SafeSVC

An example Python Notebook is available Here

Privacy protected Support Vector Classifier.

class aisdc.safemodel.classifiers.safesvc.SafeSVC(C=1.0, gamma='scale', dhat=1000, eps=10, **kwargs)

Privacy protected Support Vector Classifier.

Methods

additional_checks(curr_separate, saved_separate)	SVC specific checks.
examine_seperate_items(curr_vals, saved_vals)	Comparison of more complex structures in the super class we just check these model-specific items exist in both current and saved copies.
fit(train_features, train_labels)	Do fit and then store model dict.
get_current_and_saved_models()	Makes a copy of self.__dict__ and splits it into dicts for the current and saved versions.
get_params([deep])	Gets dictionary of parameter values restricted to those expected by base classifier.
k_hat_svm(x[, y])	Define the version which is sent to sklearn.svm.
phi_hat(input_vector)	Project a single feature.
phi_hat_multi(input_features)	Compute feature space for a matrix of inputs.
posthoc_check()	Checks whether model has been interfered with since fit() was last run.
predict(test_features)	Make predictions.
predict_proba(test_features)	Predictive probabilities.
preliminary_check([verbose, apply_constraints])	Checks whether current model parameters violate the safe rules.
request_release(path, ext[, target])	Saves model to filename specified and creates a report for the TRE output checkers.
run_attack([target, attack_name, ...])	Runs a specified attack on the trained model and saves a report to file.
save([name])	Writes model to file in appropriate format.
set_params(**kwargs)	Set params.

additional_checks(curr_separate: dict, saved_separate: dict) → tuple[str, str]

SVC specific checks.

fit(train_features: ndarray, train_labels: ndarray) → None

Do fit and then store model dict.