ValueError：传递值的形状是 (8631, 28)，索引意味着 (8631, 17)

Question

• step1：创建管道
• step2：将管道转换为数据帧
• step3：我正在尝试将管道转换为数据帧，但引发了异常。如何解决这个问题
• 第 4 步：如何解决 ValueError：传递值的形状为 (8631, 28)，索引意味着 (8631, 17) 在管道转换为数据帧之上，

from sklearn.preprocessing import FunctionTransformer, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer

import pandas as pd
from sklearn.model_selection import train_test_split

print("step1: import lib")
print("step2: loading raw data")
df = pd.read_csv("online_shoppers_intention.csv")

print("step3: data preparition")
X = df.drop(['Revenue'], axis = 1)
y = df['Revenue']

print("step4: data splitting")
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3, random_state = 0)
names = X_train.columns.tolist()

numeric_transformer = SimpleImputer(strategy = 'constant')
categorical_transformer = OneHotEncoder(handle_unknown = 'ignore')

numerical_cols = X.select_dtypes(exclude = "object").columns.values.tolist()
categorical_cols = X.select_dtypes(exclude = ['int', 'float64', 'bool']).columns.values.tolist()
    
preprocessor = ColumnTransformer(
    transformers=[
    ('num', numeric_transformer, numerical_cols)
    ,('cat', categorical_transformer, categorical_cols)
    ],
    remainder = 'passthrough')

pipe_preprocessor = Pipeline(steps = [("preprocessor", preprocessor), ("pandarizer", FunctionTransformer(lambda x: pd.DataFrame(x, columns = names)))]).fit(X_train)
    
X_train_pipe = pipe_preprocessor.transform(X_train)
X_test_pipe = pipe_preprocessor.transform(X_test)

Answer 1

我认为问题出在您的代码的一部分

pd.DataFrame(x, columns = names)

，因为在上一步中您使用

categorical_transformer = OneHotEncoder(handle_unknown = 'ignore')

预处理分类列，它增加了列数，这就是形状不匹配的原因，作为解决方案，您可以尝试使用 LabelEncoder 更改 OneHotEncoder或根据新的列数更改

names

列表