Polish absence analysis demo experience

backend/tests/test_clustering_naming.py

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+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()