Tensorflow-ranking

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](https://github.com/tensorflow/ranking/blob/master/tensorflow_ranking/extension/examples/pipeline_example.py) 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](https://github.com/tensorflow/ranking/commit/0305c0b98be76aefd012df3d7eaeee2a71fafb5f)). This is a more user-friendly format than the `ExampleInExample` one.
* Add [a demo script](https://github.com/tensorflow/ranking/blob/master/tensorflow_ranking/examples/tf_ranking_tfrecord.py) 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.