style: format
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2
benchmarks/enron_emails/data/.gitignore
vendored
2
benchmarks/enron_emails/data/.gitignore
vendored
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downloads/
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downloads/
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@@ -416,6 +416,7 @@ def main():
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# Build non-compact index for fast binary search (recompute_embeddings=False)
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# Build non-compact index for fast binary search (recompute_embeddings=False)
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from pathlib import Path
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from pathlib import Path
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index_path = Path(args.index)
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index_path = Path(args.index)
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non_compact_index_path = str(index_path.parent / f"{index_path.stem}_noncompact.leann")
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non_compact_index_path = str(index_path.parent / f"{index_path.stem}_noncompact.leann")
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enron_eval.create_non_compact_index_for_comparison(non_compact_index_path)
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enron_eval.create_non_compact_index_for_comparison(non_compact_index_path)
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@@ -428,6 +429,7 @@ def main():
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results_out["stage3"] = sweep
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results_out["stage3"] = sweep
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# Persist default stage 3 results near the index for Stage 4 auto-pickup
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# Persist default stage 3 results near the index for Stage 4 auto-pickup
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from pathlib import Path
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from pathlib import Path
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default_stage3_path = Path(args.index).parent / "enron_stage3_results.json"
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default_stage3_path = Path(args.index).parent / "enron_stage3_results.json"
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with open(default_stage3_path, "w", encoding="utf-8") as f:
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with open(default_stage3_path, "w", encoding="utf-8") as f:
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json.dump({"stage3": sweep}, f, indent=2)
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json.dump({"stage3": sweep}, f, indent=2)
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@@ -476,7 +478,9 @@ def main():
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prev = json.load(f)
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prev = json.load(f)
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complexity = prev.get("stage3", {}).get("best_complexity")
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complexity = prev.get("stage3", {}).get("best_complexity")
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if complexity is None:
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if complexity is None:
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raise SystemExit("❌ Stage 4: No --complexity and no best_complexity found in saved Stage 3 results")
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raise SystemExit(
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"❌ Stage 4: No --complexity and no best_complexity found in saved Stage 3 results"
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)
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print(f"📥 Using best complexity from saved Stage 3: {complexity}")
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print(f"📥 Using best complexity from saved Stage 3: {complexity}")
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else:
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else:
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raise SystemExit(
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raise SystemExit(
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@@ -274,7 +274,9 @@ class HNSWSearcher(BaseSearcher):
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return self._id_map[x]
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return self._id_map[x]
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return str(x)
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return str(x)
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string_labels = [[map_label(int(l)) for l in batch_labels] for batch_labels in labels]
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string_labels = [
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[map_label(int(label)) for label in batch_labels] for batch_labels in labels
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]
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else:
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else:
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string_labels = [
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string_labels = [
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[str(int_label) for int_label in batch_labels] for batch_labels in labels
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[str(int_label) for int_label in batch_labels] for batch_labels in labels
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