sklearn.covariance
.MinCovDet¶

class
sklearn.covariance.
MinCovDet
(*, store_precision=True, assume_centered=False, support_fraction=None, random_state=None)[source]¶ Minimum Covariance Determinant (MCD): robust estimator of covariance.
The Minimum Covariance Determinant covariance estimator is to be applied on Gaussiandistributed data, but could still be relevant on data drawn from a unimodal, symmetric distribution. It is not meant to be used with multimodal data (the algorithm used to fit a MinCovDet object is likely to fail in such a case). One should consider projection pursuit methods to deal with multimodal datasets.
Read more in the User Guide.
 Parameters
 store_precisionbool, default=True
Specify if the estimated precision is stored.
 assume_centeredbool, default=False
If True, the support of the robust location and the 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.
 support_fractionfloat, default=None
The proportion of points to be included in the support of the raw MCD estimate. Default is None, which implies that the minimum value of support_fraction will be used within the algorithm:
(n_sample + n_features + 1) / 2
. The parameter must be in the range (0, 1). random_stateint, RandomState instance or None, default=None
Determines the pseudo random number generator for shuffling the data. Pass an int for reproducible results across multiple function calls. See :term:
Glossary <random_state>
.
 Attributes
 raw_location_ndarray of shape (n_features,)
The raw robust estimated location before correction and reweighting.
 raw_covariance_ndarray of shape (n_features, n_features)
The raw robust estimated covariance before correction and reweighting.
 raw_support_ndarray of shape (n_samples,)
A mask of the observations that have been used to compute the raw robust estimates of location and shape, before correction and reweighting.
 location_ndarray of shape (n_features,)
Estimated robust location.
 covariance_ndarray of shape (n_features, n_features)
Estimated robust covariance matrix.
 precision_ndarray of shape (n_features, n_features)
Estimated pseudo inverse matrix. (stored only if store_precision is True)
 support_ndarray of shape (n_samples,)
A mask of the observations that have been used to compute the robust estimates of location and shape.
 dist_ndarray of shape (n_samples,)
Mahalanobis distances of the training set (on which
fit
is called) observations.
References
 Rouseeuw1984
P. J. Rousseeuw. Least median of squares regression. J. Am Stat Ass, 79:871, 1984.
 Rousseeuw
A Fast Algorithm for the Minimum Covariance Determinant Estimator, 1999, American Statistical Association and the American Society for Quality, TECHNOMETRICS
 ButlerDavies
R. W. Butler, P. L. Davies and M. Jhun, Asymptotics For The Minimum Covariance Determinant Estimator, The Annals of Statistics, 1993, Vol. 21, No. 3, 13851400
Examples
>>> import numpy as np >>> from sklearn.covariance import MinCovDet >>> from sklearn.datasets import make_gaussian_quantiles >>> real_cov = np.array([[.8, .3], ... [.3, .4]]) >>> rng = np.random.RandomState(0) >>> X = rng.multivariate_normal(mean=[0, 0], ... cov=real_cov, ... size=500) >>> cov = MinCovDet(random_state=0).fit(X) >>> cov.covariance_ array([[0.7411..., 0.2535...], [0.2535..., 0.3053...]]) >>> cov.location_ array([0.0813... , 0.0427...])
Methods
correct_covariance
(data)Apply a correction to raw Minimum Covariance Determinant estimates.
error_norm
(comp_cov[, norm, scaling, squared])Computes the Mean Squared Error between two covariance estimators.
fit
(X[, y])Fits a Minimum Covariance Determinant with the FastMCD algorithm.
get_params
([deep])Get parameters for this estimator.
Getter for the precision matrix.
mahalanobis
(X)Computes the squared Mahalanobis distances of given observations.
reweight_covariance
(data)Reweight raw Minimum Covariance Determinant estimates.
score
(X_test[, y])Computes the loglikelihood of a Gaussian data set with
self.covariance_
as an estimator of its covariance matrix.set_params
(**params)Set the parameters of this estimator.

correct_covariance
(data)[source]¶ Apply a correction to raw Minimum Covariance Determinant estimates.
Correction using the empirical correction factor suggested by Rousseeuw and Van Driessen in [RVD].
 Parameters
 dataarraylike of shape (n_samples, n_features)
The data matrix, with p features and n samples. The data set must be the one which was used to compute the raw estimates.
 Returns
 covariance_correctedndarray of shape (n_features, n_features)
Corrected robust covariance estimate.
References
 RVD
A Fast Algorithm for the Minimum Covariance Determinant Estimator, 1999, American Statistical Association and the American Society for Quality, TECHNOMETRICS

error_norm
(comp_cov, norm='frobenius', scaling=True, squared=True)[source]¶ Computes the Mean Squared Error between two covariance estimators. (In the sense of the Frobenius norm).
 Parameters
 comp_covarraylike of shape (n_features, n_features)
The covariance to compare with.
 norm{“frobenius”, “spectral”}, default=”frobenius”
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
(comp_cov  self.covariance_)
. scalingbool, default=True
If True (default), the squared error norm is divided by n_features. If False, the squared error norm is not rescaled.
 squaredbool, default=True
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.
 Returns
 resultfloat
The Mean Squared Error (in the sense of the Frobenius norm) between
self
andcomp_cov
covariance estimators.

fit
(X, y=None)[source]¶ Fits a Minimum Covariance Determinant with the FastMCD algorithm.
 Parameters
 Xarraylike of shape (n_samples, n_features)
Training data, where
n_samples
is the number of samples andn_features
is the number of features. y: Ignored
Not used, present for API consistency by convention.
 Returns
 selfobject

get_params
(deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsdict
Parameter names mapped to their values.

get_precision
()[source]¶ Getter for the precision matrix.
 Returns
 precision_arraylike of shape (n_features, n_features)
The precision matrix associated to the current covariance object.

mahalanobis
(X)[source]¶ Computes the squared Mahalanobis distances of given observations.
 Parameters
 Xarraylike of shape (n_samples, n_features)
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.
 Returns
 distndarray of shape (n_samples,)
Squared Mahalanobis distances of the observations.

reweight_covariance
(data)[source]¶ Reweight raw Minimum Covariance Determinant estimates.
Reweight observations using Rousseeuw’s method (equivalent to deleting outlying observations from the data set before computing location and covariance estimates) described in [RVDriessen].
 Parameters
 dataarraylike of shape (n_samples, n_features)
The data matrix, with p features and n samples. The data set must be the one which was used to compute the raw estimates.
 Returns
 location_reweightedndarray of shape (n_features,)
Reweighted robust location estimate.
 covariance_reweightedndarray of shape (n_features, n_features)
Reweighted robust covariance estimate.
 support_reweightedndarray of shape (n_samples,), dtype=bool
A mask of the observations that have been used to compute the reweighted robust location and covariance estimates.
References
 RVDriessen
A Fast Algorithm for the Minimum Covariance Determinant Estimator, 1999, American Statistical Association and the American Society for Quality, TECHNOMETRICS

score
(X_test, y=None)[source]¶ Computes the loglikelihood of a Gaussian data set with
self.covariance_
as an estimator of its covariance matrix. Parameters
 X_testarraylike of shape (n_samples, n_features)
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).
 yIgnored
Not used, present for API consistency by convention.
 Returns
 resfloat
The likelihood of the data set with
self.covariance_
as an estimator of its covariance matrix.

set_params
(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object. Parameters
 **paramsdict
Estimator parameters.
 Returns
 selfestimator instance
Estimator instance.