Polish absence analysis demo experience
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shuo
155
backend/tests/test_predict_explanation.py
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155
backend/tests/test_predict_explanation.py
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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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def load_predict_module():
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module_path = Path(r'D:\forsetsystem\backend\services\predict_service.py')
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fake_config = types.SimpleNamespace(
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MODELS_DIR='',
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SCALER_PATH='',
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JDR_DIMENSIONS={
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'job_demands': {'name_cn': '工作要求'},
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'job_resources': {'name_cn': '工作资源'},
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'personal_resources': {'name_cn': '个人资源'},
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'mediators': {'name_cn': '中介变量'},
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},
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)
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fake_deep_learning = types.ModuleType('core.deep_learning_model')
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fake_deep_learning.load_lstm_mlp_bundle = lambda path: None
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fake_deep_learning.predict_lstm_mlp = lambda model, data: 0.0
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fake_model_features = types.ModuleType('core.model_features')
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fake_model_features.align_feature_frame = lambda frame, names: frame
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fake_model_features.apply_label_encoders = lambda frame, encoders: frame
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fake_model_features.build_prediction_dataframe = lambda data: data
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fake_model_features.engineer_features = lambda frame: frame
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fake_model_features.to_float_array = lambda frame: frame
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sys.modules['config'] = fake_config
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sys.modules['core.deep_learning_model'] = fake_deep_learning
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sys.modules['core.model_features'] = fake_model_features
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spec = importlib.util.spec_from_file_location('test_predict_service_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 PredictExplanationTests(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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module = load_predict_module()
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cls.service = module.PredictService()
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def test_build_jdr_snapshot_marks_high_demands_and_low_resources(self):
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snapshot = self.service._build_jdr_snapshot({
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'工作要求指数': 5.8,
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'工作资源指数': 2.7,
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'个人资源指数': 2.8,
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'JD-R平衡度': -1.1,
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'倦怠风险指数': 3.1,
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'工作投入指数': 2.9,
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})
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self.assertEqual(snapshot['job_demands']['status'], '偏高')
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self.assertEqual(snapshot['job_resources']['status'], '偏低')
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self.assertEqual(snapshot['balance']['status'], '明显失衡')
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self.assertEqual(snapshot['burnout_risk']['status'], '偏高')
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def test_mechanism_summary_prefers_health_impairment_path(self):
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snapshot = self.service._build_jdr_snapshot({
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'工作要求指数': 5.6,
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'工作资源指数': 2.9,
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'个人资源指数': 2.8,
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'JD-R平衡度': -0.9,
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'倦怠风险指数': 3.0,
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'工作投入指数': 2.9,
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})
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shap_local = {
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'dimension_contribution': {
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'工作要求': 0.32,
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'中介变量': 0.18,
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'事件上下文': 0.11,
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'工作资源': -0.07,
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},
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'features': [
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{'name': 'monthly_overtime_hours', 'name_cn': '月均加班时长', 'dimension': 'job_demands', 'shap_value': 0.18},
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{'name': 'commute_minutes', 'name_cn': '通勤时长', 'dimension': 'job_demands', 'shap_value': 0.12},
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{'name': 'medical_certificate_flag', 'name_cn': '医院证明', 'dimension': 'event_context', 'shap_value': 0.08},
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{'name': 'coworker_support', 'name_cn': '同事支持', 'dimension': 'job_resources', 'shap_value': -0.05},
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],
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}
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result = {'predicted_hours': 9.4, 'risk_label': '高风险'}
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data = {
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'monthly_overtime_hours': 38,
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'commute_minutes': 62,
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'is_night_shift': 1,
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'medical_certificate_flag': 1,
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}
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summary = self.service._build_mechanism_summary(result, data, snapshot, shap_local)
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self.assertIn('健康损耗', summary['pathway_label'])
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self.assertIn('月均加班时长', summary['mechanism'])
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self.assertTrue(summary['scenario_hint'])
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def test_intervention_suggestions_cover_resource_and_personal_support(self):
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snapshot = self.service._build_jdr_snapshot({
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'工作要求指数': 4.4,
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'工作资源指数': 2.7,
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'个人资源指数': 2.6,
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'JD-R平衡度': -0.7,
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'倦怠风险指数': 2.9,
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'工作投入指数': 2.8,
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})
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suggestions = self.service._build_intervention_suggestions(
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{
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'monthly_overtime_hours': 18,
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'commute_minutes': 28,
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'chronic_disease_flag': 1,
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'medical_certificate_flag': 1,
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'leave_reason_category': '子女照护',
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},
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snapshot,
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shap_local=None,
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)
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category_map = {item['category']: item['items'] for item in suggestions}
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self.assertIn('增资源', category_map)
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self.assertIn('补个人资源', category_map)
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self.assertTrue(any('支持' in item or '弹性' in item for item in category_map['增资源']))
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self.assertTrue(any('健康' in item or '倦怠' in item for item in category_map['补个人资源']))
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def test_buffer_text_mentions_protective_factors(self):
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snapshot = self.service._build_jdr_snapshot({
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'工作要求指数': 3.9,
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'工作资源指数': 4.2,
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'个人资源指数': 4.0,
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'JD-R平衡度': 0.9,
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'倦怠风险指数': 1.8,
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'工作投入指数': 4.1,
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})
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shap_local = {
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'dimension_contribution': {
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'工作要求': 0.08,
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'工作资源': -0.12,
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'个人资源': -0.09,
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},
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'features': [
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{'name': 'supervisor_support', 'name_cn': '上级支持', 'dimension': 'job_resources', 'shap_value': -0.07},
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{'name': 'self_efficacy', 'name_cn': '自我效能感', 'dimension': 'personal_resources', 'shap_value': -0.05},
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],
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}
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summary = self.service._build_mechanism_summary({'predicted_hours': 5.3, 'risk_label': '中风险'}, {}, snapshot, shap_local)
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self.assertIn('缓冲作用', summary['buffer_text'])
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self.assertTrue(summary['protective_factors'])
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if __name__ == '__main__':
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unittest.main()
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