dcg_score#

sklearn.metrics.dcg_score(y_true,y_score,*,k=None,log_base=2,sample_weight=None,ignore_ties=False)[source]#

Compute Discounted Cumulative Gain.

Sum the true scores ranked in the order induced by the predicted scores,after applying a logarithmic discount.

This ranking metric yields a high value if true labels are ranked high byy_score.

Usually the Normalized Discounted Cumulative Gain (NDCG, computed byndcg_score) is preferred.

Parameters:

y_truearray-like of shape (n_samples, n_labels)

True targets of multilabel classification, or true scores of entitiesto be ranked.

y_scorearray-like of shape (n_samples, n_labels)

Target scores, can either be probability estimates, confidence values,or non-thresholded measure of decisions (as returned by“decision_function” on some classifiers).

kint, default=None

Only consider the highest k scores in the ranking. If None, use alloutputs.

log_basefloat, default=2

Base of the logarithm used for the discount. A low value means asharper discount (top results are more important).

sample_weightarray-like of shape (n_samples,), default=None

Sample weights. IfNone, all samples are given the same weight.

ignore_tiesbool, default=False

Assume that there are no ties in y_score (which is likely to be thecase if y_score is continuous) for efficiency gains.

Returns:

discounted_cumulative_gainfloat

The averaged sample DCG scores.

See also

ndcg_score

The Discounted Cumulative Gain divided by the Ideal Discounted Cumulative Gain (the DCG obtained for a perfect ranking), in order to have a score between 0 and 1.

References

Wikipedia entry for Discounted Cumulative Gain.

Jarvelin, K., & Kekalainen, J. (2002).Cumulated gain-based evaluation of IR techniques. ACM Transactions onInformation Systems (TOIS), 20(4), 422-446.

Wang, Y., Wang, L., Li, Y., He, D., Chen, W., & Liu, T. Y. (2013, May).A theoretical analysis of NDCG ranking measures. In Proceedings of the 26thAnnual Conference on Learning Theory (COLT 2013).

McSherry, F., & Najork, M. (2008, March). Computing information retrievalperformance measures efficiently in the presence of tied scores. InEuropean conference on information retrieval (pp. 414-421). Springer,Berlin, Heidelberg.

Examples

>>>importnumpyasnp>>>fromsklearn.metricsimportdcg_score>>># we have ground-truth relevance of some answers to a query:>>>true_relevance=np.asarray([[10,0,0,1,5]])>>># we predict scores for the answers>>>scores=np.asarray([[.1,.2,.3,4,70]])>>>dcg_score(true_relevance,scores)9.49>>># we can set k to truncate the sum; only top k answers contribute>>>dcg_score(true_relevance,scores,k=2)5.63>>># now we have some ties in our prediction>>>scores=np.asarray([[1,0,0,0,1]])>>># by default ties are averaged, so here we get the average true>>># relevance of our top predictions: (10 + 5) / 2 = 7.5>>>dcg_score(true_relevance,scores,k=1)7.5>>># we can choose to ignore ties for faster results, but only>>># if we know there aren't ties in our scores, otherwise we get>>># wrong results:>>>dcg_score(true_relevance,...scores,k=1,ignore_ties=True)5.0