refactor: 渐进式模块化重构 — 剥离可视化层、工具层、算法层到独立模块

src/core/visualization/scatter_plot.py

@@ -0,0 +1,147 @@
+# -*- coding: utf-8 -*-
+"""
+可视化模块 - 散点图生成
+"""
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from typing import Optional, Dict, List, Union
+from src.core.prediction.inference_batch import WaterQualityInference
+from src.postprocessing.visualization_reports import WaterQualityVisualization
+
+
+def generate_model_scatter_plots(
+    models_dir: str,
+    training_csv_path: str,
+    output_dir: Optional[str] = None,
+    metric: str = 'test_r2',
+    use_enhanced: bool = True,
+    feature_start_column: Union[str, int] = 13,
+    test_size: float = 0.2,
+    random_state: int = 42,
+    scatter_batch=None  # 可选：传入已实例化的 scatter_batch 对象
+) -> Dict[str, str]:
+    """
+    生成模型评估散点图（真实值vs预测值）
+
+    Args:
+        models_dir: 模型保存目录
+        training_csv_path: 训练数据CSV路径
+        output_dir: 输出目录（None则使用默认）
+        metric: 选择最佳模型的指标
+        use_enhanced: 是否使用增强版散点图（带置信区间，使用sctter_batch）
+        feature_start_column: 特征开始列名或索引
+        test_size: 测试集比例
+        random_state: 随机种子
+        scatter_batch: 可选，已实例化的 WaterQualityScatterBatch 对象
+
+    Returns:
+        散点图文件路径字典（键为目标参数名）
+    """
+    print("\n" + "="*80)
+    print("生成模型评估散点图")
+    print("="*80)
+
+    if training_csv_path is None:
+        raise ValueError("请提供 training_csv_path")
+
+    models_path = Path(models_dir)
+    if not models_path.exists():
+        raise ValueError(f"模型目录不存在: {models_dir}")
+
+    # 确定输出目录
+    if output_dir is None:
+        output_dir = str(Path(models_dir).parent / "14_visualization" / "scatter_plots")
+    Path(output_dir).mkdir(parents=True, exist_ok=True)
+
+    # 实例化可视化器
+    visualizer = WaterQualityVisualization(output_dir)
+
+    scatter_paths = {}
+
+    # 增强版散点图
+    if use_enhanced:
+        print("使用增强版散点图（带置信区间）")
+        try:
+            from src.core.prediction.sctter_batch import WaterQualityScatterBatch
+            if scatter_batch is None:
+                scatter_batch = WaterQualityScatterBatch()
+
+            results = scatter_batch.batch_plot_scatter(
+                models_root_dir=models_dir,
+                csv_path=training_csv_path,
+                output_dir=output_dir,
+                metric=metric,
+                target_column=None,
+                feature_start_column=feature_start_column,
+                test_size=test_size,
+                random_state=random_state
+            )
+
+            for target_name, result in results.items():
+                if result.get('status') == 'success':
+                    scatter_paths[target_name] = result.get('save_path', '')
+                    print(f"  ✓ {target_name}: {result.get('save_path', '')}")
+                else:
+                    print(f"  ✗ {target_name}: 失败 - {result.get('error', '未知错误')}")
+
+        except Exception as e:
+            print(f"使用增强版散点图时出错: {e}")
+            print("回退到基础版散点图")
+            use_enhanced = False
+
+    # 基础版散点图
+    if not use_enhanced or not scatter_paths:
+        print("使用基础版散点图")
+        for target_folder in models_path.iterdir():
+            if not target_folder.is_dir():
+                continue
+
+            target_name = target_folder.name
+            print(f"\n处理目标参数: {target_name}")
+
+            try:
+                inferencer = WaterQualityInference(str(target_folder))
+                eval_result = inferencer.evaluate_with_split(
+                    data_csv_path=training_csv_path,
+                    split_method="spxy",
+                    test_size=test_size,
+                    random_state=random_state,
+                    metric=metric
+                )
+
+                predictions = eval_result.get('predictions', {})
+                if predictions:
+                    y_train_true = predictions.get('y_train_true')
+                    y_train_pred = predictions.get('y_train_pred')
+                    y_test_true = predictions.get('y_test_true')
+                    y_test_pred = predictions.get('y_test_pred')
+                    metrics = eval_result.get('test_metrics', {})
+
+                    if y_train_true is not None and y_test_true is not None:
+                        y_all_true = np.concatenate([y_train_true, y_test_true])
+                        y_all_pred = np.concatenate([y_train_pred, y_test_pred])
+
+                        train_indices = np.arange(len(y_train_true))
+                        test_indices = np.arange(len(y_train_true), len(y_all_true))
+
+                        scatter_path = visualizer.plot_scatter_true_vs_pred(
+                            y_true=y_all_true,
+                            y_pred=y_all_pred,
+                            target_name=target_name,
+                            train_indices=train_indices,
+                            test_indices=test_indices,
+                            metrics={
+                                'train_r2': eval_result.get('train_metrics', {}).get('r2', 0),
+                                'test_r2': metrics.get('r2', 0),
+                                'train_rmse': eval_result.get('train_metrics', {}).get('rmse', 0),
+                                'test_rmse': metrics.get('rmse', 0)
+                            }
+                        )
+                        scatter_paths[target_name] = scatter_path
+            except Exception as e:
+                print(f"处理目标参数 {target_name} 时出错: {e}")
+                continue
+
+    print(f"\n散点图生成完成，共生成 {len(scatter_paths)} 个图表")
+    return scatter_paths