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Merge branch 'wgps_improvements' of https://github.com/beckdaniel/GPy into beckdaniel-wgps_improvements

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mzwiessele 2016-08-03 13:27:52 +01:00
commit 9be51df4f1
5 changed files with 261 additions and 242 deletions
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95
GPy/testing/model_tests.py
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@ -361,51 +361,74 @@ class MiscTests(unittest.TestCase):
preds = m.predict(self.X)
warp_k = GPy.kern.RBF(1)
warp_f = GPy.util.warping_functions.IdentityFunction()
warp_m = GPy.models.WarpedGP(self.X, self.Y, kernel=warp_k, warping_function=warp_f)
warp_f = GPy.util.warping_functions.IdentityFunction(closed_inverse=False)
warp_m = GPy.models.WarpedGP(self.X, self.Y, kernel=warp_k,
warping_function=warp_f)
warp_m.optimize()
warp_preds = warp_m.predict(self.X)
np.testing.assert_almost_equal(preds, warp_preds)
warp_k_exact = GPy.kern.RBF(1)
warp_f_exact = GPy.util.warping_functions.IdentityFunction()
warp_m_exact = GPy.models.WarpedGP(self.X, self.Y, kernel=warp_k_exact,
warping_function=warp_f_exact)
warp_m_exact.optimize()
warp_preds_exact = warp_m_exact.predict(self.X)
np.testing.assert_almost_equal(preds, warp_preds, decimal=4)
np.testing.assert_almost_equal(preds, warp_preds_exact, decimal=4)
@unittest.skip('Comment this to plot the modified sine function')
def test_warped_gp_sine(self):
def test_warped_gp_log(self):
"""
A test replicating the sine regression problem from
Snelson's paper.
A WarpedGP with the log warping function should be
equal to a standard GP with log labels.
Note that we predict the median here.
"""
k = GPy.kern.RBF(1)
Y = np.abs(self.Y)
logY = np.log(Y)
m = GPy.models.GPRegression(self.X, logY, kernel=k)
m.optimize()
preds = m.predict(self.X)[0]
warp_k = GPy.kern.RBF(1)
warp_f = GPy.util.warping_functions.LogFunction(closed_inverse=False)
warp_m = GPy.models.WarpedGP(self.X, Y, kernel=warp_k,
warping_function=warp_f)
warp_m.optimize()
warp_preds = warp_m.predict(self.X, median=True)[0]
warp_k_exact = GPy.kern.RBF(1)
warp_f_exact = GPy.util.warping_functions.LogFunction()
warp_m_exact = GPy.models.WarpedGP(self.X, Y, kernel=warp_k_exact,
warping_function=warp_f_exact)
warp_m_exact.optimize(messages=True)
warp_preds_exact = warp_m_exact.predict(self.X, median=True)[0]
np.testing.assert_almost_equal(np.exp(preds), warp_preds, decimal=4)
np.testing.assert_almost_equal(np.exp(preds), warp_preds_exact, decimal=4)
def test_warped_gp_cubic_sine(self, max_iters=100):
"""
A test replicating the cubic sine regression problem from
Snelson's paper. This test doesn't have any assertions, it's
just to ensure coverage of the tanh warping function code.
"""
X = (2 * np.pi) * np.random.random(151) - np.pi
Y = np.sin(X) + np.random.normal(0,0.1,151)
Y = np.exp(Y) - 5
#Y = np.array([np.power(abs(y),float(1)/3) * (1,-1)[y<0] for y in Y]) + 0
Y = np.sin(X) + np.random.normal(0,0.2,151)
Y = np.array([np.power(abs(y),float(1)/3) * (1,-1)[y<0] for y in Y])
X = X[:, None]
Y = Y[:, None]
#np.seterr(over='raise')
import matplotlib.pyplot as plt
warp_k = GPy.kern.RBF(1)
warp_f = GPy.util.warping_functions.TanhWarpingFunction_d(n_terms=2)
warp_m = GPy.models.WarpedGP(X[:, None], Y[:, None], kernel=warp_k, warping_function=warp_f)
#warp_m['.*variance.*'].constrain_fixed(0.25)
#warp_m['.*lengthscale.*'].constrain_fixed(1)
#warp_m['warp_tanh.d'].constrain_fixed(1)
#warp_m.randomize()
#warp_m['.*warp_tanh.psi*'][:,0:2].constrain_bounded(0,100)
#warp_m['.*warp_tanh.psi*'][:,0:1].constrain_fixed(1)
#print(warp_m.checkgrad())
#warp_m.plot()
#plt.show()
warp_m.optimize_restarts(parallel=True, robust=True)
#print(warp_m.checkgrad())
print(warp_m)
print(warp_m['.*warp.*'])
warp_m = GPy.models.WarpedGP(X, Y)#, kernel=warp_k)#, warping_function=warp_f)
warp_m['.*\.d'].constrain_fixed(1.0)
warp_m.optimize_restarts(parallel=False, robust=False, num_restarts=5,
max_iters=max_iters)
warp_m.predict(X)
warp_m.predict_quantiles(X)
warp_m.log_predictive_density(X, Y)
warp_m.predict_in_warped_space = False
warp_m.plot()
warp_m.predict_in_warped_space = True
warp_m.plot()
warp_f.plot(X.min()-10, X.max()+10)
plt.show()
warp_m.predict(X)
warp_m.predict_quantiles(X)
class GradientTests(np.testing.TestCase):