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@delta2323 delta2323 released this 24 Apr 10:10
· 1052 commits to master since this release
v0.3.0
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Learn about vigilant mode.

New features

  • Classifier (#124, #137, #151), supports predict, predict_proba methods
  • Regressor (#129, #137, #151), supports predict method
  • BaseForwardModel (#137, #151) and add save_pickle and load_pickle to it (#139)
  • Add target_index to parse method of CSVFileParser and SDFFileParser (#131)
  • Add extract_total_num to CSVFileParser and SDFFileParser (#131)
  • Add target_index option to get_qm9 (#131)
  • Add train_target_index, val_target_index, and test_target_index to get_tox21 (#131)
  • Add dropout_ratio to RSGCN.__init__ (#146).

Changes that break compatibility

  • chainer_chemistry.dataset.preprocessors.weavenet_preprocessor.DEFAULT_NUM_MAX_ATOMS has been removed. Use chainer_chemistry.WEAVE_DEFAULT_NUM_MAX_ATOMS instead (#127).

Bug fixes

  • Fix the behavior of concat hidden argument of GGNN (#117, #118)
  • Fix the order of transpose of GGNN (#143)
    • It was a serious bug that degraded the performance of GGNN. (Thank you @ir5 for the bug report!)
  • Fix examples to run with Python 2.7 (#144) (Thank you @mihaimorariu)

Example

  • Add an example of using own dataset (#114)
  • Add an example of Classifier (include saving model by pickle) using Tox21 dataset (#124, #125, #136, #141, #152, #156)
  • Add prediction script to the QM9 example (#140)
  • Add an example of Regressor (include saving model by pickle) using QM9 dataset (#129, #152, #156).

Others

  • Improve document of concat_mols (#116)
  • Import without installing RDKit (#119, #123)
  • Add introductory slide to README (#130)
  • Speed up example test scripts (#132)
  • Add tests that check invariance of Graph CNN under graph isomorphism (#145)
  • Add tests of RSGCN (#146)
  • Remove import of matplotlib (#148)
  • Add a comment on model portability (#150)
  • Apply flake8 (#155)

Also thank you @mihaimorariu (#144) and @ir5 for the implementation, documentation, bug report and example improvements!

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