Partial AUC

In [17]:

    

import matplotlib.pyplot as plt

from sklearn import svm, datasets
from sklearn.metrics import roc_auc_score, roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier


    

In [10]:

    

iris = datasets.load_iris()
X = iris.data
y = iris.target


    

In [11]:

    

y


    

    
Out[11]:





array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])

In [12]:

    

# Binarize the output
y = label_binarize(y, classes=[0, 1, 2])
n_classes = y.shape[1]  # count the number of unique class
y[7:9]


    

    
Out[12]:





array([[1, 0, 0],
       [1, 0, 0]])

In [41]:

    

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,
                                                    random_state=410, stratify=y)


    

In [42]:

    

classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,
                                 random_state=410))
y_score = classifier.fit(X_train, y_train).decision_function(X_test)


    

In [58]:

    

# Calculate for each class
fpr = {}
tpr = {}
roc_auc = {}
partial_auc = {}

for i in range(n_classes):
    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])
    partial_auc[i] = round(roc_auc_score(y_test[:, i], y_score[:, i], max_fpr=0.05), 4)


    

In [59]:

    

plt.figure()
lw = 2
plt.plot(fpr[0], tpr[0], color='purple',
         lw=lw, label='auc='+str(roc_auc[0])+', partial_auc='+str(partial_auc[0]))
plt.plot(fpr[1], tpr[1], color='r',
         lw=lw, label='auc='+str(roc_auc[1])+', partial_auc='+str(partial_auc[1]))
plt.plot(fpr[2], tpr[2], color='g',
         lw=lw, label='auc='+str(roc_auc[2])+', partial_auc='+str(partial_auc[2]))
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.axvline(x=0.05, color='grey', 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()