From 0f4fc07272e69a43eca00eb468cd5954812b0f79 Mon Sep 17 00:00:00 2001
From: Ravin Kumar <ravinsdrive@gmail.com>
Date: Sun, 15 Jul 2018 09:18:11 -0700
Subject: [PATCH] Parametrize shape and size tests

---
 pymc3/tests/test_distributions_random.py | 106 +++++++++++------------
 1 file changed, 53 insertions(+), 53 deletions(-)

diff --git a/pymc3/tests/test_distributions_random.py b/pymc3/tests/test_distributions_random.py
index bf573081a3..af87785db5 100644
--- a/pymc3/tests/test_distributions_random.py
+++ b/pymc3/tests/test_distributions_random.py
@@ -145,73 +145,73 @@ def sample_random_variable(random_variable, size):
             except AttributeError:
                 return random_variable.distribution.random(size=size)
 
-        def test_scalar_parameter_shape(self):
+        @pytest.mark.parametrize('size', [None, 5, (4, 5)], ids=str)
+        def test_scalar_parameter_shape(self, size):
             rv = self.get_random_variable(None)
-            for size in (None, 5, (4, 5)):
-                if size is None:
-                    expected = 1,
-                else:
-                    expected = np.atleast_1d(size).tolist()
-                actual = np.atleast_1d(self.sample_random_variable(rv, size)).shape
-                assert tuple(expected) == actual
+            if size is None:
+                expected = 1,
+            else:
+                expected = np.atleast_1d(size).tolist()
+            actual = np.atleast_1d(self.sample_random_variable(rv, size)).shape
+            assert tuple(expected) == actual
 
-        def test_scalar_shape(self):
+        @pytest.mark.parametrize('size', [None, 5, (4, 5)], ids=str)
+        def test_scalar_shape(self, size):
             shape = 10
             rv = self.get_random_variable(shape)
-            for size in (None, 5, (4, 5)):
-                if size is None:
-                    expected = []
-                else:
-                    expected = np.atleast_1d(size).tolist()
-                expected.append(shape)
-                actual = np.atleast_1d(self.sample_random_variable(rv, size)).shape
-                assert tuple(expected) == actual
 
-        def test_parameters_1d_shape(self):
+            if size is None:
+                expected = []
+            else:
+                expected = np.atleast_1d(size).tolist()
+            expected.append(shape)
+            actual = np.atleast_1d(self.sample_random_variable(rv, size)).shape
+            assert tuple(expected) == actual
+
+        @pytest.mark.parametrize('size', [None, 5, (4, 5)], ids=str)
+        def test_parameters_1d_shape(self, size):
             rv = self.get_random_variable(self.shape, with_vector_params=True)
-            for size in (None, 5, (4, 5)):
-                if size is None:
-                    expected = []
-                else:
-                    expected = np.atleast_1d(size).tolist()
-                expected.append(self.shape)
-                actual = self.sample_random_variable(rv, size).shape
-                assert tuple(expected) == actual
+            if size is None:
+                expected = []
+            else:
+                expected = np.atleast_1d(size).tolist()
+            expected.append(self.shape)
+            actual = self.sample_random_variable(rv, size).shape
+            assert tuple(expected) == actual
 
-        def test_broadcast_shape(self):
+        @pytest.mark.parametrize('size', [None, 5, (4, 5)], ids=str)
+        def test_broadcast_shape(self, size):
             broadcast_shape = (2 * self.shape, self.shape)
             rv = self.get_random_variable(broadcast_shape, with_vector_params=True)
-            for size in (None, 5, (4, 5)):
-                if size is None:
-                    expected = []
-                else:
-                    expected = np.atleast_1d(size).tolist()
-                expected.extend(broadcast_shape)
-                actual = np.atleast_1d(self.sample_random_variable(rv, size)).shape
-                assert tuple(expected) == actual
+            if size is None:
+                expected = []
+            else:
+                expected = np.atleast_1d(size).tolist()
+            expected.extend(broadcast_shape)
+            actual = np.atleast_1d(self.sample_random_variable(rv, size)).shape
+            assert tuple(expected) == actual
 
-        def test_different_shapes_and_sample_sizes(self):
-            shapes = [(), (1,), (1, 1), (1, 2), (10, 10, 1), (10, 10, 2)]
+        @pytest.mark.parametrize('shape', [(), (1,), (1, 1), (1, 2), (10, 10, 1), (10, 10, 2)], ids=str)
+        def test_different_shapes_and_sample_sizes(self, shape):
             prefix = self.distribution.__name__
             expected = []
             actual = []
-            for shape in shapes:
-                rv = self.get_random_variable(shape, name='%s_%s' % (prefix, shape))
-                for size in (None, 1, 5, (4, 5)):
-                    if size is None:
+            rv = self.get_random_variable(shape, name='%s_%s' % (prefix, shape))
+            for size in (None, 1, 5, (4, 5)):
+                if size is None:
+                    s = []
+                else:
+                    try:
+                        s = list(size)
+                    except TypeError:
+                        s = [size]
+                    if s == [1]:
                         s = []
-                    else:
-                        try:
-                            s = list(size)
-                        except TypeError:
-                            s = [size]
-                        if s == [1]:
-                            s = []
-                    if shape not in ((), (1,)):
-                        s.extend(shape)
-                    e = tuple(s)
-                    a = self.sample_random_variable(rv, size).shape
-                    assert e == a
+                if shape not in ((), (1,)):
+                    s.extend(shape)
+                e = tuple(s)
+                a = self.sample_random_variable(rv, size).shape
+                assert e == a
 
 
 class TestNormal(BaseTestCases.BaseTestCase):