【原创】xgboost 特征评分的计算原理

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