Add extrapolate_season.predict() function
- 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
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9 changed files with 170 additions and 43 deletions
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@ -5,11 +5,14 @@ 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 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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@ -18,85 +21,118 @@ class TestInvalidArguments:
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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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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(vertical_no_demand, frequency=VERTICAL_FREQUENCY, ns=8)
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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(vertical_no_demand, frequency=VERTICAL_FREQUENCY, ns=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((1.5 * VERTICAL_FREQUENCY) / (1 - (1.5 / NS)))
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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, frequency=VERTICAL_FREQUENCY, ns=NS, nt=nt,
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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, 159])
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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((1.5 * VERTICAL_FREQUENCY) / (1 - (1.5 / NS)))
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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, frequency=VERTICAL_FREQUENCY, ns=NS, nt=nt,
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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 > VERTICAL_FREQUENCY # sanity check
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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, frequency=VERTICAL_FREQUENCY, ns=NS, nl=nl,
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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 < VERTICAL_FREQUENCY # sanity check
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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, frequency=VERTICAL_FREQUENCY, ns=NS, nl=nl,
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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, frequency=VERTICAL_FREQUENCY, ns=NS, ds=2,
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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, frequency=VERTICAL_FREQUENCY, ns=NS, dt=2,
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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, frequency=VERTICAL_FREQUENCY, ns=NS, dl=2,
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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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@ -104,7 +140,10 @@ class TestInvalidArguments:
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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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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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@ -112,7 +151,10 @@ class TestInvalidArguments:
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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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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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@ -120,7 +162,10 @@ class TestInvalidArguments:
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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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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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@ -128,14 +173,20 @@ class TestInvalidArguments:
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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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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, frequency=VERTICAL_FREQUENCY, ns=NS, no=-1,
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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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@ -146,7 +197,7 @@ class TestValidArguments:
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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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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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@ -173,7 +224,7 @@ class TestValidArguments:
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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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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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@ -1,7 +1,4 @@
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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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"""Test all the `*.predict()` functions in the `methods` sub-package."""
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import datetime as dt
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@ -9,10 +6,10 @@ 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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from urban_meal_delivery.forecasts.methods import extrapolate_season
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@pytest.fixture
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@ -60,7 +57,9 @@ def forecast_time_step():
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@pytest.mark.r
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@pytest.mark.parametrize('func', [arima.predict, ets.predict])
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@pytest.mark.parametrize(
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'func', [arima.predict, ets.predict, extrapolate_season.predict],
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)
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class TestMakePredictions:
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"""Make predictions with `arima.predict()` and `ets.predict()`."""
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@ -74,7 +73,7 @@ class TestMakePredictions:
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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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frequency=test_config.VERTICAL_FREQUENCY_LONG,
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)
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def test_structure_of_returned_dataframe(
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@ -84,7 +83,7 @@ class TestMakePredictions:
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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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frequency=test_config.VERTICAL_FREQUENCY_LONG,
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)
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assert isinstance(result, pd.DataFrame)
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@ -123,7 +122,7 @@ class TestMakePredictions:
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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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frequency=test_config.VERTICAL_FREQUENCY_LONG,
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)
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result = predictions.sum().sum()
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