Python 如何在 sklearn 中编写自定义估算器并对其使用交叉验证？

How to write a custom estimator in sklearn and use cross-validation on it?

提问by Donbeo

I would like to check the prediction error of a new method trough cross-validation. I would like to know if I can pass my method to the cross-validation function of sklearn and in case how.

我想通过交叉验证检查新方法的预测误差。我想知道我是否可以将我的方法传递给 sklearn 的交叉验证函数，以及如何传递。

I would like something like sklearn.cross_validation(cv=10).mymethod.

我想要类似的东西sklearn.cross_validation(cv=10).mymethod。

I need also to know how to define mymethodshould it be a function and which input element and which output

我还需要知道如何定义mymethod它应该是一个函数以及哪个输入元素和哪个输出

For example we can consider as mymethodan implementation of the least square estimator (of course not the ones in sklearn) .

例如，我们可以将其视为mymethod最小二乘估计器的实现（当然不是 sklearn 中的那些）。

I found this tutorial linkbut it is not very clear to me.

我找到了这个教程链接，但对我来说不是很清楚。

In the documentationthey use

在他们使用的文档中

>>> import numpy as np
>>> from sklearn import cross_validation
>>> from sklearn import datasets
>>> from sklearn import svm

>>> iris = datasets.load_iris()
>>> iris.data.shape, iris.target.shape
((150, 4), (150,))

 >>> clf = svm.SVC(kernel='linear', C=1) 
 >>> scores = cross_validation.cross_val_score(
 ...    clf, iris.data, iris.target, cv=5)
 ...
 >>> scores      

But the problem is that they are using as estimator clfthat is obtained by a function built in sklearn. How should I define my own estimator in order that I can pass it to the cross_validation.cross_val_scorefunction?

但问题是他们使用的clf是由 sklearn 中内置的函数获得的估计器。我应该如何定义自己的估算器才能将其传递给cross_validation.cross_val_score函数？

So for example suppose a simple estimator that use a linear model $y=x\beta$ where beta is estimated as X[1,:]+alpha where alpha is a parameter. How should I complete the code?

例如，假设一个简单的估计器使用线性模型 $y=x\beta$，其中 beta 被估计为 X[1,:]+alpha ，其中 alpha 是一个参数。我应该如何完成代码？

class my_estimator():
      def fit(X,y):
          beta=X[1,:]+alpha #where can I pass alpha to the function?
          return beta
      def scorer(estimator, X, y) #what should the scorer function compute?
          return ?????

With the following code I received an error:

使用以下代码我收到一个错误：

class my_estimator():
    def fit(X, y, **kwargs):
        #alpha = kwargs['alpha']
        beta=X[1,:]#+alpha 
        return beta

---

---

>>> cv=cross_validation.cross_val_score(my_estimator,x,y,scoring="mean_squared_error")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Python27\lib\site-packages\scikit_learn-0.14.1-py2.7-win32.egg\sklearn\cross_validation.py", line 1152, in cross_val_score
    for train, test in cv)
  File "C:\Python27\lib\site-packages\scikit_learn-0.14.1-py2.7-win32.egg\sklearn\externals\joblib\parallel.py", line 516, in __call__
    for function, args, kwargs in iterable:
  File "C:\Python27\lib\site-packages\scikit_learn-0.14.1-py2.7-win32.egg\sklearn\cross_validation.py", line 1152, in <genexpr>
    for train, test in cv)
  File "C:\Python27\lib\site-packages\scikit_learn-0.14.1-py2.7-win32.egg\sklearn\base.py", line 43, in clone
    % (repr(estimator), type(estimator)))
TypeError: Cannot clone object '<class __main__.my_estimator at 0x05ACACA8>' (type <type 'classobj'>): it does not seem to be a scikit-learn estimator a it does not implement a 'get_params' methods.
>>> 

采纳答案by BartoszKP

The answer also lies in sklearn's documentation.

答案还在于 sklearn 的文档中。

You need to define two things:

您需要定义两件事：

• an estimator that implements the fit(X, y)function, Xbeing the matrix with inputs and ybeing the vector of outputs

• a scorer function, or callable object that can be used with: scorer(estimator, X, y)and returns the score of given model

• 一个实现fit(X, y)函数的估计器，X它是具有输入的矩阵和y输出的向量

• 一个计分器函数或可调用对象，可用于：scorer(estimator, X, y)并返回给定模型的分数

Referring to your example: first of all, scorershouldn't be a method of the estimator, it's a different notion. Just create a callable:

参考你的例子：首先，scorer不应该是估算器的一种方法，它是一个不同的概念。只需创建一个可调用的：

def scorer(estimator, X, y)
    return ?????  # compute whatever you want, it's up to you to define
                  # what does it mean that the given estimator is "good" or "bad"

Or even a more simple solution: you can pass a string 'mean_squared_error'or 'accuracy'(full list available in this part of the documentation) to cross_val_scorefunction to use a predefined scorer.

或者甚至更简单的解决方案：您可以传递一个字符串'mean_squared_error'或'accuracy'（文档的这一部分提供的完整列表）cross_val_score来使用预定义的记分器。

Another possibility is to use make_scorerfactory function.

另一种可能性是使用make_scorer工厂函数。

As for the second thing, you can pass parameters to your model through the fit_paramsdictparameter of the cross_val_scorefunction (as mentioned in the documentation). These parameters will be passed to the fitfunction.

至于第二件事，您可以通过函数的fit_paramsdict参数cross_val_score（如文档中所述）将参数传递给您的模型。这些参数将传递给fit函数。

class my_estimator():
    def fit(X, y, **kwargs):
        alpha = kwargs['alpha']
        beta=X[1,:]+alpha 
        return beta

After reading all the error messages, which provide quite clear idea of what's missing, here is a simple example:

在阅读了所有错误消息后，这些消息提供了对缺失内容的清晰了解，这里是一个简单的例子：

import numpy as np
from sklearn.cross_validation import cross_val_score

class RegularizedRegressor:
    def __init__(self, l = 0.01):
        self.l = l

    def combine(self, inputs):
        return sum([i*w for (i,w) in zip([1] + inputs, self.weights)])

    def predict(self, X):
        return [self.combine(x) for x in X]

    def classify(self, inputs):
        return sign(self.predict(inputs))

    def fit(self, X, y, **kwargs):
        self.l = kwargs['l']
        X = np.matrix(X)
        y = np.matrix(y)
        W = (X.transpose() * X).getI() * X.transpose() * y

        self.weights = [w[0] for w in W.tolist()]

    def get_params(self, deep = False):
        return {'l':self.l}

X = np.matrix([[0, 0], [1, 0], [0, 1], [1, 1]])
y = np.matrix([0, 1, 1, 0]).transpose()

print cross_val_score(RegularizedRegressor(),
                      X,
                      y, 
                      fit_params={'l':0.1},
                      scoring = 'mean_squared_error')