使用 lightgbm 的特征重要性

Question

我正在嘗試運行我的 lightgbm 進行功能選擇，如下所示；

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')

然后我適合模型如下

# 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_

但我收到以下錯誤

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模型時在lightgbm獲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, fig_size = (40, 20)):
    feature_imp = pd.DataFrame({'Value':model.feature_importance(),'Feature':X.columns})
    plt.figure(figsize=fig_size)
    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()

Answer 2

根據我們是使用scikit-learn還是lightgbm方法訓練model ，為了獲得重要性，我們應該分別選擇feature_importances_屬性或feature_importance()函數，就像在這個例子中一樣（其中model是lgbm.fit() / lgbm.train()和train_columns = x_train_df.columns )：

import pandas as pd

def get_lgbm_varimp(model, train_columns, max_vars=50):
    
    if "basic.Booster" in str(model.__class__):
        # lightgbm.basic.Booster was trained directly, so using feature_importance() function 
        cv_varimp_df = pd.DataFrame([train_columns, model.feature_importance()]).T
    else:
        # Scikit-learn API LGBMClassifier or LGBMRegressor was fitted, 
        # so using feature_importances_ property
        cv_varimp_df = pd.DataFrame([train_columns, model.feature_importances_]).T

    cv_varimp_df.columns = ['feature_name', 'varimp']

    cv_varimp_df.sort_values(by='varimp', ascending=False, inplace=True)

    cv_varimp_df = cv_varimp_df.iloc[0:max_vars]   

    return cv_varimp_df
    

請注意，我們依賴於這樣一個假設，即特征重要性值的排序就像訓練期間模型矩陣列的排序（包括 one-hot dummy cols），參見LightGBM #209 。

Answer 3

對於 LightGBM 的 3.1.1 版本，擴展@user3067175 的評論：

pd.DataFrame({'Value':model.feature_importance(),'Feature':features}).sort_values(by="Value",ascending=False)

是一個特征名稱列表，在您的數據集的相同順序內，可以替換為features = df_train.columns.tolist() 。 這應該以相同的繪圖順序返回特征重要性。

注意：如果你使用 LGBMRegressor，你應該使用

pd.DataFrame({'Value':model.feature_importances_,'Feature':features}).sort_values(by="Value",ascending=False)

Answer 4

如果要檢查沒有訓練數據的加載模型，可以通過以下方式獲取特征重要性和特征名稱

df_feature_importance = (
    pd.DataFrame({
        'feature': model.feature_name(),
        'importance': model.feature_importance(),
    })
    .sort_values('importance', ascending=False)
)