Merge nucleic/sleek-ember-seal-uady into dev

tests/test_audit_data.py

@@ -0,0 +1,92 @@
+import sys
+import tempfile
+import unittest
+from pathlib import Path
+
+import numpy as np
+
+
+MODULE_DIR = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(MODULE_DIR))
+
+import audit_data
+from purpose_data import SourceRecord
+
+
+def record(index, purpose, slice_name="core", language="en"):
+    return SourceRecord(
+        value={
+            "prompt": f"prompt {index}",
+            "purpose": purpose,
+            "secondary": None,
+            "mixed": False,
+            "difficulty": 0.5,
+            "slice": slice_name,
+            "lang": language,
+        },
+        source=Path("source.jsonl"),
+        line=index + 1,
+    )
+
+
+class ReviewSampleTests(unittest.TestCase):
+    def test_sample_is_exact_and_deterministic(self):
+        records = [
+            record(index, audit_data.LABELS[index % len(audit_data.LABELS)])
+            for index in range(101)
+        ]
+        first = audit_data.stratified_review_sample(records, fraction=0.1, seed=42)
+        second = audit_data.stratified_review_sample(records, fraction=0.1, seed=42)
+        self.assertEqual(10, len(first))
+        self.assertEqual(
+            [item.value["prompt"] for item in first],
+            [item.value["prompt"] for item in second],
+        )
+
+    def test_review_csv_has_blank_reviewer_fields(self):
+        records = [record(0, "planning")]
+        with tempfile.TemporaryDirectory() as temporary:
+            path = Path(temporary) / "review.csv"
+            audit_data.write_review_csv(path, records)
+            text = path.read_text(encoding="utf-8")
+        self.assertIn("reviewedPurpose", text)
+        self.assertIn("prompt 0", text)
+
+
+class SemanticCandidateTests(unittest.TestCase):
+    def test_threshold_depends_on_label_agreement(self):
+        embeddings = np.asarray(
+            [
+                [1.0, 0.0],
+                [0.98, 0.2],
+                [0.98, -0.2],
+            ],
+            dtype=np.float32,
+        )
+        candidates = audit_data.semantic_candidates(
+            embeddings,
+            ["planning", "planning", "writing"],
+            same_label_threshold=0.97,
+            cross_label_threshold=0.99,
+            neighbors=2,
+            block_size=2,
+        )
+        pairs = {(item.left, item.right) for item in candidates}
+        self.assertIn((0, 1), pairs)
+        self.assertNotIn((0, 2), pairs)
+
+    def test_candidate_pairs_are_deduplicated(self):
+        embeddings = np.asarray([[1.0, 0.0], [1.0, 0.0]], dtype=np.float32)
+        candidates = audit_data.semantic_candidates(
+            embeddings,
+            ["review", "review"],
+            same_label_threshold=0.9,
+            cross_label_threshold=0.9,
+            neighbors=1,
+            block_size=1,
+        )
+        self.assertEqual(1, len(candidates))
+
+
+if __name__ == "__main__":
+    unittest.main()