def _assert_val_error(f, *args, **kwargs):
# Legacy, didn't really assert anything. Bad news!
# try:
# f(*args, **kwargs)
# return False
# except ValueError:
# return True
assert_raises(ValueError, f, *args, **kwargs)
def test_more_elaborate():
# show we can fit this with a non-zero order
arima = ARIMA(order=(2, 1, 2), suppress_warnings=True).fit(y=hr)
_try_get_attrs(arima)
# can we fit this same arima with a made-up exogenous array?
xreg = rs.rand(hr.shape[0], 4)
arima = ARIMA(order=(2, 1, 2), suppress_warnings=True).fit(y=hr,
exogenous=xreg)
_try_get_attrs(arima)
# pickle this for the __get/setattr__ coverage.
# since the only time this is tested is in parallel in auto.py,
# this doesn't actually get any coverage proof...
fl = 'some_temp_file.pkl'
with open(fl, 'wb') as p:
pickle.dump(arima, p)
# show we can predict with this even though it's been pickled
new_xreg = rs.rand(5, 4)
_preds = arima.predict(n_periods=5, exogenous=new_xreg)
# now unpickle
with open(fl, 'rb') as p:
other = pickle.load(p)
# show we can still predict, compare
_other_preds = other.predict(n_periods=5, exogenous=new_xreg)
assert_array_almost_equal(_preds, _other_preds)
# now clear the cache and remove the pickle file
arima._clear_cached_state()
os.unlink(fl)
# now show that since we fit the ARIMA with an exogenous array,
# we need to provide one for predictions otherwise it breaks.
assert_raises(ValueError, arima.predict, n_periods=5, exogenous=None)
# show that if we DO provide an exogenous and it's the wrong dims, we
# also break things down.
assert_raises(ValueError,
arima.predict,
n_periods=5,
exogenous=rs.rand(4, 4))
def test_with_oob():
# show we can fit with CV (kinda)
arima = ARIMA(order=(2, 1, 2),
suppress_warnings=True,
out_of_sample_size=10).fit(y=hr)
assert not np.isnan(arima.oob()) # show this works
# show we can fit if ooss < 0 and oob will be nan
arima = ARIMA(order=(2, 1, 2),
suppress_warnings=True,
out_of_sample_size=-1).fit(y=hr)
assert np.isnan(arima.oob())
# This will raise since n_steps is not an int
assert_raises(TypeError, arima.predict, n_periods="5")
# But that we CAN forecast with an int...
_ = arima.predict(n_periods=5)
# Show we fail if cv > n_samples
assert_raises(ValueError,
ARIMA(order=(2, 1, 2), out_of_sample_size=1000).fit, hr)
def test_corner_cases():
assert_raises(ValueError,
auto_arima,
wineind,
error_action='some-bad-string')
# things that produce warnings
with warnings.catch_warnings(record=False):
warnings.simplefilter('ignore')
# show a constant result will result in a quick fit
auto_arima(np.ones(10), suppress_warnings=True)
# show the same thing with return_all results in the ARIMA in a list
fits = auto_arima(np.ones(10),
suppress_warnings=True,
return_valid_fits=True)
assert hasattr(fits, '__iter__')
# show we fail for n_iter < 0
assert_raises(ValueError, auto_arima, np.ones(10), random=True, n_fits=-1)
# show if max* < start* it breaks:
assert_raises(ValueError, auto_arima, np.ones(10), start_p=5, max_p=0)
def test_oob_for_issue_28():
# Continuation of above: can we do one with an exogenous array, too?
xreg = rs.rand(hr.shape[0], 4)
arima = ARIMA(order=(2, 1, 2),
suppress_warnings=True,
out_of_sample_size=10).fit(y=hr, exogenous=xreg)
oob = arima.oob()
assert not np.isnan(oob)
# Assert that the endog shapes match. First is equal to the original,
# and the second is the differenced array, with original shape - d.
assert np.allclose(arima.arima_res_.data.endog, hr, rtol=1e-2)
assert arima.arima_res_.model.endog.shape[0] == hr.shape[0] - 1
# Now assert the same for exog
assert np.allclose(arima.arima_res_.data.exog, xreg, rtol=1e-2)
assert arima.arima_res_.model.exog.shape[0] == xreg.shape[0] - 1
# Compare the OOB score to an equivalent fit on data - 10 obs, but
# without any OOB scoring, and we'll show that the OOB scoring in the
# first IS in fact only applied to the first (train - n_out_of_bag)
# samples
arima_no_oob = ARIMA(
order=(2, 1, 2), suppress_warnings=True,
out_of_sample_size=0)\
.fit(y=hr[:-10], exogenous=xreg[:-10, :])
scoring = get_callable(arima_no_oob.scoring, VALID_SCORING)
preds = arima_no_oob.predict(n_periods=10, exogenous=xreg[-10:, :])
assert np.allclose(oob, scoring(hr[-10:], preds), rtol=1e-2)
# Show that the model parameters are exactly the same
xreg_test = rs.rand(5, 4)
assert np.allclose(arima.params(), arima_no_oob.params(), rtol=1e-2)
# Now assert on the forecast differences.
with_oob_forecasts = arima.predict(n_periods=5, exogenous=xreg_test)
no_oob_forecasts = arima_no_oob.predict(n_periods=5, exogenous=xreg_test)
assert_raises(AssertionError, assert_array_almost_equal,
with_oob_forecasts, no_oob_forecasts)
# But after we update the no_oob model with the latest data, we should
# be producing the same exact forecasts
# First, show we'll fail if we try to add observations with no exogenous
assert_raises(ValueError, arima_no_oob.add_new_observations, hr[-10:],
None)
# Also show we'll fail if we try to add mis-matched shapes of data
assert_raises(ValueError, arima_no_oob.add_new_observations, hr[-10:],
xreg_test)
# Show we fail if we try to add observations with a different dim exog
assert_raises(ValueError, arima_no_oob.add_new_observations, hr[-10:],
xreg_test[:, 2])
# Actually add them now, and compare the forecasts (should be the same)
arima_no_oob.add_new_observations(hr[-10:], xreg[-10:, :])
assert np.allclose(with_oob_forecasts,
arima_no_oob.predict(n_periods=5, exogenous=xreg_test),
rtol=1e-2)