Skip to content

Conversation

icfaust
Copy link
Contributor

@icfaust icfaust commented Dec 2, 2024

Description

Temp


PR should start as a draft, then move to ready for review state after CI is passed and all applicable checkboxes are closed.
This approach ensures that reviewers don't spend extra time asking for regular requirements.

You can remove a checkbox as not applicable only if it doesn't relate to this PR in any way.
For example, PR with docs update doesn't require checkboxes for performance while PR with any change in actual code should have checkboxes and justify how this code change is expected to affect performance (or justification should be self-evident).

Checklist to comply with before moving PR from draft:

PR completeness and readability

  • I have reviewed my changes thoroughly before submitting this pull request.
  • I have commented my code, particularly in hard-to-understand areas.
  • I have updated the documentation to reflect the changes or created a separate PR with update and provided its number in the description, if necessary.
  • Git commit message contains an appropriate signed-off-by string (see CONTRIBUTING.md for details).
  • I have added a respective label(s) to PR if I have a permission for that.
  • I have resolved any merge conflicts that might occur with the base branch.

Testing

  • I have run it locally and tested the changes extensively.
  • All CI jobs are green or I have provided justification why they aren't.
  • I have extended testing suite if new functionality was introduced in this PR.

Performance

  • I have measured performance for affected algorithms using scikit-learn_bench and provided at least summary table with measured data, if performance change is expected.
  • I have provided justification why performance has changed or why changes are not expected.
  • I have provided justification why quality metrics have changed or why changes are not expected.
  • I have extended benchmarking suite and provided corresponding scikit-learn_bench PR if new measurable functionality was introduced in this PR.


class DummyEstimator(BaseEstimator):

def fit(self, X, y=None):
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

This looks like an accidental change.

multi_output=True,
accept_sparse=False,
dtype=[np.float64, np.float32],
dtype=[xp.float64, xp.float32],
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Shouldn't the call to get this 'xp' pass all of the data arrays? (X+y+sample_weight)


def _estimators_(self):
"""This attribute provides lazy creation of scikit-learn conformant
Decision Trees used for analysis in such as 'apply'. This will stay
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Suggested change
Decision Trees used for analysis in such as 'apply'. This will stay
Decision Trees used for analysis in methods such as 'apply'. This will stay

),
]
)
# TODO: Fix to support integers as input
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Would this be implemented with the changes in this PR?

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Not sure yet. we will see.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Labels

None yet

Projects

None yet

Development

Successfully merging this pull request may close these issues.

2 participants