def test_auc_extended(self):
# Test immediate calculation of AUC
metric = BinaryRecoMetrics.AUC(click_column='click')
actual = pd.DataFrame({
Constants.user_id: [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4],
Constants.item_id: [1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3],
'click': [1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1]
})
predicted = pd.DataFrame({
Constants.user_id: [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4],
Constants.item_id: [1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3],
'click':
[0.3, 0.9, 0.4, 0.3, 0.2, 0.9, 0.2, 0.6, 0.9, 0.2, 0.7, 0.8]
})
auc = metric.get_score(actual, predicted)
self.assertEqual(0.78125, auc)
# Test accumulated calculation
metric = BinaryRecoMetrics.AUC(click_column='click')
_, results = metric.get_score(actual,
predicted,
batch_accumulate=True,
return_extended_results=True)
self.assertEqual(0.78125, results['auc'])
self.assertEqual(12, results['support'])
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_auc_one_class(self):
# Test immediate calculation of AUC
metric = BinaryRecoMetrics.AUC(click_column='click')
actual = pd.DataFrame({
Constants.user_id: [1, 2, 3, 4],
Constants.item_id: [1, 2, 0, 3],
'click': [0, 0, 0, 0]
})
predicted = pd.DataFrame({
Constants.user_id: [1, 2, 3, 4],
Constants.item_id: [1, 2, 2, 3],
'click': [0.1, 0.9, 0.1, 0.1]
})
auc = metric.get_score(actual, predicted)
self.assertTrue(np.isnan(auc))