def runMarginalFW(G,alg_params,model=None,V = [],alpha = [],stepSizeComputation = False,rho_vec =-1):
quiet = alg_params['quiet']
if stepSizeComputation:
quiet = True
assert type(rho_vec)!=int,'Requires rho_vec to be a vector'
G.rhos_edge = rho_vec
G.computeNodeRhos()
######### Setup parameters $$$$$$$$$$
stdout_initial = -1
dev_null_f = open(os.devnull,'w')
if quiet:
stdout_initial = sys.stdout
sys.stdout = dev_null_f
logf,matf,fxn_params,f_obj = setupParams(G,alg_params)
#print "\t----- Starting Marginal Inference with FW ------ \t"
#Initial settings
mus = G.init_vec
MAX_STEPS = alg_params['max_steps']
smallest_marginal = np.Inf
if len(alpha)==0:
V.append(csr_matrix(mus))
alpha.append(1)
#Track statistics
statistics= setupStatistics(MAX_STEPS,mus.shape,G.nVertices)
statistics['alpha'] = alpha
#Extract gurobi model
if not alg_params['useMAP'] and model is None:
model = ILPsolver.defineModel(G,alg_params)
if alg_params['useMAP'] and alg_params['MAPsolver']!='toulbar2':
len_gap = 5
else:
len_gap = 1
objWarning =0
gap_l = [10]*len_gap
if alg_params['M_truncated_dynamic'] or alg_params['M_truncated']:
#Additional stats to track
#Updated at every iteration
statistics['gap_FW'] = []
statistics['gap_full'] = []
#Updated when epsilon modified
statistics['eps_val'] = []
statistics['primal_push'] = []
statistics['iterate_push'] = []
statistics['marker'] = []
statistics['eps_val'] =[alg_params['M_eps']]
#print "------------- M_eps with eps = ",alg_params['M_eps'],' -----------------'
if alg_params['PreCorrection']:
alg_params['correction_tol'] = 0.5
G.init_vec = mus
mus,val,bound,correctionGap = optutil.corrective(G,alg_params,V,alpha,np.inf,logf)
#For all the steps
for it in xrange(MAX_STEPS):
assert len(alpha)==len(V),'mismatch in alpha and V'
start_time = time.time()
val,grad = f_obj(mus,fxn_params,True)
################# Running MAP Inference ################
if not alg_params['useMAP']:
model = ILPsolver.updateObjective(grad,alg_params,model)
if alg_params['useMAP']:
map_potentials = -1*grad
if alg_params['MAPsolver']=='toulbar2':
vertex,toulbar2_output,solution_MAP = MAPsolver.runMAP(map_potentials,G.nVertices,G.Edges,
G.Cardinality,G.graphType,alg_params['toulbar2_uai_file'],alg_params['maxSecondsPerMAP'])
elif alg_params['MAPsolver']=='MPLP':
vertex,MPLP_output,solution_MAP = MPLPsolver.runMAP(map_potentials,G.nVertices,G.Edges,
G.Cardinality,G.graphType,alg_params['toulbar2_uai_file'],alg_params['maxSecondsPerMAP'])
else:#Use MAP solvers from openGM
if it<2:
vInit = None
else:
vInit = V[-1].toarray()[0]
vertex,MAP_output,solution_MAP = GenericSolver.runMAP(map_potentials,G.nVertices,G.Edges,
G.Cardinality,G.graphType,alg_params['toulbar2_uai_file'],'all',vInit,logf)
else:
vertex = ILPsolver.optimize(model)
################ Code to track usage and save statistics ##################
if alg_params['use_marginal_polytope']:
assert np.sum(vertex-vertex.astype(int))<np.exp(-10),"Vertex not integer. Investigate : "+str(vertex)
############### Update marginals ###############
#ipdb.set_trace()
#Check if epsilon needs to be updated
if alg_params['M_truncated_dynamic']:
g_u0 = np.dot(-1*grad,alg_params['uniform']-mus)
g_k = np.dot(-1*grad,vertex-mus)
#If the gap is negative use the correctionGap
if g_k<0:
g_k = correctionGap
if g_u0==0:
new_eps = np.inf
elif g_u0<0:
new_eps = g_k/(-4*g_u0)
else:
new_eps = alg_params['M_eps']
#Halve epsilon if the gap is *still* negative (shouldn't happen)
#and if below precision -> set to 0
if new_eps < 0:
new_eps = alg_params['M_eps']/2.
if new_eps<1e-15:
new_eps = 0
if new_eps<alg_params['M_eps']:
#Modified version to ensuring halving
new_eps = min(new_eps,alg_params['M_eps']/2.)
print "************ UPDATING DYN. EPSILON TO ",new_eps," *******************"
old_eps = alg_params['M_eps']
for i in xrange(1,len(alpha)):
alpha[i] = alpha[i]*((1-old_eps)/(1-new_eps))
alpha[0] = alpha[0] - (1-alpha[0])*(((1-old_eps)/(1-new_eps))*new_eps - old_eps)
alg_params['M_eps'] = new_eps
#Check what an away step would look like
step_size,min_fxn_val = optutil.getStepDir(f_obj,fxn_params, mus-alg_params['uniform'],mus,0,alpha[0]/(1-alpha[0]))
statistics['primal_push'].append(min_fxn_val)
statistics['iterate_push'].append(mus+step_size*(mus-alg_params['uniform']))
statistics['eps_val'].append(new_eps)
statistics['marker'].append(it)
alg_params['correction_tol'] = 0.5
G.init_vec = mus
mus,val,bound,correctionGap = optutil.corrective(G,alg_params,V,alpha,np.inf,logf)
if alg_params['pairwise']:
mus,gap,direction,step_size,extra = optutil.PFWstep(mus,grad,vertex,V,alpha,alg_params,f_obj,fxn_params)
else:
mus,gap,direction,step_size,extra = optutil.FWstep(mus,grad,vertex,V,alpha,alg_params,f_obj,fxn_params)
#if is eps -> uses extra['gap_FW'] otherwise uses gap wchich is the FW gap anyways
#Fully Corrective Variant
if np.mod(it,alg_params['correctionFreq'])==0 and alg_params['doCorrection']:
G.init_vec = mus
if gap < 1:
alg_params['correction_tol'] = 0.05
else:
alg_params['correction_tol'] = 0.5
mus,val,bound,correctionGap = optutil.corrective(G,alg_params,V,alpha,np.inf,logf)
#Local Search
if alg_params['doICM']:
mus,vertex_last,val = optutil.localsearch(vertex,mus,G,alg_params,f_obj,
fxn_params,V,alpha,logf)
#Track statistics
entropy = np.sum(-1*np.log(mus)*mus)
val_do_not_use,grad_final = f_obj(mus,fxn_params,True)
grad_norm = np.max(grad_final)
dir_norm = np.linalg.norm(direction)
if np.min(mus)<smallest_marginal:
smallest_marginal = np.min(mus)
updateStatistics(statistics,it,time.time()-start_time,len(V),mus,val,gap,step_size,entropy,grad_norm,dir_norm,np.min(mus))
#Also track statisitcs for
if alg_params['M_truncated'] or alg_params['M_truncated_dynamic']:
statistics['gap_FW'].append(extra['gap_FW'])
statistics['gap_full'].append(extra['gap_full'])
writeStatistics(statistics,it,matf,alsoWrite = ['gap_FW','gap_FW'])
print 'Gap FW: ',extra['gap_FW']
else:
writeStatistics(statistics,it,matf)
#Error : Objective not decreasing
if it>2 and statistics['Obj_Val'][it]>statistics['Obj_Val'][it-1] and np.abs(statistics['Obj_Val'][it]-statistics['Obj_Val'][it-1])>1e-7:
logf.write('####### WARNING. OBJECTIVE NOT DECREASING ######\n')
logf.write('Step Size: '+str(step_size)+' Diff: '+str(np.abs(statistics['Obj_Val'][it]-statistics['Obj_Val'][it-1]))+'\n')
print "WARNING: ",'Step Size: '+str(step_size)+'Obj: '+str(statistics['Obj_Val'][it])+' Diff: '+str(np.abs(statistics['Obj_Val'][it]-statistics['Obj_Val'][it-1]))+'\n'
objWarning +=1
if objWarning>3:
logf.write('#### EXITING due to increasing objective #####\n')
break
assert False,'Objective should be decreasing'
################## Print/Write to console ##################
entropy_mus = np.sum(obj_fxn.computeEntropy(mus[1:np.sum(G.Cardinality)]))
print "\n"
status_1 = 'It: %d, Primal: %.8g, Gap: %.8g, Bound: %.5g ' % (it,val,gap,statistics['Bound'][-1])
status_2 = 'Time(s): %.3g, StepSize: %.8g ' % (statistics['Runtime'][-1],step_size)
status_3 = 'Entropy(mus)= %.3g, Norm (p) : %.3g, Norm (g) : %.3g '% (entropy_mus,dir_norm,grad_norm)
status_4 = 'Min mu (overall) = %.5g, Min mu (current) = %.5g Sum(alphas) = %.3g' %(smallest_marginal,np.min(mus),np.sum(alpha))
status = status_1+status_2+status_3+status_4
print status
logf.write(status+'\n')
################## Check stopping criterion ################
gap_l[np.mod(it,len_gap)] = gap
#M_eps variants have different stopping criterion
if alg_params['M_truncated'] or alg_params['M_truncated_dynamic']:
#All variants break if global gap is less than tolerance
if extra['gap_FW']<alg_params['tol']:
break
#Modify eps
if alg_params['M_truncated'] and alg_params['M_eps_iterations']>1 and gap < alg_params['tol']: #Manually modify eps
old_eps = alg_params['M_eps']
new_eps = alg_params['M_eps']/2.0
print "************ UPDATING EPSILON TO ",new_eps," *******************"
for i in xrange(1,len(alpha)):
alpha[i] = alpha[i]*((1-old_eps)/(1-new_eps))
alpha[0] = alpha[0] - (1-alpha[0])*(((1-old_eps)/(1-new_eps))*new_eps - old_eps)
alg_params['M_eps'] = new_eps
#Check what an away step would look like
step_size,min_fxn_val = optutil.getStepDir(f_obj,fxn_params, mus-alg_params['uniform'],mus,0,alpha[0]/(1-alpha[0]))
statistics['primal_push'].append(min_fxn_val)
statistics['iterate_push'].append(mus+step_size*(mus-alg_params['uniform']))
statistics['eps_val'].append(new_eps)
statistics['marker'].append(it)
alg_params['M_eps_iterations'] = alg_params['M_eps_iterations'] - 1
else:
if np.max(gap_l)<alg_params['tol']:
print "Duality Gap condition reached: ",np.mean(gap_l)
break
print "\n----- Done Marginal Inference ------ \n"
################ Cleanup Code ###############
if alg_params['M_truncated'] or alg_params['M_truncated_dynamic']:
old_eps = alg_params['M_eps']
new_eps = alg_params['M_eps']/2.0
for i in xrange(1,len(alpha)):
alpha[i] = alpha[i]*((1-old_eps)/(1-new_eps))
alpha[0] = alpha[0] - (1-alpha[0])*(((1-old_eps)/(1-new_eps))*new_eps - old_eps)
alg_params['M_eps'] = new_eps
#Check what an away step would look like
#print alpha[0]
step_size,min_fxn_val = optutil.getStepDir(f_obj,fxn_params, mus-alg_params['uniform'],mus,0,alpha[0]/(1-alpha[0]))
statistics['primal_push'].append(min_fxn_val)
statistics['iterate_push'].append(mus+step_size*(mus-alg_params['uniform']))
statistics['eps_val'].append(new_eps)
statistics['marker'].append(it)
mat = writeStatistics(statistics,it,matf,returnMAT = True,alsoWrite = ['gap_FW','gap_full','primal_push','eps_val','marker'])
mat['iterate_push'] = np.vstack(statistics['iterate_push'])
else:
mat = writeStatistics(statistics,it,matf,returnMAT = True)
print "LogZ: ",val*-1+gap
print "Marginals: ",
#for m in statistics['IterSet'][it,:np.sum(G.Cardinality)].tolist():
# print ('%.4f')%(m),
n_m = statistics['IterSet'][it,:np.sum(G.Cardinality)]
print n_m.reshape(G.nVertices,2)
e_m = statistics['IterSet'][it,np.sum(G.Cardinality):]
print e_m.reshape(G.nEdges,4)
dev_null_f.close()
logf.close()
if quiet:
sys.stdout=stdout_initial
if stepSizeComputation:
return val*-1+gap
else:
return mat
def optSpanningTree(G,spanning_tree_params):
quiet = spanning_tree_params['quiet']
dev_null_f = open(os.devnull,'w')
if quiet:
stdout_initial = sys.stdout
sys.stdout = dev_null_f
print "\n----- Starting Marginal Inference - Optimizing Spanning Tree Polytope ------ \n"
#Dealing with parameters
alg_params = copy.deepcopy(spanning_tree_params['alg_params'])
spanning_tree_iter = spanning_tree_params['spanning_tree_iter']
matf = spanning_tree_params['mat_file_name']
logfname = spanning_tree_params['log_file_name']
assert 'usePreprocess' in spanning_tree_params,'usePreprocess not set'
assert 'FWStrategy' in spanning_tree_params and 'Kval' in spanning_tree_params,'FWStrategy/Kval not set'
assert 'stepSizeComputation' in spanning_tree_params,'stepSizeComputation not set'
model = None
if not alg_params['useMAP']:
model = ILPsolver.defineModel(G,alg_params)
#Keep aside a different log/mat file for every iteration of optimizing over the spanning tree
alg_matf = alg_params['mat_file_name'].replace('.mat','')
alg_logf = alg_params['log_file_name'].replace('.txt','')
logf = open(logfname,'w',0)
#Tracking unique vertices of the marginal polytope
all_data = []
vec_len = max(G.init_vec.shape)
V = []
alpha = []
prevUb = np.inf
########### Running iterations of Frank-Wolfe #################
for it in xrange(spanning_tree_iter):
print "\nIteration ",(it+1)
#Update the name of matlab/log file for every iteration
alg_params['mat_file_name'] = alg_matf+'-sp'+str(it)+'.mat'
alg_params['log_file_name'] = alg_logf+'-sp'+str(it)+'.txt'
################# Strategies for marginal inference with FW #############################
if spanning_tree_params['FWStrategy'] == 'runK' and it>0:
#Truncate the number of ILP calls
alg_params['max_steps'] = spanning_tree_params['Kval']
elif spanning_tree_params['FWStrategy'] == 'uniform':
pass
else:
pass
if spanning_tree_params['usePreprocess'] and it>0:
alg_params['PreCorrection'] = True
else:
alg_params['PreCorrection'] = False
mat = inf.runMarginalFW(G,alg_params,model,V,alpha)
if len(alpha) != len(mat['alpha']):
alpha = mat['alpha']
val = mat['Obj_Val'][-1]
mus = mat['IterSet'][-1,:]
#postprocess
start_time = time.time()
mat['Runtime'][-1] = mat['Runtime'][-1]+(time.time()-start_time)
######################## Update Status ####################################
#print Status
status = 'Stats: #Itns: %d, Primal Obj: %.6f,\
Final Gap: %.6f, Avg Runtime(seconds): %.3f,\
Last Step Size: %f, #Unique Vertices : %d' % \
(mat['Obj_Val'].shape[0],mat['Obj_Val'][-1],
mat['Dual_Gap'][-1],np.mean(mat['Runtime']),
mat['Step_Size'][-1],len(V))
#print status
if quiet:
stdout_initial.write(str(os.getpid())+" "+str(status)+"\n")
logf.write(status+'\n')
##################### Updating rho vectors ###############################
start_time = time.time()
G.init_vec = mus
spanning_tree_polytope_vertex,grad = spanUtil.computeMST(G,mus)
direction = np.reshape(spanning_tree_polytope_vertex,(spanning_tree_polytope_vertex.shape[0],1))-G.rhos_edge
rhos = G.rhos_edge
status = "Computing directed spanning tree took "+str(time.time()-start_time)+' seconds'
#print status
logf.write(status+'\n')
#Strategies for computing the step size
if spanning_tree_params['stepSizeComputation'] == 'linesearch':
G_copy = copy.deepcopy(G)
alpha_rho = sciopt.fminbound(lambda a:inf.runMarginalFW(G_copy,alg_params,model,V,alpha,stepSizeComputation=True,rho_vec = rhos+a*direction),0,1,xtol=0.0005)#,disp=3)
print "Step Size (Vertices) :",alpha_rho
elif spanning_tree_params['stepSizeComputation'] == 'standard':
alpha_rho = float(2)/(it+3)
print "Step Size (Standard) :",alpha_rho
else:
assert False,'Invalid stepSizeComputation: '+spanning_tree_params['stepSizeComputation']
#Update
G.rhos_edge = G.rhos_edge+alpha_rho*(direction)
#Update rhos_node and K_C
G.computeNodeRhos()
print "Edge Appearance\n",G.rhos_edge," \nDirection\n",spanning_tree_polytope_vertex
#print "LogZ estimate: ",inf.runMarginalFW(G_copy,alg_params,model,V,alpha,stepSizeComputation=True,rho_vec = rhos+alpha_rho*direction)
############################# Collect Data and Save ###############################
#Collect data on this iteration
data = {}
data['fw_result'] = mat
data['rhos'] = G.rhos_edge
data['alpha_rho'] = alpha_rho
data['timeTaken'] = time.time()-start_time + np.sum(mat['Runtime'])
data['dualityGap'] = -1*np.dot(grad,direction)
print "Rho Gap: ",data['dualityGap']
#Postprocessing using the vertices of the marginal polytope
if spanning_tree_params['usePreprocess'] and it<spanning_tree_iter-1:
start_time = time.time()
mus,val,prevUb,gapFW = optutil.corrective(G,alg_params,V,alpha,bound=prevUb)
G.init_vec = mus
data['timeTaken'] += (time.time()-start_time)
#Append stats at the very end
all_data.append(data)
all_data_mat = {}
all_data_mat['final_mus'] = mus
all_data_mat['final_logz'] = val
all_data_mat['rho_data'] = all_data
all_data_mat['MARG_vertices'] = scipy.sparse.vstack(V)
savemat(matf,all_data_mat)
print "\n----- Done Marginal Inference ------ \n"
logf.close()
if quiet:
sys.stdout=stdout_initial
dev_null_f.close()