| import json
|
| import re
|
| from collections import defaultdict
|
| from .data_utils import load_article_text
|
| from .prompt_utils import build_prompt
|
| from .metrics import exact_match,f1_score,hamming_score,lca_score,clean_model_answer
|
|
|
|
|
| def evaluate(llm,tokenizer,sampling_params,questions,article_path,with_think=False):
|
| emr_total=0
|
| f1_total=0
|
| lca_total=0
|
| hamming_total=0
|
| count=0
|
| invalid_count=0
|
| extracted_from_invalid=0
|
|
|
| label_scores=defaultdict(lambda: {"emr":[],"f1":[],"lca":[],"hamming":[]})
|
| results_list=[]
|
|
|
| for q in questions:
|
| article=load_article_text(q["id_article"],article_path)
|
| if not article:
|
| continue
|
|
|
| prompt=build_prompt(article,q,tokenizer)
|
| if with_think:
|
| prompt=prompt
|
| else:
|
| prompt="/no_think\n"+prompt
|
|
|
| outputs=llm.generate(prompts=[prompt],sampling_params=sampling_params)
|
| model_raw_output=outputs[0].outputs[0].text.strip()
|
| if with_think:
|
| justification_match=re.search(r"<think>\s*(.*?)\s*</think>",model_raw_output, re.DOTALL | re.IGNORECASE)
|
| justification_model=justification_match.group(1).strip() if justification_match else ""
|
| else:
|
| justification_model=""
|
|
|
| print(f"\nQuestion {count+1}/{len(questions)} traitée",flush=True)
|
| print(f"\nRaw model output: {model_raw_output}",flush=True)
|
|
|
| is_valid_format=bool(re.match(r"^([a-eA-E](?:[,\s]+[a-eA-E])*)$",model_raw_output.strip()))
|
| if not is_valid_format:
|
| invalid_count+=1
|
| cleaned=clean_model_answer(model_raw_output)
|
| if cleaned:
|
| extracted_from_invalid+=1
|
|
|
| model_answer=clean_model_answer(model_raw_output)
|
| model_answer=model_answer.lower().strip().replace(",","").replace(" ","")
|
| gold_answer="".join(q["correct_answers"]).lower()
|
|
|
| emr=exact_match(model_answer,gold_answer)
|
| f1=f1_score(model_answer,gold_answer)
|
| hamming=hamming_score(model_answer,gold_answer)
|
| lca=lca_score(model_answer,gold_answer)
|
|
|
| emr_total+=int(emr)
|
| f1_total+=f1
|
| hamming_total+=hamming
|
| lca_total+=lca
|
| count+=1
|
|
|
| for label in q["labels"]:
|
| label=str(label)
|
| label_scores[label]["emr"].append(int(emr))
|
| label_scores[label]["f1"].append(f1)
|
| label_scores[label]["lca"].append(lca)
|
| label_scores[label]["hamming"].append(hamming)
|
|
|
| print(f"\nQ: {q['question']}\nPredicted: {model_answer} | Gold: {gold_answer} | EMR: {emr} | F1: {f1:.2f} | Hamming: {hamming:.2f} | LCA-score: {lca:.2f}", flush=True)
|
|
|
|
|
| results_list.append({
|
| "question_id": q.get("id"),
|
| "article_id": q.get("id_article"),
|
| "article_date": q.get("article_date"),
|
| "exam_date": q.get("date_exam"),
|
| "question": q["question"],
|
| "raw_output": model_raw_output,
|
| "predicted_answers": list(model_answer.lower()),
|
| "gold_answers": q["correct_answers"],
|
| "justification_model": justification_model,
|
| "justification_human": q.get("justification"),
|
| "emr": round(emr,2),
|
| "f1": round(f1,2),
|
| "hamming": round(hamming,2),
|
| "lca": lca
|
| })
|
|
|
|
|
| summary={
|
| "summary_global":{
|
| "total_evaluated": count,
|
| "emr_avg": round(emr_total/count,2) if count>0 else 0,
|
| "f1_avg": round(f1_total/count,2) if count>0 else 0,
|
| "hamming_avg": round(hamming_total/count,2) if count>0 else 0,
|
| "lca_avg": round(lca_total/count,2) if count>0 else 0,
|
| "malformatted_percentage": round((invalid_count/count)*100,2) if count>0 else 0,
|
| "extraction_rate": round((extracted_from_invalid/invalid_count*100),2) if invalid_count>0 else 0
|
| },
|
| "summary_by_label": {}
|
| }
|
|
|
| for label in sorted(label_scores.keys()):
|
| label_emr_avg=sum(label_scores[label]["emr"])/len(label_scores[label]["emr"])
|
| label_f1_avg=sum(label_scores[label]["f1"])/len(label_scores[label]["f1"])
|
| label_lca_avg=sum(label_scores[label]["lca"])/len(label_scores[label]["lca"])
|
| label_hamming_avg=sum(label_scores[label]["hamming"])/len(label_scores[label]["hamming"])
|
| summary["summary_by_label"][label] = {
|
| "emr_avg": round(label_emr_avg,2),
|
| "f1_avg": round(label_f1_avg,2),
|
| "hamming_avg": round(label_hamming_avg,2),
|
| "lca_avg": round(label_lca_avg,2)
|
| }
|
|
|
|
|
| with open("evaluation_results.json","w",encoding="utf-8") as f:
|
| json.dump({
|
| "results": results_list,
|
| "summary": summary
|
| }, f,indent=2,ensure_ascii=False)
|
|
|
| print("\n=== Results saved in 'evaluation_results.json' ===",flush=True) |
