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REF-1523-Recursive feature ranking/evolution #86

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merged 16 commits into from
Oct 25, 2024
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SkBlaz
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@SkBlaz SkBlaz commented Oct 23, 2024

@SkBlaz SkBlaz requested a review from bmramor October 23, 2024 12:52
@devxappuser devxappuser changed the title Recursive feature ranking/evolution REF-1523-Recursive feature ranking/evolution Oct 23, 2024
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@SkBlaz SkBlaz requested a review from ma-zni October 24, 2024 15:39
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miha-jenko
miha-jenko previously approved these changes Oct 24, 2024
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@miha-jenko miha-jenko left a comment

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Looks good.

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@jasnaurbancic jasnaurbancic left a comment

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Can't wait to try it out!

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@bmramor bmramor left a comment

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Nice refactoring and automation of evolution!
Would be nice to note whether the MI evolution works in any way, since this seems a bit experimental.
The projections seems like a good idea, since the old evolution had a relatively large drop for longer feature sets... It might be worth investigating the optimal dimensions, though.

tmp_feature = input_dataframe[feature].tolist()
for enx, el in enumerate(tmp_feature):
combined_feature[enx] = str(
internal_hash(
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do we still need internal_hash?

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yeah, for now


logger = logging.getLogger('syn-logger')
logger.setLevel(logging.DEBUG)

num_folds = 4
NUM_FOLDS = 2
SVD_DIMS = 2
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why 2?

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Just a default for now, was fast.

@SkBlaz SkBlaz merged commit 74b042c into main Oct 25, 2024
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4 participants