# EllipticEnvelope¶

class ibex.sklearn.covariance.EllipticEnvelope(store_precision=True, assume_centered=False, support_fraction=None, contamination=0.1, random_state=None)

Bases: sklearn.covariance.outlier_detection.EllipticEnvelope, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

An object for detecting outliers in a Gaussian distributed dataset.

Read more in the User Guide.

store_precision : boolean, optional (default=True)
Specify if the estimated precision is stored.
assume_centered : boolean, optional (default=False)
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.
support_fraction : float in (0., 1.), optional (default=None)
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: [n_sample + n_features + 1] / 2.
contamination : float in (0., 0.5), optional (default=0.1)
The amount of contamination of the data set, i.e. the proportion of outliers in the data set.
random_state : int, RandomState instance or None, optional (default=None)
The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
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.

EmpiricalCovariance, MinCovDet

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 n_samples > n_features ** 2.

 [1] Rousseeuw, P.J., Van Driessen, K. “A fast algorithm for the minimum covariance determinant estimator” Technometrics 41(3), 212 (1999)
decision_function(X, raw_values=False)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Compute the decision function of the given observations.

X : array-like, shape (n_samples, n_features)

raw_values : bool
Whether or not to consider raw Mahalanobis distances as the decision function. Must be False (default) for compatibility with the others outlier detection tools.
decision : array-like, shape (n_samples, )
Decision function of the samples. It is equal to the Mahalanobis distances if raw_values is True. By default (raw_values=False), it is equal to the cubic root of the shifted Mahalanobis distances. In that case, the threshold for being an outlier is 0, which ensures a compatibility with other outlier detection tools such as the One-Class SVM.
fit(X, y=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit the EllipticEnvelope model with X.

X : numpy array or sparse matrix of shape [n_samples, n_features]
Training data

y : (ignored)

predict(X)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Outlyingness of observations in X according to the fitted model.

X : array-like, shape = (n_samples, n_features)

is_outliers : array, shape = (n_samples, ), dtype = bool
For each observation, tells whether or not it should be considered as an outlier according to the fitted model.
threshold : float,
The values of the less outlying point’s decision function.
score(X, y, sample_weight=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Returns the mean accuracy on the given test data and labels.

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.

X : array-like, shape = (n_samples, n_features)
Test samples.
y : array-like, shape = (n_samples,) or (n_samples, n_outputs)
True labels for X.
sample_weight : array-like, shape = (n_samples,), optional
Sample weights.
score : float
Mean accuracy of self.predict(X) wrt. y.