问扩展xgboost.XGBClassifier
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Stack Overflow用户

提问于 2017-08-30 01:42:59

回答 1查看 1.1K关注 0票数 1

我正在尝试定义一个名为XGBExtended的类，它扩展了类xgboost.XGBClassifier，xgboost的scikit-learn API。我遇到了一些关于get_params方法的问题。下面是一个说明该问题的IPython会话。基本上，get_params似乎只返回我在XGBExtended.__init__中定义的属性，在父初始化方法(xgboost.XGBClassifier.__init__)期间定义的属性被忽略。我正在使用IPython并运行Python2.7。完整的系统规格在底部。

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'missing‘不是XGBClassifier.get_params方法中的params字典中的关键字。我进入调试器来查看：

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>

如您所见，params只包含foo变量。但是，对象本身包含xgboost.XGBClassifier.__init__定义的所有参数。但是由于某些原因，从xgboost.XGBClassifier.get_params调用的BaseEstimator.get_params方法只能获得在XGBExtended.__init__方法中显式定义的参数。不幸的是，即使我用deep = True显式调用get_params，它仍然不能正常工作：

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

有人能说出为什么会发生这种情况吗？

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)]'}

python

python-2.7

scikit-learn

xgboost

回答 1

Stack Overflow用户

回答已采纳

发布于 2017-08-30 01:55:51

这里的问题是子类的声明不正确。当您仅使用foo声明init方法时，您将覆盖原始方法。它不会自动初始化，即使基类构造函数应该有默认值。

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}