simulation_controller.py

import instance_generator
import simulation
import analyzer
import pandas as pd
import numpy as np
import random
from utils.utils import *
from cli.cli import get_args
from configuration.config_manager import ConfigManager

config_manager = ConfigManager()

def main():
    # Get cli args
    kargs = get_args()
    print(kargs)
    instance = kargs["instance"]
    
    # Final dataframe containing data for each experiment of the simulation
    final_df = pd.DataFrame()

    # Create folder with timestamp as name for storing
    # simulation data
    dir_path = create_timestamp_folder(config_manager.SIMULATION_CONTROLLER_ARCHIVE_PATH)

    if instance == "":
        # 1) Generate instance configuration
        print("> STEP 1 - Generating instance configuration...")
        instance_generator.main()
        # Move instance generator output to final folder
        copy_dir(config_manager.OUTPUT_INSTANCE_PATH, dir_path)
    else:
        print("> STEP 1 - Skip -- instance passed as param: {}...".format(instance))
        copy_file(instance, dir_path)

    # Before simulations starts, remove all agent logs file from base foulder
    remove_dir_content(config_manager.SIMULATION_AGENT_LOGGING_BASE_PATH)

    # Execute each instance for a predefined number of times (ex. 5)
    for i in range(0, config_manager.NUMBER_OF_SIMULATION_EXECUTION):

        # Generate a random seed for the experiment
        # Note: this is a very important point for experiment reproduction
        # Is very important set seed for both library (random and np)
        seed = np.random.randint(0, 4096)
        random.seed(seed)
        np.random.seed(seed)

        # 2) Single simulation based on configuration file generated before
        print("> STEP 2 - Simulation of instance...")
        simulation.main(instance)

        #time.sleep(2)

        # 3) Analyze simulation output
        print("> STEP 3 - Analyze output...")
        analyzer.main()

        # Create a dir for each iteration of the simulation
        # Move analyzer output files to final foulder (separated for each iteration)
        path = dir_path.joinpath("iteration_{}".format(i))
        os.makedirs(path)
        copy_dir(config_manager.ANALYZER_OUTPUT_PATH, path)
        
        # Copy to this foulder also simulation results (weights for each agent)
        # Also clean src dir of all content (avoiding file overwriting)
        copy_dir(config_manager.SIMULATION_AGENT_LOGGING_BASE_PATH, path)
        remove_dir_content(config_manager.SIMULATION_AGENT_LOGGING_BASE_PATH)

        #time.sleep(2)

        # 4) Load analyzer output file that contains index for comparison
        print("> STEP 4 - Load Index df...")
        df = pd.read_csv(config_manager.INDEX_COMPARISON_FILE,
                         delimiter='\t', header=0, index_col=0)

        df.index.name = "strategy"
        df["seed"] = [seed] * len(list(df.index.values))
        df.set_index(["seed", df.index], inplace=True)
        
        print(df)
        
        if final_df.empty:
            final_df = df
        else:
            final_df = pd.concat([final_df, df])

        # print(final_df)
        # time.sleep(5)

    # 5) Export final results comparison table
    print("> STEP 5 - Export final results table...")
    print(final_df)

    final_df.to_csv(config_manager.SIMULATION_CONTROLLER_OUTPUT_FILE, sep='\t', encoding='utf-8')

    # Move instance generator output to final folder
    copy_dir(config_manager.SIMULATION_CONTROLLER_OUTPUT_PATH, dir_path)

    with open(dir_path.joinpath(config_manager.SIMULATION_CONTROLLER_ARCHIVE_COMPARISON_FILE_NAME), "w") as f:
        # Print mean result of comparison index after 5 executions
        for s in config_manager.STRATEGIES:
            print("---------------------------------------------")
            f.write("---------------------------------------------\n")
            print("> Strategy {}".format(s))
            f.write("> Strategy {}\n".format(s))
            res = final_df.query("strategy == @s")

            for index in config_manager.INDEX_TO_MEAN_FOR_COMPARISON_FOR_TXT_FILE:
                #print(res[index])
                val = res[index].mean()
                print("     > {}: {:0.2f}".format(index, val))
                f.write("     > {}: {:0.2f}\n".format(index, val))

    # Zip archive foulder with all previous gathered data
    zip_foulder(dir_path, dir_path)

    # Remove dir previously zipped
    remove_dir_with_content(dir_path)

# Call main program.
if __name__ == "__main__":
    main()