Bump AutoGluon to 1.5 and route per-version to RealTabPFNv2/v25 classes

.github/workflows/pull_request.yml

@@ -34,14 +34,13 @@ jobs:
       matrix:
         include:
           - os: ubuntu-latest
-            python-version: "3.9"
+            python-version: "3.10"
             dependency-set: minimum
-          - os: macos-15-intel # We need x86 as ARM is  python>= 3.11 only.
-            # https://github.com/actions/setup-python/issues/855
-            python-version: "3.9"
+          - os: macos-latest
+            python-version: "3.10"
             dependency-set: minimum
           - os: windows-latest
-            python-version: "3.9"
+            python-version: "3.10"
             dependency-set: minimum
           - os: ubuntu-latest
             python-version: "3.13"
@@ -64,7 +63,6 @@ jobs:
         uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
         with:
           python-version: ${{ matrix.python-version }}
-          architecture: x64
 
       - name: Install uv
         uses: astral-sh/setup-uv@08807647e7069bb48b6ef5acd8ec9567f424441b # v8.1.0

pyproject.toml

@@ -10,11 +10,11 @@ dependencies = [
     "pandas>=1.4.0",
     "scikit-learn>=1.6.0",
     "scipy>=1.11.1",
-    "tabpfn>=6.0.5",
-    "tabpfn-common-utils[telemetry-interactive]>=0.2.0",
+    "tabpfn>=6.3.2",
+    "tabpfn-common-utils[telemetry-interactive]>=0.2.13",
 ]
 
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 authors = [
     { name = "Noah Hollmann", email = "noah.hollmann@charite.de" },
     { name = "Leo Grinsztajn" },
@@ -43,7 +43,6 @@ classifiers = [
     'Operating System :: Unix',
     'Operating System :: MacOS',
     'Programming Language :: Python :: 3',
-    'Programming Language :: Python :: 3.9',
     'Programming Language :: Python :: 3.10',
     'Programming Language :: Python :: 3.11',
     'Programming Language :: Python :: 3.12',
@@ -64,7 +63,7 @@ interpretability = [
 post_hoc_ensembles = [
     "llvmlite",
     "hyperopt>=0.2.7",
-    "autogluon.tabular==1.4.0"
+    "autogluon.tabular>=1.5.0,<1.6"
 ]
 hpo = [
     "hyperopt>=0.2.7",
@@ -89,7 +88,7 @@ all = [
     "hyperopt>=0.2.7",
     # https://discuss.python.org/t/pkg-resources-removal-how-to-go-from-there/106079
     "setuptools>=67.0.0,<82",
-    "autogluon.tabular==1.4.0",
+    "autogluon.tabular>=1.5.0,<1.6",
     # scikit-survival not included; install manually if you need SurvivalTabPFN.
 ]
 
@@ -117,7 +116,7 @@ addopts = "--durations=10 -vv"
 
 # https://github.com/astral-sh/ruff
 [tool.ruff]
-target-version = "py39"
+target-version = "py310"
 line-length = 88
 output-format = "full"
 src = ["src", "tests", "examples"]
@@ -329,7 +328,7 @@ convention = "google"
 max-args = 10 # Changed from default of 5
 
 [tool.mypy]
-python_version = "3.9"
+python_version = "3.10"
 packages = ["src/tabpfn_extensions", "tests"]
 
 show_error_codes = true
@@ -368,7 +367,7 @@ ignore_missing_imports = true
 [tool.pyright]
 include = ["src", "tests"]
 
-pythonVersion = "3.9"
+pythonVersion = "3.10"
 typeCheckingMode = "strict"
 
 strictListInference = true

src/tabpfn_extensions/hpo/search_space.py

@@ -143,15 +143,24 @@ def get_param_grid_hyperopt(
     if model_dir is None:
         model_dir = get_cache_dir()
 
-    if task_type == "multiclass" and model_version == ModelVersion.V2:
-        model_source = ModelSource.get_classifier_v2()
-    elif task_type == "multiclass" and model_version == ModelVersion.V2_5:
-        model_source = ModelSource.get_classifier_v2_5()
-    elif task_type == "regression":
-        if model_version == ModelVersion.V2:
-            model_source = ModelSource.get_regressor_v2()
-        elif model_version == ModelVersion.V2_5:
-            model_source = ModelSource.get_regressor_v2_5()
+    # Resolve the model source for the (task_type, model_version) pair. Any
+    # combination we don't explicitly support below raises before we try to
+    # use `model_source`.
+    model_source_lookup = {
+        ("multiclass", ModelVersion.V2): ModelSource.get_classifier_v2,
+        ("multiclass", ModelVersion.V2_5): ModelSource.get_classifier_v2_5,
+        ("regression", ModelVersion.V2): ModelSource.get_regressor_v2,
+        ("regression", ModelVersion.V2_5): ModelSource.get_regressor_v2_5,
+    }
+    try:
+        model_source = model_source_lookup[(task_type, model_version)]()
+    except KeyError as err:
+        raise NotImplementedError(
+            f"No hpo search space is defined for task type {task_type!r} and "
+            f"model version {model_version!r}."
+        ) from err
+
+    if task_type == "regression":
         search_space["inference_config/REGRESSION_Y_PREPROCESS_TRANSFORMS"] = hp.choice(
             "REGRESSION_Y_PREPROCESS_TRANSFORMS",
             [
@@ -162,12 +171,6 @@ def get_param_grid_hyperopt(
             ],
         )
 
-    else:
-        raise NotImplementedError(
-            f"No hpo search space is defined for task type {task_type} and "
-            f"model version {model_version}."
-        )
-
     # Make sure models are downloaded.
     if download_models_if_missing:
         for ckpt_name in model_source.filenames:

src/tabpfn_extensions/hpo/tuned_tabpfn.py

@@ -39,8 +39,9 @@
 from __future__ import annotations
 
 import logging
+from collections.abc import Callable
 from enum import Enum
-from typing import Any, Callable
+from typing import Any
 
 import numpy as np
 import torch
@@ -183,7 +184,7 @@ def _optimize(self, X: np.ndarray, y: np.ndarray, task_type: str):
             for k, v_item in self.search_space.items():
                 if isinstance(v_item, list):
                     custom_space[k] = hp.choice(k, v_item)
-                elif isinstance(v_item, (int, float, bool, str)) or v_item is None:
+                elif isinstance(v_item, int | float | bool | str) or v_item is None:
                     custom_space[k] = v_item
                 else:
                     custom_space[k] = v_item

src/tabpfn_extensions/interpretability/shap.py

@@ -33,8 +33,9 @@
 
 from __future__ import annotations
 
+from collections.abc import Callable
 from multiprocessing import Pool
-from typing import Any, Callable
+from typing import Any
 
 import matplotlib.pyplot as plt
 import numpy as np

src/tabpfn_extensions/interpretability/shapiq.py

@@ -78,7 +78,7 @@ class index will be set to 1 per default for classification models. This argumen
         data = data.values
 
     # make labels to array if it is a pandas Series
-    if isinstance(labels, (pd.Series, pd.DataFrame)):
+    if isinstance(labels, pd.Series | pd.DataFrame):
         labels = labels.values
 
     # TabPFNExplainer is directly available in the shapiq module

src/tabpfn_extensions/many_class/_utils.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-import sys
 from collections.abc import Iterable, Mapping
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any
@@ -15,8 +14,7 @@
 
 def _dataclass_kwargs() -> dict[str, Any]:
     kwargs: dict[str, Any] = {}
-    if sys.version_info >= (3, 10):  # pragma: no cover - environment dependent
-        kwargs["slots"] = True
+    kwargs["slots"] = True
     return kwargs
 
 

src/tabpfn_extensions/misc/sklearn_compat.py

@@ -18,8 +18,9 @@
 import platform
 import sys
 import types
+from collections.abc import Callable
 from dataclasses import dataclass, field
-from typing import Callable, Literal
+from typing import Literal
 
 import sklearn
 from sklearn.utils.fixes import parse_version
@@ -34,8 +35,6 @@
 
 # tags infrastructure
 def _dataclass_args():
-    if sys.version_info < (3, 10):
-        return {}
     return {"slots": True}
 
 
@@ -196,7 +195,7 @@ def _is_fitted(estimator, attributes=None, all_or_any=all):
             Whether the estimator is fitted.
         """
         if attributes is not None:
-            if not isinstance(attributes, (list, tuple)):
+            if not isinstance(attributes, list | tuple):
                 attributes = [attributes]
             return all_or_any([hasattr(estimator, attr) for attr in attributes])
 

src/tabpfn_extensions/post_hoc_ensembles/sklearn_interface.py

@@ -10,6 +10,7 @@
 
 import datetime
 from enum import Enum
+from pathlib import Path
 from typing import Any, Literal
 
 import numpy as np
@@ -198,10 +199,25 @@ def fit(self, X: np.ndarray | pd.DataFrame, y: np.ndarray | pd.Series):
         This method should be called from the child class's fit method after validation.
         """
         from autogluon.tabular import TabularPredictor
-        from autogluon.tabular.models import TabPFNV2Model
+        from autogluon.tabular.models import RealTabPFNv2Model, RealTabPFNv25Model
 
         from tabpfn_extensions.post_hoc_ensembles.utils import search_space_func
 
+        # Route to the AutoGluon TabPFN model class that matches the requested
+        # TabPFN model version. Each class ships with the correct per-version
+        # max_rows/max_features/max_classes limits, so we no longer need to
+        # override them via ag_args_fit.
+        if self.model_version == ModelVersion.V2:
+            ag_model_class = RealTabPFNv2Model
+        elif self.model_version == ModelVersion.V2_5:
+            ag_model_class = RealTabPFNv25Model
+        else:
+            raise NotImplementedError(
+                f"AutoTabPFN does not support TabPFN model version "
+                f"{self.model_version.value!r} yet. Supported versions: "
+                f"{ModelVersion.V2.value!r}, {ModelVersion.V2_5.value!r}.",
+            )
+
         if isinstance(X, pd.DataFrame):
             training_df = X.copy()
             self._column_names = X.columns.tolist()
@@ -247,30 +263,33 @@ def fit(self, X: np.ndarray | pd.DataFrame, y: np.ndarray | pd.Series):
                 **self.get_task_args_(),
             }
 
-        def _patch_ag_args_fit_inplace(config: dict[str, Any]) -> None:
-            """Patch AutoGluon's per-model params_aux for TabPFN sub-models.
-
-            - Disable AutoGluon's static max_rows / max_features / max_classes
-              asserts so TabPFN's own per-checkpoint validation is the single
-              authority. TODO: Fix upstream in AutoGluon's `TabPFNV2Model`. A
-              single class handles all v2.x checkpoints with v2-era limits
-              hardcoded in `_get_default_auxiliary_params`, which is wrong for
-              v2.5+. Until that lands, we override per sub-model here.
-            - Forward the user's `ignore_pretraining_limits` flag to TabPFN.
+        def _adapt_config_for_autogluon_inplace(config: dict[str, Any]) -> None:
+            """Forward `ignore_pretraining_limits` and translate `model_path`.
+
+            - AutoGluon 1.5 expects the checkpoint to be passed as
+              ``zip_model_path=[classification_ckpt, regression_ckpt]`` (just
+              the filenames; AG joins with its own resolved cache dir). The
+              search space produces a TabPFN-compatible ``model_path=<abs path>``
+              so that it can also be consumed directly by core TabPFN; here we
+              translate it to the form AG expects and drop the original key.
+            - Forward the user's ``ignore_pretraining_limits`` flag into AG's
+              ``ag_args_fit.ignore_constraints``.
             """
             ag_args_fit = config.setdefault("ag_args_fit", {})
-            ag_args_fit["max_rows"] = None
-            ag_args_fit["max_features"] = None
-            ag_args_fit["max_classes"] = None
             ag_args_fit["ignore_constraints"] = self.ignore_pretraining_limits
 
+            full_path = config.pop("model_path", None)
+            if full_path is not None:
+                ckpt_name = Path(full_path).name
+                config["zip_model_path"] = [ckpt_name, ckpt_name]
+
         if isinstance(tabpfn_configs, list):
             for cfg in tabpfn_configs:
-                _patch_ag_args_fit_inplace(cfg)
+                _adapt_config_for_autogluon_inplace(cfg)
         else:
-            _patch_ag_args_fit_inplace(tabpfn_configs)
+            _adapt_config_for_autogluon_inplace(tabpfn_configs)
 
-        hyperparameters = {TabPFNV2Model: tabpfn_configs}
+        hyperparameters = {ag_model_class: tabpfn_configs}
         if isinstance(self.presets, str) and self.presets == "extreme_quality":
             raise ValueError(
                 "Extreme quality preset is not supported at the moment, as it does not "

src/tabpfn_extensions/post_hoc_ensembles/utils.py

@@ -67,16 +67,10 @@ def prepare_tabpfnv2_config(
     else:
         config.pop("balance_probabilities", None)
 
-    # TODO: Enable RF-PFN at some point
-    config["model_type"] = "single"
-
-    # Special case for dt_pfn
-    # TODO: This code is unused until we support RF-PFN
-    if config.get("model_type") == "dt_pfn":
-        config["n_ensemble_repeats"] = config["n_estimators"]
-        config["n_estimators"] = 1
-
-    # Remove deprecated keys
+    # Remove search-space-only keys that are not valid TabPFNClassifier kwargs.
+    # `model_type` is a placeholder for future RF-PFN routing (currently unused),
+    # and `max_depth` was for the dt_pfn variant.
+    config.pop("model_type", None)
     config.pop("max_depth", None)
 
     return config

src/tabpfn_extensions/pval_crt/crt.py

@@ -124,7 +124,7 @@ def tabpfn_crt(
     # ---------------------------
     # Multi-feature support
     # ---------------------------
-    if isinstance(j, Sequence) and not isinstance(j, (str, bytes)):
+    if isinstance(j, Sequence) and not isinstance(j, str | bytes):
         results = {}
 
         for feat in j:

src/tabpfn_extensions/pval_crt/utils.py

@@ -1,13 +1,13 @@
 from __future__ import annotations
 
 from collections.abc import Sequence
-from typing import Any, Union
+from typing import Any
 
 import numpy as np
 import pandas as pd
 import torch
 
-FeatureSpec = Union[int, str]
+FeatureSpec = int | str
 
 
 def coerce_X_y_to_numpy(
@@ -23,7 +23,7 @@ def coerce_X_y_to_numpy(
     else:
         X_np = np.asarray(X)
 
-    if pd is not None and isinstance(y, (pd.Series, pd.DataFrame)):
+    if pd is not None and isinstance(y, pd.Series | pd.DataFrame):
         y_np = np.asarray(y).reshape(-1)
     else:
         y_np = np.asarray(y).reshape(-1)

src/tabpfn_extensions/sklearn_ensembles/meta_models.py

@@ -97,6 +97,7 @@ def get_tabpfn_outer_ensemble(config: configs.TabPFNConfig, **kwargs):
     for ensemble_member_index, sub_config in zip(
         range(config.n_estimators),
         relevant_config_product,
+        strict=False,
     ):
         member_config = copy.deepcopy(config)
         for k, v in sub_config.items():

src/tabpfn_extensions/sklearn_ensembles/weighted_ensemble.py

@@ -30,7 +30,7 @@ def fit(self, X, y):
         # Prune classifiers with weights below the threshold
         pruned_classifiers = []
         pruned_weights = []
-        for clf, weight in zip(self.estimators, weights):
+        for clf, weight in zip(self.estimators, weights, strict=True):
             if weight >= self.weight_threshold:
                 pruned_classifiers.append(clf)
                 pruned_weights.append(weight)
@@ -40,7 +40,7 @@ def fit(self, X, y):
             pruned_classifiers = [
                 clf
                 for _, clf in sorted(
-                    zip(pruned_weights, pruned_classifiers),
+                    zip(pruned_weights, pruned_classifiers, strict=True),
                     key=lambda pair: pair[0],
                 )
             ]