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()