TensorFlow Ranking v0.3.0

This is the 0.3.0 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 related to the DNN Estimator Builder and Keras APIs.

A DNN Estimator Builder is available at tfr.estimator.make_dnn_ranking_estimator().

For Keras, we provide an example to showcase the use of Keras APIs to build ranking models , and a documentation providing step-by-step user instructions outlining the Keras user journey.

The new Keras components are:

• Losses: Ranking losses in Keras object oriented loss format, along with a base class and a factory method. The APIs are:

  • tfr.keras.losses.get(loss_key)
  • Base class: tfr.keras.losses._RankingLoss
  • Losses under tfr.keras.losses.*

• Metrics: Ranking metrics in Keras object oriented metric format, along with a base class and a default metrics getter method. The APIs are:

  • tfr.keras.metrics.get_default_metrics()
  • Base class: [tfr.keras.metrics._RankingMetric`
  • Metrics under tfr.keras.metrics.*

• Feature Transformations: tfr.keras.feature.EncodeListwiseFeatures, to convert sparse ranking features to dense. The APIs are:

  • tfr.keras.feature.EncodeListwiseFeatures

• Ranking Network: Base classes for building Ranking Networks, which define scoring logic. The APIs are:

  • tfr.keras.network.RankingNetwork
  • tfr.keras.network.UnivariateRankingNetwork

• Premade Networks: We support premade architectures users can access out-of-the-box. The APIs are:

  • tfr.keras.canned.DNNRankingNetwork

• Keras Model : Ranking models can be built using Keras Functional Model API. The APIs are:

  • tfr.keras.model.create_keras_model()

• Integration with Estimators and RankingPipeline: Keras model can be converted to Estimator to use Estimator’s training utilities and is compatible with RankingPipeline. The APIs for conversion are:

  • tfr.keras.estimator.model_to_estimator()