Add OrderHistory.choose_tactical_model()

- 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

tests/forecasts/timify/test_avg_daily_demand.py

@@ -1,37 +1,145 @@
-"""Tests for the `OrderHistory.avg_daily_demand()` method."""
+"""Tests for the `OrderHistory.avg_daily_demand()` and ...
+
+`OrderHistory.choose_tactical_model()` methods.
+
+We test both methods together as they take the same input and are really
+two parts of the same conceptual step.
+"""
+
+import pytest
 
 from tests import config as test_config
+from urban_meal_delivery.forecasts import models
 
 
-def test_avg_daily_demand_with_constant_demand(
-    order_history, good_pixel_id, predict_at,
-):
-    """The average daily demand must be the number of time steps ...
+class TestAverageDailyDemand:
+    """Tests for the `OrderHistory.avg_daily_demand()` method."""
 
-    ... if the demand is `1` at each time step.
+    def test_avg_daily_demand_with_constant_demand(
+        self, order_history, good_pixel_id, predict_at,
+    ):
+        """The average daily demand must be the number of time steps ...
 
-    Note: The `order_history` fixture assumes `12` time steps per day as it
-    uses `LONG_TIME_STEP=60` as the length of a time step.
-    """
-    result = order_history.avg_daily_demand(
-        pixel_id=good_pixel_id,
-        predict_day=predict_at.date(),
-        train_horizon=test_config.LONG_TRAIN_HORIZON,
-    )
+        ... if the demand is `1` at each time step.
 
-    assert result == 12.0
+        Note: The `order_history` fixture assumes `12` time steps per day as it
+        uses `LONG_TIME_STEP=60` as the length of a time step.
+        """
+        result = order_history.avg_daily_demand(
+            pixel_id=good_pixel_id,
+            predict_day=predict_at.date(),
+            train_horizon=test_config.LONG_TRAIN_HORIZON,
+        )
+
+        assert result == 12.0
+
+    def test_avg_daily_demand_with_no_demand(
+        self, order_history, good_pixel_id, predict_at,
+    ):
+        """Without demand, the average daily demand must be `0.0`."""
+        order_history._data.loc[:, 'n_orders'] = 0
+
+        result = order_history.avg_daily_demand(
+            pixel_id=good_pixel_id,
+            predict_day=predict_at.date(),
+            train_horizon=test_config.LONG_TRAIN_HORIZON,
+        )
+
+        assert result == 0.0
 
 
-def test_avg_daily_demand_with_no_demand(
-    order_history, good_pixel_id, predict_at,
-):
-    """Without demand, the average daily demand must be `0.0`."""
-    order_history._data.loc[:, 'n_orders'] = 0
+class TestChooseTacticalModel:
+    """Tests for the `OrderHistory.choose_tactical_model()` method."""
 
-    result = order_history.avg_daily_demand(
-        pixel_id=good_pixel_id,
-        predict_day=predict_at.date(),
-        train_horizon=test_config.LONG_TRAIN_HORIZON,
-    )
+    def test_best_model_with_high_demand(
+        self, order_history, good_pixel_id, predict_at,
+    ):
+        """With high demand, the average daily demand is `.>= 25.0`."""
+        # With 12 time steps per day, the ADD becomes `36.0`.
+        order_history._data.loc[:, 'n_orders'] = 3
 
-    assert result == 0.0
+        result = order_history.choose_tactical_model(
+            pixel_id=good_pixel_id,
+            predict_day=predict_at.date(),
+            train_horizon=test_config.LONG_TRAIN_HORIZON,
+        )
+
+        assert isinstance(result, models.HorizontalETSModel)
+
+    def test_best_model_with_medium_demand(
+        self, order_history, good_pixel_id, predict_at,
+    ):
+        """With medium demand, the average daily demand is `>= 10.0` and `< 25.0`."""
+        # With 12 time steps per day, the ADD becomes `24.0`.
+        order_history._data.loc[:, 'n_orders'] = 2
+
+        result = order_history.choose_tactical_model(
+            pixel_id=good_pixel_id,
+            predict_day=predict_at.date(),
+            train_horizon=test_config.LONG_TRAIN_HORIZON,
+        )
+
+        assert isinstance(result, models.HorizontalETSModel)
+
+    def test_best_model_with_low_demand(
+        self, order_history, good_pixel_id, predict_at,
+    ):
+        """With low demand, the average daily demand is `>= 2.5` and `< 10.0`."""
+        # With 12 time steps per day, the ADD becomes `12.0` ...
+        data = order_history._data
+        data.loc[:, 'n_orders'] = 1
+
+        # ... and we set three additional time steps per day to `0`.
+        data.loc[  # noqa:ECE001
+            # all `Pixel`s, all `Order`s in time steps starting at 11 am
+            (slice(None), slice(data.index.levels[1][0], None, 12)),
+            'n_orders',
+        ] = 0
+        data.loc[  # noqa:ECE001
+            # all `Pixel`s, all `Order`s in time steps starting at 12 am
+            (slice(None), slice(data.index.levels[1][1], None, 12)),
+            'n_orders',
+        ] = 0
+        data.loc[  # noqa:ECE001
+            # all `Pixel`s, all `Order`s in time steps starting at 1 pm
+            (slice(None), slice(data.index.levels[1][2], None, 12)),
+            'n_orders',
+        ] = 0
+
+        result = order_history.choose_tactical_model(
+            pixel_id=good_pixel_id,
+            predict_day=predict_at.date(),
+            train_horizon=test_config.LONG_TRAIN_HORIZON,
+        )
+
+        # TODO: this should be the future `HorizontalSMAModel`.
+        assert isinstance(result, models.HorizontalETSModel)
+
+    def test_best_model_with_no_demand(
+        self, order_history, good_pixel_id, predict_at,
+    ):
+        """Without demand, the average daily demand is `< 2.5`."""
+        order_history._data.loc[:, 'n_orders'] = 0
+
+        result = order_history.choose_tactical_model(
+            pixel_id=good_pixel_id,
+            predict_day=predict_at.date(),
+            train_horizon=test_config.LONG_TRAIN_HORIZON,
+        )
+
+        # TODO: this should be the future `HorizontalTrivialModel`.
+        assert isinstance(result, models.HorizontalETSModel)
+
+    def test_best_model_for_unknown_train_horizon(
+        self, order_history, good_pixel_id, predict_at,  # noqa:RST215
+    ):
+        """For `train_horizon`s not included in the rule-based system ...
+
+        ... the method raises a `RuntimeError`.
+        """
+        with pytest.raises(RuntimeError, match='no rule'):
+            order_history.choose_tactical_model(
+                pixel_id=good_pixel_id,
+                predict_day=predict_at.date(),
+                train_horizon=test_config.SHORT_TRAIN_HORIZON,
+            )

tests/forecasts/timify/test_make_time_series.py

@@ -36,7 +36,7 @@ def bad_predict_at():
     ... 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=2, days=1)
+    predict_day = test_config.END - datetime.timedelta(weeks=6, days=1)
     return datetime.datetime(
         predict_day.year, predict_day.month, predict_day.day, test_config.NOON, 0,
     )