使用lightgbm的重要特征

Question

我正在尝试运行我的lightgbm进行以下功能选择

初始化

# Initialize an empty array to hold feature importances
feature_importances = np.zeros(features_sample.shape[1])

# Create the model with several hyperparameters
model = lgb.LGBMClassifier(objective='binary', 
         boosting_type = 'goss', 
         n_estimators = 10000, class_weight ='balanced')

然后我把模型拟合如下

# Fit the model twice to avoid overfitting
for i in range(2):

   # Split into training and validation set
   train_features, valid_features, train_y, valid_y = train_test_split(train_X, train_Y, test_size = 0.25, random_state = i)

   # Train using early stopping
   model.fit(train_features, train_y, early_stopping_rounds=100, eval_set = [(valid_features, valid_y)], 
             eval_metric = 'auc', verbose = 200)

   # Record the feature importances
   feature_importances += model.feature_importances_

但是我得到了下面的错误

Training until validation scores don't improve for 100 rounds. 
Early stopping, best iteration is: [6]  valid_0's auc: 0.88648
ValueError: operands could not be broadcast together with shapes (87,) (83,) (87,) 

Answer 1

使用train模型时在lightgbm中获取特征重要性的示例。

import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

def plotImp(model, X , num = 20):
    feature_imp = pd.DataFrame({'Value':model.feature_importance(),'Feature':X.columns})
    plt.figure(figsize=(40, 20))
    sns.set(font_scale = 5)
    sns.barplot(x="Value", y="Feature", data=feature_imp.sort_values(by="Value", 
                                                        ascending=False)[0:num])
    plt.title('LightGBM Features (avg over folds)')
    plt.tight_layout()
    plt.savefig('lgbm_importances-01.png')
    plt.show()