dev(narugo): add unittest for ccip

    elif 'optics' in method:

        clustering = OPTICS(max_eps=eps, min_samples=min_samples, metric=_metric).fit(samples)

    else:

        raise ValueError(f'Unknown mode for CCIP clustering - {method!r}.')

        assert False, f'Unknown mode for CCIP clustering - {method!r}.'  # pragma: no cover

    return clustering.labels_.tolist()

import glob

import os.path

from typing import List, Tuple

import numpy as np

import pytest

from hbutils.testing import disable_output

from natsort import natsorted

from sklearn.metrics import adjusted_rand_score

from imgutils.metrics import ccip_difference, ccip_default_threshold, ccip_extract_feature, ccip_same, ccip_batch_same, \

    ccip_clustering

from test.testings import get_testfile

def _all_images_and_cids() -> Tuple[List[str], List[int]]:

    files = natsorted(glob.glob(get_testfile('dataset', 'images_test_v1', '*', '*.jpg')))[::2][:12]

    cids = [int(os.path.basename(os.path.dirname(file))) for file in files]

    return files, cids

@pytest.fixture()

def images_12() -> List[str]:

    return _all_images_and_cids()[0]

@pytest.fixture()

def images_cids() -> List[int]:

    return _all_images_and_cids()[1]

@pytest.fixture()

def img_1(images_12):

    return images_12[0]

@pytest.fixture()

def img_2(images_12):

    return images_12[1]

@pytest.fixture()

def img_3(images_12):

    return images_12[2]

@pytest.fixture()

def img_4(images_12):

    return images_12[3]

@pytest.fixture()

def img_5(images_12):

    return images_12[4]

@pytest.fixture()

def img_6(images_12):

    return images_12[5]

@pytest.fixture()

def img_7(images_12):

    return images_12[6]

@pytest.fixture()

def img_8(images_12):

    return images_12[7]

@pytest.fixture()

def img_9(images_12):

    return images_12[8]

@pytest.fixture()

def img_10(images_12):

    return images_12[9]

@pytest.fixture()

def img_11(images_12):

    return images_12[10]

@pytest.fixture()

def img_12(images_12):

    return images_12[11]

@pytest.fixture()

def threshold() -> float:

    return ccip_default_threshold()

@pytest.fixture()

def s_threshold(threshold) -> float:

    return threshold + 0.05

@pytest.mark.unittest

class TestMetricCCIP:

    def test_ccip_difference(self, img_1, img_2, img_3, img_4, img_5, img_6, img_7, s_threshold):

        assert ccip_difference(img_1, img_2) < s_threshold

        assert ccip_difference(img_1, img_3) < s_threshold

        assert ccip_difference(img_2, img_3) < s_threshold

        f1, f2, f3 = map(ccip_extract_feature, (img_1, img_2, img_3))

        assert ccip_difference(f1, f2) < s_threshold

        assert ccip_difference(f1, f3) < s_threshold

        assert ccip_difference(f2, f3) < s_threshold

        assert ccip_difference(img_1, img_4) >= s_threshold

        assert ccip_difference(img_1, img_5) >= s_threshold

        assert ccip_difference(img_1, img_6) >= s_threshold

        assert ccip_difference(img_6, img_7) >= s_threshold

    def test_ccip_same(self, img_1, img_2, img_3, img_4, img_5, img_6, img_7, s_threshold):

        assert ccip_same(img_1, img_2, threshold=s_threshold)

        assert ccip_same(img_1, img_3, threshold=s_threshold)

        assert ccip_same(img_2, img_3, threshold=s_threshold)

        f1, f2, f3 = map(ccip_extract_feature, (img_1, img_2, img_3))

        assert ccip_same(f1, f2, threshold=s_threshold)

        assert ccip_same(f1, f3, threshold=s_threshold)

        assert ccip_same(f2, f3, threshold=s_threshold)

        assert not ccip_same(img_1, img_4, threshold=s_threshold)

        assert not ccip_same(img_1, img_5, threshold=s_threshold)

        assert not ccip_same(img_1, img_6, threshold=s_threshold)

        assert not ccip_same(img_6, img_7, threshold=s_threshold)

    def test_ccip_batch_same(self, images_12, images_cids, s_threshold):

        matrix = ccip_batch_same(images_12, threshold=s_threshold)

        cids = np.array(images_cids)

        cmatrix = cids == cids.reshape(-1, 1)

        assert (matrix != cmatrix).sum() < 5

    def test_ccip_cluster(self, images_12, images_cids):

        with disable_output():

            assert adjusted_rand_score(

                ccip_clustering(images_12, min_samples=2),

                images_cids

            ) >= 0.98

        with disable_output():

            assert adjusted_rand_score(

                ccip_clustering(images_12, min_samples=2, method='dbscan'),

                images_cids,

            ) >= 0.98

        with disable_output():

            assert adjusted_rand_score(

                ccip_clustering(images_12, min_samples=2, method='optics_best'),

                images_cids,

            ) >= 0.98

        with pytest.raises(KeyError):

            _ = ccip_clustering(images_12, min_samples=2, method='what_the_fxxk')