Worst Case Attack
Run a worst case attack based upon predictive probabilities.
- class sacroml.attacks.worst_case_attack.WorstCaseAttack(output_dir: str = 'outputs', write_report: bool = True, n_reps: int = 10, reproduce_split: int | Iterable[int] | None = 5, p_thresh: float = 0.05, n_dummy_reps: int = 1, train_beta: int = 1, test_beta: int = 1, test_prop: float = 0.2, include_model_correct_feature: bool = False, sort_probs: bool = True, attack_model: str = 'sklearn.ensemble.RandomForestClassifier', attack_model_params: dict | None = None)[source]
Worst case attack.
Methods
attack(target)Run worst case attack.
attack_from_preds(proba_train, proba_test[, ...])Run attack based upon the predictions in proba_train and proba_test.
generate_arrays(n_rows_in, n_rows_out[, ...])Generate train and test prediction arrays, used when computing baseline.
Get parameters for this attack.
run_attack_reps(proba_train, proba_test[, ...])Run actual attack reps from train and test predictions.
- __init__(output_dir: str = 'outputs', write_report: bool = True, n_reps: int = 10, reproduce_split: int | Iterable[int] | None = 5, p_thresh: float = 0.05, n_dummy_reps: int = 1, train_beta: int = 1, test_beta: int = 1, test_prop: float = 0.2, include_model_correct_feature: bool = False, sort_probs: bool = True, attack_model: str = 'sklearn.ensemble.RandomForestClassifier', attack_model_params: dict | None = None) None[source]
Construct an object to execute a worst case attack.
- Parameters:
- output_dirstr
Name of the directory where outputs are stored.
- write_reportbool
Whether to generate a JSON and PDF report.
- n_repsint
Number of attacks to run – in each iteration an attack model is trained on a different subset of the data.
- reproduce_splitint or Iterable[int] or None
Variable that controls the reproducibility of the data split. It can be an integer or a list of integers of length n_reps. Default : 5.
- p_threshfloat
Threshold to determine significance of things. For instance auc_p_value and pdif_vals.
- n_dummy_repsint
Number of baseline (dummy) experiments to do.
- train_betaint
Value of b for beta distribution used to sample the in-sample (training) probabilities.
- test_betaint
Value of b for beta distribution used to sample the out-of-sample (test) probabilities.
- test_propfloat
Proportion of data to use as a test set for the attack model.
- include_model_correct_featurebool
Inclusion of additional feature to hold whether or not the target model made a correct prediction for each example.
- sort_probsbool
Whether to sort combined preds (from training and test) to have highest probabilities in the first column.
- attack_modelstr
Class name of the attack model.
- attack_model_paramsdict or None
Dictionary of hyperparameters for the attack_model such as min_sample_split, min_samples_leaf, etc.
- attack(target: Target) dict[source]
Run worst case attack.
- Parameters:
- targetattacks.target.Target
target as a Target class object
- Returns:
- dict
Attack report.
- attack_from_preds(proba_train: ndarray, proba_test: ndarray, train_correct: ndarray | None = None, test_correct: ndarray | None = None) None[source]
Run attack based upon the predictions in proba_train and proba_test.
- Parameters:
- proba_trainnp.ndarray
Array of train predictions. One row per example, one column per class.
- proba_testnp.ndarray
Array of test predictions. One row per example, one column per class.
- generate_arrays(n_rows_in: int, n_rows_out: int, train_beta: float = 2, test_beta: float = 2) tuple[ndarray, ndarray][source]
Generate train and test prediction arrays, used when computing baseline.
- Parameters:
- n_rows_inint
Number of rows of in-sample (training) probabilities.
- n_rows_outint
Number of rows of out-of-sample (testing) probabilities.
- train_betafloat
Beta value for generating train probabilities.
- test_betafloat:
Beta value for generating test probabilities.
- Returns:
- proba_trainnp.ndarray
Array of train predictions (n_rows x 2 columns).
- proba_testnp.ndarray
Array of test predictions (n_rows x 2 columns).
- get_params() dict
Get parameters for this attack.
- Returns:
- paramsdict
Parameter names mapped to their values.
- run_attack_reps(proba_train: ndarray, proba_test: ndarray, train_correct: ndarray | None = None, test_correct: ndarray | None = None) dict[source]
Run actual attack reps from train and test predictions.
- Parameters:
- proba_trainnp.ndarray
Predictions from the model on training (in-sample) data.
- proba_testnp.ndarray
Predictions from the model on testing (out-of-sample) data.
- Returns:
- dict
Dictionary of mia_metrics (a list of metric across repetitions).