test_ranking.py 文件源码

def test_roc_nonrepeating_thresholds():
    # Test to ensure that we don't return spurious repeating thresholds.
    # Duplicated thresholds can arise due to machine precision issues.
    dataset = datasets.load_digits()
    X = dataset['data']
    y = dataset['target']

    # This random forest classifier can only return probabilities
    # significant to two decimal places
    clf = ensemble.RandomForestClassifier(n_estimators=100, random_state=0)

    # How well can the classifier predict whether a digit is less than 5?
    # This task contributes floating point roundoff errors to the probabilities
    train, test = slice(None, None, 2), slice(1, None, 2)
    probas_pred = clf.fit(X[train], y[train]).predict_proba(X[test])
    y_score = probas_pred[:, :5].sum(axis=1)  # roundoff errors begin here
    y_true = [yy < 5 for yy in y[test]]

    # Check for repeating values in the thresholds
    fpr, tpr, thresholds = roc_curve(y_true, y_score, drop_intermediate=False)
    assert_equal(thresholds.size, np.unique(np.round(thresholds, 2)).size)