def get_forward_numerical_info(task_name, ts, create_by):
results = []
ts_series = []
for i in range(1, forward_n + 1):
ts_series.append(ts - i * time_interval)
# check if detail es of task exists
doctype = create_by + "-" + task_name
index_exist = es_user_portrait.indices.exists_type(index_sensing_task, doctype)
if not index_exist:
print "new create task detail index"
mappings_sensing_task(doctype)
if ts_series:
search_results = es_user_portrait.mget(index=index_sensing_task, doc_type=doctype, body={"ids": ts_series})[
"docs"
]
found_count = 0
average_origin = []
average_retweeted = []
average_commet = []
average_total = []
average_negetive = []
for item in search_results:
if item["found"]:
temp = item["_source"]
sentiment_dict = json.loads(temp["sentiment_distribution"])
average_total.append(int(temp["weibo_total_number"]))
average_negetive.append(
int(sentiment_dict["2"])
+ int(sentiment_dict["3"])
+ int(sentiment_dict["4"])
+ int(sentiment_dict["5"])
+ int(sentiment_dict["6"])
)
found_count += 1
if found_count > initial_count:
number_mean = np.mean(average_total)
number_std = np.std(average_total)
sentiment_mean = np.mean(average_negetive)
sentiment_std = np.mean(average_negetive)
results = [1, number_mean, number_std, sentiment_mean, sentiment_std]
else:
results = [0]
return results
def get_forward_numerical_info(task_name, ts, create_by):
results = []
ts_series = []
for i in range(1, forward_n + 1):
ts_series.append(ts - i * time_interval)
# check if detail es of task exists
doctype = create_by + "-" + task_name
index_exist = es_user_portrait.indices.exists_type(index_sensing_task,
doctype)
if not index_exist:
print "new create task detail index"
mappings_sensing_task(doctype)
if ts_series:
search_results = es_user_portrait.mget(index=index_sensing_task,
doc_type=doctype,
body={"ids": ts_series})['docs']
found_count = 0
average_origin = []
average_retweeted = []
average_commet = []
average_total = []
average_negetive = []
for item in search_results:
if item['found']:
temp = item['_source']
sentiment_dict = json.loads(temp['sentiment_distribution'])
average_total.append(int(temp['weibo_total_number']))
average_negetive.append(
int(sentiment_dict["2"]) + int(sentiment_dict['3']) +
int(sentiment_dict['4']) + int(sentiment_dict['5']) +
int(sentiment_dict['6']))
found_count += 1
if found_count > initial_count:
number_mean = np.mean(average_total)
number_std = np.std(average_total)
sentiment_mean = np.mean(average_negetive)
sentiment_std = np.mean(average_negetive)
results = [
1, number_mean, number_std, sentiment_mean, sentiment_std
]
else:
results = [0]
return results