Release of mplc v0.3.0

[arthurPignet]

We planned to release a new version of mplc on git, pip and conda.

the release note are shown below, please don't hesitated to comment !

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MPLC v0.3 Release notes

This release introduces several changes to the library which is now offering more modularity. It is also deployed on PyPI.

Features

• Multiple code refactorization: Refacto the multi-partner learning approaches in a more object oriented way. #255
• New Experiment object: An object which runs and repeats several scenarios and gathers results to simplify their analysis. #275
• New corruption methods and options: Refacto the corruption in a more object oriented way. Add random, permutation, duplication, redundancy ways to corrupt a dataset. #280 & #277
• Updated datasets integration: Change module architecture. The datasets are now a subclass of the Dataset class. #262
• Updated tutorial notebooks. #266
• Documentation: Update and format documentation. #264 & #294
• Add code coverage badge: A new badge on the README, to follow the extension of the test coverage. #268
• More tests added: There is now end-to-end tests for contributivity methods and multi-partner learning approaches. #304 & #300
• Add FederatedGradient: New multi-partner learning method. The gradients are aggregated (instead of the weights). Then the optimizer updates the model with the aggregated gradient. #299
• Add S-model: New multi-partner learning method. It adds for each partner a NoiseAdaptative layer, which is supposed to adapt to potential label flip and thus improve the shared model performances, even with corrupted partners.
  #281 & #301
• Add local/global option for test and validation datasets. The dataset is split (only once! and in a stratified way) between train/test/validation datasets. If ‘local’, these test/validation datasets are splitted between partners, in the exact same way than the training one. #288

Fixes

• Fix bugs in corruption by permutation: A transposition was missing. #284
• Update Cifar10 model optimizer: The learning rate was way too low. We also switched from RMSprop to Adam for the optimizer, as it showed better performances. #283
• Fix early stopping: Condition for early stopping was not reachable. #279
• Logs: Add epoch number when showing evaluation metrics. #274
• Dependencies updates: Removal of Keras (We now exclusively use the tensorflow's built-in Keras module), upgrade to Tensor Flow v2.4.0, Numpy v1.19.4, Scikit Learn v0.23.2, Pandas v1.1.5. #290
• Switch to Github Actions instead of Travis for Continuous Integration. #298
• Normalize contributivity scores in step-by-step methods. #295

Contributors

This release received successful contributions from:

• bowni
• arthurPignet
• RomainGoussault
• Thomas-Galtier
• celinejacques
• natct10
• jeromechambost
• JustineBoulant
• Meylina

[bowni]

The release note is really cool, well done @arthurPignet!

Fixing a few English typos:

We planned to release a new version of mplc on git, pip and conda.

the release note are shown below, please don't hesitated to comment !

---

MPLC v0.3 Release notes

This release introduces several changes to the library which is now offering more modularity. It is also deployed on PyPI.

Features

• Multiple code refactorization: Refacto the multi-partner learning approaches in a more object oriented way. #255
• New Experiment object: An object which runs and repeats several scenarios and gathers results to simplify their analysis. #275
• New corruption methods and options: Refacto the corruption in a more object oriented way. Add random, permutation, duplication, redundancy ways to corrupt a dataset. #280 & #277
• Updated datasets integration: Change module architecture. The datasets are now a subclass of the Dataset class. #262
• Updated tutorial notebooks. #266
• Documentation: Update and format documentation. #264 & #294
• Add code coverage badge: A new badge on the README, to follow the extension of the test coverage. #268
• More tests added: There is now end-to-end tests for contributivity methods and multi-partner learning approaches. #304 & #300
• Add FederatedGradient: New multi-partner learning method. The gradients are aggregated (instead of the weights). Then the optimizer updates the model with the aggregated gradient. #299
• Add S-model: New multi-partner learning method. It adds for each partner a NoiseAdaptative layer, which is supposed to adapt to potential label flip and thus improve the shared model performances, even with corrupted partners.
  #281 & #301
• Add local/global option for test and validation datasets. The dataset is split (only once! and in a stratified way) between train/test/validation datasets. If ‘local’, these test/validation datasets are splitted between partners, in the exact same way than the training one. #288

Fixes

• Fix bugs in corruption by permutation: A transposition was missing. #284
• Update Cifar10 model optimizer: The learning rate was way too low. We also switched from RMSprop to Adam for the optimizer, as it showed better performances. #283
• Fix early stopping: Condition for early stopping was not reachable. #279
• Logs: Add epoch number when showing evaluation metrics. #274
• Dependencies updates: Removal of Keras (We now exclusively use the tensorflow's built-in Keras module), upgrade to Tensor Flow v2.4.0, Numpy v1.19.4, Scikit Learn v0.23.2, Pandas v1.1.5. #290
• Switch to Github Actions instead of Travis for Continuous Integration. #298
• Normalize contributivity scores in step-by-step methods. #295

Contributors

This release received successful contributions from:

• bowni
• arthurPignet
• RomainGoussault
• Thomas-Galtier
• celinejacques
• natct10
• jeromechambost
• JustineBoulant
• Meylina