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This commit is contained in:
Martin Bubel
parent
ca2092f12e
commit
96d8ac0975
1 changed files with 283 additions and 113 deletions
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@ -1,32 +1,39 @@
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'''
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"""
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Created on 20 April 2017
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@author: pgmoren
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'''
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import unittest, itertools
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#import cPickle as pickle
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import pickle
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"""
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import numpy as np
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import tempfile
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import GPy
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from nose import SkipTest
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import numpy as np
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import os
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fixed_seed = 11
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class Test(unittest.TestCase):
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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(2, variance=2.0, lengthscale=1.0, power=2.0, active_dims = [0,1])
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k3 = GPy.kern.Bias(2, variance=2.0, active_dims = [1,0])
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k4 = GPy.kern.StdPeriodic(2, variance=2.0, lengthscale=1.0, period=1.0, active_dims = [1,1])
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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., lengthscale=2)
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k7 = GPy.kern.Matern32(2, variance=1.0, lengthscale=[1.0,3.0], ARD=True, active_dims = [1,1])
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k8 = GPy.kern.Matern52(2, variance=2.0, lengthscale=[2.0,1.0], ARD=True, active_dims = [1,0])
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k9 = GPy.kern.ExpQuad(2, variance=3.0, lengthscale=[1.0,2.0], ARD=True, active_dims = [0,1])
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k10 = GPy.kern.OU(2, variance=2.0, lengthscale=[2.0, 1.0], ARD=True, active_dims=[1, 0])
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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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@ -34,21 +41,23 @@ class Test(unittest.TestCase):
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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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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(np.array(kk.active_dims), np.array(kk_r.active_dims))
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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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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 = 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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@ -61,7 +70,9 @@ class Test(unittest.TestCase):
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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(GPy.likelihoods.link_functions.Identity(),variance=3.0)
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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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@ -87,138 +98,252 @@ class Test(unittest.TestCase):
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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(ep_mode="nested")
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e1.ga_approx_old = GPy.inference.latent_function_inference.expectation_propagation.gaussianApproximation(np.random.rand(10),np.random.rand(10))
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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(GPy.inference.latent_function_inference.expectation_propagation.posteriorParams(np.random.rand(10),np.random.rand(100).reshape((10,10))))
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e1._ep_approximation.append(GPy.inference.latent_function_inference.expectation_propagation.gaussianApproximation(np.random.rand(10),np.random.rand(10)))
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e1._ep_approximation.append(GPy.inference.latent_function_inference.expectation_propagation.cavityParams(10))
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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 = GPy.inference.latent_function_inference.LatentFunctionInference.from_dict(e1.to_dict())
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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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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(e1.ga_approx_old.tau[:], e1_r.ga_approx_old.tau[:])
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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(e1._ep_approximation[0].mu[:], e1_r._ep_approximation[0].mu[:])
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np.testing.assert_array_equal(e1._ep_approximation[0].Sigma[:], e1_r._ep_approximation[0].Sigma[:])
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np.testing.assert_array_equal(e1._ep_approximation[1].tau[:], e1_r._ep_approximation[1].tau[:])
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np.testing.assert_array_equal(e1._ep_approximation[1].v[:], e1_r._ep_approximation[1].v[:])
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np.testing.assert_array_equal(e1._ep_approximation[2].tau[:], e1_r._ep_approximation[2].tau[:])
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np.testing.assert_array_equal(e1._ep_approximation[2].v[:], e1_r._ep_approximation[2].v[:])
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np.testing.assert_array_equal(e1._ep_approximation[3][:], e1_r._ep_approximation[3][:])
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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(ep_mode="nested")
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e2.ga_approx_old = GPy.inference.latent_function_inference.expectation_propagation.gaussianApproximation(np.random.rand(10),np.random.rand(10))
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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(GPy.inference.latent_function_inference.expectation_propagation.posteriorParamsDTC(np.random.rand(10),np.random.rand(10)))
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e2._ep_approximation.append(GPy.inference.latent_function_inference.expectation_propagation.gaussianApproximation(np.random.rand(10),np.random.rand(10)))
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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 = GPy.inference.latent_function_inference.LatentFunctionInference.from_dict(e2.to_dict())
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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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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(e2.ga_approx_old.tau[:], e2_r.ga_approx_old.tau[:])
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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(e2._ep_approximation[0].mu[:], e2_r._ep_approximation[0].mu[:])
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np.testing.assert_array_equal(e2._ep_approximation[0].Sigma_diag[:], e2_r._ep_approximation[0].Sigma_diag[:])
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np.testing.assert_array_equal(e2._ep_approximation[1].tau[:], e2_r._ep_approximation[1].tau[:])
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np.testing.assert_array_equal(e2._ep_approximation[1].v[:], e2_r._ep_approximation[1].v[:])
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assert(e2._ep_approximation[2] == e2_r._ep_approximation[2])
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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 = GPy.inference.latent_function_inference.exact_gaussian_inference.ExactGaussianInference()
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e3_r = GPy.inference.latent_function_inference.LatentFunctionInference.from_dict(e3.to_dict())
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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)])[:, None]
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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=GPy.inference.latent_function_inference.expectation_propagation.EP(ep_mode="nested")
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mean_function=None
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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(X=X, Y=Y, kernel=kernel, likelihood=likelihood, inference_method=inference_method, mean_function=mean_function, normalizer=True, name='gp_classification')
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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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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(np.array(var).flatten(), np.array(var1_r).flatten())
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np.testing.assert_array_equal(np.array(var).flatten(), np.array(var2_r).flatten())
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||||
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(var2_r).flatten()
|
||||
)
|
||||
|
||||
def test_serialize_deserialize_SparseGP(self):
|
||||
np.random.seed(fixed_seed)
|
||||
N = 20
|
||||
Nhalf = int(N/2)
|
||||
X = np.hstack([np.random.normal(5, 2, Nhalf), np.random.normal(10, 2, Nhalf)])[:, None]
|
||||
Nhalf = int(N / 2)
|
||||
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)
|
||||
likelihood = GPy.likelihoods.Bernoulli()
|
||||
inference_method=GPy.inference.latent_function_inference.expectation_propagation.EPDTC(ep_mode="nested")
|
||||
mean_function=None
|
||||
inference_method = (
|
||||
GPy.inference.latent_function_inference.expectation_propagation.EPDTC(
|
||||
ep_mode="nested"
|
||||
)
|
||||
)
|
||||
mean_function = None
|
||||
|
||||
sm = GPy.core.SparseGP(X=X, Y=Y, Z=X[0:20,:], kernel=kernel, likelihood=likelihood, inference_method=inference_method, mean_function=mean_function, normalizer=True, name='sparse_gp_classification')
|
||||
sm = GPy.core.SparseGP(
|
||||
X=X,
|
||||
Y=Y,
|
||||
Z=X[0:20, :],
|
||||
kernel=kernel,
|
||||
likelihood=likelihood,
|
||||
inference_method=inference_method,
|
||||
mean_function=mean_function,
|
||||
normalizer=True,
|
||||
name="sparse_gp_classification",
|
||||
)
|
||||
sm.optimize()
|
||||
sm.save_model("temp_test_gp_with_data.json", compress=True, save_data=True)
|
||||
sm.save_model("temp_test_gp_without_data.json", compress=True, save_data=False)
|
||||
sm1_r = GPy.core.GP.load_model("temp_test_gp_with_data.json.zip")
|
||||
sm2_r = GPy.core.GP.load_model("temp_test_gp_without_data.json.zip", (X,Y))
|
||||
sm2_r = GPy.core.GP.load_model("temp_test_gp_without_data.json.zip", (X, Y))
|
||||
os.remove("temp_test_gp_with_data.json.zip")
|
||||
os.remove("temp_test_gp_without_data.json.zip")
|
||||
var = sm.predict(X)[0]
|
||||
var1_r = sm1_r.predict(X)[0]
|
||||
var2_r = sm2_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(var2_r).flatten())
|
||||
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(var2_r).flatten()
|
||||
)
|
||||
|
||||
def test_serialize_deserialize_GPRegressor(self):
|
||||
np.random.seed(fixed_seed)
|
||||
N = 50
|
||||
N_new = 50
|
||||
D = 1
|
||||
X = np.random.uniform(-3., 3., (N, 1))
|
||||
X = np.random.uniform(-3.0, 3.0, (N, 1))
|
||||
Y = np.sin(X) + np.random.randn(N, D) * 0.05
|
||||
X_new = np.random.uniform(-3., 3., (N_new, 1))
|
||||
X_new = np.random.uniform(-3.0, 3.0, (N_new, 1))
|
||||
k = GPy.kern.RBF(input_dim=1, lengthscale=10)
|
||||
m = GPy.models.GPRegression(X,Y,k)
|
||||
m = GPy.models.GPRegression(X, Y, k)
|
||||
m.optimize()
|
||||
m.save_model("temp_test_gp_regressor_with_data.json", compress=True, save_data=True)
|
||||
m.save_model("temp_test_gp_regressor_without_data.json", compress=True, save_data=False)
|
||||
m1_r = GPy.models.GPRegression.load_model("temp_test_gp_regressor_with_data.json.zip")
|
||||
m2_r = GPy.models.GPRegression.load_model("temp_test_gp_regressor_without_data.json.zip", (X,Y))
|
||||
m.save_model(
|
||||
"temp_test_gp_regressor_with_data.json", compress=True, save_data=True
|
||||
)
|
||||
m.save_model(
|
||||
"temp_test_gp_regressor_without_data.json", compress=True, save_data=False
|
||||
)
|
||||
m1_r = GPy.models.GPRegression.load_model(
|
||||
"temp_test_gp_regressor_with_data.json.zip"
|
||||
)
|
||||
m2_r = GPy.models.GPRegression.load_model(
|
||||
"temp_test_gp_regressor_without_data.json.zip", (X, Y)
|
||||
)
|
||||
os.remove("temp_test_gp_regressor_with_data.json.zip")
|
||||
os.remove("temp_test_gp_regressor_without_data.json.zip")
|
||||
|
||||
Xp = np.random.uniform(size=(int(1e5),1))
|
||||
Xp[:,0] = Xp[:,0]*15-5
|
||||
Xp = np.random.uniform(size=(int(1e5), 1))
|
||||
Xp[:, 0] = Xp[:, 0] * 15 - 5
|
||||
|
||||
_, var = m.predict(Xp)
|
||||
_, var1_r = m1_r.predict(Xp)
|
||||
|
|
@ -229,51 +354,96 @@ class Test(unittest.TestCase):
|
|||
def test_serialize_deserialize_GPClassification(self):
|
||||
np.random.seed(fixed_seed)
|
||||
N = 50
|
||||
Nhalf = int(N/2)
|
||||
X = np.hstack([np.random.normal(5, 2, Nhalf), np.random.normal(10, 2, Nhalf)])[:, None]
|
||||
Nhalf = int(N / 2)
|
||||
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.GPClassification(X, Y, kernel=kernel)
|
||||
m.optimize()
|
||||
m.save_model("temp_test_gp_classifier_with_data.json", compress=True, save_data=True)
|
||||
m.save_model("temp_test_gp_classifier_without_data.json", compress=True, save_data=False)
|
||||
m1_r = GPy.models.GPClassification.load_model("temp_test_gp_classifier_with_data.json.zip")
|
||||
self.assertTrue(type(m) == type(m1_r), "Incorrect model type. Expected: {} Actual: {}".format(type(m), type(m1_r)))
|
||||
m2_r = GPy.models.GPClassification.load_model("temp_test_gp_classifier_without_data.json.zip", (X,Y))
|
||||
self.assertTrue(type(m) == type(m2_r), "Incorrect model type. Expected: {} Actual: {}".format(type(m), type(m2_r)))
|
||||
m.save_model(
|
||||
"temp_test_gp_classifier_with_data.json", compress=True, save_data=True
|
||||
)
|
||||
m.save_model(
|
||||
"temp_test_gp_classifier_without_data.json", compress=True, save_data=False
|
||||
)
|
||||
m1_r = GPy.models.GPClassification.load_model(
|
||||
"temp_test_gp_classifier_with_data.json.zip"
|
||||
)
|
||||
self.assertTrue(
|
||||
type(m) == type(m1_r),
|
||||
"Incorrect model type. Expected: {} Actual: {}".format(type(m), type(m1_r)),
|
||||
)
|
||||
m2_r = GPy.models.GPClassification.load_model(
|
||||
"temp_test_gp_classifier_without_data.json.zip", (X, Y)
|
||||
)
|
||||
self.assertTrue(
|
||||
type(m) == type(m2_r),
|
||||
"Incorrect model type. Expected: {} Actual: {}".format(type(m), type(m2_r)),
|
||||
)
|
||||
os.remove("temp_test_gp_classifier_with_data.json.zip")
|
||||
os.remove("temp_test_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())
|
||||
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()
|
||||
)
|
||||
|
||||
def test_serialize_deserialize_SparseGPClassification(self):
|
||||
np.random.seed(fixed_seed)
|
||||
N = 50
|
||||
Nhalf = int(N/2)
|
||||
X = np.hstack([np.random.normal(5, 2, Nhalf), np.random.normal(10, 2, Nhalf)])[:, None]
|
||||
Nhalf = int(N / 2)
|
||||
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")
|
||||
self.assertTrue(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))
|
||||
self.assertTrue(type(m) == type(m2_r), "Incorrect model type. Expected: {} Actual: {}".format(type(m), type(m2_r)))
|
||||
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"
|
||||
)
|
||||
self.assertTrue(
|
||||
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)
|
||||
)
|
||||
self.assertTrue(
|
||||
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())
|
||||
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()
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
#import sys;sys.argv = ['', 'Test.test_parameter_index_operations']
|
||||
# import sys;sys.argv = ['', 'Test.test_parameter_index_operations']
|
||||
unittest.main()
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue