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Update parameter freezing #91

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Merged
davidwalter2 merged 1 commit into from
Dec 23, 2025
Merged

Update parameter freezing #91

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38 changes: 31 additions & 7 deletions rabbit/fitter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -125,8 +125,6 @@ def __init__(self, indata, poi_model, options, do_blinding=False):

self.parms = np.concatenate([self.poi_model.pois, self.indata.systs])

self.init_frozen_params(options.freezeParameters)

# tf variable containing all fit parameters
thetadefault = tf.zeros([self.indata.nsyst], dtype=self.indata.dtype)
if self.poi_model.npoi > 0:
Expand All @@ -136,6 +134,15 @@ def __init__(self, indata, poi_model, options, do_blinding=False):

self.x = tf.Variable(xdefault, trainable=True, name="x")

# for freezing parameters
self.frozen_params = []
self.frozen_params_mask = tf.Variable(
tf.zeros_like(self.x, dtype=tf.bool), trainable=False, dtype=tf.bool
)

self.frozen_indices = np.array([])
self.freeze_params(options.freezeParameters)

# observed number of events per bin
self.nobs = tf.Variable(
tf.zeros_like(self.indata.data_obs), trainable=False, name="nobs"
Expand Down Expand Up @@ -294,12 +301,26 @@ def load_fitresult(self, fitresult_file, fitresult_key):
self.x.assign(xvals)
self.cov.assign(tf.constant(covval))

def init_frozen_params(self, frozen_parmeter_expressions):
self.frozen_params = match_regexp_params(
frozen_parmeter_expressions, self.parms
def update_frozen_params(self):
new_mask_np = np.isin(self.parms, self.frozen_params)

self.frozen_params_mask.assign(new_mask_np)
self.frozen_indices = np.where(new_mask_np)[0]

def freeze_params(self, frozen_parmeter_expressions):
self.frozen_params.extend(
match_regexp_params(frozen_parmeter_expressions, self.parms)
)
self.update_frozen_params()

def defreeze_params(self, unfrozen_parmeter_expressions):
unfrozen_parmeter = match_regexp_params(
unfrozen_parmeter_expressions, self.parms
)
self.frozen_params_mask = np.isin(self.parms, self.frozen_params)
self.frozen_indices = np.where(self.frozen_params_mask)[0]
self.frozen_params = [
x for x in self.frozen_params if x not in unfrozen_parmeter
]
self.update_frozen_params()

def init_blinding_values(self, unblind_parameter_expressions=[]):

Expand Down Expand Up @@ -1265,6 +1286,9 @@ def _compute_yields_noBBB(self, full=True):
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),
Expand Down