def _itergroundings(self, simplify=False, unsatfailure=False):
global global_bpll_grounding
global_bpll_grounding = self
if self.multicore:
pool = Pool(maxtasksperchild=1)
try:
for gndresult in pool.imap(with_tracing(create_formula_groundings), self.formulas):
for fidx, stat in gndresult:
for (varidx, validx, val) in stat:
self._varidx2fidx[varidx].add(fidx)
self._addstat(fidx, varidx, validx, val)
checkmem()
yield None
except CtrlCException as e:
pool.terminate()
raise e
pool.close()
pool.join()
else:
for gndresult in imap(create_formula_groundings, self.formulas):
for fidx, stat in gndresult:
for (varidx, validx, val) in stat:
self._varidx2fidx[varidx].add(fidx)
self._addstat(fidx, varidx, validx, val)
yield None
def _itergroundings(self, simplify=False, unsatfailure=False):
global global_bpll_grounding
global_bpll_grounding = self
if self.multicore:
pool = Pool(maxtasksperchild=1)
try:
for gndresult in pool.imap(
with_tracing(create_formula_groundings),
self.formulas):
for fidx, stat in gndresult:
for (varidx, validx, val) in stat:
self._varidx2fidx[varidx].add(fidx)
self._addstat(fidx, varidx, validx, val)
checkmem()
yield None
except Exception as e:
logger.error('Error in child process. Terminating pool...')
pool.close()
raise e
finally:
pool.terminate()
pool.join()
else:
for gndresult in imap(create_formula_groundings, self.formulas):
for fidx, stat in gndresult:
for (varidx, validx, val) in stat:
self._varidx2fidx[varidx].add(fidx)
self._addstat(fidx, varidx, validx, val)
yield None
def _prepare(self):
self.watch.tag('preparing optimization', verbose=self.verbose)
if self.verbose:
bar = ProgressBar(steps=len(self.dbs), color='green')
if self.multicore:
pool = Pool(maxtasksperchild=1)
try:
for i, (_, d_) in enumerate(
pool.imap(
with_tracing(
_methodcaller('_prepare', sideeffects=True)),
self.learners)):
checkmem()
self.learners[i].__dict__ = d_
if self.verbose: 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 learner in self.learners:
checkmem()
learner._prepare()
if self.verbose: bar.inc()
def _prepare(self):
self.watch.tag("preparing optimization", verbose=self.verbose)
if self.verbose:
bar = ProgressBar(width=100, steps=len(self.dbs), color="green")
if self.multicore:
pool = Pool(maxtasksperchild=1)
try:
for i, (_, d_) in enumerate(
pool.imap(with_tracing(_methodcaller("_prepare", sideeffects=True)), self.learners)
):
checkmem()
self.learners[i].__dict__ = d_
if self.verbose:
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 learner in self.learners:
checkmem()
learner._prepare()
if self.verbose:
bar.inc()
def _prepare(self):
self.watch.tag('preparing optimization', verbose=self.verbose)
if self.verbose:
bar = ProgressBar(width=100, steps=len(self.dbs), color='green')
if self.multicore:
for i, (_, d_) in enumerate(Pool(maxtasksperchild=1).imap(with_tracing(_methodcaller('_prepare', sideeffects=True)), self.learners)):
checkmem()
self.learners[i].__dict__ = d_
if self.verbose: bar.inc()
else:
for learner in self.learners:
checkmem()
learner._prepare()
if self.verbose: bar.inc()
def _prepare(self):
self.watch.tag('preparing optimization', verbose=self.verbose)
if self.verbose:
bar = ProgressBar(width=100, steps=len(self.dbs), color='green')
if self.multicore:
for i, (_, d_) in enumerate(
Pool(maxtasksperchild=1).imap(
with_tracing(
_methodcaller('_prepare', sideeffects=True)),
self.learners)):
checkmem()
self.learners[i].__dict__ = d_
if self.verbose: bar.inc()
else:
for learner in self.learners:
checkmem()
learner._prepare()
if self.verbose: bar.inc()
def create_formula_groundings(formula, unsatfailure=True):
checkmem()
results = []
if global_bpll_grounding.mrf.mln.logic.islitconj(formula):
for res in global_bpll_grounding.itergroundings_fast(formula):
checkmem()
results.append(res)
else:
for gf in formula.itergroundings(global_bpll_grounding.mrf,
simplify=False):
checkmem()
stat = []
for gndatom in gf.gndatoms():
world = list(global_bpll_grounding.mrf.evidence)
var = global_bpll_grounding.mrf.variable(gndatom)
for validx, value in var.itervalues():
var.setval(value, world)
truth = gf(world)
if truth != 0:
stat.append((var.idx, validx, truth))
elif unsatfailure and gf.weight == HARD and gf(
global_bpll_grounding.mrf.evidence) != 1:
print
gf.print_structure(global_bpll_grounding.mrf.evidence)
raise SatisfiabilityException(
'MLN is unsatisfiable due to hard constraint violation {} (see above)'
.format(
global_bpll_grounding.mrf.formulas[gf.idx]))
results.append((gf.idx, stat))
return results
def create_formula_groundings(formula, unsatfailure=True):
checkmem()
results = []
if global_bpll_grounding.mrf.mln.logic.islitconj(formula):
for res in global_bpll_grounding.itergroundings_fast(formula):
checkmem()
results.append(res)
else:
for gf in formula.itergroundings(global_bpll_grounding.mrf, simplify=False):
checkmem()
stat = []
for gndatom in gf.gndatoms():
world = list(global_bpll_grounding.mrf.evidence)
var = global_bpll_grounding.mrf.variable(gndatom)
for validx, value in var.itervalues():
var.setval(value, world)
truth = gf(world)
if truth != 0:
stat.append((var.idx, validx, truth))
elif unsatfailure and gf.weight == HARD and gf(global_bpll_grounding.mrf.evidence) != 1:
print
gf.print_structure(global_bpll_grounding.mrf.evidence)
raise SatisfiabilityException('MLN is unsatisfiable due to hard constraint violation %s (see above)' % global_bpll_grounding.mrf.formulas[gf.idx])
results.append((gf.idx, stat))
return results
def _setup_learner((i, mln_, db, method, params)):
checkmem()
mrf = mln_.ground(db)
algo = method(mrf, **params)
return i, algo
def _setup_learner((i, mln_, db, method, params)):
checkmem()
mrf = mln_.ground(db)
algo = method(mrf, **params)
return i, algo
