继承自scikit-learn的LassoCV模型

Question

我尝试使用继承来扩展scikit-learn的RidgeCV模型:

from sklearn.linear_model import RidgeCV, LassoCV

class Extended(RidgeCV):
    def __init__(self, *args, **kwargs):
        super(Extended, self).__init__(*args, **kwargs)

    def example(self):
        print 'Foo'


x = [[1,0],[2,0],[3,0],[4,0], [30, 1]]
y = [2,4,6,8, 60]
model = Extended(alphas = [float(a)/1000.0 for a in range(1, 10000)])
model.fit(x,y)
print model.predict([[5,1]])

它运行得很好,但是当我尝试从LassoCV继承时,它产生了以下回溯:

Traceback (most recent call last):
  File "C:/Python27/so.py", line 14, in <module>
    model.fit(x,y)
  File "C:\Python27\lib\site-packages\sklearn\linear_model\coordinate_descent.py", line 1098, in fit
    path_params = self.get_params()
  File "C:\Python27\lib\site-packages\sklearn\base.py", line 214, in get_params
    for key in self._get_param_names():
  File "C:\Python27\lib\site-packages\sklearn\base.py", line 195, in _get_param_names
    % (cls, init_signature))
RuntimeError: scikit-learn estimators should always specify their parameters in the signature of their __init__ (no varargs). <class '__main__.Extended'> with constructor (<self>, *args, **kwargs) doesn't  follow this convention.

有人可以解释如何解决这个问题吗？

Answer 1

你可能想制作scikit-learn兼容模型,进一步使用scikit-learn功能.如果你这样做 - 你需要先阅读:http: //scikit-learn.org/stable/developers/contributing.html#rolling-your-own-estimator

不久:scikit学习有许多功能,如估计的克隆(克隆()函数),元算法,如GridSearch,Pipeline,交叉验证.所有这些事情都必须能够获得估算器内部的字段值,并更改这些字段的值(例如,GridSearch必须在每次评估之前更改估算器内部的参数),如参数alphain SGDClassifier.要更改某个参数的值,必须知道它的名称.为了获得在每一个分类方法的所有字段的名称get_params从BaseEstimator类(你是隐式继承)时,需要指定所有的参数__init__类的方法,因为它很容易自省的所有参数名__init__的方法(看看BaseEstimator,这是类这引发了这个错误).

所以它只是希望你删除所有的varargs

*args, **kwargs

来自__init__签名.您必须在__init__签名中列出模型的所有参数,并初始化对象的所有内部字段.

以下是SGDClassifier的__init__方法示例,它继承自BaseSGDClassifier:

def __init__(self, loss="hinge", penalty='l2', alpha=0.0001, l1_ratio=0.15,
             fit_intercept=True, n_iter=5, shuffle=True, verbose=0,
             epsilon=DEFAULT_EPSILON, n_jobs=1, random_state=None,
             learning_rate="optimal", eta0=0.0, power_t=0.5,
             class_weight=None, warm_start=False, average=False):
    super(SGDClassifier, self).__init__(
        loss=loss, penalty=penalty, alpha=alpha, l1_ratio=l1_ratio,
        fit_intercept=fit_intercept, n_iter=n_iter, shuffle=shuffle,
        verbose=verbose, epsilon=epsilon, n_jobs=n_jobs,
        random_state=random_state, learning_rate=learning_rate, eta0=eta0,
        power_t=power_t, class_weight=class_weight, warm_start=warm_start, average=average)