def run(p="../../../data/atis/atis.pkl",
wordembdim=70,
lablembdim=70,
innerdim=300,
lr=0.05,
numbats=100,
epochs=20,
validinter=1,
wreg=0.0003,
depth=1):
train, test, dics = pickle.load(open(p))
word2idx = dics["words2idx"]
table2idx = dics["tables2idx"]
label2idx = dics["labels2idx"]
label2idxrev = {v: k for k, v in label2idx.items()}
train = zip(*train)
test = zip(*test)
print "%d training examples, %d test examples" % (len(train), len(test))
#tup2text(train[0], word2idx, table2idx, label2idx)
maxlen = 0
for tup in train + test:
maxlen = max(len(tup[0]), maxlen)
numwords = max(word2idx.values()) + 2
numlabels = max(label2idx.values()) + 2
# get training data
traindata = getdatamatrix(train, maxlen, 0).astype("int32")
traingold = getdatamatrix(train, maxlen, 2).astype("int32")
trainmask = (traindata > 0).astype("float32")
# test data
testdata = getdatamatrix(test, maxlen, 0).astype("int32")
testgold = getdatamatrix(test, maxlen, 2).astype("int32")
testmask = (testdata > 0).astype("float32")
res = atiseval(testgold - 1, testgold - 1, label2idxrev)
print res #; exit()
# define model
innerdim = [innerdim] * depth
m = SimpleSeqTransDec(indim=numwords,
inpembdim=wordembdim,
outembdim=lablembdim,
innerdim=innerdim,
outdim=numlabels)
# training
m = m.train([traindata, shiftdata(traingold), trainmask], traingold).adagrad(lr=lr).grad_total_norm(5.0).seq_cross_entropy().l2(wreg)\
.split_validate(splits=5, random=True).seq_cross_entropy().seq_accuracy().validinter(validinter).takebest()\
.train(numbats, epochs)
# predict after training
s = GreedySearch(m, startsymbol=0)
testpred, _ = s.decode(testdata)
testpred = testpred * testmask
evalres = atiseval(testpred - 1, testgold - 1, label2idxrev)
print evalres
def run(p="../../../data/atis/atis.pkl", wordembdim=70, lablembdim=70, innerdim=300, lr=0.01, numbats=100, epochs=20, validinter=1, wreg=0.0001, depth=1, attdim=300):
train, test, dics = pickle.load(open(p))
word2idx = dics["words2idx"]
table2idx = dics["tables2idx"]
label2idx = dics["labels2idx"]
label2idxrev = {v: k for k, v in label2idx.items()}
train = zip(*train)
test = zip(*test)
print "%d training examples, %d test examples" % (len(train), len(test))
#tup2text(train[0], word2idx, table2idx, label2idx)
maxlen = 0
for tup in train + test:
maxlen = max(len(tup[0]), maxlen)
numwords = max(word2idx.values()) + 2
numlabels = max(label2idx.values()) + 2
# get training data
traindata = getdatamatrix(train, maxlen, 0).astype("int32")
traingold = getdatamatrix(train, maxlen, 2).astype("int32")
trainmask = (traindata > 0).astype("float32")
# test data
testdata = getdatamatrix(test, maxlen, 0).astype("int32")
testgold = getdatamatrix(test, maxlen, 2).astype("int32")
testmask = (testdata > 0).astype("float32")
res = atiseval(testgold-1, testgold-1, label2idxrev); print res#; exit()
# define model
innerdim = [innerdim] * depth
m = SimpleSeqEncDecAtt(
inpvocsize=numwords,
inpembdim=wordembdim,
outvocsize=numlabels,
outembdim=lablembdim,
encdim=innerdim,
decdim=innerdim,
attdim=attdim,
inconcat=False
)
# training
m.train([traindata, shiftdata(traingold), trainmask], traingold).adagrad(lr=lr).grad_total_norm(1.).seq_cross_entropy().l2(wreg)\
.validate_on([testdata, shiftdata(testgold), testmask], testgold).seq_cross_entropy().seq_accuracy().validinter(validinter)\
.train(numbats, epochs)
# predict after training
s = SeqEncDecAttSearch(m)
testpred = s.decode(testdata)
testpred = testpred * testmask
#testpredprobs = m.predict(testdata, shiftdata(testgold), testmask)
#testpred = np.argmax(testpredprobs, axis=2)-1
#testpred = testpred * testmask
#print np.vectorize(lambda x: label2idxrev[x] if x > -1 else " ")(testpred)
evalres = atiseval(testpred-1, testgold-1, label2idxrev); print evalres
def run(p="../../../data/atis/atis.pkl", wordembdim=70, lablembdim=70, innerdim=300, lr=0.05, numbats=100, epochs=20, validinter=1, wreg=0.0003, depth=1):
tracker = SummaryTracker()
train, test, dics = pickle.load(open(p))
word2idx = dics["words2idx"]
table2idx = dics["tables2idx"]
label2idx = dics["labels2idx"]
label2idxrev = {v: k for k, v in label2idx.items()}
train = zip(*train)
test = zip(*test)
print "%d training examples, %d test examples" % (len(train), len(test))
#tup2text(train[0], word2idx, table2idx, label2idx)
maxlen = 0
for tup in train + test:
maxlen = max(len(tup[0]), maxlen)
numwords = max(word2idx.values()) + 2
numlabels = max(label2idx.values()) + 2
# get training data
traindata = getdatamatrix(train, maxlen, 0).astype("int32")
traingold = getdatamatrix(train, maxlen, 2).astype("int32")
trainmask = (traindata > 0).astype("float32")
# test data
testdata = getdatamatrix(test, maxlen, 0).astype("int32")
testgold = getdatamatrix(test, maxlen, 2).astype("int32")
testmask = (testdata > 0).astype("float32")
res = atiseval(testgold-1, testgold-1, label2idxrev); print res#; exit()
print asizeof(traindata)
# define model
innerdim = [innerdim] * depth
m = SimpleSeqTransDec(indim=numwords, inpembdim=wordembdim, outembdim=lablembdim, innerdim=innerdim, outdim=numlabels)
# training
m = m.train([traindata, shiftdata(traingold), trainmask], traingold).adagrad(lr=lr).grad_total_norm(5.0).seq_cross_entropy().l2(wreg)\
.cross_validate(splits=5, random=True).seq_cross_entropy().seq_accuracy().validinter(validinter).takebest()\
.train(numbats, epochs)
# predict after training
s = SeqTransDecSearch(m)
testpred = s.decode(testdata)
testpred = testpred * testmask
evalres = atiseval(testpred-1, testgold-1, label2idxrev); print evalres
def run(p="../../../data/atis/atis.pkl",
wordembdim=70,
lablembdim=70,
innerdim=300,
lr=0.01,
numbats=100,
epochs=20,
validinter=1,
wreg=0.0001,
depth=1,
attdim=300):
train, test, dics = pickle.load(open(p))
word2idx = dics["words2idx"]
table2idx = dics["tables2idx"]
label2idx = dics["labels2idx"]
label2idxrev = {v: k for k, v in label2idx.items()}
train = zip(*train)
test = zip(*test)
print "%d training examples, %d test examples" % (len(train), len(test))
#tup2text(train[0], word2idx, table2idx, label2idx)
maxlen = 0
for tup in train + test:
maxlen = max(len(tup[0]), maxlen)
numwords = max(word2idx.values()) + 2
numlabels = max(label2idx.values()) + 2
# get training data
traindata = getdatamatrix(train, maxlen, 0).astype("int32")
traingold = getdatamatrix(train, maxlen, 2).astype("int32")
trainmask = (traindata > 0).astype("float32")
# test data
testdata = getdatamatrix(test, maxlen, 0).astype("int32")
testgold = getdatamatrix(test, maxlen, 2).astype("int32")
testmask = (testdata > 0).astype("float32")
res = atiseval(testgold - 1, testgold - 1, label2idxrev)
print res #; exit()
# define model
innerdim = [innerdim] * depth
m = SimpleSeqEncDecAtt(inpvocsize=numwords,
inpembdim=wordembdim,
outvocsize=numlabels,
outembdim=lablembdim,
encdim=innerdim,
decdim=innerdim,
attdim=attdim,
inconcat=False)
# training
m.train([traindata, shiftdata(traingold), trainmask], traingold).adagrad(lr=lr).grad_total_norm(1.).seq_cross_entropy().l2(wreg)\
.validate_on([testdata, shiftdata(testgold), testmask], testgold).seq_cross_entropy().seq_accuracy().validinter(validinter)\
.train(numbats, epochs)
# predict after training
s = SeqEncDecAttSearch(m)
testpred = s.decode(testdata)
testpred = testpred * testmask
#testpredprobs = m.predict(testdata, shiftdata(testgold), testmask)
#testpred = np.argmax(testpredprobs, axis=2)-1
#testpred = testpred * testmask
#print np.vectorize(lambda x: label2idxrev[x] if x > -1 else " ")(testpred)
evalres = atiseval(testpred - 1, testgold - 1, label2idxrev)
print evalres