Cap regional failure oversampling

backend/tests/test_failure_driven_yolo_sampling.py

@@ -55,7 +55,7 @@ def test_sampling_repeats_only_failed_region_train_tiles() -> None:
         "background": {"pure_empty_false_positives": 2},
     }
     paths, metadata = MODULE.build_sampling(
-        summary=summary, manifest=manifest, assessment=assessment
+        summary=summary, manifest=manifest, assessment=assessment, max_region_share=1.0
     )
     assert paths.count(str(Path("/tmp/fl-pos.png").resolve())) == 3
     assert paths.count(str(Path("/tmp/fl-neg.png").resolve())) == 4
@@ -87,7 +87,7 @@ def test_sampling_can_use_calibration_before_test_is_opened() -> None:
         "background": None,
     }
     paths, metadata = MODULE.build_sampling(
-        summary=summary, manifest=manifest, assessment=assessment
+        summary=summary, manifest=manifest, assessment=assessment, max_region_share=1.0
     )
     assert len(paths) == 3
     assert metadata["failure_evidence_source"] == "calibration"
@@ -120,6 +120,7 @@ def test_precision_correction_can_balance_positive_and_negative_tiles() -> None:
         assessment=assessment,
         precision_positive_repeat=2,
         negative_repeat=3,
+        max_region_share=1.0,
     )
 
     assert paths.count(str(Path("/tmp/fl-pos.png").resolve())) == 2
@@ -157,7 +158,9 @@ def test_sampling_targets_failed_calibration_contexts_without_using_protected_ti
         },
     }
 
-    paths, metadata = MODULE.build_sampling(summary=summary, manifest=manifest, assessment=assessment)
+    paths, metadata = MODULE.build_sampling(
+        summary=summary, manifest=manifest, assessment=assessment, max_region_share=1.0
+    )
 
     assert paths.count(str(Path("/tmp/industry-pos.png").resolve())) == 5
     assert paths.count(str(Path("/tmp/industry-neg.png").resolve())) == 6
@@ -165,3 +168,42 @@ def test_sampling_targets_failed_calibration_contexts_without_using_protected_ti
     assert not any("protected" in path for path in paths)
     assert metadata["weak_recall_contexts"] == ["flanders:industrial"]
     assert metadata["weak_precision_contexts"] == ["flanders:industrial"]
+
+
+def test_region_cap_drops_only_repeats_and_preserves_every_unique_tile() -> None:
+    manifest = {"samples": [
+        {"sample_slug": "fl", "split": "train", "region": "flanders", "context": "industrial"},
+        {"sample_slug": "wa", "split": "train", "region": "wallonia", "context": "rural-town"},
+        {"sample_slug": "br", "split": "train", "region": "brussels", "context": "dense-urban"},
+    ]}
+    summary = {"tiles": [
+        {"sample_slug": "fl", "split": "train", "label_count": 2, "image_path": f"/tmp/fl-{index}.png"}
+        for index in range(4)
+    ] + [
+        {"sample_slug": "wa", "split": "train", "label_count": 2, "image_path": f"/tmp/wa-{index}.png"}
+        for index in range(2)
+    ] + [
+        {"sample_slug": "br", "split": "train", "label_count": 2, "image_path": f"/tmp/br-{index}.png"}
+        for index in range(2)
+    ]}
+    assessment = {
+        "status": "continue_training_loop",
+        "gates": {"min_region_f1": .45, "min_region_precision": .5, "min_region_recall": .4,
+                  "max_pure_empty_false_positives": 0},
+        "calibration": {"regions": {
+            "flanders": {"f1": .2, "precision": .3, "recall": .2},
+            "wallonia": {"f1": .6, "precision": .6, "recall": .6},
+            "brussels": {"f1": .6, "precision": .6, "recall": .6},
+        }},
+    }
+
+    paths, metadata = MODULE.build_sampling(
+        summary=summary, manifest=manifest, assessment=assessment,
+        positive_repeat=5, max_region_share=.65,
+    )
+
+    assert all(str(Path(f"/tmp/fl-{index}.png").resolve()) in paths for index in range(4))
+    assert metadata["pre_cap_entries_by_region"]["flanders"] == 20
+    assert metadata["sampled_entries_by_region"]["flanders"] == 7
+    assert metadata["dropped_region_repeat_count"] == 13
+    assert metadata["sampled_entries_by_region"]["flanders"] / len(paths) <= .65

docs/BELGIUM_BUILDING_TRAINING_LOOP.md

@@ -108,6 +108,11 @@ modes without copying a protected AOI into training. The sampling evidence
 records both context sets and repeat factors. When no matching train context
 exists, regional sampling remains active and the missing context becomes a
 concrete input for the next immutable corpus expansion.
+Failure weighting may not let one region exceed 65% of the sampled entries.
+The deterministic cap removes only repeated entries and retains every unique
+train tile at least once; manifests record pre-cap counts, final counts and the
+number of dropped repeats. This keeps a weak region prominent without turning
+the national detector into a single-region expert.
 The checkpointed orchestrator invokes this builder after every rejected
 iteration, stores its checksum in `training-loop-state.json`, and uses the
 resulting dataset YAML for the next checkpoint. A restart resumes both the

docs/CODEX_EXECUTION_LOG.md

@@ -15,6 +15,10 @@
   loop now requires and hashes the train tile-quality report, and refuses
   missing counters instead of treating absent invalid/blank-label evidence as
   zero.
+- Capped failure-driven regional oversampling at 65% after the first v31
+  sampling assigned 75.5% of entries to Flanders. The cap retains every unique
+  tile, removes repeats only and writes pre/post regional counts into the
+  checksummed sampling evidence.
 
 ## 2026-07-27 - Guest demo and product professionalization
 

scripts/build_failure_driven_yolo_sampling.py

@@ -6,6 +6,7 @@ from __future__ import annotations
 import argparse
 import hashlib
 import json
+import math
 from collections import Counter
 from pathlib import Path
 from typing import Any
@@ -38,6 +39,7 @@ def build_sampling(
     precision_positive_repeat: int = 1,
     context_positive_repeat: int = 5,
     context_negative_repeat: int = 6,
+    max_region_share: float = 0.65,
 ) -> tuple[list[str], dict[str, Any]]:
     if assessment.get("status") != "continue_training_loop":
         raise ValueError("Failure-driven sampling requires a failed assessment")
@@ -49,6 +51,8 @@ def build_sampling(
         context_negative_repeat,
     ) < 1:
         raise ValueError("Repeat factors must be positive")
+    if not 0 < max_region_share <= 1:
+        raise ValueError("max_region_share must be in (0, 1]")
 
     samples = {item["sample_slug"]: item for item in manifest["samples"]}
     gates = assessment["gates"]
@@ -91,8 +95,8 @@ def build_sampling(
         ):
             weak_precision_contexts.add(key)
 
-    image_paths: list[str] = []
+    base_paths_by_region: dict[str, list[str]] = {}
-    repeat_counts: Counter[str] = Counter()
+    extra_paths_by_region: dict[str, list[str]] = {}
     selected_samples: set[str] = set()
     protected_samples: set[str] = set()
     for tile in summary["tiles"]:
@@ -120,10 +124,34 @@ def build_sampling(
                 else negative_repeat
             )
         path = str(Path(tile["image_path"]).resolve())
-        image_paths.extend([path] * repeat)
+        base_paths_by_region.setdefault(region, []).append(path)
-        repeat_counts[region] += repeat
+        extra_paths_by_region.setdefault(region, []).extend([path] * (repeat - 1))
         selected_samples.add(tile["sample_slug"])
 
+    pre_cap_counts = Counter({
+        region: len(paths) + len(extra_paths_by_region[region])
+        for region, paths in base_paths_by_region.items()
+    })
+    capped_counts = Counter(pre_cap_counts)
+    if max_region_share < 1:
+        while capped_counts:
+            region, count = max(capped_counts.items(), key=lambda item: (item[1], item[0]))
+            total = sum(capped_counts.values())
+            if count / total <= max_region_share:
+                break
+            others = total - count
+            limit = math.floor(max_region_share / (1 - max_region_share) * others)
+            limit = max(limit, len(base_paths_by_region[region]))
+            if limit >= count:
+                break
+            capped_counts[region] = limit
+    image_paths: list[str] = []
+    for region in sorted(base_paths_by_region):
+        base_paths = base_paths_by_region[region]
+        extra_limit = capped_counts[region] - len(base_paths)
+        image_paths.extend(base_paths)
+        image_paths.extend(extra_paths_by_region[region][:extra_limit])
+    repeat_counts = Counter({region: capped_counts[region] for region in capped_counts})
     if not image_paths:
         raise ValueError("No train-only tiles selected")
     metadata = {
@@ -141,6 +169,7 @@ def build_sampling(
         "precision_positive_repeat": precision_positive_repeat,
         "context_positive_repeat": context_positive_repeat,
         "context_negative_repeat": context_negative_repeat,
+        "max_region_share": max_region_share,
         "source_train_tile_count": sum(
             1
             for tile in summary["tiles"]
@@ -148,6 +177,8 @@ def build_sampling(
         ),
         "sampled_train_entry_count": len(image_paths),
         "sampled_entries_by_region": dict(sorted(repeat_counts.items())),
+        "pre_cap_entries_by_region": dict(sorted(pre_cap_counts.items())),
+        "dropped_region_repeat_count": sum(pre_cap_counts.values()) - len(image_paths),
         "selected_train_sample_count": len(selected_samples),
         "protected_sample_count": len(protected_samples),
         "protected_samples_in_training": [],
@@ -166,6 +197,7 @@ def main() -> int:
     parser.add_argument("--precision-positive-repeat", type=int, default=1)
     parser.add_argument("--context-positive-repeat", type=int, default=5)
     parser.add_argument("--context-negative-repeat", type=int, default=6)
+    parser.add_argument("--max-region-share", type=float, default=0.65)
     args = parser.parse_args()
 
     summary = json.loads(args.summary.read_text(encoding="utf-8"))
@@ -180,6 +212,7 @@ def main() -> int:
         precision_positive_repeat=args.precision_positive_repeat,
         context_positive_repeat=args.context_positive_repeat,
         context_negative_repeat=args.context_negative_repeat,
+        max_region_share=args.max_region_share,
     )
     args.output_dir.mkdir(parents=True, exist_ok=True)
     train_list = args.output_dir / "train-failure-driven.txt"