from sklearn.datasets import load_iris
from sklearn.metrics import roc_auc_score
from sklearn.ensemble import RandomForestClassifier    
iris = load_iris() #loading dataset
X, y = iris.data, iris.target
y_scores = []
for i in range(len(set(y))):
    y_true = (y == i)
    y_prob = clf.predict_proba(X)[:, i]
    score = roc_auc_score(y_true, y_prob)
    y_scores.append(score)
print("Macro-averaged AUC:", sum(y_scores) / len(y_scores))