main_uni.py

import os
import argparse
import warnings
from trainers.trainer_uni import cross_domain_trainer
import sklearn.exceptions
import pickle
from sklearn.manifold import TSNE
warnings.filterwarnings("ignore", category=sklearn.exceptions.UndefinedMetricWarning)

parser = argparse.ArgumentParser()


# ========  Experiments Name ================
parser.add_argument('--save_dir',               default='experiments_logs',         type=str, help='Directory containing all experiments')
parser.add_argument('--experiment_description', default='WISDM-RAINCOAT-uni',               type=str, help='Name of your experiment (EEG, HAR, HHAR_SA, ')
# ========= Select the DA methods ============
parser.add_argument('--da_method',              default='RAINCOAT',               type=str, help='DANCE, TFAC')
# ========= Select the DATASET ==============
parser.add_argument('--data_path',              default=r'./data',                  type=str, help='Path containing dataset')
parser.add_argument('--dataset',                default='WISDM',                      type=str, help='Dataset of choice: (WISDM - EEG - HAR - HHAR_SA, Boiler)')
# ========= Select the BACKBONE ==============
parser.add_argument('--backbone',               default='CNN',                      type=str, help='Backbone of choice: (CNN - RESNET18 - TCN)')
# ========= Experiment settings ===============
parser.add_argument('--num_runs',               default=1,                          type=int, help='Number of consecutive run with different seeds')
parser.add_argument('--device',                 default='cuda',                   type=str, help='cpu or cuda')

args = parser.parse_args()

if __name__ == "__main__":

    trainer = cross_domain_trainer(args)
    trainer.train()