def test_seqsampling_running(self):
#We need a very small instance for testing on GitHub.
optionsBM = {
'h': 1.75,
'hprime': 0.5,
'eps': 0.2,
'epsprime': 0.1,
"p": 0.1,
"q": 1.2,
"solvername": solvername,
"xhat_gen_options": {
"crops_multiplier": 3
},
"crops_multiplier": 3,
}
seq_pb = seqsampling.SeqSampling(
"mpisppy.tests.examples.farmer",
seqsampling.xhat_generator_farmer,
optionsBM,
stochastic_sampling=False,
stopping_criterion="BM",
solving_type="EF-2stage",
)
x = seq_pb.run(maxit=50)
T = x['T']
ub = round_pos_sig(x['CI'][1], 2)
self.assertEqual((T, ub), (1, 7400.0))
def test_seqsampling_creator(self):
BFs = [4, 3, 2]
xhat_gen_options = self._get_xhat_gen_options(BFs)
optionsBM = {'h':0.2,
'hprime':0.015,
'eps':0.5,
'epsprime':0.4,
"p":2,
"q":1.2,
"solvername":solvername,
"xhat_gen_options": xhat_gen_options,
"branching_factors": BFs,
}
seqsampling.SeqSampling(self.refmodelname,
aircond.xhat_generator_aircond,
optionsBM,
stochastic_sampling=False,
stopping_criterion="BM",
solving_type="EF-mstage",
)
def test_seqsampling_creator(self):
optionsBM = {
'h': 0.2,
'hprime': 0.015,
'eps': 0.5,
'epsprime': 0.4,
"p": 2,
"q": 1.2,
"solvername": solvername,
"xhat_gen_options": {
"crops_multiplier": 3
},
"crops_multiplier": 3,
}
seqsampling.SeqSampling(
"mpisppy.tests.examples.farmer",
seqsampling.xhat_generator_farmer,
optionsBM,
stochastic_sampling=False,
stopping_criterion="BM",
)
def main(args):
""" Code for farmer sequential sampling (in a function for easier testing)
Args:
args (parseargs): the command line arguments object from parseargs
Returns:
results (dict): the solution, gap confidence interval and T
"""
refmodelname = "afarmer"
scenario_creator = afarmer.scenario_creator
scen_count = args.num_scens
solver_name = args.EF_solver_name
crops_multiplier = args.crops_multiplier
scenario_names = ['Scenario' + str(i) for i in range(scen_count)]
xhat_gen_options = {
"scenario_names": scenario_names,
"solvername": solver_name,
"solver_options": None,
"use_integer": False,
"crops_multiplier": crops_multiplier,
"start_seed": 0,
}
# simply called "options" by the SeqSampling constructor
inneroptions = {
"solvername": solver_name,
"solver_options": None,
"sample_size_ratio": args.sample_size_ratio,
"xhat_gen_options": xhat_gen_options,
"ArRP": args.ArRP,
"kf_xhat": args.kf_GS,
"kf_xhat": args.kf_xhat,
"confidence_level": args.confidence_level,
}
if args.BM_vs_BPL == "BM":
# Bayraksan and Morton
optionsBM = {
'h': args.BM_h,
'hprime': args.BM_hprime,
'eps': args.BM_eps,
'epsprime': args.BM_eps_prime,
"p": args.BM_p,
"q": args.BM_q,
"xhat_gen_options": xhat_gen_options,
}
optionsBM.update(inneroptions)
sampler = seqsampling.SeqSampling(
refmodelname,
xhat_generator_farmer,
optionsBM,
stochastic_sampling=False,
stopping_criterion="BM",
solving_type="EF-2stage",
)
else: # must be BPL
optionsBPL = {
'eps': args.BPL_eps,
"c0": args.BPL_c0,
"n0min": args.BPL_n0min,
"xhat_gen_options": xhat_gen_options,
}
optionsBPL.update(inneroptions)
ss = int(args.BPL_n0min) != 0
sampler = seqsampling.SeqSampling(
refmodelname,
xhat_generator_farmer,
optionsBPL,
stochastic_sampling=ss,
stopping_criterion="BPL",
solving_type="EF-2stage",
)
xhat = sampler.run()
return xhat