Make full regression output available in AutoTabPFNRegressor

[msalem7777]

Describe the bug

setting ignore_pretraining_limits=True on model initialization creates a model that does not have the ouput_type argument defined in the .predict method.

Steps/Code to Reproduce

reg = TabPFNRegressor(ignore_pretraining_limits=True)
reg.fit(X_train, y_train)
tabpfn_full_preds = reg.predict(X_test, output_type="full")

Expected Results

No error is thrown.

Actual Results

---

TypeError Traceback (most recent call last)
Cell In[50], line 1
----> 1 tabpfn_full_preds = reg.predict(X_test, output_type="full")

TypeError: predict() got an unexpected keyword argument 'output_type'

Versions

Collecting system and dependency information...
PyTorch version: 2.3.1+cu121
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Microsoft Windows 10 Home (10.0.19045 64-bit)
GCC version: Could not collect
Clang version: Could not collect
CMake version: Could not collect
Libc version: N/A

Python version: 3.9.10 (tags/v3.9.10:f2f3f53, Jan 17 2022, 15:14:21) [MSC v.1929 64 bit (AMD64)] (64-bit runtime)
Python platform: Windows-10-10.0.19045-SP0
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1050
Nvidia driver version: 555.99
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Name: Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz
Manufacturer: GenuineIntel
Family: 198
Architecture: 9
ProcessorType: 3
DeviceID: CPU0
CurrentClockSpeed: 2800
MaxClockSpeed: 2801
L2CacheSize: 1024
L2CacheSpeed: None
Revision: None

Dependency Versions:
--------------------
tabpfn: 2.0.8
torch: 2.3.1+cu121
numpy: 1.26.3
scipy: 1.13.1
pandas: 2.2.2
scikit-learn: 1.5.0
typing_extensions: 4.12.2
einops: 0.8.0
huggingface-hub: 0.29.0

[LeoGrin]

Hello @msalem7777,

Thanks a lot for the report! I cannot reproduce this from my side, could you confirm that you're not getting this error if you pass ignore_pretraining_limits=False with the exact same setting? If you could also provide a minimal reproducible example with some data (potentially random) that would also help a lot! Thanks :)

[msalem7777]

Hi @LeoGrin, thanks for the quick response and sorry for the confusion!

After re-running and digging deeper, I realized I misreported the issue. The problem isn't related to ignore_pretraining_limits=True — the error actually occurs when using AutoTabPFNRegressor, regardless of that flag. It seems the output_type argument is not supported in the ensemble version (AutoTabPFNRegressor.predict()), unlike in the base TabPFNRegressor.

Feel free to close this if that’s expected behavior — otherwise I’d be happy to revise the issue title to reflect the actual scope. Thanks again!

[LeoGrin]

Thanks a lot for clarifying @msalem7777!

[noahho]

Moved over to tabpfn-extensions under: PriorLabs/tabpfn-extensions#102