def test_shapefactor(backend):
mc = MockConfig(
par_map={
'shapefac1': {
'paramset':
unconstrained(
name='shapefac1',
is_scalar=False,
n_parameters=1,
inits=[0],
bounds=[[0, 10]],
fixed=False,
),
'slice':
slice(0, 1),
},
'shapefac2': {
'paramset':
unconstrained(
name='shapefac2',
is_scalar=False,
n_parameters=2,
inits=[0, 0],
bounds=[[0, 10], [0, 10]],
fixed=False,
),
'slice':
slice(1, 3),
},
},
par_order=['shapefac1', 'shapefac2'],
samples=['signal', 'background'],
channels=['chan_one', 'chan_two'],
channel_nbins={
'chan_one': 1,
'chan_two': 2
},
)
mega_mods = {
'shapefactor/shapefac1': {
'signal': {
'type': 'shapefactor',
'name': 'shapefac1',
'data': {
'mask': [True, False, False]
},
},
'background': {
'type': 'shapefactor',
'name': 'shapefac1',
'data': {
'mask': [True, False, False]
},
},
},
'shapefactor/shapefac2': {
'signal': {
'type': 'shapefactor',
'name': 'shapefac2',
'data': {
'mask': [False, True, True]
},
},
'background': {
'type': 'normfactor',
'name': 'shapefac2',
'data': {
'mask': [False, True, True]
},
},
},
}
hsc = shapefactor_combined([('shapefac1', 'shapefactor'),
('shapefac2', 'shapefactor')], mc, mega_mods)
mod = hsc.apply(pyhf.tensorlib.astensor([2.0, 3.0, 4.0]))
shape = pyhf.tensorlib.shape(mod)
assert shape == (2, 2, 1, 3)
mod = np.asarray(pyhf.tensorlib.tolist(mod))
assert np.allclose(mod[0, 0, 0], [2.0, 1.0, 1.0])
assert np.allclose(mod[1, 0, 0], [1.0, 3.0, 4.0])
hsc = shapefactor_combined(
[('shapefac1', 'shapefactor'), ('shapefac2', 'shapefactor')],
mc,
mega_mods,
batch_size=4,
)
mod = hsc.apply(
pyhf.tensorlib.astensor([[2.0, 3.0, 4.0], [5.0, 6.0, 7.0],
[8.0, 9.0, 10.0], [11.0, 12.0, 13.0]]))
shape = pyhf.tensorlib.shape(mod)
assert shape == (2, 2, 4, 3)
mod = np.asarray(pyhf.tensorlib.tolist(mod))
assert np.allclose(mod[0, 0, 0], [2.0, 1.0, 1.0])
assert np.allclose(mod[0, 0, 1], [5.0, 1.0, 1.0])
assert np.allclose(mod[0, 0, 2], [8.0, 1.0, 1.0])
assert np.allclose(mod[0, 0, 3], [11.0, 1.0, 1.0])
assert np.allclose(mod[1, 0, 0], [1.0, 3.0, 4.0])
assert np.allclose(mod[1, 0, 1], [1.0, 6.0, 7.0])
assert np.allclose(mod[1, 0, 2], [1.0, 9.0, 10.0])
assert np.allclose(mod[1, 0, 3], [1.0, 12.0, 13.0])
def test_normfactor(backend):
mc = MockConfig(
par_map={
'mu1': {
'paramset':
unconstrained(
name='mu1',
is_scalar=True,
n_parameters=1,
inits=[0],
bounds=[[0, 10]],
fixed=False,
),
'slice':
slice(0, 1),
},
'mu2': {
'paramset':
unconstrained(
name='mu2',
is_scalar=True,
n_parameters=1,
inits=[0],
bounds=[[0, 10]],
fixed=False,
),
'slice':
slice(1, 2),
},
},
par_order=['mu1', 'mu2'],
samples=['signal', 'background'],
)
mega_mods = {
'normfactor/mu1': {
'signal': {
'type': 'normfactor',
'name': 'mu1',
'data': {
'mask': [True, False, False]
},
},
'background': {
'type': 'normfactor',
'name': 'mu1',
'data': {
'mask': [True, False, False]
},
},
},
'normfactor/mu2': {
'signal': {
'type': 'normfactor',
'name': 'mu2',
'data': {
'mask': [False, True, True]
},
},
'background': {
'type': 'normfactor',
'name': 'mu2',
'data': {
'mask': [False, True, True]
},
},
},
}
hsc = normfactor_combined([('mu1', 'normfactor'), ('mu2', 'normfactor')],
mc, mega_mods)
mod = hsc.apply(pyhf.tensorlib.astensor([2.0, 3.0]))
shape = pyhf.tensorlib.shape(mod)
assert shape == (2, 2, 1, 3)
mod = np.asarray(pyhf.tensorlib.tolist(mod))
assert np.allclose(mod[0, 0, 0], [2.0, 1.0, 1.0])
assert np.allclose(mod[1, 0, 0], [1.0, 3.0, 3.0])
hsc = normfactor_combined([('mu1', 'normfactor'), ('mu2', 'normfactor')],
mc,
mega_mods,
batch_size=4)
mod = hsc.apply(
pyhf.tensorlib.astensor([[1.0, 5.0], [2.0, 6.0], [3.0, 7.0],
[4.0, 8.0]]))
shape = pyhf.tensorlib.shape(mod)
assert shape == (2, 2, 4, 3)
mod = np.asarray(pyhf.tensorlib.tolist(mod))
assert np.allclose(mod[0, 0, 0], [1.0, 1.0, 1.0])
assert np.allclose(mod[0, 0, 1], [2.0, 1.0, 1.0])
assert np.allclose(mod[0, 0, 2], [3.0, 1.0, 1.0])
assert np.allclose(mod[0, 0, 3], [4.0, 1.0, 1.0])
assert np.allclose(mod[1, 0, 0], [1.0, 5.0, 5.0])
assert np.allclose(mod[1, 0, 1], [1.0, 6.0, 6.0])
assert np.allclose(mod[1, 0, 2], [1.0, 7.0, 7.0])
assert np.allclose(mod[1, 0, 3], [1.0, 8.0, 8.0])