自学习置换的重要性导致模型中零系数的非零值。

Question

我对sklearn的permutation_importance函数感到困惑。我用正则logistic回归拟合了一条管道，导致的几个特征系数为0。然而，当我想在测试数据集中计算特征的排列重要性时，中的一些特性得到了非零重要性值。如果他们对分类器不起作用，这又怎么可能呢？

下面是一些示例代码和数据：

import numpy as np    
from sklearn.datasets import make_classification
from sklearn.model_selection import RepeatedStratifiedKFold
import scipy.stats as stats
from sklearn.utils.fixes import loguniform
from sklearn.preprocessing import StandardScaler
from sklearn.impute import KNNImputer
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import RandomizedSearchCV
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.inspection import permutation_importance


# create example data with missings
X, y = make_classification(n_samples = 500,
                           n_features = 100,
                           n_informative = 25,
                           n_redundant = 75,
                           random_state = 0)
c = 10000 # number of missings
X.ravel()[np.random.choice(X.size, c, replace = False)] = np.nan # introduce random missings
Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size = 0.2, random_state = 0)

folds = 5
repeats = 5
n_iter = 25
rskfold = RepeatedStratifiedKFold(n_splits = folds, n_repeats = repeats, random_state = 1897)

scl = StandardScaler()
imp = KNNImputer(n_neighbors = 5, weights = 'uniform')
sgdc = SGDClassifier(loss = 'log', penalty = 'elasticnet', class_weight = 'balanced', random_state = 0)

pipe = Pipeline([('scaler', scl),
                 ('imputer', imp),
                 ('clf', sgdc)])
param_rand = {'clf__l1_ratio': stats.uniform(0, 1),
              'clf__alpha': loguniform(0.001, 1)}

m = RandomizedSearchCV(pipe, param_rand, n_iter = n_iter, cv = rskfold, scoring = 'accuracy', random_state = 0, verbose = 1, n_jobs = -1)
m.fit(Xtrain, ytrain)

coefs = m.best_estimator_.steps[2][1].coef_
print('Number of non-zero feature coefficients in classifier:')
print(np.sum(coefs != 0))

imps = permutation_importance(m, Xtest, ytest, n_repeats = 25, random_state = 0, n_jobs = -1)

print('Number of non-zero feature importances after permutations:')
print(np.sum(imps['importances_mean'] != 0))

你会发现第二个打印数字与第一个不匹配.

任何帮助都是非常感谢的！

Answer 1

因为你有一个KNNImputer。模型中系数为零的特征仍会影响其他列的计算，从而改变整个列的预测，从而具有非零置换的重要性。