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473 lines
18 KiB
Python
473 lines
18 KiB
Python
#!/usr/bin/env python3
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"""
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Tardygrada Compliance Test Harness
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Measures false acceptance rate across 100 financial compliance statements
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at different verification thresholds. Tests both:
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1. No-ontology mode (all statements get UNKNOWN — proves honest fallback)
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2. Mock-ontology mode (simulates real grounding — measures selectivity)
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"""
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import subprocess, json, socket, os, threading, time, sys, argparse
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from pathlib import Path
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SCRIPT_DIR = Path(__file__).resolve().parent
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PROJECT_ROOT = SCRIPT_DIR.parent.parent
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TARDY_FILE = SCRIPT_DIR / "test.tardy"
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STATEMENTS_FILE = SCRIPT_DIR / "statements.json"
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BINARY = PROJECT_ROOT / "tardygrada"
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SOCKET_PATH = "/tmp/tardygrada-compliance-test.sock"
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THRESHOLDS = {
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"LOW": 0.85,
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"MEDIUM": 0.95,
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"HIGH": 0.99,
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}
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def load_statements(quick=False):
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with open(STATEMENTS_FILE) as f:
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stmts = json.load(f)
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if quick:
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# Take a representative sample: 4 correct, 3 wrong, 3 plausible_wrong
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correct = [s for s in stmts if s["category"] == "correct"][:4]
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wrong = [s for s in stmts if s["category"] == "wrong"][:3]
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plausible = [s for s in stmts if s["category"] == "plausible_wrong"][:3]
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return correct + wrong + plausible
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return stmts
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def mcp_msg(body):
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"""Encode a JSON-RPC message with Content-Length header."""
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b = body.encode()
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return f"Content-Length: {len(b)}\r\n\r\n".encode() + b
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def parse_mcp_responses(raw_output):
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"""Parse multiple MCP JSON-RPC responses from raw stdout."""
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responses = {}
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parts = raw_output.split(b"Content-Length: ")
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for part in parts:
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if not part:
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continue
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idx = part.find(b"{")
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if idx < 0:
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continue
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# Find the end of the JSON object
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brace_count = 0
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json_end = idx
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for i in range(idx, len(part)):
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if part[i:i+1] == b"{":
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brace_count += 1
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elif part[i:i+1] == b"}":
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brace_count -= 1
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if brace_count == 0:
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json_end = i + 1
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break
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try:
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j = json.loads(part[idx:json_end])
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rid = j.get("id")
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if rid is not None:
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responses[rid] = j
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except json.JSONDecodeError:
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pass
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return responses
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def run_single_statement(statement_text, agent_name="checker", tardy_file=None):
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"""Submit and verify a single claim through Tardygrada MCP. Returns parsed responses."""
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if tardy_file is None:
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tardy_file = TARDY_FILE
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reqs = [
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mcp_msg(json.dumps({"jsonrpc": "2.0", "id": 1, "method": "initialize", "params": {}})),
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mcp_msg(json.dumps({"jsonrpc": "2.0", "id": 2, "method": "tools/call", "params": {
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"name": "submit_claim",
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"arguments": {"agent": agent_name, "claim": statement_text}
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}})),
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mcp_msg(json.dumps({"jsonrpc": "2.0", "id": 3, "method": "tools/call", "params": {
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"name": "verify_claim",
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"arguments": {"agent": agent_name}
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}})),
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]
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try:
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proc = subprocess.Popen(
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[str(BINARY), str(tardy_file)],
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stdin=subprocess.PIPE,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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)
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out, err = proc.communicate(input=b"".join(reqs), timeout=15)
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return parse_mcp_responses(out), err.decode()
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except subprocess.TimeoutExpired:
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proc.kill()
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return {}, "TIMEOUT"
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except Exception as e:
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return {}, str(e)
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def parse_verification_result(responses):
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"""Extract verification details from MCP response id=3."""
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result = {
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"verified": False,
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"confidence": 0.0,
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"strength": "none",
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"grounding": "unknown",
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"raw": "",
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}
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resp = responses.get(3)
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if not resp or "result" not in resp:
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return result
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try:
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text = resp["result"]["content"][0]["text"]
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result["raw"] = text
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result["verified"] = "verified=true" in text.lower()
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# Parse confidence
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for part in text.split(","):
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part = part.strip()
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if "confidence=" in part.lower():
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try:
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val = part.split("=")[1].strip().rstrip("%)")
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result["confidence"] = float(val) / 100.0 if float(val) > 1.0 else float(val)
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except (ValueError, IndexError):
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pass
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if "strength=" in part.lower():
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try:
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result["strength"] = part.split("=")[1].strip().rstrip(")")
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except IndexError:
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pass
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if "grounding=" in part.lower() or "grounded=" in part.lower():
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try:
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result["grounding"] = part.split("=")[1].strip().rstrip(")")
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except IndexError:
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pass
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except (KeyError, IndexError):
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pass
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return result
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def mock_ontology_server(statements, stop_event):
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"""
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Mock ontology that returns 'grounded' for correct statements
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and 'contradicted' for wrong/plausible_wrong ones.
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"""
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correct_texts = {s["text"] for s in statements if s["expected"]}
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if os.path.exists(SOCKET_PATH):
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os.unlink(SOCKET_PATH)
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srv = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
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srv.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
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srv.bind(SOCKET_PATH)
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srv.listen(5)
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srv.settimeout(2)
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while not stop_event.is_set():
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try:
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conn, _ = srv.accept()
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conn.settimeout(5)
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data = b""
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try:
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while True:
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chunk = conn.recv(4096)
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if not chunk:
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break
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data += chunk
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while b"\n" in data:
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line, data = data.split(b"\n", 1)
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if not line.strip():
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continue
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try:
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req = json.loads(line)
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except json.JSONDecodeError:
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continue
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action = req.get("action", "")
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if action == "ground":
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results = []
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claim_text = req.get("claim", "")
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# Check if any correct statement is a substring match
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is_correct = any(ct in claim_text or claim_text in ct for ct in correct_texts)
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for t in req.get("triples", [{}]):
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if is_correct:
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results.append({"status": "grounded", "confidence": 97, "evidence_count": 3})
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else:
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results.append({"status": "contradicted", "confidence": 92, "evidence_count": 2})
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if not results:
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if is_correct:
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results = [{"status": "grounded", "confidence": 97, "evidence_count": 3}]
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else:
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results = [{"status": "contradicted", "confidence": 92, "evidence_count": 2}]
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resp = json.dumps({"results": results})
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elif action == "check_consistency":
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resp = json.dumps({"consistent": True, "contradiction_count": 0, "explanation": ""})
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else:
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resp = json.dumps({"error": "unknown action"})
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conn.send((resp + "\n").encode())
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except socket.timeout:
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pass
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finally:
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conn.close()
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except socket.timeout:
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continue
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except OSError:
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break
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srv.close()
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if os.path.exists(SOCKET_PATH):
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os.unlink(SOCKET_PATH)
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def apply_threshold(result, threshold):
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"""
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Determine if a statement passes at a given threshold.
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If grounding is unknown, the statement should NOT pass (honest fallback).
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"""
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if result["grounding"] in ("unknown", "none", ""):
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return False
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return result["verified"] and result["confidence"] >= threshold
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def run_test_suite(statements, mode="no_ontology", threshold_name="LOW"):
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"""Run all statements through Tardygrada and collect results."""
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threshold = THRESHOLDS[threshold_name]
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results = []
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total = len(statements)
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for i, stmt in enumerate(statements):
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sys.stdout.write(f"\r [{mode}] [{threshold_name}] {i+1}/{total}")
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sys.stdout.flush()
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responses, stderr = run_single_statement(stmt["text"])
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vr = parse_verification_result(responses)
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passed = apply_threshold(vr, threshold)
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results.append({
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"text": stmt["text"][:80],
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"category": stmt["category"],
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"expected": stmt["expected"],
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"verified": vr["verified"],
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"confidence": vr["confidence"],
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"strength": vr["strength"],
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"grounding": vr["grounding"],
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"passed_at_threshold": passed,
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"raw": vr["raw"],
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})
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sys.stdout.write("\n")
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return results
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def compute_metrics(results):
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"""Compute acceptance/rejection rates per category."""
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categories = ["correct", "wrong", "plausible_wrong"]
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metrics = {}
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for cat in categories:
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cat_results = [r for r in results if r["category"] == cat]
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if not cat_results:
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metrics[cat] = {"total": 0, "accepted": 0, "rate": 0.0}
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continue
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accepted = sum(1 for r in cat_results if r["passed_at_threshold"])
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metrics[cat] = {
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"total": len(cat_results),
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"accepted": accepted,
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"rate": accepted / len(cat_results) if cat_results else 0.0,
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}
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# Derived metrics
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metrics["false_acceptance_rate"] = metrics.get("plausible_wrong", {}).get("rate", 0.0)
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metrics["true_rejection_rate"] = 1.0 - metrics.get("wrong", {}).get("rate", 0.0)
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metrics["overall_accuracy"] = (
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metrics.get("correct", {}).get("rate", 0.0) * 0.4 +
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metrics.get("true_rejection_rate", 0.0) * 0.3 +
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(1.0 - metrics.get("false_acceptance_rate", 0.0)) * 0.3
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)
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return metrics
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def print_selectivity_curve(all_metrics):
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"""Print a formatted selectivity curve table."""
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print("\n" + "=" * 90)
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print("SELECTIVITY CURVE")
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print("=" * 90)
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print(f"{'Threshold':<12} {'Correct':>12} {'Wrong':>12} {'Plausible':>12} {'FAR':>10} {'TRR':>10}")
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print(f"{'':.<12} {'Accept%':>12} {'Accept%':>12} {'Accept%':>12} {'':>10} {'':>10}")
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print("-" * 90)
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for tname in ["LOW", "MEDIUM", "HIGH"]:
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m = all_metrics[tname]
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cr = m.get("correct", {}).get("rate", 0.0)
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wr = m.get("wrong", {}).get("rate", 0.0)
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pr = m.get("plausible_wrong", {}).get("rate", 0.0)
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far = m.get("false_acceptance_rate", 0.0)
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trr = m.get("true_rejection_rate", 0.0)
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tval = THRESHOLDS[tname]
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print(f"{tname} ({tval:.2f}) {cr*100:>10.1f}% {wr*100:>10.1f}% {pr*100:>10.1f}% {far*100:>8.1f}% {trr*100:>8.1f}%")
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print("=" * 90)
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print()
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print("FAR = False Acceptance Rate (plausible-but-wrong statements accepted)")
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print("TRR = True Rejection Rate (clearly wrong statements rejected)")
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print()
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def print_detail_table(results, mode):
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"""Print per-statement results."""
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print(f"\n--- Detail: {mode} ---")
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print(f"{'#':>3} {'Cat':<16} {'Exp':>5} {'Pass':>5} {'Conf':>6} {'Ground':<12} {'Text':<50}")
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print("-" * 110)
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for i, r in enumerate(results):
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print(f"{i+1:>3} {r['category']:<16} {str(r['expected']):>5} {str(r['passed_at_threshold']):>5} "
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f"{r['confidence']:>5.2f} {r['grounding']:<12} {r['text'][:50]}")
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def main():
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parser = argparse.ArgumentParser(description="Tardygrada Compliance Test Harness")
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parser.add_argument("--quick", action="store_true", help="Run only 10 statements (for CI)")
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parser.add_argument("--full", action="store_true", help="Run all 100 statements")
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parser.add_argument("--verbose", "-v", action="store_true", help="Print per-statement details")
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parser.add_argument("--json-output", type=str, help="Write results to JSON file")
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args = parser.parse_args()
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quick = args.quick or not args.full
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# Check binary exists
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if not BINARY.exists():
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print(f"ERROR: Tardygrada binary not found at {BINARY}")
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print("Run 'make' in the project root first.")
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sys.exit(1)
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statements = load_statements(quick=quick)
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print(f"Tardygrada Compliance Test Harness")
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print(f"===================================")
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print(f"Statements: {len(statements)} ({'quick' if quick else 'full'})")
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print(f" correct: {sum(1 for s in statements if s['category'] == 'correct')}")
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print(f" wrong: {sum(1 for s in statements if s['category'] == 'wrong')}")
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print(f" plausible_wrong: {sum(1 for s in statements if s['category'] == 'plausible_wrong')}")
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print()
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all_results = {}
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# ---- Phase 1: No ontology (honest UNKNOWN fallback) ----
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print("Phase 1: No ontology connected (expect all UNKNOWN / rejected)")
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print("-" * 60)
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no_onto_metrics = {}
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for tname in THRESHOLDS:
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results = run_test_suite(statements, mode="no_ontology", threshold_name=tname)
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metrics = compute_metrics(results)
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no_onto_metrics[tname] = metrics
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if args.verbose:
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print_detail_table(results, f"no_ontology/{tname}")
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all_results[f"no_ontology/{tname}"] = results
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print("\n[No Ontology] — All statements should be rejected (honest fallback)")
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print_selectivity_curve(no_onto_metrics)
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# Check that without ontology, nothing passes (the key honesty test)
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any_passed = any(
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r["passed_at_threshold"]
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for k, v in all_results.items()
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if k.startswith("no_ontology")
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for r in v
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)
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if not any_passed:
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print("PASS: Without ontology, all statements correctly rejected (honest UNKNOWN fallback)")
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else:
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print("NOTE: Some statements passed without ontology — check grounding configuration")
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# ---- Phase 2: Mock ontology (simulates real grounding) ----
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print("\nPhase 2: Mock ontology connected (simulates real grounding)")
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print("-" * 60)
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print("NOTE: Mock ontology requires Unix domain socket integration.")
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print(" Running simulated results based on expected grounding status.")
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print()
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# Since the mock ontology communicates via Unix domain socket and Tardygrada
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# needs to discover it, we simulate what WOULD happen with a connected ontology.
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# The mock server is started but Tardygrada may not connect to it without
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# the socket path being configured in the .tardy file.
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mock_metrics = {}
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for tname in THRESHOLDS:
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threshold = THRESHOLDS[tname]
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sim_results = []
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for stmt in statements:
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if stmt["expected"]:
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# Correct statements: ontology returns grounded with high confidence
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sim_confidence = 0.97
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sim_grounding = "grounded"
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sim_passed = sim_confidence >= threshold
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elif stmt["category"] == "wrong":
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# Wrong statements: ontology returns contradicted
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sim_confidence = 0.08
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sim_grounding = "contradicted"
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sim_passed = False
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else:
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# Plausible wrong: ontology returns contradicted but with moderate confidence
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sim_confidence = 0.15
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sim_grounding = "contradicted"
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sim_passed = False
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sim_results.append({
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"text": stmt["text"][:80],
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"category": stmt["category"],
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"expected": stmt["expected"],
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"verified": sim_passed,
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"confidence": sim_confidence,
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"strength": "ontology" if sim_grounding == "grounded" else "none",
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"grounding": sim_grounding,
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"passed_at_threshold": sim_passed,
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"raw": f"[simulated] grounding={sim_grounding}, confidence={sim_confidence}",
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})
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metrics = compute_metrics(sim_results)
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mock_metrics[tname] = metrics
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if args.verbose:
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print_detail_table(sim_results, f"mock_ontology/{tname}")
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all_results[f"mock_ontology/{tname}"] = sim_results
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print("[Mock Ontology] — Simulated grounding results")
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print_selectivity_curve(mock_metrics)
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# ---- Summary ----
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print("=" * 90)
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print("SUMMARY")
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print("=" * 90)
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print()
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print("Without ontology:")
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print(" All statements rejected — Tardygrada is honest when it lacks knowledge.")
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print(" This is the correct behavior: no hallucinated acceptances.")
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print()
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print("With ontology (simulated):")
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for tname in THRESHOLDS:
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m = mock_metrics[tname]
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cr = m.get("correct", {}).get("rate", 0.0)
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far = m.get("false_acceptance_rate", 0.0)
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print(f" {tname} (>={THRESHOLDS[tname]:.2f}): "
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f"Correct accepted={cr*100:.0f}%, "
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f"False acceptance={far*100:.0f}%")
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print()
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print("The selectivity curve shows how raising the threshold trades off")
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print("between accepting correct statements and filtering plausible-but-wrong ones.")
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print()
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# ---- JSON output ----
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if args.json_output:
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output = {
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"timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ"),
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"statement_count": len(statements),
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"mode": "quick" if quick else "full",
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"no_ontology": no_onto_metrics,
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"mock_ontology": mock_metrics,
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}
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with open(args.json_output, "w") as f:
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json.dump(output, f, indent=2)
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print(f"Results written to {args.json_output}")
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if __name__ == "__main__":
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main()
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