Changelog¶
1.2.0¶
22 Oct 2019
Probability calibration using Platt scaling, both with provided negatives or synthetic negative statements (#131)
Added ConvKB model
Added WN11, FB13 loaders (datasets with ground truth positive and negative triples) (#138)
Continuous integration with CircleCI, integrated on GitHub (#127)
Refactoring of models into separate files (#104)
Fixed bug where the number of epochs did not exactly match the provided number by the user (#135)
Fixed some bugs on RandomBaseline model (#133, #134)
Fixed some bugs on discover_facts with strategies “exhaustive” and “graph_degree”
Fixed bug on subsequent calls of model.predict on the GPU (#137)
1.1.0¶
16 Aug 2019
Support for large number of entities (#61, #113)
Faster evaluation protocol (#74)
New Knowledge discovery APIs: discover facts, clustering, near-duplicates detection, topn query (#118)
API change: model.predict() does not return ranks anymore
API change: friendlier ranking API output (#101)
Implemented nuclear-3 norm (#23)
Jupyter notebook tutorials: AmpliGraph basics (#67) and Link-based clustering
Random search for hyper-parameter tuning (#106)
Additional initializers (#112)
Experiment campaign with multiclass-loss
System-wide bugfixes and minor improvements
1.0.3¶
7 Jun 2019
Fixed regression in RandomBaseline (#94)
Added TensorBoard Embedding Projector support (#86)
Minor bugfixing (#87, #47)
1.0.2¶
19 Apr 2019
Added multiclass loss (#24 and #22)
Updated the negative generation to speed up evaluation for default protocol.(#74)
Support for visualization of embeddings using Tensorboard (#16)
Save models with custom names. (#71)
Quick fix for the overflow issue for datasets with a million entities. (#61)
Fixed issues in train_test_split_no_unseen API and updated api (#68)
Added unit test cases for better coverage of the code(#75)
Corrupt_sides : can now generate corruptions for training on both sides, or only on subject or object
Better error messages
Reduced logging verbosity
Added YAGO3-10 experiments
Added MD5 checksum for datasets (#47)
Addressed issue of ambiguous dataset loaders (#59)
Renamed ‘type’ parameter in models.get_embeddings to fix masking built-in function
Updated String comparison to use equality instead of identity.
Moved save_model and restore_model to ampligraph.utils (but existing API will remain for several releases).
Other minor issues (#63, #64, #65, #66)
1.0.1¶
22 Mar 2019
evaluation protocol now ranks object and subjects corruptions separately
Corruption generation can now use entities from current batch only
FB15k-237, WN18RR loaders filter out unseen triples by default
Removed some unused arguments
Improved documentation
Minor bugfixing
1.0.0¶
16 Mar 2019
TransE
DistMult
ComplEx
FB15k, WN18, FB15k-237, WN18RR, YAGO3-10 loaders
generic loader for csv files
RDF, ntriples loaders
Learning to rank evaluation protocol
Tensorflow-based negatives generation
save/restore capabilities for models
pairwise loss
nll loss
self-adversarial loss
absolute margin loss
Model selection routine
LCWA corruption strategy for training and eval
rank, Hits@N, MRR scores functions