ga_script_TSP_data.py

from cifo.algorithm.genetic_algorithm import GeneticAlgorithm
from cifo.custom_problem.travel_salesman_problem import (
    TravelSalesmanProblem
)

from cifo.algorithm.ga_operators import (
    initialize_using_random, initialize_using_hc, initialize_using_greedy, initialize_using_multiple,
    roulettewheel_selection, rank_selection, tournament_selection,
    cycle_crossover, pmx_crossover, order1_crossover, heuristic_crossover, multiple_crossover,
    swap_mutation, insert_mutation, inversion_mutation, scramble_mutation, greedy_mutation,
    multiple_mutation,
    elitism_replacement, standard_replacement 
)    

from cifo.util.observer import LocalSearchObserver
import numpy as np
from copy import deepcopy

from os import listdir, path, mkdir
from os.path import isfile, join
from pandas import pandas as pd

# Problem
#--------------------------------------------------------------------------------------------------
# Decision Variables
input = [
    [0,36.96584005,51.47535512,81.37883643,67.09706382,96.25912034,33.37095504,49.05139087,82.42814829,17.53944615,28.60425589,51.23577205,81.61379496,51.51224221,49.22279957,42.77989519,52.41229411,8.559339301,29.73906448,81.02061035,47.6395033,57.71653277,58.75682446,62.03784071,31.25151882,50.1453012,24.63358841,18.94551955,30.25790504,85.21792873,36.85186187,56.53666255,62.03497797,37.68030795,16.01323158,63.58061167,63.59738826,62.15986186,23.10591688,37.37057763,8.517827435,58.29133094,69.49677904,87.08357383,34.45822295,25.69816455,34.67118709,66.44842139,85.07076451,23.63220219,17.29653852,32.6772139,71.67238993,31.40561027,74.5859657,13.37462792,74.65274849,68.78350664,28.32893632,16.45723427,40.79505789,32.14446993,22.8157847,61.17311404,67.85584464,50.50121842,17.32224642,69.64537022,8.58156977,67.23338249,38.25387274,72.79577656,45.89074932,62.00721071,21.98649411,45.34735754,23.43343436,64.20771523,40.49933354,33.55673111,49.24624472,80.86344475,47.42370856,61.25720927,57.6628477,77.27561713,51.02315985,74.39466341,48.992992,55.93722702],
    [36.96584005,0,61.91619178,85.79358509,56.34475438,68.71598221,4.09671684,36.54433609,93.72104677,44.14412909,26.24710648,19.03207804,64.5753318,27.03707842,25.97304705,6.288581842,24.73396593,45.43488009,16.97199729,53.72595597,62.78665591,62.66049676,29.58502223,68.28070695,61.67825794,38.42329848,60.23006801,37.50720546,54.44740046,55.15306618,73.80628982,47.35539658,66.44844157,73.28046004,52.53609876,42.98509658,56.44437672,45.2803504,59.30269394,31.97786358,45.30930669,27.20468134,63.6108007,55.78010425,70.4261779,15.74337821,47.21292078,74.88684535,92.9748268,16.96133125,31.60291136,5.074596635,85.38521764,54.70583033,61.30430838,47.80276173,69.84275638,76.85850099,65.22136526,46.8267315,22.40937611,57.95619821,14.16705865,38.74170375,47.70704542,62.00538466,43.02981561,36.79869334,36.12941277,45.68338293,28.53063986,47.58183111,67.32112581,65.51898315,56.29357404,67.25267976,59.14111513,28.71109276,28.38619482,36.2971628,12.36567547,89.48057197,69.15888966,32.97194384,70.14129461,88.05203116,67.93350969,78.96061085,46.92748861,30.70635332],
    [51.47535512,61.91619178,0,30.12739296,34.32168728,77.5059462,61.5682967,34.02388454,32.4626629,34.82777207,35.89770585,80.87587171,50.98237406,88.0571181,47.40236359,67.33435037,53.43310071,51.21862313,69.14400337,66.21221522,7.704441461,9.593636341,57.4876327,11.33612019,35.13486982,32.84725272,48.04081167,33.01855188,25.45017471,74.07837052,63.69004377,29.24684645,12.77136775,52.8532566,58.65344852,46.76357494,28.00948541,41.06769324,50.23083385,30.7481669,54.3915391,89.01432619,29.07080451,77.90618437,53.2270194,47.57798198,17.18198031,14.99119164,33.78825151,62.82597756,37.2387574,57.39802441,23.46906511,23.96565857,41.55026146,61.56812262,31.04718986,17.31950789,57.5602719,66.80842471,43.10727162,26.88431343,55.80029958,48.55206336,47.82748521,1.756097224,34.63436043,69.43283815,59.80526894,49.58840516,82.08238552,57.8575155,18.53404505,13.64674759,43.58962278,19.35438187,65.47198371,72.36893294,36.32124112,25.65451483,70.73797899,29.68527208,19.45055536,57.1279573,8.299614076,26.8990232,11.25378185,23.36537322,20.04259069,50.95819548],
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    [77.27561713,88.05203116,26.8990232,14.63480364,46.88289588,88.71627873,88.01687463,56.11258746,5.669493281,59.95979591,62.45048397,106.8140112,59.53548371,114.576373,70.31329117,93.1581319,76.10055527,75.74970281,96.01841079,81.03855073,29.64882139,25.88191364,78.89774663,20.27983563,54.72209858,54.48285095,68.84342907,59.45671402,48.39134637,89.49847722,81.9525446,47.66325406,22.92258114,69.47900477,82.10918015,64.48401737,41.45296053,58.32098096,71.47006697,56.20240316,79.01492666,114.872618,37.39550395,93.72900094,71.0460687,74.24098643,43.90857635,13.31164857,9.465834758,89.72334541,64.00899054,83.73977936,7.980974265,46.98057842,51.09838952,86.05824753,34.40031558,11.68689278,77.71828798,91.73331476,67.76447403,48.22603046,82.65758854,67.64090587,63.73656332,27.3847309,60.01160044,89.61867454,85.78013369,66.2411035,108.9813827,74.04551332,34.59300172,24.32850328,65.37679813,35.46589069,88.047628,94.70404902,60.93838392,52.15867362,95.95500987,5.683607743,33.91636494,77.68886603,19.65437995,0,26.97974103,14.97857497,42.40853172,72.29287347],
    [51.02315985,67.93350969,11.25378185,35.16985029,45.56794452,88.73319996,66.97812302,43.96025898,31.72658997,33.5067342,41.73238314,86.94915876,62.16705935,92.97500279,56.58831363,73.74147312,62.61515247,48.89766393,72.78963632,77.23633225,5.378550746,20.22978013,67.15078614,19.76350972,28.30250066,42.99116066,42.23869129,34.38902089,21.46216613,84.99070991,56.46596008,40.24194483,22.33176473,44.68621014,55.12970979,57.56769405,39.24712929,52.01950572,44.76983144,38.63485985,52.17105166,95.12220892,39.92978536,88.74293168,45.65102071,52.69492461,20.74522732,19.5717282,35.74465473,66.16441741,39.7005891,63.11465616,20.69499333,20.0823774,52.72778282,59.16018946,41.23650836,21.64801254,51.3967389,64.92518711,51.26970785,21.28960774,59.80631258,59.05953908,58.86621746,9.704759042,33.71038334,79.3708149,59.59818726,60.52022003,85.08970749,68.775842,8.719031149,23.60444369,38.51780217,9.650994188,61.14920075,81.43044441,44.74709378,32.1765956,77.8184477,31.45226042,8.986948887,67.09831944,9.230338235,26.97974103,0,29.50300732,30.5018279,60.76073152],
    [74.39466341,78.96061085,23.36537322,7.105278528,32.53430646,73.76075343,79.51886225,44.51508698,19.2460315,58.11563544,54.7871948,97.16755962,44.56357224,105.9332831,58.63999673,83.52018256,64.13596726,74.54581467,88.99142027,66.45315552,29.48409483,17.14009925,66.22941257,12.35688762,57.37445258,42.73204055,70.87679541,55.59717557,48.58666297,74.94274053,85.81985114,34.92764533,12.88793885,74.18636143,82.01341268,50.79050958,27.60817173,44.67043386,73.20408295,47.11933211,77.69869396,105.0326446,22.75515984,79.2117883,75.07121309,66.55062619,39.72769888,9.985902984,14.74462485,83.13998872,59.08873236,75.09180653,19.88297536,47.09707594,36.23113674,84.87963288,19.42378116,8.219114626,80.28447813,90.01889084,57.42771956,49.5782924,75.66060118,54.30171493,49.59142461,24.69874496,57.86357108,76.27576981,82.54613126,52.21937088,102.3195329,59.78441009,38.21238636,13.53482915,66.64825392,39.14618757,88.76861806,82.23610424,50.82046234,44.82411753,85.55561823,12.52539118,38.25852416,64.67745014,20.27363802,14.97857497,29.50300732,0,32.05270077,59.7345962],
    [48.992992,46.92748861,20.04259069,38.86753431,19.51990277,59.53302641,47.62984302,14.1796579,48.00984528,36.74628412,23.98383968,65.14645001,36.1473593,73.93204535,28.11631627,51.48559479,34.04965709,52.05453573,57.8373748,47.0695164,25.51473195,16.53366803,37.64940945,22.13534975,45.54075609,12.85938108,55.17286163,30.76455759,34.06010948,54.61374442,72.12954801,10.608964,19.81636675,63.6736881,60.83958983,27.14007085,14.83138779,21.86093017,56.56158711,15.34843463,54.69959905,73.05849741,20.52229521,58.29305684,62.96171387,35.28543113,19.26223053,29.11383,46.29149459,52.44498896,31.83503047,43.17426343,41.5248005,32.94047349,27.48751916,61.53858402,25.94871876,30.79161077,64.47634147,65.44560243,25.52892228,37.19754047,44.76194754,28.58191281,28.86563831,21.14826194,35.93806269,49.43169982,56.0166392,30.24548289,71.2795562,38.44301646,35.5011783,18.72626696,50.03475107,36.0675194,68.55024758,52.9240558,18.81197066,15.5522142,53.76045237,43.03645924,36.95776743,37.13234029,27.70379843,42.40853172,30.5018279,32.05270077,0,31.06246063],
    [55.93722702,30.70635332,50.95819548,65.57023747,30.04357507,40.51150177,33.87317717,16.93508883,77.69760707,53.02684118,29.06494692,43.31765491,34.37176642,54.72147414,6.721991448,31.66727078,6.231497522,62.7329447,47.21524899,25.17666698,55.48401863,46.97351916,6.627974548,52.2676687,67.95880917,18.21817412,72.77673918,44.9128289,57.28386989,29.54258423,89.17537411,24.81918894,49.39393802,84.26505137,71.54523017,12.86466693,32.75012183,17.00339351,73.04874162,24.51377144,64.09553996,49.90199756,39.35911958,31.955111,82.43336544,30.9872945,43.83605472,59.28961986,74.47897182,45.07329279,41.42506554,30.33094716,72.31921582,56.72916661,32.78967384,69.12109652,45.34639631,60.63102968,80.53919439,70.43006517,15.25700746,61.03081565,38.5835255,8.289088535,17.37015267,51.90214837,51.84508121,18.62853682,59.22554537,15.08448974,59.23325207,17.53654317,64.15406021,47.9643968,67.75881503,64.51401743,79.25640275,22.50473552,18.32054533,32.17150539,30.33065566,71.89758244,65.8376319,6.52305319,58.76340355,72.29287347,60.76073152,59.7345962,31.06246063,0]
]

tsp_decision_variables = {
    "Distances" : np.array(input), #<< Number, Mandatory
    "Item-Name" : [i for i in range(1, len(input[0]))]
        # << String, Optional, The names of the items, can be used to present the solution.
        # It starts at 1 reserving the 0 for the static city
        # We are setting the departure point, so we only have to arrange the n-1 remaining cities
}

# Problem Instance
tsp_problem_instance = TravelSalesmanProblem(
    decision_variables=tsp_decision_variables
)

# Configuration
#--------------------------------------------------------------------------------------------------
parent_selection = roulettewheel_selection

# Dictionaries to create test directories
valid_Init = {initialize_using_random: "rand", initialize_using_hc: "hc", initialize_using_greedy: "greedyI",
              initialize_using_multiple: "mixIB"}

valid_Select = {roulettewheel_selection: "rol", tournament_selection: "tourn", rank_selection: "rank"}

valid_Xover = {cycle_crossover: "cycle", pmx_crossover: "pmx",  order1_crossover: "order1", heuristic_crossover: "heur",
               multiple_crossover: "mixCB"}
                # singlepoint_crossover: "singP" should not be used
valid_Mutation = {swap_mutation: "swap", insert_mutation: "insert", inversion_mutation: "invert",
                  scramble_mutation: "scramble",  greedy_mutation: "greedyM", multiple_mutation: "mixMB"}
                    # single_point_mutation: "singP" should not be used

valid_Replacement = {elitism_replacement: "elit", standard_replacement: "std"}

# Parameters to grid search in a run
test_init = [initialize_using_multiple, initialize_using_hc,  initialize_using_greedy, initialize_using_random]
test_select = [tournament_selection, roulettewheel_selection, rank_selection]
test_xover = [heuristic_crossover, multiple_crossover,  cycle_crossover, pmx_crossover, order1_crossover]
test_mutation = [multiple_mutation, inversion_mutation, multiple_mutation, greedy_mutation, swap_mutation,
                 insert_mutation, scramble_mutation]
test_replacement = [elitism_replacement, standard_replacement]

test_xover_prob = [0.1, 0.9, 0.95, 0.05]
test_mut_prob = [0.95, 0.9, 0.1, 0.05]

test_tournament_size = [15, 10, 5]

# Initial params
params = {
        # params
        "Population-Size"           : 20,
        "Number-of-Generations"     : 1000,
        "Crossover-Probability"     : 0.8,
        "Mutation-Probability"      : 0.8,
        # operators / approaches
        "Initialization-Approach"   : initialize_using_random,
        "Selection-Approach"        : parent_selection,
        "Tournament-Size"           : 5,
        "Crossover-Approach"        : cycle_crossover,
        "Mutation-Approach"         : swap_mutation,
        "Replacement-Approach"      : elitism_replacement
    }


def one_combination():
    """
    Actually runs the algorithm with one set of parameters.
    Names the resume file from parameters of search
    Creates the resume file from all the runs for a set of parameters
    """
    log_base_dir="./log/"       # Base dir for log of initial runs
    if not path.exists(log_base_dir):
        mkdir(log_base_dir)
    all_dir = "./log_all/"      # Base dir for resume the resume files
    if not path.exists(all_dir):
        mkdir(all_dir)

    log_name =  ("I-"    + str(valid_Init.get(params.get("Initialization-Approach"))) +
                "_S-"    + str(valid_Select.get(params.get("Selection-Approach"))) +
                "_C-"    + str(valid_Xover.get(params.get("Crossover-Approach"))) +
                "_M-"    + str(valid_Mutation.get(params.get("Mutation-Approach"))) +
                "_R-"    + str(valid_Replacement.get(params.get("Replacement-Approach"))) +
                "_CP-"   + str((params.get("Crossover-Probability"))) +
                "_MP-"   + str((params.get("Mutation-Probability"))) +
                "_PS-"   + str((params.get("Population-Size"))) +
                "_TS-"   + str((params.get("Tournament-Size"))) +
                "_G-"    + str((params.get("Number-of-Generations")))
                )
    resume_name= f"{all_dir}{log_name}.xlsx"

    # checks if the same run as already been performed, if so, skips it.
    # -----------------
    log_dir = str(log_base_dir) + str(log_name)
    if not path.exists(log_dir):
       mkdir(log_dir)

    runs_made = [f for f in listdir(all_dir) if isfile(join(all_dir, f))]
    if (str(log_name)+".xlsx") in runs_made:
        print (f"Run {log_name}.xlsx already made, skipping")
        return

    # Run the same configuration many times (get distribution)
    #--------------------------------------------------------------------------------------------------
    overall_best_solution = None
    number_of_runs = 50
    for run in range(1, number_of_runs + 1):
        # Genetic Algorithm
        ga = GeneticAlgorithm(
            problem_instance=tsp_problem_instance,
            params=params,
            run=run,
            log_name=log_name,
            log_dir=log_base_dir
            )

        ga_observer = LocalSearchObserver(ga)
        ga.register_observer(ga_observer)
        ga.search()
        ga.save_log()

        # find the best solution over the runs
        if run == 1:
            overall_best_solution = deepcopy(ga.best_solution)
        else:
            if ga.best_solution.fitness < overall_best_solution.fitness:
                overall_best_solution = deepcopy(ga.best_solution)

        print('overall_best_solution: ', overall_best_solution.representation)
        print('overall_best_solution fitness: ', overall_best_solution.fitness)

    # Consolidate the runs
    #--------------------------------------------------------------------------------------------------

    log_files = [f for f in listdir(log_dir) if isfile(join(log_dir, f))]
    print(log_files)

    fitness_runs = []
    columns_name = []
    counter = 0
    generations = []

    for log_name in log_files:
        if log_name.startswith('run_'):
            df = pd.read_excel(log_dir + "/" + log_name)
            fitness_runs.append(list(df.Fitness))
            columns_name.append(log_name.strip(".xslx"))
            counter += 1

            if not generations:
                generations = list(df["Generation"])

    #fitness_sum = [sum(x) for x in zip(*fitness_runs)]

    df = pd.DataFrame(list(zip(*fitness_runs)), columns=columns_name)

    fitness_sd   = list(df.std(axis=1))
    fitness_mean = list(df.mean(axis=1))

    #df["Fitness_Sum"] = fitness_sum
    df["Generation"]  = generations
    df["Fitness_SD"]  = fitness_sd
    df["Fitness_Mean"]  = fitness_mean
    df["Fitness_Lower"] = df["Fitness_Mean"] - 1.96 * df["Fitness_SD"] / (number_of_runs ** 0.5)
    df["Fitness_Upper"] = df["Fitness_Mean"] + 1.96 * df["Fitness_SD"] / (number_of_runs ** 0.5)

    #df.to_excel(log_dir + "/all.xlsx", index=False, encoding='utf-8')
    log_name = ("I-" + str(valid_Init.get(params.get("Initialization-Approach"))) +
                "_S-" + str(
                valid_Select.get(params.get("Selection-Approach"))) +  # this one return None because of .select method
                "_C-" + str(valid_Xover.get(params.get("Crossover-Approach"))) +
                "_M-" + str(valid_Mutation.get(params.get("Mutation-Approach"))) +
                "_R-" + str(valid_Replacement.get(params.get("Replacement-Approach"))) +
                "_CP-" + str((params.get("Crossover-Probability"))) +
                "_MP-" + str((params.get("Mutation-Probability"))) +
                "_PS-" + str((params.get("Population-Size"))) +
                "_TS-" + str((params.get("Tournament-Size"))) +
                "_G-" + str((params.get("Number-of-Generations")))
                )

    # Exporting summary of configuration with best solution
    with pd.ExcelWriter(all_dir + f"{log_name}.xlsx") as writer:
        df.to_excel(writer, sheet_name='Fitness', index=False, encoding='utf-8')
        pd.DataFrame([[overall_best_solution.representation, overall_best_solution.fitness]],
                     columns=["Representation", "Fitness"]).to_excel(writer, sheet_name='Overall_Best_Solution')

# -------------------------------------------------------------------------------------------------
# RUNS Through the Several list of possible parameters
# -------------------------------------------------------------------------------------------------
for init in range(len(test_init)):
    params["Initialization-Approach"] = test_init[init]
    for select in range(len(test_select)):
        params["Selection-Approach"] = test_select[select]
        for xover in range(len(test_xover)):
            params["Crossover-Approach"] = test_xover[xover]
            for mutation in range(len(test_mutation)):
                params["Mutation-Approach"] = test_mutation[mutation]
                for replacement in range(len(test_replacement)):
                    params["Replacement-Approach"] = test_replacement[replacement]
                    for xover_prob in range(len(test_xover_prob)):
                        params["Crossover-Probability"] = test_xover_prob[xover_prob]
                        for mut_prob in range(len(test_mut_prob)):
                            params["Mutation-Probability"] = test_mut_prob[mut_prob]
                            if str(test_select[select]) == str(tournament_selection):
                                for tourn_size in range(len(test_tournament_size)):
                                    params["Tournament-Size"] = test_tournament_size[tourn_size]
                                    one_combination()
                            else:
                                one_combination()