def test_with_gauss_qtilde(n, min_x):
sigma_x = 0.032
minimizer = Minuit()
bounds = (-10, 10)
obs = zfit.Space("x", limits=bounds)
mean = zfit.Parameter("mean", n * sigma_x)
sigma = zfit.Parameter("sigma", 1.0)
model = zfit.pdf.Gauss(obs=obs, mu=mean, sigma=sigma)
data = model.sample(n=1000)
nll = UnbinnedNLL(model=model, data=data)
minimum = minimizer.minimize(loss=nll)
minimum.hesse()
x = minimum.params[mean]["value"]
x_err = minimum.params[mean]["minuit_hesse"]["error"]
x_min = x - x_err * 3
x_max = x + x_err * 3
x_min = max([x_min, min_x])
poinull = POIarray(mean, np.linspace(x_min, x_max, 50))
calculator = AsymptoticCalculator(nll, minimizer)
ci = ConfidenceInterval(calculator, poinull, qtilde=True)
ci.interval(alpha=0.05, printlevel=1)
def test_with_gauss_fluctuations():
x_true = -2.0
minimizer = Minuit()
bounds = (-10, 10)
obs = zfit.Space("x", limits=bounds)
mean = zfit.Parameter("mean", 0)
sigma = zfit.Parameter("sigma", 1.0)
model = zfit.pdf.Gauss(obs=obs, mu=mean, sigma=sigma)
npzfile = f"{notebooks_dir}/toys/FC_toys_{x_true}.npz"
data = zfit.data.Data.from_numpy(obs=obs, array=np.load(npzfile)["x"])
nll = UnbinnedNLL(model=model, data=data)
minimum = minimizer.minimize(loss=nll)
minimum.hesse()
toys_fname = f"{notebooks_dir}/toys/FC_toys_{x_true}.yml"
calculator = FrequentistCalculator.from_yaml(toys_fname, minimum,
minimizer)
keys = np.unique([k[0].value for k in calculator.keys()])
keys.sort()
poinull = POIarray(mean, keys)
ci = ConfidenceInterval(calculator, poinull, qtilde=False)
with pytest.warns(UserWarning):
ci.interval(alpha=0.05, printlevel=0)
ci = ConfidenceInterval(calculator, poinull, qtilde=True)
ci.interval(alpha=0.05, printlevel=0)
def create_loss(constraint=False):
obs = zfit.Space("x", limits=(0.1, 2.0))
data = zfit.data.Data.from_numpy(obs=obs,
array=np.random.normal(1.2, 0.1, 10000))
mean = zfit.Parameter("mu", true_mu)
sigma = zfit.Parameter("sigma", true_sigma)
model = zfit.pdf.Gauss(obs=obs, mu=mean, sigma=sigma)
loss = UnbinnedNLL(model=model, data=data)
if constraint:
loss.add_constraints(
zfit.constraint.GaussianConstraint(params=mean,
observation=true_mu,
uncertainty=0.01))
return loss, (mean, sigma)
def create_loss_1():
obs = zfit.Space("x", limits=(0.1, 2.0))
data = zfit.data.Data.from_numpy(obs=obs, array=np.random.normal(1.2, 0.1, 10000))
mean = zfit.Parameter("mu", 1.2)
sigma = zfit.Parameter("sigma", 0.1)
model = zfit.pdf.Gauss(obs=obs, mu=mean, sigma=sigma)
loss = UnbinnedNLL(model=model, data=data)
return loss
def create_loss_counting():
n = 370
nbkg = 340
Nsig = zfit.Parameter("Nsig", 0, -100.0, 100)
Nbkg = zfit.Parameter("Nbkg", nbkg, floating=False)
Nobs = zfit.ComposedParameter("Nobs",
lambda a, b: a + b,
params=[Nsig, Nbkg])
obs = zfit.Space("N", limits=(0, 800))
model = Poisson(obs=obs, lamb=Nobs)
data = zfit.data.Data.from_numpy(obs=obs, array=np.array([n]))
loss = UnbinnedNLL(model=model, data=data)
return loss, Nsig
def test_simple_kde_1d():
# test special properties here
# zfit.settings.options['numerical_grad'] = True
data = tf.random.normal(shape=(100, 1))
# data = np.random.normal(size=(100, 1))
sigma = zfit.Parameter("sigma", 0.5)
lower = ((-5, ), )
upper = ((5, ), )
obs = zfit.Space(["obs1"], limits=(lower, upper))
kde = zphys.unstable.pdf.GaussianKDE(data=data, bandwidth=sigma, obs=obs)
probs = kde.pdf(x=data + 0.03)
from zfit.loss import UnbinnedNLL
data = tf.random.normal(shape=(100, 1))
data = zfit.Data.from_tensor(tensor=data, obs=obs)
nll = UnbinnedNLL(model=kde, data=data)
minimizer = zfit.minimize.Minuit()
minimum = minimizer.minimize(loss=nll)
assert minimum.converged
def test_simple_kde_3d():
# test special properties here
data = np.random.normal(size=(100, 3))
sigma = zfit.Parameter("sigma", 0.5)
sigmas = [sigma, 1., 2]
lower = ((-5, -5, -5), )
upper = ((5, 5, 5), )
obs = zfit.Space(["obs1", "obs2", "obs3"], limits=(lower, upper))
kde = zphys.unstable.pdf.GaussianKDE(data=data, bandwidth=sigmas, obs=obs)
probs = kde.pdf(x=data + 0.03)
probs_np = probs.numpy()
from zfit.loss import UnbinnedNLL
data = np.random.normal(size=(100, 3))
data = zfit.Data.from_tensor(tensor=data, obs=obs)
nll = UnbinnedNLL(model=kde, data=data)
minimizer = zfit.minimize.Minuit()
minimum = minimizer.minimize(loss=nll)
assert minimum.converged