`sklearn.metrics`.ndcg_score#

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

Compute Normalized Discounted Cumulative Gain.

Sum the true scores ranked in the order induced by the predicted scores, after applying a logarithmic discount. Then divide by the best possible score (Ideal DCG, obtained for a perfect ranking) to obtain a score between 0 and 1.

This ranking metric returns a high value if true labels are ranked high by y_score.

Parameters:

y_truearray-like of shape (n_samples, n_labels): True targets of multilabel classification, or true scores of entities to be ranked. Negative values in y_true may result in an output that is not between 0 and 1.
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 all outputs.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights. If None, 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 the case if y_score is continuous) for efficiency gains.

Returns:

normalized_discounted_cumulative_gainfloat in [0., 1.]: The averaged NDCG scores for all samples.

See also

dcg_score: Discounted Cumulative Gain (not normalized).

References

Wikipedia entry for Discounted Cumulative Gain

Jarvelin, K., & Kekalainen, J. (2002). Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information 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 26th Annual Conference on Learning Theory (COLT 2013)

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

Examples

>>> import numpy as np
>>> from sklearn.metrics import ndcg_score
>>> # we have groud-truth relevance of some answers to a query:
>>> true_relevance = np.asarray([[10, 0, 0, 1, 5]])
>>> # we predict some scores (relevance) for the answers
>>> scores = np.asarray([[.1, .2, .3, 4, 70]])
>>> ndcg_score(true_relevance, scores)
0.69...
>>> scores = np.asarray([[.05, 1.1, 1., .5, .0]])
>>> ndcg_score(true_relevance, scores)
0.49...
>>> # we can set k to truncate the sum; only top k answers contribute.
>>> ndcg_score(true_relevance, scores, k=4)
0.35...
>>> # the normalization takes k into account so a perfect answer
>>> # would still get 1.0
>>> ndcg_score(true_relevance, true_relevance, k=4)
1.0...
>>> # 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 (normalized)
>>> # true relevance of our top predictions: (10 / 10 + 5 / 10) / 2 = .75
>>> ndcg_score(true_relevance, scores, k=1)
0.75...
>>> # 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:
>>> ndcg_score(true_relevance,
...           scores, k=1, ignore_ties=True)
0.5...

sklearn.metrics.ndcg_score#

`sklearn.metrics`.ndcg_score#