def test_xhat_eval_creator(self):
options = self._get_xhatEval_options()
MMW_options = self._get_base_options()
scenario_creator_kwargs = MMW_options['kwargs']
scenario_creator_kwargs['num_scens'] = MMW_options['batch_size']
ev = Xhat_Eval(options,
farmer.scenario_names_creator(100),
farmer.scenario_creator,
scenario_denouement=None,
scenario_creator_kwargs=scenario_creator_kwargs)
def test_gap_estimators(self):
scenario_names = farmer.scenario_names_creator(50, start=1000)
estim = ciutils.gap_estimators(
self.xhat,
self.refmodelname,
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), (110.0, 426.0))
def test_xhat_eval_evaluate(self):
options = self._get_xhatEval_options()
MMW_options = self._get_base_options()
scenario_creator_kwargs = MMW_options['kwargs']
scenario_creator_kwargs['num_scens'] = MMW_options['batch_size']
ev = Xhat_Eval(options,
farmer.scenario_names_creator(100),
farmer.scenario_creator,
scenario_denouement=None,
scenario_creator_kwargs=scenario_creator_kwargs)
xhat = ciutils.read_xhat(self.xhat_path)
obj = round_pos_sig(ev.evaluate(xhat), 2)
self.assertEqual(obj, -1300000.0)
def test_xhat_eval_evaluate_one(self):
options = self._get_xhatEval_options()
MMW_options = self._get_base_options()
xhat = ciutils.read_xhat(self.xhat_path)
scenario_creator_kwargs = MMW_options['kwargs']
scenario_creator_kwargs['num_scens'] = MMW_options['batch_size']
scenario_names = farmer.scenario_names_creator(100)
ev = Xhat_Eval(options,
scenario_names,
farmer.scenario_creator,
scenario_denouement=None,
scenario_creator_kwargs=scenario_creator_kwargs)
k = scenario_names[0]
obj = ev.evaluate_one(xhat, k, ev.local_scenarios[k])
obj = round_pos_sig(obj, 2)
self.assertEqual(obj, -48000.0)