def test_intra_list_diversity_sample_float(self):
metric = DiversityRecoMetrics.IntraListDiversity(self.item_features, click_column='score', k=4,
user_sample_size=0.8)
results = metric.get_score(self.actual, self.predicted, batch_accumulate=False, return_extended_results=True)
self.assertEqual(0.268, np.round(results['intra-list diversity'], 3))
self.assertEqual(2, results['support'])
def test_intra_list_diversity_sample_one_item(self):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
metric = DiversityRecoMetrics.IntraListDiversity(self.item_features, click_column='score', k=1)
results = metric.get_score(self.actual, self.predicted, batch_accumulate=False,
return_extended_results=True)
self.assertEqual(None, results)
def test_intra_list_diversity_sample_default(self):
metric = DiversityRecoMetrics.IntraListDiversity(self.item_features, click_column='score', k=4)
with warnings.catch_warnings(record=True) as w:
results = metric.get_score(self.actual, self.predicted, batch_accumulate=False,
return_extended_results=True)
self.assertEqual(0.267, np.round(results['intra-list diversity'], 3))
self.assertEqual(3, results['support'])
def test_intra_list_diversity_metric_euclidean(self):
metric = DiversityRecoMetrics.IntraListDiversity(self.item_features, click_column='score', k=4,
metric='euclidean')
with warnings.catch_warnings(record=True) as w:
results = metric.get_score(self.actual, self.predicted, batch_accumulate=False,
return_extended_results=True)
self.assertEqual(round(results['intra-list diversity'], 3), 1.307)
self.assertEqual(results['support'], 3)
def test_intra_list_diversity_one(self):
actual = pd.DataFrame({Constants.user_id: [0, 0],
Constants.item_id: [0, 1],
'score': [1, 1]})
predicted = pd.DataFrame({Constants.user_id: [0, 0],
Constants.item_id: [0, 1],
'score': [1, 1]})
item_features = pd.DataFrame({Constants.item_id: [0, 1],
'feat1': [0, 1]})
metric = DiversityRecoMetrics.IntraListDiversity(item_features, click_column='score', k=2)
with warnings.catch_warnings(record=True) as w:
results = metric.get_score(actual, predicted, batch_accumulate=False,
return_extended_results=True)
self.assertEqual(results['intra-list diversity'], 1)
self.assertEqual(1, results['support'])
def test_intra_list_diversity_batch_accumulate_true(self):
metric = DiversityRecoMetrics.IntraListDiversity(self.item_features, click_column='score', k=4)
with warnings.catch_warnings(record=True) as w:
results = metric.get_score(self.actual, self.predicted, batch_accumulate=True, return_extended_results=True)
def test_intra_list_diversity_sample_int_zero(self):
with self.assertRaises(ValueError):
# This should fail when user_sample_size is set to be 0.
metric = DiversityRecoMetrics.IntraListDiversity(self.item_features, click_column='score', k=4,
user_sample_size=0)