ML项目模型训练器TypeError：构造函数参数不匹配问题诊断与解决

# 错误发生在 config.get_model_trainer_config() 内部
# 进一步追溯，是在 ModelTrainerConfig 实例化时
model_trainer_config = ModelTrainerConfig(
    root_dir=config.root_dir,
    train_data_path = config.train_data_path,
    test_data_path = config.test_data_path,
    trained_model_file_path =  os.path.join('artifact', 'model'), # 这一行导致错误
    model_name = config.model_name,
    alpha = params.alpha,
    l1_ratio = params.l1_ratio,
    target_column = schema.name
)

# 假设 ModelTrainerConfig 的定义可能如下（导致错误）
# src/config/configuration.py 或其他地方
from dataclasses import dataclass
from pathlib import Path

@dataclass(frozen=True)
class ModelTrainerConfig:
    root_dir: Path
    train_data_path: Path
    test_data_path: Path
    model_name: str
    alpha: float
    l1_ratio: float
    target_column: str
    # 缺少 trained_model_file_path

import os
from dataclasses import dataclass
from pathlib import Path

@dataclass(frozen=True)
class ModelTrainerConfig:
    root_dir: Path
    train_data_path: Path
    test_data_path: Path
    trained_model_file_path: Path # 添加这一行以接受参数
    model_name: str
    alpha: float
    l1_ratio: float
    target_column: str

class ModelTrainer:
    def __init__(self):
        # 这里硬编码实例化了 ModelTrainerConfig，而不是接收外部传入的配置
        self.model_trainer_config = ModelTrainerConfig()

# src/entity/config_entity.py 或类似路径
import os
from dataclasses import dataclass
from pathlib import Path

@dataclass(frozen=True)
class DataIngestionConfig:
    root_dir: Path
    source_URL: str
    local_data_file: Path
    unzip_dir: Path

@dataclass(frozen=True)
class DataValidationConfig:
    root_dir: Path
    STATUS_FILE: Path
    unzip_data_dir: Path
    all_schema: dict

@dataclass(frozen=True)
class DataTransformationConfig:
    root_dir: Path
    data_path: Path
    processor_name: str
    target_column: str

@dataclass(frozen=True)
class ModelTrainerConfig:
    root_dir: Path
    train_data_path: Path
    test_data_path: Path
    trained_model_file_path: Path # 确保此参数已定义
    model_name: str
    alpha: float
    l1_ratio: float
    target_column: str

# ... 其他配置类 ...

# src/components/model_trainer.py
import os
import sys
import pandas as pd
import numpy as np
import joblib # 用于保存模型

from sklearn.linear_model import LinearRegression, Lasso, Ridge, ElasticNet
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.svm import SVR
from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import r2_score

# 假设 logging 模块已配置
# from your_project_path.logger import logging
# 假设 ModelTrainerConfig 已从 entity 导入
from your_project_path.entity.config_entity import ModelTrainerConfig

class ModelTrainer:
    def __init__(self, config: ModelTrainerConfig): # 接受配置对象
        self.config = config # 将传入的配置赋值给实例属性

    # 将 save_obj 和 evaluate_model 改为静态方法或辅助函数，或确保它们正确使用 self
    @staticmethod
    def save_obj(file_path, obj):
        try:
            dir_path = os.path.dirname(file_path)
            os.makedirs(dir_path, exist_ok=True)
            with open(file_path, 'wb') as file_obj:
                joblib.dump(obj, file_obj, compress=('gzip'))
        except Exception as e:
            # logging.error('Error occurred in utils save_obj', exc_info=True)
            raise e

    @staticmethod
    def evaluate_model(X_train, y_train, X_test, y_test, models):
        try:
            report = {}
            for name, model in models.items():
                model.fit(X_train, y_train)
                y_test_pred = model.predict(X_test)
                test_model_score = r2_score(y_test, y_test_pred)
                report[name] = test_model_score
            return report
        except Exception as e:
            # logging.error('Exception occurred during model evaluation', exc_info=True)
            raise e

    def initiate_model_training(self, X_train, X_test, y_train, y_test):
        # logging.info('Initiating model training...')
        try:
            models = {
                'LinearRegression': LinearRegression(),
                'Lasso': Lasso(alpha=self.config.alpha), # 使用配置中的参数
                'Ridge': Ridge(alpha=self.config.alpha),
                'Elasticnet': ElasticNet(alpha=self.config.alpha, l1_ratio=self.config.l1_ratio),
                'RandomForestRegressor': RandomForestRegressor(),
                'GradientBoostRegressor': GradientBoostingRegressor(),
                "AdaBoost": AdaBoostRegressor(),
                'DecisionTreeRegressor': DecisionTreeRegressor(),
                "SupportVectorRegressor": SVR(),
                "KNN": KNeighborsRegressor()
            }

            model_report: dict = self.evaluate_model(X_train, y_train, X_test, y_test, models)
            print(f"Model Report: {model_report}")
            # logging.info(f'Model Report : {model_report}')

            best_model_score = max(sorted(model_report.values()))
            best_model_name = [name for name, score in model_report.items() if score == best_model_score][0]
            best_model = models[best_model_name]

            print(f"Best Model Found, Model Name: {best_model_name}, R2-score: {best_model_score}")
            # logging.info(f"Best Model Found, Model name: {best_model_name}, R2-score: {best_model_score}")

            self.save_obj(
                file_path=self.config.trained_model_file_path, # 从 self.config 获取路径
                obj=best_model
            )
            # logging.info(f"Best model saved to {self.config.trained_model_file_path}")

        except Exception as e:
            # logging.error('Exception occurred at model training', exc_info=True)
            raise e

# research/04_model_trainer.ipynb 或 main.py
from your_project_path.config.configuration import ConfigurationManager
from your_project_path.components.model_trainer import ModelTrainer
# 假设 X_train, X_test, y_train, y_test 已经从之前的数据处理阶段加载或生成

try:
    config