sklearn.covariance.GraphLasso¶
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class
sklearn.covariance.GraphLasso(alpha=0.01, mode='cd', tol=0.0001, enet_tol=0.0001, max_iter=100, verbose=False, assume_centered=False)[源代码]¶ Sparse inverse covariance estimation with an l1-penalized estimator.
Read more in the User Guide.
Parameters: alpha : positive float, default 0.01
The regularization parameter: the higher alpha, the more regularization, the sparser the inverse covariance.
mode : {‘cd’, ‘lars’}, default ‘cd’
The Lasso solver to use: coordinate descent or LARS. Use LARS for very sparse underlying graphs, where p > n. Elsewhere prefer cd which is more numerically stable.
tol : positive float, default 1e-4
The tolerance to declare convergence: if the dual gap goes below this value, iterations are stopped.
enet_tol : positive float, optional
The tolerance for the elastic net solver used to calculate the descent direction. This parameter controls the accuracy of the search direction for a given column update, not of the overall parameter estimate. Only used for mode=’cd’.
max_iter : integer, default 100
The maximum number of iterations.
verbose : boolean, default False
If verbose is True, the objective function and dual gap are plotted at each iteration.
assume_centered : boolean, default False
If True, data are not centered before computation. Useful when working with data whose mean is almost, but not exactly zero. If False, data are centered before computation.
Attributes: covariance_ : array-like, shape (n_features, n_features)
Estimated covariance matrix
precision_ : array-like, shape (n_features, n_features)
Estimated pseudo inverse matrix.
n_iter_ : int
Number of iterations run.
Methods
error_norm(comp_cov[, norm, scaling, squared])Computes the Mean Squared Error between two covariance estimators. fit(X[, y])get_params([deep])Get parameters for this estimator. get_precision()Getter for the precision matrix. mahalanobis(observations)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. -
__init__(alpha=0.01, mode='cd', tol=0.0001, enet_tol=0.0001, max_iter=100, verbose=False, assume_centered=False)[源代码]¶
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error_norm(comp_cov, norm='frobenius', scaling=True, squared=True)[源代码]¶ 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. :
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get_params(deep=True)[源代码]¶ 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.
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get_precision()[源代码]¶ Getter for the precision matrix.
Returns: precision_ : array-like,
The precision matrix associated to the current covariance object.
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mahalanobis(observations)[源代码]¶ Computes the squared Mahalanobis distances of given observations.
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.
Returns: mahalanobis_distance : array, shape = [n_observations,]
Squared Mahalanobis distances of the observations.
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score(X_test, y=None)[源代码]¶ 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.
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