def test_rolling_multi_fbeta():
def tail(iterable, n):
return collections.deque(iterable, maxlen=n)
fbeta = metrics.Rolling(
metric=metrics.MultiFBeta(betas={0: 0.25, 1: 1, 2: 4}, weights={0: 1, 1: 1, 2: 2}),
window_size=3,
)
n = fbeta.window_size
sk_fbeta = sk_metrics.fbeta_score
y_true = [0, 1, 2, 2, 2]
y_pred = [0, 1, 0, 2, 1]
for i, (yt, yp) in enumerate(zip(y_true, y_pred)):
fbeta.update(yt, yp)
if i >= 2:
sk_y_true, sk_y_pred = tail(y_true[: i + 1], n), tail(y_pred[: i + 1], n)
fbeta_0, _, _ = sk_fbeta(sk_y_true, sk_y_pred, beta=0.25, average=None)
_, fbeta_1, _ = sk_fbeta(sk_y_true, sk_y_pred, beta=1, average=None)
_, _, fbeta_2 = sk_fbeta(sk_y_true, sk_y_pred, beta=4, average=None)
multi_fbeta = fbeta_0 * 1 + fbeta_1 * 1 + fbeta_2 * 2
multi_fbeta /= 1 + 1 + 2
assert math.isclose(fbeta.get(), multi_fbeta)
def test_multi_fbeta():
fbeta = metrics.MultiFBeta(betas={0: 0.25, 1: 1, 2: 4}, weights={0: 1, 1: 1, 2: 2})
sk_fbeta = sk_metrics.fbeta_score
y_true = [0, 1, 2, 2, 2]
y_pred = [0, 1, 0, 2, 1]
for i, (yt, yp) in enumerate(zip(y_true, y_pred)):
fbeta.update(yt, yp)
if i >= 2:
fbeta_0, _, _ = sk_fbeta(y_true[: i + 1], y_pred[: i + 1], beta=0.25, average=None)
_, fbeta_1, _ = sk_fbeta(y_true[: i + 1], y_pred[: i + 1], beta=1, average=None)
_, _, fbeta_2 = sk_fbeta(y_true[: i + 1], y_pred[: i + 1], beta=4, average=None)
multi_fbeta = fbeta_0 * 1 + fbeta_1 * 1 + fbeta_2 * 2
multi_fbeta /= 1 + 1 + 2
assert math.isclose(fbeta.get(), multi_fbeta)