python - Sklearn 管道转换特定列 - ValueError : too many values to unpack (expected 2)

Question

我正在尝试使用缩放器、onhotencoder、多项式特征和最后的线性回归模型制作管道

from sklearn.pipeline import Pipeline
pipeline = Pipeline([
                    ('scaler', StandardScaler(), num_cols),
                    ('polynom', PolynomialFeatures(3), num_cols), 
                    ('encoder', OneHotEncoder(), cat_cols),
                   ('linear_regression', LinearRegression() )
])

但是当我安装管道时，我遇到了 ValueError: 太多值无法解压(预期为 2)

pipeline.fit(x_train,y_train)
pipeline.score(x_test, y_test)

Answer 1

如果我理解正确，您希望将管道的某些步骤应用于特定列。您必须使用 ColumnTransformer ，而不是通过在管道阶段末尾添加列名(这是不正确的并导致错误)来完成此操作。 。 Here你可以找到另一个类似的例子。

就您而言，您可以执行以下操作:

import pandas as pd

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import OneHotEncoder
from sklearn.linear_model import LinearRegression
from sklearn.compose import ColumnTransformer

# Fake data.
train_data = pd.DataFrame({'n1': range(10), 'n2': range(10)})
train_data['c1'] = 0
train_data['c1'][5:] = 1
y_train = [0]*10
y_train[5:] = [1]*5

# Here I assumed you are using a DataFrame. If not, use integer indices instead of column names.
num_cols = ['n1', 'n2']
cat_cols = ['c1']


# Pipeline to transform the numerical features.
numerical_transformer = Pipeline([('scaler', StandardScaler()),
                                  ('polynom', PolynomialFeatures(3))
    
])

# Apply the numerical transformer only on the numerical columns.
# Spearately, apply the OneHotEncoder.
ct = ColumnTransformer([('num_transformer', numerical_transformer, num_cols),
                        ('encoder', OneHotEncoder(), cat_cols)])

# Main pipeline for fitting.
pipeline = Pipeline([
                   ('column_transformer', ct),
                   ('linear_regression', LinearRegression() )
])

pipeline.fit(train_data, y_train)

大致上，管道的布局如下所示: