def __call__(self, node, **params):
# ======================================================================
# Initialization
# ======================================================================
logger.debug('inference on {}'.format(self.name))
if self.prac.verbose > 0:
print prac_heading('Resolving Coreferences')
preds = list(
node.rdfs(
goaltest=lambda n: isinstance(n, FrameNode) and not n.children,
all=True))[:2]
dbs = node.outdbs
infstep = PRACInferenceStep(node, self)
projectpath = os.path.join(pracloc.pracmodules, self.name)
ac = None
pngs = {}
# if not preds: return []
# ======================================================================
# Preprocessing
# ======================================================================
# merge output dbs from senses_and_roles step, containing
# roles inferred from multiple sentences.
if not preds:
# no coreferencing required - forward dbs and settings
# from previous module
infstep.indbs = [db.copy() for db in dbs]
infstep.outdbs = [db.copy() for db in infstep.indbs]
logger.debug(
'%s has no predecessors. Nothing to do here. Passing db...' %
node)
return [node]
# retrieve all words from the dbs to calculate distances.
# Do not use pracinference.instructions as they are not
# annotated by the Stanford parser.
sentences = [db.words() for pred in preds for db in pred.indbs]
infstep.indbs = [db.copy() for db in dbs]
# infstep.outdbs = [db.copy() for db in infstep.indbs]
# query action core to load corresponding project
actioncore = node.frame.actioncore
# clear corefdb and unify current db with the two preceding ones
corefdb = PRACDatabase(self.prac)
corefdb = corefdb.union(dbs, self.prac.mln)
# for s in range(max(0, i - 2), i+1):
# corefdb = corefdb.union(dbs[s], self.prac.mln)
for pred in preds:
logger.debug('unifying with %s' % pred)
for db in pred.indbs:
corefdb = corefdb.union(db, self.prac.mln)
# remove all senses from the databases' domain that are not
# assigned to any word.
for q in corefdb.query('!(EXIST ?w (has_sense(?w,?sense)))'):
corefdb.rmval('sense', q['?sense'])
try:
# preprocessing: adding distance information for each
# word in the instructions
# s = words[max(0, i - 2):i+1]
# snts = list(enumerate(s))
# idx = len(snts) - 1 # idx of current sentence
# for s in snts[:-1]:
# idx2 = s[0]
# for w in s[1]:
# corefdb << 'distance({},DIST{})'.format(w, idx - idx2)
for sidx, s in enumerate(sentences):
for w in s:
cont = True
for q in corefdb.query('distance({}, ?w)'.format(w)):
cont = False
break
if not cont: continue
corefdb << 'distance({},DIST{})'.format(w, sidx)
# print 'distance({},DIST{})'.format(w, sidx)
logger.debug('loading Project: {}'.format(
colorize(actioncore, (None, 'cyan', True), True)))
project = MLNProject.open(
os.path.join(projectpath, '{}.pracmln'.format(actioncore)))
mlntext = project.mlns.get(project.queryconf['mln'], None)
mln = parse_mln(mlntext,
searchpaths=[self.module_path],
projectpath=projectpath,
logic=project.queryconf.get('logic', 'FuzzyLogic'),
grammar=project.queryconf.get(
'grammar', 'PRACGrammar'))
except MLNParsingError:
logger.warning(
'Could not use MLN in project {} for coreference resolution'.
format(colorize(actioncore, (None, 'cyan', True), True)))
infstep.outdbs = [db.copy(self.prac.mln) for db in dbs]
infstep.png = node.parent.laststep.png
infstep.applied_settings = node.parent.laststep.applied_settings
return [node]
except Exception:
infstep.outdbs = [db.copy(self.prac.mln) for db in dbs]
infstep.png = node.parent.laststep.png
infstep.applied_settings = node.parent.laststep.applied_settings
logger.warning(
'Could not load project "{}". Passing dbs to next module...'.
format(ac))
return [node]
# adding similarities
wnmod = self.prac.module('wn_senses')
newdatabase = wnmod.add_sims(corefdb, mln)
# update queries depending on missing roles
acroles = filter(lambda role: role != 'action_verb',
self.prac.actioncores[actioncore].roles)
missingroles = [
ar for ar in acroles
if len(list(newdatabase.query('{}(?w,{})'.format(ar, actioncore))))
== 0
]
conf = project.queryconf
conf.update({'queries': ','.join(missingroles)})
print colorize('querying for missing roles {}'.format(conf['queries']),
(None, 'green', True), True)
# asserting impossible role-ac combinations, leaving previously
# inferred roles untouched
fulldom = mergedom(mln.domains, newdatabase.domains)
ac_domains = [dom for dom in fulldom if '_ac' in dom]
acs = list(set([v for a in ac_domains for v in fulldom[a]]))
acs = filter(lambda ac_: ac_ != actioncore, acs)
for ac1 in acs:
for r in missingroles:
for w in newdatabase.domains['word']:
# words with no sense are asserted false
if list(
corefdb.query(
'!(EXIST ?sense (has_sense({},?sense)))'.
format(w))):
newdatabase << '!{}({},{})'.format(r, w, actioncore)
# leave previously inferred information roles
# untouched
if list(newdatabase.query('{}({},{})'.format(r, w, ac1))):
continue
else:
newdatabase << '!{}({},{})'.format(r, w, ac1)
try:
# ==========================================================
# Inference
# ==========================================================
infer = self.mlnquery(config=conf,
verbose=self.prac.verbose > 2,
db=newdatabase,
mln=mln)
if self.prac.verbose == 2:
print
print prac_heading('INFERENCE RESULTS')
infer.write()
# ==========================================================
# Postprocessing
# ==========================================================
# merge initial db with results
for db in infstep.indbs:
resultdb = db.copy()
for res in infer.results.keys():
if infer.results[res] != 1.0:
continue
resultdb << str(res)
_, _, args = self.prac.mln.logic.parse_literal(res)
w = args[0]
for q in newdatabase.query(
'has_sense({0},?s) ^ has_pos({0},?pos)'.format(w)):
resultdb << 'has_sense({},{})'.format(w, q['?s'])
resultdb << 'is_a({0},{0})'.format(q['?s'])
resultdb << 'has_pos({},{})'.format(w, q['?pos'])
resultdb = wnmod.add_sims(resultdb, mln)
# enhance the frame data
for mrole in missingroles:
for q in resultdb.query(
'{role}(?w, {actioncore}) ^ has_sense(?w, ?s)'.
format(role=mrole, actioncore=actioncore)):
for p in preds:
if p.frame.object(q['?w']) is not None:
node.frame.actionroles[mrole] = p.frame.object(
q['?w'])
break
infstep.outdbs.append(resultdb)
pprint(node.frame.tojson())
except NoConstraintsError:
logger.debug('No coreferences found. Passing db...')
infstep.outdbs.append(db)
except Exception:
logger.error('Something went wrong')
traceback.print_exc()
pngs['Coref - ' + str(node)] = get_cond_prob_png(project.queryconf.get(
'queries', ''),
dbs,
filename=self.name)
infstep.png = pngs
infstep.applied_settings = project.queryconf.config
return [node]
def __call__(self, node, **params):
# ======================================================================
# Initialization
# ======================================================================
logger.debug('inference on {}'.format(self.name))
if self.prac.verbose > 0:
print(prac_heading('Refining Actioncores'))
dbs = node.outdbs
infstep = PRACInferenceStep(node, self)
# if node.previous_module == 'achieved_by':
# raise ActionKnowledgeError('I don\'t know how to %s' % node.frame.sentence)
# ======================================================================
# Preprocessing
# ======================================================================
for olddb in dbs:
infstep.indbs.append(olddb.copy())
#To handle multiple acs in one task, we have to check if the single
# dbs contain achieved_bys which representing already plans
pngs = {}
actioncore = node.frame.actioncore
mod = self.prac.module('complex_achieved_by')
newnodes = list(mod(node))
n = None
parentframes = [
p.frame for p in node.parentspath()
if isinstance(p, FrameNode)
]
if any(n.frame in parentframes for n in newnodes):
logger.error(
'aborting reasoning because of infinite loop. (%s)' %
node.frame)
node.children = []
else:
for n in newnodes:
yield n
if n is not None: return
if n is None:
# This list is used to avoid an infinite loop during the
# achieved by inference.
# To avoid this infinite loop, the list contains the pracmlns
# which were inferenced during the process.
# Every pracmln should be used only once during the process
# because the evidence for the inference will always remain
# the same.
# So if the pracmln hadnt inferenced a plan in the first time,
# it will never do it.
# Need to remove possible achieved_by predicates from
# previous achieved_by inferences
db_ = PRACDatabase(self.prac)
for atom, truth in sorted(olddb.evidence.items()):
if 'achieved_by' in atom: continue
db_ << (atom, truth)
if params.get('project', None) is None:
logger.debug('Loading Project: {}.pracmln'.format(
colorize(actioncore, (None, 'cyan', True), True)))
projectpath = os.path.join(pracloc.pracmodules, self.name,
'{}.pracmln'.format(actioncore))
if os.path.exists(projectpath):
project = MLNProject.open(projectpath)
else:
infstep.outdbs.append(olddb)
logger.error(actioncore + ".pracmln does not exist.")
return
else:
logger.debug(
colorize('Loading Project from params',
(None, 'cyan', True), True))
projectpath = os.path.join(
params.get('projectpath', None)
or os.path.join(pracloc.pracmodules, self.name),
params.get('project').name)
project = params.get('project')
mlntext = project.mlns.get(project.queryconf['mln'], None)
mln = parse_mln(
mlntext,
searchpaths=[self.module_path],
projectpath=projectpath,
logic=project.queryconf.get('logic', 'FirstOrderLogic'),
grammar=project.queryconf.get('grammar', 'PRACGrammar'))
known_concepts = mln.domains.get('concept', [])
wnmod = self.prac.module('wn_senses')
#Merge domains of db and given mln to avoid errors due to role inference and the resulting missing fuzzy perdicates
known_concepts = list(
set(known_concepts).union(
set(db_.domains.get('concept', []))))
db = wnmod.get_senses_and_similarities(db_, known_concepts)
unified_db = db_.union(db)
dbnew = wnmod.add_sims(unified_db, unified_db)
# Inference achieved_by predicate
db_ = self.extendDBWithAchievedByEvidence(
dbnew, mln, actioncore)
# ==============================================================
# Inference
# ==============================================================
# db_.write()
try:
infer = self.mlnquery(config=project.queryconf,
verbose=self.prac.verbose > 2,
db=db_,
mln=mln)
except NoConstraintsError:
logger.error(
'achieved_by inference failed due to NoConstraintsError: %s'
% node.frame)
return
result_db = infer.resultdb
if self.prac.verbose == 2:
print()
print(prac_heading('INFERENCE RESULTS'))
infer.write()
# ==============================================================
# Postprocessing
# ==============================================================
# unified_db = result_db.union(kb.query_mln, db_)
# only add inferred achieved_by atoms, leave out
# 0-evidence atoms
for qa in result_db.query('achieved_by(?ac1,?ac2)'):
if qa['?ac2'] == 'Complex': continue
unified_db << 'achieved_by({},{})'.format(
qa['?ac1'], qa['?ac2'])
pngs[qa['?ac2']] = get_cond_prob_png(project.queryconf.get(
'queries', ''),
dbs,
filename=self.name)
newframe = Frame(self.prac,
node.frame.sidx,
'',
words=[],
syntax=[],
actioncore=qa['?ac2'],
actionroles={})
# out('->', newframe)
infstep.outdbs.append(unified_db)
yield FrameNode(node.pracinfer,
newframe,
node,
pred=None,
indbs=[unified_db],
prevmod=self.name)
return
infstep.outdbs.append(unified_db)
# raise ActionKnowledgeError('I don\'t know how to %s' % node.frame.sentence)