def get_time_steps(time_range: Union[str, datetime, Tuple[datetime, datetime]],
specs: ModelSpecs) -> pd.DatetimeIndex:
""" Get relevant datetime indices to build features for.
The time_range parameter can be one or two datetime objects, in which case this function builds a DateTimeIndex.
It can also be one of two strings: "train" or "test". In this situation, this function creates a training or
testing period from model specs.
TODO: we can check (and complain) if datetime objects are incompatible to specs.frequency
e.g. if round_datetime(dt, by_seconds=specs.frequency.total_seconds()) != dt:
raise Exception("%s is not compatible with frequency %s." % (dt, specs.frequency))
We have to discuss if we allow to use any time to start intervals or rather 15:00, 15:15, 15:30 etc ...
"""
# check valid time_range parameter
if not (isinstance(time_range, datetime) or
(isinstance(time_range, tuple)
and isinstance(time_range[0], datetime)
and isinstance(time_range[1], datetime)) or
(isinstance(time_range, str) and time_range in ("train", "test"))):
raise Exception(
"Goal for DateTimeIndex construction needs to be either a string ('train', 'test'),"
"a tuple of two datetime objects or one datetime object.")
pd_frequency = timedelta_to_pandas_freq_str(specs.frequency)
# easy cases: one or two datetime objects
if isinstance(time_range, datetime):
return pd.date_range(time_range,
time_range,
closed="left",
freq=pd_frequency)
elif isinstance(time_range, tuple):
if not timedelta_fits_into(specs.frequency,
time_range[1] - time_range[0]):
raise Exception(
"Start & end period (%s to %s) does not cleanly fit a multiple of the model frequency (%s)"
% (time_range[0], time_range[1], specs.frequency))
return pd.date_range(time_range[0],
time_range[1],
closed="left",
freq=pd_frequency)
# special cases: "train" or "test" - we have to calculate from model specs
length_of_data = specs.end_of_testing - specs.start_of_training
if time_range == "train":
end_of_training = (specs.start_of_training +
length_of_data * specs.ratio_training_testing_data)
end_of_training = round_datetime(end_of_training,
specs.frequency.total_seconds())
logger.debug("Start of training: %s" % specs.start_of_training)
logger.debug("End of training: %s" % end_of_training)
return pd.date_range(specs.start_of_training,
end_of_training,
freq=pd_frequency)
elif time_range == "test":
start_of_testing = (
specs.start_of_training +
(length_of_data * specs.ratio_training_testing_data) +
specs.frequency)
start_of_testing = round_datetime(start_of_testing,
specs.frequency.total_seconds())
logger.debug("Start of testing: %s" % start_of_testing)
logger.debug("End of testing: %s" % specs.end_of_testing)
return pd.date_range(start_of_testing,
specs.end_of_testing,
freq=pd_frequency)
def test_round_time_by_15min(dt):
round_to_hour = round_datetime(dt, by_seconds=60 * 15)
assert round_to_hour.day == dt.day
assert round_to_hour.hour == 14
assert round_to_hour.minute == 45
def test_round_time_by_15min():
dt = datetime(2018, 1, 26, 14, 40)
round_to_hour = round_datetime(dt, by_seconds=60 * 15)
assert round_to_hour.day == dt.day
assert round_to_hour.hour == 14
assert round_to_hour.minute == 45
def test_round_time_by_hour(dt):
round_to_hour = round_datetime(dt, by_seconds=60 * 60)
assert round_to_hour.day == dt.day
assert round_to_hour.hour == 15
assert round_to_hour.minute == 00
def test_round_time_by_hour():
dt = datetime(2018, 1, 26, 14, 40)
round_to_hour = round_datetime(dt, by_seconds=60 * 60)
assert round_to_hour.day == dt.day
assert round_to_hour.hour == 15
assert round_to_hour.minute == 00