def ratio():
"""子观点占比
"""
if os.path.exists(temp_file):
os.remove(temp_file)
topic_name = request.args.get('query', default_weibo_topic_name) # 话题名
news_id = request.args.get('news_id', default_weibo_news_id)
topicid = em.getEventIDByName(topic_name)
cluster_num = request.args.get('cluster_num', default_cluster_num)
if cluster_num == default_cluster_num:
cluster_num = -1
cluster_eva_min_size = request.args.get('cluster_eva_min_size', default_cluster_eva_min_size)
vsm = request.args.get('vsm', default_vsm)
calculation_label = int(request.args.get('calcu', 1)) # 默认进行重新计算, 0表示从从已有结果数据文件加载数据
eventcomment = EventComments(topicid)
comments = eventcomment.getNewsComments(news_id)
if not comments:
return json.dumps({"status":"fail"})
cal_results = comments_calculation_v2(comments, cluster_num=cluster_num, \
cluster_eva_min_size=int(cluster_eva_min_size), version=vsm)
features = cal_results['cluster_infos']['features']
item_infos = cal_results['item_infos']
cluster_ratio = dict()
senti_ratio = dict()
sentiment_results = dict()
cluster_results = dict()
for comment in item_infos:
if ('clusterid' in comment) and (comment['clusterid'][:8] != 'nonsense') :
clusterid = comment['clusterid']
try:
cluster_ratio[clusterid] += 1
except KeyError:
cluster_ratio[clusterid] = 1
try:
cluster_results[clusterid].append(comment)
except KeyError:
cluster_results[clusterid] = [comment]
if ('sentiment' in comment) and (comment['sentiment'] in emotions_vk_v1) \
and ('clusterid' in comment) \
and (comment['clusterid'][:8] != 'nonsense'):
sentiment = comment['sentiment']
try:
senti_ratio[sentiment] += 1
except KeyError:
senti_ratio[sentiment] = 1
try:
sentiment_results[sentiment].append(comment)
except KeyError:
sentiment_results[sentiment] = [comment]
ratio_results = dict()
ratio_total_count = sum(cluster_ratio.values())
for clusterid, ratio in cluster_ratio.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
ratio_results[','.join(feature[:3])] = float(ratio) / float(ratio_total_count)
sentiratio_results = dict()
sentiratio_total_count = sum(senti_ratio.values())
for sentiment, ratio in senti_ratio.iteritems():
if sentiment in emotions_vk_v1:
label = emotions_vk_v1[sentiment]
if label and len(label):
sentiratio_results[label] = float(ratio) / float(sentiratio_total_count)
# 情感分类去重
sentiment_dump_dict = dict()
for sentiment, contents in sentiment_results.iteritems():
dump_dict = dict()
for comment in contents:
same_from_sentiment = comment["same_from_sentiment"]
try:
dump_dict[same_from_sentiment].append(comment)
except KeyError:
dump_dict[same_from_sentiment] = [comment]
sentiment_dump_dict[sentiment] = dump_dict
# 子观点分类去重
cluster_dump_dict = dict()
for clusterid, contents in cluster_results.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
dump_dict = dict()
for comment in contents:
same_from_cluster = comment["same_from"]
try:
dump_dict[same_from_cluster].append(comment)
except KeyError:
dump_dict[same_from_cluster] = [comment]
cluster_dump_dict[clusterid] = dump_dict
dump_file = open(temp_file, 'w')
dump_file.write(json.dumps({"features":features, "senti_dump_dict":sentiment_dump_dict,\
"cluster_dump_dict":cluster_dump_dict}));
dump_file.close();
return json.dumps({"ratio":ratio_results, "sentiratio":sentiratio_results,})
def news_comments_list(taskid,
start_ts,
over_ts,
weibo_list,
cluster_num=-1,
cluster_eva_min_size=default_cluster_eva_min_size,
vsm=default_vsm,
calculation_label=1): #weibo_list把微博读进来
"""计算饼图数据，并将饼图数据和去重后的推荐文本写到文件
taskid = request.args.get('taskid', default_task_id)
cluster_num = request.args.get('cluster_num', default_cluster_num) #若无此参数，取-1；否则取用户设定值
if cluster_num == default_cluster_num:
cluster_num = -1
cluster_eva_min_size = request.args.get('cluster_eva_min_size', default_cluster_eva_min_size)
vsm = request.args.get('vsm', default_vsm)
calculation_label = int(request.args.get('calcu', 1)) # 默认进行重新计算, 0表示从从已有结果数据文件加载数据
"""
params = {"taskid": taskid, "cluster_num": cluster_num, "cluster_eva_min_size": cluster_eva_min_size, \
"vsm": vsm, "calculation_label": calculation_label}
comments = weibo_list
logfile = os.path.join(LOG_FOLDER, taskid + '.log')
cal_results = comments_calculation_v2(comments, logfile=logfile, cluster_num=cluster_num, \
cluster_eva_min_size=int(cluster_eva_min_size), version=vsm)
#print cal_results
features = cal_results['cluster_infos']['features']
item_infos = cal_results['item_infos']
cluster_ratio = dict()
senti_ratio = dict()
sentiment_results = dict()
cluster_results = dict()
rub_results = []
# 过滤前文本数
before_filter_count = len(item_infos)
# 过滤后文本数
after_filter_count = 0
download_items = []
for comment in item_infos:
# print comment
download_item = {}
download_item["id"] = comment["id"]
download_item["title"] = comment["title"]
download_item["text"] = comment["text"]
# download_item["timestamp"] = comment["timestamp"]
download_item["datetime"] = comment["datetime"]
download_item["clusterid"] = comment["clusterid"]
download_item["sentiment"] = comment["sentiment"]
download_item["ad_label"] = comment["ad_label"]
if (comment["clusterid"][:8] != 'nonsense') and (comment["clusterid"]
!= 'other'):
download_item["duplicate"] = comment["duplicate"]
download_item["same_from"] = comment["same_from"]
download_items.append(download_item)
if ('clusterid'
in comment) and (comment['clusterid'][:8] != 'nonsense'):
clusterid = comment['clusterid']
try:
cluster_ratio[clusterid] += 1
except KeyError:
cluster_ratio[clusterid] = 1
try:
cluster_results[clusterid].append(comment)
except KeyError:
cluster_results[clusterid] = [comment]
if ('sentiment' in comment) and (comment['sentiment'] in emotions_vk_v1) and ('clusterid' in comment) \
and (comment['clusterid'][:8] != 'nonsense'):
sentiment = comment['sentiment']
try:
senti_ratio[sentiment] += 1
except KeyError:
senti_ratio[sentiment] = 1
try:
sentiment_results[sentiment].append(comment)
except KeyError:
sentiment_results[sentiment] = [comment]
after_filter_count += 1
if comment['clusterid'][:8] == 'nonsense':
rub_results.append(comment)
ratio_results = dict()
ratio_total_count = sum(cluster_ratio.values())
for clusterid, ratio in cluster_ratio.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
ratio_results[','.join(
feature[:3])] = float(ratio) / float(ratio_total_count)
sentiratio_results = dict()
sentiratio_total_count = sum(senti_ratio.values())
for sentiment, ratio in senti_ratio.iteritems():
if sentiment in emotions_vk_v1:
label = emotions_vk_v1[sentiment]
if label and len(label):
sentiratio_results[label] = float(ratio) / float(
sentiratio_total_count)
# 情感分类去重
sentiment_dump_dict = dict()
for sentiment, contents in sentiment_results.iteritems():
dump_dict = dict()
for comment in contents:
same_from_sentiment = comment["same_from_sentiment"]
try:
dump_dict[same_from_sentiment].append(comment)
except KeyError:
dump_dict[same_from_sentiment] = [comment]
sentiment_dump_dict[sentiment] = dump_dict
# 子观点分类去重
cluster_dump_dict = dict()
for clusterid, contents in cluster_results.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
dump_dict = dict()
for comment in contents:
same_from_cluster = comment["same_from"]
try:
dump_dict[same_from_cluster].append(comment)
except KeyError:
dump_dict[same_from_cluster] = [comment]
for k, v in dump_dict.iteritems():
sort_dump_dict = sorted(v,
key=lambda x: x['weight'],
reverse=True)
cluster_dump_dict[clusterid] = sort_dump_dict
# dump_file = open(task_result_file+'_news', 'w')
# dump_file.write(json.dumps({"params": params, "features":features, "senti_dump_dict":sentiment_dump_dict, \
# "cluster_dump_dict":cluster_dump_dict, "ratio":ratio_results, "sentiratio": sentiratio_results, \
# "before_filter_count": before_filter_count, "after_filter_count": after_filter_count}))
# dump_file.close()
# new_file = open(task_result_file+'_news_2','w')
# print task_result_file+'2' #所有的微博
# for i in xrange(0,len(download_items)):
# new_file.write(json.dumps(download_items[i])+'\n')
# new_file.close
#task = taskid.split('_')
#index_body={'name':task[0],'start_ts':task[1],'end_ts':task[2],'features':json.dumps(features),'cluster_dump_dict':json.dumps(cluster_dump_dict)}
index_body = {
'name': taskid,
'start_ts': start_ts,
'end_ts': over_ts,
'features': json.dumps(features),
'cluster_dump_dict': json.dumps(cluster_dump_dict)
}
weibo_es.index(index=topics_river_index_name,
doc_type=topics_river_index_type,
id=taskid,
body=index_body)
return json.dumps({
"features": features,
"cluster_dump_dict": cluster_dump_dict
})
def news_comments_list(task_source,
taskid,
weibo_list,
cluster_num=-1,
cluster_eva_min_size=default_cluster_eva_min_size,
vsm=default_vsm,
calculation_label=1): #weibo_list把微博读进来
"""计算饼图数据，并将饼图数据和去重后的推荐文本写到文件
"""
print 'weibo_list..len...', len(weibo_list)
params = {"taskid": taskid, "cluster_num": cluster_num, "cluster_eva_min_size": cluster_eva_min_size, \
"vsm": vsm, "calculation_label": calculation_label}
comments = weibo_list
logfile = os.path.join(LOG_FOLDER, taskid + '.log')
cal_results = comments_calculation_v2(comments, logfile=logfile, cluster_num=cluster_num, \
cluster_eva_min_size=int(cluster_eva_min_size), version=vsm)
#print cal_results
features = cal_results['cluster_infos']['features']
item_infos = cal_results['item_infos']
cluster_ratio = dict()
senti_ratio = dict()
sentiment_results = dict()
cluster_results = dict()
rub_results = []
# 过滤前文本数
before_filter_count = len(item_infos)
# 过滤后文本数
after_filter_count = 0
download_items = []
for comment in item_infos:
# print comment
download_item = {}
download_item["id"] = comment["id"]
download_item["title"] = comment["title"]
download_item["text"] = comment["text"]
# download_item["timestamp"] = comment["timestamp"]
download_item["datetime"] = comment["datetime"]
download_item["clusterid"] = comment["clusterid"]
download_item["sentiment"] = comment["sentiment"]
download_item["ad_label"] = comment["ad_label"]
if (comment["clusterid"][:8] != 'nonsense') and (comment["clusterid"]
!= 'other'):
download_item["duplicate"] = comment["duplicate"]
download_item["same_from"] = comment["same_from"]
download_items.append(download_item)
if ('clusterid'
in comment) and (comment['clusterid'][:8] != 'nonsense'):
clusterid = comment['clusterid']
try:
cluster_ratio[clusterid] += 1
except KeyError:
cluster_ratio[clusterid] = 1
try:
cluster_results[clusterid].append(comment)
except KeyError:
cluster_results[clusterid] = [comment]
if ('sentiment' in comment) and (comment['sentiment'] in emotions_vk_v1) and ('clusterid' in comment) \
and (comment['clusterid'][:8] != 'nonsense'):
sentiment = comment['sentiment']
try:
senti_ratio[sentiment] += 1
except KeyError:
senti_ratio[sentiment] = 1
try:
sentiment_results[sentiment].append(comment)
except KeyError:
sentiment_results[sentiment] = [comment]
after_filter_count += 1
if comment['clusterid'][:8] == 'nonsense':
rub_results.append(comment)
ratio_results = dict()
ratio_total_count = sum(cluster_ratio.values())
for clusterid, ratio in cluster_ratio.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
ratio_results[','.join(
feature[:3])] = float(ratio) / float(ratio_total_count)
sentiratio_results = dict()
sentiratio_total_count = sum(senti_ratio.values())
for sentiment, ratio in senti_ratio.iteritems():
if sentiment in emotions_vk_v1:
label = emotions_vk_v1[sentiment]
if label and len(label):
sentiratio_results[label] = float(ratio) / float(
sentiratio_total_count)
# 情感分类去重
sentiment_dump_dict = dict()
for sentiment, contents in sentiment_results.iteritems():
dump_dict = dict()
for comment in contents:
same_from_sentiment = comment["same_from_sentiment"]
try:
dump_dict[same_from_sentiment].append(comment)
except KeyError:
dump_dict[same_from_sentiment] = [comment]
sentiment_dump_dict[sentiment] = dump_dict
# 子观点分类去重
cluster_dump_dict = dict()
for clusterid, contents in cluster_results.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
dump_dict = dict()
for comment in contents:
same_from_cluster = comment["same_from"]
try:
dump_dict[same_from_cluster].append(comment)
except KeyError:
dump_dict[same_from_cluster] = [comment]
for k, v in dump_dict.iteritems():
sort_dump_dict = sorted(v,
key=lambda x: x['weight'],
reverse=True)
cluster_dump_dict[clusterid] = sort_dump_dict
#task = taskid.split('_')
index_body = {
'name': taskid,
'features': json.dumps(features),
'cluster_dump_dict': json.dumps(cluster_dump_dict)
}
es_intel.index(index=topics_river_index_name,
doc_type=topics_river_index_type,
id=taskid,
body=index_body)
return json.dumps({
"features": features,
"cluster_dump_dict": cluster_dump_dict
})
def comments_list():
if os.path.exists(temp_file):
os.remove(temp_file)
AB_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'../../public/')
sys.path.append(AB_PATH)
from comment_module import comments_calculation_v2
topicid = request.args.get('topicid', default_topic_id)
subeventid = request.args.get('subeventid', 'global')
min_cluster_num = request.args.get('min_cluster_num',
default_min_cluster_num)
max_cluster_num = request.args.get('max_cluster_num',
default_max_cluster_num)
cluster_eva_min_size = request.args.get('cluster_eva_min_size',
default_cluster_eva_min_size)
vsm = request.args.get('vsm', default_vsm)
ec = EventComments(topicid)
if subeventid == 'global':
comments = ec.getAllNewsComments()
else:
comments = ec.getCommentsBySubeventid(subeventid)
if not comments:
return json.dumps({"status": "fail"})
cal_results = comments_calculation_v2(comments, int(min_cluster_num),
int(max_cluster_num),
int(cluster_eva_min_size), vsm)
features = cal_results['cluster_infos']['features']
item_infos = cal_results['item_infos']
senti_dict = {0: '中性', 1: '积极', 2: '愤怒', 3: '悲伤'}
cluster_ratio = dict()
senti_ratio = dict()
sentiment_results = dict()
cluster_results = dict()
for comment in item_infos:
if ('clusterid'
in comment) and (comment['clusterid'][:8] != 'nonsense'):
clusterid = comment['clusterid']
try:
cluster_ratio[clusterid] += 1
except KeyError:
cluster_ratio[clusterid] = 1
try:
cluster_results[clusterid].append(comment)
except KeyError:
cluster_results[clusterid] = [comment]
if ('sentiment' in comment) and (comment['sentiment'] in senti_dict) and ('clusterid' in comment) \
and (comment['clusterid'][:8] != 'nonsense'):
sentiment = comment['sentiment']
try:
senti_ratio[sentiment] += 1
except KeyError:
senti_ratio[sentiment] = 1
try:
sentiment_results[sentiment].append(comment)
except KeyError:
sentiment_results[sentiment] = [comment]
ratio_results = dict()
ratio_total_count = sum(cluster_ratio.values())
for clusterid, ratio in cluster_ratio.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
ratio_results[','.join(
feature[:3])] = float(ratio) / float(ratio_total_count)
sentiratio_results = dict()
sentiratio_total_count = sum(senti_ratio.values())
for sentiment, ratio in senti_ratio.iteritems():
if sentiment in senti_dict:
label = senti_dict[sentiment]
if label and len(label):
sentiratio_results[label] = float(ratio) / float(
sentiratio_total_count)
# 情感分类去重
sentiment_dump_dict = dict()
for sentiment, contents in sentiment_results.iteritems():
dump_dict = dict()
for comment in contents:
same_from_sentiment = comment["same_from_sentiment"]
try:
dump_dict[same_from_sentiment].append(comment)
except KeyError:
dump_dict[same_from_sentiment] = [comment]
sentiment_dump_dict[sentiment] = dump_dict
# 子观点分类去重
cluster_dump_dict = dict()
for clusterid, contents in cluster_results.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
dump_dict = dict()
for comment in contents:
same_from_cluster = comment["same_from"]
try:
dump_dict[same_from_cluster].append(comment)
except KeyError:
dump_dict[same_from_cluster] = [comment]
cluster_dump_dict[clusterid] = dump_dict
dump_file = open(temp_file, 'w')
dump_file.write(json.dumps({"features":features, "senti_dump_dict":sentiment_dump_dict,\
"cluster_dump_dict":cluster_dump_dict}))
dump_file.close()
return json.dumps({
"ratio": ratio_results,
"sentiratio": sentiratio_results,
})
def comments_list():
taskid = request.args.get('taskid', default_task_id)
cluster_num = request.args.get('cluster_num', '') #若无此参数，取-1；否则取用户设定值
if cluster_num == '':
cluster_num = default_cluster_num
cluster_eva_min_size = request.args.get('cluster_eva_min_size',
default_cluster_eva_min_size)
vsm = request.args.get('vsm', default_vsm)
temp_file = taskid + temp_file_post
if os.path.exists(temp_file):
os.remove(temp_file)
ec = EventComments(taskid)
comments = ec.getAllNewsComments()
if not comments:
return json.dumps({"status": "fail"})
cal_results = comments_calculation_v2(
comments, cluster_eva_min_size=int(cluster_eva_min_size), version=vsm)
features = cal_results['cluster_infos']['features']
item_infos = cal_results['item_infos']
senti_dict = {0: '中性', 1: '积极', 2: '愤怒', 3: '悲伤'}
cluster_ratio = dict()
senti_ratio = dict()
sentiment_results = dict()
cluster_results = dict()
for comment in item_infos:
if ('clusterid'
in comment) and (comment['clusterid'][:8] != 'nonsense'):
clusterid = comment['clusterid']
try:
cluster_ratio[clusterid] += 1
except KeyError:
cluster_ratio[clusterid] = 1
try:
cluster_results[clusterid].append(comment)
except KeyError:
cluster_results[clusterid] = [comment]
if ('sentiment' in comment) and (comment['sentiment'] in senti_dict) and ('clusterid' in comment) \
and (comment['clusterid'][:8] != 'nonsense'):
sentiment = comment['sentiment']
try:
senti_ratio[sentiment] += 1
except KeyError:
senti_ratio[sentiment] = 1
try:
sentiment_results[sentiment].append(comment)
except KeyError:
sentiment_results[sentiment] = [comment]
ratio_results = dict()
ratio_total_count = sum(cluster_ratio.values())
for clusterid, ratio in cluster_ratio.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
ratio_results[','.join(
feature[:3])] = float(ratio) / float(ratio_total_count)
sentiratio_results = dict()
sentiratio_total_count = sum(senti_ratio.values())
for sentiment, ratio in senti_ratio.iteritems():
if sentiment in senti_dict:
label = senti_dict[sentiment]
if label and len(label):
sentiratio_results[label] = float(ratio) / float(
sentiratio_total_count)
# 情感分类去重
sentiment_dump_dict = dict()
for sentiment, contents in sentiment_results.iteritems():
dump_dict = dict()
for comment in contents:
same_from_sentiment = comment["same_from_sentiment"]
try:
dump_dict[same_from_sentiment].append(comment)
except KeyError:
dump_dict[same_from_sentiment] = [comment]
sentiment_dump_dict[sentiment] = dump_dict
# 子观点分类去重
cluster_dump_dict = dict()
for clusterid, contents in cluster_results.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
dump_dict = dict()
for comment in contents:
same_from_cluster = comment["same_from"]
try:
dump_dict[same_from_cluster].append(comment)
except KeyError:
dump_dict[same_from_cluster] = [comment]
cluster_dump_dict[clusterid] = dump_dict
dump_file = open(temp_file, 'w')
dump_file.write(json.dumps({"features":features, "senti_dump_dict":sentiment_dump_dict,\
"cluster_dump_dict":cluster_dump_dict, "ratio":ratio_results, "sentiratio": sentiratio_results}))
dump_file.close()
return json.dumps({
"ratio": ratio_results,
"sentiratio": sentiratio_results
})
def comments_list():
if os.path.exists(temp_file):
os.remove(temp_file)
AB_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../public/')
sys.path.append(AB_PATH)
from comment_module import comments_calculation_v2
topicid = request.args.get('topicid', default_topic_id)
subeventid = request.args.get('subeventid', 'global')
min_cluster_num = request.args.get('min_cluster_num', default_min_cluster_num)
max_cluster_num = request.args.get('max_cluster_num', default_max_cluster_num)
cluster_eva_min_size = request.args.get('cluster_eva_min_size', default_cluster_eva_min_size)
vsm = request.args.get('vsm', default_vsm)
ec = EventComments(topicid)
if subeventid == 'global':
comments = ec.getAllNewsComments()
else:
comments = ec.getCommentsBySubeventid(subeventid)
if not comments:
return json.dumps({"status":"fail"})
cal_results = comments_calculation_v2(comments, int(min_cluster_num), int(max_cluster_num), int(cluster_eva_min_size), vsm)
features = cal_results['cluster_infos']['features']
item_infos = cal_results['item_infos']
senti_dict = {
0:'中性',
1:'积极',
2:'愤怒',
3:'悲伤'
}
cluster_ratio = dict()
senti_ratio = dict()
sentiment_results = dict()
cluster_results = dict()
for comment in item_infos:
if ('clusterid' in comment) and (comment['clusterid'][:8] != 'nonsense') :
clusterid = comment['clusterid']
try:
cluster_ratio[clusterid] += 1
except KeyError:
cluster_ratio[clusterid] = 1
try:
cluster_results[clusterid].append(comment)
except KeyError:
cluster_results[clusterid] = [comment]
if ('sentiment' in comment) and (comment['sentiment'] in senti_dict) and ('clusterid' in comment) \
and (comment['clusterid'][:8] != 'nonsense'):
sentiment = comment['sentiment']
try:
senti_ratio[sentiment] += 1
except KeyError:
senti_ratio[sentiment] = 1
try:
sentiment_results[sentiment].append(comment)
except KeyError:
sentiment_results[sentiment] = [comment]
ratio_results = dict()
ratio_total_count = sum(cluster_ratio.values())
for clusterid, ratio in cluster_ratio.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
ratio_results[','.join(feature[:3])] = float(ratio) / float(ratio_total_count)
sentiratio_results = dict()
sentiratio_total_count = sum(senti_ratio.values())
for sentiment, ratio in senti_ratio.iteritems():
if sentiment in senti_dict:
label = senti_dict[sentiment]
if label and len(label):
sentiratio_results[label] = float(ratio) / float(sentiratio_total_count)
# 情感分类去重
sentiment_dump_dict = dict()
for sentiment, contents in sentiment_results.iteritems():
dump_dict = dict()
for comment in contents:
same_from_sentiment = comment["same_from_sentiment"]
try:
dump_dict[same_from_sentiment].append(comment)
except KeyError:
dump_dict[same_from_sentiment] = [comment]
sentiment_dump_dict[sentiment] = dump_dict
# 子观点分类去重
cluster_dump_dict = dict()
for clusterid, contents in cluster_results.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
dump_dict = dict()
for comment in contents:
same_from_cluster = comment["same_from"]
try:
dump_dict[same_from_cluster].append(comment)
except KeyError:
dump_dict[same_from_cluster] = [comment]
cluster_dump_dict[clusterid] = dump_dict
dump_file = open(temp_file, 'w')
dump_file.write(json.dumps({"features":features, "senti_dump_dict":sentiment_dump_dict,\
"cluster_dump_dict":cluster_dump_dict}));
dump_file.close();
return json.dumps({"ratio":ratio_results, "sentiratio":sentiratio_results,})
def comments_list():
"""计算饼图数据，并将饼图数据和去重后的推荐文本写到文件
"""
taskid = request.args.get('taskid', default_task_id)
cluster_num = request.args.get('cluster_num', default_cluster_num) #若无此参数，取-1；否则取用户设定值
if cluster_num == default_cluster_num:
cluster_num = -1
cluster_eva_min_size = request.args.get('cluster_eva_min_size', default_cluster_eva_min_size)
vsm = request.args.get('vsm', default_vsm)
calculation_label = int(request.args.get('calcu', 1)) # 默认进行重新计算, 0表示从从已有结果数据文件加载数据
params = {"taskid": taskid, "cluster_num": cluster_num, "cluster_eva_min_size": cluster_eva_min_size, \
"vsm": vsm, "calculation_label": calculation_label}
task_result_file = os.path.join(RESULT_FOLDER, taskid)
if os.path.exists(task_result_file) and calculation_label == 0:
# 从已有数据文件加载结果集
with open(task_result_file) as dump_file:
dump_dict = json.loads(dump_file.read())
ratio_results = dump_dict["ratio"]
sentiratio_results = dump_dict["sentiratio"]
before_filter_count = dump_dict["before_filter_count"]
after_filter_count = dump_dict["after_filter_count"]
return json.dumps({"ratio": ratio_results, "sentiratio": sentiratio_results, \
"before_filter_count": before_filter_count, "after_filter_count": after_filter_count})
comments = []
task_input_file = os.path.join(UPLOAD_FOLDER, taskid)
if os.path.exists(task_input_file):
with open(task_input_file) as f:
for line in f:
comments.append(json.loads(line.strip()))
if not comments or not len(comments):
return json.dumps({"status":"fail"})
logfile = os.path.join(LOG_FOLDER, taskid + '.log')
cal_results = comments_calculation_v2(comments, logfile=logfile, cluster_num=cluster_num, \
cluster_eva_min_size=int(cluster_eva_min_size), version=vsm)
features = cal_results['cluster_infos']['features']
item_infos = cal_results['item_infos']
cluster_ratio = dict()
senti_ratio = dict()
sentiment_results = dict()
cluster_results = dict()
rub_results = []
# 过滤前文本数
before_filter_count = len(item_infos)
# 过滤后文本数
after_filter_count = 0
for comment in item_infos:
if ('clusterid' in comment) and (comment['clusterid'][:8] != 'nonsense') :
clusterid = comment['clusterid']
try:
cluster_ratio[clusterid] += 1
except KeyError:
cluster_ratio[clusterid] = 1
try:
cluster_results[clusterid].append(comment)
except KeyError:
cluster_results[clusterid] = [comment]
if ('sentiment' in comment) and (comment['sentiment'] in emotions_vk_v1) and ('clusterid' in comment) \
and (comment['clusterid'][:8] != 'nonsense'):
sentiment = comment['sentiment']
try:
senti_ratio[sentiment] += 1
except KeyError:
senti_ratio[sentiment] = 1
try:
sentiment_results[sentiment].append(comment)
except KeyError:
sentiment_results[sentiment] = [comment]
after_filter_count += 1
if comment['clusterid'][:8] == 'nonsense':
rub_results.append(comment)
ratio_results = dict()
ratio_total_count = sum(cluster_ratio.values())
for clusterid, ratio in cluster_ratio.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
ratio_results[','.join(feature[:3])] = float(ratio) / float(ratio_total_count)
sentiratio_results = dict()
sentiratio_total_count = sum(senti_ratio.values())
for sentiment, ratio in senti_ratio.iteritems():
if sentiment in emotions_vk_v1:
label = emotions_vk_v1[sentiment]
if label and len(label):
sentiratio_results[label] = float(ratio) / float(sentiratio_total_count)
# 情感分类去重
sentiment_dump_dict = dict()
for sentiment, contents in sentiment_results.iteritems():
dump_dict = dict()
for comment in contents:
same_from_sentiment = comment["same_from_sentiment"]
try:
dump_dict[same_from_sentiment].append(comment)
except KeyError:
dump_dict[same_from_sentiment] = [comment]
sentiment_dump_dict[sentiment] = dump_dict
# 子观点分类去重
cluster_dump_dict = dict()
for clusterid, contents in cluster_results.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
dump_dict = dict()
for comment in contents:
same_from_cluster = comment["same_from"]
try:
dump_dict[same_from_cluster].append(comment)
except KeyError:
dump_dict[same_from_cluster] = [comment]
cluster_dump_dict[clusterid] = dump_dict
dump_file = open(task_result_file, 'w')
dump_file.write(json.dumps({"params": params, "features":features, "senti_dump_dict":sentiment_dump_dict, \
"cluster_dump_dict":cluster_dump_dict, "ratio":ratio_results, "sentiratio": sentiratio_results, \
"before_filter_count": before_filter_count, "after_filter_count": after_filter_count, \
"rub_results": rub_results}))
dump_file.close()
return json.dumps({"ratio": ratio_results, "sentiratio": sentiratio_results, \
"before_filter_count": before_filter_count, "after_filter_count": after_filter_count})
def comments_list():
"""计算饼图数据，并将饼图数据和去重后的推荐文本写到文件
"""
topicid = request.args.get('topicid', default_topic_id)
subeventid = request.args.get('subeventid', 'global')
cluster_num = request.args.get('cluster_num', default_cluster_num)
if cluster_num == default_cluster_num:
cluster_num = -1
cluster_eva_min_size = request.args.get('cluster_eva_min_size', default_cluster_eva_min_size)
vsm = request.args.get('vsm', default_vsm)
ec = EventComments(topicid)
if subeventid == 'global':
comments = ec.getAllNewsComments()
else:
comments = ec.getCommentsBySubeventid(subeventid)
if not comments:
return json.dumps({"status":"fail"})
cal_results = comments_calculation_v2(comments, cluster_num=int(cluster_num), \
cluster_eva_min_size=int(cluster_eva_min_size), version=vsm)
features = cal_results['cluster_infos']['features']
item_infos = cal_results['item_infos']
cluster_ratio = dict()
senti_ratio = dict()
sentiment_results = dict()
cluster_results = dict()
for comment in item_infos:
if ('clusterid' in comment) and (comment['clusterid'][:8] != 'nonsense') :
clusterid = comment['clusterid']
try:
cluster_ratio[clusterid] += 1
except KeyError:
cluster_ratio[clusterid] = 1
try:
cluster_results[clusterid].append(comment)
except KeyError:
cluster_results[clusterid] = [comment]
if ('sentiment' in comment) and (comment['sentiment'] in emotions_vk_v1) and ('clusterid' in comment) \
and (comment['clusterid'][:8] != 'nonsense'):
sentiment = comment['sentiment']
try:
senti_ratio[sentiment] += 1
except KeyError:
senti_ratio[sentiment] = 1
try:
sentiment_results[sentiment].append(comment)
except KeyError:
sentiment_results[sentiment] = [comment]
ratio_results = dict()
ratio_total_count = sum(cluster_ratio.values())
for clusterid, ratio in cluster_ratio.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
ratio_results[','.join(feature[:3])] = float(ratio) / float(ratio_total_count)
sentiratio_results = dict()
sentiratio_total_count = sum(senti_ratio.values())
for sentiment, ratio in senti_ratio.iteritems():
if sentiment in emotions_vk_v1:
label = emotions_vk_v1[sentiment]
if label and len(label):
sentiratio_results[label] = float(ratio) / float(sentiratio_total_count)
# 情感分类去重
sentiment_dump_dict = dict()
for sentiment, contents in sentiment_results.iteritems():
dump_dict = dict()
for comment in contents:
same_from_sentiment = comment["same_from_sentiment"]
try:
dump_dict[same_from_sentiment].append(comment)
except KeyError:
dump_dict[same_from_sentiment] = [comment]
sentiment_dump_dict[sentiment] = dump_dict
# 子观点分类去重
cluster_dump_dict = dict()
for clusterid, contents in cluster_results.iteritems():
if clusterid in features:
feature = features[clusterid]
if feature and len(feature):
dump_dict = dict()
for comment in contents:
same_from_cluster = comment["same_from"]
try:
dump_dict[same_from_cluster].append(comment)
except KeyError:
dump_dict[same_from_cluster] = [comment]
cluster_dump_dict[clusterid] = dump_dict
dump_file = open(temp_file, 'w')
dump_file.write(json.dumps({"features":features, "senti_dump_dict":sentiment_dump_dict,\
"cluster_dump_dict":cluster_dump_dict}));
dump_file.close();
return json.dumps({"ratio":ratio_results, "sentiratio":sentiratio_results,})
