import numpy as np
import pytest
import tensorflow as tf

from tensorflow_similarity.retrieval_metrics import PrecisionAtK

testdata = [
    (
        "micro",
        tf.constant(0.583333333),
    ),
    (
        "macro",
        tf.constant(0.5),
    ),
]


@pytest.mark.parametrize("avg, expected", testdata, ids=["micro", "macro"])
def test_compute(avg, expected):
    query_labels = tf.constant([1, 1, 1, 0])
    match_mask = tf.constant(
        [
            [True, True, False],
            [True, True, False],
            [True, True, False],
            [False, False, True],
        ],
        dtype=bool,
    )
    rm = PrecisionAtK(k=3, average=avg)

    precision = rm.compute(query_labels=query_labels, match_mask=match_mask)
    np.testing.assert_allclose(precision, expected)

import numpy as np
import pytest
import tensorflow as tf

from tensorflow_similarity.retrieval_metrics import PrecisionAtK

testdata = [
    (
        "micro",
        tf.constant(0.583333333),
    ),
    (
        "macro",
        tf.constant(0.5),
    ),
]


@pytest.mark.parametrize("avg, expected", testdata, ids=["micro", "macro"])
def test_compute(avg, expected):
    query_labels = tf.constant([1, 1, 1, 0])
    match_mask = tf.constant(
        [
            [True, True, False],
            [True, True, False],
            [True, True, False],
            [False, False, True],
        ],
        dtype=bool,
    )
    rm = PrecisionAtK(k=3, average=avg)

    precision = rm.compute(query_labels=query_labels, match_mask=match_mask)
    np.testing.assert_allclose(precision, expected, atol=1e-05)

Update atol on precision at k test.
Update atol on precision at k test.
