def __init__(self, mln_, dbs, method, **params):
'''
:param dbs: list of :class:`mln.database.Database` objects to
be used for learning.
:param mln_: the MLN object to be used for learning
:param method: the algorithm to be used for learning. Must be a
class provided by
:class:`mln.methods.LearningMethods`.
:param **params: additional parameters handed over to the base
learners.
'''
self.dbs = dbs
self._params = edict(params)
if not mln_._materialized:
self.mln = mln_.materialize(*dbs)
else:
self.mln = mln_
self.watch = StopWatch()
self.learners = [None] * len(dbs)
self.watch.tag('setup learners', verbose=self.verbose)
if self.verbose:
bar = ProgressBar(steps=len(dbs), color='green')
if self.multicore:
pool = Pool(maxtasksperchild=1)
logger.debug(
'Setting up multi-core processing for {} cores'.format(
pool._processes))
try:
for i, learner in pool.imap(with_tracing(_setup_learner),
self._iterdbs(method)):
self.learners[i] = learner
if self.verbose:
bar.label('Database %d, %s' % ((i + 1), learner.name))
bar.inc()
except Exception as e:
logger.error('Error in child process. Terminating pool...')
pool.close()
raise e
finally:
pool.terminate()
pool.join()
# as MLNs and formulas have been copied to the separate processes,
# the mln pointers of the formulas now point to the MLNs in these child processes
# we have to copy the materialized weight back to our parent process
self.mln.weights = list(first(self.learners).mrf.mln.weights)
else:
for i, db in enumerate(self.dbs):
_, learner = _setup_learner(
(i, self.mln, db, method, self._params + {
'multicore': False
}))
self.learners[i] = learner
if self.verbose:
bar.label('Database %d, %s' % ((i + 1), learner.name))
bar.inc()
if self.verbose:
print 'set up', self.name
self.watch.finish('setup learners')
def __init__(self, mrf, queries=ALL, **params):
self.mrf = mrf
self.mln = mrf.mln
self._params = edict(params)
if not queries:
self.queries = [
self.mln.logic.gnd_lit(ga, negated=False, mln=self.mln)
for ga in self.mrf.gndatoms
if self.mrf.evidence[ga.idx] is None
]
else:
# check for single/multiple query and expand
if type(queries) is not list:
queries = [queries]
self.queries = self._expand_queries(queries)
# fill in the missing truth values of variables that have only one remaining value
for variable in self.mrf.variables:
if variable.valuecount(self.mrf.evidence_dicti(
)) == 1: # the var is fully determined by the evidence
for _, value in variable.itervalues(self.mrf.evidence):
break
self.mrf.set_evidence(variable.value2dict(value), erase=False)
# apply the closed world assumptions to the explicitly specified predicates
if self.cwpreds:
for pred in self.cwpreds:
if isinstance(self.mln.predicate(pred),
SoftFunctionalPredicate):
if self.verbose:
logger.warning(
'Closed world assumption will be applied to soft functional predicate %s'
% pred)
elif isinstance(self.mln.predicate(pred), FunctionalPredicate):
raise Exception(
'Closed world assumption is inapplicable to functional predicate %s'
% pred)
for gndatom in self.mrf.gndatoms:
if gndatom.predname != pred: continue
if self.mrf.evidence[gndatom.idx] is None:
self.mrf.evidence[gndatom.idx] = 0
# apply the closed world assumption to all remaining ground atoms that are not in the queries
if self.closedworld:
qpreds = set()
for q in self.queries:
qpreds.update(q.prednames())
for gndatom in self.mrf.gndatoms:
if isinstance(self.mln.predicate(gndatom.predname), FunctionalPredicate) \
or isinstance(self.mln.predicate(gndatom.predname), SoftFunctionalPredicate):
continue
if gndatom.predname not in qpreds and self.mrf.evidence[
gndatom.idx] is None:
self.mrf.evidence[gndatom.idx] = 0
for var in self.mrf.variables:
if isinstance(var, FuzzyVariable):
var.consistent(self.mrf.evidence, strict=True)
self._watch = StopWatch()
def __init__(self, mln_, dbs, method, **params):
'''
:param dbs: list of :class:`mln.database.Database` objects to
be used for learning.
:param mln_: the MLN object to be used for learning
:param method: the algorithm to be used for learning. Must be a
class provided by
:class:`mln.methods.LearningMethods`.
:param **params: additional parameters handed over to the base
learners.
'''
self.dbs = dbs
self._params = edict(params)
if not mln_._materialized:
self.mln = mln_.materialize(*dbs)
else:
self.mln = mln_
self.watch = StopWatch()
self.learners = [None] * len(dbs)
self.watch.tag('setup learners', verbose=self.verbose)
if self.verbose:
bar = ProgressBar(steps=len(dbs), color='green')
if self.multicore:
pool = Pool(maxtasksperchild=1)
logger.debug('Setting up multi-core processing for {} cores'.format(pool._processes))
try:
for i, learner in pool.imap(with_tracing(_setup_learner), self._iterdbs(method)):
self.learners[i] = learner
if self.verbose:
bar.label('Database %d, %s' % ((i + 1), learner.name))
bar.inc()
except Exception as e:
logger.error('Error in child process. Terminating pool...')
pool.close()
raise e
finally:
pool.terminate()
pool.join()
else:
for i, db in enumerate(self.dbs):
_, learner = _setup_learner((i, self.mln, db, method, self._params + {'multicore': False}))
self.learners[i] = learner
if self.verbose:
bar.label('Database %d, %s' % ((i + 1), learner.name))
bar.inc()
if self.verbose:
print 'set up', self.name
self.watch.finish('setup learners')
def __init__(self, mln_, dbs, method, **params):
'''
:param dbs: list of :class:`mln.database.Database` objects to be used for learning.
:param mln_: the MLN object to be used for learning
:param method: the algorithm to be used for learning. Must be a class provided by :class:`mln.methods.LearningMethods`.
:param **params: additional parameters handed over to the base learners.
'''
self.dbs = dbs
self._params = edict(params)
if not mln_._materialized:
self.mln = mln_.materialize(*dbs)
else:
self.mln = mln_
self.watch = StopWatch()
self.learners = [None] * len(dbs)
self.watch.tag('setup learners', verbose=self.verbose)
if self.verbose:
bar = ProgressBar(width=100, steps=len(dbs), color='green')
if self.multicore:
pool = Pool(maxtasksperchild=1)
logger.debug('Setting up multi-core processing for %d cores' %
pool._processes)
for i, learner in pool.imap(with_tracing(_setup_learner),
self._iterdbs(method)):
self.learners[i] = learner
if self.verbose:
bar.label('Database %d, %s' % ((i + 1), learner.name))
bar.inc()
pool.close()
pool.join()
else:
for i, db in enumerate(self.dbs):
_, learner = _setup_learner(
(i, self.mln, db, method, self._params + {
'multicore': False
}))
self.learners[i] = learner
if self.verbose:
bar.label('Database %d, %s' % ((i + 1), learner.name))
bar.inc()
if self.verbose:
print 'set up', self.name
self.watch.finish('setup learners')
def __init__(self, mrf, queries=ALL, **params):
self.mrf = mrf
self.mln = mrf.mln
self._params = edict(params)
if not queries:
self.queries = [self.mln.logic.gnd_lit(ga, negated=False, mln=self.mln) for ga in self.mrf.gndatoms if self.mrf.evidence[ga.idx] is None]
else:
# check for single/multiple query and expand
if type(queries) is not list:
queries = [queries]
self.queries = self._expand_queries(queries)
# fill in the missing truth values of variables that have only one remaining value
for variable in self.mrf.variables:
if variable.valuecount(self.mrf.evidence_dicti()) == 1: # the var is fully determined by the evidence
for _, value in variable.itervalues(self.mrf.evidence): break
self.mrf.set_evidence(variable.value2dict(value), erase=False)
# apply the closed world assumptions to the explicitly specified predicates
if self.cwpreds:
for pred in self.cwpreds:
if isinstance(self.mln.predicate(pred), SoftFunctionalPredicate):
if self.verbose: logger.warning('Closed world assumption will be applied to soft functional predicate %s' % pred)
elif isinstance(self.mln.predicate(pred), FunctionalPredicate):
raise Exception('Closed world assumption is inapplicable to functional predicate %s' % pred)
for gndatom in self.mrf.gndatoms:
if gndatom.predname != pred: continue
if self.mrf.evidence[gndatom.idx] is None:
self.mrf.evidence[gndatom.idx] = 0
# apply the closed world assumption to all remaining ground atoms that are not in the queries
if self.closedworld:
qpreds = set()
for q in self.queries:
qpreds.update(q.prednames())
for gndatom in self.mrf.gndatoms:
if isinstance(self.mln.predicate(gndatom.predname), FunctionalPredicate) \
or isinstance(self.mln.predicate(gndatom.predname), SoftFunctionalPredicate):
continue
if gndatom.predname not in qpreds and self.mrf.evidence[gndatom.idx] is None:
self.mrf.evidence[gndatom.idx] = 0
for var in self.mrf.variables:
if isinstance(var, FuzzyVariable):
var.consistent(self.mrf.evidence, strict=True)
self._watch = StopWatch()
def tojson(self):
return tojson(edict({constants.JSON_HOWTO_IMPORT_DATE: self.import_date}) +\
edict(Frame.tojson(self)) + edict({constants.JSON_HOWTO_STEPS: tojson(self.steps)}))
