ShrunkCovariance (original) (raw)
class sklearn.covariance.ShrunkCovariance(*, store_precision=True, assume_centered=False, shrinkage=0.1)[source]#
Covariance estimator with shrinkage.
Read more in the User Guide.
Parameters:
store_precisionbool, default=True
Specify if the estimated precision is stored.
assume_centeredbool, default=False
If True, data will not be centered before computation. Useful when working with data whose mean is almost, but not exactly zero. If False, data will be centered before computation.
shrinkagefloat, default=0.1
Coefficient in the convex combination used for the computation of the shrunk estimate. Range is [0, 1].
Attributes:
**covariance_**ndarray of shape (n_features, n_features)
Estimated covariance matrix
**location_**ndarray of shape (n_features,)
Estimated location, i.e. the estimated mean.
**precision_**ndarray of shape (n_features, n_features)
Estimated pseudo inverse matrix. (stored only if store_precision is True)
**n_features_in_**int
Number of features seen during fit.
Added in version 0.24.
**feature_names_in_**ndarray of shape (n_features_in_,)
Names of features seen during fit. Defined only when Xhas feature names that are all strings.
Added in version 1.0.
See also
An object for detecting outliers in a Gaussian distributed dataset.
Maximum likelihood covariance estimator.
Sparse inverse covariance estimation with an l1-penalized estimator.
Sparse inverse covariance with cross-validated choice of the l1 penalty.
LedoitWolf Estimator.
Minimum Covariance Determinant (robust estimator of covariance).
Oracle Approximating Shrinkage Estimator.
Notes
The regularized covariance is given by:
(1 - shrinkage) * cov + shrinkage * mu * np.identity(n_features)
where mu = trace(cov) / n_features
Examples
import numpy as np from sklearn.covariance import ShrunkCovariance 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 = ShrunkCovariance().fit(X) cov.covariance_ array([[0.7387..., 0.2536...], [0.2536..., 0.4110...]]) cov.location_ array([0.0622..., 0.0193...])
error_norm(comp_cov, norm='frobenius', scaling=True, squared=True)[source]#
Compute the Mean Squared Error between two covariance estimators.
Parameters:
comp_covarray-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_).
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) betweenself and comp_cov covariance estimators.
Fit the shrunk covariance model to X.
Parameters:
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
yIgnored
Not used, present for API consistency by convention.
Returns:
selfobject
Returns the instance itself.
get_metadata_routing()[source]#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Returns:
routingMetadataRequest
A MetadataRequest encapsulating routing information.
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.
Getter for the precision matrix.
Returns:
**precision_**array-like of shape (n_features, n_features)
The precision matrix associated to the current covariance object.
Compute the squared Mahalanobis distances of given observations.
Parameters:
Xarray-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:
distndarray of shape (n_samples,)
Squared Mahalanobis distances of the observations.
score(X_test, y=None)[source]#
Compute the log-likelihood of X_test under the estimated Gaussian model.
The Gaussian model is defined by its mean and covariance matrix which are represented respectively by self.location_ and self.covariance_.
Parameters:
X_testarray-like 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 log-likelihood of X_test with self.location_ and self.covariance_as estimators of the Gaussian model mean and covariance matrix respectively.
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.