def _get_classiying_rule_type(self, answer):
string_classifier_predict = (
classifier_services.StringClassifier.predict_label_for_doc)
predict_counter = test_utils.CallCounter(
string_classifier_predict)
with self.swap(
classifier_services.StringClassifier,
'predict_label_for_doc', predict_counter):
response = reader.classify(
self.exp_id, self.exp_state, answer, {'answer': answer})
answer_group_index = response['answer_group_index']
rule_spec_index = response['rule_spec_index']
answer_groups = self.exp_state.interaction.answer_groups
if answer_group_index == len(answer_groups):
return 'default'
answer_group = answer_groups[answer_group_index]
if answer_group.get_fuzzy_rule_index() == rule_spec_index:
return (
'soft' if predict_counter.times_called == 0
else 'classifier')
return 'hard'
def _is_string_classifier_called(self, answer):
string_classifier_predict = classifier_services.StringClassifier.predict_label_for_doc
predict_counter = test_utils.CallCounter(string_classifier_predict)
with self.swap(classifier_services.StringClassifier, "predict_label_for_doc", predict_counter):
response = reader.classify(self.exp_state, answer)
answer_group_index = response["answer_group_index"]
rule_spec_index = response["rule_spec_index"]
answer_groups = self.exp_state.interaction.answer_groups
if answer_group_index == len(answer_groups):
return "default"
answer_group = answer_groups[answer_group_index]
return answer_group.get_classifier_rule_index() == rule_spec_index and predict_counter.times_called == 1
def _is_string_classifier_called(self, answer):
string_classifier_predict = (
classifier_services.StringClassifier.predict_label_for_doc)
predict_counter = test_utils.CallCounter(string_classifier_predict)
with self.swap(classifier_services.StringClassifier,
'predict_label_for_doc', predict_counter):
response = reader.classify(self.exp_state, answer)
answer_group_index = response['answer_group_index']
rule_spec_index = response['rule_spec_index']
answer_groups = self.exp_state.interaction.answer_groups
if answer_group_index == len(answer_groups):
return 'default'
answer_group = answer_groups[answer_group_index]
return (answer_group.get_classifier_rule_index() == rule_spec_index
and predict_counter.times_called == 1)
