def test_ndcg_bulk_match(demo_recs, drop_rating):
"bulk and normal match"
train, test, recs = demo_recs
if drop_rating:
test = test[['user', 'item']]
rla = RecListAnalysis()
rla.add_metric(ndcg)
rla.add_metric(ndcg, name='ndcg_k', k=5)
rla.add_metric(dcg)
# metric without the bulk capabilities
rla.add_metric(lambda *a: ndcg(*a), name='ind_ndcg')
rla.add_metric(lambda *a, **k: ndcg(*a, **k), name='ind_ndcg_k', k=5)
res = rla.compute(recs, test)
res['ind_ideal'] = res['dcg'] / res['ind_ndcg']
print(res)
assert res.ndcg.values == approx(res.ind_ndcg.values)
def test_ndcg_wrong():
recs = pd.DataFrame({'item': [1, 2]})
truth = pd.DataFrame({'item': [1, 2, 3], 'rating': [3.0, 5.0, 4.0]})
truth = truth.set_index('item')
assert ndcg(recs, truth) == approx(_dcg([3.0, 5.0] / _dcg([5.0, 4.0, 3.0])))
def test_ndcg_perfect():
recs = pd.DataFrame({'item': [2, 3, 1]})
truth = pd.DataFrame({'item': [1, 2, 3], 'rating': [3.0, 5.0, 4.0]})
truth = truth.set_index('item')
assert ndcg(recs, truth) == approx(1.0)
def test_ndcg_no_match():
recs = pd.DataFrame({'item': [4]})
truth = pd.DataFrame({'item': [1, 2, 3], 'rating': [3.0, 5.0, 4.0]})
truth = truth.set_index('item')
assert ndcg(recs, truth) == approx(0.0)
