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Fix freezing parameter functionality #105

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Merged
lucalavezzo merged 1 commit into from
Jan 26, 2026
Merged

Fix freezing parameter functionality #105

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4 changes: 2 additions & 2 deletions bin/rabbit_limit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -138,8 +138,8 @@ def do_asymptotic_limits(args, fitter, ws, data_values, mapping=None, fit_data=F
idx = np.where(fitter.parms.astype(str) == key)[0][0]
if key in fitter.poi_model.pois.astype(str):
is_poi = True
xbest = fitter_asimov.get_blinded_poi()[idx]
xobs = fitter.get_blinded_poi()[idx]
xbest = fitter_asimov.get_poi()[idx]
xobs = fitter.get_poi()[idx]
elif key in fitter.parms.astype(str):
is_poi = False
xbest = fitter_asimov.get_blinded_theta()[
Expand Down
30 changes: 16 additions & 14 deletions rabbit/fitter.py
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Expand Up @@ -403,19 +403,30 @@ def set_blinding_offsets(self, blind=True):
np.zeros(self.indata.nsyst, dtype=np.float64)
)

def get_blinded_theta(self):
def get_theta(self):
theta = self.x[self.poi_model.npoi : self.poi_model.npoi + self.indata.nsyst]
theta = tf.where(
self.frozen_params_mask[
self.poi_model.npoi : self.poi_model.npoi + self.indata.nsyst
],
tf.stop_gradient(theta),
theta,
)
theta = self.x[self.poi_model.npoi :]
if self.do_blinding:
return theta + self._blinding_offsets_theta
else:
return theta

def get_blinded_poi(self):
def get_poi(self):
xpoi = self.x[: self.poi_model.npoi]
if self.poi_model.allowNegativePOI:
poi = xpoi
else:
poi = tf.square(xpoi)
poi = tf.where(
self.frozen_params_mask[: self.poi_model.npoi], tf.stop_gradient(poi), poi
)
if self.do_blinding:
return poi * self._blinding_offsets_poi
else:
Expand Down Expand Up @@ -1298,17 +1309,8 @@ def _expected_variations(

def _compute_yields_noBBB(self, full=True):
# full: compute yields inclduing masked channels
poi = self.get_blinded_poi()
theta = self.get_blinded_theta()

poi = tf.where(
self.frozen_params_mask[: self.poi_model.npoi], tf.stop_gradient(poi), poi
)
theta = tf.where(
self.frozen_params_mask[self.poi_model.npoi :],
tf.stop_gradient(theta),
theta,
)
poi = self.get_poi()
theta = self.get_theta()

rnorm = self.poi_model.compute(poi)

Expand Down Expand Up @@ -1877,7 +1879,7 @@ def reduced_nll(self):

def _compute_lc(self, full_nll=False):
# constraints
theta = self.get_blinded_theta()
theta = self.get_theta()
lc = self.indata.constraintweights * 0.5 * tf.square(theta - self.theta0)
if full_nll:
# normalization factor for normal distribution: log(1/sqrt(2*pi)) = -0.9189385332046727
Expand Down