def test_gaussian_constraint_orderbug2(
): # as raised in #162, failed before fixing
param1 = zfit.Parameter("param1", 1500)
param5 = zfit.Parameter("param2", 0.5)
param2 = zfit.Parameter("param3", 1.0)
param3 = zfit.Parameter("param4", 1.0)
param4 = zfit.Parameter("param5", 1.0)
constraint = {
"params": [param1, param2, param3, param4, param5],
"observation": [1500, 1.0, 1.0, 1.0, 0.5],
"uncertainty": [0.05 * 1500, 0.001, 0.01, 0.1, 0.05 * 0.5],
}
constr1 = GaussianConstraint(**constraint)
# param_vals = [1500, 1.0, 1.0, 1.0, 0.5]
constraint["x"] = [m.numpy() for m in constraint["params"]]
true_val = true_gauss_constr_value(x=constraint["x"],
mu=constraint["observation"],
sigma=constraint["uncertainty"])
value_tensor = constr1.value()
constr_np = value_tensor.numpy()
assert constr_np == pytest.approx(true_val)
assert true_val < 1000
assert true_val == pytest.approx(
-8.592,
abs=0.1) # if failing, change value. Hardcoded for additional layer
def test_gaussian_constraint_shape_errors():
param1 = zfit.Parameter("Param1", 5)
param2 = zfit.Parameter("Param2", 6)
with pytest.raises(ShapeIncompatibleError):
GaussianConstraint([param1, param2], mu=[4, 2, 3], sigma=5)
with pytest.raises(ShapeIncompatibleError):
GaussianConstraint([param1, param2], mu=[4, 2], sigma=5)
with pytest.raises(ShapeIncompatibleError):
GaussianConstraint([param1, param2], mu=2, sigma=[1, 4])
with pytest.raises(ShapeIncompatibleError):
GaussianConstraint(param1, mu=[4, 2], sigma=[2, 3])
def test_gaussian_constraint_matrix():
param1 = zfit.Parameter("Param1", 5)
param2 = zfit.Parameter("Param2", 6)
params = [param1, param2]
mu = [3., 6.1]
sigma = np.array([[1, 0.3], [0.3, 0.5]])
constr = GaussianConstraint(params=params, mu=mu, sigma=sigma)
constr_np = zfit.run(constr.value())
assert constr_np == pytest.approx(3.989638)
assert constr.get_dependents() == set(params)
def test_gaussian_constraint_sampling():
param1 = zfit.Parameter("Param1", 5)
params = [param1]
mu = [5]
sigma = [1]
constr = GaussianConstraint(params=params, mu=mu, sigma=sigma)
sample = constr.sample(15000)
assert np.mean(sample[param1]) == pytest.approx(mu[0], rel=0.01)
assert np.std(sample[param1]) == pytest.approx(sigma[0], rel=0.01)
def test_gaussian_constraint_orderbug(): # as raised in #162
observed = [1500, 1.0, 1.0, 1.0, 0.5]
params = [zfit.Parameter(f"param{i}", val) for i, val in enumerate(observed)]
sigma = [0.05 * 1500, 0.001, 0.01, 0.1, 0.05 * 0.5]
true_val = true_gauss_constr_value(x=observed, mu=observed, sigma=sigma)
constr1 = GaussianConstraint(params=params, observation=observed, uncertainty=sigma)
value_tensor = constr1.value()
constr_np = value_tensor.numpy()
assert constr_np == pytest.approx(true_val)
assert true_val < 10000
def test_gaussian_constraint_sampling():
param1 = zfit.Parameter("Param1", 5)
params = [param1]
observed = [5]
sigma = [1]
constr = GaussianConstraint(params=params,
observation=observed,
uncertainty=sigma)
sample = constr.sample(15000)
assert np.mean(sample[param1]) == pytest.approx(observed[0], rel=0.01)
assert np.std(sample[param1]) == pytest.approx(sigma[0], rel=0.01)
def test_gaussian_constraint_shape_errors():
param1 = zfit.Parameter("Param1", 5)
param2 = zfit.Parameter("Param2", 6)
obs1 = zfit.Parameter("obs1", 2)
obs2 = zfit.Parameter("obs2", 3)
obs3 = zfit.Parameter("obs3", 4)
with pytest.raises(ShapeIncompatibleError):
GaussianConstraint(params=[param1, param2], observation=[obs1, obs2, obs3], uncertainty=5).value()
with pytest.raises(ShapeIncompatibleError):
GaussianConstraint(params=[param1, param2], observation=[obs1, obs3], uncertainty=5).value()
with pytest.raises(ShapeIncompatibleError):
GaussianConstraint(params=[param1, param2], observation=obs1, uncertainty=[1, 4]).value()
with pytest.raises(ShapeIncompatibleError):
GaussianConstraint(params=param1, observation=[obs1, obs3], uncertainty=[2, 3]).value()
def test_gaussian_constraint_matrix():
param1 = zfit.Parameter("Param1", 5)
param2 = zfit.Parameter("Param2", 6)
params = [param1, param2]
observed = [3., 6.1]
sigma = np.array([[1, 0.3],
[0.3, 0.5]])
trueval = true_multinormal_constr_value(x=zfit.run(params)[0], mean=observed, cov=sigma)
constr = GaussianConstraint(params=params, observation=observed, uncertainty=sigma)
constr_np = zfit.run(constr.value())
assert constr_np == pytest.approx(trueval)
#assert constr_np == pytest.approx(3.989638)
assert constr.get_cache_deps() == set(params)
def test_gaussian_constraint():
param_vals = [5, 6, 3]
observed = [3, 6.1, 4.3]
sigma = [1, 0.3, 0.7]
true_val = true_gauss_constr_value(x=observed, mu=param_vals, sigma=sigma)
assert true_val == true_gauss_constr_value(x=param_vals, mu=observed, sigma=sigma)
params = [zfit.Parameter(f"Param{i}", val) for i, val in enumerate(param_vals)]
constr = GaussianConstraint(params=params, observation=observed, uncertainty=sigma)
constr_np = constr.value().numpy()
assert constr_np == pytest.approx(true_val)
assert constr.get_cache_deps() == set(params)
param_vals[0] = 2
params[0].set_value(param_vals[0])
constr2_np = constr.value().numpy()
constr2_newtensor_np = constr.value().numpy()
assert constr2_newtensor_np == pytest.approx(constr2_np)
true_val2 = true_gauss_constr_value(x=param_vals, mu=observed, sigma=sigma)
assert constr2_np == pytest.approx(true_val2)
print(true_val2)
constr.observation[0].set_value(5)
observed[0] = 5
print("x: ", param_vals, [p.numpy() for p in params])
print("mu: ", observed, [p.numpy() for p in constr.observation])
print("sigma: ", sigma, np.sqrt([p for p in np.diag(constr.covariance)]))
true_val3 = true_gauss_constr_value(x=param_vals, mu=observed, sigma=sigma)
constr3_np = constr.value().numpy()
assert constr3_np == pytest.approx(true_val3)
def test_gaussian_constraint():
param_vals = [5, 6, 3]
mu = [3, 6.1, 4.3]
sigma = [1, 0.3, 0.7]
true_val = true_gauss_constr_value(param_vals, mu, sigma)
params = [
zfit.Parameter(f"Param{i}", val) for i, val in enumerate(param_vals)
]
constr = GaussianConstraint(params=params, mu=mu, sigma=sigma)
value_tensor = constr.value()
constr_np = zfit.run(value_tensor)
assert constr_np == pytest.approx(true_val)
assert constr.get_dependents() == set(params)
param_vals[0] = 2
params[0].set_value(param_vals[0])
constr2_np = zfit.run(value_tensor)
constr2_newtensor_np = zfit.run(constr.value())
assert constr2_newtensor_np == pytest.approx(constr2_np)
true_val2 = true_gauss_constr_value(param_vals, mu, sigma)
assert constr2_np == pytest.approx(true_val2)
