def __init__(self, path, size, m, dim=2, M=255):
self.path = path
self.size = size
self.dim = dim
self.m = m
self.M = M
self.histogram = BAT.Histogram(path)
def selector(algo, func_details, popSize, Iter):
function_name = func_details[0]
lb = func_details[1]
ub = func_details[2]
dim = func_details[3]
if (algo == 0):
x = pso.PSO(getattr(benchmarks, function_name), lb, ub, dim, popSize,
Iter)
if (algo == 1):
x = mvo.MVO(getattr(benchmarks, function_name), lb, ub, dim, popSize,
Iter)
if (algo == 2):
x = gwo.GWO(getattr(benchmarks, function_name), lb, ub, dim, popSize,
Iter)
if (algo == 3):
x = mfo.MFO(getattr(benchmarks, function_name), lb, ub, dim, popSize,
Iter)
if (algo == 4):
x = cs.CS(getattr(benchmarks, function_name), lb, ub, dim, popSize,
Iter)
if (algo == 5):
x = bat.BAT(getattr(benchmarks, function_name), lb, ub, dim, popSize,
Iter)
if (algo == 6):
x = woa.WOA(getattr(benchmarks, function_name), lb, ub, dim, popSize,
Iter)
if (algo == 7):
x = ffa.FFA(getattr(benchmarks, function_name), lb, ub, dim, popSize,
Iter)
if (algo == 8):
x = ssa.SSA(getattr(benchmarks, function_name), lb, ub, dim, popSize,
Iter)
return x
def optimize(func, dim, optimization_algorithm, number_of_runs=51):
global problem
problem['func_num'] = func
problem['dimension'] = dim
if optimization_algorithm['pso']:
print("Now dealing with PSO %sD %sF" % (dim, func))
stats_of_pso, best_pso = PSO.PSO(number_of_runs, problem, test_flags)
print("PSO %sD %sF: %s" % (dim, func, best_pso))
if optimization_algorithm['bison']:
print("Now dealing with Bison %sD %sF" % (dim, func))
improvements, best_bison = BasicBison.bison_algorithm(
number_of_runs, problem, test_flags)
print("Bison %sD %sF: %s" % (dim, func, best_bison))
if optimization_algorithm['cs']:
print("Now dealing with CS %sD %sF" % (dim, func))
stats_of_cs, best_cs = CS.CS(number_of_runs, problem, test_flags)
print("CS %sD %sF: %s" % (dim, func, best_cs))
if optimization_algorithm['bat']:
print("Now dealing with BAT %sD %sF" % (dim, func))
stats_of_bat, best_bat = BAT.BAT(number_of_runs, problem, test_flags)
print("BAT %sD %sF: %s" % (dim, func, best_bat))
if optimization_algorithm['ffa']:
print("Now dealing with FFA %sD %sF" % (dim, func))
stats_of_ffa, best_ffa = FFA.FFA(number_of_runs, problem, test_flags)
print("FFA %sD %sF: %s" % (dim, func, best_ffa))
print("Yay!~")
def selector(algo, func_details, popSize, Iter):
function_name = func_details[0]
lb = func_details[1]
ub = func_details[2]
dim = 30
if (algo == 'PSO'):
x = pso.PSO(getattr(cec2005, function_name), lb, ub, dim, popSize,
Iter)
if (algo == 'SSA'):
x = ssa.SSA(getattr(cec2005, function_name), lb, ub, dim, popSize,
Iter)
if (algo == 'GOA'):
x = goa.GOA(getattr(cec2005, function_name), lb, ub, dim, popSize,
Iter)
if (algo == 'IGOA'):
x = igoa.IGOA(getattr(cec2005, function_name), lb, ub, dim, popSize,
Iter)
if (algo == 'MVO'):
x = mvo.MVO(getattr(cec2005, function_name), lb, ub, dim, popSize,
Iter)
if (algo == 'GWO'):
x = gwo.GWO(getattr(cec2005, function_name), lb, ub, dim, popSize,
Iter)
if (algo == 'MFO'):
x = mfo.MFO(getattr(cec2005, function_name), lb, ub, dim, popSize,
Iter)
if (algo == 'CS'):
x = cs.CS(getattr(cec2005, function_name), lb, ub, dim, popSize, Iter)
if (algo == 'BAT'):
x = bat.BAT(getattr(cec2005, function_name), lb, ub, dim, popSize,
Iter)
if (algo == 'WOA'):
x = woa.WOA(getattr(cec2005, function_name), lb, ub, dim, popSize,
Iter)
if (algo == 'FFA'):
x = ffa.FFA(getattr(cec2005, function_name), lb, ub, dim, popSize,
Iter)
return x
def selector(algo, func_details, popSize, Iter, trainDataset, testDataset,
actv):
function_name = func_details[0]
lb = func_details[1]
ub = func_details[2]
Dataset_train = trainDataset
Dataset_test = testDataset
numRowsTrain = numpy.shape(Dataset_train)[
0] # number of instances in the train dataset
numInputsTrain = numpy.shape(
Dataset_train)[1] - 1 #number of features in the train dataset
numRowsTest = numpy.shape(Dataset_test)[
0] # number of instances in the test dataset
numInputsTest = numpy.shape(
Dataset_test)[1] - 1 #number of features in the test dataset
trainInput = Dataset_train[0:numRowsTrain, 0:-1]
trainOutput = Dataset_train[0:numRowsTrain, -1]
testInput = Dataset_test[0:numRowsTest, 0:-1]
testOutput = Dataset_test[0:numRowsTest, -1]
#number of hidden neurons
HiddenNeurons = numInputsTrain * 2 + 1
net = nl.net.newff([[0, 1]] * numInputsTrain, [HiddenNeurons, 1])
if (actv == 1):
net = nl.net.newff(
[[0, 1]] * numInputsTrain, [HiddenNeurons, 1],
[nl.trans.LogSig(), nl.trans.LogSig()])
if (actv == 2):
net = nl.net.newff(
[[0, 1]] * numInputsTrain, [HiddenNeurons, 1],
[nl.trans.SatLinPrm(1, 0, 1),
nl.trans.SatLinPrm(1, 0, 1)])
dim = (numInputsTrain * HiddenNeurons) + (2 * HiddenNeurons) + 1
if (algo == 12):
x = adam.adamse(getattr(costNN, function_name), lb, ub, dim, popSize,
Iter, trainInput, trainOutput, net)
if (algo == 0):
x = pso.PSO(getattr(costNN, function_name), lb, ub, dim, popSize, Iter,
trainInput, trainOutput, net)
if (algo == 1):
x = mvo.MVO(getattr(costNN, function_name), lb, ub, dim, popSize, Iter,
trainInput, trainOutput, net)
if (algo == 2):
x = gwo.GWO(getattr(costNN, function_name), lb, ub, dim, popSize, Iter,
trainInput, trainOutput, net)
if (algo == 3):
x = cs.CS(getattr(costNN, function_name), lb, ub, dim, popSize, Iter,
trainInput, trainOutput, net)
if (algo == 4):
x = bat.BAT(getattr(costNN, function_name), lb, ub, dim, popSize, Iter,
trainInput, trainOutput, net)
if (algo == 5):
x = de.DE(getattr(costNN, function_name), lb, ub, dim, popSize, Iter,
trainInput, trainOutput, net)
if (algo == 6):
x = ga.GA(getattr(costNN, function_name), lb, ub, dim, popSize, Iter,
trainInput, trainOutput, net)
if (algo == 7):
x = fa.FFA(getattr(costNN, function_name), lb, ub, dim, popSize, Iter,
trainInput, trainOutput, net)
if (algo == 8):
x = bbo.BBO(getattr(costNN, function_name), lb, ub, dim, popSize, Iter,
trainInput, trainOutput, net)
if (algo == 9):
printAcc = []
printAcc2 = []
x = solution()
timerStart = time.time()
x.startTime = time.strftime("%Y-%m-%d-%H-%M-%S")
if (function_name == "costNN"):
net.trainf.defaults['trainf'] = nl.error.MSE()
elif (function_name == "costNN4"):
net.trainf.defaults['trainf'] = nl.error.CEE()
else:
return x
net.trainf = nl.train.train_gd
newOutput = [[x] for x in trainOutput]
newOutput = numpy.asarray(newOutput)
e = net.train(trainInput, newOutput, epochs=Iter * popSize)
timerEnd = time.time()
x.optimizer = "BP"
x.objfname = function_name
x.popnum = 0
x.endTime = time.strftime("%Y-%m-%d-%H-%M-%S")
x.executionTime = timerEnd - timerStart
x.convergence = e
pred = net.sim(trainInput).reshape(len(trainOutput))
pred = numpy.round(pred).astype(int)
trainOutput = trainOutput.astype(int)
pred = numpy.clip(pred, 0, 1)
ConfMatrix = confusion_matrix(trainOutput, pred)
ConfMatrix1D = ConfMatrix.flatten()
printAcc.append(accuracy_score(trainOutput, pred, normalize=True))
classification_results = numpy.concatenate((printAcc, ConfMatrix1D))
x.trainAcc = classification_results[0]
x.trainTP = classification_results[1]
x.trainFN = classification_results[2]
x.trainFP = classification_results[3]
x.trainTN = classification_results[4]
pred = net.sim(testInput).reshape(len(testOutput))
pred = numpy.round(pred).astype(int)
testOutput = testOutput.astype(int)
pred = numpy.clip(pred, 0, 1)
ConfMatrix = confusion_matrix(testOutput, pred)
ConfMatrix1D = ConfMatrix.flatten()
printAcc2.append(accuracy_score(testOutput, pred, normalize=True))
classification_results2 = numpy.concatenate((printAcc2, ConfMatrix1D))
x.testAcc = classification_results2[0]
x.testTP = classification_results2[1]
x.testFN = classification_results2[2]
x.testFP = classification_results2[3]
x.testTN = classification_results2[4]
return x
if (algo == 10):
printAcc = []
printAcc2 = []
x = solution()
timerStart = time.time()
x.startTime = time.strftime("%Y-%m-%d-%H-%M-%S")
if (function_name == "costNN"):
net.trainf.defaults['trainf'] = nl.error.MSE()
elif (function_name == "costNN4"):
net.trainf.defaults['trainf'] = nl.error.CEE()
else:
return x
net.trainf = nl.train.train_gdx
newOutput = [[x] for x in trainOutput]
newOutput = numpy.asarray(newOutput)
e = net.train(trainInput, newOutput, epochs=Iter * popSize)
timerEnd = time.time()
x.endTime = time.strftime("%Y-%m-%d-%H-%M-%S")
x.optimizer = "BPMA"
x.objfname = function_name
x.popnum = 0
x.executionTime = timerEnd - timerStart
x.convergence = e
pred = net.sim(trainInput).reshape(len(trainOutput))
pred = numpy.round(pred).astype(int)
trainOutput = trainOutput.astype(int)
pred = numpy.clip(pred, 0, 1)
ConfMatrix = confusion_matrix(trainOutput, pred)
ConfMatrix1D = ConfMatrix.flatten()
printAcc.append(accuracy_score(trainOutput, pred, normalize=True))
classification_results = numpy.concatenate((printAcc, ConfMatrix1D))
x.trainAcc = classification_results[0]
x.trainTP = classification_results[1]
x.trainFN = classification_results[2]
x.trainFP = classification_results[3]
x.trainTN = classification_results[4]
pred = net.sim(testInput).reshape(len(testOutput))
pred = numpy.round(pred).astype(int)
testOutput = testOutput.astype(int)
pred = numpy.clip(pred, 0, 1)
ConfMatrix = confusion_matrix(testOutput, pred)
ConfMatrix1D = ConfMatrix.flatten()
printAcc2.append(accuracy_score(testOutput, pred, normalize=True))
classification_results2 = numpy.concatenate((printAcc2, ConfMatrix1D))
x.testAcc = classification_results2[0]
x.testTP = classification_results2[1]
x.testFN = classification_results2[2]
x.testFP = classification_results2[3]
x.testTN = classification_results2[4]
return x
if (algo == 11):
x = solution()
printAcc = []
printAcc2 = []
if (function_name == "costNN4"):
if (actv == 0):
timerStart = time.time()
x.startTime = time.strftime("%Y-%m-%d-%H-%M-%S")
clf = MLPClassifier(hidden_layer_sizes=HiddenNeurons,
activation='tanh',
max_iter=Iter * popSize,
learning_rate_init=0.01,
n_iter_no_change=7500).fit(
trainInput, trainOutput)
timerEnd = time.time()
x.endTime = time.strftime("%Y-%m-%d-%H-%M-%S")
x.optimizer = "Adam"
x.objfname = function_name
x.popnum = 0
x.executionTime = timerEnd - timerStart
x.convergence = clf.loss_curve_
pred = clf.predict(trainInput)
ConfMatrix = confusion_matrix(trainOutput, pred)
ConfMatrix1D = ConfMatrix.flatten()
printAcc.append(
accuracy_score(trainOutput, pred, normalize=True))
classification_results = numpy.concatenate(
(printAcc, ConfMatrix1D))
x.trainAcc = classification_results[0]
x.trainTP = classification_results[1]
x.trainFN = classification_results[2]
x.trainFP = classification_results[3]
x.trainTN = classification_results[4]
pred = clf.predict(testInput)
ConfMatrix = confusion_matrix(testOutput, pred)
ConfMatrix1D = ConfMatrix.flatten()
printAcc2.append(
accuracy_score(testOutput, pred, normalize=True))
classification_results2 = numpy.concatenate(
(printAcc2, ConfMatrix1D))
x.testAcc = classification_results2[0]
x.testTP = classification_results2[1]
x.testFN = classification_results2[2]
x.testFP = classification_results2[3]
x.testTN = classification_results2[4]
return x
elif (actv == 1):
timerStart = time.time()
x.startTime = time.strftime("%Y-%m-%d-%H-%M-%S")
clf = MLPClassifier(hidden_layer_sizes=HiddenNeurons,
activation='logistic',
max_iter=Iter * popSize,
learning_rate_init=0.01,
n_iter_no_change=7500).fit(
trainInput, trainOutput)
timerEnd = time.time()
x.endTime = time.strftime("%Y-%m-%d-%H-%M-%S")
x.optimizer = "Adam"
x.objfname = function_name
x.popnum = 0
x.executionTime = timerEnd - timerStart
x.convergence = clf.loss_curve_
pred = clf.predict(trainInput)
ConfMatrix = confusion_matrix(trainOutput, pred)
ConfMatrix1D = ConfMatrix.flatten()
printAcc.append(
accuracy_score(trainOutput, pred, normalize=True))
classification_results = numpy.concatenate(
(printAcc, ConfMatrix1D))
x.trainAcc = classification_results[0]
x.trainTP = classification_results[1]
x.trainFN = classification_results[2]
x.trainFP = classification_results[3]
x.trainTN = classification_results[4]
pred = clf.predict(testInput)
ConfMatrix = confusion_matrix(testOutput, pred)
ConfMatrix1D = ConfMatrix.flatten()
printAcc2.append(
accuracy_score(testOutput, pred, normalize=True))
classification_results2 = numpy.concatenate(
(printAcc2, ConfMatrix1D))
x.testAcc = classification_results2[0]
x.testTP = classification_results2[1]
x.testFN = classification_results2[2]
x.testFP = classification_results2[3]
x.testTN = classification_results2[4]
return x
elif (actv == 2):
timerStart = time.time()
x.startTime = time.strftime("%Y-%m-%d-%H-%M-%S")
clf = MLPClassifier(hidden_layer_sizes=HiddenNeurons,
activation='relu',
max_iter=Iter * popSize,
learning_rate_init=0.01,
n_iter_no_change=7500).fit(
trainInput, trainOutput)
timerEnd = time.time()
x.endTime = time.strftime("%Y-%m-%d-%H-%M-%S")
x.optimizer = "Adam"
x.objfname = function_name
x.popnum = 0
x.executionTime = timerEnd - timerStart
x.convergence = clf.loss_curve_
pred = clf.predict(trainInput)
ConfMatrix = confusion_matrix(trainOutput, pred)
ConfMatrix1D = ConfMatrix.flatten()
printAcc.append(
accuracy_score(trainOutput, pred, normalize=True))
classification_results = numpy.concatenate(
(printAcc, ConfMatrix1D))
x.trainAcc = classification_results[0]
x.trainTP = classification_results[1]
x.trainFN = classification_results[2]
x.trainFP = classification_results[3]
x.trainTN = classification_results[4]
pred = clf.predict(testInput)
ConfMatrix = confusion_matrix(testOutput, pred)
ConfMatrix1D = ConfMatrix.flatten()
printAcc2.append(
accuracy_score(testOutput, pred, normalize=True))
classification_results2 = numpy.concatenate(
(printAcc2, ConfMatrix1D))
x.testAcc = classification_results2[0]
x.testTP = classification_results2[1]
x.testFN = classification_results2[2]
x.testFP = classification_results2[3]
x.testTN = classification_results2[4]
return x
else:
return x
# Evaluate MLP classification model based on the training set
trainClassification_results = 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]
return x
def selector(algo, func_details, popSize, Iter, trainDataset, testDataset):
function_name = func_details[0]
lb = func_details[1]
ub = func_details[2]
DatasetSplitRatio = 2 / 3
dataTrain = "datasets/" + trainDataset
dataTest = "datasets/" + testDataset
Dataset_train = numpy.loadtxt(open(dataTrain, "rb"),
delimiter=",",
skiprows=0)
Dataset_test = numpy.loadtxt(open(dataTest, "rb"),
delimiter=",",
skiprows=0)
numRowsTrain = numpy.shape(Dataset_train)[
0] # number of instances in the train dataset
numInputsTrain = numpy.shape(
Dataset_train)[1] - 1 #number of features in the train dataset
numRowsTest = numpy.shape(Dataset_test)[
0] # number of instances in the test dataset
numInputsTest = numpy.shape(
Dataset_test)[1] - 1 #number of features in the test dataset
trainInput = Dataset_train[0:numRowsTrain, 0:-1]
trainOutput = Dataset_train[0:numRowsTrain, -1]
testInput = Dataset_test[0:numRowsTest, 0:-1]
testOutput = Dataset_test[0:numRowsTest, -1]
#number of hidden neurons
HiddenNeurons = numInputsTrain * 2 + 1
net = nl.net.newff([[0, 1]] * numInputsTrain, [HiddenNeurons, 1])
dim = (numInputsTrain * HiddenNeurons) + (2 * HiddenNeurons) + 1
if (algo == 0):
x = pso.PSO(getattr(costNN, function_name), lb, ub, dim, popSize, Iter,
trainInput, trainOutput, net)
if (algo == 1):
x = mvo.MVO(getattr(costNN, function_name), lb, ub, dim, popSize, Iter,
trainInput, trainOutput, net)
if (algo == 2):
x = gwo.GWO(getattr(costNN, function_name), lb, ub, dim, popSize, Iter,
trainInput, trainOutput, net)
if (algo == 3):
x = mfo.MFO(getattr(costNN, function_name), lb, ub, dim, popSize, Iter,
trainInput, trainOutput, net)
if (algo == 4):
x = cs.CS(getattr(costNN, function_name), lb, ub, dim, popSize, Iter,
trainInput, trainOutput, net)
if (algo == 5):
x = bat.BAT(getattr(costNN, function_name), lb, ub, dim, popSize, Iter,
trainInput, trainOutput, net)
# Evaluate MLP classification model based on the training set
trainClassification_results = 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]
return x
def selector(algo, func_details, popSize, Iter, completeData):
function_name = func_details[0]
lb = func_details[1]
ub = func_details[2]
DatasetSplitRatio = 0.34 #Training 66%, Testing 34%
DataFile = "datasets/" + completeData
data_set = numpy.loadtxt(open(DataFile, "rb"), delimiter=",", skiprows=0)
numRowsData = numpy.shape(data_set)[
0] # number of instances in the dataset
numFeaturesData = numpy.shape(
data_set)[1] - 1 #number of features in the dataset
dataInput = data_set[0:numRowsData, 0:-1]
dataTarget = data_set[0:numRowsData, -1]
trainInput, testInput, trainOutput, testOutput = train_test_split(
dataInput, dataTarget, test_size=DatasetSplitRatio, random_state=1)
#
# numRowsTrain=numpy.shape(trainInput)[0] # number of instances in the train dataset
# numFeaturesTrain=numpy.shape(trainInput)[1]-1 #number of features in the train dataset
#
# numRowsTest=numpy.shape(testInput)[0] # number of instances in the test dataset
# numFeaturesTest=numpy.shape(testInput)[1]-1 #number of features in the test dataset
#
dim = numFeaturesData
if (algo == 0):
x = pso.PSO(getattr(fitnessFUNs, function_name), lb, ub, dim, popSize,
Iter, trainInput, trainOutput)
if (algo == 1):
x = mvo.MVO(getattr(fitnessFUNs, function_name), lb, ub, dim, popSize,
Iter, trainInput, trainOutput)
if (algo == 2):
x = gwo.GWO(getattr(fitnessFUNs, function_name), lb, ub, dim, popSize,
Iter, trainInput, trainOutput)
if (algo == 3):
x = mfo.MFO(getattr(fitnessFUNs, function_name), lb, ub, dim, popSize,
Iter, trainInput, trainOutput)
if (algo == 4):
x = woa.WOA(getattr(fitnessFUNs, function_name), lb, ub, dim, popSize,
Iter, trainInput, trainOutput)
if (algo == 5):
x = ffa.FFA(getattr(fitnessFUNs, function_name), lb, ub, dim, popSize,
Iter, trainInput, trainOutput)
if (algo == 6):
x = bat.BAT(getattr(fitnessFUNs, function_name), lb, ub, dim, popSize,
Iter, trainInput, trainOutput)
# Evaluate MLP classification model based on the training set
# trainClassification_results=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)
reducedfeatures = []
for index in range(0, dim):
if (x.bestIndividual[index] == 1):
reducedfeatures.append(index)
reduced_data_train_global = trainInput[:, reducedfeatures]
reduced_data_test_global = testInput[:, reducedfeatures]
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(reduced_data_train_global, trainOutput)
# Compute the accuracy of the prediction
target_pred_train = knn.predict(reduced_data_train_global)
acc_train = float(accuracy_score(trainOutput, target_pred_train))
x.trainAcc = acc_train
target_pred_test = knn.predict(reduced_data_test_global)
acc_test = float(accuracy_score(testOutput, target_pred_test))
x.testAcc = acc_test
#print('Test set accuracy: %.2f %%' % (acc * 100))
#x.testTP=testClassification_results[1]
#x.testFN=testClassification_results[2]
#x.testFP=testClassification_results[3]
#x.testTN=testClassification_results[4]
return x
