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  <div class="section" id="module-hep_ml.gradientboosting">
<span id="gradient-boosting"></span><h1>Gradient boosting<a class="headerlink" href="#module-hep_ml.gradientboosting" title="Permalink to this headline">¶</a></h1>
<p>Gradient boosting is general-purpose algorithm proposed by Friedman <a class="reference internal" href="#gb" id="id1"><span>[GB]</span></a>.
It is one of the most efficient machine learning algorithms used for classification, regression and ranking.</p>
<p>The key idea of algorithm is iterative minimization of target <strong>loss</strong> function
by training each time one more estimator to the sequence. In this implementation decision trees are taken as such estimators.</p>
<p><strong>hep_ml</strong> provides non-standard loss functions for gradient boosting.
There are for instance, loss functions to fight with correlation or loss functions for ranking.
See  <a class="reference internal" href="losses.html#module-hep_ml.losses" title="hep_ml.losses"><code class="xref py py-class docutils literal notranslate"><span class="pre">hep_ml.losses</span></code></a> for details.</p>
<p>See also libraries: XGBoost, sklearn.ensemble.GradientBoostingClassifier</p>
<dl class="citation">
<dt class="label" id="gb"><span class="brackets"><a class="fn-backref" href="#id1">GB</a></span></dt>
<dd><p>J.H. Friedman ‘Greedy function approximation: A gradient boosting machine.’, 2001.</p>
</dd>
</dl>
<dl class="py class">
<dt class="sig sig-object py" id="hep_ml.gradientboosting.UGradientBoostingClassifier">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">hep_ml.gradientboosting.</span></span><span class="sig-name descname"><span class="pre">UGradientBoostingClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">splitter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'best'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_tree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/hep_ml/gradientboosting.html#UGradientBoostingClassifier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#hep_ml.gradientboosting.UGradientBoostingClassifier" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">hep_ml.gradientboosting.UGradientBoostingBase</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.base.ClassifierMixin</span></code></p>
<p>This version of gradient boosting supports only two-class classification and only special losses
derived from AbstractLossFunction.</p>
<p><cite>max_depth</cite>, <cite>max_leaf_nodes</cite>, <cite>min_samples_leaf</cite>, <cite>min_samples_split</cite>, <cite>max_features</cite> are parameters
of regression tree, which is used as base estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>loss</strong> (<a class="reference internal" href="losses.html#hep_ml.losses.AbstractLossFunction" title="hep_ml.losses.AbstractLossFunction"><em>AbstractLossFunction</em></a>) – any descendant of AbstractLossFunction, those are very various.
See <a class="reference internal" href="losses.html#module-hep_ml.losses" title="hep_ml.losses"><code class="xref py py-class docutils literal notranslate"><span class="pre">hep_ml.losses</span></code></a> for available losses.</p></li>
<li><p><strong>n_estimators</strong> (<em>int</em>) – number of trained trees.</p></li>
<li><p><strong>subsample</strong> (<em>float</em>) – fraction of data to use on each stage</p></li>
<li><p><strong>learning_rate</strong> (<em>float</em>) – size of step.</p></li>
<li><p><strong>update_tree</strong> (<em>bool</em>) – True by default. If False, ‘improvement’ step after fitting tree will be skipped.</p></li>
<li><p><strong>train_features</strong> – features used by tree.
Note that algorithm may require also variables used by loss function, but not listed here.</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="hep_ml.gradientboosting.UGradientBoostingClassifier.fit">
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/hep_ml/gradientboosting.html#UGradientBoostingClassifier.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#hep_ml.gradientboosting.UGradientBoostingClassifier.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Train formula.
Only two-class binary classification is supported with labels 0 and 1.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>X</strong> – dataset of shape [n_samples, n_features]</p></li>
<li><p><strong>y</strong> – labels, array-like of shape [n_samples]</p></li>
<li><p><strong>sample_weight</strong> – array-like of shape [n_samples] or None</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>self</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="hep_ml.gradientboosting.UGradientBoostingClassifier.predict">
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/hep_ml/gradientboosting.html#UGradientBoostingClassifier.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#hep_ml.gradientboosting.UGradientBoostingClassifier.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predicted classes for each event</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>X</strong> – pandas.DataFrame with all train_features</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>numpy.array of shape [n_samples] with predicted classes.</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="hep_ml.gradientboosting.UGradientBoostingClassifier.predict_proba">
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/hep_ml/gradientboosting.html#UGradientBoostingClassifier.predict_proba"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#hep_ml.gradientboosting.UGradientBoostingClassifier.predict_proba" title="Permalink to this definition">¶</a></dt>
<dd><p>Predicted probabilities for each event</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>X</strong> – pandas.DataFrame with all train_features</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>numpy.array of shape [n_samples, n_classes]</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="hep_ml.gradientboosting.UGradientBoostingClassifier.staged_predict_proba">
<span class="sig-name descname"><span class="pre">staged_predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/hep_ml/gradientboosting.html#UGradientBoostingClassifier.staged_predict_proba"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#hep_ml.gradientboosting.UGradientBoostingClassifier.staged_predict_proba" title="Permalink to this definition">¶</a></dt>
<dd><p>Predicted probabilities for each event</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>X</strong> – data</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>sequence of numpy.array of shape [n_samples, n_classes]</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="hep_ml.gradientboosting.UGradientBoostingRegressor">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">hep_ml.gradientboosting.</span></span><span class="sig-name descname"><span class="pre">UGradientBoostingRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_samples_leaf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_leaf_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">splitter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'best'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_tree</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/hep_ml/gradientboosting.html#UGradientBoostingRegressor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#hep_ml.gradientboosting.UGradientBoostingRegressor" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">hep_ml.gradientboosting.UGradientBoostingBase</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.base.RegressorMixin</span></code></p>
<p>Gradient Boosted regressor. Approximates target by sum of predictions of several trees.</p>
<p><cite>max_depth</cite>, <cite>max_leaf_nodes</cite>, <cite>min_samples_leaf</cite>, <cite>min_samples_split</cite>, <cite>max_features</cite> are parameters
of regression tree, which is used as base estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>loss</strong> (<a class="reference internal" href="losses.html#hep_ml.losses.AbstractLossFunction" title="hep_ml.losses.AbstractLossFunction"><em>AbstractLossFunction</em></a>) – any descendant of AbstractLossFunction, those are very various.
See <a class="reference internal" href="losses.html#module-hep_ml.losses" title="hep_ml.losses"><code class="xref py py-class docutils literal notranslate"><span class="pre">hep_ml.losses</span></code></a> for available losses.</p></li>
<li><p><strong>n_estimators</strong> (<em>int</em>) – number of trained trees.</p></li>
<li><p><strong>subsample</strong> (<em>float</em>) – fraction of data to use on each stage</p></li>
<li><p><strong>learning_rate</strong> (<em>float</em>) – size of step.</p></li>
<li><p><strong>update_tree</strong> (<em>bool</em>) – True by default. If False, ‘improvement’ step after fitting tree will be skipped.</p></li>
<li><p><strong>train_features</strong> – features used by tree.
Note that algorithm may require also variables used by loss function, but not listed here.</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="hep_ml.gradientboosting.UGradientBoostingRegressor.fit">
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/hep_ml/gradientboosting.html#UGradientBoostingRegressor.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#hep_ml.gradientboosting.UGradientBoostingRegressor.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>X</strong> – dataset of shape [n_samples, n_features]</p></li>
<li><p><strong>y</strong> – target values, array-like of shape [n_samples]</p></li>
<li><p><strong>sample_weight</strong> – array-like of shape [n_samples] or None</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>self</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="hep_ml.gradientboosting.UGradientBoostingRegressor.predict">
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/hep_ml/gradientboosting.html#UGradientBoostingRegressor.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#hep_ml.gradientboosting.UGradientBoostingRegressor.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict values for new samples</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>X</strong> – pandas.DataFrame with all train_features</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>numpy.array of shape [n_samples]</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="hep_ml.gradientboosting.UGradientBoostingRegressor.staged_predict">
<span class="sig-name descname"><span class="pre">staged_predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/hep_ml/gradientboosting.html#UGradientBoostingRegressor.staged_predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#hep_ml.gradientboosting.UGradientBoostingRegressor.staged_predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Return predictions after each new tree</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>X</strong> – data</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>sequence of numpy.array of shape [n_samples]</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

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