day31 python打卡

作业：尝试针对之前的心脏病项目ipynb，将他按照今天的示例项目整理成规范的形式，思考下哪些部分可以未来复用。

在有多级目录时，相对导入仅在同一包内有效，尤其在下级文件导入上级文件夹中的文件。

# src/config.py
 
CONFIG = {
    "data_path": PROJECT_ROOT / "data/raw/heart.csv",
    "test_size": 0.2,
    "random_state": 42,
    "models": {
        "random_forest": {
            "n_estimators": 100,
            "max_depth": 5
        },
        "xgboost": {
            "learning_rate": 0.1,
            "max_depth": 3,
            "n_estimators": 200
        }
    }
}
 

# src/data/loader.py
from pathlib import Path
import pandas as pd
from sklearn.model_selection import train_test_split
from src.config import CONFIG
 
def load_data() -> tuple:
    """加载并拆分数据集"""
    df = pd.read_csv(CONFIG["data_path"])
    
    # 假设最后一列是目标变量
    X = df.iloc[:, :-1]
    y = df.iloc[:, -1]
    
    return train_test_split(
        X, y,
        test_size=CONFIG["test_size"],
        random_state=CONFIG["random_state"]
    )

# src/models/base_model.py
from abc import ABC, abstractmethod
import pandas as pd
 
class BaseModel(ABC):
    """所有模型的统一接口"""
    
    @abstractmethod
    def train(self, X_train: pd.DataFrame, y_train: pd.Series):
        pass
    
    @abstractmethod
    def predict(self, X_test: pd.DataFrame) -> pd.Series:
        pass
    
    @abstractmethod
    def save(self, path: str):
        pass

# src/models/random_forest.py
from sklearn.ensemble import RandomForestClassifier
from .base_model import BaseModel
from src.config import CONFIG
 
class RandomForestModel(BaseModel):
    def __init__(self):
        self.model = RandomForestClassifier(
            n_estimators=CONFIG["models"]["random_forest"]["n_estimators"],
            max_depth=CONFIG["models"]["random_forest"]["max_depth"],
            random_state=CONFIG["random_state"]
        )
    
    def train(self, X_train, y_train):
        self.model.fit(X_train, y_train)
    
    def predict(self, X_test):
        return self.model.predict(X_test)
    
    def save(self, path):
        joblib.dump(self.model, path)

# src/evaluation/metrics.py
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
 
def calculate_all_metrics(y_true, y_pred) -> dict:
    return {
        "accuracy": accuracy_score(y_true, y_pred),
        "precision": precision_score(y_true, y_pred),
        "recall": recall_score(y_true, y_pred),
        "f1": f1_score(y_true, y_pred)
    }

# scripts/train_model.py
from src.models import train
 
if __name__ == "__main__":
    train.train_all_models()