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Basic framework for serializing GPy models
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202
GPy/testing/serialization_tests.py
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GPy/testing/serialization_tests.py
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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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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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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 = k1 + k1.copy() + k2 + k3 + k4 + k5 + k6
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k11 = k1 * k2 * k2.copy() * k3 * k4 * k5
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k12 = (k1 + k2) * (k3 + k4 + k5)
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k13 = ((k1 + k2) * k3) + k4 + k5 * k7
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k14 = ((k1 + k2) * k3) + k4 * k5 + k8
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k15 = ((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]
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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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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(GPy.likelihoods.link_functions.Identity(),variance=3.0)
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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(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._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[-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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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(e1.ga_approx_old.tau[:], e1_r.ga_approx_old.tau[:])
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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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e2 = GPy.inference.latent_function_inference.exact_gaussian_inference.ExactGaussianInference()
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e2_r = GPy.inference.latent_function_inference.LatentFunctionInference.from_dict(e2.to_dict())
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assert type(e2) == type(e2_r)
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def test_serialize_deserialize_model(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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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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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.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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import os
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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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def test_serialize_deserialize_inference_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., 3., (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., 3., (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("temp_test_gp_regressor_with_data.json", compress=True, save_data=True)
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m.save_model("temp_test_gp_regressor_without_data.json", compress=True, save_data=False)
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m1_r = GPy.models.GPRegression.load_model("temp_test_gp_regressor_with_data.json.zip")
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m2_r = GPy.models.GPRegression.load_model("temp_test_gp_regressor_without_data.json.zip", (X,Y))
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import os
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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_inference_GPClassifier(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]
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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("temp_test_gp_classifier_with_data.json", compress=True, save_data=True)
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m.save_model("temp_test_gp_classifier_without_data.json", compress=True, save_data=False)
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m1_r = GPy.models.GPClassification.load_model("temp_test_gp_classifier_with_data.json.zip")
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m2_r = GPy.models.GPClassification.load_model("temp_test_gp_classifier_without_data.json.zip", (X,Y))
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import os
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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(np.array(var).flatten(), np.array(var1_r).flatten())
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np.testing.assert_array_equal(np.array(var).flatten(), np.array(var1_r).flatten())
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if __name__ == "__main__":
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#import sys;sys.argv = ['', 'Test.test_parameter_index_operations']
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unittest.main()
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