def __init__(self, fsfile,
fsfct = None,
tree_model = None,
phopts = None):
"""Initialize a StochSolver object.
"""
if fsfct is None:
# Changed in October 2018: None implies AbstractModel
args_list = _optiondict_2_list(phopts)
parser = construct_ph_options_parser("")
options = parser.parse_args(args_list)
scenario_instance_factory = \
ScenarioTreeInstanceFactory(fsfile, tree_model)
try:
self.scenario_tree = \
GenerateScenarioTreeForPH(options,
scenario_instance_factory)
except:
print ("ERROR in StochSolver called from",inspect.stack()[1][3])
raise RuntimeError("fsfct is None, so assuming",
"AbstractModel but could not find all ingredients.")
else: # concrete model
if callable(fsfct):
scen_function = fsfct
else: # better be a string
fsfile = fsfile.replace('.py','') # import does not like .py
# __import__ only gives the top level module
# probably need to be dealing with modules installed via setup.py
m = __import__(fsfile)
for n in fsfile.split(".")[1:]:
m = getattr(m, n)
scen_function = getattr(m, fsfct)
if tree_model is None:
treecbname = "pysp_scenario_tree_model_callback"
tree_maker = getattr(m, treecbname)
tree = tree_maker()
scenario_instance_factory = ScenarioTreeInstanceFactory(scen_function, tree_model)
else:
# DLW March 21: still not correct
scenario_instance_factory = \
ScenarioTreeInstanceFactory(scen_function, tree_model)
kwargs = _kwfromphopts(phopts)
self.scenario_tree = \
scenario_instance_factory.generate_scenario_tree(**kwargs) #verbose = True)
instances = scenario_instance_factory. \
construct_instances_for_scenario_tree(self.scenario_tree)
self.scenario_tree.linkInInstances(instances)
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):
###################################
print("RUNNING - run args=%s" % str(args))
import pyomo.environ
def LagrangeParametric(args=None):
class Object(object):
pass
Result = Object()
Result.status = 'LagrangeParam begins ' + datetime_string(
) + '...running new ph'
ph = None
blanks = " " # used for formatting print statements
# options used
betaMin = options.beta_min
betaMax = options.beta_max
betaTol = options.beta_tol
gapTol = options.Lagrange_gap
minProb = options.min_prob
maxIntervals = options.max_intervals
maxTime = options.max_time
IndVarName = options.indicator_var_name
multName = options.lambda_parm_name
CCStageNum = options.stage_num
csvPrefix = options.csvPrefix
verbosity = options.verbosity
verbosity = 2 # override for debug (= 3 to get super-debug)
HGdebug = 0 # special debug (not public)
# local...may become option
optTol = gapTol
####################################################################
STARTTIME = time.time()
Result.status = "options set"
if verbosity > 1:
print("From LagrangeParametric, status = %s\tSTARTTIME = %s" \
% (str(getattr(Result,'status')), str(STARTTIME)))
ph = PHFromScratch(options)
Result.ph = ph
rootnode = ph._scenario_tree._stages[0]._tree_nodes[
0] # use rootnode to loop over scenarios
ReferenceInstance = ph._instances[
rootnode._scenarios[0]._name] # arbitrary scenario
if find_active_objective(ph._scenario_tree._scenarios[0]._instance,
safety_checks=True).is_minimizing():
sense = 'min'
else:
sense = 'max'
scenario_count = len(full_scenario_tree._stages[-1]._tree_nodes)
if options.verbosity > 0:
print("%s %s scenarios" % (str(sense), str(scenario_count)))
# initialize
Result.status = 'starting at ' + datetime_string()
if verbosity > 0:
print(Result.status)
ScenarioList = []
lambdaval = 0.
lagrUtil.Set_ParmValue(ph, multName, 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()
sumprob = 0.
minprob = 1.
maxprob = 0.
# fixed = 0 to get PR point at b=0
lagrUtil.FixAllIndicatorVariables(ph, IndVarName, 0)
for scenario in rootnode._scenarios:
instance = ph._instances[scenario._name]
sname = scenario._name
sprob = scenario._probability
sumprob = sumprob + sprob
minprob = min(minprob, sprob)
maxprob = max(maxprob, sprob)
ScenarioList.append([sname, sprob])
ScenarioList.sort(
key=operator.itemgetter(1)) # sorts from min to max probability
if verbosity > 0:
print("probabilities sum to %f range: %f to %f" %
(sumprob, minprob, maxprob))
Result.ScenarioList = ScenarioList
# Write ScenarioList = name, probability in csv file sorted by probability
outName = csvPrefix + 'ScenarioList.csv'
print("writing to %s" % outName)
with open(outName, 'w') as outFile:
for scenario in ScenarioList:
outFile.write(scenario[0] + ", " + str(scenario[1]) + '\n')
Result.ScenarioList = ScenarioList
addstatus = 'Scenario List written to ' + csvPrefix + 'ScenarioList.csv'
Result.status = Result.status + '\n' + addstatus
if verbosity > 0:
print(addstatus)
if verbosity > 0:
print("solve begins %s" % datetime_string())
print("\t- lambda = %f" % lambdaval)
SolStat, zL = lagrUtil.solve_ph_code(ph, options)
if verbosity > 0:
print("solve ends %s" % datetime_string())
print("\t- status = %s" % str(SolStat))
print("\t- zL = %s" % str(zL))
bL = Compute_ExpectationforVariable(ph, IndVarName, CCStageNum)
if bL > 0:
print("** bL = %s > 0 (all %s = 0)" % (str(bL), str(IndVarName)))
return Result
if verbosity > 0:
print("Initial optimal obj = %s for bL = %s" % (str(zL), str(bL)))
# fixed = 1 to get PR point at b=1
lagrUtil.FixAllIndicatorVariables(ph, IndVarName, 1)
if verbosity > 0:
print("solve begins %s" % datetime_string())
print("\t- lambda = %s" % str(lambdaval))
SolStat, zU = lagrUtil.solve_ph_code(ph, options)
if verbosity > 0:
print("solve ends %s" % datetime_string())
print("\t- status = %s" % str(SolStat))
print("\t- zU = %s" % str(zU))
if not SolStat[0:2] == 'ok':
print(str(SolStat[0:3]) + " is not 'ok'")
addstatus = "** Solution is non-optimal...aborting"
print(addstatus)
Result.status = Result.status + "\n" + addstatus
return Result
bU = Compute_ExpectationforVariable(ph, IndVarName, CCStageNum)
if bU < 1. - betaTol and verbosity > 0:
print("** Warning: bU = %s < 1" % str(bU))
### enumerate points in PR space (all but one scenario)
# Result.lbz = [ [0,bL,zL], [None,bU,zU] ]
# for scenario in rootnode._scenarios:
# sname = scenario._name
# instance = ph._instances[sname]
# print "excluding scenario",sname
# getattr(instance,IndVarName).value = 0
# print sname,"value =",getattr(instance,IndVarName).value,getattr(instance,IndVarName).fixed
# SolStat, z = lagrUtil.solve_ph_code(ph, options)
# b = Compute_ExpectationforVariable(ph, IndVarName, CCStageNum)
# print "solve ends with status =",SolStat,"(b, z) =",b,z
# getattr(instance,IndVarName).value = 1
# Result.lbz.append([None,b,z])
# for t in instance.TimePeriods:
# print "Global at",t,"=",instance.posGlobalLoadGenerateMismatch[t].value, \
# '-',instance.negGlobalLoadGenerateMismatch[t].value,"=",\
# instance.GlobalLoadGenerateMismatch[t].value,\
# "\tDemand =",instance.TotalDemand[t].value, ",",\
# "Reserve =",instance.ReserveRequirement[t].value
#
# PrintPRpoints(Result.lbz)
# return Result
#### end enumeration
########################################################################
if verbosity > 1:
print("We have bU = %s ...about to free all %s for %d scenarios" % \
(str(bU), str(IndVarName), len(ScenarioList)))
# free scenario selection variable
lagrUtil.FreeAllIndicatorVariables(ph, IndVarName)
if verbosity > 1:
print("\tall %s freed; elapsed time = %f" %
(str(IndVarName), time.time() - STARTTIME))
# initialize with the two endpoints
Result.lbz = [[0., bL, zL], [None, bU, zU]]
Result.selections = [[], ScenarioList]
NumIntervals = 1
if verbosity > 0:
print("Initial relative Lagrangian gap = %f maxIntervals = %d" %
(1 - zL / zU, maxIntervals))
if verbosity > 1:
print("entering while loop %s" % datetime_string())
print("\n")
############ main loop to search intervals #############
########################################################
while NumIntervals < maxIntervals:
lapsedTime = time.time() - STARTTIME
if lapsedTime > maxTime:
addstatus = '** max time reached ' + str(lapsedTime)
print(addstatus)
Result.status = Result.status + '\n' + addstatus
break
if verbosity > 1:
print("Top of while with %d intervals elapsed time = %f" %
(NumIntervals, lapsedTime))
PrintPRpoints(Result.lbz)
lambdaval = None
### loop over PR points to find first unfathomed interval to search ###
for PRpoint in range(1, len(Result.lbz)):
if Result.lbz[PRpoint][0] == None:
# multiplier = None means interval with upper endpoint at PRpoint not fathomed
bL = Result.lbz[PRpoint - 1][1]
zL = Result.lbz[PRpoint - 1][2]
bU = Result.lbz[PRpoint][1]
zU = Result.lbz[PRpoint][2]
lambdaval = (zU - zL) / (bU - bL)
break
#############################
# Exited from the for loop
if verbosity > 1:
print("exited for loop with PRpoint = %s ...lambdaval = %s" %
(PRpoint, lambdaval))
if lambdaval == None: break # all intervals are fathomed
if verbosity > 1: PrintPRpoints(Result.lbz)
if verbosity > 0:
print("Searching for b in [%s, %s] with %s = %f" % (str(
round(bL, 4)), str(round(bU, 4)), multName, lambdaval))
# search interval (bL,bU)
lagrUtil.Set_ParmValue(ph, multName, lambdaval)
if verbosity > 0:
print("solve begins %s" % datetime_string())
print("\t- %s = %f" % (multName, lambdaval))
#########################################################
SolStat, Lagrangian = lagrUtil.solve_ph_code(ph, options)
#########################################################
if not SolStat[0:2] == 'ok':
addstatus = "** Solution status " + SolStat + " is not optimal"
print(addstatus)
Result.status = Result.status + "\n" + addstatus
return Result
b = Compute_ExpectationforVariable(ph, IndVarName, CCStageNum)
z = Lagrangian + lambdaval * b
if verbosity > 0:
print("solve ends %s" % datetime_string())
print("\t- Lagrangian = %f" % Lagrangian)
print("\t- b = %s" % str(b))
print("\t- z = %s" % str(z))
print("\n")
# We have PR point (b,z), which may be new or one of the endpoints
##################################################################
######### Begin tolerance tests ##########
# Test that b is in [bL,bU]
if verbosity > 1: print("\ttesting b")
if b < bL - betaTol or b > bU + betaTol:
addstatus = "** fatal error: probability (= " + str(b) + \
") is outside interval, (" + str(bL) + ", " + str(bU) + ")"
addstatus = addstatus + "\n\t(tolerance = " + str(
betaTol) + ")"
print(addstatus + '\n')
Result.status = Result.status + addstatus
return Result
# Test that z is in [zL,zU]
if verbosity > 1: print("\ttesting z")
# using optTol as absolute tolerance (not relative)
# ...if we reconsider, need to allow negative z-values
if z < zL - optTol or z > zU + optTol:
addstatus = "** fatal error: obj (= " + str(z) + \
") is outside interval, (" + str(zL) + ", " + str(zU) + ")"
print(addstatus + '\n')
Result.status = Result.status + addstatus
return Result
# Ok, we have (b,z) in [(bL,zL), (bU,zU)], at least within tolerances
oldLagrangian = zL - lambdaval * bL
# ensure lambdaval set such that endpoints have same Lagrangian value
# (this is probably unnecessary, but check anyway)
if abs(oldLagrangian -
(zU - lambdaval * bU)) > optTol * abs(oldLagrangian):
addstatus = "** fatal error: Lagrangian at (bL,zL) = " + \
str(oldLagrangian) + " not= " + str(zU-lambdaval*bU) + \
"\n\t(optTol = " + str(optTol) + ")"
Result.status = Result.status + addstatus
return Result
# no more fatal error tests...need to know if (b,z) is endpoint or new
if verbosity > 1: print("No anomalies...testing if b = bL or bU")
# Test if endpoint is an alternative optimum of Lagrangian
# ...using optTol as *relative* tolerance
# (could use other reference values -- eg, avg or max of old and new Lagrangian values)
refValue = max(min(abs(oldLagrangian), abs(Lagrangian)), 1.)
alternativeOpt = abs(oldLagrangian -
Lagrangian) <= optTol * refValue
# alternativeOpt = True means we computed point (b,z) is alternative optimum such that:
# case 1: (b,z) = endpoint, in which case we simply fathom [bL,bU] by setting PRpoint
# to [lambdaval,bU,zU] (the numeric value of multiplier means fathomed)
# case 2: (b,z) is new PR point on line segment, in which case we split into
# [bL,b] and [b,bU], with both fathomed
if verbosity > 1:
print("oldLagrangian = %s" % str(oldLagrangian))
if alternativeOpt:
print(":= Lagrangian = %s" % str(Lagrangian))
else:
print("> Lagrangian = %s" % str(Lagrangian))
if alternativeOpt:
# setting multiplier of (bU,zU) to a numeric fathoms the interval [bL,bU]
Result.lbz[PRpoint][0] = lambdaval
# test if (b,z) is an endpoint
newPRpoint = abs(b - bL) > betaTol and abs(b - bU) > betaTol
if not newPRpoint:
# ...(b,z) is NOT an endpoint (or sufficiently close), so split and fathom
if verbosity > 1:
print("\tnot an endpoint\tlbz = %s" %
str(Result.lbz[PRpoint]))
if verbosity > 0:
print("Lagangian solution is new PR point on line segment of (" \
+ str(bL) + ", " + str(bU) +")")
print(
"\tsplitting (bL,bU) into (bL,b) and (b,bU), both fathomed"
)
# note: else ==> b = bL or bU, so we do nothing, having already fathomed [bL,bU]
# (b,z) is new PR point, so split interval (still in while loop)
##########################################
# alternative optimum ==> split & fathom: (bL,b), (b,bU)
if verbosity > 1:
print("\talternativeOpt %s newPRpoint = %s" %
(alternativeOpt, newPRpoint))
if newPRpoint:
NumIntervals += 1
if alternativeOpt:
if verbosity > 1:
print("\tInsert [lambdaval,b,z] at %f" % PRpoint)
Result.lbz = Insert([lambdaval, b, z], PRpoint, Result.lbz)
addstatus = "Added PR point on line segment of envelope"
if verbosity > 0: print(addstatus + '\n')
else:
if verbosity > 1:
print("\tInsert [None,b,z] at %f" % PRpoint)
Result.lbz = Insert([None, b, z], PRpoint, Result.lbz)
addstatus = "new envelope extreme point added (interval split, not fathomed)"
Result.status = Result.status + "\n" + addstatus
if verbosity > 1:
print("...after insertion:")
PrintPRpoints(Result.lbz)
# get the selections of new point (ie, scenarios for which delta=1)
Selections = []
for scenario in ScenarioList:
instance = ph._instances[scenario[0]]
if getattr(instance, IndVarName).value == 1:
Selections.append(scenario)
Result.selections = Insert(Selections, PRpoint,
Result.selections)
if verbosity > 0:
print("Interval "+str(PRpoint)+", ["+str(bL)+", "+str(bU)+ \
"] split at ("+str(b)+", "+str(z)+")")
print("\tnew PR point has " + str(len(Selections)) +
" selections")
if verbosity > 1:
print("test that selections list aligned with lbz")
if not len(Result.lbz) == len(Result.selections):
print("** fatal error: lbz not= selections")
PrintPRpoints(Result.lbz)
print("Result.selections:")
for i in range(Result.selections):
print("%d %f" % (i, Result.selections[i]))
return Result
# ok, we have split and/or fathomed interval
if NumIntervals >= maxIntervals:
# we are about to leave while loop due to...
addstatus = "** terminating because number of intervals = " + \
str(NumIntervals) + " >= max = " + str(maxIntervals)
if verbosity > 0: print(addstatus + '\n')
Result.status = Result.status + "\n" + addstatus
# while loop continues
if verbosity > 1:
print("bottom of while loop")
PrintPRpoints(Result.lbz)
###################################################
# end while NumIntervals < maxIntervals:
# ^ this is indentation of while loop
################ end while loop ###################
if verbosity > 1: print("\nend while loop...setting multipliers")
for i in range(1, len(Result.lbz)):
db = Result.lbz[i][1] - Result.lbz[i - 1][1]
dz = Result.lbz[i][2] - Result.lbz[i - 1][2]
if dz > 0:
Result.lbz[i][0] = dz / db
else:
#print "dz =",dz," at ",i,": ",Result.lbz[i]," -",Result.lbz[i-1]
Result.lbz[i][0] = 0
if verbosity > 0: PrintPRpoints(Result.lbz)
addstatus = '\nLagrange multiplier search ends' + datetime_string()
if verbosity > 0:
print(addstatus + '\n')
Result.status = Result.status + addstatus
outName = csvPrefix + "PRoptimal.csv"
with open(outName, 'w') as outFile:
if verbosity > 0:
print("writing PR points to " + outName + '\n')
for lbz in Result.lbz:
outFile.write(str(lbz[1]) + ", " + str(lbz[2]) + '\n')
outName = csvPrefix + "OptimalSelections.csv"
with open(outName, 'w') as outFile:
if verbosity > 0:
print("writing optimal selections for each PR point to " +
csvPrefix + 'PRoptimal.csv\n')
for selections in Result.selections:
char = ""
thisSelection = ""
for slist in selections:
if slist:
thisSelection = thisSelection + char + slist[0]
char = ","
outFile.write(thisSelection + '\n')
if verbosity > 0:
print("\nReturning status:\n %s \n=======================" %
Result.status)
################################
if verbosity > 2:
print("\nAbout to return...Result attributes: %d" %
len(inspect.getmembers(Result)))
for attr in inspect.getmembers(Result):
print(attr[0])
print("\n===========================================")
# LagrangeParametric ends here
return Result
################################
####################################### start run ####################################
AllInOne = False
########################
# 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 0",
action="store",
dest="beta_min",
type=float,
default=0.)
conf_options_parser.add_argument(
"--beta-max",
help="The beta level for the chance constraint. Default is 1.",
action="store",
dest="beta_max",
type=float,
default=1.)
conf_options_parser.add_argument(
"--beta-tol",
help="Tolerance for testing equality to beta. Default is 1e-5",
action="store",
dest="beta_tol",
type=float,
default=1e-5)
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(
"--min-prob",
help="Tolerance for testing probability > 0. Default is 1e-9",
action="store",
dest="min_prob",
type=float,
default=1e-5)
conf_options_parser.add_argument(
"--max-intervals",
help=
"The max number of intervals generated; if causes termination, non-fathomed intervals have multiplier=None. Default = 100.",
action="store",
dest="max_intervals",
type=int,
default=100)
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(
"--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(
"--csvPrefix",
help="Output file name. Default is ''",
action="store",
dest="csvPrefix",
type=str,
default='')
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)
# 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)
##HG: deleted params filed as deletedParam.py
#######################################################################################################
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
# create the reference instances and the scenario tree - no
# scenario instances yet.
if options.verbosity > 0:
print("Loading reference model and scenario tree")
# Dec 18
# 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)
####
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
########## Here is where multiplier search is called from run() ############
Result = LagrangeParametric()
#####################################################################################
finally:
# delete temporary unarchived directories
scenario_instance_factory.close()
if options.verbosity > 0:
print("\n===========================================")
print("\nreturned from LagrangeParametric")
if options.verbosity > 2:
print("\nFrom run, Result should have status and ph objects...")
for attr in inspect.getmembers(Result):
print(attr)
print("\n===========================================")
try:
status = Result.status
print("status = " + str(Result.status))
except:
print("status not defined")
sys.exit()
try:
lbz = Result.lbz
PrintPRpoints(lbz)
with open(options.csvPrefix + "PRoptimal.csv", 'w') as outFile:
for lbz in Result.lbz:
outFile.write(str(lbz[1]) + ", " + str(lbz[2]) + '\n')
except:
print("Result.lbz not defined")
sys.exit()
try:
ScenarioList = Result.ScenarioList
ScenarioList.sort(key=operator.itemgetter(1))
with open(options.csvPrefix + "ScenarioList.csv", 'w') as outFile:
for scenario in ScenarioList:
outFile.write(scenario[0] + ", " + str(scenario[1]) + '\n')
except:
print("Result.ScenarioList not defined")
sys.exit()
class StochSolver:
"""A class for solving stochastic versions of concrete models and
abstract models.
Inspired by the IDAES use case and by daps ability to create tree models.
Author: David L. Woodruff, February 2017
Args:
fsfile (str): is a path to the file that contains the scenario
callback for concrete or the reference model for abstract.
fsfct (str, or fct, or None):
| str: callback function name in the file
| fct: callback function (fsfile is ignored)
| None: it is a AbstractModel
tree_model (concrete model, or networkx tree, or path):
gives the tree as a concrete model (which could be a fct)
or a valid networkx scenario tree
or path to AMPL data file.
phopts: dictionary of ph options; needed during construction
if there is bundling.
Attributes:
scenario_tree: scenario tree object (that includes data)
"""
def __init__(self, fsfile, fsfct=None, tree_model=None, phopts=None):
"""Initialize a StochSolver object.
"""
if fsfct is None:
# Changed in October 2018: None implies AbstractModel
args_list = _optiondict_2_list(phopts)
parser = construct_ph_options_parser("")
options = parser.parse_args(args_list)
scenario_instance_factory = \
ScenarioTreeInstanceFactory(fsfile, tree_model)
try:
self.scenario_tree = \
GenerateScenarioTreeForPH(options,
scenario_instance_factory)
except:
print("ERROR in StochSolver called from",
inspect.stack()[1][3])
raise RuntimeError(
"fsfct is None, so assuming",
"AbstractModel but could not find all ingredients.")
else: # concrete model
if callable(fsfct):
scen_function = fsfct
else: # better be a string
fsfile = fsfile.replace('.py', '') # import does not like .py
# __import__ only gives the top level module
# probably need to be dealing with modules installed via setup.py
m = __import__(fsfile)
for n in fsfile.split(".")[1:]:
m = getattr(m, n)
scen_function = getattr(m, fsfct)
if tree_model is None:
treecbname = "pysp_scenario_tree_model_callback"
tree_maker = getattr(m, treecbname)
tree = tree_maker()
scenario_instance_factory = ScenarioTreeInstanceFactory(
scen_function, tree_model)
else:
# DLW March 21: still not correct
scenario_instance_factory = \
ScenarioTreeInstanceFactory(scen_function, tree_model)
kwargs = _kwfromphopts(phopts)
self.scenario_tree = \
scenario_instance_factory.generate_scenario_tree(**kwargs) #verbose = True)
instances = scenario_instance_factory. \
construct_instances_for_scenario_tree(self.scenario_tree)
self.scenario_tree.linkInInstances(instances)
#=========================
def make_ef(self,
verbose=False,
generate_weighted_cvar=False,
cvar_weight=None,
risk_alpha=None,
cc_indicator_var_name=None,
cc_alpha=0.0):
""" Make an ef object (used by solve_ef); all Args are optional.
Args:
verbose (boolean): indicates verbosity to PySP for construction
generate_weighted_cvar (boolean): indicates we want weighted CVar
cvar_weight (float): weight for the cvar term
risk_alpha (float): alpha value for cvar
cc_indicator_var_name (string): name of the Var used for chance constraint
cc_alpha (float): alpha for chance constraint
Returns:
ef_instance: the ef object
"""
return create_ef_instance(
self.scenario_tree,
verbose_output=verbose,
generate_weighted_cvar=generate_weighted_cvar,
cvar_weight=cvar_weight,
risk_alpha=risk_alpha,
cc_indicator_var_name=cc_indicator_var_name,
cc_alpha=cc_alpha)
def solve_ef(self,
subsolver,
sopts=None,
tee=False,
need_gap=False,
verbose=False,
generate_weighted_cvar=False,
cvar_weight=None,
risk_alpha=None,
cc_indicator_var_name=None,
cc_alpha=0.0):
"""Solve the stochastic program directly using the extensive form.
All Args other than subsolver are optional.
Args:
subsolver (str): the solver to call (e.g., 'ipopt')
sopts (dict): solver options
tee (bool): indicates dynamic solver output to terminal.
need_gap (bool): indicates the need for the optimality gap
verbose (boolean): indicates verbosity to PySP for construction
generate_weighted_cvar (boolean): indicates we want weighted CVar
cvar_weight (float): weight for the cvar term
risk_alpha (float): alpha value for cvar
cc_indicator_var_name (string): name of the Var used for chance constraint
cc_alpha (float): alpha for chance constraint
Returns: (`Pyomo solver result`, `float`)
solve_result is the solver return value.
absgap is the absolute optimality gap (might not be valid); only if requested
Note:
Also update the scenario tree, populated with the solution.
Also attach the full ef instance to the object. So you might want
obj = pyo.value(stsolver.ef_instance.MASTER)
This needs more work to deal with solver failure (dlw, March, 2018)
"""
self.ef_instance = self.make_ef(
verbose=verbose,
generate_weighted_cvar=generate_weighted_cvar,
cvar_weight=cvar_weight,
risk_alpha=risk_alpha,
cc_indicator_var_name=cc_indicator_var_name,
cc_alpha=cc_alpha)
solver = SolverFactory(subsolver)
if sopts is not None:
for key in sopts:
solver.options[key] = sopts[key]
if need_gap:
solve_result = solver.solve(self.ef_instance,
tee=tee,
load_solutions=False)
if len(solve_result.solution) > 0:
absgap = solve_result.solution(0).gap
else:
absgap = None
self.ef_instance.solutions.load_from(solve_result)
else:
solve_result = solver.solve(self.ef_instance, tee=tee)
# note: the objective is probably called MASTER
#print ("debug value(ef_instance.MASTER)=",value(ef_instance.MASTER))
self.scenario_tree.pullScenarioSolutionsFromInstances()
self.scenario_tree.snapshotSolutionFromScenarios() # update nodes
if need_gap:
return solve_result, absgap
else:
return solve_result
#=========================
def solve_ph(self, subsolver, default_rho, phopts=None, sopts=None):
"""Solve the stochastic program given by this.scenario_tree using ph
Args:
subsolver (str): the solver to call (e.g., 'ipopt')
default_rho (float): the rho value to use by default
phopts: dictionary of ph options (optional)
sopts: dictionary of subsolver options (optional)
Returns: the ph object
Note:
Updates the scenario tree, populated with the xbar values;
however, you probably want to do
obj, xhat = ph.compute_and_report_inner_bound_using_xhat()
where ph is the return value.
"""
ph = None
# Build up the options for PH.
parser = construct_ph_options_parser("")
phargslist = ['--default-rho', str(default_rho)]
phargslist.append('--solver')
phargslist.append(str(subsolver))
phargslist = _optiondict_2_list(phopts, args_list=phargslist)
# Subproblem options go to PH as space-delimited, equals-separated pairs.
if sopts is not None:
soptstring = ""
for key in sopts:
soptstring += key + '=' + str(sopts[key]) + ' '
phargslist.append('--scenario-solver-options')
phargslist.append(soptstring)
phoptions = parser.parse_args(phargslist)
# construct the PH solver object
try:
ph = PHAlgorithmBuilder(phoptions, self.scenario_tree)
except:
print("Internal error: ph construction failed.")
if ph is not None:
ph.release_components()
raise
retval = ph.solve()
if retval is not None:
raise RuntimeError("ph Failure Encountered=" + str(retval))
# dlw May 2017: I am not sure if the next line is really needed
ph.save_solution()
return ph
#=========================
def root_Var_solution(self):
"""Generator to loop over x-bar
Yields:
name, value pair for root node solution values
"""
root_node = self.scenario_tree.findRootNode()
for variable_id in sorted(root_node._variable_ids):
var_name, index = root_node._variable_ids[variable_id]
name = var_name
if index is not None:
name += _name_index_generator(index)
yield name, root_node._solution[variable_id]
#=========================
def root_E_obj(self):
"""post solve Expected cost of the solution in the scenario tree (xbar)
Returns:
float: the expected costs of the solution in the tree (xbar)
"""
root_node = self.scenario_tree.findRootNode()
return root_node.computeExpectedNodeCost()
def run(args=None):
###################################
# to import plugins
import pyomo.environ
import pysp.plugins.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, 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 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:
# 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)))