Remove workaround in InferenceData conversion; require ArviZ >=0.11.4

conda-envs/environment-dev-py37.yml

@@ -4,7 +4,7 @@ channels:
 - defaults
 dependencies:
 - aesara>=2.1.0
-- arviz>=0.11.2
+- arviz>=0.11.4
 - cachetools>=4.2.1
 - cloudpickle
 - fastprogress>=0.2.0

conda-envs/environment-dev-py38.yml

@@ -4,7 +4,7 @@ channels:
 - defaults
 dependencies:
 - aesara>=2.1.0
-- arviz>=0.11.2
+- arviz>=0.11.4
 - cachetools>=4.2.1
 - cloudpickle
 - fastprogress>=0.2.0

conda-envs/environment-dev-py39.yml

@@ -4,7 +4,7 @@ channels:
 - defaults
 dependencies:
 - aesara>=2.1.0
-- arviz>=0.11.2
+- arviz>=0.11.4
 - cachetools>=4.2.1
 - cloudpickle
 - fastprogress>=0.2.0

conda-envs/windows-environment-dev-py38.yml

@@ -5,7 +5,7 @@ channels:
 dependencies:
  # base dependencies (see install guide for Windows)
 - aesara>=2.1.0
-- arviz>=0.11.2
+- arviz>=0.11.4
 - cachetools>=4.2.1
 - cloudpickle
 - fastprogress>=0.2.0

pymc/backends/arviz.py

@@ -7,8 +7,6 @@
     Any,
     Dict,
     Iterable,
-    List,
-    Mapping,
     Optional,
     Tuple,
     Union,
@@ -21,9 +19,7 @@
 from aesara.tensor.sharedvar import SharedVariable
 from aesara.tensor.subtensor import AdvancedIncSubtensor, AdvancedIncSubtensor1
 from arviz import InferenceData, concat, rcParams
-from arviz.data.base import CoordSpec, DimSpec
-from arviz.data.base import dict_to_dataset as _dict_to_dataset
-from arviz.data.base import generate_dims_coords, make_attrs, requires
+from arviz.data.base import CoordSpec, DimSpec, dict_to_dataset, requires
 
 import pymc
 
@@ -101,42 +97,6 @@ def insert(self, k: str, v, idx: int):
             self.trace_dict[k][idx, :] = v
 
 
-def dict_to_dataset(
-    data,
-    library=None,
-    coords=None,
-    dims=None,
-    attrs=None,
-    default_dims=None,
-    skip_event_dims=None,
-    index_origin=None,
-):
-    """Temporal workaround for dict_to_dataset.
-
-    Once ArviZ>0.11.2 release is available, only two changes are needed for everything to work.
-    1) this should be deleted, 2) dict_to_dataset should be imported as is from arviz, no underscore,
-    also remove unnecessary imports
-    """
-    if default_dims is None:
-        return _dict_to_dataset(
-            data,
-            attrs=attrs,
-            library=library,
-            coords=coords,
-            dims=dims,
-            skip_event_dims=skip_event_dims,
-        )
-    else:
-        out_data = {}
-        for name, vals in data.items():
-            vals = np.atleast_1d(vals)
-            val_dims = dims.get(name)
-            val_dims, crds = generate_dims_coords(vals.shape, name, dims=val_dims, coords=coords)
-            crds = {key: xr.IndexVariable((key,), data=crds[key]) for key in val_dims}
-            out_data[name] = xr.DataArray(vals, dims=val_dims, coords=crds)
-        return xr.Dataset(data_vars=out_data, attrs=make_attrs(attrs=attrs, library=library))
-
-
 class InferenceDataConverter:  # pylint: disable=too-many-instance-attributes
     """Encapsulate InferenceData specific logic."""
 
@@ -160,7 +120,6 @@ def __init__(
         model=None,
         save_warmup: Optional[bool] = None,
         density_dist_obs: bool = True,
-        index_origin: Optional[int] = None,
     ):
 
         self.save_warmup = rcParams["data.save_warmup"] if save_warmup is None else save_warmup
@@ -196,7 +155,6 @@ def __init__(
         self.posterior_predictive = posterior_predictive
         self.log_likelihood = log_likelihood
         self.predictions = predictions
-        self.index_origin = rcParams["data.index_origin"] if index_origin is None else index_origin
 
         def arbitrary_element(dct: Dict[Any, np.ndarray]) -> np.ndarray:
             return next(iter(dct.values()))
@@ -344,15 +302,13 @@ def posterior_to_xarray(self):
                 coords=self.coords,
                 dims=self.dims,
                 attrs=self.attrs,
-                index_origin=self.index_origin,
             ),
             dict_to_dataset(
                 data_warmup,
                 library=pymc,
                 coords=self.coords,
                 dims=self.dims,
                 attrs=self.attrs,
-                index_origin=self.index_origin,
             ),
         )
 
@@ -386,15 +342,13 @@ def sample_stats_to_xarray(self):
                 dims=None,
                 coords=self.coords,
                 attrs=self.attrs,
-                index_origin=self.index_origin,
             ),
             dict_to_dataset(
                 data_warmup,
                 library=pymc,
                 dims=None,
                 coords=self.coords,
                 attrs=self.attrs,
-                index_origin=self.index_origin,
             ),
         )
 
@@ -427,15 +381,13 @@ def log_likelihood_to_xarray(self):
                 dims=self.dims,
                 coords=self.coords,
                 skip_event_dims=True,
-                index_origin=self.index_origin,
             ),
             dict_to_dataset(
                 data_warmup,
                 library=pymc,
                 dims=self.dims,
                 coords=self.coords,
                 skip_event_dims=True,
-                index_origin=self.index_origin,
             ),
         )
 
@@ -456,9 +408,7 @@ def translate_posterior_predictive_dict_to_xarray(self, dct) -> xr.Dataset:
                     "This can mean that some draws or even whole chains are not represented.",
                     k,
                 )
-        return dict_to_dataset(
-            data, library=pymc, coords=self.coords, dims=self.dims, index_origin=self.index_origin
-        )
+        return dict_to_dataset(data, library=pymc, coords=self.coords, dims=self.dims)
 
     @requires(["posterior_predictive"])
     def posterior_predictive_to_xarray(self):
@@ -493,7 +443,6 @@ def priors_to_xarray(self):
                     library=pymc,
                     coords=self.coords,
                     dims=self.dims,
-                    index_origin=self.index_origin,
                 )
             )
         return priors_dict
@@ -510,7 +459,6 @@ def observed_data_to_xarray(self):
             coords=self.coords,
             dims=self.dims,
             default_dims=[],
-            index_origin=self.index_origin,
         )
 
     @requires(["trace", "predictions"])
@@ -557,7 +505,6 @@ def is_data(name, var) -> bool:
             coords=self.coords,
             dims=self.dims,
             default_dims=[],
-            index_origin=self.index_origin,
         )
 
     def to_inference_data(self):

pymc/tests/test_distributions.py

@@ -1114,10 +1114,6 @@ def test_wald_logp(self):
             decimal=select_by_precision(float64=6, float32=1),
         )
 
-    @pytest.mark.xfail(
-        condition=(aesara.config.floatX == "float32"),
-        reason="Poor CDF in SciPy. See scipy/scipy#869 for details.",
-    )
     def test_wald_logcdf(self):
         self.check_logcdf(
             Wald,
@@ -1273,6 +1269,10 @@ def modified_scipy_hypergeom_logcdf(value, N, k, n):
             {"N": NatSmall, "k": NatSmall, "n": NatSmall},
         )
 
+    @pytest.mark.xfail(
+        condition=(aesara.config.floatX == "float32"),
+        reason="SciPy log CDF stopped working after un-pinning NumPy version.",
+    )
     def test_negative_binomial(self):
         def scipy_mu_alpha_logpmf(value, mu, alpha):
             return sp.nbinom.logpmf(value, alpha, 1 - mu / (mu + alpha))

requirements-dev.txt

@@ -2,7 +2,7 @@
 # See that file for comments about the need/usage of each dependency.
 
 aesara>=2.1.0
-arviz>=0.11.2
+arviz>=0.11.4
 cachetools>=4.2.1
 cloudpickle
 fastprogress>=0.2.0

requirements.txt

@@ -1,5 +1,5 @@
 aesara>=2.1.0
-arviz>=0.11.2
+arviz>=0.11.4
 cachetools>=4.2.1
 cloudpickle
 fastprogress>=0.2.0