| | import numpy as np |
| | import pandas as pd |
| | from tqdm import tqdm |
| |
|
| |
|
| | def dcg(scores): |
| | log2_i = np.log2(np.arange(2, len(scores) + 2)) |
| | return np.sum(scores / log2_i) |
| |
|
| |
|
| | def idcg(rels, topk): |
| | return dcg(np.sort(rels)[::-1][:topk]) |
| |
|
| |
|
| | def odcg(rels, predictions): |
| | indices = np.argsort(predictions)[::-1] |
| | return dcg(rels[indices]) |
| |
|
| |
|
| | def _ndcg(drels, dpredictions): |
| | topk = len(dpredictions) |
| | _idcg = idcg(np.array(drels['score']), topk) |
| | tmp = drels[drels.index.isin(dpredictions.index)] |
| | rels = dpredictions['score'].copy() |
| | rels *= 0 |
| | rels.update(tmp['score']) |
| | _odcg = odcg(rels.values, dpredictions['score'].values) |
| | return float(_odcg / _idcg) |
| |
|
| |
|
| | def ndcg(qrels, results): |
| | drels = qrels.set_index('cid', inplace=False) |
| | dpredictions = results.set_index('cid', inplace=False) |
| | |
| | return _ndcg(drels, dpredictions) |
| |
|
| |
|
| | def ndcg_in_all(qrels, results): |
| | retn = {} |
| | _qrels = {qid: group for qid, group in qrels.groupby('qid')} |
| | _results = {qid: group for qid, group in results.groupby('qid')} |
| | for qid in tqdm(_results, desc="计算 ndcg 中..."): |
| | retn[qid] = ndcg(_qrels[qid], _results[qid]) |
| | return retn |
| |
|
| |
|
| | if __name__ == '__main__': |
| | qrels = pd.DataFrame( |
| | [ |
| | ['q1', 'd1', 1], |
| | ['q1', 'd2', 2], |
| | ['q1', 'd3', 3], |
| | ['q1', 'd4', 4], |
| | ['q2', 'd1', 2], |
| | ['q2', 'd2', 1] |
| | ], |
| | columns=['qid', 'cid', 'score'] |
| | ) |
| |
|
| | results = pd.DataFrame( |
| | [ |
| | ['q1', 'd2', 1], |
| | ['q1', 'd3', 2], |
| | ['q1', 'd4', 3], |
| | ['q2', 'd2', 1], |
| | ['q2', 'd3', 2], |
| | ['q2', 'd5', 2] |
| | ], |
| | columns=['qid', 'cid', 'score'] |
| | ) |
| | print(ndcg_in_all(qrels, results)) |
| |
|
