how can i get the base learners's importance?

[the-black1022]

i was useing the stacking framework to predict. here is my steps: in the first layer, i use the classifiers such as KNN, SVM, RF, in the second layer , i use LR as meta classifier, and my problems is, the first: how do I see the contribution of each base classifier's prediction result to the final result, the second: can I adjust the weight of the prediction result of the base classifier in the second layer classifier. thank you very much

[rasbt]

how do I see the contribution of each base classifier's prediction result to the final result

This can be done by generating a dataset of the meta-classifier predictions and then accessing the internally fitted meta_classifier (self.meta_clf_). E.g., here is an example:

1) Setup: Assume this is your meta-classifier:

from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB 
from sklearn.ensemble import RandomForestClassifier
from mlxtend.classifier import StackingCVClassifier
from sklearn import datasets

import numpy as np
import warnings

iris = datasets.load_iris()
X, y = iris.data[:, 1:3], iris.target

RANDOM_SEED = 42

clf1 = KNeighborsClassifier(n_neighbors=1)
clf2 = RandomForestClassifier(random_state=RANDOM_SEED)
clf3 = GaussianNB()
lr = LogisticRegression()

sclf = StackingCVClassifier(classifiers=[clf1, clf2, clf3],
                            meta_classifier=lr,
                            random_state=RANDOM_SEED)

2) Create meta-feature predictions:

sclf.fit(X, y)
meta_X = sclf.predict_meta_features(X)

3) Get feature importance

You can now use meta_X, and the fitted meta-classifier sclf.meta_clf_ with your favorite feature importance method. E.g., feature importance via permutation:

from mlxtend.evaluate import feature_importance_permutation
mean_importance_vals, all_importance_vals = feature_importance_permutation(
    meta_X, y, sclf.meta_clf_.predict, metric='accuracy')
print(mean_importance_vals)
# prints: array([0.2       , 0.26666667, 0.20666667])