def transition(self, answer, params, interactive_widget_properties):
"""Handle feedback interactions with readers."""
dest_id = None
feedback = None
recorded_answer = answer
if interactive_widget_properties['classifier']:
# Import the relevant classifier module.
classifier_module = '.'.join([
feconf.SAMPLE_CLASSIFIERS_DIR.replace('/', '.'),
interactive_widget_properties['classifier'],
interactive_widget_properties['classifier']])
Classifier = importlib.import_module(classifier_module)
logging.info(Classifier.__name__)
norm_answer = Classifier.DEFAULT_NORMALIZER().normalize(answer)
if norm_answer is None:
raise Exception(
'Invalid input: could not normalize the answer.')
answer_handler = None
for handler in self.widget.handlers:
if handler.name == 'submit':
answer_handler = handler
for ind, rule in enumerate(answer_handler.rules):
if ind == len(answer_handler.rules) - 1:
# TODO(sll): This is a special case for multiple-choice input
# which should really be handled generically. However, it's
# not very interesting anyway because the reader's answer
# in this case is already known (it's just the last of the
# multiple-choice options given).
if self.widget.widget_id == 'MultipleChoiceInput':
recorded_answer = (
self.widget.params['choices'][int(answer)])
if rule.name == 'Default':
dest_id = rule.dest
feedback = (utils.get_random_choice(rule.feedback)
if rule.feedback else '')
break
func_name, param_list = self.get_classifier_info(
self.widget.widget_id, handler.name, rule, params)
param_list = [norm_answer] + param_list
classifier_output = getattr(Classifier, func_name)(*param_list)
return_value, unused_return_data = (
utils.normalize_classifier_return(classifier_output))
if return_value:
dest_id = rule.dest
feedback = (utils.get_random_choice(rule.feedback)
if rule.feedback else '')
break
return dest_id, feedback, rule, recorded_answer
def transition(self, answer, params, handler):
"""Handle feedback interactions with readers."""
recorded_answer = answer
answer_handler = None
for wi_handler in self.widget.handlers:
if wi_handler.name == handler:
answer_handler = wi_handler
if answer_handler.classifier:
# Import the relevant classifier module.
classifier_module = '.'.join([
feconf.SAMPLE_CLASSIFIERS_DIR.replace('/', '.'),
answer_handler.classifier, answer_handler.classifier])
Classifier = importlib.import_module(classifier_module)
logging.info(Classifier.__name__)
norm_answer = Classifier.DEFAULT_NORMALIZER().normalize(answer)
if norm_answer is None:
raise Exception('Could not normalize %s.' % answer)
# TODO(sll): This is a special case for multiple-choice input
# which should really be handled generically.
if self.widget.widget_id == 'MultipleChoiceInput':
recorded_answer = self.widget.params['choices'][int(answer)]
selected_rule = None
for ind, rule in enumerate(answer_handler.rules):
if rule.name == 'Default':
selected_rule = rule
break
func_name, param_list = self.get_classifier_info(
self.widget.widget_id, answer_handler.name, rule, params)
param_list = [norm_answer] + param_list
classifier_output = getattr(Classifier, func_name)(*param_list)
match, _ = utils.normalize_classifier_return(classifier_output)
if match:
selected_rule = rule
break
feedback = (utils.get_random_choice(selected_rule.feedback)
if selected_rule.feedback else '')
return selected_rule.dest, feedback, rule, recorded_answer
def find_first_match(self, handler, Classifier, norm_answer, params):
for ind, rule in enumerate(handler.rules):
if rule.name == 'Default':
return rule
func_name, param_list = self.get_classifier_info(
self.widget.widget_id, handler.name, rule, params)
param_list = [norm_answer] + param_list
classifier_output = getattr(Classifier, func_name)(*param_list)
match, _ = utils.normalize_classifier_return(classifier_output)
if match:
return rule
raise Exception('No matching rule found for handler %s.' % handler.name)
def test_normalize_classifier_return(self):
"""Test normalize_classifier_return method."""
with self.assertRaises(utils.InvalidInputException):
utils.normalize_classifier_return()
with self.assertRaises(utils.InvalidInputException):
utils.normalize_classifier_return(1, 2, 3)
with self.assertRaises(AssertionError):
utils.normalize_classifier_return(1)
with self.assertRaises(AssertionError):
utils.normalize_classifier_return(1, {'a': 'b'})
with self.assertRaises(AssertionError):
utils.normalize_classifier_return(True, 2)
self.assertEqual(
utils.normalize_classifier_return(True), (True, {}))
self.assertEqual(
utils.normalize_classifier_return(False), (False, {}))
self.assertEqual(
utils.normalize_classifier_return(
False, {'a': 'b'}), (False, {'a': 'b'}))
