def deserialize(self, j):
self.kbs = {
k: kb.KnowledgeBase().deserialize(v, k)
for k, v in j.iteritems()
}
self.dirty = False
return self
def load():
facts = base64.decodebytes(FACTS.encode('ascii'))
facts = pickle.loads(facts)
skb = kb.KnowledgeBase()
for fact in facts:
skb.tell(fact)
return skb
def run(self):
global input
while True:
# choose kourse
while True:
i = 1
output = u'Cursos disponíveis:\n\n'
for k in kodule.all_kourses:
output = output + str(i) + '. ' + k.title + '\n'
i += 1
yield output + u"\nO que quer estudar? Digite o número do curso."
if not input.value.isdigit():
continue
selected_item = int(input.value) - 1
if selected_item < 0 or selected_item >= len(
kodule.all_kourses):
continue
break
# study kourse
output = ''
kourse = kodule.all_kourses[selected_item]
if kourse.pathname not in self.kbs:
yield kourse.title + ':\n' \
+ ' '.join(kourse.initial_material) \
+ '\n\n' \
+ u'Envie "ok" para começar o curso ou "não" para voltar para a escolha do curso.'
if util.normalize_caseless(input.value) != 'ok':
continue
self.kbs[kourse.pathname] = kb.KnowledgeBase(
) # TODO load from file/DB
else:
output += 'Vamos continuar!\n\n'
for x in study.study(input, kourse, self.kbs[kourse.pathname]):
yield output + x
output = ''
now = datetime.datetime.now()
be_back_datetime = self.kbs[
kourse.pathname].get_next_revision_datetime(
) + datetime.timedelta(seconds=59)
if be_back_datetime >= now + datetime.timedelta(days=7):
be_back_str = u'em ' + unicode(be_back_datetime.date())
elif be_back_datetime.day == (now +
datetime.timedelta(days=1)).day:
be_back_str = unicode(
datetime.datetime.strftime(be_back_datetime,
"%A")) + u' às ' + unicode(
be_back_datetime.time())[:5]
else:
be_back_str = u'às ' + unicode(be_back_datetime.time())[:5]
yield u"Já viu material o suficiente, chega de '" \
+ kourse.title \
+ u"' por enquanto.\n" \
+ u"Por favor volte " \
+ be_back_str \
+ u" para revisar o que aprendeu até agora e ver coisas novas!"
def template_obj_builder(dataset_root,
model_weight_file,
template_load_dir,
template_save_dir,
model_type,
templates_idlist,
introduce_oov,
use_hard_scoring=True,
parts=1,
offset=0):
ktrain = kb.KnowledgeBase(os.path.join(dataset_root, 'train.txt'))
if introduce_oov:
ktrain.entity_map["<OOV>"] = len(ktrain.entity_map)
ktest = kb.KnowledgeBase(os.path.join(dataset_root, 'test.txt'),
ktrain.entity_map,
ktrain.relation_map,
add_unknowns=not introduce_oov)
kvalid = kb.KnowledgeBase(os.path.join(dataset_root, 'valid.txt'),
ktrain.entity_map,
ktrain.relation_map,
add_unknowns=not introduce_oov)
if (model_type == "distmult"):
base_model = models.TypedDM(model_weight_file)
elif (model_type == "complex"):
base_model = models.TypedComplex(model_weight_file)
elif (model_type == 'trivec'):
base_model = models.TriVec(model_weight_file)
else:
message = 'Invalid Model type choice: {0} (choose from {1})'.format(
model_type, ["distmult", "complex"])
logging.error(message)
raise argparse.ArgumentTypeError(message)
templates_obj = builder.build_templates(templates_idlist,
[ktrain, kvalid, ktest],
base_model, use_hard_scoring,
template_load_dir,
template_save_dir, parts, offset)
return templates_obj
def load_knowledge_base():
"""
Loads and returns knowledge base
:return: KnowledgeBase object
"""
kb_object_path = util.relative_path('kb_data/kb_object.p')
if os.path.isfile(kb_object_path):
knowledge_base = pickle.load(open(kb_object_path, 'rb'))
else:
knowledge_base = kb.KnowledgeBase()
knowledge_base.load()
pickle.dump(knowledge_base, open(kb_object_path, 'wb'))
return knowledge_base
def readKB(filename):
content = []
with open(filename, 'r') as f:
content = f.read().splitlines()
alpha_size = int(content[0])
query_string = content[1:alpha_size + 1]
query = []
for cnf in query_string:
clause = cnf.split()
clause = list(filter(lambda x: x != 'OR', clause))
query.append(clause)
KB = kb.KnowledgeBase()
KB_size = int(content[alpha_size + 1])
KB_string = content[alpha_size + 2:]
for cnf in KB_string:
clause = cnf.split()
clause = list(filter(lambda x: x != 'OR', clause))
KB.addClause(clause)
return KB, query
if (args.y_labels != '' and args.negative_count != 0):
logging.error(
'Cannot generate random samples with y labels. If using --y_labels use flag --negative_count 0 also'
)
exit(-1)
dataset_root = os.path.join(args.data_repo_root, args.dataset)
template_objs = template_builder.template_obj_builder(
dataset_root, args.model_weights, args.template_load_dir, None,
args.model_type, args.t_ids, args.oov_entity)
ktrain = template_objs[0].kb
k_preprocess = kb.KnowledgeBase(args.preprocess_file,
ktrain.entity_map,
ktrain.relation_map,
add_unknowns=not args.oov_entity)
y_labels = [1 for _ in range(k_preprocess.facts.shape[0])]
if (args.y_labels != ''):
#y_labels = np.loadtxt(args.y_labels)
y_labels, y_multilabels = utils.read_multilabel(args.y_labels)
if (y_labels.shape[0] != k_preprocess.facts.shape[0]):
logging.error('Number of facts and their y labels do not match')
exit(-1)
new_facts = preprocess(k_preprocess, template_objs, args.negative_count,
not args.del_ids, y_labels)
write_to_file(new_facts, args.sm_data_write)
