def test_unbinned_simultaneous_nll():
test_values = tf.constant(test_values_np)
test_values = zfit.Data.from_tensor(obs=obs1, tensor=test_values)
test_values2 = tf.constant(test_values_np2)
test_values2 = zfit.Data.from_tensor(obs=obs1, tensor=test_values2)
gaussian1, mu1, sigma1 = create_gauss1()
gaussian2, mu2, sigma2 = create_gauss2()
gaussian2 = gaussian2.create_extended(zfit.Parameter('yield_gauss2', 5))
nll = zfit.loss.UnbinnedNLL(
model=[gaussian1, gaussian2],
data=[test_values, test_values2],
)
minimizer = Minuit(tolerance=1e-5)
status = minimizer.minimize(loss=nll, params=[mu1, sigma1, mu2, sigma2])
params = status.params
assert set(nll.get_params()) == {mu1, mu2, sigma1, sigma2}
assert params[mu1]['value'] == pytest.approx(np.mean(test_values_np),
rel=0.007)
assert params[mu2]['value'] == pytest.approx(np.mean(test_values_np2),
rel=0.007)
assert params[sigma1]['value'] == pytest.approx(np.std(test_values_np),
rel=0.007)
assert params[sigma2]['value'] == pytest.approx(np.std(test_values_np2),
rel=0.007)
def from_minuit(
cls, loss: ZfitLoss, params: Iterable[ZfitParameter],
result: iminuit.util.MigradResult,
minimizer: Union[ZfitMinimizer, iminuit.Minuit]) -> 'FitResult':
"""Create a `FitResult` from a :py:class:~`iminuit.util.MigradResult` returned by
:py:meth:`iminuit.Minuit.migrad` and a iminuit :py:class:~`iminuit.Minuit` instance with the corresponding
zfit objects.
Args:
loss: zfit Loss that was minimized.
params: Iterable of the zfit parameters that were floating during the minimization.
result: Return value of the iminuit migrad command.
minimizer: Instance of the iminuit Minuit that was used to minimize the loss.
Returns:
A `FitResult` as if zfit Minuit was used.
"""
from .minimizer_minuit import Minuit
if not isinstance(minimizer, Minuit):
if isinstance(minimizer, iminuit.Minuit):
minimizer_new = Minuit()
minimizer_new._minuit_minimizer = minimizer
minimizer = minimizer_new
else:
raise ValueError(
f"Minimizer {minimizer} not supported. Use `Minuit` from zfit or from iminuit."
)
params_result = [p_dict for p_dict in result[1]]
result_vals = [res["value"] for res in params_result]
set_values(params, values=result_vals)
info = {
'n_eval': result[0]['nfcn'],
'n_iter': result[0]['ncalls'],
# 'grad': result['jac'],
# 'message': result['message'],
'original': result[0]
}
edm = result[0]['edm']
fmin = result[0]['fval']
status = -999
converged = result[0]['is_valid']
params = OrderedDict(
(p, res['value']) for p, res in zip(params, params_result))
return cls(params=params,
edm=edm,
fmin=fmin,
info=info,
loss=loss,
status=status,
converged=converged,
minimizer=minimizer)
def test_extended_unbinned_nll():
test_values = ztf.constant(test_values_np)
test_values = zfit.Data.from_tensor(obs=obs1, tensor=test_values)
gaussian3, mu3, sigma3, yield3 = create_gauss3ext()
nll_object = zfit.loss.ExtendedUnbinnedNLL(model=gaussian3,
data=test_values,
fit_range=(-20, 20))
minimizer = Minuit()
status = minimizer.minimize(loss=nll_object)
params = status.params
assert params[mu3]['value'] == pytest.approx(np.mean(test_values_np), rel=0.005)
assert params[sigma3]['value'] == pytest.approx(np.std(test_values_np), rel=0.005)
assert params[yield3]['value'] == pytest.approx(yield_true, rel=0.005)
def test_unbinned_simultaneous_nll():
test_values = tf.constant(test_values_np)
test_values = zfit.Data.from_tensor(obs=obs1, tensor=test_values)
test_values2 = tf.constant(test_values_np2)
test_values2 = zfit.Data.from_tensor(obs=obs1, tensor=test_values2)
gaussian1, mu1, sigma1 = create_gauss1()
gaussian2, mu2, sigma2 = create_gauss2()
nll_object = zfit.loss.UnbinnedNLL(model=[gaussian1, gaussian2],
data=[test_values, test_values2],
fit_range=[(-np.infty, np.infty), (-np.infty, np.infty)]
)
minimizer = Minuit()
status = minimizer.minimize(loss=nll_object, params=[mu1, sigma1, mu2, sigma2])
params = status.params
assert params[mu1]['value'] == pytest.approx(np.mean(test_values_np), rel=0.005)
assert params[mu2]['value'] == pytest.approx(np.mean(test_values_np2), rel=0.005)
assert params[sigma1]['value'] == pytest.approx(np.std(test_values_np), rel=0.005)
assert params[sigma2]['value'] == pytest.approx(np.std(test_values_np2), rel=0.005)
def test_unbinned_nll(weights, sigma):
gaussian1, mu1, sigma1 = create_gauss1()
gaussian2, mu2, sigma2 = create_gauss2()
test_values = tf.constant(test_values_np)
test_values = zfit.Data.from_tensor(obs=obs1,
tensor=test_values,
weights=weights)
nll_object = zfit.loss.UnbinnedNLL(model=gaussian1, data=test_values)
minimizer = Minuit(tolerance=1e-5)
status = minimizer.minimize(loss=nll_object, params=[mu1, sigma1])
params = status.params
rel_error = 0.005 if weights is None else 0.1 # more fluctuating with weights
assert params[mu1]['value'] == pytest.approx(np.mean(test_values_np),
rel=rel_error)
assert params[sigma1]['value'] == pytest.approx(np.std(test_values_np),
rel=rel_error)
constraints = zfit.constraint.nll_gaussian(
params=[mu2, sigma2],
observation=[mu_constr[0], sigma_constr[0]],
uncertainty=sigma())
nll_object = UnbinnedNLL(model=gaussian2,
data=test_values,
constraints=constraints)
minimizer = Minuit(tolerance=1e-4)
status = minimizer.minimize(loss=nll_object, params=[mu2, sigma2])
params = status.params
if weights is None:
assert params[mu2]['value'] > np.average(test_values_np,
weights=weights)
assert params[sigma2]['value'] < np.std(test_values_np)