调整后端测试框架结构

tests/unit/test_pipeline_health_analyzer.py

@@ -0,0 +1,60 @@
+from api_ex.pipeline_health_analyzer import PipelineHealthAnalyzer
+
+
+def test_pipeline_health_analyzer():
+    # 初始化分析器，假设模型文件路径为'models/rsf_model.joblib'
+    analyzer = PipelineHealthAnalyzer()
+    # 创建示例输入数据（9个样本）
+    import pandas as pd
+    import time
+
+    base_data = pd.DataFrame(
+        {
+            "Material": [7, 11, 7, 7, 7, 7, 7, 7, 7],
+            "Diameter": [6, 6, 3, 2, 3, 3, 2, 2, 2],
+            "Flow Velocity": [0.55, 0.32, 0.25, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4],
+            "Pressure": [0.15, 0.15, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
+        }
+    )
+
+    # 复制生成10万样本
+    num_samples = 100
+    repetitions = num_samples // len(base_data) + 1
+    sample_data = pd.concat([base_data] * repetitions, ignore_index=True).head(
+        num_samples
+    )
+
+    print(f"生成了 {len(sample_data)} 个样本")
+
+    # 记录开始时间
+    start_time = time.time()
+
+    # 进行生存预测
+    survival_functions = analyzer.predict_survival(sample_data)
+
+    # 记录结束时间
+    end_time = time.time()
+    elapsed_time = end_time - start_time
+    print(f"预测耗时: {elapsed_time:.2f} 秒")
+
+    # 打印预测结果示例
+    print(f"预测了 {len(survival_functions)} 个生存函数")
+    for i, sf in enumerate(survival_functions):
+        print(
+            f"样本 {i+1}: 时间点数量={len(sf.x)}, 生存概率范围={sf.y.min():.3f} - {sf.y.max():.3f} {sf}"
+        )
+
+    # 验证返回结果类型
+    assert isinstance(survival_functions, list), "返回值应为列表"
+    assert all(
+        hasattr(sf, "x") and hasattr(sf, "y") for sf in survival_functions
+    ), "每个生存函数应包含x和y属性"
+
+    # 可选：测试绘图功能（不显示图表）
+    analyzer.plot_survival(survival_functions, show_plot=True)
+
+
+if __name__ == "__main__":
+    import conftest
+
+    conftest.run_this_test(__file__)  # 自定义运行，类似示例