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NDCG for recommender systems
from math import log
import unittest
def dcg_at_k(scores):
assert scores
return scores[0] + sum(sc / log(ind, 2) for sc, ind in zip(scores[1:], range(2, len(scores)+1)))
def ndcg_at_k(predicted_scores, user_scores):
assert len(predicted_scores) == len(user_scores)
idcg = dcg_at_k(sorted(user_scores, reverse=True))
return (dcg_at_k(predicted_scores) / idcg) if idcg > 0.0 else 0.0
class TestMetrics(unittest.TestCase):
def test_dcg_small(self):
scores = [3, 2]
self.assertAlmostEqual(dcg_at_k(scores), 5.0)
def test_dcg_large(self):
scores = [3, 2, 3, 0, 0, 1, 2, 2, 3, 0]
self.assertAlmostEqual(dcg_at_k(scores), 9.6051177391888114)
def test_ndcg(self):
predicted1 = [.4, .1, .8]
predicted2 = [.0, .1, .4]
predicted3 = [.4, .1, .0]
actual = [.8, .4, .1, .0]
self.assertAlmostEqual(ndcg_at_k(predicted1, actual[:3]), 0.795, 3)
self.assertAlmostEqual(ndcg_at_k(predicted2, actual[:3]), 0.279, 3)
self.assertAlmostEqual(ndcg_at_k(predicted3, actual[:3]), 0.396, 3)

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@suzanv suzanv commented Jul 19, 2017

This is a better implementation than because the ideal ranking should include all items in the collection, not only the retrieved items.


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@Jomonsugi Jomonsugi commented Aug 21, 2017

Can you give me an idea of how to use your function if I have a vector of binary (ground truth) labels and then an output from an ALS model, for example: [ 1.09253478e-01 1.97033856e-01 5.51080274e-01 ..., 1.77992064e-03 1.90066773e-12 1.74711004e-04]

When evaluation my model using AUC, I can just feed in the binary ground truth vector and the output from my ALS model as the predicted scores as is, but I am wondering how this would work with your model if I am considering, for example, k=10 recommendations and would like to use NDCG to evaluate the output.

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