def equal_opportunity(random_data, predicted_data, target_variable, protected_variable, unprivileged_input): random_data['Pred'] = np.random.binomial(1, .5, 1000) dataset = BinaryLabelDataset(df=random_data, label_names=[target_variable], protected_attribute_names=[protected_variable]) classified_dataset = BinaryLabelDataset(df=predicted_data, label_names=[target_variable], protected_attribute_names=[protected_variable]) privileged_group = [] for v in predicted_data[protected_variable].unique()[predicted_data[protected_variable].unique() != unprivileged_input]: privileged_group.append({protected_variable: v}) unprivileged_group = [{protected_variable: unprivileged_input}] #female=0 metric = ClassificationMetric(dataset, classified_dataset, unprivileged_group, privileged_group) print(metric.equal_opportunity_difference()) if abs(metric.equal_opportunity_difference().round(3)) < 0.2: print('The algorithm can be considered to be not biased') else: print('There is a potential bias')
def get_metric_reports(true_dataset,classfied_dataset,privileged_groups,unprivileged_groups): mirror_dataset=classfied_dataset.copy(deepcopy=True) mirror_dataset.labels=copy.deepcopy(true_dataset.labels) metric=ClassificationMetric( dataset=mirror_dataset, classified_dataset=classfied_dataset, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) #Measuring unfairness end report=OrderedDict() report['TPR']=metric.true_positive_rate() report['TNR']=metric.true_negative_rate() report['FPR']=metric.false_positive_rate() report['FNR']=metric.false_negative_rate() report['Balanced_Acc']=0.5*(report['TPR']+report['TNR']) report['Acc']=metric.accuracy() report["Statistical parity difference"]=metric.statistical_parity_difference() report["Disparate impact"]=metric.disparate_impact() report["Equal opportunity difference"]=metric.equal_opportunity_difference() report["Average odds difference"]=metric.average_odds_difference() report["Theil index"]=metric.theil_index() report["United Fairness"]=metric.generalized_entropy_index() return report
def compute_metrics(dataset_true, dataset_pred, unprivileged_groups, privileged_groups, disp=True): """ Compute the key metrics """ classified_metric_pred = ClassificationMetric( dataset_true, dataset_pred, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) metrics = OrderedDict() metrics["Balanced accuracy"] = 0.5 * ( classified_metric_pred.true_positive_rate() + classified_metric_pred.true_negative_rate()) metrics[ "Statistical parity difference"] = classified_metric_pred.statistical_parity_difference( ) metrics["Disparate impact"] = classified_metric_pred.disparate_impact() metrics[ "Average odds difference"] = classified_metric_pred.average_odds_difference( ) metrics[ "Equal opportunity difference"] = classified_metric_pred.equal_opportunity_difference( ) metrics["Theil index"] = classified_metric_pred.theil_index() if disp: for k in metrics: print("%s = %.4f" % (k, metrics[k])) return metrics
def get_classifier_metrics(test_list, prediction_list): privileged_groups = [{'sex': 1}] unprivileged_groups = [{'sex': 0}] acc_list = [] bal_acc_list = [] avg_odds_list = [] recall_diff_list = [] precision_diff_list = [] for test_, pred_ in zip(test_list, prediction_list): model_metric = ClassificationMetric( test_, pred_, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) acc_list.append(model_metric.accuracy().round(3)) bal_acc_list.append(((model_metric.true_positive_rate() + model_metric.true_negative_rate()) / 2).round(3)) avg_odds_list.append(model_metric.average_odds_difference().round(3)) recall_diff_list.append( model_metric.equal_opportunity_difference().round(3)) precision_diff_list.append( (model_metric.precision(privileged=False) - model_metric.precision(privileged=True)).round(3)) return acc_list, bal_acc_list, avg_odds_list, recall_diff_list, precision_diff_list
def show_classifier_metrics(test_list, prediction_list): privileged_groups = [{'sex': 1}] unprivileged_groups = [{'sex': 0}] counter = 1 for test_, pred_ in zip(test_list, prediction_list): display(Markdown("#### Model {} dataset metrics".format(counter))) model_metric = ClassificationMetric( test_, pred_, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) ex_model_metric = MetricTextExplainer(model_metric) print(ex_model_metric.average_odds_difference()) print( 'Difference in Recall between Unprivileged and Privileged: {:.3f}'. format(model_metric.equal_opportunity_difference())) print( 'Difference in Precision between Unprivileged and Privileged: {:.3f}.' .format( model_metric.precision(privileged=False) - model_metric.precision(privileged=True))) counter += 1
def calculate_bias_measures(data_orig_train, data_orig_vt, unprivileged_groups, privileged_groups): model = RandomForestClassifier().fit( data_orig_train.features, data_orig_train.labels.ravel(), sample_weight=data_orig_train.instance_weights) dataset = data_orig_vt dataset_pred = dataset.copy() dataset_pred.labels = model.predict(data_orig_vt.features) classified_metric_race = ClassificationMetric( dataset, dataset_pred, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) metric_pred_race = BinaryLabelDatasetMetric( dataset_pred, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) print("Mean difference {}".format(metric_pred_race.mean_difference())) print("Disparate Metric {}".format(metric_pred_race.disparate_impact())) print("Equal Opportunity Difference {}".format( classified_metric_race.equal_opportunity_difference())) print("Average Abs Odds Difference {}".format( classified_metric_race.average_abs_odds_difference())) print("Theil index {}".format(classified_metric_race.theil_index()))
def equal_ops_values(random_data, predicted_data, target_variable, protected_variable, unprivileged_input): random_data['Pred'] = np.random.binomial(1, .5, 1000) dataset = BinaryLabelDataset(df=random_data, label_names=[target_variable], protected_attribute_names=[protected_variable]) classified_dataset = BinaryLabelDataset(df=predicted_data, label_names=[target_variable], protected_attribute_names=[protected_variable]) privileged_group = [] for v in predicted_data[protected_variable].unique()[predicted_data[protected_variable].unique() != unprivileged_input]: privileged_group.append({protected_variable: v}) unprivileged_group = [{protected_variable: unprivileged_input}] #female=0 metric = ClassificationMetric(dataset, classified_dataset, unprivileged_group, privileged_group) return abs(metric.equal_opportunity_difference())
def fair_metrics(dataset, pred, pred_is_dataset=False): if pred_is_dataset: dataset_pred = pred else: dataset_pred = dataset.copy() dataset_pred.labels = pred cols = [ 'statistical_parity_difference', 'equal_opportunity_difference', 'average_abs_odds_difference', 'disparate_impact', 'theil_index' ] obj_fairness = [[0, 0, 0, 1, 0]] fair_metrics = pd.DataFrame(data=obj_fairness, index=['objective'], columns=cols) for attr in dataset_pred.protected_attribute_names: idx = dataset_pred.protected_attribute_names.index(attr) privileged_groups = [{ attr: dataset_pred.privileged_protected_attributes[idx][0] }] unprivileged_groups = [{ attr: dataset_pred.unprivileged_protected_attributes[idx][0] }] classified_metric = ClassificationMetric( dataset, dataset_pred, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) metric_pred = BinaryLabelDatasetMetric( dataset_pred, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) acc = classified_metric.accuracy() row = pd.DataFrame([[ metric_pred.mean_difference(), classified_metric.equal_opportunity_difference(), classified_metric.average_abs_odds_difference(), metric_pred.disparate_impact(), classified_metric.theil_index() ]], columns=cols, index=[attr]) fair_metrics = fair_metrics.append(row) fair_metrics = fair_metrics.replace([-np.inf, np.inf], 2) return fair_metrics
def fit_classifier(classifier, weights, lambda_values, X_train, y_train, X_test, y_test, test_pred): ''' Function to fit classifiers for range of Lambda values Args: classifier: SVM or Logistic regression weights: weights for each sample lambda_values: range of lambda values to assess X_train: training data y_train: training lables X_test: test data y_test: test labels test_pred: prepared format to store predictions Returns: accuracy_list: test accuracy for each model equal_opp_list: Equal Opportunity difference for each model stat_parity_list: Statistical Parity difference for each model ''' accuracy_list = [] equal_opp_list = [] stat_parity_list = [] for l in lambda_values: print("-------- \n", 'Lambda: ', "{0:.2f}".format(l)) if classifier == "Logistic Regression": learner = LogisticRegression(solver='liblinear', random_state=1, penalty='l2', C=1/l) else: learner = svm.SVC(C=1/l) learner.fit(X_train,y_train, sample_weight=weights) test_pred.labels = learner.predict(X_test) metric = ClassificationMetric(test, test_pred, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) print("Equal opportunity:", "{0:.3f}".format(metric.equal_opportunity_difference())) print("Statistical parity:", "{0:.3f}".format(metric.statistical_parity_difference())) print("Accuracy:", "{0:.3f}".format(metric.accuracy())) accuracy_list.append(metric.accuracy()) equal_opp_list.append(metric.equal_opportunity_difference()) stat_parity_list.append(metric.statistical_parity_difference()) return accuracy_list, equal_opp_list, stat_parity_list
def test(dataset, model, x_test, thresh_arr, unprivileged_groups, privileged_groups): bld = BinaryLabelDataset(df=dataset, label_names=['labels'], protected_attribute_names=['age']) if np.isin(k, model_AIF): y_val_pred_prob = model.predict_proba(bld) else: y_val_pred_prob, A_val_pred_prob = model.predict_proba(x_test) metric_arrs = np.empty([0, 8]) for thresh in thresh_arr: if np.isin(k, model_AIF): y_val_pred = (y_val_pred_prob > thresh).astype(np.float64) else: y_val_pred = (y_val_pred_prob.numpy() > thresh).astype(np.float64) metric_arrs = np.append(metric_arrs, roc_auc_score(y_test, y_val_pred_prob)) if np.isin(k, model_AIF): metric_arrs = np.append(metric_arrs, 0) else: metric_arrs = np.append(metric_arrs, roc_auc_score(A_test, A_val_pred_prob)) dataset_pred = dataset.copy() dataset_pred.labels = y_val_pred bld2 = BinaryLabelDataset(df=dataset_pred, label_names=['labels'], protected_attribute_names=['age']) metric = ClassificationMetric(bld, bld2, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) metric_arrs = np.append( metric_arrs, ((metric.true_positive_rate() + metric.true_negative_rate()) / 2)) metric_arrs = np.append(metric_arrs, metric.average_odds_difference()) metric_arrs = np.append(metric_arrs, metric.disparate_impact()) metric_arrs = np.append(metric_arrs, metric.statistical_parity_difference()) metric_arrs = np.append(metric_arrs, metric.equal_opportunity_difference()) metric_arrs = np.append(metric_arrs, metric.theil_index()) return metric_arrs
def get_cm_metrics(): df_pred = X.copy() df_pred[df.columns[-1]] = np.expand_dims(ypred_class, axis=1) dataset_pred = BinaryLabelDataset(df=df_pred, label_names=[ 'action_taken_name'], protected_attribute_names=['applicant_sex_name_Female']) metric_CM = ClassificationMetric( dataset, dataset_pred, privileged_groups=privileged_group, unprivileged_groups=unprivileged_group) return { "Equal Opportunity Difference": metric_CM.equal_opportunity_difference(), 'Average Odds Difference': metric_CM.average_odds_difference(), "Accuracy Male": metric_CM.accuracy(privileged=True), "Accuracy Female": metric_CM.accuracy(privileged=False) }
def metrics_form(y_val_pred_prob, y_test, A_prob, A_test, bld, dataset): metric_arrs = np.empty([0, 8]) if np.isin(k, model_AIF): y_val_pred = (y_val_pred_prob > thresh).astype(np.float64) else: y_val_pred = (y_val_pred_prob > thresh).astype(np.float64) A_pred = (A_prob > thresh).astype(np.float64) metric_arrs = np.append(metric_arrs, roc_auc_score(y_test, y_val_pred_prob)) print("y {}".format(roc_auc_score(y_test, y_val_pred_prob))) metric_arrs = np.append(metric_arrs, accuracy_score(y_test, y_val_pred)) if np.isin(k, model_AIF): metric_arrs = np.append(metric_arrs, 0) else: metric_arrs = np.append(metric_arrs, roc_auc_score(A_test, A_prob)) print("A {}".format(roc_auc_score(A_test, A_prob))) dataset_pred = dataset.copy() dataset_pred.labels = y_val_pred bld2 = BinaryLabelDataset(df=dataset_pred, label_names=['labels'], protected_attribute_names=protected) metric = ClassificationMetric(bld, bld2, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) metric_arrs = np.append( metric_arrs, ((metric.true_positive_rate() + metric.true_negative_rate()) / 2)) metric_arrs = np.append(metric_arrs, np.abs(metric.average_odds_difference())) metric_arrs = np.append(metric_arrs, metric.disparate_impact()) metric_arrs = np.append(metric_arrs, np.abs(metric.statistical_parity_difference())) metric_arrs = np.append(metric_arrs, np.abs(metric.equal_opportunity_difference())) return metric_arrs
def fairness_IBM(y_pred, Ztr, ytr, verbose=0): from aif360.datasets import BinaryLabelDataset from aif360.metrics import ClassificationMetric assert np.array_equal(np.unique(Ztr), np.array([0, 1])), "Z must contain either 0 or 1" # if len(ytr.shape) == 1: # ytr = np.expand_dims(ytr, -1) Ztr = np.squeeze(Ztr) if verbose: print(ytr.shape) print(Ztr.shape) unprivileged_groups = [{"zs": [0]}] privileged_groups = [{"zs": [1]}] metric_arrs = defaultdict(list) dict_ = {"y_true": ytr, "zs": Ztr} df = pd.DataFrame(dict_) dataset = BinaryLabelDataset(df=df, label_names=["y_true"], protected_attribute_names=["zs"], unprivileged_protected_attributes=[[0]], privileged_protected_attributes=[[1]]) dataset_pred = dataset.copy() dataset_pred.labels = y_pred metric = ClassificationMetric(dataset, dataset_pred, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) # metric_arrs['bal_acc'].append((metric.true_positive_rate() # + metric.true_negative_rate()) / 2) metric_arrs["EA"].append( metric.accuracy(privileged=False) - metric.accuracy(privileged=True)) # ASSUMING ALL OTHER METRICS RETURN U - P metric_arrs['EO'].append(metric.average_odds_difference()) # The ideal value of this metric is 1.0 # A value < 1 implies higher benefit for the privileged group # and a value >1 implies a higher metric_arrs['DI'].append(metric.disparate_impact() - 1) metric_arrs['DP'].append(metric.statistical_parity_difference()) metric_arrs['EQ'].append(metric.equal_opportunity_difference()) metric_arrs['TH'].append(metric.between_group_theil_index() * 10) results = pd.DataFrame(metric_arrs) return results
def compute_aif_metrics(dataset_true, dataset_pred, unprivileged_groups, privileged_groups,\ ret_eval_dict=True): metrics_cls = ClassificationMetric(dataset_true, dataset_pred, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) metrics_dict = {} metrics_dict["BA"] = 0.5*(metrics_cls.true_positive_rate()+ metrics_cls.true_negative_rate()) metrics_dict["SPD"] = metrics_cls.statistical_parity_difference() metrics_dict["DI"] = metrics_cls.disparate_impact() metrics_dict["AOD"] = metrics_cls.average_odds_difference() metrics_dict["EOD"] = metrics_cls.equal_opportunity_difference() metrics_dict["DFBA"] = metrics_cls.differential_fairness_bias_amplification() metrics_dict["TI"] = metrics_cls.theil_index() if ret_eval_dict: return metrics_dict, metrics_cls else: return metrics_cls
def fairness_check(s3_url, bucket_name, s3_username, s3_password, training_id): cos = boto3.resource("s3", endpoint_url=s3_url, aws_access_key_id=s3_username, aws_secret_access_key=s3_password) y_test_out = 'y_test.out' p_test_out = 'p_test.out' y_pred_out = 'y_pred.out' get_s3_item(cos, bucket_name, training_id + '/' + y_test_out, y_test_out) get_s3_item(cos, bucket_name, training_id + '/' + p_test_out, p_test_out) get_s3_item(cos, bucket_name, training_id + '/' + y_pred_out, y_pred_out) """Need to generalize the protected features""" unprivileged_groups = [{'race': 4.0}] privileged_groups = [{'race': 0.0}] favorable_label = 0.0 unfavorable_label = 1.0 """Load the necessary labels and protected features for fairness check""" y_test = np.loadtxt(y_test_out) p_test = np.loadtxt(p_test_out) y_pred = np.loadtxt(y_pred_out) """Calculate the fairness metrics""" original_test_dataset = dataset_wrapper(outcome=y_test, protected=p_test, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups, favorable_label=favorable_label, unfavorable_label=unfavorable_label) plain_predictions_test_dataset = dataset_wrapper(outcome=y_pred, protected=p_test, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups, favorable_label=favorable_label, unfavorable_label=unfavorable_label) classified_metric_nodebiasing_test = ClassificationMetric(original_test_dataset, plain_predictions_test_dataset, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) TPR = classified_metric_nodebiasing_test.true_positive_rate() TNR = classified_metric_nodebiasing_test.true_negative_rate() bal_acc_nodebiasing_test = 0.5*(TPR+TNR) print("#### Plain model - without debiasing - classification metrics on test set") metrics = { "Classification accuracy": classified_metric_nodebiasing_test.accuracy(), "Balanced classification accuracy": bal_acc_nodebiasing_test, "Statistical parity difference": classified_metric_nodebiasing_test.statistical_parity_difference(), "Disparate impact": classified_metric_nodebiasing_test.disparate_impact(), "Equal opportunity difference": classified_metric_nodebiasing_test.equal_opportunity_difference(), "Average odds difference": classified_metric_nodebiasing_test.average_odds_difference(), "Theil index": classified_metric_nodebiasing_test.theil_index(), "False negative rate difference": classified_metric_nodebiasing_test.false_negative_rate_difference() } print("metrics: ", metrics) return metrics
def k_fold_statistics(k_folds, classifier, lambda_values, dataset, unprivileged_groups, privileged_groups): ''' Function to fit classifier to k number of random train/test splits Args: k_folds: number of folds of statistics classifier: SVM or Logistic regression weights: weights for each sample lambda_value: selected level of regularisation dataset: dataset to be used Returns: accuracy_list: test accuracy for each model equal_opp_list: Equal Opportunity difference for each model stat_parity_list: Statistical Parity difference for each model ''' accuracy_list = [] equal_opp_list = [] stat_parity_list = [] for k in range(k_folds): train, test = dataset_orig.split([0.8], shuffle=True) train, validation = train.split([0.8], shuffle=True) scale_orig = StandardScaler() X_train = scale_orig.fit_transform(train.features) y_train = train.labels.ravel() X_test = scale_orig.transform(test.features) y_test = validation.labels.ravel() X_valid = scale_orig.transform(validation.features) y_valid = test.labels.ravel() test_pred = test.copy() valid_pred = validation.copy() RW = Reweighing(unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) best_mean_statistic = 0 # fit all candidate models for lambda_value in lambda_values: train = RW.fit_transform(train) if classifier == "Logistic Regression": learner = LogisticRegression(solver='liblinear', random_state=1, penalty='l2', C=1/lambda_value) else: learner = svm.SVC(C=1/lambda_value) learner.fit(X_train,y_train, sample_weight=train.instance_weights) valid_pred.labels = learner.predict(X_valid) metric = ClassificationMetric(validation, valid_pred, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) mean_statistic = (1-abs(metric.equal_opportunity_difference())+metric.accuracy())/2 if mean_statistic > best_mean_statistic: best_learner = learner test_pred.labels = best_learner.predict(X_test) metric = ClassificationMetric(test, test_pred, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) print("----------------") print("Split {}/{}".format(k, k_folds)) print("Equal opportunity:", "{0:.3f}".format(metric.equal_opportunity_difference())) print("Statistical parity:", "{0:.3f}".format(metric.statistical_parity_difference())) print("Accuracy:", "{0:.3f}".format(metric.accuracy())) accuracy_list.append(metric.accuracy()) equal_opp_list.append(metric.equal_opportunity_difference()) stat_parity_list.append(metric.statistical_parity_difference()) accuracy_list = np.array(accuracy_list) equal_opp_list = np.array(equal_opp_list) stat_parity_list = np.array(stat_parity_list) print('The mean statistics for {} folds is:'.format(k_folds)) print("Mean Accuracy: {0:.3f},".format(np.mean(accuracy_list)), "Std: {0:.3f}".format(np.std(accuracy_list))) print("Mean Equal Opportunity: {0:.3f},".format(np.mean(equal_opp_list)), "Std: {0:.3f}".format( np.std(equal_opp_list))) print("Mean Statistical Parity: {0:.3f},".format(np.mean(stat_parity_list)), "Std: {0:.3f}".format(np.std(stat_parity_list))) return accuracy_list, equal_opp_list, stat_parity_list
def fairness_check(label_dir, model_dir): """Need to generalize the protected features""" # races_to_consider = [0,4] unprivileged_groups = [{'race': 4.0}] privileged_groups = [{'race': 0.0}] favorable_label = 0.0 unfavorable_label = 1.0 """Load the necessary labels and protected features for fairness check""" # y_train = np.loadtxt(label_dir + '/y_train.out') # p_train = np.loadtxt(label_dir + '/p_train.out') y_test = np.loadtxt(label_dir + '/y_test.out') p_test = np.loadtxt(label_dir + '/p_test.out') y_pred = np.loadtxt(label_dir + '/y_pred.out') """Calculate the fairness metrics""" # original_traning_dataset = dataset_wrapper(outcome=y_train, protected=p_train, # unprivileged_groups=unprivileged_groups, # privileged_groups=privileged_groups, # favorable_label=favorable_label, # unfavorable_label=unfavorable_label) original_test_dataset = dataset_wrapper(outcome=y_test, protected=p_test, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups, favorable_label=favorable_label, unfavorable_label=unfavorable_label) plain_predictions_test_dataset = dataset_wrapper(outcome=y_pred, protected=p_test, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups, favorable_label=favorable_label, unfavorable_label=unfavorable_label) classified_metric_nodebiasing_test = ClassificationMetric(original_test_dataset, plain_predictions_test_dataset, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) TPR = classified_metric_nodebiasing_test.true_positive_rate() TNR = classified_metric_nodebiasing_test.true_negative_rate() bal_acc_nodebiasing_test = 0.5*(TPR+TNR) print("#### Plain model - without debiasing - classification metrics on test set") # print("Test set: Classification accuracy = %f" % classified_metric_nodebiasing_test.accuracy()) # print("Test set: Balanced classification accuracy = %f" % bal_acc_nodebiasing_test) # print("Test set: Statistical parity difference = %f" % classified_metric_nodebiasing_test.statistical_parity_difference()) # print("Test set: Disparate impact = %f" % classified_metric_nodebiasing_test.disparate_impact()) # print("Test set: Equal opportunity difference = %f" % classified_metric_nodebiasing_test.equal_opportunity_difference()) # print("Test set: Average odds difference = %f" % classified_metric_nodebiasing_test.average_odds_difference()) # print("Test set: Theil index = %f" % classified_metric_nodebiasing_test.theil_index()) # print("Test set: False negative rate difference = %f" % classified_metric_nodebiasing_test.false_negative_rate_difference()) metrics = { "Classification accuracy": classified_metric_nodebiasing_test.accuracy(), "Balanced classification accuracy": bal_acc_nodebiasing_test, "Statistical parity difference": classified_metric_nodebiasing_test.statistical_parity_difference(), "Disparate impact": classified_metric_nodebiasing_test.disparate_impact(), "Equal opportunity difference": classified_metric_nodebiasing_test.equal_opportunity_difference(), "Average odds difference": classified_metric_nodebiasing_test.average_odds_difference(), "Theil index": classified_metric_nodebiasing_test.theil_index(), "False negative rate difference": classified_metric_nodebiasing_test.false_negative_rate_difference() } return {"metrics": metrics}
class FairnessBoundsWarning: """Raise warnings if classifier misses specified fairness bounds. Bounds are checked using AIF360s classification metric if the specified bound is not None. """ DISPARATE_IMPACT_RATIO_BOUND = 0.8 FPR_RATIO_BOUND = 0.8 FNR_RATIO_BOUND = 0.8 ERROR_RATIO_BOUND = 0.8 EO_DIFFERENCE_BOUND = 0.1 FPR_DIFFERENCE_BOUND = None FNR_DIFFERENCE_BOUND = None ERROR_DIFFERENCE_BOUND = None def __init__( self, raw_dataset: BinaryLabelDataset, predicted_dataset: BinaryLabelDataset, privileged_groups=None, unprivileged_groups=None, ): """ Args: raw_dataset (BinaryLabelDataset): Dataset with ground-truth labels. predicted_dataset (BinaryLabelDataset): Dataset after predictions. privileged_groups (list(dict)): Privileged groups. Format is a list of `dicts` where the keys are `protected_attribute_names` and the values are values in `protected_attributes`. Each `dict` element describes a single group. unprivileged_groups (list(dict)): Unprivileged groups. Same format as privileged_groups. """ self._raw_dataset = raw_dataset self._predicted_dataset = predicted_dataset if privileged_groups is None: privileged_groups = [ dict( zip( predicted_dataset.protected_attribute_names, predicted_dataset.privileged_protected_attributes, ) ) ] if unprivileged_groups is None: unprivileged_groups = [ dict( zip( predicted_dataset.protected_attribute_names, predicted_dataset.unprivileged_protected_attributes, ) ) ] self._classification_metric = ClassificationMetric( raw_dataset, predicted_dataset, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups, ) def check_bounds(self): """Run methods checking each bound.""" self._check_disparate_impact() self._check_fpr_bound() self._check_fnr_bound() self._check_all_errors_bound() self._check_eo_bound() @staticmethod def _warn_bound(metric_name, computed_ratio, tolerated_ratio): """Raise warning with default message.""" warning_msg = ( "Classifier has " + metric_name + " of : " + str(computed_ratio) + " above threshold of " + str(tolerated_ratio) ) warnings.warn(warning_msg) @staticmethod def _maybe_scale(num): """Return inverse of num if num is larger than one.""" return num if num < 1 else 1 / num def _check_disparate_impact(self): """Raise warning if disparate impact bound is breached.""" if self.DISPARATE_IMPACT_RATIO_BOUND is not None: dsp_im = self._maybe_scale( self._classification_metric.disparate_impact() ) if dsp_im > self.DISPARATE_IMPACT_RATIO_BOUND: self._warn_bound( "disparate impact", dsp_im, self.DISPARATE_IMPACT_RATIO_BOUND, ) def _check_fpr_bound(self): """Raise warning if false positive bound is breached.""" if self.FPR_RATIO_BOUND is not None: fprr = self._maybe_scale( self._classification_metric.false_positive_rate_ratio() ) if fprr > self.FPR_RATIO_BOUND: self._warn_bound( "false positive ratio", fprr, self.FPR_RATIO_BOUND ) if self.FPR_DIFFERENCE_BOUND is not None: fprd = self._classification_metric.false_positive_rate_difference() if fprd > self.FPR_DIFFERENCE_BOUND: self._warn_bound( "false positive rate difference", fprd, self.FPR_DIFFERENCE_BOUND, ) def _check_fnr_bound(self): """Raise warning if false negative bound is breached.""" if self.FNR_RATIO_BOUND is not None: fnrr = self._maybe_scale( self._classification_metric.false_negative_rate_ratio() ) if fnrr > self.FNR_RATIO_BOUND: self._warn_bound( "false negative ratio", fnrr, self.FNR_RATIO_BOUND ) if self.FNR_DIFFERENCE_BOUND is not None: fnrd = self._classification_metric.false_positive_rate_difference() if fnrd > self.FNR_DIFFERENCE_BOUND: self._warn_bound( "false negative rate difference", fnrd, self.FNR_DIFFERENCE_BOUND, ) def _check_all_errors_bound(self): """Raise warning if overall error bound is breached.""" if self.ERROR_RATIO_BOUND is not None: err = self._maybe_scale( self._classification_metric.error_rate_ratio() ) if err > self.ERROR_RATIO_BOUND: self._warn_bound("error ratio", err, self.ERROR_RATIO_BOUND) if self.ERROR_DIFFERENCE_BOUND is not None: errd = self._classification_metric.error_rate_difference() if errd > self.ERROR_DIFFERENCE_BOUND: self._warn_bound( "error rate difference", errd, self.ERROR_DIFFERENCE_BOUND ) def _check_eo_bound(self): """Raise warning if equalized odds difference is breached.""" if self.EO_DIFFERENCE_BOUND is not None: eo = self._classification_metric.equal_opportunity_difference() if eo > self.EO_DIFFERENCE_BOUND: self._warn_bound( "true positive rate", eo, self.EO_DIFFERENCE_BOUND )
else: priv.append([]) stdDs = StandardDataset(validation_comp, 'is_violent_recid', [0], prot, priv) stdPred = StandardDataset(validation_pred, 'is_violent_recid', [0], prot, priv) bi_met = BinaryLabelDatasetMetric(stdDs, privileged_groups=[priv_dict], unprivileged_groups=[unpriv_dict]) class_met = ClassificationMetric(stdDs, stdPred, unprivileged_groups=[unpriv_dict], privileged_groups=[priv_dict]) disparate_impact = bi_met.disparate_impact() #error_rate_ratio = class_met.error_rate_ratio() eq_diff = class_met.equal_opportunity_difference() #Create 2 Bar Graphs x = [1] di_y = [disparate_impact] er_y = [error_rate_ratio] plt.ylim(bottom=0, top=2) plt.xlim(left=0, right=2) ax = plt.gca() ax.axes.xaxis.set_visible(False) plt.bar(x, di_y, width=0.6) plt.axhline(y=1.25, xmin=0, xmax=2, linestyle='--', color='black') plt.axhline(y=1, xmin=0, xmax=2, linestyle='--', color='green') plt.axhline(y=0.75, xmin=0, xmax=2, linestyle='--', color='black')
"#### Plain model - without debiasing - classification metrics on test set" ) # print("Test set: Classification accuracy = %f" % classified_metric_nodebiasing_test.accuracy()) # print("Test set: Balanced classification accuracy = %f" % bal_acc_nodebiasing_test) # print("Test set: Statistical parity difference = %f" % classified_metric_nodebiasing_test.statistical_parity_difference()) # print("Test set: Disparate impact = %f" % classified_metric_nodebiasing_test.disparate_impact()) # print("Test set: Equal opportunity difference = %f" % classified_metric_nodebiasing_test.equal_opportunity_difference()) # print("Test set: Average odds difference = %f" % classified_metric_nodebiasing_test.average_odds_difference()) # print("Test set: Theil index = %f" % classified_metric_nodebiasing_test.theil_index()) # print("Test set: False negative rate difference = %f" % classified_metric_nodebiasing_test.false_negative_rate_difference()) metrics = { "Classification accuracy": classified_metric_nodebiasing_test.accuracy(), "Balanced classification accuracy": bal_acc_nodebiasing_test, "Statistical parity difference": classified_metric_nodebiasing_test.statistical_parity_difference(), "Disparate impact": classified_metric_nodebiasing_test.disparate_impact(), "Equal opportunity difference": classified_metric_nodebiasing_test.equal_opportunity_difference(), "Average odds difference": classified_metric_nodebiasing_test.average_odds_difference(), "Theil index": classified_metric_nodebiasing_test.theil_index(), "False negative rate difference": classified_metric_nodebiasing_test.false_negative_rate_difference() } print("metrics: ", metrics)
def compute_metrics(model, X_test, y_test, X_train, y_train, dataset_test, dataset_name, model_name, unprivileged_groups, privileged_groups, position): """ Calculate and return: model accuracy and fairness metrics Parameters ---------- model: scikit-learn classifier X_test: numpy 2d array y_test: numpy 1d array X_train: numpy 2d array y_train: numpy 1d array dataset_test: aif360.datasets.BinaryLabelDataset dataset_name: string Dataset name used in the analysis model_name: string unprivileged_groups: list<dict> Dictionary where the key is the name of the sensitive column in the dataset, and the value is the value of the unprivileged group in the dataset privileged_groups: list<dict> Dictionary where the key is the name of the sensitive column in the dataset, and the value is the value of the privileged group in the dataset position: int Column position of the sensitive group in the dataset """ y_pred_test = model.predict(X_test) acc_test = accuracy_score(y_true=y_test, y_pred=y_pred_test) print("Test accuracy: ", acc_test) y_pred_train = model.predict(X_train) acc_train = accuracy_score(y_true=y_train, y_pred=y_pred_train) print("Train accuracy: ", acc_train) dataset_pred = dataset_test.copy() dataset_pred.labels = y_pred_test bin_metric = BinaryLabelDatasetMetric( dataset_pred, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) disparate_impact_bin = bin_metric.disparate_impact() print('Disparate impact: ', disparate_impact_bin) mean_difference = bin_metric.mean_difference() print('Mean difference: ', mean_difference) classif_metric = ClassificationMetric( dataset_test, dataset_pred, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) classif_disparete_impact = classif_metric.disparate_impact() avg_odds = classif_metric.average_odds_difference() print('Average odds difference:', avg_odds) equal_opport = classif_metric.equal_opportunity_difference() print('Equality of opportunity:', equal_opport) false_discovery_rate = classif_metric.false_discovery_rate_difference() print('False discovery rate difference:', false_discovery_rate) entropy_index = classif_metric.generalized_entropy_index() print('Generalized entropy index:', entropy_index) cons_comp = consitency_mod(bin_metric, position, n_neighbors=5) print('Consistency: ', cons_comp) result = (dataset_name, model_name, acc_test, disparate_impact_bin, mean_difference, classif_disparete_impact, avg_odds, equal_opport, false_discovery_rate, entropy_index, cons_comp) return result
def comb_algorithm(l, m, n, dataset_original1, privileged_groups1, unprivileged_groups1, optim_options1): dataset_original2 = copy.deepcopy(dataset_original1) privileged_groups2 = copy.deepcopy(privileged_groups1) unprivileged_groups2 = copy.deepcopy(unprivileged_groups1) optim_options2 = copy.deepcopy(optim_options1) print(l, m, n) dataset_orig_train, dataset_orig_vt = dataset_original2.split([0.7], shuffle=True) dataset_orig_valid, dataset_orig_test = dataset_orig_vt.split([0.5], shuffle=True) if l == 0: dataset_transf_train, dataset_transf_valid, dataset_transf_test = dataset_orig_train, dataset_orig_valid, dataset_orig_test else: pre_used = preAlgorithm[l - 1] dataset_transf_train, dataset_transf_valid, dataset_transf_test = Pre( pre_used, dataset_orig_train, dataset_orig_valid, dataset_orig_test, privileged_groups2, unprivileged_groups2, optim_options2) #assert (l,m,n)!=(2,0,0) #assert not np.all(dataset_transf_train.labels.flatten()==1.0) if m == 0: dataset_transf_valid_pred, dataset_transf_test_pred = train( dataset_transf_train, dataset_transf_valid, dataset_transf_test, privileged_groups2, unprivileged_groups2) else: in_used = inAlgorithm[m - 1] if in_used == "adversarial_debiasing": dataset_transf_valid_pred, dataset_transf_test_pred = adversarial_debiasing( dataset_transf_train, dataset_transf_valid, dataset_transf_test, privileged_groups2, unprivileged_groups2) elif in_used == "art_classifier": dataset_transf_valid_pred, dataset_transf_test_pred = art_classifier( dataset_transf_train, dataset_transf_valid, dataset_transf_test, privileged_groups2, unprivileged_groups2) elif in_used == "prejudice_remover": for key, value in privileged_groups2[0].items(): sens_attr = key dataset_transf_valid_pred, dataset_transf_test_pred = prejudice_remover( dataset_transf_train, dataset_transf_valid, dataset_transf_test, privileged_groups2, unprivileged_groups2, sens_attr) if n == 0: dataset_transf_test_pred_transf = dataset_transf_test_pred else: post_used = postAlgorithm[n - 1] if post_used == "calibrated_eqodds": cpp = CalibratedEqOddsPostprocessing( privileged_groups=privileged_groups2, unprivileged_groups=unprivileged_groups2, cost_constraint=cost_constraint, seed=1) cpp = cpp.fit(dataset_transf_valid, dataset_transf_valid_pred) dataset_transf_test_pred_transf = cpp.predict( dataset_transf_test_pred) elif post_used == "eqodds": EO = EqOddsPostprocessing(unprivileged_groups=unprivileged_groups2, privileged_groups=privileged_groups2, seed=1) EO = EO.fit(dataset_transf_valid, dataset_transf_valid_pred) dataset_transf_test_pred_transf = EO.predict( dataset_transf_test_pred) elif post_used == "reject_option": ROC = RejectOptionClassification( unprivileged_groups=unprivileged_groups2, privileged_groups=privileged_groups2, low_class_thresh=0.01, high_class_thresh=0.99, num_class_thresh=100, num_ROC_margin=50, metric_name=allowed_metrics[0], metric_ub=metric_ub, metric_lb=metric_lb) ROC = ROC.fit(dataset_transf_valid, dataset_transf_valid_pred) dataset_transf_test_pred_transf = ROC.predict( dataset_transf_test_pred) metric = ClassificationMetric(dataset_transf_test, dataset_transf_test_pred_transf, unprivileged_groups=unprivileged_groups2, privileged_groups=privileged_groups2) metrics = OrderedDict() metrics["Classification accuracy"] = metric.accuracy() TPR = metric.true_positive_rate() TNR = metric.true_negative_rate() bal_acc_nodebiasing_test = 0.5 * (TPR + TNR) metrics["Balanced classification accuracy"] = bal_acc_nodebiasing_test metrics[ "Statistical parity difference"] = metric.statistical_parity_difference( ) metrics["Disparate impact"] = metric.disparate_impact() metrics[ "Equal opportunity difference"] = metric.equal_opportunity_difference( ) metrics["Average odds difference"] = metric.average_odds_difference() metrics["Theil index"] = metric.theil_index() metrics["United Fairness"] = metric.generalized_entropy_index() # print(metrics) feature = "[" for m in metrics: feature = feature + " " + str(round(metrics[m], 4)) feature = feature + "]" return feature
def compute_metrics(model, X_test, y_test, X_train, y_train, dataset_test, unprivileged_groups, privileged_groups, protect_attribute, print_result): """ Calculate and return: model accuracy and fairness metrics Parameters ---------- model: scikit-learn classifier X_test: numpy 2d array y_test: numpy 1d array X_train: numpy 2d array y_train: numpy 1d array dataset_test: aif360.datasets.BinaryLabelDataset unprivileged_groups: list<dict> Dictionary where the key is the name of the sensitive column in the dataset, and the value is the value of the unprivileged group in the dataset privileged_groups: list<dict> Dictionary where the key is the name of the sensitive column in the dataset, and the value is the value of the privileged group in the dataset protect_attribute print_result """ result = {} y_pred_test = model.predict(X_test) result['acc_test'] = accuracy_score(y_true=y_test, y_pred=y_pred_test) y_pred_train = model.predict(X_train) result['acc_train'] = accuracy_score(y_true=y_train, y_pred=y_pred_train) dataset_pred = dataset_test.copy() dataset_pred.labels = y_pred_test bin_metric = BinaryLabelDatasetMetric(dataset_pred, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) result['disp_impact'] = bin_metric.disparate_impact() result['stat_parity'] = bin_metric.mean_difference() classif_metric = ClassificationMetric(dataset_test, dataset_pred, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) result['avg_odds'] = classif_metric.average_odds_difference() result['equal_opport'] = classif_metric.equal_opportunity_difference() result['false_discovery_rate'] = classif_metric.false_discovery_rate_difference() result['entropy_index'] = classif_metric.generalized_entropy_index() result['acc_test_clf'] = classif_metric.accuracy(privileged=None) result['acc_test_priv'] = classif_metric.accuracy(privileged=True) result['acc_test_unpriv'] = classif_metric.accuracy(privileged=False) result['consistency'] = consitency(X_test, y_pred_test, protect_attribute, n_neighbors=5) result['counterfactual'] = counterfactual(X_test, model, protect_attribute) if print_result: print("Train accuracy: ", result['acc_train']) print("Test accuracy: ", result['acc_test']) print("Test accuracy clf: ", result['acc_test_clf']) print("Test accuracy priv.: ", result['acc_test_priv']) print("Test accuracy unpriv.: ", result['acc_test_unpriv']) print('Disparate impact: ', result['disp_impact']) print('Mean difference: ', result['stat_parity']) print('Average odds difference:', result['avg_odds']) print('Equality of opportunity:', result['equal_opport']) print('False discovery rate difference:', result['false_discovery_rate']) print('Generalized entropy index:', result['entropy_index']) print('Consistency: ', result['consistency']) print('Counterfactual fairness: ', result['counterfactual']) return result
for c in C: predictions, _ = train_and_predict(X_train, y_train, X_test, c, norm_type) ds_te_pred = ds_te.copy() ds_te_pred.labels = predictions metric_te = ClassificationMetric(ds_te, ds_te_pred, unprivileged_groups=unpriv, privileged_groups=priv) BACC = 0.5*(metric_te.true_positive_rate()\ +metric_te.true_negative_rate()) metric_1 = metric_te.statistical_parity_difference() metric_2 = metric_te.average_odds_difference() metric_3 = metric_te.equal_opportunity_difference() accuracy.append(BACC) mean_diff.append(metric_1) average_odds_diff.append(metric_2) equal_opp_diff.append(metric_3) # save plots plot_results(C, norm_type, accuracy, mean_diff, average_odds_diff, \ equal_opp_diff, name+'_all_metrics_'+norm_type) def results_table(C, accuracy, mean_diff, avg_odds_diff, equal_opp_diff): results = pd.DataFrame() results['c'] = C results['bACC'] = accuracy
ClassificationMetric( dataset_ground_truth, dataset_classifier, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) TPR = classificaltion_metric.true_positive_rate() TNR = classificaltion_metric.true_negative_rate() bal_acc_nodebiasing_test = 0.5 * (TPR + TNR) metrics = { "classification_accuracy": classificaltion_metric.accuracy(), "balanced_classification_accuracy": bal_acc_nodebiasing_test, "statistical_parity_difference": classificaltion_metric.statistical_parity_difference(), "disparate_impact": classificaltion_metric.disparate_impact(), "equal_opportunity_difference": classificaltion_metric.equal_opportunity_difference(), "average_odds_difference": classificaltion_metric.average_odds_difference(), "theil_index": classificaltion_metric.theil_index(), "false_negative_rate_difference": classificaltion_metric.false_negative_rate_difference() } sys.stdout.write(json.dumps(metrics))
def comb_algorithm(l, m, n, dataset_original1, privileged_groups1, unprivileged_groups1, optim_options1): dataset_original2 = copy.deepcopy(dataset_original1) privileged_groups2 = copy.deepcopy(privileged_groups1) unprivileged_groups2 = copy.deepcopy(unprivileged_groups1) optim_options2 = copy.deepcopy(optim_options1) print(l, m, n) dataset_original_train, dataset_original_vt = dataset_original2.split( [0.7], shuffle=True) dataset_original_valid, dataset_original_test = dataset_original_vt.split( [0.5], shuffle=True) dataset_original_test.labels = dataset_original_test.labels print('=======================') #print(dataset_original_test.labels) dataset_orig_train = copy.deepcopy(dataset_original_train) dataset_orig_valid = copy.deepcopy(dataset_original_valid) dataset_orig_test = copy.deepcopy(dataset_original_test) if l == 0: dataset_transfer_train = copy.deepcopy(dataset_original_train) dataset_transfer_valid = copy.deepcopy(dataset_original_valid) dataset_transfer_test = copy.deepcopy(dataset_original_test) #dataset_transf_train, dataset_transf_valid, dataset_transf_test = dataset_orig_train, dataset_orig_valid, dataset_orig_test else: pre_used = preAlgorithm[l - 1] dataset_transfer_train, dataset_transfer_valid, dataset_transfer_test = Pre( pre_used, dataset_orig_train, dataset_orig_valid, dataset_orig_test, privileged_groups2, unprivileged_groups2, optim_options2) dataset_transf_train = copy.deepcopy(dataset_transfer_train) dataset_transf_valid = copy.deepcopy(dataset_transfer_valid) dataset_transf_test = copy.deepcopy(dataset_transfer_test) if m == 0: dataset_transfer_valid_pred, dataset_transfer_test_pred = plain_model( dataset_transf_train, dataset_transf_valid, dataset_transf_test, privileged_groups2, unprivileged_groups2) else: in_used = inAlgorithm[m - 1] if in_used == "adversarial_debiasing": dataset_transfer_valid_pred, dataset_transfer_test_pred = adversarial_debiasing( dataset_transf_train, dataset_transf_valid, dataset_transf_test, privileged_groups2, unprivileged_groups2) elif in_used == "art_classifier": dataset_transfer_valid_pred, dataset_transfer_test_pred = art_classifier( dataset_transf_train, dataset_transf_valid, dataset_transf_test, privileged_groups2, unprivileged_groups2) elif in_used == "prejudice_remover": for key, value in privileged_groups2[0].items(): sens_attr = key dataset_transfer_valid_pred, dataset_transfer_test_pred = prejudice_remover( dataset_transf_train, dataset_transf_valid, dataset_transf_test, privileged_groups2, unprivileged_groups2, sens_attr) dataset_transf_valid_pred = copy.deepcopy(dataset_transfer_valid_pred) dataset_transf_test_pred = copy.deepcopy(dataset_transfer_test_pred) if n == 0: dataset_transf_test_pred_transf = copy.deepcopy( dataset_transfer_test_pred) else: post_used = postAlgorithm[n - 1] if post_used == "calibrated_eqodds": cpp = CalibratedEqOddsPostprocessing( privileged_groups=privileged_groups2, unprivileged_groups=unprivileged_groups2, cost_constraint=cost_constraint) cpp = cpp.fit(dataset_transfer_valid, dataset_transf_valid_pred) dataset_transf_test_pred_transf = cpp.predict( dataset_transf_test_pred) elif post_used == "eqodds": EO = EqOddsPostprocessing(unprivileged_groups=unprivileged_groups2, privileged_groups=privileged_groups2) EO = EO.fit(dataset_transfer_valid, dataset_transf_valid_pred) dataset_transf_test_pred_transf = EO.predict( dataset_transf_test_pred) elif post_used == "reject_option": #dataset_transf_test_pred_transf = reject_option(dataset_transf_valid, dataset_transf_valid_pred, dataset_transf_test, dataset_transf_test_pred, privileged_groups2, unprivileged_groups2) ROC = RejectOptionClassification( unprivileged_groups=unprivileged_groups2, privileged_groups=privileged_groups2) ROC = ROC.fit(dataset_transfer_valid, dataset_transf_valid_pred) dataset_transf_test_pred_transf = ROC.predict( dataset_transf_test_pred) #print('=======================') org_labels = dataset_orig_test.labels print(dataset_orig_test.labels) #print(dataset_transf_test.labels) #print('=======================') pred_labels = dataset_transf_test_pred.labels print(dataset_transf_test_pred.labels) true_pred = org_labels == pred_labels print("acc after in: ", float(np.sum(true_pred)) / pred_labels.shape[1]) #print('=======================') #print(dataset_transf_test_pred_transf.labels) #print(dataset_transf_test_pred_transf.labels.shape) metric = ClassificationMetric(dataset_transfer_test, dataset_transf_test_pred_transf, unprivileged_groups=unprivileged_groups2, privileged_groups=privileged_groups2) metrics = OrderedDict() metrics["Classification accuracy"] = metric.accuracy() TPR = metric.true_positive_rate() TNR = metric.true_negative_rate() bal_acc_nodebiasing_test = 0.5 * (TPR + TNR) metrics["Balanced classification accuracy"] = bal_acc_nodebiasing_test metrics[ "Statistical parity difference"] = metric.statistical_parity_difference( ) metrics["Disparate impact"] = metric.disparate_impact() metrics[ "Equal opportunity difference"] = metric.equal_opportunity_difference( ) metrics["Average odds difference"] = metric.average_odds_difference() metrics["Theil index"] = metric.theil_index() metrics["United Fairness"] = metric.generalized_entropy_index() feature = [] feature_str = "[" for m in metrics: data = round(metrics[m], 4) feature.append(data) feature_str = feature_str + str(data) + " " feature_str = feature_str + "]" return feature, feature_str
def fit(self, dataset_true, dataset_pred): """Estimates the optimal classification threshold and margin for reject option classification that optimizes the metric provided. Note: The `fit` function is a no-op for this algorithm. Args: dataset_true (BinaryLabelDataset): Dataset containing the true `labels`. dataset_pred (BinaryLabelDataset): Dataset containing the predicted `scores`. Returns: RejectOptionClassification: Returns self. """ fair_metric_arr = np.zeros(self.num_class_thresh*self.num_ROC_margin) balanced_acc_arr = np.zeros_like(fair_metric_arr) ROC_margin_arr = np.zeros_like(fair_metric_arr) class_thresh_arr = np.zeros_like(fair_metric_arr) cnt = 0 # Iterate through class thresholds for class_thresh in np.linspace(self.low_class_thresh, self.high_class_thresh, self.num_class_thresh): self.classification_threshold = class_thresh if class_thresh <= 0.5: low_ROC_margin = 0.0 high_ROC_margin = class_thresh else: low_ROC_margin = 0.0 high_ROC_margin = (1.0-class_thresh) # Iterate through ROC margins for ROC_margin in np.linspace( low_ROC_margin, high_ROC_margin, self.num_ROC_margin): self.ROC_margin = ROC_margin # Predict using the current threshold and margin dataset_transf_pred = self.predict(dataset_pred) dataset_transf_metric_pred = BinaryLabelDatasetMetric( dataset_transf_pred, unprivileged_groups=self.unprivileged_groups, privileged_groups=self.privileged_groups) classified_transf_metric = ClassificationMetric( dataset_true, dataset_transf_pred, unprivileged_groups=self.unprivileged_groups, privileged_groups=self.privileged_groups) ROC_margin_arr[cnt] = self.ROC_margin class_thresh_arr[cnt] = self.classification_threshold # Balanced accuracy and fairness metric computations balanced_acc_arr[cnt] = 0.5*(classified_transf_metric.true_positive_rate()\ +classified_transf_metric.true_negative_rate()) if self.metric_name == "Statistical parity difference": fair_metric_arr[cnt] = dataset_transf_metric_pred.mean_difference() elif self.metric_name == "Average odds difference": fair_metric_arr[cnt] = classified_transf_metric.average_odds_difference() elif self.metric_name == "Equal opportunity difference": fair_metric_arr[cnt] = classified_transf_metric.equal_opportunity_difference() cnt += 1 rel_inds = np.logical_and(fair_metric_arr >= self.metric_lb, fair_metric_arr <= self.metric_ub) if any(rel_inds): best_ind = np.where(balanced_acc_arr[rel_inds] == np.max(balanced_acc_arr[rel_inds]))[0][0] else: warn("Unable to satisy fairness constraints") rel_inds = np.ones(len(fair_metric_arr), dtype=bool) best_ind = np.where(fair_metric_arr[rel_inds] == np.min(fair_metric_arr[rel_inds]))[0][0] self.ROC_margin = ROC_margin_arr[rel_inds][best_ind] self.classification_threshold = class_thresh_arr[rel_inds][best_ind] return self
# TODO: (1) Redo the previous cell for gender bias and recompute the corresponding # fairness metrics # (2)collect these values in a table # (3) think about a way to visualize these values # Statistical Parity difference (SPD) spd_pre_race = fairness_metrics.statistical_parity_difference() # Disparate Impact Ratio dir_pre_race = fairness_metrics.disparate_impact() # Average Odds Difference and Average absolute odds difference aod_pre_race = fairness_metrics.average_odds_difference() aaod_pre_race = fairness_metrics.average_abs_odds_difference() # Equal Opportunity Difference aka true positive rate difference eod_pre_race = fairness_metrics.equal_opportunity_difference() # Generealized entropy index with various alpha's fairness_metrics.between_all_groups_generalized_entropy_index(alpha=2) ClassificationMetric(dataset=bld_true, classified_dataset=bld_pred, unprivileged_groups=None, privileged_groups=None).false_positive_rate() df_fm.head() # TO DELETE # ============================================================================= # bld_pred.align_datasets # bld_true.temporarily_ignore('score_cat')
def fairness_check(object_storage_url, object_storage_username, object_storage_password, data_bucket_name, result_bucket_name, model_id, feature_testset_path='processed_data/X_test.npy', label_testset_path='processed_data/y_test.npy', protected_label_testset_path='processed_data/p_test.npy', model_class_file='model.py', model_class_name='model', favorable_label=0.0, unfavorable_label=1.0, privileged_groups=[{ 'race': 0.0 }], unprivileged_groups=[{ 'race': 4.0 }]): url = re.compile(r"https?://") cos = Minio(url.sub('', object_storage_url), access_key=object_storage_username, secret_key=object_storage_password, secure=False) # Local Minio server won't have HTTPS dataset_filenamex = "X_test.npy" dataset_filenamey = "y_test.npy" dataset_filenamep = "p_test.npy" weights_filename = "model.pt" model_files = model_id + '/_submitted_code/model.zip' cos.fget_object(data_bucket_name, feature_testset_path, dataset_filenamex) cos.fget_object(data_bucket_name, label_testset_path, dataset_filenamey) cos.fget_object(data_bucket_name, protected_label_testset_path, dataset_filenamep) cos.fget_object(result_bucket_name, model_id + '/' + weights_filename, weights_filename) cos.fget_object(result_bucket_name, model_files, 'model.zip') # Load PyTorch model definition from the source code. zip_ref = zipfile.ZipFile('model.zip', 'r') zip_ref.extractall('model_files') zip_ref.close() modulename = 'model_files.' + model_class_file.split('.')[0].replace( '-', '_') ''' We required users to define where the model class is located or follow some naming convention we have provided. ''' model_class = getattr(importlib.import_module(modulename), model_class_name) # load & compile model device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') model = model_class().to(device) model.load_state_dict(torch.load(weights_filename, map_location=device)) """Load the necessary labels and protected features for fairness check""" x_test = np.load(dataset_filenamex) y_test = np.load(dataset_filenamey) p_test = np.load(dataset_filenamep) _, y_pred = evaluate(model, x_test, y_test) """Calculate the fairness metrics""" original_test_dataset = dataset_wrapper( outcome=y_test, protected=p_test, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups, favorable_label=favorable_label, unfavorable_label=unfavorable_label) plain_predictions_test_dataset = dataset_wrapper( outcome=y_pred, protected=p_test, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups, favorable_label=favorable_label, unfavorable_label=unfavorable_label) classified_metric_nodebiasing_test = ClassificationMetric( original_test_dataset, plain_predictions_test_dataset, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) TPR = classified_metric_nodebiasing_test.true_positive_rate() TNR = classified_metric_nodebiasing_test.true_negative_rate() bal_acc_nodebiasing_test = 0.5 * (TPR + TNR) print( "#### Plain model - without debiasing - classification metrics on test set" ) metrics = { "Classification accuracy": classified_metric_nodebiasing_test.accuracy(), "Balanced classification accuracy": bal_acc_nodebiasing_test, "Statistical parity difference": classified_metric_nodebiasing_test.statistical_parity_difference(), "Disparate impact": classified_metric_nodebiasing_test.disparate_impact(), "Equal opportunity difference": classified_metric_nodebiasing_test.equal_opportunity_difference(), "Average odds difference": classified_metric_nodebiasing_test.average_odds_difference(), "Theil index": classified_metric_nodebiasing_test.theil_index(), "False negative rate difference": classified_metric_nodebiasing_test.false_negative_rate_difference() } print("metrics: ", metrics) return metrics
def get_fair_metrics(dataset, pred, pred_is_dataset=False): """ Measure fairness metrics. Parameters: dataset (pandas dataframe): Dataset pred (array): Model predictions pred_is_dataset, optional (bool): True if prediction is already part of the dataset, column name 'labels'. Returns: fair_metrics: Fairness metrics. """ if pred_is_dataset: dataset_pred = pred else: dataset_pred = dataset.copy() dataset_pred.labels = pred cols = [ 'statistical_parity_difference', 'equal_opportunity_difference', 'average_abs_odds_difference', 'disparate_impact', 'theil_index' ] obj_fairness = [[0, 0, 0, 1, 0]] fair_metrics = pd.DataFrame(data=obj_fairness, index=['objective'], columns=cols) for attr in dataset_pred.protected_attribute_names: idx = dataset_pred.protected_attribute_names.index(attr) privileged_groups = [{ attr: dataset_pred.privileged_protected_attributes[idx][0] }] unprivileged_groups = [{ attr: dataset_pred.unprivileged_protected_attributes[idx][0] }] classified_metric = ClassificationMetric( dataset, dataset_pred, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) metric_pred = BinaryLabelDatasetMetric( dataset_pred, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) acc = classified_metric.accuracy() row = pd.DataFrame([[ metric_pred.mean_difference(), classified_metric.equal_opportunity_difference(), classified_metric.average_abs_odds_difference(), metric_pred.disparate_impact(), classified_metric.theil_index() ]], columns=cols, index=[attr]) fair_metrics = fair_metrics.append(row) fair_metrics = fair_metrics.replace([-np.inf, np.inf], 2) return fair_metrics