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
243 lines
8 KiB
Python
243 lines
8 KiB
Python
"""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 import config as test_config
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from urban_meal_delivery.forecasts.methods import decomposition
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# The "periodic" `ns` suggested for the STL method.
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NS = 999
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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(
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time_series, frequency=test_config.VERTICAL_FREQUENCY_LONG, ns=NS,
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)
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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(
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vertical_no_demand, frequency=test_config.VERTICAL_FREQUENCY_LONG, ns=8,
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)
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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(
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vertical_no_demand,
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frequency=test_config.VERTICAL_FREQUENCY_LONG,
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ns=ns,
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)
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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(
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(1.5 * test_config.VERTICAL_FREQUENCY_LONG) / (1 - (1.5 / NS)),
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)
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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,
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frequency=test_config.VERTICAL_FREQUENCY_LONG,
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ns=NS,
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nt=nt,
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)
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@pytest.mark.parametrize('nt', [-99, -1, 0, 1, 99, 125])
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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(
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(1.5 * test_config.VERTICAL_FREQUENCY_LONG) / (1 - (1.5 / NS)),
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)
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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,
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frequency=test_config.VERTICAL_FREQUENCY_LONG,
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ns=NS,
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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 > test_config.VERTICAL_FREQUENCY_LONG # 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,
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frequency=test_config.VERTICAL_FREQUENCY_LONG,
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ns=NS,
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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 < test_config.VERTICAL_FREQUENCY_LONG # 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,
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frequency=test_config.VERTICAL_FREQUENCY_LONG,
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ns=NS,
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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,
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frequency=test_config.VERTICAL_FREQUENCY_LONG,
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ns=NS,
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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,
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frequency=test_config.VERTICAL_FREQUENCY_LONG,
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ns=NS,
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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,
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frequency=test_config.VERTICAL_FREQUENCY_LONG,
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ns=NS,
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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,
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frequency=test_config.VERTICAL_FREQUENCY_LONG,
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ns=NS,
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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,
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frequency=test_config.VERTICAL_FREQUENCY_LONG,
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ns=NS,
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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,
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frequency=test_config.VERTICAL_FREQUENCY_LONG,
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ns=NS,
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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,
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frequency=test_config.VERTICAL_FREQUENCY_LONG,
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ns=NS,
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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,
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frequency=test_config.VERTICAL_FREQUENCY_LONG,
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ns=NS,
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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=test_config.VERTICAL_FREQUENCY_LONG, 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=test_config.VERTICAL_FREQUENCY_LONG,
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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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