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