3.3.0

Added

• Random recommendations' engine for anonymous users [@Michal-Kolomanski]

Changed

• Cleaning up the main conftest [@Michal-Kolomanski]
• More metadata in autoencoders pipeline [@Michal-Kolomanski]
• Pandas warnings in the AE pipline [@Michal-Kolomanski]
• db commands divided into db and seed commands [@Michal-Kolomanski]
• seed commands only available in development and testing env [@Michal-Kolomanski]

Fixed

• Dependabot alerts [@Michal-Kolomanski]

Removed

3.2.1

Added

Changed

• Python version: 3.10 [@Michal-Kolomanski]
• Updating and cleaning up packages [@Michal-Kolomanski]

Fixed

• Dependabot alerts [@Michal-Kolomanski]

Removed

3.2.0

Added

• Integrate elastic_search results from the Marketplace [@Michal-Kolomanski]
• JMS Subscriber for user actions and flask cli for it [@michal-szostak]
• search_data migration [@Michal-Kolomanski]
• tqdm and logging in embedding pipeline [@Michal-Kolomanski]

Changed

• engine_version field in the recommendation request is required [@Michal-Kolomanski]
• Structure of the tests in the tests folder [@Michal-Kolomanski]
• There is no version specified in flask command for RL training. Use: flask train rl [@Michal-Kolomanski]
• drop_ml_models in flask train all [@Michal-Kolomanski]

Fixed

Removed

• v2 configuration of RL pipeline removed [@Michal-Kolomanski]

3.1.2

Added

• JMS connector [@Michal-Kolomanski]
• Models from m_l_component collection are deleted when necessary (/update, flask command) [@Michal-Kolomanski]
• Flask command to delete models from m_l_component collection [@Michal-Kolomanski]
• Flask train, db command and docs in the README [@Michal-Kolomanski]
• If requested recommendation model (in /recommendation body request) does not exist try to use another model [@Michal-Kolomanski]
• engine_version in recommendation collection [@Michal-Kolomanski]

Changed

• Black version = 20.8b0 - click dependency [@Michal-Kolomanski]

Fixed

• InsufficientRecommendationSpace in RL model evaluation step [@Michal-Kolomanski]

Removed

• RL v2 training in flask train all [@Michal-Kolomanski]

3.1.1

Added

• User actions documentation [@JanKapala, @Michal-Kolomanski]
• Logging in generation of SARSes [@Michal-Kolomanski]
• SARS generator - saving users with ID -1 to the database while they are not logged in [@Michal-Kolomanski]

Changed

• Limited SARSes deletion in the RL data extraction step [@Michal-Kolomanski]
• Test for regenerate SARSes are disabled due to the fact that they tend to freeze [@Michal-Kolomanski]

Fixed

• Details in the AE data preparation step [@Michal-Kolomanski]
• Multiprocessing in Celery task. Transition to billiard [@JanKapala, @Michal-Kolomanski]

Removed

• Invalid user actions progress bar [@Michal-Kolomanski]

3.1.0

Added

• Add the AI docs, describing the training, data and algorithm evaluation [@wujuu, @JanKapala, @Michal-Kolomanski]
• Flask CLI commands (testing db seed, execution of individual AI pipelines and more) [@wujuu]
• Unit and integration tests for each pipeline step of the autoencoders [@Michal-Kolomanski]
• New ENV variable, which points to default recommendation algorithm [@Michal-Kolomanski]
• The ability to modify the sizes and number of layers in the autoencoders networks.[@Michal-Kolomanski]
• Implement migration process [@wujuu]
• Split validation in autoencoder's data preparation step [@Michal-Kolomanski]
• Logging system [@Michal-Kolomanski]
• Add MongoDB to CI and to testing environment [@wujuu]
• Add adjustable min/max numerical boundaries on Actor output [@wujuu]
• Add more tests to RLInferenceComponent [@wujuu]
• DB indexes [@JanKapala]
• Support for multiprocessing-capable code testing and parallel tests [@JanKapala]
• Implement user journey creator for testing purposes [@JanKapala]
• User user journey visualizer [@JanKapala]
• Add pytest-randomly [@JanKapala]

Changed

• Methods refactoring in the AEDataPreparationStep [@Michal-Kolomanski]
• Refactoring neural network layer definitions across all pipelines [@Michal-Kolomanski]
• Reimplementation SARSes (re)generation [@JanKapala]

Fixed

• flask seed_faker command on an empty database [@Michal-Kolomanski]
• Flask db seed [@Michal-Kolomanski]

Removed

Release 3.0.1

Changed

• Training time significantly decreased, by reducing epochs and unused models [@wujuu]

Fixed

• Fixed RLInference bugs on user's history longer than max history length [@wujuu]

Release 3.0.0

Added

• fix AGENT_VERSION default in execute_training task [@JanKapala]
• Add proper policy delay to TD3 [@wujuu]
• Complex and simple reward generation [@wujuu]
• Add ENGINE_VERSION field to the /recommendations endpoint [@Michal-Kolomanski]
• Abstract Pipeline architecture [@JanKapala, @wujuu]
• NCFPipeline [@JanKapala]
• NCFInferenceComponent [@JanKapala]
• Pipeline and Step Metadata [@wujuu, @JanKapala]
• AutoencodersPipeline [@Michal-Kolomanski]
• Embedder and Normalizers implementation and integration [@JanKapala, @wujuu]
• RLPipeline [@wujuu]

Changed

• Split History Embedder into MLP and LSTM versions [@wujuu]
• Service engagement refactoring [@wujuu]
• Service Selector refactoring [@wujuu]
• Improved SARSes generator [@JanKapala]
• Project structure [@Michal-Kolomanski, @wujuu, @JanKapala]

Fixed

• Fix dynamic examples in swagger API [@JanKapala]

Removed

• Deletion of unnecessary jupyter notebooks [@JanKapala]

Release 2.2.2

Fixed

• fix mongo FTS by specifying proper fields in the service model [@JanKapala]

Release 2.2.1

Fixed

• AGENT_VER defaults to pre_agent if not in env [@JanKapala]