From 12866f12b07f280d722c287224af21b2cc89a901 Mon Sep 17 00:00:00 2001
From: duxin <duxin@example.com>
Date: Wed, 29 Jul 2026 10:20:00 +0800
Subject: [PATCH] =?UTF-8?q?fix:=20Pipeline=20=E5=AE=89=E5=85=A8=E7=BD=91?=
 =?UTF-8?q?=20=E2=80=94=20preproc=20=E4=B8=8E=20model=20=E9=97=B4=E6=8F=92?=
 =?UTF-8?q?=E5=85=A5=20=5FSafeFiniteTransformer?=
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- 新增 _SafeFiniteTransformer: np.nan_to_num + clip(±1e15)
- 放在 Pipeline preproc→model 之间，无论 MNF 数值溢出还是
  PhysicalFeatureExtractor 除零，SVR 收不到 inf/NaN
- X_train/X_test 入口先 replace(inf→NaN)，SimpleImputer 接力填充
---
 src/core/modeling/modeling_batch.py | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/src/core/modeling/modeling_batch.py b/src/core/modeling/modeling_batch.py
index 4f563eb..d84cee8 100644
--- a/src/core/modeling/modeling_batch.py
+++ b/src/core/modeling/modeling_batch.py
@@ -50,6 +50,22 @@ from src.preprocessing.spectral_Preprocessing import Preprocessing, get_preproce
 from src.core.utils.split_methods import spxy, ks
 
 
+class _SafeFiniteTransformer(BaseEstimator, TransformerMixin):
+    """Pipeline 安全网：np.nan_to_num + clip，确保无 inf/NaN 进入下游模型。
+
+    放在 Pipeline 的 preproc 与 model 之间，无论上游（MNF/比值除法）
+    产生何种极端值，SVR 等严格校验的模型都不会因 inf 拒绝输入。
+    """
+    def fit(self, X, y=None):
+        return self
+
+    def transform(self, X):
+        result = np.nan_to_num(np.asarray(X, dtype=np.float64),
+                               nan=0.0, posinf=0.0, neginf=0.0)
+        result = np.clip(result, -1e15, 1e15)
+        return result
+
+
 class WaterQualityModelingBatch:
     """水质参数反演批量建模类"""
 
@@ -619,9 +635,14 @@ class WaterQualityModelingBatch:
         pipeline = Pipeline([
             ('imputer', SimpleImputer(strategy='median')),
             ('preproc', preproc),
+            ('cleaner', _SafeFiniteTransformer()),
             ('model', base_model),
         ])
 
+        # ★ 入口清洗：inf → NaN，后续 SimpleImputer/cleaner 接力处理
+        X_train = X_train.replace([np.inf, -np.inf], np.nan)
+        X_test  = X_test.replace([np.inf, -np.inf], np.nan)
+
         # RandomizedSearchCV 需要以「步骤名__参数名」的格式索引参数网格；
         # 我们原有的 config['params'] 是模型层的（无 __），统一加 model__ 前缀。
         prefixed_params = {