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Sparse GP serialization
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Moreno
parent
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commit
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4 changed files with 146 additions and 52 deletions
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@ -11,6 +11,7 @@ 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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@ -116,46 +117,38 @@ class Test(unittest.TestCase):
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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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e1 = GPy.inference.latent_function_inference.expectation_propagation.EPDTC(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.posteriorParamsDTC(np.random.rand(10),np.random.rand(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_diag[:], e1_r._ep_approximation[0].Sigma_diag[:])
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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 = 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._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(100.0)
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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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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(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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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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assert type(e3) == type(e3_r)
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def test_serialize_deserialize_model(self):
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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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@ -165,13 +158,13 @@ class Test(unittest.TestCase):
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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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@ -180,7 +173,32 @@ class Test(unittest.TestCase):
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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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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)])[:, 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.EPDTC(ep_mode="nested")
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mean_function=None
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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')
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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(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_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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@ -195,7 +213,6 @@ class Test(unittest.TestCase):
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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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@ -208,7 +225,7 @@ class Test(unittest.TestCase):
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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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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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@ -221,7 +238,6 @@ class Test(unittest.TestCase):
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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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@ -231,6 +247,28 @@ class Test(unittest.TestCase):
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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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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]
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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.SparseGPClassification(X, Y, num_inducing=3, kernel=kernel)
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m.optimize()
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m.save_model("temp_test_sparse_gp_classifier_with_data.json", compress=True, save_data=True)
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m.save_model("temp_test_sparse_gp_classifier_without_data.json", compress=True, save_data=False)
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m1_r = GPy.models.SparseGPClassification.load_model("temp_test_sparse_gp_classifier_with_data.json.zip")
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m2_r = GPy.models.SparseGPClassification.load_model("temp_test_sparse_gp_classifier_without_data.json.zip", (X,Y))
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os.remove("temp_test_sparse_gp_classifier_with_data.json.zip")
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os.remove("temp_test_sparse_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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