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)
lbs = algo.run(instance_name=instance_name)
save_path = hydro_path + '/Output/DW_Primal/%s_LBS.pickle' % (
instance_name)
write_object_results(save_path, (algo.instance, lbs))
sup_dim = rnd_container_data.support_dimension
supp_ctrs = [{
'innovations[%i]' % (reservoir): 1
} for reservoir in range(sup_dim)]
supp_ctrs.extend(({
'innovations[%i]' % (r_id): -1
}) for r_id in range(sup_dim))
supp_rhs = rnd_container_data.get_noise_ub(
[f'innovations[{r_id}]' for r_id in range(sup_dim)])
supp_rhs.extend(
rnd_container_data.get_noise_lb(
[f'innovations[{r_id}]' for r_id in range(sup_dim)], True))
algo = SDDP(T,
model_builder,
random_builder,
risk_measure=DistRobustWassersteinCont,
radius=r_dro,
support_ctrs=supp_ctrs,
support_rhs=supp_rhs)
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)
'''
Created on Jan 3, 2018
@author: dduque
'''
from SDDP import options, LAST_CUTS_SELECTOR, load_algorithm_options,\
SLACK_BASED_CUT_SELECTOR
from SDDP.SDDP_Alg import SDDP
from SDDP.RiskMeasures import DistRobustWasserstein
from Utils.file_savers import write_object_results
from HydroModel import load_hydro_data, model_builder, random_builder, hydro_path
from InstanceGen.ReservoirChainGen import read_instance, HydroRndInstance #Necessary to unpickle file!
if __name__ == '__main__':
'''
Implementation of SDDP solving EV problem.
--T=12 --R=10 --max_iter=10000 --max_time=1800 --sim_iter=1000 --lines_freq=10 --dro_r=20 --lag=1 --N=10 --dynamic_sampling=False --multicut=True
'''
load_algorithm_options()
T, r_dro, instance_name, out_of_sample_rnd_cont = load_hydro_data(
'SDDP', 'SP')
algo = SDDP(T, model_builder, random_builder)
lbs = algo.run(instance_name=instance_name, ev=False)
sim_result = algo.simulate_policy(options['sim_iter'],
out_of_sample_rnd_cont)
dro_trials.extend([r for r in range(100, 200, 20)])
dro_trials.extend([r for r in range(200, 1000, 200)])
dro_trials.extend([r for r in range(1000, 3001, 500)])
dro_trials.extend([0.001, 0.005, 0.01, 0.05, 0.1, 0.5])
dro_trials.sort()
#dro_trials = [0.001, 0.01, 1, 5, 8, 10, 15, 20, 50, 100, 500, 1000, 5000]
print(dro_trials)
if False: # solve
for dro_r in dro_trials:
instance_name = 'DW_CapExp_N%i_m%i_n%i_r%.3f' % (n_scenarios, m, n, dro_r)
# algo_sp = SDDP(T, model_builder, random_builder)
algo_dro = SDDP(T,
model_builder,
random_builder,
risk_measure=DistRobustWasserstein,
norm=1,
radius=dro_r,
data_random_container=random_builder())
# algo_dro = SDDP(T,
# model_builder,
# random_builder,
# risk_measure=DistRobust,
# dro_inner_solver=DiscreteWassersteinInnerSolver,
# radius=dro_r,
# norm=1,
# data_random_container=random_builder())
algo = algo_dro
lbs = algo.run(instance_name=instance_name)
print(["%4.2f" % algo.stage_problems[0].model.getVarByName(f'x[{i}]').X for i in data.I])
save_path = './Output/%s_LBS.pickle' % (instance_name)
from Utils.file_savers import write_object_results
from HydroModel 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('PRIMAL', 'PhiVar')
#options['cut_selector'] = SLACK_BASED_CUT_SELECTOR#SLACK_BASED_CUT_SELECTOR#LAST_CUTS_SELECTOR
algo = SDDP(T, model_builder, random_builder, risk_measure = DistRobust, dro_inner_solver=PhilpottInnerDROSolver, set_type = DistRobust.L1_NORM , radius = r_dro, data_random_container=rnd_container_data)
lbs = algo.run(instance_name=instance_name, dynamic_sampling=options['dynamic_sampling'])
save_path = hydro_path+'/Output/Phi_Primal/%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/Phi_Primal/%s_OOS.pickle' %(instance_name)
write_object_results(save_path, sim_result)
report_stats(sim_result.sims_ub)
del(algo)
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(
'DUAL', 'DW')
#options['cut_selector'] = SLACK_BASED_CUT_SELECTOR#SLACK_BASED_CUT_SELECTOR#LAST_CUTS_SELECTOR
algo = SDDP(T,
model_builder,
random_builder,
risk_measure=DistRobustWasserstein,
norm=1,
radius=r_dro,
data_random_container=rnd_container_data)
oos_setup = {
'when': [10, 30, 60, 120, 180, 300],
'random_container': rnd_container_oos,
'output': []
}
lbs = algo.run(instance_name=instance_name, out_of_sample_setup=oos_setup)
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)
from Utils.file_savers import write_object_results
from HydroModel 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('DUAL', 'PhiVar')
#options['cut_selector'] = SLACK_BASED_CUT_SELECTOR#SLACK_BASED_CUT_SELECTOR#LAST_CUTS_SELECTOR
algo = SDDP(T, model_builder, random_builder, risk_measure = DistRobustDuality, set_type = DistRobustDuality.L1_NORM , radius = r_dro, data_random_container=rnd_container_data, cutting_planes =True)
lbs = algo.run(instance_name=instance_name, dynamic_sampling=options['dynamic_sampling'])
save_path = hydro_path+'/Output/Phi_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/Phi_Dual/%s_OOS.pickle' %(instance_name)
write_object_results(save_path, sim_result)
report_stats(sim_result.sims_ub)
del(algo)
out_states = [var.VarName for var in [P, Q, W, C, M]]
rhs_vars = [evatra.VarName, rainfall.VarName]
return m, in_states, out_states, rhs_vars
#print_model(m)
if __name__ == '__main__':
load_algorithm_options()
T = 52
N_train = 20
N_test = len(parameters["niwa_data"][0]) - N_train
W = len(parameters["niwa_data"])
Y = len(parameters["niwa_data"][0])
O = [[parameters["niwa_data"][w][y] for y in range(N_train)]
for w in range(W)]
data_OOS = [[parameters["niwa_data"][w][y] for y in range(N_train, Y)]
for w in range(W)]
algo = SDDP(T,
model_builder,
random_builder,
risk_measure=DistRobust,
dro_solver=DiscreteWassersteinInnerSolver,
dro_solver_params={
'norm': 1,
'radius': 0
})
#algo = SDDP(T, model_builder, random_builder, risk_measure =DistRobustWasserstein, radius = 10)
lbs = algo.run(instance_name='MILK_52')
out_of_sample_rnd_cont = random_builder_oos(data_OOS)
sim_result = algo.simulate_policy(5000, out_of_sample_rnd_cont)
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)