def create_merged_bayesian_network_for_event(
event,
prefix,
bayesian_network,
assert_merge_definitions=False,
logger=StdOutLogger(verbose=False)):
merge_def_list = BayesianNetworkMergeUtils.create_merge_definition_list(
event, prefix, merge_singleton_events=False)
logger.log('Merging...', includeTimestamp=True)
logger.set_carriage_reset(True)
count = len(merge_def_list)
for index, merge_def in enumerate(merge_def_list):
logger.log("\rMerging '%s' (%i of %i)" %
(merge_def.get_merged_event(), index + 1, count),
includeTimestamp=True)
merge_defs = DistributionEventMergeDefinitions(
event.name,
bayesian_network,
allow_unspecified_events=True,
assert_merge_definitions=assert_merge_definitions)
merge_defs.set_merge_definitions([merge_def])
bn_wrapper = BayesianNetworkWrapper(bayesian_network)
bayesian_network = bn_wrapper.create_network_with_merged_events(
merge_defs, bake=True)
logger.set_carriage_reset(False)
logger.log('Merging done!', includeTimestamp=True)
return bayesian_network
def test_comparisons_2__compare_structure(bayesian_network_1,
bayesian_network_2):
wrapper1 = BayesianNetworkWrapper(bayesian_network_1)
wrapper2 = BayesianNetworkWrapper(bayesian_network_2)
with pytest.raises(Exception) as e:
assert wrapper1.assert_structure_equality(wrapper2)
assert str(e.value) == 'Number of states not equal'
def merge_alleles(self, events=None, prefixes=None, assert_merge_definitions=False):
d_event = None
if events is None:
events = []
events.append(self.get_event_from_nickname(Nicknames.v_choice.value))
events.append(self.get_event_from_nickname(Nicknames.j_choice.value))
d_event = self.get_event_from_nickname(Nicknames.d_gene.value)
if d_event is not None: events.append(d_event)
if prefixes is None:
prefixes = []
prefixes.append(Nicknames.v_choice.value + ':')
prefixes.append(Nicknames.j_choice.value + ':')
if d_event is not None: prefixes.append(Nicknames.d_gene.value + ':')
pg_network = BayesianNetworkMergeUtils.create_merged_bayesian_network_for_events(events, prefixes,
self._network_wrapper._network,
assert_merge_definitions=assert_merge_definitions,
logger=self._logger)
self._network_wrapper = BayesianNetworkWrapper(pg_network)
def create_truncated_network(template):
template_wrapper = BayesianNetworkWrapper(template)
out = pom.BayesianNetwork()
v_state = template_wrapper.get_state(
'GeneChoice_V_gene_Undefined_side_prio7_size147')
#v_del_state = template_wrapper.get_state('Deletion_V_gene_Three_prime_prio5_size21')
j_state = template_wrapper.get_state(
'GeneChoice_J_gene_Undefined_side_prio7_size16')
#j_del_state = template_wrapper.get_state('Deletion_J_gene_Five_prime_prio5_size23')
d_state = template_wrapper.get_state(
'GeneChoice_D_gene_Undefined_side_prio6_size3')
out.add_states(v_state, j_state, d_state)
#out.add_states(v_state, j_state, v_del_state, j_del_state, d_state)
out.add_edge(v_state, j_state)
#out.add_edge(v_state, v_del_state)
#out.add_edge(j_state, j_del_state)
out.add_edge(v_state, d_state)
out.add_edge(j_state, d_state)
out.bake()
return out
def test_marginals_7__merge_all_alleles(bayesian_network_3):
wrapper = BayesianNetworkWrapper(bayesian_network_3)
merge_def1 = EventMergeDefinition('V1')
merge_def1.extend(['V1*01', 'V1*02'])
merge_defs = DistributionEventMergeDefinitions(
'v_gene',
wrapper.get_network(),
allow_unspecified_events=True,
assert_merge_definitions=True)
merge_defs.set_merge_definitions([merge_def1])
merged_network = wrapper.create_network_with_merged_events(merge_defs)
assert (merged_network is not None)
wrapper = BayesianNetworkWrapper(merged_network)
margs = wrapper.get_probabilities(
statename=None, probability_type=ProbabilityType.Conditional)
assert len(margs) == 7
def test_marginals_5__merge_v_alleles(bayesian_network_1):
wrapper = BayesianNetworkWrapper(bayesian_network_1)
merge_def1 = EventMergeDefinition('V1')
merge_def1.extend(['V1*01', 'V1*02', 'V1*03'])
merge_defs = DistributionEventMergeDefinitions(
'v_gene',
bayesian_network_1,
allow_unspecified_events=True,
assert_merge_definitions=True)
merge_defs.set_merge_definitions([merge_def1])
merged_network = wrapper.create_network_with_merged_events(merge_defs)
wrapper = BayesianNetworkWrapper(merged_network)
margs = wrapper.get_probabilities(
statename='v_gene', probability_type=ProbabilityType.Conditional)
assert len(margs) == 2
assert utilities.almost_equal(margs.iloc[0, 0], 0.6, 0.0001)
assert utilities.almost_equal(margs.iloc[1, 0], 0.4, 0.0001)
def test_marginals_4__merge_v_alleles(bayesian_network_1):
wrapper = BayesianNetworkWrapper(bayesian_network_1)
merge_def1 = EventMergeDefinition('V1')
merge_def1.extend(['V1*01', 'V1*02', 'V1*03'])
merge_defs = DistributionEventMergeDefinitions(
'v_gene',
bayesian_network_1,
allow_unspecified_events=True,
assert_merge_definitions=True)
merge_defs.set_merge_definitions([merge_def1])
merged_network = wrapper.create_network_with_merged_events(merge_defs)
wrapper = BayesianNetworkWrapper(merged_network)
margs = wrapper.get_probabilities(
statename=None, probability_type=ProbabilityType.Marginal)
assert len(margs) == 2
for key, value in margs.iteritems():
tot = value.sum()
assert utilities.almost_equal(tot, 1.0, 0.0001)
out_marginals = 'C:/CiR/pTCR/IGOR_models/Unproductive_models/models_imgt_ref_dir_sep2019/HC/1365_TRB/1365_TRB_marginals.txt'
out_params = 'C:/CiR/pTCR/IGOR_models/Unproductive_models/models_imgt_ref_dir_sep2019/HC/1365_TRB/1365_TRB_params.txt'
gmw = GenModelWrapper(out_params, out_marginals, '1363_TRB_', StdOutLogger())
gmw.merge_alleles(assert_merge_definitions=True)
bn = gmw.get_bayesian_network_wrapper().get_network()
# truncate_bn = create_truncated_network(bn)
# truncate_bn_wrapper = BayesianNetworkWrapper(truncate_bn)
merge_defs = create_merg_defs(bn)
merged_bayesian_network = gmw.get_bayesian_network_wrapper(
).create_network_with_merged_events(merge_defs, bake=False)
merged_bayesian_network.bake()
bnwrapper = BayesianNetworkWrapper(merged_bayesian_network)
states = {}
n = gmw.get_eventname_for_nickname('v_choice')
state = bnwrapper.get_state(n)
states[n] = state
n = gmw.get_eventname_for_nickname('j_choice')
state = bnwrapper.get_state(n)
states[n] = state
n = gmw.get_eventname_for_nickname('d_gene')
state = bnwrapper.get_state(n)
states[n] = state
n = gmw.get_eventname_for_nickname('v_del3')
state = bnwrapper.get_state(n)
def test_comparisons_1__compare_structure(bayesian_network_1):
wrapper1 = BayesianNetworkWrapper(bayesian_network_1)
wrapper2 = BayesianNetworkWrapper(bayesian_network_1)
wrapper1.assert_structure_equality(wrapper2)
def test_marginals_2__assert_marginals(bayesian_network_1):
wrapper = BayesianNetworkWrapper(bayesian_network_1)
margs = wrapper.get_probabilities('j_gene', ProbabilityType.Marginal)
assert len(margs) == 3
def _init_network_wrapper(self):
importer = BayesianNetworkImporter(self._logger)
pg_network = importer.create_network_from_genmodelwrapper(self)
pg_network.bake()
wrapper = BayesianNetworkWrapper(pg_network)
return wrapper
class GenModelWrapper(object):
_GENE_NAME_NICKNAMES = ['v_choice', 'j_choice']
_MARGINALS_DF_COL_NAME = 'Marginal'
def __init__(self, params_fn, marginals_fn, name='', logger=StdOutLogger(verbose=False)):
self._logger = logger
self._params_fn = params_fn
self._marginal_fn = marginals_fn
self._name = name
self._genmodel = GenModel(params_fn, marginals_fn)
self._logger.log('GenModelWrapper.GenModel class loaded (%s, %s)' % (params_fn, marginals_fn),
onlyIfVerbose=True)
self._init_names_and_nicknames()
self._insertion_compositions = self._init_insertion_compositions()
self._insertion_lengths = self._init_insertion_lengths()
self._network_wrapper = self._init_network_wrapper()
self._logger.log('GenModelWrapper loaded', onlyIfVerbose=True)
def _init_insertion_lengths(self):
formatter = InsertionStatisticsFormatter(self)
out = formatter.get_lengths_statistics()
return out
def get_insertion_lengths(self, nickname):
out = self._insertion_lengths.get(nickname, None)
return out
def _init_insertion_compositions(self):
calculator = SteadyStateCalculator(self._genmodel.marginals)
calculator.calculate_for_key(Nicknames.vj_dinucl.value)
calculator.calculate_for_key(Nicknames.vd_dinucl.value)
calculator.calculate_for_key(Nicknames.dj_dinucl.value)
out = calculator.get_matrices()
return out
def get_insertion_composition(self, nickname):
out = self._insertion_compositions.get(nickname, None)
return out
def _init_network_wrapper(self):
importer = BayesianNetworkImporter(self._logger)
pg_network = importer.create_network_from_genmodelwrapper(self)
pg_network.bake()
wrapper = BayesianNetworkWrapper(pg_network)
return wrapper
def merge_alleles(self, events=None, prefixes=None, assert_merge_definitions=False):
d_event = None
if events is None:
events = []
events.append(self.get_event_from_nickname(Nicknames.v_choice.value))
events.append(self.get_event_from_nickname(Nicknames.j_choice.value))
d_event = self.get_event_from_nickname(Nicknames.d_gene.value)
if d_event is not None: events.append(d_event)
if prefixes is None:
prefixes = []
prefixes.append(Nicknames.v_choice.value + ':')
prefixes.append(Nicknames.j_choice.value + ':')
if d_event is not None: prefixes.append(Nicknames.d_gene.value + ':')
pg_network = BayesianNetworkMergeUtils.create_merged_bayesian_network_for_events(events, prefixes,
self._network_wrapper._network,
assert_merge_definitions=assert_merge_definitions,
logger=self._logger)
self._network_wrapper = BayesianNetworkWrapper(pg_network)
def _init_names_and_nicknames(self):
self._names_for_nicknames = {}
self._nicknames_for_names = {}
for event in self._genmodel.events:
self._names_for_nicknames[event.nickname] = event.name
self._nicknames_for_names[event.name] = event.nickname
def __str__(self):
return self._name
def get_GenModel(self):
return self._genmodel
def get_bayesian_network_wrapper(self):
return self._network_wrapper
def get_name(self):
return self._name
def set_name(self, name):
self._name = name
def get_eventname_for_nickname(self, nickname):
return self._names_for_nicknames.get(nickname, None)
def get_event_from_name(self, name):
for event in self._genmodel.events:
if event.name == name: return event
return None
def get_event_from_nickname(self, nickname):
for event in self._genmodel.events:
if event.nickname == nickname: return event
return None
def get_bayesian_network_wrapper(self):
return self._network_wrapper
def _get_v_gene_marginal_from_primitives(self, v_gene_name):
nickname = str(Nicknames.v_choice.value)
event = self.get_event_from_nickname(nickname)
index = -1
for realization in event.realizations:
if v_gene_name == realization.name:
index = realization.index
break
margs = self._genmodel.marginals[0].get(nickname)
out = margs[index]
return out
def assert_bayesian_network_structure_equality(self, gen_model_wrapper):
try:
self._network_wrapper.assert_structure_equality(gen_model_wrapper.get_bayesian_network_wrapper())
except AssertionError as error:
id = self._name + 'vs. ' + gen_model_wrapper.get_name() + ': '
raise AssertionError(id + str(error))