Merge pull request #2011 from arokem/fix-2010

BF: Use the `sklearn.base` interface, instead of deprecated `sklearn.linear_model.base`

dipy/reconst/sfm.py

@@ -35,7 +35,8 @@
 from dipy.reconst.cache import Cache
 from dipy.core.onetime import auto_attr
 
-lm, has_sklearn, _ = optional_package('sklearn.linear_model')
+sklearn, has_sklearn, _ = optional_package('sklearn')
+lm, _, _ = optional_package('sklearn.linear_model')
 
 # If sklearn is unavailable, we can fall back on nnls (but we also warn the
 # user that we are about to do that):
@@ -307,12 +308,17 @@ def __init__(self, gtab, sphere=None, response=[0.0015, 0.0005, 0.0005],
             The eigenvalues of a canonical tensor to be used as the response
             function of single-fascicle signals.
             Default:[0.0015, 0.0005, 0.0005]
-        solver : string, dipy.core.optimize.SKLearnLinearSolver object, or sklearn.linear_model.base.LinearModel object, optional.
+        solver : string, or initialized linear model object.
             This will determine the algorithm used to solve the set of linear
             equations underlying this model. If it is a string it needs to be
             one of the following: {'ElasticNet', 'NNLS'}. Otherwise, it can be
-            an object that inherits from `dipy.optimize.SKLearnLinearSolver`.
+            an object that inherits from `dipy.optimize.SKLearnLinearSolver`
+            or an object with a similar interface from Scikit Learn:
+            `sklearn.linear_model.ElasticNet`, `sklearn.linear_model.Lasso` or
+            `sklearn.linear_model.Ridge` and other objects that inherit from
+            `sklearn.base.RegressorMixin`.
             Default: 'ElasticNet'.
+
         l1_ratio : float, optional
             Sets the balance betwee L1 and L2 regularization in ElasticNet
             [Zou2005]_. Default: 0.5
@@ -356,7 +362,7 @@ def __init__(self, gtab, sphere=None, response=[0.0015, 0.0005, 0.0005],
             self.solver = opt.NonNegativeLeastSquares()
 
         elif (isinstance(solver, opt.SKLearnLinearSolver) or
-            has_sklearn and isinstance(solver, lm.base.LinearModel)):
+              has_sklearn and isinstance(solver, sklearn.base.RegressorMixin)):
             self.solver = solver
 
         else: