def get_gp_parameter(parameter_name):
json_path = get_setting_cfg().get('path', 'gp_parameter')
core_num = get_setting_cfg().getint('GP', 'core_num')
with open(json_path) as f:
data = json.load(f)
data[parameter_name]['predictor_parameter'].update({'n_jobs': core_num})
data[parameter_name]['predictor_parameter'].update({'const_range': (data[parameter_name]['predictor_parameter'].pop(
'min_const', -1), data[parameter_name]['predictor_parameter'].pop('max_const', 1))})
return data[parameter_name]
def convert_list_to_info():
event_list = get_event_list()
new_data = {}
for event in event_list:
new_data[event['id']] = event
json_path = get_setting_cfg().get('path', 'event_info')
with io.open(json_path, 'w+', encoding='utf8') as f:
json.dump(new_data, f, ensure_ascii=False, indent=4)
def get_last_rank(event_id):
if not is_event_has_rank(event_id): return None
json_path = get_setting_cfg().get('path', 'event_rank')
data = get_json(json_path)
rank_list = {rank: data[event_id][rank][-1] for rank in data[event_id]}
for rank in rank_list:
rank_list[rank]['summaryTime'] = str_to_datetime(
rank_list[rank]['summaryTime'])
return rank_list
def save_event_list():
json_path = get_setting_cfg().get('path', 'event_list')
r = requests.get('{}/events'.format(api_url))
if r.status_code != 200:
raise Exception('get event list fail with code:{}'.format(
r.status_code))
event_list = r.json()
with io.open(json_path, 'w+', encoding='utf8') as f:
json.dump(event_list, f, ensure_ascii=False, indent=4)
convert_list_to_info()
def get_pandas_data(event_id, rank_num):
json_path = get_setting_cfg().get('path', 'event_rank')
with open(json_path) as f:
rank_data = json.load(f)
score_diff_list = []
time_list = []
for i in range(1, len(rank_data[event_id][rank_num])):
rank = rank_data[event_id][rank_num]
score_diff_list.append(rank[i]['score'] - rank[i - 1]['score'])
time_list.append(str_to_datetime(rank[i]['summaryTime']))
return pd.Series(score_diff_list, index=time_list)
def get_mylogger(log_name):
config = get_cfg.get_setting_cfg()
log_path = config.get('log', 'log_path')
log_file = config.get('log', log_name)
is_debug = config.getboolean('log', 'is_debug')
log_file = os.path.join(log_path, log_file)
my_logger = logging.getLogger(log_name)
level = logging.INFO
if is_debug:
level = logging.DEBUG
my_logger.setLevel(level)
return my_logger, level, log_file
def save_event_info(event_id):
validate_event_id(event_id)
json_path = get_setting_cfg().get('path', 'event_info')
r = requests.get('{}/events/{}'.format(api_url, event_id))
if r.status_code != 200:
raise Exception('get event info fail with code:{}'.format(
r.status_code))
info = r.json()
info_list = {}
if os.path.exists(json_path):
info_list = get_json(json_path)
info_list[event_id] = info
with io.open(json_path, 'w+', encoding='utf8') as f:
json.dump(info_list, f, ensure_ascii=False, indent=4)
def save_event_ranking(event_id):
validate_event_id(event_id)
rank_num_list = ','.join(get_monitor_rank())
json_path = get_setting_cfg().get('path', 'event_rank')
r = requests.get('{}/events/{}/rankings/logs/eventPoint/{}'.format(
api_url, event_id, rank_num_list))
if r.status_code != 200:
raise Exception('get event rank fail with code:{}'.format(
r.status_code))
rank_list = r.json()
new_rank_list = {}
if os.path.exists(json_path):
new_rank_list = get_json(json_path)
new_rank_list[event_id] = {
rank['rank']: rank['data']
for rank in rank_list
}
with io.open(json_path, 'w+', encoding='utf8') as f:
json.dump(new_rank_list, f, ensure_ascii=False, indent=4)
def get_predict_status(event_id):
return_str = ''
rank_list = mltd_api.get_monitor_rank()
info = mltd_api.get_event_info(event_id)
begin = info['schedule']['beginDate']
end = info['schedule']['endDate']
diff = timedelta(minutes=30)
last_step = int((end - begin) / diff)
n_step = get_setting_cfg().getint('ARIMA', 'n_step')
for rank in rank_list:
predict_value, predictor_info = predict.predict(
event_id, rank, last_step)
if predict_value:
return_str += '下{}個時間點{}名的預測分數\n(預測器根據{}時間點 分數變動幅度:{}產生):\n{}\n{}\n'.format(
n_step, rank,
predictor_info['last_time'].strftime("%Y-%m-%d %H:%M:%S"),
predictor_info['last_score'],
', '.join([str(value) for value in predict_value[:n_step]]),
'最終結果:{}'.format(predict_value[-1]))
if return_str == '':
return_str = '無法取得預測值，預測器尚未生成'
return return_str
def calculate_speed(event_id, interval):
if not is_event_has_rank(event_id): return None
json_path = get_setting_cfg().get('path', 'event_rank')
data = get_json(json_path)
speed_dict = {}
for rank in data[event_id]:
if len(data[event_id][rank]) >= 2:
now_score = data[event_id][rank][-1]['score']
last_score = data[event_id][rank][-2]['score']
now_time = str_to_ts(data[event_id][rank][-1]['summaryTime'])
last_time = str_to_ts(data[event_id][rank][-2]['summaryTime'])
speed = (now_score - last_score) / (now_time - last_time)
else:
speed_dict[rank] = speed = 0
if interval == 'hour':
speed_dict[rank] = speed * 3600
elif interval == 'half_hour':
speed_dict[rank] = speed * 1800
elif interval == 'quarter_hour':
speed_dict[rank] = speed * 900
elif interval == 'min':
speed_dict[rank] = speed * 60
return speed_dict
def get_predictor(train_x, train_y, predictor_parameter_type=get_setting_cfg().get('GP', 'setting')):
parameter = get_gp_parameter(predictor_parameter_type)
mltd_predictor = gp.SymbolicRegressor(
function_set=cf.get_custom_function_list(), **parameter['predictor_parameter'])
return mltd_predictor.fit(train_x, train_y)
def get_monitor_rank():
return get_setting_cfg().get('monitor', 'monitor_rank').split(',')
def get_event_list():
return get_json(get_setting_cfg().get('path', 'event_list'))
def get_event_info(event_id):
json_path = get_setting_cfg().get('path', 'event_info')
info = get_json(json_path)[event_id]
for key in info['schedule']:
info['schedule'][key] = str_to_datetime(info['schedule'][key])
return info
def get_data(event_id, rank_num):
json_path = get_setting_cfg().get('path', 'event_rank')
with open(json_path) as f:
data = json.load(f)
return [rank['score'] for rank in data[event_id][rank_num]]
def str_to_datetime(time_str,
time_zone=get_setting_cfg().getint('common', 'timezone')):
dt = parse(time_str)
local_dt = dt.astimezone(timezone(timedelta(hours=time_zone)))
return local_dt
def np_dt_to_dt(np_dt,
time_zone=get_setting_cfg().getint('common', 'timezone')):
return (pd.Timestamp(np_dt).to_pydatetime().astimezone(
timezone(timedelta(hours=time_zone)))) + timedelta(hours=time_zone)
