获取 sklearn 逻辑回归的边际效应

Question

我想从sklearn模型中获得逻辑回归的边际效应

我知道您可以使用“.get_margeff()”获取统计模型逻辑回归的这些信息。sklearn 没有什么东西吗？我想避免自己进行计算，因为我觉得会有很大的错误空间。

import statsmodels.formula.api as sm
from statsmodels.tools.tools import add_constant
from sklearn.datasets import load_breast_cancer
import pandas as pd
import numpy as np

data = load_breast_cancer()
x = data.data
y= data.target
x=add_constant(x,has_constant='add')

model = sm.Logit(y, x).fit_regularized()
margeff = model.get_margeff(dummy=True,count=True)
##print the margal effect
print(margeff.margeff)
>> [ 6.73582136e-02  2.15779589e-04  1.28857837e-02 -1.06718136e-03
 -1.96032750e+00  1.36137385e+00 -1.16303369e+00 -1.37422595e+00
  8.14539021e-01 -1.95330095e+00 -4.86235558e-01  4.84260993e-02
  7.16675627e-02 -2.89644712e-03 -5.18982198e+00 -5.93269894e-01
  3.22934080e+00 -1.28363008e+01  3.07823155e+00  5.84122170e+00
  1.92785670e-02 -9.86284081e-03 -7.53298463e-03 -3.52349287e-04
  9.13527446e-01  1.69938656e-01 -2.89245493e-01 -4.65659522e-01
 -8.32713335e-01 -1.15567833e+00]


# manual calculation, doing this as you can get the coef_ from a sklearn model and use in the function

def PDF(XB):
    var1 = np.exp(XB)
    var2 = np.power((1+np.exp(XB)),2)
    var3 = (var1 / var2) 
    return var3
arrPDF = PDF(np.dot(x,model.params))
ME=pd.DataFrame(np.dot(arrPDF[:,None],model.params[None,:]))
print(ME.iloc[:,1:].mean().to_list())

>>
[0.06735821358791198, 0.0002157795887363032, 0.012885783711597246, -0.0010671813611730326, -1.9603274961356965, 1.361373851981879, -1.1630336876543224, -1.3742259536619654, 0.8145390210646809, -1.9533009514684947, -0.48623555805230195, 0.04842609927469917, 0.07166756271689229, -0.0028964471200298475, -5.189821981601878, -0.5932698935239838, 3.229340802910038, -12.836300822253634, 3.0782315528664834, 5.8412217033605245, 0.019278567008384557, -0.009862840813512401, -0.007532984627259091, -0.0003523492868714151, 0.9135274456151128, 0.16993865598225097, -0.2892454926120402, -0.46565952159093893, -0.8327133347971125, -1.1556783345783221]

自定义函数给出的结果与“”相同.get_margeff()，但在上面的自定义函数中使用 sklearn ceof_ 时可能存在很大的错误空间。

1. sklearn 中是否有一些方法/函数/属性可以给我带来边际效应
2. 如果没有，是否有另一个库从 ceof_ 获取数据和边际效应
3. 如果上述两个问题的答案都是“否”，那么是否存在自定义函数不起作用的情况（例如，使用 sklearn 中的特定求解器或惩罚）

Answer 1

我几天前刚刚满足了这个需求。

我的主管给了我这些我想分享的信息。希望这可以帮到你。

partial_dependence：这个方法可以得到你想要的partial dependence或者marginal effects你想要的。

plot_partial_dependence：此方法可以绘制partial dependence.

以下是 API 参考中的示例代码。

scikit-learn version: 0.21.2

from sklearn.inspection import plot_partial_dependence, partial_dependence
from sklearn.datasets import make_friedman1
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import GradientBoostingRegressor


%matplotlib inline


X, y = make_friedman1()

# case1: linear model
lm = LinearRegression().fit(X, y)
# plot the partial dependence
plot_partial_dependence(lm, X, [0, (0, 1)])
# get the partial dependence
partial_dependence(lm, X, [0])

# case2: classifier
clf = GradientBoostingRegressor(n_estimators=10).fit(X, y)
# plot the partial dependence
plot_partial_dependence(clf, X, [0, (0, 1)])
# get the partial dependence
partial_dependence(clf, X, [0])