alexander/urban-meal-delivery
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Add OrderHistory.choose_tactical_model()

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- the method implements a heuristic from the first research paper
  that chooses the most promising forecasting `*Model` based on
  the average daily demand in a `Pixel` for a given `train_horizon`
- adjust the test scenario => `LONG_TRAIN_HORIZON` becomes `8`
  as that is part of the rule implemented in the heuristic
This commit is contained in:
Alexander Hess 2021-02-02 11:29:27 +01:00
commit af82951485
Signed by: alexander
GPG key ID: 344EA5AB10D868E0
6 changed files with 199 additions and 35 deletions
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13
tests/config.py
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@ -1,6 +1,6 @@
"""Globals used when testing."""
import datetime
import datetime as dt
from urban_meal_delivery import config
@ -11,10 +11,11 @@ YEAR, MONTH, DAY = 2016, 7, 1
# The hour when most test cases take place.
NOON = 12
# `START` and `END` constitute a 22-day time span.
# That implies a maximum `train_horizon` of `3` as that needs full 7-day weeks.
START = datetime.datetime(YEAR, MONTH, DAY, config.SERVICE_START, 0)
END = datetime.datetime(YEAR, MONTH, DAY + 21, config.SERVICE_END, 0)
# `START` and `END` constitute a 57-day time span, 8 full weeks plus 1 day.
# That implies a maximum `train_horizon` of `8` as that needs full 7-day weeks.
START = dt.datetime(YEAR, MONTH, DAY, config.SERVICE_START, 0)
_end = START + dt.timedelta(days=56) # `56` as `START` is not included
END = dt.datetime(_end.year, _end.month, _end.day, config.SERVICE_END, 0)
# Default time steps (in minutes), for example, for `OrderHistory` objects.
LONG_TIME_STEP = 60
@ -28,6 +29,6 @@ VERTICAL_FREQUENCY_SHORT = 7 * 24
# Default training horizons, for example, for
# `OrderHistory.make_horizontal_time_series()`.
LONG_TRAIN_HORIZON = 3
LONG_TRAIN_HORIZON = 8
SHORT_TRAIN_HORIZON = 2
TRAIN_HORIZONS = (SHORT_TRAIN_HORIZON, LONG_TRAIN_HORIZON)