feature-selection - XGBoost:xgb.importance 特征图

Question

当我尝试使用以下代码时出现以下错误。

******代码******

    importance = bst.get_fscore(fmap='xgb.fmap')
    importance = sorted(importance.items(), key=operator.itemgetter(1))

******错误******

  File "scripts/xgboost_bnp.py", line 225, in <module>
  importance = bst.get_fscore(fmap='xgb.fmap')
  File "/usr/lib/python2.7/site-packages/xgboost/core.py", line 754, in get_fscore
    trees = self.get_dump(fmap)
  File "/usr/lib/python2.7/site-packages/xgboost/core.py", line 740, in get_dump
   ctypes.byref(sarr)))
  File "/usr/lib/python2.7/site-packages/xgboost/core.py", line 92, in _check_call
raise XGBoostError(_LIB.XGBGetLastError())
xgboost.core.XGBoostError: can not open file "xgb.fmap"

Answer 1

出现此错误是因为您使用可选参数 fmap 调用 get_fscore，声明每个特征的特征重要性应从名为 的特征映射文件中获取xgb.fmap，它在你的文件系统中不存在。

这是一个返回排序后的特征名称及其重要性的函数:

import xgboost as xgb
import pandas as pd

def get_xgb_feat_importances(clf):

    if isinstance(clf, xgb.XGBModel):
        # clf has been created by calling
        # xgb.XGBClassifier.fit() or xgb.XGBRegressor().fit()
        fscore = clf.booster().get_fscore()
    else:
        # clf has been created by calling xgb.train.
        # Thus, clf is an instance of xgb.Booster.
        fscore = clf.get_fscore()

    feat_importances = []
    for ft, score in fscore.iteritems():
        feat_importances.append({'Feature': ft, 'Importance': score})
    feat_importances = pd.DataFrame(feat_importances)
    feat_importances = feat_importances.sort_values(
        by='Importance', ascending=False).reset_index(drop=True)
    # Divide the importances by the sum of all importances
    # to get relative importances. By using relative importances
    # the sum of all importances will equal to 1, i.e.,
    # np.sum(feat_importances['importance']) == 1
    feat_importances['Importance'] /= feat_importances['Importance'].sum()
    # Print the most important features and their importances
    print feat_importances.head()
    return feat_importances