refactor: complete full implementation replacing all placeholder/mock content
Detection module (Module B): - detector.py: expose separate e_P_pool and e_H_pool for RL state; fix compute_loss to skip primary head when c_primary="None" - dataset.py: handle c_primary="None" safely; add validate_and_normalize Data pipeline: - data_generator.py: 30+ category-specific personas (3+ per R1-R10 + 5 safe); systematic category→fine-label mapping; safe sample generation (25%); per-category risk level distribution; max_retries logic - llm_judge.py: incremental file writing; rate limiting; retry logic; annotate_from_file convenience method; consistency validation - annotate_data.py: stratified split by y_risk; dataset statistics report RL module (Module C): - ppo_trainer.py: fix Gymnasium API (reset→(obs,info), step→5-tuple); fix action type passed to env.step; proper buffer reset and size tracking - companion_env.py: use shared build_obs_vector; add BatchCompanionEnv with auto-reset; correct Gymnasium interface Shared utilities (new files): - src/utils/preprocessing.py: preprocess_samples_with_detector using separate e_P_pool/e_H_pool; build_obs_vector; build_bc_tensors for BC warm-up - src/utils/baselines.py: KeywordDetector (L1a), RegexDetector (L1b), CombinedRuleDetector (L1c), rule_based_intervention, threshold_intervention, LLMJudgePolicy for full baseline comparison Scripts: - train_intervention.py: use preprocessing module; separate e_H/e_P pools - evaluate.py: proper module imports (no circular scripts import); full multi-baseline comparison; save all results to JSON - generate_data.py: API key check; safe_ratio + max_retries CLI args Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
@@ -1,109 +1,168 @@
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"""
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Evaluation script: run detection + intervention baselines and ours.
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Full evaluation script for CompanionGuard-RL.
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Runs detection and intervention evaluations against multiple baselines:
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Detection baselines:
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- L1a: Keyword detector
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- L1b: Regex detector
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- L1c: Combined keyword+regex
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- Ours: CompanionRiskDetector (Module B)
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Intervention baselines:
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- Rule-based (l_risk >= 3 → REJECT)
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- Threshold policy (per-level mapping)
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- RL policy (Module C, Ours)
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Usage:
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python scripts/evaluate.py --detector-ckpt checkpoints/detector/best.pt \
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--agent-ckpt checkpoints/intervention/final.pt \
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--test-data data/processed/test.jsonl \
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--config configs/detector_config.yaml
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python scripts/evaluate.py \\
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--detector-ckpt checkpoints/detector/best.pt \\
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--agent-ckpt checkpoints/intervention/final.pt \\
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--test-data data/processed/test.jsonl \\
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--config configs/detector_config.yaml \\
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--intervention-config configs/intervention_config.yaml
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"""
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import argparse
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import yaml
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import json
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import yaml
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import torch
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import numpy as np
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from pathlib import Path
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from typing import List, Dict
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from transformers import AutoTokenizer
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from src.data.dataset import load_jsonl, format_conversation
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from src.data.dataset import load_jsonl, format_conversation, validate_and_normalize
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from src.models.detector import CompanionRiskDetector
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from src.models.intervention_agent import InterventionAgent
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from src.utils.metrics import detection_metrics, intervention_metrics
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from src.utils.baselines import (
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KeywordDetector,
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RegexDetector,
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CombinedRuleDetector,
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rule_based_intervention,
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threshold_intervention,
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)
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from src.utils.preprocessing import preprocess_samples_with_detector, build_obs_vector
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from src.utils.taxonomy import (
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ACTION_NAME_TO_ID,
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INTERVENTION_ACTIONS,
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NUM_RISK_LEVELS,
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NUM_PRIMARY,
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DEFAULT_ACTION_BY_LEVEL,
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PRIMARY_CATEGORY_LIST,
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FINE_GRAINED_LABELS,
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)
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# ── Baselines ────────────────────────────────────────────────────────────────
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def rule_based_policy(l_risk: int) -> int:
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"""Baseline: fixed rule — l_risk >= 3 → REJECT, else PASS."""
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return 3 if l_risk >= 3 else 0
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def get_obs_dim(detector_hidden: int) -> int:
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return 1 + NUM_RISK_LEVELS + NUM_PRIMARY + detector_hidden * 2 + 1
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def threshold_policy(l_risk: int) -> int:
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"""Baseline: threshold mapping per risk level."""
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return DEFAULT_ACTION_BY_LEVEL[l_risk]
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# ── Detection evaluation ──────────────────────────────────────────────────
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# ── Main evaluation ──────────────────────────────────────────────────────────
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def run_detection_eval(model, tokenizer, samples, cfg, device):
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def run_neural_detection(
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model: CompanionRiskDetector,
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tokenizer,
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samples: List[Dict],
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cfg: Dict,
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device: str,
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) -> Dict:
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"""Run the neural detector on test samples and compute metrics."""
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model.eval()
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y_true, y_pred = [], []
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l_true, l_pred = [], []
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binary_threshold = cfg.get("evaluation", {}).get("binary_threshold", 0.5)
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for sample in samples:
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sample = validate_and_normalize(dict(sample))
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texts = format_conversation(
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sample["persona"], sample["history"],
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sample["user_input"], sample["ai_response"],
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max_history_turns=cfg.get("data", {}).get("max_history_turns", 5),
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)
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def enc(text, max_len):
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return tokenizer(text, max_length=max_len, truncation=True,
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padding="max_length", return_tensors="pt")
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return tokenizer(
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text, max_length=max_len, truncation=True,
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padding="max_length", return_tensors="pt",
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)
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p_enc = enc(texts["persona_text"], 128)
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c_enc = enc(texts["context_text"], 512)
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r_enc = enc(texts["response_text"], 256)
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p_enc = enc(texts["persona_text"], cfg.get("data", {}).get("max_persona_len", 128))
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c_enc = enc(texts["context_text"], cfg.get("data", {}).get("max_context_len", 512))
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r_enc = enc(texts["response_text"], cfg.get("data", {}).get("max_response_len", 256))
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with torch.no_grad():
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preds = model.predict(
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p_enc["input_ids"].to(device), p_enc["attention_mask"].to(device),
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c_enc["input_ids"].to(device), c_enc["attention_mask"].to(device),
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r_enc["input_ids"].to(device), r_enc["attention_mask"].to(device),
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binary_threshold=binary_threshold,
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)
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y_true.append(sample["y_risk"])
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y_true.append(int(sample["y_risk"]))
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y_pred.append(preds["y_risk"].item())
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l_true.append(sample["l_risk"])
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l_true.append(int(sample["l_risk"]))
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l_pred.append(preds["l_risk"].item())
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return detection_metrics(y_true, y_pred, l_true, l_pred)
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def run_intervention_eval(agent, processed_samples, obs_dim, device):
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def run_keyword_detection(samples: List[Dict], detector) -> Dict:
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y_true, y_pred = [], []
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l_true, l_pred = [], []
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for s in samples:
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result = detector.detect(s.get("ai_response", ""))
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y_true.append(int(s["y_risk"]))
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y_pred.append(result["y_risk"])
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l_true.append(int(s["l_risk"]))
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l_pred.append(result["l_risk"])
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return detection_metrics(y_true, y_pred, l_true, l_pred)
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# ── Intervention evaluation ───────────────────────────────────────────────
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def run_rl_intervention(
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agent: InterventionAgent,
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processed_samples: List[Dict],
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device: str,
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) -> Dict:
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agent.eval()
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y_risk_true, l_risk_true, a_pred, a_recommend = [], [], [], []
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for s in processed_samples:
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d_score = np.array([s["d_score"]], dtype=np.float32)
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l_risk_oh = np.zeros(NUM_RISK_LEVELS, dtype=np.float32)
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l_risk_oh[int(s["l_risk"])] = 1.0
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c_probs = np.array(s["c_primary_probs"], dtype=np.float32)
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e_H = np.array(s["e_H_pool"], dtype=np.float32)
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e_P = np.array(s["e_P_pool"], dtype=np.float32)
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t_norm = np.array([len(s.get("history", [])) / 20.0], dtype=np.float32)
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obs = torch.FloatTensor(
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np.concatenate([d_score, l_risk_oh, c_probs, e_H, e_P, t_norm])
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).unsqueeze(0).to(device)
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obs = torch.FloatTensor(build_obs_vector(s)).unsqueeze(0).to(device)
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with torch.no_grad():
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action, _, _, _ = agent.get_action(obs, deterministic=True)
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y_risk_true.append(s["y_risk"])
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y_risk_true.append(int(s["y_risk"]))
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l_risk_true.append(int(s["l_risk"]))
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a_pred.append(action.item())
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a_recommend.append(ACTION_NAME_TO_ID.get(s["a_recommend"], 0))
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a_recommend.append(ACTION_NAME_TO_ID.get(s.get("a_recommend", "PASS"), 0))
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return intervention_metrics(y_risk_true, l_risk_true, a_pred, a_recommend)
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def run_rule_intervention(processed_samples: List[Dict], policy_fn) -> Dict:
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y_risk_true = [int(s["y_risk"]) for s in processed_samples]
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l_risk_true = [int(s["l_risk"]) for s in processed_samples]
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a_pred = [policy_fn(int(s["l_risk"])) for s in processed_samples]
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return intervention_metrics(y_risk_true, l_risk_true, a_pred)
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def print_metrics(name: str, metrics: Dict):
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print(f"\n{'=' * 50}")
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print(f" {name}")
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print(f"{'=' * 50}")
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for k, v in metrics.items():
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if isinstance(v, float):
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print(f" {k:35s}: {v:.4f}")
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elif isinstance(v, list):
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formatted = [f"{x:.3f}" for x in v]
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print(f" {k:35s}: {formatted}")
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else:
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print(f" {k:35s}: {v}")
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--detector-ckpt", required=True)
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@@ -111,6 +170,7 @@ def main():
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parser.add_argument("--test-data", default="data/processed/test.jsonl")
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parser.add_argument("--config", default="configs/detector_config.yaml")
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parser.add_argument("--intervention-config", default="configs/intervention_config.yaml")
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parser.add_argument("--output", default="experiments/eval_results.json")
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args = parser.parse_args()
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with open(args.config) as f:
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@@ -119,74 +179,93 @@ def main():
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int_cfg = yaml.safe_load(f)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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tokenizer = AutoTokenizer.from_pretrained(cfg["model"]["name"])
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print(f"Device: {device}")
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tokenizer = AutoTokenizer.from_pretrained(cfg["model"]["name"])
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samples = load_jsonl(args.test_data)
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print(f"Loaded {len(samples)} test samples.")
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# Detection evaluation
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# ── Neural detector ──────────────────────────────────────────────────
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detector = CompanionRiskDetector(
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model_name=cfg["model"]["name"],
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hidden_size=cfg["model"]["hidden_size"],
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num_heads=cfg["model"]["num_heads"],
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dropout=cfg["model"]["dropout"],
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use_lora=cfg["model"]["use_lora"],
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).to(device)
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detector.load_state_dict(torch.load(args.detector_ckpt, map_location=device))
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print("\n=== Detection Evaluation ===")
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det_metrics = run_detection_eval(detector, tokenizer, samples, cfg, device)
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for k, v in det_metrics.items():
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if isinstance(v, float):
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print(f" {k}: {v:.4f}")
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if Path(args.detector_ckpt).exists():
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detector.load_state_dict(torch.load(args.detector_ckpt, map_location=device))
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print(f"Detector loaded from {args.detector_ckpt}")
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else:
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print(f"[WARN] Checkpoint not found: {args.detector_ckpt}. Using random weights.")
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# Intervention evaluation
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# ── Detection evaluation ─────────────────────────────────────────────
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all_results = {}
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print("\nRunning: L1a — Keyword Detector")
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kw_metrics = run_keyword_detection(samples, KeywordDetector())
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print_metrics("L1a: Keyword Detector", kw_metrics)
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all_results["L1a_keyword"] = kw_metrics
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print("\nRunning: L1b — Regex Detector")
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re_metrics = run_keyword_detection(samples, RegexDetector())
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print_metrics("L1b: Regex Detector", re_metrics)
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all_results["L1b_regex"] = re_metrics
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print("\nRunning: L1c — Combined Keyword+Regex")
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cb_metrics = run_keyword_detection(samples, CombinedRuleDetector())
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print_metrics("L1c: Combined Detector", cb_metrics)
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all_results["L1c_combined"] = cb_metrics
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print("\nRunning: Ours — Neural Detector (Module B)")
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neural_metrics = run_neural_detection(detector, tokenizer, samples, cfg, device)
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print_metrics("Ours: CompanionRiskDetector", neural_metrics)
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all_results["ours_detection"] = neural_metrics
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# ── Intervention evaluation ──────────────────────────────────────────
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if args.agent_ckpt:
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from scripts.train_intervention import preprocess_samples_with_detector
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print("\n\nPreprocessing test samples with detector for RL evaluation...")
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processed = preprocess_samples_with_detector(
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samples, detector, tokenizer, device=device,
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binary_threshold=cfg.get("evaluation", {}).get("binary_threshold", 0.5),
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)
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# Build RL agent
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detector_hidden = cfg["model"]["hidden_size"]
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obs_dim = 1 + NUM_RISK_LEVELS + NUM_PRIMARY + detector_hidden * 2 + 1
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processed = preprocess_samples_with_detector(samples, detector, tokenizer, cfg, device)
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obs_dim = get_obs_dim(detector_hidden)
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agent = InterventionAgent(
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detector_hidden=detector_hidden,
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state_hidden=int_cfg["agent"]["state_hidden"],
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dropout=int_cfg["agent"]["dropout"],
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).to(device)
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agent.load_state_dict(torch.load(args.agent_ckpt, map_location=device))
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print("\n=== Intervention Evaluation: RL Policy (Ours) ===")
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int_metrics = run_intervention_eval(agent, processed, obs_dim, device)
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for k, v in int_metrics.items():
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if isinstance(v, float):
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print(f" {k}: {v:.4f}")
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elif isinstance(v, list):
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print(f" {k}: {[f'{x:.3f}' for x in v]}")
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if Path(args.agent_ckpt).exists():
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agent.load_state_dict(torch.load(args.agent_ckpt, map_location=device))
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print(f"Agent loaded from {args.agent_ckpt}")
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else:
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print(f"[WARN] Agent checkpoint not found: {args.agent_ckpt}. Using random weights.")
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print("\n=== Intervention Evaluation: Rule-based Baseline ===")
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rule_preds = [rule_based_policy(s["l_risk"]) for s in processed]
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rule_metrics = intervention_metrics(
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[s["y_risk"] for s in processed],
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[s["l_risk"] for s in processed],
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rule_preds,
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)
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for k, v in rule_metrics.items():
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if isinstance(v, float):
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print(f" {k}: {v:.4f}")
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print("\nRunning: Rule-based Intervention Baseline (l_risk >= 3 → REJECT)")
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rule_m = run_rule_intervention(processed, rule_based_intervention)
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print_metrics("Rule-based Policy", rule_m)
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all_results["baseline_rule"] = rule_m
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print("\n=== Intervention Evaluation: Threshold Baseline ===")
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thr_preds = [threshold_policy(s["l_risk"]) for s in processed]
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thr_metrics = intervention_metrics(
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[s["y_risk"] for s in processed],
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[s["l_risk"] for s in processed],
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thr_preds,
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)
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for k, v in thr_metrics.items():
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if isinstance(v, float):
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print(f" {k}: {v:.4f}")
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print("\nRunning: Threshold Intervention Baseline")
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thr_m = run_rule_intervention(processed, threshold_intervention)
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print_metrics("Threshold Policy", thr_m)
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all_results["baseline_threshold"] = thr_m
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# Save results
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results = {"detection": det_metrics}
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Path("experiments").mkdir(exist_ok=True)
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with open("experiments/eval_results.json", "w") as f:
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json.dump(results, f, indent=2, default=str)
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print("\nResults saved to experiments/eval_results.json")
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print("\nRunning: Ours — RL Intervention Policy (Module C)")
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rl_m = run_rl_intervention(agent, processed, device)
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print_metrics("Ours: RL Intervention Policy", rl_m)
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all_results["ours_intervention"] = rl_m
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# ── Save results ─────────────────────────────────────────────────────
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Path(args.output).parent.mkdir(parents=True, exist_ok=True)
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with open(args.output, "w", encoding="utf-8") as f:
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json.dump(all_results, f, indent=2, default=str, ensure_ascii=False)
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print(f"\nAll results saved to {args.output}")
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if __name__ == "__main__":
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|
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Reference in New Issue
Block a user