Package: allRank
allRank : Learning to Rank in PyTorchAboutallRank is a PyTorch-based framework for training neural Learning-to-Rank (LTR) models, featuring implementations of:common pointwise, pairwise and listwise loss functionsfully connected and Transformer-like scoring functionscommonly used evaluation metrics like Normalized Discounted Cumulative Gain (NDCG) and Mean Reciprocal Rank (MRR)click-models for experiments on simulated click-through dataMotivationallRank provides an easy and flexible way to experiment with various LTR neural network models and loss functions.
It is easy to add a custom loss, and to configure the model and the training procedure.
We hope that allRank will facilitate both research in neural LTR and its industrial applications.FeaturesImplemented loss functions:ListNet (for binary and graded relevance)ListMLERankNetOrdinal lossLambdaRankLambdaLossApproxNDCGRMSENeuralNDCG (introduced inhttps://arxiv.org/pdf/2102.07831)Getting started guideTo help you get started, we provide arun_example.shscript which generates dummy ranking data in libsvm format and trains
a Transformer model on the data using provided exampleconfig.jsonconfig file. Once you run the script, the dummy data can be found indummy_datadirectory
and the results of the experiment intest_rundirectory. To run the example, Docker is required.Configuring your model & trainingTo train your own model, configure your experiment inconfig.jsonfile and runpython allrank/main.py --config_file_name allrank/config.json --run_id <the_name_of_your_experiment> --job_dir <the_place_to_save_results>All the hyperparameters of the training procedure: i.e. model defintion, data location, loss and metrics used, training hyperparametrs etc. are controlled
by theconfig.jsonfile. We provide a template fileconfig_template.jsonwhere supported attributes, their meaning and possible values are explained.
Note that following MSLR-WEB30K convention, your libsvm file with training data should be namedtrain.txt. You can specify the name of the validation dataset
(eg. valid or test) in the config. Results will be saved under the path<job_dir>/results/<run_id>Google Cloud Storage is supported in allRank as a place for data and job results.Implementing custom loss functionsTo experiment with your own custom loss, you need to implement a function that takes two tensors (model prediction and ground truth) as input
and put it in thelossespackage, making sure it is exposed on a package level.
To use it in training, simply pass the name (and args, if your loss method has some hyperparameters) of your function in the correct place in the config file:"loss": {
    "name": "yourLoss",
    "args": {
        "arg1": val1,
        "arg2: val2
    }
  }Applying click-modelTo apply a click model you need to first have an allRank model trained.
Next, run:python allrank/rank_and_click.py --input-model-path <path_to_the_model_weights_file> --roles <comma_separated_list_of_ds_roles_to_process e.g. train,valid> --config_file_name allrank/config.json --run_id <the_name_of_your_experiment> --job_dir <the_place_to_save_results>The model will be used to rank all slates from the dataset specified in config. Next - a click model configured in config will be applied and the resulting click-through dataset will be written under<job_dir>/results/<run_id>in a libSVM format.
The path to the results directory may then be used as an input for another allRank model training.Continuous integrationYou should runscripts/ci.shto verify that code passes style guidelines and unit tests.ResearchThis framework was developed to support the research projectContext-Aware Learning to Rank with Self-Attention. If you use allRank in your research, please cite:@article{Pobrotyn2020ContextAwareLT,
  title={Context-Aware Learning to Rank with Self-Attention},
  author={Przemyslaw Pobrotyn and Tomasz Bartczak and Mikolaj Synowiec and Radoslaw Bialobrzeski and Jaroslaw Bojar},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.10084}
}Additionally, if you use the NeuralNDCG loss function, please cite the corresponding work,NeuralNDCG: Direct Optimisation of a Ranking Metric via Differentiable Relaxation of Sorting:@article{Pobrotyn2021NeuralNDCG,
  title={NeuralNDCG: Direct Optimisation of a Ranking Metric via Differentiable Relaxation of Sorting},
  author={Przemyslaw Pobrotyn and Radoslaw Bialobrzeski},
  journal={ArXiv},
  year={2021},
  volume={abs/2102.07831}
}LicenseApache 2 License