def _compute_objective_term(self):
total = 0.0
for scenario in self._ph._scenario_tree._scenarios:
instance = self._ph._instances[scenario._name]
total += -scenario._probability*value(find_active_objective(instance))
return total
def solve(self,
check_status=True,
exception_on_failure=True,
io_options=None):
# TODO: Does this import need to be delayed because
# it is in a plugins subdirectory
from pyomo.solvers.plugins.solvers.persistent_solver import \
PersistentSolver
if self.instance is None:
raise RuntimeError(
"The extensive form instance has not been constructed."
"Call the build_ef() method to construct it.")
start_time = time.time()
if self.get_option("verbose"):
print("Queuing extensive form solve")
self.objective = undefined
self.gap = undefined
self.bound = undefined
self.pyomo_solve_time = undefined
self.solve_time = undefined
self.termination_condition = undefined
self.solver_status = undefined
self.solution_status = undefined
self.solver_results = undefined
if isinstance(self._solver, PersistentSolver):
self._solver.set_instance(self.instance,
symbolic_solver_labels=self.get_option("symbolic_solver_labels"))
solve_kwds = {}
solve_kwds['load_solutions'] = False
if self.get_option("keep_solver_files"):
solve_kwds['keepfiles'] = True
if self.get_option("symbolic_solver_labels"):
solve_kwds['symbolic_solver_labels'] = True
if self.get_option("output_solver_log"):
solve_kwds['tee'] = True
if io_options is not None:
solve_kwds.update(io_options)
self.objective_sense = \
find_active_objective(self.instance).sense
if (not self.get_option("disable_warmstart")) and \
(self._solver.warm_start_capable()):
action_handle = self._solver_manager.queue(self.instance,
opt=self._solver,
warmstart=True,
**solve_kwds)
else:
action_handle = self._solver_manager.queue(self.instance,
opt=self._solver,
**solve_kwds)
if self.get_option("verbose"):
print("Waiting for extensive form solve")
results = self._solver_manager.wait_for(action_handle)
if self.get_option("verbose"):
print("Done with extensive form solve - loading results")
if self.get_option("output_solver_results"):
print("Results for ef:")
results.write(num=1)
self.solver_results = results
if hasattr(results.solver,"user_time") and \
(not isinstance(results.solver.user_time,
UndefinedData)) and \
(results.solver.user_time is not None):
# the solve time might be a string, or might
# not be - we eventually would like more
# consistency on this front from the solver
# plugins.
self.solve_time = \
float(results.solver.user_time)
elif hasattr(results.solver,"time"):
self.solve_time = \
float(results.solver.time)
else:
self.solve_time = undefined
if hasattr(results,"pyomo_solve_time"):
self.pyomo_solve_time = \
results.pyomo_solve_time
else:
self.pyomo_solve_times = undefined
self.termination_condition = \
results.solver.termination_condition
self.solver_status = \
results.solver.status
if len(results.solution) > 0:
assert len(results.solution) == 1
results_sm = results._smap
self.instance.solutions.load_from(results)
solution0 = results.solution(0)
if hasattr(solution0, "gap") and \
(solution0.gap is not None):
self.gap = solution0.gap
else:
self.gap = undefined
self.solution_status = solution0.status
if self.get_option("verbose"):
print("Storing solution in scenario tree")
for scenario in self._manager.scenario_tree.scenarios:
scenario.update_solution_from_instance()
self._manager.scenario_tree.snapshotSolutionFromScenarios()
self.objective = self._manager.scenario_tree.\
findRootNode().\
computeExpectedNodeCost()
if self.gap is not undefined:
if self.objective_sense == pyomo.core.base.minimize:
self.bound = self.objective - self.gap
else:
self.bound = self.objective + self.gap
else:
self.objective = undefined
self.gap = undefined
self.bound = undefined
self.solution_status = undefined
failure = False
if check_status:
if not ((self.solution_status == SolutionStatus.optimal) or \
(self.solution_status == SolutionStatus.feasible)):
failure = True
if self.get_option("verbose") or \
exception_on_failure:
msg = ("EF solve failed solution status check:\n"
"Solver Status: %s\n"
"Termination Condition: %s\n"
"Solution Status: %s\n"
% (self.solver_status,
self.termination_condition,
self.solution_status))
if self.get_option("verbose"):
print(msg)
if exception_on_failure:
raise RuntimeError(msg)
else:
if self.get_option("verbose"):
print("EF solve completed. Skipping status check.")
if self.get_option("verbose") or self.get_option("output_times"):
print("Time to solve and load results for the "
"extensive form=%.2f seconds"
% (time.time()-start_time))
return failure
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
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
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