From 2ebccfe7f34b9d3f7fcd2ea5d5e660b759a60e7d Mon Sep 17 00:00:00 2001 From: Ushnesha Daripa Date: Mon, 12 Jan 2026 16:03:12 -0800 Subject: [PATCH] dpa test cases update --- tests/test_predMetric.py | 104 +++++++++++++++++++-------------------- 1 file changed, 52 insertions(+), 52 deletions(-) diff --git a/tests/test_predMetric.py b/tests/test_predMetric.py index 1180cce..ed53d09 100644 --- a/tests/test_predMetric.py +++ b/tests/test_predMetric.py @@ -1,9 +1,9 @@ import torch import pytest -from metrics.PredMetrics_v1 import * -from attackerModels.ANN import simpleDenseModel -from utils.datacreator import dataCreator -from utils.losses import ModifiedBCELoss +from bias_amplification.metrics.PredMetrics import * +from bias_amplification.attacker_models.ANN import simpleDenseModel +from bias_amplification.utils.datacreator import dataCreator +from bias_amplification.utils.losses import ModifiedBCELoss from unittest.mock import Mock, patch # =============================================================================== @@ -1148,7 +1148,7 @@ def test_amortized_leakage_on_unbiased_data_unbiased_pred_with_train_and_test_de ) num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.45), torch.tensor(0.8)] - with patch("PredMetrics_v1.Leakage.calcLeak", side_effect=mock_values): + with patch("PredMetrics.Leakage.calcLeak", side_effect=mock_values): amortized_leakage = leakage.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -1175,7 +1175,7 @@ def test_amortized_leakage_on_unbiased_data_unbiased_pred_with_train_and_test_de ) num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.55), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -1203,7 +1203,7 @@ def test_amortized_leakage_on_unbiased_data_unbiased_pred_with_train_and_test_de ) num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.55), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -1237,7 +1237,7 @@ def test_amortized_leakage_on_unbiased_data_unbiased_pred_with_train_and_test_me num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.57), torch.tensor(0.6)] - with patch("PredMetrics_v1.Leakage.calcLeak", side_effect=mock_values): + with patch("PredMetrics.Leakage.calcLeak", side_effect=mock_values): amortized_leakage = leakage.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -1266,7 +1266,7 @@ def test_amortized_leakage_on_unbiased_data_unbiased_pred_with_train_and_test_me num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.54), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -1296,7 +1296,7 @@ def test_amortized_leakage_on_unbiased_data_unbiased_pred_with_train_and_test_me num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.57), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -1324,7 +1324,7 @@ def test_amortized_leakage_on_unbiased_data_unbiased_pred_default_method(self): pred = get_test_data()["M1"] num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.53), torch.tensor(0.6)] - with patch("PredMetrics_v1.Leakage.calcLeak", side_effect=mock_values): + with patch("PredMetrics.Leakage.calcLeak", side_effect=mock_values): amortized_leakage = leakage.getAmortizedLeakage( feat_train=feat, data_train=data, pred_train=pred, num_trials=num_trials ) @@ -1339,7 +1339,7 @@ def test_amortized_leakage_on_unbiased_data_unbiased_pred_default_method_dpa_Ato pred = get_test_data()["M1"] num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.59), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat, data_train=data, @@ -1358,7 +1358,7 @@ def test_amortized_leakage_on_unbiased_data_unbiased_pred_default_method_dpa_Tto pred = get_test_data()["M1"] num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.57), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat, data_train=data, @@ -1383,7 +1383,7 @@ def test_amortized_leakage_on_unbiased_data_unbiased_pred_method_median(self): num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.57), torch.tensor(0.6)] - with patch("PredMetrics_v1.Leakage.calcLeak", side_effect=mock_values): + with patch("PredMetrics.Leakage.calcLeak", side_effect=mock_values): amortized_leakage = leakage.getAmortizedLeakage( feat_train=feat, data_train=data, @@ -1403,7 +1403,7 @@ def test_amortized_leakage_on_unbiased_data_unbiased_pred_method_median_dpa_AtoT num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.54), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat, data_train=data, @@ -1424,7 +1424,7 @@ def test_amortized_leakage_on_unbiased_data_unbiased_pred_method_median_dpa_TtoA num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.57), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat, data_train=data, @@ -1455,7 +1455,7 @@ def test_amortized_leakage_on_biased_data_unbiased_pred_with_train_and_test_defa ) num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.45), torch.tensor(0.8)] - with patch("PredMetrics_v1.Leakage.calcLeak", side_effect=mock_values): + with patch("PredMetrics.Leakage.calcLeak", side_effect=mock_values): amortized_leakage = leakage.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -1482,7 +1482,7 @@ def test_amortized_leakage_on_biased_data_unbiased_pred_with_train_and_test_defa ) num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.55), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -1510,7 +1510,7 @@ def test_amortized_leakage_on_biased_data_unbiased_pred_with_train_and_test_defa ) num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.55), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -1542,7 +1542,7 @@ def test_amortized_leakage_on_biased_data_unbiased_pred_with_train_and_test_meth num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.57), torch.tensor(0.6)] - with patch("PredMetrics_v1.Leakage.calcLeak", side_effect=mock_values): + with patch("PredMetrics.Leakage.calcLeak", side_effect=mock_values): amortized_leakage = leakage.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -1571,7 +1571,7 @@ def test_amortized_leakage_on_biased_data_unbiased_pred_with_train_and_test_meth num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.54), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -1601,7 +1601,7 @@ def test_amortized_leakage_on_biased_data_unbiased_pred_with_train_and_test_meth num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.57), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -1629,7 +1629,7 @@ def test_amortized_leakage_on_biased_data_unbiased_pred_default_method(self): pred = get_test_data()["M1"] num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.53), torch.tensor(0.6)] - with patch("PredMetrics_v1.Leakage.calcLeak", side_effect=mock_values): + with patch("PredMetrics.Leakage.calcLeak", side_effect=mock_values): amortized_leakage = leakage.getAmortizedLeakage( feat_train=feat, data_train=data, pred_train=pred, num_trials=num_trials ) @@ -1644,7 +1644,7 @@ def test_amortized_leakage_on_biased_data_unbiased_pred_default_method_dpa_AtoT( pred = get_test_data()["M1"] num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.59), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat, data_train=data, @@ -1663,7 +1663,7 @@ def test_amortized_leakage_on_biased_data_unbiased_pred_default_method_dpa_TtoA( pred = get_test_data()["M1"] num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.57), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat, data_train=data, @@ -1688,7 +1688,7 @@ def test_amortized_leakage_on_biased_data_unbiased_pred_method_median(self): num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.57), torch.tensor(0.6)] - with patch("PredMetrics_v1.Leakage.calcLeak", side_effect=mock_values): + with patch("PredMetrics.Leakage.calcLeak", side_effect=mock_values): amortized_leakage = leakage.getAmortizedLeakage( feat_train=feat, data_train=data, @@ -1708,7 +1708,7 @@ def test_amortized_leakage_on_biased_data_unbiased_pred_method_median_dpa_AtoT(s num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.54), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat, data_train=data, @@ -1729,7 +1729,7 @@ def test_amortized_leakage_on_biased_data_unbiased_pred_method_median_dpa_TtoA(s num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.57), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat, data_train=data, @@ -1760,7 +1760,7 @@ def test_amortized_leakage_on_unbiased_data_biased_pred_with_train_and_test_defa ) num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.45), torch.tensor(0.8)] - with patch("PredMetrics_v1.Leakage.calcLeak", side_effect=mock_values): + with patch("PredMetrics.Leakage.calcLeak", side_effect=mock_values): amortized_leakage = leakage.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -1787,7 +1787,7 @@ def test_amortized_leakage_on_unbiased_data_biased_pred_with_train_and_test_defa ) num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.55), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -1815,7 +1815,7 @@ def test_amortized_leakage_on_unbiased_data_biased_pred_with_train_and_test_defa ) num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.55), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -1847,7 +1847,7 @@ def test_amortized_leakage_on_unbiased_data_biased_pred_with_train_and_test_meth num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.57), torch.tensor(0.6)] - with patch("PredMetrics_v1.Leakage.calcLeak", side_effect=mock_values): + with patch("PredMetrics.Leakage.calcLeak", side_effect=mock_values): amortized_leakage = leakage.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -1876,7 +1876,7 @@ def test_amortized_leakage_on_unbiased_data_biased_pred_with_train_and_test_meth num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.54), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -1906,7 +1906,7 @@ def test_amortized_leakage_on_unbiased_data_biased_pred_with_train_and_test_meth num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.57), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -1934,7 +1934,7 @@ def test_amortized_leakage_on_unbiased_data_biased_pred_default_method(self): pred = get_test_data()["M2"] num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.53), torch.tensor(0.6)] - with patch("PredMetrics_v1.Leakage.calcLeak", side_effect=mock_values): + with patch("PredMetrics.Leakage.calcLeak", side_effect=mock_values): amortized_leakage = leakage.getAmortizedLeakage( feat_train=feat, data_train=data, pred_train=pred, num_trials=num_trials ) @@ -1949,7 +1949,7 @@ def test_amortized_leakage_on_unbiased_data_biased_pred_default_method_dpa_AtoT( pred = get_test_data()["M2"] num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.59), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat, data_train=data, @@ -1968,7 +1968,7 @@ def test_amortized_leakage_on_unbiased_data_biased_pred_default_method_dpa_TtoA( pred = get_test_data()["M2"] num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.57), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat, data_train=data, @@ -1993,7 +1993,7 @@ def test_amortized_leakage_on_unbiased_data_biased_pred_method_median(self): num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.57), torch.tensor(0.6)] - with patch("PredMetrics_v1.Leakage.calcLeak", side_effect=mock_values): + with patch("PredMetrics.Leakage.calcLeak", side_effect=mock_values): amortized_leakage = leakage.getAmortizedLeakage( feat_train=feat, data_train=data, @@ -2013,7 +2013,7 @@ def test_amortized_leakage_on_unbiased_data_biased_pred_method_median_dpa_AtoT(s num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.54), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat, data_train=data, @@ -2034,7 +2034,7 @@ def test_amortized_leakage_on_unbiased_data_biased_pred_method_median_dpa_TtoA(s num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.57), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat, data_train=data, @@ -2063,7 +2063,7 @@ def test_amortized_leakage_on_biased_data_biased_pred_with_train_and_test_defaul ) num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.45), torch.tensor(0.8)] - with patch("PredMetrics_v1.Leakage.calcLeak", side_effect=mock_values): + with patch("PredMetrics.Leakage.calcLeak", side_effect=mock_values): amortized_leakage = leakage.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -2090,7 +2090,7 @@ def test_amortized_leakage_on_biased_data_biased_pred_with_train_and_test_defaul ) num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.55), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -2118,7 +2118,7 @@ def test_amortized_leakage_on_biased_data_biased_pred_with_train_and_test_defaul ) num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.55), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -2150,7 +2150,7 @@ def test_amortized_leakage_on_biased_data_biased_pred_with_train_and_test_method num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.57), torch.tensor(0.6)] - with patch("PredMetrics_v1.Leakage.calcLeak", side_effect=mock_values): + with patch("PredMetrics.Leakage.calcLeak", side_effect=mock_values): amortized_leakage = leakage.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -2179,7 +2179,7 @@ def test_amortized_leakage_on_biased_data_biased_pred_with_train_and_test_method num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.54), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -2209,7 +2209,7 @@ def test_amortized_leakage_on_biased_data_biased_pred_with_train_and_test_method num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.57), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat_train, data_train=data_train, @@ -2237,7 +2237,7 @@ def test_amortized_leakage_on_biased_data_biased_pred_default_method(self): pred = get_test_data()["M2"] num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.53), torch.tensor(0.6)] - with patch("PredMetrics_v1.Leakage.calcLeak", side_effect=mock_values): + with patch("PredMetrics.Leakage.calcLeak", side_effect=mock_values): amortized_leakage = leakage.getAmortizedLeakage( feat_train=feat, data_train=data, pred_train=pred, num_trials=num_trials ) @@ -2252,7 +2252,7 @@ def test_amortized_leakage_on_biased_data_biased_pred_default_method_dpa_AtoT(se pred = get_test_data()["M2"] num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.59), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat, data_train=data, @@ -2271,7 +2271,7 @@ def test_amortized_leakage_on_biased_data_biased_pred_default_method_dpa_TtoA(se pred = get_test_data()["M2"] num_trials = 3 mock_values = [torch.tensor(0.4), torch.tensor(0.57), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat, data_train=data, @@ -2296,7 +2296,7 @@ def test_amortized_leakage_on_biased_data_biased_pred_method_median(self): num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.57), torch.tensor(0.6)] - with patch("PredMetrics_v1.Leakage.calcLeak", side_effect=mock_values): + with patch("PredMetrics.Leakage.calcLeak", side_effect=mock_values): amortized_leakage = leakage.getAmortizedLeakage( feat_train=feat, data_train=data, @@ -2316,7 +2316,7 @@ def test_amortized_leakage_on_biased_data_biased_pred_method_median_dpa_AtoT(sel num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.54), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat, data_train=data, @@ -2337,7 +2337,7 @@ def test_amortized_leakage_on_biased_data_biased_pred_method_median_dpa_TtoA(sel num_trials = 3 method = "median" mock_values = [torch.tensor(0.4), torch.tensor(0.57), torch.tensor(0.6)] - with patch("PredMetrics_v1.DPA.calcLeak", side_effect=mock_values): + with patch("PredMetrics.DPA.calcLeak", side_effect=mock_values): amortized_leakage = dpa_1.getAmortizedLeakage( feat_train=feat, data_train=data,