Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[0.1.16] - 2023-02-06

Added

• RankMatchFailure metric for evaluation
• Statistical significance and power analysis utilities
• Stat analysis for groupwise metrics in Ranking

[0.1.15] - 2023-01-20

Changed

• Upgrading from tensorflow 2.0.x to 2.9.x
• Moving from Keras Functional API to Model Subclassing API for more customization capabilities
• Auxiliary loss is reimplemented as part of ScoringModel

Added

• AutoDAGNetwork which allows for building flexible connected architectures using config files
• SetRankEncoder keras Layer to train SetRank like Ranking models
• Support for using tf-models-official deep learning garden library
• RankMatchFailure metric for validation

[0.1.14] - 2022-11-18

Changed

• Ability to pass custom RelevanceModel class in Pipeline.

[0.1.13] - 2022-10-17

Fixed

• Bug in metrics_helper when used without secondary_labels

Added

• RankMatchFailure metric for evaluation
• RankMatchFailure auxiliary loss

[0.1.12] - 2022-04-26

[0.1.11] - 2021-01-18

Changed

• Adding rank feature to serving parse fn by default and removing dependence on required serving_info attribute

[0.1.10] - 2021-12-29

Changed

• Adding all trained features to serving parse fn by default

[0.1.9] - 2021-11-29

Changed

• Refactored secondary label metrics computation for ranking and added unit tests
• Added NDCG metric for secondary labels

[0.1.8] - 2021-10-21

Added

• New argument to model.save()

[0.1.7] - 2021-09-30

Added

• SoftmaxCrossEntropy loss for ranking models

[0.1.6] - 2021-07-16

Fixed

• Fixing required arguments in setup.py

[0.1.5] - 2021-07-15

Added

• Adding support for performing post-training steps (such as copying data) by custom class inheriting RelevancePipeline.

[0.1.4] - 2021-06-30

Changed

• Performing pre-processing step in __init__() to be able to copy files before model_config and feature_config are initiated.

[0.1.3] - 2021-06-24

Changed

• Making pyspark an optional dependency to install ml4ir

[0.1.2] - 2021-06-16

Added

• Support for performing pre-processing steps (such as copying data) by custom class inheriting RelevancePipeline.

[0.1.1] - 2021-05-20

Added

• Support for using native tf/keras feature functions from the feature config YAML

[0.1.0] - 2021-03-01

Changed

• TFRecord format changed for SequenceExample to earlier implementation.
• Removed support for max_len attribute for SequenceExample features.
• No effective changes for Example TFRecords.
• TFRecord implementation on python (training) and jvm (inference) side are now in sync.

[0.0.5] - 2021-02-17

Added

• Changelog file to track version updates for ml4ir.
• build-requirements.txt with all python dependencies needed for developing on ml4ir and the CircleCI autobuilds.
• Updated CircleCI builds to use build-requirements.txt

Fixed

• Removed build requirements from the base ml4ir requirements.txt allowing us to keep the published whl file dependencies to be minimal.