Implementation of Ranknet to LambdaRank in TensorFlow2.0

**This repository has From RankNet to LambdaRank implementation in tensorflow 2.0, **

Requirements

• tqdm==4.32.1
• numpy==1.16.4
• Click==7.0
• tensorflow_gpu==2.1.0

Setup

$ git clone https://github.com/akanyaani/ranknet-tensorflow2.0
$ cd ranknet-tensorflow2.0
$ pip install -r requirements.txt

Download data from here https://www.microsoft.com/en-us/research/project/mslr/ and pass any of the fold to pre_process.

$ python pre_process.py --help

Options:
  --data-dir TEXT           training data path  [default: /data/rank_data]
  --per-file-limit INTEGER  no of example per tfrecords  [default: 50000]
  --help                    Show this message and exit.
  

Preprocessing and and creating the TF Records of MSLR Data

>> python pre_process.py --data-dir data/path

Training learning to rank model.

$ python train_model.py --help

Options:
  --data-path TEXT       out directory  [default: ./data/tf_records]
  --out-dir TEXT         tf records path  [default:
                         /media/akanyaani/Disk2/ranknet]
  --algo TEXT            LTR algo name  [default: ranknet]
  --ranknet-type TEXT    Ranknet type (default or factor)  [default: default]
  --optimizer TEXT       optimizer type  [default: adam]
  --window-size INTEGER  window size  [default: 512]
  --batch-size INTEGER   batch size  [default: 128]
  --lr FLOAT             learning rate  [default: 0.0001]
  --graph-mode BOOLEAN   graph execution  [default: True]
  --help                 Show this message and exit.

  
  
$ python train_model.py --data-path /data/path \
                        --out-dir /model/data/path \
                        --algo lambdarank \
                        --window-size=512 \
                        --batch-size 128 \
                        --lr 1e-4 \
                        --graph-mode True

Start TensorBoard through the command line.

$ tensorboard --logdir /model/data/path

References:

• "Microsoft Learning to Rank Datasets"
• "From RankNet to LambdaRank"
• "Tensorflow-LTR"

Contribution

• Your issues and PRs are always welcome.

Author

• Abhay Kumar
• Author Email : [email protected]
• Follow me on Twitter

License

• MIT