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sklearn.covariance.MinCovDet

class sklearn.covariance.MinCovDet(store_precision=True, assume_centered=False, support_fraction=None, random_state=None)

Minimum Covariance Determinant (MCD): robust estimator of covariance.

The Minimum Covariance Determinant covariance estimator is to be applied on Gaussian-distributed data, but could still be relevant on data drawn from a unimodal, symmetric distribution. It is not meant to be used with multi-modal 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 multi-modal datasets.

Parameters:

store_precision : bool

Specify if the estimated precision is stored.

assume_centered : Boolean

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_fraction : float, 0 < support_fraction < 1

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

random_state : integer or numpy.RandomState, optional

The random generator used. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.

References

[Rouseeuw1984](1, 2, 3) P. J. Rousseeuw. Least median of squares regression. J. Am Stat Ass, 79:871, 1984.
[Rouseeuw1999]A Fast Algorithm for the Minimum Covariance Determinant Estimator, 1999, American Statistical Association and the American Society for Quality, TECHNOMETRICS
[Butler1993]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, 1385-1400

Attributes

raw_location_ array-like, shape (n_features,) The raw robust estimated location before correction and re-weighting.
raw_covariance_ array-like, shape (n_features, n_features) The raw robust estimated covariance before correction and re-weighting.
raw_support_ array-like, 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 re-weighting.
location_ array-like, shape (n_features,) Estimated robust location
covariance_ array-like, shape (n_features, n_features) Estimated robust covariance matrix
precision_ array-like, shape (n_features, n_features) Estimated pseudo inverse matrix. (stored only if store_precision is True)
support_ array-like, shape (n_samples,) A mask of the observations that have been used to compute the robust estimates of location and shape.
dist_ array-like, shape (n_samples,) Mahalanobis distances of the training set (on which fit is called) observations.

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.
get_precision() Getter for the precision matrix.
mahalanobis(observations) Computes the Mahalanobis distances of given observations.
reweight_covariance(data) Re-weight raw Minimum Covariance Determinant estimates.
score(X_test[, y]) Computes the log-likelihood of a Gaussian data set with self.covariance_ as an estimator of its covariance matrix.
set_params(**params) Set the parameters of this estimator.
__init__(store_precision=True, assume_centered=False, support_fraction=None, random_state=None)
correct_covariance(data)

Apply a correction to raw Minimum Covariance Determinant estimates.

Correction using the empirical correction factor suggested by Rousseeuw and Van Driessen in [Rouseeuw1984].

Parameters:

data : array-like, 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_corrected : array-like, shape (n_features, n_features)

Corrected robust covariance estimate.

error_norm(comp_cov, norm='frobenius', scaling=True, squared=True)

Computes the Mean Squared Error between two covariance estimators. (In the sense of the Frobenius norm).

Parameters:

comp_cov : array-like, shape = [n_features, n_features]

The covariance to compare with.

norm : str

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_).

scaling : bool

If True (default), the squared error norm is divided by n_features. If False, the squared error norm is not rescaled.

squared : bool

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:

The Mean Squared Error (in the sense of the Frobenius norm) between :

`self` and `comp_cov` covariance estimators. :

fit(X, y=None)

Fits a Minimum Covariance Determinant with the FastMCD algorithm.

Parameters:

X : array-like, shape = [n_samples, n_features]

Training data, where n_samples is the number of samples and n_features is the number of features.

y : not used, present for API consistence purpose.

Returns:

self : object

Returns self.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

get_precision()

Getter for the precision matrix.

Returns:

`precision_` : array-like,

The precision matrix associated to the current covariance object.

mahalanobis(observations)

Computes the Mahalanobis distances of given observations.

The provided observations are assumed to be centered. One may want to center them using a location estimate first.

Parameters:

observations : array-like, shape = [n_observations, 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 (including centering).

Returns:

mahalanobis_distance : array, shape = [n_observations,]

Mahalanobis distances of the observations.

reweight_covariance(data)

Re-weight raw Minimum Covariance Determinant estimates.

Re-weight observations using Rousseeuw’s method (equivalent to deleting outlying observations from the data set before computing location and covariance estimates). [Rouseeuw1984]

Parameters:

data : array-like, 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_reweighted : array-like, shape (n_features, )

Re-weighted robust location estimate.

covariance_reweighted : array-like, shape (n_features, n_features)

Re-weighted robust covariance estimate.

support_reweighted : array-like, type boolean, shape (n_samples,)

A mask of the observations that have been used to compute the re-weighted robust location and covariance estimates.

score(X_test, y=None)

Computes the log-likelihood of a Gaussian data set with self.covariance_ as an estimator of its covariance matrix.

Parameters:

X_test : array-like, 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).

y : not used, present for API consistence purpose.

Returns:

res : float

The likelihood of the data set with self.covariance_ as an estimator of its covariance matrix.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self :
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