Comments (1)
Short answer: no, the functions don't compute the same.
The query_topn
function doesn't filter the positive (i.e. known) triples of the graph, so it will return triples that the model was trained on. These triples are usually ranked quite high and therefore inflate the hits@10 metric in comparison to evaluate_performance
.
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Related Issues (20)
- 'ConvKB' object has no attribute 'corr_batch_size' HOT 1
- load_from_ntriples() doesn't work as expected HOT 1
- Migrate to tensor flow 2 HOT 5
- https://github.com/Accenture/AmpliGraph/blob/d4bf44559cb7178039f21203780be4eb946ea4eb/experiments/IJCAI-21/experiments.py#L12 HOT 1
- ConvE has no attribute tensorboard_logs_path
- generate_candidates() doesn't work as expected - generates same triplet
- Refit a trained model with new triplets HOT 3
- Implement NodePieces HOT 1
- FocusE-ComplEx: Question on using numeric edge weight attributes - prediction performance (MRR) and embeddings are very similar between models with and without edge weights
- discovery.py library, discover_facts function, when returning np.hstack if some array is empty
- Can we pass custom trained embedding as entity and then train the model?
- Multiple GPU for training
- AttributeEerro while running the code HOT 1
- ImportError: cannot import name 'ConvKB' from 'ampligraph.latent_features' HOT 3
- Train and Test Data split error HOT 2
- Ampligraph Embedding for Protein Sequences
- AttributeError: 'ScoringBasedEmbeddingModel' object has no attribute '_reset_compile_cache' HOT 5
- AttributeError: 'ScoringBasedEmbeddingModel' object has no attribute '_reset_compile_cache' HOT 1
- Question: Is it possible to integrate two embedding model? HOT 1
- file should be inside "ampligraph_datasets" directory to be able to load as dataset
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