test_common.py 文件源码

def test_sample_weight_invariance(n_samples=50):
    random_state = check_random_state(0)

    # binary
    random_state = check_random_state(0)
    y_true = random_state.randint(0, 2, size=(n_samples, ))
    y_pred = random_state.randint(0, 2, size=(n_samples, ))
    y_score = random_state.random_sample(size=(n_samples,))
    for name in ALL_METRICS:
        if (name in METRICS_WITHOUT_SAMPLE_WEIGHT or
                name in METRIC_UNDEFINED_BINARY):
            continue
        metric = ALL_METRICS[name]
        if name in THRESHOLDED_METRICS:
            yield check_sample_weight_invariance, name, metric, y_true, y_score
        else:
            yield check_sample_weight_invariance, name, metric, y_true, y_pred

    # multiclass
    random_state = check_random_state(0)
    y_true = random_state.randint(0, 5, size=(n_samples, ))
    y_pred = random_state.randint(0, 5, size=(n_samples, ))
    y_score = random_state.random_sample(size=(n_samples, 5))
    for name in ALL_METRICS:
        if (name in METRICS_WITHOUT_SAMPLE_WEIGHT or
                name in METRIC_UNDEFINED_BINARY_MULTICLASS):
            continue
        metric = ALL_METRICS[name]
        if name in THRESHOLDED_METRICS:
            yield check_sample_weight_invariance, name, metric, y_true, y_score
        else:
            yield check_sample_weight_invariance, name, metric, y_true, y_pred

    # multilabel indicator
    _, ya = make_multilabel_classification(n_features=1, n_classes=20,
                                           random_state=0, n_samples=100,
                                           allow_unlabeled=False)
    _, yb = make_multilabel_classification(n_features=1, n_classes=20,
                                           random_state=1, n_samples=100,
                                           allow_unlabeled=False)
    y_true = np.vstack([ya, yb])
    y_pred = np.vstack([ya, ya])
    y_score = random_state.randint(1, 4, size=y_true.shape)

    for name in (MULTILABELS_METRICS + THRESHOLDED_MULTILABEL_METRICS +
                 MULTIOUTPUT_METRICS):
        if name in METRICS_WITHOUT_SAMPLE_WEIGHT:
            continue

        metric = ALL_METRICS[name]
        if name in THRESHOLDED_METRICS:
            yield (check_sample_weight_invariance, name, metric, y_true,
                   y_score)
        else:
            yield (check_sample_weight_invariance, name, metric, y_true,
                   y_pred)