How to plot multiple ROC curves in one plot with legend and AUC scores in python?

I am building 2 models.

Model 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)


Model 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)


How do i plot both the ROC curves in one plot , with a legend & text of AUC scores for each model ?

2 Answers
2

Try adapting this to your data:

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)


Just by adding the models to the list will plot multiple ROC curves in one plot. Hopefully this works for you!

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


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