class_indicators.py

# -*- coding: utf-8 -*-
import json
import numpy as np
import sys

# Retrieve cumulated number of vehicle (CNV) and cumulated travel time
# (CTT) per class.

# See src/vrptw_to_json.py
VRPTW_PRECISION = 1000

CLASSES = ["C1", "C2", "R1", "R2", "RC1", "RC2"]


def s_round(v, d):
    if d == 0:
        return str(int(v))
    else:
        return str(round(v, d))


def get_class(file_name):
    number_index = 1

    if file_name[0:2].upper() == "RC":
        number_index = 2

    return file_name[0:number_index].upper() + file_name[number_index]


def log_indicators(BKS, files):
    CNV = {}
    CTT = {}
    BKS_CNV = {}
    BKS_CTT = {}

    for c in CLASSES:
        CNV[c] = []
        CTT[c] = []
        BKS_CNV[c] = []
        BKS_CTT[c] = []

    computing_times = []

    for f in files:
        instance = f[0 : f.rfind("_sol.json")]
        instance = instance[instance.rfind("/") + 1 :]

        if instance not in BKS and instance + "_distance" not in BKS:
            continue

        with open(f, "r") as sol_file:
            solution = json.load(sol_file)

        if solution["code"] != 0 or len(solution["unassigned"]) > 0:
            continue

        instance_class = get_class(instance)

        if instance + "_distance" in BKS:
            # Specific entry for approach targeting distance as optimization
            # objective.
            indicators = BKS[instance + "_distance"]
        else:
            indicators = BKS[instance]

        BKS_CNV[instance_class].append(indicators["solved_with_vehicles"])
        BKS_CTT[instance_class].append(indicators["best_known_cost"])

        CNV[instance_class].append(len(solution["routes"]))
        CTT[instance_class].append(solution["summary"]["cost"])

        computing_times.append(
            solution["summary"]["computing_times"]["loading"]
            + solution["summary"]["computing_times"]["solving"]
        )

    # Output
    print("," + ",".join(CLASSES) + "," + "Total")

    cumulated_BKS_CNV = 0
    bks_cnv_line = "BKS CNV,"
    for c in CLASSES:
        bks_cnv_line += s_round(np.mean(BKS_CNV[c]), 2) + ","
        cumulated_BKS_CNV += sum(BKS_CNV[c])
    print(bks_cnv_line + s_round(cumulated_BKS_CNV, 0))

    cumulated_BKS_CTT = 0
    bks_ctt_line = "BKS CTT,"
    for c in CLASSES:
        bks_ctt_line += s_round(np.mean(BKS_CTT[c]), 2) + ","
        cumulated_BKS_CTT += sum(BKS_CTT[c])
    print(bks_ctt_line + s_round(cumulated_BKS_CTT, 0))

    cumulated_CNV = 0
    cnv_line = "CNV,"
    for c in CLASSES:
        cnv_line += s_round(np.mean(CNV[c]), 2) + ","
        cumulated_CNV += sum(CNV[c])
    print(cnv_line + s_round(cumulated_CNV, 0))

    cumulated_CTT = 0
    ctt_line = "CTT,"
    for c in CLASSES:
        ctt_line += s_round(np.mean(CTT[c]) / VRPTW_PRECISION, 2) + ","
        cumulated_CTT += sum(CTT[c])
    print(ctt_line + s_round(cumulated_CTT / VRPTW_PRECISION, 0))

    # Computing time percentiles
    print(",")
    ct_percentiles = np.percentile(computing_times, [0, 10, 25, 50, 75, 90, 100])
    print(",Computing times")
    print("Average," + s_round(np.mean(computing_times), 0))
    print(",")

    titles = [
        "Min",
        "First decile",
        "Lower quartile",
        "Median",
        "Upper quartile",
        "Ninth decile",
        "Max",
    ]
    for i in range(len(titles)):
        print(titles[i] + "," + s_round(ct_percentiles[i], 0))


if __name__ == "__main__":
    # First argument if the best known solution file.
    with open(sys.argv[1], "r") as sol_file:
        bks = json.load(sol_file)

    # Remaining arguments are computed solution files to use.
    log_indicators(bks, sys.argv[2:])