def train(model_id, messages, hyper):
print("RETRAINING STARTED (model id: {})".format(model_id))
dtrain = build_train(TRAIN_DATA, DATAPROCESSORS_PATH, model_id, messages)
if hyper == "hyperopt":
# from train.train_hyperopt import LGBOptimizer
from train.train_hyperopt_mlflow import LGBOptimizer
elif hyper == "hyperparameterhunter":
# from train.train_hyperparameterhunter import LGBOptimizer
from train.train_hyperparameterhunter_mlfow import LGBOptimizer
LGBOpt = LGBOptimizer(dtrain, MODELS_PATH)
LGBOpt.optimize(maxevals=2, model_id=model_id)
print("RETRAINING COMPLETED (model id: {})".format(model_id))
def create_data_processor():
print("creating preprocessor...")
dataprocessor = build_train(TRAIN_PATH / 'train.csv', DATAPROCESSORS_PATH)
def create_data_processor():
create_folders()
download_data()
print("creating preprocessor...")
dataprocessor = build_train(TRAIN_PATH / 'train.csv', DATAPROCESSORS_PATH)
def train(model_id, messages):
print("RETRAINING STARTED (model id: {})".format(model_id))
dtrain = build_train(TRAIN_DATA, DATAPROCESSORS_PATH, model_id, messages)
LGBOpt = LGBOptimizer(dtrain, MODELS_PATH)
LGBOpt.optimize(maxevals=10, model_id=model_id)
print("RETRAINING COMPLETED (model id: {})".format(model_id))