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xgboost是基于GBDT原理进行改进的算法，效率高，并且可以进行并行化运算；
而且可以在训练的过程中给出各个特征的评分，从而表明每个特征对模型训练的重要性，
调用的源码就不准备详述，本文主要侧重的是计算的原理，函数get_fscore源码如下，
源码来自安装包：xgboost/python-package/xgboost/core.py
通过下面的源码可以看出，特征评分可以看成是被用来分离决策树的次数，而这个与
《统计学习基础-数据挖掘、推理与推测》中10.13.1 计算公式有写差异，此处需要注意。
注：考虑的角度不同，计算方法略有差异。
def get_fscore(self, fmap=‘‘):
"""Get feature importance of each feature.
Parameters
----------
fmap: str (optional)
The name of feature map file
"""
return self.get_score(fmap, importance_type=‘weight‘)
def get_score(self, fmap=‘‘, importance_type=‘weight‘):
"""Get feature importance of each feature.
Importance type can be defined as:
‘weight‘ - the number of times a feature is used to split the data across all trees.
‘gain‘ - the average gain of the feature when it is used in trees
‘cover‘ - the average coverage of the feature when it is used in trees
Parameters
----------
fmap: str (optional)
The name of feature map file
"""
if importance_type not in [‘weight‘, ‘gain‘, ‘cover‘]:
msg = "importance_type mismatch, got ‘{}‘, expected ‘weight‘, ‘gain‘, or ‘cover‘"
raise ValueError(msg.format(importance_type))
# if it‘s weight, then omap stores the number of missing values
if importance_type == ‘weight‘:
# do a simpler tree dump to save time
trees = self.get_dump(fmap, with_stats=False)
fmap = {}
for tree in trees:
for line in tree.split(‘\n‘):
# look for the opening square bracket
arr = line.split(‘[‘)
# if no opening bracket (leaf node), ignore this line
if len(arr) == 1:
continue
# extract feature name from string between []
fid = arr[1].split(‘]‘)[0].split(‘<‘)[0]
if fid not in fmap:
# if the feature hasn‘t been seen yet
fmap[fid] = 1
else:
fmap[fid] += 1
return fmap
else:
trees = self.get_dump(fmap, with_stats=True)
importance_type += ‘=‘
fmap = {}
gmap = {}
for tree in trees:
for line in tree.split(‘\n‘):
# look for the opening square bracket
arr = line.split(‘[‘)
# if no opening bracket (leaf node), ignore this line
if len(arr) == 1:
continue
# look for the closing bracket, extract only info within that bracket
fid = arr[1].split(‘]‘)
# extract gain or cover from string after closing bracket
g = float(fid[1].split(importance_type)[1].split(‘,‘)[0])
# extract feature name from string before closing bracket
fid = fid[0].split(‘<‘)[0]
if fid not in fmap:
# if the feature hasn‘t been seen yet
fmap[fid] = 1
gmap[fid] = g
else:
fmap[fid] += 1
gmap[fid] += g
# calculate average value (gain/cover) for each feature
for fid in gmap:
gmap[fid] = gmap[fid] / fmap[fid]
return gmap
GBDT特征评分的计算说明原理：
链接：1、http://machinelearningmastery.com/feature-importance-and-feature-selection-with-xgboost-in-python/
详细的代码说明过程：可以从上面的链接进入下面的链接：
http://stats.stackexchange.com/questions/162162/relative-variable-importance-for-boosting
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原文地址：http://www.cnblogs.com/haobang008/p/5929378.html
