Add stl() function

- `stl()` wraps R's "stl" function in Python
- STL is a decomposition method for time series

tests/forecasts/test_decomposition.py

@@ -0,0 +1,200 @@
+"""Test the `stl()` function."""
+
+import math
+
+import pandas as pd
+import pytest
+
+from tests import config as test_config
+from urban_meal_delivery import config
+from urban_meal_delivery.forecasts import decomposition
+
+
+# See remarks in `datetime_index` fixture.
+FREQUENCY = 7 * 12
+
+# The default `ns` suggested for the STL method.
+NS = 7
+
+
+@pytest.fixture
+def datetime_index():
+    """A `pd.Index` with `DateTime` values.
+
+    The times resemble a vertical time series with a
+    `frequency` of `7` times the number of daily time steps,
+    which is `12` for `LONG_TIME_STEP` values.
+    """
+    gen = (
+        start_at
+        for start_at in pd.date_range(
+            test_config.START, test_config.END, freq=f'{test_config.LONG_TIME_STEP}T',
+        )
+        if config.SERVICE_START <= start_at.hour < config.SERVICE_END
+    )
+
+    index = pd.Index(gen)
+    index.name = 'start_at'
+
+    return index
+
+
+@pytest.fixture
+def no_demand(datetime_index):
+    """A time series of order totals when there was no demand."""
+    return pd.Series(0, index=datetime_index, name='order_totals')
+
+
+class TestInvalidArguments:
+    """Test `stl()` with invalid arguments."""
+
+    def test_no_nans_in_time_series(self, datetime_index):
+        """`stl()` requires a `time_series` without `NaN` values."""
+        time_series = pd.Series(dtype=float, index=datetime_index)
+
+        with pytest.raises(ValueError, match='`NaN` values'):
+            decomposition.stl(time_series, frequency=FREQUENCY, ns=99)
+
+    def test_ns_not_odd(self, no_demand):
+        """`ns` must be odd and `>= 7`."""
+        with pytest.raises(ValueError, match='`ns`'):
+            decomposition.stl(no_demand, frequency=FREQUENCY, ns=8)
+
+    @pytest.mark.parametrize('ns', [-99, -1, 1, 5])
+    def test_ns_smaller_than_seven(self, no_demand, ns):
+        """`ns` must be odd and `>= 7`."""
+        with pytest.raises(ValueError, match='`ns`'):
+            decomposition.stl(no_demand, frequency=FREQUENCY, ns=ns)
+
+    def test_nt_not_odd(self, no_demand):
+        """`nt` must be odd and `>= default_nt`."""
+        nt = 200
+        default_nt = math.ceil((1.5 * FREQUENCY) / (1 - (1.5 / NS)))
+
+        assert nt > default_nt  # sanity check
+
+        with pytest.raises(ValueError, match='`nt`'):
+            decomposition.stl(no_demand, frequency=FREQUENCY, ns=NS, nt=nt)
+
+    @pytest.mark.parametrize('nt', [-99, -1, 0, 1, 99, 159])
+    def test_nt_not_at_least_the_default(self, no_demand, nt):
+        """`nt` must be odd and `>= default_nt`."""
+        # `default_nt` becomes 161.
+        default_nt = math.ceil((1.5 * FREQUENCY) / (1 - (1.5 / NS)))
+
+        assert nt < default_nt  # sanity check
+
+        with pytest.raises(ValueError, match='`nt`'):
+            decomposition.stl(no_demand, frequency=FREQUENCY, ns=NS, nt=nt)
+
+    def test_nl_not_odd(self, no_demand):
+        """`nl` must be odd and `>= frequency`."""
+        nl = 200
+
+        assert nl > FREQUENCY  # sanity check
+
+        with pytest.raises(ValueError, match='`nl`'):
+            decomposition.stl(no_demand, frequency=FREQUENCY, ns=NS, nl=nl)
+
+    def test_nl_at_least_the_frequency(self, no_demand):
+        """`nl` must be odd and `>= frequency`."""
+        nl = 77
+
+        assert nl < FREQUENCY  # sanity check
+
+        with pytest.raises(ValueError, match='`nl`'):
+            decomposition.stl(no_demand, frequency=FREQUENCY, ns=NS, nl=nl)
+
+    def test_ds_not_zero_or_one(self, no_demand):
+        """`ds` must be `0` or `1`."""
+        with pytest.raises(ValueError, match='`ds`'):
+            decomposition.stl(no_demand, frequency=FREQUENCY, ns=NS, ds=2)
+
+    def test_dt_not_zero_or_one(self, no_demand):
+        """`dt` must be `0` or `1`."""
+        with pytest.raises(ValueError, match='`dt`'):
+            decomposition.stl(no_demand, frequency=FREQUENCY, ns=NS, dt=2)
+
+    def test_dl_not_zero_or_one(self, no_demand):
+        """`dl` must be `0` or `1`."""
+        with pytest.raises(ValueError, match='`dl`'):
+            decomposition.stl(no_demand, frequency=FREQUENCY, ns=NS, dl=2)
+
+    @pytest.mark.parametrize('js', [-1, 0])
+    def test_js_not_positive(self, no_demand, js):
+        """`js` must be positive."""
+        with pytest.raises(ValueError, match='`js`'):
+            decomposition.stl(no_demand, frequency=FREQUENCY, ns=NS, js=js)
+
+    @pytest.mark.parametrize('jt', [-1, 0])
+    def test_jt_not_positive(self, no_demand, jt):
+        """`jt` must be positive."""
+        with pytest.raises(ValueError, match='`jt`'):
+            decomposition.stl(no_demand, frequency=FREQUENCY, ns=NS, jt=jt)
+
+    @pytest.mark.parametrize('jl', [-1, 0])
+    def test_jl_not_positive(self, no_demand, jl):
+        """`jl` must be positive."""
+        with pytest.raises(ValueError, match='`jl`'):
+            decomposition.stl(no_demand, frequency=FREQUENCY, ns=NS, jl=jl)
+
+    @pytest.mark.parametrize('ni', [-1, 0])
+    def test_ni_not_positive(self, no_demand, ni):
+        """`ni` must be positive."""
+        with pytest.raises(ValueError, match='`ni`'):
+            decomposition.stl(no_demand, frequency=FREQUENCY, ns=NS, ni=ni)
+
+    def test_no_not_non_negative(self, no_demand):
+        """`no` must be non-negative."""
+        with pytest.raises(ValueError, match='`no`'):
+            decomposition.stl(no_demand, frequency=FREQUENCY, ns=NS, no=-1)
+
+
+class TestValidArguments:
+    """Test `stl()` with valid arguments."""
+
+    def test_structure_of_returned_dataframe(self, no_demand):
+        """`stl()` returns a `pd.DataFrame` with three columns."""
+        result = decomposition.stl(no_demand, frequency=FREQUENCY, ns=NS)
+
+        assert isinstance(result, pd.DataFrame)
+        assert list(result.columns) == ['seasonal', 'trend', 'residual']
+
+    # Run the `stl()` function with all possible combinations of arguments,
+    # including default ones and explicitly set non-default ones.
+    @pytest.mark.parametrize('nt', [None, 163])
+    @pytest.mark.parametrize('nl', [None, 777])
+    @pytest.mark.parametrize('ds', [0, 1])
+    @pytest.mark.parametrize('dt', [0, 1])
+    @pytest.mark.parametrize('dl', [0, 1])
+    @pytest.mark.parametrize('js', [None, 1])
+    @pytest.mark.parametrize('jt', [None, 1])
+    @pytest.mark.parametrize('jl', [None, 1])
+    @pytest.mark.parametrize('ni', [2, 3])
+    @pytest.mark.parametrize('no', [0, 1])
+    def test_decompose_time_series_with_no_demand(  # noqa:WPS211,WPS216
+        self, no_demand, nt, nl, ds, dt, dl, js, jt, jl, ni, no,  # noqa:WPS110
+    ):
+        """Decomposing a time series with no demand ...
+
+        ... returns a `pd.DataFrame` with three columns holding only `0.0` values.
+        """
+        decomposed = decomposition.stl(
+            no_demand,
+            frequency=FREQUENCY,
+            ns=NS,
+            nt=nt,
+            nl=nl,
+            ds=ds,
+            dt=dt,
+            dl=dl,
+            js=js,
+            jt=jt,
+            jl=jl,
+            ni=ni,
+            no=no,  # noqa:WPS110
+        )
+
+        result = decomposed.sum().sum()
+
+        assert result == 0