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catch linalg errors inside model and more sopihisticated non-pd checks

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Max Zwiessele 2013-06-07 13:34:45 +01:00
parent 695fad4ed8
commit 924a319b98
2 changed files with 22 additions and 7 deletions
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21
GPy/core/model.py
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@ -10,6 +10,7 @@ from parameterised import Parameterised
import multiprocessing as mp import multiprocessing as mp
import numpy as np import numpy as np
from GPy.core.domains import POSITIVE, REAL from GPy.core.domains import POSITIVE, REAL
from numpy.linalg.linalg import LinAlgError
# import numdifftools as ndt # import numdifftools as ndt
class Model(Parameterised): class Model(Parameterised):
@ -227,20 +228,32 @@ class Model(Parameterised):
""" """
The objective function passed to the optimizer. It combines the likelihood and the priors. The objective function passed to the optimizer. It combines the likelihood and the priors.
""" """
self._set_params_transformed(x) try:
self._set_params_transformed(x)
except (LinAlgError, ZeroDivisionError) as e:
print "DEBUG: catching linalg, rejecting step" # TODO: delete
return np.inf
return -self.log_likelihood() - self.log_prior() return -self.log_likelihood() - self.log_prior()
def objective_function_gradients(self, x): def objective_function_gradients(self, x):
""" """
Gets the gradients from the likelihood and the priors. Gets the gradients from the likelihood and the priors.
""" """
self._set_params_transformed(x) try:
self._set_params_transformed(x)
except (LinAlgError, ZeroDivisionError) as e:
print "DEBUG: catching linalg, rejecting step" # TODO: delete
# return np.ones_like(x)
obj_grads = -self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients()) obj_grads = -self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients())
return obj_grads return obj_grads
def objective_and_gradients(self, x): def objective_and_gradients(self, x):
self._set_params_transformed(x) try:
obj_f = -self.log_likelihood() - self.log_prior() self._set_params_transformed(x)
obj_f = -self.log_likelihood() - self.log_prior()
except (LinAlgError, ZeroDivisionError) as e:
print "DEBUG: catching linalg, rejecting step" # TODO: delete
obj_f = np.inf
obj_grads = -self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients()) obj_grads = -self._transform_gradients(self._log_likelihood_gradients() + self._log_prior_gradients())
return obj_f, obj_grads return obj_f, obj_grads

8
GPy/util/linalg.py
View file

@ -96,15 +96,17 @@ def jitchol(A, maxtries=5):
return L return L
else: else:
diagA = np.diag(A) diagA = np.diag(A)
if np.any(diagA < 0.): if np.any(diagA <= 0.):
raise linalg.LinAlgError, "not pd: negative diagonal elements" raise linalg.LinAlgError, "not pd: non-positive diagonal elements"
jitter = diagA.mean() * 1e-6 jitter = diagA.mean() * 1e-6
for i in range(1, maxtries + 1): while maxtries > 0 and np.isfinite(jitter):
print 'Warning: adding jitter of {:.10e}'.format(jitter) print 'Warning: adding jitter of {:.10e}'.format(jitter)
try: try:
return linalg.cholesky(A + np.eye(A.shape[0]).T * jitter, lower=True) return linalg.cholesky(A + np.eye(A.shape[0]).T * jitter, lower=True)
except: except:
jitter *= 10 jitter *= 10
finally:
maxtries -= 1
raise linalg.LinAlgError, "not positive definite, even with jitter." raise linalg.LinAlgError, "not positive definite, even with jitter."