def setUp(self):
data = south()
data['n_samples'] = 0
np.random.seed(TEST_SEED)
self.trace = Trace.from_csv(FULL_PATH + '/data/south_mvcm_5000', multi=True)
self.single_trace = Trace.from_csv(FULL_PATH + '/data/south_mvcm_5000_0.csv')
self.size_known = json.load(open(FULL_PATH + '/data/effective_size.json', 'r'))
def setUp(self):
data = south()
data['n_samples'] = 0
np.random.seed(TEST_SEED)
self.trace = Trace.from_csv(FULL_PATH + '/data/south_mvcm_5000',
multi=True)
self.single_trace = Trace.from_csv(FULL_PATH +
'/data/south_mvcm_5000_0.csv')
self.geweke_known = json.load(open(FULL_PATH + '/data/geweke.json'))
def setUp(self):
data = south()
data['n_samples'] = 0
np.random.seed(TEST_SEED)
test_methods = ['obm', 'bm', 'bartlett', 'hanning', 'tukey']
self.trace = Trace.from_csv(FULL_PATH + '/data/south_mvcm_5000', multi=True)
self.single_trace = Trace.from_csv(FULL_PATH + '/data/south_mvcm_5000_0.csv')
self.bm = json.load(open(FULL_PATH + '/data/mcse_bm.json', 'r'))
self.obm = json.load(open(FULL_PATH + '/data/mcse_obm.json', 'r'))
self.tukey = json.load(open(FULL_PATH + '/data/mcse_hanning.json', 'r'))
self.bartlett = json.load(open(FULL_PATH + '/data/mcse_bartlett.json', 'r'))
self.hanning = self.tukey
def build():
models = []
for cand in M.__dict__.values():
if isinstance(cand, CLASSTYPES):
if issubclass(cand, Sampler_Mixin):
models.append(cand)
for model in models:
print('starting {}'.format(model))
env = south()
del env['M']
run_with_seed(model, env=env, seed=TEST_SEED, fprefix=FULL_PATH + '/data/')
return os.listdir(FULL_PATH + '/data/')
def setUp(self):
data = south()
data['n_samples'] = 0
with open(FULL_PATH + '/data/psrf_noburn.json', 'r') as noburn:
self.noburn = json.load(noburn)
with open(FULL_PATH + '/data/psrf_brooks.json', 'r') as brooks:
self.known_brooks = json.load(brooks)
with open(FULL_PATH + '/data/psrf_gr.json', 'r') as gr:
self.known_gr = json.load(gr)
np.random.seed(TEST_SEED)
self.trace = Trace.from_csv(FULL_PATH + '/data/south_mvcm_5000', multi=True)
self.mockmodel = Hashmap(trace=self.trace)
def setUp(self):
data = south()
data['n_samples'] = 0
with open(FULL_PATH + '/data/psrf_noburn.json', 'r') as noburn:
self.noburn = json.load(noburn)
with open(FULL_PATH + '/data/psrf_brooks.json', 'r') as brooks:
self.known_brooks = json.load(brooks)
with open(FULL_PATH + '/data/psrf_gr.json', 'r') as gr:
self.known_gr = json.load(gr)
np.random.seed(TEST_SEED)
self.trace = Trace.from_csv(FULL_PATH + '/data/south_mvcm_5000',
multi=True)
self.mockmodel = Hashmap(trace=self.trace)
def run_with_seed(cls, env=utils.south(), seed=TEST_SEED, fprefix=''):
fname = str(cls).strip("'<>'").split('.')[-1].lower()
try:
env['n_samples'] = 0
model = cls(**env)
except TypeError:
reduced = copy.deepcopy(env)
del reduced['M']
del reduced['W']
reduced['n_samples'] = 0
model = cls(**reduced)
np.random.seed(TEST_SEED)
model.draw()
model.trace.to_df().to_csv(fprefix + fname + '.csv', index=False)
def run_with_seed(cls, env=utils.south(), seed=TEST_SEED, fprefix = ''):
fname = str(cls).strip("'<>'").split('.')[-1].lower()
try:
env['n_samples'] = 0
model = cls(**env)
except TypeError:
reduced = copy.deepcopy(env)
del reduced['M']
del reduced['W']
reduced['n_samples'] = 0
model = cls(**reduced)
np.random.seed(TEST_SEED)
model.draw()
model.trace.to_df().to_csv(fprefix + fname + '.csv', index=False)
def build():
models = []
for cand in M.__dict__.values():
if isinstance(cand, CLASSTYPES):
if issubclass(cand, Sampler_Mixin):
models.append(cand)
for model in models:
print('starting {}'.format(model))
env = south()
del env['M']
run_with_seed(model,
env=env,
seed=TEST_SEED,
fprefix=FULL_PATH + '/data/')
return os.listdir(FULL_PATH + '/data/')
def setUp(self):
data = south()
data['n_samples'] = 0
np.random.seed(TEST_SEED)
test_methods = ['obm', 'bm', 'bartlett', 'hanning', 'tukey']
self.trace = Trace.from_csv(FULL_PATH + '/data/south_mvcm_5000',
multi=True)
self.single_trace = Trace.from_csv(FULL_PATH +
'/data/south_mvcm_5000_0.csv')
self.bm = json.load(open(FULL_PATH + '/data/mcse_bm.json', 'r'))
self.obm = json.load(open(FULL_PATH + '/data/mcse_obm.json', 'r'))
self.tukey = json.load(open(FULL_PATH + '/data/mcse_hanning.json',
'r'))
self.bartlett = json.load(
open(FULL_PATH + '/data/mcse_bartlett.json', 'r'))
self.hanning = self.tukey
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 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 setUp(self):
data = south()
data['n_samples'] = 0
np.random.seed(TEST_SEED)
self.trace = Trace.from_csv(FULL_PATH + '/data/south_mvcm_5000',
multi=True)
def build_self(self):
super(Model_Mixin, self).__init__()
self.inputs = utils.south()
self.__dict__.update(self.inputs)
self.ignore_shape = False
self.squeeze = True
def setUp(self):
data = south()
data['n_samples'] = 0
np.random.seed(TEST_SEED)
self.trace = Trace.from_csv(FULL_PATH + '/data/south_mvcm_5000', multi=True)
def build_self(self):
super(Model_Mixin, self).__init__()
self.inputs = utils.south()
self.__dict__.update(self.inputs)
self.ignore_shape = False
self.squeeze = True
