def trendsetter_rank(TOPK, date, topic_id, windowsize, topic, real_topic_id):
data = None
key = str(real_topic_id) + '_' + str(date) + '_' + str(windowsize)
ds_g = nx.read_gexf(str(GRAPH_PATH) + str(key) + '_ds_dg_graph.gexf')
tmp_file, ds_N = prepare_data_for_ts(topic_id, topic, date, windowsize,
ds_g)
top_N = ds_N # 选择保存TS前top_N的数据
print 'trendsetter rank start'
if not tmp_file:
return data
input_tmp_path = tmp_file.name
print 'input_tmp_path:', input_tmp_path
job_id = generate_job_id(
datetime2ts(date), windowsize, topic_id, ts_netwrok_type
) # pagerank中g与ds_g分别对应的network_type分别为1,2.Trendsetter_rank为3
print 'job_id:', job_id
iter_count = Trendsetter_iter_max # ???迭代的最大次数---PageRank的迭代次数设置为1
print 'trendsetter rank direct_superior_network'
ds_sorted_uids, ds_all_uid_tr = ts_rank(job_id, iter_count, input_tmp_path,
top_N)
print 'save trendsetter rank result'
print 'len(ds_sorted_uids):', len(ds_sorted_uids)
print 'len(ds_all_uid_tr):', len(ds_all_uid_tr)
data = save_tr_results(topic, date, windowsize, ds_sorted_uids,
ds_all_uid_tr)
return ds_all_uid_tr, data # 标识保存是否成功
def test_hadoop_job_id(self):
date = '2013-03-01'
ts = datetime2ts(date)
window_size = 1
topic_id = 1
job_id = generate_job_id(ts, window_size, topic_id)
self.assertEqual(job_id, '2013_03_01_1_1', 'wrong job id')
def pagerank_rank(top_n, date, topic_id, window_size):
data = []
tmp_file = prepare_data_for_pr(topic_id, date, window_size)
if not tmp_file:
return data
input_tmp_path = tmp_file.name
job_id = generate_job_id(datetime2ts(date), window_size, topic_id)
iter_count = PAGERANK_ITER_MAX
sorted_uids = pagerank(job_id, iter_count, input_tmp_path, top_n)
print sorted_uids
def pagerank_rank(top_n, date, topic_id, window_size):
data = []
tmp_file = prepare_data_for_pr(topic_id, date, window_size)
if not tmp_file:
return data
input_tmp_path = tmp_file.name
job_id = generate_job_id(datetime2ts(date), window_size, topic_id)
iter_count = PAGERANK_ITER_MAX
sorted_uids = pagerank(job_id, iter_count, input_tmp_path, top_n)
topicname = acquire_topic_name(topic_id)
if not topicname:
return data
data = save_rank_results(sorted_uids, 'topic', 'pagerank', date, window_size, topicname)
return data
def trendsetter_rank(TOPK, date, topic_id, windowsize, topic, real_topic_id):
data = None
key = str(real_topic_id) + '_' + str(date) + '_' + str(windowsize)
ds_g = nx.read_gexf(str(GRAPH_PATH) + str(key) + '_ds_dg_graph.gexf')
tmp_file, ds_N = prepare_data_for_ts(topic_id, topic, date, windowsize, ds_g)
top_N = ds_N # 选择保存TS前top_N的数据
print 'trendsetter rank start'
if not tmp_file:
return data
input_tmp_path = tmp_file.name
print 'input_tmp_path:', input_tmp_path
job_id = generate_job_id(datetime2ts(date), windowsize, topic_id, ts_netwrok_type) # pagerank中g与ds_g分别对应的network_type分别为1,2.Trendsetter_rank为3
print 'job_id:', job_id
iter_count = Trendsetter_iter_max # ???迭代的最大次数---PageRank的迭代次数设置为1
print 'trendsetter rank direct_superior_network'
ds_sorted_uids, ds_all_uid_tr = ts_rank(job_id, iter_count, input_tmp_path, top_N)
print 'save trendsetter rank result'
print 'len(ds_sorted_uids):', len(ds_sorted_uids)
print 'len(ds_all_uid_tr):', len(ds_all_uid_tr)
data = save_tr_results(topic, date, windowsize, ds_sorted_uids, ds_all_uid_tr)
return ds_all_uid_tr, data # 标识保存是否成功
