def test_seasonality_smoke(index, forecast_index):
s = Seasonality(12)
s.in_sample(index)
steps = 83 if forecast_index is None else len(forecast_index)
warn = None
if (
is_int_index(index)
and np.any(np.diff(index) != 1)
or (
type(index) is pd.Index
and max(index) > 2 ** 63
and forecast_index is None
)
):
warn = UserWarning
with pytest_warns(warn):
s.out_of_sample(steps, index, forecast_index)
assert isinstance(s.period, int)
str(s)
hash(s)
if isinstance(index, (pd.DatetimeIndex, pd.PeriodIndex)) and index.freq:
s = Seasonality.from_index(index)
s.in_sample(index)
s.out_of_sample(steps, index, forecast_index)
Seasonality.from_index(list(index))
def test_seasonality(index):
s = Seasonality(period=12)
exog = s.in_sample(index)
assert s.is_dummy
assert exog.shape == (index.shape[0], 12)
pd.testing.assert_index_equal(exog.index, index)
assert np.all(exog.sum(1) == 1.0)
assert list(exog.columns) == [f"s({i},12)" for i in range(1, 13)]
expected = np.zeros((index.shape[0], 12))
for i in range(12):
expected[i::12, i] = 1.0
np.testing.assert_equal(expected, np.asarray(exog))
warn = None
if type(index) is NumericIndex and np.any(np.diff(index) != 1):
warn = UserWarning
with pytest.warns(warn):
fcast = s.out_of_sample(steps=12, index=index)
assert fcast.iloc[0, len(index) % 12] == 1.0
assert np.all(fcast.sum(1) == 1)
s = Seasonality(period=7, initial_period=3)
exog = s.in_sample(index)
assert exog.iloc[0, 2] == 1.0
assert exog.iloc[0].sum() == 1.0
assert s.initial_period == 3
with pytest.raises(ValueError, match="initial_period must be in"):
Seasonality(period=12, initial_period=-3)
with pytest.raises(ValueError, match="period must be >= 2"):
Seasonality(period=1)