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sha256=48809d88893a3f17d79f8e5acbd28126de919b8ced6d1f6856a61fd6bfae571d
sha512=9e1d01c42aed436163b1ce50bee141f40cb5bc943d5dd16d6eb21f1b53d613933533c70f28675e418a550cf44e0cd66d47496e462132769b05dec64bf3db560c
doc/sklearn/Sklearn/Covariance/EllipticEnvelope/index.html
Module Covariance.EllipticEnvelopeSource
val create :
?store_precision:bool ->
?assume_centered:bool ->
?support_fraction:float ->
?contamination:float ->
?random_state:int ->
unit ->
tAn object for detecting outliers in a Gaussian distributed dataset.
Read more in the :ref:`User Guide <outlier_detection>`.
Parameters ---------- store_precision : bool, default=True Specify if the estimated precision is stored.
assume_centered : bool, 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, 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`. Range is (0, 1).
contamination : float, default=0.1 The amount of contamination of the data set, i.e. the proportion of outliers in the data set. Range is (0, 0.5).
random_state : int or RandomState instance, 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 ---------- 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.
offset_ : float Offset used to define the decision function from the raw scores. We have the relation: ``decision_function = score_samples - offset_``. 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 < 0) in training.
.. versionadded:: 0.20
raw_location_ : ndarray of shape (n_features,) The raw robust estimated location before correction and re-weighting.
raw_covariance_ : ndarray of shape (n_features, n_features) The raw robust estimated covariance before correction and re-weighting.
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 re-weighting.
dist_ : ndarray of shape (n_samples,) Mahalanobis distances of the training set (on which :meth:`fit` is called) observations.
Examples -------- >>> import numpy as np >>> from sklearn.covariance import EllipticEnvelope >>> true_cov = np.array([.8, .3], ... [.3, .4]) >>> X = np.random.RandomState(0).multivariate_normal(mean=0, 0, ... cov=true_cov, ... size=500) >>> cov = EllipticEnvelope(random_state=0).fit(X) >>> # predict returns 1 for an inlier and -1 for an outlier >>> cov.predict([0, 0], ... [3, 3]) array( 1, -1) >>> cov.covariance_ array([0.7411..., 0.2535...], [0.2535..., 0.3053...]) >>> cov.location_ array(0.0813... , 0.0427...)
See Also -------- EmpiricalCovariance, MinCovDet
Notes ----- 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``.
References ---------- .. 1 Rousseeuw, P.J., Van Driessen, K. 'A fast algorithm for the minimum covariance determinant estimator' Technometrics 41(3), 212 (1999)
val correct_covariance :
data:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
[> `ArrayLike ] Np.Obj.tApply a correction to raw Minimum Covariance Determinant estimates.
Correction using the empirical correction factor suggested by Rousseeuw and Van Driessen in RVD_.
Parameters ---------- data : array-like 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_corrected : ndarray 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
val decision_function :
x:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
[> `ArrayLike ] Np.Obj.tCompute the decision function of the given observations.
Parameters ---------- X : array-like of shape (n_samples, n_features) The data matrix.
Returns ------- decision : ndarray of shape (n_samples, ) 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.
val error_norm :
?norm:[ `Frobenius | `Spectral ] ->
?scaling:bool ->
?squared:bool ->
comp_cov:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
floatComputes the Mean Squared Error between two covariance estimators. (In the sense of the Frobenius norm).
Parameters ---------- comp_cov : array-like 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_)``.
scaling : bool, default=True If True (default), the squared error norm is divided by n_features. If False, the squared error norm is not rescaled.
squared : bool, 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 ------- result : float The Mean Squared Error (in the sense of the Frobenius norm) between `self` and `comp_cov` covariance estimators.
Fit the EllipticEnvelope model.
Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) Training data.
y : Ignored Not used, present for API consistency by convention.
val fit_predict :
?y:Py.Object.t ->
x:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
[> `ArrayLike ] Np.Obj.tPerform fit on X and returns labels for X.
Returns -1 for outliers and 1 for inliers.
Parameters ---------- X : array-like, sparse matrix, dataframe of shape (n_samples, n_features)
y : Ignored Not used, present for API consistency by convention.
Returns ------- y : ndarray of shape (n_samples,) 1 for inliers, -1 for outliers.
Get parameters for this estimator.
Parameters ---------- deep : bool, default=True 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.
Getter for the precision matrix.
Returns ------- precision_ : array-like of shape (n_features, n_features) The precision matrix associated to the current covariance object.
Computes the squared Mahalanobis distances of given observations.
Parameters ---------- X : array-like 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 ------- dist : ndarray of shape (n_samples,) Squared Mahalanobis distances of the observations.
Predict the labels (1 inlier, -1 outlier) of X according to the fitted model.
Parameters ---------- X : array-like of shape (n_samples, n_features) The data matrix.
Returns ------- is_inlier : ndarray of shape (n_samples,) Returns -1 for anomalies/outliers and +1 for inliers.
val reweight_covariance :
data:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
[> `ArrayLike ] Np.Obj.t * [> `ArrayLike ] Np.Obj.t * Py.Object.tRe-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) described in RVDriessen_.
Parameters ---------- data : array-like 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_reweighted : ndarray of shape (n_features,) Re-weighted robust location estimate.
covariance_reweighted : ndarray of shape (n_features, n_features) Re-weighted robust covariance estimate.
support_reweighted : ndarray of shape (n_samples,), dtype=bool A mask of the observations that have been used to compute the re-weighted 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
val score :
?sample_weight:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
y:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
floatReturns 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.
Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples.
y : array-like of shape (n_samples,) or (n_samples, n_outputs) True labels for X.
sample_weight : array-like of shape (n_samples,), default=None Sample weights.
Returns ------- score : float Mean accuracy of self.predict(X) w.r.t. y.
Compute the negative Mahalanobis distances.
Parameters ---------- X : array-like of shape (n_samples, n_features) The data matrix.
Returns ------- negative_mahal_distances : array-like of shape (n_samples,) Opposite of the Mahalanobis distances.
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
Attribute location_: get value or raise Not_found if None.
Attribute location_: get value as an option.
Attribute covariance_: get value or raise Not_found if None.
Attribute covariance_: get value as an option.
Attribute precision_: get value or raise Not_found if None.
Attribute precision_: get value as an option.
Attribute support_: get value or raise Not_found if None.
Attribute support_: get value as an option.
Attribute raw_location_: get value or raise Not_found if None.
Attribute raw_location_: get value as an option.
Attribute raw_covariance_: get value or raise Not_found if None.
Attribute raw_covariance_: get value as an option.
Attribute raw_support_: get value or raise Not_found if None.
Attribute raw_support_: get value as an option.
Attribute dist_: get value or raise Not_found if None.
Pretty-print the object to a formatter.