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