def make_node_scenario_dict_balanced(BFs, leaf_nodes=True, start=1):
""" creates a node-scenario dictionary for aircond problem
Args:
BFs (list of ints): branching factors for each stage
leaf_nodes (bool): include/exclude leaf nodes (default True)
start (int): starting value for scenario number (default 1)
Returns:
node_scenario_dict (dict): dictionary of node-scenario key-value pairs
"""
nodenames = make_nodenames_balanced(BFs, leaf_nodes)
num_scens = np.prod(BFs)
scenario_names = aircond.scenario_names_creator(num_scens, start)
num_stages = len(BFs) + 1
node_scenario_dict = {"ROOT": scenario_names[0]}
for i in range(BFs[0]):
node_scenario_dict[nodenames[i]] = scenario_names[i]
if num_stages >= 3:
for stage in range(2, num_stages + leaf_nodes):
node_scenario_dict = _recursive_node_dict(
node_scenario_dict, nodenames, scenario_names, BFs, stage)
return node_scenario_dict
def main():
solution_files = {"first_stage_solution":"aircond_first_stage.csv",
"tree_solution":"aircond_full_solution"
}
options = {"mstage": True, # 2stage vs. mstage
"cylinders": ['ph','xhatshuffle','lagrangian'],
"write_solution": solution_files
}
ama_options = amalgamator.Amalgamator_parser(options, aircond.inparser_adder)
#Here, we need to change something specified by the command line
#That's why we cannot use amalgamator.from_module
if ama_options['num_scens'] is not None:
raise RuntimeError("Do not use num_scens here, we want to solve the problem for the whole sample scenario tree")
num_scens = np.prod(ama_options['branching_factors'])
scenario_names = aircond.scenario_names_creator(num_scens,0)
ama =amalgamator.Amalgamator(ama_options,
scenario_names,
aircond.scenario_creator,
aircond.kw_creator)
ama.run()
if ama.on_hub:
print("first_stage_solution=", ama.first_stage_solution)
print("inner bound=", ama.best_inner_bound)
print("outer bound=", ama.best_outer_bound)
def _get_xhat_gen_options(self, BFs):
num_scens = np.prod(BFs)
scenario_names = aircond.scenario_names_creator(num_scens)
xhat_gen_options = {"scenario_names": scenario_names,
"solvername": solvername,
"solver_options": None,
"branching_factors": BFs,
"mudev": 0,
"sigmadev": 40,
"start_ups": False,
"start_seed": 0,
}
return xhat_gen_options
def test_xhat_eval_evaluate_one(self):
ev = self._eval_creator()
options = self._get_base_options()
branching_factors= options['kwoptions']['branching_factors']
full_xhat = self._make_full_xhat(branching_factors)
num_scens = np.prod(branching_factors)
scenario_creator_kwargs = options['kwargs']
scenario_creator_kwargs['num_scens'] = num_scens
all_scenario_names = aircond.scenario_names_creator(num_scens)
k = all_scenario_names[0]
obj = ev.evaluate_one(full_xhat,k,ev.local_scenarios[k])
obj = round_pos_sig(obj,2)
self.assertEqual(obj, 990.0) # rebaselined 28 Dec 2021
def _eval_creator(self):
options = self._get_xhatEval_options()
MMW_options = self._get_base_options()
branching_factors= MMW_options['kwoptions']['branching_factors']
scen_count = np.prod(branching_factors)
scenario_creator_kwargs = MMW_options['kwargs']
###scenario_creator_kwargs['num_scens'] = MMW_options['batch_size']
all_scenario_names = aircond.scenario_names_creator(scen_count)
all_nodenames = sputils.create_nodenames_from_branching_factors(branching_factors)
ev = Xhat_Eval(options,
all_scenario_names,
aircond.scenario_creator,
scenario_denouement=None,
all_nodenames=all_nodenames,
scenario_creator_kwargs=scenario_creator_kwargs
)
return ev
def test_gap_estimators(self):
options = self._get_base_options()
branching_factors= options['kwoptions']['branching_factors']
scen_count = np.prod(branching_factors)
scenario_names = aircond.scenario_names_creator(scen_count, start=1000)
sample_options = {"seed": 0,
"branching_factors": options['kwoptions']['branching_factors']}
estim = ciutils.gap_estimators(self.xhat,
self.refmodelname,
solving_type="EF-mstage",
sample_options=sample_options,
scenario_creator_kwargs = options['kwargs'],
solvername=solvername,
scenario_names=scenario_names,
)
G = estim['G']
s = estim['s']
G,s = round_pos_sig(G,3),round_pos_sig(s,3)
self.assertEqual((G,s), (64.5, 31.8)) # rebaselined 28 Dec 2021 (see also 5 dec)