def test_get_lr(): # Tests that the likelihood ratio values are not empty for extreme values and are realistic. ctx = create_cts_test_ctx() r = SimilarityRecommender(ctx) assert r.get_lr(0.0001) is not None assert r.get_lr(10.0) is not None assert r.get_lr(0.001) > r.get_lr(5.0)
def test_get_lr(test_ctx): # Tests that the likelihood ratio values are not empty for extreme values and are realistic. ctx = install_continuous_data(test_ctx) r = SimilarityRecommender(ctx) assert r.get_lr(0.0001) is not None assert r.get_lr(10.0) is not None assert r.get_lr(0.001) > r.get_lr(5.0)
def test_get_lr(test_ctx): # Tests that the likelihood ratio values are not empty for extreme values and are realistic. with mock_install_continuous_data(test_ctx): r = SimilarityRecommender(test_ctx) cache = r._get_cache({}) assert r.get_lr(0.0001, cache) is not None assert r.get_lr(10.0, cache) is not None assert r.get_lr(0.001, cache) > r.get_lr(5.0, cache)
def test_get_lr(instantiate_mocked_s3_bucket): # Tests that the likelihood ratio values are not empty for extreme values and are realistic. r = SimilarityRecommender() assert r.get_lr(0.0001) is not None assert r.get_lr(10.0) is not None assert r.get_lr(0.001) > r.get_lr(5.0)
def test_get_lr(mock_s3_continuous_data): # Tests that the likelihood ratio values are not empty for extreme values and are realistic. r = SimilarityRecommender() assert r.get_lr(0.0001) is not None assert r.get_lr(10.0) is not None assert r.get_lr(0.001) > r.get_lr(5.0)