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Add total_gain and total_cover importance measures (#3498)

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Add `'total_gain'` and `'total_cover'` as possible `importance_type`
arguments to `Booster.get_score` in the Python package.

`get_score` already accepts a `'gain'` argument, which returns each
feature's average gain over all of its splits.  `'total_gain'` does the
same, but returns a total rather than an average.  This seems more
intuitively meaningful, and also matches the behavior of the R package's
`xgb.importance` function.

I also added an analogous `'total_cover'` command for consistency.

This should resolve #3484.
This commit is contained in:
jqmp
2018-07-23 03:30:55 -04:00
committed by Philip Hyunsu Cho
parent a1505de631
commit e9a97e0d88
2 changed files with 26 additions and 7 deletions
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4
tests/python/test_shap.py
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@@ -33,10 +33,14 @@ class TestSHAP(unittest.TestCase):
scores2 = bst.get_score(importance_type='weight')
scores3 = bst.get_score(importance_type='cover')
scores4 = bst.get_score(importance_type='gain')
scores5 = bst.get_score(importance_type='total_cover')
scores6 = bst.get_score(importance_type='total_gain')
assert len(scores1) == len(features)
assert len(scores2) == len(features)
assert len(scores3) == len(features)
assert len(scores4) == len(features)
assert len(scores5) == len(features)
assert len(scores6) == len(features)
# check backwards compatibility of get_fscore
fscores = bst.get_fscore()