def process(cls, params):
Validator.process(params)
if 'error' in params:
cls.LOGGER.error(message=params['error'])
abort(404, message=params['error'])
StandardNLP.process(params)
FeatureExtractor.process(params)
Classifier.process(params)
Executor.process(params)
Logger.process(params)
Sanitizer.process(params)
def climbing(examples, services, step_size):
""" Returns the set of services to climb on if any.
"""
to_be_climbed = set()
for service, queries in examples.iteritems():
current = services[service]
for query in queries:
confidences, keywords = get_confs_kws(query, services)
chosen_name = max(confidences.keys(), key=confidences.get)
conf = confidences[chosen_name]
if chosen_name != service and conf >= THRESHOLD:
# Here the query was misclassified
chosen = services[chosen_name]
for kw in keywords:
original = kw
if kw in chosen._variables:
# traverse through dummies
while isinstance(chosen._variables[kw], str):
kw = chosen._variables[kw]
chosen._variables[kw] -= step_size
kw = original
if kw in current._variables:
# traverse through dummies
while isinstance(current._variables[kw], str):
kw = current._variables[kw]
current._variables[kw] += step_size
Logger.log_heuristic('"{}"'.format(query))
Logger.log_heuristic(
'was supposed to be in {}'.format(service))
Logger.log_heuristic('was in {}'.format(chosen_name))
Logger.log_heuristic(
'~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n')
to_be_climbed.add(chosen_name)
to_be_climbed.add(service)
return to_be_climbed