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[ ] 0% | 0.00 sec | 100 random sampling with cv | Traceback (most recent call last):
File "C:\Users\kun\Downloads\plot_mkr_1_sklearn_api.py", line 108, in<module>
pipe.fit(X_train, Y_train)
File "C:\Users\kun\miniconda3\envs\naremo\lib\site-packages\sklearn\base.py", line 1473, in wrapper
return fit_method(estimator, *args, **kwargs)
File "C:\Users\kun\miniconda3\envs\naremo\lib\site-packages\sklearn\pipeline.py", line 473, in fit
self._final_estimator.fit(Xt, y, **last_step_params["fit"])
File "C:\Users\kun\miniconda3\envs\naremo\lib\site-packages\himalaya\backend\_utils.py", line 97, in wrapper
return func(*args, **kwargs)
File "C:\Users\kun\miniconda3\envs\naremo\lib\site-packages\himalaya\kernel_ridge\_sklearn_api.py", line 935, in fit
tmp = self._call_solver(Ks=Ks, Y=y, cv=cv, return_weights="dual",
File "C:\Users\kun\miniconda3\envs\naremo\lib\site-packages\himalaya\kernel_ridge\_sklearn_api.py", line 59, in _call_solver
return function(**direct_params, **solver_params)
File "C:\Users\kun\miniconda3\envs\naremo\lib\site-packages\himalaya\kernel_ridge\_random_search.py", line 222, in solve_multiple_kernel_ridge_random_search
Ktrain, Ktest = K[train[:, None], train], K[test[:, None], train]
IndexError: tensors used as indices must be long, byte or bool tensors
Environment
Here is my torch information:
PyTorch version: 1.13.1+cu117
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A
OS: Microsoft Windows 11 Pro
GCC version: (GCC) 11.3.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: N/A
Python version: 3.10.14 | packaged by Anaconda, Inc. | (main, May 6 2024, 19:44:50) [MSC v.1916 64 bit (AMD64)] (64-bit runtime)
Python platform: Windows-10-10.0.22631-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 RTX 4060 Ti
Nvidia driver version: 560.70
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] pytorchvideo==0.1.5
[pip3] torch==1.13.1+cu117
[pip3] torchaudio==0.13.1+cu117
[pip3] torchvision==0.14.1+cu117
[conda] blas 1.0 mkl
[conda] mkl 2021.4.0 pypi_0 pypi
[conda] mkl-service 2.4.0 py310h2bbff1b_1
[conda] mkl_fft 1.3.8 py310h2bbff1b_0
[conda] mkl_random 1.2.4 py310h59b6b97_0
[conda] numpy 1.26.4 py310h055cbcc_0
[conda] numpy-base 1.26.4 py310h65a83cf_0
[conda] pytorchvideo 0.1.5 pypi_0 pypi
[conda] torch 1.13.1+cu117 pypi_0 pypi
[conda] torchaudio 0.13.1+cu117 pypi_0 pypi
[conda] torchvision 0.14.1+cu117 pypi_0 pypi
others:
Package Version
himalaya 0.4.6
scikit-learn 1.5.1
numpy 1.26.4
Please let me know if you need more information. Thank you!
The text was updated successfully, but these errors were encountered:
Thanks for your wonderful tool! I recently used himalaya for voxel-wise encoding but got the
IndexError
when usingtorch_cuda
backend.Minimal reproducible code
The example code from your document: Multiple-kernel ridge with scikit-learn API
Log
Environment
Here is my torch information:
others:
Please let me know if you need more information. Thank you!
The text was updated successfully, but these errors were encountered: