def test_arviz_to_json():
import arviz as az
data = az.load_arviz_data("centered_eight")
arviz_to_json(data, "centered_eight.npz")
check_zip("centered_eight.npz")
# check that we can write to a file descriptor as well as a filename
with open("centered_eight_as_f.npz", "wb") as f:
arviz_to_json(data, f)
check_zip("centered_eight_as_f.npz")
samples=2000
chains=2
tune=1000
geometry_model=pm.Model(coords=coords)
with geometry_model:
#to store the n-parameter of Binomial dist
#in the constant group of ArviZ InferenceData
#You should always call it n for imd to retrieve it
n = pm.Data('n', data.tries)
sigma_angle = pm.HalfNormal('sigma_angle')
p_goes_in = pm.Deterministic('p_goes_in', 2 * Phi(tt.arcsin((CUP_RADIUS - BALL_RADIUS) / data.distance) / sigma_angle) - 1, dims='distance')
successes = pm.Binomial('successes', n=n, p=p_goes_in, observed=data.successes, dims='distance')
#inference
trace_g = pm.sample(draws=samples, chains=chains, tune=tune)
prior_g= pm.sample_prior_predictive(samples=samples)
posterior_predictive_g = pm.sample_posterior_predictive(trace_g,samples=samples)
## STEP 1
# will also capture all the sampler statistics
data_g = az.from_pymc3(trace=trace_g, prior=prior_g, posterior_predictive=posterior_predictive_g)
## STEP 2
#dag
dag_g = get_dag(geometry_model)
# insert dag into sampler stat attributes
data_g.sample_stats.attrs["graph"] = str(dag_g)
## STEP 3
# save data
arviz_to_json(data_g, fileName+'.npz')
#model-inference
coords_c = {"school": ["A","B","C","D","E","F","G","H"]}
fileName_c="eight_schools_centered"
samples=4000
chains=2
tune=1000
with pm.Model(coords=coords_c) as centered_eight:
mu = pm.Normal('mu', mu=0, sigma=5)
tau = pm.HalfCauchy('tau', beta=5)
theta = pm.Normal('theta', mu=mu, sigma=tau, dims='school')
y = pm.Normal('y', mu=theta, sigma=sigma, observed=obs, dims='school')
#inference
trace_c = pm.sample(samples, chains=chains, tune=tune, random_seed=SEED)
prior_c= pm.sample_prior_predictive(samples=samples)
posterior_predictive_c = pm.sample_posterior_predictive(trace_c, samples=samples)
## STEP 1
# will also capture all the sampler statistics
data_c = az.from_pymc3(trace = trace_c, prior = prior_c, posterior_predictive = posterior_predictive_c)
## STEP 2
#dag
dag_c = get_dag(centered_eight)
# insert dag into sampler stat attributes
data_c.sample_stats.attrs["graph"] = str(dag_c)
## STEP 3
# save data
arviz_to_json(data_c, fileName_c+'.npz')
b = pm.Normal("b", mu=mu_b, sd=sigma_b, dims="driver")
sigma = pm.HalfNormal("sigma", sd=sigma_sigma, dims="driver")
y_pred = pm.Normal('y_pred',
mu=a[driver_idx] + b[driver_idx] * day_idx,
sd=sigma[driver_idx],
observed=reactions.Reaction,
dims="driver_idx_day")
## inference
trace_hi = pm.sample(draws=samples, chains=chains, tune=tune)
prior_hi = pm.sample_prior_predictive(samples=samples)
posterior_predictive_hi = pm.sample_posterior_predictive(trace_hi,
samples=samples)
## STEP 1
## export inference results in ArviZ InferenceData obj
## will also capture all the sampler statistics
data_hi = az.from_pymc3(trace=trace_hi,
prior=prior_hi,
posterior_predictive=posterior_predictive_hi)
## STEP 2
## extract dag
dag_hi = get_dag(hierarchical_model)
## insert dag into sampler stat attributes
data_hi.sample_stats.attrs["graph"] = str(dag_hi)
## STEP 3
## save data
fileName_hi = "reaction_times_hierarchical"
arviz_to_json(data_hi, fileName_hi + '.npz')
trace = pm.sample(samples=samples, chains=chains)
prior = pm.sample_prior_predictive(samples=samples)
posterior_predictive = pm.sample_posterior_predictive(trace)
dag = get_dag(model)
# will also capture all the sampler statistics
data = az.from_pymc3(trace=trace,
prior=prior,
posterior_predictive=posterior_predictive)
# insert dag into sampler stat attributes
data.sample_stats.attrs["graph"] = dag
return data
if __name__ == "__main__":
# generate a single switchpoint model
poverty = load_data()
model = define_model(poverty)
dag = get_dag(model)
data = capture_inference(model)
arviz_to_json(data, "switchpoint.npz")
# generate multiple models
models = {
"centered": az.load_arviz_data("centered_eight"),
"noncentered": az.load_arviz_data("non_centered_eight"),
}
multi_arviz_to_json(models, "multimodel.zip")
b = pm.Normal("b", mu=10, sd=250)
sigma = pm.HalfNormal("sigma", sd=200)
y_pred = pm.Normal('y_pred',
mu=a + b * day_idx,
sd=sigma,
observed=reactions.Reaction,
dims="driver_idx_day")
## inference
trace_p = pm.sample(samples, chains=chains, tune=tune)
prior_p = pm.sample_prior_predictive(samples=samples)
posterior_predictive_p = pm.sample_posterior_predictive(trace_p,
samples=samples)
## STEP 1
## export inference results in ArviZ InferenceData obj
## will also capture all the sampler statistics
data_p = az.from_pymc3(trace=trace_p,
prior=prior_p,
posterior_predictive=posterior_predictive_p)
## STEP 2
## extract dag
dag_p = get_dag(fullyPooled_model)
## insert dag into sampler stat attributes
data_p.sample_stats.attrs["graph"] = str(dag_p)
## STEP 3
## save data
fileName_p = "reaction_times_pooled"
arviz_to_json(data_p, fileName_p + '.npz')
