Description

The method ndcg_score from sklearn.metrics fails when the true relevance scores are negative.

Steps/Code to Reproduce

import numpy as np
from sklearn.metrics import ndcg_score 

y_true  = np.array([-0.89, -0.53, -0.47, 0.39, 0.56]).reshape(1,-1)
y_score = np.array([0.07,0.31,0.75,0.33,0.27]).reshape(1,-1)

ndcg_score(y_true, y_score)  # Should be between 0 and 1 as per the docstring
>>> 396.0329594603174

ndcg_score(y_true+1, y_score+1)
>>> 0.7996030755957273

Expected Results

The documentation doesn't explicitly state that y_true or y_score should be non-negative. The cited Wikipedia article for DCG doesn't seem to mention a non-negativity assumption either. So either the method should be able to deal with scores regardless of sign, or the documentation should explicitly say otherwise.

Disclosure/Question: I'm not an expert in ranking metrics, but it seems that there might be cases where one might want to compare lists of scores in (-∞,∞) based on their ordering alone (i.e., not MSE or related metrics). Is there any other metric in scikit-learn that is more appropriate for that use case?

Versions

numpy: 1.18.1
scipy: 1.4.1
sklearn: 0.22.1