Ranking Versions Save

This is the 0.2.3 release of TensorFlow Ranking. It depends on tensorflow-serving-api==2.1.0 and is fully compatible with tensorflow==2.1.0. Both will be installed as required packages when installing tensorflow-ranking.

The main changes in this release are:

• Added an EstimatorBuilder Class to encapsulate boilerplate codes when constructing a TF-ranking model Estimator. Clients can access it via tfr.estimator.EstimatorBuilder.
• Added a RankingPipeline Class to hide the boilerplate codes regarding the train and eval data reading, train and eval specs definition, dataset building, exporting strategies. With this, clients can construct a RankingPipeline object using tfr.ext.pipeline.RankingPipeline and then call train_and_eval() to run the pipeline.
• Provided an example to demo the use of tfr.ext.pipeline.RankingPipeline.

This is the 0.2.2 release of TensorFlow Ranking. It depends on tensorflow-serving-api==2.1.0 and is fully compatible with tensorflow==2.1.0. Both will be installed as required packages when installing tensorflow-ranking. The main changes in this release are:

• Fixed metric computation to include lists without any relevant examples.
• Updated demo code to be TF 2.1.0 compatible.
• Replaced deprecated dataset.output_dtypes with tf.compat.v1.get_output_dtypes(dataset).

This is the 0.2.1 release of TensorFlow Ranking. It depends on tensorflow-serving-api==2.0.0 and is fully compatible with tensorflow==2.0.0. Both will be installed as required packages when installing tensorflow-ranking.

The main changes in this release are:

• Updated demo code to use Antique data in ELWC format.
• Updated tutorial script to demonstrate using weights in metrics and losses.
• Removed LIBSVM generator from tfr.data and updated the docs.
• Make gain and discount parameters in the definition of NDCG configurable.
• Added MAP as a ranking metric.
• Added a topn parameter to MRR metric.

This is the 0.2.0 release of TensorFlow Ranking. It depends on tensorflow-serving-api>=2.0.0 and is fully compatible with tensorflow==2.0.0. Both will be installed as required packages when installing tensorflow-ranking.

There is no new functionality added compared with v0.1.6. This release marks a milestone that our future development will be based on TensorFlow 2.0.

This is the 0.1.6 release of TensorFlow Ranking. We add the dependency to tensorflow-serving-api to use tensorflow.serving.ExampleListWithContext as our input data format. It is tested and stable against TensorFlow 1.15.0 and TensorFlow 2.0.0. The main changes in this release are:

• Support tensorflow.serving.ExampleListWithContext as our input data format (commit). This is a more user-friendly format than the ExampleInExample one.
• Add a demo script for data stored in TFRecord. The stored format can be ExampleListhWithContext or other format defined in data.py.

This is the 0.1.5 release of TensorFlow Ranking. It is tested and stable against TensorFlow version 1.14.0 and TensorFlow version 2.0 RC0. The main changes in this release are:

• Support for Multi-Task Learning and Multi-Objective Learning (Issue #85).
• Deprecate the input_size argument for tfr.feature. encode_listwise_features and infer it automatically in the function.
• Fix the weighted mrr computation for doc-level weights.

This is the 0.1.4 release of TensorFlow Ranking. It is tested and stable against TensorFlow version 1.14.0 and TensorFlow version 2.0 RC0. The main changes in this release are:

• Documentation for APIs. List of symbols/operations are available here.
• Demo for using sparse and embedded features on ANTIQUE dataset.
• Example for prediction using ranking estimator in demo code.
• Code and test cases are fully TF2.0 RC0 compatible.
• Updated tfr.utils.sort_by_scores to break ties.
• Added ApproxMRR loss function.

Announcement: A hands-on tutorial for TF-Ranking, with relevant theoretical background will be presented on Oct 2 at ICTIR 2019, hosted in Santa Clara, CA. Please consider attending!

This is the 0.1.3 release of TensorFlow Ranking. It is tested and stable against TensorFlow version 1.14.0. The main changes in this release are:

• Introduced an ExampleInExample data format.
• Introduced a factory method to build tf.dataset in different data formats.
• Introduced a factory method to build serving receiving input functions for different data formats.
• Refactored the main modules to be object-oriented to increase the code extensibility.