def solve(localsolver, solvername, instance,
logfilename='logfile_loadobjective.log',
get_suffixes=True, solver_options=None, outputfile=None):
# Wall time - clock starts.
starttime_modelsolve = time.time()
if localsolver:
solver = SolverFactory(solvername)
# Configure with solver options
# file_print_levels (Output Level-of-Detail):
# 4 for just # of iterations, and final objective, infeas,etc. values
# 6 for summary information about all iterations, but not variable values
# 8 for variable values at all iterations
# 10 for all iterations
if solver_options:
for k, v in solver_options.items():
solver.options[k] = v
solver.options['OF_mumps_mem_percent'] = '5' # "OF_" prefix signals to Pyomo to create a temporary options file
solver.options['OF_output_file'] = outputfile
if get_suffixes:
instance.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT)
instance.ipopt_zL_out = pyo.Suffix(direction=pyo.Suffix.IMPORT)
instance.ipopt_zU_out = pyo.Suffix(direction=pyo.Suffix.IMPORT)
setattr(instance, 'lambda', pyo.Suffix(direction=pyo.Suffix.IMPORT)) # use setattr because 'lambda' is reserved keyword
try:
results = solver.solve(instance, tee=True, symbolic_solver_labels=True,
keepfiles=False, logfile=logfilename)
except pyutilib.common._exceptions.ApplicationError:
traceback.print_exc()
return None
else:
opt = SolverFactory("cbc")
solver_manager = SolverManagerFactory('neos')
instance.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT)
instance.rc = pyo.Suffix(direction=pyo.Suffix.IMPORT)
instance.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT_EXPORT)
# self.instance.slack = pyo.Suffix(direction=pyo.Suffix.IMPORT)
opt.options["display_width"] = 170
opt.options["display"] = '_varname, _var.rc, _var.lb, _var, _var.ub, _var.slack'
results = solver_manager.solve(instance, opt=opt, solver=solvername, logfile=logfilename)
results.write()
# Wall time - clock stops.
_endtime_modelsolve = time.time()
timefor_modelsolve = _endtime_modelsolve - starttime_modelsolve
logger.info('*solving done* <- it took %f seconds>' % timefor_modelsolve)
feasible = check_whether_feasible(instance, results)
return instance, results, feasible
def optimize_model(model, email, cet, solver=OPT_DEFAULT):
if not set_neos_email(email):
if solver is not OPT_DEFAULT:
assert_solver_available_locally(solver)
elif cet == CET_ADDI:
solver = "mosek"
elif cet == CET_MULT:
raise ValueError(
"Please specify the solver for optimizing multiplicative model locally."
)
solver_instance = SolverFactory(solver)
print("Estimating the {} locally with {} solver.".format(
CET_Model_Categories[cet], solver),
flush=True)
return solver_instance.solve(model, tee=True), 1
else:
if solver is OPT_DEFAULT and cet is CET_ADDI:
solver = "mosek"
elif solver is OPT_DEFAULT and cet == CET_MULT:
solver = "knitro"
solver_instance = SolverManagerFactory('neos')
print("Estimating the {} remotely with {} solver.".format(
CET_Model_Categories[cet], solver),
flush=True)
return solver_instance.solve(model, tee=True, opt=solver), 1
def optimize(self, remote=True):
"""Optimize the function by requested method"""
# TODO(error/warning handling): Check problem status after optimization
if remote == False:
if self.cet == "addi":
solver = SolverFactory("mosek")
self.problem_status = solver.solve(self.__model__, tee=True)
self.optimization_status = 1
elif self.cet == "mult":
# TODO(warning handling): Use log system instead of print()
print(
"Estimating the multiplicative model will be available in near future."
)
return False
else:
if self.cet == "addi":
opt = "mosek"
elif self.cet == "mult":
opt = "knitro"
solver = SolverManagerFactory('neos')
self.problem_status = solver.solve(self.__model__,
tee=True,
opt=opt)
self.optimization_status = 1
def optimize(self, remote=True):
"""Optimize the function by requested method"""
# TODO(error/warning handling): Check problem status after optimization
if not remote:
if self.cet == "addi":
solver = SolverFactory("mosek")
self.problem_status = solver.solve(self.__model__, tee=True)
self.optimization_status = 1
elif self.cet == "mult":
solver = SolverFactory('gams')
self.problem_status = solver.solve(self.__model__, solver='minos', tee=True)
self.optimization_status = 1
else:
if self.cet == "addi":
opt = "mosek"
elif self.cet == "mult":
opt = "knitro"
solver = SolverManagerFactory('neos')
self.problem_status = solver.solve(self.__model__,
tee=True,
opt=opt)
self.optimization_status = 1
def solve_ef(master_instance, options):
with SolverFactory(options.solver_type) as ef_solver:
with SolverManagerFactory(
options.solver_manager_type) as ef_solver_manager:
if ef_solver is None:
raise ValueError("Failed to create solver manager of type=" +
options.solver_type +
" for use in extensive form solve")
if len(options.ef_solver_options) > 0:
print("Initializing ef solver with options=" +
str(options.ef_solver_options))
ef_solver.set_options("".join(options.ef_solver_options))
if options.ef_mipgap is not None:
if (options.ef_mipgap < 0.0) or \
(options.ef_mipgap > 1.0):
raise ValueError("Value of the mipgap parameter for the "
"EF solve must be on the unit interval; "
"value specified=" +
str(options.ef_mipgap))
else:
ef_solver.mipgap = options.ef_mipgap
solve_kwds = {}
solve_kwds['load_solutions'] = False
if options.keep_solver_files:
solve_kwds['keepfiles'] = True
if options.symbolic_solver_labels:
solve_kwds['symbolic_solver_labels'] = True
if options.output_solver_log:
solve_kwds['tee'] = True
if options.write_fixed_variables:
solve_kwds['output_fixed_variable_bounds'] = True
if options.verbose:
print("Solving extensive form.")
if (not options.disable_warmstarts) and \
(ef_solver.warm_start_capable()):
ef_action_handle = ef_solver_manager.queue(master_instance,
opt=ef_solver,
warmstart=True,
**solve_kwds)
else:
ef_action_handle = ef_solver_manager.queue(master_instance,
opt=ef_solver,
**solve_kwds)
results = ef_solver_manager.wait_for(ef_action_handle)
# check the return code - if this is anything but "have a solution", we need to bail.
if (results.solver.status == SolverStatus.ok) and \
((results.solver.termination_condition == TerminationCondition.optimal) or \
((len(results.solution) > 0) and (results.solution(0).status == SolutionStatus.optimal))):
master_instance.solutions.load_from(results)
return results
raise RuntimeError("Extensive form was infeasible!")
def optimize(self, remote=True):
"""Optimize the function by requested method"""
if remote == False:
solver = SolverFactory("mosek")
print("Estimating the model locally with mosek solver")
self.problem_status = solver.solve(self.__model__, tee=True)
self.optimization_status = 1
else:
solver = SolverManagerFactory("neos")
print("Estimating the model remotely with mosek solver")
self.problem_status = solver.solve(self.__model__,
tee=True,
opt="mosek")
self.optimization_status = 1
def optimize(self, remote=False):
"""Optimize the function by requested method"""
# TODO(error/warning handling): Check problem status after optimization
if remote == False:
print("Estimating the model locally with mosek solver")
solver = SolverFactory("mosek")
self.problem_status = solver.solve(self.__model__, tee=True)
print(self.problem_status)
self.optimization_status = 1
else:
print("Estimating the model remotely with mosek solver")
solver = SolverManagerFactory('neos')
self.problem_status = solver.solve(self.__model__,
tee=True,
opt="mosek")
print(self.problem_status)
self.optimization_status = 1
def run(args=None):
###################################
# to import plugins
import pyomo.environ
import pyomo.solvers.plugins.smanager.phpyro
def LagrangeMorePR(args=None):
print("lagrangeMorePR begins %s" % datetime_string())
blanks = " " # used for formatting print statements
class Object(object): pass
Result = Object()
# options used
betaTol = options.beta_tol # tolerance used to separate b-values
IndVarName = options.indicator_var_name
multName = options.lambda_parm_name
CCStageNum = options.stage_num
MaxMorePR = options.max_number # max PR points to be generated (above F^* with all delta fixed)
MaxTime = options.max_time # max time before terminate
csvPrefix = options.csvPrefix # input filename prefix (eg, case name)
probFileName = options.probFileName # name of file containing probabilities
##HG override
# options.verbosity = 2
verbosity = options.verbosity
Result.status = 'starting '+datetime_string()
STARTTIME = time.time()
ph = PHFromScratch(options)
rootnode = ph._scenario_tree._stages[0]._tree_nodes[0] # use rootnode to loop over scenarios
if find_active_objective(ph._scenario_tree._scenarios[0]._instance,safety_checks=True).is_minimizing():
print("We are solving a MINIMIZATION problem.")
else:
print("We are solving a MAXIMIZATION problem.")
# initialize
ScenarioList = []
with open(csvPrefix+"ScenarioList.csv",'r') as inputFile:
for line in inputFile.readlines():
L = line.split(',')
ScenarioList.append([L[0],float(L[1])])
addstatus = str(len(ScenarioList))+' scenarios read from file: ' + csvPrefix+'ScenarioList.csv'
if verbosity > 0: print(addstatus)
Result.status = Result.status + '\n' + addstatus
PRoptimal = []
with open(csvPrefix+"PRoptimal.csv",'r') as inputFile:
for line in inputFile.readlines():
bzS = line.split(',')
PRoptimal.append( [None, float(bzS[0]), float(bzS[1])] )
addstatus = str(len(PRoptimal))+' PR points read from file: '+ csvPrefix+'PRoptimal.csv (envelope function)'
if verbosity > 0:
print(addstatus)
Result.status = Result.status + '\n' + addstatus
# ensure PR points on envelope function are sorted by probability
PRoptimal.sort(key=operator.itemgetter(1))
PRoptimal[0][0] = 0 # initial lambda (for b=0)
for p in range(1,len(PRoptimal)):
dz = PRoptimal[p][2] - PRoptimal[p-1][2]
db = PRoptimal[p][1] - PRoptimal[p-1][1]
PRoptimal[p][0] = dz/db
if verbosity > 0:
PrintPRpoints(PRoptimal)
Result.PRoptimal = PRoptimal
lambdaval = 0.
lagrUtil.Set_ParmValue(ph, options.lambda_parm_name,lambdaval)
# IMPORTANT: Preprocess the scenario instances
# before fixing variables, otherwise they
# will be preprocessed out of the expressions
# and the output_fixed_variable_bounds option
# will have no effect when we update the
# fixed variable values (and then assume we
# do not need to preprocess again because
# of this option).
ph._preprocess_scenario_instances()
## read scenarios to select for each PR point on envelope function
with open(csvPrefix+"OptimalSelections.csv",'r') as inputFile:
OptimalSelections = []
for line in inputFile.readlines():
if len(line) == 0: break # eof
selections = line.split(',')
L = len(selections)
Ls = len(selections[L-1])
selections[L-1] = selections[L-1][0:Ls-1]
if verbosity > 1:
print(str(selections))
OptimalSelections.append(selections)
Result.OptimalSelections = OptimalSelections
addstatus = str(len(OptimalSelections)) + ' Optimal selections read from file: ' \
+ csvPrefix + 'OptimalSelections.csv'
Result.status = Result.status + '\n' + addstatus
if len(OptimalSelections) == len(PRoptimal):
if verbosity > 0:
print(addstatus)
else:
addstatus = addstatus + '\n** Number of selections not equal to number of PR points'
print(addstatus)
Result.status = Result.status + '\n' + addstatus
print(str(OptimalSelections))
print((PRoptimal))
return Result
#####################################################################################
# get probabilities
if probFileName is None:
# ...generate from widest gap regions
PRlist = FindPRpoints(options, PRoptimal)
else:
# ...read probabilities
probList = []
with open(probFileName,'r') as inputFile:
if verbosity > 0:
print("reading from probList = "+probFileName)
for line in inputFile.readlines(): # 1 probability per line
if len(line) == 0:
break
prob = float(line)
probList.append(prob)
if verbosity > 0:
print("\t "+str(len(probList))+" probabilities")
if verbosity > 1:
print(str(probList))
PRlist = GetPoints(options, PRoptimal, probList)
if verbosity > 1:
print("PRlist:")
for interval in PRlist:
print(str(interval))
# We now have PRlist = [[i, b], ...], where b is in PRoptimal interval (i-1,i)
addstatus = str(len(PRlist)) + ' probabilities'
if verbosity > 0:
print(addstatus)
Result.status = Result.status + '\n' + addstatus
#####################################################################################
lapsedTime = time.time() - STARTTIME
addstatus = 'Initialize complete...lapsed time = ' + str(lapsedTime)
if verbosity > 1:
print(addstatus)
Result.status = Result.status + '\n' + addstatus
#####################################################################################
if verbosity > 1:
print("\nlooping over Intervals to generate PR points by flipping heuristic")
Result.morePR = []
for interval in PRlist:
lapsedTime = time.time() - STARTTIME
if lapsedTime > MaxTime:
addstatus = '** lapsed time = ' + str(lapsedTime) + ' > max time = ' + str(MaxTime)
if verbosity > 0: print(addstatus)
Result.status = Result.status + '\n' + addstatus
break
i = interval[0] # = PR point index
b = interval[1] # = target probability to reach by flipping from upper endpoint
bU = PRoptimal[i][1] # = upper endpoint
bL = PRoptimal[i-1][1] # = lower endpoint
if verbosity > 1:
print( "target probability = "+str(b)+" < bU = PRoptimal[" + str(i) + "][1]" \
" and > bL = PRoptimal["+str(i-1)+"][1]")
if b < bL or b > bU:
addstatus = '** probability = '+str(b) + ', not in gap interval: (' \
+ str(bL) + ', ' + str(bU) + ')'
print(addstatus)
print(str(PRoptimal))
print(str(PRlist))
Result.status = Result.status + '\n' + addstatus
return Result
if verbosity > 1:
print( "i = "+str(i)+" : Starting with bU = "+str(bU)+" having "+ \
str(len(OptimalSelections[i]))+ " selections:")
print(str(OptimalSelections[i]))
# first fix all scenarios = 0
for sname, sprob in ScenarioList:
scenario = ph._scenario_tree.get_scenario(sname)
lagrUtil.FixIndicatorVariableOneScenario(ph,
scenario,
IndVarName,
0)
# now fix optimal selections = 1
for sname in OptimalSelections[i]:
scenario = ph._scenario_tree.get_scenario(sname)
lagrUtil.FixIndicatorVariableOneScenario(ph,
scenario,
IndVarName,
1)
# flip scenario selections from bU until we reach b (target probability)
bNew = bU
for sname, sprob in ScenarioList:
scenario = ph._scenario_tree.get_scenario(sname)
if bNew - sprob < b:
continue
instance = ph._instances[sname]
if getattr(instance, IndVarName).value == 0:
continue
bNew = bNew - sprob
# flipped scenario selection
lagrUtil.FixIndicatorVariableOneScenario(ph,
scenario,
IndVarName,
0)
if verbosity > 1:
print("\tflipped "+sname+" with prob = "+str(sprob)+" ...bNew = "+str(bNew))
if verbosity > 1:
print("\tflipped selections reach "+str(bNew)+" >= target = "+str(b)+" (bL = "+str(bL)+")")
if bNew <= bL + betaTol or bNew >= bU - betaTol:
if verbosity > 0:
print("\tNot generating PR point...flipping from bU failed")
continue # to next interval in list
# ready to solve to get cost for fixed scenario selections associated with probability = bNew
if verbosity > 1:
# check that scenarios are fixed as they should be
totalprob = 0.
for scenario in ScenarioList:
sname = scenario[0]
sprob = scenario[1]
instance = ph._instances[sname]
print("fix "+sname+" = "+str(getattr(instance,IndVarName).value)+\
" is "+str(getattr(instance,IndVarName).fixed)+" probability = "+str(sprob))
if getattr(instance,IndVarName).value == 1:
totalprob = totalprob + sprob
lambdaval = getattr(instance, multName).value
print("\ttotal probability = %f" % totalprob)
# solve (all delta fixed); lambda=0, so z = Lagrangian
if verbosity > 0:
print("solve begins %s" % datetime_string())
print("\t- lambda = %f" % lambdaval)
SolStat, z = lagrUtil.solve_ph_code(ph, options)
b = Compute_ExpectationforVariable(ph, IndVarName, CCStageNum)
if verbosity > 0:
print("solve ends %s" % datetime_string())
print("\t- SolStat = %s" % str(SolStat))
print("\t- b = %s" % str(b))
print("\t- z = %s" % str(z))
print("(adding to more PR points)")
Result.morePR.append([None,b,z])
if verbosity > 1:
PrintPRpoints(Result.morePR)
######################################################
# end loop over target probabilities
with open(csvPrefix+"PRmore.csv",'w') as outFile:
for point in Result.morePR:
outFile.write(str(point[1])+','+str(point[2]))
addstatus = str(len(Result.morePR)) + ' PR points written to file: '+ csvPrefix + 'PRmore.csv'
if verbosity > 0: print(addstatus)
Result.status = Result.status + '\n' + addstatus
addstatus = 'lapsed time = ' + putcommas(time.time() - STARTTIME)
if verbosity > 0: print(addstatus)
Result.status = Result.status + '\n' + addstatus
return Result
################################
# LagrangeMorePR ends here
################################
#### start run ####
AllInOne = False
# VERYSTARTTIME=time.time()
# print "##############VERYSTARTTIME:",str(VERYSTARTTIME-VERYSTARTTIME)
##########################
# options defined here
##########################
try:
conf_options_parser = construct_ph_options_parser("lagrange [options]")
conf_options_parser.add_argument("--beta-min",
help="The min beta level for the chance constraint. Default is None",
action="store",
dest="beta_min",
type=float,
default=None)
conf_options_parser.add_argument("--beta-max",
help="The beta level for the chance constraint. Default is None",
action="store",
dest="beta_max",
type=float,
default=None)
conf_options_parser.add_argument("--min-prob",
help="Tolerance for testing probability > 0. Default is 1e-5",
action="store",
dest="min_prob",
type=float,
default=1e-5)
conf_options_parser.add_argument("--beta-tol",
help="Tolerance for testing equality to beta. Default is 10^-2",
action="store",
dest="beta_tol",
type=float,
default=1e-2)
conf_options_parser.add_argument("--Lagrange-gap",
help="The (relative) Lagrangian gap acceptable for the chance constraint. Default is 10^-4.",
action="store",
type=float,
dest="Lagrange_gap",
default=0.0001)
conf_options_parser.add_argument("--max-number",
help="The max number of PR points. Default = 10.",
action="store",
dest="max_number",
type=int,
default=10)
conf_options_parser.add_argument("--max-time",
help="Maximum time (seconds). Default is 3600.",
action="store",
dest="max_time",
type=float,
default=3600)
conf_options_parser.add_argument("--csvPrefix",
help="Input file name prefix. Default is ''",
action="store",
dest="csvPrefix",
type=str,
default="")
conf_options_parser.add_argument("--lambda-parm-name",
help="The name of the lambda parameter in the model. Default is lambdaMult",
action="store",
dest="lambda_parm_name",
type=str,
default="lambdaMult")
conf_options_parser.add_argument("--indicator-var-name",
help="The name of the indicator variable for the chance constraint. The default is delta",
action="store",
dest="indicator_var_name",
type=str,
default="delta")
conf_options_parser.add_argument("--stage-num",
help="The stage number of the CC indicator variable (number, not name). Default is 2",
action="store",
dest="stage_num",
type=int,
default=2)
conf_options_parser.add_argument("--verbosity",
help="verbosity=0 is no extra output, =1 is medium, =2 is debug, =3 super-debug. Default is 1.",
action="store",
dest="verbosity",
type=int,
default=1)
conf_options_parser.add_argument("--prob-file",
help="file name specifiying probabilities",
action="store",
dest="probFileName",
type=str,
default=None)
# The following needed for solve_ph_code in lagrangeutils
conf_options_parser.add_argument("--solve-with-ph",
help="Perform solves via PH rather than an EF solve. Default is False",
action="store_true",
dest="solve_with_ph",
default=False)
################################################################
options = conf_options_parser.parse_args(args=args)
# temporary hack
options._ef_options = conf_options_parser._ef_options
options._ef_options.import_argparse(options)
except SystemExit as _exc:
# the parser throws a system exit if "-h" is specified - catch
# it to exit gracefully.
return _exc.code
if options.verbose is True:
print("Loading reference model and scenario tree")
scenario_instance_factory = \
ScenarioTreeInstanceFactory(options.model_directory,
options.instance_directory)
full_scenario_tree = \
GenerateScenarioTreeForPH(options,
scenario_instance_factory)
solver_manager = SolverManagerFactory(options.solver_manager_type)
if solver_manager is None:
raise ValueError("Failed to create solver manager of "
"type="+options.solver_manager_type+
" specified in call to PH constructor")
if isinstance(solver_manager,
pyomo.solvers.plugins.smanager.phpyro.SolverManager_PHPyro):
solver_manager.deactivate()
raise ValueError("PHPyro can not be used as the solver manager")
try:
if (scenario_instance_factory is None) or (full_scenario_tree is None):
raise RuntimeError("***ERROR: Failed to initialize the model and/or scenario tree data.")
# load_model gets called again, so lets make sure unarchived directories are used
options.model_directory = scenario_instance_factory._model_filename
options.instance_directory = scenario_instance_factory._scenario_tree_filename
scenario_count = len(full_scenario_tree._stages[-1]._tree_nodes)
# create ph objects for finding the solution. we do this even if
# we're solving the extensive form
if options.verbose is True:
print("Loading scenario instances and initializing scenario tree for full problem.")
########## Here is where multiplier search is called ############
Result = LagrangeMorePR()
#####################################################################################
finally:
solver_manager.deactivate()
# delete temporary unarchived directories
scenario_instance_factory.close()
print("\n==================== returned from LagrangeMorePR")
print(str(Result.status))
try:
print("Envelope:")
print(str(PrintPRpoints(Result.PRoptimal)))
print("\nAdded:")
PrintPRpoints(Result.morePR)
except:
print("from run: PrintPRpoints failed")
sys.exit()
# combine tables and sort by probability
if len(Result.morePR) > 0:
PRpoints = copy.deepcopy(Result.PRoptimal)
for lbz in Result.morePR: PRpoints.append(lbz)
print("Combined table of PR points (sorted):")
PRpoints.sort(key=operator.itemgetter(1))
print(str(PrintPRpoints(PRpoints)))
def solve_model(self, model):
"""
Method to solve an optimization model using a specified solver
Returns:
:dict - solver statistics and model solution
"""
# Set a few default values
attr_to_check = [
'solver_name', 'neos', 'write_solution', 'return_solution',
'verbosity', 'solver_progress'
]
for attr in attr_to_check:
self.__chkattrt(attr)
# Get model name
model_name = model.name
self.__model_name_str = str(re.sub(" ", "_", model_name))
# Solver name to lower case characters
self.solver_name = self.solver_name.lower()
# Confirm solver paths and thus availability
self.__solvers_path_check()
# NEOS vs local solvers: check solvers are recognised/available
self.__solvers_compatibility_check() # Run solvers check
# Call solver factory
opt_solver = SolverFactory(self.solver_name)
# Change solver temporary folder path
log_folder = path.join('_log', '')
if self.solver_name in ['gurobi', 'baron', 'cplex']:
if not path.exists(log_folder):
makedirs(log_folder)
self.__set_tempdir(log_folder)
else:
pass
# Include solver-compatible options
if self.solver_name in ['cplex', 'gurobi']:
opt_solver.options['threads'] = self.threads
opt_solver.options['mipgap'] = self.rel_gap
opt_solver.options['timelimit'] = self.time_limit
elif self.solver_name in ['baron']:
opt_solver.options['threads'] = self.threads
opt_solver.options['LPSol'] = 3
opt_solver.options['EpsR'] = self.rel_gap
opt_solver.options['MaxTime'] = self.time_limit
# For Unix systems ensure "libcplexxxxx.dylib" is in the system PATH for baron to use CPLEX for MIPs
elif self.solver_name in ['cbc']:
opt_solver.options['threads'] = self.threads
opt_solver.options['ratio'] = self.rel_gap
opt_solver.options['seconds'] = self.time_limit
opt_solver.options['log'] = int(self.solver_progress) * 2
opt_solver.options['mess'] = 'on'
opt_solver.options['timeM'] = "elapsed"
opt_solver.options['preprocess'] = "equal"
elif self.solver_name in ['glpk']:
opt_solver.options['mipgap'] = self.rel_gap
opt_solver.options['tmlim'] = self.time_limit
elif self.solver_name in ['ipopt']:
opt_solver.options['max_wall_time'] = self.time_limit
elif self.solver_name in ['bonmin']:
opt_solver.options['time_limit'] = self.time_limit
opt_solver.options['number_cpx_threads'] = self.threads
opt_solver.options['allowable_fraction_gap'] = self.rel_gap
elif self.solver_name in ['couenne']:
opt_solver.options['time_limit'] = self.time_limit
opt_solver.options['threads'] = self.threads
opt_solver.options['ratio'] = self.rel_gap
opt_solver.options['seconds'] = self.time_limit
opt_solver.options['log'] = int(self.solver_progress) * 2
opt_solver.options['mess'] = 'on'
opt_solver.options['timeM'] = "elapsed"
opt_solver.options['preprocess'] = "equal"
else:
pass
# Write log to file named <model_name>/DD_MM_YY_HH_MM_xx.log
# Create (if it does not exist) the '_log' folder
log_store_folder = path.join(log_folder, self.__model_name_str, '')
if not path.exists(log_store_folder):
makedirs(log_store_folder)
self.__current_datetime_str = datetime.now().strftime(
"%d_%m_%y_%H_%M_")
file_suffix = 0
# Results filename
if self.__model_name_str == 'Unknown' or len(
self.__model_name_str) <= 10:
log_filename = self.__model_name_str + self.__current_datetime_str + str(
file_suffix) + ".log"
else:
log_filename = self.__model_name_str[:4] + '..' + self.__model_name_str[-4:] + \
self.__current_datetime_str + str(file_suffix) + ".log"
while path.exists(log_store_folder + log_filename):
file_suffix += 1
log_filename = self.__current_datetime_str + str(
file_suffix) + ".log"
log_filename = self.__current_datetime_str + str(file_suffix) + ".log"
# Solve <model> with/without writing final solution to stdout
processed_results = None
self.__page_borders() # Headers for page
try:
if self.neos:
self.solver_results = SolverManagerFactory('neos').solve(
model, opt=opt_solver, tee=self.solver_progress)
else:
self.solver_results = opt_solver.solve(
model,
tee=self.solver_progress,
logfile=log_store_folder + log_filename)
# Process results obtained
if self.neos:
self.__msg(
"Currently, results are not post-processed for NEOS server runs"
) #FIXME Find a work around
else:
processed_results = self._process_solver_results(model)
except ValueError:
self.__psmsg("Something went wrong with the solver")
# Return model solution and solver statistics
self.final_results = processed_results
self.__page_borders(bottom=True) # Footer for page
class SolverWrapper:
class __SolversInfo:
"""
Default info for solvers
"""
def __init__(self):
self.configured_solvers = {
# solver_name: ['windows executable', 'unix executable', 'solver_io']
'cbc': ['cbc', 'cbc', 'lp'],
'cplex': ['cplex', 'cplex', 'lp'],
'glpk': ['glpk', 'glpk', 'lp'],
'gurobi': ['gurobi', 'gurobi.sh',
'python'], # Configure gurobi to use python api?
'baron': ['baron', 'baron', 'nl'],
'ipopt': ['ipopt', 'ipopt', 'lp'],
'couenne': ['couenne', 'couenne', 'nl'],
'bonmin': ['bonmin', 'bonmin', 'nl']
}
self.neos_compatible = [
'bonmin', 'cbc', 'conopt', 'couenne', 'cplex', 'filmint',
'filter', 'ipopt', 'knitro', 'l-bfgs-b', 'lancelot', 'lgo',
'loqo', 'minlp', 'minos', 'minto', 'mosek', 'ooqp', 'path',
'raposa', 'snopt'
]
class __Constants:
"""
Default constants for use throughout the class
"""
def __init__(self):
self.var_defaults = {
'solver_name': 'gurobi',
'solver_path': False,
'time_limit': 1200,
'threads': 2,
'neos': False,
'verbosity': False,
'debug_mode': False,
'solver_progress': True,
'write_solution': True,
'write_solution_to_stdout': True,
'return_solution': True,
'rel_gap': 0.0,
'result_precision': 6
}
self.var_types = {
'time_limit': [int, float],
'threads': [int],
'neos': [bool],
'verbosity': [bool],
'debug_mode': [bool],
'solver_progress': [bool],
'write_solution': [bool],
'write_solution_to_stdout': [bool],
'return_solution': [bool],
'rel_gap': [int, float],
'result_precision': [int]
}
self.os_name = platform.system()
def __init__(self,
solver_name=None,
solver_path=None,
time_limit=None,
threads=None,
neos=None,
verbosity=None,
debug_mode=None,
solver_progress=None,
write_solution=None,
write_solution_to_stdout=None,
return_solution=None,
rel_gap=None,
result_precision=None,
neos_registered_email=None):
# Set methods defaults
self.solver_info = self.__SolversInfo()
self.constants = self.__Constants()
self.solver_name = self.__apattr(
solver_name, self.constants.var_defaults['solver_name'])
self.solver_path = self.__apattr(
solver_path, self.constants.var_defaults['solver_path'])
# Set solver defaults
self.time_limit = self.__apattr(
time_limit, self.constants.var_defaults['time_limit'])
self.threads = self.__apattr(threads,
self.constants.var_defaults['threads'])
self.neos = self.__apattr(neos, self.constants.var_defaults['neos'])
self.verbosity = self.__apattr(
verbosity, self.constants.var_defaults['verbosity'])
self.debug_mode = self.__apattr(
debug_mode, self.constants.var_defaults['debug_mode'])
self.verbose_debug_mode = False
self.solver_progress = self.__apattr(
solver_progress, self.constants.var_defaults['solver_progress'])
self.write_solution = self.__apattr(
write_solution, self.constants.var_defaults['write_solution'])
self.write_solution_to_stdout = self.__apattr(
write_solution_to_stdout,
self.constants.var_defaults['write_solution_to_stdout'])
self.return_solution = self.__apattr(
return_solution, self.constants.var_defaults['return_solution'])
self.rel_gap = self.__apattr(rel_gap,
self.constants.var_defaults['rel_gap'])
self.result_precision = self.__apattr(
result_precision, self.constants.var_defaults['result_precision'])
# Set other defaults
self.__model_name_str = None
self.__current_datetime_str = None
self.__pyomo_version = pyoversion.version
self.__pyutilib_version = self.__get_pkg_version("PyUtilib")
self.__dependency_check_count = 1
self.__dependency_check = self.__pyutilib_dependency_check()
self.__DEF_registered_email = "*****@*****.**"
self.__DEF_REGEX = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'
self.neos_registered_email = neos_registered_email
self.neos_registered_email = self.__sg_neos_registered_email(
defaults=False)
def __sg_neos_registered_email(self, defaults=True):
if self.neos_registered_email is None:
if defaults:
self.__msg(
"NB: An email address can be passed to 'neos_registered_email' instead of using defaults."
)
self.neos_registered_email = self.__DEF_registered_email
return self.neos_registered_email
else:
if re.match(self.__DEF_REGEX, self.neos_registered_email):
return self.neos_registered_email
else:
self.__msg(
"An invalid email address was passed. Resorting to default..."
)
self.neos_registered_email = None
self.__sg_neos_registered_email(
defaults=False
) # Tiny way to avoid repeated warnings to users
return self.neos_registered_email
def __apattr(self, attrib, value):
"""
Set value to an attrib
:param attrib:
:param value:
:return: None
"""
if attrib is None:
return value
else:
return attrib
def __chkattrt(self, attrib):
"""
Check type of attrib against requirement and set to default else
:param attrib:
:return: None
"""
# Get attrib name as string
c_var_list = [vars for vars in locals().keys() if "_" not in vars[:2]]
attrib_str = None
for var_l in c_var_list:
if id(attrib) == id(var_l):
attrib_str = var_l
break
else:
pass
if attrib_str is not None and attrib_str in self.constants.var_types.keys(
):
if type(attrib) in self.constants.var_types[attrib_str]:
pass
else:
self.__psmsg(
'Value given to ' + attrib_str + ' is invalid',
'The following value types are acceptable: ' +
str(self.constants.var_types[attrib_str]))
self.__psmsg('Setting default value. . .')
setattr(self, attrib_str,
self.constants.var_defaults[attrib_str])
else:
pass
def __msg(self, *msg, text_indent=4):
text_indent = " " * int(text_indent)
# Text wrapper function
wrapper = textwrap.TextWrapper(width=60,
initial_indent=text_indent,
subsequent_indent=text_indent)
# Print message
print("\n")
for message in msg:
message = wrapper.wrap(text=str(message))
for element in message:
print(element)
return text_indent
def __pemsg(self, *msg, exit=True):
"""
Custom error messages to print to stdout and stop execution
:param message: Error message to be printed
"""
if self.debug_mode:
exit = self.debug_mode
else:
exit = exit
text_indent = self.__msg(*msg, text_indent=4)
if exit: # Stop run
print(text_indent + "Exiting . . .")
sys.exit(1)
else:
pass
def __psmsg(self, *msg):
"""
Custom status messages to print to stdout and stop execution
:param message: Error message to be printed
"""
if self.verbosity:
self.__msg("INFO:", *msg, text_indent=1)
else:
pass
def __page_borders(self, bottom=False):
if self.verbosity:
if bottom:
self.__msg("- - " * 15, "\n", text_indent=0)
else:
self.__msg("- - " * 15, text_indent=0)
else:
pass
def __run_ext_command(self, cmd=[" "]):
return subprocess.Popen(cmd, stdout=subprocess.PIPE).communicate()[0]
def __get_solver_path(self, solver_exct):
if self.constants.os_name == 'Windows':
path = self.__run_ext_command(['where',
solver_exct]).decode('utf-8')
else:
path = self.__run_ext_command(['which',
solver_exct]).decode('utf-8')
if path != '':
return path, True
else:
return None, False
def __get_pkg_version(self, pkg):
parent_cmd = subprocess.Popen(['pip', 'freeze'],
stdout=subprocess.PIPE)
if self.constants.os_name == 'Windows':
value = subprocess.check_output(
['findstr', str(pkg)], stdin=parent_cmd.stdout).decode('utf-8')
else:
value = subprocess.check_output(
['grep', str(pkg)], stdin=parent_cmd.stdout).decode('utf-8')
if value == '':
return value
elif len(value) > len(pkg):
value = value[len(pkg) + 2:]
return value
else:
return None
def __pyutilib_dependency_check(self):
if self.__dependency_check_count <= 3:
if self.__pyomo_version == '5.6.8':
if '5.7.3' not in self.__pyutilib_version:
system('pip install pyutilib==5.7.3')
self.__pyutilib_version = self.__get_pkg_version(
"PyUtilib")
self.__dependency_check_count += 1
self.__pyutilib_dependency_check()
else:
self.__dependency_check = True
elif self.__pyomo_version == '5.6.9':
if '5.8.0' not in self.__pyutilib_version:
system('pip install pyutilib==5.8.0')
self.__pyutilib_version = self.__get_pkg_version(
"PyUtilib")
self.__dependency_check_count += 1
self.__pyutilib_dependency_check()
else:
self.__dependency_check = True
elif self.__pyomo_version > '5.6.9':
if '6.0.0' not in self.__pyutilib_version:
system('pip install pyutilib==6.0.0')
self.__pyutilib_version = self.__get_pkg_version(
"PyUtilib")
self.__dependency_check_count += 1
self.__pyutilib_dependency_check()
else:
self.__dependency_check = True
else:
self.__pemsg(
"The right version of pyutilib could not be installed.")
def __set_tempdir(self, folder):
"""
Set temporary solver folder
:param folder: path to folder
:return:
"""
if self.__pyomo_version <= '5.7.1':
from pyutilib.services import TempfileManager
TempfileManager.tempdir = folder
else:
from pyomo.common.tempfiles import TempfileManager
TempfileManager.tempdir = folder
def __get_set_list(self, pyo_obj):
if self.__pyomo_version < '5.7.0'[:len(self.__pyomo_version)]:
return [set for set in pyo_obj._index.set_tuple]
else:
return [set.name for set in pyo_obj._index.subsets()]
def __get_set_list_alt(self, pyo_obj):
if self.__pyomo_version < '5.7.0'[:len(self.__pyomo_version)]:
return [set for set in pyo_obj.domain.set_tuple]
else:
return [set.name for set in pyo_obj.domain.subsets()]
def __solvers_compatibility_check(self):
"""
NEOS vs local solvers: Check solvers are recognised/available
:return:
"""
if self.neos: # using NEOS
if self.solver_name not in self.solver_info.neos_compatible:
self.__pemsg(
"NEOS server does not seem to be configure for " +
str(self.solver_name),
"If you must used this solver, install a local version and set option 'neos' to 'False'",
exit=False)
self.__pemsg("Attempting to use a local solver instance . . .",
exit=False)
self.neos = False
self.__solvers_path_check()
self.__solvers_compatibility_check()
else:
environ['NEOS_EMAIL'] = self.__sg_neos_registered_email()
if self.verbosity:
self.__psmsg("Using NEOS server to solve model . . .")
else:
pass
else: # using a locally installed solver
if self.solver_name not in self.solver_info.configured_solvers.keys(
):
self.__pemsg(
self.solver_name +
" is not amongst those currently configured by this package"
)
elif not self.solver_avail:
if self.solver_path is False:
self.__pemsg(self.solver_name +
" is not installed or at the path specified")
else:
self.solver_path = False
self.__solvers_path_check()
self.__solvers_compatibility_check()
else:
if self.verbosity:
self.__psmsg("Solver located in {}".format(
self.solver_path))
else:
pass
def __solvers_path_check(self):
"""
# Confirm solver paths and thus availability
:return:
"""
if not self.neos:
if self.solver_path is False:
if self.constants.os_name == 'Windows':
self.solver_path, self.solver_avail = \
self.__get_solver_path(self.solver_info.configured_solvers[self.solver_name][0])
else:
self.solver_path, self.solver_avail = \
self.__get_solver_path(self.solver_info.configured_solvers[self.solver_name][1])
else:
self.solver_avail = path.exists(self.solver_path)
else:
self.solver_avail = False
def solve_model(self, model):
"""
Method to solve an optimization model using a specified solver
Returns:
:dict - solver statistics and model solution
"""
# Set a few default values
attr_to_check = [
'solver_name', 'neos', 'write_solution', 'return_solution',
'verbosity', 'solver_progress'
]
for attr in attr_to_check:
self.__chkattrt(attr)
# Get model name
model_name = model.name
self.__model_name_str = str(re.sub(" ", "_", model_name))
# Solver name to lower case characters
self.solver_name = self.solver_name.lower()
# Confirm solver paths and thus availability
self.__solvers_path_check()
# NEOS vs local solvers: check solvers are recognised/available
self.__solvers_compatibility_check() # Run solvers check
# Call solver factory
opt_solver = SolverFactory(self.solver_name)
# Change solver temporary folder path
log_folder = path.join('_log', '')
if self.solver_name in ['gurobi', 'baron', 'cplex']:
if not path.exists(log_folder):
makedirs(log_folder)
self.__set_tempdir(log_folder)
else:
pass
# Include solver-compatible options
if self.solver_name in ['cplex', 'gurobi']:
opt_solver.options['threads'] = self.threads
opt_solver.options['mipgap'] = self.rel_gap
opt_solver.options['timelimit'] = self.time_limit
elif self.solver_name in ['baron']:
opt_solver.options['threads'] = self.threads
opt_solver.options['LPSol'] = 3
opt_solver.options['EpsR'] = self.rel_gap
opt_solver.options['MaxTime'] = self.time_limit
# For Unix systems ensure "libcplexxxxx.dylib" is in the system PATH for baron to use CPLEX for MIPs
elif self.solver_name in ['cbc']:
opt_solver.options['threads'] = self.threads
opt_solver.options['ratio'] = self.rel_gap
opt_solver.options['seconds'] = self.time_limit
opt_solver.options['log'] = int(self.solver_progress) * 2
opt_solver.options['mess'] = 'on'
opt_solver.options['timeM'] = "elapsed"
opt_solver.options['preprocess'] = "equal"
elif self.solver_name in ['glpk']:
opt_solver.options['mipgap'] = self.rel_gap
opt_solver.options['tmlim'] = self.time_limit
elif self.solver_name in ['ipopt']:
opt_solver.options['max_wall_time'] = self.time_limit
elif self.solver_name in ['bonmin']:
opt_solver.options['time_limit'] = self.time_limit
opt_solver.options['number_cpx_threads'] = self.threads
opt_solver.options['allowable_fraction_gap'] = self.rel_gap
elif self.solver_name in ['couenne']:
opt_solver.options['time_limit'] = self.time_limit
opt_solver.options['threads'] = self.threads
opt_solver.options['ratio'] = self.rel_gap
opt_solver.options['seconds'] = self.time_limit
opt_solver.options['log'] = int(self.solver_progress) * 2
opt_solver.options['mess'] = 'on'
opt_solver.options['timeM'] = "elapsed"
opt_solver.options['preprocess'] = "equal"
else:
pass
# Write log to file named <model_name>/DD_MM_YY_HH_MM_xx.log
# Create (if it does not exist) the '_log' folder
log_store_folder = path.join(log_folder, self.__model_name_str, '')
if not path.exists(log_store_folder):
makedirs(log_store_folder)
self.__current_datetime_str = datetime.now().strftime(
"%d_%m_%y_%H_%M_")
file_suffix = 0
# Results filename
if self.__model_name_str == 'Unknown' or len(
self.__model_name_str) <= 10:
log_filename = self.__model_name_str + self.__current_datetime_str + str(
file_suffix) + ".log"
else:
log_filename = self.__model_name_str[:4] + '..' + self.__model_name_str[-4:] + \
self.__current_datetime_str + str(file_suffix) + ".log"
while path.exists(log_store_folder + log_filename):
file_suffix += 1
log_filename = self.__current_datetime_str + str(
file_suffix) + ".log"
log_filename = self.__current_datetime_str + str(file_suffix) + ".log"
# Solve <model> with/without writing final solution to stdout
processed_results = None
self.__page_borders() # Headers for page
try:
if self.neos:
self.solver_results = SolverManagerFactory('neos').solve(
model, opt=opt_solver, tee=self.solver_progress)
else:
self.solver_results = opt_solver.solve(
model,
tee=self.solver_progress,
logfile=log_store_folder + log_filename)
# Process results obtained
if self.neos:
self.__msg(
"Currently, results are not post-processed for NEOS server runs"
) #FIXME Find a work around
else:
processed_results = self._process_solver_results(model)
except ValueError:
self.__psmsg("Something went wrong with the solver")
# Return model solution and solver statistics
self.final_results = processed_results
self.__page_borders(bottom=True) # Footer for page
# Method for post processing solver results
def _process_solver_results(self, model):
"""
Method to post process results from 'solve_model' method
:param model: solved model
:return: dictionary of solver results
"""
from pyomo.environ import Set, RealSet, RangeSet, Param, Var
# Write solution to stdout/file
if self.write_solution and (
str(self.solver_results.solver.Status) in ['ok']
or str(self.solver_results.solver.Termination_condition)
in ['maxTimeLimit']):
if self.write_solution_to_stdout:
# Write solution to screen
self.solver_results.write()
else:
pass
# Write solution to file named <model_name>/DD_MM_YY_HH_MM_xx.json
# Create (if it does not exist) the '_results_store' folder
results_store_folder = path.join('_results_store',
self.__model_name_str, '')
if not path.exists(results_store_folder):
makedirs(results_store_folder)
if self.__pyomo_version <= '5.7.1':
model.solutions.store_to(
self.solver_results) # define solutions storage folder
else:
pass
self.__current_datetime_str = datetime.now().strftime(
"%d_%m_%y_%H_%M_")
file_suffix = 0
# Results filename
if self.__model_name_str == 'Unknown' or len(
self.__model_name_str) <= 10:
result_filename = self.__model_name_str + self.__current_datetime_str + str(
file_suffix) + ".json"
else:
result_filename = self.__model_name_str[:4] + '..' + self.__model_name_str[-4:] + \
self.__current_datetime_str + str(file_suffix) + ".json"
while path.exists(results_store_folder + result_filename):
file_suffix += 1
result_filename = self.__current_datetime_str + str(
file_suffix) + ".json"
result_filename = self.__current_datetime_str + str(
file_suffix) + ".json"
self.solver_results.write(filename=results_store_folder +
result_filename,
format="json")
else:
pass
# Create dictionary to for solver statistics and solution
final_result = dict()
# Include the default solver results from opt_solver.solve & current state of model
final_result['solver_results_def'] = self.solver_results
final_result[
'model'] = model # copy.deepcopy(model) # include all model attributes
# _include solver statistics
acceptable_termination_conditions = [
'maxTimeLimit', 'maxIterations', 'locallyOptimal',
'globallyOptimal', 'optimal', 'other'
]
if str(self.solver_results.solver.Status) == 'ok' or (
str(self.solver_results.solver.Status) == 'aborted'
and str(self.solver_results.solver.Termination_condition)
in acceptable_termination_conditions):
final_result['solver'] = dict(
) # Create dictionary for solver statistics
final_result['solver'] = {
'status':
str(self.solver_results.solver.Status),
'solver_message':
str(self.solver_results.solver.Message),
'termination_condition':
str(self.solver_results.solver.Termination_condition)
}
try:
final_result['solver'][
'wall_time'] = self.solver_results.solver.wall_time
except AttributeError:
final_result['solver']['wall_time'] = None
try:
final_result['solver'][
'wall_time'] = self.solver_results.solver.wall_time
except AttributeError:
final_result['solver']['wall_time'] = None
try:
final_result['solver'][
'cpu_time'] = self.solver_results.solver.time
except AttributeError:
final_result['solver']['cpu_time'] = None
try:
if self.solver_results.problem.Upper_bound == 0 and self.solver_results.problem.Lower_bound == 0:
final_result['solver']['gap'] = 0
elif self.solver_results.problem.Upper_bound == 0:
final_result['solver']['gap'] = abs(
round(self.solver_results.problem.Lower_bound, 2))
else:
final_result['solver']['gap'] = abs(round(
(self.solver_results.problem.Upper_bound - self.solver_results.problem.Lower_bound) \
* 100 / self.solver_results.problem.Upper_bound, 2))
except AttributeError:
final_result['solver']['gap'] = None
# Check state of available solution
if self.__pyomo_version < '5.7.1':
try:
for key, value in final_result['solver_results_def'][
'Solution'][0]['Objective'].items():
objective_value = value['Value']
final_result['solution_status'] = True
except:
final_result['solution_status'] = False
objective_value = 'Unk'
else:
try:
objective_value = model.solutions.solutions[0]._entry[
'objective'][list(
model.solutions.solutions[0]._entry['objective'].
keys())[0]][1]['Value']
final_result['solution_status'] = True
except:
final_result['solution_status'] = False
objective_value = 'Unk'
if self.return_solution and final_result['solution_status']:
# True: include values of all model objects in 'final_result'
# write list of sets, parameters and variables
final_result['sets_list'] = [
str(i) for i in chain(model.component_objects(Set),
model.component_objects(RealSet),
model.component_objects(RangeSet))
if (re.split("_", str(i))[-1] != 'index')
if (re.split("_", str(i))[-1] != 'domain')
]
final_result['parameters_list'] = [
str(i) for i in model.component_objects(Param)
]
final_result['variables_list'] = [
str(i) for i in model.component_objects(Var)
]
# Populate final results for sets, parameters and variables
# Create method to return array
def indexed_value_extract(index, object):
return array([value for value in object[index].value])
# Sets
final_result['sets'] = dict()
for set in final_result['sets_list']:
set_object = getattr(model, str(set))
final_result['sets'][set] = array(
list(set_object)) # save array of set elements
# Parameters
final_result['parameters'] = dict()
if self.verbosity:
print('\nProcessing parameters . . . ')
else:
pass
for par in final_result['parameters_list']:
if self.verbosity:
print(par, ' ', end="")
else:
pass
par_object = getattr(model, str(par))
par_object_dim = par_object.dim(
) # get dimension of parameter
if par_object_dim == 0:
final_result['parameters'][par] = par_object.value
elif par_object_dim == 1:
final_result['parameters'][par] = array(
[value for value in par_object.values()])
else:
try:
par_set_list = self.__get_set_list(par_object)
par_set_lens = [
len(getattr(model, str(set)))
for set in par_set_list
]
except AttributeError:
par_set_list = [str(par_object._index.name)]
temp_par_set = getattr(model, par_set_list[0])
if temp_par_set.dimen == 1:
par_set_lens = [len(temp_par_set)]
else:
par_set_lens = [
len(set) for set in
self.__get_set_list_alt(temp_par_set)
]
# print(par_set_lens)
final_result['parameters'][par] = zeros(
shape=par_set_lens, dtype=float)
if par_object_dim == 2:
if len(par_set_list) == par_object_dim:
for ind_i, i in enumerate(
getattr(model, str(par_set_list[0]))):
for ind_j, j in enumerate(
getattr(model,
str(par_set_list[1]))):
final_result['parameters'][par][ind_i][
ind_j] = par_object[i, j]
elif len(par_set_list) == 1:
for set in par_set_list:
for ind, (i, j) in enumerate(
getattr(model, str(set))):
# print(type(final_result['parameters'][par]),final_result['parameters'][par])
# print(i,j,final_result['parameters'][par][i-1][j-1])
# print(par_set_lens)
# print(par_set_list)
# print(par_object_dim)
if self.__pyomo_version < '5.7': # FIXME: Better way to do this?
final_result['parameters'][par][
i - 1][j - 1] = par_object[i,
j]
else:
final_result['parameters'][par][
ind] = par_object[i, j]
else:
pass
else:
pass # FIXME 3-dimensional variables are not considered yet
# Variables
final_result['variables'] = dict()
# Include objective functionv value
final_result['variables']['Objective'] = objective_value
if self.verbosity:
print('\nProcessing results of variables . . . ')
else:
pass
for variable in final_result['variables_list']:
try:
if self.verbosity:
print(variable, ' ', end="")
else:
pass
variable_object = getattr(model, str(variable))
variable_object_dim = variable_object.dim(
) # get dimension of variable
if variable_object_dim == 0:
final_result['variables'][
variable] = variable_object.value
elif variable_object_dim == 1:
final_result['variables'][variable] = array([
value.value
for value in variable_object.values()
])
else:
try:
variable_set_list = self.__get_set_list(
variable_object)
variable_set_lens = [
len(getattr(model, str(set)))
for set in variable_set_list
]
except AttributeError:
variable_set_list = [
str(variable_object._index.name)
]
temp_variable_set = getattr(
model, variable_set_list[0])
if temp_variable_set.dimen == 1:
variable_set_lens = [
len(temp_variable_set)
]
else:
variable_set_lens = [
len(set)
for set in self.__get_set_list_alt(
temp_variable_set)
]
# print(variable_set_lens)
final_result['variables'][variable] = zeros(
shape=variable_set_lens, dtype=float)
if variable_object_dim == 2:
if len(variable_set_list
) == variable_object_dim:
for ind_i, i in enumerate(
getattr(
model,
str(variable_set_list[0]))):
for ind_j, j in enumerate(
getattr(
model,
str(variable_set_list[1]))
):
final_result['variables'][
variable][ind_i][
ind_j] = variable_object[
i, j].value
elif len(variable_set_list) == 1:
for set in variable_set_list:
for ind, (i, j) in enumerate(
getattr(model, str(set))):
# print(type(final_result['variables'][variable]),final_result['variables'][variable])
# print(i,j,final_result['variables'][variable][i-1][j-1])
if self.__pyomo_version < '5.7': # FIXME: Better way to do this?
final_result['variables'][
variable][i - 1][
j -
1] = variable_object[
i, j].value
else:
final_result['variables'][
variable][
ind] = variable_object[
i, j].value
else:
pass
else:
pass # FIXME 3-dimensional variables are not considered yet
except AttributeError:
pass
print('\n')
else: # if solver_status != 'ok' or amongst acceptable termination conditions
if self.debug_mode:
if self.verbose_debug_mode: # Print troublesome constraints
from pyomo.util.infeasible import log_infeasible_constraints
log_infeasible_constraints(model)
else:
pass
self.__psmsg('An optimal solution could not be processed'
) # leave program running to debug
else:
self.__pemsg('An optimal solution could not be processed')
else: # if solver_status != ok
self.__pemsg('An optimal solution was NOT found')
return final_result
from pyomo.core import *
from pyomo.opt import SolverFactory, SolverManagerFactory
from DiseaseEstimation import model
# create the instance
instance = model.create('DiseaseEstimation.dat')
# define the solver and its options
solver = 'ipopt'
opt = SolverFactory( solver )
if opt is None:
raise ValueError, "Problem constructing solver `"+str(solver)
opt.set_options('max_iter=2')
# create the solver manager
solver_manager = SolverManagerFactory( 'serial' )
# solve
results = solver_manager.solve(instance, opt=opt, tee=True, timelimit=None)
instance.load(results)
# display results
display(instance)
def run(args=None):
##########################================================#########
# to import plugins
import pyomo.environ
import pysp.pyro.smanager_pyro
import pysp.plugins.phpyro
def partialLagrangeParametric(args=None):
print("lagrangeParam begins ")
blanks = " " # used for formatting print statements
class Object(object):
pass
Result = Object()
# options used
IndVarName = options.indicator_var_name
CCStageNum = options.stage_num
alphaTol = options.alpha_tol
MaxMorePR = options.MaxMorePR # option to include up to this many PR points above F^* with all delta fixed
outputFilePrefix = options.outputFilePrefix
# We write ScenarioList = name, probability
# PRoptimal = probability, min-cost, [selections]
# PRmore = probability, min-cost, [selections]
# ================ sorted by probability ========================
#
# These can be read to avoid re-computing points
ph = PHFromScratch(options)
Result.ph = ph
rootnode = ph._scenario_tree._stages[0]._tree_nodes[
0] # use rootnode to loop over scenarios
if find_active_objective(ph._scenario_tree._scenarios[0]._instance,
safety_checks=True).is_minimizing():
print("We are solving a MINIMIZATION problem.\n")
else:
print("We are solving a MAXIMIZATION problem.\n")
# initialize
ScenarioList = []
lambdaval = 0.
lagrUtil.Set_ParmValue(ph, options.lambda_parm_name, lambdaval)
# IMPORTANT: Preprocess the scenario instances
# before fixing variables, otherwise they
# will be preprocessed out of the expressions
# and the output_fixed_variable_bounds option
# will have no effect when we update the
# fixed variable values (and then assume we
# do not need to preprocess again because
# of this option).
ph._preprocess_scenario_instances()
lagrUtil.FixAllIndicatorVariables(ph, IndVarName, 0)
for scenario in rootnode._scenarios:
ScenarioList.append((scenario._name, scenario._probability))
# sorts from min to max probability
ScenarioList.sort(key=operator.itemgetter(1))
with open(outputFilePrefix + 'ScenarioList.csv', 'w') as outFile:
for scenario in ScenarioList:
outFile.write(scenario[0] + ", " + str(scenario[1]) + "\n")
Result.ScenarioList = ScenarioList
print("lambda= " + str(lambdaval) + " ...run begins " +
str(len(ScenarioList)) + " scenarios")
SolStat, zL = lagrUtil.solve_ph_code(ph, options)
print("\t...ends")
bL = Compute_ExpectationforVariable(ph, IndVarName, CCStageNum)
if bL > 0:
print("** bL = " + str(bL) + " > 0")
return Result
print("Initial cost = " + str(zL) + " for bL = " + str(bL))
lagrUtil.FixAllIndicatorVariables(ph, IndVarName, 1)
print("lambda= " + str(lambdaval) + " ...run begins")
SolStat, zU = lagrUtil.solve_ph_code(ph, options)
print("\t...ends")
bU = Compute_ExpectationforVariable(ph, IndVarName, CCStageNum)
if bU < 1:
print("** bU = " + str(bU) + " < 1")
lagrUtil.FreeAllIndicatorVariables(ph, IndVarName)
Result.lbz = [[0, bL, zL], [None, bU, zU]]
Result.selections = [[], ScenarioList]
NumIntervals = 1
print("initial gap = " + str(1 - zL / zU) + " \n")
print("End of test; this is only a test.")
return Result
################################
# LagrangeParametric ends here
################################
#### start run ####
AllInOne = False
##########################
# options defined here
##########################
try:
conf_options_parser = construct_ph_options_parser("lagrange [options]")
conf_options_parser.add_argument(
"--alpha",
help="The alpha level for the chance constraint. Default is 0.05",
action="store",
dest="alpha",
type=float,
default=0.05)
conf_options_parser.add_argument(
"--alpha-min",
help=
"The min alpha level for the chance constraint. Default is None",
action="store",
dest="alpha_min",
type=float,
default=None)
conf_options_parser.add_argument(
"--alpha-max",
help="The alpha level for the chance constraint. Default is None",
action="store",
dest="alpha_max",
type=float,
default=None)
conf_options_parser.add_argument(
"--min-prob",
help="Tolerance for testing probability > 0. Default is 1e-5",
action="store",
dest="min_prob",
type=float,
default=1e-5)
conf_options_parser.add_argument(
"--alpha-tol",
help="Tolerance for testing equality to alpha. Default is 1e-5",
action="store",
dest="alpha_tol",
type=float,
default=1e-5)
conf_options_parser.add_argument(
"--MaxMorePR",
help=
"Generate up to this many additional PR points after response function. Default is 0",
action="store",
dest="MaxMorePR",
type=int,
default=0)
conf_options_parser.add_argument(
"--outputFilePrefix",
help="Output file name. Default is ''",
action="store",
dest="outputFilePrefix",
type=str,
default="")
conf_options_parser.add_argument(
"--stage-num",
help=
"The stage number of the CC indicator variable (number, not name). Default is 2",
action="store",
dest="stage_num",
type=int,
default=2)
conf_options_parser.add_argument(
"--lambda-parm-name",
help=
"The name of the lambda parameter in the model. Default is lambdaMult",
action="store",
dest="lambda_parm_name",
type=str,
default="lambdaMult")
conf_options_parser.add_argument(
"--indicator-var-name",
help=
"The name of the indicator variable for the chance constraint. The default is delta",
action="store",
dest="indicator_var_name",
type=str,
default="delta")
conf_options_parser.add_argument(
"--use-Loane-cuts",
help="Add the Loane cuts if there is a gap. Default is False",
action="store_true",
dest="add_Loane_cuts",
default=False)
conf_options_parser.add_argument(
"--fofx-var-name",
help=
"(Loane) The name of the model's auxiliary variable that is constrained to be f(x). Default is fofox",
action="store",
dest="fofx_var_name",
type=str,
default="fofx")
conf_options_parser.add_argument(
"--solve-with-ph",
help=
"Perform solves via PH rather than an EF solve. Default is False",
action="store_true",
dest="solve_with_ph",
default=False)
conf_options_parser.add_argument(
"--skip-graph",
help=
"Do not show the graph at the end. Default is False (i.e. show the graph)",
action="store_true",
dest="skip_graph",
default=False)
conf_options_parser.add_argument(
"--write-xls",
help="Write results into a xls file. Default is False",
action="store_true",
dest="write_xls",
default=False)
conf_options_parser.add_argument(
"--skip-ExpFlip",
help=
"Do not show the results for flipping the indicator variable for each scenario. Default is False (i.e. show the flipping-results)",
action="store_true",
dest="skip_ExpFlip",
default=False)
conf_options_parser.add_argument(
"--HeurFlip",
help=
"The number of solutions to evaluate after the heuristic. Default is 3. For 0 the heuristic flip gets skipped.",
action="store",
type=int,
dest="HeurFlip",
default=3)
conf_options_parser.add_argument(
"--HeurMIP",
help=
"The mipgap for the scenariowise solves in the heuristic. Default is 0.0001",
action="store",
type=float,
dest="HeurMIP",
default=0.0001)
conf_options_parser.add_argument(
"--interactive",
help="Enable interactive version of the code. Default is False.",
action="store_true",
dest="interactive",
default=False)
conf_options_parser.add_argument(
"--Lgap",
help=
"The (relative) Lagrangian gap acceptable for the chance constraint. Default is 10^-4",
action="store",
type=float,
dest="LagrangeGap",
default=0.0001)
conf_options_parser.add_argument(
"--lagrange-method",
help="The Lagrange multiplier search method",
action="store",
dest="lagrange_search_method",
type=str,
default="tangential")
conf_options_parser.add_argument(
"--max-lambda",
help="The max value of the multiplier. Default=10^10",
action="store",
dest="max_lambda",
type=float,
default=10**10)
conf_options_parser.add_argument(
"--min-lambda",
help="The min value of the multiplier. Default=0.0",
action="store",
dest="min_lambda",
type=float,
default=0)
conf_options_parser.add_argument(
"--min-probability",
help="The min value of scenario probability. Default=10^-15",
action="store",
dest="min_probability",
type=float,
default=10**(-15))
################################################################
options = conf_options_parser.parse_args(args=args)
# temporary hack
options._ef_options = conf_options_parser._ef_options
options._ef_options.import_argparse(options)
except SystemExit as _exc:
# the parser throws a system exit if "-h" is specified - catch
# it to exit gracefully.
return _exc.code
# load the reference model and create the scenario tree - no
# scenario instances yet.
if options.verbose:
print("Loading reference model and scenario tree")
#scenario_instance_factory, full_scenario_tree = load_models(options)
scenario_instance_factory = \
ScenarioTreeInstanceFactory(options.model_directory,
options.instance_directory)
full_scenario_tree = \
GenerateScenarioTreeForPH(options,
scenario_instance_factory)
solver_manager = SolverManagerFactory(options.solver_manager_type)
if solver_manager is None:
raise ValueError("Failed to create solver manager of "
"type=" + options.solver_manager_type +
" specified in call to PH constructor")
if isinstance(solver_manager, pysp.plugins.phpyro.SolverManager_PHPyro):
raise ValueError("PHPyro can not be used as the solver manager")
try:
if (scenario_instance_factory is None) or (full_scenario_tree is None):
raise RuntimeError(
"***ERROR: Failed to initialize model and/or the scenario tree data."
)
# load_model gets called again, so lets make sure unarchived directories are used
options.model_directory = scenario_instance_factory._model_filename
options.instance_directory = scenario_instance_factory._scenario_tree_filename
scenario_count = len(full_scenario_tree._stages[-1]._tree_nodes)
# create ph objects for finding the solution. we do this even if
# we're solving the extensive form
if options.verbose:
print(
"Loading scenario instances and initializing scenario tree for full problem."
)
########## Here is where multiplier search is called ############
Result = partialLagrangeParametric()
#####################################################################################
finally:
# delete temporary unarchived directories
scenario_instance_factory.close()
print("\nreturned from partialLagrangeParametric")
def run(args=None):
##########################================================#########
# to import plugins
import pyomo.environ
import pyomo.solvers.plugins.smanager.phpyro
import pyomo.solvers.plugins.smanager.pyro
def partialLagrangeParametric(args=None):
print("lagrangeParam begins ")
blanks = " " # used for formatting print statements
class Object(object): pass
Result = Object()
# options used
IndVarName = options.indicator_var_name
CCStageNum = options.stage_num
alphaTol = options.alpha_tol
MaxMorePR = options.MaxMorePR # option to include up to this many PR points above F^* with all delta fixed
outputFilePrefix = options.outputFilePrefix
# We write ScenarioList = name, probability
# PRoptimal = probability, min-cost, [selections]
# PRmore = probability, min-cost, [selections]
# ================ sorted by probability ========================
#
# These can be read to avoid re-computing points
ph = PHFromScratch(options)
Result.ph = ph
rootnode = ph._scenario_tree._stages[0]._tree_nodes[0] # use rootnode to loop over scenarios
if find_active_objective(ph._scenario_tree._scenarios[0]._instance,safety_checks=True).is_minimizing():
print("We are solving a MINIMIZATION problem.\n")
else:
print("We are solving a MAXIMIZATION problem.\n")
# initialize
ScenarioList = []
lambdaval = 0.
lagrUtil.Set_ParmValue(ph,
options.lambda_parm_name,
lambdaval)
# IMPORTANT: Preprocess the scenario instances
# before fixing variables, otherwise they
# will be preprocessed out of the expressions
# and the output_fixed_variable_bounds option
# will have no effect when we update the
# fixed variable values (and then assume we
# do not need to preprocess again because
# of this option).
ph._preprocess_scenario_instances()
lagrUtil.FixAllIndicatorVariables(ph, IndVarName, 0)
for scenario in rootnode._scenarios:
ScenarioList.append((scenario._name,
scenario._probability))
# sorts from min to max probability
ScenarioList.sort(key=operator.itemgetter(1))
with open(outputFilePrefix+'ScenarioList.csv','w') as outFile:
for scenario in ScenarioList:
outFile.write(scenario[0]+ ", " +str(scenario[1])+"\n")
Result.ScenarioList = ScenarioList
print("lambda= "+str(lambdaval)+" ...run begins "+str(len(ScenarioList))+" scenarios")
SolStat, zL = lagrUtil.solve_ph_code(ph, options)
print("\t...ends")
bL = Compute_ExpectationforVariable(ph,
IndVarName,
CCStageNum)
if bL > 0:
print("** bL = "+str(bL)+" > 0")
return Result
print("Initial cost = "+str(zL)+" for bL = "+str(bL))
lagrUtil.FixAllIndicatorVariables(ph, IndVarName, 1)
print("lambda= "+str(lambdaval)+" ...run begins")
SolStat, zU = lagrUtil.solve_ph_code(ph, options)
print("\t...ends")
bU = Compute_ExpectationforVariable(ph,
IndVarName,
CCStageNum)
if bU < 1:
print("** bU = "+str(bU)+" < 1")
lagrUtil.FreeAllIndicatorVariables(ph, IndVarName)
Result.lbz = [ [0,bL,zL], [None,bU,zU] ]
Result.selections = [[], ScenarioList]
NumIntervals = 1
print("initial gap = "+str(1-zL/zU)+" \n")
print("End of test; this is only a test.")
return Result
################################
# LagrangeParametric ends here
################################
#### start run ####
AllInOne = False
##########################
# options defined here
##########################
try:
conf_options_parser = construct_ph_options_parser("lagrange [options]")
conf_options_parser.add_argument("--alpha",
help="The alpha level for the chance constraint. Default is 0.05",
action="store",
dest="alpha",
type=float,
default=0.05)
conf_options_parser.add_argument("--alpha-min",
help="The min alpha level for the chance constraint. Default is None",
action="store",
dest="alpha_min",
type=float,
default=None)
conf_options_parser.add_argument("--alpha-max",
help="The alpha level for the chance constraint. Default is None",
action="store",
dest="alpha_max",
type=float,
default=None)
conf_options_parser.add_argument("--min-prob",
help="Tolerance for testing probability > 0. Default is 1e-5",
action="store",
dest="min_prob",
type=float,
default=1e-5)
conf_options_parser.add_argument("--alpha-tol",
help="Tolerance for testing equality to alpha. Default is 1e-5",
action="store",
dest="alpha_tol",
type=float,
default=1e-5)
conf_options_parser.add_argument("--MaxMorePR",
help="Generate up to this many additional PR points after response function. Default is 0",
action="store",
dest="MaxMorePR",
type=int,
default=0)
conf_options_parser.add_argument("--outputFilePrefix",
help="Output file name. Default is ''",
action="store",
dest="outputFilePrefix",
type=str,
default="")
conf_options_parser.add_argument("--stage-num",
help="The stage number of the CC indicator variable (number, not name). Default is 2",
action="store",
dest="stage_num",
type=int,
default=2)
conf_options_parser.add_argument("--lambda-parm-name",
help="The name of the lambda parameter in the model. Default is lambdaMult",
action="store",
dest="lambda_parm_name",
type=str,
default="lambdaMult")
conf_options_parser.add_argument("--indicator-var-name",
help="The name of the indicator variable for the chance constraint. The default is delta",
action="store",
dest="indicator_var_name",
type=str,
default="delta")
conf_options_parser.add_argument("--use-Loane-cuts",
help="Add the Loane cuts if there is a gap. Default is False",
action="store_true",
dest="add_Loane_cuts",
default=False)
conf_options_parser.add_argument("--fofx-var-name",
help="(Loane) The name of the model's auxiliary variable that is constrained to be f(x). Default is fofox",
action="store",
dest="fofx_var_name",
type=str,
default="fofx")
conf_options_parser.add_argument("--solve-with-ph",
help="Perform solves via PH rather than an EF solve. Default is False",
action="store_true",
dest="solve_with_ph",
default=False)
conf_options_parser.add_argument("--skip-graph",
help="Do not show the graph at the end. Default is False (i.e. show the graph)",
action="store_true",
dest="skip_graph",
default=False)
conf_options_parser.add_argument("--write-xls",
help="Write results into a xls file. Default is False",
action="store_true",
dest="write_xls",
default=False)
conf_options_parser.add_argument("--skip-ExpFlip",
help="Do not show the results for flipping the indicator variable for each scenario. Default is False (i.e. show the flipping-results)",
action="store_true",
dest="skip_ExpFlip",
default=False)
conf_options_parser.add_argument("--HeurFlip",
help="The number of solutions to evaluate after the heuristic. Default is 3. For 0 the heuristic flip gets skipped.",
action="store",
type=int,
dest="HeurFlip",
default=3)
conf_options_parser.add_argument("--HeurMIP",
help="The mipgap for the scenariowise solves in the heuristic. Default is 0.0001",
action="store",
type=float,
dest="HeurMIP",
default=0.0001)
conf_options_parser.add_argument("--interactive",
help="Enable interactive version of the code. Default is False.",
action="store_true",
dest="interactive",
default=False)
conf_options_parser.add_argument("--Lgap",
help="The (relative) Lagrangian gap acceptable for the chance constraint. Default is 10^-4",
action="store",
type=float,
dest="LagrangeGap",
default=0.0001)
conf_options_parser.add_argument("--lagrange-method",
help="The Lagrange multiplier search method",
action="store",
dest="lagrange_search_method",
type=str,
default="tangential")
conf_options_parser.add_argument("--max-lambda",
help="The max value of the multiplier. Default=10^10",
action="store",
dest="max_lambda",
type=float,
default=10**10)
conf_options_parser.add_argument("--min-lambda",
help="The min value of the multiplier. Default=0.0",
action="store",
dest="min_lambda",
type=float,
default=0)
conf_options_parser.add_argument("--min-probability",
help="The min value of scenario probability. Default=10^-15",
action="store",
dest="min_probability",
type=float,
default=10**(-15))
################################################################
options = conf_options_parser.parse_args(args=args)
# temporary hack
options._ef_options = conf_options_parser._ef_options
options._ef_options.import_argparse(options)
except SystemExit as _exc:
# the parser throws a system exit if "-h" is specified - catch
# it to exit gracefully.
return _exc.code
# load the reference model and create the scenario tree - no
# scenario instances yet.
if options.verbose:
print("Loading reference model and scenario tree")
#scenario_instance_factory, full_scenario_tree = load_models(options)
scenario_instance_factory = \
ScenarioTreeInstanceFactory(options.model_directory,
options.instance_directory,
options.verbose)
full_scenario_tree = \
GenerateScenarioTreeForPH(options,
scenario_instance_factory)
solver_manager = SolverManagerFactory(options.solver_manager_type)
if solver_manager is None:
raise ValueError("Failed to create solver manager of "
"type="+options.solver_manager_type+
" specified in call to PH constructor")
if isinstance(solver_manager,
pyomo.solvers.plugins.smanager.phpyro.SolverManager_PHPyro):
solver_manager.deactivate()
raise ValueError("PHPyro can not be used as the solver manager")
try:
if (scenario_instance_factory is None) or (full_scenario_tree is None):
raise RuntimeError("***ERROR: Failed to initialize model and/or the scenario tree data.")
# load_model gets called again, so lets make sure unarchived directories are used
options.model_directory = scenario_instance_factory._model_filename
options.instance_directory = scenario_instance_factory._scenario_tree_filename
scenario_count = len(full_scenario_tree._stages[-1]._tree_nodes)
# create ph objects for finding the solution. we do this even if
# we're solving the extensive form
if options.verbose:
print("Loading scenario instances and initializing scenario tree for full problem.")
########## Here is where multiplier search is called ############
Result = partialLagrangeParametric()
#####################################################################################
finally:
solver_manager.deactivate()
# delete temporary unarchived directories
scenario_instance_factory.close()
print("\nreturned from partialLagrangeParametric")
return (
sum(
modelo.tiempo_produccion[planta, producto] *
modelo.produccion[producto]
for producto in modelo.productos
) <= modelo.tiempo_disponible[planta]
)
modelo.disponibilidad_tiempo = Constraint(
modelo.plantas, rule=funcion_disponibilidad_tiempo
)
def funcion_ganancias(modelo):
return sum(
modelo.ganancia_unitaria[producto] * modelo.produccion[producto]
for producto in modelo.productos
)
modelo.ganancias = Objective(rule=funcion_ganancias, sense=maximize)
instancia = modelo.create_instance("wyndor.dat")
opt = SolverFactory("cbc")
solver_manager = SolverManagerFactory('neos')
resultados = solver_manager.solve(instancia, opt=opt) # resolver en la nube
# resultados = opt.solve(instancia) # resolver localmente
instancia.display()
def solve_model(model, p_summary=False, p_log=False): # Custom Solve Method
import datetime
# Choose the best solution from the trial pool
# trial_pool = {
# 'sample1':{'solution_file':None,'aspiration':None},
# 'sample2':{'solution_file':None,'aspiration':None},
# 'sample3':{'solution_file':None,'aspiration':None}
# }
#model1 = copy.deepcopy(model)
#model2 = copy.deepcopy(model)
#model3 = copy.deepcopy(model)
import configparser
config = configparser.ConfigParser()
config.read('../start_config.ini')
#initialization Setting
mip_gap = float(config['solver']['mip_gap'])
solver_timeout = int(config['solver']['solver_timeout'])
solver_sh = config['solver']['solver_sh']
number_of_trials = int(config['solver']['number_of_trials'])
engage_neos = bool(int(
config['solver']['engage_neos'])) #initialization Setting
threads = int(config['solver']['threads'])
if solver_sh not in set(["cbc", "cplex"]):
raise AssertionError("Invalid Solver!, Error Code : 200B")
logger.info("success! \n loading solver......")
j = 1
timeout_arguments = {'cplex': 'timelimit', 'cbc': 'sec'}
gap_arguments = {
'cplex': 'mipgap',
'cbc': 'ratio'
} # Cplex Local Executable will take : "mip_tolerance_mipgap", mipgap is for neos version
# NEOS Server Library Dependency
# pyro4
# suds
# openopt
while j < number_of_trials + 1:
from pyomo.opt import SolverFactory, SolverManagerFactory
#model.pprint()
opt = SolverFactory(solver_sh, solver_io='lp')
# print ('\ninterfacing solver shell :',solver_sh)
logger.debug('interfacing solver shell : %s' % (solver_sh))
if engage_neos:
solver_manager = SolverManagerFactory('neos')
opt.options[timeout_arguments[solver_sh]] = solver_timeout
opt.options[gap_arguments[solver_sh]] = mip_gap
#opt.symbolic_solver_labels=True
#
#opt.enable = 'parallel'
print(
'\tsolver options >> \n\n\tTolerance Limits:\n\tmip_gap = %s \n\ttimeout = %s'
% (str(mip_gap), str(solver_timeout)))
# print ("\nProcessing Trial Number :",j)
# print ("\nJob Triggered at :",str(datetime.datetime.now()))
print(
'\ngenerating solution ...... !! please wait !! \n to interrupt press ctrl+C\n'
)
# logger.debug('\tsolver options >> \n\n\tTolerance Limits:\n\tmip_gap = %s \n\ttimeout = %s'%(str(mip_gap),str(solver_timeout)))
logger.debug('Processing Trial Number :%d' % (j))
logger.debug("Job Triggered")
try:
if engage_neos:
p_log = False
results = solver_manager.solve(model, opt=opt, tee=True)
else:
opt.options['threads'] = threads
if p_log:
opt.options['slog'] = 1
results = opt.solve(
model
) # Method Load Solutions is not available in pyomo versions less than 4.x
except:
j = j + 1
mip_gap = (j - 1) * mip_gap
solver_sh = 'cplex'
engage_neos = True
continue
#results.write(filename='results'+str(datetime.date.today())+'.json',format = 'json')
#print (results)
if str(results['Solver'][0]['Termination condition']) in [
'infeasible', 'maxTimeLimit', 'maxIterations',
'intermediateNonInteger', 'unbounded'
]:
j = j + 1
mip_gap = (j - 1) * mip_gap
solver_sh = 'cplex'
engage_neos = True
if j == number_of_trials + 1:
#print (results['Problem'])
#print (results['Solver'])
raise AssertionError(
"Solver Failed with Termination Status : %s \nError Code : 200C"
% (str(results['Solver'][0]['Termination condition'])))
exit(0)
# print ('Terminated by:',str(results['Solver'][0]['Termination condition']))
# print ("\n\nRetrying...\n\n")
logger.info('Terminated by:',
str(results['Solver'][0]['Termination condition']))
logger.info("\n\nRetrying...\n\n")
else:
# print ("SUCCESS: Solution Captured!")
logger.info("Solution Captured!")
model.solutions.store_to(results)
#post_process_results()
break
if p_summary:
print(results['Problem'])
# print (results['Solver'])
# print ("\nSolution Retrived at:",str(datetime.datetime.now()))
# logger.info("Solution Retrived")
return [model, results]
modelo.ganancias = Objective(rule=funcion_ganancias, sense=maximize)
def funcion_capacidad_doug(modelo):
return modelo.produccion["madera"] <= 6
modelo.capacidad_doug = Constraint(rule=funcion_capacidad_doug)
def funcion_capacidad_linda(modelo):
return modelo.produccion["aluminio"] <= 4
modelo.capacidad_linda = Constraint(rule=funcion_capacidad_linda)
def funcion_capacidad_bob(modelo):
return (
modelo.produccion["madera"]*6 +
modelo.produccion["aluminio"]*8 <= 48
)
modelo.capacidad_bob = Constraint(rule=funcion_capacidad_bob)
opt = SolverFactory("cbc")
solver_manager = SolverManagerFactory('neos')
resultados = solver_manager.solve(modelo, opt=opt) # resolver en la nube
# resultados = opt.solve(modelo) # resolver localmente
modelo.display()
def run(args=None):
###################################
# to import plugins
import pyomo.environ
import pyomo.solvers.plugins.smanager.phpyro
def LagrangeMorePR(args=None):
print("lagrangeMorePR begins %s" % datetime_string())
blanks = " " # used for formatting print statements
class Object(object): pass
Result = Object()
# options used
betaTol = options.beta_tol # tolerance used to separate b-values
IndVarName = options.indicator_var_name
multName = options.lambda_parm_name
CCStageNum = options.stage_num
MaxMorePR = options.max_number # max PR points to be generated (above F^* with all delta fixed)
MaxTime = options.max_time # max time before terminate
csvPrefix = options.csvPrefix # input filename prefix (eg, case name)
probFileName = options.probFileName # name of file containing probabilities
##HG override
# options.verbosity = 2
verbosity = options.verbosity
Result.status = 'starting '+datetime_string()
STARTTIME = time.time()
ph = PHFromScratch(options)
rootnode = ph._scenario_tree._stages[0]._tree_nodes[0] # use rootnode to loop over scenarios
if find_active_objective(ph._scenario_tree._scenarios[0]._instance,safety_checks=True).is_minimizing():
print("We are solving a MINIMIZATION problem.")
else:
print("We are solving a MAXIMIZATION problem.")
# initialize
ScenarioList = []
with open(csvPrefix+"ScenarioList.csv",'r') as inputFile:
for line in inputFile.readlines():
L = line.split(',')
ScenarioList.append([L[0],float(L[1])])
addstatus = str(len(ScenarioList))+' scenarios read from file: ' + csvPrefix+'ScenarioList.csv'
if verbosity > 0: print(addstatus)
Result.status = Result.status + '\n' + addstatus
PRoptimal = []
with open(csvPrefix+"PRoptimal.csv",'r') as inputFile:
for line in inputFile.readlines():
bzS = line.split(',')
PRoptimal.append( [None, float(bzS[0]), float(bzS[1])] )
addstatus = str(len(PRoptimal))+' PR points read from file: '+ csvPrefix+'PRoptimal.csv (envelope function)'
if verbosity > 0:
print(addstatus)
Result.status = Result.status + '\n' + addstatus
# ensure PR points on envelope function are sorted by probability
PRoptimal.sort(key=operator.itemgetter(1))
PRoptimal[0][0] = 0 # initial lambda (for b=0)
for p in range(1,len(PRoptimal)):
dz = PRoptimal[p][2] - PRoptimal[p-1][2]
db = PRoptimal[p][1] - PRoptimal[p-1][1]
PRoptimal[p][0] = dz/db
if verbosity > 0:
PrintPRpoints(PRoptimal)
Result.PRoptimal = PRoptimal
lambdaval = 0.
lagrUtil.Set_ParmValue(ph, options.lambda_parm_name,lambdaval)
# IMPORTANT: Preprocess the scenario instances
# before fixing variables, otherwise they
# will be preprocessed out of the expressions
# and the output_fixed_variable_bounds option
# will have no effect when we update the
# fixed variable values (and then assume we
# do not need to preprocess again because
# of this option).
ph._preprocess_scenario_instances()
## read scenarios to select for each PR point on envelope function
with open(csvPrefix+"OptimalSelections.csv",'r') as inputFile:
OptimalSelections = []
for line in inputFile.readlines():
if len(line) == 0: break # eof
selections = line.split(',')
L = len(selections)
Ls = len(selections[L-1])
selections[L-1] = selections[L-1][0:Ls-1]
if verbosity > 1:
print(str(selections))
OptimalSelections.append(selections)
Result.OptimalSelections = OptimalSelections
addstatus = str(len(OptimalSelections)) + ' Optimal selections read from file: ' \
+ csvPrefix + 'OptimalSelections.csv'
Result.status = Result.status + '\n' + addstatus
if len(OptimalSelections) == len(PRoptimal):
if verbosity > 0:
print(addstatus)
else:
addstatus = addstatus + '\n** Number of selections not equal to number of PR points'
print(addstatus)
Result.status = Result.status + '\n' + addstatus
print(str(OptimalSelections))
print((PRoptimal))
return Result
#####################################################################################
# get probabilities
if probFileName is None:
# ...generate from widest gap regions
PRlist = FindPRpoints(options, PRoptimal)
else:
# ...read probabilities
probList = []
with open(probFileName,'r') as inputFile:
if verbosity > 0:
print("reading from probList = "+probFileName)
for line in inputFile.readlines(): # 1 probability per line
if len(line) == 0:
break
prob = float(line)
probList.append(prob)
if verbosity > 0:
print("\t "+str(len(probList))+" probabilities")
if verbosity > 1:
print(str(probList))
PRlist = GetPoints(options, PRoptimal, probList)
if verbosity > 1:
print("PRlist:")
for interval in PRlist:
print(str(interval))
# We now have PRlist = [[i, b], ...], where b is in PRoptimal interval (i-1,i)
addstatus = str(len(PRlist)) + ' probabilities'
if verbosity > 0:
print(addstatus)
Result.status = Result.status + '\n' + addstatus
#####################################################################################
lapsedTime = time.time() - STARTTIME
addstatus = 'Initialize complete...lapsed time = ' + str(lapsedTime)
if verbosity > 1:
print(addstatus)
Result.status = Result.status + '\n' + addstatus
#####################################################################################
if verbosity > 1:
print("\nlooping over Intervals to generate PR points by flipping heuristic")
Result.morePR = []
for interval in PRlist:
lapsedTime = time.time() - STARTTIME
if lapsedTime > MaxTime:
addstatus = '** lapsed time = ' + str(lapsedTime) + ' > max time = ' + str(MaxTime)
if verbosity > 0: print(addstatus)
Result.status = Result.status + '\n' + addstatus
break
i = interval[0] # = PR point index
b = interval[1] # = target probability to reach by flipping from upper endpoint
bU = PRoptimal[i][1] # = upper endpoint
bL = PRoptimal[i-1][1] # = lower endpoint
if verbosity > 1:
print( "target probability = "+str(b)+" < bU = PRoptimal[" + str(i) + "][1]" \
" and > bL = PRoptimal["+str(i-1)+"][1]")
if b < bL or b > bU:
addstatus = '** probability = '+str(b) + ', not in gap interval: (' \
+ str(bL) + ', ' + str(bU) + ')'
print(addstatus)
print(str(PRoptimal))
print(str(PRlist))
Result.status = Result.status + '\n' + addstatus
return Result
if verbosity > 1:
print( "i = "+str(i)+" : Starting with bU = "+str(bU)+" having "+ \
str(len(OptimalSelections[i]))+ " selections:")
print(str(OptimalSelections[i]))
# first fix all scenarios = 0
for sname, sprob in ScenarioList:
scenario = ph._scenario_tree.get_scenario(sname)
lagrUtil.FixIndicatorVariableOneScenario(ph,
scenario,
IndVarName,
0)
# now fix optimal selections = 1
for sname in OptimalSelections[i]:
scenario = ph._scenario_tree.get_scenario(sname)
lagrUtil.FixIndicatorVariableOneScenario(ph,
scenario,
IndVarName,
1)
# flip scenario selections from bU until we reach b (target probability)
bNew = bU
for sname, sprob in ScenarioList:
scenario = ph._scenario_tree.get_scenario(sname)
if bNew - sprob < b:
continue
instance = ph._instances[sname]
if getattr(instance, IndVarName).value == 0:
continue
bNew = bNew - sprob
# flipped scenario selection
lagrUtil.FixIndicatorVariableOneScenario(ph,
scenario,
IndVarName,
0)
if verbosity > 1:
print("\tflipped "+sname+" with prob = "+str(sprob)+" ...bNew = "+str(bNew))
if verbosity > 1:
print("\tflipped selections reach "+str(bNew)+" >= target = "+str(b)+" (bL = "+str(bL)+")")
if bNew <= bL + betaTol or bNew >= bU - betaTol:
if verbosity > 0:
print("\tNot generating PR point...flipping from bU failed")
continue # to next interval in list
# ready to solve to get cost for fixed scenario selections associated with probability = bNew
if verbosity > 1:
# check that scenarios are fixed as they should be
totalprob = 0.
for scenario in ScenarioList:
sname = scenario[0]
sprob = scenario[1]
instance = ph._instances[sname]
print("fix "+sname+" = "+str(getattr(instance,IndVarName).value)+\
" is "+str(getattr(instance,IndVarName).fixed)+" probability = "+str(sprob))
if getattr(instance,IndVarName).value == 1:
totalprob = totalprob + sprob
lambdaval = getattr(instance, multName).value
print("\ttotal probability = %f" % totalprob)
# solve (all delta fixed); lambda=0, so z = Lagrangian
if verbosity > 0:
print("solve begins %s" % datetime_string())
print("\t- lambda = %f" % lambdaval)
SolStat, z = lagrUtil.solve_ph_code(ph, options)
b = Compute_ExpectationforVariable(ph, IndVarName, CCStageNum)
if verbosity > 0:
print("solve ends %s" % datetime_string())
print("\t- SolStat = %s" % str(SolStat))
print("\t- b = %s" % str(b))
print("\t- z = %s" % str(z))
print("(adding to more PR points)")
Result.morePR.append([None,b,z])
if verbosity > 1:
PrintPRpoints(Result.morePR)
######################################################
# end loop over target probabilities
with open(csvPrefix+"PRmore.csv",'w') as outFile:
for point in Result.morePR:
outFile.write(str(point[1])+','+str(point[2]))
addstatus = str(len(Result.morePR)) + ' PR points written to file: '+ csvPrefix + 'PRmore.csv'
if verbosity > 0: print(addstatus)
Result.status = Result.status + '\n' + addstatus
addstatus = 'lapsed time = ' + putcommas(time.time() - STARTTIME)
if verbosity > 0: print(addstatus)
Result.status = Result.status + '\n' + addstatus
return Result
################################
# LagrangeMorePR ends here
################################
#### start run ####
AllInOne = False
# VERYSTARTTIME=time.time()
# print "##############VERYSTARTTIME:",str(VERYSTARTTIME-VERYSTARTTIME)
##########################
# options defined here
##########################
try:
conf_options_parser = construct_ph_options_parser("lagrange [options]")
conf_options_parser.add_argument("--beta-min",
help="The min beta level for the chance constraint. Default is None",
action="store",
dest="beta_min",
type=float,
default=None)
conf_options_parser.add_argument("--beta-max",
help="The beta level for the chance constraint. Default is None",
action="store",
dest="beta_max",
type=float,
default=None)
conf_options_parser.add_argument("--min-prob",
help="Tolerance for testing probability > 0. Default is 1e-5",
action="store",
dest="min_prob",
type=float,
default=1e-5)
conf_options_parser.add_argument("--beta-tol",
help="Tolerance for testing equality to beta. Default is 10^-2",
action="store",
dest="beta_tol",
type=float,
default=1e-2)
conf_options_parser.add_argument("--Lagrange-gap",
help="The (relative) Lagrangian gap acceptable for the chance constraint. Default is 10^-4.",
action="store",
type=float,
dest="Lagrange_gap",
default=0.0001)
conf_options_parser.add_argument("--max-number",
help="The max number of PR points. Default = 10.",
action="store",
dest="max_number",
type=int,
default=10)
conf_options_parser.add_argument("--max-time",
help="Maximum time (seconds). Default is 3600.",
action="store",
dest="max_time",
type=float,
default=3600)
conf_options_parser.add_argument("--csvPrefix",
help="Input file name prefix. Default is ''",
action="store",
dest="csvPrefix",
type=str,
default="")
conf_options_parser.add_argument("--lambda-parm-name",
help="The name of the lambda parameter in the model. Default is lambdaMult",
action="store",
dest="lambda_parm_name",
type=str,
default="lambdaMult")
conf_options_parser.add_argument("--indicator-var-name",
help="The name of the indicator variable for the chance constraint. The default is delta",
action="store",
dest="indicator_var_name",
type=str,
default="delta")
conf_options_parser.add_argument("--stage-num",
help="The stage number of the CC indicator variable (number, not name). Default is 2",
action="store",
dest="stage_num",
type=int,
default=2)
conf_options_parser.add_argument("--verbosity",
help="verbosity=0 is no extra output, =1 is medium, =2 is debug, =3 super-debug. Default is 1.",
action="store",
dest="verbosity",
type=int,
default=1)
conf_options_parser.add_argument("--prob-file",
help="file name specifiying probabilities",
action="store",
dest="probFileName",
type=str,
default=None)
# The following needed for solve_ph_code in lagrangeutils
conf_options_parser.add_argument("--solve-with-ph",
help="Perform solves via PH rather than an EF solve. Default is False",
action="store_true",
dest="solve_with_ph",
default=False)
################################################################
options = conf_options_parser.parse_args(args=args)
# temporary hack
options._ef_options = conf_options_parser._ef_options
options._ef_options.import_argparse(options)
except SystemExit as _exc:
# the parser throws a system exit if "-h" is specified - catch
# it to exit gracefully.
return _exc.code
if options.verbose is True:
print("Loading reference model and scenario tree")
scenario_instance_factory = \
ScenarioTreeInstanceFactory(options.model_directory,
options.instance_directory,
options.verbose)
full_scenario_tree = \
GenerateScenarioTreeForPH(options,
scenario_instance_factory)
solver_manager = SolverManagerFactory(options.solver_manager_type)
if solver_manager is None:
raise ValueError("Failed to create solver manager of "
"type="+options.solver_manager_type+
" specified in call to PH constructor")
if isinstance(solver_manager,
pyomo.solvers.plugins.smanager.phpyro.SolverManager_PHPyro):
solver_manager.deactivate()
raise ValueError("PHPyro can not be used as the solver manager")
try:
if (scenario_instance_factory is None) or (full_scenario_tree is None):
raise RuntimeError("***ERROR: Failed to initialize the model and/or scenario tree data.")
# load_model gets called again, so lets make sure unarchived directories are used
options.model_directory = scenario_instance_factory._model_filename
options.instance_directory = scenario_instance_factory._scenario_tree_filename
scenario_count = len(full_scenario_tree._stages[-1]._tree_nodes)
# create ph objects for finding the solution. we do this even if
# we're solving the extensive form
if options.verbose is True:
print("Loading scenario instances and initializing scenario tree for full problem.")
########## Here is where multiplier search is called ############
Result = LagrangeMorePR()
#####################################################################################
finally:
solver_manager.deactivate()
# delete temporary unarchived directories
scenario_instance_factory.close()
print("\n==================== returned from LagrangeMorePR")
print(str(Result.status))
try:
print("Envelope:")
print(str(PrintPRpoints(Result.PRoptimal)))
print("\nAdded:")
PrintPRpoints(Result.morePR)
except:
print("from run: PrintPRpoints failed")
sys.exit()
# combine tables and sort by probability
if len(Result.morePR) > 0:
PRpoints = copy.deepcopy(Result.PRoptimal)
for lbz in Result.morePR: PRpoints.append(lbz)
print("Combined table of PR points (sorted):")
PRpoints.sort(key=operator.itemgetter(1))
print(str(PrintPRpoints(PRpoints)))
from pyomo.environ import *
from pyomo.opt import SolverFactory, SolverManagerFactory
from DiseaseEstimation import model
# create the instance
instance = model.create('DiseaseEstimation.dat')
# define the solver and its options
solver = 'ipopt'
opt = SolverFactory(solver)
if opt is None:
raise ValueError, "Problem constructing solver `" + str(solver)
opt.set_options('max_iter=2')
# create the solver manager
solver_manager = SolverManagerFactory('serial')
# solve
results = solver_manager.solve(instance, opt=opt, tee=True, timelimit=None)
instance.load(results)
# display results
display(instance)
def solveGeneratedInstance(self, instance, inst_name):
# Nutze Solver Manager None/string
if self.solver_manager_name is not None:
with SolverManagerFactory(self.solver_manager_name) as solver_manager:
if self.EXE_path is None:
opt = SolverFactory(self.solver, solver_io=self.solver_io)
else:
opt = SolverFactory(self.solver, solver_io=self.solver_io,
executable=self.EXE_path)
# Solve the instance :: Clean gets no Logs
if not self.clean:
self.result = solver_manager.solve(instance, opt=opt,
tee=self.tee,
logfile=inst_name+".log")
else:
self.result = solver_manager.solve(instance, opt=opt,
tee=self.tee,)
else:
path_log = ["logs", "logfiles", inst_name+".log"]
if self.EXE_path is None:
with SolverFactory(self.solver,
solver_io=self.solver_io) as opt:
# Set Solver Options
try:
for key, value in self.solver_options.items():
opt.options[key] = value
except AttributeError:
pass
# Solve the instance :: Clean gets no Logs
if not self.clean:
self.result = opt.solve(instance, tee=self.tee,
load_solutions=self.load_solutions,
symbolic_solver_labels=
self.symbolic_solver_labels,
logfile=os.path.join(*path_log))
else:
self.result = opt.solve(instance, tee=self.tee,
load_solutions=self.load_solutions,
symbolic_solver_labels=
self.symbolic_solver_labels)
else:
with SolverFactory(self.solver, solver_io=self.solver_io,
executable=self.EXE_path) as opt:
# Set Solver Options
try:
for key, value in self.solver_options.items():
opt.options[key] = value
except AttributeError:
pass
# Solve the instance :: Clean gets no Logs
if not self.clean:
self.result = opt.solve(instance, tee=self.tee,
load_solutions=self.load_solutions,
symbolic_solver_labels=
self.symbolic_solver_labels,
logfile=os.path.join(*path_log))
else:
self.result = opt.solve(instance, tee=self.tee,
load_solutions=self.load_solutions,
symbolic_solver_labels=
self.symbolic_solver_labels)
if not self.load_solutions:
if len(self.result.solution) > 0:
# you may need to relax these checks in certain cases
assert str(self.result.solver.status) == "ok"
assert str(self.result.solver.termination_condition) == "optimal"
self.absgap = self.result.solution(0).gap
# now load the solution
instance.solutions.load_from(self.result)
self.updateResults({inst_name: self.result})
return instance
def solve_model(model): # Custom Solve Method
import datetime
trial_pool = {
'sample1':{'solution_file':None,'aspiration':None},
'sample2':{'solution_file':None,'aspiration':None},
'sample3':{'solution_file':None,'aspiration':None}
}
#model1 = copy.deepcopy(model)
#model2 = copy.deepcopy(model)
#model3 = copy.deepcopy(model)
print ("success! \n loading solver......")
mip_gap = 0.005
solver_timeout = 300
number_of_trials = 2
solver_sh = 'cplex' #initialization Setting
engage_neos = True #initialization Setting
j = 1
timeout_arguments = {'cplex':'timelimit','cbc':'sec'}
gap_arguments = {'cplex':'mipgap','cbc':'ratio'} # Cplex Local Executable Take : "mip_tolerance_mipgap", mipgap is for neos version
# NEOS Server Library Dependency
# pyro4
# suds
# openopt
while j < number_of_trials + 1:
from pyomo.opt import SolverFactory, SolverManagerFactory
#model.pprint()
opt = SolverFactory(solver_sh, solver_io = 'lp')
print ('\ninterfacing solver shell :',solver_sh)
if engage_neos:
solver_manager = SolverManagerFactory('neos')
opt.options[timeout_arguments[solver_sh]]= solver_timeout
opt.options[gap_arguments[solver_sh]] = mip_gap
#opt.symbolic_solver_labels=True
#opt.options['slog'] = 1
#opt.enable = 'parallel'
print ('\tsolver options >> \n\n\tTolerance Limits:\n\tmip_gap = %s \n\ttimeout = %s'%(str(mip_gap),str(solver_timeout)))
print ("\nProcessing Trial Number :",j)
print ("\nJob Triggered at :",str(datetime.datetime.now()))
print ('\ngenerating production plan...... !! please wait !! \n to interrupt press ctrl+C\n')
try:
if engage_neos:
results = solver_manager.solve(model,opt = opt, tee= True)
else:
opt.options['threads'] = 3
results = opt.solve(model) #,tee=True) # Method Load Solutions is not available in pyomo versions less than 4.x
except:
j = j+1
mip_gap = (j-1)*mip_gap
solver_sh = 'cplex'
engage_neos = True
continue
#results.write(filename='results'+str(datetime.date.today())+'.json',format = 'json')
#print (results)
if str(results['Solver'][0]['Termination condition']) in ['infeasible','maxTimeLimit','maxIterations','intermediateNonInteger']:
j = j+1
mip_gap = (j-1)*mip_gap
solver_sh = 'cplex'
engage_neos = True
if j == number_of_trials + 1:
#print (results['Problem'])
#print (results['Solver'])
raise AssertionError("Solver Failed with Termination Status : %s"%(str(results['Solver'][0]['Termination condition'])))
exit(0)
print ('Terminated by:',str(results['Solver'][0]['Termination condition']))
print ("\n\nRetrying...\n\n")
else:
print ("solution captured")
model.solutions.store_to(results)
#post_process_results()
break
print (results['Problem'])
print (results['Solver'])
print ("\nSolution Retrived at:",str(datetime.datetime.now()))
return [model,results]