如何进行布尔分类处理？

import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, OneHotEncoder, OrdinalEncoder
from sklearn.pipeline import Pipeline

data = pd.read_csv('Datasets/StudentScore.csv')

target = 'MathScore'
x = data.drop(data[[target, 'Unnamed: 0']], axis=1)
y = data[target]
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)

# numerical processing
num_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())
])

x_train[['ReadingScore', 'WritingScore']] = num_transformer.fit_transform(x_train[['ReadingScore', 'WritingScore']])
x_test[['ReadingScore', 'WritingScore']] = num_transformer.transform(x_test[['ReadingScore', 'WritingScore']])

# ordinal processing
education_levels = ["high school", "some high school", "some college", "associate's degree", "bachelor's degree",
                    "master's degree"]

ord_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='most_frequent')),
    ('encoder', OrdinalEncoder(categories=[education_levels])),
])

x_train[['ParentEduc']] = ord_transformer.fit_transform(x_train[['ParentEduc']])
x_test[['ParentEduc']] = ord_transformer.transform(x_test[['ParentEduc']])

# nominal processing
nom_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='most_frequent')),
    ('encoder', OneHotEncoder())
])

x_train[['EthnicGroup']] = nom_transformer.fit_transform(x_train[['EthnicGroup']])
x_test[['EthnicGroup']] = nom_transformer.transform(x_test[['EthnicGroup']])

# boolean processing
bool_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='most_frequent')),
    ('encoder', OneHotEncoder(sparse_output=False)),
])

x_train[['Gender', 'LunchType', 'TestPrep']] = bool_transformer.fit_transform(
    x_train[['Gender', 'LunchType', 'TestPrep']])
x_test[['Gender', 'LunchType', 'TestPrep']] = bool_transformer.transform(x_train[['Gender', 'LunchType', 'TestPrep']])

我在尝试创建管道来处理布尔分类特征时遇到错误。具体来说，在训练和测试集中的特征的 fit_transform 步骤中，我收到了

ValueError：列必须与标称处理和布尔处理部分中的键长度相同

如何解决这个问题？

错误信息“ValueError: 列必须与标称处理和布尔处理部分中的键长度相同”表明在 fit_transform 和 transform 的训练数据和测试数据之间传递了不同数量的列。

在的代码中，问题出现在这些行中：

x_test[['Gender', 'LunchType', 'TestPrep']] = bool_transformer.transform(x_train[['Gender', 'LunchType', 'TestPrep']])

错误地将 x_train 传递给 transform 方法，而不是 x_test 。这会导致列不匹配，因为 transform 方法期望看到与它在 fit_transform 期间看到的数据集相同的列数。

要解决此问题，请更正代码中的错字，以便它将 x_test 传递给 transform 方法：

x_test[['Gender', 'LunchType', 'TestPrep']] = bool_transformer.transform(x_test[['Gender', 'LunchType', 'TestPrep']])

通过此更改，的代码应在训练和测试数据上使用正确数量的列运行，而不会出现任何错误。