import importlib.util
import sys
import types
import unittest
from pathlib import Path

import numpy as np


def load_clustering_module():
    module_path = Path(r'D:\forsetsystem\backend\core\clustering.py')

    fake_config = types.SimpleNamespace(
        RANDOM_STATE=42,
        TARGET_COLUMN='缺勤时长（小时）',
        EMPLOYEE_ID_COLUMN='员工工号',
        FEATURE_NAME_CN={
            '月均加班时长': '月均加班时长',
            '缺勤时长（小时）': '缺勤时长（小时）',
        },
    )
    fake_preprocessing = types.ModuleType('core.preprocessing')
    fake_preprocessing.get_clean_data = lambda: None
    fake_sklearn = types.ModuleType('sklearn')
    fake_sklearn_cluster = types.ModuleType('sklearn.cluster')
    fake_sklearn_preprocessing = types.ModuleType('sklearn.preprocessing')

    class DummyKMeans:
        def __init__(self, *args, **kwargs):
            self.cluster_centers_ = None

        def fit_predict(self, data):
            self.cluster_centers_ = np.asarray(data, dtype=float)
            return np.zeros(len(data), dtype=int)

    class DummyMinMaxScaler:
        def fit_transform(self, data):
            return np.asarray(data, dtype=float)

        def inverse_transform(self, data):
            return np.asarray(data, dtype=float)

    fake_sklearn_cluster.KMeans = DummyKMeans
    fake_sklearn_preprocessing.MinMaxScaler = DummyMinMaxScaler

    sys.modules['config'] = fake_config
    sys.modules['core.preprocessing'] = fake_preprocessing
    sys.modules['sklearn'] = fake_sklearn
    sys.modules['sklearn.cluster'] = fake_sklearn_cluster
    sys.modules['sklearn.preprocessing'] = fake_sklearn_preprocessing

    spec = importlib.util.spec_from_file_location('test_clustering_module', module_path)
    module = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(module)
    return module


class ClusterNamingTests(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        module = load_clustering_module()
        cls.analyzer = module.KMeansAnalyzer()

    def test_generate_cluster_names_avoids_generic_group_names(self):
        centers = np.array([
            [41, 11, 18, 28, 22.5, 4.2],
            [30, 3, 22, 33, 23.0, 5.8],
            [36, 7, 36, 52, 24.0, 8.6],
            [38, 6, 24, 31, 27.2, 8.1],
        ], dtype=float)

        names = self.analyzer._generate_cluster_names(centers)

        self.assertEqual(len(names), 4)
        for name in names.values():
            self.assertNotIn('群体', name)

    def test_generate_cluster_names_returns_business_labels(self):
        centers = np.array([
            [42, 10, 16, 26, 22.0, 4.1],
            [29, 2, 20, 30, 22.8, 5.6],
            [35, 6, 34, 50, 24.1, 8.8],
            [37, 7, 23, 29, 27.5, 8.0],
        ], dtype=float)

        names = self.analyzer._generate_cluster_names(centers)

        self.assertIn('稳定成熟型', names.values())
        self.assertIn('新锐成长型', names.values())
        self.assertIn('压力奔波型', names.values())
        self.assertIn('健康关注型', names.values())

    def test_duplicate_names_receive_natural_suffixes(self):
        centers = np.array([
            [44, 12, 18, 29, 22.2, 4.0],
            [39, 9, 20, 34, 23.1, 5.3],
            [32, 4, 31, 46, 24.8, 7.2],
        ], dtype=float)

        names = self.analyzer._deduplicate_cluster_names(
            {0: '稳定成熟型', 1: '稳定成熟型', 2: '负荷承压型'},
            centers,
        )

        self.assertEqual({names[0], names[1]}, {'稳定成熟型-资深组', '稳定成熟型-成熟组'})
        self.assertEqual(names[2], '负荷承压型')

    def test_description_reflects_center_traits(self):
        description = self.analyzer._generate_description(
            '压力奔波型',
            np.array([34, 5, 36, 52, 24.0, 8.3], dtype=float),
        )

        self.assertIn('加班负荷偏高', description)
        self.assertIn('通勤压力偏高', description)
        self.assertIn('缺勤时长偏高', description)


if __name__ == '__main__':
    unittest.main()