如何仅在某些值上在管道内使用 StandardScaler？

Question

我有问题。我想使用StandardScaler()，但我的数据集包含某些OneHotEncoding 值和其他应该不缩放的值。但是如果我运行StandardScaler()，所有的值都会被缩放。那么是否可以选择仅对管道内的某些值运行此方法？

我发现了这个问题：One-Hot-Encode categorical variables and scale continuous ones simultaneouely 使用以下代码

columns = ['rank']
columns_to_scale  = ['gre', 'gpa']

scaler = StandardScaler()
ohe    = OneHotEncoder(sparse=False)

# Concatenate (Column-Bind) Processed Columns Back Together
processed_data = np.concatenate([scaled_columns, encoded_columns], axis=1)

那么有没有一个选项只在 pipeline 内运行 StandardScaler() 在某些值上，而其他值应该合并到缩放值？ 所以管道应该只对值'xy', 'xyz'使用StandardScaler。

标准缩放器类

from sklearn.base import BaseEstimator, TransformerMixin
class StandardScaler_with_certain_features(BaseEstimator, TransformerMixin):
    def __init__(self, columns_to_scale):
        scaler = StandardScaler()
        

    def fit(self, X, y = None):
        scaler.fit(X_train) # only std.fit on train set
        X_train_nor = scaler.transform(X_train.values)

    def transform(self, X, y = None):
        return X

管道

columns_to_scale  = ['xy', 'xyz']
    
steps = [('standard_scaler', StandardScaler_with_certain_features(columns_to_scale)),
         ('feature_selection', SelectFromModel(estimator=LogisticRegression(max_iter=100))),
         ('lasso', Lasso(alpha=0.03))]

pipeline = Pipeline(steps) 

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=30)

parameteres = { }

grid = GridSearchCV(pipeline, param_grid=parameteres, cv=5)                
grid.fit(X_train, y_train)
                    
print("score = %3.2f" %(grid.score(X_test,y_test)))
print('Training set score: ' + str(grid.score(X_train,y_train)))
print('Test set score: ' + str(grid.score(X_test,y_test)))

# Prediction
y_pred = grid.predict(X_test)
print("RMSE Val:", metrics.mean_squared_error(y_test, y_pred, squared=False))

Answer 1

您可以在Pipeline 中包含ColumnTransformer，以便将StandardScaler 仅应用于某些列。您需要设置remainder='passthrough 以确保未缩放的列与已缩放的列连接。

import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.compose import ColumnTransformer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.linear_model import Lasso

df = pd.DataFrame({
    'y': np.random.normal(0, 1, 100),
    'x': np.random.normal(0, 1, 100),
    'z': np.random.normal(0, 1, 100),
    'xy': np.random.normal(2, 3, 100),
    'xyz': np.random.normal(4, 5, 100),
})

X = df.drop(labels=['y'], axis=1)
y = df['y']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=30)

preprocessor = ColumnTransformer(
    transformers=[('scaler', StandardScaler(), ['xy', 'xyz'])],
    remainder='passthrough'
)

pipeline = Pipeline([
    ('preprocessor', preprocessor),
    ('lasso', Lasso(alpha=0.03))
])

pipeline.fit(X_train, y_train)
pipeline.score(X_test, y_test)