feat(step8): 外部模型从单文件升级为母文件夹多模型字典扫描

src/core/steps/prediction_step.py

@@ -103,6 +103,10 @@ class PredictionStep:
         output_dir: Union[str, Path] = "./11_12_13_predictions/Machine_Learning_Prediction",
         callback: Optional[Callable] = None,
         _report_generator=None,
+        _external_model=None,
+        _external_model_path=None,
+        _external_models_dict=None,
+        _external_model_dir=None,
     ) -> Dict[str, str]:
         """将训练好的最佳机器学习模型应用到采样点光谱上，预测水质参数"""
         from src.core.prediction.inference_batch import WaterQualityInference
@@ -149,19 +153,48 @@ class PredictionStep:
                 else:
                     print(f"检测到部分预测结果文件，缺少: {missing_targets}，将继续生成...")
 
-        inferencer = WaterQualityInference(models_dir)
-        all_results = inferencer.batch_inference_multi_models(
-            models_root_dir=models_dir,
-            sampling_csv_path=sampling_csv_path,
-            output_dir=str(ml_prediction_dir),
-            metric=metric,
-            prediction_column=prediction_column,
-            output_format="csv",
-        )
-
-        for target_name, result in all_results.items():
-            if result.get("status") == "success":
-                prediction_files[target_name] = result["output_file"]
+        if _external_models_dict:
+            # 外部模型字典优先：每个 {subdir_name: model_obj} 对应一个水质参数，
+            # 手动为每个模型创建 inference 实例并调用 inference_pipeline。
+            print(f"\n使用外部导入模型字典（{len(_external_models_dict)} 个模型）...")
+            for target_name, model_obj in _external_models_dict.items():
+                try:
+                    output_file = ml_prediction_dir / f"{target_name}.csv"
+                    model_inferencer = WaterQualityInference(
+                        models_dir or "./",
+                        external_model=model_obj,
+                        external_model_path=_external_model_dir or "",
+                    )
+                    predictions, result_df = model_inferencer.inference_pipeline(
+                        sampling_csv_path=sampling_csv_path,
+                        output_csv_path=str(output_file),
+                        metric=metric,
+                        prediction_column=prediction_column,
+                    )
+                    prediction_files[target_name] = str(output_file)
+                    print(f"  ✓ {target_name}: {len(predictions)} 个预测值")
+                except Exception as e:
+                    print(f"  ✗ {target_name}: 失败 — {type(e).__name__}: {e}")
+                    prediction_files[target_name] = None
+        else:
+            inferencer = WaterQualityInference(
+                models_dir,
+                external_model=_external_model,
+                external_model_path=_external_model_path,
+            )
+            all_results = inferencer.batch_inference_multi_models(
+                models_root_dir=models_dir,
+                sampling_csv_path=sampling_csv_path,
+                output_dir=str(ml_prediction_dir),
+                metric=metric,
+                prediction_column=prediction_column,
+                output_format="csv",
+                external_model=_external_model,
+                external_model_path=_external_model_path,
+            )
+            for target_name, result in all_results.items():
+                if result.get("status") == "success":
+                    prediction_files[target_name] = result["output_file"]
 
         print(f"预测完成，结果保存在: {ml_prediction_dir}")