">
<summary class="sd-summary-title sd-card-header">
<span class="sd-summary-text">Control the propagation depth<a class="headerlink" href="#control-the-propagation-depth" title="Link to this dropdown">#</a></span><span class="sd-summary-state-marker sd-summary-chevron-right"><svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-right" viewBox="0 0 24 24" aria-hidden="true"><path d="M8.72 18.78a.75.75 0 0 1 0-1.06L14.44 12 8.72 6.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018l6.25 6.25a.75.75 0 0 1 0 1.06l-6.25 6.25a.75.75 0 0 1-1.06 0Z"></path></svg></span></summary><div class="sd-summary-content sd-card-body docutils">
<p class="sd-card-text">Auto-propagated callbacks can be distinguished from regular callbacks by the fact
that they have a <code class="docutils literal notranslate"><span class="pre">max_propagation_depth</span></code> attribute. This attribute indicates the
maximum depth of nested estimators at which the callback will be propagated. In the
previous example, the grid search is at depth 0 and the logistic regression is at
depth 1 for instance.</p>
<p class="sd-card-text">If <code class="docutils literal notranslate"><span class="pre">max_propagation_depth</span></code> is set to 0, the callback will not be propagated to any
sub-estimators and will only be invoked for the (meta-)estimator it is registered
on. If it is set to <code class="docutils literal notranslate"><span class="pre">None</span></code>, it will be propagated to all nested levels of the
estimator composition.</p>
</div>
</details></section>
</section>
<section id="scikit-learn-s-built-in-callbacks">
<h2><span class="section-number">7.4. </span>Scikit-learn’s built-in callbacks<a class="headerlink" href="#scikit-learn-s-built-in-callbacks" title="Link to this heading">#</a></h2>
<p>The built-in callbacks currently available in scikit-learn are the following:</p>
<div class="pst-scrollable-table-container"><table class="table">
<thead>
<tr class="row-odd"><th class="head"><p>Callback</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><a class="reference internal" href="modules/generated/sklearn.callback.ProgressBar.html#sklearn.callback.ProgressBar" title="sklearn.callback.ProgressBar"><code class="xref py py-class docutils literal notranslate"><span class="pre">ProgressBar</span></code></a></p></td>
<td><p>Display progress bars.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="modules/generated/sklearn.callback.ScoringMonitor.html#sklearn.callback.ScoringMonitor" title="sklearn.callback.ScoringMonitor"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScoringMonitor</span></code></a></p></td>
<td><p>Log a scoring metric at the end of each task during
fit.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="callback-support-status">
<span id="callback-compatible-estimators"></span><h2><span class="section-number">7.5. </span>Callback Support Status<a class="headerlink" href="#callback-support-status" title="Link to this heading">#</a></h2>
<p>The development of support for callbacks in estimators is in progress. Here is a list of
the estimators that support callbacks:</p>
<ul class="simple">
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.LogisticRegression</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/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">sklearn.model_selection.GridSearchCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/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">sklearn.model_selection.HalvingGridSearchCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/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">sklearn.model_selection.HalvingRandomSearchCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/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">sklearn.model_selection.RandomizedSearchCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.pipeline.Pipeline</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.preprocessing.StandardScaler</span></code></a></p></li>
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