from_pymc3 fails in certain circumstances

Describe the bug A clear and concise description of what the bug is.

To Reproduce

import arviz as az
import pymc3 as pm
print(az.__version__)
print(pm.__version__)

with pm.Model() as planetmodel:
    p_water = pm.Uniform("p", 0 ,1)
    w = pm.Binomial("w", p=p_water, n=2, observed=1)
    trace = pm.sample(5000, chains=2)

water_data = az.from_pymc3(trace=trace)

0.4.0 3.7 Auto-assigning NUTS sampler… Initializing NUTS using jitter+adapt_diag… Multiprocess sampling (2 chains in 2 jobs) NUTS: [p] Sampling 2 chains: 100%|███████████████████████████████████████████████████████████████| 11000/11000 [00:03<00:00, 2882.36draws/s] /Users/canyon/.miniconda3/envs/bayestutorial/lib/python3.6/site-packages/arviz/data/io_pymc3.py:56: FutureWarning: arrays to stack must be passed as a “sequence” type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future. chain_likelihoods.append(np.stack(log_like)) Traceback (most recent call last): File “trace_error.py”, line 11, in <module> water_data = az.from_pymc3(trace=trace) File “/Users/canyon/.miniconda3/envs/bayestutorial/lib/python3.6/site-packages/arviz/data/io_pymc3.py”, line 152, in from_pymc3 dims=dims, File “/Users/canyon/.miniconda3/envs/bayestutorial/lib/python3.6/site-packages/arviz/data/io_pymc3.py”, line 137, in to_inference_data “sample_stats”: self.sample_stats_to_xarray(), File “/Users/canyon/.miniconda3/envs/bayestutorial/lib/python3.6/site-packages/arviz/data/base.py”, line 25, in wrapped return func(cls, *args, **kwargs) File “/Users/canyon/.miniconda3/envs/bayestutorial/lib/python3.6/site-packages/arviz/data/io_pymc3.py”, line 78, in sample_stats_to_xarray log_likelihood, dims = self._extract_log_likelihood() File “/Users/canyon/.miniconda3/envs/bayestutorial/lib/python3.6/site-packages/arviz/data/base.py”, line 25, in wrapped return func(cls, *args, **kwargs) File “/Users/canyon/.miniconda3/envs/bayestutorial/lib/python3.6/site-packages/arviz/data/io_pymc3.py”, line 56, in _extract_log_likelihood chain_likelihoods.append(np.stack(log_like)) File “/Users/canyon/.miniconda3/envs/bayestutorial/lib/python3.6/site-packages/numpy/core/shape_base.py”, line 410, in stack arrays = [asanyarray(arr) for arr in arrays] File “/Users/canyon/.miniconda3/envs/bayestutorial/lib/python3.6/site-packages/numpy/core/shape_base.py”, line 410, in <listcomp> arrays = [asanyarray(arr) for arr in arrays] File “/Users/canyon/.miniconda3/envs/bayestutorial/lib/python3.6/site-packages/arviz/data/io_pymc3.py”, line 55, in <genexpr> log_like = (log_likelihood_vals_point(point) for point in self.trace.points([chain])) File “/Users/canyon/.miniconda3/envs/bayestutorial/lib/python3.6/site-packages/arviz/data/io_pymc3.py”, line 51, in log_likelihood_vals_point return np.concatenate(log_like_vals) ValueError: zero-dimensional arrays cannot be concatenated

Expected behavior An Inferencedata object should be created Additional context

[canyon289]

Tutorial has pinned version? No matter, I’ll just fix and move on with life. Thanks @ahartikainen

[ahartikainen]

In this func

    def log_likelihood_vals_point(point):
            """Compute log likelihood for each observed point."""
            log_like_vals = []
            for var, log_like in cached:
                log_like_val = log_like(point)
                if var.missing_values:
                    log_like_val = log_like_val[~var.observations.mask]
                log_like_vals.append(log_like_val)
            return np.concatenate(log_like_vals)

log_like(point) returns a 0-dim array (np.array(num)), which can’t be concatenated.

One option is to wrap that to np.atleast_1d(log_like(point))