lof_novelty_sklearn.py

clf = LocalOutlierFactor(novelty=True)
clf = clf.fit(X_train)
test_scores = clf.decision_function(X_test)

# LOF in sklearn returns negative decision scores. Multiply by -1 to approximate the original score, otherwise the roc_auc_score function doesn't work.
# Documentation: "Inliers tend to have a LOF score close to 1 (negative_outlier_factor_ close to -1), while outliers tend to have a larger LOF score"
test_scores = -1*test_scores

roc = round(roc_auc_score(y_test, test_scores), ndigits=4)
prn = round(precision_n_scores(y_test, test_scores), ndigits=4)

print(f'{clf_name} ROC:{roc}, precision @ rank n:{prn}')
#>> LOF ROC:0.9656, precision @ rank n:0.8