from module import Preprocessor, Trainer, Predictor
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Process commandline')
parser.add_argument('--config', type=str, required=True)
parser.add_argument('--log_level', type=str, default="INFO")
args = parser.parse_args()
FORMAT = '%(asctime)-15s %(message)s'
logging.basicConfig(format=FORMAT, level=args.log_level)
logger = logging.getLogger('global_logger')
with open(args.config, 'r') as config_file:
try:
config = yaml.safe_load(config_file)
preprocessor = Preprocessor(config['preprocessing'], logger)
data_x, data_y, train_x, train_y, validate_x, validate_y, test_x = preprocessor.process(
)
trainer = Trainer(config['training'], logger, preprocessor.classes)
trainer.fit(train_x, train_y)
# Returns value error: Classification metrics can't handle a mix of multilabel-indicator and binary target
# Therefore quote out these for now to have the program work
#accuracy, cls_report = trainer.validate(validate_x, validate_y)
#logger.info("accuracy:{}".format(accuracy))
#logger.info("\n{}\n".format(cls_report))
model = trainer.fit(data_x, data_y)
predictor = Predictor(config['predict'], logger, model)
probs = predictor.predict_prob(test_x)
predictor.save_result(preprocessor.test_ids, probs)
except yaml.YAMLError as err:
logger.warning('Config file err: {}'.format(err))
# Preparing optimizer
optimizer = create_optimizer(args, model)
# Preparing criterions
criterions = {
"class_1": create_criterions(args, num_class_1, device),
"class_2": create_criterions(args, num_class_2, device),
}
# Preparing metrics
metrics = {
"class_1": create_metrics(args),
"class_2": create_metrics(args)
}
# Preparing trainer
trainer = Trainer(
model=model,
optim=optimizer,
criterions=criterions,
metric=metrics,
scheduler=None,
train_dl=data["train_dataloader"],
val_dl=data["valid_dataloader"],
writer=writer,
save_dir=args.model_dir,
device=device,
)
trainer.fit(args.epochs)