使用 XGBoost 特征重要性分数打印出特征选择中使用的特征

Question

我正在使用 XGBoost 特征重要性分数在我的 KNN Model 中使用以下代码（ 摘自本文）执行特征选择：

# this section for training and testing the algorithm after feature selection

#dataset spliting
X = df.iloc[:, 0:17]
y_bin = df.iloc[:, 17]


# spliting the dataset into train, test and validate for binary classification
X_train, X_test, y_bin_train, y_bin_test = train_test_split(X, y_bin, random_state=0, test_size=0.2)

# fit model on training data
model = XGBClassifier()
model.fit(X_train, y_bin_train)

# using normalization technique to feature scale the training data
norm = MinMaxScaler()
X_train= norm.fit_transform(X_train)
X_test= norm.transform(X_test)

#oversampling
smote= SMOTE()
X_train, y_bin_train = smote.fit_resample(X_train,y_bin_train)

# Fit model using each importance as a threshold
thresholds = sort(model.feature_importances_)
for thresh in thresholds:
  # select features using threshold
  selection = SelectFromModel(model, threshold=thresh, prefit=True)
  select_X_train = selection.transform(X_train)
  
  # train model
  knn = KNeighborsClassifier(n_neighbors=3, metric='euclidean')
  knn.fit(select_X_train, y_bin_train)

  # eval model
  select_X_test = selection.transform(X_test)
  y_pred = knn.predict(select_X_test)

  report = classification_report(y_bin_test,y_pred)
  print("Thresh= {} , n= {}\n {}" .format(thresh, select_X_train.shape[1], report))
  cm = confusion_matrix(y_bin_test, y_pred)
  print(cm)

我得到的 output 向我展示了每次迭代使用的特征数量select_X_train.shape[1] ，每次删除特征时使用的阈值thresh ，分类报告和混淆矩阵：

Thresh= 0.0 , n= 17
               precision    recall  f1-score   support

           0       0.98      0.96      0.97     42930
           1       0.87      0.92      0.89     11996

    accuracy                           0.95     54926
   macro avg       0.92      0.94      0.93     54926
weighted avg       0.95      0.95      0.95     54926

[[41226  1704]
 [  909 11087]]
Thresh= 0.007143254857510328 , n= 16
               precision    recall  f1-score   support

           0       0.98      0.96      0.97     42930
           1       0.87      0.92      0.89     11996

    accuracy                           0.95     54926
   macro avg       0.92      0.94      0.93     54926
weighted avg       0.95      0.95      0.95     54926

[[41226  1704]
 [  909 11087]]

这个 output 将继续运行，直到使用的功能数量达到 1 (n=1)。 我想要做的是我还想在每次迭代中包含使用（或删除）的功能的名称，但我无法弄清楚。 有没有办法完成它？

Answer 1

你可以使用

X.columns[selector.get_support()].to_list()

提取所选特征的名称列表，其中X是具有特征值的 pandas 数据框， selector是SelectFromModel元转换器。 另请参阅此答案。

import pandas as pd
import numpy as np
from imblearn.over_sampling import SMOTE
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectFromModel
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import MinMaxScaler

# generate some data
df = pd.DataFrame({
    'x1': np.random.normal(0, 1, 100),
    'x2': np.random.normal(2, 3, 100),
    'x3': np.random.normal(4, 5, 100),
    'y': np.random.choice([0, 1], 100),
})

# extract the features and target
X = df.iloc[:, :-1]
y = df.iloc[:, -1]

# split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size=0.2)

# scale the data
scaler = MinMaxScaler().fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

# resample the data
smote = SMOTE()
X_train, y_train = smote.fit_resample(X_train, y_train)

# fit the XGBoost classifier using all the features
model = XGBClassifier()
model.fit(X_train, y_train)

# fit the KNN classifier using each feature importance 
# value as a feature selection threshold
thresholds = np.sort(model.feature_importances_)

for threshold in thresholds:

    # select the features
    selector = SelectFromModel(model, threshold=threshold, prefit=True)
    X_train_ = selector.transform(X_train)
    X_test_ = selector.transform(X_test)

    # extract the names of the selected features 
    selected_features = X.columns[selector.get_support()].to_list()

    # train the model
    knn = KNeighborsClassifier(n_neighbors=3, metric='euclidean')
    knn.fit(X_train_, y_train)

    # generate the model predictions
    y_pred = knn.predict(X_test_)

    # calculate the model performance metrics
    report = classification_report(y_test, y_pred)
    cm = confusion_matrix(y_test, y_pred)

    print('Threshold: {}'.format(threshold))
    print('Selected features: \n {}'.format(selected_features))
    print('Confusion matrix: \n {}'.format(cm))
    print('Classification report: \n {}'.format(report))
    print('----------------------------')

# Threshold: 0.2871088981628418
# Selected features: 
#  ['x1', 'x2', 'x3']
# Confusion matrix: 
#  [[6 0]
#  [7 7]]
# Classification report: 
#                precision    recall  f1-score   support
#
#            0       0.46      1.00      0.63         6
#            1       1.00      0.50      0.67        14
#
#     accuracy                           0.65        20
#    macro avg       0.73      0.75      0.65        20
# weighted avg       0.84      0.65      0.66        20
#
# ----------------------------
# Threshold: 0.34210699796676636
# Selected features: 
#  ['x1', 'x3']
# Confusion matrix: 
#  [[ 4  2]
#  [10  4]]
# Classification report: 
#                precision    recall  f1-score   support
#
#            0       0.29      0.67      0.40         6
#            1       0.67      0.29      0.40        14
#
#     accuracy                           0.40        20
#    macro avg       0.48      0.48      0.40        20
# weighted avg       0.55      0.40      0.40        20
#
# ----------------------------
# Threshold: 0.37078407406806946
# Selected features: 
#  ['x1']
# Confusion matrix: 
#  [[3 3]
#  [5 9]]
# Classification report: 
#                precision    recall  f1-score   support
#
#            0       0.38      0.50      0.43         6
#            1       0.75      0.64      0.69        14
#
#     accuracy                           0.60        20
#    macro avg       0.56      0.57      0.56        20
# weighted avg       0.64      0.60      0.61        20
#
# ----------------------------