def updateValue(self,laststate, state, lastaction,lastreward,lbda=1):
if self.t < self.horizon: return
qvalues = self.getValues()
if qvalues is None: return
qvalue = qvalues[lastaction]
next_qvalues = self.getTargetValues()
max_q_index = np.argmax(next_qvalues)
maxnext = next_qvalues[max_q_index]
if self.nn:
update = (lastreward + (self.gamma * maxnext))
qvalues[lastaction] = update
from pybrain.supervised import RPropMinusTrainer
trainer = RPropMinusTrainer(self.nn)
dataset = Sequential()
trainer.trainOnDataset(dataset)
else:
self.Q[laststate][lastaction]=qvalue + self.alpha * lbda * (lastreward + self.gamma * maxnext - qvalue)
#code for normalize data in ds
i = np.array([d[0] for d in ds])
inorm = np.array([d[0] for d in ds])
i /= np.max(np.abs(i), axis=0)
o = np.array([d[1] for d in ds])
onorm = np.array([d[1] for d in ds])
o /= np.max(np.abs(o), axis=0)
print routes_frommeasur_ids["11000602"] / np.max(np.abs(inorm), axis=0)
#creating new object for normalized data
nds = SupervisedDataSet(1, 1)
for ix in range(len(ds)):
nds.addSample(i[ix], o[ix])
#print routes_frommeasur
#creating net
net = buildNetwork(nds.indim,
3,
nds.outdim,
bias=True,
hiddenclass=TanhLayer)
#training net
trainer = RPropMinusTrainer(net, verbose=True)
trainer.trainOnDataset(nds, 10)
#trainer.testOnData(verbose=True)
p = net.activate(routes_frommeasur_ids["11000602"] /
np.max(np.abs(inorm), axis=0))
print(p[0] * np.max(np.abs(onorm), axis=0)[0])
def learn(self,
pathdataset=["dstc4_train"],
Pathdataroot="data",
numberOfHiddenUnit=20,
EPOCHS_PER_CYCLE=10,
CYCLES=40,
weightdecayw=0.01):
print "Start learning LSTM, and make dictionary file"
#Construct dictionary: variable name -> corresponding index of element in i/o vector
print "Star make dictionary: variable name -> corresponding index of element in i/o vector"
self.dictOut = {
} #"TOPIC_SLOT_VALUE" -> corresponding index of element
self.dictIn = {
} #"SPEAKER_{val}"or"TOPIC_{val}","WORD_{word}" "BIO_{BIO}", "CLASS_{slot,value}", ""{defined label}-> corresponding index of element
#-target vector dictionary
index = 0
totalNumSlot = 0
for topic in self.tagsets.keys():
for slot in self.tagsets[topic].keys():
totalNumSlot += 1
for value in self.tagsets[topic][slot]:
self.dictOut[topic + "_" + slot + "_" + value] = index
index += 1
print "totalNumSlot:" + str(totalNumSlot)
print "outputSize:" + str(len(self.dictOut.keys()))
#-input dictionry
dataset = []
for pathdat in pathdataset:
dataset.append(
dataset_walker.dataset_walker(pathdat,
dataroot=Pathdataroot,
labels=False))
#--(sub input vector 1) Class features i.e., Slot and value ratio (Similar to base line)
index = 0
for topic in self.tagsets.keys():
for slot in self.tagsets[topic].keys():
if ("CLASS_" + slot) not in self.dictIn:
self.dictIn["CLASS_" + slot] = index
index += 1
for value in self.tagsets[topic][slot]:
if ("CLASS_" + value) not in self.dictIn:
self.dictIn["CLASS_" + value] = index
index += 1
self.TOTALSIZEOFCLASSFeature = index
f = open(self.FileNameofNumClassFeature, "wb")
pickle.dump(self.TOTALSIZEOFCLASSFeature, f)
f.close()
#--(sub input vector 2) Sentence features
if not self.isUseSentenceRepresentationInsteadofBOW:
index = 0
for elemDataset in dataset:
for call in elemDataset:
for (uttr, _) in call:
#General info1 (CLASS; this feature must be rejistered at first)
if ("SPEAKER_" + uttr["speaker"]) not in self.dictIn:
self.dictIn["SPEAKER_" + uttr["speaker"]] = index
index += 1
if ("TOPIC_" + uttr["segment_info"]["topic"]
) not in self.dictIn:
self.dictIn["TOPIC_" +
uttr["segment_info"]["topic"]] = index
index += 1
#General info2
#-BIO
if ("BIO_" + uttr['segment_info']['target_bio']
) not in self.dictIn:
self.dictIn[
"BIO_" +
uttr['segment_info']['target_bio']] = index
index += 1
#BOW
if LSTMWithBOWTracker.isIgnoreUtterancesNotRelatedToMainTask:
if not (uttr['segment_info']['target_bio'] == "O"):
#-BOW
splitedtrans = self.__getRegurelisedBOW(
uttr["transcript"])
for word in splitedtrans:
if ("WORD_" + word) not in self.dictIn:
self.dictIn["WORD_" + word] = index
index += 1
self.TOTALSIZEOFSENTENCEFeature = index
f = open(self.FileNameofNumSentenceFeature, "wb")
pickle.dump(self.TOTALSIZEOFSENTENCEFeature, f)
f.close()
elif self.isUseSentenceRepresentationInsteadofBOW:
index = 0
for i in range(0, LSTMWithBOWTracker.D2V_VECTORSIZE):
self.dictIn[str(index) + "thElemPV"] = index
index += 1
index = 0
for i in range(0, LSTMWithBOWTracker.D2V_VECTORSIZE):
self.dictIn[str(index) + "thAvrWord"] = index
index += 1
assert self.D2V_VECTORSIZE == LSTMWithBOWTracker.D2V_VECTORSIZE, "D2V_VECTORSIZE is restrected to be same over the class"
else:
assert False, "Unexpected block"
#--(sub input vector 3) Features M1s defined
index = 0
if self.isEnableToUseM1sFeature:
rejisteredFeatures = self.__rejisterM1sInputFeatureLabel(
self.tagsets, dataset)
for rFeature in rejisteredFeatures:
assert rFeature not in self.dictIn, rFeature + " already registered in input vector. Use different label name. "
self.dictIn[rFeature] = index
index += 1
self.TOTALSIZEOFM1DEFINEDFeature = index
f = open(self.FileNameofNumM1Feature, "wb")
pickle.dump(self.TOTALSIZEOFM1DEFINEDFeature, f)
f.close()
print "inputSize:" + str(len(self.dictIn.keys()))
assert self.dictIn[
"CLASS_INFO"] == 0, "Unexpected index CLASS_INFO should has value 0"
assert self.dictIn[
"CLASS_Fort Siloso"] == 334, "Unexpected index CLASS_Fort Siloso should has value 334"
assert self.dictIn[
"CLASS_Yunnan"] == 1344, "Unexpected index CLASS_Yunnan should has value 1611"
#--write
fileObject = open('dictInput.pic', 'w')
pickle.dump(self.dictIn, fileObject)
fileObject.close()
fileObject = open('dictOutput.pic', 'w')
pickle.dump(self.dictOut, fileObject)
fileObject.close()
#Build RNN frame work
print "Start learning Network"
#Capability of network is: (30 hidden units can represents 1048576 relations) wherease (10 hidden units can represents 1024)
#Same to Henderson (http://www.aclweb.org/anthology/W13-4073)?
net = buildNetwork(len(self.dictIn.keys()),
numberOfHiddenUnit,
len(self.dictOut.keys()),
hiddenclass=LSTMLayer,
outclass=SigmoidLayer,
outputbias=False,
recurrent=True)
#Train network
#-convert training data into sequence of vector
convDataset = [] #[call][uttr][input,targetvec]
iuttr = 0
convCall = []
for elemDataset in dataset:
for call in elemDataset:
for (uttr, label) in call:
if self.isIgnoreUtterancesNotRelatedToMainTask:
if uttr['segment_info']['target_bio'] == "O":
continue
#-input
convInput = self._translateUtteranceIntoInputVector(
uttr, call)
#-output
convOutput = [0.0] * len(
self.dictOut.keys()) #Occured:+1, Not occured:0
if "frame_label" in label:
for slot in label["frame_label"].keys():
for value in label["frame_label"][slot]:
convOutput[self.dictOut[
uttr["segment_info"]["topic"] + "_" +
slot + "_" + value]] = 1
#-post proccess
if self.isSeparateDialogIntoSubDialog:
if uttr['segment_info']['target_bio'] == "B":
if len(convCall) > 0:
convDataset.append(convCall)
convCall = []
convCall.append([convInput, convOutput])
#print "Converted utterance" + str(iuttr)
iuttr += 1
if not self.isSeparateDialogIntoSubDialog:
if len(convCall) > 0:
convDataset.append(convCall)
convCall = []
#Online learning
trainer = RPropMinusTrainer(net, weightdecay=weightdecayw)
EPOCHS = EPOCHS_PER_CYCLE * CYCLES
for i in xrange(CYCLES):
#Shuffle order
ds = SequentialDataSet(len(self.dictIn.keys()),
len(self.dictOut.keys()))
datInd = range(0, len(convDataset))
random.shuffle(
datInd
) #Backpropergation already implemeted data shuffling, however though RpropMinus don't.
for ind in datInd:
ds.newSequence()
for convuttr in convDataset[ind]:
ds.addSample(convuttr[0], convuttr[1])
#Evaluation and Train
epoch = (i + 1) * EPOCHS_PER_CYCLE
print "\r epoch {}/{} Error={}".format(
epoch, EPOCHS, trainer.testOnData(dataset=ds))
stdout.flush()
trainer.trainOnDataset(dataset=ds, epochs=EPOCHS_PER_CYCLE)
NetworkWriter.writeToFile(
trainer.module, "LSTM_" + "Epoche" + str(i + 1) + ".rnnw")
NetworkWriter.writeToFile(trainer.module, "LSTM.rnnw")
def learn(self,pathdataset=["dstc4_train"], Pathdataroot="data",numberOfHiddenUnit=20, EPOCHS_PER_CYCLE = 10, CYCLES = 40,weightdecayw=0.01):
print "Start learning LSTM, and make dictionary file"
#Construct dictionary: variable name -> corresponding index of element in i/o vector
print "Star make dictionary: variable name -> corresponding index of element in i/o vector"
self.dictOut={}#"TOPIC_SLOT_VALUE" -> corresponding index of element
self.dictIn={}#"SPEAKER_{val}"or"TOPIC_{val}","WORD_{word}" "BIO_{BIO}", "CLASS_{slot,value}", ""{defined label}-> corresponding index of element
#-target vector dictionary
index=0
totalNumSlot=0
for topic in self.tagsets.keys():
for slot in self.tagsets[topic].keys():
totalNumSlot+=1
for value in self.tagsets[topic][slot]:
self.dictOut[topic+"_"+slot+"_"+value]=index
index+=1
print "totalNumSlot:" + str(totalNumSlot)
print "outputSize:"+str(len(self.dictOut.keys()))
#-input dictionry
dataset=[]
for pathdat in pathdataset:
dataset.append(dataset_walker.dataset_walker(pathdat,dataroot=Pathdataroot,labels=False))
#--(sub input vector 1) Class features i.e., Slot and value ratio (Similar to base line)
index=0
for topic in self.tagsets.keys():
for slot in self.tagsets[topic].keys():
if ("CLASS_"+ slot) not in self.dictIn:
self.dictIn["CLASS_"+slot]=index
index+=1
for value in self.tagsets[topic][slot]:
if ("CLASS_"+ value) not in self.dictIn:
self.dictIn["CLASS_"+value]=index
index+=1
self.TOTALSIZEOFCLASSFeature=index
f=open(self.FileNameofNumClassFeature,"wb")
pickle.dump(self.TOTALSIZEOFCLASSFeature,f)
f.close()
#--(sub input vector 2) Sentence features
if not self.isUseSentenceRepresentationInsteadofBOW:
index=0
for elemDataset in dataset:
for call in elemDataset:
for (uttr,_) in call:
#General info1 (CLASS; this feature must be rejistered at first)
if ("SPEAKER_"+uttr["speaker"]) not in self.dictIn:
self.dictIn["SPEAKER_"+uttr["speaker"]]=index
index+=1
if ("TOPIC_"+uttr["segment_info"]["topic"]) not in self.dictIn:
self.dictIn["TOPIC_"+uttr["segment_info"]["topic"]]=index
index+=1
#General info2
#-BIO
if ("BIO_"+uttr['segment_info']['target_bio']) not in self.dictIn:
self.dictIn["BIO_"+uttr['segment_info']['target_bio']]=index
index+=1
#BOW
if LSTMWithBOWTracker.isIgnoreUtterancesNotRelatedToMainTask:
if not (uttr['segment_info']['target_bio'] == "O"):
#-BOW
splitedtrans=self.__getRegurelisedBOW(uttr["transcript"])
for word in splitedtrans:
if ("WORD_"+word) not in self.dictIn:
self.dictIn["WORD_"+word]=index
index+=1
self.TOTALSIZEOFSENTENCEFeature=index
f=open(self.FileNameofNumSentenceFeature,"wb")
pickle.dump(self.TOTALSIZEOFSENTENCEFeature,f)
f.close()
elif self.isUseSentenceRepresentationInsteadofBOW:
index=0
for i in range(0,LSTMWithBOWTracker.D2V_VECTORSIZE):
self.dictIn[str(index)+"thElemPV"]=index
index+=1
index=0
for i in range(0,LSTMWithBOWTracker.D2V_VECTORSIZE):
self.dictIn[str(index)+"thAvrWord"]=index
index+=1
assert self.D2V_VECTORSIZE == LSTMWithBOWTracker.D2V_VECTORSIZE, "D2V_VECTORSIZE is restrected to be same over the class"
else:
assert False, "Unexpected block"
#--(sub input vector 3) Features M1s defined
index=0
if self.isEnableToUseM1sFeature:
rejisteredFeatures=self.__rejisterM1sInputFeatureLabel(self.tagsets,dataset)
for rFeature in rejisteredFeatures:
assert rFeature not in self.dictIn, rFeature +" already registered in input vector. Use different label name. "
self.dictIn[rFeature]=index
index+=1
self.TOTALSIZEOFM1DEFINEDFeature=index
f=open(self.FileNameofNumM1Feature,"wb")
pickle.dump(self.TOTALSIZEOFM1DEFINEDFeature,f)
f.close()
print "inputSize:"+str(len(self.dictIn.keys()))
assert self.dictIn["CLASS_INFO"] == 0, "Unexpected index CLASS_INFO should has value 0"
assert self.dictIn["CLASS_Fort Siloso"] == 334, "Unexpected index CLASS_Fort Siloso should has value 334"
assert self.dictIn["CLASS_Yunnan"] == 1344, "Unexpected index CLASS_Yunnan should has value 1611"
#--write
fileObject = open('dictInput.pic', 'w')
pickle.dump(self.dictIn, fileObject)
fileObject.close()
fileObject = open('dictOutput.pic', 'w')
pickle.dump(self.dictOut, fileObject)
fileObject.close()
#Build RNN frame work
print "Start learning Network"
#Capability of network is: (30 hidden units can represents 1048576 relations) wherease (10 hidden units can represents 1024)
#Same to Henderson (http://www.aclweb.org/anthology/W13-4073)?
net = buildNetwork(len(self.dictIn.keys()), numberOfHiddenUnit, len(self.dictOut.keys()), hiddenclass=LSTMLayer, outclass=SigmoidLayer, outputbias=False, recurrent=True)
#Train network
#-convert training data into sequence of vector
convDataset=[]#[call][uttr][input,targetvec]
iuttr=0
convCall=[]
for elemDataset in dataset:
for call in elemDataset:
for (uttr,label) in call:
if self.isIgnoreUtterancesNotRelatedToMainTask:
if uttr['segment_info']['target_bio'] == "O":
continue
#-input
convInput=self._translateUtteranceIntoInputVector(uttr,call)
#-output
convOutput=[0.0]*len(self.dictOut.keys())#Occured:+1, Not occured:0
if "frame_label" in label:
for slot in label["frame_label"].keys():
for value in label["frame_label"][slot]:
convOutput[self.dictOut[uttr["segment_info"]["topic"]+"_"+slot+"_"+value]]=1
#-post proccess
if self.isSeparateDialogIntoSubDialog:
if uttr['segment_info']['target_bio'] == "B":
if len(convCall) > 0:
convDataset.append(convCall)
convCall=[]
convCall.append([convInput,convOutput])
#print "Converted utterance" + str(iuttr)
iuttr+=1
if not self.isSeparateDialogIntoSubDialog:
if len(convCall) > 0:
convDataset.append(convCall)
convCall=[]
#Online learning
trainer = RPropMinusTrainer(net,weightdecay=weightdecayw)
EPOCHS = EPOCHS_PER_CYCLE * CYCLES
for i in xrange(CYCLES):
#Shuffle order
ds = SequentialDataSet(len(self.dictIn.keys()),len(self.dictOut.keys()))
datInd=range(0,len(convDataset))
random.shuffle(datInd)#Backpropergation already implemeted data shuffling, however though RpropMinus don't.
for ind in datInd:
ds.newSequence()
for convuttr in convDataset[ind]:
ds.addSample(convuttr[0],convuttr[1])
#Evaluation and Train
epoch = (i+1) * EPOCHS_PER_CYCLE
print "\r epoch {}/{} Error={}".format(epoch, EPOCHS,trainer.testOnData(dataset=ds))
stdout.flush()
trainer.trainOnDataset(dataset=ds,epochs=EPOCHS_PER_CYCLE)
NetworkWriter.writeToFile(trainer.module, "LSTM_"+"Epoche"+str(i+1)+".rnnw")
NetworkWriter.writeToFile(trainer.module, "LSTM.rnnw")
