Fix bugs
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@ -121,7 +121,6 @@ class DataPreparation:
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if scalers and 'feature_scaler' in scalers:
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X_scaled = scalers['feature_scaler'].transform(X)
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else:
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# 如果没有提供标准化器，直接返回处理后的数组
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X_scaled = X
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logging.info(f"Preprocessed data shape: {X_scaled.shape}")
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@ -133,11 +132,10 @@ class DataPreparation:
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'y': None # 验证数据可能没有标签
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}
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# 否则，从原始数据中提取特征
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# 从原始数据中提取特征和目标值
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if not feature_names:
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feature_names = self.feature_analyzer.get_equipment_specific_features(equipment_type)
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# 提取特征和目标值
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features = []
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targets = []
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@ -149,12 +147,17 @@ class DataPreparation:
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try:
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feature_values.append(float(value) if value is not None else 0.0)
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except (ValueError, TypeError):
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feature_values.append(0.0) # 使用0替代NaN
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feature_values.append(0.0)
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features.append(feature_values)
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# 提取目标值（成本）
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# 提取目标值（成本）并验证范围
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try:
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targets.append(float(item['actual_cost']))
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cost = float(item['actual_cost'])
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logging.info(f"Raw cost value: {cost}")
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if cost > 0: # 只使用正数成本值
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targets.append(cost)
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else:
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logging.warning(f"Skipping non-positive cost value: {cost}")
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except (ValueError, TypeError):
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logging.error(f"Invalid cost value: {item.get('actual_cost')}")
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continue
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@ -163,23 +166,35 @@ class DataPreparation:
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X = np.array(features, dtype=float)
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y = np.array(targets, dtype=float)
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# 记录数据范围
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logging.info(f"Features range: min={X.min()}, max={X.max()}")
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logging.info(f"Targets range: min={y.min()}, max={y.max()}")
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# 处理无效值
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X = np.nan_to_num(X, nan=0.0, posinf=0.0, neginf=0.0)
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# 使用训练数据的标准化器
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if scalers and 'feature_scaler' in scalers:
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X_scaled = scalers['feature_scaler'].transform(X)
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if 'target_scaler' in scalers:
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y_scaled = scalers['target_scaler'].transform(y.reshape(-1, 1)).ravel()
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else:
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y_scaled = y
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else:
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# 如果没有提供标准化器，直接返回处理后的数组
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X_scaled = X
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y_scaled = y
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logging.info(f"Preprocessed data shape: {X_scaled.shape}")
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logging.info(f"Validation features shape: {X_scaled.shape}")
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logging.info(f"Validation features type: {X_scaled.dtype}")
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# 记录标准化后的数据范围
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logging.info(f"Scaled validation X range: min={X_scaled.min()}, max={X_scaled.max()}")
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logging.info(f"Scaled validation y range: min={y_scaled.min()}, max={y_scaled.max()}")
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return {
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'X': X_scaled,
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'y': y # 返回原始成本值
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'y': y_scaled # 返回标准化后的成本值
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}
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except Exception as e:
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@ -111,19 +111,43 @@ class ModelTrainer:
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# 训练集评估
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train_pred = model.predict(X_train)
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# 如果使用了标准化，需要转换回原始范围
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if hasattr(self, 'target_scaler'):
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train_pred = self.target_scaler.inverse_transform(train_pred.reshape(-1, 1)).ravel()
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y_train_orig = self.target_scaler.inverse_transform(y_train.reshape(-1, 1)).ravel()
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else:
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y_train_orig = y_train
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# 记录预测范围
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logging.info(f"Train predictions range: min={train_pred.min()}, max={train_pred.max()}")
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logging.info(f"Train actual range: min={y_train_orig.min()}, max={y_train_orig.max()}")
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train_metrics = {
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'r2': r2_score(y_train, train_pred),
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'mae': mean_absolute_error(y_train, train_pred),
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'rmse': np.sqrt(mean_squared_error(y_train, train_pred))
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'r2': r2_score(y_train_orig, train_pred),
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'mae': mean_absolute_error(y_train_orig, train_pred),
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'rmse': np.sqrt(mean_squared_error(y_train_orig, train_pred))
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}
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# 验证集评估
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if X_val is not None and y_val is not None:
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val_pred = model.predict(X_val)
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# 如果使用了标准化，需要转换回原始范围
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if hasattr(self, 'target_scaler'):
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val_pred = self.target_scaler.inverse_transform(val_pred.reshape(-1, 1)).ravel()
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y_val_orig = self.target_scaler.inverse_transform(y_val.reshape(-1, 1)).ravel()
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else:
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y_val_orig = y_val
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# 记录预测范围
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logging.info(f"Validation predictions range: min={val_pred.min()}, max={val_pred.max()}")
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logging.info(f"Validation actual range: min={y_val_orig.min()}, max={y_val_orig.max()}")
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val_metrics = {
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'r2': r2_score(y_val, val_pred),
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'mae': mean_absolute_error(y_val, val_pred),
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'rmse': np.sqrt(mean_squared_error(y_val, val_pred))
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'r2': r2_score(y_val_orig, val_pred),
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'mae': mean_absolute_error(y_val_orig, val_pred),
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'rmse': np.sqrt(mean_squared_error(y_val_orig, val_pred))
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}
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else:
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# 使用交叉验证
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@ -285,7 +309,7 @@ class ModelTrainer:
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try:
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logging.info(f"Loading model for {equipment_type}")
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# 从数据库获<EFBFBD><EFBFBD>最新的激活模型
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# 从数据库获最新的激活模型
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with get_db_connection() as conn:
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cursor = conn.cursor(dictionary=True)
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cursor.execute("""
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