def convert_predictions_json_to_sorted_ucdays(predictions):
ucdays = []
days = predictions.keys()
sorted_days = sorted(days)
for day in sorted_days:
ucday = UCDay(day)
for i in range(0, 24):
hour_json = predictions[day][i]
ucday.hours[i] = UCHour.buildv2(hour_json)
ucdays.append(ucday)
return ucdays
def convert_records_map_to_list(records):
sorted_records_list = []
sorted_indices = sorted(records)
first_date_str = sorted_indices[0]
last_date_str = sorted_indices[-1]
# days_str contains all the days from first date to last day
days_str = []
days_str.append(first_date_str)
_date = get_date(first_date_str)
last_date = get_date(last_date_str)
while (True):
if (_date >= last_date):
break
_date += datetime.timedelta(days=1)
_date_str = _date.strftime('%Y-%m-%d')
days_str.append(_date_str)
for day_str in days_str:
if day_str in sorted_indices:
sorted_records_list.append(records[day_str])
else:
sorted_records_list.append(UCDay(day_str))
return sorted_records_list
def dl_forecast(self, serving_url, model_stats, ucdoc, ucday_list):
records_hour_price_map = {}
day_list = []
duration = model_stats['model']['duration']
predict_window = model_stats['model']['predict_window']
# ucday_list is sorted from past to present [2018-01-01, 2018-01-02, ...]
empthy_list = []
if len(ucday_list) >= duration:
ucday_list = ucday_list[len(ucday_list)-duration:]
else:
empthy_list = [0 for _ in range(0, duration-len(ucday_list))]
for ucday in ucday_list:
day_list.append(ucday.date)
for hour in range(24):
for price_cat in range(4):
key = (hour, price_cat)
if key not in records_hour_price_map:
records_hour_price_map[key] = empthy_list[:]
records_hour_price_map[key].append(
ucday.hours[hour].histogram_value(price_cat))
records_hour_price_list = []
for key, value in records_hour_price_map.items():
# Replace 0 in the value with its median
median = statistics.median(value)
value = [i if i!=0 else median for i in value]
records_hour_price_list.append((value, key[0], key[1]))
# URL = "http://10.193.217.105:8501/v1/models/faezeh:predict"
response, predict_day_list = predict(serving_url, model_stats=model_stats, day_list=day_list, uckey=ucdoc.uckey, age=ucdoc.a, si=ucdoc.si, network=ucdoc.t, gender=ucdoc.g,
media=ucdoc.m, ip_location=ucdoc.ipl, records_hour_price_list=records_hour_price_list)
ucdayMap = {}
date = day_list[-1]
prediction_records = response
i = -1
for date in predict_day_list:
i += 1
ucday = UCDay(date)
j = -1
for _, hour, price_cat in records_hour_price_list:
j += 1
count = prediction_records[j][i]
if math.isnan(count):
count = 0
if price_cat == 0:
ucday.hours[hour].h0 = count
elif price_cat == 1:
ucday.hours[hour].h1 = count
if price_cat == 2:
ucday.hours[hour].h2 = count
if price_cat == 3:
ucday.hours[hour].h3 = count
ucdayMap[date] = ucday.hours
return ucdayMap
def convert_day_hour_counts_to_ucdoc(uckey, day_hour_counts):
ucdoc = UCDoc.build_from_concat_string(uckey)
for day_map in day_hour_counts:
if len(day_map) > 0:
# There is only one key here.
day = next(iter(day_map.keys()))
hour_count_map = day_map[day]
for hour_map in hour_count_map:
if len(hour_map) > 0:
hour = next(iter(hour_map.keys()))
h = hour_map[hour]
records = ucdoc.records
if day not in records:
records[day] = UCDay(str(day))
dict = build_dict_from_counts(h, hour)
uchour = UCHour.buildv1(dict)
records[day].hours[hour] = uchour
return ucdoc
def build_from_es_doc(dict_ucdoc):
ucdoc = UCDoc(None)
ucdoc.uckey = dict_ucdoc.get('uckey')
ucdoc.m = dict_ucdoc.get('m')
ucdoc.si = dict_ucdoc.get('si')
ucdoc.t = dict_ucdoc.get('t')
ucdoc.a = dict_ucdoc.get('a')
ucdoc.g = dict_ucdoc.get('g')
#ucdoc.dpc = dict_ucdoc['dpc']
ucdoc.pm = dict_ucdoc.get('pm')
ucdoc.r = dict_ucdoc.get('r')
ucdoc.ipl = dict_ucdoc.get('ip_city_code')
ucdoc.records = {}
# ucdoc.lastUpdate = None
if dict_ucdoc.get('records') is not None:
for date, ucday_doc in dict_ucdoc['records'].items():
ucday = UCDay.build(ucday_doc)
ucdoc.records[date] = ucday
return ucdoc