Add OrderHistory.avg_daily_demand()

- the method calculates the number of daily `Order`s in a `Pixel`
  withing the `train_horizon` preceding the `predict_day`

src/urban_meal_delivery/forecasts/timify.py

@@ -467,3 +467,38 @@ class OrderHistory:
             raise LookupError('`predict_at` is not in the order history')
 
         return training_ts, frequency, actuals_ts
+
+    def avg_daily_demand(
+        self, pixel_id: int, predict_day: dt.date, train_horizon: int,
+    ) -> float:
+        """Calculate the average daily demand (ADD) for a `Pixel`.
+
+        The ADD is defined as the average number of daily `Order`s in a
+        `Pixel` within the training horizon preceding the `predict_day`.
+
+        The ADD is primarily used for the rule-based heuristic to determine
+        the best forecasting model for a `Pixel` on the `predict_day`.
+
+        Implementation note: To calculate the ADD, the order counts are
+        generated as a vertical time series. That must be so as we need to
+        include all time steps of the days before the `predict_day` and
+        no time step of the latter.
+
+        Args:
+            pixel_id: pixel for which the ADD is calculated
+            predict_day: following the `train_horizon` on which the ADD is calculated
+            train_horizon: time horizon over which the ADD is calculated
+
+        Returns:
+            average number of orders per day
+        """
+        training_ts, _, _ = self.make_vertical_ts(  # noqa:WPS434
+            pixel_id=pixel_id, predict_day=predict_day, train_horizon=train_horizon,
+        )
+
+        first_day = training_ts.index.min().date()
+        last_day = training_ts.index.max().date()
+        # `+1` as both `first_day` and `last_day` are included.
+        n_days = (last_day - first_day).days + 1
+
+        return round(training_ts.sum() / n_days, 1)

tests/forecasts/conftest.py

@@ -82,6 +82,17 @@ def good_pixel_id(pixel):
     return pixel.id  # `== 1`
 
 
+@pytest.fixture
+def predict_at() -> dt.datetime:
+    """`NOON` on the day to be predicted."""
+    return dt.datetime(
+        test_config.END.year,
+        test_config.END.month,
+        test_config.END.day,
+        test_config.NOON,
+    )
+
+
 @pytest.fixture
 def order_totals(good_pixel_id):
     """A mock for `OrderHistory.totals`.

tests/forecasts/test_models.py

@@ -1,6 +1,5 @@
 """Tests for the `urban_meal_delivery.forecasts.models` sub-package."""
 
-import datetime as dt
 
 import pandas as pd
 import pytest
@@ -59,16 +58,6 @@ class TestGenericForecastingModelProperties:
 
         self.unique_model_names.add(model.name)
 
-    @pytest.fixture
-    def predict_at(self) -> dt.datetime:
-        """`NOON` on the day to be predicted."""
-        return dt.datetime(
-            test_config.END.year,
-            test_config.END.month,
-            test_config.END.day,
-            test_config.NOON,
-        )
-
     @pytest.mark.r
     def test_make_prediction_structure(
         self, model_cls, order_history, pixel, predict_at,

tests/forecasts/timify/test_avg_daily_demand.py

@@ -0,0 +1,37 @@
+"""Tests for the `OrderHistory.avg_daily_demand()` method."""
+
+from tests import config as test_config
+
+
+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 ...
+
+    ... if the demand is `1` at each time step.
+
+    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(
+    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