def _daily_predict(self, series_id, consumption, weekdays, dates):
is_day_off = self._get_is_day_off(weekdays, series_id, dates)
is_day_off = is_day_off[-self._input_days:]
org_key = ''.join([str(i) for i in is_day_off])
pred = []
for offset in range(7):
weekday = weekdays[-1]
date = dates[-1]
for _ in range(offset + 1):
weekday = _get_next_weekday(weekday)
date = _get_next_date(date, offset + 1)
key = org_key + str(self._is_day_off(weekday, series_id, date))
while 1:
if key in self.train_data['daily'][offset]:
break
else:
# print(key, 'not found')
key = key[1:]
if not len(key):
msg = 'Key not found: %s\tWindow: %s\tOffset: %s' % (
org_key, 'daily', offset)
raise KeyError(msg)
x = consumption[-(len(key) - 1) * 24:]
x = group_sum(x, 24)
x = np.expand_dims(x, axis=0)
weights = self.train_data['daily'][offset][key]['weights']
# print(consumption.shape, x.shape, weights.shape, key)
pred.append(x.dot(weights)[0])
return np.array(pred)
def _hourly_predict(self, series_id, consumption, weekdays, dates):
is_day_off = self._get_is_day_off(weekdays, series_id, dates)
is_day_off = is_day_off[-self._input_days:]
is_day_off.append(
self._is_day_off(_get_next_weekday(weekdays[-1]), series_id,
_get_next_date(dates[-1])))
key = ''.join([str(i) for i in is_day_off])
# print(key, weekdays)
while 1:
if key in self.train_data['hourly'][0]:
break
else:
# print(key, 'not found')
key = key[1:]
if not len(key):
raise KeyError('Empty key')
consumption = consumption[-(len(key) - 1) * 24:]
x = np.zeros((24, len(key) - 1))
for i in range(len(key) - 1):
x[:, i] = consumption[i * 24:(i + 1) * 24]
pred = []
for offset in range(24):
weights = self.train_data['hourly'][offset][key]['weights']
pred.append(x[offset:offset + 1].dot(weights)[0])
return np.array(pred)
def _prepare_is_day_off(window, df, metadata, series_id):
is_day_off = df.is_holiday.values[::24].tolist()
current_date = df.timestamp.values[-1]
current_weekday = df.weekday.values[-1]
for _ in range(WINDOW_TO_PRED_DAYS[window]):
current_date = _get_next_date(current_date)
current_weekday = _get_next_weekday(current_weekday)
current_day_is_off = _is_day_off(series_id, current_weekday, metadata)
current_day_is_off = current_day_is_off or _is_holiday(current_date)
is_day_off.append(current_day_is_off)
is_day_off = np.array(is_day_off, dtype=np.float32)
is_day_off[is_day_off == 0] = -1
is_day_off = np.expand_dims(is_day_off, axis=0)
return is_day_off
def _prepare_future_weekday(window, df):
weekday = []
current_weekday = df.weekday.values[-1]
for _ in range(WINDOW_TO_PRED_DAYS[window]):
current_weekday = _get_next_weekday(current_weekday)
weekday.append(current_weekday)
weekday = [_weekday_ohe(day) for day in weekday]
weekday = np.array(weekday, dtype=np.float32)
if window == 'hourly':
weekday = np.repeat(weekday, 24, axis=0)
elif window == 'weekly':
weekday = weekday[::7]
weekday = np.expand_dims(weekday, axis=0)
return weekday
def _prepare_future_day_off(window, df, metadata, series_id):
is_day_off = []
current_date = df.timestamp.values[-1]
current_weekday = df.weekday.values[-1]
for _ in range(WINDOW_TO_PRED_DAYS[window]):
current_date = _get_next_date(current_date)
current_weekday = _get_next_weekday(current_weekday)
current_day_is_off = _is_day_off(series_id, current_weekday, metadata)
current_day_is_off = current_day_is_off or _is_holiday(current_date)
is_day_off.append(current_day_is_off)
is_day_off = np.array(is_day_off, dtype=np.float32)
if window == 'hourly':
is_day_off = np.repeat(is_day_off, 24, axis=0)
if window == 'weekly':
is_day_off = np.reshape(is_day_off, (2, -1))
else:
is_day_off = np.expand_dims(is_day_off, axis=1)
is_day_off = np.expand_dims(is_day_off, axis=0)
return is_day_off