f15329c9f0e06cb0babcf2efed758161d3f87dab,examples/ocsvm_example.py,,,#,13

Before Change

    y_test_scores = clf.decision_function(X_test)  // outlier scores

    // evaluate and print the results
    print("{clf_name} Train ROC:{roc}, precision @ rank n:{prn}".format(
        clf_name=clf_name,
        roc=np.round(roc_auc_score(y_train, y_train_scores), decimals=4),
        prn=np.round(precision_n_scores(y_train, y_train_scores), decimals=4)))

    print("{clf_name} Test ROC:{roc}, precision @ rank n:{prn}".format(
        clf_name=clf_name,
        roc=np.round(roc_auc_score(y_test, y_test_scores), decimals=4),
        prn=np.round(precision_n_scores(y_test, y_test_scores), decimals=4)))

    // visualize the results
    visualize(clf_name, X_train, y_train, X_test, y_test, y_train_pred,
              y_test_pred, save_figure=False)

After Change

    // evaluate and print the results
    print("\nOn Training Data:")
    evaluate_print(clf_name, y_train, y_train_scores)
    print("\nOn Test Data:")
    evaluate_print(clf_name, y_test, y_test_scores)

    // visualize the results
    visualize(clf_name, X_train, y_train, X_test, y_test, y_train_pred,

Instances