Skip to content

Fix compatibility with pymc and bambi #730

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
aloctavodia merged 2 commits into from
Jul 7, 2026
Merged

Fix compatibility with pymc and bambi #730

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
69980c4
fix compatibility with pymc and bambi
aloctavodia Jul 7, 2026
7e79c06
update version
aloctavodia Jul 7, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion preliz/__init__.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@
from preliz.internal.citations import citations


__version__ = "0.27.0"
__version__ = "0.27.1"


# Allow legend outside plot in maxent to be included in the saved figure
Expand Down
8 changes: 4 additions & 4 deletions preliz/ppls/agnostic.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -262,11 +262,11 @@ def looper(*args, **kwargs):
model = func(*args, **kwargs)
model.build()
if var_to_plot is None:
var_to_plot = model.observed_RVs[0].name
var_to_plot = model.backend.model.observed_RVs[0].name

with disable_pymc_sampling_logs():
idata = model.prior_predictive(iterations)
results = idata[group].stack(sample=("chain", "draw"))[var_to_plot].values.T
results = idata[group].dataset.stack(sample=("chain", "draw"))[var_to_plot].values.T
if group == "prior":
results = np.atleast_2d(results)

Expand All @@ -275,8 +275,8 @@ def looper(*args, **kwargs):
if var_to_plot is None:
var_to_plot = model.observed_RVs[0].name
with disable_pymc_sampling_logs():
idata = sample_prior_predictive(samples=iterations)
results = idata[group].stack(sample=("chain", "draw"))[var_to_plot].values.T
idata = sample_prior_predictive(draws=iterations)
results = idata[group].dataset.stack(sample=("chain", "draw"))[var_to_plot].values.T
if group == "prior":
results = np.atleast_2d(results)

Expand Down
Loading