ROC与多个分类器python的交叉验证

Question

我想在同一个图上为多个分类器绘制不同的 ROC，但我不知道如何从其中一些分类器中做到这一点：

这是我的代码的 sn-p：

# Learn to predict each class against the other
classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,
                                 random_state=random_state))
y_score = classifier.fit(X_train, y_train).decision_function(X_test)
n_classes=2
# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
    fpr[i], tpr[i], _ = roc_curve(y_test, y_score)
    roc_auc[i] = auc(fpr[i], tpr[i])

# Compute micro-average ROC curve and ROC area
fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
plt.figure(1)
lw = 1
plt.plot(fpr[1], tpr[1], color='darkorange',
         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[1])

# Learn to predict each class against the other
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
y_score = classifier.fit(X_train, y_train).decision_function(X_test)

# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
    fpr[i], tpr[i], _ = roc_curve(y_test, y_score)
    roc_auc[i] = auc(fpr[i], tpr[i])

# Compute micro-average ROC curve and ROC area
fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])

plt.figure(1)
lw = 1
plt.plot(fpr[1], tpr[1], color='darkblue',
         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[1])
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.show()

代码将编译，但仅打印第二个分类器结果
我的问题是：

1. 如何为不同的分类器绘制多条 ROC 曲线？
2. 如果我想使用一个没有decision_function() 的分类器，我该如何替换它？

谢谢！

Answer 1

对于第 1 点）这对我有用（以 pdf 格式打印结果）

ClassesList = [0,1,2,3]
y = label_binarize(y_test, classes=ClassesList)
n_classes = y.shape[1]

# Compute ROC curve and ROC area for each class
fpr = {}
tpr = {}
roc_auc = {}


for i in range(n_classes):

    fpr[i], tpr[i], _ = metrics.roc_curve(y[:,i], y_score[:, i])
    roc_auc[i] = metrics.auc(fpr[i], tpr[i])


# Compute micro-average ROC curve and ROC area
fpr["micro"], tpr["micro"], _ = metrics.roc_curve(y.ravel(), y_score.ravel())
roc_auc["micro"] = metrics.auc(fpr["micro"], tpr["micro"])

# Plot ROC curves for the multiclass problem

# Compute macro-average ROC curve and ROC area

# First aggregate all false positive rates
all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))

# Then interpolate all ROC curves at this points
mean_tpr = np.zeros_like(all_fpr)
for i in range(n_classes):
    mean_tpr += interp(all_fpr, fpr[i], tpr[i])

# Finally average it and compute AUC
mean_tpr /= n_classes

fpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
roc_auc["macro"] = metrics.auc(fpr["macro"], tpr["macro"])

# Plot all ROC curves
plt.figure()
plt.plot(fpr["micro"], tpr["micro"],
         label='micro-average ROC curve (area = {0:0.2f})'
               ''.format(roc_auc["micro"]),
         color='deeppink', linestyle=':', linewidth=4)

plt.plot(fpr["macro"], tpr["macro"],
         label='macro-average ROC curve (area = {0:0.2f})'
               ''.format(roc_auc["macro"]),
         color='navy', linestyle=':', linewidth=4)

colors = cycle(['aqua', 'darkorange', 'cornflowerblue','red'])
for i, color in zip(range(n_classes), colors):
    plt.plot(fpr[i], tpr[i], color=color, lw=2,
             label='ROC curve of class {0} (area = {1:0.2f})'
             ''.format(i, roc_auc[i]))

plt.plot([0, 1], [0, 1], 'k--', lw=2)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Some extension of Receiver operating characteristic to multi-class')
plt.legend(loc="lower right")

with PdfPages('ROCCurve.pdf') as ROCCurvePdf:
    plt.savefig(ROCCurvePdf, format='pdf')

plt.clf()

关于第 2 点）我不明白你的意思