Question

使用python和scikit-learn，我想进行网格搜索。但是我的一些模特最终都是空的。如何使网格搜索功能忽略这些模型？

我想我可以有一个评分函数，如果模型为空则返回0，但我不确定如何。

predictor = sklearn.svm.LinearSVC(penalty='l1', dual=False, class_weight='auto')
param_dist = {'C': pow(2.0, np.arange(-10, 11))}
learner = sklearn.grid_search.GridSearchCV(estimator=predictor,
                                           param_grid=param_dist,
                                           n_jobs=self.n_jobs, cv=5,
                                           verbose=0)
learner.fit(X, y)

我的数据的方式是此learner对象将选择与空模型对应的C。知道我怎么能确保模型不是空的吗？

编辑：“空模型”是指选择了0个要素的模型。特别是使用l1正则化模型，这很容易发生。因此，在这种情况下，如果SVM中的C足够小，优化问题将找到0向量作为系数的最优解。因此predictor.coef_将是0 s。

的向量

Answer 1

尝试实现自定义得分手，类似于：

import numpy as np

def scorer_(estimator, X, y):
    # Your criterion here
    if np.allclose(estimator.coef_, np.zeros_like(estimator.coef_)):
        return 0
    else:
        return estimator.score(X, y)

learner = sklearn.grid_search.GridSearchCV(...
                                           scoring=scorer_)

Answer 2

我不认为有这样的内置功能;但是，制作自定义gridsearcher很简单：

from sklearn.cross_validation import KFold                                                                                                                   
from sklearn.grid_search import GridSearchCV                                                                                                                 
from sklearn.cross_validation import cross_val_score                                                                                                         
import itertools                                                                                                                                             
from sklearn import metrics                                                                                                                                  
import operator                                                                                                                                              


def model_eval(X, y, model, cv):                                                                                                                             
        scores = []                                                                                                                                          
        for train_idx, test_idx in cv:                                                                                                                       
                X_train, y_train = X[train_idx], y[train_idx]                                                                                                
                X_test, y_test = X[test_idx], y[test_idx]                                                                                                    
                model.fit(X_train, y_train)                                                                                                                  
                nonzero_coefs = len(np.nonzero(model.coef_)[0]) #check for nonzero coefs                                                                     
                if nonzero_coefs == 0: #if they're all zero, don't evaluate any further; move to next hyperparameter combo                                   
                        return 0                                                                                                                             
                predictions = model.predict(X_test)                                                                                                          
                score = metrics.accuracy_score(y_test, predictions)                                                                                          
                scores.append(score)                                                                                                                         
        return np.array(scores).mean()                                                                                                                       


X, y = make_classification(n_samples=1000,                                                                                                                   
                           n_features=10,                                                                                                                    
                           n_informative=3,                                                                                                                  
                           n_redundant=0,                                                                                                                    
                           n_repeated=0,                                                                                                                     
                           n_classes=2,                                                                                                                      
                           random_state=0,                                                                                                                   
                           shuffle=False)                                                                                                                    


C = pow(2.0, np.arange(-20, 11))                                                                                                                             
penalty = {'l1', 'l2'}                                                                                                                                       

parameter_grid = itertools.product(C, penalty)                                                                                                               

kf = KFold(X.shape[0], n_folds=5) #use the same folds  to evaluate each hyperparameter combo                                                                 

hyperparameter_scores = {}                                                                                                                                   
for C, penalty in parameter_grid:                                                                                                                            
        model = svm.LinearSVC(dual=False, C=C, penalty=penalty)                                                                                              
        result = model_eval(X, y, model, kf)                                                                                                                 
        hyperparameter_scores[(C, penalty)] = result                                                                                                         

sorted_scores = sorted(hyperparameter_scores.items(), key=operator.itemgetter(1))                                                                            

best_parameters, best_score = sorted_scores[-1]                                                                                                              
print best_parameters                                                                                                                                        
print best_score

在sklearn中创建网格搜索功能以忽略空模型

2 个答案: