def test_simulator_metropolis_mcmc(self):
with self.SMABC_test as m:
step = pm.Metropolis([m.rvs_to_values[m["a"]], m.rvs_to_values[m["b"]]])
trace = pm.sample(step=step, return_inferencedata=False)
assert abs(self.data.mean() - trace["a"].mean()) < 0.05
assert abs(self.data.std() - trace["b"].mean()) < 0.05
def test_issue_5043_autoconvert_coord_values(self):
coords = {"city": pd.Series(["Bonn", "Berlin"])}
with pm.Model(coords=coords) as pmodel:
# The model tracks coord values as (immutable) tuples
assert isinstance(pmodel.coords["city"], tuple)
pm.Normal("x", dims="city")
mtrace = pm.sample(
return_inferencedata=False,
compute_convergence_checks=False,
step=pm.Metropolis(),
cores=1,
tune=7,
draws=15,
)
# The converter must convert coord values them to numpy arrays
# because tuples as coordinate values causes problems with xarray.
converter = InferenceDataConverter(trace=mtrace)
assert isinstance(converter.coords["city"], np.ndarray)
converter.to_inference_data()
# We're not automatically converting things other than tuple,
# so advanced use cases remain supported at the InferenceData level.
# They just can't be used in the model construction already.
converter = InferenceDataConverter(
trace=mtrace,
coords={
"city":
pd.MultiIndex.from_tuples([("Bonn", 53111),
("Berlin", 10178)],
names=["name", "zipcode"])
},
)
assert isinstance(converter.coords["city"], pd.MultiIndex)
def test_save_warmup(self, save_warmup, chains, tune, draws):
with pm.Model():
pm.Uniform("u1")
pm.Normal("n1")
idata = pm.sample(
tune=tune,
draws=draws,
chains=chains,
cores=1,
step=pm.Metropolis(),
discard_tuned_samples=False,
return_inferencedata=True,
idata_kwargs={"save_warmup": save_warmup},
)
warmup_prefix = "" if save_warmup and (tune > 0) else "~"
post_prefix = "" if draws > 0 else "~"
test_dict = {
f"{post_prefix}posterior": ["u1", "n1"],
f"{post_prefix}sample_stats": ["~tune", "accept"],
f"{warmup_prefix}warmup_posterior": ["u1", "n1"],
f"{warmup_prefix}warmup_sample_stats": ["~tune"],
"~warmup_log_likelihood": [],
"~log_likelihood": [],
}
fails = check_multiple_attrs(test_dict, idata)
assert not fails
if hasattr(idata, "posterior"):
assert idata.posterior.dims["chain"] == chains
assert idata.posterior.dims["draw"] == draws
if hasattr(idata, "warmup_posterior"):
assert idata.warmup_posterior.dims["chain"] == chains
assert idata.warmup_posterior.dims["draw"] == tune
def MCMC(model):
import time
with model:
n = 6000
START = time.time()
try:
start = pm.find_MAP()
except AssertionError:
return model, {'error':'AssertionError in pm.find_MAP()'}
init_time = time.time()-START
print 'Time to initialize: %ds' % (init_time)
START = time.time()
trace = pm.sample(n,pm.Metropolis(),start)
duration = time.time()-START
print 'Time to sample (MH): %ds' % (duration)
# START = time.time()
# trace = pm.sample(n,pm.Slice(),start)
# print 'Time to sample (Slice): %ds' % (time.time()-START)
# START = time.time()
# trace = pm.sample(n,pm.HamiltonianMC(),start)
# print 'Time to sample (HMC): %ds' % (time.time()-START)
# error_b, error_x, output = error(trace,model.data.A,model.data.x_true,
# model.data.b_obs,model.data.scaling)
# fig = pm.traceplot(trace)
# plot(error_b,error_x)
# plt.show()
return model, trace, init_time, duration
def test_return_inferencedata(self):
with self.model:
kwargs = dict(draws=100, tune=50, cores=1, chains=2, step=pm.Metropolis())
# trace with tuning
with pytest.warns(UserWarning, match="will be included"):
result = pm.sample(
**kwargs, return_inferencedata=False, discard_tuned_samples=False
)
assert isinstance(result, pm.backends.base.MultiTrace)
assert len(result) == 150
# inferencedata with tuning
result = pm.sample(**kwargs, return_inferencedata=True, discard_tuned_samples=False)
assert isinstance(result, InferenceData)
assert result.posterior.sizes["draw"] == 100
assert result.posterior.sizes["chain"] == 2
assert len(result._groups_warmup) > 0
# inferencedata without tuning, with idata_kwargs
prior = pm.sample_prior_predictive(return_inferencedata=False)
result = pm.sample(
**kwargs,
return_inferencedata=True,
discard_tuned_samples=True,
idata_kwargs={"prior": prior},
random_seed=-1,
)
assert "prior" in result
assert isinstance(result, InferenceData)
assert result.posterior.sizes["draw"] == 100
assert result.posterior.sizes["chain"] == 2
assert len(result._groups_warmup) == 0
def test_empirical_from_trace(another_simple_model):
with another_simple_model:
step = pm.Metropolis()
trace = pm.sample(100, step=step, chains=1, tune=0, return_inferencedata=False)
emp = Empirical(trace)
assert emp.histogram.shape[0].eval() == 100
trace = pm.sample(100, step=step, chains=4, tune=0, return_inferencedata=False)
emp = Empirical(trace)
assert emp.histogram.shape[0].eval() == 400
def test_spawn_densitydist_function():
with pm.Model() as model:
mu = pm.Normal("mu", 0, 1)
def func(x):
return -2 * (x ** 2).sum()
obs = pm.DensityDist("density_dist", logp=func, observed=np.random.randn(100))
pm.sample(draws=10, tune=10, step=pm.Metropolis(), cores=2, mp_ctx="spawn")
def test_metropolis_sampling(self):
"""Check if the Metropolis sampler can handle broadcasting."""
with pm.Model() as test_model:
test1 = pm.Normal("test1", mu=0.0, sigma=1.0, size=(1, 10))
test2 = pm.Normal("test2", mu=test1, sigma=1.0, size=(10, 10))
step = pm.Metropolis()
# TODO FIXME: Assert whatever it is we're testing
pm.sample(tune=5, draws=7, cores=1, step=step, compute_convergence_checks=False)
def test_sample_find_MAP_does_not_modify_start():
# see https://github.com/pymc-devs/pymc/pull/4458
with pm.Model():
pm.LogNormal("untransformed")
# make sure find_Map does not modify the start dict
start = {"untransformed": 2}
pm.find_MAP(start=start)
assert start == {"untransformed": 2}
# make sure sample does not modify the start dict
start = {"untransformed": 0.2}
pm.sample(draws=10, step=pm.Metropolis(), tune=5, start=start, chains=3)
assert start == {"untransformed": 0.2}
# make sure sample does not modify the start when passes as list of dict
start = [{"untransformed": 2}, {"untransformed": 0.2}]
pm.sample(draws=10, step=pm.Metropolis(), tune=5, start=start, chains=2)
assert start == [{"untransformed": 2}, {"untransformed": 0.2}]
def test_conversion_from_variables_subset(self):
"""This is a regression test for issue #5337."""
with pm.Model() as model:
x = pm.Normal("x")
pm.Normal("y", x, observed=5)
idata = pm.sample(
tune=10, draws=20, chains=1, step=pm.Metropolis(), compute_convergence_checks=False
)
pm.sample_posterior_predictive(idata, var_names=["x"])
pm.sample_prior_predictive(var_names=["x"])
def test_remote_pipe_closed():
master_pid = os.getpid()
with pm.Model():
x = pm.Normal("x", shape=2, mu=0.1)
at_pid = at.as_tensor_variable(np.array(master_pid, dtype="int32"))
pm.Normal("y", mu=_crash_remote_process(x, at_pid), shape=2)
step = pm.Metropolis()
with pytest.raises(RuntimeError, match="Chain [0-9] failed"):
pm.sample(step=step, mp_ctx="spawn", tune=2, draws=2, cores=2, chains=2)
def test_empirical_does_not_support_inference_data(another_simple_model):
with another_simple_model:
step = pm.Metropolis()
trace = pm.sample(100,
step=step,
chains=1,
tune=0,
return_inferencedata=True)
with pytest.raises(NotImplementedError,
match="return_inferencedata=False"):
Empirical(trace)
def test_sampler_stat_tune(self, cores):
with self.model:
tune_stat = pm.sample(
tune=5,
draws=7,
cores=cores,
discard_tuned_samples=False,
return_inferencedata=False,
step=pm.Metropolis(),
).get_sampler_stats("tune", chains=1)
assert list(tune_stat).count(True) == 5
assert list(tune_stat).count(False) == 7
def test_spawn_densitydist_bound_method():
N = 100
with pm.Model() as model:
mu = pm.Normal("mu", 0, 1)
normal_dist = pm.Normal.dist(mu, 1, size=N)
def logp(x):
out = pm.logp(normal_dist, x)
return out
obs = pm.DensityDist("density_dist", logp=logp, observed=np.random.randn(N), size=N)
pm.sample(draws=10, tune=10, step=pm.Metropolis(), cores=2, mp_ctx="spawn")
def test_autodetect_coords_from_model(self, use_context):
pd = pytest.importorskip("pandas")
df_data = pd.DataFrame(columns=["date"]).set_index("date")
dates = pd.date_range(start="2020-05-01", end="2020-05-20")
for city, mu in {"Berlin": 15, "San Marino": 18, "Paris": 16}.items():
df_data[city] = np.random.normal(loc=mu, size=len(dates))
df_data.index = dates
df_data.index.name = "date"
coords = {"date": df_data.index, "city": df_data.columns}
with pm.Model(coords=coords) as model:
europe_mean = pm.Normal("europe_mean_temp", mu=15.0, sigma=3.0)
city_offset = pm.Normal("city_offset",
mu=0.0,
sigma=3.0,
dims="city")
city_temperature = pm.Deterministic("city_temperature",
europe_mean + city_offset,
dims="city")
data_dims = ("date", "city")
data = pm.ConstantData("data", df_data, dims=data_dims)
_ = pm.Normal("likelihood",
mu=city_temperature,
sigma=0.5,
observed=data,
dims=data_dims)
trace = pm.sample(
return_inferencedata=False,
compute_convergence_checks=False,
cores=1,
chains=1,
tune=20,
draws=30,
step=pm.Metropolis(),
)
if use_context:
idata = to_inference_data(trace=trace)
if not use_context:
idata = to_inference_data(trace=trace, model=model)
assert "city" in list(idata.posterior.dims)
assert "city" in list(idata.observed_data.dims)
assert "date" in list(idata.observed_data.dims)
np.testing.assert_array_equal(idata.posterior.coords["city"],
coords["city"])
np.testing.assert_array_equal(idata.observed_data.coords["date"],
coords["date"])
np.testing.assert_array_equal(idata.observed_data.coords["city"],
coords["city"])
def test_iterator():
with pm.Model() as model:
a = pm.Normal("a", shape=1)
b = pm.HalfNormal("b")
step1 = pm.NUTS([model.rvs_to_values[a]])
step2 = pm.Metropolis([model.rvs_to_values[b]])
step = pm.CompoundStep([step1, step2])
start = {"a": floatX(np.array([1.0])), "b_log__": floatX(np.array(2.0))}
sampler = ps.ParallelSampler(10, 10, 3, 2, [2, 3, 4], [start] * 3, step, 0, False)
with sampler:
for draw in sampler:
pass
def test_save_warmup_issue_1208_after_3_9(self):
with pm.Model():
pm.Uniform("u1")
pm.Normal("n1")
trace = pm.sample(
tune=100,
draws=200,
chains=2,
cores=1,
step=pm.Metropolis(),
discard_tuned_samples=False,
return_inferencedata=False,
)
assert isinstance(trace, pm.backends.base.MultiTrace)
assert len(trace) == 300
# from original trace, warmup draws should be separated out
idata = to_inference_data(trace, save_warmup=True)
test_dict = {
"posterior": ["u1", "n1"],
"sample_stats": ["~tune", "accept"],
"warmup_posterior": ["u1", "n1"],
"warmup_sample_stats": ["~tune", "accept"],
}
fails = check_multiple_attrs(test_dict, idata)
assert not fails
assert idata.posterior.dims["chain"] == 2
assert idata.posterior.dims["draw"] == 200
# manually sliced trace triggers the same warning as <=3.8
with pytest.warns(UserWarning, match="Warmup samples"):
idata = to_inference_data(trace[-30:], save_warmup=True)
test_dict = {
"posterior": ["u1", "n1"],
"sample_stats": ["~tune", "accept"],
"~warmup_posterior": [],
"~warmup_sample_stats": [],
}
fails = check_multiple_attrs(test_dict, idata)
assert not fails
assert idata.posterior.dims["chain"] == 2
assert idata.posterior.dims["draw"] == 30
def test_abort(mp_start_method):
with pm.Model() as model:
a = pm.Normal("a", shape=1)
b = pm.HalfNormal("b")
step1 = pm.NUTS([model.rvs_to_values[a]])
step2 = pm.Metropolis([model.rvs_to_values[b]])
step = pm.CompoundStep([step1, step2])
# on Windows we cannot fork
if platform.system() == "Windows" and mp_start_method == "fork":
return
if mp_start_method == "spawn":
step_method_pickled = cloudpickle.dumps(step, protocol=-1)
else:
step_method_pickled = None
for abort in [False, True]:
ctx = multiprocessing.get_context(mp_start_method)
proc = ps.ProcessAdapter(
10,
10,
step,
chain=3,
seed=1,
mp_ctx=ctx,
start={
"a": floatX(np.array([1.0])),
"b_log__": floatX(np.array(2.0))
},
step_method_pickled=step_method_pickled,
)
proc.start()
while True:
proc.write_next()
out = ps.ProcessAdapter.recv_draw([proc])
if out[1]:
break
if abort:
proc.abort()
proc.join()
muB = pm.Normal('muB', 0, .100)
tauB = pm.Gamma('tauB', .01, .01)
udfB = pm.Uniform('udfB', 0, 1)
tdfB = 1 + tdfBgain * (-pm.log(1 - udfB))
# define the priors
tau = pm.Gamma('tau', 0.01, 0.01)
beta0 = pm.Normal('beta0', mu=0, tau=1.0E-12)
beta1 = pm.T('beta1', mu=muB, lam=tauB, nu=tdfB, shape=n_predictors)
mu = beta0 + pm.dot(beta1, x.values.T)
# define the likelihood
#mu = beta0 + beta1[0] * x.values[:,0] + beta1[1] * x.values[:,1]
yl = pm.Normal('yl', mu=mu, tau=tau, observed=y)
# Generate a MCMC chain
start = pm.find_MAP()
step1 = pm.NUTS([beta1])
step2 = pm.Metropolis([beta0, tau, muB, tauB, udfB])
trace = pm.sample(10000, [step1, step2], start, progressbar=False)
# EXAMINE THE RESULTS
burnin = 2000
thin = 1
# Print summary for each trace
#pm.summary(trace[burnin::thin])
#pm.summary(trace)
# Check for mixing and autocorrelation
#pm.autocorrplot(trace[burnin::thin], vars =[mu, tau])
#pm.autocorrplot(trace, vars =[beta0])
## Plot KDE and sampled values for each parameter.
y_sd = np.std(y)
zx = (x - x_m) / x_sd
zy = (y - y_m) / y_sd
# THE MODEL
with pm.Model() as model:
# define the priors
beta0 = pm.Normal('beta0', mu=0, tau=1.0E-12)
beta1 = pm.Normal('beta1', mu=0, tau=1.0E-12)
tau = pm.Gamma('tau', 0.001, 0.001)
# define the likelihood
mu = beta0 + beta1 * zx
yl = pm.Normal('yl', mu=mu, tau=tau, observed=zy)
# Generate a MCMC chain
start = pm.find_MAP()
step = [pm.Metropolis([rv]) for rv in model.unobserved_RVs]
trace = pm.sample(10000, step, start, progressbar=False)
# EXAMINE THE RESULTS
burnin = 5000
thin = 10
## Print summary for each trace
#pm.summary(trace[burnin::thin])
#pm.summary(trace)
## Check for mixing and autocorrelation
#pm.autocorrplot(trace[burnin::thin], vars =[tau])
#pm.autocorrplot(trace, vars =[tau])
## Plot KDE and sampled values for each parameter.
# THE MODEL.
with pm.Model() as model:
# Hyperprior on model index:
model_index = pm.DiscreteUniform('model_index', lower=0, upper=1)
# Prior
nu = pm.Normal('nu', mu=0, tau=0.1) # it is posible to use tau or sd
eta = pm.Gamma('eta', .1, .1)
theta0 = 1 / (1 + pm.exp(-nu)) # theta from model index 0
theta1 = pm.exp(-eta) # theta from model index 1
theta = pm.switch(pm.eq(model_index, 0), theta0, theta1)
# Likelihood
y = pm.Bernoulli('y', p=theta, observed=y)
# Sampling
start = pm.find_MAP()
steps = [pm.Metropolis([i]) for i in model.unobserved_RVs[1:]]
steps.append(pm.ElemwiseCategoricalStep(var=model_index, values=[0, 1]))
trace = pm.sample(10000, steps, start=start, progressbar=False)
# EXAMINE THE RESULTS.
burnin = 1000
thin = 5
## Print summary for each trace
#pm.summary(trace[burnin::thin])
#pm.summary(trace)
## Check for mixing and autocorrelation
#pm.autocorrplot(trace[burnin::thin], vars =[nu, eta])
#pm.autocorrplot(trace, vars =[nu, eta])
# THE MODEL
with pm.Model() as model:
# define the priors
beta0 = pm.Normal('beta0', mu=0, tau=1.0E-12)
beta1 = pm.Normal('beta1', mu= 0, tau=1.0E-12, shape=n_predictors)
tau = pm.Gamma('tau', 0.01, 0.01)
mu = beta0 + pm.dot(beta1, x.values.T)
# define the likelihood
yl = pm.Normal('yl', mu=mu, tau=tau, observed=y)
# Generate a MCMC chain
start = pm.find_MAP()
step1 = pm.NUTS([beta1])
step2 = pm.Metropolis([beta0, tau])
trace = pm.sample(10000, [step1, step2], start, progressbar=False)
# EXAMINE THE RESULTS
burnin = 5000
thin = 1
# Print summary for each trace
#pm.summary(trace[burnin::thin])
#pm.summary(trace)
# Check for mixing and autocorrelation
#pm.autocorrplot(trace[burnin::thin], vars =[mu, tau])
#pm.autocorrplot(trace, vars =[beta0])
## Plot KDE and sampled values for each parameter.
# THE MODEL.
with pm.Model() as model:
# Hyperprior on model index:
model_index = pm.DiscreteUniform('model_index', lower=0, upper=1)
# Prior
nu = pm.Normal('nu', mu=0, tau=0.1) # it is posible to use tau or sd
eta = pm.Gamma('eta', .1, .1)
theta0 = 1 / (1 + pm.exp(-nu)) # theta from model index 0
theta1 = pm.exp(-eta) # theta from model index 1
theta = pm.switch(pm.eq(model_index, 0), theta0, theta1)
# Likelihood
y = pm.Bernoulli('y', p=theta, observed=y)
# Sampling
start = pm.find_MAP()
step1 = pm.Metropolis(model.vars[1:])
step2 = pm.ElemwiseCategoricalStep(var=model_index, values=[0, 1])
trace = pm.sample(10000, [step1, step2], start=start, progressbar=False)
# EXAMINE THE RESULTS.
burnin = 1000
thin = 5
## Print summary for each trace
#pm.summary(trace[burnin::thin])
#pm.summary(trace)
## Check for mixing and autocorrelation
#pm.autocorrplot(trace[burnin::thin], vars =[nu, eta])
#pm.autocorrplot(trace, vars =[nu, eta])
y = np.repeat([0, 1], [3, 6]) # 3 tails 6 heads
with pm.Model() as model:
# Hyperhyperprior:
model_index = pm.DiscreteUniform('model_index', lower=0, upper=1)
# Hyperprior:
kappa_theta = 12
mu_theta = pm.switch(pm.eq(model_index, 1), 0.25, 0.75)
# Prior distribution:
a_theta = mu_theta * kappa_theta
b_theta = (1 - mu_theta) * kappa_theta
theta = pm.Beta('theta', a_theta, b_theta) # theta distributed as beta density
#likelihood
y = pm.Bernoulli('y', theta, observed=y)
start = pm.find_MAP()
step1 = pm.Metropolis([model_index])
step2 = pm.Metropolis([theta])
trace = pm.sample(10000, [step1, step2], start=start, progressbar=False)
## Check the results.
burnin = 2000 # posterior samples to discard
thin = 1 # posterior samples to discard
## Print summary for each trace
#pm.summary(trace[burnin::thin])
#pm.summary(trace)
## Check for mixing and autocorrelation
#pm.autocorrplot(trace)
import numpy as np
import pymc as pm
from plot_post import *
# Generate the data
y = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
0]) # 11 heads and 3 tails
with pm.Model() as model:
# define the prior
theta = pm.Beta('theta', 1, 1) # prior
# define the likelihood
y = pm.Bernoulli('y', p=theta, observed=y)
# Generate a MCMC chain
trace = pm.sample(5000, pm.Metropolis(),
progressbar=False) # Use Metropolis sampling
# start = pm.find_MAP() # Find starting value by optimization
# step = pm.NUTS() # Instantiate NUTS sampler
# trace = pm.sample(5000, step, start=start, progressbar=False)
# create an array with the posterior sample
theta_sample = trace['theta']
print theta_sample
plt.subplot(1, 2, 1)
plt.plot(theta_sample[:500], np.arange(500), marker='o')
plt.xlim(0, 1)
plt.xlabel(r'$\theta$')
plt.ylabel('Position in Chain')
d = pm.Gamma('d', 1, 1)
sG = m**2 / d**2
rG = m / d**2
# define the priors
sigma = pm.Uniform('sigma', 0,
10) # y values are assumed to be standardized
tau = pm.Gamma('tau', sG, rG)
a0 = pm.Normal('a0', mu=0,
tau=0.001) # y values are assumed to be standardized
a = pm.Normal('a', mu=0, tau=atau, shape=NxLvl)
mu = a0 + a
# define the likelihood
yl = pm.Normal('yl', mu[x], tau=tau, observed=z)
# Generate a MCMC chain
start = pm.find_MAP()
steps = pm.Metropolis()
trace = pm.sample(20000, steps, start, progressbar=False)
# EXAMINE THE RESULTS
burnin = 2000
thin = 50
# Print summary for each trace
#pm.summary(trace[burnin::thin])
#pm.summary(trace)
# Check for mixing and autocorrelation
pm.autocorrplot(trace[burnin::thin], vars=model.unobserved_RVs[:-1])
## Plot KDE and sampled values for each parameter.
#pm.traceplot(trace[burnin::thin])
55, 40, 46, 56, 47, 54, 54, 42, 34, 35, 41, 48, 46, 39, 55, 30, 49, 27, 51,
41, 36, 45, 41, 53, 32, 43, 33
])
condition = np.repeat([0, 1, 2, 3], nSubj)
# Specify the model in PyMC
with pm.Model() as model:
kappa = pm.Gamma('kappa', 1, 0.1, shape=ncond)
mu = pm.Beta('mu', 1, 1, shape=ncond)
theta = pm.Beta('theta',
mu[condition] * kappa[condition],
(1 - mu[condition]) * kappa[condition],
shape=len(z))
y = pm.Binomial('y', p=theta, n=N, observed=z)
start = pm.find_MAP()
step1 = pm.Metropolis([mu])
step2 = pm.Metropolis([theta])
step3 = pm.NUTS([kappa])
# samplers = [pm.Metropolis([rv]) for rv in model.unobserved_RVs]
trace = pm.sample(10000, [step1, step2, step3],
start=start,
progressbar=False)
## Check the results.
burnin = 5000 # posterior samples to discard
thin = 10 # posterior samples to discard
## Print summary for each trace
#pm.summary(trace[burnin::thin])
#pm.summary(trace)
y_sd = np.std(y)
zx = (x - x_m) / x_sd
zy = (y - y_m) / y_sd
# THE MODEL
with pm.Model() as model:
# define the priors
tau = pm.Gamma('tau', 0.001, 0.001)
beta0 = pm.Normal('beta0', mu=0, tau=1.0E-12)
beta1 = pm.Normal('beta1', mu=0, tau=1.0E-12)
mu = beta0 + beta1 * zx
# define the likelihood
yl = pm.Normal('yl', mu=mu, tau=tau, observed=zy)
# Generate a MCMC chain
start = pm.find_MAP()
step = pm.Metropolis()
trace = pm.sample(10000, step, start, progressbar=False)
# EXAMINE THE RESULTS
burnin = 5000
thin = 10
## Print summary for each trace
#pm.summary(trace[burnin::thin])
#pm.summary(trace)
## Check for mixing and autocorrelation
#pm.autocorrplot(trace[burnin::thin], vars =[tau])
#pm.autocorrplot(trace, vars =[tau])
## Plot KDE and sampled values for each parameter.
def make_step(cls):
args = {}
if hasattr(cls, "step_args"):
args.update(cls.step_args)
return pm.Metropolis(**args)
