def to_svg(self):
'''
Renders the graph to a file.
:return: the rendered graph
'''
g = self.to_dot()
return render_gv(g)
def finalgraph(self, filename=None):
finaldb = Database(self.prac.mln)
for step in self.inference_steps:
for db in step.output_dbs:
for atom, truth in list(db.evidence.items()):
if truth == 0: continue
_, predname, args = self.prac.mln.logic.parseLiteral(atom)
if predname in self.prac.roles.union(
['has_sense', 'action_core', 'achieved_by']):
finaldb << atom
# finaldb.write(sys.stdout, color=True)
g = Digraph(format='svg', engine='dot')
g.attr('node', shape='box', style='filled')
for res in finaldb.query('action_core(?w, ?a) ^ has_sense(?w, ?s)'):
actioncore = res['?a']
sense = res['?s']
predname = 'action_core'
g.node(actioncore, fillcolor='#bee280')
g.node(sense)
g.edge(actioncore, sense, label='is_a')
roles = self.prac.actioncores[actioncore].roles
for role in roles:
for res in db.query('{}(?w, {}) ^ has_sense(?w, ?s)'.format(
role, actioncore)):
sense = res['?s']
g.node(sense)
g.edge(actioncore, sense, label=role)
for res in finaldb.query('achieved_by(?a1, ?a2)'):
a1 = res['?a1']
a2 = res['?a2']
g.node(a1, fillcolor='#bee280')
g.node(a2, fillcolor='#bee280')
g.edge(a1, a2, label='achieved_by')
actioncore = a2
roles = self.prac.actionroles[actioncore].roles
for role in roles:
for res in db.query('{}(?w, {}) ^ has_sense(?w, ?s)'.format(
role, actioncore)):
sense = res['?s']
g.node(sense)
g.edge(actioncore, sense, label=role)
return render_gv(g, filename)
def role_distributions(self, step):
'''
TODO
:param step:
:return:
'''
distrs = {}
for db_ in step.output_dbs:
for word in db_.domains['word']:
for q in db_.query('action_core(?w,?ac)'):
# ==========================================================
# Initializaton
# ==========================================================
actioncore = q['?ac']
projectpath = os.path.join(self.module_path,
'{}.pracmln'.format(actioncore))
project = MLNProject.open(projectpath)
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'))
# ==========================================================
# Preprocessing
# ==========================================================
# add inferred concepts to known_concepts to display
# them in the graph. Ignore verbs and adjectives,
# as they do not have hypernym relations to nouns
concepts = self.prac.config.getlist('wordnet', 'concepts')
for con in db_.query('has_sense(?w,?s)'):
if con['?s'].split('.')[1] in ['a', 's', 'v']:
continue
concepts.append(con['?s'])
wn = WordNet(concepts=concepts)
db = db_.copy(mln=mln)
for qs in db_.query('!(EXIST ?w (has_sense(?w,?s)))'):
db.rmval('sense', qs['?s'])
for concept in db_.domains['concept']:
if concept not in mln.domains['concept']:
db.rmval('concept', concept)
for res in db_.query('has_sense({}, ?s)'.format(word)):
sense = res['?s']
if sense == 'null': continue
roles = self.prac.actioncores[actioncore].roles
role = None
for r in roles:
vars = [
'?v{}'.format(i) for i in range(
len(db_.mln.predicate(r).argdoms) - 1)
]
br = False
for qr in db_.query('{}({},{})'.format(
r, ','.join(vars), actioncore)):
for v in vars:
if qr[v] == word:
role = r
br = True
break
if br: break
if br: break
if role is None: continue
db.retract('has_sense({},{})'.format(word, sense))
add_all_wordnet_similarities(db, wn)
# ======================================================
# Inference
# ======================================================
infer = self.mlnquery(method='EnumerationAsk',
mln=mln,
db=db,
queries='has_sense',
cw=True,
multicore=True,
verbose=self.prac.verbose > 2)
result = infer.resultdb
if self.prac.verbose == 2:
print
print prac_heading('INFERENCE RESULTS')
print
infer.write()
# ======================================================
# Graph generation
# ======================================================
g = wn.to_dot()
maxprob = 0.
for atom, truth in result.gndatoms():
_, predname, args = db.mln.logic.parse_literal(
atom)
concept = args[1]
if predname == 'has_sense' and args[
0] == word and concept != 'null':
maxprob = max(maxprob, truth)
for atom, truth in result.gndatoms():
_, predname, args = db.mln.logic.parse_literal(
atom)
concept = args[1]
if predname == 'has_sense' and args[
0] == word and concept != 'null':
if concept in concepts:
g.node(concept,
fillcolor=get_prob_color(truth /
maxprob))
distrs[role] = render_gv(g)
return distrs