run_e2eaiok.py

from e2eAIOK.utils.hydroautolearner import HydroAutoLearner
import argparse
import sys
import pathlib


def parse_args(args):
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--model_name',
        type=str,
        required=True,
        help='could be in-stock model name or udm(user-define-model)')
    parser.add_argument(
        '--data_path',
        type=str,
        default="/home/vmagent/app/dataset/pipeline_test/",
        help='Dataset path')
    parser.add_argument(
        '--conf',
        type=str,
        default='conf/e2eaiok_defaults.conf',
        help='e2eaiok defaults configuration')
    parser.add_argument(
        '--custom_result_path',
        type=str,
        default=str(pathlib.Path(__file__).parent.absolute()),
        help='custom result path')
    parser.add_argument(
        '--executable_python',
        type=str,
        default='',
        help='user env python path')
    parser.add_argument(
        '--program',
        type=str,
        default='',
        help='user defined train.py')
    parser.add_argument(
        '--enable_sigopt',
        dest="enable_sigopt",
        action="store_true",
        default=False,
        help='if enable sigopt')
    parser.add_argument(
        '--no_model_cache',
        dest="enable_model_cache",
        action="store_false",
        default=True,
        help='if disable model cache')
    parser.add_argument(
        '--interactive',
        dest="interative",
        action="store_true",
        help='enable interative mode')
    return parser.parse_args(args).__dict__


def main(input_args):
    learner = HydroAutoLearner(input_args)
    learner.submit()

    model = learner.get_best_model()
    print("\nWe found the best model! Here is the model explaination")
    model.explain()


if __name__ == '__main__':
    input_args = parse_args(sys.argv[1:])
    main(input_args)