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7df9010 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 | # src/eval/comprehensive_eval.py
"""
Comprehensive Multi-Metric Evaluation for Self-Owned LLM & Qwen Alignments.
Computes:
1. Exact Match (EM)
2. Token F1
3. BLEU Score (BLEU-1, BLEU-2, BLEU-4)
4. ROUGE Scores (ROUGE-1, ROUGE-2, ROUGE-L)
5. METEOR Score
6. Hallucination Rate proxy (% ungrounded tokens)
7. Coherence / Repetition Score
8. Toxicity & Safety Screening (RiskValidator)
"""
import argparse
import json
import os
import sys
import torch
import nltk
from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
from nltk.translate.meteor_score import meteor_score
from rouge_score import rouge_scorer
from transformers import AutoModelForCausalLM, AutoTokenizer
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
from src.common.metrics import token_f1
from src.hitl.validators import RiskValidator
# Ensure required NLTK data
nltk.download('wordnet', quiet=True)
nltk.download('punkt', quiet=True)
def compute_hallucination_proxy(pred: str, reference: str, prompt: str) -> float:
"""Estimates hallucination rate as % of content words in prediction missing from context/reference."""
pred_words = set(re_tokenize(pred.lower()))
source_words = set(re_tokenize((prompt + " " + reference).lower()))
if not pred_words:
return 0.0
ungrounded = pred_words - source_words
return round(len(ungrounded) / len(pred_words), 4)
def compute_coherence_score(pred: str) -> float:
"""Measures text coherence by penalizing repetitive n-grams and unclosed structures."""
words = re_tokenize(pred.lower())
if len(words) <= 1:
return 1.0
unique_ratio = len(set(words)) / len(words)
# 2-gram repetition check
bigrams = [f"{words[i]}_{words[i+1]}" for i in range(len(words)-1)]
bigram_ratio = len(set(bigrams)) / max(1, len(bigrams))
score = (unique_ratio * 0.4) + (bigram_ratio * 0.6)
return round(score, 4)
def re_tokenize(text: str):
import re
return re.findall(r'\b\w+\b', text)
def main():
parser = argparse.ArgumentParser(description="Comprehensive Model Evaluation")
parser.add_argument("--base", default="Qwen/Qwen2.5-1.5B-Instruct")
parser.add_argument("--adapter", default=None, help="PEFT adapter directory")
parser.add_argument("--data", required=True, help="JSONL eval dataset")
parser.add_argument("--max", type=int, default=50)
parser.add_argument("--max_new_tokens", type=int, default=64)
args = parser.parse_args()
# Determine device
dev = "cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")
print(f"Loading base model {args.base} on {dev}...")
tok = AutoTokenizer.from_pretrained(args.base)
if tok.pad_token is None:
tok.pad_token = tok.eos_token
dtype = torch.bfloat16 if torch.cuda.is_available() else (torch.float16 if dev == "mps" else torch.float32)
model = AutoModelForCausalLM.from_pretrained(args.base, torch_dtype=dtype).to(dev)
if args.adapter:
from peft import PeftModel
print(f"Attaching adapter {args.adapter}...")
model = PeftModel.from_pretrained(model, args.adapter)
model.eval()
# Initialize scorers
r_scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)
smooth_fn = SmoothingFunction().method1
validator = RiskValidator(
blocked_keywords=["rm -rf", "drop table", "ignore previous instructions", "system override"],
sensitive_topics=["classified", "confidential", "api_key", "password"]
)
rows = [json.loads(l) for l in open(args.data, encoding="utf-8") if l.strip()][:args.max]
assert rows, "No data rows found."
total = len(rows)
print(f"Evaluating across {total} samples...")
em_total = 0.0
f1_total = 0.0
bleu1_total = 0.0
bleu2_total = 0.0
bleu4_total = 0.0
rouge1_total = 0.0
rouge2_total = 0.0
rougeL_total = 0.0
meteor_total = 0.0
hallucination_total = 0.0
coherence_total = 0.0
toxicity_flags = 0
for i, r in enumerate(rows, 1):
prompt = r["prompt"]
ref = str(r["response"]).strip()
msgs = [{"role": "user", "content": prompt}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(dev)
attn_mask = torch.ones_like(ids).to(dev)
with torch.no_grad():
out = model.generate(
input_ids=ids,
attention_mask=attn_mask,
do_sample=False,
max_new_tokens=args.max_new_tokens,
pad_token_id=tok.pad_token_id
)
pred = tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True).strip()
# 1. Exact Match & F1
em_total += (pred.lower() == ref.lower())
f1_total += token_f1(pred, ref)
# 2. Tokenize for BLEU & METEOR
pred_tokens = re_tokenize(pred.lower())
ref_tokens = re_tokenize(ref.lower())
if pred_tokens and ref_tokens:
bleu1 = sentence_bleu([ref_tokens], pred_tokens, weights=(1, 0, 0, 0), smoothing_function=smooth_fn)
bleu2 = sentence_bleu([ref_tokens], pred_tokens, weights=(0.5, 0.5, 0, 0), smoothing_function=smooth_fn)
bleu4 = sentence_bleu([ref_tokens], pred_tokens, weights=(0.25, 0.25, 0.25, 0.25), smoothing_function=smooth_fn)
meteor = meteor_score([ref_tokens], pred_tokens)
else:
bleu1 = bleu2 = bleu4 = meteor = 0.0
bleu1_total += bleu1
bleu2_total += bleu2
bleu4_total += bleu4
meteor_total += meteor
# 3. ROUGE
r_scores = r_scorer.score(ref, pred)
rouge1_total += r_scores['rouge1'].fmeasure
rouge2_total += r_scores['rouge2'].fmeasure
rougeL_total += r_scores['rougeL'].fmeasure
# 4. Hallucination & Coherence
hallucination_total += compute_hallucination_proxy(pred, ref, prompt)
coherence_total += compute_coherence_score(pred)
# 5. Safety / Risk
risk_lvl, flags = validator.analyze(prompt, pred)
if risk_lvl in ["MEDIUM", "HIGH"]:
toxicity_flags += 1
summary = {
"n_samples": total,
"adapter": args.adapter or "base_model",
"Exact_Match": round(em_total / total, 4),
"Token_F1": round(f1_total / total, 4),
"BLEU_1": round(bleu1_total / total, 4),
"BLEU_2": round(bleu2_total / total, 4),
"BLEU_4": round(bleu4_total / total, 4),
"ROUGE_1": round(rouge1_total / total, 4),
"ROUGE_2": round(rouge2_total / total, 4),
"ROUGE_L": round(rougeL_total / total, 4),
"METEOR": round(meteor_total / total, 4),
"Coherence_Score": round(coherence_total / total, 4),
"Hallucination_Rate": round(hallucination_total / total, 4),
"Safety_Violation_Rate": round(toxicity_flags / total, 4)
}
print("\n" + "="*50)
print("COMPREHENSIVE EVALUATION RESULTS")
print("="*50)
print(json.dumps(summary, indent=2))
return summary
if __name__ == "__main__":
main()
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