Python L-BFGS-B不满足给定约束
我试图通过使用scipy中的最小化函数来找到模型的优化权重值。如下面代码所示,我定义了返回1减去模型f1分数的错误函数Python L-BFGS-B不满足给定约束,python,optimization,scipy,minimize,Python,Optimization,Scipy,Minimize,我试图通过使用scipy中的最小化函数来找到模型的优化权重值。如下面代码所示,我定义了返回1减去模型f1分数的错误函数 def err_func(weights,x,y): undetected=0 correct=0 incorrect=0 results=fun(weights,x) for i in range(0,len(results)): if(results[i]==y[i])
def err_func(weights,x,y):
undetected=0
correct=0
incorrect=0
results=fun(weights,x)
for i in range(0,len(results)):
if(results[i]==y[i]):
correct+=1
elif(not (results[i]==y[i])):
incorrect+=1
undetected=len(y)-(correct+incorrect)
precision=float(correct) / float(correct + incorrect)
recall=float(correct) / float(correct + incorrect + undetected)
f1=2 * precision * recall / (precision + recall)
return 1.0-f1
我使用的约束条件是,权重中的每个值介于0和1之间,权重之和等于1。这些定义如下:
cons = ({'type': 'eq', 'fun': lambda x: 1 - sum(x)})
bnds = tuple((0.0, 1.0) for x in weights)
eps=1e-2
但在运行minimize方法时,我的函数不满足约束
from scipy.optimize import minimize
res = minimize(err_func, weights,method='L-BFGS-B', args=(x,y),constraints=cons,bounds=bnds,options = {'eps':eps,'maxiter':100})
print res
test_weights=res.x
print sum(test_weights)
我得到这样一个输出,权重之和大于1。我错过了什么
> fun: 0.4955555555555555 hess_inv: <11x11 LbfgsInvHessProduct with
> dtype=float64>
> jac: array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]) message: 'CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL'
> nfev: 24
> nit: 1 status: 0 success: True
> x: array([ 0. , 0.22222222, 0. , 1. , 1. ,
> 0.11111111, 1. , 1. , 1. , 0. , 1. ])
> 6.33333333333
>乐趣:0.495555赫斯_投资:dtype=float64>
>jac:array([0,0,0,0,0,0,0,0,0,0,0.])消息:“收敛:投影梯度的NORM\uL-BFGS-B
仅支持绑定约束(这就是第二个“B”的意思)。此方法不支持常规约束
摘录自:
Parameters:
...
constraints : dict or sequence of dict, optional
...
Constraints definition (only for COBYLA and SLSQP)