def setUpClass(cls):
cls.test_point, cls.model, _ = models.beta_bernoulli(cls.shape)
with cls.model:
strace0 = cls.backend(cls.name)
strace1 = cls.backend(cls.name)
cls.draws = 5
strace0.setup(cls.draws, chain=0)
strace1.setup(cls.draws, chain=1)
varnames = list(cls.test_point.keys())
shapes = {varname: value.shape
for varname, value in cls.test_point.items()}
dtypes = {varname: value.dtype
for varname, value in cls.test_point.items()}
cls.expected = {0: {}, 1: {}}
for varname in varnames:
mcmc_shape = (cls.draws,) + shapes[varname]
values = np.arange(cls.draws * np.prod(shapes[varname]),
dtype=dtypes[varname])
cls.expected[0][varname] = values.reshape(mcmc_shape)
cls.expected[1][varname] = values.reshape(mcmc_shape) * 100
for idx in range(cls.draws):
point0 = {varname: cls.expected[0][varname][idx, ...]
for varname in varnames}
point1 = {varname: cls.expected[1][varname][idx, ...]
for varname in varnames}
strace0.record(point=point0)
strace1.record(point=point1)
strace0.close()
strace1.close()
cls.mtrace = base.MultiTrace([strace0, strace1])
def setUpClass(cls):
cls.test_point, cls.model, _ = models.beta_bernoulli(cls.shape)
with cls.model:
strace0 = cls.backend(cls.name)
strace1 = cls.backend(cls.name)
cls.draws = 5
strace0.setup(cls.draws, chain=0)
strace1.setup(cls.draws, chain=1)
varnames = list(cls.test_point.keys())
shapes = {varname: value.shape
for varname, value in cls.test_point.items()}
dtypes = {varname: value.dtype
for varname, value in cls.test_point.items()}
cls.expected = {0: {}, 1: {}}
for varname in varnames:
mcmc_shape = (cls.draws,) + shapes[varname]
values = np.arange(cls.draws * np.prod(shapes[varname]),
dtype=dtypes[varname])
cls.expected[0][varname] = values.reshape(mcmc_shape)
cls.expected[1][varname] = values.reshape(mcmc_shape) * 100
for idx in range(cls.draws):
point0 = {varname: cls.expected[0][varname][idx, ...]
for varname in varnames}
point1 = {varname: cls.expected[1][varname][idx, ...]
for varname in varnames}
strace0.record(point=point0)
strace1.record(point=point1)
strace0.close()
strace1.close()
cls.mtrace = base.MultiTrace([strace0, strace1])
def setup_method(self):
self.test_point, self.model, _ = models.beta_bernoulli(self.shape)
with self.model:
self.strace = self.backend(self.name)
self.draws, self.chain = 3, 0
if not hasattr(self, "sampler_vars"):
self.sampler_vars = None
if self.sampler_vars is not None:
assert self.strace.supports_sampler_stats
self.strace.setup(self.draws, self.chain, self.sampler_vars)
else:
self.strace.setup(self.draws, self.chain)
def setup_method(self):
self.test_point, self.model, _ = models.beta_bernoulli(self.shape)
with self.model:
self.strace = self.backend(self.name)
self.draws, self.chain = 3, 0
if not hasattr(self, 'sampler_vars'):
self.sampler_vars = None
if self.sampler_vars is not None:
assert self.strace.supports_sampler_stats
self.strace.setup(self.draws, self.chain, self.sampler_vars)
else:
self.strace.setup(self.draws, self.chain)
def setUp(self):
self.test_point, self.model, _ = models.beta_bernoulli(self.shape)
with self.model:
self.strace = self.backend(self.name)
self.draws, self.chain = 3, 0
self.strace.setup(self.draws, self.chain)
def setup_class(cls):
cls.test_point, cls.model, _ = models.beta_bernoulli(cls.shape)
if hasattr(cls, "write_partial_chain") and cls.write_partial_chain is True:
cls.chain_vars = cls.model.unobserved_RVs[1:]
else:
cls.chain_vars = cls.model.unobserved_RVs
with cls.model:
strace0 = cls.backend(cls.name, vars=cls.chain_vars)
strace1 = cls.backend(cls.name, vars=cls.chain_vars)
if not hasattr(cls, "sampler_vars"):
cls.sampler_vars = None
cls.draws = 5
if cls.sampler_vars is not None:
strace0.setup(cls.draws, chain=0, sampler_vars=cls.sampler_vars)
strace1.setup(cls.draws, chain=1, sampler_vars=cls.sampler_vars)
else:
strace0.setup(cls.draws, chain=0)
strace1.setup(cls.draws, chain=1)
varnames = list(cls.test_point.keys())
shapes = {varname: value.shape for varname, value in cls.test_point.items()}
dtypes = {varname: value.dtype for varname, value in cls.test_point.items()}
cls.expected = {0: {}, 1: {}}
for varname in varnames:
mcmc_shape = (cls.draws,) + shapes[varname]
values = np.arange(cls.draws * np.prod(shapes[varname]), dtype=dtypes[varname])
cls.expected[0][varname] = values.reshape(mcmc_shape)
cls.expected[1][varname] = values.reshape(mcmc_shape) * 100
if cls.sampler_vars is not None:
cls.expected_stats = {0: [], 1: []}
for vars in cls.sampler_vars:
stats = {}
cls.expected_stats[0].append(stats)
cls.expected_stats[1].append(stats)
for key, dtype in vars.items():
if dtype == np.bool:
stats[key] = np.zeros(cls.draws, dtype=dtype)
else:
stats[key] = np.arange(cls.draws, dtype=dtype)
for idx in range(cls.draws):
point0 = {varname: cls.expected[0][varname][idx, ...] for varname in varnames}
point1 = {varname: cls.expected[1][varname][idx, ...] for varname in varnames}
if cls.sampler_vars is not None:
stats1 = [
{key: val[idx] for key, val in stats.items()} for stats in cls.expected_stats[0]
]
stats2 = [
{key: val[idx] for key, val in stats.items()} for stats in cls.expected_stats[1]
]
strace0.record(point=point0, sampler_stats=stats1)
strace1.record(point=point1, sampler_stats=stats2)
else:
strace0.record(point=point0)
strace1.record(point=point1)
strace0.close()
strace1.close()
cls.mtrace = base.MultiTrace([strace0, strace1])
cls.stat_dtypes = {}
cls.stats_counts = collections.Counter()
for stats in cls.sampler_vars or []:
cls.stat_dtypes.update(stats)
cls.stats_counts.update(stats.keys())
def setup_method(self):
self.test_point, self.model, _ = models.beta_bernoulli(self.shape)
self.draws, self.chain = 3, 0
def setUp(self):
self.test_point, self.model, _ = models.beta_bernoulli(self.shape)
with self.model:
self.strace = self.backend(self.name)
self.draws, self.chain = 3, 0
self.strace.setup(self.draws, self.chain)
def setup_method(self):
self.test_point, self.model, _ = models.beta_bernoulli(self.shape)
self.draws, self.chain = 3, 0
def setup_class(cls):
cls.test_point, cls.model, _ = models.beta_bernoulli(cls.shape)
with cls.model:
strace0 = cls.backend(cls.name)
strace1 = cls.backend(cls.name)
if not hasattr(cls, 'sampler_vars'):
cls.sampler_vars = None
cls.draws = 5
if cls.sampler_vars is not None:
strace0.setup(cls.draws, chain=0, sampler_vars=cls.sampler_vars)
strace1.setup(cls.draws, chain=1, sampler_vars=cls.sampler_vars)
else:
strace0.setup(cls.draws, chain=0)
strace1.setup(cls.draws, chain=1)
varnames = list(cls.test_point.keys())
shapes = {varname: value.shape
for varname, value in cls.test_point.items()}
dtypes = {varname: value.dtype
for varname, value in cls.test_point.items()}
cls.expected = {0: {}, 1: {}}
for varname in varnames:
mcmc_shape = (cls.draws,) + shapes[varname]
values = np.arange(cls.draws * np.prod(shapes[varname]),
dtype=dtypes[varname])
cls.expected[0][varname] = values.reshape(mcmc_shape)
cls.expected[1][varname] = values.reshape(mcmc_shape) * 100
if cls.sampler_vars is not None:
cls.expected_stats = {0: [], 1: []}
for vars in cls.sampler_vars:
stats = {}
cls.expected_stats[0].append(stats)
cls.expected_stats[1].append(stats)
for key, dtype in vars.items():
if dtype == np.bool:
stats[key] = np.zeros(cls.draws, dtype=dtype)
else:
stats[key] = np.arange(cls.draws, dtype=dtype)
for idx in range(cls.draws):
point0 = {varname: cls.expected[0][varname][idx, ...]
for varname in varnames}
point1 = {varname: cls.expected[1][varname][idx, ...]
for varname in varnames}
if cls.sampler_vars is not None:
stats1 = [dict((key, val[idx]) for key, val in stats.items())
for stats in cls.expected_stats[0]]
stats2 = [dict((key, val[idx]) for key, val in stats.items())
for stats in cls.expected_stats[1]]
strace0.record(point=point0, sampler_stats=stats1)
strace1.record(point=point1, sampler_stats=stats2)
else:
strace0.record(point=point0)
strace1.record(point=point1)
strace0.close()
strace1.close()
cls.mtrace = base.MultiTrace([strace0, strace1])
cls.stat_dtypes = {}
cls.stats_counts = collections.Counter()
for stats in cls.sampler_vars or []:
cls.stat_dtypes.update(stats)
cls.stats_counts.update(stats.keys())
