main.py

""" Neural Large Neighborhood Search
    Copyright (C) 2020  André Hottung

    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <https://www.gnu.org/licenses/>."""

import logging
import numpy as np
import datetime
import os
import sys
import config
import train
import search
from actor import VrpActorModel
from critic import VrpCriticModel

VERSION = "0.3.0"

if __name__ == '__main__':
    run_id = np.random.randint(10000, 99999)
    config = config.get_config()

    # Creating output directories
    if config.output_path == "":
        config.output_path = os.getcwd()
    now = datetime.datetime.now()
    config.output_path = os.path.join(config.output_path, "runs", f"run_{now.day}.{now.month}.{now.year}_{run_id}")
    os.makedirs(os.path.join(config.output_path, "solutions"))
    os.makedirs(os.path.join(config.output_path, "models"))
    os.makedirs(os.path.join(config.output_path, "search"))

    # Create logger and log run parameters
    logging.basicConfig(
        filename=os.path.join(config.output_path, "log_" + str(run_id) + ".txt"), filemode='w',
        level=logging.INFO, format='[%(levelname)s]%(message)s')
    logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
    logging.info("Started")
    logging.info("Call: {0}".format(''.join(sys.argv)))
    logging.info("Version: {0}".format(VERSION))
    logging.info("PARAMETERS:")
    for arg in sorted(vars(config)):
        logging.info("{0}: {1}".format(arg, getattr(config, arg)))
    logging.info("----------")

    if config.mode == "train":
        actor = VrpActorModel(config.device, hidden_size=config.pointer_hidden_size).to(config.device)
        critic = VrpCriticModel(config.critic_hidden_size).to(config.device)

        model_path = train.train_nlns(actor, critic, run_id, config)
        search.evaluate_batch_search(config, model_path)
    elif config.mode == "eval_batch":
        if config.instance_path and not config.instance_path.endswith(".pkl"):
            raise Exception("Batch mode only supports .pkl instances files.")
        search.evaluate_batch_search(config, config.model_path)
    elif config.mode == "eval_single":
        search.evaluate_single_search(config, config.model_path, config.instance_path)
    else:
        raise Exception("Unknown mode")