Add max_train_samples, fit_predict, and missing variable handling to QRF#170
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Add max_train_samples, fit_predict, and missing variable handling to QRF#170
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Adds three convenience features that downstream consumers (policyengine-us-data, policyengine-uk-data) currently implement manually: 1. max_train_samples: auto-subsample training data to reduce memory while preserving sequential covariance (the correct fix for #96) 2. fit_predict(): combines fit + predict + gc cleanup in one call 3. fit_predict() zero-fills variables missing from X_train instead of erroring Fixes #169 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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- Add .reset_index(drop=True) after subsampling to prevent index corruption during sequential imputation - Use skip_missing=True in fit_predict() instead of reimplementing _handle_missing_variables() logic - Validate max_train_samples is positive Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Summary
Adds three convenience features to
QRFthat downstream consumers (policyengine-us-data, policyengine-uk-data) currently implement manually:max_train_samplesparameter onQRF.__init__(): auto-subsamples training data to reduce memory while preserving sequential covariance (the correct fix for Sequential imputation runs out of memory with many variables #96)fit_predict()method: combines fit + predict + gc cleanup in one callfit_predict()zero-fills variables missing fromX_traininstead of erroring, so callers don't need to pre-filterFixes #169
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