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
