如何在sklearn中使用GridSearchCV和自定义估算器？

Question

我有一个估计器应该与sklearn api兼容。我试图用gridsearchcv来拟合这个估算器的一个参数，但我不明白该怎么做。

这是我的代码：

import numpy as np 
import sklearn as sk 

from sklearn.linear_model import LinearRegression, LassoLarsCV, RidgeCV 
from sklearn.linear_model.base import LinearClassifierMixin, SparseCoefMixin, BaseEstimator 


class ELM(BaseEstimator): 

    def __init__(self, n_nodes, link='rbf', output_function='lasso', n_jobs=1, c=1): 
     self.n_jobs = n_jobs 
     self.n_nodes = n_nodes 
     self.c = c 

     if link == 'rbf': 
      self.link = lambda z: np.exp(-z*z) 
     elif link == 'sig': 
      self.link = lambda z: 1./(1 + np.exp(-z)) 
     elif link == 'id': 
      self.link = lambda z: z 
     else: 
      self.link = link 

     if output_function == 'lasso': 
      self.output_function = LassoLarsCV(cv=10, n_jobs=self.n_jobs) 
     elif output_function == 'lr': 
      self.output_function = LinearRegression(n_jobs=self.n_jobs) 

     elif output_function == 'ridge': 
      self.output_function = RidgeCV(cv=10) 

     else: 
      self.output_function = output_function 

     return 


    def H(self, x): 

     n, p = x.shape 
     xw = np.dot(x, self.w.T) 
     xw = xw + np.ones((n, 1)).dot(self.b.T) 
     return self.link(xw) 

    def fit(self, x, y, w=None): 

     n, p = x.shape 
     self.mean_y = y.mean() 
     if w == None: 
      self.w = np.random.uniform(-self.c, self.c, (self.n_nodes, p)) 
     else: 
      self.w = w 

     self.b = np.random.uniform(-self.c, self.c, (self.n_nodes, 1)) 
     self.h_train = self.H(x) 
     self.output_function.fit(self.h_train, y) 

     return self 

    def predict(self, x): 
     self.h_predict = self.H(x) 
     return self.output_function.predict(self.h_predict) 

    def get_params(self, deep=True): 
     return {"n_nodes": self.n_nodes, 
       "link": self.link, 
       "output_function": self.output_function, 
       "n_jobs": self.n_jobs, 
       "c": self.c} 

    def set_params(self, **parameters): 
     for parameter, value in parameters.items(): 
      setattr(self, parameter, value) 



### Fit the c parameter ### 
X = np.random.normal(0, 1, (100,5)) 
y = X[:,1] * X[:,2] + np.random.normal(0, .1, 100) 

gs = sk.grid_search.GridSearchCV(ELM(n_nodes=20, output_function='lr'), 
           cv=5, 
           param_grid={"c":np.linspace(0.0001,1,10)}, 
           fit_params={}) 

#gs.fit(X, y) # Error 

Answer 1

有你的代码中的2个问题：

1. 您没有指定scoring参数GridSearchCV。你似乎在做回归，所以mean_squared_error是一个选项。您的set_params不会返回对象本身的引用。您应该在for循环后面添加return self。

   正如Andreas已经提到的，你很少需要在scikit-learn中重新定义set_params和get_params。刚刚从BaseEstimator继承应该就足够了。

Answer 2

您从BaseEstimator继承。它应该只是工作。见http://scikit-learn.org/dev/developers/index.html#rolling-your-own-estimator

FYI这可能是有趣的你：https://github.com/scikit-learn/scikit-learn/pull/3306