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WOA.py
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WOA.py
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import numpy as np
import random
from copy import deepcopy
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
import math,time,sys
from matplotlib import pyplot
import pandas as pd
from datetime import datetime
from functools import partial
import seaborn as sns
from sklearn.metrics import roc_auc_score
from sklearn.neural_network import MLPClassifier
from sklearn.naive_bayes import GaussianNB
MaxIter = 70
pop_size = 8
omega = 0.99
def initialise(partCount, dim, trainX, testX, trainy, testy):
population=np.zeros((partCount,dim))
minn = 1
maxx = math.floor(0.5*dim)
if maxx<minn:
maxx = minn + 1
#not(c[i].all())
for i in range(partCount):
random.seed(i**3 + 10 + time.time() )
no = random.randint(minn,maxx)
if no == 0:
no = 1
random.seed(time.time()+ 100)
pos = random.sample(range(0,dim-1),no)
for j in pos:
population[i][j]=1
return population
def fitness(agent, trainX, testX, trainy, testy):
# print(agent)
cols=np.flatnonzero(agent)
# print(cols)
val=1
if np.shape(cols)[0]==0:
return val
clf=KNeighborsClassifier(n_neighbors=5)
#clf=MLPClassifier(alpha=0.001, hidden_layer_sizes=(1000,500,100),max_iter=2000,random_state=4)
train_data=trainX[:,cols]
test_data=testX[:,cols]
clf.fit(train_data,trainy)
val=1-clf.score(test_data,testy)
#in case of multi objective []
set_cnt=sum(agent)
set_cnt=set_cnt/np.shape(agent)[0]
val=omega*val+(1-omega)*set_cnt
return val
def test_accuracy(agent, trainX, testX, trainy, testy):
cols=np.flatnonzero(agent)
val=1
if np.shape(cols)[0]==0:
return val
# clf = RandomForestClassifier(n_estimators=300)
#clf=MLPClassifier(alpha=0.001, hidden_layer_sizes=(1000,500,100),max_iter=2000,random_state=4)
clf=KNeighborsClassifier(n_neighbors=5)
# clf=MLPClassifier( alpha=0.01, max_iterno=1000) #hidden_layer_sizes=(1000,500,100)
#cross=4
#test_size=(1/cross)
#X_train, X_test, y_train, y_test = train_test_split(trainX, trainy, stratify=trainy,test_size=test_size)
train_data=trainX[:,cols]
test_data=testX[:,cols]
clf.fit(train_data,trainy)
val=clf.score(test_data,testy)
return val
def onecnt(agent):
return sum(agent)
def sigmoid(gamma):
if gamma < 0:
return 1 - 1/(1 + math.exp(gamma))
else:
return 1/(1 + math.exp(-gamma))
def WOA(dataset):
df = pd.read_csv(dataset)
a, b = np.shape(df)
data = df.values[:,0:b-1]
label = df.values[:,b-1]
dimension = data.shape[1]
cross = 5
test_size = (1/cross)
trainX, testX, trainy, testy = train_test_split(data, label,stratify=label ,test_size=test_size,random_state=(7+17*int(time.time()%1000)))
clf=KNeighborsClassifier(n_neighbors=5)
clf.fit(trainX,trainy)
val=clf.score(testX,testy)
whole_accuracy = val
print("Total Acc: ",val)
pop = initialise(pop_size, dimension, trainX, testX, trainy, testy)
fit = []
for i in range(pop_size):
fit.append(fitness(pop[i], trainX, testX, trainy, testy))
ind = np.argsort(fit)
gbest = pop[ind[0]].copy()
gbest_fit = fit[ind[0]].copy()
for n in range(MaxIter):
a = 2 - 2 * n / (MaxIter - 1) # linearly decreased from 2 to 0
for j in range(pop_size):
r = np.random.rand()
A = 2 * a * r - a
C = 2 * r
l = np.random.uniform(-1, 1)
p = np.random.rand()
b = 1
if (p < 0.5) :
if np.abs(A) < 1:
D = np.abs(C * gbest - pop[j] )
pop[j] = gbest - A * D
else :
x_rand = pop[np.random.randint(pop_size)]
D = np.abs(C * x_rand - pop[j])
pop[j] = (x_rand - A * D)
else:
D1 = np.abs(gbest - pop[j])
pop[j] = D1 * np.exp(b * l) * np.cos(2 * np.pi * l) + gbest
for i in range(pop_size):
for j in range(dimension):
if (sigmoid(pop[i][j]) > random.random()):
pop[i][j] = 1
else:
pop[i][j] = 0
ind = np.argsort(fit)
bestpop = pop[ind[0]].copy()
bestfit = fit[ind[0]].copy()
testAcc = test_accuracy(bestpop, trainX, testX, trainy, testy)
featCnt = onecnt(bestpop)
#print("best agent: ", bestpop)
print("Test Accuracy: ", testAcc)
print("#Features: ", featCnt)
return testAcc, featCnt, bestpop
datasetlist = ["BreastCancer.csv", "BreastEW.csv", "CongressEW.csv", "Exactly.csv", "Exactly2.csv", "HeartEW.csv", "Ionosphere.csv", "Lymphography.csv", "M-of-n.csv", "PenglungEW.csv", "Sonar.csv", "SpectEW.csv", "Tic-tac-toe.csv", "Vote.csv", "Wine.csv", "Zoo.csv","KrVsKpEW.csv", "WaveformEW.csv" ]
for datasetname in datasetlist:
print(datasetname)
accuArr = []
featArr = []
agenArr = []
#start_time = datetime.now()
for i in range(15):
# print(i)
testAcc, featCnt, gbest = WOA(datasetname)
# print(testAcc)
accuArr.append(testAcc)
featArr.append(featCnt)
agenArr.append(gbest)
#time_required = datetime.now() - start_time
maxx = max(accuArr)
k = np.argsort(accuArr)
bagent = agenArr[k[-1]]
currFeat= 20000
for i in range(np.shape(accuArr)[0]):
if accuArr[i]==maxx and featArr[i] < currFeat:
currFeat = featArr[i]
bagent = agenArr[i]
datasetname = datasetname.split('.')[0]
print(datasetname)
print(maxx,currFeat)
print(bagent)