def post(self):
"""Handles POST requests."""
payload_proto = (
training_job_response_payload_pb2.TrainingJobResponsePayload())
payload_proto.ParseFromString(self.request.body)
if not validate_job_result_message_proto(payload_proto.job_result):
raise self.InvalidInputException
job_id = payload_proto.job_result.job_id
classifier_training_job = (
classifier_services.get_classifier_training_job_by_id(job_id))
if classifier_training_job.status == (
feconf.TRAINING_JOB_STATUS_FAILED):
# Send email to admin and admin-specified email recipients.
# Other email recipients are specified on admin config page.
email_manager.send_job_failure_email(job_id)
raise self.InternalErrorException(
'The current status of the job cannot transition to COMPLETE.')
classifier_data_proto = getattr(
payload_proto.job_result,
payload_proto.job_result.WhichOneof('classifier_frozen_model'))
classifier_services.store_classifier_data(
job_id, classifier_data_proto)
# Update status of the training job to 'COMPLETE'.
classifier_services.mark_training_job_complete(job_id)
return self.render_json({})
def extract_request_message_vm_id_and_signature(self):
"""Returns message, vm_id and signature retrieved from incoming request.
Returns:
tuple(str). Message at index 0, vm_id at index 1 and signature at
index 2.
"""
payload_proto = (
training_job_response_payload_pb2.TrainingJobResponsePayload())
payload_proto.ParseFromString(self.request.body)
signature = payload_proto.signature
vm_id = payload_proto.vm_id
return classifier_domain.OppiaMLAuthInfo(
payload_proto.job_result.SerializeToString(), vm_id, signature)
def setUp(self):
super(TrainedClassifierHandlerTests, self).setUp()
self.exp_id = 'exp_id1'
self.title = 'Testing Classifier storing'
self.category = 'Test'
yaml_path = os.path.join(feconf.TESTS_DATA_DIR,
'string_classifier_test.yaml')
with python_utils.open_file(yaml_path, 'r') as yaml_file:
self.yaml_content = yaml_file.read()
self.signup(self.CURRICULUM_ADMIN_EMAIL,
self.CURRICULUM_ADMIN_USERNAME)
self.signup('*****@*****.**', 'mod')
assets_list = []
with self.swap(feconf, 'ENABLE_ML_CLASSIFIERS', True):
exp_services.save_new_exploration_from_yaml_and_assets(
feconf.SYSTEM_COMMITTER_ID, self.yaml_content, self.exp_id,
assets_list)
self.exploration = exp_fetchers.get_exploration_by_id(self.exp_id)
self.algorithm_id = feconf.INTERACTION_CLASSIFIER_MAPPING[
self.exploration.states['Home'].interaction.id]['algorithm_id']
self.algorithm_version = feconf.INTERACTION_CLASSIFIER_MAPPING[
self.exploration.states['Home'].interaction.
id]['algorithm_version']
self.classifier_data = {
'_alpha': 0.1,
'_beta': 0.001,
'_prediction_threshold': 0.5,
'_training_iterations': 25,
'_prediction_iterations': 5,
'_num_labels': 10,
'_num_docs': 12,
'_num_words': 20,
'_label_to_id': {
'text': 1
},
'_word_to_id': {
'hello': 2
},
'_w_dp': [],
'_b_dl': [],
'_l_dp': [],
'_c_dl': [],
'_c_lw': [],
'_c_l': [],
}
classifier_training_job = (
classifier_services.get_classifier_training_job(
self.exp_id, self.exploration.version, 'Home',
self.algorithm_id))
self.assertIsNotNone(classifier_training_job)
self.job_id = classifier_training_job.job_id
# TODO(pranavsid98): Replace the three commands below with
# mark_training_job_pending after Giritheja's PR gets merged.
classifier_training_job_model = (
classifier_models.ClassifierTrainingJobModel.get(self.job_id,
strict=False))
classifier_training_job_model.status = (
feconf.TRAINING_JOB_STATUS_PENDING)
classifier_training_job_model.update_timestamps()
classifier_training_job_model.put()
self.job_result = (training_job_response_payload_pb2.
TrainingJobResponsePayload.JobResult())
self.job_result.job_id = self.job_id
classifier_frozen_model = (
text_classifier_pb2.TextClassifierFrozenModel())
classifier_frozen_model.model_json = json.dumps(self.classifier_data)
self.job_result.text_classifier.CopyFrom(classifier_frozen_model)
self.payload_proto = (
training_job_response_payload_pb2.TrainingJobResponsePayload())
self.payload_proto.job_result.CopyFrom(self.job_result)
self.payload_proto.vm_id = feconf.DEFAULT_VM_ID
self.secret = feconf.DEFAULT_VM_SHARED_SECRET
self.payload_proto.signature = classifier_services.generate_signature(
python_utils.convert_to_bytes(self.secret),
python_utils.convert_to_bytes(
self.payload_proto.job_result.SerializeToString()),
self.payload_proto.vm_id)
self.payload_for_fetching_next_job_request = {
'vm_id': feconf.DEFAULT_VM_ID,
'message': json.dumps({})
}
self.payload_for_fetching_next_job_request['signature'] = (
classifier_services.generate_signature(
python_utils.convert_to_bytes(self.secret),
python_utils.convert_to_bytes(
self.payload_for_fetching_next_job_request['message']),
self.payload_for_fetching_next_job_request['vm_id']))