def trainListFile(self, listTrainFile, listmanualfiles):
if len(listmanualfiles) != len(listTrainFile):
print("Co loi")
sys.exit()
self.reset()
queries = dlib.ranking_pairs()
for index in range(0, len(listTrainFile)):
self.reset()
data = dlib.ranking_pair()
inputNonRelevant = " ".join([line for line in open(listTrainFile[index], 'r').readlines()])
tpAllSent = myTokenizer(inputNonRelevant)
self.inputFromString(inputNonRelevant)
inputRelevant = " ".join([line for line in open(listmanualfiles[index], 'r').readlines()])
tpRelevant = myTokenizer(inputRelevant)
tpNonRelevant = list(set(tpAllSent).difference(set(tpRelevant)))
self.genAllVector()
for sent in tpRelevant:
data.relevant.append(dlib.vector(self.dicVector.get(sent.strip())))
for sent in tpNonRelevant:
data.nonrelevant.append(dlib.vector(self.dicVector.get(sent.strip())))
queries.append(data)
trainer = dlib.svm_rank_trainer()
trainer.c = 10
rank = trainer.train(queries)
_weight = []
for i in range(0, len(rank.weights)):
_weight.append(rank.weights[i])
return _weight
# Dlib comes with a compiled python interface for python 2.7 on MS Windows. If # you are using another python version or operating system then you need to # compile the dlib python interface before you can use this file. To do this, # run compile_dlib_python_module.bat. This should work on any operating system # so long as you have CMake and boost-python installed. On Ubuntu, this can be # done easily by running the command: sudo apt-get install libboost-python-dev cmake import dlib # Now let's make some testing data. To make it really simple, let's suppose that # we are ranking 2D vectors and that vectors with positive values in the first # dimension should rank higher than other vectors. So what we do is make # examples of relevant (i.e. high ranking) and non-relevant (i.e. low ranking) # vectors and store them into a ranking_pair object like so: data = dlib.ranking_pair() # Here we add two examples. In real applications, you would want lots of # examples of relevant and non-relevant vectors. data.relevant.append(dlib.vector([1, 0])) data.nonrelevant.append(dlib.vector([0, 1])) # Now that we have some data, we can use a machine learning method to learn a # function that will give high scores to the relevant vectors and low scores to # the non-relevant vectors. trainer = dlib.svm_rank_trainer() # Note that the trainer object has some parameters that control how it behaves. # For example, since this is the SVM-Rank algorithm it has a C parameter that # controls the trade-off between trying to fit the training data exactly or # selecting a "simpler" solution which might generalize better. trainer.c = 10
def train(self, directoryPlain, directoryManual):
self.reset()
listFile = []
listPlainFile = listAllFileInFolder(directoryPlain)
listManualFile = listAllFileInFolder(directoryManual)
dicPlainFile = {}
dicManualFile = {}
for file in listPlainFile:
fname = file.strip().split('/')[-1]
listFile.append(fname)
dicPlainFile[fname] = file
for file in listManualFile:
fname = file.strip().split('/')[-1]
listFile.append(fname)
dicManualFile[fname] = file
listFile = list(set(listFile))
queries = dlib.ranking_pairs()
countt = 0
outfile = open("completefile.txt", 'w')
for file in listFile:
outvecfile = open("/home/hien/Data/Work/Wordnet_naiscorp/test/valuevector/"+file.strip().split('/')[-1], 'w')
countt = countt + 1
outfile.write(file+'\n')
print (file, countt)
self.reset()
data = dlib.ranking_pair()
inputNonRelevant = " ".join([line for line in open(dicPlainFile.get(file), 'r').readlines()])
tpAllSent = myTokenizer(inputNonRelevant)
self.inputFromString(inputNonRelevant)
inputRelevant = " ".join([line for line in open(dicManualFile.get(file), 'r').readlines()])
tpRelevant = myTokenizer(inputRelevant)
tpNonRelevant = list(set(tpAllSent).difference(set(tpRelevant)))
self.genAllVector()
for sent in tpAllSent:
outvecfile.write(str(self.dicVector.get(sent.strip()))+"\t"+sent.strip()+'\n')
outvecfile.close()
for sent in tpRelevant:
# print (sent)
# print(self.dicVector.get(sent))
# print(type(self.dicVector.get(sent)))
data.relevant.append(dlib.vector(self.dicVector.get(sent.strip())))
# outvecfile.write(str(self.dicVector.get(sent.strip()))+"\t"+sent.strip()+'\n')
# outvecfile.
for sent in tpNonRelevant:
# print(self.dicVector.get(sent))
data.nonrelevant.append(dlib.vector(self.dicVector.get(sent.strip())))
queries.append(data)
trainer = dlib.svm_rank_trainer()
trainer.c = 10
rank = trainer.train(queries)
_weight = []
for i in range(0, len(rank.weights)):
_weight.append(rank.weights[i])
# print(type(rank.weights))
# print (rank.weights[0])
# print (rank.weights)
# print(_weight)
# return rank.weights
return _weight
# # Compiling dlib should work on any operating system so long as you have # CMake installed. On Ubuntu, this can be done easily by running the # command: # sudo apt-get install cmake # import dlib # Now let's make some testing data. To make it really simple, let's suppose # that we are ranking 2D vectors and that vectors with positive values in the # first dimension should rank higher than other vectors. So what we do is make # examples of relevant (i.e. high ranking) and non-relevant (i.e. low ranking) # vectors and store them into a ranking_pair object like so: data = dlib.ranking_pair() # Here we add two examples. In real applications, you would want lots of # examples of relevant and non-relevant vectors. data.relevant.append(dlib.vector([1, 0])) data.nonrelevant.append(dlib.vector([0, 1])) # Now that we have some data, we can use a machine learning method to learn a # function that will give high scores to the relevant vectors and low scores to # the non-relevant vectors. trainer = dlib.svm_rank_trainer() # Note that the trainer object has some parameters that control how it behaves. # For example, since this is the SVM-Rank algorithm it has a C parameter that # controls the trade-off between trying to fit the training data exactly or # selecting a "simpler" solution which might generalize better. trainer.c = 10