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Add time-dependent prediction distribution data preparation - #430
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Implement `_prepare_probs_distribution_data_times` in `src/rtichoke/performance_data/probs_distribution.py` to prepare raw prediction histogram data and cutoff-region Aalen-Johansen (AJ) operating summaries. Add focused unit tests in `tests/test_probs_distribution_times.py`. Co-authored-by: uriahf <11351434+uriahf@users.noreply.github.com>
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Implement `_prepare_probs_distribution_data_times` in `src/rtichoke/performance_data/probs_distribution.py` to prepare raw prediction histogram data and cutoff-region Aalen-Johansen (AJ) operating summaries. Add focused unit tests in `tests/test_probs_distribution_times.py`. Co-authored-by: uriahf <11351434+uriahf@users.noreply.github.com>
PR Description:
src/rtichoke/performance_data/probs_distribution.pytests/test_probs_distribution_times.pyAdded
_prepare_probs_distribution_data_times(...)insrc/rtichoke/performance_data/probs_distribution.pyreturning_PredictionDistributionTimesDatawith keys:bins: Raw probability-bin observation counts (n_observations,n_real_positive,n_real_negative,n_real_competing) conserving N without censoring/competing adjustments.rank_bins: Raw probability-quantile rank-bin observation counts conserving N.cutoff_region_aj: Exactly two rows per operating cutoff (predicted_positivesandpredicted_negatives), containing estimated AJ state masses (real_positives_est,real_negatives_est,real_competing_est,real_censored_est) and derived confusion matrix counts (true_positives,false_positives,true_negatives,false_negatives).operating_points: One row per operating cutoff with performance metrics matching existing time-dependent performance output.uv run ruff check .passed.uv run ruff format --check .passed.uv run ty check src/rtichokepassed.uv run pytestpassed (412 passed, 12 skipped).PR created automatically by Jules for task 10490003742230139151 started by @uriahf