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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)
# print(drels, dpredictions)
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..."):
if qid in _qrels:
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))
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