Add default log_lik handling for pystan and cmdstan

CHANGELOG.md

@@ -6,7 +6,7 @@
 * Added `arviz.labels` module with classes and utilities ([1201](https://github.com/arviz-devs/arviz/pull/1201))
 * Added probability estimate within ROPE in `plot_posterior` ([1570](https://github.com/arviz-devs/arviz/pull/1570))
 * Added `rope_color` and `ref_val_color` arguments to `plot_posterior` ([1570](https://github.com/arviz-devs/arviz/pull/1570))
-* Improved retrieving or pointwise log likelihood in `from_cmdstanpy` ([1579](https://github.com/arviz-devs/arviz/pull/1579) and [1598](https://github.com/arviz-devs/arviz/pull/1598))
+* Improved retrieving or pointwise log likelihood in `from_cmdstanpy`, `from_cmdstan` and `from_pystan` ([1579](https://github.com/arviz-devs/arviz/pull/1579) and [1599](https://github.com/arviz-devs/arviz/pull/1599))
 * Added interactive legend to bokeh `forestplot` ([1591](https://github.com/arviz-devs/arviz/pull/1591))
 
 ### Maintenance and fixes

arviz/data/io_cmdstan.py

@@ -90,6 +90,13 @@ def __init__(
         self._parse_posterior()
         self._parse_prior()
 
+        if (
+            self.log_likelihood is None
+            and self.posterior_ is not None
+            and any(name.split(".")[0] == "log_lik" for name in self.posterior_columns)
+        ):
+            self.log_likelihood = ["log_lik"]
+
     @requires("posterior_")
     def _parse_posterior(self):
         """Read csv paths to list of ndarrays."""
@@ -871,7 +878,7 @@ def _unpack_ndarrays(arrays, columns, dtypes=None):
         for key, cols_locs in col_groups.items():
             ndim = np.array([loc for _, loc in cols_locs]).max(0) + 1
             dtype = dtypes.get(key, np.float64)
-            sample[key] = utils.full((chains, draws, *ndim), 0, dtype=dtype)
+            sample[key] = np.zeros((chains, draws, *ndim), dtype=dtype)
             for col, loc in cols_locs:
                 for chain_id, arr in enumerate(arrays):
                     draw = arr[:, col]

arviz/data/io_pystan.py

@@ -54,6 +54,13 @@ def __init__(
         self.dims = dims
         self.save_warmup = rcParams["data.save_warmup"] if save_warmup is None else save_warmup
 
+        if (
+            self.log_likelihood is None
+            and self.posterior is not None
+            and "log_lik" in self.posterior.sim["pars_oi"]
+        ):
+            self.log_likelihood = ["log_lik"]
+
         import pystan  # pylint: disable=import-error
 
         self.pystan = pystan
@@ -313,6 +320,13 @@ def __init__(
         self.coords = coords
         self.dims = dims
 
+        if (
+            self.log_likelihood is None
+            and self.posterior is not None
+            and "log_lik" in self.posterior.param_names
+        ):
+            self.log_likelihood = ["log_lik"]
+
         import stan  # pylint: disable=import-error
 
         self.stan = stan

arviz/tests/external_tests/test_data_cmdstan.py

@@ -166,7 +166,6 @@ def test_inference_data_input_types2(self, paths, observed_data_paths):
                 constant_data_var=["y"],
                 predictions_constant_data=observed_data_paths[0],
                 predictions_constant_data_var=["y"],
-                log_likelihood="log_lik",
                 coords={"school": np.arange(8)},
                 dims={
                     "theta": ["school"],
@@ -277,7 +276,7 @@ def test_inference_data_input_types5(self, paths, observed_data_paths):
                 prior_predictive=None,
                 observed_data=observed_data_paths[0],
                 observed_data_var=["y"],
-                log_likelihood=["log_lik"],
+                log_likelihood=["y_hat"],
                 coords={"school": np.arange(8), "log_lik_dim": np.arange(8)},
                 dims={
                     "theta": ["school"],
@@ -290,7 +289,7 @@ def test_inference_data_input_types5(self, paths, observed_data_paths):
             test_dict = {
                 "posterior": ["mu", "tau", "theta_tilde", "theta"],
                 "prior": ["mu", "tau", "theta_tilde", "theta"],
-                "log_likelihood": ["log_lik"],
+                "log_likelihood": ["y_hat"],
                 "observed_data": ["y"],
                 "sample_stats_prior": ["lp"],
             }

arviz/tests/external_tests/test_data_pystan.py

@@ -118,6 +118,7 @@ def get_inference_data4(self, data):
             coords=None,
             dims=None,
             posterior_model=data.model,
+            log_likelihood=[],
             prior_model=data.model,
             save_warmup=pystan_version() == 2,
         )
@@ -173,6 +174,7 @@ def test_inference_data(self, data, eight_schools_params):
             "predictions_constant_data": ["sigma", "y"],
             "sample_stats_prior": ["diverging"],
             "sample_stats": ["diverging", "lp"],
+            "log_likelihood": ["log_lik"],
             "prior_predictive": ["y_hat", "log_lik"],
         }
         fails = check_multiple_attrs(test_dict, inference_data3)
@@ -261,10 +263,10 @@ def test_index_order(self, data, eight_schools_params):
         idata = from_pystan(posterior=fit)
         assert idata is not None
         for j, fpar in enumerate(fit.sim["fnames_oi"]):
-            if fpar == "lp__":
-                continue
             par, *shape = fpar.replace("]", "").split("[")
-            assert hasattr(idata.posterior, par)
+            if par in {"lp__", "log_lik"}:
+                continue
+            assert hasattr(idata.posterior, par), (par, list(idata.posterior.data_vars))
             if shape:
                 shape = [slice(None), slice(None)] + list(map(int, shape))
                 assert idata.posterior[par][tuple(shape)].values.mean() == float(j)

arviz/utils.py

@@ -427,12 +427,6 @@ def _cov(data):
         raise ValueError("{} dimension arrays are not supported".format(data.ndim))
 
 
-@conditional_jit(nopython=True)
-def full(shape, x, dtype=None):
-    """Jitting numpy full."""
-    return np.full(shape, x, dtype=dtype)
-
-
 def flatten_inference_data_to_dict(
     data,
     var_names=None,