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