def test_options(self):
exp_brooks = psrf(trace=self.mockmodel.trace)
for k,v in exp_brooks.items():
if k == 'Alphas':
continue
np.testing.assert_allclose(v, np.squeeze(self.known_brooks[k]),
rtol=RTOL, atol=ATOL,
err_msg='Failed in {}'.format(k))
exp_brooks = psrf(chain=self.mockmodel.trace['Tau2'])
np.testing.assert_allclose(exp_brooks['parameter'],
self.known_brooks['Tau2'],
rtol=RTOL, atol=ATOL,
err_msg='Failed in Tau2')
test_completion = psrf(trace=self.trace, autoburnin=False)
for k,v in self.noburn.items():
if k == 'Alphas':
continue
np.testing.assert_allclose(v, np.squeeze(test_completion[k]),
rtol=1e-5, atol=1e-5,
err_msg='Failed in {}'.format(k))
limit_vars = psrf(trace=self.trace, varnames=['Tau2', 'Sigma2'])
for k,v in limit_vars.items():
if k == 'Alphas':
continue
np.testing.assert_allclose(v, np.squeeze(limit_vars[k]),
rtol=RTOL, atol=ATOL,
err_msg='Failed in {}'.format(k))
def test_options(self):
exp_brooks = psrf(trace=self.mockmodel.trace)
for k, v in exp_brooks.items():
if k == 'Alphas':
continue
np.testing.assert_allclose(v,
np.squeeze(self.known_brooks[k]),
rtol=RTOL,
atol=ATOL,
err_msg='Failed in {}'.format(k))
exp_brooks = psrf(chain=self.mockmodel.trace['Tau2'])
np.testing.assert_allclose(exp_brooks['parameter'],
self.known_brooks['Tau2'],
rtol=RTOL,
atol=ATOL,
err_msg='Failed in Tau2')
test_completion = psrf(trace=self.trace, autoburnin=False)
for k, v in self.noburn.items():
if k == 'Alphas':
continue
np.testing.assert_allclose(v,
np.squeeze(test_completion[k]),
rtol=1e-5,
atol=1e-5,
err_msg='Failed in {}'.format(k))
limit_vars = psrf(trace=self.trace, varnames=['Tau2', 'Sigma2'])
for k, v in limit_vars.items():
if k == 'Alphas':
continue
np.testing.assert_allclose(v,
np.squeeze(limit_vars[k]),
rtol=RTOL,
atol=ATOL,
err_msg='Failed in {}'.format(k))
def test_coef_recovery(self):
#test with:
#model=model, trace=model.trace, chain=model.trace['asdf']
#autoburnin=False, method='original'
exp_brooks = psrf(self.mockmodel)
for k,v in exp_brooks.items():
if k == 'Alphas':
continue
np.testing.assert_allclose(v, np.squeeze(self.known_brooks[k]),
rtol=RTOL, atol=ATOL,
err_msg='Failed in {}'.format(k))
exp_gr = psrf(trace=self.trace, method='original')
for k,v in exp_gr.items():
if k == 'Alphas':
continue
np.testing.assert_allclose(v, np.squeeze(self.known_gr[k]),
rtol=RTOL, atol=ATOL,
err_msg='Failed in {}'.format(k))
def build():
data = south()
del data['W']
del data['M']
model = pysal.model.spvcm.both.MVCM(**data, n_samples=0)
np.random.seed(TEST_SEED)
print('starting South 5000, njobs=4')
model.sample(5000, n_jobs=4)
print('starting PSRF')
known_brooks = psrf(model)
known_gr = psrf(model, method='original')
import json
with open(FULL_PATH + '/data/' + 'psrf_brooks.json', 'w') as brooks:
json.dump(known_brooks, brooks)
with open(FULL_PATH + '/data/' + 'psrf_gr.json', 'w') as gr:
json.dump(known_gr, gr)
for i, method in enumerate(['bm', 'obm', 'bartlett', 'hanning']):
known_mcse = mcse(model, varnames=['Tau2'], method=method)
with open(FULL_PATH + '/data/' + 'mcse_{}.json'.format(i, method),
'w') as mcse_file:
json.dump(known_mcse, mcse_file)
known_hpd = hpd_interval(model, varnames=['Sigma2'])
with open(FULL_PATH + '/data/' + 'hpd_interval.json', 'w') as hpd_file:
json.dump(known_hpd, hpd_file)
known_size = effective_size(model, varnames=['Tau2'], use_R=False)
with open(FULL_PATH + '/data/' + 'effective_size.json', 'w') as size_file:
json.dump(known_size, size_file)
known_geweke = geweke(model, varnames=['Sigma2'])
known_geweke = [{k: v.tolist()
for k, v in known.items()} for known in known_geweke]
with open(FULL_PATH + '/data/' + 'geweke.json', 'w') as geweke_file:
json.dump(known_geweke, geweke_file)
model.trace.to_csv(FULL_PATH + '/data/' + 'south_mvcm_5000.csv')
return ([
FULL_PATH + '/data/' + 'psrf_{}.json'.format(k)
for k in ['brooks', 'gr']
] + [FULL_PATH + '/data/south_mvcm_5000.csv'])
def build():
data = south()
del data["W"]
del data["M"]
model = pysal.model.spvcm.both.MVCM(**data, n_samples=0)
np.random.seed(TEST_SEED)
print("starting South 5000, njobs=4")
model.sample(5000, n_jobs=4)
print("starting PSRF")
known_brooks = psrf(model)
known_gr = psrf(model, method="original")
import json
with open(FULL_PATH + "/data/" + "psrf_brooks.json", "w") as brooks:
json.dump(known_brooks, brooks)
with open(FULL_PATH + "/data/" + "psrf_gr.json", "w") as gr:
json.dump(known_gr, gr)
for i, method in enumerate(["bm", "obm", "bartlett", "hanning"]):
known_mcse = mcse(model, varnames=["Tau2"], method=method)
with open(
FULL_PATH + "/data/" + "mcse_{}.json".format(i, method), "w"
) as mcse_file:
json.dump(known_mcse, mcse_file)
known_hpd = hpd_interval(model, varnames=["Sigma2"])
with open(FULL_PATH + "/data/" + "hpd_interval.json", "w") as hpd_file:
json.dump(known_hpd, hpd_file)
known_size = effective_size(model, varnames=["Tau2"], use_R=False)
with open(FULL_PATH + "/data/" + "effective_size.json", "w") as size_file:
json.dump(known_size, size_file)
known_geweke = geweke(model, varnames=["Sigma2"])
known_geweke = [{k: v.tolist() for k, v in known.items()} for known in known_geweke]
with open(FULL_PATH + "/data/" + "geweke.json", "w") as geweke_file:
json.dump(known_geweke, geweke_file)
model.trace.to_csv(FULL_PATH + "/data/" + "south_mvcm_5000.csv")
return [
FULL_PATH + "/data/" + "psrf_{}.json".format(k) for k in ["brooks", "gr"]
] + [FULL_PATH + "/data/south_mvcm_5000.csv"]
def test_coef_recovery(self):
#test with:
#model=model, trace=model.trace, chain=model.trace['asdf']
#autoburnin=False, method='original'
exp_brooks = psrf(self.mockmodel)
for k, v in exp_brooks.items():
if k == 'Alphas':
continue
np.testing.assert_allclose(v,
np.squeeze(self.known_brooks[k]),
rtol=RTOL,
atol=ATOL,
err_msg='Failed in {}'.format(k))
exp_gr = psrf(trace=self.trace, method='original')
for k, v in exp_gr.items():
if k == 'Alphas':
continue
np.testing.assert_allclose(v,
np.squeeze(self.known_gr[k]),
rtol=RTOL,
atol=ATOL,
err_msg='Failed in {}'.format(k))
def build():
data = south()
del data['W']
del data['M']
model = pysal.model.spvcm.both.MVCM(**data, n_samples=0)
np.random.seed(TEST_SEED)
print('starting South 5000, njobs=4')
model.sample(5000,n_jobs=4)
print('starting PSRF')
known_brooks = psrf(model)
known_gr = psrf(model, method='original')
import json
with open(FULL_PATH + '/data/' + 'psrf_brooks.json', 'w') as brooks:
json.dump(known_brooks, brooks)
with open(FULL_PATH + '/data/' + 'psrf_gr.json', 'w') as gr:
json.dump(known_gr, gr)
for i, method in enumerate(['bm', 'obm', 'bartlett', 'hanning']):
known_mcse = mcse(model, varnames=['Tau2'], method=method)
with open(FULL_PATH + '/data/' + 'mcse_{}.json'.format(i,method), 'w') as mcse_file:
json.dump(known_mcse, mcse_file)
known_hpd = hpd_interval(model, varnames=['Sigma2'])
with open(FULL_PATH + '/data/' + 'hpd_interval.json', 'w') as hpd_file:
json.dump(known_hpd, hpd_file)
known_size = effective_size(model, varnames=['Tau2'], use_R=False)
with open(FULL_PATH + '/data/' + 'effective_size.json', 'w') as size_file:
json.dump(known_size, size_file)
known_geweke = geweke(model, varnames=['Sigma2'])
known_geweke = [{k:v.tolist() for k,v in known.items()} for known in known_geweke]
with open(FULL_PATH + '/data/' + 'geweke.json', 'w') as geweke_file:
json.dump(known_geweke, geweke_file)
model.trace.to_csv(FULL_PATH + '/data/' + 'south_mvcm_5000.csv')
return ([FULL_PATH + '/data/' + 'psrf_{}.json'.format(k)
for k in ['brooks', 'gr']] + [FULL_PATH + '/data/south_mvcm_5000.csv'])