在管道中转换的特征的置换特征重要性 （sklearn）

问：

早些时候也提出了类似的问题。我需要通过 计算预处理特征的特征重要性。预处理是在管道中实现的。代码如下：sklearn.inspection.permutation_importance

import numpy as np
import pandas as pd
import os
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline

###
data load
###

X_train, X_test, y_train, y_test = train_test_split(all_features, diagnosis, test_size=0.25, random_state=42)

pca_conn = Pipeline(
    steps = [("group_whiten", StandardScaler()),
             ('pca_conn', PCA(n_components = 100)),
            ("pca_whiten", StandardScaler())]
)

pca_grad = Pipeline(
    steps = [("group_whiten", StandardScaler()),
             ('pca_grad', PCA(n_components = 100)),
            ("pca_whiten", StandardScaler())]
)

pca_centroid_disp_pca = Pipeline(
    steps = [("group_whiten", StandardScaler()),
             ('pca_grad', PCA(n_components = 10)),
            ("pca_whiten", StandardScaler())]
)

pca_cortex_disp_pca = Pipeline(
    steps = [("group_whiten", StandardScaler()),
             ('pca_grad', PCA(n_components = 100)),
            ("pca_whiten", StandardScaler())]
)

cat_encoder = Pipeline(
    steps = [("cat_encoder", OneHotEncoder(handle_unknown="ignore"))]
)
whiten = Pipeline(
    steps = [("whiten", StandardScaler())]
)

preprocessor = ColumnTransformer(
    transformers=[
        ("pca_conn", pca_conn, conn_cols),
        ("pca_grad", pca_grad, grad_cols),
        ("pca_centroid_disp_pca", pca_centroid_disp_pca, centroid_disp_cols),
        ("pca_cortex_disp_pca", pca_cortex_disp_pca, cortex_disp_cols),
        ("encode_dataset", cat_encoder, ["dataset"]),
        ("encode_sex", cat_encoder, ["sex"]),
        ("whiten_fd", whiten, ["mean_fd"]),
        ("whiten_age", whiten, ["age"])
    ]
)

lr = LogisticRegression(random_state=42, max_iter = 10000)
clf = Pipeline([('preprocessor', preprocessor),
                ('lr',lr)])

from sklearn.linear_model import LogisticRegression

trained_logreg = clf.fit(X_train, y_train)
trained_logreg.score(X_test, y_test)

perm_acc = permutation_importance(trained_logreg, X_test, y_test,n_repeats=100, random_state=42, n_jobs = -1)

默认情况下，似乎会计算原始特征的排列重要性。有没有人尝试过为变换的特征实现排列重要性？有什么提示吗？我认为单独进行预处理不是一种选择（数据泄漏）。