如何在python中使用图例和AUC分数在一个图中绘制多个ROC曲线？

Question

我正在建造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个ROC曲线,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

像这样的东西...

#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 3

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函数绘制了分类器的 roc_curve。因此，如果我们plot_roc_curve在没有指定参数的情况下使用两次，ax它将绘制两个图表。因此，在这里我们将第一个图存储在figure变量中并访问其轴并提供给下一个plot_roc_curve函数，以便仅显示第一个图的轴的图。

Answer 4

只需将模型添加到列表中，即可在一个图中绘制多个ROC曲线。希望这对您有用！

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