def run_one_big_IPSAA(numforecasts, mC, tsteps, ipoutageattr, samplesize):
IPSAAModel = exeADP.create_models_temp_framework(tsteps, mC)
nx.set_edge_attributes(mC["netobj"].dinet, "t"+str(tsteps-1)+"_capacity", ipoutageattr)
exeADP.update_models_orig_scens(IPSAAModel, mC, numscens=numforecasts*samplesize, mrange=[tsteps-1], reset=True,\
updateConstr=True)
IPSAAModel[tsteps-1].linear_constraints.add( \
lin_expr = [cplex.SparsePair(ind = mC["temp_var_insts"], \
val = [1]*len(mC["temp_var_insts"])) ], \
senses = ["L"], rhs = [tsteps*mC["pertinstbudget"]], names = ["TInstConstr"] )
try:
IPSAAModel[tsteps-1].solve()
vcontr = IPSAAModel[tsteps-1].solution.get_objective_value()
except:
print "testADP: control IP SAA model infeasible"
__dbg = raw_input("execution halted, press ENTER to exit")
sys.exit()
exeADP.trash_model(IPSAAModel)
sys.stdout.write("Done.\n")
return vcontr
def run_one_big_IPSAA(numforecasts, mC, tsteps, ipoutageattr, samplesize):
IPSAAModel = exeADP.create_models_temp_framework(tsteps, mC)
nx.set_edge_attributes(mC["netobj"].dinet,
"t" + str(tsteps - 1) + "_capacity", ipoutageattr)
exeADP.update_models_orig_scens(IPSAAModel, mC, numscens=numforecasts*samplesize, mrange=[tsteps-1], reset=True,\
updateConstr=True)
IPSAAModel[tsteps-1].linear_constraints.add( \
lin_expr = [cplex.SparsePair(ind = mC["temp_var_insts"], \
val = [1]*len(mC["temp_var_insts"])) ], \
senses = ["L"], rhs = [tsteps*mC["pertinstbudget"]], names = ["TInstConstr"] )
try:
IPSAAModel[tsteps - 1].solve()
vcontr = IPSAAModel[tsteps - 1].solution.get_objective_value()
except:
print "testADP: control IP SAA model infeasible"
__dbg = raw_input("execution halted, press ENTER to exit")
sys.exit()
exeADP.trash_model(IPSAAModel)
sys.stdout.write("Done.\n")
return vcontr
def run_one_big_LPSAA(numforecasts, mC, tsteps, adpftdmgattr, adpoutageattr, features, lpoutageattr):
policyEvalModels = [None for nf in xrange(numforecasts)]
polpicks = [None for nf in xrange(numforecasts)]
lpobjval = [None for nf in xrange(numforecasts)]
LPSAAModel = exeADP.create_models_temp_framework(tsteps, mC)
nx.set_edge_attributes(mC["netobj"].dinet, "t"+str(tsteps-1)+"_capacity", lpoutageattr)
exeADP.update_models_orig_scens(LPSAAModel, mC, numscens=len(lpoutageattr.values()[0]), mrange=[tsteps-1], \
reset=True, updateConstr=True)
LPSAAModel[tsteps-1].variables.set_types([ (mC["temp_var_insts"][j], \
LPSAAModel[tsteps-1].variables.type.continuous) \
for j in xrange(len(mC["temp_var_insts"])) ])
LPSAAModel[tsteps-1].set_problem_type(LPSAAModel[tsteps-1].problem_type.LP)
# find multiple policy evaluations for robustness
for nf in xrange(numforecasts):
# step 5: evaluation of policy
policyEvalModels[nf] = exeADP.create_models_temp_framework(tsteps, mC)
# override netobj's info with the stored scenario data
for t in xrange(tsteps):
nx.set_edge_attributes(mC["netobj"].dinet, "t"+str(t)+"_dmg_pct", adpftdmgattr[nf][t])
nx.set_edge_attributes(mC["netobj"].dinet, "t"+str(t)+"_capacity", adpoutageattr[nf][t])
mC["mcsim"].eval_adp_basis_functions(mC["netobj"], tsteps, mC)
featurelist = mC["mcsim"].calc_feature_ctrmassquad(mC["netobj"], tsteps, features)
exeADP.update_models_orig_scens(policyEvalModels[nf], mC, mrange=xrange(len(policyEvalModels[nf])), \
reset=True, updateConstr=True)
# solve policy search problem to find install arcs
mC["instchoices"] = [ [0]*len(mC["temp_var_insts"]) for i in xrange(tsteps)]
mC["previnsts"] = [[] for i in xrange(tsteps)]
for t in xrange(tsteps):
#TODO6: this may be semantically out of date
optpolicy,tflows,slks,bov,vfav = exeADP.adp_policy_search(t, featurelist, policyEvalModels[nf], mC, \
randpol=False)
# this is all we care about -- the policy evaluation at the last time step (i.e. all installed arcs)
polpicks[nf] = optpolicy
# step 6: fix the temp arcs to the decisions made in 5 and run an LP SAA over these samples at time T
LPSAAModel[tsteps-1].variables.set_lower_bounds( \
[ (mC["temp_var_insts"][i], polpicks[nf][i]) for i in xrange(len(mC["temp_var_insts"])) ])
LPSAAModel[tsteps-1].variables.set_upper_bounds( \
[ (mC["temp_var_insts"][i], polpicks[nf][i]) for i in xrange(len(mC["temp_var_insts"])) ])
try:
LPSAAModel[tsteps-1].solve()
lpobjval[nf] = LPSAAModel[tsteps-1].solution.get_objective_value()
except:
print "testADP: test LP SAA model infeasible"
LPSAAModel[tsteps-1].conflict.refine(LPSAAModel[tsteps-1].conflict.all_constraints())
conflicts = LPSAAModel[tsteps-1].conflict.get()
conflicts = [i for i in xrange(len(conflicts)) if conflicts[i] != -1]
cgs = LPSAAModel[tsteps-1].conflict.get_groups(conflicts)
ubcs=[j[0][1] for i,j in cgs if j[0][0] == 2]
lbcs=[j[0][1] for i,j in cgs if j[0][0] == 1]
lccs=[j[0][1] for i,j in cgs if j[0][0] == 3]
constrByVar = exeADP.find_constr_4_vars(LPSAAModel[tsteps-1], \
LPSAAModel[tsteps-1].variables.get_names())
conflConstrs = exeADP.find_constr_4_vars(LPSAAModel[tsteps-1], \
LPSAAModel[tsteps-1].linear_constraints.get_names(lccs), \
vartype="constraint")
__dbg = raw_input("execution halted, press ENTER to exit")
sys.exit()
exeADP.trash_model(LPSAAModel)
exeADP.trash_model([k for l in policyEvalModels for k in l])
return polpicks, lpobjval
def run_one_big_LPSAA(numforecasts, mC, tsteps, adpftdmgattr, adpoutageattr,
features, lpoutageattr):
policyEvalModels = [None for nf in xrange(numforecasts)]
polpicks = [None for nf in xrange(numforecasts)]
lpobjval = [None for nf in xrange(numforecasts)]
LPSAAModel = exeADP.create_models_temp_framework(tsteps, mC)
nx.set_edge_attributes(mC["netobj"].dinet,
"t" + str(tsteps - 1) + "_capacity", lpoutageattr)
exeADP.update_models_orig_scens(LPSAAModel, mC, numscens=len(lpoutageattr.values()[0]), mrange=[tsteps-1], \
reset=True, updateConstr=True)
LPSAAModel[tsteps-1].variables.set_types([ (mC["temp_var_insts"][j], \
LPSAAModel[tsteps-1].variables.type.continuous) \
for j in xrange(len(mC["temp_var_insts"])) ])
LPSAAModel[tsteps - 1].set_problem_type(LPSAAModel[tsteps -
1].problem_type.LP)
# find multiple policy evaluations for robustness
for nf in xrange(numforecasts):
# step 5: evaluation of policy
policyEvalModels[nf] = exeADP.create_models_temp_framework(tsteps, mC)
# override netobj's info with the stored scenario data
for t in xrange(tsteps):
nx.set_edge_attributes(mC["netobj"].dinet,
"t" + str(t) + "_dmg_pct",
adpftdmgattr[nf][t])
nx.set_edge_attributes(mC["netobj"].dinet,
"t" + str(t) + "_capacity",
adpoutageattr[nf][t])
mC["mcsim"].eval_adp_basis_functions(mC["netobj"], tsteps, mC)
featurelist = mC["mcsim"].calc_feature_ctrmassquad(
mC["netobj"], tsteps, features)
exeADP.update_models_orig_scens(policyEvalModels[nf], mC, mrange=xrange(len(policyEvalModels[nf])), \
reset=True, updateConstr=True)
# solve policy search problem to find install arcs
mC["instchoices"] = [[0] * len(mC["temp_var_insts"])
for i in xrange(tsteps)]
mC["previnsts"] = [[] for i in xrange(tsteps)]
for t in xrange(tsteps):
#TODO6: this may be semantically out of date
optpolicy,tflows,slks,bov,vfav = exeADP.adp_policy_search(t, featurelist, policyEvalModels[nf], mC, \
randpol=False)
# this is all we care about -- the policy evaluation at the last time step (i.e. all installed arcs)
polpicks[nf] = optpolicy
# step 6: fix the temp arcs to the decisions made in 5 and run an LP SAA over these samples at time T
LPSAAModel[tsteps-1].variables.set_lower_bounds( \
[ (mC["temp_var_insts"][i], polpicks[nf][i]) for i in xrange(len(mC["temp_var_insts"])) ])
LPSAAModel[tsteps-1].variables.set_upper_bounds( \
[ (mC["temp_var_insts"][i], polpicks[nf][i]) for i in xrange(len(mC["temp_var_insts"])) ])
try:
LPSAAModel[tsteps - 1].solve()
lpobjval[nf] = LPSAAModel[tsteps -
1].solution.get_objective_value()
except:
print "testADP: test LP SAA model infeasible"
LPSAAModel[tsteps - 1].conflict.refine(
LPSAAModel[tsteps - 1].conflict.all_constraints())
conflicts = LPSAAModel[tsteps - 1].conflict.get()
conflicts = [
i for i in xrange(len(conflicts)) if conflicts[i] != -1
]
cgs = LPSAAModel[tsteps - 1].conflict.get_groups(conflicts)
ubcs = [j[0][1] for i, j in cgs if j[0][0] == 2]
lbcs = [j[0][1] for i, j in cgs if j[0][0] == 1]
lccs = [j[0][1] for i, j in cgs if j[0][0] == 3]
constrByVar = exeADP.find_constr_4_vars(LPSAAModel[tsteps-1], \
LPSAAModel[tsteps-1].variables.get_names())
conflConstrs = exeADP.find_constr_4_vars(LPSAAModel[tsteps-1], \
LPSAAModel[tsteps-1].linear_constraints.get_names(lccs), \
vartype="constraint")
__dbg = raw_input("execution halted, press ENTER to exit")
sys.exit()
exeADP.trash_model(LPSAAModel)
exeADP.trash_model([k for l in policyEvalModels for k in l])
return polpicks, lpobjval
