Update to use ArviZ 1

.github/workflows/test.yml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.11", "3.12", "3.13"]
+        python-version: ["3.12", "3.13", "3.14"]
 
     name: Set up Python ${{ matrix.python-version }}
     steps:

README.md

@@ -16,7 +16,7 @@ If you find any bugs or have any feature requests, please open an [issue](https:
 
 ## Installation
 
-Kulprit requires a working Python interpreter (3.11+).
+Kulprit requires a working Python interpreter (3.12+).
 
 Assuming a standard Python environment is installed on your machine (including pip), Kulprit itself can be installed in one line using:
 

docs/index.rst

@@ -26,7 +26,7 @@ If you find any bugs or have any feature requests, please open an `issue <https:
 Installation
 ============
 
-Kulprit requires a working Python interpreter (3.11+). We recommend installing Python and key numerical libraries using the `Anaconda Distribution <https://www.anaconda.com/products/individual#Downloads>`_, which has one-click installers available on all major platforms.
+Kulprit requires a working Python interpreter (3.12+). We recommend installing Python and key numerical libraries using the `Anaconda Distribution <https://www.anaconda.com/products/individual#Downloads>`_, which has one-click installers available on all major platforms.
 
 Assuming a standard Python environment is installed on your machine (including pip), Kulprit itself can be installed in one line using pip:
 
@@ -59,7 +59,7 @@ Alternatively, if you want the bleeding-edge version of the package, you can ins
 Dependencies
 ============
 
-Kulprit is tested on Python 3.11+. Dependencies are listed in `pyproject.toml` and should all be installed by the Kulprit installer; no further action should be required.
+Kulprit is tested on Python 3.12+. Dependencies are listed in `pyproject.toml` and should all be installed by the Kulprit installer; no further action should be required.
 
 
 Contributing

kulprit/projection/arviz_io.py

@@ -80,7 +80,7 @@ def check_idata(idata, model, rng):
         raise UserWarning("Incompatible model and inference data.")
 
     # check if we have the log_likelihood group
-    if "log_likelihood" not in idata.groups():
+    if "log_likelihood" not in idata.groups:
         warnings.warn(
             "log_likelihood group is missing from idata, it will be computed.\n"
             "To avoid this message, please run Bambi's fit method with the option "
@@ -89,7 +89,7 @@ def check_idata(idata, model, rng):
         model.compute_log_likelihood(idata)
 
     # check if we have the posterior_predictive group
-    if "posterior_predictive" not in idata.groups():
+    if "posterior_predictive" not in idata.groups:
         model.predict(idata, kind="response", inplace=True, random_seed=rng)
 
     return idata

kulprit/projection/solver.py

@@ -62,7 +62,7 @@ def solve(neg_log_likelihood, preds, initial_guess, var_info, weights, tolerance
                 posterior_dict[key] = transformation(posterior_dict[key])
             size += new_size
 
-        new_idata = from_dict(posterior=posterior_dict)
+        new_idata = from_dict({"posterior": posterior_dict})
         loss = np.mean(objectives) * 0.5
     else:
         new_idata = None

kulprit/projector.py

@@ -334,15 +334,15 @@ def _project(self, term_names, clusters=True):
         # Add observed data and log-likelihood to the projected InferenceData object
         # We only do this for the selected projected model, not the intermediate ones
         if new_idata is not None:
-            new_idata.add_groups(observed_data=self._observed_dataset)
-            new_idata.add_groups(log_likelihood=compute_llk(new_idata, self._pymc_model))
+            new_idata["observed_data"] = self._observed_dataset
+            new_idata["log_likelihood"] = compute_llk(new_idata, self._pymc_model)
             # remove the variables that are not in the submodel
             vars_to_drop = [
                 var
-                for var in new_idata.posterior.data_vars
+                for var in new_idata["posterior"].ds.data_vars
                 if var not in (term_names + self._base_terms)
             ]
-            new_idata.posterior = new_idata.posterior.drop_vars(vars_to_drop)
+            new_idata["posterior"] = new_idata["posterior"].ds.drop_vars(vars_to_drop)
 
         # build SubModel object and return
         sub_model = SubModel(

pyproject.toml

@@ -17,15 +17,15 @@ classifiers = [
     "Operating System :: OS Independent",
     "Programming Language :: Python",
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
     "Programming Language :: Python :: 3.13",
+    "Programming Language :: Python :: 3.14",
 ]
 dynamic = ["version"]
 description = "Kullback-Leibler projections for Bayesian model selection."
 dependencies = [
-    "arviz_plots >=0.7.0",
-    "bambi >=0.16",
+    "arviz_plots >=1.1.0",
+    "bambi @ git+https://github.com/bambinos/bambi.git",
     "scikit-learn>=1.0.2"
     ]