FIX make sure to accept "minority" as a valid strategy in over-samplers#964
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updated majority to minority in str options:
_parameter_constraints: dict = {
"sampling_strategy": [
Interval(numbers.Real, 0, 1, closed="right"),
StrOptions({"auto", "minority", "not minority", "not majority", "all"}),
Mapping,
callable,
],
"random_state": ["random_state"],
}
update oversampler base.py
Codecov ReportBase: 96.50% // Head: 94.25% // Decreases project coverage by
Additional details and impacted files@@ Coverage Diff @@
## master #964 +/- ##
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- Coverage 96.50% 94.25% -2.25%
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Files 103 103
Lines 7264 7280 +16
Branches 1068 1071 +3
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- Hits 7010 6862 -148
- Misses 147 312 +165
+ Partials 107 106 -1
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I added some non-regression tests and an entry in the changelog. |
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Thanks @Prakhyath07 I will fix the CI builds that are failing. There are not related. |
…rs (#964) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
From my side i didn't find any other issue |
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where is 0.10.1? |
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On PyPI and conda-forge, e.g. https://pypi.org/project/imbalanced-learn/ |
Reference Issue
What does this implement/fix? Explain your changes.
while using smapling strategy ="minority" we were getting error. i found issue in base.py of oversampler where in _parameter constraint majority was used in stroptions instead of minority
Any other comments?