Do not fail with zero-sized arrays in dataset_to_point_list

Numpy does not support reshape(-1, ...) when size is zero

Author: ricardoV94

pymc/backends/arviz.py

@@ -619,10 +619,12 @@ def dataset_to_point_list(
         if not isinstance(vn, str):
             raise ValueError(f"Variable names must be str, but dataset key {vn} is a {type(vn)}.")
     num_sample_dims = len(sample_dims)
+
     stacked_dims = {dim_name: ds[var_names[0]][dim_name] for dim_name in sample_dims}
+    stacked_size = np.prod([ds.sizes[dim_name] for dim_name in sample_dims], dtype=int)
     transposed_dict = {vn: da.transpose(*sample_dims, ...) for vn, da in ds.items()}
     stacked_dict = {
-        vn: da.values.reshape((-1, *da.shape[num_sample_dims:]))
+        vn: da.values.reshape((stacked_size, *da.shape[num_sample_dims:]))
         for vn, da in transposed_dict.items()
     }
     points = [

tests/backends/test_arviz.py

@@ -837,3 +837,14 @@ def test_dataset_to_point_list_str_key(self):
         ds[3] = xarray.DataArray([1, 2, 3])
         with pytest.raises(ValueError, match="must be str"):
             dataset_to_point_list(ds, sample_dims=["chain", "draw"])
+
+    def test_zero_size(self):
+        ds = xarray.Dataset()
+        ds["x"] = xarray.DataArray(
+            np.zeros((4, 10, 0, 5)), dims=("chain", "draw", "dim_0", "dim_5")
+        )
+        pl, _ = dataset_to_point_list(ds, sample_dims=("chain", "draw"))
+        assert len(pl) == 40
+        assert tuple(pl[0]) == ("x",)
+        assert pl[0]["x"].shape == (0, 5)
+        assert pl[0]["x"].dtype == np.float64