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Martin Bubel 2023-10-04 20:38:20 +02:00
parent 4edfff6596
commit 1e5dd36bc7
1 changed files with 69 additions and 26 deletions
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95
GPy/testing/ep_likelihood_tests.py
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@ -1,4 +1,3 @@
import numpy as np
import unittest
import GPy
@ -31,18 +30,18 @@ class TestObservationModels(unittest.TestCase):
self.Y_noisy[75] += 1.3
self.init_var = 0.15
self.deg_free = 4.
self.deg_free = 4.0
censored = np.zeros_like(self.Y)
random_inds = np.random.choice(self.N, int(self.N / 2), replace=True)
censored[random_inds] = 1
self.Y_metadata = dict()
self.Y_metadata['censored'] = censored
self.Y_metadata["censored"] = censored
self.kernel1 = GPy.kern.RBF(self.X.shape[1]) + GPy.kern.White(self.X.shape[1])
def tearDown(self):
self.Y = None
self.X = None
self.binary_Y =None
self.binary_Y = None
self.positive_Y = None
self.kernel1 = None
@ -51,25 +50,51 @@ class TestObservationModels(unittest.TestCase):
bernoulli = GPy.likelihoods.Bernoulli()
laplace_inf = GPy.inference.latent_function_inference.Laplace()
ep_inf_alt = GPy.inference.latent_function_inference.EP(ep_mode='alternated')
ep_inf_nested = GPy.inference.latent_function_inference.EP(ep_mode='nested')
ep_inf_fractional = GPy.inference.latent_function_inference.EP(ep_mode='nested', eta=0.9)
ep_inf_alt = GPy.inference.latent_function_inference.EP(ep_mode="alternated")
ep_inf_nested = GPy.inference.latent_function_inference.EP(ep_mode="nested")
ep_inf_fractional = GPy.inference.latent_function_inference.EP(
ep_mode="nested", eta=0.9
)
m1 = GPy.core.GP(self.X, self.binary_Y.copy(), kernel=self.kernel1.copy(), likelihood=bernoulli.copy(), inference_method=laplace_inf)
m1 = GPy.core.GP(
self.X,
self.binary_Y.copy(),
kernel=self.kernel1.copy(),
likelihood=bernoulli.copy(),
inference_method=laplace_inf,
)
m1.randomize()
m2 = GPy.core.GP(self.X, self.binary_Y.copy(), kernel=self.kernel1.copy(), likelihood=bernoulli.copy(), inference_method=ep_inf_alt)
m2 = GPy.core.GP(
self.X,
self.binary_Y.copy(),
kernel=self.kernel1.copy(),
likelihood=bernoulli.copy(),
inference_method=ep_inf_alt,
)
m2.randomize()
m3 = GPy.core.GP(self.X, self.binary_Y.copy(), kernel=self.kernel1.copy(), likelihood=bernoulli.copy(), inference_method=ep_inf_nested)
m3 = GPy.core.GP(
self.X,
self.binary_Y.copy(),
kernel=self.kernel1.copy(),
likelihood=bernoulli.copy(),
inference_method=ep_inf_nested,
)
m3.randomize()
#
m4 = GPy.core.GP(self.X, self.binary_Y.copy(), kernel=self.kernel1.copy(), likelihood=bernoulli.copy(), inference_method=ep_inf_fractional)
m4 = GPy.core.GP(
self.X,
self.binary_Y.copy(),
kernel=self.kernel1.copy(),
likelihood=bernoulli.copy(),
inference_method=ep_inf_fractional,
)
m4.randomize()
optimizer = 'bfgs'
optimizer = "bfgs"
#do gradcheck here ...
# do gradcheck here ...
# self.assertTrue(m1.checkgrad())
# self.assertTrue(m2.checkgrad())
# self.assertTrue(m3.checkgrad())
@ -86,7 +111,7 @@ 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)
self.assertAlmostEqual(m1.log_likelihood(), m2.log_likelihood(), delta=1)
self.assertAlmostEqual(m1.log_likelihood(), m3.log_likelihood(), delta=1)
self.assertAlmostEqual(m1.log_likelihood(), m4.log_likelihood(), delta=5)
@ -99,22 +124,40 @@ class TestObservationModels(unittest.TestCase):
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!")
@unittest.skip(
"Fails as a consequence of fixing the DSYR function. Needs to be reviewed!"
)
def test_EP_with_StudentT(self):
studentT = GPy.likelihoods.StudentT(deg_free=self.deg_free, sigma2=self.init_var)
studentT = GPy.likelihoods.StudentT(
deg_free=self.deg_free, sigma2=self.init_var
)
laplace_inf = GPy.inference.latent_function_inference.Laplace()
ep_inf_alt = GPy.inference.latent_function_inference.EP(ep_mode='alternated')
ep_inf_nested = GPy.inference.latent_function_inference.EP(ep_mode='nested')
ep_inf_frac = GPy.inference.latent_function_inference.EP(ep_mode='nested', eta=0.7)
ep_inf_alt = GPy.inference.latent_function_inference.EP(ep_mode="alternated")
ep_inf_nested = GPy.inference.latent_function_inference.EP(ep_mode="nested")
ep_inf_frac = GPy.inference.latent_function_inference.EP(
ep_mode="nested", eta=0.7
)
m1 = GPy.core.GP(self.X.copy(), self.Y_noisy.copy(), kernel=self.kernel1.copy(), likelihood=studentT.copy(), inference_method=laplace_inf)
m1 = GPy.core.GP(
self.X.copy(),
self.Y_noisy.copy(),
kernel=self.kernel1.copy(),
likelihood=studentT.copy(),
inference_method=laplace_inf,
)
# optimize
m1['.*white'].constrain_fixed(1e-5)
m1[".*white"].constrain_fixed(1e-5)
m1.randomize()
m2 = GPy.core.GP(self.X.copy(), self.Y_noisy.copy(), kernel=self.kernel1.copy(), likelihood=studentT.copy(), inference_method=ep_inf_alt)
m2['.*white'].constrain_fixed(1e-5)
m2 = GPy.core.GP(
self.X.copy(),
self.Y_noisy.copy(),
kernel=self.kernel1.copy(),
likelihood=studentT.copy(),
inference_method=ep_inf_alt,
)
m2[".*white"].constrain_fixed(1e-5)
# m2.constrain_bounded('.*t_scale2', 0.001, 10)
m2.randomize()
@ -123,12 +166,12 @@ class TestObservationModels(unittest.TestCase):
# # m3.constrain_bounded('.*t_scale2', 0.001, 10)
# m3.randomize()
optimizer='bfgs'
m1.optimize(optimizer=optimizer,max_iters=400)
optimizer = "bfgs"
m1.optimize(optimizer=optimizer, max_iters=400)
m2.optimize(optimizer=optimizer, max_iters=400)
# m3.optimize(optimizer=optimizer, max_iters=500)
self.assertAlmostEqual(m1.log_likelihood(), m2.log_likelihood(),delta=200)
self.assertAlmostEqual(m1.log_likelihood(), m2.log_likelihood(), delta=200)
# self.assertAlmostEqual(m1.log_likelihood(), m3.log_likelihood(), 3)