def runInference(args):
pid = os.getpid()
#Unpack args
matf,gfile,alg_params,spanning_tree_params = args
print pid,"| Running on ",matf, " graphFile: ",gfile
#Setup objective function
alg_params['objective_fxn'] = obj_fxn.TRW
N_n,Nodes_n,Edges_n,Cardinality_n,mode_n = util.loadUAIfromMAT(matf)
start_time = time.time()
G = util.Graph(N=N_n,graphFile = gfile, type=alg_params['graphType'],
Edges=Edges_n,Nodes=Nodes_n,Cardinality=Cardinality_n,
mode='UAI',weighted=True)
spanning_tree_params['alg_params']=alg_params
inf.optSpanningTree(G,spanning_tree_params)
print pid,"| Completed ",matf," time elapsed: "
time_taken = time.time()-start_time
return (pid,time_taken,os.path.basename(matf).replace('.mat',''))
def runPerturbAndMAP(folder,numSamples):
#raw_input("WARNING: PerturbAndMAP ONLY TESTED FOR BINARY RANDOM VARIABLES. PRESS A KEY TO CONTINUE")
print "Running perturbMAP"
inputfolder = folder + '/uai_mat'
matfolder = folder+'/mat_out'
print inputfolder,matfolder
if not os.path.exists(inputfolder):
assert "Input folder not found. Investigate"
if not os.path.exists(matfolder):
os.mkdir(matfolder)
for root, dirs, files in os.walk(inputfolder): # Walk directory tree
for f in files:
if f.endswith("mat"):
print "Processing : ",f
basename = re.split('.uai',f)[0]
inputfilename = root+'/'+f
N,Nodes,Edges,Cardinality,mode = util.loadUAIfromMAT(inputfilename)
G = util.Graph(N=N,Edges=Edges,Nodes=Nodes,Cardinality=Cardinality,
mode=mode,weighted=True,type='undir')
start = timeit.default_timer()
logZ,mus,time_marg,time_logz = inferenceAlg.doInferencePerturbMAP(G,numSamples)
stop = timeit.default_timer()
print stop-start," seconds taken"
print "Saving marginals: ",mus
assert mus.shape[0]==np.sum(G.Cardinality),"Marginal vector not as expected"
matfile= matfolder+'/'+basename
if os.path.exists(matfile):
mat = loadmat(matfile)
print "Appending ",matfile
else:
mat = {}
print "Creating new mat file, ",matfile
mat['perturbMAP_node_marginals_ub'] = np.reshape(mus,(len(mus),1))
mat['perturbMAP_log_partition_ub'] = logZ
mat['perturbMAP_runtime_ub_logz'] = time_logz
mat['perturbMAP_runtime_ub_marg'] = time_marg
mat['perturbMAP_runtime_ub_total'] = stop-start
savemat(matfile, mat)
if not quiet:
print "Step 2: MAP inference with toulbar2"
marginal_polytope_vertex,toulbar2Output,MAPsoln = MAPsolver.runMAP(np.array(theta_expanded),
inflated_nVertices,inflated_List,inflated_Cardinality,
'undir',uai_fname = './temp.uai',timer=-1)
#Delete all temporary files
print "Deleting: ",
for f in glob.glob("temp*"):
print f,
os.unlink(f)
#Step 3] Compute the energy of the MAP solution as the estimate of logZ
if not quiet:
print "Step 3: Energy of MAP solution is estimate of logZ"
energyEstimate = np.dot(marginal_polytope_vertex,theta_expanded)
if not quiet:
print "Computing energy estimate"
return energyEstimate
if __name__ == '__main__':
print "10 Node Complete"
inputfilename = '/data/ml2/rahul/FrankWolfeMarginalInf/Synthetic/uai_mat/810.mat'
N,Nodes,Edges,Cardinality,mode = util.loadUAIfromMAT(inputfilename)
G = util.Graph(N=N,Edges=Edges,Nodes=Nodes,Cardinality=Cardinality,mode=mode,weighted=True,type='undir')
print doInferencePerturbMAP(G,numSamples = 10)
print ubLogZ(20,G,quiet = True)
# print "Debug 3 node"
# inputfilename = '/data/ml2/rahul/FrankWolfeMarginalInf/Debug/uai_mat/grid3x3.mat'
# N,Nodes,Edges,Cardinality,mode = util.loadUAIfromMAT(inputfilename)
# G = util.Graph(N=N,Edges=Edges,Nodes=Nodes,Cardinality=Cardinality,mode=mode,weighted=True,type='undir')
# print doInferencePerturbMAP(G,10)
