Add hard-negative proposal classifier pipeline

tests/test_building_proposal_classifier.py

@@ -0,0 +1,40 @@
+from __future__ import annotations
+
+import importlib.util
+from pathlib import Path
+
+import pytest
+
+
+ROOT = Path(__file__).resolve().parents[1]
+
+
+def load(name: str):
+    path = ROOT / "scripts" / f"{name}.py"
+    spec = importlib.util.spec_from_file_location(name, path)
+    assert spec and spec.loader
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+miner = load("build_building_proposal_classifier_dataset")
+trainer = load("train_building_proposal_classifier")
+
+
+def test_classify_proposals_consumes_reference_once() -> None:
+    reference = [(0.0, 0.0, 10.0, 10.0)]
+    proposals = [((0.0, 0.0, 10.0, 10.0), 0.9), ((0.0, 0.0, 10.0, 10.0), 0.8)]
+    assert [item[0] for item in miner.classify_proposals(proposals, reference, 0.25)] == ["positive", "negative"]
+
+
+def test_eligible_tiles_rejects_protected_manifest_split() -> None:
+    manifest = {"samples": [{"sample_slug": "x", "region": "flanders", "split": "test"}]}
+    summary = {"tiles": [{"sample_slug": "x", "split": "train", "image_path": "x.png"}]}
+    with pytest.raises(ValueError, match="protected"):
+        miner.eligible_tiles(summary, manifest, "flanders")
+
+
+def test_binary_metrics() -> None:
+    result = trainer.binary_metrics([0.9, 0.8, 0.2, 0.1], [1, 0, 1, 0])
+    assert result == {"tp": 1, "fp": 1, "fn": 1, "precision": 0.5, "recall": 0.5, "f1": 0.5}