COSMIT Mimimal refactoring of gradient_boosting.py (scikit-learn#11921)

NicolasHug · jnothman · commit d95cd9126cde · 2018-08-30T13:52:20.000+10:00
diff --git a/sklearn/ensemble/gradient_boosting.py b/sklearn/ensemble/gradient_boosting.py
@@ -1190,22 +1190,14 @@ def _fit_stage(self, i, X, y, y_pred, sample_weight, sample_mask,
                 # no inplace multiplication!
                 sample_weight = sample_weight * sample_mask.astype(np.float64)
 
-            if X_csc is not None:
-                tree.fit(X_csc, residual, sample_weight=sample_weight,
-                         check_input=False, X_idx_sorted=X_idx_sorted)
-            else:
-                tree.fit(X, residual, sample_weight=sample_weight,
-                         check_input=False, X_idx_sorted=X_idx_sorted)
+            X = X_csr if X_csr is not None else X
+            tree.fit(X, residual, sample_weight=sample_weight,
+                     check_input=False, X_idx_sorted=X_idx_sorted)
 
             # update tree leaves
-            if X_csr is not None:
-                loss.update_terminal_regions(tree.tree_, X_csr, y, residual, y_pred,
-                                             sample_weight, sample_mask,
-                                             self.learning_rate, k=k)
-            else:
-                loss.update_terminal_regions(tree.tree_, X, y, residual, y_pred,
-                                             sample_weight, sample_mask,
-                                             self.learning_rate, k=k)
+            loss.update_terminal_regions(tree.tree_, X, y, residual, y_pred,
+                                         sample_weight, sample_mask,
+                                         self.learning_rate, k=k)
 
             # add tree to ensemble
             self.estimators_[i, k] = tree
