Fair search DELTR for ElasticSearch

This is the Python library that serves as wrapper for the DELTR model for fair ranking in ElasticSearch with the Learning to Rank plugin.

Requirements

This library requires:

• Python 3.4+
  • Python dependencies are stored in the requirements.txt file
• ElasticSearch and Learning to rank plugin (LTR) for ElasticSearch
  • Start a supported version of ElasticSearch and follow the installation steps

Usage

There are several steps you need to take. In the following, we describe how to use the adapter to search on a collection of e-mails from W3C, included in /data/, which is one of the examples we used in the paper (see bibliography below).

Index the training corpus

Index the training corpus. We have a sample data set in zip files /data/candidates/candidates*.zip. Make sure to unzip them first. Then, you can index them with:

python deltr.py --index --document-dir ./data/candidates --index-name resumes

This will (re)index the JSON files under the folder /data/candidates in an index named resumes.

Later, at any point, you can add the real documents over which you want to search using the trained ranking model. Those documents do not need to be in the same index, most commonly they will be in a different index.

Setup the features

Create the features you want to use in LTR. We have created sample features in /data/features.json Next, we need to upload these features to ElasticSearch.

python deltr.py --prepare --feature-set-file ./data/features.json --feature-set-name w3c

This will upload the features defined in /data/features.json in ElasticSearch under the name w3c.

Train the model

After, we have defined and uploaded the features and indexed the data, we can now create a model to use for retrireval. In order to build a DELTR model, we need to provide it with some training data. We have created a sample train set contained in two files: /data/queries.csv and /data/judgements.csv. You can train a model with:

python deltr.py --train --queries ./data/queries.csv --judgements ./data/judgements.csv --model deltr_vanilla --feature-set-name w3c

This is going to train a DELTR model (with default parameters) name deltr_vanilla using the questions in /data/queries.csv and judgements for those queries in /data/judgements.csv, with the features defined in the feature set name w3c

Debugging the model by observing the feature values

The library will use the features we defined in LTR to train the model. So, for debugging purposes, the library creates a features.csv file in the same folder where this is executed. There you can see what features were generated for each document. It also creates a model.txt where you can see the final model, that was uploaded in LTR.

Note: You can also specify tuning parameters from the command line as well. E.g.

python deltr.py --train --queries ./data/queries.csv --judgements ./data/judgements.csv --model deltr_not_vanilla --feature-set-name w3c --gamma 0.8

This will create a new model with the same files, only it will set the gamma parameter to 0.8. Here you can see how to check all options.

Search with the model

Once we have the model, we can start using to do some searches.

python3 deltr.py --search --query html --model deltr_vanilla --index-name resumes

This will run a query with the keyword html using the model deltr_vanilla on the index resumes.

Note: You can also see a verbose output, which will contain the features calculated for each document returned.

python3 deltr.py --search --query html --model deltr_vanilla --index-name resumes --verbose

All options

Run the following command to get the full options list

python deltr.py --help

Development

1. Clone this repository git clone https://github.com/fair-search/fairsearch-deltr-for-elasticsearch
2. Change directory to the directory where you cloned the repository cd WHERE_ITS_DOWNLOADED/fairsearch-deltr-for-elasticsearch
3. Use any IDE to work with the code

Credits

The DELTR algorithm is described in this paper:

• Meike Zehlike, Gina-Theresa Diehn, Carlos Castillo. "Reducing Disparate Exposure in Ranking: A Learning to Rank Approach." preprint arXiv:1805.08716 (2018).

This library was developed by Ivan Kitanovski based on the paper. See the license file for more information.

For any questions contact Meike Zehlike.

See also

You can also see:

• DELTR Python library
• DELTR Java library
• FA*IR plug-in for ElasticSearch