grid_result = grid.fit(x, y)
print("Best: %f using %s" %
(grid_result.best_score_, grid_result.best_params_))
params = grid_result.best_params_
return create_fit_model(x_train,
y_train,
look_back,
params=params,
times_to_repeat=1)
if __name__ == "__main__":
look_back = constants.LOOK_BACK
print("Lookback using: ", look_back)
x_train, y_train = sequences_crafting_for_classification.get_train_set(
scenario=constants.EIGHTH_SCENARIO)
# if the dataset is the real one -> contrast imbalanced dataset problem
# if constants.DATASET_TYPE == constants.REAL_DATASET:
# x_train, y_train = resampling_dataset.oversample_set(x_train, y_train)
x_test, y_test = sequences_crafting_for_classification.get_test_set(
scenario=constants.EIGHTH_SCENARIO)
# model, threshold = create_fit_model(x_train, y_train, look_back, times_to_repeat=1)
model, threshold = model_selection(x_train, y_train, look_back)
testPredict = model.predict(x_test)
evaluation.evaluate(y_test, testPredict, threshold)
# explainability.explain_dataset(model, x_train, x_test, threshold, y_test)
# runtime_testing_fraud_buster(model)
def repeat_experiment_n_times(lstm,
rf,
xg_reg,
scenario,
times_to_repeat=100,
adversarial_attack=False,
evasion_attack=False,
is_white_box_attack=True,
use_lstm_for_adversarial=False):
tn_s = []
tp_s = []
fp_s = []
fn_s = []
f1_s = []
balanced_accuracies = []
precisions = []
recalls = []
aucpr_s = []
roc_aucs = []
num_decisions_taken_by_lstm,\
num_decisions_taken_by_rf, \
num_decisions_taken_by_xgb, \
num_decisions_correctly_taken_from_lstm, \
num_decisions_correctly_taken_from_lstm_and_not_from_xgb_or_rf = 0, 0, 0, 0, 0
for i in range(times_to_repeat):
print("Iteration", i)
x_test_set, y_test_set = sequences_crafting_for_classification.get_test_set(
scenario=scenario)
x_val, y_val, x_test, y_test = evaluation.get_val_test_set(
x_test_set, y_test_set, val_size=0.25)
x_val_supervised = x_val[:, len(x_val[0]) - 1, :]
x_test_supervised = x_test[:, len(x_val[0]) - 1, :]
if adversarial_attack or evasion_attack:
# getting train set for training
if is_white_box_attack:
print("Using as training set, the real one - whitebox attack")
dataset_type = REAL_DATASET
else:
print("Using as training set, the old one - blackbox attack")
dataset_type = OLD_DATASET
x_train, y_train = sequences_crafting_for_classification.get_train_set(
dataset_type=dataset_type)
x_train_supervised = x_train[:, look_back, :]
if adversarial_attack:
print("Crafting an adversarial attack")
if not use_lstm_for_adversarial:
print("The attacker will use a Multilayer perceptron")
# training multilayer perceptron
# todo: hyper param tuning multilayer perceptron
adversarial_model = MultiLayerPerceptron.create_fit_model(
x_train_supervised, y_train)
# crafting adversarial samples
x_test_supervised = x_test[:, len(x_test[0]) - 1, :]
frauds = x_test_supervised[np.where(y_test == 1)]
adversarial_samples = fgsm.craft_sample(frauds,
adversarial_model,
epsilon=0.01)
x_test[np.where(y_test == 1),
len(x_test[0]) - 1] = adversarial_samples
x_test_supervised = x_test[:, len(x_test[0]) - 1, :]
else:
print("The attacker will use a LSTM network")
# train the network using the right params
if is_white_box_attack:
if USING_AGGREGATED_FEATURES:
params = BEST_PARAMS_LSTM_REAL_DATASET_AGGREGATED
else:
params = BEST_PARAMS_LSTM_REAL_DATASET_NO_AGGREGATED
else:
if USING_AGGREGATED_FEATURES:
params = BEST_PARAMS_LSTM_OLD_DATASET_AGGREGATED
else:
params = BEST_PARAMS_LSTM_OLD_DATASET_NO_AGGREGATED
adversarial_model = LSTM_classifier.create_fit_model(
x_train, y_train, look_back, params=params)
frauds = x_test[np.where(y_test == 1)]
adversarial_samples = fgsm.craft_sample(frauds,
adversarial_model,
epsilon=0.01)
x_test[np.where(y_test == 1)] = adversarial_samples
x_test_supervised = x_test[:, len(x_test[0]) - 1, :]
if evasion_attack:
print("Crafting an evasion attack")
# train the network using the right params
if is_white_box_attack:
if USING_AGGREGATED_FEATURES:
params = BEST_PARAMS_RF_REAL_DATASET_AGGREGATED
else:
params = BEST_PARAMS_RF_REAL_DATASET_NO_AGGREGATED
else:
if USING_AGGREGATED_FEATURES:
params = BEST_PARAMS_RF_OLD_DATASET_AGGREGATED
else:
params = BEST_PARAMS_RF_OLD_DATASET_NO_AGGREGATED
# training the oracle
oracle = RF.create_model(x_train_supervised,
y_train,
params=params)
# get the oracle threshold
y_val_pred_oracle = oracle.predict_proba(x_val_supervised)
oracle_threshold = evaluation.find_best_threshold_fixed_fpr(
y_val, y_val_pred_oracle[:, 1])
# if the oracle predicts the fraud as fraud -> discard it, otherwise inject in real bank system
y_pred_oracle = rf.predict_proba(x_test_supervised)
y_pred_oracle = y_pred_oracle[:, 1].ravel()
y_pred_oracle = np.array(
evaluation.adjusted_classes(y_pred_oracle,
oracle_threshold))
x_test = x_test[(np.where((
(y_test == 1) & (y_pred_oracle == 0)) | (y_test == 0)))]
y_test = y_test[(np.where((
(y_test == 1) & (y_pred_oracle == 0)) | (y_test == 0)))]
x_test_supervised = x_test[:, len(x_test[0]) - 1, :]
try:
# a, b, c, d, e = 0, 0, 0, 0, 0
# y_test_pred, not_by_xgb, not_by_rf, not_found_by_others = predict_test_based_on_voting(lstm, rf, xg_reg, x_val, x_val_supervised, y_val, x_test, x_test_supervised, y_test)
y_test_pred, a, b, c, d, e = predict_test_based_on_more_confident(
lstm, rf, xg_reg, x_val, x_val_supervised, y_val, x_test,
x_test_supervised, y_test)
# y_test_pred, a, b, c, d, e = predict_test_based_on_expon(lstm, rf, xg_reg, x_val, x_val_supervised, y_val, x_test, x_test_supervised, y_test)
# y_test_pred = predict_test_based_on_sum(lstm, rf, xg_reg, x_val, x_val_supervised, y_val, x_test, x_test_supervised)
# y_test_pred = predict_test_based_on_more_confident_and_majority_voting(lstm, rf, xg_reg, x_val, x_val_supervised, y_val, x_test, x_test_supervised, y_test)
# not_found_by_xgboost += not_by_xgb
# not_by_rf += not_by_rf
# not_found_by_others += not_by_others
y_test_pred = np.array(y_test_pred)
confusion, f1, balanced_accuracy, precision, recall, aucpr, roc_auc = evaluation.get_performance(
y_test, y_test_pred, threshold=True)
tn = confusion[0, 0]
tp = confusion[1, 1]
fp = confusion[0, 1]
fn = confusion[1, 0]
tn_s.append(tn)
tp_s.append(tp)
fp_s.append(fp)
fn_s.append(fn)
f1_s.append(f1)
num_decisions_taken_by_lstm += a
num_decisions_taken_by_rf += b
num_decisions_taken_by_xgb += c
num_decisions_correctly_taken_from_lstm += d
num_decisions_correctly_taken_from_lstm_and_not_from_xgb_or_rf += e
balanced_accuracies.append(balanced_accuracy)
precisions.append(precision)
recalls.append(recall)
aucpr_s.append(aucpr)
roc_aucs.append(roc_auc)
except RuntimeError:
i -= 1
print("Num decisions taken from lstm: ",
num_decisions_taken_by_lstm / times_to_repeat)
print("Num decisions taken by rf: ",
num_decisions_taken_by_rf / times_to_repeat)
print("Num decisions taken by xgb: ",
num_decisions_taken_by_xgb / times_to_repeat)
print("Num decisions taken by lstm correctly taken: ",
num_decisions_correctly_taken_from_lstm / times_to_repeat)
print(
"Num decisions taken by lstm correctly taken and not by others: ",
num_decisions_correctly_taken_from_lstm_and_not_from_xgb_or_rf /
times_to_repeat)
evaluation.print_results(
np.array(tn_s).mean(),
np.array(fp_s).mean(),
np.array(fn_s).mean(),
np.array(tp_s).mean(),
np.array(f1_s).mean(),
np.array(balanced_accuracies).mean(),
np.array(precisions).mean(),
np.array(recalls).mean(),
np.array(aucpr_s).mean(),
np.array(roc_aucs).mean())
def experiment_with_cdf(lstm,
scale_lstm,
loc_lstm,
mean_lstm,
std_lstm,
threshold_lstm,
rf,
scale_rf,
loc_rf,
mean_rf,
std_rf,
threshold_rf,
xg_reg,
scale_xgb,
loc_xgb,
mean_xgb,
std_xgb,
threshold_xgb,
scenario,
adversarial_attack=False,
evasion_attack=False,
is_white_box_attack=True,
use_lstm_for_adversarial=False):
x_test, y_test = sequences_crafting_for_classification.get_test_set(
scenario=scenario)
x_test_supervised = x_test[:, len(x_test[0]) - 1, :]
if adversarial_attack or evasion_attack:
# getting train set for training
if is_white_box_attack:
print("whitebox attack")
dataset_type = INJECTED_DATASET
else:
print("blackbox attack")
dataset_type = OLD_DATASET
x_train, y_train = sequences_crafting_for_classification.get_train_set(
dataset_type=dataset_type)
x_train_supervised = x_train[:, look_back, :]
if adversarial_attack:
print("Crafting an adversarial attack")
if not use_lstm_for_adversarial:
print("The attacker will use a Multilayer perceptron")
# training multilayer perceptron
# todo: hyper param tuning multilayer perceptron
adversarial_model = MultiLayerPerceptron.create_fit_model(
x_train_supervised, y_train)
# crafting adversarial samples
x_test_supervised = x_test[:, len(x_test[0]) - 1, :]
frauds = x_test_supervised[np.where(y_test == 1)]
adversarial_samples = fgsm.craft_sample(frauds,
adversarial_model,
epsilon=0.01)
x_test[np.where(y_test == 1),
len(x_test[0]) - 1] = adversarial_samples
x_test_supervised = x_test[:, len(x_test[0]) - 1, :]
else:
print("The attacker will use a LSTM network")
# train the network using the right params
if is_white_box_attack:
if USING_AGGREGATED_FEATURES:
params = BEST_PARAMS_LSTM_AGGREGATED
else:
params = BEST_PARAMS_LSTM_NO_AGGREGATED
else:
if USING_AGGREGATED_FEATURES:
params = BEST_PARAMS_LSTM_OLD_DATASET_AGGREGATED
else:
params = BEST_PARAMS_LSTM_OLD_DATASET_NO_AGGREGATED
adversarial_model, _ = LSTM_classifier.create_fit_model(
x_train, y_train, look_back, params=params)
frauds = x_test[np.where(y_test == 1)]
adversarial_samples = fgsm.craft_sample(frauds,
adversarial_model,
epsilon=0.1)
x_test[np.where(y_test == 1)] = adversarial_samples
x_test_supervised = x_test[:, len(x_test[0]) - 1, :]
if evasion_attack:
print("Crafting an evasion attack")
# train the network using the right params
if is_white_box_attack:
if USING_AGGREGATED_FEATURES:
params = BEST_PARAMS_RF_AGGREGATED
else:
params = BEST_PARAMS_RF_NO_AGGREGATED
else:
if USING_AGGREGATED_FEATURES:
params = BEST_PARAMS_RF_OLD_DATASET_AGGREGATED
else:
params = BEST_PARAMS_RF_OLD_DATASET_NO_AGGREGATED
# training the oracle
oracle, oracle_threshold = RF.create_model(x_train_supervised,
y_train,
params=params)
# if the oracle predicts the fraud as fraud -> discard it, otherwise inject in real bank system
y_pred_oracle = oracle.predict_proba(x_test_supervised)
y_pred_oracle = y_pred_oracle[:, 1].ravel()
y_pred_oracle = np.array(
evaluation.adjusted_classes(y_pred_oracle, oracle_threshold))
x_test = x_test[(np.where(((y_test == 1) & (y_pred_oracle == 0))
| (y_test == 0)))]
y_test = y_test[(np.where(((y_test == 1) & (y_pred_oracle == 0))
| (y_test == 0)))]
x_test_supervised = x_test[:, len(x_test[0]) - 1, :]
y_test_pred, thresholds, num_decisions_taken_by_lstm, num_decisions_taken_by_rf, num_decisions_taken_by_xgb = predict_test_based_on_expon(
lstm, scale_lstm, loc_lstm, mean_lstm, std_lstm, threshold_lstm, rf,
scale_rf, loc_rf, mean_rf, std_rf, threshold_rf, xg_reg, scale_xgb,
loc_xgb, mean_xgb, std_xgb, threshold_xgb, x_test, x_test_supervised,
y_test)
y_test_pred = np.array(y_test_pred)
confusion, f1, balanced_accuracy, precision, recall, aucpr, roc_auc, fpr_values, tpr_values, accuracy, matthews_coeff = evaluation.get_performance(
y_test, y_test_pred, thresholds)
tn = confusion[0, 0]
tp = confusion[1, 1]
fp = confusion[0, 1]
fn = confusion[1, 0]
print("Num decisions taken from lstm: ", num_decisions_taken_by_lstm)
print("Num decisions taken by rf: ", num_decisions_taken_by_rf)
print("Num decisions taken by xgb: ", num_decisions_taken_by_xgb)
evaluation.print_results(tn, fp, fn, tp, f1, balanced_accuracy, precision,
recall, aucpr, roc_auc, fpr_values, tpr_values,
accuracy, matthews_coeff)
evaluation.print_results(
np.array(tn_s).mean(),
np.array(fp_s).mean(),
np.array(fn_s).mean(),
np.array(tp_s).mean(),
np.array(f1_s).mean(),
np.array(balanced_accuracies).mean(),
np.array(precisions).mean(),
np.array(recalls).mean(),
np.array(aucpr_s).mean(),
np.array(roc_aucs).mean())
look_back = LOOK_BACK
print("Lookback using: ", look_back)
x_train, y_train = sequences_crafting_for_classification.get_train_set()
# if the dataset is the real one -> contrast imbalanced dataset problem
if DATASET_TYPE == REAL_DATASET:
x_train, y_train = resampling_dataset.oversample_set(x_train, y_train)
# train model for supervised models (xgboost/rf)
x_train_supervised = x_train[:, look_back, :]
y_train_supervised = y_train
print("Training models...")
lstm = LSTM_classifier.create_fit_model(x_train, y_train, look_back)
rf = RF.create_model(x_train_supervised, y_train_supervised)
xg_reg = xgboost_classifier.create_model(x_train_supervised,
y_train_supervised)
def experiment(lstm,
threshold_lstm,
xg_reg,
threshold_xgb,
rf,
threshold_rf,
scenario,
adversarial_attack=False,
evasion_attack=False,
is_white_box_attack=True,
use_lstm_for_adversarial=False):
x_test, y_test = sequences_crafting_for_classification.get_test_set(
scenario=scenario)
if adversarial_attack or evasion_attack:
# getting train set for training
if is_white_box_attack:
print("Using as traing set, the real one - whitebox attack")
dataset_type = INJECTED_DATASET
else:
print("Using as traing set, the old one - blackbox attack")
dataset_type = OLD_DATASET
x_train, y_train = sequences_crafting_for_classification.get_train_set(
dataset_type=dataset_type)
x_train_supervised = x_train[:, look_back, :]
x_test_supervised = x_test[:, len(x_test[0]) - 1, :]
if adversarial_attack:
print("Crafting an adversarial attack")
if not use_lstm_for_adversarial:
print("The attacker will use a Multilayer perceptron")
adversarial_model = MultiLayerPerceptron.create_fit_model(
x_train_supervised, y_train)
frauds = x_test_supervised[np.where(y_test == 1)]
adversarial_samples = fgsm.craft_sample(frauds,
adversarial_model,
epsilon=0.01)
# in lstm samples, must be changed the last transaction of the sequence
x_test[np.where(y_test == 1),
len(x_test[0]) - 1] = adversarial_samples
x_test_supervised = x_test[:, len(x_test[0]) - 1, :]
else:
print("The attacker will use a LSTM network")
# train the network using the right params
if is_white_box_attack:
if USING_AGGREGATED_FEATURES:
params = BEST_PARAMS_LSTM_REAL_DATASET_AGGREGATED
else:
params = BEST_PARAMS_LSTM_REAL_DATASET_NO_AGGREGATED
else:
if USING_AGGREGATED_FEATURES:
params = BEST_PARAMS_LSTM_OLD_DATASET_AGGREGATED
else:
params = BEST_PARAMS_LSTM_OLD_DATASET_NO_AGGREGATED
frauds = x_test[np.where(y_test == 1)]
adversarial_model, _ = LSTM_classifier.create_fit_model(
x_train, y_train, look_back, params=params)
adversarial_samples = fgsm.craft_sample(frauds,
adversarial_model,
epsilon=0.1)
# in lstm samples, must be changed the last transaction of the sequence
x_test[np.where(y_test == 1)] = adversarial_samples
x_test_supervised = x_test[:, len(x_test[0]) - 1, :]
if evasion_attack:
print("Crafting an evasion attack")
# train the network using the right params
if is_white_box_attack:
if USING_AGGREGATED_FEATURES:
params = BEST_PARAMS_RF_AGGREGATED
else:
params = BEST_PARAMS_RF_NO_AGGREGATED
else:
if USING_AGGREGATED_FEATURES:
params = BEST_PARAMS_RF_OLD_DATASET_AGGREGATED
else:
params = BEST_PARAMS_RF_OLD_DATASET_NO_AGGREGATED
# training the oracle
oracle, oracle_threshold = RF.create_model(x_train_supervised,
y_train,
params=params)
# if the oracle predicts the fraud as fraud -> discard it, otherwise inject in real bank system
y_pred_oracle = rf.predict_proba(x_test_supervised)
y_pred_oracle = y_pred_oracle[:, 1].ravel()
y_pred_oracle = np.array(
evaluation.adjusted_classes(y_pred_oracle, oracle_threshold))
x_test = x_test[(np.where((y_test == 1) & (y_pred_oracle == 0)
| (y_test == 0)))]
y_test = y_test[(np.where((y_test == 1) & (y_pred_oracle == 0)
| (y_test == 0)))]
x_test_supervised = x_test[:, len(x_test[0]) - 1, :]
# predicting test set
y_pred_lstm = lstm.predict(x_test)
y_pred_rf = rf.predict_proba(x_test_supervised)
y_pred_xgb = xg_reg.predict_proba(x_test_supervised)
y_pred_lstm = y_pred_lstm.ravel()
y_pred_rf = y_pred_rf[:, 1].ravel()
y_pred_xgb = y_pred_xgb[:, 1].ravel()
print("LSTM")
evaluation.evaluate(y_test, y_pred_lstm, threshold_lstm)
print("RF")
evaluation.evaluate(y_test, y_pred_rf, threshold_rf)
print("Xgboost")
evaluation.evaluate(y_test, y_pred_xgb, threshold_xgb)
if not adversarial_attack and not evasion_attack:
x_test_supervised = x_test[:, len(x_test[0]) - 1, :]
x_train, y_train = sequences_crafting_for_classification.get_train_set(
)
x_train_supervised = x_train[:, look_back, :]
print("LSTM")
y_pred_lstm = lstm.predict(x_test)
evaluation.evaluate(y_test, y_pred_lstm, threshold_lstm)
explainability.explain_dataset(lstm, x_train, x_test, threshold_lstm,
y_test)
print("RF")
y_pred_rf = rf.predict_proba(x_test_supervised)[:, 1]
evaluation.evaluate(y_test, y_pred_rf, threshold_rf)
explainability.explain_dataset(rf, x_train_supervised,
x_test_supervised, threshold_rf, y_test)
print("Xgboost")
y_pred_xgb = xg_reg.predict_proba(x_test_supervised)[:, 1]
evaluation.evaluate(y_test, y_pred_xgb, threshold_xgb)
explainability.explain_dataset(xg_reg, x_train_supervised,
x_test_supervised, threshold_xgb,
y_test)
y_pred_lstm = evaluation.adjusted_classes(y_pred_lstm, threshold_lstm)
y_pred_rf = evaluation.adjusted_classes(y_pred_rf, threshold_rf)
y_pred_xgb = evaluation.adjusted_classes(y_pred_xgb, threshold_xgb)
lstm_fraud_indices = evaluation.get_fraud_indices(y_test, y_pred_lstm)
rf_fraud_indices = evaluation.get_fraud_indices(y_test, y_pred_rf)
xgboost_fraud_indices = evaluation.get_fraud_indices(y_test, y_pred_xgb)
evaluation.print_frauds_stats(lstm_fraud_indices, rf_fraud_indices,
xgboost_fraud_indices)
lstm_genuine_indices = evaluation.get_genuine_indices(y_test, y_pred_lstm)
rf_genuine_indices = evaluation.get_genuine_indices(y_test, y_pred_rf)
xgboost_genuine_indices = evaluation.get_genuine_indices(
y_test, y_pred_xgb)
evaluation.print_genuine_stats(lstm_genuine_indices, rf_genuine_indices,
xgboost_genuine_indices)
