Alexander Hess
b8952213d8
- the function implements a forecasting "method" similar to the
seasonal naive method
=> instead of simply taking the last observation given a seasonal lag,
it linearly extrapolates all observations of the same seasonal lag
from the past into the future; conceptually, it is like the
seasonal naive method with built-in smoothing
- the function is tested just like the `arima.predict()` and
`ets.predict()` functions
+ rename the `tests.forecasts.methods.test_ts_methods` module
into `tests.forecasts.methods.test_predictions`
- re-organize some constants in the `tests` package
- streamline some docstrings
57 lines
1.5 KiB
Python
57 lines
1.5 KiB
Python
"""Fixture for testing the `urban_meal_delivery.forecast.timify` module."""
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import pandas as pd
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import pytest
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from tests import config as test_config
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from urban_meal_delivery import config
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from urban_meal_delivery.forecasts import timify
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@pytest.fixture
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def good_pixel_id(pixel):
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"""A `pixel_id` that is on the `grid`."""
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return pixel.id # `== 1`
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@pytest.fixture
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def order_totals(good_pixel_id):
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"""A mock for `OrderHistory.totals`.
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To be a bit more realistic, we sample two pixels on the `grid`.
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Uses the LONG_TIME_STEP as the length of a time step.
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"""
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pixel_ids = [good_pixel_id, good_pixel_id + 1]
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gen = (
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(pixel_id, start_at)
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for pixel_id in pixel_ids
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for start_at in pd.date_range(
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test_config.START, test_config.END, freq=f'{test_config.LONG_TIME_STEP}T',
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)
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if config.SERVICE_START <= start_at.hour < config.SERVICE_END
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)
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# Re-index `data` filling in `0`s where there is no demand.
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index = pd.MultiIndex.from_tuples(gen)
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index.names = ['pixel_id', 'start_at']
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df = pd.DataFrame(data={'n_orders': 1}, index=index)
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# Sanity check: n_pixels * n_time_steps_per_day * n_weekdays * n_weeks.
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assert len(df) == 2 * 12 * (7 * 2 + 1)
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return df
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@pytest.fixture
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def order_history(order_totals, grid):
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"""An `OrderHistory` object that does not need the database.
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Uses the LONG_TIME_STEP as the length of a time step.
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"""
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oh = timify.OrderHistory(grid=grid, time_step=test_config.LONG_TIME_STEP)
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oh._data = order_totals
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return oh
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