Update conversion of observations and disable log likelihood

pymc3/backends/arviz.py

@@ -118,7 +118,6 @@ def __init__(
         dims: Optional[DimSpec] = None,
         model=None,
         save_warmup: Optional[bool] = None,
-        density_dist_obs: bool = True,
         index_origin: Optional[int] = None,
     ):
 
@@ -190,28 +189,18 @@ def arbitrary_element(dct: Dict[Any, np.ndarray]) -> np.ndarray:
             model_dims = {k: list(v) for k, v in self.model.RV_dims.items()}
             self.dims = {**model_dims, **self.dims}
 
-        self.density_dist_obs = density_dist_obs
-        self.observations, self.multi_observations = self.find_observations()
+        self.observations = self.find_observations()
 
-    def find_observations(self) -> Tuple[Optional[Dict[str, Var]], Optional[Dict[str, Var]]]:
+    def find_observations(self) -> Optional[Dict[str, Var]]:
         """If there are observations available, return them as a dictionary."""
         if self.model is None:
-            return (None, None)
+            return None
         observations = {}
-        multi_observations = {}
         for obs in self.model.observed_RVs:
-            if hasattr(obs, "observations"):
-                aux_obs = obs.observations
-                observations[obs.name] = (
-                    aux_obs.get_value() if hasattr(aux_obs, "get_value") else aux_obs
-                )
-            elif hasattr(obs, "data") and self.density_dist_obs:
-                for key, val in obs.data.items():
-                    aux_obs = val.eval() if hasattr(val, "eval") else val
-                    multi_observations[key] = (
-                        aux_obs.get_value() if hasattr(aux_obs, "get_value") else aux_obs
-                    )
-        return observations, multi_observations
+            if hasattr(obs.tag, "observations"):
+                aux_obs = obs.tag.observations
+                observations[obs.name] = aux_obs.data if hasattr(aux_obs, "data") else aux_obs
+        return observations
 
     def split_trace(self) -> Tuple[Union[None, "MultiTrace"], Union[None, "MultiTrace"]]:
         """Split MultiTrace object into posterior and warmup.
@@ -233,41 +222,6 @@ def split_trace(self) -> Tuple[Union[None, "MultiTrace"], Union[None, "MultiTrac
             trace_posterior = self.trace[self.ntune :]
         return trace_posterior, trace_warmup
 
-    def log_likelihood_vals_point(self, point, var, log_like_fun):
-        """Compute log likelihood for each observed point."""
-        log_like_val = utils.one_de(log_like_fun(point))
-        if var.missing_values:
-            mask = var.observations.mask
-            if np.ndim(mask) > np.ndim(log_like_val):
-                mask = np.any(mask, axis=-1)
-            log_like_val = np.where(mask, np.nan, log_like_val)
-        return log_like_val
-
-    def _extract_log_likelihood(self, trace):
-        """Compute log likelihood of each observation."""
-        if self.trace is None:
-            return None
-        if self.model is None:
-            return None
-
-        if self.log_likelihood is True:
-            cached = [(var, var.logp_elemwise) for var in self.model.observed_RVs]
-        else:
-            cached = [
-                (var, var.logp_elemwise)
-                for var in self.model.observed_RVs
-                if var.name in self.log_likelihood
-            ]
-        log_likelihood_dict = _DefaultTrace(len(trace.chains))
-        for var, log_like_fun in cached:
-            for k, chain in enumerate(trace.chains):
-                log_like_chain = [
-                    self.log_likelihood_vals_point(point, var, log_like_fun)
-                    for point in trace.points([chain])
-                ]
-                log_likelihood_dict.insert(var.name, np.stack(log_like_chain), k)
-        return log_likelihood_dict.trace_dict
-
     @requires("trace")
     def posterior_to_xarray(self):
         """Convert the posterior to an xarray dataset."""
@@ -348,6 +302,8 @@ def sample_stats_to_xarray(self):
     @requires("model")
     def log_likelihood_to_xarray(self):
         """Extract log likelihood and log_p data from PyMC3 trace."""
+        # TODO: add pointwise log likelihood extraction to the converter
+        return None
         if self.predictions or not self.log_likelihood:
             return None
         data_warmup = {}
@@ -540,7 +496,6 @@ def to_inference_data(
     dims: Optional[DimSpec] = None,
     model: Optional["Model"] = None,
     save_warmup: Optional[bool] = None,
-    density_dist_obs: bool = True,
 ) -> InferenceData:
     """Convert pymc3 data into an InferenceData object.
 
@@ -590,7 +545,6 @@ def to_inference_data(
         dims=dims,
         model=model,
         save_warmup=save_warmup,
-        density_dist_obs=density_dist_obs,
     ).to_inference_data()