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  <section id="ellipticenvelope">
<h1>EllipticEnvelope<a class="headerlink" href="#ellipticenvelope" title="Link to this heading">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.covariance.</span></span><span class="sig-name descname"><span class="pre">EllipticEnvelope</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_precision</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">assume_centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">support_fraction</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">contamination</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">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 external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/covariance/_elliptic_envelope.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope" title="Link to this definition">#</a></dt>
<dd><p>An object for detecting outliers in a Gaussian distributed dataset.</p>
<p>Read more in the <a class="reference internal" href="../outlier_detection.html#outlier-detection"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>store_precision</strong><span class="classifier">bool, default=True</span></dt><dd><p>Specify if the estimated precision is stored.</p>
</dd>
<dt><strong>assume_centered</strong><span class="classifier">bool, default=False</span></dt><dd><p>If True, the support of robust location and covariance estimates
is computed, and a covariance estimate is recomputed from it,
without centering the data.
Useful to work with data whose mean is significantly equal to
zero but is not exactly zero.
If False, the robust location and covariance are directly computed
with the FastMCD algorithm without additional treatment.</p>
</dd>
<dt><strong>support_fraction</strong><span class="classifier">float, default=None</span></dt><dd><p>The proportion of points to be included in the support of the raw
MCD estimate. If None, the minimum value of support_fraction will
be used within the algorithm: <code class="docutils literal notranslate"><span class="pre">(n_samples</span> <span class="pre">+</span> <span class="pre">n_features</span> <span class="pre">+</span> <span class="pre">1)</span> <span class="pre">/</span> <span class="pre">2</span> <span class="pre">*</span> <span class="pre">n_samples</span></code>.
Range is (0, 1).</p>
</dd>
<dt><strong>contamination</strong><span class="classifier">float, default=0.1</span></dt><dd><p>The amount of contamination of the data set, i.e. the proportion
of outliers in the data set. Range is (0, 0.5].</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Determines the pseudo random number generator for shuffling
the data. Pass an int for reproducible results across multiple function
calls. See <a class="reference internal" href="../../glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>location_</strong><span class="classifier">ndarray of shape (n_features,)</span></dt><dd><p>Estimated robust location.</p>
</dd>
<dt><strong>covariance_</strong><span class="classifier">ndarray of shape (n_features, n_features)</span></dt><dd><p>Estimated robust covariance matrix.</p>
</dd>
<dt><strong>precision_</strong><span class="classifier">ndarray of shape (n_features, n_features)</span></dt><dd><p>Estimated pseudo inverse matrix.
(stored only if store_precision is True)</p>
</dd>
<dt><strong>support_</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>A mask of the observations that have been used to compute the
robust estimates of location and shape.</p>
</dd>
<dt><strong>offset_</strong><span class="classifier">float</span></dt><dd><p>Offset used to define the decision function from the raw scores.
We have the relation: <code class="docutils literal notranslate"><span class="pre">decision_function</span> <span class="pre">=</span> <span class="pre">score_samples</span> <span class="pre">-</span> <span class="pre">offset_</span></code>.
The offset depends on the contamination parameter and is defined in
such a way we obtain the expected number of outliers (samples with
decision function &lt; 0) in training.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.20.</span></p>
</div>
</dd>
<dt><strong>raw_location_</strong><span class="classifier">ndarray of shape (n_features,)</span></dt><dd><p>The raw robust estimated location before correction and re-weighting.</p>
</dd>
<dt><strong>raw_covariance_</strong><span class="classifier">ndarray of shape (n_features, n_features)</span></dt><dd><p>The raw robust estimated covariance before correction and re-weighting.</p>
</dd>
<dt><strong>raw_support_</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>A mask of the observations that have been used to compute
the raw robust estimates of location and shape, before correction
and re-weighting.</p>
</dd>
<dt><strong>dist_</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Mahalanobis distances of the training set (on which <a class="reference internal" href="#sklearn.covariance.EllipticEnvelope.fit" title="sklearn.covariance.EllipticEnvelope.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> is
called) observations.</p>
</dd>
<dt><strong>n_features_in_</strong><span class="classifier">int</span></dt><dd><p>Number of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.24.</span></p>
</div>
</dd>
<dt><strong>feature_names_in_</strong><span class="classifier">ndarray of shape (<code class="docutils literal notranslate"><span class="pre">n_features_in_</span></code>,)</span></dt><dd><p>Names of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>. Defined only when <code class="docutils literal notranslate"><span class="pre">X</span></code>
has feature names that are all strings.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.0.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.covariance.EmpiricalCovariance.html#sklearn.covariance.EmpiricalCovariance" title="sklearn.covariance.EmpiricalCovariance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EmpiricalCovariance</span></code></a></dt><dd><p>Maximum likelihood covariance estimator.</p>
</dd>
<dt><a class="reference internal" href="sklearn.covariance.GraphicalLasso.html#sklearn.covariance.GraphicalLasso" title="sklearn.covariance.GraphicalLasso"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GraphicalLasso</span></code></a></dt><dd><p>Sparse inverse covariance estimation with an l1-penalized estimator.</p>
</dd>
<dt><a class="reference internal" href="sklearn.covariance.LedoitWolf.html#sklearn.covariance.LedoitWolf" title="sklearn.covariance.LedoitWolf"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LedoitWolf</span></code></a></dt><dd><p>LedoitWolf Estimator.</p>
</dd>
<dt><a class="reference internal" href="sklearn.covariance.MinCovDet.html#sklearn.covariance.MinCovDet" title="sklearn.covariance.MinCovDet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MinCovDet</span></code></a></dt><dd><p>Minimum Covariance Determinant (robust estimator of covariance).</p>
</dd>
<dt><a class="reference internal" href="sklearn.covariance.OAS.html#sklearn.covariance.OAS" title="sklearn.covariance.OAS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OAS</span></code></a></dt><dd><p>Oracle Approximating Shrinkage Estimator.</p>
</dd>
<dt><a class="reference internal" href="sklearn.covariance.ShrunkCovariance.html#sklearn.covariance.ShrunkCovariance" title="sklearn.covariance.ShrunkCovariance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ShrunkCovariance</span></code></a></dt><dd><p>Covariance estimator with shrinkage.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>Outlier detection from covariance estimation may break or not
perform well in high-dimensional settings. In particular, one will
always take care to work with <code class="docutils literal notranslate"><span class="pre">n_samples</span> <span class="pre">&gt;</span> <span class="pre">n_features</span> <span class="pre">**</span> <span class="pre">2</span></code>.</p>
<p class="rubric">References</p>
<div role="list" class="citation-list">
<div class="citation" id="r68ae096da0e4-1" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>Rousseeuw, P.J., Van Driessen, K. “A fast algorithm for the
minimum covariance determinant estimator” Technometrics 41(3), 212
(1999)</p>
</div>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.covariance</span><span class="w"> </span><span class="kn">import</span> <span class="n">EllipticEnvelope</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">true_cov</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">.8</span><span class="p">,</span> <span class="mf">.3</span><span class="p">],</span>
<span class="gp">... </span>                     <span class="p">[</span><span class="mf">.3</span><span class="p">,</span> <span class="mf">.4</span><span class="p">]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">multivariate_normal</span><span class="p">(</span><span class="n">mean</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="gp">... </span>                                                 <span class="n">cov</span><span class="o">=</span><span class="n">true_cov</span><span class="p">,</span>
<span class="gp">... </span>                                                 <span class="n">size</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">cov</span> <span class="o">=</span> <span class="n">EllipticEnvelope</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</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="gp">&gt;&gt;&gt; </span><span class="c1"># predict returns 1 for an inlier and -1 for an outlier</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">cov</span><span class="o">.</span><span class="n">predict</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="gp">... </span>             <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
<span class="go">array([ 1, -1])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">cov</span><span class="o">.</span><span class="n">covariance_</span>
<span class="go">array([[0.7411..., 0.2535...],</span>
<span class="go">       [0.2535..., 0.3053...]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">cov</span><span class="o">.</span><span class="n">location_</span>
<span class="go">array([0.0813... , 0.0427...])</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.correct_covariance">
<span class="sig-name descname"><span class="pre">correct_covariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/covariance/_robust_covariance.py#L780"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.correct_covariance" title="Link to this definition">#</a></dt>
<dd><p>Apply a correction to raw Minimum Covariance Determinant estimates.</p>
<p>Correction using the empirical correction factor suggested
by Rousseeuw and Van Driessen in <a class="reference internal" href="#rbb2ba44703ed-rvd" id="id2">[RVD]</a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>data</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>The data matrix, with p features and n samples.
The data set must be the one which was used to compute
the raw estimates.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>covariance_corrected</strong><span class="classifier">ndarray of shape (n_features, n_features)</span></dt><dd><p>Corrected robust covariance estimate.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">References</p>
<div role="list" class="citation-list">
<div class="citation" id="rbb2ba44703ed-rvd" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">RVD</a><span class="fn-bracket">]</span></span>
<p>A Fast Algorithm for the Minimum Covariance
Determinant Estimator, 1999, American Statistical Association
and the American Society for Quality, TECHNOMETRICS</p>
</div>
</div>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.decision_function">
<span class="sig-name descname"><span class="pre">decision_function</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 external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/covariance/_elliptic_envelope.py#L187"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.decision_function" title="Link to this definition">#</a></dt>
<dd><p>Compute the decision function of the given observations.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>The data matrix.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>decision</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Decision function of the samples.
It is equal to the shifted Mahalanobis distances.
The threshold for being an outlier is 0, which ensures a
compatibility with other outlier detection algorithms.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.error_norm">
<span class="sig-name descname"><span class="pre">error_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">comp_cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'frobenius'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaling</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">squared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/covariance/_empirical_covariance.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.error_norm" title="Link to this definition">#</a></dt>
<dd><p>Compute the Mean Squared Error between two covariance estimators.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>comp_cov</strong><span class="classifier">array-like of shape (n_features, n_features)</span></dt><dd><p>The covariance to compare with.</p>
</dd>
<dt><strong>norm</strong><span class="classifier">{“frobenius”, “spectral”}, default=”frobenius”</span></dt><dd><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 notranslate"><span class="pre">(comp_cov</span> <span class="pre">-</span> <span class="pre">self.covariance_)</span></code>.</p>
</dd>
<dt><strong>scaling</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True (default), the squared error norm is divided by n_features.
If False, the squared error norm is not rescaled.</p>
</dd>
<dt><strong>squared</strong><span class="classifier">bool, default=True</span></dt><dd><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>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>result</strong><span class="classifier">float</span></dt><dd><p>The Mean Squared Error (in the sense of the Frobenius norm) between
<code class="docutils literal notranslate"><span class="pre">self</span></code> and <code class="docutils literal notranslate"><span class="pre">comp_cov</span></code> covariance estimators.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.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><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 external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/covariance/_elliptic_envelope.py#L166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.fit" title="Link to this definition">#</a></dt>
<dd><p>Fit the EllipticEnvelope model.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Training data.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present for API consistency by convention.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Returns the instance itself.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.fit_predict">
<span class="sig-name descname"><span class="pre">fit_predict</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><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="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/base.py#L1037"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.fit_predict" title="Link to this definition">#</a></dt>
<dd><p>Perform fit on X and returns labels for X.</p>
<p>Returns -1 for outliers and 1 for inliers.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The input samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present for API consistency by convention.</p>
</dd>
<dt><strong>**kwargs</strong><span class="classifier">dict</span></dt><dd><p>Arguments to be passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.4.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>1 for inliers, -1 for outliers.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.get_metadata_routing">
<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/utils/_metadata_requests.py#L1508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.get_metadata_routing" title="Link to this definition">#</a></dt>
<dd><p>Get metadata routing of this object.</p>
<p>Please check <a class="reference internal" href="../../metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
mechanism works.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>routing</strong><span class="classifier">MetadataRequest</span></dt><dd><p>A <a class="reference internal" href="sklearn.utils.metadata_routing.MetadataRequest.html#sklearn.utils.metadata_routing.MetadataRequest" title="sklearn.utils.metadata_routing.MetadataRequest"><code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRequest</span></code></a> encapsulating
routing information.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/base.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.get_params" title="Link to this definition">#</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">dict</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.get_precision">
<span class="sig-name descname"><span class="pre">get_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/covariance/_empirical_covariance.py#L219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.get_precision" title="Link to this definition">#</a></dt>
<dd><p>Getter for the precision matrix.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>precision_</strong><span class="classifier">array-like of shape (n_features, n_features)</span></dt><dd><p>The precision matrix associated to the current covariance object.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.mahalanobis">
<span class="sig-name descname"><span class="pre">mahalanobis</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 external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/covariance/_empirical_covariance.py#L343"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.mahalanobis" title="Link to this definition">#</a></dt>
<dd><p>Compute the squared Mahalanobis distances of given observations.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><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>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>dist</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Squared Mahalanobis distances of the observations.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.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 external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/covariance/_elliptic_envelope.py#L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.predict" title="Link to this definition">#</a></dt>
<dd><p>Predict labels (1 inlier, -1 outlier) of X according to fitted model.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>The data matrix.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>is_inlier</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Returns -1 for anomalies/outliers and +1 for inliers.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.reweight_covariance">
<span class="sig-name descname"><span class="pre">reweight_covariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/covariance/_robust_covariance.py#L820"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.reweight_covariance" title="Link to this definition">#</a></dt>
<dd><p>Re-weight raw Minimum Covariance Determinant estimates.</p>
<p>Re-weight observations using Rousseeuw’s method (equivalent to
deleting outlying observations from the data set before
computing location and covariance estimates) described
in <a class="reference internal" href="#rd2c89e63f1c9-rvdriessen" id="id4">[RVDriessen]</a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>data</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>The data matrix, with p features and n samples.
The data set must be the one which was used to compute
the raw estimates.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>location_reweighted</strong><span class="classifier">ndarray of shape (n_features,)</span></dt><dd><p>Re-weighted robust location estimate.</p>
</dd>
<dt><strong>covariance_reweighted</strong><span class="classifier">ndarray of shape (n_features, n_features)</span></dt><dd><p>Re-weighted robust covariance estimate.</p>
</dd>
<dt><strong>support_reweighted</strong><span class="classifier">ndarray of shape (n_samples,), dtype=bool</span></dt><dd><p>A mask of the observations that have been used to compute
the re-weighted robust location and covariance estimates.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">References</p>
<div role="list" class="citation-list">
<div class="citation" id="rd2c89e63f1c9-rvdriessen" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">RVDriessen</a><span class="fn-bracket">]</span></span>
<p>A Fast Algorithm for the Minimum Covariance
Determinant Estimator, 1999, American Statistical Association
and the American Society for Quality, TECHNOMETRICS</p>
</div>
</div>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.score">
<span class="sig-name descname"><span class="pre">score</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 external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/covariance/_elliptic_envelope.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.score" title="Link to this definition">#</a></dt>
<dd><p>Return 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>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Test samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>True labels for X.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">float</span></dt><dd><p>Mean accuracy of self.predict(X) w.r.t. y.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.score_samples">
<span class="sig-name descname"><span class="pre">score_samples</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 external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/covariance/_elliptic_envelope.py#L207"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.score_samples" title="Link to this definition">#</a></dt>
<dd><p>Compute the negative Mahalanobis distances.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>The data matrix.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>negative_mahal_distances</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>Opposite of the Mahalanobis distances.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="sklearn.covariance.EllipticEnvelope.set_params">
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/base.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.covariance.EllipticEnvelope.set_params" title="Link 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 <a class="reference internal" href="sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
parameters of the form <code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s
possible to update each component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<section id="gallery-examples">
<h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link to this heading">#</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the need for robust covariance estimation on a real data set. It is useful both for outlier detection and for a better understanding of the data structure."><img alt="" src="../../_images/sphx_glr_plot_outlier_detection_wine_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/applications/plot_outlier_detection_wine.html#sphx-glr-auto-examples-applications-plot-outlier-detection-wine-py"><span class="std std-ref">Outlier detection on a real data set</span></a></p>
  <div class="sphx-glr-thumbnail-title">Outlier detection on a real data set</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different anomaly detection algorithms on 2D datasets. Datasets contain one or two modes (regions of high density) to illustrate the ability of algorithms to cope with multimodal data."><img alt="" src="../../_images/sphx_glr_plot_anomaly_comparison_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_anomaly_comparison.html#sphx-glr-auto-examples-miscellaneous-plot-anomaly-comparison-py"><span class="std std-ref">Comparing anomaly detection algorithms for outlier detection on toy datasets</span></a></p>
  <div class="sphx-glr-thumbnail-title">Comparing anomaly detection algorithms for outlier detection on toy datasets</div>
</div></div></section>
</section>


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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.covariance.EllipticEnvelope"><code class="docutils literal notranslate"><span class="pre">EllipticEnvelope</span></code></a><ul class="nav section-nav flex-column visible">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.covariance.EllipticEnvelope.correct_covariance"><code class="docutils literal notranslate"><span class="pre">correct_covariance</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.covariance.EllipticEnvelope.decision_function"><code class="docutils literal notranslate"><span class="pre">decision_function</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.covariance.EllipticEnvelope.error_norm"><code class="docutils literal notranslate"><span class="pre">error_norm</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.covariance.EllipticEnvelope.fit"><code class="docutils literal notranslate"><span class="pre">fit</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.covariance.EllipticEnvelope.fit_predict"><code class="docutils literal notranslate"><span class="pre">fit_predict</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.covariance.EllipticEnvelope.get_metadata_routing"><code class="docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.covariance.EllipticEnvelope.get_params"><code class="docutils literal notranslate"><span class="pre">get_params</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.covariance.EllipticEnvelope.get_precision"><code class="docutils literal notranslate"><span class="pre">get_precision</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.covariance.EllipticEnvelope.mahalanobis"><code class="docutils literal notranslate"><span class="pre">mahalanobis</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.covariance.EllipticEnvelope.predict"><code class="docutils literal notranslate"><span class="pre">predict</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.covariance.EllipticEnvelope.reweight_covariance"><code class="docutils literal notranslate"><span class="pre">reweight_covariance</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.covariance.EllipticEnvelope.score"><code class="docutils literal notranslate"><span class="pre">score</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.covariance.EllipticEnvelope.score_samples"><code class="docutils literal notranslate"><span class="pre">score_samples</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.covariance.EllipticEnvelope.set_params"><code class="docutils literal notranslate"><span class="pre">set_params</span></code></a></li>
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