def test_recommendation_evaluation_15(model, interactions_ds):
"""Evaluation with invalid number of k (< 0)."""
try:
recommendation_evaluation(model,
interactions_ds[1],
cn_test_users=None,
k=-1,
n_pos_interactions=None,
novelty=False,
metrics=[NDCG(), HitRatio()],
verbose=False)
assert False
except Exception as e:
assert str(e) == 'k (-1) should be > 0.'
def test_recommendation_evaluation_16(model, interactions_ds):
"""Invalid metrics value (not a list)."""
try:
recommendation_evaluation(model,
interactions_ds[1],
cn_test_users=None,
k=5,
n_pos_interactions=None,
novelty=False,
metrics={},
verbose=False)
assert False
except Exception as e:
assert str(e) == 'Expected "metrics" argument to be a list and found <class \'dict\'>. ' \
'Should contain instances of RankingMetricABC.'
def test_recommendation_evaluation_13(model, interactions_ds):
"""Evaluation with invalid number of positive interactions (< 0)."""
try:
recommendation_evaluation(model,
interactions_ds[1],
cn_test_users=None,
k=[1, 2],
n_pos_interactions=-1,
novelty=False,
metrics=[NDCG(), HitRatio()],
verbose=False)
assert False
except Exception as e:
assert str(
e
) == 'The number of positive interactions (-1) should be None or an integer > 0.'
def test_recommendation_evaluation_18(model, interactions_ds):
"""Evaluation with a custom ignore low predictions threshold."""
assert recommendation_evaluation(model, interactions_ds[1], cn_test_users=None, k=2, n_pos_interactions=None,
novelty=False, ignore_low_predictions_threshold=2) == \
{'HitRatio@2': 0.0167, 'NDCG@2': 0.0189, 'Precision@2': 0.02, 'Recall@2': 0.0167}
