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<div class="section" id="lightning-regression-fistaregressor">
<h1>lightning.regression.FistaRegressor<a class="headerlink" href="#lightning-regression-fistaregressor" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="lightning.regression.FistaRegressor">
<em class="property">class </em><tt class="descclassname">lightning.regression.</tt><tt class="descname">FistaRegressor</tt><big>(</big><em>C=1.0</em>, <em>alpha=1.0</em>, <em>penalty='l1'</em>, <em>multiclass=False</em>, <em>max_iter=100</em>, <em>max_steps=30</em>, <em>eta=2.0</em>, <em>sigma=1e-05</em>, <em>callback=None</em>, <em>verbose=0</em><big>)</big><a class="headerlink" href="#lightning.regression.FistaRegressor" title="Permalink to this definition">¶</a></dt>
<dd><p>Estimator for learning linear classifiers by FISTA.</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>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).</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>max_steps</strong> : int</p>
<blockquote>
<div><p>Maximum number of steps to use during the line search.</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>eta</strong> : float</p>
<blockquote>
<div><p>Decrease factor for line-search procedure. For example, eta=2.
will decrease the step size by a factor of 2 at each iteration
of the line-search routine.</p>
</div></blockquote>
<p><strong>callback</strong> : callable</p>
<blockquote>
<div><p>Callback function.</p>
</div></blockquote>
<p><strong>verbose</strong> : int</p>
<blockquote class="last">
<div><p>Verbosity level.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<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">fit</span></tt>(X, y)</td>
<td></td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#lightning.regression.FistaRegressor.get_params" title="lightning.regression.FistaRegressor.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-odd"><td><tt class="xref py py-obj docutils literal"><span class="pre">n_nonzero</span></tt>([percentage])</td>
<td></td>
</tr>
<tr class="row-even"><td><tt class="xref py py-obj docutils literal"><span class="pre">predict</span></tt>(X)</td>
<td></td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#lightning.regression.FistaRegressor.score" title="lightning.regression.FistaRegressor.score"><tt class="xref py py-obj docutils literal"><span class="pre">score</span></tt></a>(X, y[, sample_weight])</td>
<td>Returns the coefficient of determination R^2 of the prediction.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#lightning.regression.FistaRegressor.set_params" title="lightning.regression.FistaRegressor.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.regression.FistaRegressor.__init__">
<tt class="descname">__init__</tt><big>(</big><em>C=1.0</em>, <em>alpha=1.0</em>, <em>penalty='l1'</em>, <em>multiclass=False</em>, <em>max_iter=100</em>, <em>max_steps=30</em>, <em>eta=2.0</em>, <em>sigma=1e-05</em>, <em>callback=None</em>, <em>verbose=0</em><big>)</big><a class="headerlink" href="#lightning.regression.FistaRegressor.__init__" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="lightning.regression.FistaRegressor.get_params">
<tt class="descname">get_params</tt><big>(</big><em>deep=True</em><big>)</big><a class="headerlink" href="#lightning.regression.FistaRegressor.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.regression.FistaRegressor.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.regression.FistaRegressor.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the coefficient of determination R^2 of the prediction.</p>
<p>The coefficient R^2 is defined as (1 - u/v), where u is the regression
sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual
sum of squares ((y_true - y_true.mean()) ** 2).sum().
Best possible score is 1.0 and it can be negative (because the
model can be arbitrarily worse). A constant model that always
predicts the expected value of y, disregarding the input features,
would get a R^2 score of 0.0.</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 values 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>R^2 of self.predict(X) wrt. y.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="lightning.regression.FistaRegressor.set_params">
<tt class="descname">set_params</tt><big>(</big><em>**params</em><big>)</big><a class="headerlink" href="#lightning.regression.FistaRegressor.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>
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