migrate ep_likelihood_test to pytest

GPy/testing/ep_likelihood_tests.py

@@ -3,14 +3,18 @@ import unittest
 import GPy
 from GPy.models import GradientChecker
 
+
 fixed_seed = 10
-from nose.tools import with_setup, nottest
+
+
+def rmse(Y, Ystar):
+    return np.sqrt(np.mean((Y - Ystar) ** 2))
 
 
 # this file will contain some high level tests, this is not unit testing, but will give us a higher level estimate
 # if things are going well under the hood.
-class TestObservationModels(unittest.TestCase):
+class TestObservationModels:
-    def setUp(self):
+    def setup(self):
         np.random.seed(fixed_seed)
         self.N = 100
         self.D = 2
@@ -38,15 +42,17 @@ class TestObservationModels(unittest.TestCase):
         self.Y_metadata["censored"] = censored
         self.kernel1 = GPy.kern.RBF(self.X.shape[1]) + GPy.kern.White(self.X.shape[1])
 
-    def tearDown(self):
+    def tear_down(self):
         self.Y = None
         self.X = None
         self.binary_Y = None
         self.positive_Y = None
         self.kernel1 = None
 
-    @with_setup(setUp, tearDown)
+    def test_epccassification(self):
-    def testEPClassification(self):
+        self.setup()
+        self.tear_down()
+
         bernoulli = GPy.likelihoods.Bernoulli()
         laplace_inf = GPy.inference.latent_function_inference.Laplace()
 
@@ -111,23 +117,22 @@ class TestObservationModels(unittest.TestCase):
         probs_mean_ep_nested, probs_var_ep_nested = m3.predict(self.X)
 
         # for simple single dimension data , marginal likelihood for laplace and EP approximations should not be so far apart.
-        self.assertAlmostEqual(m1.log_likelihood(), m2.log_likelihood(), delta=1)
+        # TODO: the below were assertAlmostEqual, not sure if allclose will do the job here
-        self.assertAlmostEqual(m1.log_likelihood(), m3.log_likelihood(), delta=1)
+        assert np.allclose(m1.log_likelihood(), m2.log_likelihood())  # , delta=1
-        self.assertAlmostEqual(m1.log_likelihood(), m4.log_likelihood(), delta=5)
+        assert np.allcose(m1.log_likelihood(), m3.log_likelihood())  # , delta=1
+        assert np.allclose(m1.log_likelihood(), m4.log_likelihood())  # , delta=5
 
         GPy.util.classification.conf_matrix(probs_mean_lap, self.binary_Y)
         GPy.util.classification.conf_matrix(probs_mean_ep_alt, self.binary_Y)
         GPy.util.classification.conf_matrix(probs_mean_ep_nested, self.binary_Y)
 
-    @nottest
+    @pytest.skip(
-    def rmse(self, Y, Ystar):
-        return np.sqrt(np.mean((Y - Ystar) ** 2))
-
-    @with_setup(setUp, tearDown)
-    @unittest.skip(
         "Fails as a consequence of fixing the DSYR function. Needs to be reviewed!"
     )
-    def test_EP_with_StudentT(self):
+    def test_ep_with_studentt(self):
+        self.setup()
+        self.tear_down()
+
         studentT = GPy.likelihoods.StudentT(
             deg_free=self.deg_free, sigma2=self.init_var
         )
@@ -171,7 +176,8 @@ class TestObservationModels(unittest.TestCase):
         m2.optimize(optimizer=optimizer, max_iters=400)
         # m3.optimize(optimizer=optimizer, max_iters=500)
 
-        self.assertAlmostEqual(m1.log_likelihood(), m2.log_likelihood(), delta=200)
+        # TODO: this was assertAlmostEqual, not sure if allclose will do the job here
+        assert np.allclose(m1.log_likelihood(), m2.log_likelihood())  # , delta=200
 
         # self.assertAlmostEqual(m1.log_likelihood(), m3.log_likelihood(), 3)
 
@@ -183,9 +189,6 @@ class TestObservationModels(unittest.TestCase):
         # rmse_nested = self.rmse(preds_mean_nested, self.Y_noisy)
 
         if rmse_alt > rmse_lap:
-            self.assertAlmostEqual(rmse_lap, rmse_alt, delta=1.5)
+            # TODO: this was assertAlmostEqual, not sure if allclose will do the job here
+            assert np.allclose(rmse_lap, rmse_alt)  # , delta=1.5
         # m3.optimize(optimizer=optimizer, max_iters=500)
-
-
-if __name__ == "__main__":
-    unittest.main()