From 03fcf7311db34143c4ccd27780c0be6eb8b04167 Mon Sep 17 00:00:00 2001
From: Martin Bubel <martin.bubel@itwm.fraunhofer.de>
Date: Fri, 6 Oct 2023 08:16:39 +0200
Subject: [PATCH] mirgrate inference_tests to pytest

---
 GPy/testing/inference_tests.py | 46 ++++++++++++++++------------------
 1 file changed, 22 insertions(+), 24 deletions(-)

diff --git a/GPy/testing/inference_tests.py b/GPy/testing/inference_tests.py
index fcb4bd39..96be2834 100644
--- a/GPy/testing/inference_tests.py
+++ b/GPy/testing/inference_tests.py
@@ -12,8 +12,8 @@ import GPy
 # np.seterr(invalid='raise')
 
 
-class InferenceXTestCase(unittest.TestCase):
-    def genData(self):
+class TestInferenceXCase:
+    def get_data(self):
         np.random.seed(1111)
         Ylist = GPy.examples.dimensionality_reduction._simulate_matern(
             5, 1, 1, 10, 3, False
@@ -21,7 +21,7 @@ class InferenceXTestCase(unittest.TestCase):
         return Ylist[0]
 
     def test_inferenceX_BGPLVM_Linear(self):
-        Ys = self.genData()
+        Ys = self.get_data()
         m = GPy.models.BayesianGPLVM(Ys, 3, kernel=GPy.kern.Linear(3, ARD=True))
         m.optimize()
         x, mi = m.infer_newX(m.Y, optimize=True)
@@ -29,34 +29,34 @@ class InferenceXTestCase(unittest.TestCase):
         np.testing.assert_array_almost_equal(m.X.variance, mi.X.variance, decimal=2)
 
     def test_inferenceX_BGPLVM_RBF(self):
-        Ys = self.genData()
+        Ys = self.get_data()
         m = GPy.models.BayesianGPLVM(Ys, 3, kernel=GPy.kern.RBF(3, ARD=True))
         import warnings
 
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
             m.optimize()
-        x, mi = m.infer_newX(m.Y, optimize=True)
+        _x, mi = m.infer_newX(m.Y, optimize=True)
         np.testing.assert_array_almost_equal(m.X.mean, mi.X.mean, decimal=2)
         np.testing.assert_array_almost_equal(m.X.variance, mi.X.variance, decimal=2)
 
     def test_inferenceX_GPLVM_Linear(self):
-        Ys = self.genData()
+        Ys = self.get_data()
         m = GPy.models.GPLVM(Ys, 3, kernel=GPy.kern.Linear(3, ARD=True))
         m.optimize()
-        x, mi = m.infer_newX(m.Y, optimize=True)
+        _x, mi = m.infer_newX(m.Y, optimize=True)
         np.testing.assert_array_almost_equal(m.X, mi.X, decimal=2)
 
     def test_inferenceX_GPLVM_RBF(self):
-        Ys = self.genData()
+        Ys = self.get_data()
         m = GPy.models.GPLVM(Ys, 3, kernel=GPy.kern.RBF(3, ARD=True))
         m.optimize()
-        x, mi = m.infer_newX(m.Y, optimize=True)
+        _x, mi = m.infer_newX(m.Y, optimize=True)
         np.testing.assert_array_almost_equal(m.X, mi.X, decimal=2)
 
 
-class InferenceGPEP(unittest.TestCase):
-    def genData(self):
+class TestInferenceGPEP:
+    def get_data(self):
         np.random.seed(1)
         k = GPy.kern.RBF(1, variance=7.0, lengthscale=0.2)
         X = np.random.rand(200, 1)
@@ -64,11 +64,11 @@ class InferenceGPEP(unittest.TestCase):
             np.zeros(200), k.K(X) + 1e-5 * np.eye(X.shape[0])
         )
         lik = GPy.likelihoods.Bernoulli()
-        p = lik.gp_link.transf(f)  # squash the latent function
+        _p = lik.gp_link.transf(f)  # squash the latent function
         Y = lik.samples(f).reshape(-1, 1)
         return X, Y
 
-    def genNoisyData(self):
+    def get_noisy_data(self):
         np.random.seed(1)
         X = np.random.rand(100, 1)
         self.real_std = 0.1
@@ -83,7 +83,7 @@ class InferenceGPEP(unittest.TestCase):
     def test_inference_EP(self):
         from paramz import ObsAr
 
-        X, Y = self.genData()
+        X, Y = self.get_data()
         lik = GPy.likelihoods.Bernoulli()
         k = GPy.kern.RBF(1, variance=7.0, lengthscale=0.2)
         inf = GPy.inference.latent_function_inference.expectation_propagation.EP(
@@ -158,7 +158,7 @@ class InferenceGPEP(unittest.TestCase):
     def test_inference_EP_non_classification(self):
         from paramz import ObsAr
 
-        X, Y, Y_extra_noisy = self.genNoisyData()
+        X, _Y, Y_extra_noisy = self.get_noisy_data()
         deg_freedom = 5.0
         init_noise_var = 0.08
         lik_studentT = GPy.likelihoods.StudentT(
@@ -234,7 +234,7 @@ class InferenceGPEP(unittest.TestCase):
         )
 
 
-class VarDtcTest(unittest.TestCase):
+class TestVarDtc:
     def test_var_dtc_inference_with_mean(self):
         """Check dL_dm in var_dtc is calculated correctly"""
         np.random.seed(1)
@@ -243,10 +243,10 @@ class VarDtcTest(unittest.TestCase):
         m = GPy.models.SparseGPRegression(
             x, y, mean_function=GPy.mappings.Linear(input_dim=1, output_dim=1)
         )
-        self.assertTrue(m.checkgrad())
+        assert m.checkgrad()
 
 
-class HMCSamplerTest(unittest.TestCase):
+class TestHMCSampler:
     def test_sampling(self):
         np.random.seed(1)
         x = np.linspace(0.0, 2 * np.pi, 100)[:, None]
@@ -258,10 +258,11 @@ class HMCSamplerTest(unittest.TestCase):
         m.likelihood.variance.set_prior(GPy.priors.Gamma.from_EV(1.0, 10.0))
 
         hmc = GPy.inference.mcmc.HMC(m, stepsize=1e-2)
-        s = hmc.sample(num_samples=3)
+        _s = hmc.sample(num_samples=3)
+        # TODO: seems like there is no test here?
 
 
-class MCMCSamplerTest(unittest.TestCase):
+class TestMCMCSampler:
     def test_sampling(self):
         np.random.seed(1)
         x = np.linspace(0.0, 2 * np.pi, 100)[:, None]
@@ -274,7 +275,4 @@ class MCMCSamplerTest(unittest.TestCase):
 
         mcmc = GPy.inference.mcmc.Metropolis_Hastings(m)
         mcmc.sample(Ntotal=100, Nburn=10)
-
-
-if __name__ == "__main__":
-    unittest.main()
+        # TODO: seems like there is no test here?