def run_ucm(name):
true = getattr(results_structural, name)
for model in true['models']:
kwargs = model.copy()
kwargs.update(true['kwargs'])
# Make a copy of the data
values = dta.copy()
freq = kwargs.pop('freq', None)
if freq is not None:
values.index = pd.date_range(start='1959-01-01', periods=len(dta),
freq=freq)
# Test pandas exog
if 'exog' in kwargs:
# Default value here is pd.Series object
exog = np.log(values['realgdp'])
# Also allow a check with a 1-dim numpy array
if kwargs['exog'] == 'numpy':
exog = exog.values.squeeze()
kwargs['exog'] = exog
# Create the model
mod = UnobservedComponents(values['unemp'], **kwargs)
# Smoke test for starting parameters, untransform, transform
# Also test that transform and untransform are inverses
mod.start_params
roundtrip = mod.transform_params(
mod.untransform_params(mod.start_params))
assert_allclose(mod.start_params, roundtrip)
# Fit the model at the true parameters
res_true = mod.filter(true['params'])
# Check that the cycle bounds were computed correctly
freqstr = freq[0] if freq is not None else values.index.freqstr[0]
if 'cycle_period_bounds' in kwargs:
cycle_period_bounds = kwargs['cycle_period_bounds']
elif freqstr == 'A':
cycle_period_bounds = (1.5, 12)
elif freqstr == 'Q':
cycle_period_bounds = (1.5*4, 12*4)
elif freqstr == 'M':
cycle_period_bounds = (1.5*12, 12*12)
else:
# If we have no information on data frequency, require the
# cycle frequency to be between 0 and pi
cycle_period_bounds = (2, np.inf)
# Test that the cycle frequency bound is correct
assert_equal(mod.cycle_frequency_bound,
(2*np.pi / cycle_period_bounds[1],
2*np.pi / cycle_period_bounds[0]))
# Test that the likelihood is correct
rtol = true.get('rtol', 1e-7)
atol = true.get('atol', 0)
assert_allclose(res_true.llf, true['llf'], rtol=rtol, atol=atol)
# Optional smoke test for plot_components
try:
import matplotlib.pyplot as plt
try:
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
except ImportError:
pass
fig = plt.figure()
res_true.plot_components(fig=fig)
except ImportError:
pass
# Now fit the model via MLE
with warnings.catch_warnings(record=True):
res = mod.fit(disp=-1)
# If we found a higher likelihood, no problem; otherwise check
# that we're very close to that found by R
if res.llf <= true['llf']:
assert_allclose(res.llf, true['llf'], rtol=1e-4)
# Smoke test for summary
res.summary()
def run_ucm(name):
true = getattr(results_structural, name)
for model in true['models']:
kwargs = model.copy()
kwargs.update(true['kwargs'])
# Make a copy of the data
values = dta.copy()
freq = kwargs.pop('freq', None)
if freq is not None:
values.index = pd.date_range(start='1959-01-01', periods=len(dta),
freq=freq)
# Test pandas exog
if 'exog' in kwargs:
# Default value here is pd.Series object
exog = np.log(values['realgdp'])
# Also allow a check with a 1-dim numpy array
if kwargs['exog'] == 'numpy':
exog = exog.values.squeeze()
kwargs['exog'] = exog
# Create the model
mod = UnobservedComponents(values['unemp'], **kwargs)
# Smoke test for starting parameters, untransform, transform
# Also test that transform and untransform are inverses
mod.start_params
assert_allclose(mod.start_params, mod.transform_params(mod.untransform_params(mod.start_params)))
# Fit the model at the true parameters
res_true = mod.filter(true['params'])
# Check that the cycle bounds were computed correctly
freqstr = freq[0] if freq is not None else values.index.freqstr[0]
if 'cycle_period_bounds' in kwargs:
cycle_period_bounds = kwargs['cycle_period_bounds']
elif freqstr == 'A':
cycle_period_bounds = (1.5, 12)
elif freqstr == 'Q':
cycle_period_bounds = (1.5*4, 12*4)
elif freqstr == 'M':
cycle_period_bounds = (1.5*12, 12*12)
else:
# If we have no information on data frequency, require the
# cycle frequency to be between 0 and pi
cycle_period_bounds = (2, np.inf)
# Test that the cycle frequency bound is correct
assert_equal(mod.cycle_frequency_bound,
(2*np.pi / cycle_period_bounds[1],
2*np.pi / cycle_period_bounds[0])
)
# Test that the likelihood is correct
rtol = true.get('rtol', 1e-7)
atol = true.get('atol', 0)
assert_allclose(res_true.llf, true['llf'], rtol=rtol, atol=atol)
# Smoke test for plot_components
if have_matplotlib:
fig = res_true.plot_components()
plt.close(fig)
# Now fit the model via MLE
with warnings.catch_warnings(record=True) as w:
res = mod.fit(disp=-1)
# If we found a higher likelihood, no problem; otherwise check
# that we're very close to that found by R
if res.llf <= true['llf']:
assert_allclose(res.llf, true['llf'], rtol=1e-4)
# Smoke test for summary
res.summary()
def run_ucm(name, use_exact_diffuse=False):
true = getattr(results_structural, name)
for model in true['models']:
kwargs = model.copy()
kwargs.update(true['kwargs'])
kwargs['use_exact_diffuse'] = use_exact_diffuse
# Make a copy of the data
values = dta.copy()
freq = kwargs.pop('freq', None)
if freq is not None:
values.index = pd.date_range(start='1959-01-01',
periods=len(dta),
freq=freq)
# Test pandas exog
if 'exog' in kwargs:
# Default value here is pd.Series object
exog = np.log(values['realgdp'])
# Also allow a check with a 1-dim numpy array
if kwargs['exog'] == 'numpy':
exog = exog.values.squeeze()
kwargs['exog'] = exog
# Create the model
mod = UnobservedComponents(values['unemp'], **kwargs)
# Smoke test for starting parameters, untransform, transform
# Also test that transform and untransform are inverses
mod.start_params
roundtrip = mod.transform_params(
mod.untransform_params(mod.start_params))
assert_allclose(mod.start_params, roundtrip)
# Fit the model at the true parameters
res_true = mod.filter(true['params'])
# Check that the cycle bounds were computed correctly
freqstr = freq[0] if freq is not None else values.index.freqstr[0]
if 'cycle_period_bounds' in kwargs:
cycle_period_bounds = kwargs['cycle_period_bounds']
elif freqstr == 'A':
cycle_period_bounds = (1.5, 12)
elif freqstr == 'Q':
cycle_period_bounds = (1.5 * 4, 12 * 4)
elif freqstr == 'M':
cycle_period_bounds = (1.5 * 12, 12 * 12)
else:
# If we have no information on data frequency, require the
# cycle frequency to be between 0 and pi
cycle_period_bounds = (2, np.inf)
# Test that the cycle frequency bound is correct
assert_equal(mod.cycle_frequency_bound,
(2 * np.pi / cycle_period_bounds[1],
2 * np.pi / cycle_period_bounds[0]))
# Test that the likelihood is correct
rtol = true.get('rtol', 1e-7)
atol = true.get('atol', 0)
if use_exact_diffuse:
# If we are using exact diffuse initialization, then we need to
# adjust for the fact that KFAS does not include the constant in
# the likelihood function for the diffuse periods
# (see note to test_exact_diffuse_filtering.py for details).
res_llf = (res_true.llf_obs.sum() +
res_true.nobs_diffuse * 0.5 * np.log(2 * np.pi))
else:
# If we are using approximate diffuse initialization, then we need
# to ignore the first period, and this will agree with KFAS (since
# it does not include the constant in the likelihood function for
# diffuse periods).
res_llf = res_true.llf_obs[res_true.loglikelihood_burn:].sum()
assert_allclose(res_llf, true['llf'], rtol=rtol, atol=atol)
# Optional smoke test for plot_components
try:
import matplotlib.pyplot as plt
try:
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
except ImportError:
pass
fig = plt.figure()
res_true.plot_components(fig=fig)
except ImportError:
pass
# Now fit the model via MLE
with warnings.catch_warnings(record=True):
fit_kwargs = {}
if 'maxiter' in true:
fit_kwargs['maxiter'] = true['maxiter']
res = mod.fit(start_params=true.get('start_params', None),
disp=-1,
**fit_kwargs)
# If we found a higher likelihood, no problem; otherwise check
# that we're very close to that found by R
# See note above about these computation
if use_exact_diffuse:
res_llf = (res.llf_obs.sum() +
res.nobs_diffuse * 0.5 * np.log(2 * np.pi))
else:
res_llf = res.llf_obs[res_true.loglikelihood_burn:].sum()
if res_llf <= true['llf']:
assert_allclose(res_llf, true['llf'], rtol=1e-4)
# Smoke test for summary
res.summary()
