def test_recreate_model():
nobs = 100
endog = np.ones(nobs) * 2.0
exog = np.ones(nobs)
levels = [
'irregular', 'ntrend', 'fixed intercept', 'deterministic constant',
'dconstant', 'local level', 'llevel', 'random walk', 'rwalk',
'fixed slope', 'deterministic trend', 'dtrend',
'local linear deterministic trend', 'lldtrend',
'random walk with drift', 'rwdrift', 'local linear trend',
'lltrend', 'smooth trend', 'strend', 'random trend', 'rtrend']
for level in levels:
# Note: have to add in some stochastic component, otherwise we have
# problems with entirely deterministic models
# level + stochastic seasonal
mod = UnobservedComponents(endog, level=level, seasonal=2,
stochastic_seasonal=True, exog=exog)
mod2 = UnobservedComponents(endog, exog=exog, **mod._get_init_kwds())
check_equivalent_models(mod, mod2)
# level + autoregressive
mod = UnobservedComponents(endog, level=level, exog=exog,
autoregressive=1)
mod2 = UnobservedComponents(endog, exog=exog, **mod._get_init_kwds())
check_equivalent_models(mod, mod2)
# level + stochastic cycle
mod = UnobservedComponents(endog, level=level, exog=exog,
cycle=True, stochastic_cycle=True,
damped_cycle=True)
mod2 = UnobservedComponents(endog, exog=exog, **mod._get_init_kwds())
check_equivalent_models(mod, mod2)
def test_default_get_reference_model():
endog, exog = [0, 0, 0], [1, 1, 1]
signature = inspect.getargspec(UnobservedComponents.__init__)
default_args = dict(zip(signature.args[::-1], signature.defaults[::-1]))
default_args['level'] = True
default_args['stochastic_level'] = True
default_args['endog'] = endog
model = UnobservedComponents(**default_args)
ref_model = get_reference_model(model, [1, 1, 1], [2, 2, 2])
assert ref_model._get_init_kwds() == model._get_init_kwds()
assert array_equivalent(ref_model.endog, [[1], [1], [1]])
assert ref_model.exog is None
default_args['exog'] = exog
model = UnobservedComponents(**default_args)
ref_model = get_reference_model(model, [1, 1, 1], [2, 2, 2])
ref_model_kwds = ref_model._get_init_kwds()
model_kwds = model._get_init_kwds()
ref_model_kwds.pop('exog')
model_kwds.pop('exog')
assert ref_model_kwds == model_kwds
assert array_equivalent(ref_model.endog, [[1], [1], [1]])
assert array_equivalent(ref_model.exog, [[2], [2], [2]])