def execute_seims_model(self):
"""Run SEIMS for evaluating environmental effectiveness.
If execution fails, the `self.economy` and `self.environment` will be set the worst values.
"""
scoop_log('Scenario ID: %d, running SEIMS model...' % self.ID)
self.model.scenario_id = self.ID
self.modelout_dir = self.model.OutputDirectory
self.model.run()
self.modelrun = True
return self.model.run_success
def main():
wtsd_name = get_watershed_name(
'Specify watershed name to run scenario analysis.')
if wtsd_name not in list(DEMO_MODELS.keys()):
print('%s is not one of the available demo watershed: %s' %
(wtsd_name, ','.join(list(DEMO_MODELS.keys()))))
exit(-1)
cur_path = UtilClass.current_path(lambda: 0)
SEIMS_path = os.path.abspath(cur_path + '../../..')
model_paths = ModelPaths(SEIMS_path, wtsd_name, DEMO_MODELS[wtsd_name])
cf = write_scenario_analysis_config_file(model_paths,
'scenario_analysis.ini')
base_cfg = SAConfig(cf) # type: SAConfig
if base_cfg.bmps_cfg_unit == BMPS_CFG_UNITS[3]: # SLPPOS
cfg = SASlpPosConfig(cf)
elif base_cfg.bmps_cfg_unit == BMPS_CFG_UNITS[2]: # CONNFIELD
cfg = SAConnFieldConfig(cf)
else: # Common spatial units, e.g., HRU and EXPLICITHRU
cfg = SACommUnitConfig(cf)
cfg.construct_indexes_units_gene()
sce = SUScenario(cfg)
scoop_log('### START TO SCENARIOS OPTIMIZING ###')
start_t = time.time()
fpop, fstats = sa_nsga2.main(sce)
fpop.sort(key=lambda x: x.fitness.values)
scoop_log(fstats)
with open(cfg.opt.logbookfile, 'w', encoding='utf-8') as f:
# In case of 'TypeError: write() argument 1 must be unicode, not str' in Python2.7
# when using unicode_literals, please use '%s' to concatenate string!
f.write('%s' % fstats.__str__())
end_t = time.time()
scoop_log('Running time: %.2fs' % (end_t - start_t))
def main(cfg):
"""Main workflow of NSGA-II based Scenario analysis."""
random.seed()
scoop_log('Population: %d, Generation: %d' % (cfg.opt.npop, cfg.opt.ngens))
# Initial timespan variables
stime = time.time()
plot_time = 0.
allmodels_exect = list() # execute time of all model runs
# create reference point for hypervolume
ref_pt = numpy.array(worse_objects) * multi_weight * -1
stats = tools.Statistics(lambda sind: sind.fitness.values)
stats.register('min', numpy.min, axis=0)
stats.register('max', numpy.max, axis=0)
stats.register('avg', numpy.mean, axis=0)
stats.register('std', numpy.std, axis=0)
logbook = tools.Logbook()
logbook.header = 'gen', 'evals', 'min', 'max', 'avg', 'std'
# read observation data from MongoDB
cali_obj = Calibration(cfg)
# Read observation data just once
model_cfg_dict = cali_obj.model.ConfigDict
model_obj = MainSEIMS(args_dict=model_cfg_dict)
obs_vars, obs_data_dict = model_obj.ReadOutletObservations(object_vars)
# Initialize population
param_values = cali_obj.initialize(cfg.opt.npop)
pop = list()
for i in range(cfg.opt.npop):
ind = creator.Individual(param_values[i])
ind.gen = 0
ind.id = i
ind.obs.vars = obs_vars[:]
ind.obs.data = deepcopy(obs_data_dict)
pop.append(ind)
param_values = numpy.array(param_values)
# Write calibrated values to MongoDB
# TODO, extract this function, which is same with `Sensitivity::write_param_values_to_mongodb`.
write_param_values_to_mongodb(cfg.model.host, cfg.model.port,
cfg.model.db_name, cali_obj.ParamDefs,
param_values)
# get the low and up bound of calibrated parameters
bounds = numpy.array(cali_obj.ParamDefs['bounds'])
low = bounds[:, 0]
up = bounds[:, 1]
low = low.tolist()
up = up.tolist()
pop_select_num = int(cfg.opt.npop * cfg.opt.rsel)
init_time = time.time() - stime
def check_validation(fitvalues):
"""Check the validation of the fitness values of an individual."""
flag = True
for condidx, condstr in enumerate(conditions):
if condstr is None:
continue
if not eval('%f%s' % (fitvalues[condidx], condstr)):
flag = False
return flag
def evaluate_parallel(invalid_pops):
"""Evaluate model by SCOOP or map, and set fitness of individuals
according to calibration step."""
popnum = len(invalid_pops)
labels = list()
try: # parallel on multi-processors or clusters using SCOOP
from scoop import futures
invalid_pops = list(
futures.map(toolbox.evaluate, [cali_obj] * popnum,
invalid_pops))
except ImportError or ImportWarning: # Python build-in map (serial)
invalid_pops = list(
toolbox.map(toolbox.evaluate, [cali_obj] * popnum,
invalid_pops))
for tmpind in invalid_pops:
tmpfitnessv = list()
for k, v in list(multiobj.items()):
tmpvalues, tmplabel = tmpind.cali.efficiency_values(
k, object_names[k])
tmpfitnessv += tmpvalues[:]
labels += tmplabel[:]
tmpind.fitness.values = tuple(tmpfitnessv)
# Filter for a valid solution
if filter_ind:
invalid_pops = [
tmpind for tmpind in invalid_pops
if check_validation(tmpind.fitness.values)
]
if len(invalid_pops) < 2:
print(
'The initial population should be greater or equal than 2. '
'Please check the parameters ranges or change the sampling strategy!'
)
exit(2)
return invalid_pops, labels # Currently, `invalid_pops` contains evaluated individuals
# Record the count and execute timespan of model runs during the optimization
modelruns_count = {0: len(pop)}
modelruns_time = {
0: 0.
} # Total time counted according to evaluate_parallel()
modelruns_time_sum = {
0: 0.
} # Summarize time of every model runs according to pop
# Generation 0 before optimization
stime = time.time()
pop, plotlables = evaluate_parallel(pop)
modelruns_time[0] = time.time() - stime
for ind in pop:
allmodels_exect.append(
[ind.io_time, ind.comp_time, ind.simu_time, ind.runtime])
modelruns_time_sum[0] += ind.runtime
# currently, len(pop) may less than pop_select_num
pop = toolbox.select(pop, pop_select_num)
# Output simulated data to json or pickle files for future use.
output_population_details(pop, cfg.opt.simdata_dir, 0)
record = stats.compile(pop)
logbook.record(gen=0, evals=len(pop), **record)
scoop_log(logbook.stream)
# Begin the generational process
output_str = '### Generation number: %d, Population size: %d ###\n' % (
cfg.opt.ngens, cfg.opt.npop)
scoop_log(output_str)
UtilClass.writelog(cfg.opt.logfile, output_str, mode='replace')
modelsel_count = {
0: len(pop)
} # type: Dict[int, int] # newly added Pareto fronts
for gen in range(1, cfg.opt.ngens + 1):
output_str = '###### Generation: %d ######\n' % gen
scoop_log(output_str)
offspring = [toolbox.clone(ind) for ind in pop]
# method1: use crowding distance (normalized as 0~1) as eta
# tools.emo.assignCrowdingDist(offspring)
# method2: use the index of individual at the sorted offspring list as eta
if len(offspring
) >= 2: # when offspring size greater than 2, mate can be done
for i, ind1, ind2 in zip(range(len(offspring) // 2),
offspring[::2], offspring[1::2]):
if random.random() > cfg.opt.rcross:
continue
eta = i
toolbox.mate(ind1, ind2, eta, low, up)
toolbox.mutate(ind1, eta, low, up, cfg.opt.rmut)
toolbox.mutate(ind2, eta, low, up, cfg.opt.rmut)
del ind1.fitness.values, ind2.fitness.values
else:
toolbox.mutate(offspring[0], 1., low, up, cfg.opt.rmut)
del offspring[0].fitness.values
# Evaluate the individuals with an invalid fitness
invalid_inds = [ind for ind in offspring if not ind.fitness.valid]
valid_inds = [ind for ind in offspring if ind.fitness.valid]
if len(invalid_inds) == 0: # No need to continue
scoop_log(
'Note: No invalid individuals available, the NSGA2 will be terminated!'
)
break
# Write new calibrated parameters to MongoDB
param_values = list()
for idx, ind in enumerate(invalid_inds):
ind.gen = gen
ind.id = idx
param_values.append(ind[:])
param_values = numpy.array(param_values)
write_param_values_to_mongodb(cfg.model.host, cfg.model.port,
cfg.model.db_name, cali_obj.ParamDefs,
param_values)
# Count the model runs, and execute models
invalid_ind_size = len(invalid_inds)
modelruns_count.setdefault(gen, invalid_ind_size)
stime = time.time()
invalid_inds, plotlables = evaluate_parallel(invalid_inds)
curtimespan = time.time() - stime
modelruns_time.setdefault(gen, curtimespan)
modelruns_time_sum.setdefault(gen, 0.)
for ind in invalid_inds:
allmodels_exect.append(
[ind.io_time, ind.comp_time, ind.simu_time, ind.runtime])
modelruns_time_sum[gen] += ind.runtime
# Select the next generation population
# Previous version may result in duplications of the same scenario in one Pareto front,
# thus, I decided to check and remove the duplications first.
# pop = toolbox.select(pop + valid_inds + invalid_inds, pop_select_num)
tmppop = pop + valid_inds + invalid_inds
pop = list()
unique_sces = dict()
for tmpind in tmppop:
if tmpind.gen in unique_sces and tmpind.id in unique_sces[
tmpind.gen]:
continue
if tmpind.gen not in unique_sces:
unique_sces.setdefault(tmpind.gen, [tmpind.id])
elif tmpind.id not in unique_sces[tmpind.gen]:
unique_sces[tmpind.gen].append(tmpind.id)
pop.append(tmpind)
pop = toolbox.select(pop, pop_select_num)
output_population_details(pop, cfg.opt.simdata_dir, gen)
hyper_str = 'Gen: %d, New model runs: %d, ' \
'Execute timespan: %.4f, Sum of model run timespan: %.4f, ' \
'Hypervolume: %.4f\n' % (gen, invalid_ind_size,
curtimespan, modelruns_time_sum[gen],
hypervolume(pop, ref_pt))
scoop_log(hyper_str)
UtilClass.writelog(cfg.opt.hypervlog, hyper_str, mode='append')
record = stats.compile(pop)
logbook.record(gen=gen, evals=len(invalid_inds), **record)
scoop_log(logbook.stream)
# Count the newly generated near Pareto fronts
new_count = 0
for ind in pop:
if ind.gen == gen:
new_count += 1
modelsel_count.setdefault(gen, new_count)
# Plot 2D near optimal pareto front graphs,
# i.e., (NSE, RSR), (NSE, PBIAS), and (RSR,PBIAS)
# And 3D near optimal pareto front graphs, i.e., (NSE, RSR, PBIAS)
stime = time.time()
front = numpy.array([ind.fitness.values for ind in pop])
plot_pareto_front_single(front, plotlables, cfg.opt.out_dir, gen,
'Near Pareto optimal solutions')
plot_time += time.time() - stime
# save in file
# Header information
output_str += 'generation\tcalibrationID\t'
for kk, vv in list(object_names.items()):
output_str += pop[0].cali.output_header(kk, vv, 'Cali')
if cali_obj.cfg.calc_validation:
for kkk, vvv in list(object_names.items()):
output_str += pop[0].vali.output_header(kkk, vvv, 'Vali')
output_str += 'gene_values\n'
for ind in pop:
output_str += '%d\t%d\t' % (ind.gen, ind.id)
for kk, vv in list(object_names.items()):
output_str += ind.cali.output_efficiency(kk, vv)
if cali_obj.cfg.calc_validation:
for kkk, vvv in list(object_names.items()):
output_str += ind.vali.output_efficiency(kkk, vvv)
output_str += str(ind)
output_str += '\n'
UtilClass.writelog(cfg.opt.logfile, output_str, mode='append')
# TODO: Figure out if we should terminate the evolution
# Plot hypervolume and newly executed model count
plot_hypervolume_single(cfg.opt.hypervlog, cfg.opt.out_dir)
# Save newly added Pareto fronts of each generations
new_fronts_count = numpy.array(list(modelsel_count.items()))
numpy.savetxt('%s/new_pareto_fronts_count.txt' % cfg.opt.out_dir,
new_fronts_count,
delimiter=str(','),
fmt=str('%d'))
# Save and print timespan information
allmodels_exect = numpy.array(allmodels_exect)
numpy.savetxt('%s/exec_time_allmodelruns.txt' % cfg.opt.out_dir,
allmodels_exect,
delimiter=str(' '),
fmt=str('%.4f'))
scoop_log('Running time of all SEIMS models:\n'
'\tIO\tCOMP\tSIMU\tRUNTIME\n'
'MAX\t%s\n'
'MIN\t%s\n'
'AVG\t%s\n'
'SUM\t%s\n' %
('\t'.join('%.3f' % t for t in allmodels_exect.max(0)),
'\t'.join('%.3f' % t
for t in allmodels_exect.min(0)), '\t'.join(
'%.3f' % t
for t in allmodels_exect.mean(0)), '\t'.join(
'%.3f' % t for t in allmodels_exect.sum(0))))
exec_time = 0.
for genid, tmptime in list(modelruns_time.items()):
exec_time += tmptime
exec_time_sum = 0.
for genid, tmptime in list(modelruns_time_sum.items()):
exec_time_sum += tmptime
allcount = 0
for genid, tmpcount in list(modelruns_count.items()):
allcount += tmpcount
scoop_log('Initialization timespan: %.4f\n'
'Model execution timespan: %.4f\n'
'Sum of model runs timespan: %.4f\n'
'Plot Pareto graphs timespan: %.4f' %
(init_time, exec_time, exec_time_sum, plot_time))
return pop, logbook
for genid, tmpcount in list(modelruns_count.items()):
allcount += tmpcount
scoop_log('Initialization timespan: %.4f\n'
'Model execution timespan: %.4f\n'
'Sum of model runs timespan: %.4f\n'
'Plot Pareto graphs timespan: %.4f' %
(init_time, exec_time, exec_time_sum, plot_time))
return pop, logbook
if __name__ == "__main__":
cf, method = get_optimization_config()
cali_cfg = CaliConfig(cf, method=method)
scoop_log('### START TO CALIBRATION OPTIMIZING ###')
startT = time.time()
fpop, fstats = main(cali_cfg)
fpop.sort(key=lambda x: x.fitness.values)
scoop_log(fstats)
with open(cali_cfg.opt.logbookfile, 'w', encoding='utf-8') as f:
# In case of 'TypeError: write() argument 1 must be unicode, not str' in Python2.7
# when using unicode_literals, please use '%s' to concatenate string!
f.write('%s' % fstats.__str__())
endT = time.time()
scoop_log('### END OF CALIBRATION OPTIMIZING ###')
scoop_log('Running time: %.2fs' % (endT - startT))
def main(sceobj):
# type: (SUScenario) -> ()
"""Main workflow of NSGA-II based Scenario analysis."""
if sceobj.cfg.eval_info['BASE_ENV'] < 0:
run_base_scenario(sceobj)
print('The environment effectiveness value of the '
'base scenario is %.2f' % sceobj.cfg.eval_info['BASE_ENV'])
random.seed()
# Initial timespan variables
stime = time.time()
plot_time = 0.
allmodels_exect = list() # execute time of all model runs
pop_size = sceobj.cfg.opt.npop
gen_num = sceobj.cfg.opt.ngens
cx_rate = sceobj.cfg.opt.rcross
mut_perc = sceobj.cfg.opt.pmut
mut_rate = sceobj.cfg.opt.rmut
sel_rate = sceobj.cfg.opt.rsel
pop_select_num = int(pop_size * sel_rate)
ws = sceobj.cfg.opt.out_dir
cfg_unit = sceobj.cfg.bmps_cfg_unit
cfg_method = sceobj.cfg.bmps_cfg_method
worst_econ = sceobj.worst_econ
worst_env = sceobj.worst_env
# available gene value list
possible_gene_values = list(sceobj.bmps_params.keys())
if 0 not in possible_gene_values:
possible_gene_values.append(0)
units_info = sceobj.cfg.units_infos
suit_bmps = sceobj.suit_bmps
gene_to_unit = sceobj.cfg.gene_to_unit
unit_to_gene = sceobj.cfg.unit_to_gene
updown_units = sceobj.cfg.updown_units
scoop_log('Population: %d, Generation: %d' % (pop_size, gen_num))
scoop_log('BMPs configure unit: %s, configuration method: %s' % (cfg_unit, cfg_method))
# create reference point for hypervolume
ref_pt = numpy.array([worst_econ, worst_env]) * multi_weight * -1
stats = tools.Statistics(lambda sind: sind.fitness.values)
stats.register('min', numpy.min, axis=0)
stats.register('max', numpy.max, axis=0)
stats.register('avg', numpy.mean, axis=0)
stats.register('std', numpy.std, axis=0)
logbook = tools.Logbook()
logbook.header = 'gen', 'evals', 'min', 'max', 'avg', 'std'
# Initialize population
initialize_byinputs = False
if sceobj.cfg.initial_byinput and sceobj.cfg.input_pareto_file is not None and \
sceobj.cfg.input_pareto_gen > 0: # Initial by input Pareto solutions
inpareto_file = sceobj.modelcfg.model_dir + os.sep + sceobj.cfg.input_pareto_file
if os.path.isfile(inpareto_file):
inpareto_solutions = read_pareto_solutions_from_txt(inpareto_file,
sce_name='scenario',
field_name='gene_values')
if sceobj.cfg.input_pareto_gen in inpareto_solutions:
pareto_solutions = inpareto_solutions[sceobj.cfg.input_pareto_gen]
pop = toolbox.population_byinputs(sceobj.cfg, pareto_solutions) # type: List
initialize_byinputs = True
if not initialize_byinputs:
pop = toolbox.population(sceobj.cfg, n=pop_size) # type: List
init_time = time.time() - stime
def delete_fitness(new_ind):
"""Delete the fitness and other information of new individual."""
del new_ind.fitness.values
new_ind.gen = -1
new_ind.id = -1
new_ind.io_time = 0.
new_ind.comp_time = 0.
new_ind.simu_time = 0.
new_ind.runtime = 0.
def check_validation(fitvalues):
"""Check the validation of the fitness values of an individual."""
flag = True
for condidx, condstr in enumerate(conditions):
if condstr is None:
continue
if not eval('%f%s' % (fitvalues[condidx], condstr)):
flag = False
return flag
def evaluate_parallel(invalid_pops):
"""Evaluate model by SCOOP or map, and get fitness of individuals."""
popnum = len(invalid_pops)
try:
# parallel on multiprocesor or clusters using SCOOP
from scoop import futures
invalid_pops = list(futures.map(toolbox.evaluate, [sceobj.cfg] * popnum, invalid_pops))
except ImportError or ImportWarning:
# serial
invalid_pops = list(map(toolbox.evaluate, [sceobj.cfg] * popnum, invalid_pops))
# Filter for a valid solution
if filter_ind:
invalid_pops = [tmpind for tmpind in invalid_pops
if check_validation(tmpind.fitness.values)]
if len(invalid_pops) < 2:
print('The initial population should be greater or equal than 2. '
'Please check the parameters ranges or change the sampling strategy!')
exit(2)
return invalid_pops # Currently, `invalid_pops` contains evaluated individuals
# Record the count and execute timespan of model runs during the optimization
modelruns_count = {0: len(pop)}
modelruns_time = {0: 0.} # Total time counted according to evaluate_parallel()
modelruns_time_sum = {0: 0.} # Summarize time of every model runs according to pop
# Generation 0 before optimization
stime = time.time()
pop = evaluate_parallel(pop)
modelruns_time[0] = time.time() - stime
for ind in pop:
ind.gen = 0
allmodels_exect.append([ind.io_time, ind.comp_time, ind.simu_time, ind.runtime])
modelruns_time_sum[0] += ind.runtime
# Currently, len(pop) may less than pop_select_num
pop = toolbox.select(pop, pop_select_num)
record = stats.compile(pop)
logbook.record(gen=0, evals=len(pop), **record)
scoop_log(logbook.stream)
front = numpy.array([ind.fitness.values for ind in pop])
# save front for further possible use
numpy.savetxt(sceobj.scenario_dir + os.sep + 'pareto_front_gen0.txt',
front, delimiter=str(' '), fmt=str('%.4f'))
# Begin the generational process
output_str = '### Generation number: %d, Population size: %d ###\n' % (gen_num, pop_size)
scoop_log(output_str)
UtilClass.writelog(sceobj.cfg.opt.logfile, output_str, mode='replace')
modelsel_count = {0: len(pop)} # type: Dict[int, int] # newly added Pareto fronts
for gen in range(1, gen_num + 1):
output_str = '###### Generation: %d ######\n' % gen
scoop_log(output_str)
offspring = [toolbox.clone(ind) for ind in pop]
if len(offspring) >= 2: # when offspring size greater than 2, mate can be done
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
old_ind1 = toolbox.clone(ind1)
old_ind2 = toolbox.clone(ind2)
if random.random() <= cx_rate:
if cfg_method == BMPS_CFG_METHODS[3]: # SLPPOS method
toolbox.mate_slppos(ind1, ind2, sceobj.cfg.hillslp_genes_num)
elif cfg_method == BMPS_CFG_METHODS[2]: # UPDOWN method
toolbox.mate_updown(updown_units, gene_to_unit, unit_to_gene, ind1, ind2)
else:
toolbox.mate_rdm(ind1, ind2)
if cfg_method == BMPS_CFG_METHODS[0]:
toolbox.mutate_rdm(possible_gene_values, ind1, perc=mut_perc, indpb=mut_rate)
toolbox.mutate_rdm(possible_gene_values, ind2, perc=mut_perc, indpb=mut_rate)
else:
tagnames = None
if sceobj.cfg.bmps_cfg_unit == BMPS_CFG_UNITS[3]:
tagnames = sceobj.cfg.slppos_tagnames
toolbox.mutate_rule(units_info, gene_to_unit, unit_to_gene,
suit_bmps, ind1,
perc=mut_perc, indpb=mut_rate,
unit=cfg_unit, method=cfg_method,
tagnames=tagnames,
thresholds=sceobj.cfg.boundary_adaptive_threshs)
toolbox.mutate_rule(units_info, gene_to_unit, unit_to_gene,
suit_bmps, ind2,
perc=mut_perc, indpb=mut_rate,
unit=cfg_unit, method=cfg_method,
tagnames=tagnames,
thresholds=sceobj.cfg.boundary_adaptive_threshs)
if check_individual_diff(old_ind1, ind1):
delete_fitness(ind1)
if check_individual_diff(old_ind2, ind2):
delete_fitness(ind2)
# Evaluate the individuals with an invalid fitness
invalid_inds = [ind for ind in offspring if not ind.fitness.valid]
valid_inds = [ind for ind in offspring if ind.fitness.valid]
invalid_ind_size = len(invalid_inds)
if invalid_ind_size == 0: # No need to continue
scoop_log('Note: No invalid individuals available, the NSGA2 will be terminated!')
break
modelruns_count.setdefault(gen, invalid_ind_size)
stime = time.time()
invalid_inds = evaluate_parallel(invalid_inds)
curtimespan = time.time() - stime
modelruns_time.setdefault(gen, curtimespan)
modelruns_time_sum.setdefault(gen, 0.)
for ind in invalid_inds:
ind.gen = gen
allmodels_exect.append([ind.io_time, ind.comp_time, ind.simu_time, ind.runtime])
modelruns_time_sum[gen] += ind.runtime
# Select the next generation population
# Previous version may result in duplications of the same scenario in one Pareto front,
# thus, I decided to check and remove the duplications first.
# pop = toolbox.select(pop + valid_inds + invalid_inds, pop_select_num)
tmppop = pop + valid_inds + invalid_inds
pop = list()
unique_sces = dict()
for tmpind in tmppop:
if tmpind.gen in unique_sces and tmpind.id in unique_sces[tmpind.gen]:
continue
if tmpind.gen not in unique_sces:
unique_sces.setdefault(tmpind.gen, [tmpind.id])
elif tmpind.id not in unique_sces[tmpind.gen]:
unique_sces[tmpind.gen].append(tmpind.id)
pop.append(tmpind)
pop = toolbox.select(pop, pop_select_num)
hyper_str = 'Gen: %d, New model runs: %d, ' \
'Execute timespan: %.4f, Sum of model run timespan: %.4f, ' \
'Hypervolume: %.4f\n' % (gen, invalid_ind_size,
curtimespan, modelruns_time_sum[gen],
hypervolume(pop, ref_pt))
scoop_log(hyper_str)
UtilClass.writelog(sceobj.cfg.opt.hypervlog, hyper_str, mode='append')
record = stats.compile(pop)
logbook.record(gen=gen, evals=len(invalid_inds), **record)
scoop_log(logbook.stream)
# Count the newly generated near Pareto fronts
new_count = 0
for ind in pop:
if ind.gen == gen:
new_count += 1
modelsel_count.setdefault(gen, new_count)
# Plot 2D near optimal pareto front graphs
stime = time.time()
front = numpy.array([ind.fitness.values for ind in pop])
# save front for further possible use
numpy.savetxt(sceobj.scenario_dir + os.sep + 'pareto_front_gen%d.txt' % gen,
front, delimiter=str(' '), fmt=str('%.4f'))
# Comment out the following plot code if matplotlib does not work.
try:
from scenario_analysis.visualization import plot_pareto_front_single
pareto_title = 'Near Pareto optimal solutions'
xlabel = 'Economy'
ylabel = 'Environment'
if sceobj.cfg.plot_cfg.plot_cn:
xlabel = r'经济净投入'
ylabel = r'环境效益'
pareto_title = r'近似最优Pareto解集'
plot_pareto_front_single(front, [xlabel, ylabel],
ws, gen, pareto_title,
plot_cfg=sceobj.cfg.plot_cfg)
except Exception as e:
scoop_log('Exception caught: %s' % str(e))
plot_time += time.time() - stime
# save in file
output_str += 'generation\tscenario\teconomy\tenvironment\tgene_values\n'
for indi in pop:
output_str += '%d\t%d\t%f\t%f\t%s\n' % (indi.gen, indi.id, indi.fitness.values[0],
indi.fitness.values[1], str(indi))
UtilClass.writelog(sceobj.cfg.opt.logfile, output_str, mode='append')
# Plot hypervolume and newly executed model count
# Comment out the following plot code if matplotlib does not work.
try:
from scenario_analysis.visualization import plot_hypervolume_single
plot_hypervolume_single(sceobj.cfg.opt.hypervlog, ws, plot_cfg=sceobj.cfg.plot_cfg)
except Exception as e:
scoop_log('Exception caught: %s' % str(e))
# Save newly added Pareto fronts of each generations
new_fronts_count = numpy.array(list(modelsel_count.items()))
numpy.savetxt('%s/new_pareto_fronts_count.txt' % ws,
new_fronts_count, delimiter=str(','), fmt=str('%d'))
# Save and print timespan information
allmodels_exect = numpy.array(allmodels_exect)
numpy.savetxt('%s/exec_time_allmodelruns.txt' % ws, allmodels_exect,
delimiter=str(' '), fmt=str('%.4f'))
scoop_log('Running time of all SEIMS models:\n'
'\tIO\tCOMP\tSIMU\tRUNTIME\n'
'MAX\t%s\n'
'MIN\t%s\n'
'AVG\t%s\n'
'SUM\t%s\n' % ('\t'.join('%.3f' % v for v in allmodels_exect.max(0)),
'\t'.join('%.3f' % v for v in allmodels_exect.min(0)),
'\t'.join('%.3f' % v for v in allmodels_exect.mean(0)),
'\t'.join('%.3f' % v for v in allmodels_exect.sum(0))))
exec_time = 0.
for genid, tmptime in list(modelruns_time.items()):
exec_time += tmptime
exec_time_sum = 0.
for genid, tmptime in list(modelruns_time_sum.items()):
exec_time_sum += tmptime
allcount = 0
for genid, tmpcount in list(modelruns_count.items()):
allcount += tmpcount
scoop_log('Initialization timespan: %.4f\n'
'Model execution timespan: %.4f\n'
'Sum of model runs timespan: %.4f\n'
'Plot Pareto graphs timespan: %.4f' % (init_time, exec_time,
exec_time_sum, plot_time))
return pop, logbook
return pop, logbook
if __name__ == "__main__":
in_cf = get_config_parser()
base_cfg = SAConfig(in_cf) # type: SAConfig
if base_cfg.bmps_cfg_unit == BMPS_CFG_UNITS[3]: # SLPPOS
sa_cfg = SASlpPosConfig(in_cf)
elif base_cfg.bmps_cfg_unit == BMPS_CFG_UNITS[2]: # CONNFIELD
sa_cfg = SAConnFieldConfig(in_cf)
else: # Common spatial units, e.g., HRU and EXPLICITHRU
sa_cfg = SACommUnitConfig(in_cf)
sa_cfg.construct_indexes_units_gene()
sce = SUScenario(sa_cfg)
scoop_log('### START TO SCENARIOS OPTIMIZING ###')
startT = time.time()
fpop, fstats = main(sce)
fpop.sort(key=lambda x: x.fitness.values)
scoop_log(fstats)
with open(sa_cfg.opt.logbookfile, 'w', encoding='utf-8') as f:
# In case of 'TypeError: write() argument 1 must be unicode, not str' in Python2.7
# when using unicode_literals, please use '%s' to concatenate string!
f.write('%s' % fstats.__str__())
endT = time.time()
scoop_log('Running time: %.2fs' % (endT - startT))
