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v0.5.45 — Active-learning batch-2 selector (Loop 24) - #143
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Add batch-2 candidate selector for the calibration loop: - active_learning/selector.py: select_batch_2() with uncertainty sampling, diversity vs batch-1, configurable safety/selectivity gates, and min-uncertainty-probe guarantee - CLI : reads pool CSV and batch-1 IDs, writes JSON manifest - 11 tests covering: basic selection, safety gate, selectivity gate, uncertainty probe, diversity, empty pool, all-gated-out, selection_reason, metadata shape, CLI success/error paths - 1824 total tests passing
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Loop 24 — Second-batch selection logic
Bottleneck
The recalibration pipeline could compute weight updates and produce a reviewable report, but had no way to pick the next 8-12 candidates for lab testing.
Changes
active_learning/selector.py:select_batch_2()with uncertainty sampling (disagreement + ensemble proximity to 0.5), diversity vs batch 1 (sequence similarity), safety/selectivity gates, and min-uncertainty-probe guaranteeselect-batch: reads pool CSV and batch-1 IDs, writes JSON manifest withBatchSelectiondataclassVerification
Next
Loop 25 — Active-learning benchmark on synthetic data