Parameter settings for pre-processing models

(Default values are in ~/search/Pre-processing.yaml)

The benchmark provides several arguments for describing:

  • Basic setting of the parameters

See below for the details:

Pre-processing required parameters

  • preprocessing_model (str) : The name of the pre-processing model, which automatically loads the configs from search/properties/models/<model_name>.yaml.

Ranklib parameters

  • name (str) : The name of the ranker class.

  • ranker_path (str) : The path to the Ranklib library.

  • rel_max (int) : The maximum relevance to be assigned to items.

  • ranker (str) : The name of the ranking model.

  • ranker_id (int) : The ID of the ranking model as per the Ranklib library.

  • metric (str) : The evaluation metric.

  • top_k (int) : The evaluation at top-k.

  • lr (float) : The learning rate for training.

  • epochs (int) : The number of epochs for training.

  • train_data (list) : The data types for training [“original”, “fair”].

  • test_data (list) : The data types for testing [“original”, “fair”].

Evaluation parameters

  • metrics (list) : The list of fairness evaluation metrics having the following values [“diversity”, “select_rate”, “exposure”, “individual”, “igf”].

  • rankings (list) : The list of rankings to perform the evaluation on, having the following values: “score_col” - original data, “<score_col>__<score_col>” - trained and tested on original data, “<score_col>_fair” - transformed data, “<score_col>fair_<score_col>_fair” - trained and tested on transformed data.

  • k_list (list) : The top-k values for evaluation.