def load_data(dataset):
train_data = [[],[]]
test_data = [[],[]]
valid_data = [[],[]]
train = 1000
test = 500
vectors = readVectors('bc3_vectors.txt')
corpus = 'bc3/bc3corpus.1.0/corpus.xml'
annotation = 'bc3/bc3corpus.1.0/annotation.xml'
mails = parse_bc3(corpus,annotation)
assignVectors(mails,vectors)
count = 0
for i in mails:
for j in i.text:
if j.score > 0:
score = 1
else:
score = 0
if count<train:
train_data[0].append(j.vector + i.vector)
train_data[1].append(score)
elif count>train and count<train+test:
test_data[0].append(j.vector + i.vector)
test_data[1].append(score)
else:
valid_data[0].append(j.vector + i.vector)
valid_data[1].append(score)
count += 1
return (process_dataset(train_data,test_data,valid_data),len(train_data[0][0]))
def load_data(datasets):
train_data = [[],[]]
test_data = [[],[]]
valid_data = [[],[]]
train = 1000
test = 500
vectors = readVectors('bc3_vector_with_subject')
corpus = 'bc3/bc3corpus.1.0/corpus.xml'
annotation = 'bc3/bc3corpus.1.0/annotation.xml'
mails = parse_bc3(corpus,annotation)
assignVectors(mails,vectors)
count = 0
trainSet,validSet,testSet = divide_data(len(mails),0.6,0.2,mails)
def assignBinaryScore(dataset,output):
for i in dataset:
for j in i.text:
if j.score>=0.32 and j.score<0.65:
score = 1
elif j.score >= 0.65 and j.score<0.99:
score = 2
elif j.score == 0:
score = 0
else:
score = 3
subject = i.subject_feature[int(j.index.split('.')[0]) - 1]
thread = i.thread_feature[int(j.index.split('.')[0]) - 1]
output[0].append(j.vector + subject + thread)
output[1].append(score)
assignBinaryScore(trainSet,train_data)
assignBinaryScore(validSet,valid_data)
assignBinaryScore(testSet,test_data)
'''
for i in mails:
for j in i.text:
if j.score > 0:
score = 1
else:
score = 0
if count<train:
train_data[0].append(j.vector + i.vector)
train_data[1].append(score)
elif count>train and count<train+test:
test_data[0].append(j.vector + i.vector)
test_data[1].append(score)
else:
valid_data[0].append(j.vector + i.vector)
valid_data[1].append(score)
count += 1
'''
return (process_dataset(train_data,test_data,valid_data),len(train_data[0][0]),testSet)
index += 1 def m_rel_pos(thread): index = 0.0 for i in thread.sentences: i.m_rel_pos_score = index/float(len(thread.sentences)) index += 1 def pre_sim(thread,sim_func): for i in range(len(thread.sentences)): if i == 0: if (i+1) < len(thread.sentences): thread.sentences[i].seq_sim_score = sim_func(thread.sentences[i],thread.sentences[i+1]) else: thread.sentences[i].seq_sim_score = 0.0 elif i == (len(thread.sentences) - 1): thread.sentences[i].seq_sim_score = sim_func(thread.sentences[i],thread.sentences[i-1]) else: thread.sentences[i].seq_sim_score = max(sim_func(thread.sentences[i],thread.sentences[i+1]),sim_func(thread.sentences[i],thread.sentences[i-1])) ''' def next_sim(thread,sim_func): def centroid_sim(thread,sim_func): ''' corpus = 'bc3/bc3corpus.1.0/corpus.xml' annotation = 'bc3/bc3corpus.1.0/annotation.xml' mails = parse_bc3(corpus,annotation) #jvm.start() calculate_sentence_features(mails) #jvm.stop()
