Issue 2808 - Fix sklearn .fit method incorrectly applies arg max bug

[wasibabi]

Description

This is a proposed fix to issue 2808

• The issue is that ScikitlearnClassifier.fit unconditionally applies np.argmax. This works when y are one-hot encoded labels but fails for 1D vectors of class indices. Based on the docs I believe ScikitlearnClassifier.fit is supposed to support both
• The fix is to conditionally apply np.argmax only for one-hot encoded lables

Type of change

Please check all relevant options.

• Improvement (non-breaking)
• Bug fix (non-breaking)
• New feature (non-breaking)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

Testing

Please describe the tests that you ran to verify your changes. Consider listing any relevant details of your test configuration.

• Reproduce the bug on main
  • with y = np.array([0, 0, 0, 1, 1, 1])
  • run clf.fit(...)
  • Get `numpy.exceptions.AxisError: axis 1 is

out of bounds for array of dimension 1`

• Reproduce one-hot encoding working on main
  • with y = np.array([[1, 0], [1, 0], [1, 0], [0, 1], [0, 1], [0, 1]])
  • run clf.fit(...)
  • run succeeds. Can print out clf.nb_classes and clf.model.classes

• Demo fix on bugfix branch
  • same test

• Demo bugfix branch handles one-hot encoding
  • same test

Test Configuration:

• OS: MacOS Tahoe
• Python version: 3.11.8
• ART version or commit number: latest master
• TensorFlow / Keras / PyTorch version

Checklist

• My code follows the style guidelines of this project
• I have performed a self-review of my own code
• I have commented my code
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes
• My changes have been tested using both CPU and GPU devices