如何在pandas中正确使用稀疏向量特征和数值特征来训练sklearn模型？

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.preprocessing import StandardScaler
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression

# 加载数据集
data = pd.read_csv('data.csv')

# 分割特征和标签
X = data.drop('target', axis=1)
y = data['target']

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建稀疏向量特征处理器
sparse_transformer = CountVectorizer()

# 创建数值特征处理器
numeric_transformer = StandardScaler()

# 创建特征处理管道
preprocessor = ColumnTransformer(
    transformers=[
        ('sparse', sparse_transformer, ['sparse_feature']),
        ('numeric', numeric_transformer, ['numeric_feature'])
    ])

# 创建模型
model = LogisticRegression()

# 创建模型训练管道
pipeline = Pipeline(steps=[('preprocessor', preprocessor),
                           ('model', model)])

# 拟合模型
pipeline.fit(X_train, y_train)

# 预测
y_pred = pipeline.predict(X_test)