def test_hyndman_blog():
# This is the exact code Hyndman ran in his blog post on the matter:
# https://robjhyndman.com/hyndsight/longseasonality/
n = 2000
m = 200
y = np.random.RandomState(1).normal(size=n) + \
(np.arange(1, n + 1) % 100 / 30)
trans = FourierFeaturizer(m=m, k=5).fit(y)
_, xreg = trans.transform(y)
arima = pm.auto_arima(
y,
X=xreg,
seasonal=False,
maxiter=2, # very short
start_p=4,
max_p=5,
d=0,
max_q=1,
start_q=0,
simple_differencing=True) # type: pm.ARIMA
# Show we can forecast 10 in the future
_, xreg_test = trans.transform(y, n_periods=10)
arima.predict(n_periods=10, X=xreg_test)
def test_r_equivalency(self, exog):
y = self.y
expected = self.expected
trans = FourierFeaturizer(m=5, k=2).fit(y)
_, xreg = trans.transform(y, exog)
# maybe subset
assert_array_almost_equal(expected, xreg[:, -4:])
# maybe assert on exog
if exog is not None:
assert_array_almost_equal(exog, xreg[:, :3])
# Test a bad forecast (exog dim does not match n_periods dim)
with pytest.raises(ValueError):
trans.transform(y, exogenous=np.random.rand(5, 3), n_periods=2)
def test_r_equivalency(self, X):
y = self.y
expected = self.expected
trans = FourierFeaturizer(m=5, k=2).fit(y)
_, xreg = trans.transform(y, X)
# maybe subset
if hasattr(xreg, 'iloc'):
xreg = xreg.values
assert_array_almost_equal(expected, xreg[:, -4:])
# maybe assert on X
if X is not None:
assert_array_almost_equal(X, xreg[:, :3])
# Test a bad forecast (X dim does not match n_periods dim)
with pytest.raises(ValueError):
trans.transform(y, X=np.random.rand(5, 3), n_periods=2)
def test_update_transform():
n = 150
m = 10
y = np.random.RandomState(1).normal(size=n) + \
(np.arange(1, n + 1) % 100 / 30)
train, test = y[:100], y[100:]
trans = FourierFeaturizer(m=m, k=5).fit(train)
_, xreg = trans.transform(train)
# Now update with the test set and show the xreg is diff
yt, Xt = trans.update_and_transform(test, X=None)
assert yt is test
assert Xt.shape[0] == test.shape[0]
assert trans.n_ == y.shape[0]
# Now assert that if we do a vanilla transform with no n_periods, the last
# 50 are the same as the Xt we just got and the first 100 are the same as
# we got earlier
_, xreg2 = trans.transform(y)
assert_array_almost_equal(xreg2[:100], xreg)
assert_array_almost_equal(xreg2[100:], Xt)
def test_value_error_on_fit():
feat = FourierFeaturizer(m=12, k=8)
with pytest.raises(ValueError) as ve:
feat.fit_transform(wineind)
assert 'k must be' in pytest_error_str(ve)
def test_value_error_check():
feat = FourierFeaturizer(m=12)
with pytest.raises(ValueError) as ve:
feat._check_y_X(wineind, None, null_allowed=False)
assert 'non-None' in pytest_error_str(ve)