fix: guard against all-NA scores in calc_correlations#316
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drussellmrichie wants to merge 2 commits intolarsiusprime:masterfrom
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fix: guard against all-NA scores in calc_correlations#316drussellmrichie wants to merge 2 commits intolarsiusprime:masterfrom
drussellmrichie wants to merge 2 commits intolarsiusprime:masterfrom
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When a model group has very few training samples (e.g. sparse commercial or post-valuation sub-models), KFold raises ValueError if n_splits exceeds the number of rows. Cap n_splits at len(X) and return a penalty MAPE of 1.0 when fewer than 2 samples are available, so Optuna can still complete gracefully. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When a model group has very few sales (e.g. vacant land with 9 records),
pandas' idxmin() raises ValueError('Encountered all NA values') instead
of returning NaN as in older pandas versions. Break out of the variable
elimination loop early when all scores are NA so downstream code is
unaffected.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Problem
calc_correlationscrashes with aKeyErrororValueErroron.idxmin()when the computedscoreSeries is all-NA. This occurs when there are too few samples to compute meaningful correlations (e.g. very small model groups or sparse data).Fix
Add an early
breakwhenscore.isna().all()is detected, exiting the variable-dropping loop gracefully instead of crashing.Note: this branch also includes the fix from #315 (
fix/tuning-min-samples-cv), on which it stacks.