def TASC_2_topic(
instance_addr='../data/out_domain/10000_review_no3.txt.gz',
vecModel_addr='../data/word_vector_data/word2vec_glove.twitter.27B.100d.txt',
para_w=[1, 1, 1],
saveAddr='../data/result/TASC_2_topic.txt',
topic_addr='../data/topic'):
topicData = readTopicData(topic_addr)
tasc = TASC(instance_addr=instance_addr,
vecModel_addr=vecModel_addr,
para_w=para_w)
resDict = {}
for k, v in topicData.iteritems():
print 'test for topic ' + k
selected_num = len(v) * 4 if len(v) > 100 else 400
shortlist_num = selected_num * 4
selected_instances = tasc.get_instance_TASC(k, v, selected_num,
shortlist_num)
test_set, test_label = v['text'], v['label']
train_set, train_label = selected_instances[
'text'], selected_instances['label']
res = classificationTest(train_set,
train_label,
test_set,
test_label,
lowFreqK=2,
classifier=MultinomialNB())
resDict[k] = res
if saveAddr != '':
print resDict
saveResult(resDict, saveAddr)
return resDict
def TASC_2_topic(saveAddr='', para_w=[1, 1, 1]):
tasc = TASC(para_w=para_w)
resDict = {}
topicData = readTopicData()
for k, v in topicData.iteritems():
selected_num = len(v) * 4
shortlist_num = selected_num * 4
selected_instances = tasc.get_instance_TASC(k, v, selected_num,
shortlist_num)
test_set, test_label = v['text'], v['label']
train_set, train_label = selected_instances[
'text'], selected_instances['label']
res = classificationTest(train_set, train_label, test_set, test_label)
resDict[k] = res
if saveAddr != '':
saveResult(res, saveAddr)
return resDict
def mixdomain_2_topic():
nonTopicData = readNonTopicText()
tasc = TASC()
resDict = {}
topicData = readTopicData()
for k, v in topicData.iteritems():
selected_num = len(v) * 4
shortlist_num = selected_num * 4
selected_instances = tasc.get_instance_TASC(k, v, selected_num,
shortlist_num)
selected_instances = pd.concat([selected_instances, nonTopicData],
axis=0)
test_set, test_label = v['text'], v['label']
train_set, train_label = selected_instances[
'text'], selected_instances['label']
res = classificationTest(train_set, train_label, test_set, test_label)
resDict[k] = res
saveResult(resDict, saveAddr)
return resDict
for k, v in topicData.iteritems():
print 'test in topic "{}"'.format(k)
test_set, test_label = v['text'], v['label']
train_set, train_label = nonTopicData['text'], nonTopicData['label']
res = classificationTest(train_set,
train_label,
test_set,
test_label,
classifier=MultinomialNB())
resDict_non2topic[k] = res
# ------------ transfer
# 这里放在console执行时，要显示设置路径。console设置的默认当前路径是项目所在路径而不是py文件路径
tasc = TASC(
instance_addr='../data/out_domain/10000_review_no3.txt.gz',
vecModel_addr='../data/word_vector_data/word2vec_glove.twitter.27B.100d.txt'
)
# tasc.get_instance_TASC('apple',topicData['pure_dealed2016all'],10000,15000)
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
resDict_mix = {}
size = 1500
for k, v in topicData.iteritems():
print 'test in topic "{}"'.format(k)
selected_num = len(v) * 5 if len(v) > size else size * 5
shortlist_num = selected_num * 2
selected_instances = tasc.get_instance_TASC(k, v, selected_num,
def main_expt(
topic_addr='./data/topic/final',
non_addr='./data/non_topic/nontopicTrain.txt',
size=1500,
instance_addr='./data/out_domain/10000_review_no3.txt.gz',
vecModel_addr='./data/word_vector_data/word2vec_glove.twitter.27B.100d.txt'
):
topicData = readTopicData(topic_addr)
# ------------ non to topic
nonTopicData = readNonTopicText(addr=non_addr)
print 'nontopic test'
resDict_non2topic = {}
for k, v in topicData.iteritems():
print 'test in topic "{}"'.format(k)
test_set, test_label = v['text'], v['label']
train_set, train_label = nonTopicData['text'], nonTopicData['label']
res = classificationTest(train_set,
train_label,
test_set,
test_label,
classifier=MultinomialNB())
resDict_non2topic[k] = res
# 这里放在console执行时，要显示设置路径。console设置的默认当前路径是项目所在路径而不是py文件路径
tasc = TASC(instance_addr=instance_addr, vecModel_addr=vecModel_addr)
# tasc.get_instance_TASC('apple',topicData['pure_dealed2016all'],10000,15000)
print 'mix test'
resDict_mix = {}
for k, v in topicData.iteritems():
print 'test in topic "{}"'.format(k)
selected_num = len(v) * 5 if len(v) > size else size * 5
shortlist_num = selected_num * 2
selected_instances = tasc.get_instance_TASC(k, v, selected_num,
shortlist_num)
print 'len(selected_instances): ', len(selected_instances)
test_set, test_label = v['text'], v['label']
train_set, train_label = pd.concat([
nonTopicData['text'], selected_instances['text']
]), pd.concat([nonTopicData['label'], selected_instances['label']])
res = classificationTest(train_set,
train_label,
test_set,
test_label,
lowFreqK=10,
classifier=MultinomialNB()) #
resDict_mix[k] = res
print 'transfer test'
resDict_transfer = {}
for k, v in topicData.iteritems():
print 'test in topic "{}"'.format(k)
selected_num = len(v) * 5 if len(v) > size else size * 5
shortlist_num = selected_num * 2
selected_instances = tasc.get_instance_TASC(k, v, selected_num,
shortlist_num)
print 'len(selected_instances): ', len(selected_instances)
test_set, test_label = v['text'], v['label']
train_set, train_label = selected_instances[
'text'], selected_instances['label']
res = classificationTest(train_set,
train_label,
test_set,
test_label,
lowFreqK=10,
classifier=MultinomialNB())
resDict_transfer[k] = res
print 'resDict_non2topic'
print resDict_non2topic
print 'resDict_transfer'
print resDict_transfer
print 'resDict_mix'
print resDict_mix
datestr = datetime.datetime.now().strftime('%y_%m_%d_%H_%M_%S')
topicAccuRes = {
'*topic': resDict_non2topic.keys(),
'resDict_non2topic': [v[0] for v in resDict_non2topic.values()],
'resDict_mix': [v[0] for v in resDict_mix.values()],
'resDict_transfer': [v[0] for v in resDict_transfer.values()]
}
def getAVGRes(x):
return [
sum([v[i] for v in x.values()]) / len(topicData)
for i in range(0, 7)
]
avgRes = {
'resDict_non2topic': getAVGRes(resDict_non2topic),
'resDict_mix': getAVGRes(resDict_mix),
'resDict_transfer': getAVGRes(resDict_transfer)
}
allres = {
'resDict_non2topic': resDict_non2topic,
'resDict_mix': resDict_mix,
'resDict_transfer': resDict_transfer,
'topicAccuRes': topicAccuRes,
'avgRes': avgRes
}
print topicAccuRes.keys()
for line in [[x[i] for x in topicAccuRes.values()]
for i in range(0, len(topicAccuRes.values()[0]))]:
print line
print avgRes
if not os.path.isdir(topic_addr + '/result'):
os.mkdir(topic_addr + '/result')
saveResult(allres,
save_addr=topic_addr + '/result/' + datestr + '_allres.txt')
from final.feature_handle import Source
from sentiment_classify_method import ngram_sa_method
from experiment.expl_util import readTopicData, readNonTopicText, csv_to_train_test, classificationTest, saveResult
topicData = readTopicData()
# ------------ non to topic
nonTopicData = readNonTopicText()
resDict_non2topic = {}
for k, v in topicData.iteritems():
print 'test in topic "{}"'.format(k)
test_set, test_label = v['text'], v['label']
train_set, train_label = nonTopicData['text'], nonTopicData['label']
res = classificationTest(train_set, train_label, test_set, test_label)
resDict_non2topic[k] = res
# ------------ transfer
tasc = TASC(
) # tasc.get_instance_TASC('apple',topicData['pure_dealed2016all'],10000,15000)
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
resDict_tasc1 = {}
for k, v in topicData.iteritems():
print 'test in topic "{}"'.format(k)
selected_num = len(v) * 4 if len(v) > 1000 else 4000
shortlist_num = selected_num * 4
selected_instances = tasc.get_instance_TASC(k, v, selected_num,
shortlist_num)
print 'len(selected_instances): ', len(selected_instances)
test_set, test_label = v['text'], v['label']
train_set, train_label = selected_instances['text'], selected_instances[
def run(instance_addr='./data/out_domain/all_review_no3.txt.gz',
vecModel_addr='./data/word_vector_data/word2vec_glove.twitter.27B.100d.txt',
para_w=[1, 1, 1],
save_addr='./data/result/TASC_2_topic.txt',
topic_addr='./data/topic/final'):
topicData = readTopicData_final()
# ------------ non to topic
nonTopicData = readNonTopicText(addr='./data/non_topic/nontopicTrain.txt')
resDict_non2topic = {}
for k, v in topicData.iteritems():
print 'test in topic "{}"'.format(k)
test_set, test_label = v['text'], v['label']
train_set, train_label = nonTopicData['text'], nonTopicData['label']
res = classificationTest(train_set,
train_label,
test_set,
test_label,
lowFreqK=10,
classifier=MultinomialNB())
resDict_non2topic[k] = res
saveResult(resDict_non2topic,
save_addr=topic_addr + '/result/' + 'non2topic.txt')
# ------------ transfer
# 这里放在console执行时，要显示设置路径。console设置的默认当前路径是项目所在路径而不是py文件路径
tasc = TASC(instance_addr=instance_addr, vecModel_addr=vecModel_addr)
resDict_tasc_mix = {}
size = 1200
for k, v in topicData.iteritems():
print 'test in topic "{}"'.format(k)
selected_num = len(v) * 5 if len(v) > size else size * 5
shortlist_num = selected_num * 2
selected_instances = tasc.get_instance_TASC(k, v, selected_num,
shortlist_num)
print 'len(selected_instances): ', len(selected_instances)
test_set, test_label = v['text'], v['label']
train_set, train_label = pd.concat([
nonTopicData['text'], selected_instances['text']
]), pd.concat([nonTopicData['label'], selected_instances['label']])
res = classificationTest(train_set,
train_label,
test_set,
test_label,
lowFreqK=10,
classifier=MultinomialNB()) #
resDict_tasc_mix[k] = res
saveResult(resDict_tasc_mix,
save_addr=topic_addr + '/result/all/' + str(size) +
'_TASC_topic_transfer.txt')
#-----------
resDict_tasc_trans = {}
for k, v in topicData.iteritems():
print 'test in topic "{}"'.format(k)
selected_num = len(v) * 5 if len(v) > size else size * 5
shortlist_num = selected_num * 2
selected_instances = tasc.get_instance_TASC(k, v, selected_num,
shortlist_num)
print 'len(selected_instances): ', len(selected_instances)
test_set, test_label = v['text'], v['label']
train_set, train_label = selected_instances[
'text'], selected_instances['label']
res = classificationTest(train_set,
train_label,
test_set,
test_label,
lowFreqK=10,
classifier=MultinomialNB())
resDict_tasc_trans[k] = res
saveResult(resDict_tasc_trans,
save_addr=topic_addr + '/result/all/' + str(size) +
'_TASC_topic_mix.txt')
print resDict_non2topic
print resDict_tasc_trans
print resDict_tasc_mix