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[ENH] Return word vocabulary/histogram when fitting IndividualBOSS
#8600
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IndividualBOSS
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This is great!
small change requests:
- can you kindly add this new attribute to the docstring, in the "Attributes" section?
- can you kindly add a test that the expected type of attribute and size is returned? Add it in
test_boss
as a new test. - please revert changes to the
docs/source/examples
symlink
Added a new test in test_boss.py and also updated the Attributes section in _boss.py
I removed get_fitted_params(self) |
docs/source/examples
Outdated
@@ -1 +1 @@ | |||
../../examples | |||
../../examples |
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please revert this change
classes_ : list | ||
The classes labels. | ||
histograms_ : list | ||
Instance word histograms. |
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should say, a list of what, length, etc. How the elements are to be interpreted
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Great, thanks! Minor requests above.
I realize that for large datasets, this fills the estimator memory.
Could we add a parameter store_histogram: bool
(default=False
) that executes the new code only if it is True
?
added a new test in test_boss for testing new changes. added a method
I added a parameter store_histogram and also added a test in test_boss for testing (if not required it can be removed). |
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Great!
Small requests only:
- can you move the
store_histogram
parameter to beforen_jobs
? - can you please revert the changes to the "examples" file?
I tried to resolve that symlink thing but I was not able to do. So I think I should stop working on this and hand it to you or you have any other suggestion on it. |
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I reverted it for you now, thanks for the contribution!
@Pradyumn-cloud, why did you delete the branch? I wanted to merge this. |
Reference Issues/PRs
Fixes #8580
fix-
Return word vocabulary/histogram when fitting IndividualBOSS:
Added a histograms_ attribute to the IndividualBOSS classifier to provide a user-friendly, dictionary-based representation of the SFA transformation. This improves the interpretability of the model.
Robustness: Added defensive checks to handle cases where feature selection may result in an empty set of words, preventing potential runtime errors.