def test_frequentist_calculator_one_poi(constraint):
with pytest.raises(TypeError):
FrequentistCalculator()
loss, (mean, sigma) = create_loss(constraint=constraint)
calc = FrequentistCalculator(loss, Minuit(), ntoysnull=100, ntoysalt=100)
assert calc.ntoysnull == 100
assert calc.ntoysalt == 100
samplers = calc.sampler(floating_params=[mean])
assert all(is_valid_data(s) for s in samplers)
loss = calc.toys_loss(mean.name)
assert is_valid_loss(loss)
def test_asymptotic_calculator_one_poi():
with pytest.raises(TypeError):
AsymptoticCalculator()
loss, (mean, sigma) = create_loss()
calc = AsymptoticCalculator(loss, Minuit())
poi_null = POIarray(mean, [1.15, 1.2, 1.25])
poi_alt = POI(mean, 1.2)
dataset = calc.asimov_dataset(poi_alt)
assert all(is_valid_data(d) for d in dataset)
loss = calc.asimov_loss(poi_alt)
assert is_valid_loss(loss)
null_nll = calc.asimov_nll(pois=poi_null, poialt=poi_alt)
assert null_nll[0] >= null_nll[1]
assert null_nll[2] >= null_nll[1]
def test_toymanager_attributes():
loss, (Nsig, poigen, poieval) = create_loss()
tm = ToysManager.from_yaml(
f"{notebooks_dir}/toys/discovery_freq_zfit_toys.yml", loss, Minuit()
)
with pytest.raises(ParameterNotFound):
ToysManager.from_yaml(
f"{notebooks_dir}/toys/discovery_freq_zfit_toys.yml",
create_loss_1(),
Minuit(),
)
tr = list(tm.values())[0]
assert isinstance(tr, ToyResult)
assert list(tm.keys())[0] == (poigen, poigen)
assert (poigen, poieval) in tm.keys()
assert tm.get_toyresult(poigen, poieval) == tr
tr1 = ToyResult(poigen, poieval.append(1))
tm.add_toyresult(tr1)
with pytest.raises(TypeError):
tm.add_toyresult("tr1")
assert (tr1.poigen, tr1.poieval) in tm.keys()
tm.to_yaml(f"{pwd}/test_toyutils.yml")
tm.to_yaml(f"{pwd}/test_toyutils.yml")
tmc = ToysManager.from_yaml(f"{pwd}/test_toyutils.yml", loss, Minuit())
assert (
tm.get_toyresult(poigen, poieval).ntoys
== tmc.get_toyresult(poigen, poieval).ntoys
)
samplers = tm.sampler(floating_params=[poigen.parameter])
assert all(is_valid_data(s) for s in samplers)
loss = tm.toys_loss(poigen.name)
assert is_valid_loss(loss)
os.remove(f"{pwd}/test_toyutils.yml")
def test_base_calculator(calculator):
with pytest.raises(TypeError):
calculator()
loss, (mean, sigma) = create_loss()
with pytest.raises(ValueError):
calculator("loss", Minuit())
with pytest.raises(ValueError):
calculator(loss, "Minuit()")
calc_loss = calculator(loss, Minuit())
with pytest.raises(ValueError):
calc_loss.bestfit = "bestfit"
bestfit = calc_loss.bestfit
calc_fitresult = calculator(bestfit, calc_loss.minimizer)
assert calc_loss.bestfit == calc_fitresult.bestfit
assert calc_loss.loss == calc_fitresult.loss
mean_poi = POIarray(mean, [1.15, 1.2, 1.25])
mean_nll = calc_loss.obs_nll(pois=mean_poi)
calc_loss.obs_nll(pois=mean_poi) # get from cache
assert mean_nll[0] >= mean_nll[1]
assert mean_nll[2] >= mean_nll[1]
assert calc_loss.obs_nll(mean_poi[0]) == mean_nll[0]
assert calc_loss.obs_nll(mean_poi[1]) == mean_nll[1]
assert calc_loss.obs_nll(mean_poi[2]) == mean_nll[2]
mean_poialt = POI(mean, 1.2)
pvalue = lambda: calc_loss.pvalue(poinull=mean_poi, poialt=mean_poialt)
exp_pvalue = lambda: calc_loss.expected_pvalue(
poinull=mean_poi, poialt=mean_poialt, nsigma=np.arange(-2, 3, 1)
)
exp_poi = lambda: calc_loss.expected_poi(
poinull=mean_poi, poialt=mean_poialt, nsigma=np.arange(-2, 3, 1)
)
if calculator == BaseCalculator:
with pytest.raises(NotImplementedError):
pvalue()
with pytest.raises(NotImplementedError):
exp_pvalue()
else:
pvalue()
exp_pvalue()
model = calc_loss.model[0]
sampler = model.create_sampler(n=10000)
assert is_valid_data(sampler)
loss = calc_loss.lossbuilder(model=[model], data=[sampler], weights=None)
assert is_valid_loss(loss)
with pytest.raises(ValueError):
calc_loss.lossbuilder(model=[model, model], data=[sampler])
with pytest.raises(ValueError):
calc_loss.lossbuilder(model=[model], data=[sampler, calc_loss.data[0]])
with pytest.raises(ValueError):
calc_loss.lossbuilder(model=[model], data=[sampler], weights=[])
with pytest.raises(ValueError):
calc_loss.lossbuilder(
model=[model], data=[sampler], weights=[np.ones(10000), np.ones(10000)]
)
assert calc_loss.get_parameter(mean_poi.name) == mean
with pytest.raises(KeyError):
calc_loss.get_parameter("dummy_parameter")