def test_BinnedNLL_mask():
c = BinnedNLL([5, 1000, 1], [0, 1, 2, 3], expon_cdf)
c_unmasked = c(1)
c.mask = np.arange(3) != 1
assert c(1) < c_unmasked
def test_weighted_BinnedNLL():
def cdf(x, a):
return 1 - np.exp(-a * x)
xe = np.array([0, 1, 10])
p = np.diff(cdf(xe, 1))
n = p * 1000
m1 = Minuit(BinnedNLL(n, xe, cdf), 1)
m1.migrad()
assert m1.values[0] == pytest.approx(1, rel=1e-2)
w = np.transpose((n, 4 * n))
m2 = Minuit(BinnedNLL(w, xe, cdf), 1)
m2.migrad()
assert m2.values[0] == pytest.approx(1, rel=1e-2)
assert m2.errors[0] == pytest.approx(2 * m1.errors[0], rel=1e-2)
def test_BinnedNLL(binned, verbose):
mle, nx, xe = binned
def cdf(x, mu, sigma):
return norm(mu, sigma).cdf(x)
cost = BinnedNLL(nx, xe, cdf, verbose=verbose)
m = Minuit(cost, mu=0, sigma=1, limit_sigma=(0, None))
m.migrad()
# binning loses information compared to unbinned case
assert_allclose(m.args, mle[1:], rtol=0.15)
assert m.errors["mu"] == pytest.approx(1000**-0.5, rel=0.05)
def test_BinnedNLL(binned, verbose):
mle, nx, xe = binned
def cdf(x, mu, sigma):
return norm(mu, sigma).cdf(x)
cost = BinnedNLL(nx, xe, cdf, verbose=verbose)
assert cost.ndata == len(nx)
m = Minuit(cost, mu=0, sigma=1)
m.limits["sigma"] = (0, None)
m.migrad()
# binning loses information compared to unbinned case
assert_allclose(m.values, mle[1:], rtol=0.15)
assert m.errors["mu"] == pytest.approx(1000**-0.5, rel=0.05)
assert m.ndof == len(nx) - 2
assert_allclose(m.fmin.reduced_chi2, 1, atol=0.15)
def test_BinnedNLL_properties():
def cdf(x, a, b):
return 0
c = BinnedNLL([1], [1, 2], cdf)
assert c.cdf is cdf
with pytest.raises(AttributeError):
c.cdf = None
assert_equal(c.n, [1])
assert_equal(c.xe, [1, 2])
c.n = [2]
c.xe = [2, 3]
assert_equal(c.n, [2])
assert_equal(c.xe, [2, 3])
with pytest.raises(ValueError):
c.n = [1, 2]
with pytest.raises(ValueError):
c.xe = [1, 2, 3]
def test_BinnedNLL_bad_input_3():
with pytest.raises(ValueError):
BinnedNLL([[1, 2, 3]], [1], lambda x, a: 0)
def test_BinnedNLL_mask():
c = BinnedNLL([1, 1000, 2], [0, 1, 2, 3], lambda x, a: x + a)
assert c(2) == pytest.approx(-7000, rel=0.1)
c.mask = np.arange(3) != 1
assert c(2) == pytest.approx(-3, rel=0.1)
