def cross_validate_one(idx):
global gdata
(M,b,trainlist,threads)=gdata
if idx==0:
(M2,b2)=testGrad(M,b,trainlist,idx)
res=0
else:
random.shuffle(trainlist)
trainvalborder=len(trainlist)*(threads-2)/(threads-1)
(M2,b2)=testGrad(M,b,trainlist[:trainvalborder],idx)
print 'MMR after unigram learning train(idx=',idx,'):',mrr(M2,b2,trainlist)
res=mrr(M2,b2,trainlist[trainvalborder:])
print 'MMR after unigram learning val(idx=',idx,'):',res
return (res,M2,b2)
def cross_validate_one(idx):
global gdata
(M, b, trainlist, threads) = gdata
if idx == 0:
(M2, b2) = testGrad(M, b, trainlist, idx)
res = 0
else:
random.shuffle(trainlist)
trainvalborder = len(trainlist) * (threads - 2) / (threads - 1)
(M2, b2) = testGrad(M, b, trainlist[:trainvalborder], idx)
print 'MMR after unigram learning train(idx=', idx, '):', mrr(
M2, b2, trainlist)
res = mrr(M2, b2, trainlist[trainvalborder:])
print 'MMR after unigram learning val(idx=', idx, '):', res
return (res, M2, b2)
def trainMb(trainlist, ans1, ans0):
"""Unigram training from saved Qlist files, returns Mb weights.
You can play with the learning constants in trainConsts() of basicgrad.py"""
t0 = time.time()
M = np.random.normal(0, 0.01, (GLOVELEN, GLOVELEN))
b = -0.0001
# M=np.loadtxt('data/M58prop')
# b=np.loadtxt('data/b58prop')
mrrs, crossmrr, M, b = cross_validate_all(M, b, trainlist)
t1 = time.time()
print "time spent training =", t1 - t0
print "MRR after crossvalidation=", crossmrr
# XXX: This has a sideeffect, setting resolutions in trainlist
trainmrr = mrr(M, b, trainlist)
print 'Mb MRR on train:', trainmrr
l, alpha = trainConsts()
results = [crossmrr, mrrs, l, alpha, trainmrr]
return (M, b, results)
def trainMb(trainlist,ans1,ans0):
"""Unigram training from saved Qlist files, returns Mb weights.
You can play with the learning constants in trainConsts() of basicgrad.py"""
t0=time.time()
M=np.random.normal(0,0.01,(GLOVELEN,GLOVELEN))
b=-0.0001
# M=np.loadtxt('data/M58prop')
# b=np.loadtxt('data/b58prop')
mrrs,crossmrr,M,b=cross_validate_all(M,b,trainlist)
t1=time.time()
print "time spent training =",t1-t0
print "MRR after crossvalidation=",crossmrr
# XXX: This has a sideeffect, setting resolutions in trainlist
trainmrr=mrr(M,b,trainlist)
print 'Mb MRR on train:', trainmrr
l,alpha=trainConsts()
results=[crossmrr,mrrs,l,alpha,trainmrr]
return (M,b,results)