def test_make_mean_reciprocal_rank_fn(self):
scores = [[1., 3., 2.], [1., 2., 3.]]
# Note that scores are ranked in descending order.
# ranks = [[3, 1, 2], [3, 2, 1]]
labels = [[0., 0., 1.], [0., 1., 2.]]
# Note that the definition of MRR only uses the highest ranked
# relevant item, where an item is relevant if its label is > 0.
rel_rank = [2, 1]
weights = [[1., 2., 3.], [4., 5., 6.]]
num_queries = len(scores)
weights_feature_name = 'weights'
features = {weights_feature_name: torch.tensor(weights)}
m = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.MRR)
m_w = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.MRR,
weights_feature_name=weights_feature_name)
m_2 = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.MRR, topn=1)
self._check_metrics([
(m(torch.tensor([labels[0]]), torch.tensor([scores[0]]),
features), 0.5),
(m(torch.tensor(labels), torch.tensor(scores),
features), (0.5 + 1.0) / 2),
(m_w(torch.tensor(labels), torch.tensor(scores), features),
(3. * 0.5 + (6. + 5.) / 2. * 1.) / (3. + (6. + 5.) / 2.)),
(m_2(torch.tensor(labels), torch.tensor(scores),
features), (sum([0., 1. / rel_rank[1], 0.]) / num_queries)),
])
def test_make_discounted_cumulative_gain_fn(self):
scores = [[1., 3., 2.], [1., 2., 3.]]
# Note that scores are ranked in descending order.
ranks = [[3, 1, 2], [3, 2, 1]]
labels = [[0., 0., 1.], [0., 1., 2.]]
weights = [[1., 1., 1.], [2., 2., 1.]]
weights_feature_name = 'weights'
features = {weights_feature_name: torch.tensor(weights)}
m = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.DCG)
m_w = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.DCG,
weights_feature_name=weights_feature_name)
expected_dcg_1 = _dcg(0., 1) + _dcg(1., 2) + _dcg(0., 3)
self._check_metrics([
(m(torch.tensor([labels[0]]), torch.tensor([scores[0]]),
features), expected_dcg_1),
])
expected_dcg_2 = _dcg(2., 1) + _dcg(1., 2)
expected_dcg_2_weighted = _dcg(2., 1) + _dcg(1., 2) * 2.
expected_weight_2 = ((4 - 1) * 1. + (2 - 1) * 2.) / (4 - 1 + 2 - 1)
self._check_metrics([
(m(torch.tensor(labels), torch.tensor(scores),
features), (expected_dcg_1 + expected_dcg_2) / 2.0),
(m_w(torch.tensor(labels), torch.tensor(scores),
features), (expected_dcg_1 + expected_dcg_2_weighted) /
(1. + expected_weight_2)),
])
# Testing different gain and discount functions
gain_fn = lambda rel: rel
rank_discount_fn = lambda rank: rank
def mod_dcg_fn(l, r):
return _dcg(l,
r,
gain_fn=gain_fn,
rank_discount_fn=rank_discount_fn)
m_mod = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.DCG,
gain_fn=gain_fn,
rank_discount_fn=rank_discount_fn)
list_size = len(scores[0])
expected_modified_dcg_1 = sum([
mod_dcg_fn(labels[0][ind], ranks[0][ind])
for ind in range(list_size)
])
self._check_metrics([
(m_mod(torch.tensor([labels[0]]), torch.tensor([scores[0]]),
features), expected_modified_dcg_1),
])
def test_make_average_relevance_position_fn(self):
scores = [[1., 3., 2.], [1., 2., 3.]]
labels = [[0., 0., 1.], [0., 1., 2.]]
weights = [[1., 2., 3.], [4., 5., 6.]]
weights_feature_name = 'weights'
features = {weights_feature_name: torch.tensor(weights)}
m = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.ARP)
m_w = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.ARP,
weights_feature_name=weights_feature_name)
self._check_metrics([
(m(torch.tensor([labels[0]]), torch.tensor([scores[0]]),
features), 2.),
(m(torch.tensor(labels), torch.tensor(scores),
features), (1. * 2. + 2. * 1. + 1. * 2.) / 4.),
(m_w(torch.tensor(labels), torch.tensor(scores), features),
(3. * 1. * 2. + 6. * 2. * 1. + 5 * 1. * 2.) / (3. + 12. + 5.)),
])
def test_make_precision_fn(self):
scores = [[1., 3., 2.], [1., 2., 3.]]
labels = [[0., 0., 1.], [0., 1., 2.]]
features = {}
m = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.PRECISION)
m_top_1 = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.PRECISION, topn=1)
m_top_2 = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.PRECISION, topn=2)
self._check_metrics([
(m(torch.tensor([labels[0]]), torch.tensor([scores[0]]),
features), 1. / 3.),
(m_top_1(torch.tensor([labels[0]]), torch.tensor([scores[0]]),
features), 0. / 1.),
(m_top_2(torch.tensor([labels[0]]), torch.tensor([scores[0]]),
features), 1. / 2.),
(m(torch.tensor(labels), torch.tensor(scores),
features), (1. / 3. + 2. / 3.) / 2.),
])
def test_make_ordered_pair_accuracy_fn(self):
scores = [[1., 3., 2.], [1., 2., 3.]]
labels = [[0., 0., 1.], [0., 1., 2.]]
m = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.ORDERED_PAIR_ACCURACY)
self._check_metrics([
(m(torch.tensor([labels[0]]), torch.tensor([scores[0]]),
{}), 1. / 2.),
(m(torch.tensor([labels[1]]), torch.tensor([scores[1]]), {}), 1.),
(m(torch.tensor(labels), torch.tensor(scores),
{}), (1. + 3.) / (2. + 3.)),
])
def test_eval(self):
metric_fns = {
'metric/precision@1':
metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.PRECISION, topn=1),
}
head = ranking_head.Head(loss_fn=_make_loss_fn(),
eval_metric_fns=metric_fns)
loss, metrics_values = head.run(ranking_head.ModeKeys.EVAL,
self._default_labels,
self._default_logits,
features={})
self.assertAlmostEqual(loss.item(), self._default_loss, 5)
def test_make_mean_average_precision_fn(self):
scores = [[1., 3., 2.], [1., 2., 3.]]
# Note that scores are ranked in descending order, so the ranks are
# [[3, 1, 2], [3, 2, 1]]
labels = [[0., 0., 1.], [0., 1., 2.]]
rels = [[0, 0, 1], [0, 1, 1]]
features = {}
m = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.MAP)
m_top_1 = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.MAP, topn=1)
m_top_2 = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.MAP, topn=2)
self._check_metrics([
(m(torch.tensor([labels[0]]), torch.tensor([scores[0]]),
features), _ap(rels[0], scores[0])),
(m_top_1(torch.tensor([labels[0]]), torch.tensor([scores[0]]),
features), _ap(rels[0], scores[0], topn=1)),
(m_top_2(torch.tensor([labels[0]]), torch.tensor([scores[0]]),
features), _ap(rels[0], scores[0], topn=2)),
(m(torch.tensor(labels), torch.tensor(scores),
features), sum(_ap(rels[i], scores[i]) for i in range(2)) / 2.),
])
def test_make_normalized_discounted_cumulative_gain_fn(self):
scores = [[1., 3., 2.], [1., 2., 3.]]
# Note that scores are ranked in descending order.
ranks = [[3, 1, 2], [3, 2, 1]]
labels = [[0., 0., 1.], [0., 1., 2.]]
weights = [[1., 2., 3.], [4., 5., 6.]]
weights_3d = [[[1.], [2.], [3.]], [[4.], [5.], [6.]]]
list_weights = [1., 0.]
list_weights_2d = [[1.], [0.]]
weights_feature_name = 'weights'
weights_invalid_feature_name = 'weights_invalid'
weights_3d_feature_name = 'weights_3d'
list_weights_name = 'list_weights'
list_weights_2d_name = 'list_weights_2d'
features = {
weights_feature_name: torch.tensor([weights[0]]),
weights_invalid_feature_name: torch.tensor(weights[0]),
weights_3d_feature_name: torch.tensor([weights_3d[0]]),
list_weights_name: torch.tensor(list_weights),
list_weights_2d_name: torch.tensor(list_weights_2d)
}
m = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.NDCG)
expected_ndcg = (_dcg(0., 1) + _dcg(1., 2) + _dcg(0., 3)) / (
_dcg(1., 1) + _dcg(0., 2) + _dcg(0., 3))
self._check_metrics([
(m(torch.tensor([labels[0]]), torch.tensor([scores[0]]),
features), expected_ndcg),
])
expected_ndcg_1 = (_dcg(0., 1) + _dcg(1., 2) + _dcg(0., 3)) / (
_dcg(1., 1) + _dcg(0., 2) + _dcg(0., 3))
expected_ndcg_2 = 1.0
expected_ndcg = (expected_ndcg_1 + expected_ndcg_2) / 2.0
self._check_metrics([
(m(torch.tensor(labels), torch.tensor(scores),
features), expected_ndcg),
])
# With item-wise weights.
m_top = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.NDCG,
weights_feature_name=weights_feature_name,
topn=1)
m_weight = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.NDCG,
weights_feature_name=weights_feature_name)
m_weights_3d = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.NDCG,
weights_feature_name=weights_3d_feature_name)
self._check_metrics([
(m_top(torch.tensor([labels[0]]), torch.tensor([scores[0]]),
features), _dcg(0., 1, 2.) / _dcg(1., 1, 3.)),
(m_weight(torch.tensor([labels[0]]), torch.tensor([scores[0]]),
features),
(_dcg(0., 1, 2.) + _dcg(1., 2, 3.) + _dcg(0., 3, 1.)) /
(_dcg(1., 1, 3.) + _dcg(0., 2, 1.) + _dcg(0., 3, 2.))),
(m_weights_3d(torch.tensor([labels[0]]), torch.tensor([scores[0]]),
features),
(_dcg(0., 1, 2.) + _dcg(1., 2, 3.) + _dcg(0., 3, 1.)) /
(_dcg(1., 1, 3.) + _dcg(0., 2, 1.) + _dcg(0., 3, 2.))),
])
with self.assertRaises(ValueError):
m_weight_invalid = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.NDCG,
weights_feature_name=weights_invalid_feature_name)
m_weight_invalid(torch.tensor([labels[0]]),
torch.tensor([scores[0]]), features)
# With list-wise weights.
m_list_weight = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.NDCG,
weights_feature_name=list_weights_name)
m_list_weight_2d = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.NDCG,
weights_feature_name=list_weights_2d_name)
self._check_metrics([
(m_list_weight(torch.tensor(labels), torch.tensor(scores),
features),
(_dcg(0., 1, 2.) + _dcg(1., 2, 3.) + _dcg(0., 3, 1.)) /
(_dcg(1., 1, 3.) + _dcg(0., 2, 1.) + _dcg(0., 3, 2.))),
(m_list_weight_2d(torch.tensor(labels), torch.tensor(scores),
features),
(_dcg(0., 1, 2.) + _dcg(1., 2, 3.) + _dcg(0., 3, 1.)) /
(_dcg(1., 1, 3.) + _dcg(0., 2, 1.) + _dcg(0., 3, 2.))),
])
# Testing different gain and discount functions
gain_fn = lambda rel: rel
rank_discount_fn = lambda rank: 1. / rank
def mod_dcg_fn(l, r):
return _dcg(l,
r,
gain_fn=gain_fn,
rank_discount_fn=rank_discount_fn)
m_mod = metrics_lib.make_ranking_metric_fn(
metrics_lib.RankingMetricKey.NDCG,
gain_fn=gain_fn,
rank_discount_fn=rank_discount_fn)
list_size = len(scores[0])
expected_modified_dcg_1 = sum([
mod_dcg_fn(labels[0][ind], ranks[0][ind])
for ind in range(list_size)
])
self._check_metrics([
(m_mod(torch.tensor([labels[0]]), torch.tensor([scores[0]]),
features), expected_modified_dcg_1),
])
