Move decomposition module into methods sub-package
- move the module - unify the corresponding tests in `tests.forecasts.methods` sub-package - make all `predict()` and the `stl()` function round results - streamline documentation
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9 changed files with 21 additions and 14 deletions
1
tests/forecasts/methods/__init__.py
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1
tests/forecasts/methods/__init__.py
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"""Tests for the `urban_meal_delivery.forecasts.methods` sub-package."""
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192
tests/forecasts/methods/test_decomposition.py
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tests/forecasts/methods/test_decomposition.py
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"""Test the `stl()` function."""
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import math
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import pandas as pd
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import pytest
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from tests.forecasts.conftest import NS
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from tests.forecasts.conftest import VERTICAL_FREQUENCY
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from urban_meal_delivery.forecasts.methods import decomposition
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class TestInvalidArguments:
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"""Test `stl()` with invalid arguments."""
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def test_no_nans_in_time_series(self, vertical_datetime_index):
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"""`stl()` requires a `time_series` without `NaN` values."""
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time_series = pd.Series(dtype=float, index=vertical_datetime_index)
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with pytest.raises(ValueError, match='`NaN` values'):
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decomposition.stl(time_series, frequency=VERTICAL_FREQUENCY, ns=99)
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def test_ns_not_odd(self, vertical_no_demand):
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"""`ns` must be odd and `>= 7`."""
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with pytest.raises(ValueError, match='`ns`'):
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decomposition.stl(vertical_no_demand, frequency=VERTICAL_FREQUENCY, ns=8)
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@pytest.mark.parametrize('ns', [-99, -1, 1, 5])
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def test_ns_smaller_than_seven(self, vertical_no_demand, ns):
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"""`ns` must be odd and `>= 7`."""
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with pytest.raises(ValueError, match='`ns`'):
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decomposition.stl(vertical_no_demand, frequency=VERTICAL_FREQUENCY, ns=ns)
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def test_nt_not_odd(self, vertical_no_demand):
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"""`nt` must be odd and `>= default_nt`."""
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nt = 200
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default_nt = math.ceil((1.5 * VERTICAL_FREQUENCY) / (1 - (1.5 / NS)))
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assert nt > default_nt # sanity check
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with pytest.raises(ValueError, match='`nt`'):
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decomposition.stl(
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vertical_no_demand, frequency=VERTICAL_FREQUENCY, ns=NS, nt=nt,
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)
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@pytest.mark.parametrize('nt', [-99, -1, 0, 1, 99, 159])
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def test_nt_not_at_least_the_default(self, vertical_no_demand, nt):
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"""`nt` must be odd and `>= default_nt`."""
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# `default_nt` becomes 161.
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default_nt = math.ceil((1.5 * VERTICAL_FREQUENCY) / (1 - (1.5 / NS)))
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assert nt < default_nt # sanity check
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with pytest.raises(ValueError, match='`nt`'):
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decomposition.stl(
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vertical_no_demand, frequency=VERTICAL_FREQUENCY, ns=NS, nt=nt,
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)
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def test_nl_not_odd(self, vertical_no_demand):
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"""`nl` must be odd and `>= frequency`."""
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nl = 200
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assert nl > VERTICAL_FREQUENCY # sanity check
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with pytest.raises(ValueError, match='`nl`'):
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decomposition.stl(
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vertical_no_demand, frequency=VERTICAL_FREQUENCY, ns=NS, nl=nl,
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)
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def test_nl_at_least_the_frequency(self, vertical_no_demand):
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"""`nl` must be odd and `>= frequency`."""
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nl = 77
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assert nl < VERTICAL_FREQUENCY # sanity check
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with pytest.raises(ValueError, match='`nl`'):
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decomposition.stl(
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vertical_no_demand, frequency=VERTICAL_FREQUENCY, ns=NS, nl=nl,
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)
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def test_ds_not_zero_or_one(self, vertical_no_demand):
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"""`ds` must be `0` or `1`."""
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with pytest.raises(ValueError, match='`ds`'):
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decomposition.stl(
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vertical_no_demand, frequency=VERTICAL_FREQUENCY, ns=NS, ds=2,
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)
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def test_dt_not_zero_or_one(self, vertical_no_demand):
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"""`dt` must be `0` or `1`."""
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with pytest.raises(ValueError, match='`dt`'):
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decomposition.stl(
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vertical_no_demand, frequency=VERTICAL_FREQUENCY, ns=NS, dt=2,
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)
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def test_dl_not_zero_or_one(self, vertical_no_demand):
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"""`dl` must be `0` or `1`."""
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with pytest.raises(ValueError, match='`dl`'):
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decomposition.stl(
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vertical_no_demand, frequency=VERTICAL_FREQUENCY, ns=NS, dl=2,
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)
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@pytest.mark.parametrize('js', [-1, 0])
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def test_js_not_positive(self, vertical_no_demand, js):
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"""`js` must be positive."""
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with pytest.raises(ValueError, match='`js`'):
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decomposition.stl(
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vertical_no_demand, frequency=VERTICAL_FREQUENCY, ns=NS, js=js,
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)
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@pytest.mark.parametrize('jt', [-1, 0])
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def test_jt_not_positive(self, vertical_no_demand, jt):
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"""`jt` must be positive."""
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with pytest.raises(ValueError, match='`jt`'):
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decomposition.stl(
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vertical_no_demand, frequency=VERTICAL_FREQUENCY, ns=NS, jt=jt,
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)
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@pytest.mark.parametrize('jl', [-1, 0])
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def test_jl_not_positive(self, vertical_no_demand, jl):
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"""`jl` must be positive."""
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with pytest.raises(ValueError, match='`jl`'):
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decomposition.stl(
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vertical_no_demand, frequency=VERTICAL_FREQUENCY, ns=NS, jl=jl,
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)
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@pytest.mark.parametrize('ni', [-1, 0])
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def test_ni_not_positive(self, vertical_no_demand, ni):
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"""`ni` must be positive."""
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with pytest.raises(ValueError, match='`ni`'):
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decomposition.stl(
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vertical_no_demand, frequency=VERTICAL_FREQUENCY, ns=NS, ni=ni,
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)
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def test_no_not_non_negative(self, vertical_no_demand):
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"""`no` must be non-negative."""
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with pytest.raises(ValueError, match='`no`'):
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decomposition.stl(
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vertical_no_demand, frequency=VERTICAL_FREQUENCY, ns=NS, no=-1,
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)
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@pytest.mark.r
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class TestValidArguments:
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"""Test `stl()` with valid arguments."""
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def test_structure_of_returned_dataframe(self, vertical_no_demand):
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"""`stl()` returns a `pd.DataFrame` with three columns."""
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result = decomposition.stl(
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vertical_no_demand, frequency=VERTICAL_FREQUENCY, ns=NS,
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)
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assert isinstance(result, pd.DataFrame)
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assert list(result.columns) == ['seasonal', 'trend', 'residual']
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# Run the `stl()` function with all possible combinations of arguments,
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# including default ones and explicitly set non-default ones.
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@pytest.mark.parametrize('nt', [None, 163])
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@pytest.mark.parametrize('nl', [None, 777])
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@pytest.mark.parametrize('ds', [0, 1])
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@pytest.mark.parametrize('dt', [0, 1])
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@pytest.mark.parametrize('dl', [0, 1])
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@pytest.mark.parametrize('js', [None, 1])
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@pytest.mark.parametrize('jt', [None, 1])
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@pytest.mark.parametrize('jl', [None, 1])
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@pytest.mark.parametrize('ni', [2, 3])
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@pytest.mark.parametrize('no', [0, 1])
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def test_decompose_time_series_with_no_demand( # noqa:WPS211,WPS216
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self, vertical_no_demand, nt, nl, ds, dt, dl, js, jt, jl, ni, no, # noqa:WPS110
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):
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"""Decomposing a time series with no demand ...
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... returns a `pd.DataFrame` with three columns holding only `0.0` values.
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"""
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decomposed = decomposition.stl(
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vertical_no_demand,
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frequency=VERTICAL_FREQUENCY,
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ns=NS,
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nt=nt,
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nl=nl,
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ds=ds,
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dt=dt,
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dl=dl,
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js=js,
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jt=jt,
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jl=jl,
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ni=ni,
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no=no, # noqa:WPS110
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)
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result = decomposed.sum().sum()
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assert result == 0
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131
tests/forecasts/methods/test_ts_methods.py
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tests/forecasts/methods/test_ts_methods.py
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"""Test the `arima.predict()` and `ets.predict()` functions.
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We consider both "classical" time series prediction models.
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"""
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import datetime as dt
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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 tests.forecasts.conftest import VERTICAL_FREQUENCY
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from urban_meal_delivery import config
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from urban_meal_delivery.forecasts.methods import arima
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from urban_meal_delivery.forecasts.methods import ets
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@pytest.fixture
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def forecast_interval():
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"""A `pd.Index` with `DateTime` values ...
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... that takes place one day after the `START`-`END` horizon and
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resembles an entire day (`12` "start_at" values as we use `LONG_TIME_STEP`).
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"""
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future_day = test_config.END.date() + dt.timedelta(days=1)
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first_start_at = dt.datetime(
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future_day.year, future_day.month, future_day.day, config.SERVICE_START, 0,
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)
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end_of_day = dt.datetime(
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future_day.year, future_day.month, future_day.day, config.SERVICE_END, 0,
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)
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gen = (
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start_at
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for start_at in pd.date_range(
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first_start_at, end_of_day, 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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index = pd.Index(gen)
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index.name = 'start_at'
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return index
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@pytest.fixture
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def forecast_time_step():
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"""A `pd.Index` with one `DateTime` value, resembling `NOON`."""
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future_day = test_config.END.date() + dt.timedelta(days=1)
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start_at = dt.datetime(
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future_day.year, future_day.month, future_day.day, test_config.NOON, 0,
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)
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index = pd.Index([start_at])
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index.name = 'start_at'
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return index
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@pytest.mark.r
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@pytest.mark.parametrize('func', [arima.predict, ets.predict])
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class TestMakePredictions:
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"""Make predictions with `arima.predict()` and `ets.predict()`."""
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def test_training_data_contains_nan_values(
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self, func, vertical_no_demand, forecast_interval,
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):
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"""`training_ts` must not contain `NaN` values."""
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vertical_no_demand.iloc[0] = pd.NA
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with pytest.raises(ValueError, match='must not contain `NaN`'):
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func(
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training_ts=vertical_no_demand,
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forecast_interval=forecast_interval,
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frequency=VERTICAL_FREQUENCY,
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)
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def test_structure_of_returned_dataframe(
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self, func, vertical_no_demand, forecast_interval,
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):
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"""Both `.predict()` return a `pd.DataFrame` with five columns."""
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result = func(
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training_ts=vertical_no_demand,
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forecast_interval=forecast_interval,
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frequency=VERTICAL_FREQUENCY,
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)
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assert isinstance(result, pd.DataFrame)
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assert list(result.columns) == [
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'prediction',
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'low80',
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'high80',
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'low95',
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'high95',
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]
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def test_predict_horizontal_time_series_with_no_demand(
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self, func, horizontal_no_demand, forecast_time_step,
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):
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"""Predicting a horizontal time series with no demand ...
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... returns a `pd.DataFrame` with five columns holding only `0.0` values.
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"""
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predictions = func(
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training_ts=horizontal_no_demand,
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forecast_interval=forecast_time_step,
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frequency=7,
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)
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result = predictions.sum().sum()
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assert result == 0
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def test_predict_vertical_time_series_with_no_demand(
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self, func, vertical_no_demand, forecast_interval,
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):
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"""Predicting a vertical time series with no demand ...
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... returns a `pd.DataFrame` with five columns holding only `0.0` values.
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"""
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predictions = func(
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training_ts=vertical_no_demand,
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forecast_interval=forecast_interval,
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frequency=VERTICAL_FREQUENCY,
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)
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result = predictions.sum().sum()
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assert result == 0
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