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<ul>
<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.covariance</span></code>.GraphicalLasso</a></li>
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<div class="section" id="sklearn-covariance-graphicallasso">
<h1><a class="reference internal" href="../classes.html#module-sklearn.covariance" title="sklearn.covariance"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.covariance</span></code></a>.GraphicalLasso<a class="headerlink" href="#sklearn-covariance-graphicallasso" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.covariance.GraphicalLasso">
<em class="property">class </em><code class="descclassname">sklearn.covariance.</code><code class="descname">GraphicalLasso</code><span class="sig-paren">(</span><em>alpha=0.01</em>, <em>mode='cd'</em>, <em>tol=0.0001</em>, <em>enet_tol=0.0001</em>, <em>max_iter=100</em>, <em>verbose=False</em>, <em>assume_centered=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/covariance/graph_lasso_.py#L280"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso" title="Permalink to this definition">¶</a></dt>
<dd><p>Sparse inverse covariance estimation with an l1-penalized estimator.</p>
<p>Read more in the <a class="reference internal" href="../covariance.html#sparse-inverse-covariance"><span class="std std-ref">User Guide</span></a>.</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>alpha</strong> : positive float, default 0.01</p>
<blockquote>
<div><p>The regularization parameter: the higher alpha, the more
regularization, the sparser the inverse covariance.</p>
</div></blockquote>
<p><strong>mode</strong> : {‘cd’, ‘lars’}, default ‘cd’</p>
<blockquote>
<div><p>The Lasso solver to use: coordinate descent or LARS. Use LARS for
very sparse underlying graphs, where p > n. Elsewhere prefer cd
which is more numerically stable.</p>
</div></blockquote>
<p><strong>tol</strong> : positive float, default 1e-4</p>
<blockquote>
<div><p>The tolerance to declare convergence: if the dual gap goes below
this value, iterations are stopped.</p>
</div></blockquote>
<p><strong>enet_tol</strong> : positive float, optional</p>
<blockquote>
<div><p>The tolerance for the elastic net solver used to calculate the descent
direction. This parameter controls the accuracy of the search direction
for a given column update, not of the overall parameter estimate. Only
used for mode=’cd’.</p>
</div></blockquote>
<p><strong>max_iter</strong> : integer, default 100</p>
<blockquote>
<div><p>The maximum number of iterations.</p>
</div></blockquote>
<p><strong>verbose</strong> : boolean, default False</p>
<blockquote>
<div><p>If verbose is True, the objective function and dual gap are
plotted at each iteration.</p>
</div></blockquote>
<p><strong>assume_centered</strong> : boolean, default False</p>
<blockquote class="last">
<div><p>If True, data are not centered before computation.
Useful when working with data whose mean is almost, but not exactly
zero.
If False, data are centered before computation.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Attributes</p>
<table border="1" class="docutils">
<colgroup>
<col width="16%" />
<col width="84%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><strong>covariance_</strong></td>
<td>(array-like, shape (n_features, n_features)) Estimated covariance matrix</td>
</tr>
<tr class="row-even"><td><strong>precision_</strong></td>
<td>(array-like, shape (n_features, n_features)) Estimated pseudo inverse matrix.</td>
</tr>
<tr class="row-odd"><td><strong>n_iter_</strong></td>
<td>(int) Number of iterations run.</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<p class="last"><a class="reference internal" href="sklearn.covariance.graphical_lasso.html#sklearn.covariance.graphical_lasso" title="sklearn.covariance.graphical_lasso"><code class="xref py py-obj docutils literal"><span class="pre">graphical_lasso</span></code></a>, <a class="reference internal" href="sklearn.covariance.GraphicalLassoCV.html#sklearn.covariance.GraphicalLassoCV" title="sklearn.covariance.GraphicalLassoCV"><code class="xref py py-obj docutils literal"><span class="pre">GraphicalLassoCV</span></code></a></p>
</div>
<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><a class="reference internal" href="#sklearn.covariance.GraphicalLasso.error_norm" title="sklearn.covariance.GraphicalLasso.error_norm"><code class="xref py py-obj docutils literal"><span class="pre">error_norm</span></code></a>(comp_cov[, norm, scaling, squared])</td>
<td>Computes the Mean Squared Error between two covariance estimators.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.covariance.GraphicalLasso.fit" title="sklearn.covariance.GraphicalLasso.fit"><code class="xref py py-obj docutils literal"><span class="pre">fit</span></code></a>(X[, y])</td>
<td>Fits the GraphicalLasso model to X.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.covariance.GraphicalLasso.get_params" title="sklearn.covariance.GraphicalLasso.get_params"><code class="xref py py-obj docutils literal"><span class="pre">get_params</span></code></a>([deep])</td>
<td>Get parameters for this estimator.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.covariance.GraphicalLasso.get_precision" title="sklearn.covariance.GraphicalLasso.get_precision"><code class="xref py py-obj docutils literal"><span class="pre">get_precision</span></code></a>()</td>
<td>Getter for the precision matrix.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.covariance.GraphicalLasso.mahalanobis" title="sklearn.covariance.GraphicalLasso.mahalanobis"><code class="xref py py-obj docutils literal"><span class="pre">mahalanobis</span></code></a>(X)</td>
<td>Computes the squared Mahalanobis distances of given observations.</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="#sklearn.covariance.GraphicalLasso.score" title="sklearn.covariance.GraphicalLasso.score"><code class="xref py py-obj docutils literal"><span class="pre">score</span></code></a>(X_test[, y])</td>
<td>Computes the log-likelihood of a Gaussian data set with <cite>self.covariance_</cite> as an estimator of its covariance matrix.</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="#sklearn.covariance.GraphicalLasso.set_params" title="sklearn.covariance.GraphicalLasso.set_params"><code class="xref py py-obj docutils literal"><span class="pre">set_params</span></code></a>(**params)</td>
<td>Set the parameters of this estimator.</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.covariance.GraphicalLasso.__init__">
<code class="descname">__init__</code><span class="sig-paren">(</span><em>alpha=0.01</em>, <em>mode='cd'</em>, <em>tol=0.0001</em>, <em>enet_tol=0.0001</em>, <em>max_iter=100</em>, <em>verbose=False</em>, <em>assume_centered=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/covariance/graph_lasso_.py#L335"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.__init__" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="sklearn.covariance.GraphicalLasso.error_norm">
<code class="descname">error_norm</code><span class="sig-paren">(</span><em>comp_cov</em>, <em>norm='frobenius'</em>, <em>scaling=True</em>, <em>squared=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/covariance/empirical_covariance_.py#L215"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.error_norm" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the Mean Squared Error between two covariance estimators.
(In the sense of the Frobenius norm).</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>comp_cov</strong> : array-like, shape = [n_features, n_features]</p>
<blockquote>
<div><p>The covariance to compare with.</p>
</div></blockquote>
<p><strong>norm</strong> : str</p>
<blockquote>
<div><p>The type of norm used to compute the error. Available error types:
- ‘frobenius’ (default): sqrt(tr(A^t.A))
- ‘spectral’: sqrt(max(eigenvalues(A^t.A))
where A is the error <code class="docutils literal"><span class="pre">(comp_cov</span> <span class="pre">-</span> <span class="pre">self.covariance_)</span></code>.</p>
</div></blockquote>
<p><strong>scaling</strong> : bool</p>
<blockquote>
<div><p>If True (default), the squared error norm is divided by n_features.
If False, the squared error norm is not rescaled.</p>
</div></blockquote>
<p><strong>squared</strong> : bool</p>
<blockquote>
<div><p>Whether to compute the squared error norm or the error norm.
If True (default), the squared error norm is returned.
If False, the error norm is returned.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The Mean Squared Error (in the sense of the Frobenius norm) between</p>
<p class="last"><cite>self</cite> and <cite>comp_cov</cite> covariance estimators.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.covariance.GraphicalLasso.fit">
<code class="descname">fit</code><span class="sig-paren">(</span><em>X</em>, <em>y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/covariance/graph_lasso_.py#L345"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fits the GraphicalLasso model to X.</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> : ndarray, shape (n_samples, n_features)</p>
<blockquote>
<div><p>Data from which to compute the covariance estimate</p>
</div></blockquote>
<p class="last"><strong>y</strong> : (ignored)</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.covariance.GraphicalLasso.get_params">
<code class="descname">get_params</code><span class="sig-paren">(</span><em>deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/base.py#L213"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.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</strong> : boolean, optional</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="sklearn.covariance.GraphicalLasso.get_precision">
<code class="descname">get_precision</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/covariance/empirical_covariance_.py#L141"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.get_precision" title="Permalink to this definition">¶</a></dt>
<dd><p>Getter for the precision matrix.</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"><p class="first"><strong>precision_</strong> : array-like</p>
<blockquote class="last">
<div><p>The precision matrix associated to the current covariance object.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.covariance.GraphicalLasso.mahalanobis">
<code class="descname">mahalanobis</code><span class="sig-paren">(</span><em>X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/covariance/empirical_covariance_.py#L267"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.mahalanobis" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the squared Mahalanobis distances of given observations.</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>The observations, the Mahalanobis distances of the which we
compute. Observations are assumed to be drawn from the same
distribution than the data used in fit.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>dist</strong> : array, shape = [n_samples,]</p>
<blockquote class="last">
<div><p>Squared Mahalanobis distances of the observations.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.covariance.GraphicalLasso.score">
<code class="descname">score</code><span class="sig-paren">(</span><em>X_test</em>, <em>y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/covariance/empirical_covariance_.py#L185"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the log-likelihood of a Gaussian data set with
<cite>self.covariance_</cite> as an estimator of its covariance matrix.</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_test</strong> : array-like, shape = [n_samples, n_features]</p>
<blockquote>
<div><p>Test data of which we compute the likelihood, where n_samples is
the number of samples and n_features is the number of features.
X_test is assumed to be drawn from the same distribution than
the data used in fit (including centering).</p>
</div></blockquote>
<p><strong>y</strong></p>
<blockquote>
<div><p>not used, present for API consistence purpose.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>res</strong> : float</p>
<blockquote class="last">
<div><p>The likelihood of the data set with <cite>self.covariance_</cite> as an
estimator of its covariance matrix.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="sklearn.covariance.GraphicalLasso.set_params">
<code class="descname">set_params</code><span class="sig-paren">(</span><em>**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/base.py#L236"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.covariance.GraphicalLasso.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 latter have parameters of the form
<code class="docutils literal"><span class="pre"><component>__<parameter></span></code> 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">self</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
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