class SVMImpl:
def __init__(self):
self.Features = SVMFeatures()
self.svminstance = SVM()
def domaintrain(self, annotatedxmllist):
datalist = list()
labelslist = list()
for annotatedxml in annotatedxmllist:
for page in annotatedxml[0]:
for col in page:
if(len(col) < 2):
continue
for i in xrange(0, len(col)):
if(int(col[i][0]) == SparseType.OTHERSPARSE):
labelslist.append("S")
else:
labelslist.append("NS")
datalist.append(self.Features.domainfindfeatureFunction(i, col, annotatedxml[1]))
self.train(datalist, labelslist)
def domainpredict(self, col, fontdict):
for i in xrange(0, len(col)):
test_list = list()
test_list.append(self.Features.domainfindfeatureFunction(i, col, fontdict))
if(self.predict(test_list) == 'S'):
col[i][0] = SparseType.OTHERSPARSE
else:
col[i][0] = SparseType.NONSPARSE
return col
def train(self, datalist, labelslist):
data = SparseDataSet(datalist, L = labelslist)
self.svminstance.C = 20
data.attachKernel('gaussian', degree = 5)
self.svminstance.train(data)
#result = self.svminstance.cv(data, 2)
#print result.getPredictedLabels()
def predict(self, datalist):
data = SparseDataSet(datalist)
results = self.svminstance.test(data)
return results.getPredictedLabels()[0]
def __init__(self):
self.Features = SVMFeatures()
self.TDFeatures = SVMTDFeatures()
self.svminstance = SVM()
class SVMImpl:
def __init__(self):
self.Features = SVMFeatures()
self.TDFeatures = SVMTDFeatures()
self.svminstance = SVM()
def domaintrain(self, annotatedxmllist):
datalist = list()
labelslist = list()
for annotatedxml in annotatedxmllist:
for page in annotatedxml[0]:
for col in page:
if(len(col) < 2):
continue
for tup in col:
if(tup[1].text is None or tup[1].text.strip() == ''):
col.remove(tup)
for i in xrange(0, len(col)):
if(int(col[i][0]) == SparseType.TABLELINE):
labelslist.append("S")
else:
labelslist.append("NS")
datalist.append(self.Features.domainfindfeatureFunction(i, col, annotatedxml[1]))
self.train(datalist, labelslist)
def domaintrainforTableDecomposition(self, tableslist):
labelslist = list()
datalist = list()
for table in tableslist:
for i in xrange(0, len(table)):
if(int(table[i][0]) == SparseType.HEADER):
labelslist.append("HEADER")
else:
labelslist.append("DATA")
datalist.append(self.TDFeatures.domainfindfeatureFunction(i, table, None))
self.trainforTD(datalist, labelslist)
def domainpredictforTableDecomposition(self, table):
errorcount = 0
sparseerror = 0
for i in xrange(0, len(table)):
test_list = list()
test_list.append(self.TDFeatures.domainfindfeatureFunction(i, table, None))
if(self.predict(test_list) == 'HEADER'):
predicted = SparseType.HEADER
else:
predicted = SparseType.DATA
if((predicted) != int(table[i][0])):
errorcount += 1
if((predicted) == SparseType.HEADER):
sparseerror += 1
table[i][0] = predicted
return [table, errorcount, sparseerror]
def domainpredict(self, col, fontdict):
errorcount = 0
sparseerror = 0
for i in xrange(0, len(col)):
test_list = list()
test_list.append(self.Features.domainfindfeatureFunction(i, col, fontdict))
if(self.predict(test_list) == 'S'):
predicted = SparseType.TABLELINE
else:
predicted = SparseType.NONTABLELINE
if((predicted) != int(col[i][0])):
errorcount += 1
if((predicted) == SparseType.NONTABLELINE):
sparseerror += 1
col[i][0] = predicted
return [col, errorcount, sparseerror]
def train(self, datalist, labelslist):
data = SparseDataSet(datalist, L = labelslist)
self.svminstance.C = 20
data.attachKernel('gaussian', degree = 5)
self.svminstance.train(data)
#result = self.svminstance.cv(data, 5)
#print result
def trainforTD(self, datalist, labelslist):
data = SparseDataSet(datalist, L = labelslist)
self.svminstance.train(data)
#result = self.svminstance.cv(data, 6)
#print result
def predict(self, datalist):
data = SparseDataSet(datalist)
results = self.svminstance.test(data)
return results.getPredictedLabels()[0]
def save(self, filename):
self.svminstance.save(filename)
def __init__(self):
self.Features = SVMFeatures()
self.TDFeatures = SVMTDFeatures()
self.svminstance = SVM()
class SVMImpl:
def __init__(self):
self.Features = SVMFeatures()
self.TDFeatures = SVMTDFeatures()
self.svminstance = SVM()
def domaintrain(self, annotatedxmllist):
datalist = list()
labelslist = list()
for annotatedxml in annotatedxmllist:
for page in annotatedxml[0]:
for col in page:
if (len(col) < 2):
continue
for tup in col:
if (tup[1].text is None or tup[1].text.strip() == ''):
col.remove(tup)
for i in xrange(0, len(col)):
if (int(col[i][0]) == SparseType.TABLELINE):
labelslist.append("S")
else:
labelslist.append("NS")
datalist.append(
self.Features.domainfindfeatureFunction(
i, col, annotatedxml[1]))
self.train(datalist, labelslist)
def domaintrainforTableDecomposition(self, tableslist):
labelslist = list()
datalist = list()
for table in tableslist:
for i in xrange(0, len(table)):
if (int(table[i][0]) == SparseType.HEADER):
labelslist.append("HEADER")
else:
labelslist.append("DATA")
datalist.append(
self.TDFeatures.domainfindfeatureFunction(i, table, None))
self.trainforTD(datalist, labelslist)
def domainpredictforTableDecomposition(self, table):
errorcount = 0
sparseerror = 0
for i in xrange(0, len(table)):
test_list = list()
test_list.append(
self.TDFeatures.domainfindfeatureFunction(i, table, None))
if (self.predict(test_list) == 'HEADER'):
predicted = SparseType.HEADER
else:
predicted = SparseType.DATA
if ((predicted) != int(table[i][0])):
errorcount += 1
if ((predicted) == SparseType.HEADER):
sparseerror += 1
table[i][0] = predicted
return [table, errorcount, sparseerror]
def domainpredict(self, col, fontdict):
errorcount = 0
sparseerror = 0
for i in xrange(0, len(col)):
test_list = list()
test_list.append(
self.Features.domainfindfeatureFunction(i, col, fontdict))
if (self.predict(test_list) == 'S'):
predicted = SparseType.TABLELINE
else:
predicted = SparseType.NONTABLELINE
if ((predicted) != int(col[i][0])):
errorcount += 1
if ((predicted) == SparseType.NONTABLELINE):
sparseerror += 1
col[i][0] = predicted
return [col, errorcount, sparseerror]
def train(self, datalist, labelslist):
data = SparseDataSet(datalist, L=labelslist)
self.svminstance.C = 20
data.attachKernel('gaussian', degree=5)
self.svminstance.train(data)
#result = self.svminstance.cv(data, 5)
#print result
def trainforTD(self, datalist, labelslist):
data = SparseDataSet(datalist, L=labelslist)
self.svminstance.train(data)
#result = self.svminstance.cv(data, 6)
#print result
def predict(self, datalist):
data = SparseDataSet(datalist)
results = self.svminstance.test(data)
return results.getPredictedLabels()[0]
def save(self, filename):
self.svminstance.save(filename)
