def __call__(self, node, **params):
# ======================================================================
# Initialization
# ======================================================================
dbs = node.outdbs
infstep = PRACInferenceStep(node, self)
infstep.indbs = [db.copy() for db in dbs]
infstep.outdbs = [db.copy() for db in dbs]
logger.debug('Running {}'.format(self.name))
if self.prac.verbose > 0:
print(prac_heading('Generating CRAM Plan(s)'))
if not hasattr(self.prac.actioncores[node.frame.actioncore], 'plan'):
raise ActionKnowledgeError('I don\'t know how to %s' %
node.frame.sentence)
yield
ac = self.prac.actioncores[node.frame.actioncore]
# fill dictionary with all inferred roles...
acdict = dict([(k, v.type)
for k, v in list(node.frame.actionroles.items())])
# ..and their properties
acdict.update(
dict([('{}_props'.format(k), ' '.join([
'({} {})'.format(pkey, pval)
for pkey, pval in list(v.props.tojson().items())
])) for k, v in list(node.frame.actionroles.items())]))
# update dictionary with missing roles and roles properties
for role in ac.roles:
if acdict.get(role) is None:
acdict[role] = 'Unknown'
acdict['{}_props'.format(role)] = ''
node.plan = ac.parameterize_plan(**acdict)
if self.prac.verbose:
print()
print(prac_heading('PLAN GENERATION RESULTS'))
print(colorize('actioncore:', (None, 'white', True), True),
colorize(ac.name, (None, 'cyan', True), True))
print(colorize('assignments:', (None, 'white', True), True))
for x in acdict:
print('\t{}: {}'.format(
colorize(x, (None, 'white', True), True),
colorize(acdict[x], (None, 'cyan', True), True)))
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]