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vectorized_optimizer.py
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vectorized_optimizer.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Dec 7 20:44:52 2016
@author: hossam
"""
import csv
import numpy
import time
import vectorized_optimizers.PSO_ as pso
import vectorized_optimizers.MVO_ as mvo
import vectorized_optimizers.GWO as gwo
import vectorized_optimizers.MFO as mfo
import vectorized_optimizers.CS_ as cs
import vectorized_optimizers.BAT_ as bat
import vectorized_optimizers.WOA as woa
import vectorized_optimizers.FFA_ as ffa
import vectorized_optimizers.SSA as ssa
import vectorized_optimizers.GA_ as ga
import vectorized_optimizers.HHO_ as hho
import vectorized_optimizers.SCA as sca
import vectorized_optimizers.JAYA_ as jaya
import vectorized_optimizers.DE_ as de
import csv
import numpy
import time
import os
import neurolab as nl
import vectorized_costNN as costNN
import evaluateNetClassifier as evalNet
import solution
import plot_convergence as conv_plot
import plot_boxplot as box_plot
from sklearn.model_selection import train_test_split
from pathlib import Path
import warnings
warnings.filterwarnings("ignore")
def run(optimizer, datasets, NumOfRuns, params):
#Export results ?
Export=True
#ExportToFile="YourResultsAreHere.csv"
#Automaticly generated file name by date and time
results_directory = time.strftime("%Y-%m-%d-%H-%M-%S")
os.mkdir(results_directory)
ExportToFile=results_directory + "/experiment.csv"
# Check if it works at least once
Flag=False
# CSV Header for for the cinvergence
CnvgHeader=[]
PopulationSize = params["PopulationSize"]
Iterations = params["Iterations"]
for l in range(0,Iterations):
CnvgHeader.append("Iter"+str(l+1))
for j in range (0, len(datasets)): # specfiy the number of the datasets
for i in range (0, len(optimizer)):
for k in range (0,NumOfRuns):
func_details=["costNN",-1,1]
dataset="datasets/"+datasets[j]+".csv"
with open(dataset,"rb") as dataset_v:
dataset_values=numpy.loadtxt(dataset_v,delimiter=",")
X = numpy.array(dataset_values)[:,:-1]
y = numpy.array(dataset_values)[:,-1]
trainInput, testInput, trainOutput, testOutput = train_test_split(X, y, test_size=0.33, random_state=42)
numFeatures=numpy.shape(trainInput)[1]#number of features in the train dataset
#number of hidden neurons
HiddenNeurons = numFeatures*2+1
net = nl.net.newff([[0, 1]]*numFeatures, [HiddenNeurons, 1])
dim=(numFeatures*HiddenNeurons)+(2*HiddenNeurons)+1;
x = selector(optimizer[i], func_details, dim, PopulationSize, Iterations, trainInput,trainOutput,net)
# Evaluate MLP classification model based on the training set
#trainClassification_results=evalNet.evaluateNetClassifier(x,trainInput,trainOutput,net)
ConfMatrix, acc, prec, rec, f1, gm=evalNet.evaluateNetClassifier(x,trainInput,trainOutput,net)
'''
x.trainAcc=trainClassification_results[0]
x.trainTP=trainClassification_results[1]
x.trainFN=trainClassification_results[2]
x.trainFP=trainClassification_results[3]
x.trainTN=trainClassification_results[4]
# Evaluate MLP classification model based on the testing set
testClassification_results=evalNet.evaluateNetClassifier(x,testInput,testOutput,net)
x.testAcc=testClassification_results[0]
x.testTP=testClassification_results[1]
x.testFN=testClassification_results[2]
x.testFP=testClassification_results[3]
x.testTN=testClassification_results[4]
'''
with open(ExportToFile, 'a',newline='\n') as out:
writer = csv.writer(out,delimiter=',')
if (Flag==False): # just one time to write the header of the CSV file
#header= numpy.concatenate([["Optimizer","Dataset","objfname","Experiment","startTime","EndTime","ExecutionTime","trainAcc", "trainTP","trainFN","trainFP","trainTN", "testAcc", "testTP","testFN","testFP","testTN"],CnvgHeader])
header= numpy.concatenate([["Optimizer","Dataset","Experiment","startTime","EndTime","ExecutionTime","ConfMatrix", "Accuracy", "Precision","Recall","F1score","Gmean"],CnvgHeader])
writer.writerow(header)
Flag=True # at least one experiment
#a=numpy.concatenate([[x.optimizer,datasets[j],x.objfname,k+1,x.startTime,x.endTime,x.executionTime,x.trainAcc, x.trainTP,x.trainFN,x.trainFP,x.trainTN, x.testAcc, x.testTP,x.testFN,x.testFP,x.testTN],x.convergence])
a=numpy.concatenate([[x.optimizer,datasets[j],k+1,x.startTime,x.endTime,x.executionTime,ConfMatrix, acc,prec,rec,f1, gm],x.convergence])
writer.writerow(a)
out.close()
conv_plot.run(results_directory, optimizer, datasets, Iterations)
ev_measures=['Accuracy','Gmean']
box_plot.run(results_directory, optimizer, datasets, ev_measures, Iterations)
if (Flag==False): # Faild to run at least one experiment
print("No Optomizer or Cost function is selected. Check lists of available optimizers and cost functions")
def selector(algo, func_details, dim, popSize, Iter, trainInput,trainOutput,net):
function_name=func_details[0]
lb=func_details[1]
ub=func_details[2]
if(algo=="PSO"):
x=pso.PSO( getattr(costNN, function_name),lb,ub,dim,popSize,Iter,trainInput,trainOutput,net)
elif(algo=="MVO"):
x=mvo.MVO(getattr(costNN, function_name),lb,ub,dim,popSize,Iter,trainInput,trainOutput,net)
elif(algo=="GWO"):
x=gwo.GWO( getattr(costNN, function_name),lb,ub,dim,popSize,Iter,trainInput,trainOutput,net)
elif(algo=="MFO"):
x=mfo.MFO(getattr(costNN, function_name),lb,ub,dim,popSize,Iter,trainInput,trainOutput,net)
elif(algo=="CS"):
x=cs.CS(getattr(costNN, function_name),lb,ub,dim,popSize,Iter,trainInput,trainOutput,net)
elif(algo=="BAT"):
x=bat.BAT(getattr(costNN, function_name),lb,ub,dim,popSize,Iter,trainInput,trainOutput,net)
elif(algo=="WOA"):
x=woa.WOA(getattr(costNN, function_name),lb,ub,dim,popSize,Iter,trainInput,trainOutput,net)
elif(algo=="FFA"):
x=ffa.FFA(getattr(costNN, function_name),lb,ub,dim,popSize,Iter,trainInput,trainOutput,net)
elif(algo=="SSA"):
x=ssa.SSA(getattr(costNN, function_name),lb,ub,dim,popSize,Iter,trainInput,trainOutput,net)
elif(algo=="GA"):
x=ga.GA(getattr(costNN, function_name),lb,ub,dim,popSize,Iter,trainInput,trainOutput,net)
elif(algo=="HHO"):
x=hho.HHO(getattr(costNN, function_name),lb,ub,dim,popSize,Iter,trainInput,trainOutput,net)
elif(algo=="SCA"):
x=sca.SCA(getattr(costNN, function_name),lb,ub,dim,popSize,Iter,trainInput,trainOutput,net)
elif(algo=="JAYA"):
x=jaya.JAYA(getattr(costNN, function_name),lb,ub,dim,popSize,Iter,trainInput,trainOutput,net)
elif(algo=="DE"):
x=de.DE(getattr(costNN, function_name),lb,ub,dim,popSize,Iter,trainInput,trainOutput,net)
else:
return None
return x
#####################################################################