import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
import pickle
from sklearn.ensemble import RandomForestRegressor

# 加载数据集
df = pd.read_csv('auto-mpg.csv')

# 显示前五行数据
print(df.head())

# 处理缺失值
# 将 'horsepower' 列中的所有值转换为数值类型
df['horsepower'] = pd.to_numeric(df['horsepower'], errors='coerce')
# 删除包含缺失值的行
df = df.dropna()

# 选择相关特征进行建模
X = df[['cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'model year', 'origin']]
y = df['mpg']

# 将数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建包含标准化和线性回归的管道
pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('linreg', LinearRegression())
])

# 训练模型
pipeline.fit(X_train, y_train)

# 保存训练好的模型
with open('2.2.2_model.pkl', 'wb') as model_file:
    pickle.dump(pipeline, model_file)

# 预测并保存结果
y_pred = pipeline.predict(X_test)
results_df = pd.DataFrame(y_pred, columns=['预测结果'])
results_df.to_csv('2.2.2_results.txt', index=False)

# 测试模型
with open('2.2.2_report.txt', 'w') as results_file:
    results_file.write(f'训练集得分: {pipeline.score(X_train, y_train)}\n')
    results_file.write(f'测试集得分: {pipeline.score(X_test, y_test)}\n')

# 训练一个随机森林回归模型作为替代模型
rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)

# 使用随机森林模型进行预测
y_pred_rf = rf_model.predict(X_test)

# 保存新的结果
results_rf_df = pd.DataFrame(y_pred_rf, columns=['预测结果'])
results_rf_df.to_csv('2.2.2_results_rf.txt', index=False)

# 测试模型并保存得分
with open('2.2.2_report_rf.txt', 'w') as results_rf_file:
    results_rf_file.write(f'训练集得分: {rf_model.score(X_train, y_train)}\n')
    results_rf_file.write(f'测试集得分: {rf_model.score(X_test, y_test)}\n')

    mpg  cylinders  displacement horsepower  weight  acceleration  model year  \
0  18.0          8         307.0        130    3504          12.0          70   
1  15.0          8         350.0        165    3693          11.5          70   
2  18.0          8         318.0        150    3436          11.0          70   
3  16.0          8         304.0        150    3433          12.0          70   
4  17.0          8         302.0        140    3449          10.5          70   

   origin                   car name  
0       1  chevrolet chevelle malibu  
1       1          buick skylark 320  
2       1         plymouth satellite  
3       1              amc rebel sst  
4       1                ford torino