import logging
from . import modifier
from .. import get_backend, default_backend, events
from ..parameters import unconstrained, ParamViewer
log = logging.getLogger(__name__)
[docs]@modifier(name='shapefactor', op_code='multiplication')
class shapefactor(object):
[docs] @classmethod
def required_parset(cls, n_parameters):
return {
'paramset_type': unconstrained,
'n_parameters': n_parameters,
'modifier': cls.__name__,
'is_constrained': cls.is_constrained,
'is_shared': True,
'inits': (1.0,) * n_parameters,
'bounds': ((0.0, 10.0),) * n_parameters,
}
class shapefactor_combined(object):
def __init__(self, shapefactor_mods, pdfconfig, mega_mods, batch_size=None):
"""
Imagine a situation where we have 2 channels (SR, CR), 3 samples (sig1,
bkg1, bkg2), and 2 shapefactor modifiers (coupled_shapefactor,
uncoupled_shapefactor). Let's say this is the set-up:
SR(nbins=2)
sig1 -> subscribes to normfactor
bkg1 -> subscribes to coupled_shapefactor
CR(nbins=3)
bkg2 -> subscribes to coupled_shapefactor, uncoupled_shapefactor
The coupled_shapefactor needs to have 3 nuisance parameters to account
for the CR, with 2 of them shared in the SR. The uncoupled_shapefactor
just has 3 nuisance parameters.
self._parindices will look like
[0, 1, 2, 3, 4, 5, 6]
self._shapefactor_indices will look like
[[1,2,3],[4,5,6]]
^^^^^^^ = coupled_shapefactor
^^^^^^^ = uncoupled_shapefactor
with the 0th par-index corresponding to the normfactor. Because
channel1 has 2 bins, and channel2 has 3 bins (with channel1 before
channel2), global_concatenated_bin_indices looks like
[0, 1, 0, 1, 2]
^^^^^ = channel1
^^^^^^^^^ = channel2
So now we need to gather the corresponding shapefactor indices
according to global_concatenated_bin_indices. Therefore
self._shapefactor_indices now looks like
[[1, 2, 1, 2, 3], [4, 5, 4, 5, 6]]
and at that point can be used to compute the effect of shapefactor.
"""
self.batch_size = batch_size
keys = ['{}/{}'.format(mtype, m) for m, mtype in shapefactor_mods]
shapefactor_mods = [m for m, _ in shapefactor_mods]
parfield_shape = (self.batch_size or 1, pdfconfig.npars)
self.param_viewer = ParamViewer(
parfield_shape, pdfconfig.par_map, shapefactor_mods
)
self._shapefactor_mask = [
[[mega_mods[m][s]['data']['mask']] for s in pdfconfig.samples] for m in keys
]
global_concatenated_bin_indices = [
[[j for c in pdfconfig.channels for j in range(pdfconfig.channel_nbins[c])]]
]
self._access_field = default_backend.tile(
global_concatenated_bin_indices,
(len(shapefactor_mods), self.batch_size or 1, 1),
)
# acess field is now
# e.g. for a 3 channnel (3 bins, 2 bins, 5 bins) model
# [
# [0 1 2 0 1 0 1 2 3 4] (number of rows according to batch_size but at least 1)
# [0 1 2 0 1 0 1 2 3 4]
# [0 1 2 0 1 0 1 2 3 4]
# ]
# the index selection of param_viewer is a
# list of (batch_size, par_slice) tensors
# so self.param_viewer.index_selection[s][t]
# points to the indices for a given systematic
# at a given position in the batch
# we thus populate the access field with these indices
# up to the point where we run out of bins (in case)
# the paramset slice is larger than the number of bins
# in which case we use a dummy index that will be masked
# anyways in apply (here: 0)
# access field is shape (sys, batch, globalbin)
for s, syst_access in enumerate(self._access_field):
for t, batch_access in enumerate(syst_access):
selection = self.param_viewer.index_selection[s][t]
for b, bin_access in enumerate(batch_access):
self._access_field[s, t, b] = (
selection[bin_access] if bin_access < len(selection) else 0
)
self._precompute()
events.subscribe('tensorlib_changed')(self._precompute)
def _precompute(self):
if not self.param_viewer.index_selection:
return
tensorlib, _ = get_backend()
self.shapefactor_mask = tensorlib.tile(
tensorlib.astensor(self._shapefactor_mask, dtype="bool"),
(1, 1, self.batch_size or 1, 1),
)
self.access_field = tensorlib.astensor(self._access_field, dtype='int')
self.shapefactor_default = tensorlib.ones(
tensorlib.shape(self.shapefactor_mask)
)
self.sample_ones = tensorlib.ones(tensorlib.shape(self.shapefactor_mask)[1])
def apply(self, pars):
'''
Returns:
modification tensor: Shape (n_modifiers, n_global_samples, n_alphas, n_global_bin)
'''
if not self.param_viewer.index_selection:
return
tensorlib, _ = get_backend()
if self.batch_size is None:
flat_pars = pars
else:
flat_pars = tensorlib.reshape(pars, (-1,))
shapefactors = tensorlib.gather(flat_pars, self.access_field)
results_shapefactor = tensorlib.einsum(
'mab,s->msab', shapefactors, self.sample_ones
)
results_shapefactor = tensorlib.where(
self.shapefactor_mask, results_shapefactor, self.shapefactor_default
)
return results_shapefactor