Alexander Hess
af82951485
- 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
399 lines
14 KiB
Python
399 lines
14 KiB
Python
"""Test the code generating time series with the order totals.
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Unless otherwise noted, each `time_step` is 60 minutes long implying
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12 time steps per day (i.e., we use `LONG_TIME_STEP` by default).
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"""
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import datetime
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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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@pytest.fixture
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def good_predict_at():
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"""A `predict_at` within `START`-`END` and ...
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... a long enough history so that either `SHORT_TRAIN_HORIZON`
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or `LONG_TRAIN_HORIZON` works.
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"""
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return datetime.datetime(
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test_config.END.year,
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test_config.END.month,
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test_config.END.day,
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test_config.NOON,
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0,
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)
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@pytest.fixture
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def bad_predict_at():
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"""A `predict_at` within `START`-`END` but ...
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... not a long enough history so that both `SHORT_TRAIN_HORIZON`
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and `LONG_TRAIN_HORIZON` do not work.
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"""
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predict_day = test_config.END - datetime.timedelta(weeks=6, days=1)
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return datetime.datetime(
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predict_day.year, predict_day.month, predict_day.day, test_config.NOON, 0,
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)
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class TestMakeHorizontalTimeSeries:
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"""Test the `OrderHistory.make_horizontal_ts()` method."""
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@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
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def test_wrong_pixel(self, order_history, good_predict_at, train_horizon):
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"""A `pixel_id` that is not in the `grid`."""
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with pytest.raises(LookupError):
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order_history.make_horizontal_ts(
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pixel_id=999_999,
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predict_at=good_predict_at,
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train_horizon=train_horizon,
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)
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@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
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def test_time_series_are_series(
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self, order_history, good_pixel_id, good_predict_at, train_horizon,
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):
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"""The time series come as a `pd.Series`."""
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result = order_history.make_horizontal_ts(
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pixel_id=good_pixel_id,
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predict_at=good_predict_at,
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train_horizon=train_horizon,
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)
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training_ts, _, actuals_ts = result
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assert isinstance(training_ts, pd.Series)
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assert training_ts.name == 'n_orders'
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assert isinstance(actuals_ts, pd.Series)
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assert actuals_ts.name == 'n_orders'
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@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
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def test_time_series_have_correct_length(
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self, order_history, good_pixel_id, good_predict_at, train_horizon,
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):
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"""The length of a training time series must be a multiple of `7` ...
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... whereas the time series with the actual order counts has only `1` value.
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"""
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result = order_history.make_horizontal_ts(
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pixel_id=good_pixel_id,
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predict_at=good_predict_at,
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train_horizon=train_horizon,
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)
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training_ts, _, actuals_ts = result
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assert len(training_ts) == 7 * train_horizon
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assert len(actuals_ts) == 1
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@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
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def test_frequency_is_number_of_weekdays(
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self, order_history, good_pixel_id, good_predict_at, train_horizon,
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):
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"""The `frequency` must be `7`."""
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result = order_history.make_horizontal_ts(
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pixel_id=good_pixel_id,
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predict_at=good_predict_at,
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train_horizon=train_horizon,
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)
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_, frequency, _ = result # noqa:WPS434
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assert frequency == 7
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@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
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def test_no_long_enough_history1(
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self, order_history, good_pixel_id, bad_predict_at, train_horizon,
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):
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"""If the `predict_at` day is too early in the `START`-`END` horizon ...
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... the history of order totals is not long enough.
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"""
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with pytest.raises(RuntimeError):
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order_history.make_horizontal_ts(
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pixel_id=good_pixel_id,
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predict_at=bad_predict_at,
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train_horizon=train_horizon,
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)
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def test_no_long_enough_history2(
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self, order_history, good_pixel_id, good_predict_at,
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):
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"""If the `train_horizon` is longer than the `START`-`END` horizon ...
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... the history of order totals can never be long enough.
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"""
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with pytest.raises(RuntimeError):
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order_history.make_horizontal_ts(
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pixel_id=good_pixel_id, predict_at=good_predict_at, train_horizon=999,
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)
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class TestMakeVerticalTimeSeries:
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"""Test the `OrderHistory.make_vertical_ts()` method."""
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@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
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def test_wrong_pixel(self, order_history, good_predict_at, train_horizon):
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"""A `pixel_id` that is not in the `grid`."""
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with pytest.raises(LookupError):
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order_history.make_vertical_ts(
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pixel_id=999_999,
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predict_day=good_predict_at.date(),
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train_horizon=train_horizon,
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)
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@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
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def test_time_series_are_series(
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self, order_history, good_pixel_id, good_predict_at, train_horizon,
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):
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"""The time series come as `pd.Series`."""
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result = order_history.make_vertical_ts(
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pixel_id=good_pixel_id,
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predict_day=good_predict_at.date(),
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train_horizon=train_horizon,
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)
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training_ts, _, actuals_ts = result
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assert isinstance(training_ts, pd.Series)
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assert training_ts.name == 'n_orders'
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assert isinstance(actuals_ts, pd.Series)
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assert actuals_ts.name == 'n_orders'
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@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
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def test_time_series_have_correct_length(
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self, order_history, good_pixel_id, good_predict_at, train_horizon,
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):
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"""The length of a training time series is the product of the ...
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... weekly time steps (i.e., product of `7` and the number of daily time steps)
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and the `train_horizon` in weeks.
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The time series with the actual order counts always holds one observation
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per time step of a day.
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"""
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result = order_history.make_vertical_ts(
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pixel_id=good_pixel_id,
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predict_day=good_predict_at.date(),
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train_horizon=train_horizon,
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)
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training_ts, _, actuals_ts = result
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n_daily_time_steps = (
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60
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* (config.SERVICE_END - config.SERVICE_START)
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// test_config.LONG_TIME_STEP
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)
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assert len(training_ts) == 7 * n_daily_time_steps * train_horizon
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assert len(actuals_ts) == n_daily_time_steps
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@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
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def test_frequency_is_number_number_of_weekly_time_steps(
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self, order_history, good_pixel_id, good_predict_at, train_horizon,
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):
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"""The `frequency` is the number of weekly time steps."""
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result = order_history.make_vertical_ts(
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pixel_id=good_pixel_id,
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predict_day=good_predict_at.date(),
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train_horizon=train_horizon,
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)
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_, frequency, _ = result # noqa:WPS434
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n_daily_time_steps = (
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60
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* (config.SERVICE_END - config.SERVICE_START)
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// test_config.LONG_TIME_STEP
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)
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assert frequency == 7 * n_daily_time_steps
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@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
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def test_no_long_enough_history1(
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self, order_history, good_pixel_id, bad_predict_at, train_horizon,
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):
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"""If the `predict_at` day is too early in the `START`-`END` horizon ...
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... the history of order totals is not long enough.
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"""
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with pytest.raises(RuntimeError):
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order_history.make_vertical_ts(
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pixel_id=good_pixel_id,
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predict_day=bad_predict_at.date(),
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train_horizon=train_horizon,
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)
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def test_no_long_enough_history2(
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self, order_history, good_pixel_id, good_predict_at,
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):
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"""If the `train_horizon` is longer than the `START`-`END` horizon ...
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... the history of order totals can never be long enough.
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"""
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with pytest.raises(RuntimeError):
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order_history.make_vertical_ts(
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pixel_id=good_pixel_id,
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predict_day=good_predict_at.date(),
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train_horizon=999,
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)
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class TestMakeRealTimeTimeSeries:
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"""Test the `OrderHistory.make_realtime_ts()` method."""
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@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
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def test_wrong_pixel(self, order_history, good_predict_at, train_horizon):
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"""A `pixel_id` that is not in the `grid`."""
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with pytest.raises(LookupError):
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order_history.make_realtime_ts(
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pixel_id=999_999,
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predict_at=good_predict_at,
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train_horizon=train_horizon,
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)
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@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
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def test_time_series_are_series(
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self, order_history, good_pixel_id, good_predict_at, train_horizon,
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):
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"""The time series come as `pd.Series`."""
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result = order_history.make_realtime_ts(
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pixel_id=good_pixel_id,
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predict_at=good_predict_at,
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train_horizon=train_horizon,
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)
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training_ts, _, actuals_ts = result
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assert isinstance(training_ts, pd.Series)
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assert training_ts.name == 'n_orders'
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assert isinstance(actuals_ts, pd.Series)
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assert actuals_ts.name == 'n_orders'
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@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
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def test_time_series_have_correct_length1(
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self, order_history, good_pixel_id, good_predict_at, train_horizon,
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):
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"""The length of a training time series is the product of the ...
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... weekly time steps (i.e., product of `7` and the number of daily time steps)
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and the `train_horizon` in weeks; however, this assertion only holds if
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we predict the first `time_step` of the day.
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The time series with the actual order counts always holds `1` value.
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"""
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predict_at = datetime.datetime(
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good_predict_at.year,
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good_predict_at.month,
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good_predict_at.day,
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config.SERVICE_START,
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0,
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)
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result = order_history.make_realtime_ts(
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pixel_id=good_pixel_id, predict_at=predict_at, train_horizon=train_horizon,
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)
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training_ts, _, actuals_ts = result
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n_daily_time_steps = (
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60
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* (config.SERVICE_END - config.SERVICE_START)
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// test_config.LONG_TIME_STEP
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)
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assert len(training_ts) == 7 * n_daily_time_steps * train_horizon
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assert len(actuals_ts) == 1
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@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
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def test_time_series_have_correct_length2(
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self, order_history, good_pixel_id, good_predict_at, train_horizon,
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):
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"""The length of a training time series is the product of the ...
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... weekly time steps (i.e., product of `7` and the number of daily time steps)
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and the `train_horizon` in weeks; however, this assertion only holds if
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we predict the first `time_step` of the day. Predicting any other `time_step`
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means that the training time series becomes longer by the number of time steps
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before the one being predicted.
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The time series with the actual order counts always holds `1` value.
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"""
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assert good_predict_at.hour == test_config.NOON
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result = order_history.make_realtime_ts(
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pixel_id=good_pixel_id,
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predict_at=good_predict_at,
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train_horizon=train_horizon,
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)
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training_ts, _, actuals_ts = result
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n_daily_time_steps = (
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60
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* (config.SERVICE_END - config.SERVICE_START)
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// test_config.LONG_TIME_STEP
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)
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n_time_steps_before = (
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60 * (test_config.NOON - config.SERVICE_START) // test_config.LONG_TIME_STEP
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)
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assert (
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len(training_ts)
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== 7 * n_daily_time_steps * train_horizon + n_time_steps_before
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)
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assert len(actuals_ts) == 1
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@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
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def test_frequency_is_number_number_of_weekly_time_steps(
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self, order_history, good_pixel_id, good_predict_at, train_horizon,
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):
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"""The `frequency` is the number of weekly time steps."""
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result = order_history.make_realtime_ts(
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pixel_id=good_pixel_id,
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predict_at=good_predict_at,
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train_horizon=train_horizon,
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)
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_, frequency, _ = result # noqa:WPS434
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n_daily_time_steps = (
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60
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* (config.SERVICE_END - config.SERVICE_START)
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// test_config.LONG_TIME_STEP
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)
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assert frequency == 7 * n_daily_time_steps
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@pytest.mark.parametrize('train_horizon', test_config.TRAIN_HORIZONS)
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def test_no_long_enough_history1(
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self, order_history, good_pixel_id, bad_predict_at, train_horizon,
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):
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"""If the `predict_at` day is too early in the `START`-`END` horizon ...
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... the history of order totals is not long enough.
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"""
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with pytest.raises(RuntimeError):
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order_history.make_realtime_ts(
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pixel_id=good_pixel_id,
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predict_at=bad_predict_at,
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train_horizon=train_horizon,
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)
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def test_no_long_enough_history2(
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self, order_history, good_pixel_id, good_predict_at,
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):
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"""If the `train_horizon` is longer than the `START`-`END` horizon ...
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... the history of order totals can never be long enough.
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"""
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with pytest.raises(RuntimeError):
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order_history.make_realtime_ts(
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pixel_id=good_pixel_id, predict_at=good_predict_at, train_horizon=999,
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)
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