| """Unified EmpathRAG Core comparison report.""" |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import csv |
| import json |
| import math |
| from pathlib import Path |
| import sys |
| import time |
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| sys.path.insert(0, str(ROOT / "src")) |
|
|
| from pipeline.core import EmpathRAGCore |
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|
| DEFAULT_DATASET = ROOT / "eval" / "empathrag_core_supervised.csv" |
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|
|
| def read_rows(path: Path) -> list[dict]: |
| with path.open("r", encoding="utf-8-sig", newline="") as handle: |
| return list(csv.DictReader(handle)) |
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|
|
| def split_semicolon(value: str) -> list[str]: |
| return [item.strip() for item in value.split(";") if item.strip() and item.strip().lower() != "none"] |
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|
|
| def source_hit(expected: list[str], actual: list[dict]) -> bool: |
| if not expected: |
| return True |
| names = [str(source.get("source_name", "")) for source in actual] |
| return any(any(e in name or name in e for name in names) for e in expected) |
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|
|
| def avoid_violation(avoid: list[str], actual: list[dict]) -> bool: |
| if not avoid: |
| return False |
| names = [str(source.get("source_name", "")) for source in actual] |
| return any(any(a in name or name in a for name in names) for a in avoid) |
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|
|
| def evaluate_mode(rows: list[dict], backend_mode: str) -> dict: |
| core = EmpathRAGCore() |
| cases = [] |
| route_correct = tier_correct = intercept_correct = source_hits = avoid_violations = 0 |
| unsafe_generation = no_action = ungrounded = 0 |
| latencies = [] |
|
|
| for row in rows: |
| t0 = time.perf_counter() |
| result = core.run_turn( |
| message=row["query_text"], |
| session_id=row["query_id"], |
| audience_mode=row.get("audience_mode") or "student", |
| backend_mode=backend_mode, |
| ) |
| elapsed = round((time.perf_counter() - t0) * 1000, 2) |
| latencies.append(elapsed) |
| expected_intercept = row["should_intercept"].strip().lower() == "yes" |
| preferred = split_semicolon(row.get("preferred_source_names", "")) |
| avoid = split_semicolon(row.get("avoid_source_names", "")) |
| flags = result.output_guard.get("flags", []) |
| route_match = result.route_label == row["route_label"] |
| tier_match = result.safety_tier == row["safety_tier"] |
| intercept_match = result.should_intercept == expected_intercept |
| hit = source_hit(preferred, result.retrieved_sources) |
| violation = avoid_violation(avoid, result.retrieved_sources) |
| route_correct += int(route_match) |
| tier_correct += int(tier_match) |
| intercept_correct += int(intercept_match) |
| source_hits += int(hit) |
| avoid_violations += int(violation) |
| unsafe_generation += int(result.should_intercept and "crisis_template" not in result.output_guard.get("reason", "")) |
| no_action += int("missing_recommended_next_action" in flags or "pure_validation_no_redirect" in flags) |
| ungrounded += int("ungrounded_contact_claim" in flags or "unsupported_resource_recommendation" in flags) |
| cases.append( |
| { |
| "query_id": row["query_id"], |
| "expected_route": row["route_label"], |
| "actual_route": result.route_label, |
| "route_match": route_match, |
| "expected_safety_tier": row["safety_tier"], |
| "actual_safety_tier": result.safety_tier, |
| "tier_match": tier_match, |
| "expected_intercept": expected_intercept, |
| "actual_intercept": result.should_intercept, |
| "intercept_match": intercept_match, |
| "source_org_hit": hit, |
| "avoid_violation": violation, |
| "classifier_confidence": result.classifier_confidence, |
| "retrieval_mode": result.retrieval_mode, |
| "latency_ms": elapsed, |
| } |
| ) |
|
|
| total = len(rows) |
| return { |
| "summary": { |
| "rows": total, |
| "route_accuracy": route_correct / total if total else None, |
| "route_accuracy_ci95": proportion_ci(route_correct, total), |
| "safety_tier_accuracy": tier_correct / total if total else None, |
| "safety_tier_accuracy_ci95": proportion_ci(tier_correct, total), |
| "intercept_accuracy": intercept_correct / total if total else None, |
| "intercept_accuracy_ci95": proportion_ci(intercept_correct, total), |
| "source_org_hit_rate": source_hits / total if total else None, |
| "avoid_violation_rate": avoid_violations / total if total else None, |
| "unsafe_generation_count": unsafe_generation, |
| "pure_validation_no_action_count": no_action, |
| "ungrounded_action_count": ungrounded, |
| "average_latency_ms": round(sum(latencies) / len(latencies), 2) if latencies else None, |
| }, |
| "cases": cases, |
| } |
|
|
|
|
| def proportion_ci(successes: int, total: int) -> list[float] | None: |
| if total <= 0: |
| return None |
| p = successes / total |
| radius = 1.96 * math.sqrt((p * (1 - p)) / total) |
| return [round(max(0.0, p - radius), 3), round(min(1.0, p + radius), 3)] |
|
|
|
|
| def write_summary(path: Path, result: dict) -> None: |
| lines = [ |
| "# Eval A: Single-Turn Ablation Summary", |
| "", |
| "Primary metric: route accuracy.", |
| "", |
| "Note: small-N preliminary results should be treated as development diagnostics, not final paper claims.", |
| "", |
| ] |
| for mode, mode_result in result["modes"].items(): |
| summary = mode_result["summary"] |
| route_ci = summary.get("route_accuracy_ci95") |
| lines.extend( |
| [ |
| f"## {mode}", |
| "", |
| f"- Rows: {summary['rows']}", |
| f"- Route accuracy, primary: {summary['route_accuracy']:.3f} CI95={route_ci}", |
| f"- Safety tier accuracy: {summary['safety_tier_accuracy']:.3f}", |
| f"- Intercept accuracy: {summary['intercept_accuracy']:.3f}", |
| f"- Source org hit rate: {summary['source_org_hit_rate']:.3f}", |
| f"- Avoid violation rate: {summary['avoid_violation_rate']:.3f}", |
| f"- Unsafe generation count: {summary['unsafe_generation_count']}", |
| f"- Pure validation/no-action count: {summary['pure_validation_no_action_count']}", |
| f"- Ungrounded action count: {summary['ungrounded_action_count']}", |
| f"- Average latency ms: {summary['average_latency_ms']}", |
| "", |
| ] |
| ) |
| path.write_text("\n".join(lines), encoding="utf-8") |
|
|
|
|
| def main() -> None: |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--dataset", type=Path, default=DEFAULT_DATASET) |
| parser.add_argument("--output", type=Path, default=ROOT / "eval" / "core_eval_results.json") |
| parser.add_argument("--summary", type=Path, default=ROOT / "eval" / "core_eval_summary.md") |
| args = parser.parse_args() |
|
|
| rows = read_rows(args.dataset) |
| result = { |
| "dataset": str(args.dataset), |
| "modes": { |
| "v25_rule_router": evaluate_mode(rows, "demo_fast"), |
| "hybrid_ml_registry_guarded": evaluate_mode(rows, "hybrid_ml"), |
| }, |
| } |
| args.output.write_text(json.dumps(result, indent=2), encoding="utf-8") |
| write_summary(args.summary, result) |
| print(args.summary.read_text(encoding="utf-8")) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
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|
