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finish the debug of sparsegp_mpi

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This commit is contained in:
Zhenwen Dai 2014-09-12 10:36:01 +01:00
parent 31f11eda40
commit d7eee6aa00
2 changed files with 10 additions and 20 deletions
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15
GPy/core/sparse_gp_mpi.py
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@ -3,6 +3,7 @@
import numpy as np import numpy as np
from sparse_gp import SparseGP from sparse_gp import SparseGP
from numpy.linalg.linalg import LinAlgError
from ..inference.latent_function_inference.var_dtc_parallel import update_gradients, VarDTC_minibatch from ..inference.latent_function_inference.var_dtc_parallel import update_gradients, VarDTC_minibatch
import logging import logging
@ -83,11 +84,7 @@ class SparseGP_MPI(SparseGP):
if self.mpi_comm != None: if self.mpi_comm != None:
if self._IN_OPTIMIZATION_ and self.mpi_comm.rank==0: if self._IN_OPTIMIZATION_ and self.mpi_comm.rank==0:
self.mpi_comm.Bcast(np.int32(1),root=0) self.mpi_comm.Bcast(np.int32(1),root=0)
self.mpi_comm.Bcast(p, root=0) self.mpi_comm.Bcast(p, root=0)
from ..util.debug import checkFinite
checkFinite(p, 'optimizer_array')
SparseGP.optimizer_array.fset(self,p) SparseGP.optimizer_array.fset(self,p)
def optimize(self, optimizer=None, start=None, **kwargs): def optimize(self, optimizer=None, start=None, **kwargs):
@ -103,7 +100,13 @@ class SparseGP_MPI(SparseGP):
while True: while True:
self.mpi_comm.Bcast(flag,root=0) self.mpi_comm.Bcast(flag,root=0)
if flag==1: if flag==1:
self.optimizer_array = x try:
self.optimizer_array = x
self._fail_count = 0
except (LinAlgError, ZeroDivisionError, ValueError):
if self._fail_count >= self._allowed_failures:
raise
self._fail_count += 1
elif flag==-1: elif flag==-1:
break break
else: else:

15
GPy/inference/latent_function_inference/var_dtc_parallel.py
View file

@ -167,18 +167,12 @@ class VarDTC_minibatch(LatentFunctionInference):
# Compute Common Components # Compute Common Components
#====================================================================== #======================================================================
from ...util.debug import checkFullRank
Kmm = kern.K(Z).copy() Kmm = kern.K(Z).copy()
diag.add(Kmm, self.const_jitter) diag.add(Kmm, self.const_jitter)
r1 = checkFullRank(Kmm,name='Kmm')
KmmInv,Lm,LmInv,_ = pdinv(Kmm) KmmInv,Lm,LmInv,_ = pdinv(Kmm)
LmInvPsi2LmInvT = LmInv.dot(psi2_full).dot(LmInv.T) LmInvPsi2LmInvT = LmInv.dot(psi2_full).dot(LmInv.T)
Lambda = np.eye(Kmm.shape[0])+LmInvPsi2LmInvT Lambda = np.eye(Kmm.shape[0])+LmInvPsi2LmInvT
r2 = checkFullRank(Lambda,name='Lambda')
# if (not r1) or (not r2):
# raise
LInv,LL,LLInv,logdet_L = pdinv(Lambda) LInv,LL,LLInv,logdet_L = pdinv(Lambda)
b = LLInv.dot(LmInv.dot(psi1Y_full.T)) b = LLInv.dot(LmInv.dot(psi1Y_full.T))
bbt = np.square(b).sum() bbt = np.square(b).sum()
@ -203,7 +197,6 @@ class VarDTC_minibatch(LatentFunctionInference):
# Compute dL_dKmm # Compute dL_dKmm
#====================================================================== #======================================================================
# dL_dKmm = -(output_dim*np.einsum('md,od->mo',KmmInvPsi2LLInvT,KmmInvPsi2LLInvT) + vvt)/2.
dL_dKmm = dL_dpsi2R - output_dim*KmmInv.dot(psi2_full).dot(KmmInv)/2. dL_dKmm = dL_dpsi2R - output_dim*KmmInv.dot(psi2_full).dot(KmmInv)/2.
#====================================================================== #======================================================================
@ -336,13 +329,7 @@ def update_gradients(model, mpi_comm=None):
Y = model.Y_local Y = model.Y_local
X = model.X[model.N_range[0]:model.N_range[1]] X = model.X[model.N_range[0]:model.N_range[1]]
try: model._log_marginal_likelihood, dL_dKmm, model.posterior = model.inference_method.inference_likelihood(model.kern, X, model.Z, model.likelihood, Y)
model._log_marginal_likelihood, dL_dKmm, model.posterior = model.inference_method.inference_likelihood(model.kern, X, model.Z, model.likelihood, Y)
except Exception:
if model.mpi_comm is None or model.mpi_comm.rank==0:
import time
model.pickle('model_'+str(int(time.time()))+'.pickle')
raise
het_noise = model.likelihood.variance.size > 1 het_noise = model.likelihood.variance.size > 1