'''
Created on 20 April 2017
@author: pgmoren
'''
import unittest, itertools
#import cPickle as pickle
import pickle
import numpy as np
import tempfile
import GPy
from nose import SkipTest
import numpy as np
import os
fixed_seed = 11
[docs]class Test(unittest.TestCase):
[docs] def test_serialize_deserialize_kernels(self):
k1 = GPy.kern.RBF(2, variance=1.0, lengthscale=[1.0,1.0], ARD=True)
k2 = GPy.kern.RatQuad(2, variance=2.0, lengthscale=1.0, power=2.0, active_dims = [0,1])
k3 = GPy.kern.Bias(2, variance=2.0, active_dims = [1,0])
k4 = GPy.kern.StdPeriodic(2, variance=2.0, lengthscale=1.0, period=1.0, active_dims = [1,1])
k5 = GPy.kern.Linear(2, variances=[2.0, 1.0], ARD=True, active_dims = [1,1])
k6 = GPy.kern.Exponential(2, variance=1., lengthscale=2)
k7 = GPy.kern.Matern32(2, variance=1.0, lengthscale=[1.0,3.0], ARD=True, active_dims = [1,1])
k8 = GPy.kern.Matern52(2, variance=2.0, lengthscale=[2.0,1.0], ARD=True, active_dims = [1,0])
k9 = GPy.kern.ExpQuad(2, variance=3.0, lengthscale=[1.0,2.0], ARD=True, active_dims = [0,1])
k10 = GPy.kern.OU(2, variance=2.0, lengthscale=[2.0, 1.0], ARD=True, active_dims=[1, 0])
k11 = k1 + k1.copy() + k2 + k3 + k4 + k5 + k6
k12 = k1 * k2 * k2.copy() * k3 * k4 * k5
k13 = (k1 + k2) * (k3 + k4 + k5)
k14 = ((k1 + k2) * k3) + k4 + k5 * k7
k15 = ((k1 + k2) * k3) + k4 * k5 + k8 * k10
k16 = ((k1 * k2) * k3) + k4 * k5 + k8 + k9
k_list = [k1,k2,k3,k4,k5,k6,k7,k8,k9,k10,k11,k12,k13,k14,k15,k16]
for kk in k_list:
kk_dict = kk.to_dict()
kk_r = GPy.kern.Kern.from_dict(kk_dict)
assert type(kk) == type(kk_r)
np.testing.assert_array_equal(kk[:], kk_r[:])
np.testing.assert_array_equal(np.array(kk.active_dims), np.array(kk_r.active_dims))
[docs] def test_serialize_deserialize_mappings(self):
m1 = GPy.mappings.Identity(3,2)
m2 = GPy.mappings.Constant(3,2,1)
m2_r = GPy.core.mapping.Mapping.from_dict(m2.to_dict())
np.testing.assert_array_equal(m2.C.values[:], m2_r.C.values[:])
m3 = GPy.mappings.Linear(3,2)
m3_r = GPy.core.mapping.Mapping.from_dict(m3.to_dict())
assert np.all(m3.A == m3_r.A)
m_list = [m1, m2, m3]
for mm in m_list:
mm_dict = mm.to_dict()
mm_r = GPy.core.mapping.Mapping.from_dict(mm_dict)
assert type(mm) == type(mm_r)
assert type(mm.input_dim) == type(mm_r.input_dim)
assert type(mm.output_dim) == type(mm_r.output_dim)
[docs] def test_serialize_deserialize_likelihoods(self):
l1 = GPy.likelihoods.Gaussian(GPy.likelihoods.link_functions.Identity(),variance=3.0)
l1_r = GPy.likelihoods.likelihood.Likelihood.from_dict(l1.to_dict())
l2 = GPy.likelihoods.Bernoulli(GPy.likelihoods.link_functions.Probit())
l2_r = GPy.likelihoods.likelihood.Likelihood.from_dict(l2.to_dict())
assert type(l1) == type(l1_r)
assert np.all(l1.variance == l1_r.variance)
assert type(l2) == type(l2_r)
[docs] def test_serialize_deserialize_normalizers(self):
n1 = GPy.util.normalizer.Standardize()
n1.scale_by(np.random.rand(10))
n1_r = GPy.util.normalizer._Norm.from_dict((n1.to_dict()))
assert type(n1) == type(n1_r)
assert np.all(n1.mean == n1_r.mean)
assert np.all(n1.std == n1_r.std)
[docs] def test_serialize_deserialize_link_functions(self):
l1 = GPy.likelihoods.link_functions.Identity()
l2 = GPy.likelihoods.link_functions.Probit()
l_list = [l1, l2]
for ll in l_list:
ll_dict = ll.to_dict()
ll_r = GPy.likelihoods.link_functions.GPTransformation.from_dict(ll_dict)
assert type(ll) == type(ll_r)
[docs] def test_serialize_deserialize_inference_methods(self):
e1 = GPy.inference.latent_function_inference.expectation_propagation.EP(ep_mode="nested")
e1.ga_approx_old = GPy.inference.latent_function_inference.expectation_propagation.gaussianApproximation(np.random.rand(10),np.random.rand(10))
e1._ep_approximation = []
e1._ep_approximation.append(GPy.inference.latent_function_inference.expectation_propagation.posteriorParams(np.random.rand(10),np.random.rand(100).reshape((10,10))))
e1._ep_approximation.append(GPy.inference.latent_function_inference.expectation_propagation.gaussianApproximation(np.random.rand(10),np.random.rand(10)))
e1._ep_approximation.append(GPy.inference.latent_function_inference.expectation_propagation.cavityParams(10))
e1._ep_approximation[-1].v = np.random.rand(10)
e1._ep_approximation[-1].tau = np.random.rand(10)
e1._ep_approximation.append(np.random.rand(10))
e1_r = GPy.inference.latent_function_inference.LatentFunctionInference.from_dict(e1.to_dict())
assert type(e1) == type(e1_r)
assert e1.epsilon==e1_r.epsilon
assert e1.eta==e1_r.eta
assert e1.delta==e1_r.delta
assert e1.always_reset==e1_r.always_reset
assert e1.max_iters==e1_r.max_iters
assert e1.ep_mode==e1_r.ep_mode
assert e1.parallel_updates==e1_r.parallel_updates
np.testing.assert_array_equal(e1.ga_approx_old.tau[:], e1_r.ga_approx_old.tau[:])
np.testing.assert_array_equal(e1.ga_approx_old.v[:], e1_r.ga_approx_old.v[:])
np.testing.assert_array_equal(e1._ep_approximation[0].mu[:], e1_r._ep_approximation[0].mu[:])
np.testing.assert_array_equal(e1._ep_approximation[0].Sigma[:], e1_r._ep_approximation[0].Sigma[:])
np.testing.assert_array_equal(e1._ep_approximation[1].tau[:], e1_r._ep_approximation[1].tau[:])
np.testing.assert_array_equal(e1._ep_approximation[1].v[:], e1_r._ep_approximation[1].v[:])
np.testing.assert_array_equal(e1._ep_approximation[2].tau[:], e1_r._ep_approximation[2].tau[:])
np.testing.assert_array_equal(e1._ep_approximation[2].v[:], e1_r._ep_approximation[2].v[:])
np.testing.assert_array_equal(e1._ep_approximation[3][:], e1_r._ep_approximation[3][:])
e2 = GPy.inference.latent_function_inference.expectation_propagation.EPDTC(ep_mode="nested")
e2.ga_approx_old = GPy.inference.latent_function_inference.expectation_propagation.gaussianApproximation(np.random.rand(10),np.random.rand(10))
e2._ep_approximation = []
e2._ep_approximation.append(GPy.inference.latent_function_inference.expectation_propagation.posteriorParamsDTC(np.random.rand(10),np.random.rand(10)))
e2._ep_approximation.append(GPy.inference.latent_function_inference.expectation_propagation.gaussianApproximation(np.random.rand(10),np.random.rand(10)))
e2._ep_approximation.append(100.0)
e2_r = GPy.inference.latent_function_inference.LatentFunctionInference.from_dict(e2.to_dict())
assert type(e2) == type(e2_r)
assert e2.epsilon==e2_r.epsilon
assert e2.eta==e2_r.eta
assert e2.delta==e2_r.delta
assert e2.always_reset==e2_r.always_reset
assert e2.max_iters==e2_r.max_iters
assert e2.ep_mode==e2_r.ep_mode
assert e2.parallel_updates==e2_r.parallel_updates
np.testing.assert_array_equal(e2.ga_approx_old.tau[:], e2_r.ga_approx_old.tau[:])
np.testing.assert_array_equal(e2.ga_approx_old.v[:], e2_r.ga_approx_old.v[:])
np.testing.assert_array_equal(e2._ep_approximation[0].mu[:], e2_r._ep_approximation[0].mu[:])
np.testing.assert_array_equal(e2._ep_approximation[0].Sigma_diag[:], e2_r._ep_approximation[0].Sigma_diag[:])
np.testing.assert_array_equal(e2._ep_approximation[1].tau[:], e2_r._ep_approximation[1].tau[:])
np.testing.assert_array_equal(e2._ep_approximation[1].v[:], e2_r._ep_approximation[1].v[:])
assert(e2._ep_approximation[2] == e2_r._ep_approximation[2])
e3 = GPy.inference.latent_function_inference.exact_gaussian_inference.ExactGaussianInference()
e3_r = GPy.inference.latent_function_inference.LatentFunctionInference.from_dict(e3.to_dict())
assert type(e3) == type(e3_r)
[docs] def test_serialize_deserialize_GP(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]
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.EP(ep_mode="nested")
mean_function=None
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')
m.optimize()
m.save_model("temp_test_gp_with_data.json", compress=True, save_data=True)
m.save_model("temp_test_gp_without_data.json", compress=True, save_data=False)
m1_r = GPy.core.GP.load_model("temp_test_gp_with_data.json.zip")
m2_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 = 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(var2_r).flatten())
[docs] 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]
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
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))
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())
[docs] 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))
Y = np.sin(X) + np.random.randn(N, D) * 0.05
X_new = np.random.uniform(-3., 3., (N_new, 1))
k = GPy.kern.RBF(input_dim=1, lengthscale=10)
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))
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
_, var = m.predict(Xp)
_, var1_r = m1_r.predict(Xp)
_, var2_r = m2_r.predict(Xp)
np.testing.assert_array_equal(var.flatten(), var1_r.flatten())
np.testing.assert_array_equal(var.flatten(), var2_r.flatten())
[docs] 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]
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)))
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())
[docs] 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]
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)))
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())
if __name__ == "__main__":
#import sys;sys.argv = ['', 'Test.test_parameter_index_operations']
unittest.main()