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https://github.com/SheffieldML/GPy.git
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440 lines
17 KiB
Python
440 lines
17 KiB
Python
"""
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Created on 20 April 2017
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@author: pgmoren
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"""
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import numpy as np
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import GPy
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import os
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fixed_seed = 11
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class TestSerialization:
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def test_serialize_deserialize_kernels(self):
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k1 = GPy.kern.RBF(2, variance=1.0, lengthscale=[1.0, 1.0], ARD=True)
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k2 = GPy.kern.RatQuad(
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2, variance=2.0, lengthscale=1.0, power=2.0, active_dims=[0, 1]
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)
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k3 = GPy.kern.Bias(2, variance=2.0, active_dims=[1, 0])
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k4 = GPy.kern.StdPeriodic(
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2, variance=2.0, lengthscale=1.0, period=1.0, active_dims=[1, 1]
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)
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k5 = GPy.kern.Linear(2, variances=[2.0, 1.0], ARD=True, active_dims=[1, 1])
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k6 = GPy.kern.Exponential(2, variance=1.0, lengthscale=2)
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k7 = GPy.kern.Matern32(
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2, variance=1.0, lengthscale=[1.0, 3.0], ARD=True, active_dims=[1, 1]
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)
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k8 = GPy.kern.Matern52(
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2, variance=2.0, lengthscale=[2.0, 1.0], ARD=True, active_dims=[1, 0]
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)
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k9 = GPy.kern.ExpQuad(
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2, variance=3.0, lengthscale=[1.0, 2.0], ARD=True, active_dims=[0, 1]
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)
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k10 = GPy.kern.OU(
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2, variance=2.0, lengthscale=[2.0, 1.0], ARD=True, active_dims=[1, 0]
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)
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k11 = k1 + k1.copy() + k2 + k3 + k4 + k5 + k6
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k12 = k1 * k2 * k2.copy() * k3 * k4 * k5
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k13 = (k1 + k2) * (k3 + k4 + k5)
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k14 = ((k1 + k2) * k3) + k4 + k5 * k7
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k15 = ((k1 + k2) * k3) + k4 * k5 + k8 * k10
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k16 = ((k1 * k2) * k3) + k4 * k5 + k8 + k9
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k_list = [k1, k2, k3, k4, k5, k6, k7, k8, k9, k10, k11, k12, k13, k14, k15, k16]
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for kk in k_list:
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kk_dict = kk.to_dict()
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kk_r = GPy.kern.Kern.from_dict(kk_dict)
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assert type(kk) == type(kk_r)
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np.testing.assert_array_equal(kk[:], kk_r[:])
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np.testing.assert_array_equal(
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np.array(kk.active_dims), np.array(kk_r.active_dims)
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)
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def test_serialize_deserialize_mappings(self):
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m1 = GPy.mappings.Identity(3, 2)
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m2 = GPy.mappings.Constant(3, 2, 1)
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m2_r = GPy.core.mapping.Mapping.from_dict(m2.to_dict())
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np.testing.assert_array_equal(m2.C.values[:], m2_r.C.values[:])
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m3 = GPy.mappings.Linear(3, 2)
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m3_r = GPy.core.mapping.Mapping.from_dict(m3.to_dict())
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assert np.all(m3.A == m3_r.A)
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m_list = [m1, m2, m3]
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for mm in m_list:
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mm_dict = mm.to_dict()
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mm_r = GPy.core.mapping.Mapping.from_dict(mm_dict)
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assert type(mm) == type(mm_r)
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assert type(mm.input_dim) == type(mm_r.input_dim)
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assert type(mm.output_dim) == type(mm_r.output_dim)
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def test_serialize_deserialize_likelihoods(self):
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l1 = GPy.likelihoods.Gaussian(
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GPy.likelihoods.link_functions.Identity(), variance=3.0
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)
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l1_r = GPy.likelihoods.likelihood.Likelihood.from_dict(l1.to_dict())
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l2 = GPy.likelihoods.Bernoulli(GPy.likelihoods.link_functions.Probit())
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l2_r = GPy.likelihoods.likelihood.Likelihood.from_dict(l2.to_dict())
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assert type(l1) == type(l1_r)
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assert np.all(l1.variance == l1_r.variance)
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assert type(l2) == type(l2_r)
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def test_serialize_deserialize_normalizers(self):
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n1 = GPy.util.normalizer.Standardize()
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n1.scale_by(np.random.rand(10))
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n1_r = GPy.util.normalizer._Norm.from_dict((n1.to_dict()))
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assert type(n1) == type(n1_r)
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assert np.all(n1.mean == n1_r.mean)
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assert np.all(n1.std == n1_r.std)
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def test_serialize_deserialize_link_functions(self):
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l1 = GPy.likelihoods.link_functions.Identity()
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l2 = GPy.likelihoods.link_functions.Probit()
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l_list = [l1, l2]
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for ll in l_list:
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ll_dict = ll.to_dict()
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ll_r = GPy.likelihoods.link_functions.GPTransformation.from_dict(ll_dict)
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assert type(ll) == type(ll_r)
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def test_serialize_deserialize_inference_methods(self):
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e1 = GPy.inference.latent_function_inference.expectation_propagation.EP(
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ep_mode="nested"
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)
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e1.ga_approx_old = GPy.inference.latent_function_inference.expectation_propagation.gaussianApproximation(
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np.random.rand(10), np.random.rand(10)
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)
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e1._ep_approximation = []
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e1._ep_approximation.append(
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GPy.inference.latent_function_inference.expectation_propagation.posteriorParams(
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np.random.rand(10), np.random.rand(100).reshape((10, 10))
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)
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)
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e1._ep_approximation.append(
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GPy.inference.latent_function_inference.expectation_propagation.gaussianApproximation(
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np.random.rand(10), np.random.rand(10)
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)
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)
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e1._ep_approximation.append(
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GPy.inference.latent_function_inference.expectation_propagation.cavityParams(
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10
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)
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)
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e1._ep_approximation[-1].v = np.random.rand(10)
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e1._ep_approximation[-1].tau = np.random.rand(10)
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e1._ep_approximation.append(np.random.rand(10))
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e1_r = (
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GPy.inference.latent_function_inference.LatentFunctionInference.from_dict(
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e1.to_dict()
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)
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)
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assert type(e1) == type(e1_r)
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assert e1.epsilon == e1_r.epsilon
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assert e1.eta == e1_r.eta
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assert e1.delta == e1_r.delta
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assert e1.always_reset == e1_r.always_reset
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assert e1.max_iters == e1_r.max_iters
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assert e1.ep_mode == e1_r.ep_mode
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assert e1.parallel_updates == e1_r.parallel_updates
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np.testing.assert_array_equal(
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e1.ga_approx_old.tau[:], e1_r.ga_approx_old.tau[:]
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)
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np.testing.assert_array_equal(e1.ga_approx_old.v[:], e1_r.ga_approx_old.v[:])
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np.testing.assert_array_equal(
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e1._ep_approximation[0].mu[:], e1_r._ep_approximation[0].mu[:]
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)
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np.testing.assert_array_equal(
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e1._ep_approximation[0].Sigma[:], e1_r._ep_approximation[0].Sigma[:]
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)
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np.testing.assert_array_equal(
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e1._ep_approximation[1].tau[:], e1_r._ep_approximation[1].tau[:]
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)
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np.testing.assert_array_equal(
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e1._ep_approximation[1].v[:], e1_r._ep_approximation[1].v[:]
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)
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np.testing.assert_array_equal(
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e1._ep_approximation[2].tau[:], e1_r._ep_approximation[2].tau[:]
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)
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np.testing.assert_array_equal(
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e1._ep_approximation[2].v[:], e1_r._ep_approximation[2].v[:]
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)
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np.testing.assert_array_equal(
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e1._ep_approximation[3][:], e1_r._ep_approximation[3][:]
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)
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e2 = GPy.inference.latent_function_inference.expectation_propagation.EPDTC(
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ep_mode="nested"
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)
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e2.ga_approx_old = GPy.inference.latent_function_inference.expectation_propagation.gaussianApproximation(
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np.random.rand(10), np.random.rand(10)
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)
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e2._ep_approximation = []
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e2._ep_approximation.append(
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GPy.inference.latent_function_inference.expectation_propagation.posteriorParamsDTC(
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np.random.rand(10), np.random.rand(10)
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)
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)
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e2._ep_approximation.append(
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GPy.inference.latent_function_inference.expectation_propagation.gaussianApproximation(
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np.random.rand(10), np.random.rand(10)
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)
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)
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e2._ep_approximation.append(100.0)
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e2_r = (
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GPy.inference.latent_function_inference.LatentFunctionInference.from_dict(
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e2.to_dict()
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)
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)
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assert type(e2) == type(e2_r)
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assert e2.epsilon == e2_r.epsilon
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assert e2.eta == e2_r.eta
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assert e2.delta == e2_r.delta
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assert e2.always_reset == e2_r.always_reset
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assert e2.max_iters == e2_r.max_iters
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assert e2.ep_mode == e2_r.ep_mode
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assert e2.parallel_updates == e2_r.parallel_updates
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np.testing.assert_array_equal(
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e2.ga_approx_old.tau[:], e2_r.ga_approx_old.tau[:]
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)
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np.testing.assert_array_equal(e2.ga_approx_old.v[:], e2_r.ga_approx_old.v[:])
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np.testing.assert_array_equal(
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e2._ep_approximation[0].mu[:], e2_r._ep_approximation[0].mu[:]
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)
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np.testing.assert_array_equal(
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e2._ep_approximation[0].Sigma_diag[:],
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e2_r._ep_approximation[0].Sigma_diag[:],
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)
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np.testing.assert_array_equal(
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e2._ep_approximation[1].tau[:], e2_r._ep_approximation[1].tau[:]
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)
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np.testing.assert_array_equal(
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e2._ep_approximation[1].v[:], e2_r._ep_approximation[1].v[:]
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)
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assert e2._ep_approximation[2] == e2_r._ep_approximation[2]
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e3 = (
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GPy.inference.latent_function_inference.exact_gaussian_inference.ExactGaussianInference()
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)
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e3_r = (
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GPy.inference.latent_function_inference.LatentFunctionInference.from_dict(
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e3.to_dict()
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)
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)
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assert type(e3) == type(e3_r)
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def test_serialize_deserialize_GP(self):
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np.random.seed(fixed_seed)
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N = 20
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Nhalf = int(N / 2)
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X = np.hstack([np.random.normal(5, 2, Nhalf), np.random.normal(10, 2, Nhalf)])[
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:, None
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]
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Y = np.hstack([np.ones(Nhalf), np.zeros(Nhalf)])[:, None]
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kernel = GPy.kern.RBF(1)
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likelihood = GPy.likelihoods.Bernoulli()
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inference_method = (
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GPy.inference.latent_function_inference.expectation_propagation.EP(
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ep_mode="nested"
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)
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)
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mean_function = None
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m = GPy.core.GP(
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X=X,
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Y=Y,
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kernel=kernel,
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likelihood=likelihood,
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inference_method=inference_method,
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mean_function=mean_function,
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normalizer=True,
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name="gp_classification",
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)
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m.optimize()
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m.save_model("temp_test_gp_with_data.json", compress=True, save_data=True)
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m.save_model("temp_test_gp_without_data.json", compress=True, save_data=False)
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m1_r = GPy.core.GP.load_model("temp_test_gp_with_data.json.zip")
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m2_r = GPy.core.GP.load_model("temp_test_gp_without_data.json.zip", (X, Y))
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os.remove("temp_test_gp_with_data.json.zip")
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os.remove("temp_test_gp_without_data.json.zip")
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var = m.predict(X)[0]
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var1_r = m1_r.predict(X)[0]
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var2_r = m2_r.predict(X)[0]
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np.testing.assert_array_equal(
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np.array(var).flatten(), np.array(var1_r).flatten()
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)
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np.testing.assert_array_equal(
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np.array(var).flatten(), np.array(var2_r).flatten()
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)
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def test_serialize_deserialize_SparseGP(self):
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np.random.seed(fixed_seed)
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N = 20
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Nhalf = int(N / 2)
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X = np.hstack([np.random.normal(5, 2, Nhalf), np.random.normal(10, 2, Nhalf)])[
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:, None
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]
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Y = np.hstack([np.ones(Nhalf), np.zeros(Nhalf)])[:, None]
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kernel = GPy.kern.RBF(1)
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likelihood = GPy.likelihoods.Bernoulli()
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inference_method = (
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GPy.inference.latent_function_inference.expectation_propagation.EPDTC(
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ep_mode="nested"
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)
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)
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mean_function = None
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sm = GPy.core.SparseGP(
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X=X,
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Y=Y,
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Z=X[0:20, :],
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kernel=kernel,
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likelihood=likelihood,
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inference_method=inference_method,
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mean_function=mean_function,
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normalizer=True,
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name="sparse_gp_classification",
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)
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sm.optimize()
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sm.save_model("temp_test_gp_with_data.json", compress=True, save_data=True)
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sm.save_model("temp_test_gp_without_data.json", compress=True, save_data=False)
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sm1_r = GPy.core.GP.load_model("temp_test_gp_with_data.json.zip")
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sm2_r = GPy.core.GP.load_model("temp_test_gp_without_data.json.zip", (X, Y))
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os.remove("temp_test_gp_with_data.json.zip")
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os.remove("temp_test_gp_without_data.json.zip")
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var = sm.predict(X)[0]
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var1_r = sm1_r.predict(X)[0]
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var2_r = sm2_r.predict(X)[0]
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np.testing.assert_array_equal(
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np.array(var).flatten(), np.array(var1_r).flatten()
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)
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np.testing.assert_array_equal(
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np.array(var).flatten(), np.array(var2_r).flatten()
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)
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def test_serialize_deserialize_GPRegressor(self):
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np.random.seed(fixed_seed)
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N = 50
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N_new = 50
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D = 1
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X = np.random.uniform(-3.0, 3.0, (N, 1))
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Y = np.sin(X) + np.random.randn(N, D) * 0.05
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X_new = np.random.uniform(-3.0, 3.0, (N_new, 1))
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k = GPy.kern.RBF(input_dim=1, lengthscale=10)
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m = GPy.models.GPRegression(X, Y, k)
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m.optimize()
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m.save_model(
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"temp_test_gp_regressor_with_data.json", compress=True, save_data=True
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)
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m.save_model(
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"temp_test_gp_regressor_without_data.json", compress=True, save_data=False
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)
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m1_r = GPy.models.GPRegression.load_model(
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"temp_test_gp_regressor_with_data.json.zip"
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)
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m2_r = GPy.models.GPRegression.load_model(
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"temp_test_gp_regressor_without_data.json.zip", (X, Y)
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)
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os.remove("temp_test_gp_regressor_with_data.json.zip")
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os.remove("temp_test_gp_regressor_without_data.json.zip")
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Xp = np.random.uniform(size=(int(1e5), 1))
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Xp[:, 0] = Xp[:, 0] * 15 - 5
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_, var = m.predict(Xp)
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_, var1_r = m1_r.predict(Xp)
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_, var2_r = m2_r.predict(Xp)
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np.testing.assert_array_equal(var.flatten(), var1_r.flatten())
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np.testing.assert_array_equal(var.flatten(), var2_r.flatten())
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def test_serialize_deserialize_GPClassification(self):
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np.random.seed(fixed_seed)
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N = 50
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Nhalf = int(N / 2)
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X = np.hstack([np.random.normal(5, 2, Nhalf), np.random.normal(10, 2, Nhalf)])[
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:, None
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]
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Y = np.hstack([np.ones(Nhalf), np.zeros(Nhalf)])[:, None]
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kernel = GPy.kern.RBF(1)
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m = GPy.models.GPClassification(X, Y, kernel=kernel)
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m.optimize()
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m.save_model(
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"temp_test_gp_classifier_with_data.json", compress=True, save_data=True
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)
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m.save_model(
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"temp_test_gp_classifier_without_data.json", compress=True, save_data=False
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)
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m1_r = GPy.models.GPClassification.load_model(
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"temp_test_gp_classifier_with_data.json.zip"
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)
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assert type(m) == type(
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m1_r
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), "Incorrect model type. Expected: {} Actual: {}".format(type(m), type(m1_r))
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m2_r = GPy.models.GPClassification.load_model(
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"temp_test_gp_classifier_without_data.json.zip", (X, Y)
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)
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assert type(m) == type(
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m2_r
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), "Incorrect model type. Expected: {} Actual: {}".format(type(m), type(m2_r))
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os.remove("temp_test_gp_classifier_with_data.json.zip")
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os.remove("temp_test_gp_classifier_without_data.json.zip")
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var = m.predict(X)[0]
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var1_r = m1_r.predict(X)[0]
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_var2_r = m2_r.predict(X)[0]
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np.testing.assert_array_equal(
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np.array(var).flatten(), np.array(var1_r).flatten()
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)
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np.testing.assert_array_equal(
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np.array(var).flatten(), np.array(var1_r).flatten()
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)
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def test_serialize_deserialize_SparseGPClassification(self):
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np.random.seed(fixed_seed)
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N = 50
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Nhalf = int(N / 2)
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X = np.hstack([np.random.normal(5, 2, Nhalf), np.random.normal(10, 2, Nhalf)])[
|
|
:, None
|
|
]
|
|
Y = np.hstack([np.ones(Nhalf), np.zeros(Nhalf)])[:, None]
|
|
kernel = GPy.kern.RBF(1)
|
|
m = GPy.models.SparseGPClassification(X, Y, num_inducing=3, kernel=kernel)
|
|
m.optimize()
|
|
m.save_model(
|
|
"temp_test_sparse_gp_classifier_with_data.json",
|
|
compress=True,
|
|
save_data=True,
|
|
)
|
|
m.save_model(
|
|
"temp_test_sparse_gp_classifier_without_data.json",
|
|
compress=True,
|
|
save_data=False,
|
|
)
|
|
m1_r = GPy.models.SparseGPClassification.load_model(
|
|
"temp_test_sparse_gp_classifier_with_data.json.zip"
|
|
)
|
|
assert type(m) == type(
|
|
m1_r
|
|
), "Incorrect model type. Expected: {} Actual: {}".format(type(m), type(m1_r))
|
|
m2_r = GPy.models.SparseGPClassification.load_model(
|
|
"temp_test_sparse_gp_classifier_without_data.json.zip", (X, Y)
|
|
)
|
|
assert type(m) == type(
|
|
m2_r
|
|
), "Incorrect model type. Expected: {} Actual: {}".format(type(m), type(m2_r))
|
|
os.remove("temp_test_sparse_gp_classifier_with_data.json.zip")
|
|
os.remove("temp_test_sparse_gp_classifier_without_data.json.zip")
|
|
|
|
var = m.predict(X)[0]
|
|
var1_r = m1_r.predict(X)[0]
|
|
var2_r = m2_r.predict(X)[0]
|
|
np.testing.assert_array_equal(
|
|
np.array(var).flatten(), np.array(var1_r).flatten()
|
|
)
|
|
np.testing.assert_array_equal(
|
|
np.array(var).flatten(), np.array(var1_r).flatten()
|
|
)
|