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[CI] Various CI fixes #694

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Merged
chyunsu3 merged 2 commits into from
Aug 26, 2026
Merged

[CI] Various CI fixes #694

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3f2dc0f
[CI] Fix pre-commit failures
chyunsu3 Aug 26, 2026
c557ec7
Pin lcov<2.0
chyunsu3 Aug 26, 2026
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2 changes: 1 addition & 1 deletion .pre-commit-config.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -52,7 +52,7 @@ repos:
rev: v2.3.1
hooks:
- id: mypy
additional_dependencies: [types-setuptools]
additional_dependencies: [types-setuptools, xgboost]
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.16.4
hooks:
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4 changes: 2 additions & 2 deletions ops/conda_env/dev.yml
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Expand Up @@ -2,7 +2,7 @@ name: dev
channels:
- conda-forge
dependencies:
- python=3.12
- python=3.13
- numpy
- scipy
- pandas
Expand All @@ -13,7 +13,7 @@ dependencies:
- coverage
- codecov
- ninja
- lcov
- lcov<2.0
- cmake
- llvm-openmp
- cython
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3 changes: 0 additions & 3 deletions ops/cpp-python-coverage.sh
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Expand Up @@ -2,9 +2,6 @@

set -euo pipefail

echo "##[section]Installing lcov and Ninja..."
sudo apt-get install lcov ninja-build

echo "##[section]Building Treelite..."
mkdir build/
cd build/
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4 changes: 1 addition & 3 deletions python/treelite/sklearn/importer.py
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Expand Up @@ -431,9 +431,7 @@ def _import_hist_gradient_boosting(sklearn_model) -> Model:
feat_remapper[n_categorical + num_idx] = i
num_idx += 1
else:
feat_remapper = np.arange(
start=0, stop=sklearn_model.n_features_in_, dtype=np.int32
)
feat_remapper = np.arange(0, stop=sklearn_model.n_features_in_, dtype=np.int32)

@chyunsu3 chyunsu3 Aug 26, 2026

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The start parameter in np.arange is now a positional-only argument. See numpy/numpy#30368 (comment)


n_categorical_splits = known_cat_bitsets.shape[0]
n_trees = 0
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4 changes: 3 additions & 1 deletion tests/python/util.py
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Expand Up @@ -94,7 +94,9 @@ def to_categorical(
cat_cols = (cat_cols * rough_n_categories).astype(int)

# Mix categorical and numerical columns in a random order
new_col_idx = rng.choice(n_features, n_features, replace=False, shuffle=True)
new_col_idx = np.asarray(
rng.choice(n_features, n_features, replace=False, shuffle=True)
)
Comment on lines +97 to +99

@chyunsu3 chyunsu3 Aug 26, 2026

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mypy is conservative and assuming int return type for rng.choice(). We explicitly annotate the return type (np.ndarray) here.

df_cols = {}
for icol in range(n_categorical):
col = cat_cols[:, icol]
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