MNT api update for benchopt 1.4 (#8)

objective.py

@@ -4,7 +4,7 @@
 
 
 class Objective(BaseObjective):
-    min_benchopt_version = "1.3"
+    min_benchopt_version = "1.4"
     name = "Ridge Regression"
 
     parameters = {
@@ -21,13 +21,13 @@ def set_data(self, X, y, X_test=None, y_test=None):
         self.X_test, self.y_test = X_test, y_test
         self.n_features = self.X.shape[1]
 
-    def get_one_solution(self):
+    def get_one_result(self):
         n_features = self.n_features
         if self.fit_intercept:
             n_features += 1
-        return np.zeros(n_features)
+        return dict(beta=np.zeros(n_features))
 
-    def compute(self, beta):
+    def evaluate_result(self, beta):
         # compute residuals
         test_loss = None
         if self.X_test is not None:

solvers/cd.py

@@ -89,4 +89,4 @@ def sparse_cd(X_data, X_indices, X_indptr, y, lmbd, L, n_iter):
         return w
 
     def get_result(self):
-        return self.w
+        return dict(beta=self.w)

solvers/glmnet.py

@@ -88,4 +88,5 @@ def get_result(self):
         as_matrix = robjects.r["as"]
         coefs = np.array(as_matrix(results["beta"], "matrix"))
         beta = coefs.flatten()
-        return np.r_[beta, results["a0"]] if self.fit_intercept else beta
+        beta = np.r_[beta, results["a0"]] if self.fit_intercept else beta
+        return dict(beta=beta)

solvers/lightning.py

@@ -47,4 +47,4 @@ def get_result(self):
         beta = self.ridge.coef_.flatten()
         if self.fit_intercept:
             beta = np.r_[beta, self.ridge.intercept_]
-        return beta
+        return dict(beta=beta)

solvers/sklearn.py

@@ -44,4 +44,4 @@ def run(self, n_iter):
             self.coef = coef
 
     def get_result(self):
-        return self.coef
+        return dict(beta=self.coef)

solvers/snapml.py

@@ -52,4 +52,4 @@ def run(self, n_iter):
         self.coef = coef
 
     def get_result(self):
-        return self.coef
+        return dict(beta=self.coef)