def test_merge_traces_diff_lengths(self):
with self.model:
strace0 = self.backend(self.name)
strace0.setup(self.draws, 1)
for i in range(self.draws):
strace0.record(self.test_point)
strace0.close()
mtrace0 = base.MultiTrace([self.strace0])
with self.model:
strace1 = self.backend(self.name)
strace1.setup(2 * self.draws, 1)
for i in range(2 * self.draws):
strace1.record(self.test_point)
strace1.close()
mtrace1 = base.MultiTrace([strace1])
with pytest.raises(ValueError):
base.merge_traces([mtrace0, mtrace1])
def sample_model(model,
step=None,
num_samples=MAX_NUM_SAMPLES,
advi=False,
n_chains=NUM_CHAINS,
raw_trace=False,
single_chain=True,
num_scale1_iters=NUM_SCALE1_ITERS,
num_scale0_iters=NUM_SCALE0_ITERS):
"""
Sample parallel chains from constructed Bayesian model.
Returns tuple of Multitrace and diagnostics object.
"""
sample_chain_with_args = partial(sample_chain,
step=step,
num_samples=num_samples,
advi=advi,
num_scale1_iters=num_scale1_iters,
num_scale0_iters=num_scale0_iters)
diagnostics = None
if not advi:
if single_chain:
trace = sample_chain_with_args(model)
diagnostics = get_diagnostics(trace, model, single_chain=True)
else:
traces = []
for i in range(n_chains):
print('chain {} of {}'.format(i + 1, n_chains))
traces.append(sample_chain_with_args(model, chain_i=i))
# copy and rebuild traces list because merge_traces modifies
# the first trace in the list
trace0 = deepcopy(traces[0])
trace = merge_traces(traces)
traces = [trace0] + traces[1:]
diagnostics = get_diagnostics(merge_truncated_traces(traces),
model,
single_chain=False)
else:
trace = sample_chain_with_args(model)
diagnostics = get_diagnostics(trace, model, single_chain=True)
if raw_trace:
return trace, diagnostics
else:
return format_trace(trace, to_df=True), diagnostics
def main():
X, Y = generate_sample()
with pm.Model() as model:
alpha = pm.Normal('alpha', mu=0, sd=20)
beta = pm.Normal('beta', mu=0, sd=20)
sigma = pm.Uniform('sigma', lower=0)
y = pm.Normal('y', mu=beta*X+alpha, sd=sigma, observed=Y)
start = pm.find_MAP()
step = pm.NUTS(state=start)
with model:
if (multicore):
trace = pm.sample(itenum, step, start=start,
njobs=chainnum, random_seed=range(chainnum), progressbar=progress)
else:
ts = [pm.sample(itenum, step, chain=i, progressbar=progress)
for i in range(chainnum)]
trace = merge_traces(ts)
if (saveimage):
pm.traceplot(trace).savefig("simple_linear_trace.png")
print "Rhat = {0}".format(pm.gelman_rubin(trace))
t1 = time.clock()
print "elapsed time = {0}".format(t1 - t0)
#trace
if(not multicore):
trace=ts[0]
with model:
pm.traceplot(trace,model.vars)
pm.forestplot(trace)
with open("simplelinearregression_model.pkl","w") as fpw:
pkl.dump(model,fpw)
with open("simplelinearregression_trace.pkl","w") as fpw:
pkl.dump(trace,fpw)
with open("simplelinearregression_model.pkl") as fp:
model=pkl.load(fp)
with open("simplelinearregression_trace.pkl") as fp:
trace=pkl.load(fp)
def model_inference(model,
niter=2000,
nadvi=200000,
ntraceadvi=1000,
seed=123,
nchains=2):
with model:
v_params = pm.variational.advi(n=nadvi, random_seed=seed)
tracevi = pm.variational.sample_vp(v_params,
draws=ntraceadvi,
random_seed=seed)
traces = []
for chain in range(nchains):
step = pm.NUTS(scaling=np.power(model.dict_to_array(v_params.stds),
2),
is_cov=True,
target_accept=0.95)
trace = pm.sample(niter, chain=chain, step=step, random_seed=seed)
trace = trace[niter // 2::2]
traces.append(trace)
trace = merge_traces(traces)
return trace, v_params, tracevi
def test_merge_traces_nonunique(self):
mtrace0 = base.MultiTrace([self.strace0])
mtrace1 = base.MultiTrace([self.strace1])
with pytest.raises(ValueError):
base.merge_traces([mtrace0, mtrace1])
def _mp_sample(njobs, args):
p = mp.Pool(njobs)
traces = p.map(argsample, args)
p.close()
return merge_traces(traces)
def test_merge_traces_nonunique(self):
mtrace0 = base.MultiTrace([self.strace0])
mtrace1 = base.MultiTrace([self.strace1])
with pytest.raises(ValueError):
base.merge_traces([mtrace0, mtrace1])
def test_merge_traces_no_traces(self):
with pytest.raises(ValueError):
base.merge_traces([])
def merge_truncated_traces(traces):
min_chain_length = min(map(len, traces))
truncated_traces = list(
map(lambda trace: trace[-min_chain_length:], traces))
return merge_traces(truncated_traces)
