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[BUG] make_column_transformer fails on a two-dimensional Python list input #8502
Description
Describe the bug
cuml.compose.make_column_transformer fails when fit_transform() receives a regular two-dimensional Python list.
During remainder validation, cuML accesses X.shape[1] without first converting or validating the array-like input. A Python list has no .shape attribute, so the call raises:
AttributeError: 'list' object has no attribute 'shape'
The equivalent sklearn.compose.make_column_transformer call accepts the same list input and returns the expected standardized column.
Steps/Code to reproduce bug
cuML reproducer:
from cuml.compose import make_column_transformer from cuml.preprocessing import StandardScaler a = [[1, 2],[3, 4],[5, 6],] print(make_column_transformer((StandardScaler(), [0])).fit_transform(a))
Output:
Traceback (most recent call last):
File "/workspace/apibughub/cuml/make_column_transformer/error_bug1/error_bug_cuml.py", line 6, in <module>
print(make_column_transformer((StandardScaler(), [0])).fit_transform(a))
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^
File "/opt/conda/envs/rapids-26.08/lib/python3.14/site-packages/cuml/internals/outputs.py", line 874, in inner
res = func(*args, **kwargs)
File "/opt/conda/envs/rapids-26.08/lib/python3.14/site-packages/cuml/_thirdparty/sklearn/preprocessing/_column_transformer.py", line 901, in fit_transform
self._validate_remainder(X)
~~~~~~~~~~~~~~~~~~~~~~~~^^^
File "/opt/conda/envs/rapids-26.08/lib/python3.14/site-packages/cuml/_thirdparty/sklearn/preprocessing/_column_transformer.py", line 729, in _validate_remainder
self._n_features = X.shape[1]
^^^^^^^
AttributeError: 'list' object has no attribute 'shape'
For comparison, the equivalent scikit-learn code:
from sklearn.compose import make_column_transformer from sklearn.preprocessing import StandardScaler a = [[1, 2],[3, 4],[5, 6],] print(make_column_transformer((StandardScaler(), [0])).fit_transform(a))
Output:
[[-1.22474487]
[ 0. ]
[ 1.22474487]]
Expected behavior
make_column_transformer(...).fit_transform() should accept a rectangular two-dimensional Python list as array-like input, convert or validate it appropriately, and apply StandardScaler to the selected column.
For this input, the transformed output should be equivalent to:
[[-1.22474487]
[ 0. ]
[ 1.22474487]]
If Python lists are intentionally unsupported, the API should raise a clear input-validation error describing the supported input types rather than leaking an internal AttributeError.
Environment details (please complete the following information):
- Environment location: Docker
- Linux Distro/Architecture: Ubuntu 24.04 / x86_64
- GPU Model/Driver: NVIDIA GeForce RTX 4090 / 595.71.05
- CUDA: 13.2
- Method of cuDF & cuML install: conda
conda list:
# Name Version Build Channel
python 3.14.6 h242f9ac_102_cp314 conda-forge
numpy 2.4.6 py314h2b28147_0 conda-forge
scipy 1.16.3 py314hf07bd8e_2 conda-forge
scikit-learn 1.9.0 np2py314hf09ca88_0 conda-forge
rapids 26.08.00 cuda13_260806_c2656556 rapidsai
cuml 26.08.00 cuda13_cp311_abi3_260805_265b9da6 rapidsai
libcuml 26.08.00 cuda13_260805_265b9da6 rapidsai
cudf 26.08.00 cuda13_cp311_abi3_260805_ff5b362d rapidsai
libraft 26.08.00 cuda13_260805_ebf92684 rapidsai
libraft-headers 26.08.00 cuda13_260805_ebf92684 rapidsai
pylibraft 26.08.00 cuda13_cp311_abi3_260805_ebf92684 rapidsai
cuvs 26.08.01 cuda13_cp311_abi3_260806_25b1be43 rapidsai
libcuvs 26.08.01 cuda13_260806_25b1be43 rapidsai
cupy 14.1.1 py314hdea9c46_0 conda-forge
cupy-core 14.1.1 py314hcd3b49b_0 conda-forge
numba 0.64.0 py314h8169c2f_0 conda-forge
numba-cuda 0.30.4 py314h42812f9_0 conda-forge
rmm 26.08.00 cuda13_cp311_abi3_260805_42d059f1 rapidsai
librmm 26.08.00 cuda13_260805_42d059f1 rapidsai
cuda-version 13.3 hcbadf70_3 conda-forge
cuda-bindings 13.3.1 py314h42812f9_1 conda-forge
cuda-cudart 13.3.29 hecca717_0 conda-forge
cuda-nvrtc 13.3.33 hecca717_0 conda-forge
libcublas 13.6.0.2 h676940d_0 conda-forge
libcusolver 12.2.6.9 h676940d_0 conda-forge
libcusparse 12.8.2.51 hecca717_0 conda-forge
libcurand 10.4.3.29 h676940d_0 conda-forge