def produce_additional_models(self):
model = self.create_random_model()
if not self.check_consistency(model):
out = self.revise(model)
if out == False:
self.archive.record(AdditModProdFail())
else:
self.archive.record(AdditionalModels(out[0]))
else:
self.archive.record(AdditionalModels([model]))
if out[1] == True:
raise ValueError(
'produce_additional_models: revised set of ignored results instead of model itself'
)
def setUp(self):
self.archive = Archive()
exd = ExperimentDescription(DetectionEntity(None))
res1 = Result('res_0', exd, None)
res2 = Result('res_1', exd, None)
res3 = Result('res_2', exd, None)
res4 = Result('res_3', exd, None)
exp1 = Experiment('exp_0', [res1])
exp2 = Experiment('exp_1', [res2])
exp3 = Experiment('exp_2', [res3])
exp4 = Experiment('exp_3', [res4])
r1 = NewResults(exp1)
r2 = NewResults(exp2)
r3 = NewResults(exp3)
r4 = NewResults(exp4)
self.mod1 = Model('m_0', [1], [], [])
self.mod1.ignored_results = frozenset([res3])
self.mod1.results_covered = frozenset([res1, res4])
self.mod2 = Model('m_1', [2], [], [])
self.mod2.ignored_results = frozenset([res3])
self.mod2.results_covered = frozenset([res1, res2, res4])
m = AdditionalModels([self.mod1, self.mod2])
self.archive.record(r1)
self.archive.record(m)
self.archive.record(r2)
self.archive.record(r3)
self.archive.record(r4)
def test_was_new_model_produced_since_last_check_positive(self):
self.overseer_qual.archive.development_history.append(
CheckPointSuccess())
self.overseer_qual.archive.development_history.append(
AdditionalModels([]))
out = self.overseer_qual.was_new_model_produced_since_last_check()
self.assertEqual(out, True)
def produce_additional_models(self):
model = self.get_current_best_model()
out = self.revise(model, True)
if out == False:
self.archive.record(AdditModProdFail())
else:
self.archive.record(AdditionalModels(out[0]))
if out[1] == True:
raise ValueError(
'produce_additional_models: revised set of ignored results instead of model itself'
)
def test_check_consistency_negative(self):
met1 = mnm_repr.Metabolite('met1')
met2 = mnm_repr.Metabolite('met2')
comp1 = mnm_repr.Medium()
cond_subst_1 = mnm_repr.PresentEntity(met1, comp1)
cond_subst_2 = mnm_repr.PresentEntity(met2, comp1)
base_model = mnm_repr.Model('m1', [cond_subst_1, cond_subst_2], [], [])
exd = exp_repr.ExperimentDescription(exp_repr.DetectionEntity('met1'),
[])
res = exp_repr.Result('r1', exd, 'false')
exp = exp_repr.Experiment('exp1', [res])
arch = Archive()
arch.mnm_entities = [met1, met2]
arch.mnm_compartments = [comp1]
ev1 = AdditionalModels([base_model])
ev2 = AcceptedResults(exp)
arch.record(ev1)
arch.record(ev2)
rev = RevisionModule(arch)
out = rev.check_consistency(base_model)
self.assertEqual(False, out)