Add TrivialModel

- the trivial model simply predicts `0` demand for all time steps

src/urban_meal_delivery/forecasts/models/__init__.py

@@ -32,5 +32,6 @@ A future `planning` sub-package will contain the `*Model`s used to plan the
 from urban_meal_delivery.forecasts.models.base import ForecastingModelABC
 from urban_meal_delivery.forecasts.models.tactical.horizontal import HorizontalETSModel
 from urban_meal_delivery.forecasts.models.tactical.horizontal import HorizontalSMAModel
+from urban_meal_delivery.forecasts.models.tactical.other import TrivialModel
 from urban_meal_delivery.forecasts.models.tactical.realtime import RealtimeARIMAModel
 from urban_meal_delivery.forecasts.models.tactical.vertical import VerticalARIMAModel

src/urban_meal_delivery/forecasts/models/tactical/other.py

@@ -0,0 +1,75 @@
+"""Forecasting `*Model`s to predict demand for tactical purposes ...
+
+... that cannot be classified into either "horizontal", "vertical",
+or "real-time".
+"""  # noqa:RST215
+
+import datetime as dt
+
+import pandas as pd
+
+from urban_meal_delivery import db
+from urban_meal_delivery.forecasts.models import base
+
+
+class TrivialModel(base.ForecastingModelABC):
+    """A trivial model predicting `0` demand.
+
+    No need to distinguish between a "horizontal", "vertical", or
+    "real-time" model here as all give the same prediction for all time steps.
+    """
+
+    name = 'trivial'
+
+    def predict(
+        self, pixel: db.Pixel, predict_at: dt.datetime, train_horizon: int,
+    ) -> pd.DataFrame:
+        """Predict demand for a time step.
+
+        Args:
+            pixel: pixel in which the prediction is made
+            predict_at: time step (i.e., "start_at") to make the prediction for
+            train_horizon: weeks of historic data used to predict `predict_at`
+
+        Returns:
+            actual order counts (i.e., the "actual" column) and
+                point forecasts (i.e., the "prediction" column);
+                this model does not support confidence intervals;
+                contains one row for the `predict_at` time step
+
+        # noqa:DAR401 RuntimeError
+        """
+        # Generate the historic order time series mainly to check if a valid
+        # `training_ts` exists (i.e., the demand history is long enough).
+        _, frequency, actuals_ts = self._order_history.make_horizontal_ts(
+            pixel_id=pixel.id, predict_at=predict_at, train_horizon=train_horizon,
+        )
+
+        # Sanity checks.
+        if frequency != 7:  # pragma: no cover
+            raise RuntimeError('`frequency` should be `7`')
+        if len(actuals_ts) != 1:  # pragma: no cover
+            raise RuntimeError(
+                'the trivial model can only predict one step into the future',
+            )
+
+        # The "prediction" is simply `0.0`.
+        predictions = pd.DataFrame(
+            data={
+                'actual': actuals_ts,
+                'prediction': 0.0,
+                'low80': float('NaN'),
+                'high80': float('NaN'),
+                'low95': float('NaN'),
+                'high95': float('NaN'),
+            },
+            index=actuals_ts.index,
+        )
+
+        # Sanity checks.
+        if predictions['actual'].isnull().any():  # pragma: no cover
+            raise RuntimeError('missing actuals in trivial model')
+        if predict_at not in predictions.index:  # pragma: no cover
+            raise RuntimeError('missing prediction for `predict_at`')
+
+        return predictions

tests/forecasts/test_models.py

@@ -14,6 +14,7 @@ MODELS = (
     models.HorizontalSMAModel,
     models.RealtimeARIMAModel,
     models.VerticalARIMAModel,
+    models.TrivialModel,
 )