Fix matcher recall threshold

[zhiqiangdon]

Issue #, if available:

Description of changes:
Normalize the accumulated recall to (0, 1) to satisfy the stopping threshold.

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[github-actions]

Job PR-2652-c79bfd5 is done.
Docs are uploaded to http://autogluon-staging.s3-website-us-west-2.amazonaws.com/PR-2652/c79bfd5/index.html

[sxjscience]

Consider to add a test-case via the hit_rate implemented in torchmetrics: https://torchmetrics.readthedocs.io/en/stable/retrieval/hit_rate.html. I've implemented the test-case as follows and have verified the new implementation. Feel free to add it intest_utils.py.

from torchmetrics import RetrievalHitRate
import numpy.testing as npt


def ref_symmetric_hit_rate(features_a, features_b, logit_scale, top_ks=[1, 5, 10]):
    assert len(features_a) == len(features_b)
    hit_rate = 0
    logits_per_a = (logit_scale * features_a @ features_b.t()).detach().cpu()
    logits_per_b = logits_per_a.t().detach().cpu()
    num_elements = len(features_a)
    for logits in [logits_per_a, logits_per_b]:
        preds = logits.reshape(-1)
        indexes = torch.broadcast_to(torch.arange(num_elements).reshape(-1, 1), (num_elements, num_elements)).reshape(-1)
        target = torch.eye(num_elements, dtype=bool).reshape(-1)
        for k in top_ks:
            hr_k = RetrievalHitRate(k=k)
            hit_rate += hr_k(preds, target, indexes=indexes)
    return hit_rate / (2 * len(top_ks))


def test_symmetric_hit_rate():
    generator = torch.Generator()
    generator.manual_seed(0)
    for repeat in range(3):
        for top_ks in [[1, 5, 10], [20], [3, 7, 9]]:
            features_a = torch.randn(50, 2, generator=generator)
            features_b = torch.randn(50, 2, generator=generator)
            hit_rate_impl = compute_hit_rate(features_a, features_b, logit_scale=1.0, top_ks=top_ks)
            hit_rate_ref = ref_symmetric_hit_rate(features_a, features_b, logit_scale=1.0, top_ks=top_ks)
            npt.assert_equal(hit_rate_impl.item(), hit_rate_ref.item())

[zhiqiangdon]

Added. Thanks.

[github-actions]

Job PR-2652-582d50a is done.
Docs are uploaded to http://autogluon-staging.s3-website-us-west-2.amazonaws.com/PR-2652/582d50a/index.html