Spread capped failure samples across weak contexts
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@@ -279,6 +279,42 @@ def test_region_cap_rotates_repeats_between_sampling_rounds() -> None:
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assert second_metadata["sampling_round"] == 2
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def test_region_cap_preserves_failed_context_positive_before_hard_negative() -> None:
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manifest = {"samples": [
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{"sample_slug": "target", "split": "train", "region": "flanders", "context": "industrial"},
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{"sample_slug": "negative", "split": "train", "region": "flanders", "context": "industrial-hard-negative"},
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{"sample_slug": "wa", "split": "train", "region": "wallonia", "context": "rural-town"},
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{"sample_slug": "br", "split": "train", "region": "brussels", "context": "dense-urban"},
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]}
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summary = {"tiles": [
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{"sample_slug": "target", "split": "train", "label_count": 2, "image_path": "/tmp/target.png"},
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{"sample_slug": "negative", "split": "train", "label_count": 0, "image_path": "/tmp/negative.png"},
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{"sample_slug": "wa", "split": "train", "label_count": 1, "image_path": "/tmp/wa.png"},
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{"sample_slug": "br", "split": "train", "label_count": 1, "image_path": "/tmp/br.png"},
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]}
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assessment = {
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"status": "continue_training_loop",
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"gates": {"min_region_f1": .45, "min_region_precision": .5, "min_region_recall": .4,
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"max_pure_empty_false_positives": 0},
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"calibration": {
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"regions": {
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"flanders": {"f1": .2, "precision": .2, "recall": .3},
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"wallonia": {"f1": .6, "precision": .6, "recall": .6},
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"brussels": {"f1": .6, "precision": .6, "recall": .6},
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},
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"samples": {"target": {"f1": .2, "precision": .2, "recall": .3}},
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},
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}
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paths, metadata = MODULE.build_sampling(
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summary=summary, manifest=manifest, assessment=assessment, max_region_share=.65,
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)
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assert paths.count(str(Path("/tmp/target.png").resolve())) == 2
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assert paths.count(str(Path("/tmp/negative.png").resolve())) == 1
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assert metadata["priority_positive_repeat_count"] == 4
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def test_precision_guard_band_keeps_near_gate_region_stabilized() -> None:
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manifest = {"samples": [
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{"sample_slug": "wa-positive", "split": "train", "region": "wallonia", "context": "rural-town"},
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