def run(
epochs=10,
numbats=100,
numsam=10000,
lr=0.1,
datap="../../../data/simplequestions/datamat.char.pkl",
innerdim=200,
wreg=0.00005,
bidir=False,
keepmincount=5,
mem=False,
sameenc=False,
memaddr="dot",
memattdim=100,
membidir=False,
memlayers=1,
memmaxwords=5,
memmaxchars=20,
layers=1,
):
(traindata, traingold), (validdata, validgold), (testdata, testgold), chardic, entdic\
= readdata(datap)
if mem:
memdata = getcharmemdata(entdic,
chardic,
maxwords=memmaxwords,
maxchar=memmaxchars)
print traindata.shape, testdata.shape
numchars = max(chardic.values()) + 1
numrels = max(entdic.values()) + 1
print numchars, numrels
if bidir:
encinnerdim = [innerdim / 2] * layers
else:
encinnerdim = [innerdim] * layers
enc = SimpleSeq2Vec(indim=numchars,
inpembdim=None,
innerdim=encinnerdim,
maskid=-1,
bidir=bidir)
if mem:
if membidir:
innerdim = [innerdim / 2] * memlayers
else:
innerdim = [innerdim] * memlayers
memindim = numchars
memenc = SimpleSeq2Vec(indim=memindim,
inpembdim=None,
innerdim=innerdim,
maskid=-1,
bidir=membidir)
if memaddr is None or memaddr == "dot":
memaddr = DotMemAddr
elif memaddr == "lin":
memaddr = LinearGateMemAddr
dec = MemVec2Idx(memenc,
memdata,
memdim=innerdim,
memaddr=memaddr,
memattdim=memattdim)
else:
dec = SimpleVec2Idx(indim=innerdim, outdim=numrels)
m = Seq2Idx(enc, dec)
m = m.train([traindata], traingold).adagrad(lr=lr).l2(wreg).grad_total_norm(1.0).cross_entropy()\
.validate_on([validdata], validgold).accuracy().cross_entropy().takebest()\
.train(numbats=numbats, epochs=epochs)
pred = m.predict(testdata)
print pred.shape
evalres = evaluate(np.argmax(pred, axis=1), testgold)
print str(evalres) + "%"
def run(epochs=10,
numbats=100,
numsam=10000,
lr=0.1,
datap="../../../data/simplequestions/datamat.wordchar.pkl",
embdim=50,
encdim=50,
innerdim=200,
wreg=0.00005,
bidir=False,
keepmincount=5,
sameenc=False,
memaddr="dot",
memattdim=100,
layers=1,
embtrainfrac=0.0,
mem=False,
membidir=False,
memlayers=1,
sharedwordenc=False):
""" Memory match-based glove-based word-level relation classification """
(traindata, traingold), (validdata, validgold), (testdata, testgold), worddic, chardic, entdic\
= readdata(datap)
# get words from relation names, update word dic
memdata = getmemdata(entdic, worddic, chardic)
# get glove and transform word mats to glove index space
d2g, newdic, glove = getdic2glove(worddic,
dim=embdim,
trainfrac=embtrainfrac)
traindata, validdata, testdata, memdata = \
[np.concatenate([np.vectorize(d2g)(x[..., 0]).reshape(x.shape[:2] + (1,)), x[..., 1:]], axis=2)
for x in [traindata, validdata, testdata, memdata]]
print traindata.shape, testdata.shape
#embed()
numwords = max(worddic.values()) + 1 # don't use this, use glove
numchars = max(chardic.values()) + 1
numrels = max(entdic.values()) + 1
if bidir:
encinnerdim = [innerdim / 2] * layers
else:
encinnerdim = [innerdim] * layers
wordemb = WordEncoderPlusGlove(numchars=numchars,
encdim=encdim,
embdim=embdim,
maskid=-1,
embtrainfrac=embtrainfrac)
rnn, lastdim = SimpleSeq2Vec.makernu(embdim + encdim,
encinnerdim,
bidir=bidir)
enc = Seq2Vec(wordemb, rnn, maskid=-1)
if mem:
memembdim = embdim
memencdim = encdim
if membidir:
innerdim = [innerdim / 2] * memlayers
else:
innerdim = [innerdim] * memlayers
if not sharedwordenc:
memwordemb = WordEncoderPlusGlove(numchars=numchars,
encdim=encdim,
embdim=embdim,
maskid=-1,
embtrainfrac=embtrainfrac)
else:
memwordemb = wordemb
memrnn, memlastdim = SimpleSeq2Vec.makernu(memembdim + memencdim,
innerdim,
bidir=membidir)
memenc = Seq2Vec(memwordemb, memrnn, maskid=-1)
if memaddr is None or memaddr == "dot":
memaddr = DotMemAddr
elif memaddr == "lin":
memaddr = LinearGateMemAddr
dec = MemVec2Idx(memenc,
memdata,
memdim=innerdim,
memaddr=memaddr,
memattdim=memattdim)
else:
dec = SimpleVec2Idx(indim=innerdim, outdim=numrels)
m = Seq2Idx(enc, dec)
m = m.train([traindata], traingold).adagrad(lr=lr).l2(wreg).grad_total_norm(1.0).cross_entropy()\
.validate_on([validdata], validgold).accuracy().cross_entropy().takebest()\
.train(numbats=numbats, epochs=epochs)
pred = m.predict(testdata)
print pred.shape
evalres = evaluate(np.argmax(pred, axis=1), testgold)
print str(evalres) + "%"