sklearn.covariance.LedoitWolf  
class sklearn.covariance.LedoitWolf(*, store_precision=True, assume_centered=False, block_size=1000) [source]
 
LedoitWolf Estimator Ledoit-Wolf is a particular form of shrinkage, where the shrinkage coefficient is computed using O. Ledoit and M. Wolf’s formula as described in “A Well-Conditioned Estimator for Large-Dimensional Covariance Matrices”, Ledoit and Wolf, Journal of Multivariate Analysis, Volume 88, Issue 2, February 2004, pages 365-411. 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 (default), data will be centered before computation.  
block_sizeint, default=1000 

Size of blocks into which the covariance matrix will be split during its Ledoit-Wolf estimation. This is purely a memory optimization and does not affect results.    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)  
shrinkage_float 

Coefficient in the convex combination used for the computation of the shrunk estimate. Range is [0, 1].     Notes The regularised covariance is: (1 - shrinkage) * cov + shrinkage * mu * np.identity(n_features) where mu = trace(cov) / n_features and shrinkage is given by the Ledoit and Wolf formula (see References) References “A Well-Conditioned Estimator for Large-Dimensional Covariance Matrices”, Ledoit and Wolf, Journal of Multivariate Analysis, Volume 88, Issue 2, February 2004, pages 365-411. Examples >>> import numpy as np
>>> from sklearn.covariance import LedoitWolf
>>> real_cov = np.array([[.4, .2],
...                      [.2, .8]])
>>> np.random.seed(0)
>>> X = np.random.multivariate_normal(mean=[0, 0],
...                                   cov=real_cov,
...                                   size=50)
>>> cov = LedoitWolf().fit(X)
>>> cov.covariance_
array([[0.4406..., 0.1616...],
       [0.1616..., 0.8022...]])
>>> cov.location_
array([ 0.0595... , -0.0075...])
 Methods  
error_norm(comp_cov[, norm, scaling, squared]) Computes the Mean Squared Error between two covariance estimators.  
fit(X[, y]) Fit the Ledoit-Wolf shrunk covariance model according to the given training data and parameters.  
get_params([deep]) Get parameters for this estimator.  
get_precision() Getter for the precision matrix.  
mahalanobis(X) Computes the squared Mahalanobis distances of given observations.  
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.    
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_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) between self and comp_cov covariance estimators.     
  
fit(X, y=None) [source]
 
Fit the Ledoit-Wolf shrunk covariance model according to the given training data and parameters.  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 
   
  
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_array-like 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 
 
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]
 
Computes the log-likelihood of a Gaussian data set with self.covariance_ as an estimator of its covariance matrix.  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 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.     
 
 Examples using sklearn.covariance.LedoitWolf
 
  Ledoit-Wolf vs OAS estimation  

  Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood  

  Model selection with Probabilistic PCA and Factor Analysis (FA)