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<div class="section" id="lightning-classification-cdclassifier">
<h1>lightning.classification.CDClassifier<a class="headerlink" href="#lightning-classification-cdclassifier" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="lightning.classification.CDClassifier">
<em class="property">class </em><tt class="descclassname">lightning.classification.</tt><tt class="descname">CDClassifier</tt><big>(</big><em>loss='squared_hinge'</em>, <em>penalty='l2'</em>, <em>multiclass=False</em>, <em>C=1.0</em>, <em>alpha=1.0</em>, <em>max_iter=50</em>, <em>tol=0.001</em>, <em>termination='violation_sum'</em>, <em>shrinking=True</em>, <em>max_steps='auto'</em>, <em>sigma=0.01</em>, <em>beta=0.5</em>, <em>warm_start=False</em>, <em>debiasing=False</em>, <em>Cd=1.0</em>, <em>warm_debiasing=False</em>, <em>selection='cyclic'</em>, <em>permute=True</em>, <em>callback=None</em>, <em>n_calls=100</em>, <em>random_state=None</em>, <em>verbose=0</em>, <em>n_jobs=1</em><big>)</big><a class="headerlink" href="#lightning.classification.CDClassifier" title="Permalink to this definition">¶</a></dt>
<dd><p>Estimator for learning linear classifiers by (block) coordinate descent.</p>
<p>The objective functions considered take the form</p>
<p>minimize F(W) = C * L(W) + alpha * R(W),</p>
<p>where L(W) is a loss term and R(W) is a penalty term.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>loss</strong> : str, ‘squared_hinge’, ‘log’, ‘modified_huber’, ‘squared’</p>
<blockquote>
<div><p>The loss function to be used.</p>
</div></blockquote>
<p><strong>penalty</strong> : str, ‘l2’, ‘l1’, ‘l1/l2’</p>
<blockquote>
<div><p>The penalty to be used.</p>
<ul class="simple">
<li>l2: ridge</li>
<li>l1: lasso</li>
<li>l1/l2: group lasso</li>
</ul>
</div></blockquote>
<p><strong>multiclass</strong> : bool</p>
<blockquote>
<div><p>Whether to use a direct multiclass formulation (True) or one-vs-rest
(False). Direct formulations are only available for loss=’squared_hinge’
and loss=’log’.</p>
</div></blockquote>
<p><strong>C</strong> : float</p>
<blockquote>
<div><p>Weight of the loss term.</p>
</div></blockquote>
<p><strong>alpha</strong> : float</p>
<blockquote>
<div><p>Weight of the penalty term.</p>
</div></blockquote>
<p><strong>max_iter</strong> : int</p>
<blockquote>
<div><p>Maximum number of iterations to perform.</p>
</div></blockquote>
<p><strong>tol</strong> : float</p>
<blockquote>
<div><p>Tolerance of the stopping criterion.</p>
</div></blockquote>
<p><strong>termination</strong> : str, ‘violation_sum’, ‘violation_max’</p>
<blockquote>
<div><p>Stopping criterion to use.</p>
</div></blockquote>
<p><strong>shrinking</strong> : bool</p>
<blockquote>
<div><p>Whether to activate shrinking or not.</p>
</div></blockquote>
<p><strong>max_steps</strong> : int or “auto”</p>
<blockquote>
<div><p>Maximum number of steps to use during the line search. Use max_steps=0
to use a constant step size instead of the line search. Use
max_steps=”auto” to let CDClassifier choose the best value.</p>
</div></blockquote>
<p><strong>sigma</strong> : float</p>
<blockquote>
<div><p>Constant used in the line search sufficient decrease condition.</p>
</div></blockquote>
<p><strong>beta</strong> : float</p>
<blockquote>
<div><p>Multiplicative constant used in the backtracking line search.</p>
</div></blockquote>
<p><strong>warm_start</strong> : bool</p>
<blockquote>
<div><p>Whether to activate warm-start or not.</p>
</div></blockquote>
<p><strong>debiasing</strong> : bool</p>
<blockquote>
<div><p>Whether to refit the model using l2 penalty (only useful if penalty=’l1’
or penalty=’l1/l2’).</p>
</div></blockquote>
<p><strong>Cd</strong> : float</p>
<blockquote>
<div><p>Value of <cite>C</cite> when doing debiasing.</p>
</div></blockquote>
<p><strong>warm_debiasing</strong> : bool</p>
<blockquote>
<div><p>Whether to warm-start the model or not when doing debiasing.</p>
</div></blockquote>
<p><strong>selection</strong> : str, ‘cyclic’, ‘uniform’</p>
<blockquote>
<div><p>Strategy to use for selecting coordinates.</p>
</div></blockquote>
<p><strong>permute</strong> : bool</p>
<blockquote>
<div><p>Whether to permute coordinates or not before cycling (only when
selection=’cyclic’).</p>
</div></blockquote>
<p><strong>callback</strong> : callable</p>
<blockquote>
<div><p>Callback function.</p>
</div></blockquote>
<p><strong>n_calls</strong> : int</p>
<blockquote>
<div><p>Frequency with which <cite>callback</cite> must be called.</p>
</div></blockquote>
<p><strong>random_state</strong> : RandomState or int</p>
<blockquote>
<div><p>The seed of the pseudo random number generator to use.</p>
</div></blockquote>
<p><strong>verbose</strong> : int</p>
<blockquote>
<div><p>Verbosity level.</p>
</div></blockquote>
<p><strong>n_jobs</strong> : int</p>
<blockquote class="last">
<div><p>Number of CPU’s to be used when <cite>multiclass=False</cite> and when
penalty is a non group-lasso penalty. By default use one CPU.
If set to -1, use all CPU’s</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">References</p>
<p>Block Coordinate Descent Algorithms for Large-scale Sparse Multiclass
Classification. Mathieu Blondel, Kazuhiro Seki, and Kuniaki Uehara.
Machine Learning, May 2013.</p>
<p class="rubric">Methods</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><tt class="xref py py-obj docutils literal"><span class="pre">decision_function</span></tt>(X)</td>
<td></td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#lightning.classification.CDClassifier.fit" title="lightning.classification.CDClassifier.fit"><tt class="xref py py-obj docutils literal"><span class="pre">fit</span></tt></a>(X, y)</td>
<td>Fit model according to X and y.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#lightning.classification.CDClassifier.get_params" title="lightning.classification.CDClassifier.get_params"><tt class="xref py py-obj docutils literal"><span class="pre">get_params</span></tt></a>([deep])</td>
<td>Get parameters for this estimator.</td>
</tr>
<tr class="row-even"><td><tt class="xref py py-obj docutils literal"><span class="pre">n_nonzero</span></tt>([percentage])</td>
<td></td>
</tr>
<tr class="row-odd"><td><tt class="xref py py-obj docutils literal"><span class="pre">predict</span></tt>(X)</td>
<td></td>
</tr>
<tr class="row-even"><td><tt class="xref py py-obj docutils literal"><span class="pre">predict_proba</span></tt>(X)</td>
<td></td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#lightning.classification.CDClassifier.score" title="lightning.classification.CDClassifier.score"><tt class="xref py py-obj docutils literal"><span class="pre">score</span></tt></a>(X, y[, sample_weight])</td>
<td>Returns the mean accuracy on the given test data and labels.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#lightning.classification.CDClassifier.set_params" title="lightning.classification.CDClassifier.set_params"><tt class="xref py py-obj docutils literal"><span class="pre">set_params</span></tt></a>(**params)</td>
<td>Set the parameters of this estimator.</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="lightning.classification.CDClassifier.__init__">
<tt class="descname">__init__</tt><big>(</big><em>loss='squared_hinge'</em>, <em>penalty='l2'</em>, <em>multiclass=False</em>, <em>C=1.0</em>, <em>alpha=1.0</em>, <em>max_iter=50</em>, <em>tol=0.001</em>, <em>termination='violation_sum'</em>, <em>shrinking=True</em>, <em>max_steps='auto'</em>, <em>sigma=0.01</em>, <em>beta=0.5</em>, <em>warm_start=False</em>, <em>debiasing=False</em>, <em>Cd=1.0</em>, <em>warm_debiasing=False</em>, <em>selection='cyclic'</em>, <em>permute=True</em>, <em>callback=None</em>, <em>n_calls=100</em>, <em>random_state=None</em>, <em>verbose=0</em>, <em>n_jobs=1</em><big>)</big><a class="headerlink" href="#lightning.classification.CDClassifier.__init__" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="lightning.classification.CDClassifier.fit">
<tt class="descname">fit</tt><big>(</big><em>X</em>, <em>y</em><big>)</big><a class="headerlink" href="#lightning.classification.CDClassifier.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit model according to X and y.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>X</strong> : array-like, shape = [n_samples, n_features]</p>
<blockquote>
<div><p>Training vectors, where n_samples is the number of samples
and n_features is the number of features.</p>
</div></blockquote>
<p><strong>y</strong> : array-like, shape = [n_samples]</p>
<blockquote>
<div><p>Target values.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>self</strong> : classifier</p>
<blockquote class="last">
<div><p>Returns self.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="lightning.classification.CDClassifier.get_params">
<tt class="descname">get_params</tt><big>(</big><em>deep=True</em><big>)</big><a class="headerlink" href="#lightning.classification.CDClassifier.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>deep: boolean, optional</strong> :</p>
<blockquote>
<div><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>params</strong> : mapping of string to any</p>
<blockquote class="last">
<div><p>Parameter names mapped to their values.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="lightning.classification.CDClassifier.score">
<tt class="descname">score</tt><big>(</big><em>X</em>, <em>y</em>, <em>sample_weight=None</em><big>)</big><a class="headerlink" href="#lightning.classification.CDClassifier.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the mean accuracy on the given test data and labels.</p>
<p>In multi-label classification, this is the subset accuracy
which is a harsh metric since you require for each sample that
each label set be correctly predicted.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>X</strong> : array-like, shape = (n_samples, n_features)</p>
<blockquote>
<div><p>Test samples.</p>
</div></blockquote>
<p><strong>y</strong> : array-like, shape = (n_samples) or (n_samples, n_outputs)</p>
<blockquote>
<div><p>True labels for X.</p>
</div></blockquote>
<p><strong>sample_weight</strong> : array-like, shape = [n_samples], optional</p>
<blockquote>
<div><p>Sample weights.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>score</strong> : float</p>
<blockquote class="last">
<div><p>Mean accuracy of self.predict(X) wrt. y.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="lightning.classification.CDClassifier.set_params">
<tt class="descname">set_params</tt><big>(</big><em>**params</em><big>)</big><a class="headerlink" href="#lightning.classification.CDClassifier.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The former have parameters of the form
<tt class="docutils literal"><span class="pre"><component>__<parameter></span></tt> so that it’s possible to update each
component of a nested object.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body"><strong>self</strong> :</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
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