def main(filename, scaler, MAXFEATURENUM, **svmParameter):
# 获取基本参数
kernel = svmParameter["kernel"]
C = svmParameter["C"]
gamma = svmParameter["gamma"]
# 读取数据,Data1一定是多数类
dataSet1, labels1, dataSet2, labels2 = basef.readData(filename, scaler)
# 鲁棒测试开关
flag = 0
if flag == 1:
pickoutArray = range(len(labels1))
pickoutNum = round(len(labels1) * 0.1)
if len(labels2) < pickoutNum:
dataSet2 = dataSet2[:]
labels2 = labels2[:]
else:
PICKdata = []
PICKlabels = []
x = 0
for i in pickoutArray:
if random.randrange(0, 2) == 1 and x <= pickoutNum:
PICKdata.append(dataSet2[i])
PICKlabels.append(labels2[i])
x += 1
dataSet2 = np.array(PICKdata[:])
labels2 = np.array(PICKlabels[:])
# print "step 2: 预处理数据..."
# print "\tstep2.1: 计算指标信息增益"
initArray = en.featureSample(dataSet1, labels1, dataSet2, labels2, MAXFEATURENUM)
print "初始化指标:", initArray
preTestX = np.vstack([dataSet1, dataSet2])
preLabels = np.hstack([labels1, labels2])
# print "\tstep2.2: 平衡数据"
# Todo:此处可以直接调用遗传算法模块了 -- By nevin47
data = (dataSet1, labels1, dataSet2, labels2, initArray)
arrayValue = [0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 1]
train_X, train_label = Hyper.balanceDataforGA(arrayValue, *data)
# print "step 3: 训练..."
clf = HSVM.trainSVM(train_X, train_label, kernel=kernel, C=C, gamma=gamma)
# print "step 4: 测试..."
pre = HSVM.testSVM(preTestX, clf)
proba = HSVM.testSVMwithProb(preTestX, clf)
tt = np.array(preLabels, dtype="float64")
print "Final G-means:", basef.testSample(pre, tt)
print "\nPRO:", proba, "\n"
print "\nPRE:", pre, "\n"
print basef.testSampleShow(pre, tt)
return basef.testSampleShow(pre, tt)
def main(filename, testfilename, scaler, MAXFEATURENUM, **svmParameter):
kernel = svmParameter['kernel']
C = svmParameter['C']
gamma = svmParameter['gamma']
dataSet1, labels1, dataSet2, labels2 = basef.readData(filename, scaler)
initArray = en.featureSample(dataSet1, labels1, dataSet2, labels2, MAXFEATURENUM)
#preTestX = np.vstack([dataSet1,dataSet2])
#preLabels = np.hstack([labels1,labels2])
preTestX = basef.readTestData(testfilename, scaler)
# Todo:此处可以直接调用遗传算法模块了 -- By nevin47
data = (dataSet1, labels1, dataSet2, labels2, initArray)
arrayValue = [0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 1]
train_X, train_label = Hyper.balanceDataforGA(arrayValue,*data)
clf = HSVM.trainSVM(train_X, train_label, kernel=kernel, C=C, gamma= gamma)
pre = HSVM.testSVM(preTestX , clf)
proba = HSVM.testSVMwithProb(preTestX, clf)
return proba, pre, initArray
# if __name__ == "__main__":
# # demo
# filename1 = '/Users/nevin47/Desktop/Project/Academic/Code/Python/SVM/UnbalancedDataSVM/DataSet/test/wpbc.csv' # 设置读取文件
# filename = '/Users/nevin47/Desktop/Project/Academic/Code/Python/SVM/UnbalancedDataSVM/DataSet/CreditOriginData2.csv'
# # filename = '/Users/nevin47/Desktop/Project/Academic/Code/Python/SVM/UnbalancedDataSVM/DataSet/Heart2.csv'
# scaler = 1 # 决定是否归一化数据
# MAXFEATURENUM = 5 # 设置指标离散最大值
# SUMG = []
# SUMF = []
# for i in range(30):
# tempG,tempF = main(filename, scaler, MAXFEATURENUM, kernel='rbf', C=15.0, gamma= 1)
# SUMG.append(tempG)
# SUMF.append(tempF)
# print "AVG-G: %f,AVG-F %f",sum(SUMG)/30.0,sum(SUMF)/30.0
#
# # test
#
# # print dataSet1
def main(filename, scaler, MAXFEATURENUM, **svmParameter):
# 获取基本参数
kernel = svmParameter['kernel']
C = svmParameter['C']
gamma = svmParameter['gamma']
print "step 1: 读取数据..."
cloudReader = csv.reader(file("/Users/nevin47/Desktop/Project/Academic/Code/Python/SVM/cloudSVM/Dataset/cloud.csv",'rb'))
Cloud = []
Origin = []
for i in cloudReader:
Cloud.append(i)
originReader = csv.reader(file("/Users/nevin47/Desktop/Project/Academic/Code/Python/SVM/cloudSVM/Dataset/Origin.csv",'rb'))
for j in originReader:
Origin.append(j)
Cloud = np.array(Cloud)
Origin = np.array(Origin,dtype='float64')
for i in range(10):
testSample = Origin[:,i]
# print testSample
lowIndex = []
highIndex = []
x = 0
for index,score in enumerate(testSample):
if score > 3:
highIndex.append(index)
x += 1
elif score < 3 and score != 0:
lowIndex.append(index)
highCloud = []
lowCloud = []
# 导入云数据
for m in lowIndex:
lowCloud.append(Cloud[m])
for n in highIndex:
highCloud.append(Cloud[n])
highCloud = np.array(highCloud)
lowCloud = np.array(lowCloud)
if len(highCloud) > len(lowCloud):
highlabels = [1 for i in range(len(highCloud))]
lowlabels = [-1 for i in range(len(lowCloud))]
dataSet1, labels1, dataSet2, labels2 = highCloud,highlabels,lowCloud,lowlabels
else:
highlabels = [-1 for i in range(len(highCloud))]
lowlabels = [1 for i in range(len(lowCloud))]
dataSet1, labels1, dataSet2, labels2 = lowCloud,lowlabels,highCloud,highlabels
initArray = en.featureSample(dataSet1, labels1, dataSet2, labels2, MAXFEATURENUM)
preTestX = np.vstack([dataSet1,dataSet2])
preLabels = np.hstack([labels1,labels2])
# GMM = []
# for i in range(500):
# ii = i + 1
# tempi = ii/500.0
# arrayValue = [0.1,0.1,1,0.1]
# arrayValue[1] = tempi
# print "step2.2: 平衡数据"
# data = (dataSet1, labels1, dataSet2, labels2, initArray)
#
# train_X, train_label = Hyper.balanceDataforGA(arrayValue,*data)
#
# print "step 3: 训练..."
# clf = HSVM.trainSVM(train_X, train_label, kernel=kernel, C=C, gamma= gamma)
#
# print "step 4: 测试..."
# pre = HSVM.testSVM(preTestX , clf)
# tt = np.array(preLabels, dtype="float64")
# Gm = basef.testSample(pre, tt)
# GMM.append(Gm)
# pl.plot(range(500),GMM)
# pl.show()
print "\tstep2.2: 平衡数据"
# Todo:此处可以直接调用遗传算法模块了 -- By nevin47
data = (dataSet1, labels1, dataSet2, labels2, initArray)
arrayValue = [1,1,1,1,1,1,1]
train_X, train_label = Hyper.balanceDataforGA(arrayValue,*data)
# train_X, train_label = Hyper.balanceData(dataSet1, labels1, dataSet2, labels2, initArray)
print "step 3: 训练..."
clf = HSVM.trainSVM(train_X, train_label, kernel=kernel, C=C, gamma= gamma)
print "step 4: 测试..."
pre = HSVM.testSVM(preTestX , clf)
trainLabel = np.array(preLabels, dtype="float64")
#######
testCount = co.Counter(trainLabel)
allN = testCount[1]
allP = testCount[-1]
testResult = trainLabel - pre
FN = 0
FP = 0
for index, num in enumerate(testResult):
if (num == 2):
FN += 1
elif (num == -2):
FP += 1
TP = allP - FP
TN = allN - FN
if (TP+FN) == 0:
TPR = 0
else:
TPR = float(TP) / (TP + FN)
TNR = float(TN) / (TN + FP)
precision = float(TP) / (TP + FP)
Gmeans = math.sqrt(TPR * TNR)
if (precision+TPR) == 0:
Fmeasure = 0
else:
Fmeasure = 2 * TPR * precision / (TPR + precision)
print Gmeans,Fmeasure