Changelog¶
2.0.0¶
7 March 2023
Switched to TensorFlow 2 back-end.
Keras style APIs.
Unique model class ScoringBasedEmbeddingModel for all scoring functions that can be specified as a parameter when initializing the class.
Change of the data input/output pipeline.
Extension of supported optimizers, regularizers and initializer.
Different data storage support: no-backend (in memory) and SQLite-based backend.
Codex-M Knowledge Graph included in the APIs for automatic download.
ConvKB, ConvE, ConvE(1-N) not supported anymore as they are computationally expensive and thus not commonly used.
Support AmpliGraph 1.4 API within ampligraph.compat module.
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