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Reference Issue
No existing issue found. This concerns validation of dictionary values, separately from the zero-shrinkage bootstrap behavior in #1228.
What does this implement/fix? Explain your changes.
RandomOverSampler(shrinkage={0: np.nan})accepts the factor and generates NaN samples from finite input. Positive infinity is also accepted, while strings, None and complex values raise a TypeError during comparison. Scalar shrinkage already rejects these values through its parameter constraint.Apply the same nonnegative real-number Interval constraint to each dictionary value and report the offending class in a ValueError. Document the finite-value requirement and add dense/CSR regression cases plus scalar/dictionary equivalence checks.
Before the fix, 10 new cases fail and 6 pass. With the fix, all 117 tests under
imblearn/over_samplingpass on Windows with Python 3.12.14, NumPy 2.5.4, SciPy 1.17.1 and scikit-learn 1.9.1. The same 117 tests also pass with the minimum supported scikit-learn 1.4.2, NumPy 1.26.4, SciPy 1.11.4 and pandas 2.2.3. Ruff 0.4.8 and Black 23.3.0 checks pass for both changed Python files.Any other comments?
The existing support for non-finite input with
shrinkage=Noneis preserved. The release note includes this PR number.