def __init__(self, config, batch_size):
self.batch_size = batch_size
self.config = config
self.constraints_gaussian = gaussian_constraint_combined(
config, batch_size=self.batch_size)
self.constraints_poisson = poisson_constraint_combined(
config, batch_size=self.batch_size)
self.viewer_aux = ParamViewer(
(self.batch_size or 1, self.config.npars),
self.config.par_map,
self.config.auxdata_order,
)
assert self.constraints_gaussian.batch_size == self.batch_size
assert self.constraints_poisson.batch_size == self.batch_size
indices = []
if self.constraints_gaussian.has_pdf():
indices.append(self.constraints_gaussian._normal_data)
if self.constraints_poisson.has_pdf():
indices.append(self.constraints_poisson._poisson_data)
if self.has_pdf():
self.constraints_tv = _TensorViewer(indices, self.batch_size)
def test_batched_constraints(backend):
config = MockConfig(
par_order=['pois1', 'pois2', 'norm1', 'norm2'],
par_map={
'pois1': {
'paramset':
constrained_by_poisson(
name='pois1',
is_scalar=False,
n_parameters=1,
inits=[1.0],
bounds=[[0, 10]],
auxdata=[12],
factors=[12],
fixed=False,
),
'slice':
slice(0, 1),
'auxdata': [1],
},
'pois2': {
'paramset':
constrained_by_poisson(
name='pois2',
is_scalar=False,
n_parameters=2,
inits=[1.0] * 2,
bounds=[[0, 10]] * 2,
auxdata=[13, 14],
factors=[13, 14],
fixed=False,
),
'slice':
slice(1, 3),
},
'norm1': {
'paramset':
constrained_by_normal(
name='norm1',
is_scalar=False,
n_parameters=2,
inits=[0] * 2,
bounds=[[0, 10]] * 2,
auxdata=[0, 0],
sigmas=[1.5, 2.0],
fixed=False,
),
'slice':
slice(3, 5),
},
'norm2': {
'paramset':
constrained_by_normal(
name='norm2',
is_scalar=False,
n_parameters=3,
inits=[0] * 3,
bounds=[[0, 10]] * 3,
auxdata=[0, 0, 0],
fixed=False,
),
'slice':
slice(5, 8),
},
},
)
suggested_pars = [1.0] * 3 + [0.0] * 5 # 2 pois 5 norm
constraint = poisson_constraint_combined(config)
result = default_backend.astensor(
pyhf.tensorlib.tolist(
constraint.logpdf(
pyhf.tensorlib.astensor(config.auxdata),
pyhf.tensorlib.astensor(suggested_pars),
)))
assert np.isclose(
result,
sum([
default_backend.poisson_logpdf(data, rate)
for data, rate in zip([12, 13, 14], [12, 13, 14])
]),
)
assert result.shape == ()
suggested_pars = [1.1] * 3 + [0.0] * 5 # 2 pois 5 norm
constraint = poisson_constraint_combined(config)
result = default_backend.astensor(
pyhf.tensorlib.tolist(
constraint.logpdf(
pyhf.tensorlib.astensor(config.auxdata),
pyhf.tensorlib.astensor(suggested_pars),
)))
assert np.isclose(
result,
sum([
default_backend.poisson_logpdf(data, rate)
for data, rate in zip([12, 13, 14], [12 * 1.1, 13 * 1.1, 14 * 1.1])
]),
)
assert result.shape == ()
constraint = poisson_constraint_combined(config, batch_size=10)
result = constraint.logpdf(
pyhf.tensorlib.astensor(config.auxdata),
pyhf.tensorlib.astensor([suggested_pars] * 10),
)
assert result.shape == (10, )
suggested_pars = [
[1.1, 1.2, 1.3] + [0.0] * 5, # 2 pois 5 norm
[0.7, 0.8, 0.9] + [0.0] * 5, # 2 pois 5 norm
[0.4, 0.5, 0.6] + [0.0] * 5, # 2 pois 5 norm
]
constraint = poisson_constraint_combined(config, batch_size=3)
result = default_backend.astensor(
pyhf.tensorlib.tolist(
constraint.logpdf(
pyhf.tensorlib.astensor(config.auxdata),
pyhf.tensorlib.astensor(suggested_pars),
)))
assert np.all(
np.isclose(
result,
np.sum(
[
[
default_backend.poisson_logpdf(data, rate)
for data, rate in zip([12, 13, 14],
[12 * 1.1, 13 * 1.2, 14 * 1.3])
],
[
default_backend.poisson_logpdf(data, rate)
for data, rate in zip([12, 13, 14],
[12 * 0.7, 13 * 0.8, 14 * 0.9])
],
[
default_backend.poisson_logpdf(data, rate)
for data, rate in zip([12, 13, 14],
[12 * 0.4, 13 * 0.5, 14 * 0.6])
],
],
axis=1,
),
))
assert result.shape == (3, )
suggested_pars = [1.0] * 3 + [0.0] * 5 # 2 pois 5 norm
constraint = gaussian_constraint_combined(config, batch_size=1)
result = default_backend.astensor(
pyhf.tensorlib.tolist(
constraint.logpdf(
pyhf.tensorlib.astensor(config.auxdata),
pyhf.tensorlib.astensor(suggested_pars),
)))
assert np.isclose(
result[0],
sum([
default_backend.normal_logpdf(data, mu, sigma) for data, mu, sigma
in zip([0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [1.5, 2.0, 1.0, 1.0, 1.0])
]),
)
assert result.shape == (1, )
suggested_pars = [1.0] * 3 + [1, 2, 3, 4, 5] # 2 pois 5 norm
constraint = gaussian_constraint_combined(config, batch_size=1)
result = default_backend.astensor(
pyhf.tensorlib.tolist(
constraint.logpdf(
pyhf.tensorlib.astensor(config.auxdata),
pyhf.tensorlib.astensor(suggested_pars),
)))
assert np.isclose(
result[0],
sum([
default_backend.normal_logpdf(data, mu, sigma) for data, mu, sigma
in zip([0, 0, 0, 0, 0], [1, 2, 3, 4, 5], [1.5, 2.0, 1.0, 1.0, 1.0])
]),
)
assert result.shape == (1, )
suggested_pars = [
[1.0] * 3 + [1, 2, 3, 4, 5], # 2 pois 5 norm
[1.0] * 3 + [-1, -2, -3, -4, -5], # 2 pois 5 norm
[1.0] * 3 + [-1, -2, 0, 1, 2], # 2 pois 5 norm
]
constraint = gaussian_constraint_combined(config, batch_size=3)
result = default_backend.astensor(
pyhf.tensorlib.tolist(
constraint.logpdf(
pyhf.tensorlib.astensor(config.auxdata),
pyhf.tensorlib.astensor(suggested_pars),
)))
assert np.all(
np.isclose(
result,
np.sum(
[
[
default_backend.normal_logpdf(data, mu, sigma)
for data, mu, sigma in
zip([0, 0, 0, 0, 0], [1, 2, 3, 4, 5],
[1.5, 2.0, 1.0, 1.0, 1.0])
],
[
default_backend.normal_logpdf(data, mu, sigma)
for data, mu, sigma in zip(
[0, 0, 0, 0, 0],
[-1, -2, -3, -4, -5],
[1.5, 2.0, 1.0, 1.0, 1.0],
)
],
[
default_backend.normal_logpdf(data, mu, sigma)
for data, mu, sigma in zip(
[0, 0, 0, 0, 0],
[-1, -2, 0, 1, 2],
[1.5, 2.0, 1.0, 1.0, 1.0],
)
],
],
axis=1,
),
))
assert result.shape == (3, )
constraint = gaussian_constraint_combined(config, batch_size=10)
result = constraint.logpdf(
pyhf.tensorlib.astensor(config.auxdata),
pyhf.tensorlib.astensor([suggested_pars] * 10),
)
assert result.shape == (10, )