fix: use PY311 models for Python 3.12+ to fix monotonic_cst AttributeError

predictor.py hardcoded version checks for [9, 10, 11], causing Python 3.12
and 3.13 to fall through to the root-level pkl files which were serialized
with an old sklearn (<1.4) that lacks the monotonic_cst attribute.

Fix: clamp to the newest versioned directory (PY311) for any unknown minor
version. sklearn is pinned at >=1.6.1,<2.0 for all Python versions, so the
PY311 models (re-serialized in the uv migration, #113) are pickle-compatible
with Python 3.12 and 3.13.

Fixes: AttributeError: 'DecisionTreeClassifier' object has no attribute 'monotonic_cst'

Author: openhands-agent

sapientml_core/seeding/predictor.py

@@ -267,23 +267,18 @@ def predict(task: Task, dataset_summary: DatasetSummary) -> PipelineSkeleton:
     # check python version and store as a variable
     python_minor_version = sys.version_info.minor
 
-    # Load all the pkl files based on python version
-    if python_minor_version in [9, 10, 11]:
-        base_path = Path(os.path.dirname(__file__)) / ("../models/PY3" + str(python_minor_version))
-        with open(base_path / "pp_models.pkl", "rb") as f:
-            pp_model = pickle.load(f)
-
-        with open(base_path / "mp_model_1.pkl", "rb") as f1:
-            with open(base_path / "mp_model_2.pkl", "rb") as f2:
-                m_model = (pickle.load(f1), pickle.load(f2))
-
-    else:  # Default
-        with open(Path(os.path.dirname(__file__)) / "../models/pp_models.pkl", "rb") as f:
-            pp_model = pickle.load(f)
-
-        with open(Path(os.path.dirname(__file__)) / "../models/mp_model_1.pkl", "rb") as f1:
-            with open(Path(os.path.dirname(__file__)) / "../models/mp_model_2.pkl", "rb") as f2:
-                m_model = (pickle.load(f1), pickle.load(f2))
+    # Load all the pkl files based on python version.
+    # For Python versions beyond the newest versioned directory, use the newest
+    # available models (sklearn version is pinned, so pkl is cross-version compatible).
+    _versioned = [9, 10, 11]
+    version_key = python_minor_version if python_minor_version in _versioned else max(_versioned)
+    base_path = Path(os.path.dirname(__file__)) / f"../models/PY3{version_key}"
+    with open(base_path / "pp_models.pkl", "rb") as f:
+        pp_model = pickle.load(f)
+
+    with open(base_path / "mp_model_1.pkl", "rb") as f1:
+        with open(base_path / "mp_model_2.pkl", "rb") as f2:
+            m_model = (pickle.load(f1), pickle.load(f2))
     preprocessor_labels = _predict_preprocessors(pp_model, p_meta_feature_test, search_space.target_labels)
     all_labels = _predict_models(m_model, task_type, m_meta_feature_test, preprocessor_labels)