"""The main module of pyhf."""
import copy
import logging
from . import get_backend, default_backend
from . import exceptions
from . import modifiers
from . import utils
from . import events
from . import probability as prob
from .constraints import gaussian_constraint_combined, poisson_constraint_combined
from .parameters import reduce_paramsets_requirements, ParamViewer
from .tensor.common import _TensorViewer, _tensorviewer_from_sizes
from .mixins import _ChannelSummaryMixin
log = logging.getLogger(__name__)
def _paramset_requirements_from_channelspec(spec, channel_nbins):
# bookkeep all requirements for paramsets we need to build
_paramsets_requirements = {}
# need to keep track in which order we added the constraints
# so that we can generate correctly-ordered data
for channel in spec['channels']:
for sample in channel['samples']:
if len(sample['data']) != channel_nbins[channel['name']]:
raise exceptions.InvalidModel(
f"The sample {sample['name']:s} has {len(sample['data']):d} bins, but the channel it belongs to ({channel['name']:s}) has {channel_nbins[channel['name']]:d} bins."
)
for modifier_def in sample['modifiers']:
# get the paramset requirements for the given modifier. If
# modifier does not exist, we'll have a KeyError
try:
paramset_requirements = modifiers.registry[
modifier_def['type']
].required_parset(sample['data'], modifier_def['data'])
except KeyError:
log.exception(
f"Modifier not implemented yet (processing {modifier_def['type']:s}). Available modifiers: {modifiers.registry.keys()}"
)
raise exceptions.InvalidModifier()
# check the shareability (e.g. for shapesys for example)
is_shared = paramset_requirements['is_shared']
if not (is_shared) and modifier_def['name'] in _paramsets_requirements:
raise ValueError(
"Trying to add unshared-paramset but other paramsets exist with the same name."
)
if is_shared and not (
_paramsets_requirements.get(
modifier_def['name'], [{'is_shared': True}]
)[0]['is_shared']
):
raise ValueError(
"Trying to add shared-paramset but other paramset of same name is indicated to be unshared."
)
_paramsets_requirements.setdefault(modifier_def['name'], []).append(
paramset_requirements
)
return _paramsets_requirements
def _paramset_requirements_from_modelspec(spec, channel_nbins):
_paramsets_requirements = _paramset_requirements_from_channelspec(
spec, channel_nbins
)
# build up a dictionary of the parameter configurations provided by the user
_paramsets_user_configs = {}
for parameter in spec.get('parameters', []):
if parameter['name'] in _paramsets_user_configs:
raise exceptions.InvalidModel(
f"Multiple parameter configurations for {parameter['name']} were found."
)
_paramsets_user_configs[parameter.get('name')] = parameter
_reqs = reduce_paramsets_requirements(
_paramsets_requirements, _paramsets_user_configs
)
_sets = {}
for param_name, paramset_requirements in _reqs.items():
paramset_type = paramset_requirements.get('paramset_type')
paramset = paramset_type(**paramset_requirements)
_sets[param_name] = paramset
return _sets
def _nominal_and_modifiers_from_spec(config, spec):
default_data_makers = {
'histosys': lambda: {'hi_data': [], 'lo_data': [], 'nom_data': [], 'mask': []},
'lumi': lambda: {'mask': []},
'normsys': lambda: {'hi': [], 'lo': [], 'nom_data': [], 'mask': []},
'normfactor': lambda: {'mask': []},
'shapefactor': lambda: {'mask': []},
'shapesys': lambda: {'mask': [], 'uncrt': [], 'nom_data': []},
'staterror': lambda: {'mask': [], 'uncrt': [], 'nom_data': []},
}
# the mega-channel will consist of mega-samples that subscribe to
# mega-modifiers. i.e. while in normal histfactory, each sample might
# be affected by some modifiers and some not, here we change it so that
# samples are affected by all modifiers, but we set up the modifier
# data such that the application of the modifier does not actually
# change the bin value for bins that are not originally affected by
# that modifier
#
# We don't actually set up the modifier data here for no-ops, but we do
# set up the entire structure
mega_mods = {}
for m, mtype in config.modifiers:
for s in config.samples:
key = f'{mtype}/{m}'
mega_mods.setdefault(key, {})[s] = {
'type': mtype,
'name': m,
'data': default_data_makers[mtype](),
}
# helper maps channel-name/sample-name to pairs of channel-sample structs
helper = {}
for c in spec['channels']:
for s in c['samples']:
helper.setdefault(c['name'], {})[s['name']] = (c, s)
mega_samples = {}
for s in config.samples:
mega_nom = []
for c in config.channels:
defined_samp = helper.get(c, {}).get(s)
defined_samp = None if not defined_samp else defined_samp[1]
# set nominal to 0 for channel/sample if the pair doesn't exist
nom = (
defined_samp['data']
if defined_samp
else [0.0] * config.channel_nbins[c]
)
mega_nom += nom
defined_mods = (
{f"{x['type']}/{x['name']}": x for x in defined_samp['modifiers']}
if defined_samp
else {}
)
for m, mtype in config.modifiers:
key = f'{mtype}/{m}'
# this is None if modifier doesn't affect channel/sample.
thismod = defined_mods.get(key)
# print('key',key,thismod['data'] if thismod else None)
if mtype == 'histosys':
lo_data = thismod['data']['lo_data'] if thismod else nom
hi_data = thismod['data']['hi_data'] if thismod else nom
maskval = bool(thismod)
mega_mods[key][s]['data']['lo_data'] += lo_data
mega_mods[key][s]['data']['hi_data'] += hi_data
mega_mods[key][s]['data']['nom_data'] += nom
mega_mods[key][s]['data']['mask'] += [maskval] * len(
nom
) # broadcasting
elif mtype == 'normsys':
maskval = bool(thismod)
lo_factor = thismod['data']['lo'] if thismod else 1.0
hi_factor = thismod['data']['hi'] if thismod else 1.0
mega_mods[key][s]['data']['nom_data'] += [1.0] * len(nom)
mega_mods[key][s]['data']['lo'] += [lo_factor] * len(
nom
) # broadcasting
mega_mods[key][s]['data']['hi'] += [hi_factor] * len(nom)
mega_mods[key][s]['data']['mask'] += [maskval] * len(
nom
) # broadcasting
elif mtype in ['normfactor', 'shapefactor', 'lumi']:
maskval = bool(thismod)
mega_mods[key][s]['data']['mask'] += [maskval] * len(
nom
) # broadcasting
elif mtype in ['shapesys', 'staterror']:
uncrt = thismod['data'] if thismod else [0.0] * len(nom)
if mtype == 'shapesys':
maskval = [(x > 0 and y > 0) for x, y in zip(uncrt, nom)]
else:
maskval = [bool(thismod)] * len(nom)
mega_mods[key][s]['data']['mask'] += maskval
mega_mods[key][s]['data']['uncrt'] += uncrt
mega_mods[key][s]['data']['nom_data'] += nom
sample_dict = {'name': f'mega_{s}', 'nom': mega_nom}
mega_samples[s] = sample_dict
nominal_rates = default_backend.astensor(
[mega_samples[s]['nom'] for s in config.samples]
)
_nominal_rates = default_backend.reshape(
nominal_rates,
(
1, # modifier dimension.. nominal_rates is the base
len(config.samples),
1, # alphaset dimension
sum(list(config.channel_nbins.values())),
),
)
return mega_mods, _nominal_rates
[docs]class _ModelConfig(_ChannelSummaryMixin):
[docs] def __init__(self, spec, **config_kwargs):
super().__init__(channels=spec['channels'])
_required_paramsets = _paramset_requirements_from_modelspec(
spec, self.channel_nbins
)
poi_name = config_kwargs.pop('poi_name', 'mu')
default_modifier_settings = {
'normsys': {'interpcode': 'code4'},
'histosys': {'interpcode': 'code4p'},
}
self.modifier_settings = config_kwargs.pop(
'modifier_settings', default_modifier_settings
)
if config_kwargs:
raise exceptions.Unsupported(
f"Unsupported options were passed in: {list(config_kwargs.keys())}."
)
self.par_map = {}
self.par_order = []
self.poi_name = None
self.poi_index = None
self.auxdata = []
self.auxdata_order = []
self._create_and_register_paramsets(_required_paramsets)
if poi_name is not None:
self.set_poi(poi_name)
self.npars = len(self.suggested_init())
self.nmaindata = sum(self.channel_nbins.values())
[docs] def suggested_init(self):
init = []
for name in self.par_order:
init = init + self.par_map[name]['paramset'].suggested_init
return init
[docs] def suggested_bounds(self):
bounds = []
for name in self.par_order:
bounds = bounds + self.par_map[name]['paramset'].suggested_bounds
return bounds
[docs] def par_slice(self, name):
return self.par_map[name]['slice']
[docs] def param_set(self, name):
return self.par_map[name]['paramset']
[docs] def suggested_fixed(self):
"""
Identify the fixed parameters in the model.
Returns:
List: A list of booleans, ``True`` for fixed and ``False`` for not fixed.
Something like the following to build fixed_vals appropriately:
.. code:: python
fixed_pars = pdf.config.suggested_fixed()
inits = pdf.config.suggested_init()
fixed_vals = [
(index, init)
for index, (init, is_fixed) in enumerate(zip(inits, fixed_pars))
if is_fixed
]
"""
fixed = []
for name in self.par_order:
paramset = self.par_map[name]['paramset']
fixed = fixed + [paramset.suggested_fixed] * paramset.n_parameters
return fixed
[docs] def set_poi(self, name):
if name not in [x for x, _ in self.modifiers]:
raise exceptions.InvalidModel(
f"The parameter of interest '{name:s}' cannot be fit as it is not declared in the model specification."
)
s = self.par_slice(name)
assert s.stop - s.start == 1
self.poi_name = name
self.poi_index = s.start
[docs] def _create_and_register_paramsets(self, required_paramsets):
next_index = 0
for param_name, paramset in required_paramsets.items():
log.info(
'adding modifier %s (%s new nuisance parameters)',
param_name,
paramset.n_parameters,
)
sl = slice(next_index, next_index + paramset.n_parameters)
next_index = next_index + paramset.n_parameters
self.par_order.append(param_name)
self.par_map[param_name] = {'slice': sl, 'paramset': paramset}
class _ConstraintModel:
"""Factory class to create pdfs for the constraint terms."""
def __init__(self, config, batch_size):
self.batch_size = batch_size
self.config = config
self.constraints_gaussian = gaussian_constraint_combined(
config, batch_size=self.batch_size
)
self.constraints_poisson = poisson_constraint_combined(
config, batch_size=self.batch_size
)
self.viewer_aux = ParamViewer(
(self.batch_size or 1, self.config.npars),
self.config.par_map,
self.config.auxdata_order,
)
assert self.constraints_gaussian.batch_size == self.batch_size
assert self.constraints_poisson.batch_size == self.batch_size
indices = []
if self.constraints_gaussian.has_pdf():
indices.append(self.constraints_gaussian._normal_data)
if self.constraints_poisson.has_pdf():
indices.append(self.constraints_poisson._poisson_data)
if self.has_pdf():
self.constraints_tv = _TensorViewer(indices, self.batch_size)
def has_pdf(self):
"""
Indicate whether this model has a constraint.
Returns:
Bool: Whether the model has a constraint term
"""
return self.constraints_gaussian.has_pdf() or self.constraints_poisson.has_pdf()
def make_pdf(self, pars):
"""
Construct a pdf object for a given set of parameter values.
Args:
pars (:obj:`tensor`): The model parameters
Returns:
pdf: A distribution object implementing the constraint pdf of HistFactory.
Either a Poissonn, a Gaussian or a joint pdf of both depending on the
constraints used in the specification.
"""
pdfobjs = []
gaussian_pdf = self.constraints_gaussian.make_pdf(pars)
if gaussian_pdf:
pdfobjs.append(gaussian_pdf)
poisson_pdf = self.constraints_poisson.make_pdf(pars)
if poisson_pdf:
pdfobjs.append(poisson_pdf)
if pdfobjs:
simpdf = prob.Simultaneous(pdfobjs, self.constraints_tv, self.batch_size)
return simpdf
def logpdf(self, auxdata, pars):
"""
Compute the logarithm of the value of the probability density.
Args:
auxdata (:obj:`tensor`): The auxiliary data (a subset of the full data in a HistFactory model)
pars (:obj:`tensor`): The model parameters
Returns:
Tensor: The log of the pdf value
"""
simpdf = self.make_pdf(pars)
return simpdf.log_prob(auxdata)
class _MainModel:
"""Factory class to create pdfs for the main measurement."""
def __init__(self, config, mega_mods, nominal_rates, batch_size):
self.config = config
self._factor_mods = [
modtype
for modtype, mod in modifiers.uncombined.items()
if mod.op_code == 'multiplication'
]
self._delta_mods = [
modtype
for modtype, mod in modifiers.uncombined.items()
if mod.op_code == 'addition'
]
self.batch_size = batch_size
self._nominal_rates = default_backend.tile(
nominal_rates, (1, 1, self.batch_size or 1, 1)
)
self.modifiers_appliers = {
k: c(
[x for x in config.modifiers if x[1] == k], # x[1] is mtype
config,
mega_mods,
batch_size=self.batch_size,
**config.modifier_settings.get(k, {}),
)
for k, c in modifiers.combined.items()
}
self._precompute()
events.subscribe('tensorlib_changed')(self._precompute)
def _precompute(self):
tensorlib, _ = get_backend()
self.nominal_rates = tensorlib.astensor(self._nominal_rates)
def has_pdf(self):
"""
Indicate whether the main model exists.
Returns:
Bool: Whether the model has a Main Model component (yes it does)
"""
return True
def make_pdf(self, pars):
lambdas_data = self.expected_data(pars)
return prob.Independent(prob.Poisson(lambdas_data))
def logpdf(self, maindata, pars):
"""
Compute the logarithm of the value of the probability density.
Args:
maindata (:obj:`tensor`): The main channnel data (a subset of the full data in a HistFactory model)
pars (:obj:`tensor`): The model parameters
Returns:
Tensor: The log of the pdf value
"""
return self.make_pdf(pars).log_prob(maindata)
def _modifications(self, pars):
deltas = list(
filter(
lambda x: x is not None,
[self.modifiers_appliers[k].apply(pars) for k in self._delta_mods],
)
)
factors = list(
filter(
lambda x: x is not None,
[self.modifiers_appliers[k].apply(pars) for k in self._factor_mods],
)
)
return deltas, factors
def expected_data(self, pars, return_by_sample=False):
"""
Compute the expected rates for given values of parameters.
For a single channel single sample, we compute:
Pois(d | fac(pars) * (delta(pars) + nom) ) * Gaus(a | pars[is_gaus], sigmas) * Pois(a * cfac | pars[is_poi] * cfac)
where:
- delta(pars) is the result of an apply(pars) of combined modifiers
with 'addition' op_code
- factor(pars) is the result of apply(pars) of combined modifiers
with 'multiplication' op_code
- pars[is_gaus] are the subset of parameters that are constrained by
gauss (with sigmas accordingly, some of which are computed by
modifiers)
- pars[is_pois] are the poissons and their rates (they come with
their own additional factors unrelated to factor(pars) which are
also computed by the finalize() of the modifier)
So in the end we only make 3 calls to pdfs
1. The pdf of data and modified rates
2. All Gaussian constraint as one call
3. All Poisson constraints as one call
"""
tensorlib, _ = get_backend()
pars = tensorlib.astensor(pars)
deltas, factors = self._modifications(pars)
allsum = tensorlib.concatenate(deltas + [self.nominal_rates])
nom_plus_delta = tensorlib.sum(allsum, axis=0)
nom_plus_delta = tensorlib.reshape(
nom_plus_delta, (1,) + tensorlib.shape(nom_plus_delta)
)
allfac = tensorlib.concatenate(factors + [nom_plus_delta])
newbysample = tensorlib.product(allfac, axis=0)
if return_by_sample:
batch_first = tensorlib.einsum('ij...->ji...', newbysample)
if self.batch_size is None:
return batch_first[0]
return batch_first
newresults = tensorlib.sum(newbysample, axis=0)
if self.batch_size is None:
return newresults[0]
return newresults
[docs]class Model:
"""The main pyhf model class."""
[docs] def __init__(self, spec, batch_size=None, **config_kwargs):
"""
Construct a HistFactory Model.
Args:
spec (:obj:`jsonable`): The HistFactory JSON specification
batch_size (:obj:`None` or :obj:`int`): Number of simultaneous (batched) Models to compute.
config_kwargs: Possible keyword arguments for the model configuration
Returns:
model (:class:`~pyhf.pdf.Model`): The Model instance.
"""
self.batch_size = batch_size
# deep-copy "spec" as it may be modified by config
self.spec = copy.deepcopy(spec)
self.schema = config_kwargs.pop('schema', 'model.json')
self.version = config_kwargs.pop('version', None)
# run jsonschema validation of input specification against the (provided) schema
log.info(f"Validating spec against schema: {self.schema:s}")
utils.validate(self.spec, self.schema, version=self.version)
# build up our representation of the specification
self.config = _ModelConfig(spec, **config_kwargs)
mega_mods, _nominal_rates = _nominal_and_modifiers_from_spec(self.config, spec)
self.main_model = _MainModel(
self.config,
mega_mods=mega_mods,
nominal_rates=_nominal_rates,
batch_size=self.batch_size,
)
# this is tricky, must happen before constraint
# terms try to access auxdata but after
# combined mods have been created that
# set the aux data
for k in sorted(self.config.par_map.keys()):
parset = self.config.param_set(k)
if hasattr(parset, 'pdf_type'): # is constrained
self.config.auxdata += parset.auxdata
self.config.auxdata_order.append(k)
self.config.nauxdata = len(self.config.auxdata)
self.constraint_model = _ConstraintModel(
config=self.config, batch_size=self.batch_size
)
sizes = []
if self.main_model.has_pdf():
sizes.append(self.config.nmaindata)
if self.constraint_model.has_pdf():
sizes.append(self.config.nauxdata)
self.fullpdf_tv = _tensorviewer_from_sizes(
sizes, ['main', 'aux'], self.batch_size
)
[docs] def expected_auxdata(self, pars):
"""
Compute the expected value of the auxiliary measurements.
Args:
pars (:obj:`tensor`): The parameter values
Returns:
Tensor: The expected data of the auxiliary pdf
"""
tensorlib, _ = get_backend()
pars = tensorlib.astensor(pars)
return self.make_pdf(pars)[1].expected_data()
[docs] def _modifications(self, pars):
return self.main_model._modifications(pars)
@property
def nominal_rates(self):
"""Nominal value of bin rates of the main model."""
return self.main_model.nominal_rates
[docs] def expected_actualdata(self, pars):
"""
Compute the expected value of the main model.
Args:
pars (:obj:`tensor`): The parameter values
Returns:
Tensor: The expected data of the main model (no auxiliary data)
"""
tensorlib, _ = get_backend()
pars = tensorlib.astensor(pars)
return self.make_pdf(pars)[0].expected_data()
[docs] def expected_data(self, pars, include_auxdata=True):
"""
Compute the expected value of the main model
Args:
pars (:obj:`tensor`): The parameter values
Returns:
Tensor: The expected data of the main and auxiliary model
"""
tensorlib, _ = get_backend()
pars = tensorlib.astensor(pars)
if not include_auxdata:
return self.make_pdf(pars)[0].expected_data()
return self.make_pdf(pars).expected_data()
[docs] def constraint_logpdf(self, auxdata, pars):
"""
Compute the log value of the constraint pdf.
Args:
auxdata (:obj:`tensor`): The auxiliary measurement data
pars (:obj:`tensor`): The parameter values
Returns:
Tensor: The log density value
"""
return self.make_pdf(pars)[1].log_prob(auxdata)
[docs] def mainlogpdf(self, maindata, pars):
"""
Compute the log value of the main term.
Args:
maindata (:obj:`tensor`): The main measurement data
pars (:obj:`tensor`): The parameter values
Returns:
Tensor: The log density value
"""
return self.make_pdf(pars)[0].log_prob(maindata)
[docs] def make_pdf(self, pars):
"""
Construct a pdf object for a given set of parameter values.
Args:
pars (:obj:`tensor`): The model parameters
Returns:
pdf: A distribution object implementing the main measurement pdf of HistFactory
"""
tensorlib, _ = get_backend()
pdfobjs = []
mainpdf = self.main_model.make_pdf(pars)
if mainpdf:
pdfobjs.append(mainpdf)
constraintpdf = self.constraint_model.make_pdf(pars)
if constraintpdf:
pdfobjs.append(constraintpdf)
simpdf = prob.Simultaneous(pdfobjs, self.fullpdf_tv, self.batch_size)
return simpdf
[docs] def logpdf(self, pars, data):
"""
Compute the log value of the full density.
Args:
pars (:obj:`tensor`): The parameter values
data (:obj:`tensor`): The measurement data
Returns:
Tensor: The log density value
"""
try:
tensorlib, _ = get_backend()
pars, data = tensorlib.astensor(pars), tensorlib.astensor(data)
# Verify parameter and data shapes
if pars.shape[-1] != self.config.npars:
raise exceptions.InvalidPdfParameters(
f'eval failed as pars has len {pars.shape[-1]} but {self.config.npars} was expected'
)
if data.shape[-1] != self.nominal_rates.shape[-1] + len(
self.config.auxdata
):
raise exceptions.InvalidPdfData(
f'eval failed as data has len {data.shape[-1]} but {self.config.nmaindata + self.config.nauxdata} was expected'
)
result = self.make_pdf(pars).log_prob(data)
if (
not self.batch_size
): # force to be not scalar, should we changed with #522
return tensorlib.reshape(result, (1,))
return result
except Exception:
log.error(
f"Eval failed for data {tensorlib.tolist(data)} pars: {tensorlib.tolist(pars)}",
exc_info=True,
)
raise
[docs] def pdf(self, pars, data):
"""
Compute the density at a given observed point in data space of the full model.
Args:
pars (:obj:`tensor`): The parameter values
data (:obj:`tensor`): The measurement data
Returns:
Tensor: The density value
"""
tensorlib, _ = get_backend()
return tensorlib.exp(self.logpdf(pars, data))