python - 属性错误: 'LGBMRegressor' object has no attribute 'feature_name_'

Question

我试图在使用下面的代码训练模型后获取特征名称，然后遇到了这样的错误。

我已经检查了 lightgbm 的文档，lightgbm.LGBMRegressor 具有属性“feature_name_”，

(这是链接 https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html#lightgbm.LGBMRegressor )

我在 jupyter 笔记本上运行这个，我的 lightGBM 版本是 2.3.1

我真的不知道，有人可以给我线索吗？

from lightgbm import LGBMRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.externals import joblib

# load data
iris = load_iris()
data = iris.data
target = iris.target

# split dataset
X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2)

# training
gbm = LGBMRegressor(objective='regression', num_leaves=31, learning_rate=0.05, n_estimators=20)
gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], eval_metric='l1', early_stopping_rounds=5)

# save the model
joblib.dump(gbm, 'loan_model.pkl')


# load the model
gbm = joblib.load('loan_model.pkl')

y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_)

print('The rmse of prediction is:', mean_squared_error(y_test, y_pred) ** 0.5)

# importances and feature_name_
print('Feature importances:', list(gbm.feature_importances_))

print('Feature names',gbm.feature_name_)# this is where went wrong

这是错误日志

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-1-d982fd40dcd0> in <module>
     32 print('Feature importances:', list(gbm.feature_importances_))
     33 
---> 34 print('Feature names',gbm.feature_name_)

AttributeError: 'LGBMRegressor' object has no attribute 'feature_name_'

非常感谢你!

Answer 1

要获取 LGBMRegressor 或 lightgbm 的任何其他 ML 模型类的特征名称，您可以使用存储底层 Booster 的 booster_ 属性该模型的。

gbm = LGBMRegressor(objective='regression', num_leaves=31, learning_rate=0.05, n_estimators=20) 
gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], eval_metric='l1', early_stopping_rounds=5)

boost = gbm.booster_
print('Feature names',boost.feature_name())