def create_model():
database_url = os.environ[DATABASE_URL]
database_replica_set = os.environ[DATABASE_REPLICA_SET]
database_name = os.environ[DATABASE_NAME]
train_filename = request.json[TRAINING_FILENAME]
test_filename = request.json[TEST_FILENAME]
classifiers_name = request.json[CLASSIFIERS_NAME]
database = Database(
database_url,
database_replica_set,
os.environ[DATABASE_PORT],
database_name,
)
request_validator = UserRequest(database)
request_errors = analyse_request_errors(
request_validator,
train_filename,
test_filename,
classifiers_name)
if request_errors is not None:
return request_errors
database_url_training = Database.collection_database_url(
database_url,
database_name,
train_filename,
database_replica_set,
)
database_url_test = Database.collection_database_url(
database_url,
database_name,
test_filename,
database_replica_set,
)
metadata_creator = Metadata(database, train_filename, test_filename)
model_builder = Model(database,
metadata_creator,
database_url_training,
database_url_test)
model_builder.build(
request.json[MODELING_CODE_NAME],
classifiers_name
)
return (
jsonify({
MESSAGE_RESULT:
create_prediction_files_uri(
classifiers_name,
test_filename)}),
HTTP_STATUS_CODE_SUCCESS_CREATED,
)
def create_projection():
database_url = os.environ[DATABASE_URL]
database_replica_set = os.environ[DATABASE_REPLICA_SET]
database_name = os.environ[DATABASE_NAME]
parent_filename = request.json[PARENT_FILENAME_NAME]
projection_filename = request.json[PROJECTION_FILENAME_NAME]
projection_fields = request.json[FIELDS_NAME]
database = Database(
database_url,
database_replica_set,
os.environ[DATABASE_PORT],
database_name,
)
request_validator = UserRequest(database)
request_errors = analyse_request_errors(request_validator, parent_filename,
projection_filename,
projection_fields)
if request_errors is not None:
return request_errors
database_url_input = Database.collection_database_url(
database_url,
database_name,
parent_filename,
database_replica_set,
)
database_url_output = Database.collection_database_url(
database_url,
database_name,
projection_filename,
database_replica_set,
)
metadata_creator = Metadata(database)
projection = Projection(metadata_creator, database_url_input,
database_url_output)
projection.create(parent_filename, projection_filename, projection_fields)
return (
jsonify({
MESSAGE_RESULT:
MICROSERVICE_URI_GET + projection_filename +
MICROSERVICE_URI_GET_PARAMS
}),
HTTP_STATUS_CODE_SUCCESS_CREATED,
)
def create_tsne():
database = Database(os.environ[DATABASE_URL],
os.environ[DATABASE_REPLICA_SET],
os.environ[DATABASE_PORT], os.environ[DATABASE_NAME])
request_validator = UserRequest(database)
request_errors = analyse_request_errors(request_validator,
request.json[PARENT_FILENAME_NAME],
request.json[TSNE_FILENAME_NAME],
request.json[LABEL_NAME])
if request_errors is not None:
return request_errors
database_url_input = Database.collection_database_url(
os.environ[DATABASE_URL],
os.environ[DATABASE_NAME],
request.json[PARENT_FILENAME_NAME],
os.environ[DATABASE_REPLICA_SET],
)
thread_pool.submit(tsne_async_processing, database_url_input,
request.json[LABEL_NAME],
request.json[TSNE_FILENAME_NAME])
return (
jsonify({
MESSAGE_RESULT:
MICROSERVICE_URI_GET + request.json[TSNE_FILENAME_NAME]
}),
HTTP_STATUS_CODE_SUCCESS_CREATED,
)
def create_projection():
parent_filename = request.json[PARENT_FILENAME_NAME]
projection_filename = request.json[PROJECTION_FILENAME_NAME]
projection_fields = request.json[FIELDS_NAME]
request_errors = analyse_request_errors(request_validator, parent_filename,
projection_filename,
projection_fields)
if request_errors is not None:
return request_errors
database_url_input = Database.collection_database_url(
database_url,
database_name,
parent_filename,
database_replica_set,
)
database_url_output = Database.collection_database_url(
database_url,
database_name,
projection_filename,
database_replica_set,
)
metadata_creator = Metadata(database)
projection = Projection(metadata_creator, database_url_input,
database_url_output)
projection.create(parent_filename, projection_filename, projection_fields)
return (
jsonify({
MESSAGE_RESULT:
f'{MICROSERVICE_URI_GET}{projection_filename}'
f'{MICROSERVICE_URI_GET_PARAMS}'
}),
HTTP_STATUS_CODE_SUCCESS_CREATED,
)
def create_model():
train_filename = request.json[TRAINING_FILENAME]
test_filename = request.json[TEST_FILENAME]
classifiers_name = request.json[CLASSIFIERS_NAME]
request_errors = analyse_request_errors(request_validator, train_filename,
test_filename, classifiers_name)
if request_errors is not None:
return request_errors
database_url_training = Database.collection_database_url(
database_url,
database_name,
train_filename,
database_replica_set,
)
database_url_test = Database.collection_database_url(
database_url,
database_name,
test_filename,
database_replica_set,
)
builder = Builder(database, metadata_creator, spark_session)
builder.build(request.json[MODELING_CODE_NAME], classifiers_name,
train_filename, test_filename, database_url_training,
database_url_test)
return (
jsonify({
MESSAGE_RESULT:
create_prediction_files_uri(classifiers_name, test_filename)
}),
HTTP_STATUS_CODE_SUCCESS_CREATED,
)
