from SDDP.SDDP_Alg import SDDP
from SDDP.RiskMeasures import DistRobust, DiscreteWassersteinInnerSolver
from Utils.file_savers import write_object_results
#from HydroModel import load_hydro_data, hydro_path
from HydroValley import load_hydro_data, hydro_path
from InstanceGen.ReservoirChainGen import read_instance, HydroRndInstance #Necessary to unpickle file!
from SDDP.SDDP_utils import report_stats
if __name__ == '__main__':
'''
Implementation of Wasserstein uncertainty set based on Philpott et al.
Inner worst-case expectation problem is solved in the backward pass to
compute the cuts. This approach is regarded as Primal.
'''
load_algorithm_options()
T, model_builder, random_builder, rnd_container_data, rnd_container_oos, r_dro, instance_name, instance_name_gen = load_hydro_data(
'PRIMAL', 'DW')
#options['cut_selector'] = SLACK_BASED_CUT_SELECTOR
algo = SDDP(T,
model_builder,
random_builder,
risk_measure=DistRobust,
dro_inner_solver=DiscreteWassersteinInnerSolver,
radius=0,
norm=1,
data_random_container=rnd_container_data)
lbs = algo.run(instance_name=instance_name)
sim_result = algo.simulate_policy(rnd_container_oos)
report_stats(sim_result.sims_ub)
for r_DRO in dro_radii:
instance_name = instance_name_gen(r_DRO)
algo.change_dro_radius(r_DRO)
SLACK_BASED_CUT_SELECTOR
from SDDP.SDDP_Alg import SDDP
from Utils.file_savers import write_object_results
from HydroValley import load_hydro_data, hydro_path
from InstanceGen.ReservoirChainGen import read_instance, HydroRndInstance # Necessary to unpickle file!
from SDDP.SDDP_utils import report_stats
if __name__ == '__main__':
'''
Implementation of Wasserstein uncertainty set based on taking
the dual problem of the inner max problem that represents the
worst-case expectation.
'''
load_algorithm_options()
T, model_builder, random_builder, rnd_container_data, rnd_container_oos, r_dro, instance_name, _ = load_hydro_data(
'SP', '')
#options['cut_selector'] = SLACK_BASED_CUT_SELECTOR#SLACK_BASED_CUT_SELECTOR#LAST_CUTS_SELECTOR
#options['expected_value_problem'] = False
algo = SDDP(T, model_builder, random_builder, lower_bound=-1E10)
lbs = algo.run(instance_name=instance_name)
save_path = hydro_path + '/Output/DW_Dual/%s_LBS.pickle' % (instance_name)
write_object_results(save_path, (algo.instance, lbs))
sim_result = algo.simulate_policy(rnd_container_oos)
save_path = hydro_path + '/Output/DW_Dual/%s_OOS.pickle' % (instance_name)
write_object_results(save_path, sim_result)
report_stats(sim_result.sims_ub)
del (algo)