Summary

The following are existing issues & suggestions in the pymc3-examples repo after going through an iteration of renaming plot dependencies from pm. to arviz.

Note: This is similar to the pre-existing #34 issue.

Installs

• Pymc3: v3.11
• theano-pymc (aesara): v 1.1.2

Issues

General Issues

• Have param returninference =True

File-specific Issues

• examples/pymc3_howto/lasso_block_update.ipynb

"return_inferencedata should be manually set, either to True or to False, but one of the two to avoid the warning.

If False, an inferencedata should be created within the model context, and passed to arviz. This will

1. avoid the warning of conversion without the model context and
2. push forward arviz best practices, it is probably not too relevant here but conversion may not be cheap for some models because it requires computing all the pointwise log likelihood values. az.plot_xyz(trace) works because ArviZ internally converts the data to inferencedata, then plots."

• examples/pymc3_howto/data_container.ipynb

• examples/pymc3_howto/sampling_conjugate_step.ipynb

traces = []
models = []
names = ["Partial conjugate sampling", "Full NUTS"]

for use_conjugate in [True, False]:
    with pm.Model() as model:
        tau = pm.Exponential("tau", lam=1, testval=1.0)
        alpha = pm.Deterministic("alpha", tau * np.ones([N, J]))
        p = pm.Dirichlet("p", a=alpha)

        if use_conjugate:
            # If we use the conjugate sampling, we don't need to define the likelihood
            # as it's already taken into account in our custom step method
            step = [ConjugateStep(p.transformed, counts, tau)]

        else:
            x = pm.Multinomial("x", n=ncounts, p=p, observed=counts)
            step = []

        trace = pm.sample(step=step, chains=2, cores=1, return_inferencedata=True)
        traces.append(trace)

    assert all(az.summary(trace)["r_hat"] < 1.1)
    models.append(model)

"Since we are not storing the summary dataframe anywhere and we only want the rhat, we should use rhat instead. The assertion can be done with:

assert (az.rhat(trace).to_array() < 1.1).all()"

• examples/ode_models/ODE_with_manual_gradients.ipynb

Similar to PR 43, for line 33 at variable trace =

    Y_obs = pm.Lognormal("Y_obs", mu=pm.math.log(forward), sigma=sigma, observed=Y)

    trace = pm.sample(1500, init="jitter+adapt_diag", cores=1)
trace["diverging"].sum()

I changed init from adapt_diag to jitter+adapt_diag & added param cores=1.

The error:

SamplingError: Bad initial energy

Seen here

• examples/generalized_linear_models/GLM-model-selection.ipynb

Issue 1

ax = (
    dfll["log_likelihood"]
    .unstack()
    .plot.bar(subplots=True, layout=(1, 2), figsize=(12, 6), sharex=True)
)

ax[0, 0].set_xticks(range(5))
ax[0, 0].set_xticklabels(["k1", "k2", "k3", "k4", "k5"])
ax[0, 0].set_xlim(-0.25, 4.25);

One dependency errors out, this making remainder of notebook not run. Errors on missing sd_log__, and therefore cannot run entire notebook due to dependency.

Particularly

GLM-model-selection KeyError: 'var names: "['sd_log__'] are not present" in dataset'

More details here

Issue 2

dfll = pd.DataFrame(index=["k1", "k2", "k3", "k4", "k5"], columns=["lin", "quad"])
dfll.index.name = "model"

for nm in dfll.index:
    dfll.loc[nm, "lin"] = -models_lin[nm].logp(
        az.summary(traces_lin[nm], traces_lin[nm].varnames)["mean"].to_dict()
    )

    dfll.loc[nm, "quad"] = -models_quad[nm].logp(
        az.summary(traces_quad[nm], traces_quad[nm].varnames)["mean"].to_dict()
    )

dfll = pd.melt(dfll.reset_index(), id_vars=["model"], var_name="poly", value_name="log_likelihood")
dfll.index = pd.MultiIndex.from_frame(dfll[["model", "poly"]])