sklearn.covariance
.GraphLassoCV¶
Warning
DEPRECATED
-
class
sklearn.covariance.
GraphLassoCV
(**kwargs)[source]¶ Sparse inverse covariance w/ cross-validated choice of the l1 penalty.
See glossary entry for cross-validation estimator.
This class implements the Graphical Lasso algorithm.
Read more in the User Guide.
- Parameters
- alphasinteger, or list positive float, optional
If an integer is given, it fixes the number of points on the grids of alpha to be used. If a list is given, it gives the grid to be used. See the notes in the class docstring for more details.
- n_refinementsstrictly positive integer
The number of times the grid is refined. Not used if explicit values of alphas are passed.
- cvint, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy. Possible inputs for cv are:
None, to use the default 3-fold cross-validation,
integer, to specify the number of folds.
An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs
KFold
is used.Refer User Guide for the various cross-validation strategies that can be used here.
Changed in version 0.20:
cv
default value if None will change from 3-fold to 5-fold in v0.22.- tolpositive float, optional
The tolerance to declare convergence: if the dual gap goes below this value, iterations are stopped.
- enet_tolpositive 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_iterinteger, optional
Maximum number of iterations.
- mode{‘cd’, ‘lars’}
The Lasso solver to use: coordinate descent or LARS. Use LARS for very sparse underlying graphs, where number of features is greater than number of samples. Elsewhere prefer cd which is more numerically stable.
- n_jobsint or None, optional (default=None)
number of jobs to run in parallel.
None
means 1 unless in ajoblib.parallel_backend
context.-1
means using all processors. See Glossary for more details.- verboseboolean, optional
If verbose is True, the objective function and duality gap are printed at each iteration.
- assume_centeredBoolean
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_numpy.ndarray, shape (n_features, n_features)
Estimated covariance matrix.
- precision_numpy.ndarray, shape (n_features, n_features)
Estimated precision matrix (inverse covariance).
- alpha_float
Penalization parameter selected.
- cv_alphas_list of float
All penalization parameters explored.
- grid_scores_2D numpy.ndarray (n_alphas, n_folds)
Log-likelihood score on left-out data across folds.
- n_iter_int
Number of iterations run for the optimal alpha.
See also
Notes
The search for the optimal penalization parameter (alpha) is done on an iteratively refined grid: first the cross-validated scores on a grid are computed, then a new refined grid is centered around the maximum, and so on.
One of the challenges which is faced here is that the solvers can fail to converge to a well-conditioned estimate. The corresponding values of alpha then come out as missing values, but the optimum may be close to these missing values.
Methods
error_norm
(self, comp_cov[, norm, scaling, …])Computes the Mean Squared Error between two covariance estimators.
fit
(self, X[, y])Fits the GraphicalLasso covariance model to X.
get_params
(self[, deep])Get parameters for this estimator.
get_precision
(self)Getter for the precision matrix.
mahalanobis
(self, X)Computes the squared Mahalanobis distances of given observations.
score
(self, X_test[, y])Computes the log-likelihood of a Gaussian data set with
self.covariance_
as an estimator of its covariance matrix.set_params
(self, \*\*params)Set the parameters of this estimator.
-
__init__
(*args, **kwargs)[source]¶ DEPRECATED: The ‘GraphLassoCV’ was renamed to ‘GraphicalLassoCV’ in version 0.20 and will be removed in 0.22.
-
error_norm
(self, 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, shape = [n_features, n_features]
The covariance to compare with.
- normstr
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
If True (default), the squared error norm is divided by n_features. If False, the squared error norm is not rescaled.
- squaredbool
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
andcomp_cov
covariance estimators.
-
fit
(self, X, y=None)[source]¶ Fits the GraphicalLasso covariance model to X.
- Parameters
- Xndarray, shape (n_samples, n_features)
Data from which to compute the covariance estimate
- y(ignored)
-
get_params
(self, deep=True)[source]¶ Get parameters for this estimator.
- Parameters
- deepboolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsmapping of string to any
Parameter names mapped to their values.
-
get_precision
(self)[source]¶ Getter for the precision matrix.
- Returns
- precision_array-like
The precision matrix associated to the current covariance object.
-
mahalanobis
(self, X)[source]¶ Computes the squared Mahalanobis distances of given observations.
- Parameters
- Xarray-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
- distarray, shape = [n_samples,]
Squared Mahalanobis distances of the observations.
-
score
(self, 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, 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
- resfloat
The likelihood of the data set with
self.covariance_
as an estimator of its covariance matrix.
-
set_params
(self, **params)[source]¶ 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.- Returns
- self