From 95d30d8d81ca34375bd7bc0957f8113334097864 Mon Sep 17 00:00:00 2001 From: DXC Date: Sun, 10 May 2026 15:45:56 +0800 Subject: [PATCH] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E8=AE=AD=E7=BB=83=E6=91=98?= =?UTF-8?q?=E8=A6=81=E6=8A=A5=E5=91=8A=E6=97=A0=E6=B3=95=E8=AF=86=E5=88=AB?= =?UTF-8?q?=20.joblib=20=E6=A8=A1=E5=9E=8B=E7=9A=84=20Bug?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/postprocessing/visualization_reports.py | 101 ++++++++++++-------- 1 file changed, 59 insertions(+), 42 deletions(-) diff --git a/src/postprocessing/visualization_reports.py b/src/postprocessing/visualization_reports.py index 5726d88..327d29f 100644 --- a/src/postprocessing/visualization_reports.py +++ b/src/postprocessing/visualization_reports.py @@ -1004,66 +1004,83 @@ class ReportGenerator: 保存的文件路径 """ from src.core.modeling.modeling_batch import WaterQualityModelingBatch - + import joblib + modeler = WaterQualityModelingBatch(models_dir) - - # 需要先加载训练结果 - # 这里假设results已经存储在modeler中，或者需要从保存的文件中读取 - # 由于modeling_batch.py的结构，我们需要另一种方式来获取所有结果 - - # 尝试遍历模型目录，查找所有保存的结果 models_path = Path(models_dir) all_results = [] - - # 遍历所有目标参数文件夹 - for target_folder in models_path.iterdir(): - if not target_folder.is_dir(): - continue - - target_name = target_folder.name - - # 查找所有模型文件 - for model_file in target_folder.rglob("*.pkl"): - # 从文件名提取信息（假设格式为：{preprocess}_{model}_{split}.pkl） - model_info = { - 'target': target_name, - 'model_file': str(model_file), - 'preprocess': 'Unknown', - 'model': 'Unknown', - 'split_method': 'Unknown' + + # 递归扫描 *.joblib 和 *.pkl，兼容 artifacts_dir/target_name/ 的所有子目录层级 + model_files = list(models_path.rglob("*.joblib")) + list(models_path.rglob("*.pkl")) + + for model_file in model_files: + # 目标参数取直系父目录名（符合 artifacts_dir/target_name/ 结构） + target_name = model_file.parent.name + stem = model_file.stem + + # 文件名格式：{safe_target}_{preprocess}_{model_name}.joblib + # 使用 split('_', 2) 最多切 3 段，目标 1 段、预处理 1 段、模型 1 段 + parts = stem.split('_', 2) + preprocess = parts[1] if len(parts) > 1 else 'Unknown' + model_name_str = parts[2] if len(parts) > 2 else stem + + # 尝试从 joblib/pkl 读取元数据，提取性能指标 + metrics = {} + try: + data = joblib.load(model_file) + metadata = data.get('metadata', {}) + metrics = { + 'train_r2': metadata.get('train_r2', 'N/A'), + 'test_r2': metadata.get('test_r2', 'N/A'), + 'test_rmse': metadata.get('test_rmse', 'N/A'), + 'train_rmse': metadata.get('train_rmse', 'N/A'), + 'train_mae': metadata.get('train_mae', 'N/A'), + 'test_mae': metadata.get('test_mae', 'N/A'), + 'cv_mean': metadata.get('cv_mean', 'N/A'), } - - # 尝试从文件名解析 - parts = model_file.stem.split('_') - if len(parts) >= 3: - model_info['preprocess'] = parts[0] - model_info['model'] = parts[1] - model_info['split_method'] = parts[2] - - all_results.append(model_info) - - # 如果有训练结果数据，使用实际指标 - # 否则创建一个基本的摘要 + except Exception: + pass # 加载失败时 metrics 保持为空字典，摘要中该列为 N/A + + all_results.append({ + 'target': target_name, + 'model_file': str(model_file), + 'preprocess': preprocess, + 'model': model_name_str, + **metrics, + }) + summary_data = [] for result in all_results: summary_data.append({ '目标参数': result['target'], '预处理方法': result['preprocess'], '模型名称': result['model'], - '划分方法': result['split_method'], - '模型文件': result['model_file'] + '模型文件': result['model_file'], + '训练集R²': result.get('train_r2', 'N/A'), + '测试集R²': result.get('test_r2', 'N/A'), + '测试集RMSE': result.get('test_rmse', 'N/A'), + '训练集RMSE': result.get('train_rmse', 'N/A'), + '训练集MAE': result.get('train_mae', 'N/A'), + '测试集MAE': result.get('test_mae', 'N/A'), + 'CV均值': result.get('cv_mean', 'N/A'), }) - + if not summary_data: print("警告：未找到模型文件，生成空摘要") summary_data = [{ '目标参数': 'No Data', '预处理方法': 'N/A', '模型名称': 'N/A', - '划分方法': 'N/A', - '模型文件': 'N/A' + '模型文件': 'N/A', + '训练集R²': 'N/A', + '测试集R²': 'N/A', + '测试集RMSE': 'N/A', + '训练集RMSE': 'N/A', + '训练集MAE': 'N/A', + '测试集MAE': 'N/A', + 'CV均值': 'N/A', }] - + df_summary = pd.DataFrame(summary_data) if output_path is None: