10000 [BUG] _joblib_parallel_args removed in latest scikit-learn dev build · Issue #894 · scikit-learn-contrib/imbalanced-learn · GitHub
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[BUG] _joblib_parallel_args removed in latest scikit-learn dev build #894

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torshind opened this issue Feb 24, 2022 · 12 comments
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[BUG] _joblib_parallel_args removed in latest scikit-learn dev build #894

torshind opened this issue Feb 24, 2022 · 12 comments

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@torshind
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Describe the bug

Unable to import SMOTE using scikit-learn nightly

Steps/Code to Reproduce

from imblearn.over_sampling import SMOTE

Expected Results

No error is thrown.

Actual Results

  File "/opt/conda/lib/python3.9/site-packages/imblearn/__init__.py", line 53, in <module>
    from . import ensemble
  File "/opt/conda/lib/python3.9/site-packages/imblearn/ensemble/__init__.py", line 8, in <module>
    from ._forest import BalancedRandomForestClassifier
  File "/opt/conda/lib/python3.9/site-packages/imblearn/ensemble/_forest.py", line 28, in <module>
    from sklearn.utils.fixes import _joblib_parallel_args
ImportError: cannot import name '_joblib_parallel_args' from 'sklearn.utils.fixes' (/opt/conda/lib/python3.9/site-packages/sklearn/utils/fixes.py)

Versions

/opt/conda/lib/python3.9/site-packages/_distutils_hack/__init__.py:36: UserWarning: Setuptools is replacing distutils.
  warnings.warn("Setuptools is replacing distutils.")

System:
    python: 3.9.7 | packaged by conda-forge | (default, Sep 29 2021, 19:20:46)  [GCC 9.4.0]
executable: /opt/conda/bin/python
   machine: Linux-5.4.0-91-generic-x86_64-with-glibc2.31

Python dependencies:
          pip: 21.3.1
   setuptools: 60.2.0
      sklearn: 1.1.dev0
        numpy: 1.22.2
        scipy: 1.7.3
       Cython: None
       pandas: 1.4.1
   matplotlib: 3.5.1
       joblib: 1.1.0
threadpoolctl: 3.0.0

Built with OpenMP: True

threadpoolctl info:
       user_api: openmp
   internal_api: openmp
         prefix: libgomp
       filepath: /opt/conda/lib/python3.9/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0
        version: None
    num_threads: 64

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: /opt/conda/lib/python3.9/site-packages/numpy.libs/libopenblas64_p-r0-2f7c42d4.3.18.so
        version: 0.3.18
threading_layer: pthreads
   architecture: SkylakeX
    num_threads: 64

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: /opt/conda/lib/python3.9/site-packages/scipy.libs/libopenblasp-r0-8b9e111f.3.17.so
        version: 0.3.17
threading_layer: pthreads
   architecture: SkylakeX
    num_threads: 64

imbalanced-learn==0.9.0
@chkoar
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chkoar commented Feb 24, 2022

Thanks for reporting. This PR bumbed the version of joblib and removed the_joblib_parallel_args.

@glemaitre
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We should merge the fix just after scikit-learn is released.

@4d30
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4d30 commented May 12, 2022

What can we do in the meantime?

@glemaitre
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Just do not update yet. I will make a release in the weekend as scikit-learn has been released today.

@MaximeKan
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Any update? This seems to be broken now :/

@glemaitre
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This will not solve the issue. You just need to wait for the release.

@lesteve lesteve mentioned this issue May 16, 2022
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@glemaitre
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This has been fixed in master and the release is already on PyPI and soon in conda-forge.

@ngupta23
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Is there a way to still have imblearn be compatible with sklearn 1.0.2?

@glemaitre
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You can install pin to the 0.9.0 version. There is no difference in the features available.

@Genarito
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Hi, is there any update on this issue? I need to use the n_iter_ attribute in SVM (present in 1.1.1 version) but if upgrade, this breaks the project.

@glemaitre
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glemaitre commented Jun 27, 2022 •
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The last version of imbalanced-learn (0.9.1) is compatible with the last version of scikit-learn (1.1.1).

@now-youre-gittin-it
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[Solution] I've been facing ImportError: cannot import name '_joblib_parallel_args' from 'sklearn.utils.fixes' while trying to import ADASYN from imblearn to perform oversampling. The strange thing is there was no error the first one or two times I ran my code, but it started showing up subsequently.

This was my code for reference:

from imblearn.over_sampling import ADASYN
print('Before')
print(y_train.value_counts())

# Oversampling the training dataset
ada= ADASYN (random_state=130)
X_train_ada, y_train_ada= ada.fit_resample(X_train, y_train)

print('After')
print(y_train_ada.value_counts())

Package Versions: sckit-learn v0.9.0 and scikit-learn v1.1.2
I can't downgrade scikit-learn to 1.1.1 because it might have ramifications for other projects.

Strange solution that worked:
I moved the import statement i.e. from imblearn.over_sampling import ADASYN to a separate cell (VSCode notebook).
The rest of the code went in a separate cell, and now when I exceute the 2 cells, voila, no error!

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8 participants
@glemaitre @chkoar @Genarito @torshind @MaximeKan @ngupta23 @4d30 @now-youre-gittin-it
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