如何从MultiOutputRegressor获取系数和特征重要性? [英] How to get coefficients and feature importances from MultiOutputRegressor?

问题描述

我正在尝试使用ElasticNet和Random Forests执行多输出回归，如下所示:

I am trying to perform a MultiOutput Regression using ElasticNet and Random Forests as follows:

from sklearn.ensemble import RandomForestRegressor
from sklearn.multioutput import MultiOutputRegressor
from sklearn.linear_model import ElasticNet

X_train, X_test, y_train, y_test = train_test_split(X_features, y, test_size=0.30,random_state=0)

弹性网

l1_range=np.arange(0.1,1.05,0.1).tolist()

regr_Enet=ElasticNetCV(cv=5,copy_X=True,n_alphas=100,l1_ratio=l1_range,selection='cyclic',normalize=False,verbose =2,n_jobs=1)

regr_multi_Enet= MultiOutputRegressor(regr_Enet)##ElasticNetCV

regr_multi_Enet.fit(X_train, y_train)

随机森林

max_depth = 20
number_of_trees=100

regr_multi_RF=MultiOutputRegressor(RandomForestRegressor(n_estimators=number_of_trees,max_depth=max_depth,random_state=0,n_jobs=1,verbose=1))

regr_multi_RF.fit(X_train, y_train)

y_multirf = regr_multi_RF.predict(X_test)

一切进展顺利，但是我还没有找到一种方法来获取模型的系数(coef_)或最重要的特征(feature_importances_).当我写的时候:

Everything is going well, however I haven't found a way to obtain the coefficients (coef_ ) or most important features (feature_importances_) of the model. When I write:

regr_multi_Enet.coef_
regr_multi_RF.feature_importances_

它显示以下错误:

AttributeError: 'MultiOutputRegressor' object has no attribute 'feature_importances_'
AttributeError: 'MultiOutputRegressor' object has no attribute 'coef_'

我已经阅读了MultiOutputRegressor上的文档，但是我找不到提取系数的方法.有人知道如何找回它们吗?

I have read the documentation on MultiOutputRegressor but I cannot find a way to extract the coefficients. Anyone knows how to retrieve them?

推荐答案

MultiOutputRegressor本身不具有这些属性-您需要首先使用estimators_属性(尽管在文档，它确实存在-请参阅 MultiOutputClassifier ).这是一个可重现的示例:

MultiOutputRegressor itself doesn't have these attributes - you need to access the underlying estimators first using the estimators_ attribute (which, although not mentioned in the docs, it exists indeed - see the docs for MultiOutputClassifier). Here is a reproducible example:

from sklearn.multioutput import MultiOutputRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import ElasticNet

# dummy data
X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
W = np.array([[1, 1], [1, 1], [2, 2], [2, 2]])

regr_multi_RF=MultiOutputRegressor(RandomForestRegressor())
regr_multi_RF.fit(X,W)

# how many estimators?
len(regr_multi_RF.estimators_)
# 2

regr_multi_RF.estimators_[0].feature_importances_
# array([ 0.4,  0.6])

regr_multi_RF.estimators_[1].feature_importances_
# array([ 0.4,  0.4])

regr_Enet = ElasticNet()
regr_multi_Enet= MultiOutputRegressor(regr_Enet)
regr_multi_Enet.fit(X, W)

regr_multi_Enet.estimators_[0].coef_
# array([ 0.08333333,  0.        ])

regr_multi_Enet.estimators_[1].coef_
# array([ 0.08333333,  0.        ])

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