Merge pull request scikit-learn#4686 from bnaul/graph_lasso_tests

[MRG+1] Add enet_tol parameter to GraphLasso class/methods

Author: GaelVaroquaux

sklearn/covariance/graph_lasso_.py

@@ -107,7 +107,9 @@ def graph_lasso(emp_cov, alpha, cov_init=None, mode='cd', tol=1e-4,
 
     enet_tol : positive float, optional
         The tolerance for the elastic net solver used to calculate the descent
-        direction. Only used for mode='cd'.
+        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, optional
         The maximum number of iterations.
@@ -280,6 +282,12 @@ class GraphLasso(EmpiricalCovariance):
         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.
 
@@ -309,11 +317,12 @@ class GraphLasso(EmpiricalCovariance):
     graph_lasso, GraphLassoCV
     """
 
-    def __init__(self, alpha=.01, mode='cd', tol=1e-4, max_iter=100,
-                 verbose=False, assume_centered=False):
+    def __init__(self, alpha=.01, mode='cd', tol=1e-4, enet_tol=1e-4,
+                 max_iter=100, verbose=False, assume_centered=False):
         self.alpha = alpha
         self.mode = mode
         self.tol = tol
+        self.enet_tol = enet_tol
         self.max_iter = max_iter
         self.verbose = verbose
         self.assume_centered = assume_centered
@@ -330,14 +339,14 @@ def fit(self, X, y=None):
             X, assume_centered=self.assume_centered)
         self.covariance_, self.precision_, self.n_iter_ = graph_lasso(
             emp_cov, alpha=self.alpha, mode=self.mode, tol=self.tol,
-            max_iter=self.max_iter, verbose=self.verbose,
-            return_n_iter=True)
+            enet_tol=self.enet_tol, max_iter=self.max_iter,
+            verbose=self.verbose, return_n_iter=True)
         return self
 
 
 # Cross-validation with GraphLasso
 def graph_lasso_path(X, alphas, cov_init=None, X_test=None, mode='cd',
-                     tol=1e-4, max_iter=100, verbose=False):
+                     tol=1e-4, enet_tol=1e-4, max_iter=100, verbose=False):
     """l1-penalized covariance estimator along a path of decreasing alphas
 
     Parameters
@@ -360,6 +369,12 @@ def graph_lasso_path(X, alphas, cov_init=None, X_test=None, mode='cd',
         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, optional
         The maximum number of iterations.
 
@@ -396,7 +411,7 @@ def graph_lasso_path(X, alphas, cov_init=None, X_test=None, mode='cd',
             # Capture the errors, and move on
             covariance_, precision_ = graph_lasso(
                 emp_cov, alpha=alpha, cov_init=covariance_, mode=mode, tol=tol,
-                max_iter=max_iter, verbose=inner_verbose)
+                enet_tol=enet_tol, max_iter=max_iter, verbose=inner_verbose)
             covariances_.append(covariance_)
             precisions_.append(precision_)
             if X_test is not None:
@@ -445,6 +460,12 @@ class GraphLassoCV(GraphLasso):
         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, optional
         Maximum number of iterations.
 
@@ -506,12 +527,13 @@ class GraphLassoCV(GraphLasso):
     """
 
     def __init__(self, alphas=4, n_refinements=4, cv=None, tol=1e-4,
-                 max_iter=100, mode='cd', n_jobs=1, verbose=False,
-                 assume_centered=False):
+                 enet_tol=1e-4, max_iter=100, mode='cd', n_jobs=1,
+                 verbose=False, assume_centered=False):
         self.alphas = alphas
         self.n_refinements = n_refinements
         self.mode = mode
         self.tol = tol
+        self.enet_tol = enet_tol
         self.max_iter = max_iter
         self.verbose = verbose
         self.cv = cv
@@ -572,7 +594,7 @@ def fit(self, X, y=None):
                     delayed(graph_lasso_path)(
                         X[train], alphas=alphas,
                         X_test=X[test], mode=self.mode,
-                        tol=self.tol,
+                        tol=self.tol, enet_tol=self.enet_tol,
                         max_iter=int(.1 * self.max_iter),
                         verbose=inner_verbose)
                     for train, test in cv)
@@ -644,6 +666,6 @@ def fit(self, X, y=None):
         # Finally fit the model with the selected alpha
         self.covariance_, self.precision_, self.n_iter_ = graph_lasso(
             emp_cov, alpha=best_alpha, mode=self.mode, tol=self.tol,
-            max_iter=self.max_iter, verbose=inner_verbose,
-            return_n_iter=True)
+            enet_tol=self.enet_tol, max_iter=self.max_iter,
+            verbose=inner_verbose, return_n_iter=True)
         return self