121 lines
4.0 KiB
Python
121 lines
4.0 KiB
Python
import importlib.util
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import sys
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import types
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import unittest
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from pathlib import Path
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import numpy as np
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def load_clustering_module():
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module_path = Path(r'D:\forsetsystem\backend\core\clustering.py')
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fake_config = types.SimpleNamespace(
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RANDOM_STATE=42,
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TARGET_COLUMN='缺勤时长（小时）',
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EMPLOYEE_ID_COLUMN='员工工号',
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FEATURE_NAME_CN={
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'月均加班时长': '月均加班时长',
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'缺勤时长（小时）': '缺勤时长（小时）',
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},
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)
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fake_preprocessing = types.ModuleType('core.preprocessing')
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fake_preprocessing.get_clean_data = lambda: None
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fake_sklearn = types.ModuleType('sklearn')
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fake_sklearn_cluster = types.ModuleType('sklearn.cluster')
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fake_sklearn_preprocessing = types.ModuleType('sklearn.preprocessing')
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class DummyKMeans:
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def __init__(self, *args, **kwargs):
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self.cluster_centers_ = None
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def fit_predict(self, data):
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self.cluster_centers_ = np.asarray(data, dtype=float)
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return np.zeros(len(data), dtype=int)
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class DummyMinMaxScaler:
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def fit_transform(self, data):
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return np.asarray(data, dtype=float)
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def inverse_transform(self, data):
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return np.asarray(data, dtype=float)
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fake_sklearn_cluster.KMeans = DummyKMeans
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fake_sklearn_preprocessing.MinMaxScaler = DummyMinMaxScaler
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sys.modules['config'] = fake_config
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sys.modules['core.preprocessing'] = fake_preprocessing
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sys.modules['sklearn'] = fake_sklearn
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sys.modules['sklearn.cluster'] = fake_sklearn_cluster
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sys.modules['sklearn.preprocessing'] = fake_sklearn_preprocessing
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spec = importlib.util.spec_from_file_location('test_clustering_module', module_path)
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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return module
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class ClusterNamingTests(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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module = load_clustering_module()
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cls.analyzer = module.KMeansAnalyzer()
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def test_generate_cluster_names_avoids_generic_group_names(self):
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centers = np.array([
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[41, 11, 18, 28, 22.5, 4.2],
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[30, 3, 22, 33, 23.0, 5.8],
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[36, 7, 36, 52, 24.0, 8.6],
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[38, 6, 24, 31, 27.2, 8.1],
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], dtype=float)
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names = self.analyzer._generate_cluster_names(centers)
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self.assertEqual(len(names), 4)
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for name in names.values():
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self.assertNotIn('群体', name)
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def test_generate_cluster_names_returns_business_labels(self):
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centers = np.array([
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[42, 10, 16, 26, 22.0, 4.1],
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[29, 2, 20, 30, 22.8, 5.6],
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[35, 6, 34, 50, 24.1, 8.8],
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[37, 7, 23, 29, 27.5, 8.0],
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], dtype=float)
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names = self.analyzer._generate_cluster_names(centers)
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self.assertIn('稳定成熟型', names.values())
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self.assertIn('新锐成长型', names.values())
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self.assertIn('压力奔波型', names.values())
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self.assertIn('健康关注型', names.values())
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def test_duplicate_names_receive_natural_suffixes(self):
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centers = np.array([
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[44, 12, 18, 29, 22.2, 4.0],
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[39, 9, 20, 34, 23.1, 5.3],
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[32, 4, 31, 46, 24.8, 7.2],
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], dtype=float)
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names = self.analyzer._deduplicate_cluster_names(
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{0: '稳定成熟型', 1: '稳定成熟型', 2: '负荷承压型'},
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centers,
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)
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self.assertEqual({names[0], names[1]}, {'稳定成熟型-资深组', '稳定成熟型-成熟组'})
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self.assertEqual(names[2], '负荷承压型')
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def test_description_reflects_center_traits(self):
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description = self.analyzer._generate_description(
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'压力奔波型',
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np.array([34, 5, 36, 52, 24.0, 8.3], dtype=float),
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)
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self.assertIn('加班负荷偏高', description)
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self.assertIn('通勤压力偏高', description)
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self.assertIn('缺勤时长偏高', description)
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if __name__ == '__main__':
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unittest.main()
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