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mirgrate inference_tests to pytest

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This commit is contained in:
Martin Bubel 2023-10-06 08:16:39 +02:00
parent 0b92d3a57c
commit 03fcf7311d
1 changed files with 22 additions and 24 deletions
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46
GPy/testing/inference_tests.py
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@ -12,8 +12,8 @@ import GPy
# np.seterr(invalid='raise') # np.seterr(invalid='raise')
class InferenceXTestCase(unittest.TestCase): class TestInferenceXCase:
def genData(self): def get_data(self):
np.random.seed(1111) np.random.seed(1111)
Ylist = GPy.examples.dimensionality_reduction._simulate_matern( Ylist = GPy.examples.dimensionality_reduction._simulate_matern(
5, 1, 1, 10, 3, False 5, 1, 1, 10, 3, False
@ -21,7 +21,7 @@ class InferenceXTestCase(unittest.TestCase):
return Ylist[0] return Ylist[0]
def test_inferenceX_BGPLVM_Linear(self): 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 = GPy.models.BayesianGPLVM(Ys, 3, kernel=GPy.kern.Linear(3, ARD=True))
m.optimize() m.optimize()
x, mi = m.infer_newX(m.Y, optimize=True) 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) np.testing.assert_array_almost_equal(m.X.variance, mi.X.variance, decimal=2)
def test_inferenceX_BGPLVM_RBF(self): 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)) m = GPy.models.BayesianGPLVM(Ys, 3, kernel=GPy.kern.RBF(3, ARD=True))
import warnings import warnings
with warnings.catch_warnings(): with warnings.catch_warnings():
warnings.simplefilter("ignore") warnings.simplefilter("ignore")
m.optimize() 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.mean, mi.X.mean, decimal=2)
np.testing.assert_array_almost_equal(m.X.variance, mi.X.variance, decimal=2) np.testing.assert_array_almost_equal(m.X.variance, mi.X.variance, decimal=2)
def test_inferenceX_GPLVM_Linear(self): 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 = GPy.models.GPLVM(Ys, 3, kernel=GPy.kern.Linear(3, ARD=True))
m.optimize() 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) np.testing.assert_array_almost_equal(m.X, mi.X, decimal=2)
def test_inferenceX_GPLVM_RBF(self): 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 = GPy.models.GPLVM(Ys, 3, kernel=GPy.kern.RBF(3, ARD=True))
m.optimize() 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) np.testing.assert_array_almost_equal(m.X, mi.X, decimal=2)
class InferenceGPEP(unittest.TestCase): class TestInferenceGPEP:
def genData(self): def get_data(self):
np.random.seed(1) np.random.seed(1)
k = GPy.kern.RBF(1, variance=7.0, lengthscale=0.2) k = GPy.kern.RBF(1, variance=7.0, lengthscale=0.2)
X = np.random.rand(200, 1) 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]) np.zeros(200), k.K(X) + 1e-5 * np.eye(X.shape[0])
) )
lik = GPy.likelihoods.Bernoulli() 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) Y = lik.samples(f).reshape(-1, 1)
return X, Y return X, Y
def genNoisyData(self): def get_noisy_data(self):
np.random.seed(1) np.random.seed(1)
X = np.random.rand(100, 1) X = np.random.rand(100, 1)
self.real_std = 0.1 self.real_std = 0.1
@ -83,7 +83,7 @@ class InferenceGPEP(unittest.TestCase):
def test_inference_EP(self): def test_inference_EP(self):
from paramz import ObsAr from paramz import ObsAr
X, Y = self.genData() X, Y = self.get_data()
lik = GPy.likelihoods.Bernoulli() lik = GPy.likelihoods.Bernoulli()
k = GPy.kern.RBF(1, variance=7.0, lengthscale=0.2) k = GPy.kern.RBF(1, variance=7.0, lengthscale=0.2)
inf = GPy.inference.latent_function_inference.expectation_propagation.EP( inf = GPy.inference.latent_function_inference.expectation_propagation.EP(
@ -158,7 +158,7 @@ class InferenceGPEP(unittest.TestCase):
def test_inference_EP_non_classification(self): def test_inference_EP_non_classification(self):
from paramz import ObsAr from paramz import ObsAr
X, Y, Y_extra_noisy = self.genNoisyData() X, _Y, Y_extra_noisy = self.get_noisy_data()
deg_freedom = 5.0 deg_freedom = 5.0
init_noise_var = 0.08 init_noise_var = 0.08
lik_studentT = GPy.likelihoods.StudentT( 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): def test_var_dtc_inference_with_mean(self):
"""Check dL_dm in var_dtc is calculated correctly""" """Check dL_dm in var_dtc is calculated correctly"""
np.random.seed(1) np.random.seed(1)
@ -243,10 +243,10 @@ class VarDtcTest(unittest.TestCase):
m = GPy.models.SparseGPRegression( m = GPy.models.SparseGPRegression(
x, y, mean_function=GPy.mappings.Linear(input_dim=1, output_dim=1) 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): def test_sampling(self):
np.random.seed(1) np.random.seed(1)
x = np.linspace(0.0, 2 * np.pi, 100)[:, None] 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)) m.likelihood.variance.set_prior(GPy.priors.Gamma.from_EV(1.0, 10.0))
hmc = GPy.inference.mcmc.HMC(m, stepsize=1e-2) 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): def test_sampling(self):
np.random.seed(1) np.random.seed(1)
x = np.linspace(0.0, 2 * np.pi, 100)[:, None] 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 = GPy.inference.mcmc.Metropolis_Hastings(m)
mcmc.sample(Ntotal=100, Nburn=10) mcmc.sample(Ntotal=100, Nburn=10)
# TODO: seems like there is no test here?
if __name__ == "__main__":
unittest.main()