Changelog

1.4.0

26 May 2021

  • Added support for numerical attributes on edges (FocusE) (#235)
  • Added loaders for benchmark datasets with numeric values on edges (O*NET20K, PPI5K, NL27K, CN15K)
  • Added discovery API to find nearest neighbors in embedding space (#240)
  • Change of optimizer (from BFSG to Adam) to calibrate models with ground truth negatives (#239)
  • 10x speed improvement on train_test_split_unseen API (#242)
  • Added support to visualize training progression via tensorboard (#230)
  • Bug fix in large graph mode (when evaluate_performance with entities_subset is used) (#231)
  • Updated save model api to save embedding matrix > 6GB (#233)
  • Doc updates (#247, #221)
  • Fixed ntriples loader spurious trailing dot.
  • Add tensorboard_logs_path to model.fit() for tracking training loss and early stopping criteria.

1.3.2

25 Aug 2020

  • Added constant initializer (#205)
  • Ranking strategies for breaking ties (#212)
  • ConvE Bug Fixes (#210, #194)
  • Efficient batch sampling (#202)
  • Added pointer to documentation for large graph mode and Docs for Optimizer (#216)

1.3.1

18 Mar 2020

  • Minor bug fix in ConvE (#189)

1.3.0

9 Mar 2020

  • ConvE model Implementation (#178)
  • Changes to evaluate_performance API (#183)
  • Option to add reciprocal relations (#181)
  • Minor fixes to ConvKB (#168, #167)
  • Minor fixes in large graph mode (#174, #172, #169)
  • Option to skip unseen entities checks when train_test_split is used (#163)
  • Stability of NLL losses (#170)
  • ICLR-20 calibration paper experiments added in branch paper/ICLR-20

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