[MRG+1] Added sample_weight parameter to ransac.fit #6140
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Please make sure no-one else is working on it, before issuing a pull request next time. |
sklearn/linear_model/ransac.py
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you can reuse has_fit_parameter from sklearn.utils.validation
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Can you add a test, testing this new feature? |
…d raise an exception when sample_weight is passed to base_estimators that dont support it.
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Thanks for the feedback @MechCoder , |
sklearn/linear_model/ransac.py
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you should document sample_weight as well
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Also, can you remove this block and document it under sample_weight?, i.e
sample_weight: array-like,
blah, blah.. raises Error if balh blah
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I have modified the tests as you suggested , they all passed. I'm still not sure about considering sample_weight when sampling ie subset_idxs |
sklearn/linear_model/ransac.py
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numpy array -> array-like
Also indent the next line just 4 spaces to the first line.
Look at how all the other parameters are documented
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@imaculate please run pep8 on both the files. (just the above cosmetic comments and that's it from me) |
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Thanks .. Done! |
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just make this a comment starting with #
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Done. |
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LGTM. Well Done. |
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Merged as 41526cb (after updating whatsnew). Thanks 🍻 🍷 !! |
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Thanks @MechCoder @ahojnnes .First contribution! 🍻 . |
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🍻 :) |
Resolved issue #6113 and #5871.
For estimators that used sample_weight parameter in the fit method, it was requested that similar should be applied to ransacRegressor. This has been intergrated.