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https://github.com/SheffieldML/GPy.git
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fix: merge
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mzwiessele
commit
717d3f56f2
8 changed files with 624 additions and 64 deletions
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@ -812,6 +812,45 @@ class GradientTests(np.testing.TestCase):
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rbflin = GPy.kern.RBF(1) + GPy.kern.White(1)
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self.check_model(rbflin, model_type='SparseGPRegression', dimension=1, uncertain_inputs=1)
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def test_TPRegression_matern52_1D(self):
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''' Testing the TP regression with matern52 kernel on 1d data '''
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matern52 = GPy.kern.Matern52(1) + GPy.kern.White(1)
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self.check_model(matern52, model_type='TPRegression', dimension=1)
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def test_TPRegression_rbf_2D(self):
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''' Testing the TP regression with rbf kernel on 2d data '''
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rbf = GPy.kern.RBF(2)
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self.check_model(rbf, model_type='TPRegression', dimension=2)
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def test_TPRegression_rbf_ARD_2D(self):
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''' Testing the GP regression with rbf kernel on 2d data '''
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k = GPy.kern.RBF(2, ARD=True)
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self.check_model(k, model_type='TPRegression', dimension=2)
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def test_TPRegression_matern52_2D(self):
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''' Testing the TP regression with matern52 kernel on 2d data '''
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matern52 = GPy.kern.Matern52(2)
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self.check_model(matern52, model_type='TPRegression', dimension=2)
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def test_TPRegression_matern52_ARD_2D(self):
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''' Testing the TP regression with matern52 kernel on 2d data '''
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matern52 = GPy.kern.Matern52(2, ARD=True)
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self.check_model(matern52, model_type='TPRegression', dimension=2)
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def test_TPRegression_matern32_1D(self):
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''' Testing the TP regression with matern32 kernel on 1d data '''
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matern32 = GPy.kern.Matern32(1)
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self.check_model(matern32, model_type='TPRegression', dimension=1)
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def test_TPRegression_matern32_2D(self):
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''' Testing the TP regression with matern32 kernel on 2d data '''
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matern32 = GPy.kern.Matern32(2)
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self.check_model(matern32, model_type='TPRegression', dimension=2)
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def test_TPRegression_matern32_ARD_2D(self):
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''' Testing the TP regression with matern32 kernel on 2d data '''
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matern32 = GPy.kern.Matern32(2, ARD=True)
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self.check_model(matern32, model_type='TPRegression', dimension=2)
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def test_GPLVM_rbf_bias_white_kern_2D(self):
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""" Testing GPLVM with rbf + bias kernel """
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145
GPy/testing/tp_tests.py
Normal file
145
GPy/testing/tp_tests.py
Normal file
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@ -0,0 +1,145 @@
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'''
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Created on 14 Jul 2017, based on gp_tests
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@author: javdrher
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'''
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import unittest
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import numpy as np, GPy
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class Test(unittest.TestCase):
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def setUp(self):
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np.random.seed(12345)
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self.N = 20
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self.N_new = 50
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self.D = 1
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self.X = np.random.uniform(-3., 3., (self.N, 1))
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self.Y = np.sin(self.X) + np.random.randn(self.N, self.D) * 0.05
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self.X_new = np.random.uniform(-3., 3., (self.N_new, 1))
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def test_setxy_gp(self):
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k = GPy.kern.RBF(1) + GPy.kern.White(1)
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m = GPy.models.TPRegression(self.X, self.Y, kernel=k)
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mu, var = m.predict(m.X)
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X = m.X.copy()
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m.set_XY(m.X[:10], m.Y[:10])
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assert (m.checkgrad(tolerance=1e-2))
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m.set_XY(X, self.Y)
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mu2, var2 = m.predict(m.X)
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np.testing.assert_allclose(mu, mu2)
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np.testing.assert_allclose(var, var2)
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def test_mean_function(self):
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from GPy.core.parameterization.param import Param
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from GPy.core.mapping import Mapping
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class Parabola(Mapping):
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def __init__(self, variance, degree=2, name='parabola'):
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super(Parabola, self).__init__(1, 1, name)
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self.variance = Param('variance', np.ones(degree + 1) * variance)
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self.degree = degree
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self.link_parameter(self.variance)
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def f(self, X):
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p = self.variance[0] * np.ones(X.shape)
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for i in range(1, self.degree + 1):
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p += self.variance[i] * X ** (i)
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return p
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def gradients_X(self, dL_dF, X):
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grad = np.zeros(X.shape)
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for i in range(1, self.degree + 1):
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grad += (i) * self.variance[i] * X ** (i - 1)
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return grad
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def update_gradients(self, dL_dF, X):
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for i in range(self.degree + 1):
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self.variance.gradient[i] = (dL_dF * X ** (i)).sum(0)
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X = np.linspace(-2, 2, 100)[:, None]
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k = GPy.kern.RBF(1) + GPy.kern.White(1)
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k.randomize()
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p = Parabola(.3)
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p.randomize()
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Y = p.f(X) + np.random.multivariate_normal(np.zeros(X.shape[0]), k.K(X) + np.eye(X.shape[0]) * 1e-8)[:,
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None] + np.random.normal(0, .1, (X.shape[0], 1))
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m = GPy.models.TPRegression(X, Y, kernel=k, mean_function=p)
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assert (m.checkgrad(tolerance=2e-1))
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_ = m.predict(m.X)
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def test_normalizer(self):
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k = GPy.kern.RBF(1) + GPy.kern.White(1)
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Y = self.Y
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mu, std = Y.mean(0), Y.std(0)
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m = GPy.models.TPRegression(self.X, Y, kernel=k, normalizer=True)
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m.optimize()
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assert (m.checkgrad())
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k = GPy.kern.RBF(1) + GPy.kern.White(1)
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m2 = GPy.models.TPRegression(self.X, (Y - mu) / std, kernel=k, normalizer=False)
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m2[:] = m[:]
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mu1, var1 = m.predict(m.X, full_cov=True)
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mu2, var2 = m2.predict(m2.X, full_cov=True)
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np.testing.assert_allclose(mu1, (mu2 * std) + mu)
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np.testing.assert_allclose(var1, var2 * std ** 2)
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mu1, var1 = m.predict(m.X, full_cov=False)
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mu2, var2 = m2.predict(m2.X, full_cov=False)
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np.testing.assert_allclose(mu1, (mu2 * std) + mu)
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np.testing.assert_allclose(var1, var2 * std ** 2)
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q50n = m.predict_quantiles(m.X, (50,))
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q50 = m2.predict_quantiles(m2.X, (50,))
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np.testing.assert_allclose(q50n[0], (q50[0] * std) + mu)
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# Test variance component:
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qs = np.array([2.5, 97.5])
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# The quantiles get computed before unormalization
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# And transformed using the mean transformation:
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c = np.random.choice(self.X.shape[0])
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q95 = m2.predict_quantiles(self.X[[c]], qs)
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mu, var = m2.predict(self.X[[c]])
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from scipy.stats import t
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np.testing.assert_allclose((mu + (t.ppf(qs / 100., m2.nu + m2.num_data) * np.sqrt(var))).flatten(),
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np.array(q95).flatten())
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def test_predict_equivalence(self):
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k = GPy.kern.RBF(1) + GPy.kern.White(1)
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m = GPy.models.TPRegression(self.X, self.Y, kernel=k)
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m.optimize()
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mu1, var1 = m.predict(m.X)
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mu2, var2 = m.predict_noiseless(m.X)
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mu3, var3 = m._raw_predict(m.X)
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np.testing.assert_allclose(mu1, mu2)
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np.testing.assert_allclose(var1, var2)
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np.testing.assert_allclose(mu1, mu3)
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np.testing.assert_allclose(var1, var3)
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m2 = GPy.models.TPRegression(self.X, self.Y, kernel=k, normalizer=True)
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m2.optimize()
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mu1, var1 = m2.predict(m.X)
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mu2, var2 = m2.predict_noiseless(m.X)
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mu3, var3 = m2._raw_predict(m.X)
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np.testing.assert_allclose(mu1, mu2)
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np.testing.assert_allclose(var1, var2)
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self.assertFalse(np.allclose(mu1, mu3))
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self.assertFalse(np.allclose(var1, var3))
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def test_gp_equivalence(self):
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k = GPy.kern.RBF(1)
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m = GPy.models.GPRegression(self.X, self.Y, kernel=k)
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m.optimize()
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mu1, var1 = m.predict(self.X)
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k1 = GPy.kern.RBF(1)
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k1[:] = k[:]
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k2 = GPy.kern.White(1, variance=m.likelihood.variance)
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m2 = GPy.models.TPRegression(self.X, self.Y, kernel=k1 + k2, deg_free=1e6)
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mu2, var2 = m2.predict(self.X)
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np.testing.assert_allclose(mu1, mu2)
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np.testing.assert_allclose(var1, var2)
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if __name__ == "__main__":
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unittest.main()
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