什么是_passthrough_scorer以及如何更改GridsearchCV中的记分器（sklearn）？

Question

http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html（供参考）

x = [[2], [1], [3], [1] ... ] # about 1000 data 
grid = GridSearchCV(KernelDensity(), {'bandwidth': np.linspace(0.1, 1.0, 10)}, cv=10) 
grid.fit(x) 

当我使用GridSearchCV而不指定像评分函数，grid.scorer_的值。你能否解释_passthrough_scorer是什么类型的函数？

除此之外，我想将计分函数更改为mean_squared_error或其他。

grid = GridSearchCV(KernelDensity(), {'bandwidth': np.linspace(0.1, 1.0, 10)}, cv=10, scoring='mean_squared_error') 

而行，grid.fit（X），总是给我此错误消息：

TypeError: __call__() missing 1 required positional argument: 'y_true' 

我无法弄清楚如何给y_true的功能，因为我不知道真实分配。你能告诉我如何改变评分功能吗？我感谢您的帮助。

Answer 1

KernelDensity的默认度量是minkowski，其中p = 2这是一个欧几里德度量。如果您未指定任何其他评分方法，则GridSearchCV将使用KernelDensity指标进行评分。

均方误差公式为：sum（（y_true - y_estimated）^ 2）/ n。你得到了错误，因为你需要有一个y_true来计算它。

这里是施加到GridSearchCV KernelDensity的制造的例子：

from sklearn.neighbors import KernelDensity 
from sklearn.grid_search import GridSearchCV 
import numpy as np 

N = 20 
X = np.concatenate((np.random.randint(0, 10, 50), 
        np.random.randint(5, 10, 50)))[:, np.newaxis] 

params = {'bandwidth': np.logspace(-1.0, 1.0, 10)} 
grid = GridSearchCV(KernelDensity(), params) 
grid.fit(X) 
print(grid.grid_scores_) 
print('Best parameter: ',grid.best_params_) 
print('Best score: ',grid.best_score_) 
print('Best estimator: ',grid.best_estimator_) 

和输出是：

[mean: -96.94890, std: 100.60046, params: {'bandwidth': 0.10000000000000001}, 


mean: -70.44643, std: 40.44537, params: {'bandwidth': 0.16681005372000587}, 
mean: -71.75293, std: 18.97729, params: {'bandwidth': 0.27825594022071243}, 
mean: -77.83446, std: 11.24102, params: {'bandwidth': 0.46415888336127786}, 
mean: -78.65182, std: 8.72507, params: {'bandwidth': 0.774263682681127}, 
mean: -79.78828, std: 6.98582, params: {'bandwidth': 1.2915496650148841}, 
mean: -81.65532, std: 4.77806, params: {'bandwidth': 2.1544346900318834}, 
mean: -86.27481, std: 2.71635, params: {'bandwidth': 3.5938136638046259}, 
mean: -95.86093, std: 1.84887, params: {'bandwidth': 5.9948425031894086}, 
mean: -109.52306, std: 1.71232, params: {'bandwidth': 10.0}] 
Best parameter: {'bandwidth': 0.16681005372000587} 
Best score: -70.4464315885 
Best estimator: KernelDensity(algorithm='auto', atol=0, bandwidth=0.16681005372000587, 
     breadth_first=True, kernel='gaussian', leaf_size=40, 
     metric='euclidean', metric_params=None, rtol=0) 

为GridSeachCV有效评分方法通常需要y_true。在您的情况下，您可能希望将网格搜索将的度量标准更改为其他度量标准（例如sklearn.metrics.pairwise.pairwise_kernels,sklearn.metrics.pairwise.pairwise_distances），以便将其用于评分。