Second set of test fix

Author: sarakodeiri

examples/ept_attack/run_ept_attack.py

@@ -21,6 +21,7 @@
 from midst_toolkit.attacks.ensemble.data_utils import load_dataframe, save_dataframe
 from midst_toolkit.attacks.ept.classification import train_attack_classifier
 from midst_toolkit.attacks.ept.feature_extraction import extract_features
+from midst_toolkit.common.random import set_all_random_seeds
 from midst_toolkit.common.logger import log
 
 
@@ -255,6 +256,10 @@ def main(config: DictConfig) -> None:
     """
     log(INFO, "Running EPT-MIA Attack Example Pipeline.")
 
+    if config.random_seed is not None:
+        set_all_random_seeds(seed=config.random_seed)
+        log(INFO, f"Training phase random seed set to {config.random_seed}.")
+
     if config.attack_settings.single_table:
         log(INFO, "Data: Single-table.")
     else:

tests/unit/evaluation/quality/test_mean_f1_score_difference.py

@@ -50,7 +50,7 @@ def test_mean_f1_score_diff_with_preprocess() -> None:
         assert pytest.approx(-0.06789999999999999, abs=1e-8) == score["mean_f1_difference"]
     else:
         assert pytest.approx(0.7656, abs=1e-8) == score["random_forest_real_train_f1"]
-        assert pytest.approx(-0.06829999999999997, abs=1e-8) == score["mean_f1_difference"]
+        assert pytest.approx(-0.06759999999999997, abs=1e-8) == score["mean_f1_difference"]
     assert pytest.approx(0.5008, abs=1e-8) == score["random_forest_synthetic_train_f1"]
     assert pytest.approx(0.5, abs=1e-8) == score["adaboost_real_train_f1"]
     assert pytest.approx(0.49879999999999997, abs=1e-8) == score["adaboost_synthetic_train_f1"]
@@ -78,7 +78,7 @@ def test_mean_f1_score_diff_with_no_categorical() -> None:
     if is_apple_silicon():
         assert pytest.approx(-0.0792, abs=1e-8) == score["mean_f1_difference"]
     else:
-        assert pytest.approx(-0.0793, abs=1e-8) == score["mean_f1_difference"]
+        assert pytest.approx(-0.0794, abs=1e-8) == score["mean_f1_difference"]
     unset_all_random_seeds()
 
 
@@ -103,7 +103,7 @@ def test_mean_f1_score_diff_with_holdout_difference_f1() -> None:
         assert pytest.approx(-0.17912194553312424, abs=1e-8) == score["mean_f1_difference"]
     else:
         assert pytest.approx(0.7655658771879633, abs=1e-8) == score["random_forest_real_train_f1"]
-        assert pytest.approx(-0.1795346169714074, abs=1e-8) == score["mean_f1_difference"]
+        assert pytest.approx(-0.17883409886106752, abs=1e-8) == score["mean_f1_difference"]
     assert pytest.approx(0.40831722022666145, abs=1e-8) == score["random_forest_synthetic_train_f1"]
     assert pytest.approx(0.3632940727026944, abs=1e-8) == score["adaboost_real_train_f1"]
     assert pytest.approx(0.33490261584802905, abs=1e-8) == score["adaboost_synthetic_train_f1"]