Source code for pyhf.modifiers.shapesys

import logging

import pyhf
from pyhf import events
from pyhf.exceptions import InvalidModifier
from pyhf.parameters import ParamViewer
from pyhf.tensor.manager import get_backend

log = logging.getLogger(__name__)


[docs] def required_parset(sample_data, modifier_data): # count the number of bins with nonzero, positive yields valid_bins = [ (sample_bin > 0 and modifier_bin > 0) for sample_bin, modifier_bin in zip(modifier_data, sample_data) ] factors = [ (nom_yield**2 / unc**2) if (is_valid) else 1.0 for is_valid, nom_yield, unc in zip(valid_bins, sample_data, modifier_data) ] fixed = tuple(not is_valid for is_valid in valid_bins) n_parameters = len(factors) return { "paramset_type": "constrained_by_poisson", "n_parameters": n_parameters, "is_scalar": False, "inits": (1.0,) * n_parameters, "bounds": ((1e-10, 10.0),) * n_parameters, "fixed": fixed, "auxdata": tuple(factors), "factors": tuple(factors), }
[docs] class shapesys_builder: """Builder class for collecting shapesys modifier data""" is_shared = False
[docs] def __init__(self, config): self.builder_data = {} self.config = config self.required_parsets = {}
[docs] def collect(self, thismod, nom): uncrt = thismod['data'] if thismod else [0.0] * len(nom) mask = [True] * len(nom) if thismod else [False] * len(nom) return {'mask': mask, 'nom_data': nom, 'uncrt': uncrt}
[docs] def append(self, key, channel, sample, thismod, defined_samp): self.builder_data.setdefault(key, {}).setdefault(sample, {}).setdefault( 'data', {'uncrt': [], 'nom_data': [], 'mask': []} ) nom = ( 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'].append(moddata['mask']) self.builder_data[key][sample]['data']['uncrt'].append(moddata['uncrt']) self.builder_data[key][sample]['data']['nom_data'].append(moddata['nom_data']) if thismod: self.required_parsets.setdefault( thismod['name'], [required_parset(defined_samp['data'], thismod['data'])], )
[docs] def finalize(self): default_backend = pyhf.default_backend for modifier_name, modifier in self.builder_data.items(): for sample_name, sample in modifier.items(): sample["data"]["mask"] = default_backend.concatenate( sample["data"]["mask"] ) sample["data"]["uncrt"] = default_backend.concatenate( sample["data"]["uncrt"] ) sample["data"]["nom_data"] = default_backend.concatenate( sample["data"]["nom_data"] ) if len(sample["data"]["nom_data"]) != len(sample["data"]["uncrt"]): _modifier_type, _modifier_name = modifier_name.split("/") _sample_data_len = len(sample["data"]["nom_data"]) _uncrt_len = len(sample["data"]["uncrt"]) raise InvalidModifier( f"The '{sample_name}' sample {_modifier_type} modifier" + f" '{_modifier_name}' has data shape inconsistent with the sample.\n" + f"{sample_name} has 'data' of length {_sample_data_len} but {_modifier_name}" + f" has 'data' of length {_uncrt_len}." ) return self.builder_data
[docs] class shapesys_combined: name = 'shapesys' op_code = 'multiplication'
[docs] def __init__(self, modifiers, pdfconfig, builder_data, batch_size=None): default_backend = pyhf.default_backend self.batch_size = batch_size keys = [f'{mtype}/{m}' for m, mtype in modifiers] self._shapesys_mods = [m for m, _ in modifiers] parfield_shape = (self.batch_size or 1, pdfconfig.npars) self.param_viewer = ParamViewer( parfield_shape, pdfconfig.par_map, self._shapesys_mods ) self._shapesys_mask = [ [[builder_data[m][s]['data']['mask']] for s in pdfconfig.samples] for m in keys ] self.__shapesys_info = default_backend.astensor( [ [ [ builder_data[m][s]['data']['mask'], builder_data[m][s]['data']['nom_data'], builder_data[m][s]['data']['uncrt'], ] 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(self._shapesys_mods), self.batch_size or 1, 1), ) # access field is shape (sys, batch, globalbin) # reindex it based on current masking self._reindex_access_field(pdfconfig) self._precompute() events.subscribe('tensorlib_changed')(self._precompute)
[docs] def _reindex_access_field(self, pdfconfig): default_backend = pyhf.default_backend for syst_index, syst_access in enumerate(self._access_field): singular_sample_index = [ idx for idx, syst in enumerate( default_backend.astensor(self._shapesys_mask)[syst_index, :, 0] ) if any(syst) ][-1] for batch_index, batch_access in enumerate(syst_access): selection = self.param_viewer.index_selection[syst_index][batch_index] access_field_for_syst_and_batch = default_backend.zeros( len(batch_access) ) sample_mask = self._shapesys_mask[syst_index][singular_sample_index][0] access_field_for_syst_and_batch[sample_mask] = selection self._access_field[ syst_index, batch_index ] = access_field_for_syst_and_batch
[docs] def _precompute(self): tensorlib, _ = get_backend() if not self.param_viewer.index_selection: return self.shapesys_mask = tensorlib.astensor(self._shapesys_mask, dtype="bool") self.shapesys_mask = tensorlib.tile( self.shapesys_mask, (1, 1, self.batch_size or 1, 1) ) self.access_field = tensorlib.astensor(self._access_field, dtype='int') self.sample_ones = tensorlib.ones(tensorlib.shape(self.shapesys_mask)[1]) self.shapesys_default = tensorlib.ones(tensorlib.shape(self.shapesys_mask))
[docs] def apply(self, pars): """ Returns: modification tensor: Shape (n_modifiers, n_global_samples, n_alphas, n_global_bin) """ tensorlib, _ = get_backend() 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_shapesys = tensorlib.einsum( 'mab,s->msab', shapefactors, self.sample_ones ) results_shapesys = tensorlib.where( self.shapesys_mask, results_shapesys, self.shapesys_default ) return results_shapesys