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扩展xgboost。XGB分类器

xgboost scikit-learn python-2.7 python

andrew · 技术社区 · 7 年前

XGBExtended 这扩展了类 xgboost.XGBClassifier ,xgboost的scikit学习API。我遇到了一些问题 get_params 获取参数 似乎只返回我在其中定义的属性 XGBExtended.__init__ ,以及在父初始化方法期间定义的属性( xgboost.XGBClassifier.__init__

In [182]: import xgboost as xgb
     ...: 
     ...: class XGBExtended(xgb.XGBClassifier):
     ...:   def __init__(self, foo):
     ...:     super(XGBExtended, self).__init__()
     ...:     self.foo = foo
     ...: 
     ...: clf = XGBExtended(foo = 1)
     ...: 
     ...: clf.get_params()
     ...: 
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-182-431c4c3f334b> in <module>()
      8 clf = XGBExtended(foo = 1)
      9 
---> 10 clf.get_params()

/Users/andrewhannigan/lib/xgboost/python-package/xgboost/sklearn.pyc in get_params(self, deep)
    188         if isinstance(self.kwargs, dict):  # if kwargs is a dict, update params accordingly
    189             params.update(self.kwargs)
--> 190         if params['missing'] is np.nan:
    191             params['missing'] = None  # sklearn doesn't handle nan. see #4725
    192         if not params.get('eval_metric', True):

KeyError: 'missing'

所以我遇到了一个错误,因为“缺失”不是 params dict在 XGBClassifier.get_params

In [183]: %debug
> /Users/andrewhannigan/lib/xgboost/python-package/xgboost/sklearn.py(190)get_params()
    188         if isinstance(self.kwargs, dict):  # if kwargs is a dict, update params accordingly
    189             params.update(self.kwargs)
--> 190         if params['missing'] is np.nan:
    191             params['missing'] = None  # sklearn doesn't handle nan. see #4725
    192         if not params.get('eval_metric', True):

ipdb> params
{'foo': 1}
ipdb> self.__dict__
{'n_jobs': 1, 'seed': None, 'silent': True, 'missing': nan, 'nthread': None, 'min_child_weight': 1, 'random_state': 0, 'kwargs': {}, 'objective': 'binary:logistic', 'foo': 1, 'max_depth': 3, 'reg_alpha': 0, 'colsample_bylevel': 1, 'scale_pos_weight': 1, '_Booster': None, 'learning_rate': 0.1, 'max_delta_step': 0, 'base_score': 0.5, 'n_estimators': 100, 'booster': 'gbtree', 'colsample_bytree': 1, 'subsample': 1, 'reg_lambda': 1, 'gamma': 0}
ipdb>

正如你所见 参数 foo . 但由于某种原因 BaseEstimator.get_params 从调用的方法 xgboost.XGBClassifier.get_params 方法不幸的是,即使我明确要求 获取参数 具有 deep = True ,它仍然无法正常工作:

ipdb> super(XGBModel, self).get_params(deep=True)
{'foo': 1}
ipdb>

系统规格:

In [186]: print IPython.sys_info()
{'commit_hash': u'1149d1700',
 'commit_source': 'installation',
 'default_encoding': 'UTF-8',
 'ipython_path': '/Users/andrewhannigan/virtualenvironment/nimble_ai/lib/python2.7/site-packages/IPython',
 'ipython_version': '5.4.1',
 'os_name': 'posix',
 'platform': 'Darwin-14.5.0-x86_64-i386-64bit',
 'sys_executable': '/usr/local/Cellar/python/2.7.10/Frameworks/Python.framework/Versions/2.7/Resources/Python.app/Contents/MacOS/Python',
 'sys_platform': 'darwin',
 'sys_version': '2.7.10 (default, Jul  3 2015, 12:05:53) \n[GCC 4.2.1 Compatible Apple LLVM 6.1.0 (clang-602.0.53)]'}

1 回复 | 直到 7 年前

Vivek Kumar 7 年前

这里的问题是子类的声明不正确。当您仅使用 foo

您应该使用以下内容:

class XGBExtended(xgb.XGBClassifier):
    def __init__(self, foo, max_depth=3, learning_rate=0.1,
                 n_estimators=100, silent=True,
                 objective="binary:logistic",
                 nthread=-1, gamma=0, min_child_weight=1,
                 max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1,
                 reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
                 base_score=0.5, seed=0, missing=None, **kwargs):

        # Pass the required parameters to super class
        super(XGBExtended, self).__init__(max_depth, learning_rate,
                                            n_estimators, silent, objective,
                                            nthread, gamma, min_child_weight,
                                            max_delta_step, subsample,
                                            colsample_bytree, colsample_bylevel,
                                            reg_alpha, reg_lambda,
scale_pos_weight, base_score, seed, missing, **kwargs)

        # Use other custom parameters
        self.foo = foo

在此之后,您将不会得到任何错误。

clf = XGBExtended(foo = 1)
print(clf.get_params(deep=True))

>>> {'reg_alpha': 0, 'colsample_bytree': 1, 'silent': True, 
     'colsample_bylevel': 1, 'scale_pos_weight': 1, 'learning_rate': 0.1, 
     'missing': None, 'max_delta_step': 0, 'nthread': -1, 'base_score': 0.5, 
     'n_estimators': 100, 'subsample': 1, 'reg_lambda': 1, 'seed': 0, 
     'min_child_weight': 1, 'objective': 'binary:logistic', 
     'foo': 1, 'max_depth': 3, 'gamma': 0}