python - 将缩放和 pca 应用于 ColumnTransformer 中的列子集

Question

我有一个数据集，想要对 pandas 数据帧的子集应用缩放，然后应用 PCA，并仅返回未转换的组件和列。因此，使用来自seaborn的mpg数据集，我可以看到尝试预测mpg的训练集如下所示:

现在假设我想保留气缸和排量，并缩放其他所有内容并将其减少为 2 个分量。我预计结果总共为 4 列，即原始 2 列加上 2 个分量。

如何使用 ColumnTransformer 缩放列的子集，然后使用 PCA 并仅返回组件和 2 个直通列？

MWE

import seaborn as sns
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import (StandardScaler)
from sklearn.decomposition import PCA
from sklearn.compose import ColumnTransformer

df = sns.load_dataset('mpg').drop(["origin", "name"], axis = 1).dropna()

X = df.loc[:, ~df.columns.isin(['mpg'])]
y = df.iloc[:,0].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 21) 


scaler = StandardScaler()
pca = PCA(n_components = 2)
dtm_i = list(range(2, len(X_train.columns)))

preprocess = ColumnTransformer(transformers=[('scaler', scaler, dtm_i), ('PCA DTM', pca, dtm_i)], remainder='passthrough')
trans = preprocess.fit_transform(X_train)

pd.DataFrame(trans)

我强烈怀疑我对此步骤工作原理的误解是错误的:preprocess = ColumnTransformer(transformers=[('scaler', scaler, dtm_i), ('PCA DTM', pca, dtm_i)].我认为它在最后 4 列上运行，首先进行缩放，然后进行 PCA，最后返回 2 个分量，但我得到 8 列，前 4 列是缩放，接下来的 2 似乎是分量(可能它们不是)首先是缩放)，最后是两列“passthrough”。

Answer 1

我认为这可行，但不知道这是否是 Python/scikit 解决它的方法:

import seaborn as sns
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import (StandardScaler)
from sklearn.decomposition import PCA
from sklearn.compose import ColumnTransformer

df = sns.load_dataset('mpg').drop(["origin", "name"], axis = 1).dropna()

X = df.loc[:, ~df.columns.isin(['mpg'])]
y = df.iloc[:,0].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 21) 


scaler = StandardScaler()
pca = PCA(n_components = 2)
dtm_i = list(range(2, len(X_train.columns)))
dtm_i2 = list(range(0, len(X_train.columns)-2))

preprocess = ColumnTransformer(transformers=[('scaler', scaler, dtm_i)], remainder='passthrough')
preprocess2 = ColumnTransformer(transformers=[('PCA DTM', pca, dtm_i2)], remainder='passthrough')
trans = preprocess.fit_transform(X_train)
trans = preprocess2.fit_transform(trans)

pd.DataFrame(trans)