def __generate_model(self, data, task_id):
"""
Start train a model
:param data: Training dataset.
:param task_id: The id of the training task.
"""
xgb_obj = xgboosting.XGBoosting()
# pylint: disable=unused-variable
ret_code, ret_data = xgb_obj.xgb_train(data, task_id)
current_timestamp = int(time.time())
train_op_obj = train_op.TrainOperation()
if ret_code == 0:
train_status = "complete"
params = {
"task_id": task_id,
"end_time": current_timestamp,
"status": train_status,
"model_name": task_id + "_model"
}
else:
train_status = "failed"
params = {
"task_id": task_id,
"end_time": current_timestamp,
"status": train_status,
"model_name": ""
}
train_op_obj.update_model_info(params)
def __init__(self):
self.sample_op_obj = sample_op.SampleOperation()
self.anomaly_op_obj = anomaly_op.AbnormalOperation()
self.iforest_obj = isolation_forest.IForest()
self.ewma_obj = ewma.Ewma()
self.polynomial_obj = polynomial_interpolation.PolynomialInterpolation()
self.statistic_obj = statistic.Statistic()
self.supervised_obj = xgboosting.XGBoosting()