def test_reco_quick_start_example(self):
# Data
actual = pd.DataFrame({
"user_id": [1, 2, 3, 4],
"item_id": [1, 2, 0, 3],
"clicks": [0, 1, 0, 0]
})
predicted = pd.DataFrame({
"user_id": [1, 2, 3, 4],
"item_id": [1, 2, 2, 3],
"clicks": [0.8, 0.7, 0.8, 0.7]
})
# Metrics
auc = BinaryRecoMetrics.AUC(click_column="clicks")
ctr = BinaryRecoMetrics.CTR(click_column="clicks")
ncdg_k = RankingRecoMetrics.NDCG(click_column="clicks", k=3)
precision_k = RankingRecoMetrics.Precision(click_column="clicks", k=2)
recall_k = RankingRecoMetrics.Recall(click_column="clicks", k=2)
map_k = RankingRecoMetrics.MAP(click_column="clicks", k=2)
# Scores
self.assertEqual(auc.get_score(actual, predicted), 0.25)
self.assertEqual(ctr.get_score(actual, predicted), 0.3333333333333333)
self.assertEqual(ncdg_k.get_score(actual, predicted), 1)
self.assertEqual(precision_k.get_score(actual, predicted), 1)
self.assertEqual(recall_k.get_score(actual, predicted), 1)
self.assertEqual(map_k.get_score(actual, predicted), 1)
def test_ndcg(self):
# First, test the IDCG value
idcg_val = idcg(3)
self.assertEqual(1. / np.log2(2) + 1. / np.log2(3) + 1. / np.log2(4),
idcg_val)
# Then, test NDCG
# User 1 has items 1, 4, 2 relevant and was recommended items 1, 3, 2
# User 2 checks for no relevant items, shouldn't contribute to the metric
# User 3 checks for no recommendations, is 0
# User 4 has items 1, 2 relevant and was recommended items 3, 4; is 0
actual = pd.DataFrame({
Constants.user_id: [1, 1, 1, 1, 3, 4, 4],
Constants.item_id: [1, 2, 3, 4, 3, 1, 2],
'click': [1, 1, 0, 1, 1, 1, 1]
})
predicted = pd.DataFrame({
Constants.user_id: [1, 1, 1, 2, 4, 4],
Constants.item_id: [1, 2, 3, 3, 3, 4],
'click': [0.8, 0.7, 0.75, 0.7, 0.6, 0.4]
})
metric = RankingRecoMetrics.NDCG(click_column='click', k=3)
results = metric.get_score(actual,
predicted,
return_extended_results=True)
self.assertEqual(((1. / np.log2(2) + 1. / np.log2(4)) / idcg_val) / 3,
results['ndcg'])
self.assertEqual(3, results['support'])
def test_change_column_names(self):
user_id_column = 'uid'
item_id_column = 'iid'
actual = pd.DataFrame({
user_id_column: [0, 0],
item_id_column: [1, 2],
'click': [True, True]
})
predicted = pd.DataFrame({
user_id_column: [0, 0, 0, 0],
item_id_column: [0, 1, 2, 3],
'click': [0, -1, -2, -3]
})
# Test that the output is the same
recall_1 = RankingRecoMetrics.Recall('click',
k=1,
user_id_column=user_id_column,
item_id_column=item_id_column)
self.assertEqual(0., recall_1.get_score(actual, predicted))
recall_2 = RankingRecoMetrics.Recall('click',
k=2,
user_id_column=user_id_column,
item_id_column=item_id_column)
self.assertEqual(0.5, recall_2.get_score(actual, predicted))
# Test that none of the metrics crash
metrics = CombinedMetrics(
BinaryRecoMetrics.CTR('click',
k=1,
user_id_column=user_id_column,
item_id_column=item_id_column),
RankingRecoMetrics.MAP('click',
k=1,
user_id_column=user_id_column,
item_id_column=item_id_column),
RankingRecoMetrics.NDCG('click',
k=1,
user_id_column=user_id_column,
item_id_column=item_id_column),
RankingRecoMetrics.Precision('click',
k=1,
user_id_column=user_id_column,
item_id_column=item_id_column),
RankingRecoMetrics.Recall('click',
k=1,
user_id_column=user_id_column,
item_id_column=item_id_column))
metrics.get_score(actual, predicted)