ROC Curve With Leave-One-Out Cross Validation In Sklearn

I want to plot a ROC curve of a classifier using leave-one-out cross validation. It seems that a similar question has been asked here but without any answer. In another question he

Solution 1:

I believe the code is correct and the splitting too. I've added a few lines for validation purposes of both the implementation and the results:

from sklearn.model_selection import cross_val_score,cross_val_predict,  KFold,  LeaveOneOut, StratifiedKFold
from sklearn.metrics import roc_curve, auc
from sklearn import datasets

# Import some data to play with
iris = datasets.load_iris()
X_svc = iris.data
y = iris.target
X_svc, y = X_svc[y != 2], y[y != 2]

clf = SVC(kernel='linear', class_weight='balanced', probability=True, random_state=0)
kf = LeaveOneOut()
if kf.get_n_splits(X_svc) == len(X_svc):
    print("They are the same length, splitting correct")
else:
    print("Something is wrong")
all_y = []
all_probs=[]
for train, test in kf.split(X_svc, y):
    all_y.append(y[test])
    all_probs.append(clf.fit(X_svc[train], y[train]).predict_proba(X_svc[test])[:,1])
all_y = np.array(all_y)
all_probs = np.array(all_probs)
#print(all_y) #For validation 
#print(all_probs) #For validation

fpr, tpr, thresholds = roc_curve(all_y,all_probs)
print(fpr, tpr, thresholds) #For validation
roc_auc = auc(fpr, tpr)
plt.figure(1, figsize=(12,6))
plt.plot(fpr, tpr, lw=2, alpha=0.5, label='LOOCV ROC (AUC = %0.2f)' % (roc_auc))
plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='k', label='Chance level', alpha=.8)
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.grid()
plt.show()

The If line is meant to only make sure that the splitting is made n times, where n is the number of observations for the given dataset. This is because as the documentation states, LeaveOneOut works the same as Kfold(n_splits=n) and LeaveOneOut(p=1). Also when printing the predicted proba values they were good, making sense of the curve. Congratz on your 1.00AUC!