mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-06-14 15:25:15 +02:00
Zhenwen Dai
GitHub
commit
44a232ff6a
19 changed files with 517 additions and 76 deletions
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@ -16,8 +16,9 @@ addons:
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env:
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- PYTHON_VERSION=2.7
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#- PYTHON_VERSION=3.3
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- PYTHON_VERSION=3.4
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#- PYTHON_VERSION=3.4
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- PYTHON_VERSION=3.5
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- PYTHON_VERSION=3.6
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before_install:
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- wget https://github.com/mzwiessele/travis_scripts/raw/master/download_miniconda.sh
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92
CHANGELOG.md
92
CHANGELOG.md
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@ -1,5 +1,97 @@
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# Changelog
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## v1.7.6 (2017-06-19)
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### Fix
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* Appveyor not uploading to testpypi for now. [mzwiessele]
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### Other
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* Bump version: 1.7.5 → 1.7.6. [mzwiessele]
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## v1.7.5 (2017-06-19)
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### Fix
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* Splitting forecast tests into 3 to circumvent 10 minute stop of travis. [mzwiessele]
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### Other
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* Bump version: 1.7.4 → 1.7.5. [mzwiessele]
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## v1.7.4 (2017-06-19)
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### Fix
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* Paramz version for parallel optimization fix. [mzwiessele]
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### Other
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* Bump version: 1.7.3 → 1.7.4. [mzwiessele]
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## v1.7.3 (2017-06-19)
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### Fix
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* Appveyor build failing. [mzwiessele]
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### Other
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* Bump version: 1.7.2 → 1.7.3. [mzwiessele]
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## v1.7.2 (2017-06-17)
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### Fix
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* Appveyor build python 3.6. [mzwiessele]
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### Other
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* Bump version: 1.7.1 → 1.7.2. [mzwiessele]
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## v1.7.1 (2017-06-17)
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### Fix
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* Appveyor build python 3.6. [mzwiessele]
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### Other
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* Bump version: 1.7.0 → 1.7.1. [mzwiessele]
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## v1.7.0 (2017-06-17)
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### Fix
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* Support for 3.5 and higher now that 3.6 is out. [mzwiessele]
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### Other
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* Bump version: 1.6.3 → 1.7.0. [mzwiessele]
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## v1.6.3 (2017-06-17)
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### Other
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* Bump version: 1.6.2 → 1.6.3. [mzwiessele]
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* Merge pull request #504 from rmcantin/devel. [Max Zwiessele]
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* Fix python 2-3 compatibility. [Ruben Martinez-Cantin]
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* Merge pull request #511 from dirmeier/devel. [Max Zwiessele]
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* Added LICENSE file to MANIFEST.in. [dirmeier]
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## v1.6.2 (2017-04-12)
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### Fix
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@ -1 +1 @@
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__version__ = "1.6.2"
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__version__ = "1.7.7"
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@ -6,6 +6,7 @@ from paramz import ObsAr
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from . import ExactGaussianInference, VarDTC
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from ...util import diag
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from .posterior import PosteriorEP as Posterior
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from ...likelihoods import Gaussian
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log_2_pi = np.log(2*np.pi)
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@ -174,18 +175,18 @@ class EP(EPBase, ExactGaussianInference):
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if self.ep_mode=="nested":
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#Force EP at each step of the optimization
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self._ep_approximation = None
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post_params, ga_approx, log_Z_tilde = self._ep_approximation = self.expectation_propagation(K, Y, likelihood, Y_metadata)
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post_params, ga_approx, cav_params, log_Z_tilde = self._ep_approximation = self.expectation_propagation(K, Y, likelihood, Y_metadata)
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elif self.ep_mode=="alternated":
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if getattr(self, '_ep_approximation', None) is None:
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#if we don't yet have the results of runnign EP, run EP and store the computed factors in self._ep_approximation
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post_params, ga_approx, log_Z_tilde = self._ep_approximation = self.expectation_propagation(K, Y, likelihood, Y_metadata)
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post_params, ga_approx, cav_params, log_Z_tilde = self._ep_approximation = self.expectation_propagation(K, Y, likelihood, Y_metadata)
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else:
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#if we've already run EP, just use the existing approximation stored in self._ep_approximation
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post_params, ga_approx, log_Z_tilde = self._ep_approximation
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post_params, ga_approx, cav_params, log_Z_tilde = self._ep_approximation
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else:
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raise ValueError("ep_mode value not valid")
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return self._inference(K, ga_approx, likelihood, Y_metadata=Y_metadata, Z_tilde=log_Z_tilde)
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return self._inference(Y, K, ga_approx, cav_params, likelihood, Y_metadata=Y_metadata, Z_tilde=log_Z_tilde)
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def expectation_propagation(self, K, Y, likelihood, Y_metadata):
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@ -220,7 +221,7 @@ class EP(EPBase, ExactGaussianInference):
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# This terms cancel with the coreresponding terms in the marginal loglikelihood
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log_Z_tilde = self._log_Z_tilde(marg_moments, ga_approx, cav_params)
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# - 0.5*np.log(tau_tilde) + 0.5*(v_tilde*v_tilde*1./tau_tilde)
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return (post_params, ga_approx, log_Z_tilde)
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return (post_params, ga_approx, cav_params, log_Z_tilde)
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def _init_approximations(self, K, num_data):
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#initial values - Gaussian factors
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@ -280,7 +281,7 @@ class EP(EPBase, ExactGaussianInference):
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return log_marginal, post_params
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def _inference(self, K, ga_approx, likelihood, Z_tilde, Y_metadata=None):
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def _inference(self, Y, K, ga_approx, cav_params, likelihood, Z_tilde, Y_metadata=None):
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log_marginal, post_params = self._ep_marginal(K, ga_approx, Z_tilde)
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tau_tilde_root = np.sqrt(ga_approx.tau)
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@ -293,8 +294,7 @@ class EP(EPBase, ExactGaussianInference):
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symmetrify(Wi) #(K + Sigma^(\tilde))^(-1)
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dL_dK = 0.5 * (tdot(alpha) - Wi)
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dL_dthetaL = likelihood.exact_inference_gradients(np.diag(dL_dK), Y_metadata)
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dL_dthetaL = likelihood.ep_gradients(Y, cav_params.tau, cav_params.v, np.diag(dL_dK), Y_metadata=Y_metadata, quad_mode='gh')
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return Posterior(woodbury_inv=Wi, woodbury_vector=alpha, K=K), log_marginal, {'dL_dK':dL_dK, 'dL_dthetaL':dL_dthetaL, 'dL_dm':alpha}
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@ -66,7 +66,14 @@ class Binomial(Likelihood):
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np.testing.assert_array_equal(N.shape, y.shape)
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nchoosey = special.gammaln(N+1) - special.gammaln(y+1) - special.gammaln(N-y+1)
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return nchoosey + y*np.log(inv_link_f) + (N-y)*np.log(1.-inv_link_f)
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Ny = N-y
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t1 = np.zeros(y.shape)
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t2 = np.zeros(y.shape)
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t1[y>0] = y[y>0]*np.log(inv_link_f[y>0])
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t2[Ny>0] = Ny[Ny>0]*np.log(1.-inv_link_f[Ny>0])
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return nchoosey + t1 + t2
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def dlogpdf_dlink(self, inv_link_f, y, Y_metadata=None):
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"""
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@ -86,7 +93,13 @@ class Binomial(Likelihood):
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N = Y_metadata['trials']
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np.testing.assert_array_equal(N.shape, y.shape)
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return y/inv_link_f - (N-y)/(1.-inv_link_f)
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Ny = N-y
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t1 = np.zeros(y.shape)
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t2 = np.zeros(y.shape)
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t1[y>0] = y[y>0]/inv_link_f[y>0]
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t2[Ny>0] = (Ny[Ny>0])/(1.-inv_link_f[Ny>0])
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return t1 - t2
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def d2logpdf_dlink2(self, inv_link_f, y, Y_metadata=None):
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"""
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@ -111,7 +124,13 @@ class Binomial(Likelihood):
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"""
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N = Y_metadata['trials']
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np.testing.assert_array_equal(N.shape, y.shape)
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return -y/np.square(inv_link_f) - (N-y)/np.square(1.-inv_link_f)
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Ny = N-y
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t1 = np.zeros(y.shape)
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t2 = np.zeros(y.shape)
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t1[y>0] = -y[y>0]/np.square(inv_link_f[y>0])
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t2[Ny>0] = -(Ny[Ny>0])/np.square(1.-inv_link_f[Ny>0])
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return t1+t2
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def d3logpdf_dlink3(self, inv_link_f, y, Y_metadata=None):
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"""
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@ -135,8 +154,14 @@ class Binomial(Likelihood):
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N = Y_metadata['trials']
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np.testing.assert_array_equal(N.shape, y.shape)
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inv_link_f2 = np.square(inv_link_f)
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return 2*y/inv_link_f**3 - 2*(N-y)/(1.-inv_link_f)**3
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#inv_link_f2 = np.square(inv_link_f) #TODO Remove. Why is this here?
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Ny = N-y
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t1 = np.zeros(y.shape)
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t2 = np.zeros(y.shape)
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t1[y>0] = 2*y[y>0]/inv_link_f[y>0]**3
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t2[Ny>0] = - 2*(Ny[Ny>0])/(1.-inv_link_f[Ny>0])**3
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return t1 + t2
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def samples(self, gp, Y_metadata=None, **kw):
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"""
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@ -57,7 +57,10 @@ class Gaussian(Likelihood):
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def update_gradients(self, grad):
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self.variance.gradient = grad
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def exact_inference_gradients(self, dL_dKdiag,Y_metadata=None):
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def ep_gradients(self, Y, cav_tau, cav_v, dL_dKdiag, Y_metadata=None, quad_mode='gk', boost_grad=1.):
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return self.exact_inference_gradients(dL_dKdiag)
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def exact_inference_gradients(self, dL_dKdiag, Y_metadata=None):
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return dL_dKdiag.sum()
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def _preprocess_values(self, Y):
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@ -6,8 +6,12 @@ from scipy import stats,special
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import scipy as sp
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from . import link_functions
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from ..util.misc import chain_1, chain_2, chain_3, blockify_dhess_dtheta, blockify_third, blockify_hessian, safe_exp
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from ..util.quad_integrate import quadgk_int
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from scipy.integrate import quad
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from functools import partial
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import warnings
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from ..core.parameterization import Parameterized
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class Likelihood(Parameterized):
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@ -223,6 +227,91 @@ class Likelihood(Parameterized):
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self.__gh_points = np.polynomial.hermite.hermgauss(T)
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return self.__gh_points
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def ep_gradients(self, Y, cav_tau, cav_v, dL_dKdiag, Y_metadata=None, quad_mode='gk', boost_grad=1.):
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if self.size > 0:
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shape = Y.shape
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tau,v,Y = cav_tau.flatten(), cav_v.flatten(),Y.flatten()
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mu = v/tau
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sigma2 = 1./tau
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# assert Y.shape == v.shape
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dlik_dtheta = np.empty((self.size, Y.shape[0]))
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# for j in range(self.size):
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Y_metadata_list = []
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for index in range(len(Y)):
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Y_metadata_i = {}
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if Y_metadata is not None:
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for key in Y_metadata.keys():
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Y_metadata_i[key] = Y_metadata[key][index,:]
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Y_metadata_list.append(Y_metadata_i)
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if quad_mode == 'gk':
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f = partial(self.integrate_gk)
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quads = zip(*map(f, Y.flatten(), mu.flatten(), np.sqrt(sigma2.flatten()), Y_metadata_list))
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quads = np.vstack(quads)
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quads.reshape(self.size, shape[0], shape[1])
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elif quad_mode == 'gh':
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f = partial(self.integrate_gh)
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quads = zip(*map(f, Y.flatten(), mu.flatten(), np.sqrt(sigma2.flatten())))
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quads = np.hstack(quads)
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quads = quads.T
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else:
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raise Exception("no other quadrature mode available")
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# do a gaussian-hermite integration
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dL_dtheta_avg = boost_grad * np.nanmean(quads, axis=1)
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dL_dtheta = boost_grad * np.nansum(quads, axis=1)
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# dL_dtheta = boost_grad * np.nansum(dlik_dtheta, axis=1)
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else:
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dL_dtheta = np.zeros(self.num_params)
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return dL_dtheta
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def integrate_gk(self, Y, mu, sigma, Y_metadata_i=None):
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# gaussian-kronrod integration.
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fmin = -np.inf
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fmax = np.inf
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SQRT_2PI = np.sqrt(2.*np.pi)
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def generate_integral(f):
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a = np.exp(self.logpdf_link(f, Y, Y_metadata_i)) * np.exp(-0.5 * np.square((f - mu) / sigma)) / (
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SQRT_2PI * sigma)
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fn1 = a * self.dlogpdf_dtheta(f, Y, Y_metadata_i)
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fn = fn1
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return fn
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dF_dtheta_i = quadgk_int(generate_integral, fmin=fmin, fmax=fmax)
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return dF_dtheta_i
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def integrate_gh(self, Y, mu, sigma, Y_metadata_i=None, gh_points=None):
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# gaussian-hermite quadrature.
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# "calculate site derivatives E_f{d logp(y_i|f_i)/da} where a is a likelihood parameter
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# and the expectation is over the exact marginal posterior, which is not gaussian- and is
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# unnormalised product of the cavity distribution(a Gaussian) and the exact likelihood term.
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#
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# calculate the expectation wrt the approximate marginal posterior, which should be approximately the same.
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# . This term is needed for evaluating the
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# gradients of the marginal likelihood estimate Z_EP wrt likelihood parameters."
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# "writing it explicitly "
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# use them for gaussian-hermite quadrature
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SQRT_2PI = np.sqrt(2.*np.pi)
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if gh_points is None:
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gh_x, gh_w = self._gh_points(32)
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else:
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gh_x, gh_w = gh_points
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X = gh_x[None,:]*np.sqrt(2.)*sigma + mu
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# Here X is a grid vector of possible fi values, while Y is just a single value which will be broadcasted.
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a = np.exp(self.logpdf_link(X, Y, Y_metadata_i))
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a = a.repeat(self.num_params,0)
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b = self.dlogpdf_dtheta(X, Y, Y_metadata_i)
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old_shape = b.shape
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fn = np.array([i*j for i,j in zip(a.flatten(), b.flatten())])
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fn = fn.reshape(old_shape)
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dF_dtheta_i = np.dot(fn, gh_w)/np.sqrt(np.pi)
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return dF_dtheta_i
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def variational_expectations(self, Y, m, v, gh_points=None, Y_metadata=None):
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"""
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Use Gauss-Hermite Quadrature to compute
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|
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@ -28,10 +28,10 @@ class TestObservationModels(unittest.TestCase):
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self.Y_noisy = self.Y.copy()
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self.Y_verynoisy = self.Y.copy()
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self.Y_noisy[75:80] += 1.3
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self.Y_noisy[75] += 1.3
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self.init_var = 0.3
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self.deg_free = 5.
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self.init_var = 0.15
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self.deg_free = 4.
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censored = np.zeros_like(self.Y)
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random_inds = np.random.choice(self.N, int(self.N / 2), replace=True)
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censored[random_inds] = 1
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@ -83,7 +83,7 @@ class TestObservationModels(unittest.TestCase):
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# taking laplace predictions as the ground truth
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probs_mean_lap, probs_var_lap = m1.predict(self.X)
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probs_mean_ep_alt, probs_var_ep_alt = m2.predict(self.X)
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probs_mean_ep_nested, probs_var_ep_nested = m2.predict(self.X)
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probs_mean_ep_nested, probs_var_ep_nested = m3.predict(self.X)
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# for simple single dimension data , marginal likelihood for laplace and EP approximations should not be so far apart.
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self.assertAlmostEqual(m1.log_likelihood(), m2.log_likelihood(),delta=1)
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@ -107,12 +107,12 @@ class TestObservationModels(unittest.TestCase):
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ep_inf_nested = GPy.inference.latent_function_inference.EP(ep_mode='nested')
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ep_inf_frac = GPy.inference.latent_function_inference.EP(ep_mode='nested', eta=0.7)
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m1 = GPy.core.GP(self.X, self.Y_noisy.copy(), kernel=self.kernel1, likelihood=studentT.copy(), inference_method=laplace_inf)
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m1 = GPy.core.GP(self.X.copy(), self.Y_noisy.copy(), kernel=self.kernel1.copy(), likelihood=studentT.copy(), inference_method=laplace_inf)
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# optimize
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m1['.*white'].constrain_fixed(1e-5)
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m1.randomize()
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m2 = GPy.core.GP(self.X, self.Y_noisy.copy(), kernel=self.kernel1, likelihood=studentT.copy(), inference_method=ep_inf_alt)
|
||||
m2 = GPy.core.GP(self.X.copy(), self.Y_noisy.copy(), kernel=self.kernel1.copy(), likelihood=studentT.copy(), inference_method=ep_inf_alt)
|
||||
m2['.*white'].constrain_fixed(1e-5)
|
||||
# m2.constrain_bounded('.*t_scale2', 0.001, 10)
|
||||
m2.randomize()
|
||||
|
|
@ -124,20 +124,22 @@ class TestObservationModels(unittest.TestCase):
|
|||
|
||||
optimizer='bfgs'
|
||||
m1.optimize(optimizer=optimizer,max_iters=400)
|
||||
m2.optimize(optimizer=optimizer, max_iters=500)
|
||||
m2.optimize(optimizer=optimizer, max_iters=400)
|
||||
# m3.optimize(optimizer=optimizer, max_iters=500)
|
||||
|
||||
self.assertAlmostEqual(m1.log_likelihood(), m2.log_likelihood(),delta=200)
|
||||
|
||||
self.assertAlmostEqual(m1.log_likelihood(), m2.log_likelihood(),delta=10)
|
||||
# self.assertAlmostEqual(m1.log_likelihood(), m3.log_likelihood(), 3)
|
||||
|
||||
preds_mean_lap, preds_var_lap = m1.predict(self.X)
|
||||
preds_mean_alt, preds_var_alt = m2.predict(self.X)
|
||||
# preds_mean_nested, preds_var_nested = m3.predict(self.X)
|
||||
rmse_lap = self.rmse(preds_mean_lap, self.Y_noisy)
|
||||
rmse_alt = self.rmse(preds_mean_alt, self.Y_noisy)
|
||||
rmse_lap = self.rmse(preds_mean_lap, self.Y)
|
||||
rmse_alt = self.rmse(preds_mean_alt, self.Y)
|
||||
# rmse_nested = self.rmse(preds_mean_nested, self.Y_noisy)
|
||||
|
||||
if rmse_alt > rmse_alt:
|
||||
self.assertAlmostEqual(rmse_lap, rmse_alt, delta=1.)
|
||||
if rmse_alt > rmse_lap:
|
||||
self.assertAlmostEqual(rmse_lap, rmse_alt, delta=1.5)
|
||||
# m3.optimize(optimizer=optimizer, max_iters=500)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -306,11 +306,7 @@ class StateSpaceKernelsTests(np.testing.TestCase):
|
|||
gp_kernel=gp_kernel,
|
||||
mean_compare_decimal=2, var_compare_decimal=2)
|
||||
|
||||
def test_forecast(self,):
|
||||
"""
|
||||
Test time-series forecasting.
|
||||
"""
|
||||
|
||||
def test_forecast_regular(self,):
|
||||
# Generate data ->
|
||||
np.random.seed(339) # seed the random number generator
|
||||
#import pdb; pdb.set_trace()
|
||||
|
|
@ -334,37 +330,102 @@ class StateSpaceKernelsTests(np.testing.TestCase):
|
|||
|
||||
#import pdb; pdb.set_trace()
|
||||
|
||||
def get_new_kernels():
|
||||
periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,])
|
||||
gp_kernel = GPy.kern.Linear(1, active_dims=[0,]) + GPy.kern.Bias(1, active_dims=[0,]) + periodic_kernel
|
||||
gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
|
||||
gp_kernel.std_periodic.period.constrain_bounded(0.15, 100)
|
||||
periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,])
|
||||
gp_kernel = GPy.kern.Linear(1, active_dims=[0,]) + GPy.kern.Bias(1, active_dims=[0,]) + periodic_kernel
|
||||
gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
|
||||
gp_kernel.std_periodic.period.constrain_bounded(0.15, 100)
|
||||
|
||||
periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
|
||||
ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \
|
||||
GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel
|
||||
periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
|
||||
ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \
|
||||
GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel
|
||||
|
||||
ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
|
||||
ss_kernel.std_periodic.period.constrain_bounded(0.15, 100)
|
||||
ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
|
||||
ss_kernel.std_periodic.period.constrain_bounded(0.15, 100)
|
||||
|
||||
return ss_kernel, gp_kernel
|
||||
|
||||
ss_kernel, gp_kernel = get_new_kernels()
|
||||
self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'regular',
|
||||
use_cython=False, optimize_max_iters=30, check_gradients=True,
|
||||
predict_X=X_test,
|
||||
gp_kernel=gp_kernel,
|
||||
mean_compare_decimal=2, var_compare_decimal=2)
|
||||
|
||||
def test_forecast_svd(self,):
|
||||
# Generate data ->
|
||||
np.random.seed(339) # seed the random number generator
|
||||
#import pdb; pdb.set_trace()
|
||||
(X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=5.0, noise_var=2.0,
|
||||
plot = False, points_num=100, x_interval = (0, 40), random=True)
|
||||
|
||||
(X1,Y1) = generate_linear_data(x_points=X, tangent=1.0, add_term=20.0, noise_var=0.0,
|
||||
plot = False, points_num=100, x_interval = (0, 40), random=True)
|
||||
|
||||
Y = Y + Y1
|
||||
|
||||
X_train = X[X <= 20]
|
||||
Y_train = Y[X <= 20]
|
||||
X_test = X[X > 20]
|
||||
Y_test = Y[X > 20]
|
||||
|
||||
X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
|
||||
X_train.shape = (X_train.shape[0],1); Y_train.shape = (Y_train.shape[0],1)
|
||||
X_test.shape = (X_test.shape[0],1); Y_test.shape = (Y_test.shape[0],1)
|
||||
# Generate data <-
|
||||
|
||||
#import pdb; pdb.set_trace()
|
||||
|
||||
periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,])
|
||||
gp_kernel = GPy.kern.Linear(1, active_dims=[0,]) + GPy.kern.Bias(1, active_dims=[0,]) + periodic_kernel
|
||||
gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
|
||||
gp_kernel.std_periodic.period.constrain_bounded(0.15, 100)
|
||||
|
||||
periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
|
||||
ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \
|
||||
GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel
|
||||
|
||||
ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
|
||||
ss_kernel.std_periodic.period.constrain_bounded(0.15, 100)
|
||||
|
||||
ss_kernel, gp_kernel = get_new_kernels()
|
||||
self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'svd',
|
||||
use_cython=False, optimize_max_iters=30, check_gradients=False,
|
||||
predict_X=X_test,
|
||||
gp_kernel=gp_kernel,
|
||||
mean_compare_decimal=2, var_compare_decimal=2)
|
||||
|
||||
ss_kernel, gp_kernel = get_new_kernels()
|
||||
def test_forecast_svd_cython(self,):
|
||||
# Generate data ->
|
||||
np.random.seed(339) # seed the random number generator
|
||||
#import pdb; pdb.set_trace()
|
||||
(X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=5.0, noise_var=2.0,
|
||||
plot = False, points_num=100, x_interval = (0, 40), random=True)
|
||||
|
||||
(X1,Y1) = generate_linear_data(x_points=X, tangent=1.0, add_term=20.0, noise_var=0.0,
|
||||
plot = False, points_num=100, x_interval = (0, 40), random=True)
|
||||
|
||||
Y = Y + Y1
|
||||
|
||||
X_train = X[X <= 20]
|
||||
Y_train = Y[X <= 20]
|
||||
X_test = X[X > 20]
|
||||
Y_test = Y[X > 20]
|
||||
|
||||
X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
|
||||
X_train.shape = (X_train.shape[0],1); Y_train.shape = (Y_train.shape[0],1)
|
||||
X_test.shape = (X_test.shape[0],1); Y_test.shape = (Y_test.shape[0],1)
|
||||
# Generate data <-
|
||||
|
||||
#import pdb; pdb.set_trace()
|
||||
|
||||
periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,])
|
||||
gp_kernel = GPy.kern.Linear(1, active_dims=[0,]) + GPy.kern.Bias(1, active_dims=[0,]) + periodic_kernel
|
||||
gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
|
||||
gp_kernel.std_periodic.period.constrain_bounded(0.15, 100)
|
||||
|
||||
periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
|
||||
ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \
|
||||
GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel
|
||||
|
||||
ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
|
||||
ss_kernel.std_periodic.period.constrain_bounded(0.15, 100)
|
||||
|
||||
self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'svd',
|
||||
use_cython=True, optimize_max_iters=30, check_gradients=False,
|
||||
predict_X=X_test,
|
||||
|
|
|
|||
|
|
@ -64,13 +64,13 @@ class InferenceGPEP(unittest.TestCase):
|
|||
def genNoisyData(self):
|
||||
np.random.seed(1)
|
||||
X = np.random.rand(100,1)
|
||||
self.real_std = 0.2
|
||||
self.real_std = 0.1
|
||||
noise = np.random.randn(*X[:, 0].shape)*self.real_std
|
||||
Y = (np.sin(X[:, 0]*2*np.pi) + noise)[:, None]
|
||||
self.f = np.random.rand(X.shape[0],1)
|
||||
Y_extra_noisy = Y.copy()
|
||||
Y_extra_noisy[50:53] += 4.
|
||||
Y_extra_noisy[80:83] -= 2.
|
||||
Y_extra_noisy[50] += 4.
|
||||
# Y_extra_noisy[80:83] -= 2.
|
||||
return X, Y, Y_extra_noisy
|
||||
|
||||
def test_inference_EP(self):
|
||||
|
|
@ -85,10 +85,11 @@ class InferenceGPEP(unittest.TestCase):
|
|||
inference_method=inf,
|
||||
likelihood=lik)
|
||||
K = self.model.kern.K(X)
|
||||
post_params, ga_approx, log_Z_tilde = self.model.inference_method.expectation_propagation(K, ObsAr(Y), lik, None)
|
||||
|
||||
post_params, ga_approx, cav_params, log_Z_tilde = self.model.inference_method.expectation_propagation(K, ObsAr(Y), lik, None)
|
||||
|
||||
mu_tilde = ga_approx.v / ga_approx.tau.astype(float)
|
||||
p, m, d = self.model.inference_method._inference(K, ga_approx, lik, Y_metadata=None, Z_tilde=log_Z_tilde)
|
||||
p, m, d = self.model.inference_method._inference(Y, K, ga_approx, cav_params, lik, Y_metadata=None, Z_tilde=log_Z_tilde)
|
||||
p0, m0, d0 = super(GPy.inference.latent_function_inference.expectation_propagation.EP, inf).inference(k, X,lik ,mu_tilde[:,None], mean_function=None, variance=1./ga_approx.tau, K=K, Z_tilde=log_Z_tilde + np.sum(- 0.5*np.log(ga_approx.tau) + 0.5*(ga_approx.v*ga_approx.v*1./ga_approx.tau)))
|
||||
|
||||
assert (np.sum(np.array([m - m0,
|
||||
|
|
@ -109,19 +110,19 @@ class InferenceGPEP(unittest.TestCase):
|
|||
def test_inference_EP_non_classification(self):
|
||||
from paramz import ObsAr
|
||||
X, Y, Y_extra_noisy = self.genNoisyData()
|
||||
deg_freedom = 5
|
||||
init_noise_var = 0.4
|
||||
deg_freedom = 5.
|
||||
init_noise_var = 0.08
|
||||
lik_studentT = GPy.likelihoods.StudentT(deg_free=deg_freedom, sigma2=init_noise_var)
|
||||
# like_gaussian_noise = GPy.likelihoods.MixedNoise()
|
||||
k = GPy.kern.RBF(1, variance=2., lengthscale=1.1)
|
||||
ep_inf_alt = GPy.inference.latent_function_inference.expectation_propagation.EP(max_iters=100, delta=0.5)
|
||||
ep_inf_nested = GPy.inference.latent_function_inference.expectation_propagation.EP(ep_mode='nested', max_iters=100, delta=0.5)
|
||||
ep_inf_alt = GPy.inference.latent_function_inference.expectation_propagation.EP(max_iters=4, delta=0.5)
|
||||
# ep_inf_nested = GPy.inference.latent_function_inference.expectation_propagation.EP(ep_mode='nested', max_iters=100, delta=0.5)
|
||||
m = GPy.core.GP(X=X,Y=Y_extra_noisy,kernel=k,likelihood=lik_studentT,inference_method=ep_inf_alt)
|
||||
K = m.kern.K(X)
|
||||
post_params, ga_approx, log_Z_tilde = m.inference_method.expectation_propagation(K, ObsAr(Y_extra_noisy), lik_studentT, None)
|
||||
post_params, ga_approx, cav_params, log_Z_tilde = m.inference_method.expectation_propagation(K, ObsAr(Y_extra_noisy), lik_studentT, None)
|
||||
|
||||
mu_tilde = ga_approx.v / ga_approx.tau.astype(float)
|
||||
p, m, d = m.inference_method._inference(K, ga_approx, lik_studentT, Y_metadata=None, Z_tilde=log_Z_tilde)
|
||||
p, m, d = m.inference_method._inference(Y_extra_noisy, K, ga_approx, cav_params, lik_studentT, Y_metadata=None, Z_tilde=log_Z_tilde)
|
||||
p0, m0, d0 = super(GPy.inference.latent_function_inference.expectation_propagation.EP, ep_inf_alt).inference(k, X,lik_studentT ,mu_tilde[:,None], mean_function=None, variance=1./ga_approx.tau, K=K, Z_tilde=log_Z_tilde + np.sum(- 0.5*np.log(ga_approx.tau) + 0.5*(ga_approx.v*ga_approx.v*1./ga_approx.tau)))
|
||||
|
||||
assert (np.sum(np.array([m - m0,
|
||||
|
|
|
|||
39
GPy/testing/quadrature_tests.py
Normal file
39
GPy/testing/quadrature_tests.py
Normal file
|
|
@ -0,0 +1,39 @@
|
|||
from __future__ import print_function, division
|
||||
import numpy as np
|
||||
import GPy
|
||||
import warnings
|
||||
from ..util.quad_integrate import quadgk_int, quadvgk
|
||||
|
||||
|
||||
|
||||
class QuadTests(np.testing.TestCase):
|
||||
"""
|
||||
test file for checking implementation of gaussian-kronrod quadrature.
|
||||
we will take a function which can be integrated analytically and check if quadgk result is similar or not!
|
||||
through this file we can test how numerically accurate quadrature implementation in native numpy or manual code is.
|
||||
"""
|
||||
def setUp(self):
|
||||
pass
|
||||
|
||||
def test_infinite_quad(self):
|
||||
def f(x):
|
||||
return np.exp(-0.5*x**2)*np.power(x,np.arange(3)[:,None])
|
||||
quad_int_val = quadgk_int(f)
|
||||
real_val = np.sqrt(np.pi * 2)
|
||||
np.testing.assert_almost_equal(real_val, quad_int_val[0], decimal=7)
|
||||
|
||||
def test_finite_quad(self):
|
||||
def f2(x):
|
||||
return x**2
|
||||
quad_int_val = quadvgk(f2, 1.,2.)
|
||||
real_val = 7/3.
|
||||
np.testing.assert_almost_equal(real_val, quad_int_val, decimal=5)
|
||||
|
||||
if __name__ == '__main__':
|
||||
def f(x):
|
||||
return np.exp(-0.5 * x ** 2) * np.power(x, np.arange(3)[:, None])
|
||||
|
||||
quad_int_val = quadgk_int(f)
|
||||
real_val = np.sqrt(np.pi*2)
|
||||
np.testing.assert_almost_equal(real_val, quad_int_val[0], decimal=7)
|
||||
print(quadgk_int(f))
|
||||
|
|
@ -17,3 +17,4 @@ from . import multioutput
|
|||
from . import parallel
|
||||
from . import functions
|
||||
from . import cluster_with_offset
|
||||
from . import quad_integrate
|
||||
|
|
|
|||
|
|
@ -206,7 +206,10 @@ def authorize_download(dataset_name=None):
|
|||
|
||||
def download_data(dataset_name=None):
|
||||
"""Check with the user that the are happy with terms and conditions for the data set, then download it."""
|
||||
import itertools
|
||||
try:
|
||||
from itertools import zip_longest
|
||||
except ImportError:
|
||||
from itertools import izip_longest as zip_longest
|
||||
|
||||
dr = data_resources[dataset_name]
|
||||
if not authorize_download(dataset_name):
|
||||
|
|
@ -220,8 +223,8 @@ def download_data(dataset_name=None):
|
|||
if 'suffices' in dr: zip_urls += (dr['suffices'], )
|
||||
else: zip_urls += ([],)
|
||||
|
||||
for url, files, save_names, suffices in itertools.zip_longest(*zip_urls, fillvalue=[]):
|
||||
for f, save_name, suffix in itertools.zip_longest(files, save_names, suffices, fillvalue=None):
|
||||
for url, files, save_names, suffices in zip_longest(*zip_urls, fillvalue=[]):
|
||||
for f, save_name, suffix in zip_longest(files, save_names, suffices, fillvalue=None):
|
||||
download_url(os.path.join(url,f), dataset_name, save_name, suffix=suffix)
|
||||
|
||||
return True
|
||||
|
|
|
|||
119
GPy/util/quad_integrate.py
Normal file
119
GPy/util/quad_integrate.py
Normal file
|
|
@ -0,0 +1,119 @@
|
|||
"""
|
||||
The file for utilities related to integration by quadrature methods
|
||||
- will contain implementation for gaussian-kronrod integration.
|
||||
|
||||
"""
|
||||
import numpy as np
|
||||
|
||||
def getSubs(Subs, XK, NK=1):
|
||||
M = (Subs[1, :] - Subs[0, :]) / 2
|
||||
C = (Subs[1, :] + Subs[0, :]) / 2
|
||||
I = XK[:, None] * M + np.ones((NK, 1)) * C
|
||||
# A = [Subs(1,:); I]
|
||||
A = np.vstack((Subs[0, :], I))
|
||||
# B = [I;Subs(2,:)]
|
||||
B = np.vstack((I, Subs[1, :]))
|
||||
# Subs = [reshape(A, 1, []);
|
||||
A = A.flatten()
|
||||
# reshape(B, 1, [])];
|
||||
B = B.flatten()
|
||||
Subs = np.vstack((A,B))
|
||||
# Subs = np.concatenate((A, B), axis=0)
|
||||
return Subs
|
||||
|
||||
def quadvgk(feval, fmin, fmax, tol1=1e-5, tol2=1e-5):
|
||||
"""
|
||||
numpy implementation makes use of the code here: http://se.mathworks.com/matlabcentral/fileexchange/18801-quadvgk
|
||||
We here use gaussian kronrod integration already used in gpstuff for evaluating one dimensional integrals.
|
||||
This is vectorised quadrature which means that several functions can be evaluated at the same time over a grid of
|
||||
points.
|
||||
:param f:
|
||||
:param fmin:
|
||||
:param fmax:
|
||||
:param difftol:
|
||||
:return:
|
||||
"""
|
||||
|
||||
XK = np.array([-0.991455371120813, -0.949107912342759, -0.864864423359769, -0.741531185599394,
|
||||
-0.586087235467691, -0.405845151377397, -0.207784955007898, 0.,
|
||||
0.207784955007898, 0.405845151377397, 0.586087235467691,
|
||||
0.741531185599394, 0.864864423359769, 0.949107912342759, 0.991455371120813])
|
||||
WK = np.array([0.022935322010529, 0.063092092629979, 0.104790010322250, 0.140653259715525,
|
||||
0.169004726639267, 0.190350578064785, 0.204432940075298, 0.209482141084728,
|
||||
0.204432940075298, 0.190350578064785, 0.169004726639267,
|
||||
0.140653259715525, 0.104790010322250, 0.063092092629979, 0.022935322010529])
|
||||
# 7-point Gaussian weightings
|
||||
WG = np.array([0.129484966168870, 0.279705391489277, 0.381830050505119, 0.417959183673469,
|
||||
0.381830050505119, 0.279705391489277, 0.129484966168870])
|
||||
|
||||
NK = WK.size
|
||||
G = np.arange(2,NK,2)
|
||||
tol1 = 1e-4
|
||||
tol2 = 1e-4
|
||||
Subs = np.array([[fmin],[fmax]])
|
||||
# number of functions to evaluate in the feval vector of functions.
|
||||
NF = feval(np.zeros(1)).size
|
||||
Q = np.zeros(NF)
|
||||
neval = 0
|
||||
while Subs.size > 0:
|
||||
Subs = getSubs(Subs,XK)
|
||||
M = (Subs[1,:] - Subs[0,:]) / 2
|
||||
C = (Subs[1,:] + Subs[0,:]) / 2
|
||||
# NM = length(M);
|
||||
NM = M.size
|
||||
# x = reshape(XK * M + ones(NK, 1) * C, 1, []);
|
||||
x = XK[:,None]*M + C
|
||||
x = x.flatten()
|
||||
FV = feval(x)
|
||||
# FV = FV[:,None]
|
||||
Q1 = np.zeros((NF, NM))
|
||||
Q2 = np.zeros((NF, NM))
|
||||
|
||||
# for n=1:NF
|
||||
# F = reshape(FV(n,:), NK, []);
|
||||
# Q1(n,:) = M. * sum((WK * ones(1, NM)). * F);
|
||||
# Q2(n,:) = M. * sum((WG * ones(1, NM)). * F(G,:));
|
||||
# end
|
||||
# for i in range(NF):
|
||||
# F = FV
|
||||
# F = F.reshape((NK,-1))
|
||||
# temp_mat = np.sum(np.multiply(WK[:,None]*np.ones((1,NM)), F),axis=0)
|
||||
# Q1[i,:] = np.multiply(M, temp_mat)
|
||||
# temp_mat = np.sum(np.multiply(WG[:,None]*np.ones((1, NM)), F[G-1,:]), axis=0)
|
||||
# Q2[i,:] = np.multiply(M, temp_mat)
|
||||
# ind = np.where(np.logical_or(np.max(np.abs(Q1 -Q2) / Q1) < tol1, (Subs[1,:] - Subs[0,:]) <= tol2) > 0)[0]
|
||||
# Q = Q + np.sum(Q1[:,ind], axis=1)
|
||||
# np.delete(Subs, ind,axis=1)
|
||||
|
||||
Q1 = np.dot(FV.reshape(NF, NK, NM).swapaxes(2,1),WK)*M
|
||||
Q2 = np.dot(FV.reshape(NF, NK, NM).swapaxes(2,1)[:,:,1::2],WG)*M
|
||||
#ind = np.nonzero(np.logical_or(np.max(np.abs((Q1-Q2)/Q1), 0) < difftol , M < xtol))[0]
|
||||
ind = np.nonzero(np.logical_or(np.max(np.abs((Q1-Q2)), 0) < tol1 , (Subs[1,:] - Subs[0,:]) < tol2))[0]
|
||||
Q = Q + np.sum(Q1[:,ind], axis=1)
|
||||
Subs = np.delete(Subs, ind, axis=1)
|
||||
return Q
|
||||
|
||||
def quadgk_int(f, fmin=-np.inf, fmax=np.inf, difftol=0.1):
|
||||
"""
|
||||
Integrate f from fmin to fmax,
|
||||
do integration by substitution
|
||||
x = r / (1-r**2)
|
||||
when r goes from -1 to 1 , x goes from -inf to inf.
|
||||
the interval for quadgk function is from -1 to +1, so we transform the space from (-inf,inf) to (-1,1)
|
||||
:param f:
|
||||
:param fmin:
|
||||
:param fmax:
|
||||
:param difftol:
|
||||
:return:
|
||||
"""
|
||||
difftol = 1e-4
|
||||
def trans_func(r):
|
||||
r2 = np.square(r)
|
||||
x = r / (1-r2)
|
||||
dx_dr = (1 + r2)/(1-r2)**2
|
||||
return f(x)*dx_dr
|
||||
|
||||
integrand = quadvgk(trans_func, -1., 1., difftol, difftol)
|
||||
return integrand
|
||||
|
||||
|
||||
|
|
@ -16,6 +16,9 @@ recursive-include GPy *.c
|
|||
recursive-include GPy *.h
|
||||
recursive-include GPy *.pyx
|
||||
|
||||
# LICENSE
|
||||
include LICENSE.txt
|
||||
|
||||
# Testing
|
||||
#include GPy/testing/baseline/*.png
|
||||
#include GPy/testing/pickle_test.pickle
|
||||
|
|
|
|||
|
|
@ -76,7 +76,7 @@ If that is the case, it is best to clean the repo and reinstall.
|
|||
[<img src="https://upload.wikimedia.org/wikipedia/commons/8/8e/OS_X-Logo.svg" height=40px>](http://www.apple.com/osx/)
|
||||
[<img src="https://upload.wikimedia.org/wikipedia/commons/3/35/Tux.svg" height=40px>](https://en.wikipedia.org/wiki/List_of_Linux_distributions)
|
||||
|
||||
Python 2.7, 3.4 and higher
|
||||
Python 2.7, 3.5 and higher
|
||||
|
||||
## Citation
|
||||
|
||||
|
|
|
|||
22
appveyor.yml
22
appveyor.yml
|
|
@ -3,12 +3,14 @@ environment:
|
|||
secure: 8/ZjXFwtd1S7ixd7PJOpptupKKEDhm2da/q3unabJ00=
|
||||
COVERALLS_REPO_TOKEN:
|
||||
secure: d3Luic/ESkGaWnZrvWZTKrzO+xaVwJWaRCEP0F+K/9DQGPSRZsJ/Du5g3s4XF+tS
|
||||
gpy_version: 1.6.2
|
||||
gpy_version: 1.7.7
|
||||
matrix:
|
||||
- PYTHON_VERSION: 2.7
|
||||
MINICONDA: C:\Miniconda-x64
|
||||
- PYTHON_VERSION: 3.5
|
||||
MINICONDA: C:\Miniconda35-x64
|
||||
- PYTHON_VERSION: 3.6
|
||||
MINICONDA: C:\Miniconda36-x64
|
||||
|
||||
#configuration:
|
||||
# - Debug
|
||||
|
|
@ -62,21 +64,21 @@ deploy_script:
|
|||
- echo test >> %USERPROFILE%\\.pypirc
|
||||
- echo[
|
||||
- echo [pypi] >> %USERPROFILE%\\.pypirc
|
||||
- echo username:maxz >> %USERPROFILE%\\.pypirc
|
||||
- echo password:%pip_access% >> %USERPROFILE%\\.pypirc
|
||||
- echo username = maxz >> %USERPROFILE%\\.pypirc
|
||||
- echo password = %pip_access% >> %USERPROFILE%\\.pypirc
|
||||
- echo[
|
||||
- echo [test] >> %USERPROFILE%\\.pypirc
|
||||
- echo repository:https://test.pypi.org/legacy/ >> %USERPROFILE%\\.pypirc
|
||||
- echo username:maxz >> %USERPROFILE%\\.pypirc
|
||||
- echo password:%pip_access% >> %USERPROFILE%\\.pypirc
|
||||
- echo repository = https://testpypi.python.org/pypi >> %USERPROFILE%\\.pypirc
|
||||
- echo username = maxz >> %USERPROFILE%\\.pypirc
|
||||
- echo password = %pip_access% >> %USERPROFILE%\\.pypirc
|
||||
- ps: >-
|
||||
if ($env:APPVEYOR_REPO_BRANCH -eq 'devel') {
|
||||
twine upload --skip-existing -r test dist/*
|
||||
If ($env:APPVEYOR_REPO_BRANCH -eq 'devel') {
|
||||
echo not deploying on devel # twine upload --skip-existing -r test dist/*
|
||||
}
|
||||
elseif ($env:APPVEYOR_REPO_BRANCH -eq 'deploy') {
|
||||
ElseIf ($env:APPVEYOR_REPO_BRANCH -eq 'deploy') {
|
||||
twine upload --skip-existing dist/*
|
||||
}
|
||||
else {
|
||||
Else {
|
||||
echo not deploying on other branches
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
[bumpversion]
|
||||
current_version = 1.6.2
|
||||
current_version = 1.7.7
|
||||
tag = True
|
||||
commit = True
|
||||
|
||||
|
|
|
|||
4
setup.py
4
setup.py
|
|
@ -150,7 +150,7 @@ setup(name = 'GPy',
|
|||
py_modules = ['GPy.__init__'],
|
||||
test_suite = 'GPy.testing',
|
||||
setup_requires = ['numpy>=1.7'],
|
||||
install_requires = ['numpy>=1.7', 'scipy>=0.16', 'six', 'paramz>=0.6.9'],
|
||||
install_requires = ['numpy>=1.7', 'scipy>=0.16', 'six', 'paramz>=0.7.4'],
|
||||
extras_require = {'docs':['sphinx'],
|
||||
'optional':['mpi4py',
|
||||
'ipython>=4.0.0',
|
||||
|
|
@ -169,8 +169,8 @@ setup(name = 'GPy',
|
|||
'Operating System :: Microsoft :: Windows',
|
||||
'Operating System :: POSIX :: Linux',
|
||||
'Programming Language :: Python :: 2.7',
|
||||
'Programming Language :: Python :: 3.3',
|
||||
'Programming Language :: Python :: 3.5',
|
||||
'Programming Language :: Python :: 3.6',
|
||||
'Framework :: IPython',
|
||||
'Intended Audience :: Science/Research',
|
||||
'Intended Audience :: Developers',
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue