"""
tests.unit.test_pipeline_health_analyzer 的 Docstring
"""


def test_pipeline_health_analyzer():
    from app.algorithms.api_ex.pipeline_health_analyzer import PipelineHealthAnalyzer

    # 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=False)


if __name__ == "__main__":
    import conftest

    conftest.run_this_test(__file__)  # 自定义运行，类似示例