NeuroLGP/dCGPANN.py at main · Fergal-Stapleton/NeuroLGP · GitHub

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"""
Author: Fergal Stapleton
"""

import sys
import os
import json
import random
import numpy as np
from src import linearGenProgTree
from problem import neuroevolution
# randomGenerationNeuroLGP can be used to test method is better than random,
from algorithm import singleObjNeuroLGP
#from algorithm import randomGenerationNeuroLGP as singleObjNeuroLGP

def read_json(file_path):
    with open(file_path, "r") as f:
        return json.load(f)

def main():
    print("Welcome to GenProgMO library")
    paramsLoad = read_json('experimentCGPANN.json')
    print(json.dumps(paramsLoad, indent=4))
    # First and foremost 'what is our problem domain?''
    problem_type = paramsLoad["PROBLEM DOMAIN"]["PROBLEM TYPE"]
    experiment = paramsLoad["EXPERIMENTAL SETUP"]
    try:
        problemCode = paramsLoad["PROBLEM DOMAIN"]["PROBLEM CODE"]
        dataSource = paramsLoad["PROBLEM DOMAIN"]["DATA SOURCE"] # Not really needed, but will leave in for consistency or if needed in future
    except:
        print("Error: one of the problem domain properties has not been specified in experiment.json")
        sys.exit(1)
    try:
        opt = paramsLoad["OPTIMIZATION"]
    except:
        print("Error: one of the optimization properties has not been specified in experiment.json")
        sys.exit(1)

    # Set up class files
    probDom = neuroevolution.CGPANN(run, gp) # problem domain


    if (opt["ALGORITHM"] == 'singleObjNeuroLGP'):
        print("")
        print("...Loading single objective neuroevolution LGP algorithm")
        print("")
        fitnessList = []
        modelList = []
        diversityList = []
        # 30 runs
        for i in range(15):
            alg = singleObjCGPANNSurrogate.Standard(probDom, gp, run, experiment, opt, problemCode)
            alg.evolve()
            fitnessList.append(alg.finalFitness)
            modelList.append(alg.finalProgram)

        print("----------------------")
        print("")
        print("Fitness for every run:")
        print(fitnessList)
        print("")
        print("Mean and std dev for fitness across all runs:")
        print(np.mean(fitnessList))
        print(np.std(fitnessList))
        print("")
        print("Best run fitness:")
        best_run_idx = fitnessList.index(min(fitnessList))
        print(fitnessList[best_run_idx])
        print("Best run model:")

        best_list = [gene[0] for gene in modelList[best_run_idx]]

        pseudo_acc = gp.levenshtein_dist(best_list, probDom.psuedo_best)
        print(pseudo_acc)
        alg.gp.printProgram(modelList[best_run_idx])
        print("")

if __name__ == "__main__":
    random.seed(12)
    main()