def __init__(self, demo_raw,
train_start_time = '2014-02-01',train_end_time = '2018-10-31',
test_start_time = '2018-11-01 00:00:00', test_end_time = '2019-05-01 23:00:00' ):
# self.raw_df = raw_df
# demongraphic data [32, 32, 14]
self.demo_raw = demo_raw
self.train_start_time = train_start_time
#self.train_end_time = '2018-03-31'
self.train_end_time = train_end_time
# set train/test set
self.test_start_time = test_start_time
self.test_end_time = test_end_time
# prediction window: use one week's data to predict next hour
self.window = datetime.timedelta(hours=24 * 7)
self.step = datetime.timedelta(hours=1)
# predict_start_time should be '2018-04-08 00:00:00'
# e.g. use '2018-04-01 00:00:00' -> '2018-04-07 23:00:00', in total 168 time stamps
# to predict '2018-04-08 00:00:00'
# however, test_start_time + window = predict_start_time
# e.g. '2018-04-01 00:00:00' + 168 hour window = '2018-04-08 00:00:00'
# this is calculated by time interval, there is 1 hour shift between timestamp and time interval
self.predict_start_time = datetime_utils.str_to_datetime(self.test_start_time) + self.window
# predict_end_time = test_end_time = '2018-04-30 23:00:00'
self.predict_end_time = datetime_utils.str_to_datetime(self.test_end_time)
# if window = 7 days, test_end_time = '2018-04-30 23:00:00', actual_end_time = 04/23 - 23:00
self.actual_end_time = self.predict_end_time - self.window
# 41616
self.train_hours = datetime_utils.get_total_hour_range(self.train_start_time, self.train_end_time)
def __init__(self, raw_df, window=168):
self.raw_df = raw_df
self.train_start_time = '2014-02-01'
self.train_end_time = '2018-10-31'
self.test_start_time = '2018-11-01 00:00:00'
self.test_end_time = '2019-04-30 23:00:00'
# if not speficied, prediction window: use one week's data to predict next hour
#self.window = datetime.timedelta(hours=24 * 7)
self.window = datetime.timedelta(hours=window)
self.step = datetime.timedelta(hours=1)
# predict_start_time should be '2018-04-08 00:00:00'
# e.g. use '2018-04-01 00:00:00' -> '2018-04-07 23:00:00', in total 168 time stamps
# to predict '2018-04-08 00:00:00'
# however, test_start_time + window = predict_start_time
# e.g. '2018-04-01 00:00:00' + 168 hour window = '2018-04-08 00:00:00'
# this is calculated by time interval, there is 1 hour shift between timestamp and time interval
self.predict_start_time = datetime_utils.str_to_datetime(
self.test_start_time) + self.window
# predict_end_time = test_end_time = '2018-04-30 23:00:00'
self.predict_end_time = datetime_utils.str_to_datetime(
self.test_end_time)
# if window = 7 days, test_end_time = '2018-04-30 23:00:00', actual_end_time = 04/23 - 23:00
self.actual_end_time = self.predict_end_time - self.window
self.train_df = raw_df[self.train_start_time:self.train_end_time]
self.test_df = raw_df[self.test_start_time:self.test_end_time]
def __init__(self,
raw_df,
demo_raw,
train_start_time='2014-02-01',
train_end_time='2018-10-31',
test_start_time='2018-11-01',
test_end_time='2019-05-01'):
self.raw_df = raw_df
# demongraphic data [32, 32, 14]
self.demo_raw = demo_raw
self.train_start_time = train_start_time
#self.train_end_time = '2018-03-31'
self.train_end_time = train_end_time
# set train/test set
self.test_start_time = test_start_time
self.test_end_time = test_end_time
# prediction window: use one week's data to predict next hour
self.window = datetime.timedelta(days=7)
self.step = datetime.timedelta(hours=1)
# this is calculated by time interval, there is 1 hour shift between timestamp and time interval
self.predict_start_time = datetime_utils.str_to_datetime(
self.test_start_time) + self.window
# predict_end_time = test_end_time = '2018-04-30 23:00:00'
self.predict_end_time = datetime_utils.str_to_datetime(
self.test_end_time)
# if window = 7 days, test_end_time = '2018-04-30 23:00:00', actual_end_time = 04/23 - 23:00
self.actual_end_time = self.predict_end_time - self.window
self.train_df = raw_df[self.train_start_time:self.train_end_time]
self.test_df = raw_df[self.test_start_time:self.test_end_time]
self.grid_list = list(raw_df)
def arr_to_df(self):
convlstm_predicted = pd.DataFrame(
np.nan,
index=self.test_df_cut[self.train_obj.predict_start_time:self.
train_obj.predict_end_time].index,
columns=list(self.test_df_cut))
sample_pred_squeeze = np.squeeze(self.predicted_vals)
# test_squeeze = np.squeeze(self.test_data.y)
pred_shape = self.predicted_vals.shape
# loop through time stamps
for i in range(0, pred_shape[0]):
temp_image = sample_pred_squeeze[i]
# test_image = test_squeeze[i]
# rotate
temp_rot = np.rot90(temp_image, axes=(1, 0))
# test_rot= np.rot90(test_image, axes=(1,0))
dt = datetime_utils.str_to_datetime(
self.train_obj.test_start_time) + datetime.timedelta(hours=i)
# dt_str = pd.to_datetime(datetime_utils.datetime_to_str(dt))
predicted_timestamp = dt + self.train_obj.window
predicted_timestamp_str = pd.to_datetime(
datetime_utils.datetime_to_str(predicted_timestamp))
# print('predicted_timestamp_str: ', predicted_timestamp_str)
for c in range(pred_shape[1]):
for r in range(pred_shape[2]):
temp_str = str(r) + '_' + str(c)
if temp_str in self.intersect_pos_set:
#print('temp_str: ', temp_str)
# count +=1
convlstm_predicted.loc[predicted_timestamp_str,
temp_str] = temp_rot[r][c]
return convlstm_predicted
