如何优化使用DASK搜索参数空间？（无交叉验证）

下面是代码（在这里没有DASK）：

def build(ntries,param,niter,func,score,train,test):
    res=[]
    for i in range(ntries):
        cparam=param.rvs(size=niter,random_state=i)
        res.append( func(cparam, train, test, score) )
    return res

def score(test,correct):
    return np.linalg.norm(test-correct)

def compute_optimal(res):
    from operator import itemgetter
    _sorted=sorted(res,None,itemgetter(1))
    return _sorted

def func(c,train,test,score):
    dt=1.0/len(c)
    for cc in c:
        train=train - cc*dt
    return (c,score(train,test))

这是我如何使用它：

from dask import delayed
from distributed import LocalCluster, Client
cluster=LocalCluster(n_workers=4, threads_per_worker=1)
cli=Client(cluster)

from scipy.stats import uniform
import numpy as np

niter=500
loc=1.0e-09
scale=1.0
ntries=1000
sched=uniform(loc=loc,scale=scale)
train=np.arange(1000)+0.5
test=np.arange(1000)

# HERE IS THE DASK
graph=build(ntries,sched,niter,delayed(func),score,train,test)

# THE QUESTION SECTION
# I do these steps to bring back all the values so that I could search for the score-wise optimal pair: (parameter, score)
res=[cli.compute(g) for g in graph]
results=[r.result() for r in res]
# Actual search for the optimal pair
optimal=compute_optimal(results)
best,worst=optimal[0],optimal[-1]