<p>我找到的最佳解决方案(不确定直接访问成员是否很好):</p>
<pre><code>$ cat test_ttr.py
#!/usr/bin/python
# -*- coding: UTF-8 -*-
from sklearn.datasets import make_regression
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn import linear_model
from sklearn.pipeline import Pipeline
from sklearn.compose import TransformedTargetRegressor
def main():
x, y = make_regression()
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
model = linear_model.Ridge(alpha=1)
pipe = Pipeline([('scale', preprocessing.StandardScaler()), ('model', model)])
treg = TransformedTargetRegressor(regressor=pipe, transformer=preprocessing.MinMaxScaler())
treg.fit(x_train, y_train)
print(treg.regressor_['model'].coef_)
print(treg.regressor_['model'].alpha)
if __name__ == '__main__':
main()
$ python test_ttr.py
[-1.13077347e-02 4.44189754e-03 2.39262548e-03 1.72868998e-02
9.98554629e-03 4.66877821e-02 -4.25349208e-03 1.94027088e-03
5.64007062e-05 3.08491096e-03 -3.50818087e-05 -1.11165790e-02
-6.67893402e-03 -3.01372675e-03 3.70455557e-03 5.05148384e-03
9.39056280e-03 5.63774373e-03 -4.07545049e-03 -5.98363493e-03
-8.21146459e-03 1.20560099e-02 5.79147139e-03 -3.87135045e-03
3.62289162e-03 -5.32527728e-03 1.05227189e-02 -3.32636550e-03
2.24062002e-02 5.36611024e-03 4.42517510e-03 2.98492436e-04
-3.48722166e-03 -8.16323005e-03 -1.74921354e-03 -2.47793718e-03
2.00056722e-02 9.02842425e-03 -4.22978758e-03 2.37737450e-03
-7.93388529e-03 1.22910175e-02 1.34225568e-03 -3.51697078e-03
4.20992326e-03 4.35675123e-03 -8.07619773e-04 1.13628592e-02
4.12219590e-03 6.92190818e-03 -2.44482599e-03 -3.12429604e-03
-5.43930166e-03 3.27253280e-02 4.11909724e-03 3.83302056e-03
1.34754164e-02 -8.62591922e-04 -4.14770516e-03 -7.02794996e-03
-2.04141679e-03 -8.93807591e-04 -1.50736158e-03 3.51801088e-03
-1.26757035e-02 -8.46096567e-04 6.70465585e-02 -1.12191639e-02
6.08120935e-03 -9.07017386e-03 -2.13280853e-03 -2.24764380e-03
6.98012623e-03 -9.26042982e-03 -2.93708218e-03 5.74605237e-04
-1.41308272e-03 5.24419314e-03 3.41054848e-02 7.80090716e-03
7.33259527e-02 -4.78241365e-03 2.38806342e-04 3.84449219e-04
5.49127586e-02 -6.91505707e-04 -4.14642042e-04 3.43961614e-03
5.20966922e-04 -5.47828158e-03 -7.04740862e-04 4.68760531e-02
4.12140344e-03 -5.16221700e-03 -7.35235898e-03 7.68674585e-03
-4.39094201e-03 5.05034775e-03 5.75523532e-03 -6.17177294e-03]
1
</code></pre>
<p>如果可能的话,请随时改进这个答案</p>