def test_gradients(chunksize):
zfit.run.chunking.active = True
zfit.run.chunking.max_n_points = chunksize
param1 = zfit.Parameter("param1", 1.)
param2 = zfit.Parameter("param2", 2.)
gauss1 = Gauss(param1, 4, obs=obs1)
gauss1.set_norm_range((-5, 5))
gauss2 = Gauss(param2, 5, obs=obs1)
gauss2.set_norm_range((-5, 5))
data1 = zfit.Data.from_tensor(obs=obs1, tensor=ztf.constant(1., shape=(100,)))
data1.set_data_range((-5, 5))
data2 = zfit.Data.from_tensor(obs=obs1, tensor=ztf.constant(1., shape=(100,)))
data2.set_data_range((-5, 5))
nll = UnbinnedNLL(model=[gauss1, gauss2], data=[data1, data2])
gradient1 = nll.gradients(params=param1)
assert zfit.run(gradient1) == zfit.run(tf.gradients(ys=nll.value(), xs=param1))
gradient2 = nll.gradients(params=[param2, param1])
both_gradients_true = zfit.run(tf.gradients(ys=nll.value(), xs=[param2, param1]))
assert zfit.run(gradient2) == both_gradients_true
gradient3 = nll.gradients()
assert frozenset(zfit.run(gradient3)) == frozenset(both_gradients_true)
def test_gradients(chunksize):
from numdifftools import Gradient
zfit.run.chunking.active = True
zfit.run.chunking.max_n_points = chunksize
initial1 = 1.
initial2 = 2
param1 = zfit.Parameter("param1", initial1)
param2 = zfit.Parameter("param2", initial2)
gauss1 = Gauss(param1, 4, obs=obs1)
gauss1.set_norm_range((-5, 5))
gauss2 = Gauss(param2, 5, obs=obs1)
gauss2.set_norm_range((-5, 5))
data1 = zfit.Data.from_tensor(obs=obs1,
tensor=z.constant(1., shape=(100, )))
data1.set_data_range((-5, 5))
data2 = zfit.Data.from_tensor(obs=obs1,
tensor=z.constant(1., shape=(100, )))
data2.set_data_range((-5, 5))
nll = UnbinnedNLL(model=[gauss1, gauss2], data=[data1, data2])
def loss_func(values):
for val, param in zip(values, nll.get_cache_deps(only_floating=True)):
param.set_value(val)
return nll.value().numpy()
# theoretical, numerical = tf.test.compute_gradient(loss_func, list(params))
gradient1 = nll.gradients(params=param1)
gradient_func = Gradient(loss_func)
# gradient_func = lambda *args, **kwargs: list(gradient_func_numpy(*args, **kwargs))
assert gradient1[0].numpy() == pytest.approx(
gradient_func([param1.numpy()]))
param1.set_value(initial1)
param2.set_value(initial2)
params = [param2, param1]
gradient2 = nll.gradients(params=params)
both_gradients_true = list(
reversed(list(gradient_func([initial1, initial2
])))) # because param2, then param1
assert [g.numpy() for g in gradient2] == pytest.approx(both_gradients_true)
param1.set_value(initial1)
param2.set_value(initial2)
gradient3 = nll.gradients()
assert frozenset([g.numpy() for g in gradient3
]) == pytest.approx(frozenset(both_gradients_true))
def test_gradients():
param1 = Parameter("param111", 1.)
param2 = Parameter("param222", 2.)
gauss1 = Gauss(param1, 4, obs=obs1)
gauss1.set_norm_range((-5, 5))
gauss2 = Gauss(param2, 5, obs=obs1)
gauss2.set_norm_range((-5, 5))
data1 = zfit.data.Data.from_tensor(obs=obs1, tensor=ztf.constant(1., shape=(100,)))
data1.set_data_range((-5, 5))
data2 = zfit.data.Data.from_tensor(obs=obs1, tensor=ztf.constant(1., shape=(100,)))
data2.set_data_range((-5, 5))
nll = UnbinnedNLL(model=[gauss1, gauss2], data=[data1, data2])
gradient1 = nll.gradients(params=param1)
assert zfit.run(gradient1) == zfit.run(tf.gradients(nll.value(), param1))
gradient2 = nll.gradients(params=[param2, param1])
both_gradients_true = zfit.run(tf.gradients(nll.value(), [param2, param1]))
assert zfit.run(gradient2) == both_gradients_true
gradient3 = nll.gradients()
assert frozenset(zfit.run(gradient3)) == frozenset(both_gradients_true)