输出结果
设计思路
核心代码
#(4)交叉验证
for ixval in range(nxval):
idxTest = [a for a in range(nrow) if a%nxval == ixval%nxval]
idxTrain = [a for a in range(nrow) if a%nxval != ixval%nxval]
xTrain = numpy.array([xNormalized[r] for r in idxTrain])
xTest = numpy.array([xNormalized[r] for r in idxTest])
labelTrain = numpy.array([labelNormalized[r] for r in idxTrain])
labelTest = numpy.array([labelNormalized[r] for r in idxTest])
alphas, coefs, _ = enet_path(xTrain, labelTrain,l1_ratio=0.8, fit_intercept=False, return_models=False)
if ixval == 0:
pred = numpy.dot(xTest, coefs)
yOut = labelTest
else:
#accumulate predictions累积预测
yTemp = numpy.array(yOut)
yOut = numpy.concatenate((yTemp, labelTest), axis=0)
#accumulate predictions
predTemp = numpy.array(pred)
pred = numpy.concatenate((predTemp, numpy.dot(xTest, coefs)), axis = 0)
#三处采样
P = len(idxPos) #P = Positive cases
N = nrow - P #N = Negative cases
#第52处采样
TP = tpr[52] * P #TP = True positives = tpr * P
FN = P - TP #FN = False negatives = P - TP
FP = fpr[52] * N #FP = False positives = fpr * N
TN = N - FP #TN = True negatives = N - FP
print('52：Threshold Value = ', thresh[52])
print('TP = ', TP, 'FP = ', FP)
print('FN = ', FN, 'TN = ', TN)
#第104处采样
TP = tpr[104] * P
FN = P - TP
FP = fpr[104] * N
TN = N - FP
print('104：Threshold Value = ', thresh[104])
print('TP = ', TP, 'FP = ', FP)
print('FN = ', FN, 'TN = ', TN)
#第156处采样
TP = tpr[156] * P
FN = P - TP
FP = fpr[156] * N
TN = N - FP
print('156：Threshold Value = ', thresh[156])
print('TP = ', TP, 'FP = ', FP)
print('FN = ', FN, 'TN = ', TN)
