python - 如何在一个带有图例和AUC分数的图中绘制多条ROC曲线？

Question

我正在构建 2 个模型。

模型 1

modelgb = GradientBoostingClassifier()
modelgb.fit(x_train,y_train)
predsgb = modelgb.predict_proba(x_test)[:,1]
metrics.roc_auc_score(y_test,predsgb, average='macro', sample_weight=None)

模型 2

model = LogisticRegression()
model = model.fit(x_train,y_train)
predslog = model.predict_proba(x_test)[:,1]
metrics.roc_auc_score(y_test,predslog, average='macro', sample_weight=None)

如何在一个图中绘制两条 ROC 曲线，以及每个模型的 AUC 分数的图例和文本？

Answer 1

尝试根据您的数据进行调整：

from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt

plt.figure(0).clf()

pred = np.random.rand(1000)
label = np.random.randint(2, size=1000)
fpr, tpr, thresh = metrics.roc_curve(label, pred)
auc = metrics.roc_auc_score(label, pred)
plt.plot(fpr,tpr,label="data 1, auc="+str(auc))

pred = np.random.rand(1000)
label = np.random.randint(2, size=1000)
fpr, tpr, thresh = metrics.roc_curve(label, pred)
auc = metrics.roc_auc_score(label, pred)
plt.plot(fpr,tpr,label="data 2, auc="+str(auc))

plt.legend(loc=0)

Answer 2

只需将模型添加到列表中，即可在一个图中绘制多条 ROC 曲线。希望这对你有用！

from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import GradientBoostingClassifier
from sklearn import metrics
import matplotlib.pyplot as plt

plt.figure()

# Add the models to the list that you want to view on the ROC plot
models = [
{
    'label': 'Logistic Regression',
    'model': LogisticRegression(),
},
{
    'label': 'Gradient Boosting',
    'model': GradientBoostingClassifier(),
}
]

# Below for loop iterates through your models list
for m in models:
    model = m['model'] # select the model
    model.fit(x_train, y_train) # train the model
    y_pred=model.predict(x_test) # predict the test data
# Compute False postive rate, and True positive rate
    fpr, tpr, thresholds = metrics.roc_curve(y_test, model.predict_proba(x_test)[:,1])
# Calculate Area under the curve to display on the plot
    auc = metrics.roc_auc_score(y_test,model.predict(x_test))
# Now, plot the computed values
    plt.plot(fpr, tpr, label='%s ROC (area = %0.2f)' % (m['label'], auc))
# Custom settings for the plot 
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('1-Specificity(False Positive Rate)')
plt.ylabel('Sensitivity(True Positive Rate)')
plt.title('Receiver Operating Characteristic')
plt.legend(loc="lower right")
plt.show()   # Display

Answer 3

类似这样的...

#ROC Curve
from sklearn.metrics import roc_curve
y_pred_prob1 = classifier1.predict_proba(X_test)[:,1]
fpr1 , tpr1, thresholds1 = roc_curve(Y_test, y_pred_prob1)

y_pred_prob2 = classifier2.predict_proba(X_test)[:,1]
fpr2 , tpr2, thresholds2 = roc_curve(Y_test, y_pred_prob2)


y_pred_prob3 = classifier3.predict_proba(X_test)[:,1]
fpr3 , tpr3, thresholds3 = roc_curve(Y_test, y_pred_prob3)

y_pred_prob4 = classifier4.predict_proba(X_test)[:,1]
fpr4 , tpr4, thresholds4 = roc_curve(Y_test, y_pred_prob4)


plt.plot([0,1],[0,1], 'k--')
plt.plot(fpr1, tpr1, label= "Linear")
plt.plot(fpr2, tpr2, label= "Poly")
plt.plot(fpr3, tpr3, label= "RBF")
plt.plot(fpr4, tpr4, label= "Sigmoid")
plt.legend()
plt.xlabel("FPR")
plt.ylabel("TPR")
plt.title('Receiver Operating Characteristic')
plt.show()

Answer 4

from sklearn.metrics import plot_roc_curve


fig = plot_roc_curve( clf, x_train_bow, y_train)
fig = plot_roc_curve( clf, x_test_bow, y_test, ax = fig.ax_)
fig.figure_.suptitle("ROC curve comparison")
plt.show() 

基本上plot_roc_curve 函数绘制分类器的 roc_curve。因此，如果我们使用plot_roc_curve 两次而不指定ax 参数，它将绘制两个图形。所以这里我们将第一个图形存储在图形变量中并访问它的轴并提供给下一个plot_roc_curve 函数，以便仅显示第一个图形的轴的图。

Answer 5

from sklearn.metrics import plot_roc_curve

classifiers = [log_reg, decision_tree, decision_forest]
ax = plt.gca()
for i in classifiers:
    plot_roc_curve(i, X_test, y_test, ax=ax)