Closed
Description
The test test_sparse_input from sklearn/linear_model/tests/test_quantile.py fails when I run the test suite locally (Windows).
It fails for the fit_intercept=True parametrization but passes for fit_intercept=False. It fails for all solvers. Here's the error message
> assert 0.45 <= np.mean(y < quant_sparse.predict(X_sparse)) <= 0.55
E AssertionError: assert 0.56 <= 0.55Maybe 0.55 was too tight ?
Ping @lorentzenchr who reviewed the PR introducing this test #21086
System:
python: 3.9.13 | packaged by conda-forge | (main, May 27 2022, 16:50:36) [MSC v.1929 64 bit (AMD64)]
executable: C:\Users\J\miniconda3\envs\dev\python.exe
machine: Windows-10-10.0.19045-SP0
Python dependencies:
sklearn: 1.3.dev0
pip: 22.2.2
setuptools: 65.3.0
numpy: 1.23.3
scipy: 1.9.2
Cython: 0.29.33
pandas: 1.4.4
matplotlib: 3.5.3
joblib: 1.2.0
threadpoolctl: 3.1.0
Built with OpenMP: True
threadpoolctl info:
user_api: blas
internal_api: mkl
prefix: libblas
filepath: C:\Users\J\miniconda3\envs\dev\Library\bin\libblas.dll
version: 2022.1-Product
threading_layer: intel
num_threads: 8
user_api: blas
internal_api: openblas
prefix: libopenblas
filepath: C:\Users\J\miniconda3\envs\dev\Lib\site-packages\scipy.libs\libopenblas-57db09cfe174768fb409a6bb5a530d4c.dll
version: 0.3.18
threading_layer: pthreads
architecture: Zen
num_threads: 16
user_api: openmp
internal_api: openmp
prefix: vcomp
filepath: C:\Users\J\miniconda3\envs\dev\vcomp140.dll
version: None
num_threads: 16