cf-shap-facct22/experiments.sh at main · jpmorganchase/cf-shap-facct22 · GitHub

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# Run experiments.
python experiments.py --dataset heloc --results_version v5_close --action_strategy random --methods training_100 diff_pred_100 diff_label_100 knn1_qL1 knn3_qL1 knn5_qL1 knn10_qL1 knn20_qL1 knn50_qL1 knn100_qL1 knn250_qL1 knn500_qL1 knn1000_qL1 cone-knn100_qL1
python experiments.py --dataset wines --results_version v5_far --action_strategy proportional --methods training_100 diff_pred_100 diff_label_100 knn1_qL1 knn3_qL1 knn5_qL1 knn10_qL1 knn20_qL1 knn50_qL1 knn100_qL1 knn250_qL1 knn500_qL1 knn1000_qL1 cone-knn100_qL1
python experiments.py --dataset lendingclub --results_version v5_far --action_strategy random --methods training_100 diff_pred_100 diff_label_100 knn1_qL1 knn3_qL1 knn5_qL1 knn10_qL1 knn20_qL1 knn50_qL1 knn100_qL1 knn250_qL1 knn500_qL1 knn1000_qL1 cone-knn100_qL1

python experiments.py --dataset lendingclub --results_version v5_close --action_strategy proportional --methods training_100 diff_pred_100 diff_label_100 knn1_qL1 knn3_qL1 knn5_qL1 knn10_qL1 knn20_qL1 knn50_qL1 knn100_qL1 knn250_qL1 knn500_qL1 knn1000_qL1 cone-knn100_qL1
python experiments.py --dataset lendingclub --results_version v5_far --action_strategy proportional --methods training_100 diff_pred_100 diff_label_100 knn1_qL1 knn3_qL1 knn5_qL1 knn10_qL1 knn20_qL1 knn50_qL1 knn100_qL1 knn250_qL1 knn500_qL1 knn1000_qL1 cone-knn100_qL1
python experiments.py --dataset lendingclub --results_version v5_close --action_strategy random --methods training_100 diff_pred_100 diff_label_100 knn1_qL1 knn3_qL1 knn5_qL1 knn10_qL1 knn20_qL1 knn50_qL1 knn100_qL1 knn250_qL1 knn500_qL1 knn1000_qL1 cone-knn100_qL1

python experiments.py --dataset heloc --results_version v5_close --action_strategy proportional --methods training_100 diff_pred_100 diff_label_100 knn1_qL1 knn3_qL1 knn5_qL1 knn10_qL1 knn20_qL1 knn50_qL1 knn100_qL1 knn250_qL1 knn500_qL1 knn1000_qL1 cone-knn100_qL1
python experiments.py --dataset heloc --results_version v5_far --action_strategy proportional --methods training_100 diff_pred_100 diff_label_100 knn1_qL1 knn3_qL1 knn5_qL1 knn10_qL1 knn20_qL1 knn50_qL1 knn100_qL1 knn250_qL1 knn500_qL1 knn1000_qL1 cone-knn100_qL1
python experiments.py --dataset heloc --results_version v5_far --action_strategy random --methods training_100 diff_pred_100 diff_label_100 knn1_qL1 knn3_qL1 knn5_qL1 knn10_qL1 knn20_qL1 knn50_qL1 knn100_qL1 knn250_qL1 knn500_qL1 knn1000_qL1 cone-knn100_qL1

python experiments.py --dataset wines --results_version v5_close --action_strategy proportional --methods training_100 diff_pred_100 diff_label_100 knn1_qL1 knn3_qL1 knn5_qL1 knn10_qL1 knn20_qL1 knn50_qL1 knn100_qL1 knn250_qL1 knn500_qL1 knn1000_qL1 cone-knn100_qL1
python experiments.py --dataset wines --results_version v5_close --action_strategy random --methods training_100 diff_pred_100 diff_label_100 knn1_qL1 knn3_qL1 knn5_qL1 knn10_qL1 knn20_qL1 knn50_qL1 knn100_qL1 knn250_qL1 knn500_qL1 knn1000_qL1 cone-knn100_qL1
python experiments.py --dataset wines --results_version v5_far --action_strategy random --methods training_100 diff_pred_100 diff_label_100 knn1_qL1 knn3_qL1 knn5_qL1 knn10_qL1 knn20_qL1 knn50_qL1 knn100_qL1 knn250_qL1 knn500_qL1 knn1000_qL1 cone-knn100_qL1


# Aggregate overriding.
python experiments.py --dataset heloc --results_version v5_close --override_costs --override_nn --methods training_100 diff_pred_100 diff_label_100 knn1_qL1 knn3_qL1 knn5_qL1 knn10_qL1 knn20_qL1 knn50_qL1 knn100_qL1 knn250_qL1 knn500_qL1 knn1000_qL1 cone-knn100_qL1
python experiments.py --dataset heloc --results_version v5_far --override_costs --override_nn --methods training_100 diff_pred_100 diff_label_100 knn1_qL1 knn3_qL1 knn5_qL1 knn10_qL1 knn20_qL1 knn50_qL1 knn100_qL1 knn250_qL1 knn500_qL1 knn1000_qL1 cone-knn100_qL1
python experiments.py --dataset wines --results_version v5_close --override_costs --override_nn --methods training_100 diff_pred_100 diff_label_100 knn1_qL1 knn3_qL1 knn5_qL1 knn10_qL1 knn20_qL1 knn50_qL1 knn100_qL1 knn250_qL1 knn500_qL1 knn1000_qL1 cone-knn100_qL1
python experiments.py --dataset wines --results_version v5_far --override_costs --override_nn --methods training_100 diff_pred_100 diff_label_100 knn1_qL1 knn3_qL1 knn5_qL1 knn10_qL1 knn20_qL1 knn50_qL1 knn100_qL1 knn250_qL1 knn500_qL1 knn1000_qL1 cone-knn100_qL1
python experiments.py --dataset lendingclub --results_version v5_close --override_costs --override_nn --methods training_100 diff_pred_100 diff_label_100 knn1_qL1 knn3_qL1 knn5_qL1 knn10_qL1 knn20_qL1 knn50_qL1 knn100_qL1 knn250_qL1 knn500_qL1 knn1000_qL1 cone-knn100_qL1
python experiments.py --dataset lendingclub --results_version v5_far --override_costs --override_nn --methods training_100 diff_pred_100 diff_label_100 knn1_qL1 knn3_qL1 knn5_qL1 knn10_qL1 knn20_qL1 knn50_qL1 knn100_qL1 knn250_qL1 knn500_qL1 knn1000_qL1 cone-knn100_qL1