def test_spec_group_cols():
rla = topn.RecListAnalysis(group_cols=['data', 'user'])
rla.add_metric(topn.precision)
rla.add_metric(topn.recall)
rla.add_metric(topn.ndcg)
recs = pd.DataFrame({
'data': 'a',
'user': ['a', 'a', 'a', 'b', 'b'],
'item': [2, 3, 1, 4, 5],
'rank': [1, 2, 3, 1, 2],
'wombat': np.random.randn(5)
})
truth = pd.DataFrame({
'user': ['a', 'a', 'a', 'b', 'b', 'b'],
'item': [1, 2, 3, 1, 5, 6],
'rating': [3.0, 5.0, 4.0, 3.0, 5.0, 4.0]
})
res = rla.compute(recs, truth)
print(res)
assert len(res) == 2
assert res.index.nlevels == 2
assert res.index.names == ['data', 'user']
assert all(res.index.levels[0] == 'a')
assert all(res.index.levels[1] == ['a', 'b'])
assert all(res.reset_index().user == ['a', 'b'])
partial_ndcg = _dcg([0.0, 5.0]) / _dcg([5, 4, 3])
assert res.ndcg.values == approx([1.0, partial_ndcg])
assert res.precision.values == approx([1.0, 1/2])
assert res.recall.values == approx([1.0, 1/3])
def test_run_two():
rla = topn.RecListAnalysis()
rla.add_metric(topn.precision)
rla.add_metric(topn.recall)
rla.add_metric(topn.ndcg)
recs = pd.DataFrame({
'data': 'a',
'user': ['a', 'a', 'a', 'b', 'b'],
'item': [2, 3, 1, 4, 5],
'rank': [1, 2, 3, 1, 2]
})
truth = pd.DataFrame({
'user': ['a', 'a', 'a', 'b', 'b', 'b'],
'item': [1, 2, 3, 1, 5, 6],
'rating': [3.0, 5.0, 4.0, 3.0, 5.0, 4.0]
})
def prog(inner):
assert len(inner) == 2
return inner
res = rla.compute(recs, truth, progress=prog)
print(res)
assert len(res) == 2
assert res.index.nlevels == 2
assert res.index.names == ['data', 'user']
assert all(res.index.levels[0] == 'a')
assert all(res.index.levels[1] == ['a', 'b'])
assert all(res.reset_index().user == ['a', 'b'])
partial_ndcg = _dcg([0.0, 5.0]) / _dcg([5, 4, 3])
assert res.ndcg.values == approx([1.0, partial_ndcg])
assert res.precision.values == approx([1.0, 1 / 2])
assert res.recall.values == approx([1.0, 1 / 3])
def test_dcg_empty():
"empty should be zero"
assert lm._dcg(np.array([])) == approx(0)
def test_dcg_mult():
"multiple elements should score correctly"
assert lm._dcg(np.array([np.e, np.pi])) == approx(np.e + np.pi)
assert lm._dcg(np.array([np.e, 0, 0,
np.pi])) == approx(np.e + np.pi / np.log2(4))
def test_dcg_single():
"a single element should be scored at the right place"
assert lm._dcg(np.array([0.5])) == approx(0.5)
assert lm._dcg(np.array([0, 0.5])) == approx(0.5)
assert lm._dcg(np.array([0, 0, 0.5])) == approx(0.5 / np.log2(3))
assert lm._dcg(np.array([0, 0, 0.5, 0])) == approx(0.5 / np.log2(3))
def test_dcg_zeros():
assert lm._dcg(np.zeros(10)) == approx(0)
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_dcg_series():
"The DCG function should work on a series"
assert _dcg(pd.Series([np.e, 0, 0, np.pi])) == \
approx((np.e + np.pi / np.log2(4)))
def test_dcg_nan():
"NANs should be 0"
assert _dcg(np.array([np.nan, 0.5])) == approx(0.5)
