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@matthewfeickert
matthewfeickert committed Apr 7, 2023
1 parent 5296e75 commit f49120f
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Showing 2 changed files with 43 additions and 43 deletions.
48 changes: 24 additions & 24 deletions src/pyhf/experimental/modifiers.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -19,13 +19,13 @@ def _allocate_new_param(
p: dict[str, Sequence[float]]
) -> dict[str, str | bool | int | Sequence[float]]:
return {
'paramset_type': 'unconstrained',
'n_parameters': 1,
'is_shared': True,
'inits': p['inits'],
'bounds': p['bounds'],
'is_scalar': True,
'fixed': False,
"paramset_type": "unconstrained",
"n_parameters": 1,
"is_shared": True,
"inits": p["inits"],
"bounds": p["bounds"],
"is_scalar": True,
"fixed": False,
}


Expand All @@ -45,30 +45,30 @@ class _builder(BaseBuilder):
is_shared = False

def __init__(self, config):
self.builder_data = {'funcs': {}}
self.builder_data = {"funcs": {}}
self.config = config

def collect(self, thismod, nom):
maskval = True if thismod else False
mask = [maskval] * len(nom)
return {'mask': mask}
return {"mask": mask}

def append(self, key, channel, sample, thismod, defined_samp):
self.builder_data.setdefault(key, {}).setdefault(sample, {}).setdefault(
'data', {'mask': []}
"data", {"mask": []}
)
nom = (
defined_samp['data']
defined_samp["data"]
if defined_samp
else [0.0] * self.config.channel_nbins[channel]
)
moddata = self.collect(thismod, nom)
self.builder_data[key][sample]['data']['mask'] += moddata['mask']
self.builder_data[key][sample]["data"]["mask"] += moddata["mask"]
if thismod:
if thismod['name'] != funcname:
if thismod["name"] != funcname:
print(thismod)
self.builder_data['funcs'].setdefault(
thismod['name'], thismod['data']['expr']
self.builder_data["funcs"].setdefault(
thismod["name"], thismod["data"]["expr"]
)
self.required_parsets = {
k: [_allocate_new_param(v)] for k, v in newparams.items()
Expand All @@ -85,14 +85,14 @@ def make_applier(
) -> BaseApplier:
class _applier(BaseApplier):
name = funcname
op_code = 'multiplication'
op_code = "multiplication"

def __init__(self, modifiers, pdfconfig, builder_data, batch_size=None):
self.funcs = [make_func(v, deps) for v in builder_data['funcs'].values()]
self.funcs = [make_func(v, deps) for v in builder_data["funcs"].values()]

self.batch_size = batch_size
pars_for_applier = deps
_modnames = [f'{mtype}/{m}' for m, mtype in modifiers]
_modnames = [f"{mtype}/{m}" for m, mtype in modifiers]

parfield_shape = (
(self.batch_size, pdfconfig.npars)
Expand All @@ -103,11 +103,11 @@ def __init__(self, modifiers, pdfconfig, builder_data, batch_size=None):
parfield_shape, pdfconfig.par_map, pars_for_applier
)
self._custommod_mask = [
[[builder_data[modname][s]['data']['mask']] for s in pdfconfig.samples]
[[builder_data[modname][s]["data"]["mask"]] for s in pdfconfig.samples]
for modname in _modnames
]
self._precompute()
events.subscribe('tensorlib_changed')(self._precompute)
events.subscribe("tensorlib_changed")(self._precompute)

def _precompute(self):
tensorlib, _ = get_backend()
Expand All @@ -132,15 +132,15 @@ def apply(self, pars):
tensorlib, _ = get_backend()
if self.batch_size is None:
deps = self.param_viewer.get(pars)
print('deps', deps.shape)
print("deps", deps.shape)
results = tensorlib.astensor([f(deps) for f in self.funcs])
results = tensorlib.einsum('msab,m->msab', self.custommod_mask, results)
results = tensorlib.einsum("msab,m->msab", self.custommod_mask, results)
else:
deps = self.param_viewer.get(pars)
print('deps', deps.shape)
print("deps", deps.shape)
results = tensorlib.astensor([f(deps) for f in self.funcs])
results = tensorlib.einsum(
'msab,ma->msab', self.custommod_mask, results
"msab,ma->msab", self.custommod_mask, results
)
results = tensorlib.where(
self.custommod_mask_bool, results, self.custommod_default
Expand Down
38 changes: 19 additions & 19 deletions tests/test_experimental.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -6,38 +6,38 @@ def test_add_custom_modifier(backend):
tensorlib, _ = backend

new_params = {
'm1': {'inits': (1.0,), 'bounds': ((-5.0, 5.0),)},
'm2': {'inits': (1.0,), 'bounds': ((-5.0, 5.0),)},
"m1": {"inits": (1.0,), "bounds": ((-5.0, 5.0),)},
"m2": {"inits": (1.0,), "bounds": ((-5.0, 5.0),)},
}

expanded_pyhf = pyhf.experimental.modifiers.add_custom_modifier(
'customfunc', ['m1', 'm2'], new_params
"customfunc", ["m1", "m2"], new_params
)
model = pyhf.Model(
{
'channels': [
"channels": [
{
'name': 'singlechannel',
'samples': [
"name": "singlechannel",
"samples": [
{
'name': 'signal',
'data': [10] * 20,
'modifiers': [
"name": "signal",
"data": [10] * 20,
"modifiers": [
{
'name': 'f2',
'type': 'customfunc',
'data': {'expr': 'm1'},
"name": "f2",
"type": "customfunc",
"data": {"expr": "m1"},
},
],
},
{
'name': 'background',
'data': [100] * 20,
'modifiers': [
"name": "background",
"data": [100] * 20,
"modifiers": [
{
'name': 'f1',
'type': 'customfunc',
'data': {'expr': 'm1+(m2**2)'},
"name": "f1",
"type": "customfunc",
"data": {"expr": "m1+(m2**2)"},
},
],
},
Expand All @@ -46,7 +46,7 @@ def test_add_custom_modifier(backend):
]
},
modifier_set=expanded_pyhf,
poi_name='m1',
poi_name="m1",
validate=False,
batch_size=1,
)
Expand Down

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