sklearn.covariance.GraphLasso
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class sklearn.covariance.GraphLasso(*args, **kwargs)
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Sparse inverse covariance estimation with an l1-penalized estimator.
This class implements the Graphical Lasso algorithm.
Read more in the User Guide.
Parameters: |
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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: |
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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]) | Fits the GraphicalLasso model to X. |
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. |
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__init__(*args, **kwargs)
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DEPRECATED: The ‘GraphLasso’ was renamed to ‘GraphicalLasso’ in version 0.20 and will be removed in 0.22.
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error_norm(comp_cov, norm=’frobenius’, scaling=True, squared=True)
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Computes the Mean Squared Error between two covariance estimators. (In the sense of the Frobenius norm).
Parameters: |
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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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fit(X, y=None)
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Fits the GraphicalLasso model to X.
Parameters: |
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X : ndarray, shape (n_samples, n_features) -
Data from which to compute the covariance estimate -
y : (ignored) |
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get_params(deep=True)
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Get parameters for this estimator.
Parameters: |
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deep : boolean, optional -
If True, will return the parameters for this estimator and contained subobjects that are estimators. |
Returns: |
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params : mapping of string to any -
Parameter names mapped to their values. |
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get_precision()
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Getter for the precision matrix.
Returns: |
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precision_ : array-like -
The precision matrix associated to the current covariance object. |
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mahalanobis(X)
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Computes the squared Mahalanobis distances of given observations.
Parameters: |
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X : array-like, 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: |
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dist : array, shape = [n_samples,] -
Squared Mahalanobis distances of the observations. |
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score(X_test, y=None)
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Computes the log-likelihood of a Gaussian data set with self.covariance_
as an estimator of its covariance matrix.
Parameters: |
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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
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not used, present for API consistence purpose. |
Returns: |
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res : float -
The likelihood of the data set with self.covariance_ as an estimator of its covariance matrix. |
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set_params(**params)
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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.