def test_iforest_performance():

    # Generate train/test data
    rng = check_random_state(2)
    X = 0.3 * rng.randn(120, 2)
    X_train = np.r_[X + 2, X - 2]
    X_train = X[:100]

    # Generate some abnormal novel observations
    X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))
    X_test = np.r_[X[100:], X_outliers]
    y_test = np.array([0] * 20 + [1] * 20)

    # fit the model
    clf = IsolationForest(max_samples=100, random_state=rng).fit(X_train)

    # predict scores (the lower, the more normal)
    y_pred = clf.predict(X_test)

    # check that there is at most 6 errors (false positive or false negative)
    assert_greater(roc_auc_score(y_test, y_pred), 0.98)
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dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/ensemble/tests/test_iforest.py/test_iforest_performance--crossover with--dataset/ETHPy150Open treeio/treeio/treeio/projects/forms.py/TaskForm.old_save