Add urban_meal_delivery.forecasts.models sub-package

- `*Model`s use the `methods.*.predict()` functions to predict demand
  given an order time series generated by `timify.OrderHistory`
- `models.base.ForecastingModelABC` unifies how all `*Model`s work
  and implements a caching strategy
- implement three `*Model`s for tactical forecasting, based on the
  hets, varima, and rtarima models described in the first research paper
- add overall documentation for `urban_meal_delivery.forecasts` package
- move the fixtures in `tests.forecasts.timify.conftest` to
  `tests.forecasts.conftest` and adjust the horizon of the test horizon
  from two to three weeks

tests/forecasts/timify/conftest.py

@@ -1,57 +0,0 @@
-"""Fixture for testing the `urban_meal_delivery.forecast.timify` module."""
-
-import pandas as pd
-import pytest
-
-from tests import config as test_config
-from urban_meal_delivery import config
-from urban_meal_delivery.forecasts import timify
-
-
-@pytest.fixture
-def good_pixel_id(pixel):
-    """A `pixel_id` that is on the `grid`."""
-    return pixel.id  # `== 1`
-
-
-@pytest.fixture
-def order_totals(good_pixel_id):
-    """A mock for `OrderHistory.totals`.
-
-    To be a bit more realistic, we sample two pixels on the `grid`.
-
-    Uses the LONG_TIME_STEP as the length of a time step.
-    """
-    pixel_ids = [good_pixel_id, good_pixel_id + 1]
-
-    gen = (
-        (pixel_id, start_at)
-        for pixel_id in pixel_ids
-        for start_at in pd.date_range(
-            test_config.START, test_config.END, freq=f'{test_config.LONG_TIME_STEP}T',
-        )
-        if config.SERVICE_START <= start_at.hour < config.SERVICE_END
-    )
-
-    # Re-index `data` filling in `0`s where there is no demand.
-    index = pd.MultiIndex.from_tuples(gen)
-    index.names = ['pixel_id', 'start_at']
-
-    df = pd.DataFrame(data={'n_orders': 1}, index=index)
-
-    # Sanity check: n_pixels * n_time_steps_per_day * n_weekdays * n_weeks.
-    assert len(df) == 2 * 12 * (7 * 2 + 1)
-
-    return df
-
-
-@pytest.fixture
-def order_history(order_totals, grid):
-    """An `OrderHistory` object that does not need the database.
-
-    Uses the LONG_TIME_STEP as the length of a time step.
-    """
-    oh = timify.OrderHistory(grid=grid, time_step=test_config.LONG_TIME_STEP)
-    oh._data = order_totals
-
-    return oh

tests/forecasts/timify/test_make_time_series.py

@@ -17,8 +17,8 @@ from urban_meal_delivery import config
 def good_predict_at():
     """A `predict_at` within `START`-`END` and ...
 
-    ... a long enough history so that either `train_horizon=1`
-    or `train_horizon=2` works.
+    ... a long enough history so that either `SHORT_TRAIN_HORIZON`
+    or `LONG_TRAIN_HORIZON` works.
     """
     return datetime.datetime(
         test_config.END.year,
@@ -33,10 +33,10 @@ def good_predict_at():
 def bad_predict_at():
     """A `predict_at` within `START`-`END` but ...
 
-    ... not a long enough history so that both `train_horizon=1`
-    and `train_horizon=2` do not work.
+    ... not a long enough history so that both `SHORT_TRAIN_HORIZON`
+    and `LONG_TRAIN_HORIZON` do not work.
     """
-    predict_day = test_config.END - datetime.timedelta(weeks=1, days=1)
+    predict_day = test_config.END - datetime.timedelta(weeks=2, days=1)
     return datetime.datetime(
         predict_day.year, predict_day.month, predict_day.day, test_config.NOON, 0,
     )