Feature importances - Bagging, scikit-learn

Question

For a project I am comparing a number of decision trees, using the regression algorithms (Random Forest, Extra Trees, Adaboost and Bagging) of scikit-learn. To compare and interpret them I use the feature importance , though for the bagging decision tree this does not look to be available.

My question: Does anybody know how to get the feature importances list for Bagging?

Greetings, Kornee

Answer 1

Are you talking about BaggingClassifier? It can be used with many base estimators, so there is no feature importances implemented. There are model-independent methods for computing feature importances (see e.g. https://github.com/scikit-learn/scikit-learn/issues/8898), scikit-learn doesn't use them.

In case of decision trees as base estimators you can compute feature importances yourselves: it'd be just an average of tree.feature_importances_ among all trees in bagging.estimators_:

import numpy as np
from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_iris

X, y = load_iris(return_X_y=True)
clf = BaggingClassifier(DecisionTreeClassifier())
clf.fit(X, y)

feature_importances = np.mean([
    tree.feature_importances_ for tree in clf.estimators_
], axis=0)

RandomForestClassifer does the same computation internally.

Answer 2

Extending what CharlesG posted, here's my solution for overloading the BaggingRegressor (same should work for BaggingClassifier).

class myBaggingRegressor(BaggingRegressor):
    def fit(self, X, y):
        fitd = super().fit(X, y)
        # need to pad features?
        if self.max_features == 1.0:
            # compute feature importances or coefficients
            if hasattr(fitd.estimators_[0], 'feature_importances_'):
                self.feature_importances_ =  np.mean([est.feature_importances_ for est in fitd.estimators_], axis=0)
            else:
                self.coef_ =  np.mean([est.coef_ for est in fitd.estimators_], axis=0)
                self.intercept_ =  np.mean([est.intercept_ for est in fitd.estimators_], axis=0)
        else:
            # need to process results into the right shape
            coefsImports = np.empty(shape=(self.n_features_, self.n_estimators), dtype=float)
            coefsImports.fill(np.nan)
            if hasattr(fitd.estimators_[0], 'feature_importances_'):
                # store the feature importances
                for idx, thisEstim in enumerate(fitd.estimators_):
                    coefsImports[fitd.estimators_features_[idx], idx] = thisEstim.feature_importances_
                # compute average
                self.feature_importances_ = np.nanmean(coefsImports, axis=1)
            else:
                # store the coefficients & intercepts
                self.intercept_ = 0
                for idx, thisEstim in enumerate(fitd.estimators_):
                    coefsImports[fitd.estimators_features_[idx], idx] = thisEstim.coefs_
                    self.intercept += thisEstim.intercept_
                # compute
                self.intercept /= self.n_estimators
                # average
                self.coefs_ = np.mean(coefsImports, axis=1)                
        return fitd

This correctly handles if max_features <> 1.0, though I suppose won't work exactly if bootstrap_features=True.

I suppose it's because sklearn has evolved a lot since 2017, but couldn't get it to work with the constructor, and it doesn't seem entirely necessary - the only reason to have that is to pre-specify the feature_importances_ attribute as None. However, it shouldn't even exist until fit() is called anyway.