removed type error from metrics and metrics always return tuple even …

…if None. Ensures the same format for all metrics. Tests updated.

gandy/tests/test_metrics/test_metrics.py

@@ -41,13 +41,6 @@ def test_calculate(self):
             subject = metrics.MSE(predictions=np.array([0, 1, 2]),
                                   real="0, 1, 2").calculate()
 
-        # failure case: Uncertainties given when none expected
-        with self.assertRaises(TypeError):
-            subject = metrics.MSE(predictions=np.array([0, 1, 2]),
-                                  real=np.array([0, 1, 2]),
-                                  uncertainties=np.array([0, 0.5, 1])).\
-                                  calculate()
-
         # check to make sure necessary attributes are inputted
         subject = metrics.MSE(predictions=np.array([0, 1, 2]),
                               real=np.array([0, 1, 2]))
@@ -75,13 +68,6 @@ def test_calculate(self):
             subject = metrics.RMSE(predictions=np.array([0, 1, 2]),
                                    real="0, 1, 2").calculate()
 
-        # failure case: Uncertainties given when none expected
-        with self.assertRaises(TypeError):
-            subject = metrics.RMSE(predictions=np.array([0, 1, 2]),
-                                   real=np.array([0, 1, 2]),
-                                   uncertainties=np.array([0, 0.5, 1])).\
-                                   calculate()
-
         # check to make sure necessary attributes are inputted
         subject = metrics.RMSE(predictions=np.array([0, 1, 2]),
                                real=np.array([0, 1, 2]))
@@ -109,12 +95,6 @@ def test_calculate(self):
             subject = metrics.F1(predictions=np.array([0, 1, 2]),
                                  real="0, 1, 2").calculate()
 
-        # failure case: Uncertainties given when none expected
-        with self.assertRaises(TypeError):
-            subject = metrics.F1(predictions=np.array([0, 1, 2]),
-                                 real=np.array([0, 1, 2]),
-                                 uncertainties=np.array([0, 0.5, 1])).\
-                                 calculate()
 
         # check to make sure necessary attributes are inputted
         subject = metrics.F1(predictions=np.array([0, 1, 2]),
@@ -125,7 +105,7 @@ def test_calculate(self):
         self.assertTrue(subject.uncertainties is None)
 
         # check to make sure output is correct type
-        self.assertTrue(isinstance(subject.calculate(), (float, int)))
+        self.assertTrue(isinstance(subject.calculate(), tuple))
 
 
 class TestAccuracy(unittest.TestCase):
@@ -143,12 +123,6 @@ def test_calculate(self):
             subject = metrics.Accuracy(predictions=np.array([0, 1, 2]),
                                        real="0, 1, 2").calculate()
 
-        # failure case: Uncertainties given when none expected
-        with self.assertRaises(TypeError):
-            subject = metrics.Accuracy(predictions=np.array([0, 1, 2]),
-                                       real=np.array([0, 1, 2]),
-                                       uncertainties=np.array([0, 0.5, 1])).\
-                                       calculate()
 
         # check to make sure necessary attributes are inputted
         subject = metrics.Accuracy(predictions=np.array([0, 1, 2]),
@@ -159,7 +133,7 @@ def test_calculate(self):
         self.assertTrue(subject.uncertainties is None)
 
         # check to make sure output is correct type
-        self.assertTrue(isinstance(subject.calculate(), (float, int)))
+        self.assertTrue(isinstance(subject.calculate(), tuple))
 
 
 class TestUCP(unittest.TestCase):
@@ -203,4 +177,4 @@ def test_calculate(self):
         self.assertTrue(subject.uncertainties is not None)
 
         # check to make sure output is correct type
-        self.assertTrue(isinstance(subject.calculate(), (float, int)))
+        self.assertTrue(isinstance(subject.calculate(), tuple))