如何在Python 3.6.1中继承类属性

I'm trying to figure out how to inherit all of the attributes/methods from one class into another class. I'm basing it off of How to inherit a python base class? but I can't figure out how to get it to work for my simple example. In this example, I just want to make a new class that has all the functionality of RandomForestClassifier but with a new attribute (called new_attribute). In this method, I can't use the arguments of the original RandomForestClassifier but I can add my new attribute.

我试图弄清楚如何从一个类继承所有属性/方法到另一个类。我基于如何继承python基类?但我无法弄清楚如何让它适用于我的简单例子。在这个例子中,我只想创建一个具有RandomForestClassifier的所有功能但具有新属性(称为new_attribute)的新类。在这个方法中,我不能使用原始RandomForestClassifier的参数,但我可以添加我的新属性。

How can I set it up so I can use all of the parameters from the original RandomForestClassifier along with adding this new_attribute?

如何设置它以便我可以使用原始RandomForestClassifier中的所有参数并添加此new_attribute?

from sklearn.ensemble import RandomForestClassifier

class NewClassifier(RandomForestClassifier):
    def __init__(self, new_attribute):
        Super(RandomForestClassifier, self).__init__()
        self.new_attribute = new_attribute
A = NewClassifier(n_estimators=1, new_attribute=0)

Error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-221-686839873f88> in <module>()
      5         Super(RandomForestClassifier, self).__init__()
      6         self.new_attribute = new_attribute
----> 7 A = NewClassifier(n_estimators=1, new_attribute=0)

TypeError: __init__() got an unexpected keyword argument 'n_estimators'

Hindsight: This was a poorly constructed question. I got the above to work with the code below. However, @Mseifert has a better representation in the answers:

后见之明:这是一个构造不良的问题。我得到了上面的代码来处理下面的代码。但是,@ Mysfrt在答案中的表现更好:

class NewClassifier(RandomForestClassifier):
    def __init__(self, new_attribute, n_estimators=10, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_split=1e-07, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False, class_weight=None):
        RandomForestClassifier.__init__(self, n_estimators, criterion, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_features, max_leaf_nodes, min_impurity_split, bootstrap, oob_score, n_jobs, random_state, verbose, warm_start, class_weight)
        self.new_attribute = new_attribute
A = NewClassifier(n_estimators=1, new_attribute=0)

1 个解决方案

#1

The easiest way would be to accept new_attribute as first and must-have argument:

最简单的方法是接受new_attribute作为first和must-have参数:

from sklearn.ensemble import RandomForestClassifier

class NewClassifier(RandomForestClassifier):
    def __init__(self, *args, **kwargs):  # just accept any argument
        # Find out what the value of "new_argument is" and remove it either from
        # the positional arguments (args) or keyword arguments (kwargs) so that
        # the remaining arguments can simply be passed to the super-class.
        if args:  # if there are positional arguments
            new_attribute = args[0]
            args = args[1:]
        else:     # no positional arguments
            new_attribute = kwargs.pop('new_attribute')

        super().__init__(*args, **kwargs)
        self.new_attribute = new_attribute

Note that it's super not Super and you don't need the arguments in python-3.x

请注意,它不是Super而且你不需要python-3.x中的参数

#1