Merge pull request #348 from bsc-wdc/csvm_score_collect

Synchronization of the score in csvm

dislib/classification/csvm/base.py

@@ -182,7 +182,7 @@ def decision_function(self, x):
                      reg_shape=(x._reg_shape[0], 1),
                      shape=(x.shape[0], 1), sparse=False)
 
-    def score(self, x, y):
+    def score(self, x, y, collect=False):
         """
         Returns the mean accuracy on the given test data and labels.
 
@@ -207,7 +207,9 @@ def score(self, x, y):
             partial = _score(x_row._blocks, y_row._blocks, self._clf)
             partial_scores.append(partial)
 
-        return _merge_scores(*partial_scores)
+        score = _merge_scores(*partial_scores)
+
+        return compss_wait_on(score) if collect else score
 
     def _check_initial_parameters(self):
         gamma = self.gamma

tests/test_csvm.py

@@ -1,6 +1,7 @@
 import unittest
 
 import numpy as np
+from parameterized import parameterized
 from pycompss.api.api import compss_wait_on
 
 import dislib as ds
@@ -136,7 +137,8 @@ def test_predict(self):
         self.assertTrue(l1 == l2 == l5 == 0)
         self.assertTrue(l3 == l4 == l6 == 1)
 
-    def test_score(self):
+    @parameterized.expand([(True,), (False,)])
+    def test_score(self, collect):
         seed = 666
 
         # negative points belong to class 1, positives to 0
@@ -157,7 +159,9 @@ def test_score(self):
         x_test = ds.array(np.array([p1, p2, p3, p4]), (2, 2))
         y_test = ds.array(np.array([0, 0, 1, 1]).reshape(-1, 1), (2, 1))
 
-        accuracy = compss_wait_on(csvm.score(x_test, y_test))
+        accuracy = csvm.score(x_test, y_test, collect)
+        if not collect:
+            accuracy = compss_wait_on(accuracy)
 
         self.assertEqual(accuracy, 1.0)