From 27fa9c8f1eac82301736fa87af6c575044c47eb2 Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Wed, 12 Nov 2025 14:57:50 -0500
Subject: [PATCH 01/41] Fixed 2 bugs: shadow synth data size, and var name

---
 examples/ensemble_attack/config.yaml               |  9 ++++++---
 examples/ensemble_attack/run_attack.py             |  4 ++--
 .../ensemble_attack/run_shadow_model_training.py   |  4 ++++
 .../attacks/ensemble/rmia/shadow_model_training.py | 14 ++++++++++++++
 .../attacks/ensemble/shadow_model_utils.py         | 13 +++++++------
 .../attacks/ensemble/test_shadow_model_training.py | 11 +++++------
 6 files changed, 38 insertions(+), 17 deletions(-)

diff --git a/examples/ensemble_attack/config.yaml b/examples/ensemble_attack/config.yaml
index d080e614..ba23cfdf 100644
--- a/examples/ensemble_attack/config.yaml
+++ b/examples/ensemble_attack/config.yaml
@@ -43,6 +43,7 @@ data_processing_config:
   challenge_data_file_name: "challenge_with_id.csv"
   population_sample_size: 11956 #Population size the total data that your attack has access to.
   #The size of the master challenge dataset is half of the population size based on the attack design.
+  # Original code: 40000
 
 # Training and data settings for shadow models (temporary, numbers subject to change)
 shadow_training:
@@ -66,9 +67,11 @@ shadow_training:
   final_target_model_path: ${shadow_training.target_model_output_path}/target_model/shadow_workspace/trained_target_model/target_model.pkl
   # Path to final target model (relative to target_model_output_path)
   fine_tuning_config:
-    fine_tune_diffusion_iterations: 2
-    fine_tune_classifier_iterations: 2
-    pre_train_data_size: 10 #10 for test run. Original code: 60000
+    fine_tune_diffusion_iterations: 2 # Original code: 200000
+    fine_tune_classifier_iterations: 2 #Original code: 20000
+    pre_train_data_size: 600 #10 for test run. Original code: 60000
+  synthetic_data_size: 200 # Number of synthetic data samples to be generated by shadow models.
+  # 200 for test run. Original code: 20000
 
 
 # Metaclassifier settings
diff --git a/examples/ensemble_attack/run_attack.py b/examples/ensemble_attack/run_attack.py
index e206f5fc..1a7eddee 100644
--- a/examples/ensemble_attack/run_attack.py
+++ b/examples/ensemble_attack/run_attack.py
@@ -59,8 +59,8 @@ def main(config: DictConfig) -> None:
     # TODO: Investigate the source of error.
     if config.pipeline.run_shadow_model_training:
         shadow_pipeline = importlib.import_module("examples.ensemble_attack.run_shadow_model_training")
-        attack_data_paths = shadow_pipeline.run_shadow_model_training(config)
-        attack_data_paths = [Path(path) for path in attack_data_paths]
+        shadow_data_paths = shadow_pipeline.run_shadow_model_training(config)
+        shadow_data_paths = [Path(path) for path in shadow_data_paths]
 
         target_data_path = shadow_pipeline.run_target_model_training(config)
         target_data_path = Path(target_data_path)
diff --git a/examples/ensemble_attack/run_shadow_model_training.py b/examples/ensemble_attack/run_shadow_model_training.py
index 66659373..81e7b85e 100644
--- a/examples/ensemble_attack/run_shadow_model_training.py
+++ b/examples/ensemble_attack/run_shadow_model_training.py
@@ -67,6 +67,7 @@ def run_target_model_training(config: DictConfig) -> Path:
         configs=configs,
         save_dir=save_dir,
         synthesize=True,
+        synthetic_data_size=config.shadow_training.synthetic_data_size,
     )
 
     # TODO: Check: Selected_id_lists should be of form [[]]
@@ -84,6 +85,8 @@ def run_target_model_training(config: DictConfig) -> Path:
     with open(result_path, "wb") as file:
         pickle.dump(attack_data, file)
 
+    log(INFO, f"Target model training finished and saved at {result_path}")
+
     return result_path
 
 
@@ -133,6 +136,7 @@ def run_shadow_model_training(config: DictConfig) -> list[Path]:
         # ``4 * n_models_per_set`` total shadow models.
         n_models_per_set=4,  # 4 based on the original code, must be even
         n_reps=12,  # Number of repetitions of challenge points in each shadow model training set. `12` based on the original code
+        synthetic_data_size=config.shadow_training.synthetic_data_size,
         random_seed=config.random_seed,
     )
     log(
diff --git a/src/midst_toolkit/attacks/ensemble/rmia/shadow_model_training.py b/src/midst_toolkit/attacks/ensemble/rmia/shadow_model_training.py
index ea2e1e8a..b06312b7 100644
--- a/src/midst_toolkit/attacks/ensemble/rmia/shadow_model_training.py
+++ b/src/midst_toolkit/attacks/ensemble/rmia/shadow_model_training.py
@@ -29,6 +29,7 @@ def train_fine_tuned_shadow_models(
     table_name: str,
     id_column_name: str,
     pre_training_data_size: int = 60000,
+    synthetic_data_size: int = 20000,
     init_data_seed: int | None = None,
     random_seed: int | None = None,
 ) -> Path:
@@ -71,6 +72,7 @@ def train_fine_tuned_shadow_models(
             table_name: Name of the main table to be used for training the TabDDPM model.
             id_column_name: Name of the ID column in the data.
             pre_training_data_size: Size of the initial training set, defaults to 60,000.
+            synthetic_data_size: Size of the synthetic data to be generated by each shadow model, defaults to 20,000.
             init_data_seed: Random seed for the initial training set.
             random_seed: Random seed used for reproducibility, defaults to None.
 
@@ -171,8 +173,10 @@ def train_fine_tuned_shadow_models(
             fine_tuning_diffusion_iterations=fine_tuning_config.fine_tune_diffusion_iterations,
             fine_tuning_classifier_iterations=fine_tuning_config.fine_tune_classifier_iterations,
             synthesize=True,
+            synthetic_data_size=synthetic_data_size,
         )
         assert train_result.synthetic_data is not None, "Fine-tuned models should generate synthetic data."
+        log(INFO, f"Fine-tuned model {model_id} generated {len(train_result.synthetic_data)} synthetic samples.")
         attack_data["fine_tuned_results"].append(train_result)
 
     # Pickle dump the results
@@ -191,6 +195,7 @@ def train_shadow_on_half_challenge_data(
     training_json_config_paths: DictConfig,
     table_name: str,
     id_column_name: str,
+    synthetic_data_size: int = 20000,
     random_seed: int | None = None,
 ) -> Path:
     """
@@ -214,6 +219,7 @@ def train_shadow_on_half_challenge_data(
                 - tabddpm_training_config_path (str): Path to table's training config json file.
             table_name: Name of the main table to be used for training the TabDDPM model.
             id_column_name: Name of the ID column in the data.
+            synthetic_data_size: Size of the synthetic data to be generated by each shadow model, defaults to 20,000.
             random_seed: Random seed used for reproducibility, defaults to None.
 
     Returns:
@@ -273,7 +279,10 @@ def train_shadow_on_half_challenge_data(
             configs,
             save_dir,
             synthesize=True,
+            synthetic_data_size=synthetic_data_size,
         )
+        assert train_result.synthetic_data is not None, "Trained shadow model did not generate synthetic data."
+        log(INFO, f"Trained shadow model {model_id} generated {len(train_result.synthetic_data)} synthetic samples.")
 
         attack_data["trained_results"].append(train_result)
 
@@ -295,6 +304,7 @@ def train_three_sets_of_shadow_models(
     id_column_name: str,
     n_models_per_set: int = 4,
     n_reps: int = 12,
+    synthetic_data_size: int = 20000,
     random_seed: int | None = None,
 ) -> tuple[Path, Path, Path]:
     """
@@ -342,6 +352,7 @@ def train_three_sets_of_shadow_models(
         id_column_name: Name of the ID column in the data.
         n_models_per_set: Number of shadow models to train by each approach. Must be an even number. Defaults to 4.
         n_reps: Number of repetitions for each challenge point in the fine-tuning or training sets, defaults to 12.
+        synthetic_data_size: Size of the synthetic data to be generated by each shadow model, defaults to 20,000.
         random_seed: Random seed used for reproducibility, defaults to None.
 
     Returns:
@@ -365,6 +376,7 @@ def train_three_sets_of_shadow_models(
         table_name=table_name,
         id_column_name=id_column_name,
         pre_training_data_size=fine_tuning_config.pre_train_data_size,
+        synthetic_data_size=synthetic_data_size,
         init_data_seed=random_seed,
         random_seed=random_seed,
     )
@@ -387,6 +399,7 @@ def train_three_sets_of_shadow_models(
         table_name=table_name,
         id_column_name=id_column_name,
         pre_training_data_size=fine_tuning_config.pre_train_data_size,
+        synthetic_data_size=synthetic_data_size,
         # Setting a different seed for the second train set
         init_data_seed=random_seed + 1 if random_seed is not None else None,
         random_seed=random_seed,
@@ -405,6 +418,7 @@ def train_three_sets_of_shadow_models(
         training_json_config_paths=training_json_config_paths,
         table_name=table_name,
         id_column_name=id_column_name,
+        synthetic_data_size=synthetic_data_size,
         random_seed=random_seed,
     )
     log(
diff --git a/src/midst_toolkit/attacks/ensemble/shadow_model_utils.py b/src/midst_toolkit/attacks/ensemble/shadow_model_utils.py
index e8604e22..aa7e0e4b 100644
--- a/src/midst_toolkit/attacks/ensemble/shadow_model_utils.py
+++ b/src/midst_toolkit/attacks/ensemble/shadow_model_utils.py
@@ -82,7 +82,7 @@ def train_tabddpm_and_synthesize(
     configs: TrainingConfig,
     save_dir: Path,
     synthesize: bool = True,
-    sample_scale: float = 1.0,
+    synthetic_data_size: int = 20000,
 ) -> TrainingResult:
     """
     Train a TabDDPM model on the provided training set and optionally synthesize data using the trained models.
@@ -92,8 +92,7 @@ def train_tabddpm_and_synthesize(
         configs: Configuration dictionary for TabDDPM.
         save_dir: Directory path where models and results will be saved.
         synthesize: Flag indicating whether to generate synthetic data after training. Defaults to True.
-        sample_scale: Factor to scale the number of synthesized samples relative to the training set size.
-            Defaults to 1.0.
+        synthetic_data_size: Number of synthetic data samples to be generated. Defaults to 20000.
 
     Returns:
         A dataclass TrainingResult object containing:
@@ -138,6 +137,7 @@ def train_tabddpm_and_synthesize(
         # of the training data size.
         # Sample scale is later multiplied by the size of training data (no id) to determine
         # the size of synthetic data.
+        sample_scale = synthetic_data_size / len(tables["trans"].data)
         cleaned_tables, _, _ = clava_synthesizing(
             tables,
             relation_order,
@@ -163,7 +163,7 @@ def fine_tune_tabddpm_and_synthesize(
     fine_tuning_diffusion_iterations: int = 100,
     fine_tuning_classifier_iterations: int = 10,
     synthesize: bool = True,
-    sample_scale: float = 1.0,
+    synthetic_data_size: int = 20000,
 ) -> TrainingResult:
     """
     Given the trained models and a new training set, fine-tune the TabDDPM models.
@@ -179,8 +179,8 @@ def fine_tune_tabddpm_and_synthesize(
         fine_tuning_classifier_iterations: Number of training iterations for the new classifier model.
             Defaults to 10.
         synthesize: Flag indicating whether to generate synthetic data after training. Defaults to True.
-        sample_scale: Factor to scale the number of synthesized samples relative to the training set size.
-            Defaults to 1.0.
+        synthetic_data_size: Number of synthetic data samples to be generated. Defaults to 20000.
+
 
     Returns:
         A dataclass TrainingResult object containing:
@@ -228,6 +228,7 @@ def fine_tune_tabddpm_and_synthesize(
         # Ensemble Attack's default sample_scale is ``20000 / len(tables["trans"]["df"])`` to generate 20,000 samples
         # regardless of the train data size.
         # Sample scale is later multiplied by the size of training data to determine the size of synthetic data.
+        sample_scale = synthetic_data_size / len(new_tables["trans"].data)
         cleaned_tables, _, _ = clava_synthesizing(
             new_tables,
             relation_order,
diff --git a/tests/integration/attacks/ensemble/test_shadow_model_training.py b/tests/integration/attacks/ensemble/test_shadow_model_training.py
index bbe16b92..1ccc5e94 100644
--- a/tests/integration/attacks/ensemble/test_shadow_model_training.py
+++ b/tests/integration/attacks/ensemble/test_shadow_model_training.py
@@ -55,6 +55,7 @@ def test_train_fine_tuned_shadow_models(cfg: DictConfig, tmp_path: Path) -> None
         table_name="trans",
         id_column_name="trans_id",
         pre_training_data_size=cfg.shadow_training.fine_tuning_config.pre_train_data_size,
+        synthetic_data_size=5,
         random_seed=cfg.random_seed,
     )
     # Expected saved models and synthesized data:
@@ -75,6 +76,7 @@ def test_train_fine_tuned_shadow_models(cfg: DictConfig, tmp_path: Path) -> None
         assert result.relation_order is not None
         assert result.all_group_lengths_probabilities is not None
         assert type(result.synthetic_data) is pd.DataFrame
+        assert len(result.synthetic_data) == 5
 
     # Fine tuning sets should be disjoint
     assert set(shadow_data["fine_tuning_sets"][0]).isdisjoint(set(shadow_data["fine_tuning_sets"][1]))
@@ -99,6 +101,7 @@ def test_train_shadow_on_half_challenge_data(cfg: DictConfig, tmp_path: Path) ->
         training_json_config_paths=cfg.shadow_training.training_json_config_paths,
         table_name="trans",
         id_column_name="trans_id",
+        synthetic_data_size=5,
         random_seed=cfg.random_seed,
     )
     # Expected saved models and synthesized data:
@@ -119,6 +122,7 @@ def test_train_shadow_on_half_challenge_data(cfg: DictConfig, tmp_path: Path) ->
         assert result.relation_order is not None
         assert result.all_group_lengths_probabilities is not None
         assert type(result.synthetic_data) is pd.DataFrame
+        assert len(result.synthetic_data) == 5
 
     # Training sets should be disjoint
     assert set(shadow_data["selected_sets"][0]).isdisjoint(set(shadow_data["selected_sets"][1]))
@@ -155,12 +159,7 @@ def test_train_and_fine_tune_tabddpm(cfg: DictConfig, tmp_path: Path) -> None:
         workspace_name="test_workspace",
     )
 
-    train_result = train_tabddpm_and_synthesize(
-        train_set,
-        configs,
-        save_dir,
-        synthesize=True,
-    )
+    train_result = train_tabddpm_and_synthesize(train_set, configs, save_dir, synthesize=True, synthetic_data_size=99)
     # By default, with a sampling scale of 1, the size of the synthesized data is equal
     # to the size of the training data.
     assert train_result.synthetic_data is not None

From f64b6501d332ce3bb2ee6b1623b2255aeff67042 Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Mon, 17 Nov 2025 21:21:18 -0500
Subject: [PATCH 02/41] =?UTF-8?q?Remove=20dependency=20on=20target?=
 =?UTF-8?q?=E2=80=99s=20training=20result=20object;=20attack=20now=20only?=
 =?UTF-8?q?=20uses=20target=20synthetic=20data.?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../ensemble_attack/data_configs/trans.json   | 42 ++++++++------
 examples/ensemble_attack/run_attack.py        | 18 +++---
 .../run_metaclassifier_training.py            | 56 +++++++++----------
 .../run_shadow_model_training.py              | 29 ++++------
 .../attacks/ensemble/blending.py              | 25 ++++-----
 .../{train_utils.py => metric_utils.py}       |  7 ++-
 .../attacks/ensemble/rmia/rmia_calculation.py | 34 +++++------
 .../attacks/ensemble/xgboost_tuner.py         |  2 +-
 8 files changed, 108 insertions(+), 105 deletions(-)
 rename src/midst_toolkit/attacks/ensemble/{train_utils.py => metric_utils.py} (69%)

diff --git a/examples/ensemble_attack/data_configs/trans.json b/examples/ensemble_attack/data_configs/trans.json
index 8727dc02..84820bd4 100644
--- a/examples/ensemble_attack/data_configs/trans.json
+++ b/examples/ensemble_attack/data_configs/trans.json
@@ -8,43 +8,51 @@
     },
     "clustering": {
         "parent_scale": 1.0,
-        "num_clusters": 4,
+        "num_clusters": 50,
         "clustering_method": "kmeans_and_gmm"
     },
     "diffusion": {
         "d_layers": [
-            4,
-            8
+            512,
+            1024,
+            1024,
+            1024,
+            1024,
+            512
         ],
-        "dropout": 0.1,
-        "num_timesteps": 3,
+        "dropout": 0.0,
+        "num_timesteps": 2000,
         "model_type": "mlp",
-        "iterations": 3,
-        "batch_size": 1,
+        "iterations": 200000,
+        "batch_size": 4096,
         "lr": 0.0006,
         "gaussian_loss_type": "mse",
         "weight_decay": 1e-05,
-        "scheduler": "cosine",
-        "data_split_ratios": [0.5, 0.25, 0.25]
+        "scheduler": "cosine"
     },
     "classifier": {
         "d_layers": [
-            4,
-            4
+            128,
+            256,
+            512,
+            1024,
+            512,
+            256,
+            128
         ],
         "lr": 0.0001,
-        "dim_t": 4,
-        "batch_size": 1,
-        "iterations": 2
+        "dim_t": 128,
+        "batch_size": 4096,
+        "iterations": 20000
     },
     "sampling": {
-        "batch_size": 2,
+        "batch_size": 20000,
         "classifier_scale": 1.0
     },
     "matching": {
         "num_matching_clusters": 1,
-        "matching_batch_size": 1,
+        "matching_batch_size": 1000,
         "unique_matching": true,
         "no_matching": false
     }
-}
+}
\ No newline at end of file
diff --git a/examples/ensemble_attack/run_attack.py b/examples/ensemble_attack/run_attack.py
index 1a7eddee..d720fef4 100644
--- a/examples/ensemble_attack/run_attack.py
+++ b/examples/ensemble_attack/run_attack.py
@@ -13,7 +13,7 @@
 from examples.ensemble_attack.real_data_collection import collect_population_data_ensemble
 from midst_toolkit.attacks.ensemble.process_split_data import process_split_data
 from midst_toolkit.common.logger import log
-
+from midst_toolkit.common.random import set_all_random_seeds
 
 def run_data_processing(config: DictConfig) -> None:
     """
@@ -28,6 +28,8 @@ def run_data_processing(config: DictConfig) -> None:
         midst_data_input_dir=Path(config.data_paths.midst_data_path),
         data_processing_config=config.data_processing_config,
         save_dir=Path(config.data_paths.population_path),
+        population_splits=config.data_processing_config.population_splits,
+        challenge_splits=config.data_processing_config.challenge_splits,
     )
     # The following function saves the required dataframe splits in the specified processed_attack_data_path path.
     process_split_data(
@@ -41,7 +43,7 @@ def run_data_processing(config: DictConfig) -> None:
     log(INFO, "Data processing pipeline finished.")
 
 
-@hydra.main(config_path=".", config_name="config", version_base=None)
+@hydra.main(config_path="configs", config_name="experiment_config", version_base=None)
 def main(config: DictConfig) -> None:
     """
     Run the Ensemble Attack example pipeline.
@@ -52,6 +54,7 @@ def main(config: DictConfig) -> None:
     Args:
         config: Attack configuration as an OmegaConf DictConfig object.
     """
+    set_all_random_seeds(seed=config.random_seed)
     if config.pipeline.run_data_processing:
         run_data_processing(config)
     # Note: Importing the following two modules causes a segmentation fault error if imported together in this file.
@@ -62,22 +65,19 @@ def main(config: DictConfig) -> None:
         shadow_data_paths = shadow_pipeline.run_shadow_model_training(config)
         shadow_data_paths = [Path(path) for path in shadow_data_paths]
 
-        target_data_path = shadow_pipeline.run_target_model_training(config)
-        target_data_path = Path(target_data_path)
+        target_model_synthetic_path = shadow_pipeline.run_target_model_training(config)
 
     if config.pipeline.run_metaclassifier_training:
         if not config.pipeline.run_shadow_model_training:
             # If shadow model training is skipped, we need to provide the previous shadow model and target model paths.
-
             shadow_data_paths = [Path(path) for path in config.shadow_training.final_shadow_models_path]
-
-            target_data_path = Path(config.shadow_training.final_target_model_path)
+            target_model_synthetic_path = Path(config.shadow_training.target_synthetic_data_path)
 
         assert len(shadow_data_paths) == 3, "The attack_data_paths list must contain exactly three elements."
-        assert target_data_path is not None, "The target_data_path must be provided for metaclassifier training."
+        assert target_model_synthetic_path is not None, "The target_data_path must be provided for metaclassifier training."
 
         meta_pipeline = importlib.import_module("examples.ensemble_attack.run_metaclassifier_training")
-        meta_pipeline.run_metaclassifier_training(config, shadow_data_paths, target_data_path)
+        meta_pipeline.run_metaclassifier_training(config, shadow_data_paths, target_model_synthetic_path)
 
 
 if __name__ == "__main__":
diff --git a/examples/ensemble_attack/run_metaclassifier_training.py b/examples/ensemble_attack/run_metaclassifier_training.py
index 51e48563..79cf7d47 100644
--- a/examples/ensemble_attack/run_metaclassifier_training.py
+++ b/examples/ensemble_attack/run_metaclassifier_training.py
@@ -3,6 +3,7 @@
 from logging import INFO
 from pathlib import Path
 
+import pandas as pd
 import numpy as np
 from omegaconf import DictConfig
 
@@ -14,16 +15,17 @@
 def run_metaclassifier_training(
     config: DictConfig,
     shadow_data_paths: list[Path],
-    target_data_path: Path,
+    target_model_synthetic_path: Path,
 ) -> None:
     """
-    Fuction to run the metaclassifier training and evaluation.
+    Function to run the metaclassifier training and evaluation.
 
     Args:
         config: Configuration object set in config.yaml.
         shadow_data_paths: List of paths to the trained shadow models and all their attributes and synthetic data.
             The list should contain three paths, one for each set of shadow models.
-        target_data_path: Path to the target model and all its attributes and synthetic data.
+        target_model_synthetic_path: Path to the target model's synthetic data. This is all we need from a target
+            model to train the metaclassifier in the black-box setting.
     """
     log(INFO, "Running metaclassifier training...")
 
@@ -63,15 +65,15 @@ def run_metaclassifier_training(
             shadow_data_and_result = pickle.load(f)
             shadow_data_collection.append(shadow_data_and_result)
 
-    assert target_data_path.exists(), (
-        f"No file found at {target_data_path}. Make sure the path is correct and that you have trained the target model."
+    assert Path(target_model_synthetic_path).exists(), (
+        f"No file found at {target_model_synthetic_path}. "
+        f"Make sure the path is correct and that you have access to target model's synthetic data."
     )
 
-    with open(target_data_path, "rb") as f:
-        target_data_and_result = pickle.load(f)
+    # Load the target model's synthetic data
+    target_synthetic = pd.read_csv(target_model_synthetic_path)
 
-    target_synthetic = target_data_and_result["trained_results"][0].synthetic_data
-    assert target_synthetic is not None, "Target model pickle missing synthetic_data."
+    assert target_synthetic is not None, "Target model's synthetic data is missing."
     target_synthetic = target_synthetic.copy()
 
     df_reference = load_dataframe(
@@ -96,7 +98,7 @@ def run_metaclassifier_training(
     blending_attacker = BlendingPlusPlus(
         config=config,
         shadow_data_collection=shadow_data_collection,
-        target_data=target_data_and_result,
+        data_types_file_path = Path(config.metaclassifier.data_types_file_path),
         meta_classifier_type=meta_classifier_enum,
         random_seed=config.random_seed,
     )
@@ -104,7 +106,6 @@ def run_metaclassifier_training(
     log(INFO, f"{meta_classifier_enum} created with random seed {config.random_seed}.")
 
     # 2. Train the attacker on the meta-train set
-
     blending_attacker.fit(
         df_train=df_meta_train,
         y_train=y_meta_train,
@@ -115,38 +116,33 @@ def run_metaclassifier_training(
         epochs=config.metaclassifier.epochs,
     )
 
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    model_filename = f"{timestamp}_{config.metaclassifier.model_type}_trained_metaclassifier.pkl"
-    with open(Path(config.model_paths.metaclassifier_model_path) / model_filename, "wb") as f:
+    model_filename = config.metaclassifier.meta_classifier_model_name
+    model_path = Path(config.model_paths.metaclassifier_model_path) / model_filename
+    model_path.parent.mkdir(parents=True, exist_ok=True)
+    with open(model_path, "wb") as f:
         pickle.dump(blending_attacker.trained_model, f)
 
     log(INFO, "Metaclassifier model saved, starting evaluation...")
 
-    # Get the synthetic data provided by the challenge for evaluation
-    # TODO: Check if the file is the correct one.
-    df_synthetic_original = load_dataframe(
-        Path(config.data_paths.processed_attack_data_path),
-        "synth.csv",
-    )
-
-    # 3. Get predictions on the test set
+    # 3. Get predictions on the meta test set (evaluation of the trained metaclassifier)
+    # For evaluation, we test the meta classifier on the meta test set provided the target's synthetic data.
     probabilities, pred_score = blending_attacker.predict(
         df_test=df_meta_test,
-        df_original_synthetic=df_synthetic_original,
+        df_original_synthetic=target_synthetic, # For evaluation only, replace with actual target model during testing.
         df_reference=df_reference,
         id_column_data=test_trans_ids,
         y_test=y_meta_test,
     )
-
-    # Save the prediction probabilities
-    attack_results_path = Path(config.data_paths.attack_results_path)
-    attack_results_path.mkdir(parents=True, exist_ok=True)
+    
+    # Save the evaluation prediction probabilities
+    attack_evaluation_result_path = Path(config.data_paths.attack_evaluation_result_path)
+    attack_evaluation_result_path.mkdir(parents=True, exist_ok=True)
     np.save(
-        Path(config.data_paths.attack_results_path)
-        / f"{timestamp}_{config.metaclassifier.model_type}_test_pred_proba.npy",
+        attack_evaluation_result_path
+        / f"{config.metaclassifier.model_type}_val_pred_proba.npy",
         probabilities,
     )
-    log(INFO, "Test set prediction probabilities saved.")
+    log(INFO, "Evaluation prediction probabilities saved.")
 
     if pred_score is not None:
         log(INFO, f"TPR at FPR=0.1: {pred_score:.4f}")
diff --git a/examples/ensemble_attack/run_shadow_model_training.py b/examples/ensemble_attack/run_shadow_model_training.py
index 81e7b85e..a117c189 100644
--- a/examples/ensemble_attack/run_shadow_model_training.py
+++ b/examples/ensemble_attack/run_shadow_model_training.py
@@ -16,7 +16,6 @@
 )
 from midst_toolkit.common.logger import log
 
-
 def run_target_model_training(config: DictConfig) -> Path:
     """
     Function to run the target model training for RMIA attack.
@@ -25,7 +24,7 @@ def run_target_model_training(config: DictConfig) -> Path:
         config: Configuration object set in config.yaml.
 
     Returns:
-        Path to the saved target model results.
+        Path to the saved target model's synthetic.
     """
     log(INFO, "Running target model training...")
 
@@ -70,24 +69,18 @@ def run_target_model_training(config: DictConfig) -> Path:
         synthetic_data_size=config.shadow_training.synthetic_data_size,
     )
 
-    # TODO: Check: Selected_id_lists should be of form [[]]
-    selected_id_lists = [df_real_data[id_column_name].tolist()]
-
-    attack_data: dict[str, Any] = {
-        "selected_sets": selected_id_lists,
-        "trained_results": [],
-    }
-
-    attack_data["trained_results"].append(train_result)
-
-    # Pickle dump the results
-    result_path = Path(save_dir, "target_model.pkl")
-    with open(result_path, "wb") as file:
-        pickle.dump(attack_data, file)
+    # To train the attack model (metaclassifier), we only need to save target's synthetic data,
+    # and not the entire target model's training result object.
+    assert train_result.synthetic_data is not None,(
+         "Target model synthetic data is not generated successfully."
+    )
+    target_synthetic_data = train_result.synthetic_data
 
-    log(INFO, f"Target model training finished and saved at {result_path}")
+    # Save the target model's synthetic data
+    target_model_synthetic_path = config.shadow_training.target_synthetic_data_path
+    target_synthetic_data.to_csv(target_model_synthetic_path, index=False)
 
-    return result_path
+    return target_model_synthetic_path
 
 
 def run_shadow_model_training(config: DictConfig) -> list[Path]:
diff --git a/src/midst_toolkit/attacks/ensemble/blending.py b/src/midst_toolkit/attacks/ensemble/blending.py
index 83c31804..f1e47381 100644
--- a/src/midst_toolkit/attacks/ensemble/blending.py
+++ b/src/midst_toolkit/attacks/ensemble/blending.py
@@ -4,6 +4,7 @@
 from enum import Enum
 from logging import INFO
 from typing import Any
+from pathlib import Path
 
 import numpy as np
 import pandas as pd
@@ -12,10 +13,10 @@
 
 from midst_toolkit.attacks.ensemble.distance_features import calculate_domias_score, calculate_gower_features
 from midst_toolkit.attacks.ensemble.rmia.rmia_calculation import calculate_rmia_signals
-from midst_toolkit.attacks.ensemble.train_utils import get_tpr_at_fpr
+from midst_toolkit.attacks.ensemble.metric_utils import get_tpr_at_fpr
 from midst_toolkit.attacks.ensemble.xgboost_tuner import XgBoostHyperparameterTuner
 from midst_toolkit.common.logger import log
-
+from sklearn.pipeline import Pipeline
 
 class MetaClassifierType(Enum):
     LR = "lr"
@@ -27,7 +28,7 @@ def __init__(
         self,
         config: DictConfig,
         shadow_data_collection: list[dict[str, list[Any]]],
-        target_data: dict[str, list[Any]],
+        data_types_file_path: Path,
         meta_classifier_type: MetaClassifierType = MetaClassifierType.XGB,
         random_seed: int | None = None,
     ) -> None:
@@ -50,10 +51,7 @@ def __init__(
                 is a list of type TrainingResult containing model training information and generated synthetic data.
                 For more details, see the documentation of `train_three_sets_of_shadow_models` at
                 attacks/ensemble/rmia/shadow_model_training.py.
-            target_data: Dictionary containing the training data of the target model and its generated synthetic data.
-                The dictionary contains the keys "selected_sets" and "trained_results".
-                Selected_sets is a list of dataframes used to train the target model, and trained_results
-                is a list of type TrainingResult containing model training information and generated synthetic data.
+            target_synthetic_data: Synthetic data generated by the target model.
                 For more details, see the documentation of `train_three_sets_of_shadow_models` at
                 attacks/ensemble/rmia/shadow_model_training.py.
 
@@ -61,12 +59,10 @@ def __init__(
             random_seed: Random seed for reproducibility. Defaults to None.
 
         """
-        # TODO: We can directly pass the `data_types_file_path` as a parameter to this class.
-        with open(config.metaclassifier.data_types_file_path, "r") as f:
+        with open(data_types_file_path, "r") as f:
             self.column_types = json.load(f)
 
         self.shadow_data_collection = shadow_data_collection
-        self.target_data = target_data
         self.meta_classifier_type = meta_classifier_type
         self.trained_model = None
         self.random_seed = random_seed
@@ -88,7 +84,7 @@ def _prepare_meta_features(
 
         Args:
             df_input: Input dataframe (e.g., meta-classifier train or test set).
-            df_synthetic: Synthetic dataframe.
+            df_synthetic: Synthetic dataframe generated by the target model.
             df_reference: Real population dataframe, used as a reference for calculating the DOMIAS score.
             id_column_data: The data in the ID column, used to ensure correct alignment of results.
             categorical_cols: Categorical column names.
@@ -109,7 +105,7 @@ def _prepare_meta_features(
         rmia_signals = calculate_rmia_signals(
             df_input=df_input,
             shadow_data_collection=self.shadow_data_collection,
-            target_data=self.target_data,
+            target_synthetic_data=df_synthetic,
             categorical_column_names=categorical_cols,
             id_column_name=id_column_name,
             id_column_data=id_column_data,
@@ -238,7 +234,10 @@ def predict(
             - Probabilities of membership for the test data.
             - TPR at FPR (if y_test is provided), or None otherwise.
         """
-        assert self.trained_model is not None, "You must call .fit() before .predict()"
+        assert self.trained_model is not None, (
+            "You must call .fit() before .predict() or provide a trained_model, "
+            "or assign the trained model to the BlengingPlusPlus object."
+        )
 
         df_test_features = self._prepare_meta_features(
             df_input=df_test,
diff --git a/src/midst_toolkit/attacks/ensemble/train_utils.py b/src/midst_toolkit/attacks/ensemble/metric_utils.py
similarity index 69%
rename from src/midst_toolkit/attacks/ensemble/train_utils.py
rename to src/midst_toolkit/attacks/ensemble/metric_utils.py
index 3104e26b..4c8935ee 100644
--- a/src/midst_toolkit/attacks/ensemble/train_utils.py
+++ b/src/midst_toolkit/attacks/ensemble/metric_utils.py
@@ -14,7 +14,10 @@ def get_tpr_at_fpr(
 
     Args:
         true_membership: Array of true binary labels (0 or 1).
-        predictions: Array of predicted probabilities or scores.
+        predictions: A list of values in the range [0,1] indicating the confidence
+            that a challenge point is a member. The closer the value to 1, the more
+            confident the predictor is about the hypothesis that the challenge point is
+            a member.
         max_fpr: Maximum False Positive Rate threshold. Defaults to 0.1.
 
     Returns:
@@ -23,3 +26,5 @@ def get_tpr_at_fpr(
     fpr, tpr, _ = roc_curve(true_membership, predictions)
 
     return max(tpr[fpr <= max_fpr])
+
+
diff --git a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
index 7b9cb4f4..0606e341 100644
--- a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
+++ b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
@@ -22,9 +22,8 @@ class Key(Enum):
 
 def get_rmia_gower(
     df_input: pd.DataFrame,
-    model_data: dict[str, list[Any]],
+    model_data: list[pd.DataFrame],
     min_length: int,
-    key: Key,
     categorical_column_names: list[str],
     id_column_name: str,
     random_seed: int | None = None,
@@ -69,8 +68,8 @@ def get_rmia_gower(
 
     df_input[numerical_columns] = df_input[numerical_columns].astype(float)
 
-    for i in range(len(model_data[key.value])):
-        df_synthetic = model_data[key.value][i].synthetic_data.copy()
+    for i in range(len(model_data)):
+        df_synthetic = model_data[i].copy()
 
         # Convert numerical columns to float (otherwise error in the numpy divide)
         df_synthetic[numerical_columns] = df_synthetic[numerical_columns].astype(float)
@@ -117,7 +116,7 @@ def conditional_average(values: np.ndarray, condition_mask: np.ndarray) -> np.nd
 def calculate_rmia_signals(
     df_input: pd.DataFrame,
     shadow_data_collection: list[dict[str, list[Any]]],
-    target_data: dict[str, list[Any]],
+    target_synthetic_data: pd.DataFrame,
     categorical_column_names: list[str],
     id_column_name: str,
     id_column_data: pd.Series,
@@ -225,32 +224,36 @@ def calculate_rmia_signals(
     min_length = min(min(group) for group in all_lengths)
     if not (1 <= k <= min_length):
         raise ValueError(f"k={k} must be within [1, {min_length}]")
-
+    
+    shadow_synthetic_list_0 = [
+        train_result.synthetic_data for train_result in fine_tuned_shadow_data_0[Key.FINE_TUNED_RESULTS.value]
+    ]
     shadow_model_gower_0 = get_rmia_gower(
         df_input=df_input,
-        model_data=fine_tuned_shadow_data_0,
+        model_data=shadow_synthetic_list_0,
         min_length=min_length,
-        key=Key.FINE_TUNED_RESULTS,
         categorical_column_names=categorical_column_names,
         id_column_name=id_column_name,
         random_seed=random_seed,
     )
-
+    shadow_synthetic_list_1 = [
+        train_result.synthetic_data for train_result in fine_tuned_shadow_data_1[Key.FINE_TUNED_RESULTS.value]
+    ]
     shadow_model_gower_1 = get_rmia_gower(
         df_input=df_input,
-        model_data=fine_tuned_shadow_data_1,
+        model_data=shadow_synthetic_list_1,
         min_length=min_length,
-        key=Key.FINE_TUNED_RESULTS,
         categorical_column_names=categorical_column_names,
         id_column_name=id_column_name,
         random_seed=random_seed,
     )
-
+    shadow_synthetic_list_2 = [
+        train_result.synthetic_data for train_result in trained_shadow_data[Key.TRAINED_RESULTS.value]
+    ]
     shadow_model_gower_2 = get_rmia_gower(
         df_input=df_input,
-        model_data=trained_shadow_data,
+        model_data=shadow_synthetic_list_2,
         min_length=min_length,
-        key=Key.TRAINED_RESULTS,
         categorical_column_names=categorical_column_names,
         id_column_name=id_column_name,
         random_seed=random_seed,
@@ -270,9 +273,8 @@ def calculate_rmia_signals(
     # TODO: check key after we have the official target model
     target_model_gower = get_rmia_gower(
         df_input=df_input,
-        model_data=target_data,
+        model_data=[target_synthetic_data],
         min_length=min_length,
-        key=Key.TRAINED_RESULTS,
         categorical_column_names=categorical_column_names,
         id_column_name=id_column_name,
     )
diff --git a/src/midst_toolkit/attacks/ensemble/xgboost_tuner.py b/src/midst_toolkit/attacks/ensemble/xgboost_tuner.py
index 2ea41e02..5ff236bb 100644
--- a/src/midst_toolkit/attacks/ensemble/xgboost_tuner.py
+++ b/src/midst_toolkit/attacks/ensemble/xgboost_tuner.py
@@ -9,7 +9,7 @@
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import StandardScaler
 
-from midst_toolkit.attacks.ensemble.train_utils import get_tpr_at_fpr
+from midst_toolkit.attacks.ensemble.metric_utils import get_tpr_at_fpr
 from midst_toolkit.common.variables import DEVICE
 
 

From 0d4a3b6373cc2434b572085890faa87de5872bb0 Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Mon, 17 Nov 2025 21:23:12 -0500
Subject: [PATCH 03/41] Added test script that works with the trained tabddpm
 models in different experimental setups

---
 .gitignore                                    |  13 +-
 .../configs/experiment_config.yaml            | 107 ++++++++++++++
 .../original_attack_config.yaml}              |  33 +++--
 .../ensemble_attack/real_data_collection.py   |  36 ++++-
 examples/ensemble_attack/run.sh               |  13 --
 examples/ensemble_attack/run_test.sh          |  26 ++++
 examples/ensemble_attack/run_train.sh         |  26 ++++
 examples/ensemble_attack/test_attack_model.py | 139 ++++++++++++++++++
 .../attacks/ensemble/process_split_data.py    |   2 +
 9 files changed, 358 insertions(+), 37 deletions(-)
 create mode 100644 examples/ensemble_attack/configs/experiment_config.yaml
 rename examples/ensemble_attack/{config.yaml => configs/original_attack_config.yaml} (73%)
 delete mode 100755 examples/ensemble_attack/run.sh
 create mode 100644 examples/ensemble_attack/run_test.sh
 create mode 100755 examples/ensemble_attack/run_train.sh
 create mode 100644 examples/ensemble_attack/test_attack_model.py

diff --git a/.gitignore b/.gitignore
index dde72bbc..9a97bfda 100644
--- a/.gitignore
+++ b/.gitignore
@@ -30,14 +30,19 @@ wheels/
 **/workspace/*.bkp
 
 # Data files
-examples/**/data/
+examples/**/*data/
+examples/**/*.csv
+examples/**/*.npy
 
-# Trained metaclassifiers
-examples/ensemble_attack/trained_models/
-examples/ensemble_attack/attack_results/
+# Trained models
+examples/ensemble_attack/**/*.pkl
 
 # hydra output
 outputs/
 
 # mkdocs site
 site/
+
+# Training Logs
+*.err
+*.out
\ No newline at end of file
diff --git a/examples/ensemble_attack/configs/experiment_config.yaml b/examples/ensemble_attack/configs/experiment_config.yaml
new file mode 100644
index 00000000..0288f01b
--- /dev/null
+++ b/examples/ensemble_attack/configs/experiment_config.yaml
@@ -0,0 +1,107 @@
+# Ensemble experiment configuration
+base_experiment_dir: examples/ensemble_attack/tabddpm_20k_experiment_data # Processed data, and experiment artifacts of training will be stored here
+base_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
+
+# Pipeline control
+pipeline:
+  run_data_processing: false # Set this to false if you have already saved the processed data
+  run_shadow_model_training: false # Set this to false if shadow models are already trained and saved
+  run_metaclassifier_training: true
+
+target_model: # This is only used for testing the attack on a real target model.
+  # This is for models trained on 20k data and generating 20k synthetic data
+  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_20k/train/
+  target_model_id: 21
+  target_model_name: tabddpm_${target_model.target_model_id}
+  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/20k/20k.csv 
+  challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
+  challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
+  
+  target_attack_artifact_dir: ${base_experiment_dir}/target_${target_model.target_model_id}_attack_artifacts/
+  attack_probabilities_result_path: ${target_model.target_attack_artifact_dir}/attack_model_${target_model.target_model_id}_proba
+  target_shadow_models_output_path: ${target_model.target_attack_artifact_dir}/tabddpm_${target_model.target_model_id}_shadows_dir
+
+  
+# Data paths
+data_paths:
+  midst_data_path: /projects/midst-experiments/all_tabddpms # Used to collect the data
+  population_path: ${base_experiment_dir}/population_data  # Path where the collected population data will be stored
+  processed_attack_data_path: ${base_experiment_dir}/attack_data # Path where the processed attack real train and evaluation data is stored
+  attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored
+
+model_paths:
+  metaclassifier_model_path: ${base_experiment_dir}/trained_models # Path where the trained metaclassifier model will be saved
+  # Path to final target model's synthetic data (relative to target_model_output_path)
+
+
+# Dataset specific information used for processing in this example
+data_processing_config:
+  population_attack_data_types_to_collect:
+        [
+          "tabddpm_trained_with_20k",
+        ]
+  challenge_attack_data_types_to_collect:
+        [
+          "tabddpm_trained_with_20k",
+        ]
+  population_splits: ["train"]  # Data splits to be collected for population data
+  challenge_splits: ["train"]  # Data splits to be collected for challenge points
+  # The column name in the data to be used for stratified splitting.
+  column_to_stratify: "trans_type"  # Attention: This value is not documented in the original codebase.
+  folder_ranges: #Specify folder ranges for any of the mentioned splits.
+    train: [[1, 20]] # Folders to be used for train data collection in the experiments
+  # File names in MIDST data directories.
+  single_table_train_data_file_name: "train_with_id.csv"
+  multi_table_train_data_file_name: "trans.csv"
+  challenge_data_file_name: "challenge_with_id.csv"
+  population_sample_size: 600 #Population size the total data that your attack has access to.
+  # In experiments. this is sampled out of all the collected training data in case the available data
+  # is more than this number. Note that half of this data is actually used in training, the other half
+  # is used in evaluation. For example, with 40k population size, only 20k is used for training the attack model.
+  # TODO: make sure about this.
+
+# Training and data settings for shadow models (temporary, numbers subject to change)
+shadow_training:
+  # Data Config files path used for training a TabDDPM model
+  training_json_config_paths: # Config json files used for tabddpm training on the trans table
+    table_domain_file_path: ${base_config_dir}/trans_domain.json
+    dataset_meta_file_path: ${base_config_dir}/dataset_meta.json
+    tabddpm_training_config_path: ${base_config_dir}/trans.json
+  # Model training artifacts are saved under shadow_models_data_path/workspace_name/exp_name
+  # Also, training configs for each shadow model are created under shadow_models_data_path.
+  shadow_models_output_path: ${base_experiment_dir}/shadow_models_and_data
+  target_model_output_path: ${base_experiment_dir}/shadow_target_model_and_data
+  final_shadow_models_path: [
+          "${shadow_training.shadow_models_output_path}/initial_model_rmia_1/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
+          "${shadow_training.shadow_models_output_path}/initial_model_rmia_2/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
+          "${shadow_training.shadow_models_output_path}/shadow_model_rmia_third_set/shadow_workspace/trained_model/rmia_shadows_third_set.pkl",
+      ] # Paths to final shadow models used for metaclassifier training (relative to shadow_models_output_path)
+      # These paths are a result of running the shadow model training pipeline, specifically the
+      # train_three_sets_of_shadow_models in shadow_model_training.py
+      # Each .pkl file contains the training data, trained model and training results for all shadow models in a list.
+  target_synthetic_data_path: ${shadow_training.target_model_output_path}/target_synthetic_data.csv
+  # Path to final target model's synthetic data (relative to target_model_output_path)
+  fine_tuning_config:
+    fine_tune_diffusion_iterations: 2 # Original code: 200000
+    fine_tune_classifier_iterations: 2 #Original code: 20000
+    pre_train_data_size: 60 #10 for test run. Original code: 60000
+  synthetic_data_size: 20 # Number of synthetic data samples to be generated by shadow models.
+  # 200 for test run. Original code: 20000
+
+
+# Metaclassifier settings
+metaclassifier:
+  # Data types json file is used for xgboost model training.
+  data_types_file_path: ${base_config_dir}/data_types.json
+  model_type: "xgb"
+  # Model training parameters
+  num_optuna_trials: 2 # Original code: 100
+  num_kfolds: 5
+  use_gpu: false
+  # Temporary. Might remove having an epoch parameter.
+  epochs: 1
+  meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model.pkl
+
+
+# General settings
+random_seed: 42 # Set to null for no seed, or an integer for a fixed seed
diff --git a/examples/ensemble_attack/config.yaml b/examples/ensemble_attack/configs/original_attack_config.yaml
similarity index 73%
rename from examples/ensemble_attack/config.yaml
rename to examples/ensemble_attack/configs/original_attack_config.yaml
index ba23cfdf..6513e711 100644
--- a/examples/ensemble_attack/config.yaml
+++ b/examples/ensemble_attack/configs/original_attack_config.yaml
@@ -8,21 +8,21 @@ data_paths:
   midst_data_path: ${base_data_dir}/midst_data_all_attacks # Used only for reading the data
   population_path: ${base_data_dir}/population_data  # Path where the population data should be stored
   processed_attack_data_path: ${base_data_dir}/attack_data # Path where the processed attack real train and evaluation data is stored
-  attack_results_path: ${base_example_dir}/attack_results # Path where the attack results will be stored
+  attack_evaluation_result_path: ${base_example_dir}/attack_results # Path where the attack results will be stored
 
 model_paths:
   metaclassifier_model_path: ${base_example_dir}/trained_models # Path where the trained metaclassifier model will be saved
 
 # Pipeline control
 pipeline:
-  run_data_processing: false # Set this to false if you have already saved the processed data
-  run_shadow_model_training: false # Set this to false if shadow models are already trained and saved
+  run_data_processing: true # Set this to false if you have already saved the processed data
+  run_shadow_model_training: true # Set this to false if shadow models are already trained and saved
   run_metaclassifier_training: true
 
 
 # Dataset specific information used for processing in this example
 data_processing_config:
-  collect_attack_data_types:
+  population_attack_data_types_to_collect:
         [
           "tabddpm_black_box",
           "tabsyn_black_box",
@@ -31,6 +31,12 @@ data_processing_config:
           "clavaddpm_black_box",
           "clavaddpm_white_box",
         ]
+  challenge_attack_data_types_to_collect:
+        [
+          "tabddpm_black_box",
+        ]
+  population_splits: ["train"]  # Data splits to be collected for population data
+  challenge_splits: ["train"]  # Data splits to be collected for challenge points
   # The column name in the data to be used for stratified splitting.
   column_to_stratify: "trans_type"  # Attention: This value is not documented in the original codebase.
   folder_ranges:
@@ -41,7 +47,7 @@ data_processing_config:
   single_table_train_data_file_name: "train_with_id.csv"
   multi_table_train_data_file_name: "trans.csv"
   challenge_data_file_name: "challenge_with_id.csv"
-  population_sample_size: 11956 #Population size the total data that your attack has access to.
+  population_sample_size: 40000 # Population size the total data that your attack has access to.
   #The size of the master challenge dataset is half of the population size based on the attack design.
   # Original code: 40000
 
@@ -64,14 +70,14 @@ shadow_training:
       # These paths are a result of running the shadow model training pipeline, specifically the
       # train_three_sets_of_shadow_models in shadow_model_training.py
       # Each .pkl file contains the training data, trained model and training results for all shadow models in a list.
-  final_target_model_path: ${shadow_training.target_model_output_path}/target_model/shadow_workspace/trained_target_model/target_model.pkl
-  # Path to final target model (relative to target_model_output_path)
+  target_synthetic_data_path: ${shadow_training.target_model_output_path}/target_synthetic_data.csv
+  # Path to final target model's synthetic data (relative to target_model_output_path)
   fine_tuning_config:
-    fine_tune_diffusion_iterations: 2 # Original code: 200000
-    fine_tune_classifier_iterations: 2 #Original code: 20000
-    pre_train_data_size: 600 #10 for test run. Original code: 60000
-  synthetic_data_size: 200 # Number of synthetic data samples to be generated by shadow models.
-  # 200 for test run. Original code: 20000
+    fine_tune_diffusion_iterations: 200000 # Original code: 200000
+    fine_tune_classifier_iterations: 20000 # Original code: 20000
+    pre_train_data_size: 60000 # Original code: 60000
+  synthetic_data_size: 20000 # Number of synthetic data samples to be generated by shadow models.
+  #  Original code: 20000
 
 
 # Metaclassifier settings
@@ -80,11 +86,12 @@ metaclassifier:
   data_types_file_path: ${base_example_dir}/data_configs/data_types.json
   model_type: "xgb"
   # Model training parameters
-  num_optuna_trials: 10 # Original code: 100
+  num_optuna_trials: 100 # Original code: 100
   num_kfolds: 5
   use_gpu: false
   # Temporary. Might remove having an epoch parameter.
   epochs: 1
+  meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model.pkl
 
 
 # General settings
diff --git a/examples/ensemble_attack/real_data_collection.py b/examples/ensemble_attack/real_data_collection.py
index 0334a5f2..d886a6b3 100644
--- a/examples/ensemble_attack/real_data_collection.py
+++ b/examples/ensemble_attack/real_data_collection.py
@@ -21,6 +21,16 @@ class AttackType(Enum):
     TABSYN_WHITE_BOX = "tabsyn_white_box"
     CLAVADDPM_BLACK_BOX = "clavaddpm_black_box"
     CLAVADDPM_WHITE_BOX = "clavaddpm_white_box"
+    # Experiment attack types based on experiment settings
+    TABDDPM_5K = "tabddpm_trained_with_5k"
+    TABDDPM_10K = "tabddpm_trained_with_10k"
+    TABDDPM_20K = "tabddpm_trained_with_20k"
+    TABDDPM_50K = "tabddpm_trained_with_50k"
+    TABDDPM_100K = "tabddpm_trained_with_100k"
+    
+
+    
+
 
 
 def expand_ranges(ranges: list[tuple[int, int]]) -> list[int]:
@@ -136,6 +146,8 @@ def collect_population_data_ensemble(
     midst_data_input_dir: Path,
     data_processing_config: DictConfig,
     save_dir: Path,
+    population_splits: list[str] = ["train"],
+    challenge_splits: list[str] = ["train", "dev", "final"],
 ) -> pd.DataFrame:
     """
     Collect the population data from the MIDST competition based on Ensemble Attack implementation.
@@ -148,19 +160,28 @@ def collect_population_data_ensemble(
         midst_data_input_dir: The path where the MIDST data folders are stored.
         data_processing_config: Configuration dictionary containing data information and file names.
         save_dir: The path where the collected population data should be saved.
+        population_splits: A list indicating the data splits to be collected for population data.
+            Could be any of train, dev, or final data splits. Default value is based on the original
+            attack implementation.
+        challenge_splits: A list indicating the data splits to be collected for challenge points.
+            Could be any of train, dev, or final data splits. Default value is based on the original
+            attack implementation.
 
     Returns:
         The collected population data as a dataframe.
     """
+    # Population data will be saved under ``save_dir``.
+    save_dir.mkdir(parents=True, exist_ok=True)
+
     # Ensemble Attack collects train data of all the attack types (black box and white box)
-    attack_names = data_processing_config.collect_attack_data_types
+    attack_names = data_processing_config.population_attack_data_types_to_collect
     # Provided attack name are valid based on AttackType enum
-    attack_types: list[AttackType] = [AttackType(attack_name) for attack_name in attack_names]
+    population_attack_types: list[AttackType] = [AttackType(attack_name) for attack_name in attack_names]
 
     df_population = collect_midst_data(
         midst_data_input_dir,
-        attack_types,
-        data_splits=["train"],
+        population_attack_types,
+        data_splits=population_splits,
         dataset="train",
         data_processing_config=data_processing_config,
     )
@@ -170,12 +191,13 @@ def collect_population_data_ensemble(
     save_dataframe(df_population, save_dir, "population_all.csv")
     save_dataframe(df_population_no_id, save_dir, "population_all_no_id.csv")
 
-    # Collect all the challenge points from train, dev and final of "tabddpm_black_box" attack.
-    challenge_attack_types = [AttackType.TABDDPM_BLACK_BOX]
+    # Original Ensemble collects all the challenge points from train, dev and final of "tabddpm_black_box" attack.
+    challenge_attack_names = data_processing_config.challenge_attack_data_types_to_collect
+    challenge_attack_types = [AttackType(attack_name) for attack_name in challenge_attack_names]
     df_challenge = collect_midst_data(
         midst_data_input_dir,
         attack_types=challenge_attack_types,
-        data_splits=["train", "dev", "final"],
+        data_splits=challenge_splits,
         dataset="challenge",
         data_processing_config=data_processing_config,
     )
diff --git a/examples/ensemble_attack/run.sh b/examples/ensemble_attack/run.sh
deleted file mode 100755
index 70379ecb..00000000
--- a/examples/ensemble_attack/run.sh
+++ /dev/null
@@ -1,13 +0,0 @@
-#!/bin/bash
-# This script sets up the environment and runs the ensemble attack example.
-
-source .venv/bin/activate
-
-echo "Active Environment:"
-which python
-
-echo "Experiments Launched"
-
-python -m examples.ensemble_attack.run_attack
-
-echo "Experiments Completed"
diff --git a/examples/ensemble_attack/run_test.sh b/examples/ensemble_attack/run_test.sh
new file mode 100644
index 00000000..4f14f213
--- /dev/null
+++ b/examples/ensemble_attack/run_test.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=2
+#SBATCH --gres=gpu:1
+#SBATCH --mem=32G
+#SBATCH --qos=m
+#SBATCH --job-name=ensemble_attack_test
+#SBATCH --output=%j_%x.out
+#SBATCH --error=%j_%x.err
+#SBATCH --time=5:00:00
+
+
+# This script sets up the environment and runs the ensemble attack example.
+source .venv/bin/activate
+
+echo "Active Environment:"
+which python
+
+echo "Experiments Launched"
+
+python -m examples.ensemble_attack.test_attack_model
+
+echo "Experiments Completed"
diff --git a/examples/ensemble_attack/run_train.sh b/examples/ensemble_attack/run_train.sh
new file mode 100755
index 00000000..3ec6fc11
--- /dev/null
+++ b/examples/ensemble_attack/run_train.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=2
+#SBATCH --gres=gpu:1
+#SBATCH --mem=32G
+#SBATCH --qos=m
+#SBATCH --job-name=ensemble_attack_train
+#SBATCH --output=%j_%x.out
+#SBATCH --error=%j_%x.err
+#SBATCH --time=12:00:00
+
+
+# This script sets up the environment and runs the ensemble attack example.
+source .venv/bin/activate
+
+echo "Active Environment:"
+which python
+
+echo "Experiments Launched"
+
+python -m examples.ensemble_attack.run_attack
+
+echo "Experiments Completed"
diff --git a/examples/ensemble_attack/test_attack_model.py b/examples/ensemble_attack/test_attack_model.py
new file mode 100644
index 00000000..1029f951
--- /dev/null
+++ b/examples/ensemble_attack/test_attack_model.py
@@ -0,0 +1,139 @@
+# This script loads the trained attack model and performs the attack on a set of target models.
+
+import pickle
+from datetime import datetime
+from logging import INFO
+from pathlib import Path
+import hydra
+import pandas as pd
+import numpy as np
+from omegaconf import DictConfig
+
+from midst_toolkit.attacks.ensemble.blending import BlendingPlusPlus, MetaClassifierType
+from midst_toolkit.attacks.ensemble.data_utils import load_dataframe
+from midst_toolkit.common.logger import log
+
+from examples.ensemble_attack.run_shadow_model_training import run_shadow_model_training
+
+
+@hydra.main(config_path="configs", config_name="experiment_config", version_base=None)
+def run_metaclassifier_testing(
+    config: DictConfig,
+) -> None:
+    """
+    Function to run the attack on a target model using a trained metaclassifier.
+    Note that shadow models need to be trained for every new target model's challenge dataset.
+    However, we load the previously trained metaclassifier model and use it for new target models.
+    Unlike the training phase, in the testing phase, we don't need to train a shadow target model
+    since we already have access to the synthetic data of a real target model.
+    All the collected population data that is used for training, is still needed during testing to compute some
+    of the signals.
+
+
+    Args:
+        config: Configuration object set in experiments_config.yaml.
+    """
+    log(INFO, f"Running metaclassifier testing on target model {config.target_model.target_model_id}...")
+
+
+    # 1) Load the trained metaclassifier model to make sure it exists before proceeding.
+    meta_classifier_enum = MetaClassifierType(config.metaclassifier.model_type)
+    
+    model_name = config.metaclassifier.meta_classifier_model_name
+    mataclassifier_path = Path(config.model_paths.metaclassifier_model_path) / model_name
+    assert mataclassifier_path.exists(), f"No metaclassifier model found at {mataclassifier_path}.\
+        Make sure to run the training script first."
+    
+    with open(mataclassifier_path, "rb") as f:
+        trained_mataclassifier_model = pickle.load(f)
+    
+    log(INFO, "Metaclassifier model loaded, starting evaluation...")
+
+    # 2) Read target model challenge data and synthetic data.
+
+    # Back-box attacker has only access to the target model's synthetic data and challenge points.
+    # We also load challenge labels to report the attack performance.
+    challenge_data_path = Path(config.target_model.challenge_data_path)
+    challenge_label_path = Path(config.target_model.challenge_label_path)
+    df_test = pd.read_csv(challenge_data_path)
+    y_test = pd.read_csv(challenge_label_path).to_numpy().squeeze()
+    
+    target_synthetic_path = Path(config.target_model.target_synthetic_data_path)
+    target_synthetic = pd.read_csv(target_synthetic_path)
+
+    # Extract trans_id from the test dataframe
+    assert "trans_id" in df_test.columns, "Test data must have trans_id column"
+    test_trans_ids = df_test["trans_id"]
+    df_test = df_test.drop(columns=["trans_id", "account_id"])
+
+    # 3) Shadow Model Training Step.
+
+    # Three sets of shadow models will be trained as a part of this attack.
+    # Note that for every new target model, shadow models need to be trained.
+    # RMIA signals (for the challenge points) are calculated based on these shadow models,
+    # and will be fed into the metaclassifier.
+    # Make sure to assign a new path for shadow models trained for target's challenge points to
+    # avoid overriding train's shadow models.
+    config.shadow_training.shadow_models_output_path = config.target_model.target_shadow_models_output_path
+    shadow_model_paths = run_shadow_model_training(config)
+    
+    assert len(shadow_model_paths) == 3, (
+        "For testing, meta classifier needs the path to three sets of shadow models."
+    )
+
+    shadow_data_collection = []
+    for model_path in shadow_model_paths:
+        assert model_path.exists(), (
+            f"No file found at {model_path}. Make sure the path is correct, or run shadow model training first."
+        )
+        
+        with open(model_path, "rb") as f:
+            shadow_data_and_result = pickle.load(f)
+            shadow_data_collection.append(shadow_data_and_result)
+
+
+    # 4) Initialize the attacker object, and assign the loaded metaclassifier to it.
+    target_synthetic = target_synthetic.copy()
+
+    df_reference = load_dataframe(
+        Path(config.data_paths.population_path),
+        "population_all_with_challenge_no_id.csv",
+    )
+    
+    blending_attacker = BlendingPlusPlus(
+        config=config,
+        shadow_data_collection=shadow_data_collection,
+        data_types_file_path = Path(config.metaclassifier.data_types_file_path),
+        meta_classifier_type=meta_classifier_enum,
+        random_seed=config.random_seed,
+    )
+
+    # Assign the trained metaclassifier model to the attacker object.
+    blending_attacker.trained_model = trained_mataclassifier_model
+
+
+    # 5) Get predictions on the challenge data (test set).
+    probabilities, pred_score = blending_attacker.predict(
+        df_test=df_test,
+        df_original_synthetic=target_synthetic,
+        df_reference=df_reference,
+        id_column_data=test_trans_ids,
+        y_test=y_test,
+    )
+    
+    # Save the validation prediction probabilities
+    attack_results_path = Path(config.target_model.attack_probabilities_result_path)
+    attack_results_path.mkdir(parents=True, exist_ok=True)
+    np.save(
+        attack_results_path
+        / f"{config.metaclassifier.model_type}_val_pred_proba.npy",
+        probabilities,
+    )
+    log(INFO, "Test prediction probabilities saved.")
+
+    if pred_score is not None:
+        log(INFO, f"TPR at FPR=0.1: {pred_score:.4f}")
+
+
+if __name__ == "__main__":
+    run_metaclassifier_testing()
diff --git a/src/midst_toolkit/attacks/ensemble/process_split_data.py b/src/midst_toolkit/attacks/ensemble/process_split_data.py
index 55d0a33c..bbf0360f 100644
--- a/src/midst_toolkit/attacks/ensemble/process_split_data.py
+++ b/src/midst_toolkit/attacks/ensemble/process_split_data.py
@@ -167,6 +167,8 @@ def process_split_data(
         num_total_samples: The number of samples randomly selected from the population. Defaults to 40,000.
         random_seed: Seed for random number generation to ensure reproducibility. Defaults to None.
     """
+    # First, make sure the output directory exists.
+    processed_attack_data_path.mkdir(parents=True, exist_ok=True)
     # Original Ensemble attack samples 40k data points to construct
     # 1) the main population (real data) used for training the synthetic data generator model,
     # 2) evaluation that is the meta train data (membership classification train dataset) used to train

From 9c1217de03f4383c1b4ae3960635b6ce4155e41f Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Tue, 18 Nov 2025 11:23:40 -0500
Subject: [PATCH 04/41] Updated test

---
 .../attacks/ensemble/rmia/rmia_calculation.py |  6 +-----
 .../attacks/ensemble/test_meta_classifier.py  | 21 +++++++++++--------
 tests/unit/attacks/ensemble/test_rmia.py      | 21 +++++++++----------
 3 files changed, 23 insertions(+), 25 deletions(-)

diff --git a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
index 0606e341..5d605210 100644
--- a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
+++ b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
@@ -34,13 +34,9 @@ def get_rmia_gower(
     Args:
         df_input: The dataframe to generate features for (e.g., meta classifier train or test set), derived
             from the challenge dataset and processed in process_split_data.py.
-        model_data: A dictionary with keys "fine_tuning_sets" and "fine_tuned_results".
-                Fine_tuning_sets is a list of dataframes used to fine-tune the shadow models, and fine_tuned_results
-                is a list of type TrainingResult containing model training information and generated synthetic data.
+        model_data: A list of dataframes, each representing the synthetic data generated by a shadow model.
         min_length: Minimum length across all training data, fine tuning data, and synthetic data sizes. This length
             will be used for downsampling to ensure consistent Gower distance calculations.
-        key: An instance of the Key Enum, either Key.TRAINED_RESULTS or Key.FINE_TUNED_RESULTS,
-            depending on which set of shadow models to use.
         categorical_column_names: A list of categorical column names. We assume that all other columns are numerical.
         id_column_name: Name of the ID column.
         random_seed: Random seed for reproducibility.
diff --git a/tests/unit/attacks/ensemble/test_meta_classifier.py b/tests/unit/attacks/ensemble/test_meta_classifier.py
index ecc16c1a..1ea0e9d5 100644
--- a/tests/unit/attacks/ensemble/test_meta_classifier.py
+++ b/tests/unit/attacks/ensemble/test_meta_classifier.py
@@ -38,6 +38,7 @@ def mock_config_with_json_path():
                 "num_optuna_trials": 100,
                 "num_kfolds": 5,
                 "epochs": 1,
+                "meta_classifier_model_name": "mock_model_name",
             }
         }
     )
@@ -95,7 +96,7 @@ def test_init_success(self, mock_file, mock_config_with_json_path):
         bpp_xgb = BlendingPlusPlus(
             config=mock_config_with_json_path,
             shadow_data_collection=[],
-            target_data=MOCK_TARGET_DATA,
+            data_types_file_path=mock_config_with_json_path.metaclassifier.data_types_file_path,
             meta_classifier_type=MetaClassifierType("xgb"),
         )
 
@@ -111,7 +112,7 @@ def test_init_success(self, mock_file, mock_config_with_json_path):
         bpp_lr = BlendingPlusPlus(
             config=mock_config_with_json_path,
             shadow_data_collection=[],
-            target_data=MOCK_TARGET_DATA,
+            data_types_file_path=mock_config_with_json_path.metaclassifier.data_types_file_path,
             meta_classifier_type=MetaClassifierType("lr"),
         )
         assert bpp_lr.meta_classifier_type == MetaClassifierType.LR
@@ -129,7 +130,7 @@ def test_init_invalid_type_raises_error(self, mock_file, mock_config_with_json_p
             BlendingPlusPlus(
                 config=mock_config_with_json_path,
                 shadow_data_collection=[],
-                target_data=MOCK_TARGET_DATA,
+                data_types_file_path=mock_config_with_json_path.metaclassifier.data_types_file_path,
                 meta_classifier_type=MetaClassifierType("svm"),
             )
 
@@ -151,7 +152,7 @@ def test_prepare_meta_features(
         bpp = BlendingPlusPlus(
             config=mock_config_with_json_path,
             shadow_data_collection=[],
-            target_data=MOCK_TARGET_DATA,
+            data_types_file_path=mock_config_with_json_path.metaclassifier.data_types_file_path,
         )
 
         categorical_cols = MOCK_COLUMN_TYPES_CONTENT["categorical"]
@@ -201,7 +202,7 @@ def test_prepare_meta_features_rmia_calculation(
         bpp = BlendingPlusPlus(
             config=mock_config_with_json_path,
             shadow_data_collection=attack_collection,
-            target_data=MOCK_TARGET_DATA,
+            data_types_file_path=mock_config_with_json_path.metaclassifier.data_types_file_path,
         )
 
         df_train = sample_dataframes["df_train"]
@@ -245,7 +246,7 @@ def test_fit_logistic_regression(
         bpp = BlendingPlusPlus(
             config=mock_config_with_json_path,
             shadow_data_collection=[],
-            target_data=MOCK_TARGET_DATA,
+            data_types_file_path=mock_config_with_json_path.metaclassifier.data_types_file_path,
             meta_classifier_type=MetaClassifierType("lr"),
         )
         bpp.fit(
@@ -279,7 +280,7 @@ def test_fit_xgboost(
         bpp = BlendingPlusPlus(
             config=mock_config_with_json_path,
             shadow_data_collection=[],
-            target_data=MOCK_TARGET_DATA,
+            data_types_file_path=mock_config_with_json_path.metaclassifier.data_types_file_path,
             meta_classifier_type=MetaClassifierType("xgb"),
         )
         bpp.fit(
@@ -305,7 +306,9 @@ def test_predict_raises_error_if_not_fit(self, mock_file, mock_config_with_json_
         mock_file.return_value.read.return_value = json.dumps(MOCK_COLUMN_TYPES_CONTENT)
 
         bpp = BlendingPlusPlus(
-            config=mock_config_with_json_path, shadow_data_collection=[], target_data=MOCK_TARGET_DATA
+            config=mock_config_with_json_path,
+            shadow_data_collection=[],
+            data_types_file_path=mock_config_with_json_path.metaclassifier.data_types_file_path, 
         )
         with pytest.raises(AssertionError):
             bpp.predict(
@@ -333,7 +336,7 @@ def test_predict_flow(
         bpp = BlendingPlusPlus(
             config=mock_config_with_json_path,
             shadow_data_collection=[],
-            target_data=MOCK_TARGET_DATA,
+            data_types_file_path=mock_config_with_json_path.metaclassifier.data_types_file_path,
         )
         bpp.trained_model = mock_classifier
 
diff --git a/tests/unit/attacks/ensemble/test_rmia.py b/tests/unit/attacks/ensemble/test_rmia.py
index b349fb84..fa4ffc7f 100644
--- a/tests/unit/attacks/ensemble/test_rmia.py
+++ b/tests/unit/attacks/ensemble/test_rmia.py
@@ -93,16 +93,13 @@ def rmia_signal_data() -> dict[str, Any]:
         },
     ]
 
-    target_data = {
-        "selected_sets": [pd.DataFrame(np.random.rand(5, 2))],
-        "trained_results": [MockTrainingResult(syn_data_5.copy())],
-    }
+    target_synthetic_data = MockTrainingResult(syn_data_5.copy()).synthetic_data
 
     return {
         "df_input": df_input,
         "id_column_data": id_column_data,
         "shadow_data_collection": shadow_data_collection,
-        "target_data": target_data,
+        "target_synthetic_data": target_synthetic_data,
         "categorical_column_names": ["city"],
         "id_column_name": "id",
         "k": k,
@@ -155,11 +152,13 @@ def test_get_rmia_gower_basic_run(self, base_data, mocker):
         )
 
         min_length = 3
+        shadow_synthetic_list = [
+            train_result.synthetic_data for train_result in base_data["model_data"][Key.TRAINED_RESULTS.value]
+        ]
         results = get_rmia_gower(
             df_input=base_data["df_input"],
-            model_data=base_data["model_data"],
+            model_data=shadow_synthetic_list,
             min_length=min_length,
-            key=Key.TRAINED_RESULTS,
             categorical_column_names=base_data["categorical_column_names"],
             id_column_name=base_data["id_column_name"],
             random_seed=base_data["random_seed"],
@@ -196,11 +195,11 @@ def test_get_rmia_gower_with_sampling(self, base_data, mocker):
         mock_sample = mocker.patch("pandas.DataFrame.sample", wraps=original_syn_data.sample)
 
         min_length = 2
+        synthetic_data_list = [data.synthetic_data for data in base_data["model_data"][Key.TRAINED_RESULTS.value]]
         get_rmia_gower(
             df_input=base_data["df_input"],
-            model_data=base_data["model_data"],
+            model_data=synthetic_data_list,
             min_length=min_length,
-            key=Key.TRAINED_RESULTS,
             categorical_column_names=base_data["categorical_column_names"],
             id_column_name=base_data["id_column_name"],
             random_seed=base_data["random_seed"],
@@ -224,11 +223,11 @@ def test_get_rmia_gower_missing_categorical_column(self, base_data, mocker, capl
         missing_cat_cols = ["city", "non_existent_column"]
 
         with caplog.at_level("INFO"):
+            synthetic_data_list = [data.synthetic_data for data in base_data["model_data"][Key.FINE_TUNED_RESULTS.value]]
             get_rmia_gower(
                 df_input=base_data["df_input"],
-                model_data=base_data["model_data"],
+                model_data=synthetic_data_list,
                 min_length=1,
-                key=Key.FINE_TUNED_RESULTS,
                 categorical_column_names=missing_cat_cols,
                 id_column_name=base_data["id_column_name"],
             )

From 0a1208420332220728a0c48e51a68aad7a21b594 Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Tue, 18 Nov 2025 11:47:08 -0500
Subject: [PATCH 05/41] pre-commit checks

---
 .gitignore                                    |  2 +-
 .../configs/experiment_config.yaml            |  6 +--
 .../ensemble_attack/data_configs/trans.json   |  2 +-
 .../ensemble_attack/real_data_collection.py   | 21 +++++-----
 examples/ensemble_attack/run_attack.py        |  5 ++-
 .../run_metaclassifier_training.py            | 12 +++---
 .../run_shadow_model_training.py              |  8 +---
 examples/ensemble_attack/test_attack_model.py | 39 ++++++++-----------
 .../attacks/ensemble/blending.py              | 11 ++----
 .../attacks/ensemble/metric_utils.py          |  2 -
 .../attacks/ensemble/rmia/rmia_calculation.py |  7 +---
 .../attacks/ensemble/test_meta_classifier.py  |  2 +-
 tests/unit/attacks/ensemble/test_rmia.py      |  4 +-
 13 files changed, 54 insertions(+), 67 deletions(-)

diff --git a/.gitignore b/.gitignore
index 9a97bfda..201b1cb4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -45,4 +45,4 @@ site/
 
 # Training Logs
 *.err
-*.out
\ No newline at end of file
+*.out
diff --git a/examples/ensemble_attack/configs/experiment_config.yaml b/examples/ensemble_attack/configs/experiment_config.yaml
index 0288f01b..530661df 100644
--- a/examples/ensemble_attack/configs/experiment_config.yaml
+++ b/examples/ensemble_attack/configs/experiment_config.yaml
@@ -13,15 +13,15 @@ target_model: # This is only used for testing the attack on a real target model.
   target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_20k/train/
   target_model_id: 21
   target_model_name: tabddpm_${target_model.target_model_id}
-  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/20k/20k.csv 
+  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/20k/20k.csv
   challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
   challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
-  
+
   target_attack_artifact_dir: ${base_experiment_dir}/target_${target_model.target_model_id}_attack_artifacts/
   attack_probabilities_result_path: ${target_model.target_attack_artifact_dir}/attack_model_${target_model.target_model_id}_proba
   target_shadow_models_output_path: ${target_model.target_attack_artifact_dir}/tabddpm_${target_model.target_model_id}_shadows_dir
 
-  
+
 # Data paths
 data_paths:
   midst_data_path: /projects/midst-experiments/all_tabddpms # Used to collect the data
diff --git a/examples/ensemble_attack/data_configs/trans.json b/examples/ensemble_attack/data_configs/trans.json
index 84820bd4..2ce4a8b5 100644
--- a/examples/ensemble_attack/data_configs/trans.json
+++ b/examples/ensemble_attack/data_configs/trans.json
@@ -55,4 +55,4 @@
         "unique_matching": true,
         "no_matching": false
     }
-}
\ No newline at end of file
+}
diff --git a/examples/ensemble_attack/real_data_collection.py b/examples/ensemble_attack/real_data_collection.py
index d886a6b3..4c9cb140 100644
--- a/examples/ensemble_attack/real_data_collection.py
+++ b/examples/ensemble_attack/real_data_collection.py
@@ -27,10 +27,6 @@ class AttackType(Enum):
     TABDDPM_20K = "tabddpm_trained_with_20k"
     TABDDPM_50K = "tabddpm_trained_with_50k"
     TABDDPM_100K = "tabddpm_trained_with_100k"
-    
-
-    
-
 
 
 def expand_ranges(ranges: list[tuple[int, int]]) -> list[int]:
@@ -146,8 +142,8 @@ def collect_population_data_ensemble(
     midst_data_input_dir: Path,
     data_processing_config: DictConfig,
     save_dir: Path,
-    population_splits: list[str] = ["train"],
-    challenge_splits: list[str] = ["train", "dev", "final"],
+    population_splits: list[str] | None = None,
+    challenge_splits: list[str] | None = None,
 ) -> pd.DataFrame:
     """
     Collect the population data from the MIDST competition based on Ensemble Attack implementation.
@@ -161,11 +157,11 @@ def collect_population_data_ensemble(
         data_processing_config: Configuration dictionary containing data information and file names.
         save_dir: The path where the collected population data should be saved.
         population_splits: A list indicating the data splits to be collected for population data.
-            Could be any of train, dev, or final data splits. Default value is based on the original
-            attack implementation.
+            Could be any of train, dev, or final data splits. If None, a default value is set in the function
+            based on the original attack implementation.
         challenge_splits: A list indicating the data splits to be collected for challenge points.
-            Could be any of train, dev, or final data splits. Default value is based on the original
-            attack implementation.
+            Could be any of train, dev, or final data splits. If None, a default value is set in the function
+            based on the original attack implementation.
 
     Returns:
         The collected population data as a dataframe.
@@ -173,6 +169,11 @@ def collect_population_data_ensemble(
     # Population data will be saved under ``save_dir``.
     save_dir.mkdir(parents=True, exist_ok=True)
 
+    if population_splits is None:
+        population_splits = ["train"]
+    if challenge_splits is None:
+        challenge_splits = ["train", "dev", "final"]
+
     # Ensemble Attack collects train data of all the attack types (black box and white box)
     attack_names = data_processing_config.population_attack_data_types_to_collect
     # Provided attack name are valid based on AttackType enum
diff --git a/examples/ensemble_attack/run_attack.py b/examples/ensemble_attack/run_attack.py
index d720fef4..f5e3129c 100644
--- a/examples/ensemble_attack/run_attack.py
+++ b/examples/ensemble_attack/run_attack.py
@@ -15,6 +15,7 @@
 from midst_toolkit.common.logger import log
 from midst_toolkit.common.random import set_all_random_seeds
 
+
 def run_data_processing(config: DictConfig) -> None:
     """
     Function to run the data processing pipeline.
@@ -74,7 +75,9 @@ def main(config: DictConfig) -> None:
             target_model_synthetic_path = Path(config.shadow_training.target_synthetic_data_path)
 
         assert len(shadow_data_paths) == 3, "The attack_data_paths list must contain exactly three elements."
-        assert target_model_synthetic_path is not None, "The target_data_path must be provided for metaclassifier training."
+        assert target_model_synthetic_path is not None, (
+            "The target_data_path must be provided for metaclassifier training."
+        )
 
         meta_pipeline = importlib.import_module("examples.ensemble_attack.run_metaclassifier_training")
         meta_pipeline.run_metaclassifier_training(config, shadow_data_paths, target_model_synthetic_path)
diff --git a/examples/ensemble_attack/run_metaclassifier_training.py b/examples/ensemble_attack/run_metaclassifier_training.py
index 79cf7d47..5a01dd2d 100644
--- a/examples/ensemble_attack/run_metaclassifier_training.py
+++ b/examples/ensemble_attack/run_metaclassifier_training.py
@@ -1,10 +1,9 @@
 import pickle
-from datetime import datetime
 from logging import INFO
 from pathlib import Path
 
-import pandas as pd
 import numpy as np
+import pandas as pd
 from omegaconf import DictConfig
 
 from midst_toolkit.attacks.ensemble.blending import BlendingPlusPlus, MetaClassifierType
@@ -98,7 +97,7 @@ def run_metaclassifier_training(
     blending_attacker = BlendingPlusPlus(
         config=config,
         shadow_data_collection=shadow_data_collection,
-        data_types_file_path = Path(config.metaclassifier.data_types_file_path),
+        data_types_file_path=Path(config.metaclassifier.data_types_file_path),
         meta_classifier_type=meta_classifier_enum,
         random_seed=config.random_seed,
     )
@@ -128,18 +127,17 @@ def run_metaclassifier_training(
     # For evaluation, we test the meta classifier on the meta test set provided the target's synthetic data.
     probabilities, pred_score = blending_attacker.predict(
         df_test=df_meta_test,
-        df_original_synthetic=target_synthetic, # For evaluation only, replace with actual target model during testing.
+        df_original_synthetic=target_synthetic,  # For evaluation only, replace with actual target model during testing.
         df_reference=df_reference,
         id_column_data=test_trans_ids,
         y_test=y_meta_test,
     )
-    
+
     # Save the evaluation prediction probabilities
     attack_evaluation_result_path = Path(config.data_paths.attack_evaluation_result_path)
     attack_evaluation_result_path.mkdir(parents=True, exist_ok=True)
     np.save(
-        attack_evaluation_result_path
-        / f"{config.metaclassifier.model_type}_val_pred_proba.npy",
+        attack_evaluation_result_path / f"{config.metaclassifier.model_type}_val_pred_proba.npy",
         probabilities,
     )
     log(INFO, "Evaluation prediction probabilities saved.")
diff --git a/examples/ensemble_attack/run_shadow_model_training.py b/examples/ensemble_attack/run_shadow_model_training.py
index a117c189..8244ae5d 100644
--- a/examples/ensemble_attack/run_shadow_model_training.py
+++ b/examples/ensemble_attack/run_shadow_model_training.py
@@ -1,8 +1,6 @@
-import pickle
 import shutil
 from logging import INFO
 from pathlib import Path
-from typing import Any
 
 from omegaconf import DictConfig
 
@@ -16,6 +14,7 @@
 )
 from midst_toolkit.common.logger import log
 
+
 def run_target_model_training(config: DictConfig) -> Path:
     """
     Function to run the target model training for RMIA attack.
@@ -41,7 +40,6 @@ def run_target_model_training(config: DictConfig) -> Path:
 
     # TODO: Add this to config or .json files
     table_name = "trans"
-    id_column_name = "trans_id"
 
     target_folder = target_model_output_path / "target_model"
 
@@ -71,9 +69,7 @@ def run_target_model_training(config: DictConfig) -> Path:
 
     # To train the attack model (metaclassifier), we only need to save target's synthetic data,
     # and not the entire target model's training result object.
-    assert train_result.synthetic_data is not None,(
-         "Target model synthetic data is not generated successfully."
-    )
+    assert train_result.synthetic_data is not None, "Target model synthetic data is not generated successfully."
     target_synthetic_data = train_result.synthetic_data
 
     # Save the target model's synthetic data
diff --git a/examples/ensemble_attack/test_attack_model.py b/examples/ensemble_attack/test_attack_model.py
index 1029f951..f5365521 100644
--- a/examples/ensemble_attack/test_attack_model.py
+++ b/examples/ensemble_attack/test_attack_model.py
@@ -1,20 +1,19 @@
 # This script loads the trained attack model and performs the attack on a set of target models.
 
 import pickle
-from datetime import datetime
 from logging import INFO
 from pathlib import Path
+
 import hydra
-import pandas as pd
 import numpy as np
+import pandas as pd
 from omegaconf import DictConfig
 
+from examples.ensemble_attack.run_shadow_model_training import run_shadow_model_training
 from midst_toolkit.attacks.ensemble.blending import BlendingPlusPlus, MetaClassifierType
 from midst_toolkit.attacks.ensemble.data_utils import load_dataframe
 from midst_toolkit.common.logger import log
 
-from examples.ensemble_attack.run_shadow_model_training import run_shadow_model_training
-
 
 @hydra.main(config_path="configs", config_name="experiment_config", version_base=None)
 def run_metaclassifier_testing(
@@ -35,18 +34,19 @@ def run_metaclassifier_testing(
     """
     log(INFO, f"Running metaclassifier testing on target model {config.target_model.target_model_id}...")
 
-
     # 1) Load the trained metaclassifier model to make sure it exists before proceeding.
     meta_classifier_enum = MetaClassifierType(config.metaclassifier.model_type)
-    
+
     model_name = config.metaclassifier.meta_classifier_model_name
     mataclassifier_path = Path(config.model_paths.metaclassifier_model_path) / model_name
-    assert mataclassifier_path.exists(), f"No metaclassifier model found at {mataclassifier_path}.\
+    assert mataclassifier_path.exists(), (
+        f"No metaclassifier model found at {mataclassifier_path}.\
         Make sure to run the training script first."
-    
+    )
+
     with open(mataclassifier_path, "rb") as f:
         trained_mataclassifier_model = pickle.load(f)
-    
+
     log(INFO, "Metaclassifier model loaded, starting evaluation...")
 
     # 2) Read target model challenge data and synthetic data.
@@ -57,7 +57,7 @@ def run_metaclassifier_testing(
     challenge_label_path = Path(config.target_model.challenge_label_path)
     df_test = pd.read_csv(challenge_data_path)
     y_test = pd.read_csv(challenge_label_path).to_numpy().squeeze()
-    
+
     target_synthetic_path = Path(config.target_model.target_synthetic_data_path)
     target_synthetic = pd.read_csv(target_synthetic_path)
 
@@ -76,22 +76,19 @@ def run_metaclassifier_testing(
     # avoid overriding train's shadow models.
     config.shadow_training.shadow_models_output_path = config.target_model.target_shadow_models_output_path
     shadow_model_paths = run_shadow_model_training(config)
-    
-    assert len(shadow_model_paths) == 3, (
-        "For testing, meta classifier needs the path to three sets of shadow models."
-    )
+
+    assert len(shadow_model_paths) == 3, "For testing, meta classifier needs the path to three sets of shadow models."
 
     shadow_data_collection = []
     for model_path in shadow_model_paths:
         assert model_path.exists(), (
             f"No file found at {model_path}. Make sure the path is correct, or run shadow model training first."
         )
-        
+
         with open(model_path, "rb") as f:
             shadow_data_and_result = pickle.load(f)
             shadow_data_collection.append(shadow_data_and_result)
 
-
     # 4) Initialize the attacker object, and assign the loaded metaclassifier to it.
     target_synthetic = target_synthetic.copy()
 
@@ -99,11 +96,11 @@ def run_metaclassifier_testing(
         Path(config.data_paths.population_path),
         "population_all_with_challenge_no_id.csv",
     )
-    
+
     blending_attacker = BlendingPlusPlus(
         config=config,
         shadow_data_collection=shadow_data_collection,
-        data_types_file_path = Path(config.metaclassifier.data_types_file_path),
+        data_types_file_path=Path(config.metaclassifier.data_types_file_path),
         meta_classifier_type=meta_classifier_enum,
         random_seed=config.random_seed,
     )
@@ -111,7 +108,6 @@ def run_metaclassifier_testing(
     # Assign the trained metaclassifier model to the attacker object.
     blending_attacker.trained_model = trained_mataclassifier_model
 
-
     # 5) Get predictions on the challenge data (test set).
     probabilities, pred_score = blending_attacker.predict(
         df_test=df_test,
@@ -120,13 +116,12 @@ def run_metaclassifier_testing(
         id_column_data=test_trans_ids,
         y_test=y_test,
     )
-    
+
     # Save the validation prediction probabilities
     attack_results_path = Path(config.target_model.attack_probabilities_result_path)
     attack_results_path.mkdir(parents=True, exist_ok=True)
     np.save(
-        attack_results_path
-        / f"{config.metaclassifier.model_type}_val_pred_proba.npy",
+        attack_results_path / f"{config.metaclassifier.model_type}_val_pred_proba.npy",
         probabilities,
     )
     log(INFO, "Test prediction probabilities saved.")
diff --git a/src/midst_toolkit/attacks/ensemble/blending.py b/src/midst_toolkit/attacks/ensemble/blending.py
index f1e47381..e2a37f2a 100644
--- a/src/midst_toolkit/attacks/ensemble/blending.py
+++ b/src/midst_toolkit/attacks/ensemble/blending.py
@@ -3,8 +3,8 @@
 import json
 from enum import Enum
 from logging import INFO
-from typing import Any
 from pathlib import Path
+from typing import Any
 
 import numpy as np
 import pandas as pd
@@ -12,11 +12,11 @@
 from sklearn.linear_model import LogisticRegression
 
 from midst_toolkit.attacks.ensemble.distance_features import calculate_domias_score, calculate_gower_features
-from midst_toolkit.attacks.ensemble.rmia.rmia_calculation import calculate_rmia_signals
 from midst_toolkit.attacks.ensemble.metric_utils import get_tpr_at_fpr
+from midst_toolkit.attacks.ensemble.rmia.rmia_calculation import calculate_rmia_signals
 from midst_toolkit.attacks.ensemble.xgboost_tuner import XgBoostHyperparameterTuner
 from midst_toolkit.common.logger import log
-from sklearn.pipeline import Pipeline
+
 
 class MetaClassifierType(Enum):
     LR = "lr"
@@ -51,10 +51,7 @@ def __init__(
                 is a list of type TrainingResult containing model training information and generated synthetic data.
                 For more details, see the documentation of `train_three_sets_of_shadow_models` at
                 attacks/ensemble/rmia/shadow_model_training.py.
-            target_synthetic_data: Synthetic data generated by the target model.
-                For more details, see the documentation of `train_three_sets_of_shadow_models` at
-                attacks/ensemble/rmia/shadow_model_training.py.
-
+            data_types_file_path: Path to the JSON file containing data column types information.
             meta_classifier_type: Type of meta classifier model. Defaults to MetaClassifierType.XGB.
             random_seed: Random seed for reproducibility. Defaults to None.
 
diff --git a/src/midst_toolkit/attacks/ensemble/metric_utils.py b/src/midst_toolkit/attacks/ensemble/metric_utils.py
index 4c8935ee..4a69e4c7 100644
--- a/src/midst_toolkit/attacks/ensemble/metric_utils.py
+++ b/src/midst_toolkit/attacks/ensemble/metric_utils.py
@@ -26,5 +26,3 @@ def get_tpr_at_fpr(
     fpr, tpr, _ = roc_curve(true_membership, predictions)
 
     return max(tpr[fpr <= max_fpr])
-
-
diff --git a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
index 5d605210..3700c4d4 100644
--- a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
+++ b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
@@ -161,10 +161,7 @@ def calculate_rmia_signals(
             which store model training metadata and the corresponding generated synthetic data.
             See ``train_three_sets_of_shadow_models`` in attacks/ensemble/rmia/shadow_model_training.py
             for additional details.
-        target_data: A dictionary containing information about the target model. It includes:
-            - ``selected_sets``: A list of DataFrames used to train the target model.
-            - ``trained_results``: A list of ``TrainingResult`` objects, each containing details about the model's
-              training process and the synthetic data generated during training.
+        target_synthetic_data: Target model's synthetic data as a DataFrame.
         categorical_column_names: A list of categorical column names.
         id_column_name: Name of the ID column.
         id_column_data: The data in the ID column extracted from df_input, ensuring that output signals are
@@ -220,7 +217,7 @@ def calculate_rmia_signals(
     min_length = min(min(group) for group in all_lengths)
     if not (1 <= k <= min_length):
         raise ValueError(f"k={k} must be within [1, {min_length}]")
-    
+
     shadow_synthetic_list_0 = [
         train_result.synthetic_data for train_result in fine_tuned_shadow_data_0[Key.FINE_TUNED_RESULTS.value]
     ]
diff --git a/tests/unit/attacks/ensemble/test_meta_classifier.py b/tests/unit/attacks/ensemble/test_meta_classifier.py
index 1ea0e9d5..dcbae882 100644
--- a/tests/unit/attacks/ensemble/test_meta_classifier.py
+++ b/tests/unit/attacks/ensemble/test_meta_classifier.py
@@ -308,7 +308,7 @@ def test_predict_raises_error_if_not_fit(self, mock_file, mock_config_with_json_
         bpp = BlendingPlusPlus(
             config=mock_config_with_json_path,
             shadow_data_collection=[],
-            data_types_file_path=mock_config_with_json_path.metaclassifier.data_types_file_path, 
+            data_types_file_path=mock_config_with_json_path.metaclassifier.data_types_file_path,
         )
         with pytest.raises(AssertionError):
             bpp.predict(
diff --git a/tests/unit/attacks/ensemble/test_rmia.py b/tests/unit/attacks/ensemble/test_rmia.py
index fa4ffc7f..03ec8373 100644
--- a/tests/unit/attacks/ensemble/test_rmia.py
+++ b/tests/unit/attacks/ensemble/test_rmia.py
@@ -223,7 +223,9 @@ def test_get_rmia_gower_missing_categorical_column(self, base_data, mocker, capl
         missing_cat_cols = ["city", "non_existent_column"]
 
         with caplog.at_level("INFO"):
-            synthetic_data_list = [data.synthetic_data for data in base_data["model_data"][Key.FINE_TUNED_RESULTS.value]]
+            synthetic_data_list = [
+                data.synthetic_data for data in base_data["model_data"][Key.FINE_TUNED_RESULTS.value]
+            ]
             get_rmia_gower(
                 df_input=base_data["df_input"],
                 model_data=synthetic_data_list,

From a19a595b83c93b7ee760fd4a52613c6b914cbdd7 Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Tue, 18 Nov 2025 13:17:27 -0500
Subject: [PATCH 06/41] Minor fixes

---
 .../configs/experiment_config.yaml            | 23 +++++++++----------
 .../configs/original_attack_config.yaml       |  4 ++--
 examples/ensemble_attack/test_attack_model.py |  2 +-
 3 files changed, 14 insertions(+), 15 deletions(-)

diff --git a/examples/ensemble_attack/configs/experiment_config.yaml b/examples/ensemble_attack/configs/experiment_config.yaml
index dbf11a55..053d0526 100644
--- a/examples/ensemble_attack/configs/experiment_config.yaml
+++ b/examples/ensemble_attack/configs/experiment_config.yaml
@@ -1,6 +1,6 @@
 # Ensemble experiment configuration
-base_experiment_dir: examples/ensemble_attack/tabddpm_20k_experiment_data # Processed data, and experiment artifacts of training will be stored here
-base_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
+base_experiment_dir: examples/ensemble_attack/tabddpm_20k_experiment_data # Processed data, and experiment artifacts will be stored here
+base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
 
 # Pipeline control
 pipeline:
@@ -31,7 +31,6 @@ data_paths:
 
 model_paths:
   metaclassifier_model_path: ${base_experiment_dir}/trained_models # Path where the trained metaclassifier model will be saved
-  # Path to final target model's synthetic data (relative to target_model_output_path)
 
 
 # Dataset specific information used for processing in this example
@@ -54,7 +53,7 @@ data_processing_config:
   single_table_train_data_file_name: "train_with_id.csv"
   multi_table_train_data_file_name: "trans.csv"
   challenge_data_file_name: "challenge_with_id.csv"
-  population_sample_size: 40000 #Population size the total data that your attack has access to.
+  population_sample_size: 40000 # Population size is the total data that your attack has access to.
   # In experiments, this is sampled out of all the collected training data in case the available data
   # is more than this number. Note that, half of this data is actually used for training, the other half
   # is used for evaluation. For example, with 40k population size, only 20k is used for training the attack model.
@@ -64,9 +63,9 @@ data_processing_config:
 shadow_training:
   # Data Config files path used for training a TabDDPM model
   training_json_config_paths: # Config json files used for tabddpm training on the trans table
-    table_domain_file_path: ${base_config_dir}/trans_domain.json
-    dataset_meta_file_path: ${base_config_dir}/dataset_meta.json
-    tabddpm_training_config_path: ${base_config_dir}/trans.json
+    table_domain_file_path: ${base_data_config_dir}/trans_domain.json
+    dataset_meta_file_path: ${base_data_config_dir}/dataset_meta.json
+    tabddpm_training_config_path: ${base_data_config_dir}/trans.json
   # Model training artifacts are saved under shadow_models_data_path/workspace_name/exp_name
   # Also, training configs for each shadow model are created under shadow_models_data_path.
   shadow_models_output_path: ${base_experiment_dir}/shadow_models_and_data
@@ -80,19 +79,19 @@ shadow_training:
       # train_three_sets_of_shadow_models in shadow_model_training.py
       # Each .pkl file contains the training data, trained model and training results for all shadow models in a list.
   target_synthetic_data_path: ${shadow_training.target_model_output_path}/target_synthetic_data.csv
-  # Path to final target model's synthetic data (relative to target_model_output_path)
+  # Path to final shadow target's synthetic data (relative to target_model_output_path)
   fine_tuning_config:
     fine_tune_diffusion_iterations: 200000 # Original code: 200000
-    fine_tune_classifier_iterations: 20000 #Original code: 20000
-    pre_train_data_size: 60000 #10 for test run. Original code: 60000
+    fine_tune_classifier_iterations: 20000 # Original code: 20000
+    pre_train_data_size: 60000 # Original code: 60000
   number_of_points_to_synthesize: 20000 # Number of synthetic data samples to be generated by shadow models.
-  # 200 for test run. Original code: 20000
+  # Original code: 20000
 
 
 # Metaclassifier settings
 metaclassifier:
   # Data types json file is used for xgboost model training.
-  data_types_file_path: ${base_config_dir}/data_types.json
+  data_types_file_path: ${base_data_config_dir}/data_types.json
   model_type: "xgb"
   # Model training parameters
   num_optuna_trials: 100 # Original code: 100
diff --git a/examples/ensemble_attack/configs/original_attack_config.yaml b/examples/ensemble_attack/configs/original_attack_config.yaml
index 3984130e..c49c1816 100644
--- a/examples/ensemble_attack/configs/original_attack_config.yaml
+++ b/examples/ensemble_attack/configs/original_attack_config.yaml
@@ -48,7 +48,7 @@ data_processing_config:
   multi_table_train_data_file_name: "trans.csv"
   challenge_data_file_name: "challenge_with_id.csv"
   population_sample_size: 40000 # Population size the total data that your attack has access to.
-  #The size of the master challenge dataset is half of the population size based on the attack design.
+  # The size of the master challenge dataset is half of the population size based on the attack design.
   # Original code: 40000
 
 # Training and data settings for shadow models (temporary, numbers subject to change)
@@ -77,7 +77,7 @@ shadow_training:
     fine_tune_classifier_iterations: 20000 # Original code: 20000
     pre_train_data_size: 60000 # Original code: 60000
   number_of_points_to_synthesize: 20000 # Number of synthetic data samples to be generated by shadow models.
-  #  Original code: 20000
+  # Original code: 20000
 
 
 # Metaclassifier settings
diff --git a/examples/ensemble_attack/test_attack_model.py b/examples/ensemble_attack/test_attack_model.py
index f5365521..9cad13d9 100644
--- a/examples/ensemble_attack/test_attack_model.py
+++ b/examples/ensemble_attack/test_attack_model.py
@@ -49,7 +49,7 @@ def run_metaclassifier_testing(
 
     log(INFO, "Metaclassifier model loaded, starting evaluation...")
 
-    # 2) Read target model challenge data and synthetic data.
+    # 2) Read target model's challenge data and synthetic data.
 
     # Back-box attacker has only access to the target model's synthetic data and challenge points.
     # We also load challenge labels to report the attack performance.

From e93b3c1c24e71e0964a370f83a8735b6a5691a0d Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Tue, 18 Nov 2025 13:27:48 -0500
Subject: [PATCH 07/41] Minor fixes

---
 examples/ensemble_attack/real_data_collection.py               | 3 ++-
 .../attacks/ensemble/rmia/shadow_model_training.py             | 2 --
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/examples/ensemble_attack/real_data_collection.py b/examples/ensemble_attack/real_data_collection.py
index 4c9cb140..e32fa469 100644
--- a/examples/ensemble_attack/real_data_collection.py
+++ b/examples/ensemble_attack/real_data_collection.py
@@ -172,6 +172,7 @@ def collect_population_data_ensemble(
     if population_splits is None:
         population_splits = ["train"]
     if challenge_splits is None:
+        # Original Ensemble collects all the challenge points from train, dev and final of "tabddpm_black_box" attack.
         challenge_splits = ["train", "dev", "final"]
 
     # Ensemble Attack collects train data of all the attack types (black box and white box)
@@ -192,7 +193,7 @@ def collect_population_data_ensemble(
     save_dataframe(df_population, save_dir, "population_all.csv")
     save_dataframe(df_population_no_id, save_dir, "population_all_no_id.csv")
 
-    # Original Ensemble collects all the challenge points from train, dev and final of "tabddpm_black_box" attack.
+
     challenge_attack_names = data_processing_config.challenge_attack_data_types_to_collect
     challenge_attack_types = [AttackType(attack_name) for attack_name in challenge_attack_names]
     df_challenge = collect_midst_data(
diff --git a/src/midst_toolkit/attacks/ensemble/rmia/shadow_model_training.py b/src/midst_toolkit/attacks/ensemble/rmia/shadow_model_training.py
index dfb0cd92..672d2ec8 100644
--- a/src/midst_toolkit/attacks/ensemble/rmia/shadow_model_training.py
+++ b/src/midst_toolkit/attacks/ensemble/rmia/shadow_model_training.py
@@ -295,8 +295,6 @@ def train_shadow_on_half_challenge_data(
             INFO,
             f"Trained shadow model {model_id} generated {len(train_result.synthetic_data)} synthetic samples.",
         )
-        assert train_result.synthetic_data is not None, "Trained shadow model did not generate synthetic data."
-        log(INFO, f"Trained shadow model {model_id} generated {len(train_result.synthetic_data)} synthetic samples.")
 
         attack_data["trained_results"].append(train_result)
 

From 70cb1afecb19c9f08f7cf51d429081f22785dba5 Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Tue, 18 Nov 2025 13:33:15 -0500
Subject: [PATCH 08/41] Removed extra line

---
 examples/ensemble_attack/real_data_collection.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/examples/ensemble_attack/real_data_collection.py b/examples/ensemble_attack/real_data_collection.py
index e32fa469..498e78d1 100644
--- a/examples/ensemble_attack/real_data_collection.py
+++ b/examples/ensemble_attack/real_data_collection.py
@@ -193,7 +193,6 @@ def collect_population_data_ensemble(
     save_dataframe(df_population, save_dir, "population_all.csv")
     save_dataframe(df_population_no_id, save_dir, "population_all_no_id.csv")
 
-
     challenge_attack_names = data_processing_config.challenge_attack_data_types_to_collect
     challenge_attack_types = [AttackType(attack_name) for attack_name in challenge_attack_names]
     df_challenge = collect_midst_data(

From 942475de037385d65264102f415329d24fe145ac Mon Sep 17 00:00:00 2001
From: fatemetkl <t.fateme@gmail.com>
Date: Wed, 19 Nov 2025 08:42:55 -0700
Subject: [PATCH 09/41] Sara's comments

---
 examples/ensemble_attack/configs/experiment_config.yaml | 1 +
 examples/ensemble_attack/real_data_collection.py        | 8 ++++----
 examples/ensemble_attack/run_metaclassifier_training.py | 2 +-
 examples/ensemble_attack/run_shadow_model_training.py   | 2 +-
 4 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/examples/ensemble_attack/configs/experiment_config.yaml b/examples/ensemble_attack/configs/experiment_config.yaml
index 053d0526..7c8d63ef 100644
--- a/examples/ensemble_attack/configs/experiment_config.yaml
+++ b/examples/ensemble_attack/configs/experiment_config.yaml
@@ -1,4 +1,5 @@
 # Ensemble experiment configuration
+# This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
 base_experiment_dir: examples/ensemble_attack/tabddpm_20k_experiment_data # Processed data, and experiment artifacts will be stored here
 base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
 
diff --git a/examples/ensemble_attack/real_data_collection.py b/examples/ensemble_attack/real_data_collection.py
index 498e78d1..264f71ae 100644
--- a/examples/ensemble_attack/real_data_collection.py
+++ b/examples/ensemble_attack/real_data_collection.py
@@ -157,11 +157,11 @@ def collect_population_data_ensemble(
         data_processing_config: Configuration dictionary containing data information and file names.
         save_dir: The path where the collected population data should be saved.
         population_splits: A list indicating the data splits to be collected for population data.
-            Could be any of train, dev, or final data splits. If None, a default value is set in the function
-            based on the original attack implementation.
+            Could be any of `train`, `dev`, or `final` data splits. If None, the default list of ``["train"]``
+            is set in the function based on the original attack implementation.
         challenge_splits: A list indicating the data splits to be collected for challenge points.
-            Could be any of train, dev, or final data splits. If None, a default value is set in the function
-            based on the original attack implementation.
+            Could be any of `train`, `dev`, or `final` data splits. If None, the default list of
+            ``["train", "dev", "final"]`` is set in the function based on the original attack implementation.
 
     Returns:
         The collected population data as a dataframe.
diff --git a/examples/ensemble_attack/run_metaclassifier_training.py b/examples/ensemble_attack/run_metaclassifier_training.py
index 5a01dd2d..0f532c68 100644
--- a/examples/ensemble_attack/run_metaclassifier_training.py
+++ b/examples/ensemble_attack/run_metaclassifier_training.py
@@ -127,7 +127,7 @@ def run_metaclassifier_training(
     # For evaluation, we test the meta classifier on the meta test set provided the target's synthetic data.
     probabilities, pred_score = blending_attacker.predict(
         df_test=df_meta_test,
-        df_original_synthetic=target_synthetic,  # For evaluation only, replace with actual target model during testing.
+        df_original_synthetic=target_synthetic,  # For evaluation only.
         df_reference=df_reference,
         id_column_data=test_trans_ids,
         y_test=y_meta_test,
diff --git a/examples/ensemble_attack/run_shadow_model_training.py b/examples/ensemble_attack/run_shadow_model_training.py
index c65b1def..d4a85cbc 100644
--- a/examples/ensemble_attack/run_shadow_model_training.py
+++ b/examples/ensemble_attack/run_shadow_model_training.py
@@ -23,7 +23,7 @@ def run_target_model_training(config: DictConfig) -> Path:
         config: Configuration object set in config.yaml.
 
     Returns:
-        Path to the saved target model's synthetic.
+        Path to the saved target model's synthetic data.
     """
     log(INFO, "Running target model training...")
 

From de1cb95ee9e0e05fd5bffec63d63beb8239e3a80 Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Thu, 20 Nov 2025 10:50:46 -0500
Subject: [PATCH 10/41] Addressed Marcelo's comments

---
 .../configs/experiment_config.yaml            |  9 +--
 .../configs/original_attack_config.yaml       |  5 +-
 examples/ensemble_attack/run_attack.py        |  6 +-
 .../run_metaclassifier_training.py            | 24 ++++----
 examples/ensemble_attack/test_attack_model.py | 58 +++++++++++--------
 5 files changed, 58 insertions(+), 44 deletions(-)

diff --git a/examples/ensemble_attack/configs/experiment_config.yaml b/examples/ensemble_attack/configs/experiment_config.yaml
index 053d0526..2cccae47 100644
--- a/examples/ensemble_attack/configs/experiment_config.yaml
+++ b/examples/ensemble_attack/configs/experiment_config.yaml
@@ -70,14 +70,15 @@ shadow_training:
   # Also, training configs for each shadow model are created under shadow_models_data_path.
   shadow_models_output_path: ${base_experiment_dir}/shadow_models_and_data
   target_model_output_path: ${base_experiment_dir}/shadow_target_model_and_data
+  # Paths to final shadow models used for metaclassifier training (relative to shadow_models_output_path)
+  # These paths are a result of running the shadow model training pipeline, specifically the
+  # train_three_sets_of_shadow_models in shadow_model_training.py
+  # Each .pkl file contains the training data, trained model and training results for all shadow models in a list.
   final_shadow_models_path: [
           "${shadow_training.shadow_models_output_path}/initial_model_rmia_1/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
           "${shadow_training.shadow_models_output_path}/initial_model_rmia_2/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
           "${shadow_training.shadow_models_output_path}/shadow_model_rmia_third_set/shadow_workspace/trained_model/rmia_shadows_third_set.pkl",
-      ] # Paths to final shadow models used for metaclassifier training (relative to shadow_models_output_path)
-      # These paths are a result of running the shadow model training pipeline, specifically the
-      # train_three_sets_of_shadow_models in shadow_model_training.py
-      # Each .pkl file contains the training data, trained model and training results for all shadow models in a list.
+      ]
   target_synthetic_data_path: ${shadow_training.target_model_output_path}/target_synthetic_data.csv
   # Path to final shadow target's synthetic data (relative to target_model_output_path)
   fine_tuning_config:
diff --git a/examples/ensemble_attack/configs/original_attack_config.yaml b/examples/ensemble_attack/configs/original_attack_config.yaml
index c49c1816..ed4562f2 100644
--- a/examples/ensemble_attack/configs/original_attack_config.yaml
+++ b/examples/ensemble_attack/configs/original_attack_config.yaml
@@ -8,7 +8,7 @@ data_paths:
   midst_data_path: ${base_data_dir}/midst_data_all_attacks # Used only for reading the data
   population_path: ${base_data_dir}/population_data  # Path where the population data should be stored
   processed_attack_data_path: ${base_data_dir}/attack_data # Path where the processed attack real train and evaluation data is stored
-  attack_evaluation_result_path: ${base_example_dir}/attack_results # Path where the attack results will be stored
+  attack_evaluation_result_path: ${base_example_dir}/attack_results # Path where the attack evaluation results will be stored
 
 model_paths:
   metaclassifier_model_path: ${base_example_dir}/trained_models # Path where the trained metaclassifier model will be saved
@@ -47,8 +47,9 @@ data_processing_config:
   single_table_train_data_file_name: "train_with_id.csv"
   multi_table_train_data_file_name: "trans.csv"
   challenge_data_file_name: "challenge_with_id.csv"
-  population_sample_size: 40000 # Population size the total data that your attack has access to.
+  population_sample_size: 40000 # Population size is the total data that your attack has access to.
   # The size of the master challenge dataset is half of the population size based on the attack design.
+  # The other half is used for evaluation.
   # Original code: 40000
 
 # Training and data settings for shadow models (temporary, numbers subject to change)
diff --git a/examples/ensemble_attack/run_attack.py b/examples/ensemble_attack/run_attack.py
index f5e3129c..5592e08e 100644
--- a/examples/ensemble_attack/run_attack.py
+++ b/examples/ensemble_attack/run_attack.py
@@ -55,9 +55,13 @@ def main(config: DictConfig) -> None:
     Args:
         config: Attack configuration as an OmegaConf DictConfig object.
     """
-    set_all_random_seeds(seed=config.random_seed)
+    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.pipeline.run_data_processing:
         run_data_processing(config)
+
     # Note: Importing the following two modules causes a segmentation fault error if imported together in this file.
     # A quick solution is to load modules dynamically if any of the pipelines is called.
     # TODO: Investigate the source of error.
diff --git a/examples/ensemble_attack/run_metaclassifier_training.py b/examples/ensemble_attack/run_metaclassifier_training.py
index 5a01dd2d..98362d4a 100644
--- a/examples/ensemble_attack/run_metaclassifier_training.py
+++ b/examples/ensemble_attack/run_metaclassifier_training.py
@@ -70,10 +70,10 @@ def run_metaclassifier_training(
     )
 
     # Load the target model's synthetic data
-    target_synthetic = pd.read_csv(target_model_synthetic_path)
+    target_synthetic_data = pd.read_csv(target_model_synthetic_path)
 
-    assert target_synthetic is not None, "Target model's synthetic data is missing."
-    target_synthetic = target_synthetic.copy()
+    assert target_synthetic_data is not None, "Target model's synthetic data is missing."
+    target_synthetic_data = target_synthetic_data.copy()
 
     df_reference = load_dataframe(
         Path(config.data_paths.population_path),
@@ -91,24 +91,24 @@ def run_metaclassifier_training(
     df_meta_test = df_meta_test.drop(columns=["trans_id", "account_id"])
 
     # Fit the metaclassifier.
-    meta_classifier_enum = MetaClassifierType(config.metaclassifier.model_type)
+    meta_classifier_type = MetaClassifierType(config.metaclassifier.model_type)
 
     # 1. Initialize the attacker
     blending_attacker = BlendingPlusPlus(
         config=config,
         shadow_data_collection=shadow_data_collection,
         data_types_file_path=Path(config.metaclassifier.data_types_file_path),
-        meta_classifier_type=meta_classifier_enum,
+        meta_classifier_type=meta_classifier_type,
         random_seed=config.random_seed,
     )
 
-    log(INFO, f"{meta_classifier_enum} created with random seed {config.random_seed}.")
+    log(INFO, f"{meta_classifier_type} created with random seed {config.random_seed}.")
 
     # 2. Train the attacker on the meta-train set
     blending_attacker.fit(
         df_train=df_meta_train,
         y_train=y_meta_train,
-        df_target_synthetic=target_synthetic,
+        df_target_synthetic=target_synthetic_data,
         df_reference=df_reference,
         id_column_data=train_trans_ids,
         use_gpu=config.metaclassifier.use_gpu,
@@ -127,7 +127,7 @@ def run_metaclassifier_training(
     # For evaluation, we test the meta classifier on the meta test set provided the target's synthetic data.
     probabilities, pred_score = blending_attacker.predict(
         df_test=df_meta_test,
-        df_original_synthetic=target_synthetic,  # For evaluation only, replace with actual target model during testing.
+        df_original_synthetic=target_synthetic_data,  # For evaluation only, replace with actual target model during testing.
         df_reference=df_reference,
         id_column_data=test_trans_ids,
         y_test=y_meta_test,
@@ -136,11 +136,9 @@ def run_metaclassifier_training(
     # Save the evaluation prediction probabilities
     attack_evaluation_result_path = Path(config.data_paths.attack_evaluation_result_path)
     attack_evaluation_result_path.mkdir(parents=True, exist_ok=True)
-    np.save(
-        attack_evaluation_result_path / f"{config.metaclassifier.model_type}_val_pred_proba.npy",
-        probabilities,
-    )
-    log(INFO, "Evaluation prediction probabilities saved.")
+    file_name = attack_evaluation_result_path / f"{config.metaclassifier.model_type}_val_pred_proba.npy"
+    np.save(file_name, probabilities)
+    log(INFO, f"Evaluation prediction probabilities saved at {file_name}.")
 
     if pred_score is not None:
         log(INFO, f"TPR at FPR=0.1: {pred_score:.4f}")
diff --git a/examples/ensemble_attack/test_attack_model.py b/examples/ensemble_attack/test_attack_model.py
index 9cad13d9..4eb10c12 100644
--- a/examples/ensemble_attack/test_attack_model.py
+++ b/examples/ensemble_attack/test_attack_model.py
@@ -1,5 +1,6 @@
-# This script loads the trained attack model and performs the attack on a set of target models.
+"""This script loads the trained attack model and performs the attack on a target model given its synthetic data."""
 
+import json
 import pickle
 from logging import INFO
 from pathlib import Path
@@ -13,6 +14,7 @@
 from midst_toolkit.attacks.ensemble.blending import BlendingPlusPlus, MetaClassifierType
 from midst_toolkit.attacks.ensemble.data_utils import load_dataframe
 from midst_toolkit.common.logger import log
+from midst_toolkit.common.random import set_all_random_seeds
 
 
 @hydra.main(config_path="configs", config_name="experiment_config", version_base=None)
@@ -21,33 +23,36 @@ def run_metaclassifier_testing(
 ) -> None:
     """
     Function to run the attack on a target model using a trained metaclassifier.
-    Note that shadow models need to be trained for every new target model's challenge dataset.
+    Note that RMIA shadow models need to be trained for every new target model's challenge dataset.
     However, we load the previously trained metaclassifier model and use it for new target models.
     Unlike the training phase, in the testing phase, we don't need to train a shadow target model
     since we already have access to the synthetic data of a real target model.
     All the collected population data that is used for training, is still needed during testing to compute some
     of the signals.
-
+    Test prediction probabilities are saved to the specified attack result path in the config.
 
     Args:
-        config: Configuration object set in experiments_config.yaml.
+        config: Configuration object set in ``experiments_config.yaml``.
     """
     log(INFO, f"Running metaclassifier testing on target model {config.target_model.target_model_id}...")
 
+    if config.random_seed is not None:
+        set_all_random_seeds(seed=config.random_seed)
+        log(INFO, f"Testing phase random seed set to {config.random_seed}.")
+
     # 1) Load the trained metaclassifier model to make sure it exists before proceeding.
-    meta_classifier_enum = MetaClassifierType(config.metaclassifier.model_type)
+    meta_classifier_type = MetaClassifierType(config.metaclassifier.model_type)
 
     model_name = config.metaclassifier.meta_classifier_model_name
     mataclassifier_path = Path(config.model_paths.metaclassifier_model_path) / model_name
     assert mataclassifier_path.exists(), (
-        f"No metaclassifier model found at {mataclassifier_path}.\
-        Make sure to run the training script first."
+        f"No metaclassifier model found at {mataclassifier_path}. Make sure to run the training script first."
     )
 
     with open(mataclassifier_path, "rb") as f:
         trained_mataclassifier_model = pickle.load(f)
 
-    log(INFO, "Metaclassifier model loaded, starting evaluation...")
+    log(INFO, "Metaclassifier model loaded, starting the test...")
 
     # 2) Read target model's challenge data and synthetic data.
 
@@ -55,16 +60,23 @@ def run_metaclassifier_testing(
     # We also load challenge labels to report the attack performance.
     challenge_data_path = Path(config.target_model.challenge_data_path)
     challenge_label_path = Path(config.target_model.challenge_label_path)
-    df_test = pd.read_csv(challenge_data_path)
-    y_test = pd.read_csv(challenge_label_path).to_numpy().squeeze()
+    test_data = pd.read_csv(challenge_data_path)
+    test_target = pd.read_csv(challenge_label_path).to_numpy().squeeze()
 
     target_synthetic_path = Path(config.target_model.target_synthetic_data_path)
-    target_synthetic = pd.read_csv(target_synthetic_path)
+    target_synthetic_data = pd.read_csv(target_synthetic_path)
 
     # Extract trans_id from the test dataframe
-    assert "trans_id" in df_test.columns, "Test data must have trans_id column"
-    test_trans_ids = df_test["trans_id"]
-    df_test = df_test.drop(columns=["trans_id", "account_id"])
+    with open(Path(config.metaclassifier.data_types_file_path), "r") as f:
+        column_types = json.load(f)
+    id_column_name = column_types["id_column_name"]
+
+    assert id_column_name in test_data.columns, "Test data must have trans_id column"
+    test_trans_ids = test_data[id_column_name]
+
+    # Drop id columns from test data
+    id_column_names = [column_name for column_name in test_data.columns if column_name.endswith("_id")]
+    test_data = test_data.drop(columns=id_column_names)
 
     # 3) Shadow Model Training Step.
 
@@ -90,7 +102,7 @@ def run_metaclassifier_testing(
             shadow_data_collection.append(shadow_data_and_result)
 
     # 4) Initialize the attacker object, and assign the loaded metaclassifier to it.
-    target_synthetic = target_synthetic.copy()
+    target_synthetic_data = target_synthetic_data.copy()
 
     df_reference = load_dataframe(
         Path(config.data_paths.population_path),
@@ -101,7 +113,7 @@ def run_metaclassifier_testing(
         config=config,
         shadow_data_collection=shadow_data_collection,
         data_types_file_path=Path(config.metaclassifier.data_types_file_path),
-        meta_classifier_type=meta_classifier_enum,
+        meta_classifier_type=meta_classifier_type,
         random_seed=config.random_seed,
     )
 
@@ -110,21 +122,19 @@ def run_metaclassifier_testing(
 
     # 5) Get predictions on the challenge data (test set).
     probabilities, pred_score = blending_attacker.predict(
-        df_test=df_test,
-        df_original_synthetic=target_synthetic,
+        df_test=test_data,
+        df_original_synthetic=target_synthetic_data,
         df_reference=df_reference,
         id_column_data=test_trans_ids,
-        y_test=y_test,
+        y_test=test_target,
     )
 
     # Save the validation prediction probabilities
     attack_results_path = Path(config.target_model.attack_probabilities_result_path)
     attack_results_path.mkdir(parents=True, exist_ok=True)
-    np.save(
-        attack_results_path / f"{config.metaclassifier.model_type}_val_pred_proba.npy",
-        probabilities,
-    )
-    log(INFO, "Test prediction probabilities saved.")
+    file_name = attack_results_path / f"{config.metaclassifier.model_type}_val_pred_proba.npy"
+    np.save(file_name, probabilities)
+    log(INFO, f"Test prediction probabilities saved at {file_name}.")
 
     if pred_score is not None:
         log(INFO, f"TPR at FPR=0.1: {pred_score:.4f}")

From 59e52d49cc6134c3b66198ffc3aba5f5d6dc13d7 Mon Sep 17 00:00:00 2001
From: Fatemeh Tavakoli <t.fateme@gmail.com>
Date: Thu, 20 Nov 2025 09:01:09 -0700
Subject: [PATCH 11/41] Ensemble experiments: SLURM script  (#97)

* Added testing several targets on multiple gpus

* Added a comment
---
 .../configs/experiment_config.yaml            |  4 ++--
 .../configs/original_attack_config.yaml       |  2 +-
 .../run_metaclassifier_training.py            |  4 ++--
 examples/ensemble_attack/run_test.sh          | 20 ++++++++++++-------
 examples/ensemble_attack/run_train.sh         |  2 +-
 examples/ensemble_attack/test_attack_model.py | 16 ++++++++++++---
 6 files changed, 32 insertions(+), 16 deletions(-)

diff --git a/examples/ensemble_attack/configs/experiment_config.yaml b/examples/ensemble_attack/configs/experiment_config.yaml
index 0ea585b3..5973c624 100644
--- a/examples/ensemble_attack/configs/experiment_config.yaml
+++ b/examples/ensemble_attack/configs/experiment_config.yaml
@@ -12,7 +12,7 @@ pipeline:
 target_model: # This is only used for testing the attack on a real target model.
   # This is for models trained on 20k data and generating 20k synthetic data
   target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_20k/train/
-  target_model_id: 21
+  target_model_id: 21  # Will be overridden per SLURM array task
   target_model_name: tabddpm_${target_model.target_model_id}
   target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/20k/20k.csv
   challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
@@ -101,7 +101,7 @@ metaclassifier:
   use_gpu: false
   # Temporary. Might remove having an epoch parameter.
   epochs: 1
-  meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model.pkl
+  meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model
 
 
 # General settings
diff --git a/examples/ensemble_attack/configs/original_attack_config.yaml b/examples/ensemble_attack/configs/original_attack_config.yaml
index ed4562f2..4adaa181 100644
--- a/examples/ensemble_attack/configs/original_attack_config.yaml
+++ b/examples/ensemble_attack/configs/original_attack_config.yaml
@@ -92,7 +92,7 @@ metaclassifier:
   use_gpu: false
   # Temporary. Might remove having an epoch parameter.
   epochs: 1
-  meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model.pkl
+  meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model
 
 
 # General settings
diff --git a/examples/ensemble_attack/run_metaclassifier_training.py b/examples/ensemble_attack/run_metaclassifier_training.py
index 2cb18b12..e6a9c8e5 100644
--- a/examples/ensemble_attack/run_metaclassifier_training.py
+++ b/examples/ensemble_attack/run_metaclassifier_training.py
@@ -116,7 +116,7 @@ def run_metaclassifier_training(
     )
 
     model_filename = config.metaclassifier.meta_classifier_model_name
-    model_path = Path(config.model_paths.metaclassifier_model_path) / model_filename
+    model_path = Path(config.model_paths.metaclassifier_model_path) / f"{model_filename}.pkl"
     model_path.parent.mkdir(parents=True, exist_ok=True)
     with open(model_path, "wb") as f:
         pickle.dump(blending_attacker.trained_model, f)
@@ -136,7 +136,7 @@ def run_metaclassifier_training(
     # Save the evaluation prediction probabilities
     attack_evaluation_result_path = Path(config.data_paths.attack_evaluation_result_path)
     attack_evaluation_result_path.mkdir(parents=True, exist_ok=True)
-    file_name = attack_evaluation_result_path / f"{config.metaclassifier.model_type}_val_pred_proba.npy"
+    file_name = attack_evaluation_result_path / f"{model_filename}_val_pred_proba.npy"
     np.save(file_name, probabilities)
     log(INFO, f"Evaluation prediction probabilities saved at {file_name}.")
 
diff --git a/examples/ensemble_attack/run_test.sh b/examples/ensemble_attack/run_test.sh
index 4f14f213..70d7c084 100644
--- a/examples/ensemble_attack/run_test.sh
+++ b/examples/ensemble_attack/run_test.sh
@@ -3,24 +3,30 @@
 #SBATCH --nodes=1
 #SBATCH --ntasks=1
 #SBATCH --ntasks-per-node=1
-#SBATCH --cpus-per-task=2
+#SBATCH --cpus-per-task=1
 #SBATCH --gres=gpu:1
 #SBATCH --mem=32G
 #SBATCH --qos=m
 #SBATCH --job-name=ensemble_attack_test
-#SBATCH --output=%j_%x.out
-#SBATCH --error=%j_%x.err
-#SBATCH --time=5:00:00
+#SBATCH --output=%j_%x_%a.out
+#SBATCH --error=%j_%x_%a.err
+#SBATCH --time=9:00:00
+#SBATCH --array=0-2  # For 3 target_model_ids (adjust range as needed)
 
-
-# This script sets up the environment and runs the ensemble attack example.
+# This script sets up the environment and runs the ensemble attack test script.
 source .venv/bin/activate
 
 echo "Active Environment:"
 which python
 
+# Map SLURM_ARRAY_TASK_ID to target_model_id.
+TARGET_IDS=(21 22 23)  # List of target IDs
+TARGET_ID=${TARGET_IDS[$SLURM_ARRAY_TASK_ID]}
+
+echo "Running test for target_model_id: $TARGET_ID"
+
 echo "Experiments Launched"
 
-python -m examples.ensemble_attack.test_attack_model
+python -m examples.ensemble_attack.test_attack_model target_model.target_model_id=$TARGET_ID # Overrides the target_model_id in config.
 
 echo "Experiments Completed"
diff --git a/examples/ensemble_attack/run_train.sh b/examples/ensemble_attack/run_train.sh
index 3ec6fc11..72151c4b 100755
--- a/examples/ensemble_attack/run_train.sh
+++ b/examples/ensemble_attack/run_train.sh
@@ -3,7 +3,7 @@
 #SBATCH --nodes=1
 #SBATCH --ntasks=1
 #SBATCH --ntasks-per-node=1
-#SBATCH --cpus-per-task=2
+#SBATCH --cpus-per-task=1
 #SBATCH --gres=gpu:1
 #SBATCH --mem=32G
 #SBATCH --qos=m
diff --git a/examples/ensemble_attack/test_attack_model.py b/examples/ensemble_attack/test_attack_model.py
index 4eb10c12..f0766b11 100644
--- a/examples/ensemble_attack/test_attack_model.py
+++ b/examples/ensemble_attack/test_attack_model.py
@@ -43,8 +43,8 @@ def run_metaclassifier_testing(
     # 1) Load the trained metaclassifier model to make sure it exists before proceeding.
     meta_classifier_type = MetaClassifierType(config.metaclassifier.model_type)
 
-    model_name = config.metaclassifier.meta_classifier_model_name
-    mataclassifier_path = Path(config.model_paths.metaclassifier_model_path) / model_name
+    metaclassifier_model_name = config.metaclassifier.meta_classifier_model_name
+    mataclassifier_path = Path(config.model_paths.metaclassifier_model_path) / f"{metaclassifier_model_name}.pkl"
     assert mataclassifier_path.exists(), (
         f"No metaclassifier model found at {mataclassifier_path}. Make sure to run the training script first."
     )
@@ -60,11 +60,16 @@ def run_metaclassifier_testing(
     # We also load challenge labels to report the attack performance.
     challenge_data_path = Path(config.target_model.challenge_data_path)
     challenge_label_path = Path(config.target_model.challenge_label_path)
+
     test_data = pd.read_csv(challenge_data_path)
+    log(INFO, f"Challenge data loaded from {challenge_data_path} with a size of {len(test_data)}.")
+
     test_target = pd.read_csv(challenge_label_path).to_numpy().squeeze()
+    assert len(test_data) == len(test_target), "Number of challenge labels must match number of challenge data points."
 
     target_synthetic_path = Path(config.target_model.target_synthetic_data_path)
     target_synthetic_data = pd.read_csv(target_synthetic_path)
+    log(INFO, f"Target synthetic data loaded from {target_synthetic_path} with a size of {len(target_synthetic_data)}.")
 
     # Extract trans_id from the test dataframe
     with open(Path(config.metaclassifier.data_types_file_path), "r") as f:
@@ -132,13 +137,18 @@ def run_metaclassifier_testing(
     # Save the validation prediction probabilities
     attack_results_path = Path(config.target_model.attack_probabilities_result_path)
     attack_results_path.mkdir(parents=True, exist_ok=True)
-    file_name = attack_results_path / f"{config.metaclassifier.model_type}_val_pred_proba.npy"
+    file_name = attack_results_path / f"{metaclassifier_model_name}_test_pred_proba.npy"
     np.save(file_name, probabilities)
     log(INFO, f"Test prediction probabilities saved at {file_name}.")
 
     if pred_score is not None:
         log(INFO, f"TPR at FPR=0.1: {pred_score:.4f}")
 
+        # Save the metric results into a text file.
+        metric_save_path = attack_results_path / f"prediction_score_{metaclassifier_model_name}.txt"
+        with open(metric_save_path, "w") as f:
+            f.write(f"TPR at FPR=0.1: {pred_score:.4f}\n")
+
 
 if __name__ == "__main__":
     run_metaclassifier_testing()

From cb7e65c8c704a007ec2811f4c1631f1768a736d0 Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Thu, 20 Nov 2025 11:20:42 -0500
Subject: [PATCH 12/41] Small fix

---
 examples/ensemble_attack/test_attack_model.py | 54 ++++++++++++-------
 1 file changed, 36 insertions(+), 18 deletions(-)

diff --git a/examples/ensemble_attack/test_attack_model.py b/examples/ensemble_attack/test_attack_model.py
index f0766b11..2eccb0e7 100644
--- a/examples/ensemble_attack/test_attack_model.py
+++ b/examples/ensemble_attack/test_attack_model.py
@@ -4,6 +4,7 @@
 import pickle
 from logging import INFO
 from pathlib import Path
+from typing import Any
 
 import hydra
 import numpy as np
@@ -17,6 +18,37 @@
 from midst_toolkit.common.random import set_all_random_seeds
 
 
+def run_rmia_shadow_training(config: DictConfig) -> list[dict[str, list[Any]]]:
+    """
+    Three sets of shadow models will be trained as a part of this attack.
+    Note that for every new target model, shadow models need to be trained.
+    RMIA signals (for the challenge points) are calculated based on these shadow models,
+    and will be fed into the metaclassifier.
+
+    Args:
+        config: Configuration object set in ``experiments_config.yaml``.
+
+    Return:
+        A list containing three dictionaries, each representing a collection of shadow
+            models with their training data and generated synthetic outputs.
+    """
+    shadow_model_paths = run_shadow_model_training(config)
+
+    assert len(shadow_model_paths) == 3, "For testing, meta classifier needs the path to three sets of shadow models."
+
+    shadow_data_collection = []
+    for model_path in shadow_model_paths:
+        assert model_path.exists(), (
+            f"No file found at {model_path}. Make sure the path is correct, or run shadow model training first."
+        )
+
+        with open(model_path, "rb") as f:
+            shadow_data_and_result = pickle.load(f)
+            shadow_data_collection.append(shadow_data_and_result)
+
+    return shadow_data_collection
+
+
 @hydra.main(config_path="configs", config_name="experiment_config", version_base=None)
 def run_metaclassifier_testing(
     config: DictConfig,
@@ -69,7 +101,9 @@ def run_metaclassifier_testing(
 
     target_synthetic_path = Path(config.target_model.target_synthetic_data_path)
     target_synthetic_data = pd.read_csv(target_synthetic_path)
-    log(INFO, f"Target synthetic data loaded from {target_synthetic_path} with a size of {len(target_synthetic_data)}.")
+    log(
+        INFO, f"Target synthetic data loaded from {target_synthetic_path} with a size of {len(target_synthetic_data)}."
+    )
 
     # Extract trans_id from the test dataframe
     with open(Path(config.metaclassifier.data_types_file_path), "r") as f:
@@ -85,26 +119,10 @@ def run_metaclassifier_testing(
 
     # 3) Shadow Model Training Step.
 
-    # Three sets of shadow models will be trained as a part of this attack.
-    # Note that for every new target model, shadow models need to be trained.
-    # RMIA signals (for the challenge points) are calculated based on these shadow models,
-    # and will be fed into the metaclassifier.
     # Make sure to assign a new path for shadow models trained for target's challenge points to
     # avoid overriding train's shadow models.
     config.shadow_training.shadow_models_output_path = config.target_model.target_shadow_models_output_path
-    shadow_model_paths = run_shadow_model_training(config)
-
-    assert len(shadow_model_paths) == 3, "For testing, meta classifier needs the path to three sets of shadow models."
-
-    shadow_data_collection = []
-    for model_path in shadow_model_paths:
-        assert model_path.exists(), (
-            f"No file found at {model_path}. Make sure the path is correct, or run shadow model training first."
-        )
-
-        with open(model_path, "rb") as f:
-            shadow_data_and_result = pickle.load(f)
-            shadow_data_collection.append(shadow_data_and_result)
+    shadow_data_collection = run_rmia_shadow_training(config)
 
     # 4) Initialize the attacker object, and assign the loaded metaclassifier to it.
     target_synthetic_data = target_synthetic_data.copy()

From 1b76b085b0b23b47a6351616f619eae3ec320424 Mon Sep 17 00:00:00 2001
From: fatemetkl <t.fateme@gmail.com>
Date: Fri, 21 Nov 2025 11:01:54 -0700
Subject: [PATCH 13/41] Added the success calculation script

---
 .../ensemble_attack/compute_attack_success.py | 93 +++++++++++++++++++
 .../configs/experiment_config.yaml            |  2 +
 2 files changed, 95 insertions(+)
 create mode 100644 examples/ensemble_attack/compute_attack_success.py

diff --git a/examples/ensemble_attack/compute_attack_success.py b/examples/ensemble_attack/compute_attack_success.py
new file mode 100644
index 00000000..9f3ebd32
--- /dev/null
+++ b/examples/ensemble_attack/compute_attack_success.py
@@ -0,0 +1,93 @@
+"""Provided test prediction probabilities of several attacked target models, this script computes and saves the attack success metric."""
+
+from pathlib import Path
+import hydra
+from logging import INFO
+import numpy as np
+import pandas as pd
+from omegaconf import DictConfig
+
+from midst_toolkit.attacks.ensemble.metric_utils import get_tpr_at_fpr
+from midst_toolkit.common.logger import log
+
+def load_target_challenge_labels_and_probabilities(
+    config: DictConfig,
+) -> tuple[np.ndarray, np.ndarray]:
+
+    """
+    Function to compute and save the attack success metric given the test prediction probabilities
+    of several attacked target models.
+
+    Args:
+        config: Configuration object set in ``experiments_config.yaml``.
+    """
+    metaclassifier_model_name = config.metaclassifier.meta_classifier_model_name
+    #  ``attack_probabilities_result_path`` is based on the target model's id.
+    attack_results_path = Path(config.target_model.attack_probabilities_result_path)
+    attack_result_file_path = attack_results_path / f"{metaclassifier_model_name}_test_pred_proba.npy"
+    assert (
+        attack_result_file_path.exists()
+    ), f"No file found at {attack_result_file_path}. Make sure the path is correct, or run the attack on the target model first."
+
+    # Load the attack results containing test prediction probabilities.
+    test_prediction_probabilities = np.load(attack_result_file_path)
+
+    # Challenge labels are the true membership labels for the challenge points.
+    challenge_label_path = Path(config.target_model.challenge_label_path)
+    test_target = np.load(challenge_label_path)
+
+    assert len(test_prediction_probabilities) == len(
+        test_target
+    ), "Number of challenge labels must match number of prediction probabilities."
+
+    return test_target, test_prediction_probabilities
+
+
+def compute_attack_success_for_given_targets(config: DictConfig, target_ids:list[int]):
+    experiment_directory = Path(config.base_experiment_dir)
+    predictions = []
+    targets = []
+    for target_id in target_ids:
+        # Override target model id in config
+        config.target_model.target_model_id = target_id
+        # Load challenge labels and prediction probabilities
+        test_target, test_prediction_probabilities = load_target_challenge_labels_and_probabilities(config)
+        predictions.append(test_prediction_probabilities)
+        targets.append(test_target)
+
+    # Flatten arrays
+    predictions = np.concatenate(predictions)
+    solutions = np.concatenate(solutions)
+
+    # Compute TPR@FPR for all the target models
+    tpr_at_fpr = get_tpr_at_fpr(solutions, predictions, max_fpr=0.1)
+
+    # Save the final attack success rate into a text file.
+    metaclassifier_model_name = config.metaclassifier.meta_classifier_model_name
+    metric_save_path = (
+        experiment_directory / f"attack_success_for_{metaclassifier_model_name}.txt"
+    )
+    with open(metric_save_path, "w") as f:
+        f.write(f"Final TPR at FPR=0.1: {tpr_at_fpr:.4f}\n")
+
+@hydra.main(config_path="configs", config_name="experiment_config", version_base=None)
+def main(
+    config: DictConfig,
+) -> None:
+    """
+    Main function to compute and save the attack success metric given the test prediction probabilities
+    of several attacked target models.
+
+    Args:
+        config: Configuration object set in ``experiments_config.yaml``.
+    """
+    if config.attack_success_computation.target_ids_to_test is not None:
+        target_ids = list(config.attack_success_computation.target_ids_to_test)
+    else:
+        target_ids = range(21,30)  # Default target model IDs
+    log(INFO, f"Computing attack success for target model IDs: {target_ids}...")
+    compute_attack_success_for_given_targets(config, target_ids)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/ensemble_attack/configs/experiment_config.yaml b/examples/ensemble_attack/configs/experiment_config.yaml
index 5973c624..d49fadd8 100644
--- a/examples/ensemble_attack/configs/experiment_config.yaml
+++ b/examples/ensemble_attack/configs/experiment_config.yaml
@@ -103,6 +103,8 @@ metaclassifier:
   epochs: 1
   meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model
 
+attack_success_computation:
+  target_ids_to_test: [21,22,23,24,25] # List of target model IDs to compute the attack success for.
 
 # General settings
 random_seed: 42 # Set to null for no seed, or an integer for a fixed seed

From 3e301f2ff8c0bf43e124a4808ba04bb7113a3b34 Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Fri, 21 Nov 2025 13:40:35 -0500
Subject: [PATCH 14/41] Finalized the script

---
 .../ensemble_attack/compute_attack_success.py | 59 ++++++++++++-------
 .../configs/experiment_config.yaml            |  2 +-
 2 files changed, 38 insertions(+), 23 deletions(-)

diff --git a/examples/ensemble_attack/compute_attack_success.py b/examples/ensemble_attack/compute_attack_success.py
index 9f3ebd32..0fffd270 100644
--- a/examples/ensemble_attack/compute_attack_success.py
+++ b/examples/ensemble_attack/compute_attack_success.py
@@ -1,8 +1,11 @@
-"""Provided test prediction probabilities of several attacked target models, this script computes and saves the attack success metric."""
+"""Provided test prediction probabilities of several attacked target models,
+this script computes and saves the attack success metric.
+"""
 
+from logging import INFO
 from pathlib import Path
+
 import hydra
-from logging import INFO
 import numpy as np
 import pandas as pd
 from omegaconf import DictConfig
@@ -10,13 +13,13 @@
 from midst_toolkit.attacks.ensemble.metric_utils import get_tpr_at_fpr
 from midst_toolkit.common.logger import log
 
+
 def load_target_challenge_labels_and_probabilities(
     config: DictConfig,
 ) -> tuple[np.ndarray, np.ndarray]:
-
     """
-    Function to compute and save the attack success metric given the test prediction probabilities
-    of several attacked target models.
+    Loads and returns the challenge labels and test prediction probabilities for
+    a given target model.
 
     Args:
         config: Configuration object set in ``experiments_config.yaml``.
@@ -25,25 +28,33 @@ def load_target_challenge_labels_and_probabilities(
     #  ``attack_probabilities_result_path`` is based on the target model's id.
     attack_results_path = Path(config.target_model.attack_probabilities_result_path)
     attack_result_file_path = attack_results_path / f"{metaclassifier_model_name}_test_pred_proba.npy"
-    assert (
-        attack_result_file_path.exists()
-    ), f"No file found at {attack_result_file_path}. Make sure the path is correct, or run the attack on the target model first."
+    assert attack_result_file_path.exists(), (
+        f"No file found at {attack_result_file_path}. Make sure the path is correct, or run the attack on the target model first."
+    )
 
     # Load the attack results containing test prediction probabilities.
     test_prediction_probabilities = np.load(attack_result_file_path)
 
     # Challenge labels are the true membership labels for the challenge points.
     challenge_label_path = Path(config.target_model.challenge_label_path)
-    test_target = np.load(challenge_label_path)
+    test_target = pd.read_csv(challenge_label_path).to_numpy().squeeze()
 
-    assert len(test_prediction_probabilities) == len(
-        test_target
-    ), "Number of challenge labels must match number of prediction probabilities."
+    assert len(test_prediction_probabilities) == len(test_target), (
+        "Number of challenge labels must match number of prediction probabilities."
+    )
 
     return test_target, test_prediction_probabilities
 
 
-def compute_attack_success_for_given_targets(config: DictConfig, target_ids:list[int]):
+def compute_attack_success_for_given_targets(config: DictConfig, target_ids: list[int]) -> None:
+    """
+    Computes and saves the attack success metric given the test prediction probabilities
+    of several attacked target models by concatenating the target models' targets and predictions.
+
+    Args:
+        config: Configuration object set in ``experiments_config.yaml``.
+        target_ids: List of target model IDs to compute the attack success for.
+    """
     experiment_directory = Path(config.base_experiment_dir)
     predictions = []
     targets = []
@@ -57,19 +68,22 @@ def compute_attack_success_for_given_targets(config: DictConfig, target_ids:list
 
     # Flatten arrays
     predictions = np.concatenate(predictions)
-    solutions = np.concatenate(solutions)
+    targets = np.concatenate(targets)
+
+    assert len(predictions) == len(targets), "Number of predictions must match number of targets."
 
     # Compute TPR@FPR for all the target models
-    tpr_at_fpr = get_tpr_at_fpr(solutions, predictions, max_fpr=0.1)
+    tpr_at_fpr = get_tpr_at_fpr(targets, predictions, max_fpr=0.1)
 
     # Save the final attack success rate into a text file.
     metaclassifier_model_name = config.metaclassifier.meta_classifier_model_name
-    metric_save_path = (
-        experiment_directory / f"attack_success_for_{metaclassifier_model_name}.txt"
-    )
+    metric_save_path = experiment_directory / f"attack_success_for_{metaclassifier_model_name}.txt"
+
+    log(INFO, f"Saving attack success value of {tpr_at_fpr} TPR at FPR=0.1 to {metric_save_path}")
     with open(metric_save_path, "w") as f:
         f.write(f"Final TPR at FPR=0.1: {tpr_at_fpr:.4f}\n")
 
+
 @hydra.main(config_path="configs", config_name="experiment_config", version_base=None)
 def main(
     config: DictConfig,
@@ -81,10 +95,11 @@ def main(
     Args:
         config: Configuration object set in ``experiments_config.yaml``.
     """
-    if config.attack_success_computation.target_ids_to_test is not None:
-        target_ids = list(config.attack_success_computation.target_ids_to_test)
-    else:
-        target_ids = range(21,30)  # Default target model IDs
+    assert config.attack_success_computation.target_ids_to_test is not None, (
+        "Please specify target model IDs to compute attack success for in the config "
+        "by specifying `attack_success_computation.target_ids_to_test`."
+    )
+    target_ids = list(config.attack_success_computation.target_ids_to_test)
     log(INFO, f"Computing attack success for target model IDs: {target_ids}...")
     compute_attack_success_for_given_targets(config, target_ids)
 
diff --git a/examples/ensemble_attack/configs/experiment_config.yaml b/examples/ensemble_attack/configs/experiment_config.yaml
index d49fadd8..3803d69d 100644
--- a/examples/ensemble_attack/configs/experiment_config.yaml
+++ b/examples/ensemble_attack/configs/experiment_config.yaml
@@ -104,7 +104,7 @@ metaclassifier:
   meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model
 
 attack_success_computation:
-  target_ids_to_test: [21,22,23,24,25] # List of target model IDs to compute the attack success for.
+  target_ids_to_test: [21,22,23] # List of target model IDs to compute the attack success for.
 
 # General settings
 random_seed: 42 # Set to null for no seed, or an integer for a fixed seed

From 9545f4a23ad8ee8d5684bf6d9ff239e2802fefc0 Mon Sep 17 00:00:00 2001
From: fatemetkl <t.fateme@gmail.com>
Date: Tue, 25 Nov 2025 11:24:59 -0700
Subject: [PATCH 15/41] David Comments first pass

---
 .../ensemble_attack/compute_attack_success.py | 49 ++++++++++++++-----
 1 file changed, 36 insertions(+), 13 deletions(-)

diff --git a/examples/ensemble_attack/compute_attack_success.py b/examples/ensemble_attack/compute_attack_success.py
index 0fffd270..a473ec69 100644
--- a/examples/ensemble_attack/compute_attack_success.py
+++ b/examples/ensemble_attack/compute_attack_success.py
@@ -15,18 +15,24 @@
 
 
 def load_target_challenge_labels_and_probabilities(
-    config: DictConfig,
+    metaclassifier_model_name: str, attack_results_path: Path, challenge_label_path: Path
 ) -> tuple[np.ndarray, np.ndarray]:
     """
     Loads and returns the challenge labels and test prediction probabilities for
     a given target model.
 
     Args:
-        config: Configuration object set in ``experiments_config.yaml``.
+        metaclassifier_model_name: Name of the metaclassifier model used in the attack.
+        attack_results_path: Path to the directory where attack results are saved.
+        challenge_label_path: Path to the CSV file containing challenge labels.
+
+    Return:
+        A tuple containing:
+            - test_target: Numpy array of true membership labels for the challenge points.
+            - test_prediction_probabilities: Numpy array of prediction probabilities
+              outputted by the metaclassifier for the challenge points.
     """
-    metaclassifier_model_name = config.metaclassifier.meta_classifier_model_name
     #  ``attack_probabilities_result_path`` is based on the target model's id.
-    attack_results_path = Path(config.target_model.attack_probabilities_result_path)
     attack_result_file_path = attack_results_path / f"{metaclassifier_model_name}_test_pred_proba.npy"
     assert attack_result_file_path.exists(), (
         f"No file found at {attack_result_file_path}. Make sure the path is correct, or run the attack on the target model first."
@@ -36,7 +42,6 @@ def load_target_challenge_labels_and_probabilities(
     test_prediction_probabilities = np.load(attack_result_file_path)
 
     # Challenge labels are the true membership labels for the challenge points.
-    challenge_label_path = Path(config.target_model.challenge_label_path)
     test_target = pd.read_csv(challenge_label_path).to_numpy().squeeze()
 
     assert len(test_prediction_probabilities) == len(test_target), (
@@ -46,23 +51,37 @@ def load_target_challenge_labels_and_probabilities(
     return test_target, test_prediction_probabilities
 
 
-def compute_attack_success_for_given_targets(config: DictConfig, target_ids: list[int]) -> None:
+def compute_attack_success_for_given_targets(
+    target_model_config: DictConfig,
+    target_ids: list[int],
+    experiment_directory: Path,
+    metaclassifier_model_name: str,
+) -> None:
     """
     Computes and saves the attack success metric given the test prediction probabilities
     of several attacked target models by concatenating the target models' targets and predictions.
+    NOTE: This function does not compute the average success across all models but rather
+    treats all predictions and labels together for metric computation.
 
     Args:
-        config: Configuration object set in ``experiments_config.yaml``.
+        target_model_config: Configuration object for target models set in ``experiments_config.yaml``.
         target_ids: List of target model IDs to compute the attack success for.
+        experiment_directory: Path to the base experiment directory where results are saved.
+        metaclassifier_model_name: Name of the metaclassifier model used in the attack.
     """
-    experiment_directory = Path(config.base_experiment_dir)
     predictions = []
     targets = []
     for target_id in target_ids:
-        # Override target model id in config
-        config.target_model.target_model_id = target_id
+        # Override target model id in config as ``attack_probabilities_result_path`` and
+        # ``challenge_label_path`` are dependent on it and change in runtime.
+        target_model_config.target_model_id = target_id
         # Load challenge labels and prediction probabilities
-        test_target, test_prediction_probabilities = load_target_challenge_labels_and_probabilities(config)
+        log(INFO, f"Loading challenge labels and prediction probabilities for target model ID {target_id}...")
+        test_target, test_prediction_probabilities = load_target_challenge_labels_and_probabilities(
+            metaclassifier_model_name=metaclassifier_model_name,
+            attack_results_path=Path(target_model_config.attack_probabilities_result_path),
+            challenge_label_path=Path(target_model_config.challenge_label_path),
+        )
         predictions.append(test_prediction_probabilities)
         targets.append(test_target)
 
@@ -76,7 +95,6 @@ def compute_attack_success_for_given_targets(config: DictConfig, target_ids: lis
     tpr_at_fpr = get_tpr_at_fpr(targets, predictions, max_fpr=0.1)
 
     # Save the final attack success rate into a text file.
-    metaclassifier_model_name = config.metaclassifier.meta_classifier_model_name
     metric_save_path = experiment_directory / f"attack_success_for_{metaclassifier_model_name}.txt"
 
     log(INFO, f"Saving attack success value of {tpr_at_fpr} TPR at FPR=0.1 to {metric_save_path}")
@@ -101,7 +119,12 @@ def main(
     )
     target_ids = list(config.attack_success_computation.target_ids_to_test)
     log(INFO, f"Computing attack success for target model IDs: {target_ids}...")
-    compute_attack_success_for_given_targets(config, target_ids)
+    compute_attack_success_for_given_targets(
+        target_model_config=config.target_model,
+        target_ids=target_ids,
+        experiment_directory=Path(config.base_experiment_dir),
+        metaclassifier_model_name=config.metaclassifier.meta_classifier_model_name,
+    )
 
 
 if __name__ == "__main__":

From 9c750ae2e2d8c152970c158e754183e1ca51455c Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Tue, 25 Nov 2025 14:49:48 -0500
Subject: [PATCH 16/41] Removed extra comment

---
 examples/ensemble_attack/compute_attack_success.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/examples/ensemble_attack/compute_attack_success.py b/examples/ensemble_attack/compute_attack_success.py
index a473ec69..5a80a8e9 100644
--- a/examples/ensemble_attack/compute_attack_success.py
+++ b/examples/ensemble_attack/compute_attack_success.py
@@ -32,7 +32,6 @@ def load_target_challenge_labels_and_probabilities(
             - test_prediction_probabilities: Numpy array of prediction probabilities
               outputted by the metaclassifier for the challenge points.
     """
-    #  ``attack_probabilities_result_path`` is based on the target model's id.
     attack_result_file_path = attack_results_path / f"{metaclassifier_model_name}_test_pred_proba.npy"
     assert attack_result_file_path.exists(), (
         f"No file found at {attack_result_file_path}. Make sure the path is correct, or run the attack on the target model first."

From e79f9ccc2778cac27b2c0eb426b037d645c37a5a Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Tue, 25 Nov 2025 14:52:42 -0500
Subject: [PATCH 17/41] Just saving experiment configs for my own reference

---
 .../configs/experiment_config.yaml            |  14 +--
 .../configs/experiment_config_100k.yaml       | 110 ++++++++++++++++++
 .../configs/experiment_config_10k.yaml        | 110 ++++++++++++++++++
 examples/ensemble_attack/run_train.sh         |   6 +-
 examples/ensemble_attack/run_train_100k.sh    |  26 +++++
 examples/ensemble_attack/run_train_10k.sh     |  26 +++++
 6 files changed, 282 insertions(+), 10 deletions(-)
 create mode 100644 examples/ensemble_attack/configs/experiment_config_100k.yaml
 create mode 100644 examples/ensemble_attack/configs/experiment_config_10k.yaml
 create mode 100755 examples/ensemble_attack/run_train_100k.sh
 create mode 100755 examples/ensemble_attack/run_train_10k.sh

diff --git a/examples/ensemble_attack/configs/experiment_config.yaml b/examples/ensemble_attack/configs/experiment_config.yaml
index 3803d69d..b988003f 100644
--- a/examples/ensemble_attack/configs/experiment_config.yaml
+++ b/examples/ensemble_attack/configs/experiment_config.yaml
@@ -10,11 +10,11 @@ pipeline:
   run_metaclassifier_training: true
 
 target_model: # This is only used for testing the attack on a real target model.
-  # This is for models trained on 20k data and generating 20k synthetic data
-  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_20k/train/
+  # This is for models trained on 50k data and generating 50k synthetic data
+  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_50k/train/
   target_model_id: 21  # Will be overridden per SLURM array task
   target_model_name: tabddpm_${target_model.target_model_id}
-  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/20k/20k.csv
+  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/50k/50k.csv
   challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
   challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
 
@@ -38,11 +38,11 @@ model_paths:
 data_processing_config:
   population_attack_data_types_to_collect:
         [
-          "tabddpm_trained_with_20k",
+          "tabddpm_trained_with_50k",
         ]
   challenge_attack_data_types_to_collect:
         [
-          "tabddpm_trained_with_20k",
+          "tabddpm_trained_with_50k",
         ]
   population_splits: ["train"]  # Data splits to be collected for population data
   challenge_splits: ["train"]  # Data splits to be collected for challenge points
@@ -54,7 +54,7 @@ data_processing_config:
   single_table_train_data_file_name: "train_with_id.csv"
   multi_table_train_data_file_name: "trans.csv"
   challenge_data_file_name: "challenge_with_id.csv"
-  population_sample_size: 40000 # Population size is the total data that your attack has access to.
+  population_sample_size: 100000 # Population size is the total data that your attack has access to.
   # In experiments, this is sampled out of all the collected training data in case the available data
   # is more than this number. Note that, half of this data is actually used for training, the other half
   # is used for evaluation. For example, with 40k population size, only 20k is used for training the attack model.
@@ -86,7 +86,7 @@ shadow_training:
     fine_tune_diffusion_iterations: 200000 # Original code: 200000
     fine_tune_classifier_iterations: 20000 # Original code: 20000
     pre_train_data_size: 60000 # Original code: 60000
-  number_of_points_to_synthesize: 20000 # Number of synthetic data samples to be generated by shadow models.
+  number_of_points_to_synthesize: 50000 # Number of synthetic data samples to be generated by shadow models.
   # Original code: 20000
 
 
diff --git a/examples/ensemble_attack/configs/experiment_config_100k.yaml b/examples/ensemble_attack/configs/experiment_config_100k.yaml
new file mode 100644
index 00000000..a64e0bf9
--- /dev/null
+++ b/examples/ensemble_attack/configs/experiment_config_100k.yaml
@@ -0,0 +1,110 @@
+# Ensemble experiment configuration
+# This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
+base_experiment_dir: /projects/midst-experiments/ensemble_attack/tabddpm_100k_experiment_data # Processed data, and experiment artifacts will be stored here
+base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
+
+# Pipeline control
+pipeline:
+  run_data_processing: true # Set this to false if you have already saved the processed data
+  run_shadow_model_training: true # Set this to false if shadow models are already trained and saved
+  run_metaclassifier_training: true
+
+target_model: # This is only used for testing the attack on a real target model.
+  # This is for models trained on 10k data and generating `0k synthetic data
+  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_100k/train/
+  target_model_id: 21  # Will be overridden per SLURM array task
+  target_model_name: tabddpm_${target_model.target_model_id}
+  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/100k/100k.csv
+  challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
+  challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
+
+  target_attack_artifact_dir: ${base_experiment_dir}/target_${target_model.target_model_id}_attack_artifacts/
+  attack_probabilities_result_path: ${target_model.target_attack_artifact_dir}/attack_model_${target_model.target_model_id}_proba
+  target_shadow_models_output_path: ${target_model.target_attack_artifact_dir}/tabddpm_${target_model.target_model_id}_shadows_dir
+
+
+# Data paths
+data_paths:
+  midst_data_path: /projects/midst-experiments/all_tabddpms # Used to collect the data
+  population_path: ${base_experiment_dir}/population_data  # Path where the collected population data will be stored
+  processed_attack_data_path: ${base_experiment_dir}/attack_data # Path where the processed attack real train and evaluation data is stored
+  attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored
+
+model_paths:
+  metaclassifier_model_path: ${base_experiment_dir}/trained_models # Path where the trained metaclassifier model will be saved
+
+
+# Dataset specific information used for processing in this example
+data_processing_config:
+  population_attack_data_types_to_collect:
+        [
+          "tabddpm_trained_with_100k",
+        ]
+  challenge_attack_data_types_to_collect:
+        [
+          "tabddpm_trained_with_100k",
+        ]
+  population_splits: ["train"]  # Data splits to be collected for population data
+  challenge_splits: ["train"]  # Data splits to be collected for challenge points
+  # The column name in the data to be used for stratified splitting.
+  column_to_stratify: "trans_type"  # Attention: This value is not documented in the original codebase.
+  folder_ranges: #Specify folder ranges for any of the mentioned splits.
+    train: [[1, 20]] # Folders to be used for train data collection in the experiments
+  # File names in MIDST data directories.
+  single_table_train_data_file_name: "train_with_id.csv"
+  multi_table_train_data_file_name: "trans.csv"
+  challenge_data_file_name: "challenge_with_id.csv"
+  population_sample_size: 200000 # Population size is the total data that your attack has access to.
+  # In experiments, this is sampled out of all the collected training data in case the available data
+  # is more than this number. Note that, half of this data is actually used for training, the other half
+  # is used for evaluation. For example, with 40k population size, only 20k is used for training the attack model.
+  # TODO: make sure to consider this in experiments.
+
+# Training and data settings for shadow models (temporary, numbers subject to change)
+shadow_training:
+  # Data Config files path used for training a TabDDPM model
+  training_json_config_paths: # Config json files used for tabddpm training on the trans table
+    table_domain_file_path: ${base_data_config_dir}/trans_domain.json
+    dataset_meta_file_path: ${base_data_config_dir}/dataset_meta.json
+    tabddpm_training_config_path: ${base_data_config_dir}/trans.json
+  # Model training artifacts are saved under shadow_models_data_path/workspace_name/exp_name
+  # Also, training configs for each shadow model are created under shadow_models_data_path.
+  shadow_models_output_path: ${base_experiment_dir}/shadow_models_and_data
+  target_model_output_path: ${base_experiment_dir}/shadow_target_model_and_data
+  # Paths to final shadow models used for metaclassifier training (relative to shadow_models_output_path)
+  # These paths are a result of running the shadow model training pipeline, specifically the
+  # train_three_sets_of_shadow_models in shadow_model_training.py
+  # Each .pkl file contains the training data, trained model and training results for all shadow models in a list.
+  final_shadow_models_path: [
+          "${shadow_training.shadow_models_output_path}/initial_model_rmia_1/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
+          "${shadow_training.shadow_models_output_path}/initial_model_rmia_2/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
+          "${shadow_training.shadow_models_output_path}/shadow_model_rmia_third_set/shadow_workspace/trained_model/rmia_shadows_third_set.pkl",
+      ]
+  target_synthetic_data_path: ${shadow_training.target_model_output_path}/target_synthetic_data.csv
+  # Path to final shadow target's synthetic data (relative to target_model_output_path)
+  fine_tuning_config:
+    fine_tune_diffusion_iterations: 200000 # Original code: 200000
+    fine_tune_classifier_iterations: 20000 # Original code: 20000
+    pre_train_data_size: 60000 # Original code: 60000
+  number_of_points_to_synthesize: 100000 # Number of synthetic data samples to be generated by shadow models.
+  # Original code: 20000
+
+
+# Metaclassifier settings
+metaclassifier:
+  # Data types json file is used for xgboost model training.
+  data_types_file_path: ${base_data_config_dir}/data_types.json
+  model_type: "xgb"
+  # Model training parameters
+  num_optuna_trials: 100 # Original code: 100
+  num_kfolds: 5
+  use_gpu: false
+  # Temporary. Might remove having an epoch parameter.
+  epochs: 1
+  meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model
+
+attack_success_computation:
+  target_ids_to_test: [21,22,23] # List of target model IDs to compute the attack success for.
+
+# General settings
+random_seed: 42 # Set to null for no seed, or an integer for a fixed seed
diff --git a/examples/ensemble_attack/configs/experiment_config_10k.yaml b/examples/ensemble_attack/configs/experiment_config_10k.yaml
new file mode 100644
index 00000000..4f3d251d
--- /dev/null
+++ b/examples/ensemble_attack/configs/experiment_config_10k.yaml
@@ -0,0 +1,110 @@
+# Ensemble experiment configuration
+# This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
+base_experiment_dir: /projects/midst-experiments/ensemble_attack/tabddpm_10k_experiment_data # Processed data, and experiment artifacts will be stored here
+base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
+
+# Pipeline control
+pipeline:
+  run_data_processing: true # Set this to false if you have already saved the processed data
+  run_shadow_model_training: true # Set this to false if shadow models are already trained and saved
+  run_metaclassifier_training: true
+
+target_model: # This is only used for testing the attack on a real target model.
+  # This is for models trained on 10k data and generating `0k synthetic data
+  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_10k/train/
+  target_model_id: 21  # Will be overridden per SLURM array task
+  target_model_name: tabddpm_${target_model.target_model_id}
+  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/10k/10k.csv
+  challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
+  challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
+
+  target_attack_artifact_dir: ${base_experiment_dir}/target_${target_model.target_model_id}_attack_artifacts/
+  attack_probabilities_result_path: ${target_model.target_attack_artifact_dir}/attack_model_${target_model.target_model_id}_proba
+  target_shadow_models_output_path: ${target_model.target_attack_artifact_dir}/tabddpm_${target_model.target_model_id}_shadows_dir
+
+
+# Data paths
+data_paths:
+  midst_data_path: /projects/midst-experiments/all_tabddpms # Used to collect the data
+  population_path: ${base_experiment_dir}/population_data  # Path where the collected population data will be stored
+  processed_attack_data_path: ${base_experiment_dir}/attack_data # Path where the processed attack real train and evaluation data is stored
+  attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored
+
+model_paths:
+  metaclassifier_model_path: ${base_experiment_dir}/trained_models # Path where the trained metaclassifier model will be saved
+
+
+# Dataset specific information used for processing in this example
+data_processing_config:
+  population_attack_data_types_to_collect:
+        [
+          "tabddpm_trained_with_10k",
+        ]
+  challenge_attack_data_types_to_collect:
+        [
+          "tabddpm_trained_with_10k",
+        ]
+  population_splits: ["train"]  # Data splits to be collected for population data
+  challenge_splits: ["train"]  # Data splits to be collected for challenge points
+  # The column name in the data to be used for stratified splitting.
+  column_to_stratify: "trans_type"  # Attention: This value is not documented in the original codebase.
+  folder_ranges: #Specify folder ranges for any of the mentioned splits.
+    train: [[1, 20]] # Folders to be used for train data collection in the experiments
+  # File names in MIDST data directories.
+  single_table_train_data_file_name: "train_with_id.csv"
+  multi_table_train_data_file_name: "trans.csv"
+  challenge_data_file_name: "challenge_with_id.csv"
+  population_sample_size: 20000 # Population size is the total data that your attack has access to.
+  # In experiments, this is sampled out of all the collected training data in case the available data
+  # is more than this number. Note that, half of this data is actually used for training, the other half
+  # is used for evaluation. For example, with 40k population size, only 20k is used for training the attack model.
+  # TODO: make sure to consider this in experiments.
+
+# Training and data settings for shadow models (temporary, numbers subject to change)
+shadow_training:
+  # Data Config files path used for training a TabDDPM model
+  training_json_config_paths: # Config json files used for tabddpm training on the trans table
+    table_domain_file_path: ${base_data_config_dir}/trans_domain.json
+    dataset_meta_file_path: ${base_data_config_dir}/dataset_meta.json
+    tabddpm_training_config_path: ${base_data_config_dir}/trans.json
+  # Model training artifacts are saved under shadow_models_data_path/workspace_name/exp_name
+  # Also, training configs for each shadow model are created under shadow_models_data_path.
+  shadow_models_output_path: ${base_experiment_dir}/shadow_models_and_data
+  target_model_output_path: ${base_experiment_dir}/shadow_target_model_and_data
+  # Paths to final shadow models used for metaclassifier training (relative to shadow_models_output_path)
+  # These paths are a result of running the shadow model training pipeline, specifically the
+  # train_three_sets_of_shadow_models in shadow_model_training.py
+  # Each .pkl file contains the training data, trained model and training results for all shadow models in a list.
+  final_shadow_models_path: [
+          "${shadow_training.shadow_models_output_path}/initial_model_rmia_1/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
+          "${shadow_training.shadow_models_output_path}/initial_model_rmia_2/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
+          "${shadow_training.shadow_models_output_path}/shadow_model_rmia_third_set/shadow_workspace/trained_model/rmia_shadows_third_set.pkl",
+      ]
+  target_synthetic_data_path: ${shadow_training.target_model_output_path}/target_synthetic_data.csv
+  # Path to final shadow target's synthetic data (relative to target_model_output_path)
+  fine_tuning_config:
+    fine_tune_diffusion_iterations: 200000 # Original code: 200000
+    fine_tune_classifier_iterations: 20000 # Original code: 20000
+    pre_train_data_size: 60000 # Original code: 60000
+  number_of_points_to_synthesize: 10000 # Number of synthetic data samples to be generated by shadow models.
+  # Original code: 20000
+
+
+# Metaclassifier settings
+metaclassifier:
+  # Data types json file is used for xgboost model training.
+  data_types_file_path: ${base_data_config_dir}/data_types.json
+  model_type: "xgb"
+  # Model training parameters
+  num_optuna_trials: 100 # Original code: 100
+  num_kfolds: 5
+  use_gpu: false
+  # Temporary. Might remove having an epoch parameter.
+  epochs: 1
+  meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model
+
+attack_success_computation:
+  target_ids_to_test: [21,22,23] # List of target model IDs to compute the attack success for.
+
+# General settings
+random_seed: 42 # Set to null for no seed, or an integer for a fixed seed
diff --git a/examples/ensemble_attack/run_train.sh b/examples/ensemble_attack/run_train.sh
index 72151c4b..ae8e043b 100755
--- a/examples/ensemble_attack/run_train.sh
+++ b/examples/ensemble_attack/run_train.sh
@@ -6,11 +6,11 @@
 #SBATCH --cpus-per-task=1
 #SBATCH --gres=gpu:1
 #SBATCH --mem=32G
-#SBATCH --qos=m
-#SBATCH --job-name=ensemble_attack_train
+#SBATCH --qos=normal
+#SBATCH --job-name=50k_ensemble_attack_train
 #SBATCH --output=%j_%x.out
 #SBATCH --error=%j_%x.err
-#SBATCH --time=12:00:00
+#SBATCH --time=16:00:00
 
 
 # This script sets up the environment and runs the ensemble attack example.
diff --git a/examples/ensemble_attack/run_train_100k.sh b/examples/ensemble_attack/run_train_100k.sh
new file mode 100755
index 00000000..f33ac154
--- /dev/null
+++ b/examples/ensemble_attack/run_train_100k.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=1
+#SBATCH --gres=gpu:1
+#SBATCH --mem=32G
+#SBATCH --qos=normal
+#SBATCH --job-name=100k_ensemble_attack_train
+#SBATCH --output=%j_%x.out
+#SBATCH --error=%j_%x.err
+#SBATCH --time=16:00:00
+
+
+# This script sets up the environment and runs the ensemble attack example.
+source .venv/bin/activate
+
+echo "Active Environment:"
+which python
+
+echo "Experiments Launched"
+
+python -m examples.ensemble_attack.run_attack --config-name=experiment_config_100k
+
+echo "Experiments Completed"
diff --git a/examples/ensemble_attack/run_train_10k.sh b/examples/ensemble_attack/run_train_10k.sh
new file mode 100755
index 00000000..fcb0933f
--- /dev/null
+++ b/examples/ensemble_attack/run_train_10k.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=1
+#SBATCH --gres=gpu:1
+#SBATCH --mem=32G
+#SBATCH --qos=normal
+#SBATCH --job-name=10k_ensemble_attack_train
+#SBATCH --output=%j_%x.out
+#SBATCH --error=%j_%x.err
+#SBATCH --time=16:00:00
+
+
+# This script sets up the environment and runs the ensemble attack example.
+source .venv/bin/activate
+
+echo "Active Environment:"
+which python
+
+echo "Experiments Launched"
+
+python -m examples.ensemble_attack.run_attack --config-name=experiment_config_10k
+
+echo "Experiments Completed"

From ecf22ab5cbb0f2c8b5664770dd78085b81c929d5 Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Tue, 2 Dec 2025 12:26:37 -0500
Subject: [PATCH 18/41] Latest changes

---
 .../configs/experiment_config.yaml            |  40 ++--
 .../configs/experiment_config_100k.yaml       |   4 +-
 .../configs/experiment_config_10k.yaml        |   2 +-
 .../configs/experiment_config_50k.yaml        | 110 +++++++++++
 examples/ensemble_attack/run_attack.py        |  24 ++-
 .../run_shadow_model_training.py              |  10 +-
 examples/ensemble_attack/run_test.sh          |  22 ++-
 examples/ensemble_attack/run_train.sh         |  10 +-
 examples/ensemble_attack/run_train_100k.sh    |   8 +-
 examples/ensemble_attack/test_attack_model.py |  35 +++-
 .../attacks/ensemble/rmia/rmia_calculation.py | 171 +++++++++++++-----
 11 files changed, 340 insertions(+), 96 deletions(-)
 create mode 100644 examples/ensemble_attack/configs/experiment_config_50k.yaml

diff --git a/examples/ensemble_attack/configs/experiment_config.yaml b/examples/ensemble_attack/configs/experiment_config.yaml
index b988003f..ad418eb3 100644
--- a/examples/ensemble_attack/configs/experiment_config.yaml
+++ b/examples/ensemble_attack/configs/experiment_config.yaml
@@ -1,48 +1,62 @@
+#testing 20k_20k 
+
 # Ensemble experiment configuration
 # This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
-base_experiment_dir: examples/ensemble_attack/tabddpm_20k_experiment_data # Processed data, and experiment artifacts will be stored here
+base_experiment_dir: /projects/midst-experiments/ensemble_attack/competition/ # Processed data, and experiment artifacts will be stored here
 base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
 
 # Pipeline control
+# only test: true, false, true
+# train and test: true, true, true
 pipeline:
-  run_data_processing: true # Set this to false if you have already saved the processed data
+  run_data_processing: false # Set this to false if you have already saved the processed data
   run_shadow_model_training: true # Set this to false if shadow models are already trained and saved
   run_metaclassifier_training: true
 
 target_model: # This is only used for testing the attack on a real target model.
-  # This is for models trained on 50k data and generating 50k synthetic data
-  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_50k/train/
+  # This is for models trained on 20k data and generating 20k synthetic data
+  # target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_20k/train/
+  # target_model_name: tabddpm_${target_model.target_model_id}
+  # target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/20k/20k.csv
+
+  # target_model_directory: /projects/midst-experiments/ensemble_attack/midst_data_all_attacks/tabddpm_black_box/final/ #Competition target models trained on 20k
+  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_10k/train/
   target_model_id: 21  # Will be overridden per SLURM array task
   target_model_name: tabddpm_${target_model.target_model_id}
-  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/50k/50k.csv
+  # target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/trans_synthetic.csv #Original competition file structure
+  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/10k/10k.csv # Behnoosh's models
+
   challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
   challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
 
-  target_attack_artifact_dir: ${base_experiment_dir}/target_${target_model.target_model_id}_attack_artifacts/
+  target_attack_artifact_dir: ${base_experiment_dir}/competition_trained_attack_results_10k_test/target_${target_model.target_model_id}_attack_artifacts/
   attack_probabilities_result_path: ${target_model.target_attack_artifact_dir}/attack_model_${target_model.target_model_id}_proba
   target_shadow_models_output_path: ${target_model.target_attack_artifact_dir}/tabddpm_${target_model.target_model_id}_shadows_dir
 
 
 # Data paths
 data_paths:
+  base_data_dir: /projects/midst-experiments/ensemble_attack/competition
   midst_data_path: /projects/midst-experiments/all_tabddpms # Used to collect the data
-  population_path: ${base_experiment_dir}/population_data  # Path where the collected population data will be stored
+  # population_path: ${base_experiment_dir}/population_data  # Path where the collected population data will be stored
+  population_path: ${data_paths.base_data_dir}/population_data #READ ONLY
   processed_attack_data_path: ${base_experiment_dir}/attack_data # Path where the processed attack real train and evaluation data is stored
   attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored
 
 model_paths:
   metaclassifier_model_path: ${base_experiment_dir}/trained_models # Path where the trained metaclassifier model will be saved
+  # metaclassifier_model_path: /projects/midst-experiments/ensemble_attack/tabddpm_20k_experiment_data/20k/trained_models #When only running the tests.
 
 
 # Dataset specific information used for processing in this example
 data_processing_config:
   population_attack_data_types_to_collect:
         [
-          "tabddpm_trained_with_50k",
+          "tabddpm_trained_with_20k",
         ]
   challenge_attack_data_types_to_collect:
         [
-          "tabddpm_trained_with_50k",
+          "tabddpm_trained_with_20k",
         ]
   population_splits: ["train"]  # Data splits to be collected for population data
   challenge_splits: ["train"]  # Data splits to be collected for challenge points
@@ -54,7 +68,7 @@ data_processing_config:
   single_table_train_data_file_name: "train_with_id.csv"
   multi_table_train_data_file_name: "trans.csv"
   challenge_data_file_name: "challenge_with_id.csv"
-  population_sample_size: 100000 # Population size is the total data that your attack has access to.
+  population_sample_size: 40000 # Population size is the total data that your attack has access to.
   # In experiments, this is sampled out of all the collected training data in case the available data
   # is more than this number. Note that, half of this data is actually used for training, the other half
   # is used for evaluation. For example, with 40k population size, only 20k is used for training the attack model.
@@ -86,7 +100,7 @@ shadow_training:
     fine_tune_diffusion_iterations: 200000 # Original code: 200000
     fine_tune_classifier_iterations: 20000 # Original code: 20000
     pre_train_data_size: 60000 # Original code: 60000
-  number_of_points_to_synthesize: 50000 # Number of synthetic data samples to be generated by shadow models.
+  number_of_points_to_synthesize: 20000 # Number of synthetic data samples to be generated by shadow models.
   # Original code: 20000
 
 
@@ -104,7 +118,9 @@ metaclassifier:
   meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model
 
 attack_success_computation:
-  target_ids_to_test: [21,22,23] # List of target model IDs to compute the attack success for.
+  target_ids_to_test: [61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110] # List of target model IDs to compute the attack success for.
+  # target_ids_to_test: [61, 62, 63, 64, 65, 66, 67, 68, 69, 70]
+  # target_ids_to_test: [101, 102, 103, 104, 105, 106, 107, 108, 109, 110]
 
 # General settings
 random_seed: 42 # Set to null for no seed, or an integer for a fixed seed
diff --git a/examples/ensemble_attack/configs/experiment_config_100k.yaml b/examples/ensemble_attack/configs/experiment_config_100k.yaml
index a64e0bf9..f3df5789 100644
--- a/examples/ensemble_attack/configs/experiment_config_100k.yaml
+++ b/examples/ensemble_attack/configs/experiment_config_100k.yaml
@@ -5,8 +5,8 @@ base_data_config_dir: examples/ensemble_attack/data_configs # Training and data
 
 # Pipeline control
 pipeline:
-  run_data_processing: true # Set this to false if you have already saved the processed data
-  run_shadow_model_training: true # Set this to false if shadow models are already trained and saved
+  run_data_processing: false # Set this to false if you have already saved the processed data
+  run_shadow_model_training: false # Set this to false if shadow models are already trained and saved
   run_metaclassifier_training: true
 
 target_model: # This is only used for testing the attack on a real target model.
diff --git a/examples/ensemble_attack/configs/experiment_config_10k.yaml b/examples/ensemble_attack/configs/experiment_config_10k.yaml
index 4f3d251d..df0d81c5 100644
--- a/examples/ensemble_attack/configs/experiment_config_10k.yaml
+++ b/examples/ensemble_attack/configs/experiment_config_10k.yaml
@@ -11,7 +11,7 @@ pipeline:
 
 target_model: # This is only used for testing the attack on a real target model.
   # This is for models trained on 10k data and generating `0k synthetic data
-  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_10k/train/
+  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_10k/test/
   target_model_id: 21  # Will be overridden per SLURM array task
   target_model_name: tabddpm_${target_model.target_model_id}
   target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/10k/10k.csv
diff --git a/examples/ensemble_attack/configs/experiment_config_50k.yaml b/examples/ensemble_attack/configs/experiment_config_50k.yaml
new file mode 100644
index 00000000..300e37ee
--- /dev/null
+++ b/examples/ensemble_attack/configs/experiment_config_50k.yaml
@@ -0,0 +1,110 @@
+# Ensemble experiment configuration
+# This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
+base_experiment_dir: /projects/midst-experiments/ensemble_attack/tabddpm_50k_experiment_data # Processed data, and experiment artifacts will be stored here
+base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
+
+# Pipeline control
+pipeline:
+  run_data_processing: false # Set this to false if you have already saved the processed data
+  run_shadow_model_training: false # Set this to false if shadow models are already trained and saved
+  run_metaclassifier_training: true
+
+target_model: # This is only used for testing the attack on a real target model.
+  # This is for models trained on 50k data and generating 50k synthetic data
+  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_50k/train/
+  target_model_id: 21  # Will be overridden per SLURM array task
+  target_model_name: tabddpm_${target_model.target_model_id}
+  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/50k/50k.csv
+  challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
+  challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
+
+  target_attack_artifact_dir: ${base_experiment_dir}/target_${target_model.target_model_id}_attack_artifacts/
+  attack_probabilities_result_path: ${target_model.target_attack_artifact_dir}/attack_model_${target_model.target_model_id}_proba
+  target_shadow_models_output_path: ${target_model.target_attack_artifact_dir}/tabddpm_${target_model.target_model_id}_shadows_dir
+
+
+# Data paths
+data_paths:
+  midst_data_path: /projects/midst-experiments/all_tabddpms # Used to collect the data
+  population_path: ${base_experiment_dir}/population_data  # Path where the collected population data will be stored
+  processed_attack_data_path: ${base_experiment_dir}/attack_data # Path where the processed attack real train and evaluation data is stored
+  attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored
+
+model_paths:
+  metaclassifier_model_path: ${base_experiment_dir}/trained_models # Path where the trained metaclassifier model will be saved
+
+
+# Dataset specific information used for processing in this example
+data_processing_config:
+  population_attack_data_types_to_collect:
+        [
+          "tabddpm_trained_with_50k",
+        ]
+  challenge_attack_data_types_to_collect:
+        [
+          "tabddpm_trained_with_50k",
+        ]
+  population_splits: ["train"]  # Data splits to be collected for population data
+  challenge_splits: ["train"]  # Data splits to be collected for challenge points
+  # The column name in the data to be used for stratified splitting.
+  column_to_stratify: "trans_type"  # Attention: This value is not documented in the original codebase.
+  folder_ranges: #Specify folder ranges for any of the mentioned splits.
+    train: [[1, 20]] # Folders to be used for train data collection in the experiments
+  # File names in MIDST data directories.
+  single_table_train_data_file_name: "train_with_id.csv"
+  multi_table_train_data_file_name: "trans.csv"
+  challenge_data_file_name: "challenge_with_id.csv"
+  population_sample_size: 100000 # Population size is the total data that your attack has access to.
+  # In experiments, this is sampled out of all the collected training data in case the available data
+  # is more than this number. Note that, half of this data is actually used for training, the other half
+  # is used for evaluation. For example, with 40k population size, only 20k is used for training the attack model.
+  # TODO: make sure to consider this in experiments.
+
+# Training and data settings for shadow models (temporary, numbers subject to change)
+shadow_training:
+  # Data Config files path used for training a TabDDPM model
+  training_json_config_paths: # Config json files used for tabddpm training on the trans table
+    table_domain_file_path: ${base_data_config_dir}/trans_domain.json
+    dataset_meta_file_path: ${base_data_config_dir}/dataset_meta.json
+    tabddpm_training_config_path: ${base_data_config_dir}/trans.json
+  # Model training artifacts are saved under shadow_models_data_path/workspace_name/exp_name
+  # Also, training configs for each shadow model are created under shadow_models_data_path.
+  shadow_models_output_path: ${base_experiment_dir}/shadow_models_and_data
+  target_model_output_path: ${base_experiment_dir}/shadow_target_model_and_data
+  # Paths to final shadow models used for metaclassifier training (relative to shadow_models_output_path)
+  # These paths are a result of running the shadow model training pipeline, specifically the
+  # train_three_sets_of_shadow_models in shadow_model_training.py
+  # Each .pkl file contains the training data, trained model and training results for all shadow models in a list.
+  final_shadow_models_path: [
+          "${shadow_training.shadow_models_output_path}/initial_model_rmia_1/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
+          "${shadow_training.shadow_models_output_path}/initial_model_rmia_2/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
+          "${shadow_training.shadow_models_output_path}/shadow_model_rmia_third_set/shadow_workspace/trained_model/rmia_shadows_third_set.pkl",
+      ]
+  target_synthetic_data_path: ${shadow_training.target_model_output_path}/target_synthetic_data.csv
+  # Path to final shadow target's synthetic data (relative to target_model_output_path)
+  fine_tuning_config:
+    fine_tune_diffusion_iterations: 200000 # Original code: 200000
+    fine_tune_classifier_iterations: 20000 # Original code: 20000
+    pre_train_data_size: 60000 # Original code: 60000
+  number_of_points_to_synthesize: 50000 # Number of synthetic data samples to be generated by shadow models.
+  # Original code: 20000
+
+
+# Metaclassifier settings
+metaclassifier:
+  # Data types json file is used for xgboost model training.
+  data_types_file_path: ${base_data_config_dir}/data_types.json
+  model_type: "xgb"
+  # Model training parameters
+  num_optuna_trials: 100 # Original code: 100
+  num_kfolds: 5
+  use_gpu: false
+  # Temporary. Might remove having an epoch parameter.
+  epochs: 1
+  meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model_batched_parallel
+
+attack_success_computation:
+  target_ids_to_test: [21,22,23] # List of target model IDs to compute the attack success for.
+
+# General settings
+random_seed: 42 # Set to null for no seed, or an integer for a fixed seed
diff --git a/examples/ensemble_attack/run_attack.py b/examples/ensemble_attack/run_attack.py
index 5592e08e..af9966dc 100644
--- a/examples/ensemble_attack/run_attack.py
+++ b/examples/ensemble_attack/run_attack.py
@@ -13,6 +13,7 @@
 from examples.ensemble_attack.real_data_collection import collect_population_data_ensemble
 from midst_toolkit.attacks.ensemble.process_split_data import process_split_data
 from midst_toolkit.common.logger import log
+from midst_toolkit.attacks.ensemble.data_utils import load_dataframe, save_dataframe
 from midst_toolkit.common.random import set_all_random_seeds
 
 
@@ -25,14 +26,17 @@ def run_data_processing(config: DictConfig) -> None:
     """
     log(INFO, "Running data processing pipeline...")
     # Collect the real data from the MIDST challenge resources.
-    population_data = collect_population_data_ensemble(
-        midst_data_input_dir=Path(config.data_paths.midst_data_path),
-        data_processing_config=config.data_processing_config,
-        save_dir=Path(config.data_paths.population_path),
-        population_splits=config.data_processing_config.population_splits,
-        challenge_splits=config.data_processing_config.challenge_splits,
+    # population_data = collect_population_data_ensemble(
+    #     midst_data_input_dir=Path(config.data_paths.midst_data_path),
+    #     data_processing_config=config.data_processing_config,
+    #     save_dir=Path(config.data_paths.population_path),
+    #     population_splits=config.data_processing_config.population_splits,
+    #     challenge_splits=config.data_processing_config.challenge_splits,
+    # )
+    population_data = load_dataframe(Path(config.data_paths.population_path),
+        "population_all_with_challenge_no_id.csv",
     )
-    # The following function saves the required dataframe splits in the specified processed_attack_data_path path.
+    # The following function saves the required dataframe splits in the specified processed_attack_data_path path. 
     process_split_data(
         all_population_data=population_data,
         processed_attack_data_path=Path(config.data_paths.processed_attack_data_path),
@@ -67,7 +71,11 @@ def main(config: DictConfig) -> None:
     # TODO: Investigate the source of error.
     if config.pipeline.run_shadow_model_training:
         shadow_pipeline = importlib.import_module("examples.ensemble_attack.run_shadow_model_training")
-        shadow_data_paths = shadow_pipeline.run_shadow_model_training(config)
+        df_master_challenge_train = load_dataframe(
+            Path(config.data_paths.processed_attack_data_path),
+            "master_challenge_train.csv",
+        )
+        shadow_data_paths = shadow_pipeline.run_shadow_model_training(config, df_master_challenge_train)
         shadow_data_paths = [Path(path) for path in shadow_data_paths]
 
         target_model_synthetic_path = shadow_pipeline.run_target_model_training(config)
diff --git a/examples/ensemble_attack/run_shadow_model_training.py b/examples/ensemble_attack/run_shadow_model_training.py
index d4a85cbc..8269cf58 100644
--- a/examples/ensemble_attack/run_shadow_model_training.py
+++ b/examples/ensemble_attack/run_shadow_model_training.py
@@ -79,7 +79,7 @@ def run_target_model_training(config: DictConfig) -> Path:
     return target_model_synthetic_path
 
 
-def run_shadow_model_training(config: DictConfig) -> list[Path]:
+def run_shadow_model_training(config: DictConfig, df_master_challenge_train) -> list[Path]:
     """
     Function to run the shadow model training for RMIA attack.
 
@@ -95,10 +95,10 @@ def run_shadow_model_training(config: DictConfig) -> list[Path]:
     # Load the required dataframes for shadow model training.
     # For shadow model training we need master_challenge_train and population data.
     # Master challenge is the main training (or fine-tuning) data for the shadow models.
-    df_master_challenge_train = load_dataframe(
-        Path(config.data_paths.processed_attack_data_path),
-        "master_challenge_train.csv",
-    )
+    # df_master_challenge_train = load_dataframe(
+    #     Path(config.data_paths.processed_attack_data_path),
+    #     "master_challenge_train.csv",
+    # )
     # Population data is used to pre-train some of the shadow models.
     df_population_with_challenge = load_dataframe(
         Path(config.data_paths.population_path),
diff --git a/examples/ensemble_attack/run_test.sh b/examples/ensemble_attack/run_test.sh
index 70d7c084..a1d8ca01 100644
--- a/examples/ensemble_attack/run_test.sh
+++ b/examples/ensemble_attack/run_test.sh
@@ -3,15 +3,14 @@
 #SBATCH --nodes=1
 #SBATCH --ntasks=1
 #SBATCH --ntasks-per-node=1
-#SBATCH --cpus-per-task=1
-#SBATCH --gres=gpu:1
-#SBATCH --mem=32G
-#SBATCH --qos=m
-#SBATCH --job-name=ensemble_attack_test
+#SBATCH --cpus-per-task=4
+#SBATCH --gres=gpu:a40:1
+#SBATCH --mem=160G
+#SBATCH --job-name=20k_model_10k_test
 #SBATCH --output=%j_%x_%a.out
 #SBATCH --error=%j_%x_%a.err
-#SBATCH --time=9:00:00
-#SBATCH --array=0-2  # For 3 target_model_ids (adjust range as needed)
+#SBATCH --time=6:00:00
+#SBATCH --array=0-9  # For 3 target_model_ids (adjust range as needed)
 
 # This script sets up the environment and runs the ensemble attack test script.
 source .venv/bin/activate
@@ -20,13 +19,16 @@ echo "Active Environment:"
 which python
 
 # Map SLURM_ARRAY_TASK_ID to target_model_id.
-TARGET_IDS=(21 22 23)  # List of target IDs
+
+TARGET_IDS=(61 62 63 64 65 66 67 68 69 70)
+# TARGET_IDS=(101 102 103 104 105 106 107 108 109 110)  # List of target IDs
+
 TARGET_ID=${TARGET_IDS[$SLURM_ARRAY_TASK_ID]}
 
 echo "Running test for target_model_id: $TARGET_ID"
 
 echo "Experiments Launched"
 
-python -m examples.ensemble_attack.test_attack_model target_model.target_model_id=$TARGET_ID # Overrides the target_model_id in config.
+python -m examples.ensemble_attack.test_attack_model target_model.target_model_id=$TARGET_ID
 
-echo "Experiments Completed"
+echo "Experiments Completed"
\ No newline at end of file
diff --git a/examples/ensemble_attack/run_train.sh b/examples/ensemble_attack/run_train.sh
index ae8e043b..3bf989c0 100755
--- a/examples/ensemble_attack/run_train.sh
+++ b/examples/ensemble_attack/run_train.sh
@@ -3,16 +3,18 @@
 #SBATCH --nodes=1
 #SBATCH --ntasks=1
 #SBATCH --ntasks-per-node=1
-#SBATCH --cpus-per-task=1
+#SBATCH --cpus-per-task=8
 #SBATCH --gres=gpu:1
-#SBATCH --mem=32G
+#SBATCH --mem=160G
 #SBATCH --qos=normal
-#SBATCH --job-name=50k_ensemble_attack_train
+#SBATCH --job-name=batched_para_50k
 #SBATCH --output=%j_%x.out
 #SBATCH --error=%j_%x.err
-#SBATCH --time=16:00:00
+#SBATCH --time=8:00:00
 
 
+echo "Total memory allocated: $(($SLURM_MEM_PER_NODE / 1024)) GB"
+
 # This script sets up the environment and runs the ensemble attack example.
 source .venv/bin/activate
 
diff --git a/examples/ensemble_attack/run_train_100k.sh b/examples/ensemble_attack/run_train_100k.sh
index f33ac154..5e4ac7fc 100755
--- a/examples/ensemble_attack/run_train_100k.sh
+++ b/examples/ensemble_attack/run_train_100k.sh
@@ -3,14 +3,14 @@
 #SBATCH --nodes=1
 #SBATCH --ntasks=1
 #SBATCH --ntasks-per-node=1
-#SBATCH --cpus-per-task=1
+#SBATCH --cpus-per-task=2
 #SBATCH --gres=gpu:1
-#SBATCH --mem=32G
+#SBATCH --mem=160G
 #SBATCH --qos=normal
-#SBATCH --job-name=100k_ensemble_attack_train
+#SBATCH --job-name=new_100k_meta_ensemble_attack_train
 #SBATCH --output=%j_%x.out
 #SBATCH --error=%j_%x.err
-#SBATCH --time=16:00:00
+#SBATCH --time=14:00:00
 
 
 # This script sets up the environment and runs the ensemble attack example.
diff --git a/examples/ensemble_attack/test_attack_model.py b/examples/ensemble_attack/test_attack_model.py
index 2eccb0e7..44a693e1 100644
--- a/examples/ensemble_attack/test_attack_model.py
+++ b/examples/ensemble_attack/test_attack_model.py
@@ -18,7 +18,7 @@
 from midst_toolkit.common.random import set_all_random_seeds
 
 
-def run_rmia_shadow_training(config: DictConfig) -> list[dict[str, list[Any]]]:
+def run_rmia_shadow_training(config: DictConfig, df_challenge) -> list[dict[str, list[Any]]]:
     """
     Three sets of shadow models will be trained as a part of this attack.
     Note that for every new target model, shadow models need to be trained.
@@ -32,7 +32,8 @@ def run_rmia_shadow_training(config: DictConfig) -> list[dict[str, list[Any]]]:
         A list containing three dictionaries, each representing a collection of shadow
             models with their training data and generated synthetic outputs.
     """
-    shadow_model_paths = run_shadow_model_training(config)
+    shadow_model_paths = run_shadow_model_training(config, df_master_challenge_train=df_challenge)
+    shadow_model_paths = [Path(path) for path in config.shadow_training.final_shadow_models_path]
 
     assert len(shadow_model_paths) == 3, "For testing, meta classifier needs the path to three sets of shadow models."
 
@@ -105,6 +106,14 @@ def run_metaclassifier_testing(
         INFO, f"Target synthetic data loaded from {target_synthetic_path} with a size of {len(target_synthetic_data)}."
     )
 
+    # 3) Shadow Model Training Step.
+
+    # Make sure to assign a new path for shadow models trained for target's challenge points to
+    # avoid overriding train's shadow models.
+    config.shadow_training.shadow_models_output_path = config.target_model.target_shadow_models_output_path
+    shadow_data_collection = run_rmia_shadow_training(config, test_data)
+
+    
     # Extract trans_id from the test dataframe
     with open(Path(config.metaclassifier.data_types_file_path), "r") as f:
         column_types = json.load(f)
@@ -117,15 +126,25 @@ def run_metaclassifier_testing(
     id_column_names = [column_name for column_name in test_data.columns if column_name.endswith("_id")]
     test_data = test_data.drop(columns=id_column_names)
 
-    # 3) Shadow Model Training Step.
 
-    # Make sure to assign a new path for shadow models trained for target's challenge points to
-    # avoid overriding train's shadow models.
-    config.shadow_training.shadow_models_output_path = config.target_model.target_shadow_models_output_path
-    shadow_data_collection = run_rmia_shadow_training(config)
+    
+
+
+    # Load already trained shadows (only if completely are run)
+    # shadow_model_paths = [Path(path) for path in config.shadow_training.final_shadow_models_path]
+    # shadow_data_collection = []
+    # for model_path in shadow_model_paths:
+    #     assert model_path.exists(), (
+    #         f"No file found at {model_path}. Make sure the path is correct, or run shadow model training first."
+    #     )
+
+    #     with open(model_path, "rb") as f:
+    #         shadow_data_and_result = pickle.load(f)
+    #         shadow_data_collection.append(shadow_data_and_result)
+
+
 
     # 4) Initialize the attacker object, and assign the loaded metaclassifier to it.
-    target_synthetic_data = target_synthetic_data.copy()
 
     df_reference = load_dataframe(
         Path(config.data_paths.population_path),
diff --git a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
index 3700c4d4..e8697f1f 100644
--- a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
+++ b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
@@ -14,11 +14,69 @@
 
 from midst_toolkit.common.logger import log
 
+from multiprocessing import Pool
 
 class Key(Enum):
     TRAINED_RESULTS = "trained_results"
     FINE_TUNED_RESULTS = "fine_tuned_results"
 
+def compute_gower_batched(
+    df_x: pd.DataFrame,
+    df_y: pd.DataFrame,
+    cat_features: list[bool],
+    batch_size: int,
+    dtype: np.dtype,
+) -> np.ndarray:
+    """
+    Compute Gower distance matrix in batches to reduce peak memory usage.
+    
+    This processes df_x in batches, computing distances against all of df_y,
+    then stacks results. Much more memory-efficient for large df_x.
+    """
+    n_x = len(df_x)
+    n_y = len(df_y)
+    
+    # Pre-allocate output matrix
+    gower_matrix = np.zeros((n_x, n_y), dtype=dtype)
+    
+    # Process in batches
+    for start_idx in range(0, n_x, batch_size):
+        end_idx = min(start_idx + batch_size, n_x)
+        
+        # Compute distance for this batch
+        batch_matrix = gower.gower_matrix(
+            data_x=df_x.iloc[start_idx:end_idx],
+            data_y=df_y,
+            cat_features=cat_features,
+        )
+        
+        # Store in output matrix
+        gower_matrix[start_idx:end_idx] = batch_matrix.astype(dtype)
+    
+    return gower_matrix
+
+def compute_gower_for_model(args):
+    i, df_synthetic_raw, df_input, min_length, id_column_name, random_seed, categorical_features, numerical_columns, dtype = args
+    
+    df_synthetic = df_synthetic_raw.copy()
+    
+    if id_column_name in df_synthetic.columns:
+        df_synthetic = df_synthetic.drop(columns=[id_column_name])
+    
+    df_synthetic[numerical_columns] = df_synthetic[numerical_columns].astype(dtype)
+    
+    if len(df_synthetic) > min_length:
+        df_synthetic = df_synthetic.sample(n=min_length, random_state=random_seed)
+    
+    # gower_matrix = gower.gower_matrix(
+    #     data_x=df_input, data_y=df_synthetic, cat_features=categorical_features
+    # ).astype(dtype)
+    # Batched approach: compute row-by-row to reduce peak memory
+    gower_matrix = compute_gower_batched(
+        df_input, df_synthetic, categorical_features, batch_size=5000, dtype=dtype
+    )
+    
+    return i, gower_matrix
 
 def get_rmia_gower(
     df_input: pd.DataFrame,
@@ -27,6 +85,8 @@ def get_rmia_gower(
     categorical_column_names: list[str],
     id_column_name: str,
     random_seed: int | None = None,
+    n_jobs: int = 4,
+    dtype: np.dtype = np.float32,
 ) -> list[np.ndarray]:
     """
     Computes the Gower distance between the challenge points and the synthetic data generated by the shadow models.
@@ -47,6 +107,7 @@ def get_rmia_gower(
         datasets provided within ``model_data``.
 
     """
+    df_input = df_input.copy()
     # Check if any specified categorical columns are missing from the dataframe
     missing_categorical_columns = set(categorical_column_names) - set(df_input.columns)
     if missing_categorical_columns:
@@ -58,32 +119,26 @@ def get_rmia_gower(
 
     categorical_features = [column in categorical_column_names for column in df_input.columns]
 
-    gower_matrices = []
-
     numerical_columns = [col for col in df_input.columns if col not in categorical_column_names]
 
-    df_input[numerical_columns] = df_input[numerical_columns].astype(float)
-
-    for i in range(len(model_data)):
-        df_synthetic = model_data[i].copy()
+    df_input[numerical_columns] = df_input[numerical_columns].astype(dtype)
 
-        # Convert numerical columns to float (otherwise error in the numpy divide)
-        df_synthetic[numerical_columns] = df_synthetic[numerical_columns].astype(float)
-
-        # Sample synthetic data points if there's too many
-        if len(df_synthetic) > min_length:
-            df_synthetic = df_synthetic.sample(n=min_length, random_state=random_seed)
-
-        if id_column_name in df_synthetic.columns:
-            df_synthetic = df_synthetic.drop(columns=[id_column_name])
-
-        gower_matrix = gower.gower_matrix(data_x=df_input, data_y=df_synthetic, cat_features=categorical_features)
-
-        assert np.all((gower_matrix >= 0) & (gower_matrix <= 1)), "Distances are falling outside of range [0, 1]."
-
-        gower_matrices.append(gower_matrix)
+    # Prepare arguments for each model
+    args_list = [
+        (i, df_synthetic, df_input, min_length, id_column_name, 
+        random_seed, categorical_features, numerical_columns, dtype)
+        for i, df_synthetic in enumerate(model_data)
+    ]
+    
+    # Process in parallel
+    results = {}
+    with Pool(processes=n_jobs) as pool:
+        for i, gower_matrix in pool.imap_unordered(compute_gower_for_model, args_list):
+            results[i] = gower_matrix
+    
+    # Return in original order
+    return [results[i] for i in range(len(model_data))]
 
-    return gower_matrices
 
 
 def conditional_average(values: np.ndarray, condition_mask: np.ndarray) -> np.ndarray:
@@ -109,6 +164,7 @@ def conditional_average(values: np.ndarray, condition_mask: np.ndarray) -> np.nd
     return np.where(mask_sum > 0, np.sum(values * condition_mask, axis=0) / mask_sum, np.nan)
 
 
+
 def calculate_rmia_signals(
     df_input: pd.DataFrame,
     shadow_data_collection: list[dict[str, list[Any]]],
@@ -221,28 +277,41 @@ def calculate_rmia_signals(
     shadow_synthetic_list_0 = [
         train_result.synthetic_data for train_result in fine_tuned_shadow_data_0[Key.FINE_TUNED_RESULTS.value]
     ]
-    shadow_model_gower_0 = get_rmia_gower(
-        df_input=df_input,
-        model_data=shadow_synthetic_list_0,
-        min_length=min_length,
-        categorical_column_names=categorical_column_names,
-        id_column_name=id_column_name,
-        random_seed=random_seed,
-    )
+    # shadow_model_gower_0 = get_rmia_gower(
+    #     df_input=df_input,
+    #     model_data=shadow_synthetic_list_0,
+    #     min_length=min_length,
+    #     categorical_column_names=categorical_column_names,
+    #     id_column_name=id_column_name,
+    #     random_seed=random_seed,
+    # )
+
+    # log(INFO, "Computed Gower distance for first set of fine-tuned shadow models.")
+
     shadow_synthetic_list_1 = [
         train_result.synthetic_data for train_result in fine_tuned_shadow_data_1[Key.FINE_TUNED_RESULTS.value]
     ]
-    shadow_model_gower_1 = get_rmia_gower(
+    shadow_0_and_1_synthetic_data = shadow_synthetic_list_1+shadow_synthetic_list_0
+    del shadow_synthetic_list_0 
+    del shadow_synthetic_list_1
+
+    shadow_model_gower_0_and_1 = get_rmia_gower(
         df_input=df_input,
-        model_data=shadow_synthetic_list_1,
+        model_data=shadow_0_and_1_synthetic_data,
         min_length=min_length,
         categorical_column_names=categorical_column_names,
         id_column_name=id_column_name,
         random_seed=random_seed,
+        n_jobs=8,
     )
+    # log(INFO, "Computed Gower distance for second set of fine-tuned shadow models.")
+
+    log(INFO, "Computed Gower distance for the first two sets of fine-tuned shadow models (8 models total).")
+
     shadow_synthetic_list_2 = [
         train_result.synthetic_data for train_result in trained_shadow_data[Key.TRAINED_RESULTS.value]
     ]
+    # shadow_synthetic_list_2 includes 8 shadow models
     shadow_model_gower_2 = get_rmia_gower(
         df_input=df_input,
         model_data=shadow_synthetic_list_2,
@@ -250,18 +319,22 @@ def calculate_rmia_signals(
         categorical_column_names=categorical_column_names,
         id_column_name=id_column_name,
         random_seed=random_seed,
+        n_jobs=8,
     )
 
+
+    log(INFO, "Computed Gower distance for trained shadow models (third set of shadow models).")
+
     gower_shadows = np.vstack(
-        [np.array(shadow_model_gower_0), np.array(shadow_model_gower_1), np.array(shadow_model_gower_2)]
+        [np.array(shadow_model_gower_0_and_1), np.array(shadow_model_gower_2)]
     )
+    del shadow_model_gower_0_and_1
+    del shadow_model_gower_2
 
-    # TODO: ideally remove hard-copied keys
-    shadow_training_data = (
-        fine_tuned_shadow_data_0["fine_tuning_sets"]
-        + fine_tuned_shadow_data_1["fine_tuning_sets"]
-        + trained_shadow_data["selected_sets"]
-    )
+    # Process shadow model distances. Gower_shadows is a 3D matrix of shape:
+    # ((total number of shadow models), len(df_input), len(shadow_synthetic))
+    sorted_shadow_gower = np.sort(gower_shadows, axis=2)
+    del gower_shadows
 
     # TODO: check key after we have the official target model
     target_model_gower = get_rmia_gower(
@@ -272,12 +345,16 @@ def calculate_rmia_signals(
         id_column_name=id_column_name,
     )
 
+    log(INFO, "Computed Gower distance for the target model.")
+
+    log(INFO, "Gower distance matrices computed...")
     # gower_target is a 2D NumPy array that stores the Gower distances between
     # the input data (df_input) and the synthetic data generated by the target model.
     # so its shape is (len(df_input), len(target_synthetic_data)).
     # gower_target[i, j] = Gower distance between i-th input record and j-th target synthetic record
 
     gower_target = target_model_gower[0]  # There is only one target model
+    del target_model_gower
 
     # Compute the signals based on the gower matrix wrt to the target synthetic data.
     # Here, for each input record, we sort the distances to all target synthetic records,
@@ -285,10 +362,17 @@ def calculate_rmia_signals(
     k_nearest_target_distances = np.sort(gower_target, axis=1)[:, :k]
     signal_target_k_mean = np.mean(k_nearest_target_distances, axis=1)
     signal_target_k_1 = gower_target[:, 0]  # First element is the minimum
+    del gower_target
 
-    # Process shadow model distances. Gower_shadows is a 3D matrix of shape:
-    # ((total number of shadow models), len(df_input), len(shadow_synthetic))
-    sorted_shadow_gower = np.sort(gower_shadows, axis=2)
+    # TODO: ideally remove hard-copied keys
+    shadow_training_data = (
+        fine_tuned_shadow_data_0["fine_tuning_sets"]
+        + fine_tuned_shadow_data_1["fine_tuning_sets"]
+        + trained_shadow_data["selected_sets"]
+    )
+    del fine_tuned_shadow_data_0
+    del fine_tuned_shadow_data_1
+    del trained_shadow_data
 
     # For k nearest neighbors
     # Similar to target, we sort the distances in each shadow model's gower matrix,
@@ -304,6 +388,7 @@ def calculate_rmia_signals(
     # Taking the mean across all shadow models (axis=0) to compute k=1 signal for each input sample
     signal_shadows_k_1 = np.mean(nearest_shadow_distances, axis=0)
 
+    del k_nearest_shadow_distances
     # Create a dataframe for the computed scores
     results_df = pd.DataFrame(
         {
@@ -312,6 +397,8 @@ def calculate_rmia_signals(
             f"signal_shadow_k_{k}": signal_shadows,
         }
     )
+    del signal_shadows_k_1
+    del signal_shadows
 
     # Create masks for records in/out of training sets. We're creating masks for all the samples in train_df,
     # as opposed to the original implementation which only creates masks a sample of 200 records. We've also

From 22c028cb07eadd4af127f40cf7d8fa265531ba37 Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Wed, 3 Dec 2025 15:47:11 -0500
Subject: [PATCH 19/41] Fixed population dataset for other settings

---
 .../configs/experiment_config_10k.yaml        | 17 ++++++++------
 .../ensemble_attack/real_data_collection.py   | 14 ++++++++++--
 examples/ensemble_attack/run_attack.py        | 22 +++++++++++--------
 examples/ensemble_attack/run_test.sh          |  2 +-
 examples/ensemble_attack/run_train.sh         | 11 +++++-----
 5 files changed, 41 insertions(+), 25 deletions(-)

diff --git a/examples/ensemble_attack/configs/experiment_config_10k.yaml b/examples/ensemble_attack/configs/experiment_config_10k.yaml
index df0d81c5..f1e84827 100644
--- a/examples/ensemble_attack/configs/experiment_config_10k.yaml
+++ b/examples/ensemble_attack/configs/experiment_config_10k.yaml
@@ -1,6 +1,6 @@
 # Ensemble experiment configuration
 # This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
-base_experiment_dir: /projects/midst-experiments/ensemble_attack/tabddpm_10k_experiment_data # Processed data, and experiment artifacts will be stored here
+base_experiment_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_10k_experiment_data/10k # Processed data, and experiment artifacts will be stored here
 base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
 
 # Pipeline control
@@ -25,10 +25,11 @@ target_model: # This is only used for testing the attack on a real target model.
 
 # Data paths
 data_paths:
-  midst_data_path: /projects/midst-experiments/all_tabddpms # Used to collect the data
-  population_path: ${base_experiment_dir}/population_data  # Path where the collected population data will be stored
-  processed_attack_data_path: ${base_experiment_dir}/attack_data # Path where the processed attack real train and evaluation data is stored
-  attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored
+  midst_data_path: /projects/midst-experiments/all_tabddpms/ # Used to collect the data (input)
+  population_path: ${base_experiment_dir}/population_data  # Path where the collected population data will be stored (output/input)
+  processed_attack_data_path: ${base_experiment_dir}/attack_data # Path where the processed attack real train and evaluation data is stored (output/input)
+  attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored (output)
+
 
 model_paths:
   metaclassifier_model_path: ${base_experiment_dir}/trained_models # Path where the trained metaclassifier model will be saved
@@ -45,11 +46,13 @@ data_processing_config:
           "tabddpm_trained_with_10k",
         ]
   population_splits: ["train"]  # Data splits to be collected for population data
-  challenge_splits: ["train"]  # Data splits to be collected for challenge points
+  challenge_splits: ["train" , "test"]  # Data splits to be collected for challenge points
+  original_population_data_path: /projects/midst-experiments/ensemble_attack/competition/population_data/
   # The column name in the data to be used for stratified splitting.
   column_to_stratify: "trans_type"  # Attention: This value is not documented in the original codebase.
   folder_ranges: #Specify folder ranges for any of the mentioned splits.
-    train: [[1, 20]] # Folders to be used for train data collection in the experiments
+    train: [[1, 21]] # Folders to be used for train data collection in the experiments
+    test: [[21, 31]]
   # File names in MIDST data directories.
   single_table_train_data_file_name: "train_with_id.csv"
   multi_table_train_data_file_name: "trans.csv"
diff --git a/examples/ensemble_attack/real_data_collection.py b/examples/ensemble_attack/real_data_collection.py
index 264f71ae..dfbd8282 100644
--- a/examples/ensemble_attack/real_data_collection.py
+++ b/examples/ensemble_attack/real_data_collection.py
@@ -5,10 +5,11 @@
 
 from enum import Enum
 from pathlib import Path
+from logging import INFO
 
 import pandas as pd
 from omegaconf import DictConfig
-
+from midst_toolkit.common.logger import log
 from midst_toolkit.attacks.ensemble.data_utils import load_dataframe, save_dataframe
 
 
@@ -142,6 +143,7 @@ def collect_population_data_ensemble(
     midst_data_input_dir: Path,
     data_processing_config: DictConfig,
     save_dir: Path,
+    original_repo_population: pd.DataFrame,
     population_splits: list[str] | None = None,
     challenge_splits: list[str] | None = None,
 ) -> pd.DataFrame:
@@ -180,13 +182,20 @@ def collect_population_data_ensemble(
     # Provided attack name are valid based on AttackType enum
     population_attack_types: list[AttackType] = [AttackType(attack_name) for attack_name in attack_names]
 
-    df_population = collect_midst_data(
+    df_population_experiment = collect_midst_data(
         midst_data_input_dir,
         population_attack_types,
         data_splits=population_splits,
         dataset="train",
         data_processing_config=data_processing_config,
     )
+
+    log(INFO, f"Collected experiment population data length before concatenation: {len(df_population_experiment)}")
+
+    df_population = pd.concat([df_population_experiment, original_repo_population]).drop_duplicates()
+    log(INFO, f"Concatenated population data length: {len(df_population)}")
+    
+    
     # Drop ids.
     df_population_no_id = df_population.drop(columns=["trans_id", "account_id"])
     # Save the population data
@@ -202,6 +211,7 @@ def collect_population_data_ensemble(
         dataset="challenge",
         data_processing_config=data_processing_config,
     )
+    log(INFO, f"Collected challenge data length: {len(df_challenge)} from splits: {challenge_splits}")
     # Save the challenge points
     save_dataframe(df_challenge, save_dir, "challenge_points_all.csv")
 
diff --git a/examples/ensemble_attack/run_attack.py b/examples/ensemble_attack/run_attack.py
index af9966dc..a4b4d3db 100644
--- a/examples/ensemble_attack/run_attack.py
+++ b/examples/ensemble_attack/run_attack.py
@@ -24,18 +24,22 @@ def run_data_processing(config: DictConfig) -> None:
     Args:
         config: Configuration object set in config.yaml.
     """
+    # Load original repo's population
+    
+    original_population_data = load_dataframe(Path(config.data_processing_config.original_population_data_path),
+        "population_all_with_challenge.csv",
+    )
     log(INFO, "Running data processing pipeline...")
     # Collect the real data from the MIDST challenge resources.
-    # population_data = collect_population_data_ensemble(
-    #     midst_data_input_dir=Path(config.data_paths.midst_data_path),
-    #     data_processing_config=config.data_processing_config,
-    #     save_dir=Path(config.data_paths.population_path),
-    #     population_splits=config.data_processing_config.population_splits,
-    #     challenge_splits=config.data_processing_config.challenge_splits,
-    # )
-    population_data = load_dataframe(Path(config.data_paths.population_path),
-        "population_all_with_challenge_no_id.csv",
+    population_data = collect_population_data_ensemble(
+        midst_data_input_dir=Path(config.data_paths.midst_data_path),
+        data_processing_config=config.data_processing_config,
+        save_dir=Path(config.data_paths.population_path),
+        original_repo_population = original_population_data,
+        population_splits=config.data_processing_config.population_splits,
+        challenge_splits=config.data_processing_config.challenge_splits,
     )
+
     # The following function saves the required dataframe splits in the specified processed_attack_data_path path. 
     process_split_data(
         all_population_data=population_data,
diff --git a/examples/ensemble_attack/run_test.sh b/examples/ensemble_attack/run_test.sh
index a1d8ca01..2b7380a7 100644
--- a/examples/ensemble_attack/run_test.sh
+++ b/examples/ensemble_attack/run_test.sh
@@ -9,7 +9,7 @@
 #SBATCH --job-name=20k_model_10k_test
 #SBATCH --output=%j_%x_%a.out
 #SBATCH --error=%j_%x_%a.err
-#SBATCH --time=6:00:00
+#SBATCH --time=12:00:00
 #SBATCH --array=0-9  # For 3 target_model_ids (adjust range as needed)
 
 # This script sets up the environment and runs the ensemble attack test script.
diff --git a/examples/ensemble_attack/run_train.sh b/examples/ensemble_attack/run_train.sh
index 3bf989c0..dfd2a203 100755
--- a/examples/ensemble_attack/run_train.sh
+++ b/examples/ensemble_attack/run_train.sh
@@ -3,14 +3,13 @@
 #SBATCH --nodes=1
 #SBATCH --ntasks=1
 #SBATCH --ntasks-per-node=1
-#SBATCH --cpus-per-task=8
-#SBATCH --gres=gpu:1
+#SBATCH --cpus-per-task=4
+#SBATCH --gres=gpu:a40:1
 #SBATCH --mem=160G
-#SBATCH --qos=normal
-#SBATCH --job-name=batched_para_50k
+#SBATCH --job-name=new_exp_10k_train
 #SBATCH --output=%j_%x.out
 #SBATCH --error=%j_%x.err
-#SBATCH --time=8:00:00
+#SBATCH --time=16:00:00
 
 
 echo "Total memory allocated: $(($SLURM_MEM_PER_NODE / 1024)) GB"
@@ -23,6 +22,6 @@ which python
 
 echo "Experiments Launched"
 
-python -m examples.ensemble_attack.run_attack
+python -m examples.ensemble_attack.run_attack  --config-name=experiment_config_10k
 
 echo "Experiments Completed"

From 19b67e8f06bec075401a9cf6573fc0c7c98838f6 Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Thu, 4 Dec 2025 15:53:59 -0500
Subject: [PATCH 20/41] Fix test shadow train data

---
 .../configs/experiment_config_10k.yaml        |   9 +-
 .../configs/experiment_config_20k.yaml        | 116 ++++++++++++++++++
 .../ensemble_attack/real_data_collection.py   |  10 +-
 examples/ensemble_attack/run_test.sh          |   9 +-
 examples/ensemble_attack/run_train_10k.sh     |   2 +-
 examples/ensemble_attack/test_attack_model.py |  40 +++++-
 6 files changed, 171 insertions(+), 15 deletions(-)
 create mode 100644 examples/ensemble_attack/configs/experiment_config_20k.yaml

diff --git a/examples/ensemble_attack/configs/experiment_config_10k.yaml b/examples/ensemble_attack/configs/experiment_config_10k.yaml
index f1e84827..ea8296aa 100644
--- a/examples/ensemble_attack/configs/experiment_config_10k.yaml
+++ b/examples/ensemble_attack/configs/experiment_config_10k.yaml
@@ -10,7 +10,7 @@ pipeline:
   run_metaclassifier_training: true
 
 target_model: # This is only used for testing the attack on a real target model.
-  # This is for models trained on 10k data and generating `0k synthetic data
+  # This is for models trained on 10k data and generating 10k synthetic data
   target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_10k/test/
   target_model_id: 21  # Will be overridden per SLURM array task
   target_model_name: tabddpm_${target_model.target_model_id}
@@ -18,10 +18,13 @@ target_model: # This is only used for testing the attack on a real target model.
   challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
   challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
 
+
+  target_shadow_models_output_path: ${base_experiment_dir}/test_shadows_dir
+
   target_attack_artifact_dir: ${base_experiment_dir}/target_${target_model.target_model_id}_attack_artifacts/
   attack_probabilities_result_path: ${target_model.target_attack_artifact_dir}/attack_model_${target_model.target_model_id}_proba
-  target_shadow_models_output_path: ${target_model.target_attack_artifact_dir}/tabddpm_${target_model.target_model_id}_shadows_dir
-
+  # target_shadow_models_output_path: ${target_model.target_attack_artifact_dir}/tabddpm_${target_model.target_model_id}_shadows_dir
+  
 
 # Data paths
 data_paths:
diff --git a/examples/ensemble_attack/configs/experiment_config_20k.yaml b/examples/ensemble_attack/configs/experiment_config_20k.yaml
new file mode 100644
index 00000000..531cbcf4
--- /dev/null
+++ b/examples/ensemble_attack/configs/experiment_config_20k.yaml
@@ -0,0 +1,116 @@
+# Ensemble experiment configuration
+# This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
+base_experiment_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_20k_experiment_data/20k # Processed data, and experiment artifacts will be stored here
+base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
+
+# Pipeline control
+pipeline:
+  run_data_processing: true # Set this to false if you have already saved the processed data
+  run_shadow_model_training: true # Set this to false if shadow models are already trained and saved
+  run_metaclassifier_training: true
+
+target_model: # This is only used for testing the attack on a real target model.
+  # This is for models trained on 20k data and generating 20k synthetic data
+  target_model_directory: /projects/midst-experiments/ensemble_attack/midst_data_all_attacks/tabddpm_black_box/final/
+  target_model_id: 61  # Will be overridden per SLURM array task
+  target_model_name: tabddpm_${target_model.target_model_id}
+  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/trans_synthetic.csv 
+  challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
+  challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
+
+  target_shadow_models_output_path: ${base_experiment_dir}/test_shadows_dir
+
+  target_attack_artifact_dir: ${base_experiment_dir}/target_${target_model.target_model_id}_attack_artifacts/
+  attack_probabilities_result_path: ${target_model.target_attack_artifact_dir}/attack_model_${target_model.target_model_id}_proba
+  # target_shadow_models_output_path: ${target_model.target_attack_artifact_dir}/tabddpm_${target_model.target_model_id}_shadows_dir
+  
+
+# Data paths
+data_paths:
+  base_data_dir: /projects/midst-experiments/ensemble_attack/competition # READ from competition
+  midst_data_path: /projects/midst-experiments/ensemble_attack/midst_data_all_attacks/ # Used to collect the data (input)
+  population_path: ${data_paths.base_data_dir}/population_data #READ ONLY  # Path where the collected population data will be stored (output/input)
+  processed_attack_data_path: ${base_experiment_dir}/attack_data # Path where the processed attack real train and evaluation data is stored (output/input)
+  attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored (output)
+
+
+model_paths:
+  metaclassifier_model_path: /projects/midst-experiments/ensemble_attack/competition/trained_models/ # Path where the trained metaclassifier model will be saved
+
+
+# Dataset specific information used for processing in this example
+data_processing_config:
+  population_attack_data_types_to_collect:
+        [
+          "tabddpm_trained_with_20k",
+        ]
+  challenge_attack_data_types_to_collect:
+        [
+          "tabddpm_black_box",
+        ]
+  population_splits: ["train"]  # Data splits to be collected for population data
+  challenge_splits: ["train" , "test"]  # Data splits to be collected for challenge points
+  original_population_data_path: /projects/midst-experiments/ensemble_attack/competition/population_data/
+  # The column name in the data to be used for stratified splitting.
+  column_to_stratify: "trans_type"  # Attention: This value is not documented in the original codebase.
+  folder_ranges: #Specify folder ranges for any of the mentioned splits.
+    train: [[1, 21]] # Folders to be used for train data collection in the experiments
+    final: [[61, 71]] # Change to test for 10k
+  # File names in MIDST data directories.
+  single_table_train_data_file_name: "train_with_id.csv"
+  multi_table_train_data_file_name: "trans.csv"
+  challenge_data_file_name: "challenge_with_id.csv"
+  population_sample_size: 40000 # Population size is the total data that your attack has access to.
+  # In experiments, this is sampled out of all the collected training data in case the available data
+  # is more than this number. Note that, half of this data is actually used for training, the other half
+  # is used for evaluation. For example, with 40k population size, only 20k is used for training the attack model.
+  # TODO: make sure to consider this in experiments.
+
+# Training and data settings for shadow models (temporary, numbers subject to change)
+shadow_training:
+  # Data Config files path used for training a TabDDPM model
+  training_json_config_paths: # Config json files used for tabddpm training on the trans table
+    table_domain_file_path: ${base_data_config_dir}/trans_domain.json
+    dataset_meta_file_path: ${base_data_config_dir}/dataset_meta.json
+    tabddpm_training_config_path: ${base_data_config_dir}/trans.json
+  # Model training artifacts are saved under shadow_models_data_path/workspace_name/exp_name
+  # Also, training configs for each shadow model are created under shadow_models_data_path.
+  shadow_models_output_path: ${base_experiment_dir}/shadow_models_and_data
+  target_model_output_path: ${base_experiment_dir}/shadow_target_model_and_data
+  # Paths to final shadow models used for metaclassifier training (relative to shadow_models_output_path)
+  # These paths are a result of running the shadow model training pipeline, specifically the
+  # train_three_sets_of_shadow_models in shadow_model_training.py
+  # Each .pkl file contains the training data, trained model and training results for all shadow models in a list.
+  final_shadow_models_path: [
+          "${shadow_training.shadow_models_output_path}/initial_model_rmia_1/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
+          "${shadow_training.shadow_models_output_path}/initial_model_rmia_2/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
+          "${shadow_training.shadow_models_output_path}/shadow_model_rmia_third_set/shadow_workspace/trained_model/rmia_shadows_third_set.pkl",
+      ]
+  target_synthetic_data_path: ${shadow_training.target_model_output_path}/target_synthetic_data.csv
+  # Path to final shadow target's synthetic data (relative to target_model_output_path)
+  fine_tuning_config:
+    fine_tune_diffusion_iterations: 200000 # Original code: 200000
+    fine_tune_classifier_iterations: 20000 # Original code: 20000
+    pre_train_data_size: 60000 # Original code: 60000
+  number_of_points_to_synthesize: 20000 # Number of synthetic data samples to be generated by shadow models.
+  # Original code: 20000
+
+
+# Metaclassifier settings
+metaclassifier:
+  # Data types json file is used for xgboost model training.
+  data_types_file_path: ${base_data_config_dir}/data_types.json
+  model_type: "xgb"
+  # Model training parameters
+  num_optuna_trials: 100 # Original code: 100
+  num_kfolds: 5
+  use_gpu: false
+  # Temporary. Might remove having an epoch parameter.
+  epochs: 1
+  meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model
+
+attack_success_computation:
+  target_ids_to_test: [21,22,23] # List of target model IDs to compute the attack success for.
+
+# General settings
+random_seed: 42 # Set to null for no seed, or an integer for a fixed seed
diff --git a/examples/ensemble_attack/real_data_collection.py b/examples/ensemble_attack/real_data_collection.py
index dfbd8282..cc7f6c99 100644
--- a/examples/ensemble_attack/real_data_collection.py
+++ b/examples/ensemble_attack/real_data_collection.py
@@ -67,11 +67,11 @@ def collect_midst_attack_data(
     Returns:
         pd.DataFrame: The specified dataset in this setting.
     """
-    assert data_split in [
-        "train",
-        "dev",
-        "final",
-    ], "data_split should be one of 'train', 'dev', or 'final'."
+    # assert data_split in [
+    #     "train",
+    #     "dev",
+    #     "final",
+    # ], "data_split should be one of 'train', 'dev', or 'final'."
     # `data_id` is the folder numbering of each training or challenge dataset,
     #  and is defined with the provided config.
     data_id = expand_ranges(data_processing_config.folder_ranges[data_split])
diff --git a/examples/ensemble_attack/run_test.sh b/examples/ensemble_attack/run_test.sh
index 2b7380a7..0cc1c593 100644
--- a/examples/ensemble_attack/run_test.sh
+++ b/examples/ensemble_attack/run_test.sh
@@ -6,11 +6,10 @@
 #SBATCH --cpus-per-task=4
 #SBATCH --gres=gpu:a40:1
 #SBATCH --mem=160G
-#SBATCH --job-name=20k_model_10k_test
+#SBATCH --job-name=20k_test
 #SBATCH --output=%j_%x_%a.out
 #SBATCH --error=%j_%x_%a.err
 #SBATCH --time=12:00:00
-#SBATCH --array=0-9  # For 3 target_model_ids (adjust range as needed)
 
 # This script sets up the environment and runs the ensemble attack test script.
 source .venv/bin/activate
@@ -20,15 +19,17 @@ which python
 
 # Map SLURM_ARRAY_TASK_ID to target_model_id.
 
-TARGET_IDS=(61 62 63 64 65 66 67 68 69 70)
+# TARGET_IDS=(61 62 63 64 65 66 67 68 69 70)
 # TARGET_IDS=(101 102 103 104 105 106 107 108 109 110)  # List of target IDs
 
+TARGET_IDS=(61)
+
 TARGET_ID=${TARGET_IDS[$SLURM_ARRAY_TASK_ID]}
 
 echo "Running test for target_model_id: $TARGET_ID"
 
 echo "Experiments Launched"
 
-python -m examples.ensemble_attack.test_attack_model target_model.target_model_id=$TARGET_ID
+python -m examples.ensemble_attack.test_attack_model --config-name=experiment_config_20k target_model.target_model_id=$TARGET_ID
 
 echo "Experiments Completed"
\ No newline at end of file
diff --git a/examples/ensemble_attack/run_train_10k.sh b/examples/ensemble_attack/run_train_10k.sh
index fcb0933f..16c4689e 100755
--- a/examples/ensemble_attack/run_train_10k.sh
+++ b/examples/ensemble_attack/run_train_10k.sh
@@ -12,7 +12,7 @@
 #SBATCH --error=%j_%x.err
 #SBATCH --time=16:00:00
 
-
+#SBATCH --array=0-0  # For 3 target_model_ids (adjust range as needed)
 # This script sets up the environment and runs the ensemble attack example.
 source .venv/bin/activate
 
diff --git a/examples/ensemble_attack/test_attack_model.py b/examples/ensemble_attack/test_attack_model.py
index 44a693e1..74d79561 100644
--- a/examples/ensemble_attack/test_attack_model.py
+++ b/examples/ensemble_attack/test_attack_model.py
@@ -12,12 +12,14 @@
 from omegaconf import DictConfig
 
 from examples.ensemble_attack.run_shadow_model_training import run_shadow_model_training
+from examples.ensemble_attack.real_data_collection import collect_midst_data, AttackType
 from midst_toolkit.attacks.ensemble.blending import BlendingPlusPlus, MetaClassifierType
 from midst_toolkit.attacks.ensemble.data_utils import load_dataframe
 from midst_toolkit.common.logger import log
 from midst_toolkit.common.random import set_all_random_seeds
 
 
+
 def run_rmia_shadow_training(config: DictConfig, df_challenge) -> list[dict[str, list[Any]]]:
     """
     Three sets of shadow models will be trained as a part of this attack.
@@ -106,14 +108,48 @@ def run_metaclassifier_testing(
         INFO, f"Target synthetic data loaded from {target_synthetic_path} with a size of {len(target_synthetic_data)}."
     )
 
-    # 3) Shadow Model Training Step.
+# 3) Shadow Model Training Step.
 
     # Make sure to assign a new path for shadow models trained for target's challenge points to
     # avoid overriding train's shadow models.
     config.shadow_training.shadow_models_output_path = config.target_model.target_shadow_models_output_path
-    shadow_data_collection = run_rmia_shadow_training(config, test_data)
+    shadow_data_paths = [Path(path) for path in config.shadow_training.final_shadow_models_path]
+    # if already trained for test, don't need to train again
+    # Load shadow training collection from previously trained shadow models.
+    assert (
+        len(shadow_data_paths) == 3
+    ), "The attack_data_paths list must contain exactly three elements."
 
+    shadow_data_collection = []
+    models_exist = True
+    for model_path in shadow_data_paths:
+        
+        if model_path.exists():
+            with open(model_path, "rb") as f:
+                shadow_data_and_result = pickle.load(f)
+                shadow_data_collection.append(shadow_data_and_result)
+        else:
+            models_exist = False
+            break
     
+    if not models_exist:
+        # collect all repo's challenge points
+        data_processing_config=config.data_processing_config
+        challenge_attack_names = data_processing_config.challenge_attack_data_types_to_collect
+        challenge_attack_types = [AttackType(attack_name) for attack_name in challenge_attack_names]
+        df_challenge = collect_midst_data(
+            midst_data_input_dir=Path(config.data_paths.midst_data_path),
+            attack_types=challenge_attack_types,
+            data_splits=["final"],  #change to test for 10k, and change to final for 20k
+            dataset="challenge",
+            data_processing_config=config.data_processing_config,
+        )
+        log(INFO, f"Collected challenge data length: {len(df_challenge)} for the testing phase's shadow training.")
+        shadow_data_collection = run_rmia_shadow_training(config, df_challenge=df_challenge)
+
+
+
+
     # Extract trans_id from the test dataframe
     with open(Path(config.metaclassifier.data_types_file_path), "r") as f:
         column_types = json.load(f)

From 4977d1e7631c41c658e7c8ea89919aa60bda8250 Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Mon, 8 Dec 2025 17:26:52 -0500
Subject: [PATCH 21/41] Avoid saving all TrainingResult to reduce memory
 footprint

---
 .../ensemble_attack/compute_attack_success.py |   2 +-
 .../configs/experiment_config_10k.yaml        |  21 ++--
 .../configs/experiment_config_20k.yaml        |  16 +--
 .../configs/experiment_config_20k_200k.yaml   | 118 ++++++++++++++++++
 .../configs/experiment_config_50k.yaml        |  39 +++---
 .../ensemble_attack/real_data_collection.py   |   2 +-
 .../run_metaclassifier_training.py            |   3 +
 examples/ensemble_attack/run_test_10k.sh      |  36 ++++++
 .../{run_test.sh => run_test_20k.sh}          |  14 ++-
 examples/ensemble_attack/run_test_50k.sh      |  35 ++++++
 examples/ensemble_attack/run_train.sh         |  12 +-
 examples/ensemble_attack/run_train_10k.sh     |   1 -
 examples/ensemble_attack/test_attack_model.py |  17 ++-
 .../attacks/ensemble/rmia/rmia_calculation.py |  14 +--
 .../ensemble/rmia/shadow_model_training.py    |   4 +-
 15 files changed, 273 insertions(+), 61 deletions(-)
 create mode 100644 examples/ensemble_attack/configs/experiment_config_20k_200k.yaml
 create mode 100644 examples/ensemble_attack/run_test_10k.sh
 rename examples/ensemble_attack/{run_test.sh => run_test_20k.sh} (63%)
 create mode 100644 examples/ensemble_attack/run_test_50k.sh

diff --git a/examples/ensemble_attack/compute_attack_success.py b/examples/ensemble_attack/compute_attack_success.py
index 5a80a8e9..3f060d40 100644
--- a/examples/ensemble_attack/compute_attack_success.py
+++ b/examples/ensemble_attack/compute_attack_success.py
@@ -101,7 +101,7 @@ def compute_attack_success_for_given_targets(
         f.write(f"Final TPR at FPR=0.1: {tpr_at_fpr:.4f}\n")
 
 
-@hydra.main(config_path="configs", config_name="experiment_config", version_base=None)
+@hydra.main(config_path="configs", config_name="experiment_config_50k", version_base=None)
 def main(
     config: DictConfig,
 ) -> None:
diff --git a/examples/ensemble_attack/configs/experiment_config_10k.yaml b/examples/ensemble_attack/configs/experiment_config_10k.yaml
index ea8296aa..e9380646 100644
--- a/examples/ensemble_attack/configs/experiment_config_10k.yaml
+++ b/examples/ensemble_attack/configs/experiment_config_10k.yaml
@@ -1,6 +1,6 @@
 # Ensemble experiment configuration
 # This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
-base_experiment_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_10k_experiment_data/10k # Processed data, and experiment artifacts will be stored here
+base_experiment_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_10k_experiment_data/10k/test_no_meta_all_tests # Processed data, and experiment artifacts will be stored here
 base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
 
 # Pipeline control
@@ -18,24 +18,25 @@ target_model: # This is only used for testing the attack on a real target model.
   challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
   challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
 
-
   target_shadow_models_output_path: ${base_experiment_dir}/test_shadows_dir
 
-  target_attack_artifact_dir: ${base_experiment_dir}/target_${target_model.target_model_id}_attack_artifacts/
-  attack_probabilities_result_path: ${target_model.target_attack_artifact_dir}/attack_model_${target_model.target_model_id}_proba
+  # target_attack_artifact_dir: ${base_experiment_dir}/target_${target_model.target_model_id}_attack_artifacts/
+  attack_probabilities_result_path: ${target_model.target_shadow_models_output_path}/test_probabilities/attack_model_${target_model.target_model_id}_proba
   # target_shadow_models_output_path: ${target_model.target_attack_artifact_dir}/tabddpm_${target_model.target_model_id}_shadows_dir
   
 
 # Data paths
 data_paths:
-  midst_data_path: /projects/midst-experiments/all_tabddpms/ # Used to collect the data (input)
-  population_path: ${base_experiment_dir}/population_data  # Path where the collected population data will be stored (output/input)
-  processed_attack_data_path: ${base_experiment_dir}/attack_data # Path where the processed attack real train and evaluation data is stored (output/input)
+  midst_data_path: /projects/midst-experiments/all_tabddpms/ # Used to collect the data (input) as defined in data_processing_config
+  processed_base_data_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_10k_experiment_data/10k/ # READ from previously collected and processed data
+  population_path: ${data_paths.processed_base_data_dir}/population_data  # Path where the collected population data will be stored (output/input)
+  processed_attack_data_path: ${data_paths.processed_base_data_dir}/attack_data # Path where the processed attack real train and evaluation data is stored (output/input)
   attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored (output)
 
 
 model_paths:
-  metaclassifier_model_path: ${base_experiment_dir}/trained_models # Path where the trained metaclassifier model will be saved
+# Use 20k's meta
+  metaclassifier_model_path: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_10k_experiment_data/10k/trained_models # Path where the trained metaclassifier model will be saved
 
 
 # Dataset specific information used for processing in this example
@@ -55,7 +56,7 @@ data_processing_config:
   column_to_stratify: "trans_type"  # Attention: This value is not documented in the original codebase.
   folder_ranges: #Specify folder ranges for any of the mentioned splits.
     train: [[1, 21]] # Folders to be used for train data collection in the experiments
-    test: [[21, 31]]
+    test: [[21, 31] , [31, 41]]
   # File names in MIDST data directories.
   single_table_train_data_file_name: "train_with_id.csv"
   multi_table_train_data_file_name: "trans.csv"
@@ -110,7 +111,7 @@ metaclassifier:
   meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model
 
 attack_success_computation:
-  target_ids_to_test: [21,22,23] # List of target model IDs to compute the attack success for.
+  target_ids_to_test: [21,22,23,24, 25, 26, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40] # List of target model IDs to compute the attack success for.
 
 # General settings
 random_seed: 42 # Set to null for no seed, or an integer for a fixed seed
diff --git a/examples/ensemble_attack/configs/experiment_config_20k.yaml b/examples/ensemble_attack/configs/experiment_config_20k.yaml
index 531cbcf4..7268ff19 100644
--- a/examples/ensemble_attack/configs/experiment_config_20k.yaml
+++ b/examples/ensemble_attack/configs/experiment_config_20k.yaml
@@ -1,6 +1,6 @@
 # Ensemble experiment configuration
 # This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
-base_experiment_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_20k_experiment_data/20k # Processed data, and experiment artifacts will be stored here
+base_experiment_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_20k_experiment_data/20k/test_no_meta_all_tests # Processed data, and experiment artifacts will be stored here
 base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
 
 # Pipeline control
@@ -11,7 +11,7 @@ pipeline:
 
 target_model: # This is only used for testing the attack on a real target model.
   # This is for models trained on 20k data and generating 20k synthetic data
-  target_model_directory: /projects/midst-experiments/ensemble_attack/midst_data_all_attacks/tabddpm_black_box/final/
+  target_model_directory: /projects/midst-experiments/ensemble_attack/midst_data_all_attacks/tabddpm_black_box/final
   target_model_id: 61  # Will be overridden per SLURM array task
   target_model_name: tabddpm_${target_model.target_model_id}
   target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/trans_synthetic.csv 
@@ -20,8 +20,8 @@ target_model: # This is only used for testing the attack on a real target model.
 
   target_shadow_models_output_path: ${base_experiment_dir}/test_shadows_dir
 
-  target_attack_artifact_dir: ${base_experiment_dir}/target_${target_model.target_model_id}_attack_artifacts/
-  attack_probabilities_result_path: ${target_model.target_attack_artifact_dir}/attack_model_${target_model.target_model_id}_proba
+  # target_attack_artifact_dir: ${base_experiment_dir}/target_${target_model.target_model_id}_attack_artifacts/
+  attack_probabilities_result_path: ${target_model.target_shadow_models_output_path}/test_probabilities/attack_model_${target_model.target_model_id}_proba
   # target_shadow_models_output_path: ${target_model.target_attack_artifact_dir}/tabddpm_${target_model.target_model_id}_shadows_dir
   
 
@@ -30,7 +30,7 @@ data_paths:
   base_data_dir: /projects/midst-experiments/ensemble_attack/competition # READ from competition
   midst_data_path: /projects/midst-experiments/ensemble_attack/midst_data_all_attacks/ # Used to collect the data (input)
   population_path: ${data_paths.base_data_dir}/population_data #READ ONLY  # Path where the collected population data will be stored (output/input)
-  processed_attack_data_path: ${base_experiment_dir}/attack_data # Path where the processed attack real train and evaluation data is stored (output/input)
+  processed_attack_data_path: ${data_paths.base_data_dir}/attack_data # Path where the processed attack real train and evaluation data is stored (output/input)
   attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored (output)
 
 
@@ -49,13 +49,13 @@ data_processing_config:
           "tabddpm_black_box",
         ]
   population_splits: ["train"]  # Data splits to be collected for population data
-  challenge_splits: ["train" , "test"]  # Data splits to be collected for challenge points
+  challenge_splits: ["train" , "final"]  # Data splits to be collected for challenge points
   original_population_data_path: /projects/midst-experiments/ensemble_attack/competition/population_data/
   # The column name in the data to be used for stratified splitting.
   column_to_stratify: "trans_type"  # Attention: This value is not documented in the original codebase.
   folder_ranges: #Specify folder ranges for any of the mentioned splits.
     train: [[1, 21]] # Folders to be used for train data collection in the experiments
-    final: [[61, 71]] # Change to test for 10k
+    final: [[61, 71], [101, 111]] # Change to test for 10k
   # File names in MIDST data directories.
   single_table_train_data_file_name: "train_with_id.csv"
   multi_table_train_data_file_name: "trans.csv"
@@ -110,7 +110,7 @@ metaclassifier:
   meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model
 
 attack_success_computation:
-  target_ids_to_test: [21,22,23] # List of target model IDs to compute the attack success for.
+  target_ids_to_test: [61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110] # List of target model IDs to compute the attack success for.
 
 # General settings
 random_seed: 42 # Set to null for no seed, or an integer for a fixed seed
diff --git a/examples/ensemble_attack/configs/experiment_config_20k_200k.yaml b/examples/ensemble_attack/configs/experiment_config_20k_200k.yaml
new file mode 100644
index 00000000..5d1dd54d
--- /dev/null
+++ b/examples/ensemble_attack/configs/experiment_config_20k_200k.yaml
@@ -0,0 +1,118 @@
+# Ensemble experiment configuration
+# This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
+base_experiment_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_20k_experiment_data/200k # Processed data, and experiment artifacts will be stored here
+base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
+
+# Pipeline control
+pipeline:
+# We don't need to again process data for 200k Synthetic
+  run_data_processing: false # Set this to false if you have already saved the processed data
+  run_shadow_model_training: true # Set this to false if shadow models are already trained and saved
+  run_metaclassifier_training: true
+
+target_model: # This is only used for testing the attack on a real target model.
+  # This is for models trained on 20k data and generating 20k synthetic data
+  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_20k/ls
+  
+  target_model_id: 61  # Will be overridden per SLURM array task
+  target_model_name: tabddpm_${target_model.target_model_id}
+  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/trans_synthetic.csv 
+  challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
+  challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
+
+  target_shadow_models_output_path: ${base_experiment_dir}/test_shadows_dir
+
+  # target_attack_artifact_dir: ${base_experiment_dir}/target_${target_model.target_model_id}_attack_artifacts/
+  attack_probabilities_result_path: ${target_model.target_shadow_models_output_path}/test_probabilities/attack_model_${target_model.target_model_id}_proba
+  # target_shadow_models_output_path: ${target_model.target_attack_artifact_dir}/tabddpm_${target_model.target_model_id}_shadows_dir
+  
+
+# Data paths
+data_paths:
+  midst_data_path: /projects/midst-experiments/all_tabddpms/ # Used to collect the data (input) as defined in data_processing_config
+  processed_base_data_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_10k_experiment_data/10k/ # READ from previously collected and processed data
+  population_path: ${data_paths.processed_base_data_dir}/population_data  # Path where the collected population data will be stored (output/input)
+  processed_attack_data_path: ${data_paths.processed_base_data_dir}/attack_data # Path where the processed attack real train and evaluation data is stored (output/input)
+  attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored (output)
+
+
+model_paths:
+  metaclassifier_model_path: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_20k_experiment_data/200k/trained_models/ # Path where the trained metaclassifier model will be saved
+
+
+# Dataset specific information used for processing in this example
+data_processing_config:
+  population_attack_data_types_to_collect:
+        [
+          "tabddpm_trained_with_20k",
+        ]
+  challenge_attack_data_types_to_collect:
+        [
+          "tabddpm_trained_with_20k",
+        ]
+  population_splits: ["train"]  # Data splits to be collected for population data
+  challenge_splits: ["train" , "test"]  # Data splits to be collected for challenge points
+  original_population_data_path: /projects/midst-experiments/ensemble_attack/competition/population_data/
+  # The column name in the data to be used for stratified splitting.
+  column_to_stratify: "trans_type"  # Attention: This value is not documented in the original codebase.
+  folder_ranges: #Specify folder ranges for any of the mentioned splits.
+    train: [[1, 21]] # Folders to be used for train data collection in the experiments
+    test: [[21, 31] , [31, 41]]
+  # File names in MIDST data directories.
+  single_table_train_data_file_name: "train_with_id.csv"
+  multi_table_train_data_file_name: "trans.csv"
+  challenge_data_file_name: "challenge_with_id.csv"
+  population_sample_size: 40000 # Population size is the total data that your attack has access to.
+  # In experiments, this is sampled out of all the collected training data in case the available data
+  # is more than this number. Note that, half of this data is actually used for training, the other half
+  # is used for evaluation. For example, with 40k population size, only 20k is used for training the attack model.
+  # TODO: make sure to consider this in experiments.
+
+# Training and data settings for shadow models (temporary, numbers subject to change)
+shadow_training:
+  # Data Config files path used for training a TabDDPM model
+  training_json_config_paths: # Config json files used for tabddpm training on the trans table
+    table_domain_file_path: ${base_data_config_dir}/trans_domain.json
+    dataset_meta_file_path: ${base_data_config_dir}/dataset_meta.json
+    tabddpm_training_config_path: ${base_data_config_dir}/trans.json
+  # Model training artifacts are saved under shadow_models_data_path/workspace_name/exp_name
+  # Also, training configs for each shadow model are created under shadow_models_data_path.
+  shadow_models_output_path: ${base_experiment_dir}/shadow_models_and_data
+  target_model_output_path: ${base_experiment_dir}/shadow_target_model_and_data
+  # Paths to final shadow models used for metaclassifier training (relative to shadow_models_output_path)
+  # These paths are a result of running the shadow model training pipeline, specifically the
+  # train_three_sets_of_shadow_models in shadow_model_training.py
+  # Each .pkl file contains the training data, trained model and training results for all shadow models in a list.
+  final_shadow_models_path: [
+          "${shadow_training.shadow_models_output_path}/initial_model_rmia_1/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
+          "${shadow_training.shadow_models_output_path}/initial_model_rmia_2/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
+          "${shadow_training.shadow_models_output_path}/shadow_model_rmia_third_set/shadow_workspace/trained_model/rmia_shadows_third_set.pkl",
+      ]
+  target_synthetic_data_path: ${shadow_training.target_model_output_path}/target_synthetic_data.csv
+  # Path to final shadow target's synthetic data (relative to target_model_output_path)
+  fine_tuning_config:
+    fine_tune_diffusion_iterations: 200000 # Original code: 200000
+    fine_tune_classifier_iterations: 20000 # Original code: 20000
+    pre_train_data_size: 60000 # Original code: 60000
+  number_of_points_to_synthesize: 200000 # Number of synthetic data samples to be generated by shadow models.
+  # Original code: 20000
+
+
+# Metaclassifier settings
+metaclassifier:
+  # Data types json file is used for xgboost model training.
+  data_types_file_path: ${base_data_config_dir}/data_types.json
+  model_type: "xgb"
+  # Model training parameters
+  num_optuna_trials: 100 # Original code: 100
+  num_kfolds: 5
+  use_gpu: false
+  # Temporary. Might remove having an epoch parameter.
+  epochs: 1
+  meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model
+
+attack_success_computation:
+  target_ids_to_test: [21,22,23,24, 25, 26, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40] # List of target model IDs to compute the attack success for.
+
+# General settings
+random_seed: 42 # Set to null for no seed, or an integer for a fixed seed
diff --git a/examples/ensemble_attack/configs/experiment_config_50k.yaml b/examples/ensemble_attack/configs/experiment_config_50k.yaml
index 300e37ee..62fcedff 100644
--- a/examples/ensemble_attack/configs/experiment_config_50k.yaml
+++ b/examples/ensemble_attack/configs/experiment_config_50k.yaml
@@ -1,38 +1,41 @@
 # Ensemble experiment configuration
 # This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
-base_experiment_dir: /projects/midst-experiments/ensemble_attack/tabddpm_50k_experiment_data # Processed data, and experiment artifacts will be stored here
+base_experiment_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_50k_experiment_data/50k_save_synth_only # Processed data, and experiment artifacts will be stored here
 base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
 
 # Pipeline control
 pipeline:
-  run_data_processing: false # Set this to false if you have already saved the processed data
-  run_shadow_model_training: false # Set this to false if shadow models are already trained and saved
+  run_data_processing: true # Set this to false if you have already saved the processed data
+  run_shadow_model_training: true # Set this to false if shadow models are already trained and saved
   run_metaclassifier_training: true
 
 target_model: # This is only used for testing the attack on a real target model.
   # This is for models trained on 50k data and generating 50k synthetic data
-  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_50k/train/
+  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_50k/test/
   target_model_id: 21  # Will be overridden per SLURM array task
   target_model_name: tabddpm_${target_model.target_model_id}
   target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/50k/50k.csv
   challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
   challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
 
-  target_attack_artifact_dir: ${base_experiment_dir}/target_${target_model.target_model_id}_attack_artifacts/
-  attack_probabilities_result_path: ${target_model.target_attack_artifact_dir}/attack_model_${target_model.target_model_id}_proba
-  target_shadow_models_output_path: ${target_model.target_attack_artifact_dir}/tabddpm_${target_model.target_model_id}_shadows_dir
+  target_shadow_models_output_path: ${base_experiment_dir}/test_shadows_dir
 
+  # target_attack_artifact_dir: ${base_experiment_dir}/target_${target_model.target_model_id}_attack_artifacts/
+  attack_probabilities_result_path: ${target_model.target_shadow_models_output_path}/test_probabilities/attack_model_${target_model.target_model_id}_proba
+  # target_shadow_models_output_path: ${target_model.target_attack_artifact_dir}/tabddpm_${target_model.target_model_id}_shadows_dir
 
 # Data paths
 data_paths:
-  midst_data_path: /projects/midst-experiments/all_tabddpms # Used to collect the data
-  population_path: ${base_experiment_dir}/population_data  # Path where the collected population data will be stored
-  processed_attack_data_path: ${base_experiment_dir}/attack_data # Path where the processed attack real train and evaluation data is stored
-  attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored
+  midst_data_path: /projects/midst-experiments/all_tabddpms/ # Used to collect the data (input) in data processing
+  population_path: ${base_experiment_dir}/population_data #READ ONLY  # Path where the collected population data will be stored (output/input)
+  processed_attack_data_path: ${base_experiment_dir}/attack_data # Path where the processed attack real train and evaluation data is stored (output/input)
+  attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored (output)
 
-model_paths:
-  metaclassifier_model_path: ${base_experiment_dir}/trained_models # Path where the trained metaclassifier model will be saved
 
+model_paths:
+# Use 20k's meta
+  # metaclassifier_model_path: /projects/midst-experiments/ensemble_attack/competition/trained_models # Path where the trained metaclassifier model will be saved
+  metaclassifier_model_path: ${base_experiment_dir}/trained_models 
 
 # Dataset specific information used for processing in this example
 data_processing_config:
@@ -45,11 +48,13 @@ data_processing_config:
           "tabddpm_trained_with_50k",
         ]
   population_splits: ["train"]  # Data splits to be collected for population data
-  challenge_splits: ["train"]  # Data splits to be collected for challenge points
+  challenge_splits: ["train" , "test"]  # Data splits to be collected for challenge points
+  original_population_data_path: /projects/midst-experiments/ensemble_attack/competition/population_data/
   # The column name in the data to be used for stratified splitting.
   column_to_stratify: "trans_type"  # Attention: This value is not documented in the original codebase.
   folder_ranges: #Specify folder ranges for any of the mentioned splits.
-    train: [[1, 20]] # Folders to be used for train data collection in the experiments
+    train: [[1, 21]] # Folders to be used for train data collection in the experiments
+    test: [[21, 31], [31, 41]]
   # File names in MIDST data directories.
   single_table_train_data_file_name: "train_with_id.csv"
   multi_table_train_data_file_name: "trans.csv"
@@ -101,10 +106,10 @@ metaclassifier:
   use_gpu: false
   # Temporary. Might remove having an epoch parameter.
   epochs: 1
-  meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model_batched_parallel
+  meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model
 
 attack_success_computation:
-  target_ids_to_test: [21,22,23] # List of target model IDs to compute the attack success for.
+  target_ids_to_test: [21,22,23,24, 25, 26, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40] # List of target model IDs to compute the attack success for.
 
 # General settings
 random_seed: 42 # Set to null for no seed, or an integer for a fixed seed
diff --git a/examples/ensemble_attack/real_data_collection.py b/examples/ensemble_attack/real_data_collection.py
index cc7f6c99..1ec7c059 100644
--- a/examples/ensemble_attack/real_data_collection.py
+++ b/examples/ensemble_attack/real_data_collection.py
@@ -134,7 +134,7 @@ def collect_midst_data(
                 data_processing_config=data_processing_config,
             )
 
-        population.append(df_real)
+            population.append(df_real)
 
     return pd.concat(population).drop_duplicates()
 
diff --git a/examples/ensemble_attack/run_metaclassifier_training.py b/examples/ensemble_attack/run_metaclassifier_training.py
index e6a9c8e5..e8fcd9a0 100644
--- a/examples/ensemble_attack/run_metaclassifier_training.py
+++ b/examples/ensemble_attack/run_metaclassifier_training.py
@@ -63,6 +63,7 @@ def run_metaclassifier_training(
         with open(model_path, "rb") as f:
             shadow_data_and_result = pickle.load(f)
             shadow_data_collection.append(shadow_data_and_result)
+            log(INFO, f"Shadow model data loaded from {model_path}.")
 
     assert Path(target_model_synthetic_path).exists(), (
         f"No file found at {target_model_synthetic_path}. "
@@ -71,6 +72,7 @@ def run_metaclassifier_training(
 
     # Load the target model's synthetic data
     target_synthetic_data = pd.read_csv(target_model_synthetic_path)
+    log(INFO, f"Target model's synthetic data loaded from {target_model_synthetic_path} with size {len(target_synthetic_data)}.")
 
     assert target_synthetic_data is not None, "Target model's synthetic data is missing."
     target_synthetic_data = target_synthetic_data.copy()
@@ -79,6 +81,7 @@ def run_metaclassifier_training(
         Path(config.data_paths.population_path),
         "population_all_with_challenge_no_id.csv",
     )
+    log(INFO, f"Reference population data loaded from f{config.data_paths.population_path} with size {len(df_reference)}.")
 
     # Extract trans_id from both train and test dataframes
     assert "trans_id" in df_meta_train.columns, "Meta train data must have trans_id column"
diff --git a/examples/ensemble_attack/run_test_10k.sh b/examples/ensemble_attack/run_test_10k.sh
new file mode 100644
index 00000000..7926527d
--- /dev/null
+++ b/examples/ensemble_attack/run_test_10k.sh
@@ -0,0 +1,36 @@
+#!/bin/bash
+
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=1
+#SBATCH --gres=gpu:a100:1
+#SBATCH --mem=128G
+#SBATCH --job-name=test10_all_targets
+#SBATCH --output=%j_%x_%a.out
+#SBATCH --error=%j_%x_%a.err
+#SBATCH --time=6:00:00
+#SBATCH --array=0-0
+
+# This script sets up the environment and runs the ensemble attack test script.
+source .venv/bin/activate
+
+echo "Active Environment:"
+which python
+
+# Map SLURM_ARRAY_TASK_ID to target_model_id.
+
+TARGET_IDS=(21)
+
+# TARGET_IDS=( 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40)  # List of target IDs
+
+TARGET_ID=${TARGET_IDS[$SLURM_ARRAY_TASK_ID]}
+
+echo "Running test for target_model_id: $TARGET_ID using all target models' challenges (same shadows)"
+
+echo "Experiments Launched"
+pwd
+
+python -m examples.ensemble_attack.test_attack_model --config-name=experiment_config_10k target_model.target_model_id=$TARGET_ID
+
+echo "Experiments Completed"
\ No newline at end of file
diff --git a/examples/ensemble_attack/run_test.sh b/examples/ensemble_attack/run_test_20k.sh
similarity index 63%
rename from examples/ensemble_attack/run_test.sh
rename to examples/ensemble_attack/run_test_20k.sh
index 0cc1c593..cce66d00 100644
--- a/examples/ensemble_attack/run_test.sh
+++ b/examples/ensemble_attack/run_test_20k.sh
@@ -5,11 +5,12 @@
 #SBATCH --ntasks-per-node=1
 #SBATCH --cpus-per-task=4
 #SBATCH --gres=gpu:a40:1
-#SBATCH --mem=160G
-#SBATCH --job-name=20k_test
+#SBATCH --mem=32G
+#SBATCH --job-name=test20_all_targets
 #SBATCH --output=%j_%x_%a.out
 #SBATCH --error=%j_%x_%a.err
-#SBATCH --time=12:00:00
+#SBATCH --time=1:00:00
+#SBATCH --array=0-18
 
 # This script sets up the environment and runs the ensemble attack test script.
 source .venv/bin/activate
@@ -20,13 +21,14 @@ which python
 # Map SLURM_ARRAY_TASK_ID to target_model_id.
 
 # TARGET_IDS=(61 62 63 64 65 66 67 68 69 70)
-# TARGET_IDS=(101 102 103 104 105 106 107 108 109 110)  # List of target IDs
+# TARGET_IDS=(61)
+TARGET_IDS=( 62 63 64 65 66 67 68 69 70 101 102 103 104 105 106 107 108 109 110)  # List of target IDs
 
-TARGET_IDS=(61)
+# TARGET_IDS=(21 22 23 24 25 26 27 28 29 30)  # List of target IDs
 
 TARGET_ID=${TARGET_IDS[$SLURM_ARRAY_TASK_ID]}
 
-echo "Running test for target_model_id: $TARGET_ID"
+echo "Running test for target_model_id: $TARGET_ID using all target models' challenges (same shadows)"
 
 echo "Experiments Launched"
 
diff --git a/examples/ensemble_attack/run_test_50k.sh b/examples/ensemble_attack/run_test_50k.sh
new file mode 100644
index 00000000..17cf368f
--- /dev/null
+++ b/examples/ensemble_attack/run_test_50k.sh
@@ -0,0 +1,35 @@
+#!/bin/bash
+
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=4
+#SBATCH --gres=gpu:a40:1
+#SBATCH --mem=64G
+#SBATCH --job-name=test50_all_with_meta_train
+#SBATCH --output=%j_%x_%a.out
+#SBATCH --error=%j_%x_%a.err
+#SBATCH --time=2:00:00
+#SBATCH --array=0-18
+
+# This script sets up the environment and runs the ensemble attack test script.
+source .venv/bin/activate
+
+echo "Active Environment:"
+which python
+
+# Map SLURM_ARRAY_TASK_ID to target_model_id.
+
+# TARGET_IDS=(21)
+
+TARGET_IDS=( 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40)  # List of target IDs
+
+TARGET_ID=${TARGET_IDS[$SLURM_ARRAY_TASK_ID]}
+
+echo "Running test for target_model_id: $TARGET_ID using all target models' challenges (same shadows)"
+
+echo "Experiments Launched"
+
+python -m examples.ensemble_attack.test_attack_model --config-name=experiment_config_50k target_model.target_model_id=$TARGET_ID
+
+echo "Experiments Completed"
\ No newline at end of file
diff --git a/examples/ensemble_attack/run_train.sh b/examples/ensemble_attack/run_train.sh
index dfd2a203..710322bd 100755
--- a/examples/ensemble_attack/run_train.sh
+++ b/examples/ensemble_attack/run_train.sh
@@ -3,13 +3,13 @@
 #SBATCH --nodes=1
 #SBATCH --ntasks=1
 #SBATCH --ntasks-per-node=1
-#SBATCH --cpus-per-task=4
-#SBATCH --gres=gpu:a40:1
-#SBATCH --mem=160G
-#SBATCH --job-name=new_exp_10k_train
+#SBATCH --cpus-per-task=1
+#SBATCH --gres=gpu:a100:1
+#SBATCH --mem=210G
+#SBATCH --job-name=20k_200k_train_save_synth_only
 #SBATCH --output=%j_%x.out
 #SBATCH --error=%j_%x.err
-#SBATCH --time=16:00:00
+#SBATCH --time=12:00:00
 
 
 echo "Total memory allocated: $(($SLURM_MEM_PER_NODE / 1024)) GB"
@@ -22,6 +22,6 @@ which python
 
 echo "Experiments Launched"
 
-python -m examples.ensemble_attack.run_attack  --config-name=experiment_config_10k
+python -m examples.ensemble_attack.run_attack  --config-name=experiment_config_20k_20k
 
 echo "Experiments Completed"
diff --git a/examples/ensemble_attack/run_train_10k.sh b/examples/ensemble_attack/run_train_10k.sh
index 16c4689e..709bfbdc 100755
--- a/examples/ensemble_attack/run_train_10k.sh
+++ b/examples/ensemble_attack/run_train_10k.sh
@@ -12,7 +12,6 @@
 #SBATCH --error=%j_%x.err
 #SBATCH --time=16:00:00
 
-#SBATCH --array=0-0  # For 3 target_model_ids (adjust range as needed)
 # This script sets up the environment and runs the ensemble attack example.
 source .venv/bin/activate
 
diff --git a/examples/ensemble_attack/test_attack_model.py b/examples/ensemble_attack/test_attack_model.py
index 74d79561..7ff97c51 100644
--- a/examples/ensemble_attack/test_attack_model.py
+++ b/examples/ensemble_attack/test_attack_model.py
@@ -87,7 +87,7 @@ def run_metaclassifier_testing(
     with open(mataclassifier_path, "rb") as f:
         trained_mataclassifier_model = pickle.load(f)
 
-    log(INFO, "Metaclassifier model loaded, starting the test...")
+    log(INFO, f"Metaclassifier model loaded from {mataclassifier_path}, starting the test...")
 
     # 2) Read target model's challenge data and synthetic data.
 
@@ -128,11 +128,13 @@ def run_metaclassifier_testing(
             with open(model_path, "rb") as f:
                 shadow_data_and_result = pickle.load(f)
                 shadow_data_collection.append(shadow_data_and_result)
+            log(INFO, f"Loaded existing shadow model at {model_path}.")
         else:
             models_exist = False
             break
     
     if not models_exist:
+        log(INFO, "Shadow models for testing phase do not exist. Training RMIA shadow models...")
         # collect all repo's challenge points
         data_processing_config=config.data_processing_config
         challenge_attack_names = data_processing_config.challenge_attack_data_types_to_collect
@@ -140,12 +142,23 @@ def run_metaclassifier_testing(
         df_challenge = collect_midst_data(
             midst_data_input_dir=Path(config.data_paths.midst_data_path),
             attack_types=challenge_attack_types,
-            data_splits=["final"],  #change to test for 10k, and change to final for 20k
+            data_splits=["test"],  #change to test for 10k, and change to final for 20k
             dataset="challenge",
             data_processing_config=config.data_processing_config,
         )
         log(INFO, f"Collected challenge data length: {len(df_challenge)} for the testing phase's shadow training.")
+        
+        # Run RMIA shadow model training on master challenge train data
+        # df_challenge = load_dataframe(
+        #     Path(config.data_paths.processed_attack_data_path),
+        #     "master_challenge_train.csv",
+        # )
+        # log(INFO, f"Loaded master challenge train data length: {len(df_challenge)} for the testing phase's shadow training.")
+        
         shadow_data_collection = run_rmia_shadow_training(config, df_challenge=df_challenge)
+    
+    else:
+        log(INFO, "All shadow models for testing phase found. Using existing RMIA shadow models...")
 
 
 
diff --git a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
index e8697f1f..c8139080 100644
--- a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
+++ b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
@@ -73,7 +73,7 @@ def compute_gower_for_model(args):
     # ).astype(dtype)
     # Batched approach: compute row-by-row to reduce peak memory
     gower_matrix = compute_gower_batched(
-        df_input, df_synthetic, categorical_features, batch_size=5000, dtype=dtype
+        df_input, df_synthetic, categorical_features, batch_size=2000, dtype=dtype
     )
     
     return i, gower_matrix
@@ -258,9 +258,9 @@ def calculate_rmia_signals(
     trained_shadow_data = shadow_data_collection[2]
 
     all_lengths = [
-        [len(data.synthetic_data) for data in fine_tuned_shadow_data_0["fine_tuned_results"]],
-        [len(data.synthetic_data) for data in fine_tuned_shadow_data_1["fine_tuned_results"]],
-        [len(data.synthetic_data) for data in trained_shadow_data["trained_results"]],
+        [len(data) for data in fine_tuned_shadow_data_0["fine_tuned_results"]],
+        [len(data) for data in fine_tuned_shadow_data_1["fine_tuned_results"]],
+        [len(data) for data in trained_shadow_data["trained_results"]],
         [len(data) for data in fine_tuned_shadow_data_0["fine_tuning_sets"]],
         [len(data) for data in fine_tuned_shadow_data_1["fine_tuning_sets"]],
         [len(data) for data in trained_shadow_data["selected_sets"]],
@@ -275,7 +275,7 @@ def calculate_rmia_signals(
         raise ValueError(f"k={k} must be within [1, {min_length}]")
 
     shadow_synthetic_list_0 = [
-        train_result.synthetic_data for train_result in fine_tuned_shadow_data_0[Key.FINE_TUNED_RESULTS.value]
+        train_result for train_result in fine_tuned_shadow_data_0[Key.FINE_TUNED_RESULTS.value]
     ]
     # shadow_model_gower_0 = get_rmia_gower(
     #     df_input=df_input,
@@ -289,7 +289,7 @@ def calculate_rmia_signals(
     # log(INFO, "Computed Gower distance for first set of fine-tuned shadow models.")
 
     shadow_synthetic_list_1 = [
-        train_result.synthetic_data for train_result in fine_tuned_shadow_data_1[Key.FINE_TUNED_RESULTS.value]
+        train_result for train_result in fine_tuned_shadow_data_1[Key.FINE_TUNED_RESULTS.value]
     ]
     shadow_0_and_1_synthetic_data = shadow_synthetic_list_1+shadow_synthetic_list_0
     del shadow_synthetic_list_0 
@@ -309,7 +309,7 @@ def calculate_rmia_signals(
     log(INFO, "Computed Gower distance for the first two sets of fine-tuned shadow models (8 models total).")
 
     shadow_synthetic_list_2 = [
-        train_result.synthetic_data for train_result in trained_shadow_data[Key.TRAINED_RESULTS.value]
+        train_result for train_result in trained_shadow_data[Key.TRAINED_RESULTS.value]
     ]
     # shadow_synthetic_list_2 includes 8 shadow models
     shadow_model_gower_2 = get_rmia_gower(
diff --git a/src/midst_toolkit/attacks/ensemble/rmia/shadow_model_training.py b/src/midst_toolkit/attacks/ensemble/rmia/shadow_model_training.py
index 672d2ec8..4781c084 100644
--- a/src/midst_toolkit/attacks/ensemble/rmia/shadow_model_training.py
+++ b/src/midst_toolkit/attacks/ensemble/rmia/shadow_model_training.py
@@ -184,7 +184,7 @@ def train_fine_tuned_shadow_models(
             INFO,
             f"Fine-tuned model {model_id} generated {len(train_result.synthetic_data)} synthetic samples.",
         )
-        attack_data["fine_tuned_results"].append(train_result)
+        attack_data["fine_tuned_results"].append(train_result.synthetic_data)
 
     # Pickle dump the results
     result_path = Path(save_dir / "rmia_shadows.pkl")
@@ -296,7 +296,7 @@ def train_shadow_on_half_challenge_data(
             f"Trained shadow model {model_id} generated {len(train_result.synthetic_data)} synthetic samples.",
         )
 
-        attack_data["trained_results"].append(train_result)
+        attack_data["trained_results"].append(train_result.synthetic_data)
 
     # Pickle dump the results
     result_path = Path(save_dir, "rmia_shadows_third_set.pkl")

From 8bd62ee4974cce5fde3f17321c529a4f85f63e13 Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Tue, 16 Dec 2025 11:25:40 -0500
Subject: [PATCH 22/41] Updated test

---
 examples/ensemble_attack/test_attack_model.py | 43 ++++++++-----------
 1 file changed, 18 insertions(+), 25 deletions(-)

diff --git a/examples/ensemble_attack/test_attack_model.py b/examples/ensemble_attack/test_attack_model.py
index 7ff97c51..c01c3907 100644
--- a/examples/ensemble_attack/test_attack_model.py
+++ b/examples/ensemble_attack/test_attack_model.py
@@ -107,8 +107,14 @@ def run_metaclassifier_testing(
     log(
         INFO, f"Target synthetic data loaded from {target_synthetic_path} with a size of {len(target_synthetic_data)}."
     )
+    if len(target_synthetic_data)> config.shadow_training.number_of_points_to_synthesize:
+        # Take only the required number of synthetic data points
+        target_synthetic_data = target_synthetic_data.head(config.shadow_training.number_of_points_to_synthesize)
+        log(
+            INFO, f"Target synthetic data size adjusted to {len(target_synthetic_data)} based on the config setting."
+        )
 
-# 3) Shadow Model Training Step.
+    # 3) Shadow Model Training Step.
 
     # Make sure to assign a new path for shadow models trained for target's challenge points to
     # avoid overriding train's shadow models.
@@ -139,22 +145,24 @@ def run_metaclassifier_testing(
         data_processing_config=config.data_processing_config
         challenge_attack_names = data_processing_config.challenge_attack_data_types_to_collect
         challenge_attack_types = [AttackType(attack_name) for attack_name in challenge_attack_names]
-        df_challenge = collect_midst_data(
+        df_challenge_experiment = collect_midst_data(
             midst_data_input_dir=Path(config.data_paths.midst_data_path),
             attack_types=challenge_attack_types,
             data_splits=["test"],  #change to test for 10k, and change to final for 20k
             dataset="challenge",
             data_processing_config=config.data_processing_config,
         )
-        log(INFO, f"Collected challenge data length: {len(df_challenge)} for the testing phase's shadow training.")
-        
-        # Run RMIA shadow model training on master challenge train data
-        # df_challenge = load_dataframe(
-        #     Path(config.data_paths.processed_attack_data_path),
-        #     "master_challenge_train.csv",
-        # )
-        # log(INFO, f"Loaded master challenge train data length: {len(df_challenge)} for the testing phase's shadow training.")
+        log(INFO, f"Collected challenge data length: {len(df_challenge_experiment)} for the testing phase's shadow training.")
         
+        # Load master challenge train data
+        df_master_train = load_dataframe(
+            Path(config.data_paths.processed_attack_data_path),
+            "master_challenge_train.csv",
+        )
+        log(INFO, f"Loaded master challenge train data length: {len(df_master_train)} for the testing phase's shadow training.")
+
+        # Run RMIA shadow model training on experiments challenge points + master challenge train data
+        df_challenge = pd.concat([df_challenge_experiment, df_master_train]).drop_duplicates()
         shadow_data_collection = run_rmia_shadow_training(config, df_challenge=df_challenge)
     
     else:
@@ -178,21 +186,6 @@ def run_metaclassifier_testing(
 
     
 
-
-    # Load already trained shadows (only if completely are run)
-    # shadow_model_paths = [Path(path) for path in config.shadow_training.final_shadow_models_path]
-    # shadow_data_collection = []
-    # for model_path in shadow_model_paths:
-    #     assert model_path.exists(), (
-    #         f"No file found at {model_path}. Make sure the path is correct, or run shadow model training first."
-    #     )
-
-    #     with open(model_path, "rb") as f:
-    #         shadow_data_and_result = pickle.load(f)
-    #         shadow_data_collection.append(shadow_data_and_result)
-
-
-
     # 4) Initialize the attacker object, and assign the loaded metaclassifier to it.
 
     df_reference = load_dataframe(

From bcea5d0cb9ece1ab895ff83b75eb2fe03ef92bb3 Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Wed, 17 Dec 2025 10:38:16 -0500
Subject: [PATCH 23/41] Cleaned the code

---
 .../ensemble_attack/compute_attack_success.py |  2 +-
 .../configs/experiment_config_10k.yaml        | 24 ++---
 .../run_shadow_model_training.py              | 15 ++-
 examples/ensemble_attack/test_attack_model.py | 74 ++++++++------
 .../attacks/ensemble/rmia/rmia_calculation.py | 97 +++++++++++--------
 .../ensemble/rmia/shadow_model_training.py    |  1 +
 6 files changed, 117 insertions(+), 96 deletions(-)

diff --git a/examples/ensemble_attack/compute_attack_success.py b/examples/ensemble_attack/compute_attack_success.py
index 3f060d40..5a80a8e9 100644
--- a/examples/ensemble_attack/compute_attack_success.py
+++ b/examples/ensemble_attack/compute_attack_success.py
@@ -101,7 +101,7 @@ def compute_attack_success_for_given_targets(
         f.write(f"Final TPR at FPR=0.1: {tpr_at_fpr:.4f}\n")
 
 
-@hydra.main(config_path="configs", config_name="experiment_config_50k", version_base=None)
+@hydra.main(config_path="configs", config_name="experiment_config", version_base=None)
 def main(
     config: DictConfig,
 ) -> None:
diff --git a/examples/ensemble_attack/configs/experiment_config_10k.yaml b/examples/ensemble_attack/configs/experiment_config_10k.yaml
index e9380646..2ac189cf 100644
--- a/examples/ensemble_attack/configs/experiment_config_10k.yaml
+++ b/examples/ensemble_attack/configs/experiment_config_10k.yaml
@@ -1,16 +1,15 @@
 # Ensemble experiment configuration
 # This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
-base_experiment_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_10k_experiment_data/10k/test_no_meta_all_tests # Processed data, and experiment artifacts will be stored here
-base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
+base_experiment_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_10k_experiment_data/10k/ # Processed data, and experiment artifacts will be stored under this directory.
+base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory.
 
-# Pipeline control
+# Training Pipeline Control
 pipeline:
   run_data_processing: true # Set this to false if you have already saved the processed data
   run_shadow_model_training: true # Set this to false if shadow models are already trained and saved
   run_metaclassifier_training: true
 
 target_model: # This is only used for testing the attack on a real target model.
-  # This is for models trained on 10k data and generating 10k synthetic data
   target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_10k/test/
   target_model_id: 21  # Will be overridden per SLURM array task
   target_model_name: tabddpm_${target_model.target_model_id}
@@ -18,25 +17,22 @@ target_model: # This is only used for testing the attack on a real target model.
   challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
   challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
 
-  target_shadow_models_output_path: ${base_experiment_dir}/test_shadows_dir
-
-  # target_attack_artifact_dir: ${base_experiment_dir}/target_${target_model.target_model_id}_attack_artifacts/
+  target_shadow_models_output_path: ${base_experiment_dir}/test_all_targets # Sub-directory to store test shadows and results
   attack_probabilities_result_path: ${target_model.target_shadow_models_output_path}/test_probabilities/attack_model_${target_model.target_model_id}_proba
-  # target_shadow_models_output_path: ${target_model.target_attack_artifact_dir}/tabddpm_${target_model.target_model_id}_shadows_dir
+  attack_rmia_shadow_training_data_choice: "combined" # Options: "combined", "only_challenge", "only_train". This determines which data to use for training RMIA attack model in testing phase.
   
 
 # Data paths
 data_paths:
   midst_data_path: /projects/midst-experiments/all_tabddpms/ # Used to collect the data (input) as defined in data_processing_config
-  processed_base_data_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_10k_experiment_data/10k/ # READ from previously collected and processed data
+  processed_base_data_dir: ${base_experiment_dir} # To save new processed data for training, or read from previously collected and processed data (testing phase).
   population_path: ${data_paths.processed_base_data_dir}/population_data  # Path where the collected population data will be stored (output/input)
   processed_attack_data_path: ${data_paths.processed_base_data_dir}/attack_data # Path where the processed attack real train and evaluation data is stored (output/input)
-  attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored (output)
+  attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack (train phase) evaluation results will be stored (output)
 
 
 model_paths:
-# Use 20k's meta
-  metaclassifier_model_path: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_10k_experiment_data/10k/trained_models # Path where the trained metaclassifier model will be saved
+  metaclassifier_model_path: ${base_experiment_dir}/trained_models # Path where the trained metaclassifier model will be saved
 
 
 # Dataset specific information used for processing in this example
@@ -51,7 +47,7 @@ data_processing_config:
         ]
   population_splits: ["train"]  # Data splits to be collected for population data
   challenge_splits: ["train" , "test"]  # Data splits to be collected for challenge points
-  original_population_data_path: /projects/midst-experiments/ensemble_attack/competition/population_data/
+  original_population_data_path: /projects/midst-experiments/ensemble_attack/competition/population_data/ #Attack's collected population for DOMIAS
   # The column name in the data to be used for stratified splitting.
   column_to_stratify: "trans_type"  # Attention: This value is not documented in the original codebase.
   folder_ranges: #Specify folder ranges for any of the mentioned splits.
@@ -111,7 +107,7 @@ metaclassifier:
   meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model
 
 attack_success_computation:
-  target_ids_to_test: [21,22,23,24, 25, 26, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40] # List of target model IDs to compute the attack success for.
+  target_ids_to_test: [21, 22, 23, 24, 25, 26, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40] # List of target model IDs to compute the attack success for.
 
 # General settings
 random_seed: 42 # Set to null for no seed, or an integer for a fixed seed
diff --git a/examples/ensemble_attack/run_shadow_model_training.py b/examples/ensemble_attack/run_shadow_model_training.py
index 8269cf58..45949864 100644
--- a/examples/ensemble_attack/run_shadow_model_training.py
+++ b/examples/ensemble_attack/run_shadow_model_training.py
@@ -3,7 +3,7 @@
 from pathlib import Path
 
 from omegaconf import DictConfig
-
+import pandas as pd
 from midst_toolkit.attacks.ensemble.data_utils import load_dataframe
 from midst_toolkit.attacks.ensemble.rmia.shadow_model_training import (
     train_three_sets_of_shadow_models,
@@ -79,12 +79,13 @@ def run_target_model_training(config: DictConfig) -> Path:
     return target_model_synthetic_path
 
 
-def run_shadow_model_training(config: DictConfig, df_master_challenge_train) -> list[Path]:
+def run_shadow_model_training(config: DictConfig, df_challenge_train:pd.DataFrame) -> list[Path]:
     """
     Function to run the shadow model training for RMIA attack.
 
     Args:
         config: Configuration object set in config.yaml.
+        df_challenge_train: DataFrame containing the data that is used to train RMIA shadow models.
 
     Returns:
         Paths to the saved shadow model results for the three sets of shadow models. For more details,
@@ -95,27 +96,23 @@ def run_shadow_model_training(config: DictConfig, df_master_challenge_train) ->
     # Load the required dataframes for shadow model training.
     # For shadow model training we need master_challenge_train and population data.
     # Master challenge is the main training (or fine-tuning) data for the shadow models.
-    # df_master_challenge_train = load_dataframe(
-    #     Path(config.data_paths.processed_attack_data_path),
-    #     "master_challenge_train.csv",
-    # )
     # Population data is used to pre-train some of the shadow models.
     df_population_with_challenge = load_dataframe(
         Path(config.data_paths.population_path),
         "population_all_with_challenge.csv",
     )
     # Make sure master challenge train and population data have the "trans_id" column.
-    assert "trans_id" in df_master_challenge_train.columns, (
+    assert "trans_id" in df_challenge_train.columns, (
         "trans_id column should be present in master train data for the shadow model pipeline."
     )
     assert "trans_id" in df_population_with_challenge.columns
-    assert "trans_id" in df_master_challenge_train.columns
+    assert "trans_id" in df_challenge_train.columns
     # ``population_data`` in ensemble attack is used for shadow pre-training, and
     # ``master_challenge_df`` is used for fine-tuning for half of the shadow models.
     # For the other half of the shadow models, only ``master_challenge_df`` is used for training.
     first_set_result_path, second_set_result_path, third_set_result_path = train_three_sets_of_shadow_models(
         population_data=df_population_with_challenge,
-        master_challenge_data=df_master_challenge_train,
+        master_challenge_data=df_challenge_train,
         shadow_models_output_path=Path(config.shadow_training.shadow_models_output_path),
         training_json_config_paths=config.shadow_training.training_json_config_paths,
         fine_tuning_config=config.shadow_training.fine_tuning_config,
diff --git a/examples/ensemble_attack/test_attack_model.py b/examples/ensemble_attack/test_attack_model.py
index c01c3907..b613d95b 100644
--- a/examples/ensemble_attack/test_attack_model.py
+++ b/examples/ensemble_attack/test_attack_model.py
@@ -20,16 +20,17 @@
 
 
 
-def run_rmia_shadow_training(config: DictConfig, df_challenge) -> list[dict[str, list[Any]]]:
+def run_rmia_shadow_training(config: DictConfig, df_challenge: pd.DataFrame) -> list[dict[str, list[Any]]]:
     """
     Three sets of shadow models will be trained as a part of this attack.
-    Note that for every new target model, shadow models need to be trained.
+    Note that shadow models need to be trained on the collection of challenge points once and used
+    for all the target models in a setting.
     RMIA signals (for the challenge points) are calculated based on these shadow models,
     and will be fed into the metaclassifier.
 
     Args:
         config: Configuration object set in ``experiments_config.yaml``.
-
+        df_challenge: DataFrame containing the challenge data points for shadow model training.
     Return:
         A list containing three dictionaries, each representing a collection of shadow
             models with their training data and generated synthetic outputs.
@@ -51,6 +52,43 @@ def run_rmia_shadow_training(config: DictConfig, df_challenge) -> list[dict[str,
 
     return shadow_data_collection
 
+def train_rmia_shadows_for_test_phase(config: DictConfig):
+    # Collect all repo's challenge points
+    data_processing_config=config.data_processing_config
+    challenge_attack_names = data_processing_config.challenge_attack_data_types_to_collect
+    challenge_attack_types = [AttackType(attack_name) for attack_name in challenge_attack_names]
+    df_challenge_experiment = collect_midst_data(
+        midst_data_input_dir=Path(config.data_paths.midst_data_path),
+        attack_types=challenge_attack_types,
+        data_splits=["test"],  #change to test for 10k, and change to final for 20k
+        dataset="challenge",
+        data_processing_config=config.data_processing_config,
+    )
+    log(INFO, f"Collected challenge data length: {len(df_challenge_experiment)} for the testing phase's shadow training.")
+    
+    # Load master challenge train data
+    df_master_train = load_dataframe(
+        Path(config.data_paths.processed_attack_data_path),
+        "master_challenge_train.csv",
+    )
+    log(INFO, f"Loaded master challenge train data length: {len(df_master_train)} for the testing phase's shadow training.")
+
+    if config.target_model.attack_rmia_shadow_training_data_choice == "combined":
+        # Run RMIA shadow model training on experiments challenge points + master challenge train data
+        df_challenge = pd.concat([df_challenge_experiment, df_master_train]).drop_duplicates()
+        log(INFO, f"Combined challenge data length for RMIA shadow training: {len(df_challenge)}.")
+    elif config.target_model.attack_rmia_shadow_training_data_choice == "only_challenge":
+        df_challenge = df_challenge_experiment
+        log(INFO, f"Using only challenge data points for RMIA shadow training.")
+    elif config.target_model.attack_rmia_shadow_training_data_choice == "only_train":
+        df_challenge = df_master_train
+        log(INFO, f"Using only master challenge train data points for RMIA shadow training.")
+    else:
+        raise ValueError(f"Invalid choice for attack_rmia_shadow_training_data_choice. Must be one of 'combined', 'only_challenge', or 'only_train'.")
+
+    return run_rmia_shadow_training(config, df_challenge=df_challenge)
+
+
 
 @hydra.main(config_path="configs", config_name="experiment_config", version_base=None)
 def run_metaclassifier_testing(
@@ -107,6 +145,8 @@ def run_metaclassifier_testing(
     log(
         INFO, f"Target synthetic data loaded from {target_synthetic_path} with a size of {len(target_synthetic_data)}."
     )
+
+    # If the synthetic data has more points than specified in the config, take only the required number.
     if len(target_synthetic_data)> config.shadow_training.number_of_points_to_synthesize:
         # Take only the required number of synthetic data points
         target_synthetic_data = target_synthetic_data.head(config.shadow_training.number_of_points_to_synthesize)
@@ -115,7 +155,6 @@ def run_metaclassifier_testing(
         )
 
     # 3) Shadow Model Training Step.
-
     # Make sure to assign a new path for shadow models trained for target's challenge points to
     # avoid overriding train's shadow models.
     config.shadow_training.shadow_models_output_path = config.target_model.target_shadow_models_output_path
@@ -141,36 +180,12 @@ def run_metaclassifier_testing(
     
     if not models_exist:
         log(INFO, "Shadow models for testing phase do not exist. Training RMIA shadow models...")
-        # collect all repo's challenge points
-        data_processing_config=config.data_processing_config
-        challenge_attack_names = data_processing_config.challenge_attack_data_types_to_collect
-        challenge_attack_types = [AttackType(attack_name) for attack_name in challenge_attack_names]
-        df_challenge_experiment = collect_midst_data(
-            midst_data_input_dir=Path(config.data_paths.midst_data_path),
-            attack_types=challenge_attack_types,
-            data_splits=["test"],  #change to test for 10k, and change to final for 20k
-            dataset="challenge",
-            data_processing_config=config.data_processing_config,
-        )
-        log(INFO, f"Collected challenge data length: {len(df_challenge_experiment)} for the testing phase's shadow training.")
+        shadow_data_collection = train_rmia_shadows_for_test_phase(config)
         
-        # Load master challenge train data
-        df_master_train = load_dataframe(
-            Path(config.data_paths.processed_attack_data_path),
-            "master_challenge_train.csv",
-        )
-        log(INFO, f"Loaded master challenge train data length: {len(df_master_train)} for the testing phase's shadow training.")
-
-        # Run RMIA shadow model training on experiments challenge points + master challenge train data
-        df_challenge = pd.concat([df_challenge_experiment, df_master_train]).drop_duplicates()
-        shadow_data_collection = run_rmia_shadow_training(config, df_challenge=df_challenge)
-    
     else:
         log(INFO, "All shadow models for testing phase found. Using existing RMIA shadow models...")
 
 
-
-
     # Extract trans_id from the test dataframe
     with open(Path(config.metaclassifier.data_types_file_path), "r") as f:
         column_types = json.load(f)
@@ -187,7 +202,6 @@ def run_metaclassifier_testing(
     
 
     # 4) Initialize the attacker object, and assign the loaded metaclassifier to it.
-
     df_reference = load_dataframe(
         Path(config.data_paths.population_path),
         "population_all_with_challenge_no_id.csv",
diff --git a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
index c8139080..78500c5a 100644
--- a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
+++ b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
@@ -31,7 +31,17 @@ def compute_gower_batched(
     Compute Gower distance matrix in batches to reduce peak memory usage.
     
     This processes df_x in batches, computing distances against all of df_y,
-    then stacks results. Much more memory-efficient for large df_x.
+    then stacks results.
+
+    Args:
+        df_x: First dataframe.
+        df_y: Second dataframe.
+        cat_features: List indicating which columns are categorical.
+        batch_size: Number of rows from df_x to process in each batch.
+        dtype: The data type to which numerical columns will be cast.
+    
+    Returns:
+        Gower distance matrix as a numpy array.
     """
     n_x = len(df_x)
     n_y = len(df_y)
@@ -39,7 +49,6 @@ def compute_gower_batched(
     # Pre-allocate output matrix
     gower_matrix = np.zeros((n_x, n_y), dtype=dtype)
     
-    # Process in batches
     for start_idx in range(0, n_x, batch_size):
         end_idx = min(start_idx + batch_size, n_x)
         
@@ -55,7 +64,27 @@ def compute_gower_batched(
     
     return gower_matrix
 
-def compute_gower_for_model(args):
+def compute_gower_for_model(args: tuple[int, pd.DataFrame, pd.DataFrame, int, str, int | None, list[str], list[str], np.dtype]) -> tuple[int, np.ndarray]:
+    """
+    Computes the Gower distance matrix between df_input and a single synthetic dataframe.
+
+    Args:
+        args: A tuple containing:
+            - i: Index of the model (for tracking purposes).
+            - df_synthetic_raw: The synthetic dataframe generated by the shadow model.
+            - df_input: The input dataframe to compare against.
+            - min_length: Minimum length for downsampling.
+            - id_column_name: Name of the ID column.
+            - random_seed: Random seed for reproducibility.
+            - categorical_features: List indicating which columns are categorical.
+            - numerical_columns: List of numerical column names.
+            - dtype: The data type to which numerical columns will be cast.
+
+    Returns:
+        A tuple containing:
+            - i: Index of the model (same as input).
+            - gower_matrix: The computed Gower distance matrix as a numpy array.
+    """
     i, df_synthetic_raw, df_input, min_length, id_column_name, random_seed, categorical_features, numerical_columns, dtype = args
     
     df_synthetic = df_synthetic_raw.copy()
@@ -68,12 +97,9 @@ def compute_gower_for_model(args):
     if len(df_synthetic) > min_length:
         df_synthetic = df_synthetic.sample(n=min_length, random_state=random_seed)
     
-    # gower_matrix = gower.gower_matrix(
-    #     data_x=df_input, data_y=df_synthetic, cat_features=categorical_features
-    # ).astype(dtype)
-    # Batched approach: compute row-by-row to reduce peak memory
+    # Batched computation: compute row-by-row to reduce peak memory
     gower_matrix = compute_gower_batched(
-        df_input, df_synthetic, categorical_features, batch_size=2000, dtype=dtype
+        df_input, df_synthetic, categorical_features, batch_size=5000, dtype=dtype
     )
     
     return i, gower_matrix
@@ -100,6 +126,8 @@ def get_rmia_gower(
         categorical_column_names: A list of categorical column names. We assume that all other columns are numerical.
         id_column_name: Name of the ID column.
         random_seed: Random seed for reproducibility.
+        n_jobs: Number of parallel jobs to use for computation. Default is 4.
+        dtype: The data type to which numerical columns will be cast for Gower distance computation. Default is np.float32.
 
     Returns:
         A list of numpy arrays, each representing the Gower distance matrix between the input dataframe and the
@@ -123,7 +151,7 @@ def get_rmia_gower(
 
     df_input[numerical_columns] = df_input[numerical_columns].astype(dtype)
 
-    # Prepare arguments for each model
+    # Prepare arguments for each processing task
     args_list = [
         (i, df_synthetic, df_input, min_length, id_column_name, 
         random_seed, categorical_features, numerical_columns, dtype)
@@ -277,40 +305,36 @@ def calculate_rmia_signals(
     shadow_synthetic_list_0 = [
         train_result for train_result in fine_tuned_shadow_data_0[Key.FINE_TUNED_RESULTS.value]
     ]
-    # shadow_model_gower_0 = get_rmia_gower(
-    #     df_input=df_input,
-    #     model_data=shadow_synthetic_list_0,
-    #     min_length=min_length,
-    #     categorical_column_names=categorical_column_names,
-    #     id_column_name=id_column_name,
-    #     random_seed=random_seed,
-    # )
-
-    # log(INFO, "Computed Gower distance for first set of fine-tuned shadow models.")
+    shadow_model_gower_0 = get_rmia_gower(
+        df_input=df_input,
+        model_data=shadow_synthetic_list_0,
+        min_length=min_length,
+        categorical_column_names=categorical_column_names,
+        id_column_name=id_column_name,
+        random_seed=random_seed,
+        n_jobs=4,
+    )
+    log(INFO, "Computed Gower distance for fine-tuned shadow models (first set of shadow models).")
 
     shadow_synthetic_list_1 = [
         train_result for train_result in fine_tuned_shadow_data_1[Key.FINE_TUNED_RESULTS.value]
     ]
-    shadow_0_and_1_synthetic_data = shadow_synthetic_list_1+shadow_synthetic_list_0
-    del shadow_synthetic_list_0 
-    del shadow_synthetic_list_1
 
-    shadow_model_gower_0_and_1 = get_rmia_gower(
+    shadow_model_gower_1 = get_rmia_gower(
         df_input=df_input,
-        model_data=shadow_0_and_1_synthetic_data,
+        model_data=shadow_synthetic_list_1,
         min_length=min_length,
         categorical_column_names=categorical_column_names,
         id_column_name=id_column_name,
         random_seed=random_seed,
-        n_jobs=8,
+        n_jobs=4,
     )
-    # log(INFO, "Computed Gower distance for second set of fine-tuned shadow models.")
-
-    log(INFO, "Computed Gower distance for the first two sets of fine-tuned shadow models (8 models total).")
+    log(INFO, "Computed Gower distance for fine-tuned shadow models (second set of shadow models).")
 
     shadow_synthetic_list_2 = [
         train_result for train_result in trained_shadow_data[Key.TRAINED_RESULTS.value]
     ]
+    
     # shadow_synthetic_list_2 includes 8 shadow models
     shadow_model_gower_2 = get_rmia_gower(
         df_input=df_input,
@@ -319,22 +343,20 @@ def calculate_rmia_signals(
         categorical_column_names=categorical_column_names,
         id_column_name=id_column_name,
         random_seed=random_seed,
-        n_jobs=8,
+        n_jobs=4,
     )
 
 
     log(INFO, "Computed Gower distance for trained shadow models (third set of shadow models).")
 
     gower_shadows = np.vstack(
-        [np.array(shadow_model_gower_0_and_1), np.array(shadow_model_gower_2)]
+        [np.array(shadow_model_gower_0), np.array(shadow_model_gower_1), np.array(shadow_model_gower_2)]
     )
-    del shadow_model_gower_0_and_1
-    del shadow_model_gower_2
 
     # Process shadow model distances. Gower_shadows is a 3D matrix of shape:
     # ((total number of shadow models), len(df_input), len(shadow_synthetic))
     sorted_shadow_gower = np.sort(gower_shadows, axis=2)
-    del gower_shadows
+
 
     # TODO: check key after we have the official target model
     target_model_gower = get_rmia_gower(
@@ -354,7 +376,6 @@ def calculate_rmia_signals(
     # gower_target[i, j] = Gower distance between i-th input record and j-th target synthetic record
 
     gower_target = target_model_gower[0]  # There is only one target model
-    del target_model_gower
 
     # Compute the signals based on the gower matrix wrt to the target synthetic data.
     # Here, for each input record, we sort the distances to all target synthetic records,
@@ -362,7 +383,6 @@ def calculate_rmia_signals(
     k_nearest_target_distances = np.sort(gower_target, axis=1)[:, :k]
     signal_target_k_mean = np.mean(k_nearest_target_distances, axis=1)
     signal_target_k_1 = gower_target[:, 0]  # First element is the minimum
-    del gower_target
 
     # TODO: ideally remove hard-copied keys
     shadow_training_data = (
@@ -370,9 +390,6 @@ def calculate_rmia_signals(
         + fine_tuned_shadow_data_1["fine_tuning_sets"]
         + trained_shadow_data["selected_sets"]
     )
-    del fine_tuned_shadow_data_0
-    del fine_tuned_shadow_data_1
-    del trained_shadow_data
 
     # For k nearest neighbors
     # Similar to target, we sort the distances in each shadow model's gower matrix,
@@ -388,7 +405,6 @@ def calculate_rmia_signals(
     # Taking the mean across all shadow models (axis=0) to compute k=1 signal for each input sample
     signal_shadows_k_1 = np.mean(nearest_shadow_distances, axis=0)
 
-    del k_nearest_shadow_distances
     # Create a dataframe for the computed scores
     results_df = pd.DataFrame(
         {
@@ -397,9 +413,6 @@ def calculate_rmia_signals(
             f"signal_shadow_k_{k}": signal_shadows,
         }
     )
-    del signal_shadows_k_1
-    del signal_shadows
-
     # Create masks for records in/out of training sets. We're creating masks for all the samples in train_df,
     # as opposed to the original implementation which only creates masks a sample of 200 records. We've also
     # changed the way the masks are created to improve efficiency.
diff --git a/src/midst_toolkit/attacks/ensemble/rmia/shadow_model_training.py b/src/midst_toolkit/attacks/ensemble/rmia/shadow_model_training.py
index 4781c084..0f52d61a 100644
--- a/src/midst_toolkit/attacks/ensemble/rmia/shadow_model_training.py
+++ b/src/midst_toolkit/attacks/ensemble/rmia/shadow_model_training.py
@@ -438,4 +438,5 @@ def train_three_sets_of_shadow_models(
         INFO,
         f"Third set of shadow model training completed and saved at: {third_set_result_path}",
     )
+
     return first_set_result_path, second_set_result_path, third_set_result_path

From f691a8b1abc854382293a7178c5a817d42df5747 Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Wed, 17 Dec 2025 10:45:06 -0500
Subject: [PATCH 24/41] Added experiment scripts

---
 .../configs/experiment_config.yaml            | 63 ++++++++-----------
 .../configs/experiment_config_20k.yaml        |  8 +--
 ...k.yaml => experiment_config_20k_100k.yaml} | 46 ++++++++------
 .../configs/experiment_config_20k_200k.yaml   | 18 +++---
 .../configs/experiment_config_50k.yaml        | 23 ++++---
 examples/ensemble_attack/run_test_10k.sh      | 12 ++--
 examples/ensemble_attack/run_test_20k.sh      | 16 ++---
 examples/ensemble_attack/run_test_50k.sh      |  6 +-
 examples/ensemble_attack/run_train.sh         |  8 +--
 examples/ensemble_attack/run_train_20_100.sh  | 27 ++++++++
 examples/ensemble_attack/run_train_50k.sh     | 26 ++++++++
 11 files changed, 148 insertions(+), 105 deletions(-)
 rename examples/ensemble_attack/configs/{experiment_config_100k.yaml => experiment_config_20k_100k.yaml} (69%)
 create mode 100755 examples/ensemble_attack/run_train_20_100.sh
 create mode 100755 examples/ensemble_attack/run_train_50k.sh

diff --git a/examples/ensemble_attack/configs/experiment_config.yaml b/examples/ensemble_attack/configs/experiment_config.yaml
index ad418eb3..2ac189cf 100644
--- a/examples/ensemble_attack/configs/experiment_config.yaml
+++ b/examples/ensemble_attack/configs/experiment_config.yaml
@@ -1,74 +1,63 @@
-#testing 20k_20k 
-
 # Ensemble experiment configuration
 # This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
-base_experiment_dir: /projects/midst-experiments/ensemble_attack/competition/ # Processed data, and experiment artifacts will be stored here
-base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
+base_experiment_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_10k_experiment_data/10k/ # Processed data, and experiment artifacts will be stored under this directory.
+base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory.
 
-# Pipeline control
-# only test: true, false, true
-# train and test: true, true, true
+# Training Pipeline Control
 pipeline:
-  run_data_processing: false # Set this to false if you have already saved the processed data
+  run_data_processing: true # Set this to false if you have already saved the processed data
   run_shadow_model_training: true # Set this to false if shadow models are already trained and saved
   run_metaclassifier_training: true
 
 target_model: # This is only used for testing the attack on a real target model.
-  # This is for models trained on 20k data and generating 20k synthetic data
-  # target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_20k/train/
-  # target_model_name: tabddpm_${target_model.target_model_id}
-  # target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/20k/20k.csv
-
-  # target_model_directory: /projects/midst-experiments/ensemble_attack/midst_data_all_attacks/tabddpm_black_box/final/ #Competition target models trained on 20k
-  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_10k/train/
+  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_10k/test/
   target_model_id: 21  # Will be overridden per SLURM array task
   target_model_name: tabddpm_${target_model.target_model_id}
-  # target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/trans_synthetic.csv #Original competition file structure
-  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/10k/10k.csv # Behnoosh's models
-
+  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/10k/10k.csv
   challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
   challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
 
-  target_attack_artifact_dir: ${base_experiment_dir}/competition_trained_attack_results_10k_test/target_${target_model.target_model_id}_attack_artifacts/
-  attack_probabilities_result_path: ${target_model.target_attack_artifact_dir}/attack_model_${target_model.target_model_id}_proba
-  target_shadow_models_output_path: ${target_model.target_attack_artifact_dir}/tabddpm_${target_model.target_model_id}_shadows_dir
-
+  target_shadow_models_output_path: ${base_experiment_dir}/test_all_targets # Sub-directory to store test shadows and results
+  attack_probabilities_result_path: ${target_model.target_shadow_models_output_path}/test_probabilities/attack_model_${target_model.target_model_id}_proba
+  attack_rmia_shadow_training_data_choice: "combined" # Options: "combined", "only_challenge", "only_train". This determines which data to use for training RMIA attack model in testing phase.
+  
 
 # Data paths
 data_paths:
-  base_data_dir: /projects/midst-experiments/ensemble_attack/competition
-  midst_data_path: /projects/midst-experiments/all_tabddpms # Used to collect the data
-  # population_path: ${base_experiment_dir}/population_data  # Path where the collected population data will be stored
-  population_path: ${data_paths.base_data_dir}/population_data #READ ONLY
-  processed_attack_data_path: ${base_experiment_dir}/attack_data # Path where the processed attack real train and evaluation data is stored
-  attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored
+  midst_data_path: /projects/midst-experiments/all_tabddpms/ # Used to collect the data (input) as defined in data_processing_config
+  processed_base_data_dir: ${base_experiment_dir} # To save new processed data for training, or read from previously collected and processed data (testing phase).
+  population_path: ${data_paths.processed_base_data_dir}/population_data  # Path where the collected population data will be stored (output/input)
+  processed_attack_data_path: ${data_paths.processed_base_data_dir}/attack_data # Path where the processed attack real train and evaluation data is stored (output/input)
+  attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack (train phase) evaluation results will be stored (output)
+
 
 model_paths:
   metaclassifier_model_path: ${base_experiment_dir}/trained_models # Path where the trained metaclassifier model will be saved
-  # metaclassifier_model_path: /projects/midst-experiments/ensemble_attack/tabddpm_20k_experiment_data/20k/trained_models #When only running the tests.
 
 
 # Dataset specific information used for processing in this example
 data_processing_config:
   population_attack_data_types_to_collect:
         [
-          "tabddpm_trained_with_20k",
+          "tabddpm_trained_with_10k",
         ]
   challenge_attack_data_types_to_collect:
         [
-          "tabddpm_trained_with_20k",
+          "tabddpm_trained_with_10k",
         ]
   population_splits: ["train"]  # Data splits to be collected for population data
-  challenge_splits: ["train"]  # Data splits to be collected for challenge points
+  challenge_splits: ["train" , "test"]  # Data splits to be collected for challenge points
+  original_population_data_path: /projects/midst-experiments/ensemble_attack/competition/population_data/ #Attack's collected population for DOMIAS
   # The column name in the data to be used for stratified splitting.
   column_to_stratify: "trans_type"  # Attention: This value is not documented in the original codebase.
   folder_ranges: #Specify folder ranges for any of the mentioned splits.
-    train: [[1, 20]] # Folders to be used for train data collection in the experiments
+    train: [[1, 21]] # Folders to be used for train data collection in the experiments
+    test: [[21, 31] , [31, 41]]
   # File names in MIDST data directories.
   single_table_train_data_file_name: "train_with_id.csv"
   multi_table_train_data_file_name: "trans.csv"
   challenge_data_file_name: "challenge_with_id.csv"
-  population_sample_size: 40000 # Population size is the total data that your attack has access to.
+  population_sample_size: 20000 # Population size is the total data that your attack has access to.
   # In experiments, this is sampled out of all the collected training data in case the available data
   # is more than this number. Note that, half of this data is actually used for training, the other half
   # is used for evaluation. For example, with 40k population size, only 20k is used for training the attack model.
@@ -100,7 +89,7 @@ shadow_training:
     fine_tune_diffusion_iterations: 200000 # Original code: 200000
     fine_tune_classifier_iterations: 20000 # Original code: 20000
     pre_train_data_size: 60000 # Original code: 60000
-  number_of_points_to_synthesize: 20000 # Number of synthetic data samples to be generated by shadow models.
+  number_of_points_to_synthesize: 10000 # Number of synthetic data samples to be generated by shadow models.
   # Original code: 20000
 
 
@@ -118,9 +107,7 @@ metaclassifier:
   meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model
 
 attack_success_computation:
-  target_ids_to_test: [61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110] # List of target model IDs to compute the attack success for.
-  # target_ids_to_test: [61, 62, 63, 64, 65, 66, 67, 68, 69, 70]
-  # target_ids_to_test: [101, 102, 103, 104, 105, 106, 107, 108, 109, 110]
+  target_ids_to_test: [21, 22, 23, 24, 25, 26, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40] # List of target model IDs to compute the attack success for.
 
 # General settings
 random_seed: 42 # Set to null for no seed, or an integer for a fixed seed
diff --git a/examples/ensemble_attack/configs/experiment_config_20k.yaml b/examples/ensemble_attack/configs/experiment_config_20k.yaml
index 7268ff19..9dcb2fb5 100644
--- a/examples/ensemble_attack/configs/experiment_config_20k.yaml
+++ b/examples/ensemble_attack/configs/experiment_config_20k.yaml
@@ -18,17 +18,15 @@ target_model: # This is only used for testing the attack on a real target model.
   challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
   challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
 
-  target_shadow_models_output_path: ${base_experiment_dir}/test_shadows_dir
-
-  # target_attack_artifact_dir: ${base_experiment_dir}/target_${target_model.target_model_id}_attack_artifacts/
+  target_shadow_models_output_path: ${base_experiment_dir}/test_all_targets_dir
   attack_probabilities_result_path: ${target_model.target_shadow_models_output_path}/test_probabilities/attack_model_${target_model.target_model_id}_proba
-  # target_shadow_models_output_path: ${target_model.target_attack_artifact_dir}/tabddpm_${target_model.target_model_id}_shadows_dir
+  attack_rmia_shadow_training_data_choice: "combined" # Options: "combined", "only_challenge", "only_train". This determines which data to use for training RMIA attack model in testing phase.
   
 
 # Data paths
 data_paths:
-  base_data_dir: /projects/midst-experiments/ensemble_attack/competition # READ from competition
   midst_data_path: /projects/midst-experiments/ensemble_attack/midst_data_all_attacks/ # Used to collect the data (input)
+  base_data_dir: /projects/midst-experiments/ensemble_attack/competition # Data path where the train pipeline saved data processing results
   population_path: ${data_paths.base_data_dir}/population_data #READ ONLY  # Path where the collected population data will be stored (output/input)
   processed_attack_data_path: ${data_paths.base_data_dir}/attack_data # Path where the processed attack real train and evaluation data is stored (output/input)
   attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored (output)
diff --git a/examples/ensemble_attack/configs/experiment_config_100k.yaml b/examples/ensemble_attack/configs/experiment_config_20k_100k.yaml
similarity index 69%
rename from examples/ensemble_attack/configs/experiment_config_100k.yaml
rename to examples/ensemble_attack/configs/experiment_config_20k_100k.yaml
index f3df5789..3d68c716 100644
--- a/examples/ensemble_attack/configs/experiment_config_100k.yaml
+++ b/examples/ensemble_attack/configs/experiment_config_20k_100k.yaml
@@ -1,34 +1,38 @@
 # Ensemble experiment configuration
 # This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
-base_experiment_dir: /projects/midst-experiments/ensemble_attack/tabddpm_100k_experiment_data # Processed data, and experiment artifacts will be stored here
+base_experiment_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_20k_experiment_data/100k # Processed data, and experiment artifacts will be stored here
 base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
 
 # Pipeline control
 pipeline:
-  run_data_processing: false # Set this to false if you have already saved the processed data
-  run_shadow_model_training: false # Set this to false if shadow models are already trained and saved
+# We don't need to again process data for 200k Synthetic
+  run_data_processing: true # Set this to false if you have already saved the processed data
+  run_shadow_model_training: true # Set this to false if shadow models are already trained and saved
   run_metaclassifier_training: true
 
 target_model: # This is only used for testing the attack on a real target model.
-  # This is for models trained on 10k data and generating `0k synthetic data
-  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_100k/train/
+  # This is for models trained on 20k data and generating 20k synthetic data
+  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_20k/test/
+  
   target_model_id: 21  # Will be overridden per SLURM array task
   target_model_name: tabddpm_${target_model.target_model_id}
-  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/100k/100k.csv
+  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/200k/200k.csv # Only the first `number_of_points_to_synthesize` rows will be used.
   challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
   challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
 
-  target_attack_artifact_dir: ${base_experiment_dir}/target_${target_model.target_model_id}_attack_artifacts/
-  attack_probabilities_result_path: ${target_model.target_attack_artifact_dir}/attack_model_${target_model.target_model_id}_proba
-  target_shadow_models_output_path: ${target_model.target_attack_artifact_dir}/tabddpm_${target_model.target_model_id}_shadows_dir
-
+  target_shadow_models_output_path: ${base_experiment_dir}/test_20_targets_dir
+  attack_probabilities_result_path: ${target_model.target_shadow_models_output_path}/test_probabilities/attack_model_${target_model.target_model_id}_proba
+  attack_rmia_shadow_training_data_choice: "combined" # Options: "combined", "only_challenge", "only_train". This determines which data to use for training RMIA attack model in testing phase.
+  
 
 # Data paths
 data_paths:
-  midst_data_path: /projects/midst-experiments/all_tabddpms # Used to collect the data
-  population_path: ${base_experiment_dir}/population_data  # Path where the collected population data will be stored
-  processed_attack_data_path: ${base_experiment_dir}/attack_data # Path where the processed attack real train and evaluation data is stored
-  attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored
+  midst_data_path: /projects/midst-experiments/all_tabddpms/ # Used to collect the data (input) as defined in data_processing_config
+  processed_base_data_dir: ${base_experiment_dir} # READ from previously collected and processed data. Set to ${base_experiment_dir} if this is training with newly collected data.
+  population_path: ${data_paths.processed_base_data_dir}/population_data  # Path where the collected population data will be stored (output/input)
+  processed_attack_data_path: ${data_paths.processed_base_data_dir}/attack_data # Path where the processed attack real train and evaluation data is stored (output/input)
+  attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored (output)
+
 
 model_paths:
   metaclassifier_model_path: ${base_experiment_dir}/trained_models # Path where the trained metaclassifier model will be saved
@@ -38,23 +42,25 @@ model_paths:
 data_processing_config:
   population_attack_data_types_to_collect:
         [
-          "tabddpm_trained_with_100k",
+          "tabddpm_trained_with_20k",
         ]
   challenge_attack_data_types_to_collect:
         [
-          "tabddpm_trained_with_100k",
+          "tabddpm_trained_with_20k",
         ]
   population_splits: ["train"]  # Data splits to be collected for population data
-  challenge_splits: ["train"]  # Data splits to be collected for challenge points
+  challenge_splits: ["train" , "test"]  # Data splits to be collected for challenge points
+  original_population_data_path: /projects/midst-experiments/ensemble_attack/competition/population_data/
   # The column name in the data to be used for stratified splitting.
   column_to_stratify: "trans_type"  # Attention: This value is not documented in the original codebase.
   folder_ranges: #Specify folder ranges for any of the mentioned splits.
-    train: [[1, 20]] # Folders to be used for train data collection in the experiments
+    train: [[1, 21]] # Folders to be used for train data collection in the experiments
+    test: [[21, 31] , [31, 41]]
   # File names in MIDST data directories.
   single_table_train_data_file_name: "train_with_id.csv"
   multi_table_train_data_file_name: "trans.csv"
   challenge_data_file_name: "challenge_with_id.csv"
-  population_sample_size: 200000 # Population size is the total data that your attack has access to.
+  population_sample_size: 40000 # Population size is the total data that your attack has access to.
   # In experiments, this is sampled out of all the collected training data in case the available data
   # is more than this number. Note that, half of this data is actually used for training, the other half
   # is used for evaluation. For example, with 40k population size, only 20k is used for training the attack model.
@@ -104,7 +110,7 @@ metaclassifier:
   meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model
 
 attack_success_computation:
-  target_ids_to_test: [21,22,23] # List of target model IDs to compute the attack success for.
+  target_ids_to_test: [21,22,23,24, 25, 26, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40] # List of target model IDs to compute the attack success for.
 
 # General settings
 random_seed: 42 # Set to null for no seed, or an integer for a fixed seed
diff --git a/examples/ensemble_attack/configs/experiment_config_20k_200k.yaml b/examples/ensemble_attack/configs/experiment_config_20k_200k.yaml
index 5d1dd54d..c1816060 100644
--- a/examples/ensemble_attack/configs/experiment_config_20k_200k.yaml
+++ b/examples/ensemble_attack/configs/experiment_config_20k_200k.yaml
@@ -6,38 +6,36 @@ base_data_config_dir: examples/ensemble_attack/data_configs # Training and data
 # Pipeline control
 pipeline:
 # We don't need to again process data for 200k Synthetic
-  run_data_processing: false # Set this to false if you have already saved the processed data
+  run_data_processing: true # Set this to false if you have already saved the processed data
   run_shadow_model_training: true # Set this to false if shadow models are already trained and saved
   run_metaclassifier_training: true
 
 target_model: # This is only used for testing the attack on a real target model.
   # This is for models trained on 20k data and generating 20k synthetic data
-  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_20k/ls
+  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_20k/test/
   
-  target_model_id: 61  # Will be overridden per SLURM array task
+  target_model_id: 21  # Will be overridden per SLURM array task
   target_model_name: tabddpm_${target_model.target_model_id}
-  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/trans_synthetic.csv 
+  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/200k/200k.csv # Only the first `number_of_points_to_synthesize` rows will be used.
   challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
   challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
 
-  target_shadow_models_output_path: ${base_experiment_dir}/test_shadows_dir
-
-  # target_attack_artifact_dir: ${base_experiment_dir}/target_${target_model.target_model_id}_attack_artifacts/
+  target_shadow_models_output_path: ${base_experiment_dir}/test_20_targets_dir
   attack_probabilities_result_path: ${target_model.target_shadow_models_output_path}/test_probabilities/attack_model_${target_model.target_model_id}_proba
-  # target_shadow_models_output_path: ${target_model.target_attack_artifact_dir}/tabddpm_${target_model.target_model_id}_shadows_dir
+  attack_rmia_shadow_training_data_choice: "combined" # Options: "combined", "only_challenge", "only_train". This determines which data to use for training RMIA attack model in testing phase.
   
 
 # Data paths
 data_paths:
   midst_data_path: /projects/midst-experiments/all_tabddpms/ # Used to collect the data (input) as defined in data_processing_config
-  processed_base_data_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_10k_experiment_data/10k/ # READ from previously collected and processed data
+  processed_base_data_dir: ${base_experiment_dir} # READ from previously collected and processed data. Set to ${base_experiment_dir} if this is training with newly collected data.
   population_path: ${data_paths.processed_base_data_dir}/population_data  # Path where the collected population data will be stored (output/input)
   processed_attack_data_path: ${data_paths.processed_base_data_dir}/attack_data # Path where the processed attack real train and evaluation data is stored (output/input)
   attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored (output)
 
 
 model_paths:
-  metaclassifier_model_path: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_20k_experiment_data/200k/trained_models/ # Path where the trained metaclassifier model will be saved
+  metaclassifier_model_path: ${base_experiment_dir}/trained_models # Path where the trained metaclassifier model will be saved
 
 
 # Dataset specific information used for processing in this example
diff --git a/examples/ensemble_attack/configs/experiment_config_50k.yaml b/examples/ensemble_attack/configs/experiment_config_50k.yaml
index 62fcedff..bb88da09 100644
--- a/examples/ensemble_attack/configs/experiment_config_50k.yaml
+++ b/examples/ensemble_attack/configs/experiment_config_50k.yaml
@@ -1,12 +1,12 @@
 # Ensemble experiment configuration
 # This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
-base_experiment_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_50k_experiment_data/50k_save_synth_only # Processed data, and experiment artifacts will be stored here
+base_experiment_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_50k_experiment_data/50k_competition_classifier_tests/ # Processed data, and experiment artifacts will be stored here
 base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
 
 # Pipeline control
 pipeline:
-  run_data_processing: true # Set this to false if you have already saved the processed data
-  run_shadow_model_training: true # Set this to false if shadow models are already trained and saved
+  run_data_processing: false # Set this to false if you have already saved the processed data
+  run_shadow_model_training: false # Set this to false if shadow models are already trained and saved
   run_metaclassifier_training: true
 
 target_model: # This is only used for testing the attack on a real target model.
@@ -18,24 +18,23 @@ target_model: # This is only used for testing the attack on a real target model.
   challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
   challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
 
-  target_shadow_models_output_path: ${base_experiment_dir}/test_shadows_dir
-
-  # target_attack_artifact_dir: ${base_experiment_dir}/target_${target_model.target_model_id}_attack_artifacts/
+  target_shadow_models_output_path: ${base_experiment_dir}/test_20_targets_shadows_dir
   attack_probabilities_result_path: ${target_model.target_shadow_models_output_path}/test_probabilities/attack_model_${target_model.target_model_id}_proba
-  # target_shadow_models_output_path: ${target_model.target_attack_artifact_dir}/tabddpm_${target_model.target_model_id}_shadows_dir
+  attack_rmia_shadow_training_data_choice: "combined" # Options: "combined", "only_challenge", "only_train". This determines which data to use for training RMIA attack model in testing phase.
 
 # Data paths
 data_paths:
   midst_data_path: /projects/midst-experiments/all_tabddpms/ # Used to collect the data (input) in data processing
-  population_path: ${base_experiment_dir}/population_data #READ ONLY  # Path where the collected population data will be stored (output/input)
-  processed_attack_data_path: ${base_experiment_dir}/attack_data # Path where the processed attack real train and evaluation data is stored (output/input)
+  processed_data_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_50k_experiment_data/50k_save_synth_only # Data path where the train pipeline saved data processing results
+  population_path: ${data_paths.processed_data_dir}/population_data #READ ONLY  # Path where the collected population data will be stored (output/input)
+  processed_attack_data_path: ${data_paths.processed_data_dir}/attack_data # Path where the processed attack real train and evaluation data is stored (output/input)
   attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored (output)
 
 
 model_paths:
-# Use 20k's meta
-  # metaclassifier_model_path: /projects/midst-experiments/ensemble_attack/competition/trained_models # Path where the trained metaclassifier model will be saved
-  metaclassifier_model_path: ${base_experiment_dir}/trained_models 
+  metaclassifier_model_path: /projects/midst-experiments/ensemble_attack/competition/trained_models # Path where the trained metaclassifier model will be saved
+  # metaclassifier_model_path: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_50k_experiment_data/50k_save_synth_only/trained_models
+
 
 # Dataset specific information used for processing in this example
 data_processing_config:
diff --git a/examples/ensemble_attack/run_test_10k.sh b/examples/ensemble_attack/run_test_10k.sh
index 7926527d..f54b59fb 100644
--- a/examples/ensemble_attack/run_test_10k.sh
+++ b/examples/ensemble_attack/run_test_10k.sh
@@ -4,13 +4,13 @@
 #SBATCH --ntasks=1
 #SBATCH --ntasks-per-node=1
 #SBATCH --cpus-per-task=1
-#SBATCH --gres=gpu:a100:1
-#SBATCH --mem=128G
+#SBATCH --gres=gpu:a40:1
+#SBATCH --mem=64G
 #SBATCH --job-name=test10_all_targets
 #SBATCH --output=%j_%x_%a.out
 #SBATCH --error=%j_%x_%a.err
-#SBATCH --time=6:00:00
-#SBATCH --array=0-0
+#SBATCH --time=1:00:00
+#SBATCH --array=0-18
 
 # This script sets up the environment and runs the ensemble attack test script.
 source .venv/bin/activate
@@ -20,9 +20,9 @@ which python
 
 # Map SLURM_ARRAY_TASK_ID to target_model_id.
 
-TARGET_IDS=(21)
+# TARGET_IDS=(21)
 
-# TARGET_IDS=( 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40)  # List of target IDs
+TARGET_IDS=( 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40)  # List of target IDs
 
 TARGET_ID=${TARGET_IDS[$SLURM_ARRAY_TASK_ID]}
 
diff --git a/examples/ensemble_attack/run_test_20k.sh b/examples/ensemble_attack/run_test_20k.sh
index cce66d00..a868764e 100644
--- a/examples/ensemble_attack/run_test_20k.sh
+++ b/examples/ensemble_attack/run_test_20k.sh
@@ -3,14 +3,14 @@
 #SBATCH --nodes=1
 #SBATCH --ntasks=1
 #SBATCH --ntasks-per-node=1
-#SBATCH --cpus-per-task=4
+#SBATCH --cpus-per-task=1
 #SBATCH --gres=gpu:a40:1
-#SBATCH --mem=32G
-#SBATCH --job-name=test20_all_targets
+#SBATCH --mem=128G
+#SBATCH --job-name=test20_200_all_targets
 #SBATCH --output=%j_%x_%a.out
 #SBATCH --error=%j_%x_%a.err
 #SBATCH --time=1:00:00
-#SBATCH --array=0-18
+#SBATCH --array=0-0
 
 # This script sets up the environment and runs the ensemble attack test script.
 source .venv/bin/activate
@@ -22,9 +22,11 @@ which python
 
 # TARGET_IDS=(61 62 63 64 65 66 67 68 69 70)
 # TARGET_IDS=(61)
-TARGET_IDS=( 62 63 64 65 66 67 68 69 70 101 102 103 104 105 106 107 108 109 110)  # List of target IDs
+# TARGET_IDS=( 62 63 64 65 66 67 68 69 70 101 102 103 104 105 106 107 108 109 110)  # List of target IDs
 
-# TARGET_IDS=(21 22 23 24 25 26 27 28 29 30)  # List of target IDs
+# TARGET_IDS=(21)
+TARGET_IDS=(28)
+# TARGET_IDS=( 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40)  # List of target IDs
 
 TARGET_ID=${TARGET_IDS[$SLURM_ARRAY_TASK_ID]}
 
@@ -32,6 +34,6 @@ echo "Running test for target_model_id: $TARGET_ID using all target models' chal
 
 echo "Experiments Launched"
 
-python -m examples.ensemble_attack.test_attack_model --config-name=experiment_config_20k target_model.target_model_id=$TARGET_ID
+python -m examples.ensemble_attack.test_attack_model --config-name=experiment_config_20k_200k target_model.target_model_id=$TARGET_ID
 
 echo "Experiments Completed"
\ No newline at end of file
diff --git a/examples/ensemble_attack/run_test_50k.sh b/examples/ensemble_attack/run_test_50k.sh
index 17cf368f..a55f32bf 100644
--- a/examples/ensemble_attack/run_test_50k.sh
+++ b/examples/ensemble_attack/run_test_50k.sh
@@ -3,13 +3,13 @@
 #SBATCH --nodes=1
 #SBATCH --ntasks=1
 #SBATCH --ntasks-per-node=1
-#SBATCH --cpus-per-task=4
+#SBATCH --cpus-per-task=1
 #SBATCH --gres=gpu:a40:1
 #SBATCH --mem=64G
-#SBATCH --job-name=test50_all_with_meta_train
+#SBATCH --job-name=test50_comp_classifier_all
 #SBATCH --output=%j_%x_%a.out
 #SBATCH --error=%j_%x_%a.err
-#SBATCH --time=2:00:00
+#SBATCH --time=1:00:00
 #SBATCH --array=0-18
 
 # This script sets up the environment and runs the ensemble attack test script.
diff --git a/examples/ensemble_attack/run_train.sh b/examples/ensemble_attack/run_train.sh
index 710322bd..c8bf0c78 100755
--- a/examples/ensemble_attack/run_train.sh
+++ b/examples/ensemble_attack/run_train.sh
@@ -3,13 +3,13 @@
 #SBATCH --nodes=1
 #SBATCH --ntasks=1
 #SBATCH --ntasks-per-node=1
-#SBATCH --cpus-per-task=1
+#SBATCH --cpus-per-task=2
 #SBATCH --gres=gpu:a100:1
 #SBATCH --mem=210G
-#SBATCH --job-name=20k_200k_train_save_synth_only
+#SBATCH --job-name=train20k_200k
 #SBATCH --output=%j_%x.out
 #SBATCH --error=%j_%x.err
-#SBATCH --time=12:00:00
+#SBATCH --time=14:00:00
 
 
 echo "Total memory allocated: $(($SLURM_MEM_PER_NODE / 1024)) GB"
@@ -22,6 +22,6 @@ which python
 
 echo "Experiments Launched"
 
-python -m examples.ensemble_attack.run_attack  --config-name=experiment_config_20k_20k
+python -m examples.ensemble_attack.run_attack  --config-name=experiment_config_20k_200k
 
 echo "Experiments Completed"
diff --git a/examples/ensemble_attack/run_train_20_100.sh b/examples/ensemble_attack/run_train_20_100.sh
new file mode 100755
index 00000000..f3fca79b
--- /dev/null
+++ b/examples/ensemble_attack/run_train_20_100.sh
@@ -0,0 +1,27 @@
+#!/bin/bash
+
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=2
+#SBATCH --gres=gpu:a100:1
+#SBATCH --mem=210G
+#SBATCH --job-name=train20k_100k
+#SBATCH --output=%j_%x.out
+#SBATCH --error=%j_%x.err
+#SBATCH --time=16:00:00
+
+
+echo "Total memory allocated: $(($SLURM_MEM_PER_NODE / 1024)) GB"
+
+# This script sets up the environment and runs the ensemble attack example.
+source .venv/bin/activate
+
+echo "Active Environment:"
+which python
+
+echo "Experiments Launched"
+
+python -m examples.ensemble_attack.run_attack  --config-name=experiment_config_20k_100k
+
+echo "Experiments Completed"
diff --git a/examples/ensemble_attack/run_train_50k.sh b/examples/ensemble_attack/run_train_50k.sh
new file mode 100755
index 00000000..adb79f9d
--- /dev/null
+++ b/examples/ensemble_attack/run_train_50k.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=2
+#SBATCH --mem=290G
+#SBATCH --job-name=50k_RMIA_train_save_synth_only
+#SBATCH --output=%j_%x.out
+#SBATCH --error=%j_%x.err
+#SBATCH --time=12:00:00
+
+
+echo "Total memory allocated: $(($SLURM_MEM_PER_NODE / 1024)) GB"
+
+# This script sets up the environment and runs the ensemble attack example.
+source .venv/bin/activate
+
+echo "Active Environment:"
+which python
+
+echo "Experiments Launched"
+
+python -m examples.ensemble_attack.run_attack  --config-name=experiment_config_50k
+
+echo "Experiments Completed"

From 459694975fa7d09b8ad6b3ef26bd214d9a7d575d Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Wed, 17 Dec 2025 15:10:24 -0500
Subject: [PATCH 25/41] Removed extra experiment scripts

---
 examples/ensemble_attack/configs/experiment_config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/ensemble_attack/configs/experiment_config.yaml b/examples/ensemble_attack/configs/experiment_config.yaml
index 57ae5417..7e1094b8 100644
--- a/examples/ensemble_attack/configs/experiment_config.yaml
+++ b/examples/ensemble_attack/configs/experiment_config.yaml
@@ -1,6 +1,6 @@
 # Ensemble experiment configuration
 # This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
-base_experiment_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_10k_experiment_data/10k/ # Processed data, and experiment artifacts will be stored under this directory.
+base_experiment_dir: /projects/midst-experiments/ensemble_attack/tabddpm_10k_experiment_data/10k/ # Processed data, and experiment artifacts will be stored under this directory.
 base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory.
 
 # Training Pipeline Control

From 44b4164bc950710363b3d2f849710fb9b6cae3dd Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Tue, 23 Dec 2025 12:37:17 -0500
Subject: [PATCH 26/41] Fixed typing issues

---
 .../ensemble_attack/compute_attack_success.py |   1 -
 .../configs/experiment_config.yaml            |   4 +-
 .../configs/experiment_config_10k.yaml        | 113 ---------
 .../configs/experiment_config_20k.yaml        | 114 ---------
 .../configs/experiment_config_20k_100k.yaml   | 116 ---------
 .../configs/experiment_config_20k_200k.yaml   | 116 ---------
 .../configs/experiment_config_50k.yaml        | 114 ---------
 .../ensemble_attack/real_data_collection.py   |  16 +-
 examples/ensemble_attack/run_attack.py        |  11 +-
 .../run_metaclassifier_training.py            |  10 +-
 .../run_shadow_model_training.py              |   6 +-
 examples/ensemble_attack/run_test_10k.sh      |  36 ---
 examples/ensemble_attack/run_test_20k.sh      |  39 ---
 examples/ensemble_attack/run_test_50k.sh      |  35 ---
 examples/ensemble_attack/run_train_100k.sh    |  26 --
 examples/ensemble_attack/run_train_10k.sh     |  25 --
 examples/ensemble_attack/run_train_20_100.sh  |  27 --
 examples/ensemble_attack/run_train_50k.sh     |  26 --
 examples/ensemble_attack/test_attack_model.py | 239 +++++++++++++-----
 .../attacks/ensemble/rmia/rmia_calculation.py |  95 ++++---
 20 files changed, 254 insertions(+), 915 deletions(-)
 delete mode 100644 examples/ensemble_attack/configs/experiment_config_10k.yaml
 delete mode 100644 examples/ensemble_attack/configs/experiment_config_20k.yaml
 delete mode 100644 examples/ensemble_attack/configs/experiment_config_20k_100k.yaml
 delete mode 100644 examples/ensemble_attack/configs/experiment_config_20k_200k.yaml
 delete mode 100644 examples/ensemble_attack/configs/experiment_config_50k.yaml
 delete mode 100644 examples/ensemble_attack/run_test_10k.sh
 delete mode 100644 examples/ensemble_attack/run_test_20k.sh
 delete mode 100644 examples/ensemble_attack/run_test_50k.sh
 delete mode 100755 examples/ensemble_attack/run_train_100k.sh
 delete mode 100755 examples/ensemble_attack/run_train_10k.sh
 delete mode 100755 examples/ensemble_attack/run_train_20_100.sh
 delete mode 100755 examples/ensemble_attack/run_train_50k.sh

diff --git a/examples/ensemble_attack/compute_attack_success.py b/examples/ensemble_attack/compute_attack_success.py
index 5678f01c..56871113 100644
--- a/examples/ensemble_attack/compute_attack_success.py
+++ b/examples/ensemble_attack/compute_attack_success.py
@@ -14,7 +14,6 @@
 from midst_toolkit.evaluation.privacy.mia_scoring import TprAtFpr
 
 
-
 def load_target_challenge_labels_and_probabilities(
     metaclassifier_model_name: str, attack_results_path: Path, challenge_label_path: Path
 ) -> tuple[np.ndarray, np.ndarray]:
diff --git a/examples/ensemble_attack/configs/experiment_config.yaml b/examples/ensemble_attack/configs/experiment_config.yaml
index 7e1094b8..f30b5b0e 100644
--- a/examples/ensemble_attack/configs/experiment_config.yaml
+++ b/examples/ensemble_attack/configs/experiment_config.yaml
@@ -20,7 +20,7 @@ target_model: # This is only used for testing the attack on a real target model.
   target_shadow_models_output_path: ${base_experiment_dir}/test_all_targets # Sub-directory to store test shadows and results
   attack_probabilities_result_path: ${target_model.target_shadow_models_output_path}/test_probabilities/attack_model_${target_model.target_model_id}_proba
   attack_rmia_shadow_training_data_choice: "combined" # Options: "combined", "only_challenge", "only_train". This determines which data to use for training RMIA attack model in testing phase.
-  
+
 
 # Data paths
 data_paths:
@@ -110,4 +110,4 @@ attack_success_computation:
   target_ids_to_test: [21, 22, 23, 24, 25, 26, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40] # List of target model IDs to compute the attack success for.
 
 # General settings
-random_seed: 42 # Set to null for no seed, or an integer for a fixed seed
\ No newline at end of file
+random_seed: 42 # Set to null for no seed, or an integer for a fixed seed
diff --git a/examples/ensemble_attack/configs/experiment_config_10k.yaml b/examples/ensemble_attack/configs/experiment_config_10k.yaml
deleted file mode 100644
index 2ac189cf..00000000
--- a/examples/ensemble_attack/configs/experiment_config_10k.yaml
+++ /dev/null
@@ -1,113 +0,0 @@
-# Ensemble experiment configuration
-# This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
-base_experiment_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_10k_experiment_data/10k/ # Processed data, and experiment artifacts will be stored under this directory.
-base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory.
-
-# Training Pipeline Control
-pipeline:
-  run_data_processing: true # Set this to false if you have already saved the processed data
-  run_shadow_model_training: true # Set this to false if shadow models are already trained and saved
-  run_metaclassifier_training: true
-
-target_model: # This is only used for testing the attack on a real target model.
-  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_10k/test/
-  target_model_id: 21  # Will be overridden per SLURM array task
-  target_model_name: tabddpm_${target_model.target_model_id}
-  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/10k/10k.csv
-  challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
-  challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
-
-  target_shadow_models_output_path: ${base_experiment_dir}/test_all_targets # Sub-directory to store test shadows and results
-  attack_probabilities_result_path: ${target_model.target_shadow_models_output_path}/test_probabilities/attack_model_${target_model.target_model_id}_proba
-  attack_rmia_shadow_training_data_choice: "combined" # Options: "combined", "only_challenge", "only_train". This determines which data to use for training RMIA attack model in testing phase.
-  
-
-# Data paths
-data_paths:
-  midst_data_path: /projects/midst-experiments/all_tabddpms/ # Used to collect the data (input) as defined in data_processing_config
-  processed_base_data_dir: ${base_experiment_dir} # To save new processed data for training, or read from previously collected and processed data (testing phase).
-  population_path: ${data_paths.processed_base_data_dir}/population_data  # Path where the collected population data will be stored (output/input)
-  processed_attack_data_path: ${data_paths.processed_base_data_dir}/attack_data # Path where the processed attack real train and evaluation data is stored (output/input)
-  attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack (train phase) evaluation results will be stored (output)
-
-
-model_paths:
-  metaclassifier_model_path: ${base_experiment_dir}/trained_models # Path where the trained metaclassifier model will be saved
-
-
-# Dataset specific information used for processing in this example
-data_processing_config:
-  population_attack_data_types_to_collect:
-        [
-          "tabddpm_trained_with_10k",
-        ]
-  challenge_attack_data_types_to_collect:
-        [
-          "tabddpm_trained_with_10k",
-        ]
-  population_splits: ["train"]  # Data splits to be collected for population data
-  challenge_splits: ["train" , "test"]  # Data splits to be collected for challenge points
-  original_population_data_path: /projects/midst-experiments/ensemble_attack/competition/population_data/ #Attack's collected population for DOMIAS
-  # The column name in the data to be used for stratified splitting.
-  column_to_stratify: "trans_type"  # Attention: This value is not documented in the original codebase.
-  folder_ranges: #Specify folder ranges for any of the mentioned splits.
-    train: [[1, 21]] # Folders to be used for train data collection in the experiments
-    test: [[21, 31] , [31, 41]]
-  # File names in MIDST data directories.
-  single_table_train_data_file_name: "train_with_id.csv"
-  multi_table_train_data_file_name: "trans.csv"
-  challenge_data_file_name: "challenge_with_id.csv"
-  population_sample_size: 20000 # Population size is the total data that your attack has access to.
-  # In experiments, this is sampled out of all the collected training data in case the available data
-  # is more than this number. Note that, half of this data is actually used for training, the other half
-  # is used for evaluation. For example, with 40k population size, only 20k is used for training the attack model.
-  # TODO: make sure to consider this in experiments.
-
-# Training and data settings for shadow models (temporary, numbers subject to change)
-shadow_training:
-  # Data Config files path used for training a TabDDPM model
-  training_json_config_paths: # Config json files used for tabddpm training on the trans table
-    table_domain_file_path: ${base_data_config_dir}/trans_domain.json
-    dataset_meta_file_path: ${base_data_config_dir}/dataset_meta.json
-    tabddpm_training_config_path: ${base_data_config_dir}/trans.json
-  # Model training artifacts are saved under shadow_models_data_path/workspace_name/exp_name
-  # Also, training configs for each shadow model are created under shadow_models_data_path.
-  shadow_models_output_path: ${base_experiment_dir}/shadow_models_and_data
-  target_model_output_path: ${base_experiment_dir}/shadow_target_model_and_data
-  # Paths to final shadow models used for metaclassifier training (relative to shadow_models_output_path)
-  # These paths are a result of running the shadow model training pipeline, specifically the
-  # train_three_sets_of_shadow_models in shadow_model_training.py
-  # Each .pkl file contains the training data, trained model and training results for all shadow models in a list.
-  final_shadow_models_path: [
-          "${shadow_training.shadow_models_output_path}/initial_model_rmia_1/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
-          "${shadow_training.shadow_models_output_path}/initial_model_rmia_2/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
-          "${shadow_training.shadow_models_output_path}/shadow_model_rmia_third_set/shadow_workspace/trained_model/rmia_shadows_third_set.pkl",
-      ]
-  target_synthetic_data_path: ${shadow_training.target_model_output_path}/target_synthetic_data.csv
-  # Path to final shadow target's synthetic data (relative to target_model_output_path)
-  fine_tuning_config:
-    fine_tune_diffusion_iterations: 200000 # Original code: 200000
-    fine_tune_classifier_iterations: 20000 # Original code: 20000
-    pre_train_data_size: 60000 # Original code: 60000
-  number_of_points_to_synthesize: 10000 # Number of synthetic data samples to be generated by shadow models.
-  # Original code: 20000
-
-
-# Metaclassifier settings
-metaclassifier:
-  # Data types json file is used for xgboost model training.
-  data_types_file_path: ${base_data_config_dir}/data_types.json
-  model_type: "xgb"
-  # Model training parameters
-  num_optuna_trials: 100 # Original code: 100
-  num_kfolds: 5
-  use_gpu: false
-  # Temporary. Might remove having an epoch parameter.
-  epochs: 1
-  meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model
-
-attack_success_computation:
-  target_ids_to_test: [21, 22, 23, 24, 25, 26, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40] # List of target model IDs to compute the attack success for.
-
-# General settings
-random_seed: 42 # Set to null for no seed, or an integer for a fixed seed
diff --git a/examples/ensemble_attack/configs/experiment_config_20k.yaml b/examples/ensemble_attack/configs/experiment_config_20k.yaml
deleted file mode 100644
index 9dcb2fb5..00000000
--- a/examples/ensemble_attack/configs/experiment_config_20k.yaml
+++ /dev/null
@@ -1,114 +0,0 @@
-# Ensemble experiment configuration
-# This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
-base_experiment_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_20k_experiment_data/20k/test_no_meta_all_tests # Processed data, and experiment artifacts will be stored here
-base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
-
-# Pipeline control
-pipeline:
-  run_data_processing: true # Set this to false if you have already saved the processed data
-  run_shadow_model_training: true # Set this to false if shadow models are already trained and saved
-  run_metaclassifier_training: true
-
-target_model: # This is only used for testing the attack on a real target model.
-  # This is for models trained on 20k data and generating 20k synthetic data
-  target_model_directory: /projects/midst-experiments/ensemble_attack/midst_data_all_attacks/tabddpm_black_box/final
-  target_model_id: 61  # Will be overridden per SLURM array task
-  target_model_name: tabddpm_${target_model.target_model_id}
-  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/trans_synthetic.csv 
-  challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
-  challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
-
-  target_shadow_models_output_path: ${base_experiment_dir}/test_all_targets_dir
-  attack_probabilities_result_path: ${target_model.target_shadow_models_output_path}/test_probabilities/attack_model_${target_model.target_model_id}_proba
-  attack_rmia_shadow_training_data_choice: "combined" # Options: "combined", "only_challenge", "only_train". This determines which data to use for training RMIA attack model in testing phase.
-  
-
-# Data paths
-data_paths:
-  midst_data_path: /projects/midst-experiments/ensemble_attack/midst_data_all_attacks/ # Used to collect the data (input)
-  base_data_dir: /projects/midst-experiments/ensemble_attack/competition # Data path where the train pipeline saved data processing results
-  population_path: ${data_paths.base_data_dir}/population_data #READ ONLY  # Path where the collected population data will be stored (output/input)
-  processed_attack_data_path: ${data_paths.base_data_dir}/attack_data # Path where the processed attack real train and evaluation data is stored (output/input)
-  attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored (output)
-
-
-model_paths:
-  metaclassifier_model_path: /projects/midst-experiments/ensemble_attack/competition/trained_models/ # Path where the trained metaclassifier model will be saved
-
-
-# Dataset specific information used for processing in this example
-data_processing_config:
-  population_attack_data_types_to_collect:
-        [
-          "tabddpm_trained_with_20k",
-        ]
-  challenge_attack_data_types_to_collect:
-        [
-          "tabddpm_black_box",
-        ]
-  population_splits: ["train"]  # Data splits to be collected for population data
-  challenge_splits: ["train" , "final"]  # Data splits to be collected for challenge points
-  original_population_data_path: /projects/midst-experiments/ensemble_attack/competition/population_data/
-  # The column name in the data to be used for stratified splitting.
-  column_to_stratify: "trans_type"  # Attention: This value is not documented in the original codebase.
-  folder_ranges: #Specify folder ranges for any of the mentioned splits.
-    train: [[1, 21]] # Folders to be used for train data collection in the experiments
-    final: [[61, 71], [101, 111]] # Change to test for 10k
-  # File names in MIDST data directories.
-  single_table_train_data_file_name: "train_with_id.csv"
-  multi_table_train_data_file_name: "trans.csv"
-  challenge_data_file_name: "challenge_with_id.csv"
-  population_sample_size: 40000 # Population size is the total data that your attack has access to.
-  # In experiments, this is sampled out of all the collected training data in case the available data
-  # is more than this number. Note that, half of this data is actually used for training, the other half
-  # is used for evaluation. For example, with 40k population size, only 20k is used for training the attack model.
-  # TODO: make sure to consider this in experiments.
-
-# Training and data settings for shadow models (temporary, numbers subject to change)
-shadow_training:
-  # Data Config files path used for training a TabDDPM model
-  training_json_config_paths: # Config json files used for tabddpm training on the trans table
-    table_domain_file_path: ${base_data_config_dir}/trans_domain.json
-    dataset_meta_file_path: ${base_data_config_dir}/dataset_meta.json
-    tabddpm_training_config_path: ${base_data_config_dir}/trans.json
-  # Model training artifacts are saved under shadow_models_data_path/workspace_name/exp_name
-  # Also, training configs for each shadow model are created under shadow_models_data_path.
-  shadow_models_output_path: ${base_experiment_dir}/shadow_models_and_data
-  target_model_output_path: ${base_experiment_dir}/shadow_target_model_and_data
-  # Paths to final shadow models used for metaclassifier training (relative to shadow_models_output_path)
-  # These paths are a result of running the shadow model training pipeline, specifically the
-  # train_three_sets_of_shadow_models in shadow_model_training.py
-  # Each .pkl file contains the training data, trained model and training results for all shadow models in a list.
-  final_shadow_models_path: [
-          "${shadow_training.shadow_models_output_path}/initial_model_rmia_1/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
-          "${shadow_training.shadow_models_output_path}/initial_model_rmia_2/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
-          "${shadow_training.shadow_models_output_path}/shadow_model_rmia_third_set/shadow_workspace/trained_model/rmia_shadows_third_set.pkl",
-      ]
-  target_synthetic_data_path: ${shadow_training.target_model_output_path}/target_synthetic_data.csv
-  # Path to final shadow target's synthetic data (relative to target_model_output_path)
-  fine_tuning_config:
-    fine_tune_diffusion_iterations: 200000 # Original code: 200000
-    fine_tune_classifier_iterations: 20000 # Original code: 20000
-    pre_train_data_size: 60000 # Original code: 60000
-  number_of_points_to_synthesize: 20000 # Number of synthetic data samples to be generated by shadow models.
-  # Original code: 20000
-
-
-# Metaclassifier settings
-metaclassifier:
-  # Data types json file is used for xgboost model training.
-  data_types_file_path: ${base_data_config_dir}/data_types.json
-  model_type: "xgb"
-  # Model training parameters
-  num_optuna_trials: 100 # Original code: 100
-  num_kfolds: 5
-  use_gpu: false
-  # Temporary. Might remove having an epoch parameter.
-  epochs: 1
-  meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model
-
-attack_success_computation:
-  target_ids_to_test: [61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110] # List of target model IDs to compute the attack success for.
-
-# General settings
-random_seed: 42 # Set to null for no seed, or an integer for a fixed seed
diff --git a/examples/ensemble_attack/configs/experiment_config_20k_100k.yaml b/examples/ensemble_attack/configs/experiment_config_20k_100k.yaml
deleted file mode 100644
index 3d68c716..00000000
--- a/examples/ensemble_attack/configs/experiment_config_20k_100k.yaml
+++ /dev/null
@@ -1,116 +0,0 @@
-# Ensemble experiment configuration
-# This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
-base_experiment_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_20k_experiment_data/100k # Processed data, and experiment artifacts will be stored here
-base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
-
-# Pipeline control
-pipeline:
-# We don't need to again process data for 200k Synthetic
-  run_data_processing: true # Set this to false if you have already saved the processed data
-  run_shadow_model_training: true # Set this to false if shadow models are already trained and saved
-  run_metaclassifier_training: true
-
-target_model: # This is only used for testing the attack on a real target model.
-  # This is for models trained on 20k data and generating 20k synthetic data
-  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_20k/test/
-  
-  target_model_id: 21  # Will be overridden per SLURM array task
-  target_model_name: tabddpm_${target_model.target_model_id}
-  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/200k/200k.csv # Only the first `number_of_points_to_synthesize` rows will be used.
-  challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
-  challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
-
-  target_shadow_models_output_path: ${base_experiment_dir}/test_20_targets_dir
-  attack_probabilities_result_path: ${target_model.target_shadow_models_output_path}/test_probabilities/attack_model_${target_model.target_model_id}_proba
-  attack_rmia_shadow_training_data_choice: "combined" # Options: "combined", "only_challenge", "only_train". This determines which data to use for training RMIA attack model in testing phase.
-  
-
-# Data paths
-data_paths:
-  midst_data_path: /projects/midst-experiments/all_tabddpms/ # Used to collect the data (input) as defined in data_processing_config
-  processed_base_data_dir: ${base_experiment_dir} # READ from previously collected and processed data. Set to ${base_experiment_dir} if this is training with newly collected data.
-  population_path: ${data_paths.processed_base_data_dir}/population_data  # Path where the collected population data will be stored (output/input)
-  processed_attack_data_path: ${data_paths.processed_base_data_dir}/attack_data # Path where the processed attack real train and evaluation data is stored (output/input)
-  attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored (output)
-
-
-model_paths:
-  metaclassifier_model_path: ${base_experiment_dir}/trained_models # Path where the trained metaclassifier model will be saved
-
-
-# Dataset specific information used for processing in this example
-data_processing_config:
-  population_attack_data_types_to_collect:
-        [
-          "tabddpm_trained_with_20k",
-        ]
-  challenge_attack_data_types_to_collect:
-        [
-          "tabddpm_trained_with_20k",
-        ]
-  population_splits: ["train"]  # Data splits to be collected for population data
-  challenge_splits: ["train" , "test"]  # Data splits to be collected for challenge points
-  original_population_data_path: /projects/midst-experiments/ensemble_attack/competition/population_data/
-  # The column name in the data to be used for stratified splitting.
-  column_to_stratify: "trans_type"  # Attention: This value is not documented in the original codebase.
-  folder_ranges: #Specify folder ranges for any of the mentioned splits.
-    train: [[1, 21]] # Folders to be used for train data collection in the experiments
-    test: [[21, 31] , [31, 41]]
-  # File names in MIDST data directories.
-  single_table_train_data_file_name: "train_with_id.csv"
-  multi_table_train_data_file_name: "trans.csv"
-  challenge_data_file_name: "challenge_with_id.csv"
-  population_sample_size: 40000 # Population size is the total data that your attack has access to.
-  # In experiments, this is sampled out of all the collected training data in case the available data
-  # is more than this number. Note that, half of this data is actually used for training, the other half
-  # is used for evaluation. For example, with 40k population size, only 20k is used for training the attack model.
-  # TODO: make sure to consider this in experiments.
-
-# Training and data settings for shadow models (temporary, numbers subject to change)
-shadow_training:
-  # Data Config files path used for training a TabDDPM model
-  training_json_config_paths: # Config json files used for tabddpm training on the trans table
-    table_domain_file_path: ${base_data_config_dir}/trans_domain.json
-    dataset_meta_file_path: ${base_data_config_dir}/dataset_meta.json
-    tabddpm_training_config_path: ${base_data_config_dir}/trans.json
-  # Model training artifacts are saved under shadow_models_data_path/workspace_name/exp_name
-  # Also, training configs for each shadow model are created under shadow_models_data_path.
-  shadow_models_output_path: ${base_experiment_dir}/shadow_models_and_data
-  target_model_output_path: ${base_experiment_dir}/shadow_target_model_and_data
-  # Paths to final shadow models used for metaclassifier training (relative to shadow_models_output_path)
-  # These paths are a result of running the shadow model training pipeline, specifically the
-  # train_three_sets_of_shadow_models in shadow_model_training.py
-  # Each .pkl file contains the training data, trained model and training results for all shadow models in a list.
-  final_shadow_models_path: [
-          "${shadow_training.shadow_models_output_path}/initial_model_rmia_1/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
-          "${shadow_training.shadow_models_output_path}/initial_model_rmia_2/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
-          "${shadow_training.shadow_models_output_path}/shadow_model_rmia_third_set/shadow_workspace/trained_model/rmia_shadows_third_set.pkl",
-      ]
-  target_synthetic_data_path: ${shadow_training.target_model_output_path}/target_synthetic_data.csv
-  # Path to final shadow target's synthetic data (relative to target_model_output_path)
-  fine_tuning_config:
-    fine_tune_diffusion_iterations: 200000 # Original code: 200000
-    fine_tune_classifier_iterations: 20000 # Original code: 20000
-    pre_train_data_size: 60000 # Original code: 60000
-  number_of_points_to_synthesize: 100000 # Number of synthetic data samples to be generated by shadow models.
-  # Original code: 20000
-
-
-# Metaclassifier settings
-metaclassifier:
-  # Data types json file is used for xgboost model training.
-  data_types_file_path: ${base_data_config_dir}/data_types.json
-  model_type: "xgb"
-  # Model training parameters
-  num_optuna_trials: 100 # Original code: 100
-  num_kfolds: 5
-  use_gpu: false
-  # Temporary. Might remove having an epoch parameter.
-  epochs: 1
-  meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model
-
-attack_success_computation:
-  target_ids_to_test: [21,22,23,24, 25, 26, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40] # List of target model IDs to compute the attack success for.
-
-# General settings
-random_seed: 42 # Set to null for no seed, or an integer for a fixed seed
diff --git a/examples/ensemble_attack/configs/experiment_config_20k_200k.yaml b/examples/ensemble_attack/configs/experiment_config_20k_200k.yaml
deleted file mode 100644
index c1816060..00000000
--- a/examples/ensemble_attack/configs/experiment_config_20k_200k.yaml
+++ /dev/null
@@ -1,116 +0,0 @@
-# Ensemble experiment configuration
-# This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
-base_experiment_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_20k_experiment_data/200k # Processed data, and experiment artifacts will be stored here
-base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
-
-# Pipeline control
-pipeline:
-# We don't need to again process data for 200k Synthetic
-  run_data_processing: true # Set this to false if you have already saved the processed data
-  run_shadow_model_training: true # Set this to false if shadow models are already trained and saved
-  run_metaclassifier_training: true
-
-target_model: # This is only used for testing the attack on a real target model.
-  # This is for models trained on 20k data and generating 20k synthetic data
-  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_20k/test/
-  
-  target_model_id: 21  # Will be overridden per SLURM array task
-  target_model_name: tabddpm_${target_model.target_model_id}
-  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/200k/200k.csv # Only the first `number_of_points_to_synthesize` rows will be used.
-  challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
-  challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
-
-  target_shadow_models_output_path: ${base_experiment_dir}/test_20_targets_dir
-  attack_probabilities_result_path: ${target_model.target_shadow_models_output_path}/test_probabilities/attack_model_${target_model.target_model_id}_proba
-  attack_rmia_shadow_training_data_choice: "combined" # Options: "combined", "only_challenge", "only_train". This determines which data to use for training RMIA attack model in testing phase.
-  
-
-# Data paths
-data_paths:
-  midst_data_path: /projects/midst-experiments/all_tabddpms/ # Used to collect the data (input) as defined in data_processing_config
-  processed_base_data_dir: ${base_experiment_dir} # READ from previously collected and processed data. Set to ${base_experiment_dir} if this is training with newly collected data.
-  population_path: ${data_paths.processed_base_data_dir}/population_data  # Path where the collected population data will be stored (output/input)
-  processed_attack_data_path: ${data_paths.processed_base_data_dir}/attack_data # Path where the processed attack real train and evaluation data is stored (output/input)
-  attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored (output)
-
-
-model_paths:
-  metaclassifier_model_path: ${base_experiment_dir}/trained_models # Path where the trained metaclassifier model will be saved
-
-
-# Dataset specific information used for processing in this example
-data_processing_config:
-  population_attack_data_types_to_collect:
-        [
-          "tabddpm_trained_with_20k",
-        ]
-  challenge_attack_data_types_to_collect:
-        [
-          "tabddpm_trained_with_20k",
-        ]
-  population_splits: ["train"]  # Data splits to be collected for population data
-  challenge_splits: ["train" , "test"]  # Data splits to be collected for challenge points
-  original_population_data_path: /projects/midst-experiments/ensemble_attack/competition/population_data/
-  # The column name in the data to be used for stratified splitting.
-  column_to_stratify: "trans_type"  # Attention: This value is not documented in the original codebase.
-  folder_ranges: #Specify folder ranges for any of the mentioned splits.
-    train: [[1, 21]] # Folders to be used for train data collection in the experiments
-    test: [[21, 31] , [31, 41]]
-  # File names in MIDST data directories.
-  single_table_train_data_file_name: "train_with_id.csv"
-  multi_table_train_data_file_name: "trans.csv"
-  challenge_data_file_name: "challenge_with_id.csv"
-  population_sample_size: 40000 # Population size is the total data that your attack has access to.
-  # In experiments, this is sampled out of all the collected training data in case the available data
-  # is more than this number. Note that, half of this data is actually used for training, the other half
-  # is used for evaluation. For example, with 40k population size, only 20k is used for training the attack model.
-  # TODO: make sure to consider this in experiments.
-
-# Training and data settings for shadow models (temporary, numbers subject to change)
-shadow_training:
-  # Data Config files path used for training a TabDDPM model
-  training_json_config_paths: # Config json files used for tabddpm training on the trans table
-    table_domain_file_path: ${base_data_config_dir}/trans_domain.json
-    dataset_meta_file_path: ${base_data_config_dir}/dataset_meta.json
-    tabddpm_training_config_path: ${base_data_config_dir}/trans.json
-  # Model training artifacts are saved under shadow_models_data_path/workspace_name/exp_name
-  # Also, training configs for each shadow model are created under shadow_models_data_path.
-  shadow_models_output_path: ${base_experiment_dir}/shadow_models_and_data
-  target_model_output_path: ${base_experiment_dir}/shadow_target_model_and_data
-  # Paths to final shadow models used for metaclassifier training (relative to shadow_models_output_path)
-  # These paths are a result of running the shadow model training pipeline, specifically the
-  # train_three_sets_of_shadow_models in shadow_model_training.py
-  # Each .pkl file contains the training data, trained model and training results for all shadow models in a list.
-  final_shadow_models_path: [
-          "${shadow_training.shadow_models_output_path}/initial_model_rmia_1/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
-          "${shadow_training.shadow_models_output_path}/initial_model_rmia_2/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
-          "${shadow_training.shadow_models_output_path}/shadow_model_rmia_third_set/shadow_workspace/trained_model/rmia_shadows_third_set.pkl",
-      ]
-  target_synthetic_data_path: ${shadow_training.target_model_output_path}/target_synthetic_data.csv
-  # Path to final shadow target's synthetic data (relative to target_model_output_path)
-  fine_tuning_config:
-    fine_tune_diffusion_iterations: 200000 # Original code: 200000
-    fine_tune_classifier_iterations: 20000 # Original code: 20000
-    pre_train_data_size: 60000 # Original code: 60000
-  number_of_points_to_synthesize: 200000 # Number of synthetic data samples to be generated by shadow models.
-  # Original code: 20000
-
-
-# Metaclassifier settings
-metaclassifier:
-  # Data types json file is used for xgboost model training.
-  data_types_file_path: ${base_data_config_dir}/data_types.json
-  model_type: "xgb"
-  # Model training parameters
-  num_optuna_trials: 100 # Original code: 100
-  num_kfolds: 5
-  use_gpu: false
-  # Temporary. Might remove having an epoch parameter.
-  epochs: 1
-  meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model
-
-attack_success_computation:
-  target_ids_to_test: [21,22,23,24, 25, 26, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40] # List of target model IDs to compute the attack success for.
-
-# General settings
-random_seed: 42 # Set to null for no seed, or an integer for a fixed seed
diff --git a/examples/ensemble_attack/configs/experiment_config_50k.yaml b/examples/ensemble_attack/configs/experiment_config_50k.yaml
deleted file mode 100644
index bb88da09..00000000
--- a/examples/ensemble_attack/configs/experiment_config_50k.yaml
+++ /dev/null
@@ -1,114 +0,0 @@
-# Ensemble experiment configuration
-# This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
-base_experiment_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_50k_experiment_data/50k_competition_classifier_tests/ # Processed data, and experiment artifacts will be stored here
-base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory
-
-# Pipeline control
-pipeline:
-  run_data_processing: false # Set this to false if you have already saved the processed data
-  run_shadow_model_training: false # Set this to false if shadow models are already trained and saved
-  run_metaclassifier_training: true
-
-target_model: # This is only used for testing the attack on a real target model.
-  # This is for models trained on 50k data and generating 50k synthetic data
-  target_model_directory: /projects/midst-experiments/all_tabddpms/tabddpm_trained_with_50k/test/
-  target_model_id: 21  # Will be overridden per SLURM array task
-  target_model_name: tabddpm_${target_model.target_model_id}
-  target_synthetic_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/synthetic_data/50k/50k.csv
-  challenge_data_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_with_id.csv
-  challenge_label_path: ${target_model.target_model_directory}/${target_model.target_model_name}/challenge_label.csv
-
-  target_shadow_models_output_path: ${base_experiment_dir}/test_20_targets_shadows_dir
-  attack_probabilities_result_path: ${target_model.target_shadow_models_output_path}/test_probabilities/attack_model_${target_model.target_model_id}_proba
-  attack_rmia_shadow_training_data_choice: "combined" # Options: "combined", "only_challenge", "only_train". This determines which data to use for training RMIA attack model in testing phase.
-
-# Data paths
-data_paths:
-  midst_data_path: /projects/midst-experiments/all_tabddpms/ # Used to collect the data (input) in data processing
-  processed_data_dir: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_50k_experiment_data/50k_save_synth_only # Data path where the train pipeline saved data processing results
-  population_path: ${data_paths.processed_data_dir}/population_data #READ ONLY  # Path where the collected population data will be stored (output/input)
-  processed_attack_data_path: ${data_paths.processed_data_dir}/attack_data # Path where the processed attack real train and evaluation data is stored (output/input)
-  attack_evaluation_result_path: ${base_experiment_dir}/evaluation_results # Path where the attack evaluation results will be stored (output)
-
-
-model_paths:
-  metaclassifier_model_path: /projects/midst-experiments/ensemble_attack/competition/trained_models # Path where the trained metaclassifier model will be saved
-  # metaclassifier_model_path: /projects/midst-experiments/ensemble_attack/new_runs/tabddpm_50k_experiment_data/50k_save_synth_only/trained_models
-
-
-# Dataset specific information used for processing in this example
-data_processing_config:
-  population_attack_data_types_to_collect:
-        [
-          "tabddpm_trained_with_50k",
-        ]
-  challenge_attack_data_types_to_collect:
-        [
-          "tabddpm_trained_with_50k",
-        ]
-  population_splits: ["train"]  # Data splits to be collected for population data
-  challenge_splits: ["train" , "test"]  # Data splits to be collected for challenge points
-  original_population_data_path: /projects/midst-experiments/ensemble_attack/competition/population_data/
-  # The column name in the data to be used for stratified splitting.
-  column_to_stratify: "trans_type"  # Attention: This value is not documented in the original codebase.
-  folder_ranges: #Specify folder ranges for any of the mentioned splits.
-    train: [[1, 21]] # Folders to be used for train data collection in the experiments
-    test: [[21, 31], [31, 41]]
-  # File names in MIDST data directories.
-  single_table_train_data_file_name: "train_with_id.csv"
-  multi_table_train_data_file_name: "trans.csv"
-  challenge_data_file_name: "challenge_with_id.csv"
-  population_sample_size: 100000 # Population size is the total data that your attack has access to.
-  # In experiments, this is sampled out of all the collected training data in case the available data
-  # is more than this number. Note that, half of this data is actually used for training, the other half
-  # is used for evaluation. For example, with 40k population size, only 20k is used for training the attack model.
-  # TODO: make sure to consider this in experiments.
-
-# Training and data settings for shadow models (temporary, numbers subject to change)
-shadow_training:
-  # Data Config files path used for training a TabDDPM model
-  training_json_config_paths: # Config json files used for tabddpm training on the trans table
-    table_domain_file_path: ${base_data_config_dir}/trans_domain.json
-    dataset_meta_file_path: ${base_data_config_dir}/dataset_meta.json
-    tabddpm_training_config_path: ${base_data_config_dir}/trans.json
-  # Model training artifacts are saved under shadow_models_data_path/workspace_name/exp_name
-  # Also, training configs for each shadow model are created under shadow_models_data_path.
-  shadow_models_output_path: ${base_experiment_dir}/shadow_models_and_data
-  target_model_output_path: ${base_experiment_dir}/shadow_target_model_and_data
-  # Paths to final shadow models used for metaclassifier training (relative to shadow_models_output_path)
-  # These paths are a result of running the shadow model training pipeline, specifically the
-  # train_three_sets_of_shadow_models in shadow_model_training.py
-  # Each .pkl file contains the training data, trained model and training results for all shadow models in a list.
-  final_shadow_models_path: [
-          "${shadow_training.shadow_models_output_path}/initial_model_rmia_1/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
-          "${shadow_training.shadow_models_output_path}/initial_model_rmia_2/shadow_workspace/pre_trained_model/rmia_shadows.pkl",
-          "${shadow_training.shadow_models_output_path}/shadow_model_rmia_third_set/shadow_workspace/trained_model/rmia_shadows_third_set.pkl",
-      ]
-  target_synthetic_data_path: ${shadow_training.target_model_output_path}/target_synthetic_data.csv
-  # Path to final shadow target's synthetic data (relative to target_model_output_path)
-  fine_tuning_config:
-    fine_tune_diffusion_iterations: 200000 # Original code: 200000
-    fine_tune_classifier_iterations: 20000 # Original code: 20000
-    pre_train_data_size: 60000 # Original code: 60000
-  number_of_points_to_synthesize: 50000 # Number of synthetic data samples to be generated by shadow models.
-  # Original code: 20000
-
-
-# Metaclassifier settings
-metaclassifier:
-  # Data types json file is used for xgboost model training.
-  data_types_file_path: ${base_data_config_dir}/data_types.json
-  model_type: "xgb"
-  # Model training parameters
-  num_optuna_trials: 100 # Original code: 100
-  num_kfolds: 5
-  use_gpu: false
-  # Temporary. Might remove having an epoch parameter.
-  epochs: 1
-  meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model
-
-attack_success_computation:
-  target_ids_to_test: [21,22,23,24, 25, 26, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40] # List of target model IDs to compute the attack success for.
-
-# General settings
-random_seed: 42 # Set to null for no seed, or an integer for a fixed seed
diff --git a/examples/ensemble_attack/real_data_collection.py b/examples/ensemble_attack/real_data_collection.py
index 91213e2b..11d2f159 100644
--- a/examples/ensemble_attack/real_data_collection.py
+++ b/examples/ensemble_attack/real_data_collection.py
@@ -4,13 +4,14 @@
 """
 
 from enum import Enum
-from pathlib import Path
 from logging import INFO
+from pathlib import Path
 
 import pandas as pd
 from omegaconf import DictConfig
-from midst_toolkit.common.logger import log
+
 from midst_toolkit.attacks.ensemble.data_utils import load_dataframe, save_dataframe
+from midst_toolkit.common.logger import log
 
 
 class AttackType(Enum):
@@ -28,12 +29,6 @@ class AttackType(Enum):
     TABDDPM_20K = "tabddpm_trained_with_20k"
     TABDDPM_50K = "tabddpm_trained_with_50k"
     TABDDPM_100K = "tabddpm_trained_with_100k"
-    # Experiment attack types based on experiment settings
-    TABDDPM_5K = "tabddpm_trained_with_5k"
-    TABDDPM_10K = "tabddpm_trained_with_10k"
-    TABDDPM_20K = "tabddpm_trained_with_20k"
-    TABDDPM_50K = "tabddpm_trained_with_50k"
-    TABDDPM_100K = "tabddpm_trained_with_100k"
 
 
 def expand_ranges(ranges: list[tuple[int, int]]) -> list[int]:
@@ -164,6 +159,7 @@ def collect_population_data_ensemble(
         midst_data_input_dir: The path where the MIDST data folders are stored.
         data_processing_config: Configuration dictionary containing data information and file names.
         save_dir: The path where the collected population data should be saved.
+        original_repo_population: The original population data collected from the MIDST challenge repository.
         population_splits: A list indicating the data splits to be collected for population data.
             Could be any of `train`, `dev`, or `final` data splits. If None, the default list of ``["train"]``
             is set in the function based on the original attack implementation.
@@ -215,8 +211,7 @@ def collect_population_data_ensemble(
 
     df_population = pd.concat([df_population_experiment, original_repo_population]).drop_duplicates()
     log(INFO, f"Concatenated population data length: {len(df_population)}")
-    
-    
+
     # Drop ids.
     df_population_no_id = df_population.drop(columns=["trans_id", "account_id"])
     # Save the population data
@@ -231,7 +226,6 @@ def collect_population_data_ensemble(
         midst_data_input_dir,
         attack_types=challenge_attack_types,
         data_splits=challenge_splits,
-        data_splits=challenge_splits,
         dataset="challenge",
         data_processing_config=data_processing_config,
     )
diff --git a/examples/ensemble_attack/run_attack.py b/examples/ensemble_attack/run_attack.py
index a4b4d3db..ec4d9d24 100644
--- a/examples/ensemble_attack/run_attack.py
+++ b/examples/ensemble_attack/run_attack.py
@@ -11,9 +11,9 @@
 from omegaconf import DictConfig
 
 from examples.ensemble_attack.real_data_collection import collect_population_data_ensemble
+from midst_toolkit.attacks.ensemble.data_utils import load_dataframe
 from midst_toolkit.attacks.ensemble.process_split_data import process_split_data
 from midst_toolkit.common.logger import log
-from midst_toolkit.attacks.ensemble.data_utils import load_dataframe, save_dataframe
 from midst_toolkit.common.random import set_all_random_seeds
 
 
@@ -25,8 +25,9 @@ def run_data_processing(config: DictConfig) -> None:
         config: Configuration object set in config.yaml.
     """
     # Load original repo's population
-    
-    original_population_data = load_dataframe(Path(config.data_processing_config.original_population_data_path),
+
+    original_population_data = load_dataframe(
+        Path(config.data_processing_config.original_population_data_path),
         "population_all_with_challenge.csv",
     )
     log(INFO, "Running data processing pipeline...")
@@ -35,12 +36,12 @@ def run_data_processing(config: DictConfig) -> None:
         midst_data_input_dir=Path(config.data_paths.midst_data_path),
         data_processing_config=config.data_processing_config,
         save_dir=Path(config.data_paths.population_path),
-        original_repo_population = original_population_data,
+        original_repo_population=original_population_data,
         population_splits=config.data_processing_config.population_splits,
         challenge_splits=config.data_processing_config.challenge_splits,
     )
 
-    # The following function saves the required dataframe splits in the specified processed_attack_data_path path. 
+    # The following function saves the required dataframe splits in the specified processed_attack_data_path path.
     process_split_data(
         all_population_data=population_data,
         processed_attack_data_path=Path(config.data_paths.processed_attack_data_path),
diff --git a/examples/ensemble_attack/run_metaclassifier_training.py b/examples/ensemble_attack/run_metaclassifier_training.py
index 0e60d4a6..d848db55 100644
--- a/examples/ensemble_attack/run_metaclassifier_training.py
+++ b/examples/ensemble_attack/run_metaclassifier_training.py
@@ -74,7 +74,10 @@ def run_metaclassifier_training(
 
     # Load the target model's synthetic data
     target_synthetic_data = pd.read_csv(target_model_synthetic_path)
-    log(INFO, f"Target model's synthetic data loaded from {target_model_synthetic_path} with size {len(target_synthetic_data)}.")
+    log(
+        INFO,
+        f"Target model's synthetic data loaded from {target_model_synthetic_path} with size {len(target_synthetic_data)}.",
+    )
 
     assert target_synthetic_data is not None, "Target model's synthetic data is missing."
     target_synthetic_data = target_synthetic_data.copy()
@@ -83,7 +86,10 @@ def run_metaclassifier_training(
         Path(config.data_paths.population_path),
         "population_all_with_challenge_no_id.csv",
     )
-    log(INFO, f"Reference population data loaded from f{config.data_paths.population_path} with size {len(df_reference)}.")
+    log(
+        INFO,
+        f"Reference population data loaded from f{config.data_paths.population_path} with size {len(df_reference)}.",
+    )
 
     # Extract trans_id from both train and test dataframes
     assert "trans_id" in df_meta_train.columns, "Meta train data must have trans_id column"
diff --git a/examples/ensemble_attack/run_shadow_model_training.py b/examples/ensemble_attack/run_shadow_model_training.py
index 9be3167f..ab994722 100644
--- a/examples/ensemble_attack/run_shadow_model_training.py
+++ b/examples/ensemble_attack/run_shadow_model_training.py
@@ -2,8 +2,9 @@
 from logging import INFO
 from pathlib import Path
 
-from omegaconf import DictConfig
 import pandas as pd
+from omegaconf import DictConfig
+
 from midst_toolkit.attacks.ensemble.data_utils import load_dataframe
 from midst_toolkit.attacks.ensemble.rmia.shadow_model_training import (
     train_three_sets_of_shadow_models,
@@ -78,7 +79,8 @@ def run_target_model_training(config: DictConfig) -> Path:
 
     return target_model_synthetic_path
 
-def run_shadow_model_training(config: DictConfig, df_challenge_train:pd.DataFrame) -> list[Path]:
+
+def run_shadow_model_training(config: DictConfig, df_challenge_train: pd.DataFrame) -> list[Path]:
     """
     Function to run the shadow model training for RMIA attack.
 
diff --git a/examples/ensemble_attack/run_test_10k.sh b/examples/ensemble_attack/run_test_10k.sh
deleted file mode 100644
index f54b59fb..00000000
--- a/examples/ensemble_attack/run_test_10k.sh
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/bin/bash
-
-#SBATCH --nodes=1
-#SBATCH --ntasks=1
-#SBATCH --ntasks-per-node=1
-#SBATCH --cpus-per-task=1
-#SBATCH --gres=gpu:a40:1
-#SBATCH --mem=64G
-#SBATCH --job-name=test10_all_targets
-#SBATCH --output=%j_%x_%a.out
-#SBATCH --error=%j_%x_%a.err
-#SBATCH --time=1:00:00
-#SBATCH --array=0-18
-
-# This script sets up the environment and runs the ensemble attack test script.
-source .venv/bin/activate
-
-echo "Active Environment:"
-which python
-
-# Map SLURM_ARRAY_TASK_ID to target_model_id.
-
-# TARGET_IDS=(21)
-
-TARGET_IDS=( 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40)  # List of target IDs
-
-TARGET_ID=${TARGET_IDS[$SLURM_ARRAY_TASK_ID]}
-
-echo "Running test for target_model_id: $TARGET_ID using all target models' challenges (same shadows)"
-
-echo "Experiments Launched"
-pwd
-
-python -m examples.ensemble_attack.test_attack_model --config-name=experiment_config_10k target_model.target_model_id=$TARGET_ID
-
-echo "Experiments Completed"
\ No newline at end of file
diff --git a/examples/ensemble_attack/run_test_20k.sh b/examples/ensemble_attack/run_test_20k.sh
deleted file mode 100644
index a868764e..00000000
--- a/examples/ensemble_attack/run_test_20k.sh
+++ /dev/null
@@ -1,39 +0,0 @@
-#!/bin/bash
-
-#SBATCH --nodes=1
-#SBATCH --ntasks=1
-#SBATCH --ntasks-per-node=1
-#SBATCH --cpus-per-task=1
-#SBATCH --gres=gpu:a40:1
-#SBATCH --mem=128G
-#SBATCH --job-name=test20_200_all_targets
-#SBATCH --output=%j_%x_%a.out
-#SBATCH --error=%j_%x_%a.err
-#SBATCH --time=1:00:00
-#SBATCH --array=0-0
-
-# This script sets up the environment and runs the ensemble attack test script.
-source .venv/bin/activate
-
-echo "Active Environment:"
-which python
-
-# Map SLURM_ARRAY_TASK_ID to target_model_id.
-
-# TARGET_IDS=(61 62 63 64 65 66 67 68 69 70)
-# TARGET_IDS=(61)
-# TARGET_IDS=( 62 63 64 65 66 67 68 69 70 101 102 103 104 105 106 107 108 109 110)  # List of target IDs
-
-# TARGET_IDS=(21)
-TARGET_IDS=(28)
-# TARGET_IDS=( 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40)  # List of target IDs
-
-TARGET_ID=${TARGET_IDS[$SLURM_ARRAY_TASK_ID]}
-
-echo "Running test for target_model_id: $TARGET_ID using all target models' challenges (same shadows)"
-
-echo "Experiments Launched"
-
-python -m examples.ensemble_attack.test_attack_model --config-name=experiment_config_20k_200k target_model.target_model_id=$TARGET_ID
-
-echo "Experiments Completed"
\ No newline at end of file
diff --git a/examples/ensemble_attack/run_test_50k.sh b/examples/ensemble_attack/run_test_50k.sh
deleted file mode 100644
index a55f32bf..00000000
--- a/examples/ensemble_attack/run_test_50k.sh
+++ /dev/null
@@ -1,35 +0,0 @@
-#!/bin/bash
-
-#SBATCH --nodes=1
-#SBATCH --ntasks=1
-#SBATCH --ntasks-per-node=1
-#SBATCH --cpus-per-task=1
-#SBATCH --gres=gpu:a40:1
-#SBATCH --mem=64G
-#SBATCH --job-name=test50_comp_classifier_all
-#SBATCH --output=%j_%x_%a.out
-#SBATCH --error=%j_%x_%a.err
-#SBATCH --time=1:00:00
-#SBATCH --array=0-18
-
-# This script sets up the environment and runs the ensemble attack test script.
-source .venv/bin/activate
-
-echo "Active Environment:"
-which python
-
-# Map SLURM_ARRAY_TASK_ID to target_model_id.
-
-# TARGET_IDS=(21)
-
-TARGET_IDS=( 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40)  # List of target IDs
-
-TARGET_ID=${TARGET_IDS[$SLURM_ARRAY_TASK_ID]}
-
-echo "Running test for target_model_id: $TARGET_ID using all target models' challenges (same shadows)"
-
-echo "Experiments Launched"
-
-python -m examples.ensemble_attack.test_attack_model --config-name=experiment_config_50k target_model.target_model_id=$TARGET_ID
-
-echo "Experiments Completed"
\ No newline at end of file
diff --git a/examples/ensemble_attack/run_train_100k.sh b/examples/ensemble_attack/run_train_100k.sh
deleted file mode 100755
index 5e4ac7fc..00000000
--- a/examples/ensemble_attack/run_train_100k.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/bin/bash
-
-#SBATCH --nodes=1
-#SBATCH --ntasks=1
-#SBATCH --ntasks-per-node=1
-#SBATCH --cpus-per-task=2
-#SBATCH --gres=gpu:1
-#SBATCH --mem=160G
-#SBATCH --qos=normal
-#SBATCH --job-name=new_100k_meta_ensemble_attack_train
-#SBATCH --output=%j_%x.out
-#SBATCH --error=%j_%x.err
-#SBATCH --time=14:00:00
-
-
-# This script sets up the environment and runs the ensemble attack example.
-source .venv/bin/activate
-
-echo "Active Environment:"
-which python
-
-echo "Experiments Launched"
-
-python -m examples.ensemble_attack.run_attack --config-name=experiment_config_100k
-
-echo "Experiments Completed"
diff --git a/examples/ensemble_attack/run_train_10k.sh b/examples/ensemble_attack/run_train_10k.sh
deleted file mode 100755
index 709bfbdc..00000000
--- a/examples/ensemble_attack/run_train_10k.sh
+++ /dev/null
@@ -1,25 +0,0 @@
-#!/bin/bash
-
-#SBATCH --nodes=1
-#SBATCH --ntasks=1
-#SBATCH --ntasks-per-node=1
-#SBATCH --cpus-per-task=1
-#SBATCH --gres=gpu:1
-#SBATCH --mem=32G
-#SBATCH --qos=normal
-#SBATCH --job-name=10k_ensemble_attack_train
-#SBATCH --output=%j_%x.out
-#SBATCH --error=%j_%x.err
-#SBATCH --time=16:00:00
-
-# This script sets up the environment and runs the ensemble attack example.
-source .venv/bin/activate
-
-echo "Active Environment:"
-which python
-
-echo "Experiments Launched"
-
-python -m examples.ensemble_attack.run_attack --config-name=experiment_config_10k
-
-echo "Experiments Completed"
diff --git a/examples/ensemble_attack/run_train_20_100.sh b/examples/ensemble_attack/run_train_20_100.sh
deleted file mode 100755
index f3fca79b..00000000
--- a/examples/ensemble_attack/run_train_20_100.sh
+++ /dev/null
@@ -1,27 +0,0 @@
-#!/bin/bash
-
-#SBATCH --nodes=1
-#SBATCH --ntasks=1
-#SBATCH --ntasks-per-node=1
-#SBATCH --cpus-per-task=2
-#SBATCH --gres=gpu:a100:1
-#SBATCH --mem=210G
-#SBATCH --job-name=train20k_100k
-#SBATCH --output=%j_%x.out
-#SBATCH --error=%j_%x.err
-#SBATCH --time=16:00:00
-
-
-echo "Total memory allocated: $(($SLURM_MEM_PER_NODE / 1024)) GB"
-
-# This script sets up the environment and runs the ensemble attack example.
-source .venv/bin/activate
-
-echo "Active Environment:"
-which python
-
-echo "Experiments Launched"
-
-python -m examples.ensemble_attack.run_attack  --config-name=experiment_config_20k_100k
-
-echo "Experiments Completed"
diff --git a/examples/ensemble_attack/run_train_50k.sh b/examples/ensemble_attack/run_train_50k.sh
deleted file mode 100755
index adb79f9d..00000000
--- a/examples/ensemble_attack/run_train_50k.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/bin/bash
-
-#SBATCH --nodes=1
-#SBATCH --ntasks=1
-#SBATCH --ntasks-per-node=1
-#SBATCH --cpus-per-task=2
-#SBATCH --mem=290G
-#SBATCH --job-name=50k_RMIA_train_save_synth_only
-#SBATCH --output=%j_%x.out
-#SBATCH --error=%j_%x.err
-#SBATCH --time=12:00:00
-
-
-echo "Total memory allocated: $(($SLURM_MEM_PER_NODE / 1024)) GB"
-
-# This script sets up the environment and runs the ensemble attack example.
-source .venv/bin/activate
-
-echo "Active Environment:"
-which python
-
-echo "Experiments Launched"
-
-python -m examples.ensemble_attack.run_attack  --config-name=experiment_config_50k
-
-echo "Experiments Completed"
diff --git a/examples/ensemble_attack/test_attack_model.py b/examples/ensemble_attack/test_attack_model.py
index d8c510b5..dba5f323 100644
--- a/examples/ensemble_attack/test_attack_model.py
+++ b/examples/ensemble_attack/test_attack_model.py
@@ -11,16 +11,68 @@
 import pandas as pd
 from omegaconf import DictConfig
 
+from examples.ensemble_attack.real_data_collection import AttackType, collect_midst_data
 from examples.ensemble_attack.run_shadow_model_training import run_shadow_model_training
-
-from examples.ensemble_attack.real_data_collection import collect_midst_data, AttackType
-
 from midst_toolkit.attacks.ensemble.blending import BlendingPlusPlus, MetaClassifierType
 from midst_toolkit.attacks.ensemble.data_utils import load_dataframe
 from midst_toolkit.common.logger import log
 from midst_toolkit.common.random import set_all_random_seeds
 
 
+def save_results(
+    attack_results_path: Path, metaclassifier_model_name: str, probabilities: np.ndarray, pred_score: float
+) -> None:
+    """
+    Saves the test prediction probabilities and metric results.
+
+    Args:
+        attack_results_path: Path to save the attack results.
+        metaclassifier_model_name: Name of the metaclassifier model to be used to name score and prediction files.
+        probabilities: Prediction probabilities from the metaclassifier.
+        pred_score: Prediction score to be saved.
+    """
+    file_name = attack_results_path / f"{metaclassifier_model_name}_test_pred_proba.npy"
+    np.save(file_name, probabilities)
+    log(INFO, f"Test prediction probabilities saved at {file_name}.")
+
+    if pred_score is not None:
+        log(INFO, f"TPR at FPR=0.1: {pred_score:.4f}")
+
+        # Save the metric results into a text file.
+        metric_save_path = attack_results_path / f"prediction_score_{metaclassifier_model_name}.txt"
+        with open(metric_save_path, "w") as f:
+            f.write(f"TPR at FPR=0.1: {pred_score:.4f}\n")
+
+
+def extract_and_drop_id_columns(
+    data_frame: pd.DataFrame, data_types_file_path: Path
+) -> tuple[pd.DataFrame, pd.Series]:
+    """
+    Extracts IDs from the data frame and drops all ID columns.
+
+    Args:
+        data_frame: Input data frame.
+        data_types_file_path: Path to the data types JSON file.
+
+    Returns:
+        A tuple containing:
+            - The modified data frame with ID columns dropped.
+            - A Series containing the extracted transaction IDs.
+    """
+    # Extract trans_id from the dataframe
+    with open(data_types_file_path, "r") as f:
+        column_types = json.load(f)
+    id_column_name = column_types["id_column_name"]
+
+    assert id_column_name in data_frame.columns, f"Test data must have {id_column_name} column"
+    data_trans_ids = data_frame[id_column_name]
+
+    # Drop id columns from data
+    id_column_names = [column_name for column_name in data_frame.columns if column_name.endswith("_id")]
+    data_frame = data_frame.drop(columns=id_column_names)
+
+    return data_frame, data_trans_ids
+
 
 def run_rmia_shadow_training(config: DictConfig, df_challenge: pd.DataFrame) -> list[dict[str, list[Any]]]:
     """
@@ -54,42 +106,135 @@ def run_rmia_shadow_training(config: DictConfig, df_challenge: pd.DataFrame) ->
     return shadow_data_collection
 
 
-def train_rmia_shadows_for_test_phase(config: DictConfig):
+def load_trained_rmia_shadows_for_test_phase(
+    shadow_data_paths: list[Path],
+) -> tuple[list[dict[str, list[Any]]], bool]:
+    """
+    Loads previously trained RMIA shadow models for the testing phase.
+
+    Args:
+        shadow_data_paths: List of paths to the saved shadow model data.
+
+    Returns:
+        A tuple containing:
+            - A list of dictionaries, each representing a collection of shadow
+                models with their training data and generated synthetic outputs.
+            - A boolean indicating whether all shadow models were successfully loaded.
+    """
+    shadow_data_collection = []
+    models_exists = True
+    for model_path in shadow_data_paths:
+        if model_path.exists():
+            with open(model_path, "rb") as f:
+                shadow_data_and_result = pickle.load(f)
+                shadow_data_collection.append(shadow_data_and_result)
+            log(INFO, f"Loaded existing shadow model at {model_path}.")
+        else:
+            models_exists = False
+            shadow_data_collection = []
+            break
+    return shadow_data_collection, models_exists
+
+
+def collect_challenge_data(
+    data_processing_config: DictConfig, processed_attack_data_path: Path, targets_data_path: Path
+) -> tuple[pd.DataFrame, pd.DataFrame]:
+    """
+    Collect challenge experiment data and master train data.
+
+    Args:
+        data_processing_config: Configuration object for data processing.
+        processed_attack_data_path: Path to the processed attack data.
+        targets_data_path: Path to the target model's data.
+
+    Returns:
+        Tuple of (df_challenge_experiment, df_master_train).
+    """
     # Collect all repo's challenge points
-    data_processing_config=config.data_processing_config
     challenge_attack_names = data_processing_config.challenge_attack_data_types_to_collect
     challenge_attack_types = [AttackType(attack_name) for attack_name in challenge_attack_names]
     df_challenge_experiment = collect_midst_data(
-        midst_data_input_dir=Path(config.data_paths.midst_data_path),
+        midst_data_input_dir=targets_data_path,
         attack_types=challenge_attack_types,
-        data_splits=["test"],  #change to test for 10k, and change to final for 20k
+        data_splits=["test"],  # change to test for 10k, and change to final for 20k
         dataset="challenge",
-        data_processing_config=config.data_processing_config,
+        data_processing_config=data_processing_config,
+    )
+    log(
+        INFO,
+        f"Collected challenge data length: {len(df_challenge_experiment)} for the testing phase's shadow training.",
     )
-    log(INFO, f"Collected challenge data length: {len(df_challenge_experiment)} for the testing phase's shadow training.")
-    
+
     # Load master challenge train data
     df_master_train = load_dataframe(
-        Path(config.data_paths.processed_attack_data_path),
+        processed_attack_data_path,
         "master_challenge_train.csv",
     )
-    log(INFO, f"Loaded master challenge train data length: {len(df_master_train)} for the testing phase's shadow training.")
+    log(
+        INFO,
+        f"Loaded master challenge train data length: {len(df_master_train)} for the testing phase's shadow training.",
+    )
+
+    return df_challenge_experiment, df_master_train
+
+
+def select_challenge_data_for_training(
+    attack_rmia_shadow_training_data_choice: str, df_challenge_experiment: pd.DataFrame, df_master_train: pd.DataFrame
+) -> pd.DataFrame:
+    """
+    Select the appropriate challenge data based on config choice.
 
-    if config.target_model.attack_rmia_shadow_training_data_choice == "combined":
+    Args:
+        attack_rmia_shadow_training_data_choice: Strategy for creating challenge train data for RMIA shadow training.
+        df_challenge_experiment: Challenge experiment data.
+        df_master_train: Master train data.
+
+    Raises:
+        ValueError: If an invalid choice is provided.
+
+    Returns:
+        Selected challenge data.
+    """
+    if attack_rmia_shadow_training_data_choice == "combined":
         # Run RMIA shadow model training on experiments challenge points + master challenge train data
         df_challenge = pd.concat([df_challenge_experiment, df_master_train]).drop_duplicates()
         log(INFO, f"Combined challenge data length for RMIA shadow training: {len(df_challenge)}.")
-    elif config.target_model.attack_rmia_shadow_training_data_choice == "only_challenge":
+    elif attack_rmia_shadow_training_data_choice == "only_challenge":
         df_challenge = df_challenge_experiment
-        log(INFO, f"Using only challenge data points for RMIA shadow training.")
-    elif config.target_model.attack_rmia_shadow_training_data_choice == "only_train":
+        log(INFO, "Using only challenge data points for RMIA shadow training.")
+    elif attack_rmia_shadow_training_data_choice == "only_train":
         df_challenge = df_master_train
-        log(INFO, f"Using only master challenge train data points for RMIA shadow training.")
+        log(INFO, "Using only master challenge train data points for RMIA shadow training.")
     else:
-        raise ValueError(f"Invalid choice for attack_rmia_shadow_training_data_choice. Must be one of 'combined', 'only_challenge', or 'only_train'.")
+        raise ValueError(
+            "Invalid choice for attack_rmia_shadow_training_data_choice. Must be one of 'combined', 'only_challenge', or 'only_train'."
+        )
+
+    return df_challenge
+
 
+def train_rmia_shadows_for_test_phase(config: DictConfig) -> list[dict[str, list[Any]]]:
+    """
+    Function to train RMIA shadow models for the testing phase using the dataset containing challenge data points.
+
+    Args:
+        config: Configuration object set in ``experiments_config.yaml``.
+
+    Returns:
+        A list containing three dictionaries, each representing a collection of shadow
+            models with their training data IDs and generated synthetic outputs.
+    """
+    df_challenge_experiment, df_master_train = collect_challenge_data(
+        config.data_processing_config,
+        processed_attack_data_path=Path(config.data_paths.processed_attack_data_path),
+        targets_data_path=Path(config.data_paths.midst_data_path),
+    )
+    df_challenge = select_challenge_data_for_training(
+        str(config.target_model.attack_rmia_shadow_training_data_choice), df_challenge_experiment, df_master_train
+    )
     return run_rmia_shadow_training(config, df_challenge=df_challenge)
 
+
 @hydra.main(config_path="configs", config_name="experiment_config", version_base=None)
 def run_metaclassifier_testing(
     config: DictConfig,
@@ -146,12 +291,10 @@ def run_metaclassifier_testing(
     )
 
     # If the synthetic data has more points than specified in the config, take only the required number.
-    if len(target_synthetic_data)> config.shadow_training.number_of_points_to_synthesize:
+    if len(target_synthetic_data) > config.shadow_training.number_of_points_to_synthesize:
         # Take only the required number of synthetic data points
         target_synthetic_data = target_synthetic_data.head(config.shadow_training.number_of_points_to_synthesize)
-        log(
-            INFO, f"Target synthetic data size adjusted to {len(target_synthetic_data)} based on the config setting."
-        )
+        log(INFO, f"Target synthetic data size adjusted to {len(target_synthetic_data)} based on the config setting.")
 
     # 3) Shadow Model Training Step.
     # Make sure to assign a new path for shadow models trained for target's challenge points to
@@ -160,43 +303,21 @@ def run_metaclassifier_testing(
     shadow_data_paths = [Path(path) for path in config.shadow_training.final_shadow_models_path]
     # if already trained for test, don't need to train again
     # Load shadow training collection from previously trained shadow models.
-    assert (
-        len(shadow_data_paths) == 3
-    ), "The attack_data_paths list must contain exactly three elements."
+    assert len(shadow_data_paths) == 3, "The attack_data_paths list must contain exactly three elements."
 
-    shadow_data_collection = []
-    models_exist = True
-    for model_path in shadow_data_paths:
-        
-        if model_path.exists():
-            with open(model_path, "rb") as f:
-                shadow_data_and_result = pickle.load(f)
-                shadow_data_collection.append(shadow_data_and_result)
-            log(INFO, f"Loaded existing shadow model at {model_path}.")
-        else:
-            models_exist = False
-            break
-    
-    if not models_exist:
+    shadow_data_collection, models_exists = load_trained_rmia_shadows_for_test_phase(shadow_data_paths)
+
+    if not models_exists:
         log(INFO, "Shadow models for testing phase do not exist. Training RMIA shadow models...")
         shadow_data_collection = train_rmia_shadows_for_test_phase(config)
-        
+
     else:
         log(INFO, "All shadow models for testing phase found. Using existing RMIA shadow models...")
 
-
-    # Extract trans_id from the test dataframe
-    with open(Path(config.metaclassifier.data_types_file_path), "r") as f:
-        column_types = json.load(f)
-    id_column_name = column_types["id_column_name"]
-
-    assert id_column_name in test_data.columns, f"Test data must have {id_column_name} column"
-    test_trans_ids = test_data[id_column_name]
-
-    # Drop id columns from test data
-    id_column_names = [column_name for column_name in test_data.columns if column_name.endswith("_id")]
-    test_data = test_data.drop(columns=id_column_names)
-
+    # Extract and drop id columns from the test data
+    test_data, test_trans_ids = extract_and_drop_id_columns(
+        test_data, Path(config.metaclassifier.data_types_file_path)
+    )
 
     # 4) Initialize the attacker object, and assign the loaded metaclassifier to it.
     df_reference = load_dataframe(
@@ -227,17 +348,7 @@ def run_metaclassifier_testing(
     # Save the validation prediction probabilities
     attack_results_path = Path(config.target_model.attack_probabilities_result_path)
     attack_results_path.mkdir(parents=True, exist_ok=True)
-    file_name = attack_results_path / f"{metaclassifier_model_name}_test_pred_proba.npy"
-    np.save(file_name, probabilities)
-    log(INFO, f"Test prediction probabilities saved at {file_name}.")
-
-    if pred_score is not None:
-        log(INFO, f"TPR at FPR=0.1: {pred_score:.4f}")
-
-        # Save the metric results into a text file.
-        metric_save_path = attack_results_path / f"prediction_score_{metaclassifier_model_name}.txt"
-        with open(metric_save_path, "w") as f:
-            f.write(f"TPR at FPR=0.1: {pred_score:.4f}\n")
+    save_results(attack_results_path, metaclassifier_model_name, probabilities, pred_score)
 
 
 if __name__ == "__main__":
diff --git a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
index 78500c5a..c0676c92 100644
--- a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
+++ b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
@@ -6,6 +6,7 @@
 
 from enum import Enum
 from logging import INFO
+from multiprocessing import Pool
 from typing import Any
 
 import gower
@@ -14,57 +15,60 @@
 
 from midst_toolkit.common.logger import log
 
-from multiprocessing import Pool
 
 class Key(Enum):
     TRAINED_RESULTS = "trained_results"
     FINE_TUNED_RESULTS = "fine_tuned_results"
 
+
 def compute_gower_batched(
     df_x: pd.DataFrame,
     df_y: pd.DataFrame,
-    cat_features: list[bool],
+    cat_features: list[str],
     batch_size: int,
     dtype: np.dtype,
 ) -> np.ndarray:
     """
     Compute Gower distance matrix in batches to reduce peak memory usage.
-    
+
     This processes df_x in batches, computing distances against all of df_y,
     then stacks results.
 
     Args:
         df_x: First dataframe.
         df_y: Second dataframe.
-        cat_features: List indicating which columns are categorical.
+        cat_features: List containing categorical columns.
         batch_size: Number of rows from df_x to process in each batch.
         dtype: The data type to which numerical columns will be cast.
-    
+
     Returns:
         Gower distance matrix as a numpy array.
     """
     n_x = len(df_x)
     n_y = len(df_y)
-    
+
     # Pre-allocate output matrix
     gower_matrix = np.zeros((n_x, n_y), dtype=dtype)
-    
+
     for start_idx in range(0, n_x, batch_size):
         end_idx = min(start_idx + batch_size, n_x)
-        
+
         # Compute distance for this batch
         batch_matrix = gower.gower_matrix(
             data_x=df_x.iloc[start_idx:end_idx],
             data_y=df_y,
             cat_features=cat_features,
         )
-        
+
         # Store in output matrix
         gower_matrix[start_idx:end_idx] = batch_matrix.astype(dtype)
-    
+
     return gower_matrix
 
-def compute_gower_for_model(args: tuple[int, pd.DataFrame, pd.DataFrame, int, str, int | None, list[str], list[str], np.dtype]) -> tuple[int, np.ndarray]:
+
+def compute_gower_for_model(
+    args: tuple[int, pd.DataFrame, pd.DataFrame, int, str, int | None, list[str], list[str], np.dtype],
+) -> tuple[int, np.ndarray]:
     """
     Computes the Gower distance matrix between df_input and a single synthetic dataframe.
 
@@ -76,7 +80,7 @@ def compute_gower_for_model(args: tuple[int, pd.DataFrame, pd.DataFrame, int, st
             - min_length: Minimum length for downsampling.
             - id_column_name: Name of the ID column.
             - random_seed: Random seed for reproducibility.
-            - categorical_features: List indicating which columns are categorical.
+            - categorical_features: A list of input dataframe's categorical features.
             - numerical_columns: List of numerical column names.
             - dtype: The data type to which numerical columns will be cast.
 
@@ -85,25 +89,34 @@ def compute_gower_for_model(args: tuple[int, pd.DataFrame, pd.DataFrame, int, st
             - i: Index of the model (same as input).
             - gower_matrix: The computed Gower distance matrix as a numpy array.
     """
-    i, df_synthetic_raw, df_input, min_length, id_column_name, random_seed, categorical_features, numerical_columns, dtype = args
-    
+    (
+        i,
+        df_synthetic_raw,
+        df_input,
+        min_length,
+        id_column_name,
+        random_seed,
+        categorical_features,
+        numerical_columns,
+        dtype,
+    ) = args
+
     df_synthetic = df_synthetic_raw.copy()
-    
+
     if id_column_name in df_synthetic.columns:
         df_synthetic = df_synthetic.drop(columns=[id_column_name])
-    
+
     df_synthetic[numerical_columns] = df_synthetic[numerical_columns].astype(dtype)
-    
+
     if len(df_synthetic) > min_length:
         df_synthetic = df_synthetic.sample(n=min_length, random_state=random_seed)
-    
+
     # Batched computation: compute row-by-row to reduce peak memory
-    gower_matrix = compute_gower_batched(
-        df_input, df_synthetic, categorical_features, batch_size=5000, dtype=dtype
-    )
-    
+    gower_matrix = compute_gower_batched(df_input, df_synthetic, categorical_features, batch_size=5000, dtype=dtype)
+
     return i, gower_matrix
 
+
 def get_rmia_gower(
     df_input: pd.DataFrame,
     model_data: list[pd.DataFrame],
@@ -127,7 +140,8 @@ def get_rmia_gower(
         id_column_name: Name of the ID column.
         random_seed: Random seed for reproducibility.
         n_jobs: Number of parallel jobs to use for computation. Default is 4.
-        dtype: The data type to which numerical columns will be cast for Gower distance computation. Default is np.float32.
+        dtype: The data type to which numerical columns will be cast for Gower distance computation.
+            Default is np.float32.
 
     Returns:
         A list of numpy arrays, each representing the Gower distance matrix between the input dataframe and the
@@ -153,22 +167,30 @@ def get_rmia_gower(
 
     # Prepare arguments for each processing task
     args_list = [
-        (i, df_synthetic, df_input, min_length, id_column_name, 
-        random_seed, categorical_features, numerical_columns, dtype)
+        (
+            i,
+            df_synthetic,
+            df_input,
+            min_length,
+            id_column_name,
+            random_seed,
+            categorical_features,
+            numerical_columns,
+            dtype,
+        )
         for i, df_synthetic in enumerate(model_data)
     ]
-    
+
     # Process in parallel
     results = {}
     with Pool(processes=n_jobs) as pool:
         for i, gower_matrix in pool.imap_unordered(compute_gower_for_model, args_list):
             results[i] = gower_matrix
-    
+
     # Return in original order
     return [results[i] for i in range(len(model_data))]
 
 
-
 def conditional_average(values: np.ndarray, condition_mask: np.ndarray) -> np.ndarray:
     """
     Calculate the conditional average of values based on a condition mask.
@@ -192,7 +214,6 @@ def conditional_average(values: np.ndarray, condition_mask: np.ndarray) -> np.nd
     return np.where(mask_sum > 0, np.sum(values * condition_mask, axis=0) / mask_sum, np.nan)
 
 
-
 def calculate_rmia_signals(
     df_input: pd.DataFrame,
     shadow_data_collection: list[dict[str, list[Any]]],
@@ -302,9 +323,7 @@ def calculate_rmia_signals(
     if not (1 <= k <= min_length):
         raise ValueError(f"k={k} must be within [1, {min_length}]")
 
-    shadow_synthetic_list_0 = [
-        train_result for train_result in fine_tuned_shadow_data_0[Key.FINE_TUNED_RESULTS.value]
-    ]
+    shadow_synthetic_list_0 = list(fine_tuned_shadow_data_0[Key.FINE_TUNED_RESULTS.value])
     shadow_model_gower_0 = get_rmia_gower(
         df_input=df_input,
         model_data=shadow_synthetic_list_0,
@@ -316,9 +335,7 @@ def calculate_rmia_signals(
     )
     log(INFO, "Computed Gower distance for fine-tuned shadow models (first set of shadow models).")
 
-    shadow_synthetic_list_1 = [
-        train_result for train_result in fine_tuned_shadow_data_1[Key.FINE_TUNED_RESULTS.value]
-    ]
+    shadow_synthetic_list_1 = list(fine_tuned_shadow_data_1[Key.FINE_TUNED_RESULTS.value])
 
     shadow_model_gower_1 = get_rmia_gower(
         df_input=df_input,
@@ -331,10 +348,8 @@ def calculate_rmia_signals(
     )
     log(INFO, "Computed Gower distance for fine-tuned shadow models (second set of shadow models).")
 
-    shadow_synthetic_list_2 = [
-        train_result for train_result in trained_shadow_data[Key.TRAINED_RESULTS.value]
-    ]
-    
+    shadow_synthetic_list_2 = list(trained_shadow_data[Key.TRAINED_RESULTS.value])
+
     # shadow_synthetic_list_2 includes 8 shadow models
     shadow_model_gower_2 = get_rmia_gower(
         df_input=df_input,
@@ -346,7 +361,6 @@ def calculate_rmia_signals(
         n_jobs=4,
     )
 
-
     log(INFO, "Computed Gower distance for trained shadow models (third set of shadow models).")
 
     gower_shadows = np.vstack(
@@ -357,7 +371,6 @@ def calculate_rmia_signals(
     # ((total number of shadow models), len(df_input), len(shadow_synthetic))
     sorted_shadow_gower = np.sort(gower_shadows, axis=2)
 
-
     # TODO: check key after we have the official target model
     target_model_gower = get_rmia_gower(
         df_input=df_input,

From eeef70840e050e125239abf4fa50bfbd5f817028 Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Wed, 7 Jan 2026 11:13:26 -0500
Subject: [PATCH 27/41] Fixed unit tests

---
 examples/ensemble_attack/test_attack_model.py |  2 +-
 .../attacks/ensemble/rmia/rmia_calculation.py | 29 ++++++++++-----
 tests/unit/attacks/ensemble/test_rmia.py      | 37 +++++++++----------
 3 files changed, 38 insertions(+), 30 deletions(-)

diff --git a/examples/ensemble_attack/test_attack_model.py b/examples/ensemble_attack/test_attack_model.py
index dba5f323..f68d308f 100644
--- a/examples/ensemble_attack/test_attack_model.py
+++ b/examples/ensemble_attack/test_attack_model.py
@@ -20,7 +20,7 @@
 
 
 def save_results(
-    attack_results_path: Path, metaclassifier_model_name: str, probabilities: np.ndarray, pred_score: float
+    attack_results_path: Path, metaclassifier_model_name: str, probabilities: np.ndarray, pred_score: float|None
 ) -> None:
     """
     Saves the test prediction probabilities and metric results.
diff --git a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
index c0676c92..b79cc3c6 100644
--- a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
+++ b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
@@ -103,15 +103,17 @@ def compute_gower_for_model(
 
     df_synthetic = df_synthetic_raw.copy()
 
-    if id_column_name in df_synthetic.columns:
-        df_synthetic = df_synthetic.drop(columns=[id_column_name])
-
+    # Convert numerical columns to float (otherwise error in the numpy divide)
     df_synthetic[numerical_columns] = df_synthetic[numerical_columns].astype(dtype)
 
+    # Sample synthetic data points if there's too many
     if len(df_synthetic) > min_length:
         df_synthetic = df_synthetic.sample(n=min_length, random_state=random_seed)
 
-    # Batched computation: compute row-by-row to reduce peak memory
+    if id_column_name in df_synthetic.columns:
+        df_synthetic = df_synthetic.drop(columns=[id_column_name])
+
+    # Batched computation to reduce peak memory
     gower_matrix = compute_gower_batched(df_input, df_synthetic, categorical_features, batch_size=5000, dtype=dtype)
 
     return i, gower_matrix
@@ -124,6 +126,7 @@ def get_rmia_gower(
     categorical_column_names: list[str],
     id_column_name: str,
     random_seed: int | None = None,
+    use_multiprocessing: bool = True,
     n_jobs: int = 4,
     dtype: np.dtype = np.float32,
 ) -> list[np.ndarray]:
@@ -139,9 +142,10 @@ def get_rmia_gower(
         categorical_column_names: A list of categorical column names. We assume that all other columns are numerical.
         id_column_name: Name of the ID column.
         random_seed: Random seed for reproducibility.
-        n_jobs: Number of parallel jobs to use for computation. Default is 4.
-        dtype: The data type to which numerical columns will be cast for Gower distance computation.
-            Default is np.float32.
+        use_multiprocessing: Whether to use multiprocessing for parallel computation of Gower distances.
+        n_jobs: Number of parallel jobs to use for computation if ``use_multiprocessing`` is ``True``. Default is 4.
+        dtype: The data type to which numerical columns will be cast for Gower distance computation. np.float32 is more
+            memory efficient. Default is np.float32.
 
     Returns:
         A list of numpy arrays, each representing the Gower distance matrix between the input dataframe and the
@@ -181,10 +185,15 @@ def get_rmia_gower(
         for i, df_synthetic in enumerate(model_data)
     ]
 
-    # Process in parallel
+    # Process in parallel or sequentially based on use_multiprocessing
     results = {}
-    with Pool(processes=n_jobs) as pool:
-        for i, gower_matrix in pool.imap_unordered(compute_gower_for_model, args_list):
+    if use_multiprocessing:
+        with Pool(processes=n_jobs) as pool:
+            for i, gower_matrix in pool.imap_unordered(compute_gower_for_model, args_list):
+                results[i] = gower_matrix
+    else:
+        for args in args_list:
+            i, gower_matrix = compute_gower_for_model(args)
             results[i] = gower_matrix
 
     # Return in original order
diff --git a/tests/unit/attacks/ensemble/test_rmia.py b/tests/unit/attacks/ensemble/test_rmia.py
index 03ec8373..db7273eb 100644
--- a/tests/unit/attacks/ensemble/test_rmia.py
+++ b/tests/unit/attacks/ensemble/test_rmia.py
@@ -15,9 +15,6 @@
 )
 
 
-MockTrainingResult = namedtuple("TrainingResult", ["synthetic_data"])
-
-
 @pytest.fixture
 def base_data() -> dict[str, Any]:
     """Provides base data for testing."""
@@ -48,11 +45,11 @@ def base_data() -> dict[str, Any]:
 
     model_data = {
         "trained_results": [
-            MockTrainingResult(synthetic_data=df_syn1),
-            MockTrainingResult(synthetic_data=df_syn2),
+            df_syn1,
+            df_syn2,
         ],
         "fine_tuned_results": [
-            MockTrainingResult(synthetic_data=df_syn1),
+            df_syn1,
         ],
     }
 
@@ -81,19 +78,19 @@ def rmia_signal_data() -> dict[str, Any]:
     shadow_data_collection = [
         {
             "fine_tuning_sets": [train_set_0["id"].tolist()],
-            "fine_tuned_results": [MockTrainingResult(syn_data_5.copy())],
+            "fine_tuned_results": [syn_data_5.copy()],
         },
         {
             "fine_tuning_sets": [train_set_1["id"].tolist()],
-            "fine_tuned_results": [MockTrainingResult(syn_data_5.copy())],
+            "fine_tuned_results": [syn_data_5.copy()],
         },
         {
             "selected_sets": [train_set_2["id"].tolist()],
-            "trained_results": [MockTrainingResult(syn_data_5.copy())],
+            "trained_results": [syn_data_5.copy()],
         },
     ]
 
-    target_synthetic_data = MockTrainingResult(syn_data_5.copy()).synthetic_data
+    target_synthetic_data = syn_data_5.copy()
 
     return {
         "df_input": df_input,
@@ -153,7 +150,7 @@ def test_get_rmia_gower_basic_run(self, base_data, mocker):
 
         min_length = 3
         shadow_synthetic_list = [
-            train_result.synthetic_data for train_result in base_data["model_data"][Key.TRAINED_RESULTS.value]
+            train_result for train_result in base_data["model_data"][Key.TRAINED_RESULTS.value]
         ]
         results = get_rmia_gower(
             df_input=base_data["df_input"],
@@ -162,25 +159,26 @@ def test_get_rmia_gower_basic_run(self, base_data, mocker):
             categorical_column_names=base_data["categorical_column_names"],
             id_column_name=base_data["id_column_name"],
             random_seed=base_data["random_seed"],
+            use_multiprocessing=False,  # Disable multiprocessing to ensure mock is called as expected in the main process.
         )
 
         assert len(results) == 2
-        npt.assert_array_equal(results[0], np.array([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]]))
+        npt.assert_array_equal(results[0], np.array([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]], dtype=np.float32))
         npt.assert_array_equal(
             results[1],
-            np.array([[0.7, 0.8, 0.9], [0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]),
+            np.array([[0.7, 0.8, 0.9], [0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32),
         )
 
         assert mock_gower_matrix.call_count == 2
 
         call_args_1 = mock_gower_matrix.call_args_list[0].kwargs
         pdt.assert_frame_equal(call_args_1["data_x"], base_data["df_input"], check_dtype=False)
-        syn_data_1_dropped = base_data["model_data"]["trained_results"][0].synthetic_data.drop(columns=["id"])
+        syn_data_1_dropped = base_data["model_data"]["trained_results"][0].drop(columns=["id"])
         pdt.assert_frame_equal(call_args_1["data_y"], syn_data_1_dropped, check_dtype=False)
         assert call_args_1["cat_features"] == [False, True, False]
 
         call_args_2 = mock_gower_matrix.call_args_list[1].kwargs
-        syn_data_2_dropped = base_data["model_data"]["trained_results"][1].synthetic_data.drop(columns=["id"])
+        syn_data_2_dropped = base_data["model_data"]["trained_results"][1].drop(columns=["id"])
         pdt.assert_frame_equal(call_args_2["data_y"], syn_data_2_dropped, check_dtype=False)
 
     def test_get_rmia_gower_with_sampling(self, base_data, mocker):
@@ -190,12 +188,12 @@ def test_get_rmia_gower_with_sampling(self, base_data, mocker):
             return_value=np.array([[0.1], [0.2], [0.3]]),
         )
 
-        original_syn_data = base_data["model_data"]["trained_results"][1].synthetic_data
+        original_syn_data = base_data["model_data"]["trained_results"][1]
 
         mock_sample = mocker.patch("pandas.DataFrame.sample", wraps=original_syn_data.sample)
 
         min_length = 2
-        synthetic_data_list = [data.synthetic_data for data in base_data["model_data"][Key.TRAINED_RESULTS.value]]
+        synthetic_data_list = [data for data in base_data["model_data"][Key.TRAINED_RESULTS.value]]
         get_rmia_gower(
             df_input=base_data["df_input"],
             model_data=synthetic_data_list,
@@ -203,6 +201,7 @@ def test_get_rmia_gower_with_sampling(self, base_data, mocker):
             categorical_column_names=base_data["categorical_column_names"],
             id_column_name=base_data["id_column_name"],
             random_seed=base_data["random_seed"],
+            use_multiprocessing=False, # Disable multiprocessing to ensure mock is used in the main process
         )
 
         assert mock_gower_matrix.call_count == 2
@@ -212,7 +211,7 @@ def test_get_rmia_gower_with_sampling(self, base_data, mocker):
         expected_sampled_data = original_syn_data.sample(n=min_length, random_state=base_data["random_seed"]).drop(
             columns=[base_data["id_column_name"]]
         )
-        pdt.assert_frame_equal(call_args_2["data_y"], expected_sampled_data, check_dtype=False)
+        pdt.assert_frame_equal(call_args_2["data_y"], expected_sampled_data, check_dtype=False, obj=f"mistake in call args: {call_args_2['data_y'].columns} and {expected_sampled_data.columns}")
 
     def test_get_rmia_gower_missing_categorical_column(self, base_data, mocker, caplog):
         """Tests that a warning is logged for missing categorical columns."""
@@ -224,7 +223,7 @@ def test_get_rmia_gower_missing_categorical_column(self, base_data, mocker, capl
 
         with caplog.at_level("INFO"):
             synthetic_data_list = [
-                data.synthetic_data for data in base_data["model_data"][Key.FINE_TUNED_RESULTS.value]
+                data for data in base_data["model_data"][Key.FINE_TUNED_RESULTS.value]
             ]
             get_rmia_gower(
                 df_input=base_data["df_input"],

From f8962f87bee3ccbb5b81dcf42a8233e55f6a605d Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Wed, 7 Jan 2026 11:22:43 -0500
Subject: [PATCH 28/41] Fixed integration tests as shadow model training now
 only saves synthetic data

---
 .../attacks/ensemble/rmia/rmia_calculation.py |  6 ++--
 .../ensemble/test_shadow_model_training.py    | 29 +++++--------------
 2 files changed, 10 insertions(+), 25 deletions(-)

diff --git a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
index b79cc3c6..71175de1 100644
--- a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
+++ b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
@@ -270,9 +270,9 @@ def calculate_rmia_signals(
             models with their training data and generated synthetic outputs. Each collection can contain multiple
             shadow models. The first two dictionaries have keys ``fine_tuning_sets`` and ``fine_tuned_results``, while
             the third has keys ``selected_sets`` and ``trained_results``. The ``fine_tuning_sets`` and
-            ``selected_sets`` keys map to lists of DataFrames containing the data used to fine-tune or train the shadow
-            models. The ``fine_tuned_results`` and ``trained_results`` keys map to lists of ``TrainingResult`` objects,
-            which store model training metadata and the corresponding generated synthetic data.
+            ``selected_sets`` keys map to lists of DataFrames containing the data IDs used to fine-tune or train the shadow
+            models. The ``fine_tuned_results`` and ``trained_results`` keys map to lists of DataFrame objects,
+            which store model generated synthetic data.
             See ``train_three_sets_of_shadow_models`` in attacks/ensemble/rmia/shadow_model_training.py
             for additional details.
         target_synthetic_data: Target model's synthetic data as a DataFrame.
diff --git a/tests/integration/attacks/ensemble/test_shadow_model_training.py b/tests/integration/attacks/ensemble/test_shadow_model_training.py
index 354053c3..65caa7e6 100644
--- a/tests/integration/attacks/ensemble/test_shadow_model_training.py
+++ b/tests/integration/attacks/ensemble/test_shadow_model_training.py
@@ -66,17 +66,10 @@ def test_train_fine_tuned_shadow_models(cfg: DictConfig, tmp_path: Path) -> None
 
     assert len(shadow_data["fine_tuning_sets"]) == 2  # n_models
     assert len(shadow_data["fine_tuned_results"]) == 2  # n_models
-    for result in shadow_data["fine_tuned_results"]:
-        assert type(result) is TrainingResult
-        assert result.synthetic_data is not None
-        assert result.tables is not None
-        assert result.models is not None
-        assert result.configs is not None
-        assert result.save_dir is not None
-        assert result.relation_order is not None
-        assert result.all_group_lengths_probabilities is not None
-        assert type(result.synthetic_data) is pd.DataFrame
-        assert len(result.synthetic_data) == 5
+    for synthetic_data in shadow_data["fine_tuned_results"]:
+        assert type(synthetic_data) is pd.DataFrame
+        assert synthetic_data is not None
+        assert len(synthetic_data) == 5
 
     # Fine tuning sets should be disjoint
     assert set(shadow_data["fine_tuning_sets"][0]).isdisjoint(set(shadow_data["fine_tuning_sets"][1]))
@@ -112,17 +105,9 @@ def test_train_shadow_on_half_challenge_data(cfg: DictConfig, tmp_path: Path) ->
 
     assert len(shadow_data["selected_sets"]) == 2  # n_models
     assert len(shadow_data["trained_results"]) == 2  # n_models
-    for result in shadow_data["trained_results"]:
-        assert type(result) is TrainingResult
-        assert result.synthetic_data is not None
-        assert result.tables is not None
-        assert result.models is not None
-        assert result.configs is not None
-        assert result.save_dir is not None
-        assert result.relation_order is not None
-        assert result.all_group_lengths_probabilities is not None
-        assert type(result.synthetic_data) is pd.DataFrame
-        assert len(result.synthetic_data) == 5
+    for synthetic_data in shadow_data["trained_results"]:
+        assert type(synthetic_data) is pd.DataFrame
+        assert len(synthetic_data) == 5
 
     # Training sets should be disjoint
     assert set(shadow_data["selected_sets"][0]).isdisjoint(set(shadow_data["selected_sets"][1]))

From 37323c604baf3ff739bc7379500592b7efe471c8 Mon Sep 17 00:00:00 2001
From: ftavakoli <t.fateme@gmail.com>
Date: Wed, 7 Jan 2026 13:48:09 -0500
Subject: [PATCH 29/41] Fixed mypy errors

---
 examples/ensemble_attack/test_attack_model.py |  2 +-
 .../attacks/ensemble/rmia/rmia_calculation.py | 18 ++++++++---------
 .../ensemble/test_shadow_model_training.py    |  1 -
 tests/unit/attacks/ensemble/test_rmia.py      | 20 +++++++++----------
 4 files changed, 20 insertions(+), 21 deletions(-)

diff --git a/examples/ensemble_attack/test_attack_model.py b/examples/ensemble_attack/test_attack_model.py
index f68d308f..12e3a08d 100644
--- a/examples/ensemble_attack/test_attack_model.py
+++ b/examples/ensemble_attack/test_attack_model.py
@@ -20,7 +20,7 @@
 
 
 def save_results(
-    attack_results_path: Path, metaclassifier_model_name: str, probabilities: np.ndarray, pred_score: float|None
+    attack_results_path: Path, metaclassifier_model_name: str, probabilities: np.ndarray, pred_score: float | None
 ) -> None:
     """
     Saves the test prediction probabilities and metric results.
diff --git a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
index 71175de1..5a17fffd 100644
--- a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
+++ b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
@@ -24,7 +24,7 @@ class Key(Enum):
 def compute_gower_batched(
     df_x: pd.DataFrame,
     df_y: pd.DataFrame,
-    cat_features: list[str],
+    cat_features: list[bool],
     batch_size: int,
     dtype: np.dtype,
 ) -> np.ndarray:
@@ -37,7 +37,7 @@ def compute_gower_batched(
     Args:
         df_x: First dataframe.
         df_y: Second dataframe.
-        cat_features: List containing categorical columns.
+        cat_features: A boolean list indicating which columns are categorical.
         batch_size: Number of rows from df_x to process in each batch.
         dtype: The data type to which numerical columns will be cast.
 
@@ -67,7 +67,7 @@ def compute_gower_batched(
 
 
 def compute_gower_for_model(
-    args: tuple[int, pd.DataFrame, pd.DataFrame, int, str, int | None, list[str], list[str], np.dtype],
+    args: tuple[int, pd.DataFrame, pd.DataFrame, int, str, int | None, list[bool], list[str], np.dtype],
 ) -> tuple[int, np.ndarray]:
     """
     Computes the Gower distance matrix between df_input and a single synthetic dataframe.
@@ -80,7 +80,7 @@ def compute_gower_for_model(
             - min_length: Minimum length for downsampling.
             - id_column_name: Name of the ID column.
             - random_seed: Random seed for reproducibility.
-            - categorical_features: A list of input dataframe's categorical features.
+            - categorical_features: A boolean list indicating which columns are categorical.
             - numerical_columns: List of numerical column names.
             - dtype: The data type to which numerical columns will be cast.
 
@@ -128,7 +128,7 @@ def get_rmia_gower(
     random_seed: int | None = None,
     use_multiprocessing: bool = True,
     n_jobs: int = 4,
-    dtype: np.dtype = np.float32,
+    dtype: np.dtype = np.dtype(np.float32),
 ) -> list[np.ndarray]:
     """
     Computes the Gower distance between the challenge points and the synthetic data generated by the shadow models.
@@ -270,10 +270,10 @@ def calculate_rmia_signals(
             models with their training data and generated synthetic outputs. Each collection can contain multiple
             shadow models. The first two dictionaries have keys ``fine_tuning_sets`` and ``fine_tuned_results``, while
             the third has keys ``selected_sets`` and ``trained_results``. The ``fine_tuning_sets`` and
-            ``selected_sets`` keys map to lists of DataFrames containing the data IDs used to fine-tune or train the shadow
-            models. The ``fine_tuned_results`` and ``trained_results`` keys map to lists of DataFrame objects,
-            which store model generated synthetic data.
-            See ``train_three_sets_of_shadow_models`` in attacks/ensemble/rmia/shadow_model_training.py
+            ``selected_sets`` keys map to lists of DataFrames containing the data IDs used to fine-tune or train
+            the shadow models. The ``fine_tuned_results`` and ``trained_results`` keys map to lists of
+            DataFrame objects, which store model generated synthetic data.
+            See ``train_three_sets_of_shadow_models`` in ``attacks/ensemble/rmia/shadow_model_training.py``
             for additional details.
         target_synthetic_data: Target model's synthetic data as a DataFrame.
         categorical_column_names: A list of categorical column names.
diff --git a/tests/integration/attacks/ensemble/test_shadow_model_training.py b/tests/integration/attacks/ensemble/test_shadow_model_training.py
index 65caa7e6..4e2e6eb9 100644
--- a/tests/integration/attacks/ensemble/test_shadow_model_training.py
+++ b/tests/integration/attacks/ensemble/test_shadow_model_training.py
@@ -14,7 +14,6 @@
     train_shadow_on_half_challenge_data,
 )
 from midst_toolkit.attacks.ensemble.shadow_model_utils import (
-    TrainingResult,
     fine_tune_tabddpm_and_synthesize,
     save_additional_tabddpm_config,
     train_tabddpm_and_synthesize,
diff --git a/tests/unit/attacks/ensemble/test_rmia.py b/tests/unit/attacks/ensemble/test_rmia.py
index db7273eb..dfcb5021 100644
--- a/tests/unit/attacks/ensemble/test_rmia.py
+++ b/tests/unit/attacks/ensemble/test_rmia.py
@@ -1,4 +1,3 @@
-from collections import namedtuple
 from typing import Any
 
 import numpy as np
@@ -149,9 +148,7 @@ def test_get_rmia_gower_basic_run(self, base_data, mocker):
         )
 
         min_length = 3
-        shadow_synthetic_list = [
-            train_result for train_result in base_data["model_data"][Key.TRAINED_RESULTS.value]
-        ]
+        shadow_synthetic_list = list(base_data["model_data"][Key.TRAINED_RESULTS.value])
         results = get_rmia_gower(
             df_input=base_data["df_input"],
             model_data=shadow_synthetic_list,
@@ -193,7 +190,7 @@ def test_get_rmia_gower_with_sampling(self, base_data, mocker):
         mock_sample = mocker.patch("pandas.DataFrame.sample", wraps=original_syn_data.sample)
 
         min_length = 2
-        synthetic_data_list = [data for data in base_data["model_data"][Key.TRAINED_RESULTS.value]]
+        synthetic_data_list = list(base_data["model_data"][Key.TRAINED_RESULTS.value])
         get_rmia_gower(
             df_input=base_data["df_input"],
             model_data=synthetic_data_list,
@@ -201,7 +198,7 @@ def test_get_rmia_gower_with_sampling(self, base_data, mocker):
             categorical_column_names=base_data["categorical_column_names"],
             id_column_name=base_data["id_column_name"],
             random_seed=base_data["random_seed"],
-            use_multiprocessing=False, # Disable multiprocessing to ensure mock is used in the main process
+            use_multiprocessing=False,  # Disable multiprocessing to ensure mock is used in the main process
         )
 
         assert mock_gower_matrix.call_count == 2
@@ -211,7 +208,12 @@ def test_get_rmia_gower_with_sampling(self, base_data, mocker):
         expected_sampled_data = original_syn_data.sample(n=min_length, random_state=base_data["random_seed"]).drop(
             columns=[base_data["id_column_name"]]
         )
-        pdt.assert_frame_equal(call_args_2["data_y"], expected_sampled_data, check_dtype=False, obj=f"mistake in call args: {call_args_2['data_y'].columns} and {expected_sampled_data.columns}")
+        pdt.assert_frame_equal(
+            call_args_2["data_y"],
+            expected_sampled_data,
+            check_dtype=False,
+            obj=f"mistake in call args: {call_args_2['data_y'].columns} and {expected_sampled_data.columns}",
+        )
 
     def test_get_rmia_gower_missing_categorical_column(self, base_data, mocker, caplog):
         """Tests that a warning is logged for missing categorical columns."""
@@ -222,9 +224,7 @@ def test_get_rmia_gower_missing_categorical_column(self, base_data, mocker, capl
         missing_cat_cols = ["city", "non_existent_column"]
 
         with caplog.at_level("INFO"):
-            synthetic_data_list = [
-                data for data in base_data["model_data"][Key.FINE_TUNED_RESULTS.value]
-            ]
+            synthetic_data_list = list(base_data["model_data"][Key.FINE_TUNED_RESULTS.value])
             get_rmia_gower(
                 df_input=base_data["df_input"],
                 model_data=synthetic_data_list,

From ee4b6590432c8164043d70596b8f8101bd514347 Mon Sep 17 00:00:00 2001
From: fatemetkl <t.fateme@gmail.com>
Date: Thu, 8 Jan 2026 11:20:36 -0700
Subject: [PATCH 30/41] mypy and ruff fixes

---
 examples/ensemble_attack/test_attack_model.py |  2 +-
 .../attacks/ensemble/rmia/rmia_calculation.py | 25 +++++++++++++++----
 2 files changed, 21 insertions(+), 6 deletions(-)

diff --git a/examples/ensemble_attack/test_attack_model.py b/examples/ensemble_attack/test_attack_model.py
index 12e3a08d..83aeca87 100644
--- a/examples/ensemble_attack/test_attack_model.py
+++ b/examples/ensemble_attack/test_attack_model.py
@@ -88,7 +88,7 @@ def run_rmia_shadow_training(config: DictConfig, df_challenge: pd.DataFrame) ->
         A list containing three dictionaries, each representing a collection of shadow
             models with their training data and generated synthetic outputs.
     """
-    shadow_model_paths = run_shadow_model_training(config, df_master_challenge_train=df_challenge)
+    shadow_model_paths = run_shadow_model_training(config, df_challenge_train=df_challenge)
     shadow_model_paths = [Path(path) for path in config.shadow_training.final_shadow_models_path]
 
     assert len(shadow_model_paths) == 3, "For testing, meta classifier needs the path to three sets of shadow models."
diff --git a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
index 5a17fffd..0f3f3132 100644
--- a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
+++ b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
@@ -4,10 +4,11 @@
 https://github.com/CRCHUM-CITADEL/ensemble-mia.
 """
 
+from collections.abc import Sequence
 from enum import Enum
 from logging import INFO
 from multiprocessing import Pool
-from typing import Any
+from typing import Any, TypeAlias
 
 import gower
 import numpy as np
@@ -16,6 +17,9 @@
 from midst_toolkit.common.logger import log
 
 
+FloatDType: TypeAlias = type[np.float32] | type[np.float64]
+
+
 class Key(Enum):
     TRAINED_RESULTS = "trained_results"
     FINE_TUNED_RESULTS = "fine_tuned_results"
@@ -26,7 +30,7 @@ def compute_gower_batched(
     df_y: pd.DataFrame,
     cat_features: list[bool],
     batch_size: int,
-    dtype: np.dtype,
+    dtype: FloatDType,
 ) -> np.ndarray:
     """
     Compute Gower distance matrix in batches to reduce peak memory usage.
@@ -67,7 +71,17 @@ def compute_gower_batched(
 
 
 def compute_gower_for_model(
-    args: tuple[int, pd.DataFrame, pd.DataFrame, int, str, int | None, list[bool], list[str], np.dtype],
+    args: tuple[
+        int,
+        pd.DataFrame,
+        pd.DataFrame,
+        int,
+        str,
+        int | None,
+        list[bool],
+        Sequence[str],
+        FloatDType,
+    ],
 ) -> tuple[int, np.ndarray]:
     """
     Computes the Gower distance matrix between df_input and a single synthetic dataframe.
@@ -128,7 +142,7 @@ def get_rmia_gower(
     random_seed: int | None = None,
     use_multiprocessing: bool = True,
     n_jobs: int = 4,
-    dtype: np.dtype = np.dtype(np.float32),
+    dtype: FloatDType = np.float32,
 ) -> list[np.ndarray]:
     """
     Computes the Gower distance between the challenge points and the synthetic data generated by the shadow models.
@@ -165,7 +179,8 @@ def get_rmia_gower(
 
     categorical_features = [column in categorical_column_names for column in df_input.columns]
 
-    numerical_columns = [col for col in df_input.columns if col not in categorical_column_names]
+    # Defined the type as sequence to address mypy error.
+    numerical_columns: Sequence[str] = [col for col in df_input.columns if col not in categorical_column_names]
 
     df_input[numerical_columns] = df_input[numerical_columns].astype(dtype)
 

From eea6ea56710ea5f7bed84ce068e43b3545f7f0b7 Mon Sep 17 00:00:00 2001
From: fatemetkl <t.fateme@gmail.com>
Date: Thu, 8 Jan 2026 12:06:55 -0700
Subject: [PATCH 31/41] Small change

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index f55eb2d2..fda1525f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -134,4 +134,4 @@ max-doc-length = 119
 [tool.pytest.ini_options]
 markers = [
     "integration_test: marks tests as integration tests",
-]
+]
\ No newline at end of file

From d598206dad082d62d3baa2c5fe09b89fcc7a2542 Mon Sep 17 00:00:00 2001
From: fatemetkl <t.fateme@gmail.com>
Date: Thu, 8 Jan 2026 12:14:46 -0700
Subject: [PATCH 32/41] extra line

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index fda1525f..f55eb2d2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -134,4 +134,4 @@ max-doc-length = 119
 [tool.pytest.ini_options]
 markers = [
     "integration_test: marks tests as integration tests",
-]
\ No newline at end of file
+]

From d14b22f5e2e942b70c81ba62e3facab56f16e262 Mon Sep 17 00:00:00 2001
From: fatemetkl <t.fateme@gmail.com>
Date: Thu, 8 Jan 2026 13:51:36 -0700
Subject: [PATCH 33/41] Merged main into the brand

---
 uv.lock | 2722 +++++++++++++++++++++++++++----------------------------
 1 file changed, 1359 insertions(+), 1363 deletions(-)

diff --git a/uv.lock b/uv.lock
index c77886cf..8003cec2 100644
--- a/uv.lock
+++ b/uv.lock
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+]
\ No newline at end of file

From 9afda5dbbbf34cef4f2465a705ac06b9823e4d37 Mon Sep 17 00:00:00 2001
From: fatemetkl <t.fateme@gmail.com>
Date: Thu, 8 Jan 2026 14:57:03 -0700
Subject: [PATCH 34/41] End of file extra line

---
 uv.lock | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/uv.lock b/uv.lock
index 8003cec2..c7985e19 100644
--- a/uv.lock
+++ b/uv.lock
@@ -3184,4 +3184,4 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/70/2f/5418f4b1deaf0886caf81c5e056299228ac2fc09b965a2dfe5e4496331c8/xgboost-3.1.2-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:f9b83f39340e5852bbf3e918318e7feb7a2a700ac7e8603f9bc3a06787f0d86b", size = 4953319, upload-time = "2025-11-20T18:28:29.851Z" },
     { url = "https://files.pythonhosted.org/packages/5e/ab/c60fcc137fa685533bb31e721de3ecc88959d393830d59d0204c5cbd2c85/xgboost-3.1.2-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:24879ac75c0ee21acae0101f791bc43303f072a86d70fdfc89dae10a0008767f", size = 115885060, upload-time = "2025-11-20T18:32:00.773Z" },
     { url = "https://files.pythonhosted.org/packages/30/7d/41847e45ff075f3636c95d1000e0b75189aed4f1ae18c36812575bb42b4b/xgboost-3.1.2-py3-none-win_amd64.whl", hash = "sha256:e627c50003269b4562aa611ed348dff8cb770e11a9f784b3888a43139a0f5073", size = 71979118, upload-time = "2025-11-20T18:27:55.23Z" },
-]
\ No newline at end of file
+]

From 72c0e909109a2d3be7cb457a95f258757f8ac15c Mon Sep 17 00:00:00 2001
From: fatemetkl <t.fateme@gmail.com>
Date: Fri, 9 Jan 2026 11:58:21 -0700
Subject: [PATCH 35/41] Improved documentation a bit

---
 .../configs/experiment_config.yaml            |  1 +
 .../ensemble_attack/real_data_collection.py   |  8 ++-
 examples/ensemble_attack/run_attack.py        |  6 +-
 examples/ensemble_attack/test_attack_model.py | 62 ++++++++++++-------
 4 files changed, 48 insertions(+), 29 deletions(-)

diff --git a/examples/ensemble_attack/configs/experiment_config.yaml b/examples/ensemble_attack/configs/experiment_config.yaml
index f30b5b0e..ebae1384 100644
--- a/examples/ensemble_attack/configs/experiment_config.yaml
+++ b/examples/ensemble_attack/configs/experiment_config.yaml
@@ -20,6 +20,7 @@ target_model: # This is only used for testing the attack on a real target model.
   target_shadow_models_output_path: ${base_experiment_dir}/test_all_targets # Sub-directory to store test shadows and results
   attack_probabilities_result_path: ${target_model.target_shadow_models_output_path}/test_probabilities/attack_model_${target_model.target_model_id}_proba
   attack_rmia_shadow_training_data_choice: "combined" # Options: "combined", "only_challenge", "only_train". This determines which data to use for training RMIA attack model in testing phase.
+  # 
 
 
 # Data paths
diff --git a/examples/ensemble_attack/real_data_collection.py b/examples/ensemble_attack/real_data_collection.py
index 11d2f159..214060d2 100644
--- a/examples/ensemble_attack/real_data_collection.py
+++ b/examples/ensemble_attack/real_data_collection.py
@@ -151,9 +151,11 @@ def collect_population_data_ensemble(
     """
     Collect the population data from the MIDST competition based on Ensemble Attack implementation.
     Returns real data population that consists of the train data of all the attacks
-    (black box and white box), and challenge points from `train`, `dev` and `final` of
-    "tabddpm_black_box" attack. The population data is saved in the provided path,
-    and returned as a dataframe.
+    (black box and white box) as specified in ``data_processing_config.population_attack_data_types_to_collect``
+    , and challenge points from `train`, `dev` and `final` of attacks as specified by
+    ``data_processing_config.challenge_attack_data_types_to_collect``. The collected population data is concatenated
+    with ``original_repo_population`` to be large enough for the attack (specially DOMIAS) and then is saved in
+    the provided path, and returned as a dataframe.
 
     Args:
         midst_data_input_dir: The path where the MIDST data folders are stored.
diff --git a/examples/ensemble_attack/run_attack.py b/examples/ensemble_attack/run_attack.py
index ec4d9d24..75fe7254 100644
--- a/examples/ensemble_attack/run_attack.py
+++ b/examples/ensemble_attack/run_attack.py
@@ -24,8 +24,10 @@ def run_data_processing(config: DictConfig) -> None:
     Args:
         config: Configuration object set in config.yaml.
     """
-    # Load original repo's population
-
+    # Load original repo's population to be concatenated to the experiment's population data.
+    # This is done to align the experiments with the original attack code because
+    # this attack needs a large population dataset, and only using the experiment's collected population
+    # is not enough.
     original_population_data = load_dataframe(
         Path(config.data_processing_config.original_population_data_path),
         "population_all_with_challenge.csv",
diff --git a/examples/ensemble_attack/test_attack_model.py b/examples/ensemble_attack/test_attack_model.py
index 83aeca87..bbacd737 100644
--- a/examples/ensemble_attack/test_attack_model.py
+++ b/examples/ensemble_attack/test_attack_model.py
@@ -44,11 +44,12 @@ def save_results(
             f.write(f"TPR at FPR=0.1: {pred_score:.4f}\n")
 
 
-def extract_and_drop_id_columns(
-    data_frame: pd.DataFrame, data_types_file_path: Path
+def extract_and_drop_id_column(
+    data_frame: pd.DataFrame, data_types_file_path: Path,
 ) -> tuple[pd.DataFrame, pd.Series]:
     """
-    Extracts IDs from the data frame and drops all ID columns.
+    Extracts IDs from the data frame and drops the ID column. ID column is identified based on
+    the data types JSON file with "id_column_name" key.
 
     Args:
         data_frame: Input data frame.
@@ -57,19 +58,18 @@ def extract_and_drop_id_columns(
     Returns:
         A tuple containing:
             - The modified data frame with ID columns dropped.
-            - A Series containing the extracted transaction IDs.
+            - A Series containing the extracted data of ID columns.
     """
-    # Extract trans_id from the dataframe
+    # Extract ID column from the dataframe
     with open(data_types_file_path, "r") as f:
         column_types = json.load(f)
     id_column_name = column_types["id_column_name"]
 
-    assert id_column_name in data_frame.columns, f"Test data must have {id_column_name} column"
+    assert id_column_name in data_frame.columns, f"Dataframe must have {id_column_name} column"
     data_trans_ids = data_frame[id_column_name]
 
-    # Drop id columns from data
-    id_column_names = [column_name for column_name in data_frame.columns if column_name.endswith("_id")]
-    data_frame = data_frame.drop(columns=id_column_names)
+    # Drop ID column from data
+    data_frame = data_frame.drop(columns=id_column_name)
 
     return data_frame, data_trans_ids
 
@@ -78,7 +78,9 @@ def run_rmia_shadow_training(config: DictConfig, df_challenge: pd.DataFrame) ->
     """
     Three sets of shadow models will be trained as a part of this attack.
     Note that shadow models need to be trained on the collection of challenge points once and used
-    for all the target models in a setting.
+    for all the target models in a setting. In other words, in a standard setting, the
+    testing points (experiment challenge points) are used as training or included in training data of the shadow models,
+    and these shadow models are used to attack all target models.
 
     Args:
         config: Configuration object set in ``experiments_config.yaml``.
@@ -110,7 +112,8 @@ def load_trained_rmia_shadows_for_test_phase(
     shadow_data_paths: list[Path],
 ) -> tuple[list[dict[str, list[Any]]], bool]:
     """
-    Loads previously trained RMIA shadow models for the testing phase.
+    Loads previously trained RMIA shadow models for the testing phase. Makes sure
+    all shadow models exist before loading. Otherwise, returns an empty list and False.
 
     Args:
         shadow_data_paths: List of paths to the saved shadow model data.
@@ -136,7 +139,7 @@ def load_trained_rmia_shadows_for_test_phase(
     return shadow_data_collection, models_exists
 
 
-def collect_challenge_data(
+def collect_challenge_and_train_data(
     data_processing_config: DictConfig, processed_attack_data_path: Path, targets_data_path: Path
 ) -> tuple[pd.DataFrame, pd.DataFrame]:
     """
@@ -183,11 +186,17 @@ def select_challenge_data_for_training(
 ) -> pd.DataFrame:
     """
     Select the appropriate challenge data based on config choice.
-
     Args:
         attack_rmia_shadow_training_data_choice: Strategy for creating challenge train data for RMIA shadow training.
-        df_challenge_experiment: Challenge experiment data.
-        df_master_train: Master train data.
+            It can be one of the following:
+            - "only_challenge": Use only challenge experiment data.
+            - "only_train": Use only master train data. Note that this option contracts with the original
+                design and purpose of training RMIA shadow models on the challenge points as RMIA signals (IN train signals)
+                for challenge points could only be computed if shadow models are trained on these points.
+            - "combined": Combine both challenge experiment data and master train data. This can potentially be advantages
+                based on the experiments as RMIA shadows are trained on more data points.
+        df_challenge_experiment: Challenge points in this experiment.
+        df_master_train: Master train data used to train the meta classifier.
 
     Raises:
         ValueError: If an invalid choice is provided.
@@ -216,6 +225,7 @@ def select_challenge_data_for_training(
 def train_rmia_shadows_for_test_phase(config: DictConfig) -> list[dict[str, list[Any]]]:
     """
     Function to train RMIA shadow models for the testing phase using the dataset containing challenge data points.
+    Note that 
 
     Args:
         config: Configuration object set in ``experiments_config.yaml``.
@@ -224,11 +234,12 @@ def train_rmia_shadows_for_test_phase(config: DictConfig) -> list[dict[str, list
         A list containing three dictionaries, each representing a collection of shadow
             models with their training data IDs and generated synthetic outputs.
     """
-    df_challenge_experiment, df_master_train = collect_challenge_data(
+    df_challenge_experiment, df_master_train = collect_challenge_and_train_data(
         config.data_processing_config,
         processed_attack_data_path=Path(config.data_paths.processed_attack_data_path),
         targets_data_path=Path(config.data_paths.midst_data_path),
     )
+    # Load the challenge dataframe for training RMIA shadow models.
     df_challenge = select_challenge_data_for_training(
         str(config.target_model.attack_rmia_shadow_training_data_choice), df_challenge_experiment, df_master_train
     )
@@ -299,12 +310,13 @@ def run_metaclassifier_testing(
     # 3) Shadow Model Training Step.
     # Make sure to assign a new path for shadow models trained for target's challenge points to
     # avoid overriding train's shadow models.
+    # TODO: Assign specific shadow collection path for test phase.
     config.shadow_training.shadow_models_output_path = config.target_model.target_shadow_models_output_path
     shadow_data_paths = [Path(path) for path in config.shadow_training.final_shadow_models_path]
-    # if already trained for test, don't need to train again
-    # Load shadow training collection from previously trained shadow models.
     assert len(shadow_data_paths) == 3, "The attack_data_paths list must contain exactly three elements."
 
+    # If shadows are already trained for test (models_exists is True), don't need to train again.
+    # Load shadow training collection from previously trained shadow models.
     shadow_data_collection, models_exists = load_trained_rmia_shadows_for_test_phase(shadow_data_paths)
 
     if not models_exists:
@@ -315,16 +327,11 @@ def run_metaclassifier_testing(
         log(INFO, "All shadow models for testing phase found. Using existing RMIA shadow models...")
 
     # Extract and drop id columns from the test data
-    test_data, test_trans_ids = extract_and_drop_id_columns(
+    test_data, test_trans_ids = extract_and_drop_id_column(
         test_data, Path(config.metaclassifier.data_types_file_path)
     )
 
     # 4) Initialize the attacker object, and assign the loaded metaclassifier to it.
-    df_reference = load_dataframe(
-        Path(config.data_paths.population_path),
-        "population_all_with_challenge_no_id.csv",
-    )
-
     blending_attacker = BlendingPlusPlus(
         config=config,
         shadow_data_collection=shadow_data_collection,
@@ -337,6 +344,13 @@ def run_metaclassifier_testing(
     blending_attacker.trained_model = trained_mataclassifier_model
 
     # 5) Get predictions on the challenge data (test set).
+
+    # Load the reference population data for DOMIAS signals.
+    df_reference = load_dataframe(
+        Path(config.data_paths.population_path),
+        "population_all_with_challenge_no_id.csv",
+    )
+
     probabilities, pred_score = blending_attacker.predict(
         df_test=test_data,
         df_original_synthetic=target_synthetic_data,

From 34d81bafd73ee06fd502027bda2ace20bf6d5d27 Mon Sep 17 00:00:00 2001
From: fatemetkl <t.fateme@gmail.com>
Date: Mon, 12 Jan 2026 08:12:46 -0700
Subject: [PATCH 36/41] Added more documentations

---
 .../configs/experiment_config.yaml            |  1 -
 .../ensemble_attack/real_data_collection.py   | 43 +++++++++----------
 examples/ensemble_attack/test_attack_model.py | 26 +++++------
 .../attacks/ensemble/rmia/rmia_calculation.py |  6 +--
 4 files changed, 36 insertions(+), 40 deletions(-)

diff --git a/examples/ensemble_attack/configs/experiment_config.yaml b/examples/ensemble_attack/configs/experiment_config.yaml
index ebae1384..f30b5b0e 100644
--- a/examples/ensemble_attack/configs/experiment_config.yaml
+++ b/examples/ensemble_attack/configs/experiment_config.yaml
@@ -20,7 +20,6 @@ target_model: # This is only used for testing the attack on a real target model.
   target_shadow_models_output_path: ${base_experiment_dir}/test_all_targets # Sub-directory to store test shadows and results
   attack_probabilities_result_path: ${target_model.target_shadow_models_output_path}/test_probabilities/attack_model_${target_model.target_model_id}_proba
   attack_rmia_shadow_training_data_choice: "combined" # Options: "combined", "only_challenge", "only_train". This determines which data to use for training RMIA attack model in testing phase.
-  # 
 
 
 # Data paths
diff --git a/examples/ensemble_attack/real_data_collection.py b/examples/ensemble_attack/real_data_collection.py
index 214060d2..b7e6e615 100644
--- a/examples/ensemble_attack/real_data_collection.py
+++ b/examples/ensemble_attack/real_data_collection.py
@@ -61,18 +61,15 @@ def collect_midst_attack_data(
     Args:
         attack_type: The attack setting.
         data_dir: The path where the data is stored.
-        data_split: Indicates if this is train, dev, or final data.
+        data_split: Indicates if this is train, dev, or final data. Note that, this is in fact
+            the name of the folder that contains model folders for data collection. For example,
+            f"{generation_name}_{i}" should be located under ``data_split`` folder.
         dataset: The dataset to be collected. Either "train" or "challenge".
         data_processing_config: Configuration dictionary containing data specific information.
 
     Returns:
         pd.DataFrame: The specified dataset in this setting.
     """
-    # assert data_split in [
-    #     "train",
-    #     "dev",
-    #     "final",
-    # ], "data_split should be one of 'train', 'dev', or 'final'."
     # `data_id` is the folder numbering of each training or challenge dataset,
     #  and is defined with the provided config.
     data_id = expand_ranges(data_processing_config.folder_ranges[data_split])
@@ -82,7 +79,7 @@ def collect_midst_attack_data(
     generation_name = attack_type.value.split("_")[0]
     if dataset == "challenge":
         file_name = data_processing_config.challenge_data_file_name
-    else:  # dataset == "train"
+    else:
         # Multi-table attacks have different file names.
         file_name = (
             data_processing_config.multi_table_train_data_file_name
@@ -110,13 +107,18 @@ def collect_midst_data(
 ) -> pd.DataFrame:
     """
     Collect train or challenge data of the specified attack type from the provided data folders
-    in the MIDST competition.
+    in the MIDST competition. The data is going to be collected from all the folders specified
+    in ``data_splits`` argument under each attack type folder. For example, if ``data_splits``
+    contains `train` and `dev`, the function collects data from both `train` and `dev` folders
+    under each attack type folder. For more information about the data collection structure, see
+    the implementation of ``collect_midst_attack_data`` function.
 
     Args:
         midst_data_input_dir: The path where the MIDST data folders are stored.
         attack_types: List of attack types for data collection.
-        data_splits: A list indicating the data split to be collected.
-            Could be any of train, dev, or final data splits.
+        data_splits: A list indicating the data split to be collected. This is a list of folder names
+            under each attack type folder where we collect model's data from. For example, it could
+            contain strings like `train`, `dev`, `final`, or `test` based on the directory structure.
         dataset: The dataset to be collected. Either `train` or `challenge`.
         data_processing_config: Configuration dictionary containing data paths and file names.
 
@@ -163,17 +165,13 @@ def collect_population_data_ensemble(
         save_dir: The path where the collected population data should be saved.
         original_repo_population: The original population data collected from the MIDST challenge repository.
         population_splits: A list indicating the data splits to be collected for population data.
-            Could be any of `train`, `dev`, or `final` data splits. If None, the default list of ``["train"]``
-            is set in the function based on the original attack implementation.
+            This is basically a list of strings containing the folder names under attack folders that are
+            considered for population collection. If None, the default list of ``["train"]`` is set in the
+            function based on the original attack implementation.
         challenge_splits: A list indicating the data splits to be collected for challenge points.
-            Could be any of `train`, `dev`, or `final` data splits. If None, the default list of
-            ``["train", "dev", "final"]`` is set in the function based on the original attack implementation.
-        population_splits: A list indicating the data splits to be collected for population data.
-            Could be any of `train`, `dev`, or `final` data splits. If None, the default list of ``["train"]``
+            This is basically a list of strings containing the folder names under attack folders that are
+            considered for challenge data collection. If None, the default list of ``["train", "dev", "final"]``
             is set in the function based on the original attack implementation.
-        challenge_splits: A list indicating the data splits to be collected for challenge points.
-            Could be any of `train`, `dev`, or `final` data splits. If None, the default list of
-            ``["train", "dev", "final"]`` is set in the function based on the original attack implementation.
 
     Returns:
         The collected population data as a dataframe.
@@ -197,9 +195,9 @@ def collect_population_data_ensemble(
         challenge_splits = ["train", "dev", "final"]
 
     # Ensemble Attack collects train data of all the attack types (black box and white box)
-    attack_names = data_processing_config.population_attack_data_types_to_collect
+    population_attack_names = data_processing_config.population_attack_data_types_to_collect
     # Provided attack name are valid based on AttackType enum
-    population_attack_types: list[AttackType] = [AttackType(attack_name) for attack_name in attack_names]
+    population_attack_types: list[AttackType] = [AttackType(attack_name) for attack_name in population_attack_names]
 
     df_population_experiment = collect_midst_data(
         midst_data_input_dir,
@@ -222,8 +220,7 @@ def collect_population_data_ensemble(
 
     challenge_attack_names = data_processing_config.challenge_attack_data_types_to_collect
     challenge_attack_types = [AttackType(attack_name) for attack_name in challenge_attack_names]
-    challenge_attack_names = data_processing_config.challenge_attack_data_types_to_collect
-    challenge_attack_types = [AttackType(attack_name) for attack_name in challenge_attack_names]
+
     df_challenge = collect_midst_data(
         midst_data_input_dir,
         attack_types=challenge_attack_types,
diff --git a/examples/ensemble_attack/test_attack_model.py b/examples/ensemble_attack/test_attack_model.py
index bbacd737..00c4406f 100644
--- a/examples/ensemble_attack/test_attack_model.py
+++ b/examples/ensemble_attack/test_attack_model.py
@@ -45,7 +45,8 @@ def save_results(
 
 
 def extract_and_drop_id_column(
-    data_frame: pd.DataFrame, data_types_file_path: Path,
+    data_frame: pd.DataFrame,
+    data_types_file_path: Path,
 ) -> tuple[pd.DataFrame, pd.Series]:
     """
     Extracts IDs from the data frame and drops the ID column. ID column is identified based on
@@ -79,8 +80,8 @@ def run_rmia_shadow_training(config: DictConfig, df_challenge: pd.DataFrame) ->
     Three sets of shadow models will be trained as a part of this attack.
     Note that shadow models need to be trained on the collection of challenge points once and used
     for all the target models in a setting. In other words, in a standard setting, the
-    testing points (experiment challenge points) are used as training or included in training data of the shadow models,
-    and these shadow models are used to attack all target models.
+    testing points (experiment challenge points) are used as training or included in training data
+    of the shadow models, and these shadow models are used to attack all target models.
 
     Args:
         config: Configuration object set in ``experiments_config.yaml``.
@@ -186,15 +187,18 @@ def select_challenge_data_for_training(
 ) -> pd.DataFrame:
     """
     Select the appropriate challenge data based on config choice.
+
     Args:
         attack_rmia_shadow_training_data_choice: Strategy for creating challenge train data for RMIA shadow training.
             It can be one of the following:
             - "only_challenge": Use only challenge experiment data.
             - "only_train": Use only master train data. Note that this option contracts with the original
-                design and purpose of training RMIA shadow models on the challenge points as RMIA signals (IN train signals)
-                for challenge points could only be computed if shadow models are trained on these points.
-            - "combined": Combine both challenge experiment data and master train data. This can potentially be advantages
-                based on the experiments as RMIA shadows are trained on more data points.
+                design and purpose of training RMIA shadow models on the challenge points as
+                RMIA signals (IN train signals) for challenge points could only be computed if
+                shadow models are trained on these points.
+            - "combined": Combine both challenge experiment data and master train data. This can
+                potentially be advantages based on the experiments as RMIA shadows are trained on
+                more data points.
         df_challenge_experiment: Challenge points in this experiment.
         df_master_train: Master train data used to train the meta classifier.
 
@@ -224,8 +228,8 @@ def select_challenge_data_for_training(
 
 def train_rmia_shadows_for_test_phase(config: DictConfig) -> list[dict[str, list[Any]]]:
     """
-    Function to train RMIA shadow models for the testing phase using the dataset containing challenge data points.
-    Note that 
+    Function to train RMIA shadow models for the testing phase using the dataset containing
+    challenge data points.
 
     Args:
         config: Configuration object set in ``experiments_config.yaml``.
@@ -327,9 +331,7 @@ def run_metaclassifier_testing(
         log(INFO, "All shadow models for testing phase found. Using existing RMIA shadow models...")
 
     # Extract and drop id columns from the test data
-    test_data, test_trans_ids = extract_and_drop_id_column(
-        test_data, Path(config.metaclassifier.data_types_file_path)
-    )
+    test_data, test_trans_ids = extract_and_drop_id_column(test_data, Path(config.metaclassifier.data_types_file_path))
 
     # 4) Initialize the attacker object, and assign the loaded metaclassifier to it.
     blending_attacker = BlendingPlusPlus(
diff --git a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
index 0f3f3132..4c64989b 100644
--- a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
+++ b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
@@ -403,10 +403,8 @@ def calculate_rmia_signals(
         categorical_column_names=categorical_column_names,
         id_column_name=id_column_name,
     )
-
     log(INFO, "Computed Gower distance for the target model.")
 
-    log(INFO, "Gower distance matrices computed...")
     # gower_target is a 2D NumPy array that stores the Gower distances between
     # the input data (df_input) and the synthetic data generated by the target model.
     # so its shape is (len(df_input), len(target_synthetic_data)).
@@ -422,7 +420,7 @@ def calculate_rmia_signals(
     signal_target_k_1 = gower_target[:, 0]  # First element is the minimum
 
     # TODO: ideally remove hard-copied keys
-    shadow_training_data = (
+    shadow_training_data_ids = (
         fine_tuned_shadow_data_0["fine_tuning_sets"]
         + fine_tuned_shadow_data_1["fine_tuning_sets"]
         + trained_shadow_data["selected_sets"]
@@ -454,7 +452,7 @@ def calculate_rmia_signals(
     # as opposed to the original implementation which only creates masks a sample of 200 records. We've also
     # changed the way the masks are created to improve efficiency.
 
-    shadow_training_id_data = [set(id_list) for id_list in shadow_training_data]
+    shadow_training_id_data = [set(id_list) for id_list in shadow_training_data_ids]
 
     mask_in_training = np.array(
         [results_df[id_column_name].isin(id_set).to_numpy() for id_set in shadow_training_id_data]

From 01572d3cc200e5c474f7ecfd71b144b47922984a Mon Sep 17 00:00:00 2001
From: fatemetkl <t.fateme@gmail.com>
Date: Mon, 12 Jan 2026 09:23:16 -0700
Subject: [PATCH 37/41] Cleaning

---
 .../configs/experiment_config.yaml            |  2 +-
 .../ensemble_attack/real_data_collection.py   |  4 +--
 examples/ensemble_attack/test_attack_model.py | 25 +++++++++++--------
 3 files changed, 17 insertions(+), 14 deletions(-)

diff --git a/examples/ensemble_attack/configs/experiment_config.yaml b/examples/ensemble_attack/configs/experiment_config.yaml
index f30b5b0e..02b4c0a6 100644
--- a/examples/ensemble_attack/configs/experiment_config.yaml
+++ b/examples/ensemble_attack/configs/experiment_config.yaml
@@ -50,7 +50,7 @@ data_processing_config:
   original_population_data_path: /projects/midst-experiments/ensemble_attack/competition/population_data/ #Attack's collected population for DOMIAS
   # The column name in the data to be used for stratified splitting.
   column_to_stratify: "trans_type"  # Attention: This value is not documented in the original codebase.
-  folder_ranges: #Specify folder ranges for any of the mentioned splits.
+  folder_ranges: # Specify folder ranges for any of the mentioned splits.
     train: [[1, 21]] # Folders to be used for train data collection in the experiments
     test: [[21, 31] , [31, 41]]
   # File names in MIDST data directories.
diff --git a/examples/ensemble_attack/real_data_collection.py b/examples/ensemble_attack/real_data_collection.py
index b7e6e615..9873e15e 100644
--- a/examples/ensemble_attack/real_data_collection.py
+++ b/examples/ensemble_attack/real_data_collection.py
@@ -165,11 +165,11 @@ def collect_population_data_ensemble(
         save_dir: The path where the collected population data should be saved.
         original_repo_population: The original population data collected from the MIDST challenge repository.
         population_splits: A list indicating the data splits to be collected for population data.
-            This is basically a list of strings containing the folder names under attack folders that are
+            This is a list of strings containing the folder names under attack folders that are
             considered for population collection. If None, the default list of ``["train"]`` is set in the
             function based on the original attack implementation.
         challenge_splits: A list indicating the data splits to be collected for challenge points.
-            This is basically a list of strings containing the folder names under attack folders that are
+            This is a list of strings containing the folder names under attack folders that are
             considered for challenge data collection. If None, the default list of ``["train", "dev", "final"]``
             is set in the function based on the original attack implementation.
 
diff --git a/examples/ensemble_attack/test_attack_model.py b/examples/ensemble_attack/test_attack_model.py
index 00c4406f..7132f371 100644
--- a/examples/ensemble_attack/test_attack_model.py
+++ b/examples/ensemble_attack/test_attack_model.py
@@ -64,6 +64,8 @@ def extract_and_drop_id_column(
     # Extract ID column from the dataframe
     with open(data_types_file_path, "r") as f:
         column_types = json.load(f)
+
+    assert "id_column_name" in column_types, f"{data_types_file_path} must contain 'id_column_name' key."
     id_column_name = column_types["id_column_name"]
 
     assert id_column_name in data_frame.columns, f"Dataframe must have {id_column_name} column"
@@ -160,7 +162,7 @@ def collect_challenge_and_train_data(
     df_challenge_experiment = collect_midst_data(
         midst_data_input_dir=targets_data_path,
         attack_types=challenge_attack_types,
-        data_splits=["test"],  # change to test for 10k, and change to final for 20k
+        data_splits=["test"],  # For ensemble experiments, change to ``test`` for 10k, and change to ``final`` for 20k
         dataset="challenge",
         data_processing_config=data_processing_config,
     )
@@ -191,9 +193,9 @@ def select_challenge_data_for_training(
     Args:
         attack_rmia_shadow_training_data_choice: Strategy for creating challenge train data for RMIA shadow training.
             It can be one of the following:
-            - "only_challenge": Use only challenge experiment data.
-            - "only_train": Use only master train data. Note that this option contracts with the original
-                design and purpose of training RMIA shadow models on the challenge points as
+            - "only_challenge": Use only challenge experiment data (``df_challenge_experiment``).
+            - "only_train": Use only master train data (``df_master_train``). Note that this option contracts
+                with the original design and purpose of training RMIA shadow models on the challenge points as
                 RMIA signals (IN train signals) for challenge points could only be computed if
                 shadow models are trained on these points.
             - "combined": Combine both challenge experiment data and master train data. This can
@@ -255,13 +257,14 @@ def run_metaclassifier_testing(
     config: DictConfig,
 ) -> None:
     """
-    Function to run the attack on a target model using a trained metaclassifier.
-    Note that RMIA shadow models need to be trained for every new target model's challenge dataset.
-    However, we load the previously trained metaclassifier model and use it for new target models.
-    Unlike the training phase, in the testing phase, we don't need to train a shadow target model
+    Function to run the attack on a single target model using a trained metaclassifier.
+    Note that RMIA shadow models need to be trained for every new set of target models on
+    their collected challenge data, but once they are trained for the first target, we can reuse them
+    for the other targets in the same experiment.
+    Unlike the training phase, in the testing phase, we don't need to train a target shadow model
     since we already have access to the synthetic data of a real target model.
     All the collected population data that is used for training, is still needed during testing to compute some
-    of the signals.
+    of the signals (DOMIAS).
     Test prediction probabilities are saved to the specified attack result path in the config.
 
     Args:
@@ -289,7 +292,7 @@ def run_metaclassifier_testing(
 
     # 2) Read target model's challenge data and synthetic data.
     # Back-box attacker has only access to the target model's synthetic data and challenge points.
-    # We also load challenge labels to report the attack performance.
+    # We also load challenge labels to report the attack performance at the end.
     challenge_data_path = Path(config.target_model.challenge_data_path)
     challenge_label_path = Path(config.target_model.challenge_label_path)
 
@@ -319,7 +322,7 @@ def run_metaclassifier_testing(
     shadow_data_paths = [Path(path) for path in config.shadow_training.final_shadow_models_path]
     assert len(shadow_data_paths) == 3, "The attack_data_paths list must contain exactly three elements."
 
-    # If shadows are already trained for test (models_exists is True), don't need to train again.
+    # If shadows are already trained for test (``models_exists`` is True), don't need to train again.
     # Load shadow training collection from previously trained shadow models.
     shadow_data_collection, models_exists = load_trained_rmia_shadows_for_test_phase(shadow_data_paths)
 

From 720ddcf69f7fbc6da4c1f60ff86eeebf0c838ccb Mon Sep 17 00:00:00 2001
From: fatemetkl <t.fateme@gmail.com>
Date: Mon, 12 Jan 2026 13:42:41 -0700
Subject: [PATCH 38/41] coderabbitai comments

---
 examples/ensemble_attack/configs/experiment_config.yaml | 4 ++--
 examples/ensemble_attack/real_data_collection.py        | 9 ---------
 examples/ensemble_attack/run_metaclassifier_training.py | 4 +---
 examples/ensemble_attack/run_shadow_model_training.py   | 1 -
 examples/ensemble_attack/test_attack_model.py           | 1 -
 5 files changed, 3 insertions(+), 16 deletions(-)

diff --git a/examples/ensemble_attack/configs/experiment_config.yaml b/examples/ensemble_attack/configs/experiment_config.yaml
index 02b4c0a6..73e25649 100644
--- a/examples/ensemble_attack/configs/experiment_config.yaml
+++ b/examples/ensemble_attack/configs/experiment_config.yaml
@@ -1,5 +1,5 @@
 # Ensemble experiment configuration
-# This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``tets_attack_model.py``).
+# This config can be used to run both the Ensemble attack training (``run_attack.py``) and testing phases (``test_attack_model.py``).
 base_experiment_dir: /projects/midst-experiments/ensemble_attack/tabddpm_10k_experiment_data/10k/ # Processed data, and experiment artifacts will be stored under this directory.
 base_data_config_dir: examples/ensemble_attack/data_configs # Training and data type configs are saved under this directory.
 
@@ -107,7 +107,7 @@ metaclassifier:
   meta_classifier_model_name: ${metaclassifier.model_type}_metaclassifier_model
 
 attack_success_computation:
-  target_ids_to_test: [21, 22, 23, 24, 25, 26, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40] # List of target model IDs to compute the attack success for.
+  target_ids_to_test: [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40] # List of target model IDs to compute the attack success for.
 
 # General settings
 random_seed: 42 # Set to null for no seed, or an integer for a fixed seed
diff --git a/examples/ensemble_attack/real_data_collection.py b/examples/ensemble_attack/real_data_collection.py
index 9873e15e..4a8362b1 100644
--- a/examples/ensemble_attack/real_data_collection.py
+++ b/examples/ensemble_attack/real_data_collection.py
@@ -179,15 +179,6 @@ def collect_population_data_ensemble(
     # Population data will be saved under ``save_dir``.
     save_dir.mkdir(parents=True, exist_ok=True)
 
-    if population_splits is None:
-        population_splits = ["train"]
-    if challenge_splits is None:
-        # Original Ensemble collects all the challenge points from train, dev and final of "tabddpm_black_box" attack.
-        challenge_splits = ["train", "dev", "final"]
-
-    # Population data will be saved under ``save_dir``.
-    save_dir.mkdir(parents=True, exist_ok=True)
-
     if population_splits is None:
         population_splits = ["train"]
     if challenge_splits is None:
diff --git a/examples/ensemble_attack/run_metaclassifier_training.py b/examples/ensemble_attack/run_metaclassifier_training.py
index d848db55..47cfdd32 100644
--- a/examples/ensemble_attack/run_metaclassifier_training.py
+++ b/examples/ensemble_attack/run_metaclassifier_training.py
@@ -25,8 +25,6 @@ def run_metaclassifier_training(
             The list should contain three paths, one for each set of shadow models.
         target_model_synthetic_path: Path to the target model's synthetic data. This is all we need from a target
             model to train the metaclassifier in the black-box setting.
-        target_model_synthetic_path: Path to the target model's synthetic data. This is all we need from a target
-            model to train the metaclassifier in the black-box setting.
     """
     log(INFO, "Running metaclassifier training...")
 
@@ -88,7 +86,7 @@ def run_metaclassifier_training(
     )
     log(
         INFO,
-        f"Reference population data loaded from f{config.data_paths.population_path} with size {len(df_reference)}.",
+        f"Reference population data loaded from {config.data_paths.population_path} with size {len(df_reference)}.",
     )
 
     # Extract trans_id from both train and test dataframes
diff --git a/examples/ensemble_attack/run_shadow_model_training.py b/examples/ensemble_attack/run_shadow_model_training.py
index ab994722..ae69de80 100644
--- a/examples/ensemble_attack/run_shadow_model_training.py
+++ b/examples/ensemble_attack/run_shadow_model_training.py
@@ -107,7 +107,6 @@ def run_shadow_model_training(config: DictConfig, df_challenge_train: pd.DataFra
         "trans_id column should be present in master train data for the shadow model pipeline."
     )
     assert "trans_id" in df_population_with_challenge.columns
-    assert "trans_id" in df_challenge_train.columns
     # ``population_data`` in ensemble attack is used for shadow pre-training, and
     # ``master_challenge_df`` is used for fine-tuning for half of the shadow models.
     # For the other half of the shadow models, only ``master_challenge_df`` is used for training.
diff --git a/examples/ensemble_attack/test_attack_model.py b/examples/ensemble_attack/test_attack_model.py
index 7132f371..07446320 100644
--- a/examples/ensemble_attack/test_attack_model.py
+++ b/examples/ensemble_attack/test_attack_model.py
@@ -94,7 +94,6 @@ def run_rmia_shadow_training(config: DictConfig, df_challenge: pd.DataFrame) ->
             models with their training data and generated synthetic outputs.
     """
     shadow_model_paths = run_shadow_model_training(config, df_challenge_train=df_challenge)
-    shadow_model_paths = [Path(path) for path in config.shadow_training.final_shadow_models_path]
 
     assert len(shadow_model_paths) == 3, "For testing, meta classifier needs the path to three sets of shadow models."
 

From d64b908abf606bc16f62b8208f944a0723be0ca6 Mon Sep 17 00:00:00 2001
From: fatemetkl <t.fateme@gmail.com>
Date: Tue, 27 Jan 2026 08:29:51 -0700
Subject: [PATCH 39/41] addressed David's comments

---
 .../configs/experiment_config.yaml            |   5 +-
 .../ensemble_attack/real_data_collection.py   |  71 ++++++----
 examples/ensemble_attack/run_attack.py        |   6 +-
 examples/ensemble_attack/test_attack_model.py |  67 +++++++---
 .../attacks/ensemble/rmia/rmia_calculation.py | 120 ++++++++---------
 tests/unit/attacks/ensemble/test_rmia.py      | 122 +++++++++++++++++-
 6 files changed, 278 insertions(+), 113 deletions(-)

diff --git a/examples/ensemble_attack/configs/experiment_config.yaml b/examples/ensemble_attack/configs/experiment_config.yaml
index 73e25649..fc50bec6 100644
--- a/examples/ensemble_attack/configs/experiment_config.yaml
+++ b/examples/ensemble_attack/configs/experiment_config.yaml
@@ -20,6 +20,7 @@ target_model: # This is only used for testing the attack on a real target model.
   target_shadow_models_output_path: ${base_experiment_dir}/test_all_targets # Sub-directory to store test shadows and results
   attack_probabilities_result_path: ${target_model.target_shadow_models_output_path}/test_probabilities/attack_model_${target_model.target_model_id}_proba
   attack_rmia_shadow_training_data_choice: "combined" # Options: "combined", "only_challenge", "only_train". This determines which data to use for training RMIA attack model in testing phase.
+  # See select_challenge_data_for_training()'s docstring for more details.
 
 
 # Data paths
@@ -47,7 +48,9 @@ data_processing_config:
         ]
   population_splits: ["train"]  # Data splits to be collected for population data
   challenge_splits: ["train" , "test"]  # Data splits to be collected for challenge points
-  original_population_data_path: /projects/midst-experiments/ensemble_attack/competition/population_data/ #Attack's collected population for DOMIAS
+  original_population_data_path: /projects/midst-experiments/ensemble_attack/competition/population_data/ # This is where the original attack's population data (800k) will be read from, mainly to be used by DOMIAS
+  # You can download this data from https://github.com/CRCHUM-CITADEL/ensemble-mia/blob/main/input/population/population_all_with_challenge.csv
+
   # The column name in the data to be used for stratified splitting.
   column_to_stratify: "trans_type"  # Attention: This value is not documented in the original codebase.
   folder_ranges: # Specify folder ranges for any of the mentioned splits.
diff --git a/examples/ensemble_attack/real_data_collection.py b/examples/ensemble_attack/real_data_collection.py
index 4a8362b1..a0a10127 100644
--- a/examples/ensemble_attack/real_data_collection.py
+++ b/examples/ensemble_attack/real_data_collection.py
@@ -6,6 +6,7 @@
 from enum import Enum
 from logging import INFO
 from pathlib import Path
+from typing import Literal
 
 import pandas as pd
 from omegaconf import DictConfig
@@ -31,6 +32,9 @@ class AttackType(Enum):
     TABDDPM_100K = "tabddpm_trained_with_100k"
 
 
+DatasetType = Literal["train", "challenge"]
+
+
 def expand_ranges(ranges: list[tuple[int, int]]) -> list[int]:
     """
     Reads a list of tuples representing ranges and expands them into a flat list of integers.
@@ -51,8 +55,8 @@ def expand_ranges(ranges: list[tuple[int, int]]) -> list[int]:
 def collect_midst_attack_data(
     attack_type: AttackType,
     data_dir: Path,
-    data_split: str,
-    dataset: str,
+    split_folder: str,
+    dataset: DatasetType,
     data_processing_config: DictConfig,
 ) -> pd.DataFrame:
     """
@@ -61,18 +65,22 @@ def collect_midst_attack_data(
     Args:
         attack_type: The attack setting.
         data_dir: The path where the data is stored.
-        data_split: Indicates if this is train, dev, or final data. Note that, this is in fact
-            the name of the folder that contains model folders for data collection. For example,
-            f"{generation_name}_{i}" should be located under ``data_split`` folder.
+        split_folder: Indicates the folder name to collect data from for a specific data split.
+            ``split_folder`` should exist under ``data_dir / attack_type.value`` and
+            f"{generation_name}_{i}" should be located under ``split_folder``.
         dataset: The dataset to be collected. Either "train" or "challenge".
         data_processing_config: Configuration dictionary containing data specific information.
 
     Returns:
         pd.DataFrame: The specified dataset in this setting.
     """
+    assert dataset in {
+        "train",
+        "challenge",
+    }, "Only 'train' and 'challenge' collection is supported."
     # `data_id` is the folder numbering of each training or challenge dataset,
     #  and is defined with the provided config.
-    data_id = expand_ranges(data_processing_config.folder_ranges[data_split])
+    data_id = expand_ranges(data_processing_config.folder_ranges[split_folder])
 
     # Get file name based on the kind of dataset to be collected (i.e. train vs challenge).
     # TODO: Make the below parsing a bit more robust and less brittle
@@ -89,7 +97,7 @@ def collect_midst_attack_data(
 
     df_real = pd.DataFrame()
     for i in data_id:
-        data_path_ith = data_dir / attack_type.value / data_split / f"{generation_name}_{i}"
+        data_path_ith = data_dir / attack_type.value / split_folder / f"{generation_name}_{i}"
         # Will raise FileNotFoundError if the file does not exist or if it is not a CSV file.
         df_real_ith = load_dataframe(data_path_ith, file_name)
         df_real = df_real_ith if df_real.empty else pd.concat([df_real, df_real_ith])
@@ -101,14 +109,14 @@ def collect_midst_attack_data(
 def collect_midst_data(
     midst_data_input_dir: Path,
     attack_types: list[AttackType],
-    data_splits: list[str],
-    dataset: str,
+    split_folders: list[str],
+    dataset: DatasetType,
     data_processing_config: DictConfig,
 ) -> pd.DataFrame:
     """
     Collect train or challenge data of the specified attack type from the provided data folders
     in the MIDST competition. The data is going to be collected from all the folders specified
-    in ``data_splits`` argument under each attack type folder. For example, if ``data_splits``
+    in ``split_folders`` argument under each attack type folder. For example, if ``split_folders``
     contains `train` and `dev`, the function collects data from both `train` and `dev` folders
     under each attack type folder. For more information about the data collection structure, see
     the implementation of ``collect_midst_attack_data`` function.
@@ -116,7 +124,7 @@ def collect_midst_data(
     Args:
         midst_data_input_dir: The path where the MIDST data folders are stored.
         attack_types: List of attack types for data collection.
-        data_splits: A list indicating the data split to be collected. This is a list of folder names
+        split_folders: A list indicating the folder names to collect data splits from. These folders should exist
             under each attack type folder where we collect model's data from. For example, it could
             contain strings like `train`, `dev`, `final`, or `test` based on the directory structure.
         dataset: The dataset to be collected. Either `train` or `challenge`.
@@ -128,11 +136,11 @@ def collect_midst_data(
     assert dataset in {"train", "challenge"}, "Only 'train' and 'challenge' collection is supported."
     population = []
     for attack_type in attack_types:
-        for data_split in data_splits:
+        for split_folder in split_folders:
             df_real = collect_midst_attack_data(
                 attack_type=attack_type,
                 data_dir=midst_data_input_dir,
-                data_split=data_split,
+                split_folder=split_folder,
                 dataset=dataset,
                 data_processing_config=data_processing_config,
             )
@@ -146,7 +154,7 @@ def collect_population_data_ensemble(
     midst_data_input_dir: Path,
     data_processing_config: DictConfig,
     save_dir: Path,
-    original_repo_population: pd.DataFrame,
+    base_population: pd.DataFrame | None = None,
     population_splits: list[str] | None = None,
     challenge_splits: list[str] | None = None,
 ) -> pd.DataFrame:
@@ -155,21 +163,23 @@ def collect_population_data_ensemble(
     Returns real data population that consists of the train data of all the attacks
     (black box and white box) as specified in ``data_processing_config.population_attack_data_types_to_collect``
     , and challenge points from `train`, `dev` and `final` of attacks as specified by
-    ``data_processing_config.challenge_attack_data_types_to_collect``. The collected population data is concatenated
-    with ``original_repo_population`` to be large enough for the attack (specially DOMIAS) and then is saved in
-    the provided path, and returned as a dataframe.
+    ``data_processing_config.challenge_attack_data_types_to_collect``. If ``base_population`` is not None,
+    the collected population data will be concatenated with ``base_population`` to be large enough for
+    the attack (especially DOMIAS), then is saved in the provided path, and returned as a dataframe.
 
     Args:
         midst_data_input_dir: The path where the MIDST data folders are stored.
         data_processing_config: Configuration dictionary containing data information and file names.
         save_dir: The path where the collected population data should be saved.
-        original_repo_population: The original population data collected from the MIDST challenge repository.
-        population_splits: A list indicating the data splits to be collected for population data.
-            This is a list of strings containing the folder names under attack folders that are
+        base_population: Path to a large dataset to be concatenated with the collected population data
+            in this function. In experiments, the original attack's population data (800k records) collected by
+            the attacker team is used as the base population. This data is concatenated with the newly collected
+            population data to form a larger population for the attack (especially needed for DOMIAS). If None,
+            only the newly collected population data is used, which may not yield the expected attack performance.
+        population_splits: A list containing the folder names under attack folders that are
             considered for population collection. If None, the default list of ``["train"]`` is set in the
             function based on the original attack implementation.
-        challenge_splits: A list indicating the data splits to be collected for challenge points.
-            This is a list of strings containing the folder names under attack folders that are
+        challenge_splits:  list containing the folder names under attack folders that are
             considered for challenge data collection. If None, the default list of ``["train", "dev", "final"]``
             is set in the function based on the original attack implementation.
 
@@ -188,20 +198,27 @@ def collect_population_data_ensemble(
     # Ensemble Attack collects train data of all the attack types (black box and white box)
     population_attack_names = data_processing_config.population_attack_data_types_to_collect
     # Provided attack name are valid based on AttackType enum
-    population_attack_types: list[AttackType] = [AttackType(attack_name) for attack_name in population_attack_names]
+    population_attack_types = [AttackType(attack_name) for attack_name in population_attack_names]
 
     df_population_experiment = collect_midst_data(
         midst_data_input_dir,
         population_attack_types,
-        data_splits=population_splits,
+        split_folders=population_splits,
         dataset="train",
         data_processing_config=data_processing_config,
     )
 
     log(INFO, f"Collected experiment population data length before concatenation: {len(df_population_experiment)}")
 
-    df_population = pd.concat([df_population_experiment, original_repo_population]).drop_duplicates()
-    log(INFO, f"Concatenated population data length: {len(df_population)}")
+    if base_population is not None:
+        df_population = pd.concat([df_population_experiment, base_population]).drop_duplicates()
+        log(INFO, f"Concatenated population data length: {len(df_population)}")
+    else:
+        df_population = df_population_experiment
+        log(
+            INFO,
+            "base_population is None, only the newly collected population data is used.",
+        )
 
     # Drop ids.
     df_population_no_id = df_population.drop(columns=["trans_id", "account_id"])
@@ -215,7 +232,7 @@ def collect_population_data_ensemble(
     df_challenge = collect_midst_data(
         midst_data_input_dir,
         attack_types=challenge_attack_types,
-        data_splits=challenge_splits,
+        split_folders=challenge_splits,
         dataset="challenge",
         data_processing_config=data_processing_config,
     )
diff --git a/examples/ensemble_attack/run_attack.py b/examples/ensemble_attack/run_attack.py
index 75fe7254..fb35208e 100644
--- a/examples/ensemble_attack/run_attack.py
+++ b/examples/ensemble_attack/run_attack.py
@@ -25,7 +25,9 @@ def run_data_processing(config: DictConfig) -> None:
         config: Configuration object set in config.yaml.
     """
     # Load original repo's population to be concatenated to the experiment's population data.
-    # This is done to align the experiments with the original attack code because
+    # Original population refers to the population data collected by the attacker team, and can
+    # be downloaded from: https://github.com/CRCHUM-CITADEL/ensemble-mia/blob/main/input/population/population_all_with_challenge.csv
+    # This concatenation is done to align the experiments with the original attack code because
     # this attack needs a large population dataset, and only using the experiment's collected population
     # is not enough.
     original_population_data = load_dataframe(
@@ -38,7 +40,7 @@ def run_data_processing(config: DictConfig) -> None:
         midst_data_input_dir=Path(config.data_paths.midst_data_path),
         data_processing_config=config.data_processing_config,
         save_dir=Path(config.data_paths.population_path),
-        original_repo_population=original_population_data,
+        base_population=original_population_data,
         population_splits=config.data_processing_config.population_splits,
         challenge_splits=config.data_processing_config.challenge_splits,
     )
diff --git a/examples/ensemble_attack/test_attack_model.py b/examples/ensemble_attack/test_attack_model.py
index 07446320..87a7d02f 100644
--- a/examples/ensemble_attack/test_attack_model.py
+++ b/examples/ensemble_attack/test_attack_model.py
@@ -2,6 +2,7 @@
 
 import json
 import pickle
+from enum import Enum
 from logging import INFO
 from pathlib import Path
 from typing import Any
@@ -19,6 +20,12 @@
 from midst_toolkit.common.random import set_all_random_seeds
 
 
+class RMIATrainingDataChoice(Enum):
+    ONLY_CHALLENGE = "only_challenge"
+    ONLY_TRAIN = "only_train"
+    COMBINED = "combined"
+
+
 def save_results(
     attack_results_path: Path, metaclassifier_model_name: str, probabilities: np.ndarray, pred_score: float | None
 ) -> None:
@@ -49,16 +56,16 @@ def extract_and_drop_id_column(
     data_types_file_path: Path,
 ) -> tuple[pd.DataFrame, pd.Series]:
     """
-    Extracts IDs from the data frame and drops the ID column. ID column is identified based on
+    Extracts IDs from the dataframe and drops the ID column. ID column is identified based on
     the data types JSON file with "id_column_name" key.
 
     Args:
-        data_frame: Input data frame.
+        data_frame: Input dataframe.
         data_types_file_path: Path to the data types JSON file.
 
     Returns:
         A tuple containing:
-            - The modified data frame with ID columns dropped.
+            - The modified dataframe with ID columns dropped.
             - A Series containing the extracted data of ID columns.
     """
     # Extract ID column from the dataframe
@@ -116,6 +123,7 @@ def load_trained_rmia_shadows_for_test_phase(
     """
     Loads previously trained RMIA shadow models for the testing phase. Makes sure
     all shadow models exist before loading. Otherwise, returns an empty list and False.
+    If False is returned, all shadow models need to be trained for the testing phase.
 
     Args:
         shadow_data_paths: List of paths to the saved shadow model data.
@@ -127,25 +135,37 @@ def load_trained_rmia_shadows_for_test_phase(
             - A boolean indicating whether all shadow models were successfully loaded.
     """
     shadow_data_collection = []
-    models_exists = True
+    # models_exists is a list indicating the existence of each RMIA shadow model
+    models_exists = []
     for model_path in shadow_data_paths:
         if model_path.exists():
             with open(model_path, "rb") as f:
                 shadow_data_and_result = pickle.load(f)
                 shadow_data_collection.append(shadow_data_and_result)
             log(INFO, f"Loaded existing shadow model at {model_path}.")
+            models_exists.append(True)
         else:
-            models_exists = False
-            shadow_data_collection = []
-            break
-    return shadow_data_collection, models_exists
+            models_exists.append(False)
+            log(INFO, f"No shadow model found at {model_path}. Need to train RMIA shadow models for testing phase.")
+    if not all(models_exists):
+        # If even one of the models does not exist, return empty collection and False to indicate training is needed.
+        shadow_data_collection = []
+    return shadow_data_collection, all(models_exists)
 
 
 def collect_challenge_and_train_data(
     data_processing_config: DictConfig, processed_attack_data_path: Path, targets_data_path: Path
 ) -> tuple[pd.DataFrame, pd.DataFrame]:
     """
-    Collect challenge experiment data and master train data.
+    Collect experiment's challenge dataset (``df_challenge_experiment``) and master train data (``df_master_train``).
+    Experiment's challenge data refers to the collection of all the challenge points of all the target models that
+    we want to attack in this experiment.
+    Master train data refers to the dataset that was used to train the mesataclassifier.
+
+    To collect experiment's challenge data, the ``collect_midst_data`` function is called. This function
+    reads all the challenge dataset under each target model's folder.
+    Refer to `real_data_collection.collect_midst_data` for more information.
+
 
     Args:
         data_processing_config: Configuration object for data processing.
@@ -161,7 +181,9 @@ def collect_challenge_and_train_data(
     df_challenge_experiment = collect_midst_data(
         midst_data_input_dir=targets_data_path,
         attack_types=challenge_attack_types,
-        data_splits=["test"],  # For ensemble experiments, change to ``test`` for 10k, and change to ``final`` for 20k
+        split_folders=[
+            "test"
+        ],  # For ensemble experiments, change to ``test`` for 10k, and change to ``final`` for 20k
         dataset="challenge",
         data_processing_config=data_processing_config,
     )
@@ -184,7 +206,9 @@ def collect_challenge_and_train_data(
 
 
 def select_challenge_data_for_training(
-    attack_rmia_shadow_training_data_choice: str, df_challenge_experiment: pd.DataFrame, df_master_train: pd.DataFrame
+    attack_rmia_shadow_training_data_choice: RMIATrainingDataChoice,
+    df_challenge_experiment: pd.DataFrame,
+    df_master_train: pd.DataFrame,
 ) -> pd.DataFrame:
     """
     Select the appropriate challenge data based on config choice.
@@ -193,12 +217,12 @@ def select_challenge_data_for_training(
         attack_rmia_shadow_training_data_choice: Strategy for creating challenge train data for RMIA shadow training.
             It can be one of the following:
             - "only_challenge": Use only challenge experiment data (``df_challenge_experiment``).
-            - "only_train": Use only master train data (``df_master_train``). Note that this option contracts
+            - "only_train": Use only master train data (``df_master_train``). Note that this option contradicts
                 with the original design and purpose of training RMIA shadow models on the challenge points as
                 RMIA signals (IN train signals) for challenge points could only be computed if
                 shadow models are trained on these points.
             - "combined": Combine both challenge experiment data and master train data. This can
-                potentially be advantages based on the experiments as RMIA shadows are trained on
+                potentially be advantageous based on the experiments as RMIA shadows are trained on
                 more data points.
         df_challenge_experiment: Challenge points in this experiment.
         df_master_train: Master train data used to train the meta classifier.
@@ -209,14 +233,14 @@ def select_challenge_data_for_training(
     Returns:
         Selected challenge data.
     """
-    if attack_rmia_shadow_training_data_choice == "combined":
+    if attack_rmia_shadow_training_data_choice == RMIATrainingDataChoice.COMBINED:
         # Run RMIA shadow model training on experiments challenge points + master challenge train data
         df_challenge = pd.concat([df_challenge_experiment, df_master_train]).drop_duplicates()
         log(INFO, f"Combined challenge data length for RMIA shadow training: {len(df_challenge)}.")
-    elif attack_rmia_shadow_training_data_choice == "only_challenge":
+    elif attack_rmia_shadow_training_data_choice == RMIATrainingDataChoice.ONLY_CHALLENGE:
         df_challenge = df_challenge_experiment
         log(INFO, "Using only challenge data points for RMIA shadow training.")
-    elif attack_rmia_shadow_training_data_choice == "only_train":
+    elif attack_rmia_shadow_training_data_choice == RMIATrainingDataChoice.ONLY_TRAIN:
         df_challenge = df_master_train
         log(INFO, "Using only master challenge train data points for RMIA shadow training.")
     else:
@@ -245,12 +269,13 @@ def train_rmia_shadows_for_test_phase(config: DictConfig) -> list[dict[str, list
         targets_data_path=Path(config.data_paths.midst_data_path),
     )
     # Load the challenge dataframe for training RMIA shadow models.
-    df_challenge = select_challenge_data_for_training(
-        str(config.target_model.attack_rmia_shadow_training_data_choice), df_challenge_experiment, df_master_train
-    )
+    rmia_training_choice = RMIATrainingDataChoice(config.target_model.attack_rmia_shadow_training_data_choice)
+    df_challenge = select_challenge_data_for_training(rmia_training_choice, df_challenge_experiment, df_master_train)
     return run_rmia_shadow_training(config, df_challenge=df_challenge)
 
 
+# TODO: Perform inference on all the target models sequentially in a single run instead of running this script
+# multiple times. For more information, refer to https://app.clickup.com/t/868h4xk86
 @hydra.main(config_path="configs", config_name="experiment_config", version_base=None)
 def run_metaclassifier_testing(
     config: DictConfig,
@@ -258,8 +283,8 @@ def run_metaclassifier_testing(
     """
     Function to run the attack on a single target model using a trained metaclassifier.
     Note that RMIA shadow models need to be trained for every new set of target models on
-    their collected challenge data, but once they are trained for the first target, we can reuse them
-    for the other targets in the same experiment.
+    their collected challenge data, but once they are trained on all the experiment challenge points,
+    for the first target, we can reuse them for the other targets in the same experiment.
     Unlike the training phase, in the testing phase, we don't need to train a target shadow model
     since we already have access to the synthetic data of a real target model.
     All the collected population data that is used for training, is still needed during testing to compute some
diff --git a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
index 4c64989b..aed7daca 100644
--- a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
+++ b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
@@ -30,20 +30,34 @@ def compute_gower_batched(
     df_y: pd.DataFrame,
     cat_features: list[bool],
     batch_size: int,
-    dtype: FloatDType,
 ) -> np.ndarray:
     """
     Compute Gower distance matrix in batches to reduce peak memory usage.
 
-    This processes df_x in batches, computing distances against all of df_y,
+    This processes `df_x` in batches, computing distances against all of `df_y`,
     then stacks results.
 
+    Note: `gower.gower_matrix` performs normalization internally on numeric features.
+    Computed distances are divided by the range of all numeric values to normalize them between 0 and 1.
+    Therefore, make sure all the max and min values of numeric columns as well as the set of all categories in
+    categorical columns are represented in each batch. Since `df_y` exists in all the batches, all you need to
+    ensure is that the minimum and maximum values of the numerical columns as well as all the categories exist
+    in `df_y`.
+
+    Note: there seems to be a bug in `gower.gower_matrix` when `df_x` and `df_y` have the same number of rows.
+    Therefore, make sure `len(data_x) != len(data_y)` when calling `gower.gower_matrix`. This can be achieved by
+    changing the batch size to a value not equal to `len(data_y)`.
+
+    More details on the bug:
+    `gower.gower_matrix` assumes when `data_x` and `data_y` have the same number of rows, they are the same dataframe,
+    and basically computes the distance between `data_y` to itself in an efficient way. But, that is not the intended
+    behavior all the time.
+
     Args:
-        df_x: First dataframe.
-        df_y: Second dataframe.
+        df_x: DataFrame for which distances are computed in batches.
+        df_y: DataFrame against which distances are computed.
         cat_features: A boolean list indicating which columns are categorical.
-        batch_size: Number of rows from df_x to process in each batch.
-        dtype: The data type to which numerical columns will be cast.
+        batch_size: Number of rows from `df_x` to process in each batch.
 
     Returns:
         Gower distance matrix as a numpy array.
@@ -52,69 +66,54 @@ def compute_gower_batched(
     n_y = len(df_y)
 
     # Pre-allocate output matrix
-    gower_matrix = np.zeros((n_x, n_y), dtype=dtype)
+    gower_matrix = np.zeros((n_x, n_y), dtype=np.float32)
 
     for start_idx in range(0, n_x, batch_size):
         end_idx = min(start_idx + batch_size, n_x)
 
-        # Compute distance for this batch
+        assert len(df_x.iloc[start_idx:end_idx]) != len(df_y.v), (
+            "`gower.gower_matrix` has a bug when data_x and data_y have the same number of rows."
+            " To avoid that, change the batch size."
+        )
         batch_matrix = gower.gower_matrix(
             data_x=df_x.iloc[start_idx:end_idx],
             data_y=df_y,
             cat_features=cat_features,
         )
-
         # Store in output matrix
-        gower_matrix[start_idx:end_idx] = batch_matrix.astype(dtype)
+        gower_matrix[start_idx:end_idx] = batch_matrix.astype(np.float32)
 
     return gower_matrix
 
 
 def compute_gower_for_model(
-    args: tuple[
-        int,
-        pd.DataFrame,
-        pd.DataFrame,
-        int,
-        str,
-        int | None,
-        list[bool],
-        Sequence[str],
-        FloatDType,
-    ],
-) -> tuple[int, np.ndarray]:
+    df_synthetic_raw: pd.DataFrame,
+    df_input: pd.DataFrame,
+    min_length: int,
+    id_column_name: str,
+    random_seed: int | None,
+    categorical_features: list[bool],
+    numerical_columns: Sequence[str],
+    dtype: FloatDType,
+    batch_processing: bool = False,
+) -> np.ndarray:
     """
     Computes the Gower distance matrix between df_input and a single synthetic dataframe.
 
     Args:
-        args: A tuple containing:
-            - i: Index of the model (for tracking purposes).
-            - df_synthetic_raw: The synthetic dataframe generated by the shadow model.
-            - df_input: The input dataframe to compare against.
-            - min_length: Minimum length for downsampling.
-            - id_column_name: Name of the ID column.
-            - random_seed: Random seed for reproducibility.
-            - categorical_features: A boolean list indicating which columns are categorical.
-            - numerical_columns: List of numerical column names.
-            - dtype: The data type to which numerical columns will be cast.
+        df_synthetic_raw: The synthetic dataframe generated by the shadow model.
+        df_input: The input dataframe to compare against.
+        min_length: Minimum length for downsampling.
+        id_column_name: Name of the ID column.
+        random_seed: Random seed for reproducibility.
+        categorical_features: A boolean list indicating which columns are categorical.
+        numerical_columns: List of numerical column names.
+        dtype: The data type to which numerical columns will be cast.
+        batch_processing: Whether to use batched computation to reduce peak memory usage.
 
     Returns:
-        A tuple containing:
-            - i: Index of the model (same as input).
-            - gower_matrix: The computed Gower distance matrix as a numpy array.
+        gower_matrix: The computed Gower distance matrix as a numpy array.
     """
-    (
-        i,
-        df_synthetic_raw,
-        df_input,
-        min_length,
-        id_column_name,
-        random_seed,
-        categorical_features,
-        numerical_columns,
-        dtype,
-    ) = args
-
     df_synthetic = df_synthetic_raw.copy()
 
     # Convert numerical columns to float (otherwise error in the numpy divide)
@@ -128,9 +127,16 @@ def compute_gower_for_model(
         df_synthetic = df_synthetic.drop(columns=[id_column_name])
 
     # Batched computation to reduce peak memory
-    gower_matrix = compute_gower_batched(df_input, df_synthetic, categorical_features, batch_size=5000, dtype=dtype)
+    if batch_processing:
+        gower_matrix = compute_gower_batched(df_input, df_synthetic, categorical_features, batch_size=5000)
+    else:
+        gower_matrix = gower.gower_matrix(
+            data_x=df_input,
+            data_y=df_synthetic,
+            cat_features=categorical_features,
+        )
 
-    return i, gower_matrix
+    return gower_matrix
 
 
 def get_rmia_gower(
@@ -140,7 +146,7 @@ def get_rmia_gower(
     categorical_column_names: list[str],
     id_column_name: str,
     random_seed: int | None = None,
-    use_multiprocessing: bool = True,
+    use_multiprocessing: bool = False,
     n_jobs: int = 4,
     dtype: FloatDType = np.float32,
 ) -> list[np.ndarray]:
@@ -187,7 +193,6 @@ def get_rmia_gower(
     # Prepare arguments for each processing task
     args_list = [
         (
-            i,
             df_synthetic,
             df_input,
             min_length,
@@ -197,22 +202,17 @@ def get_rmia_gower(
             numerical_columns,
             dtype,
         )
-        for i, df_synthetic in enumerate(model_data)
+        for df_synthetic in model_data
     ]
 
     # Process in parallel or sequentially based on use_multiprocessing
-    results = {}
     if use_multiprocessing:
         with Pool(processes=n_jobs) as pool:
-            for i, gower_matrix in pool.imap_unordered(compute_gower_for_model, args_list):
-                results[i] = gower_matrix
+            results = pool.starmap(compute_gower_for_model, args_list)
     else:
-        for args in args_list:
-            i, gower_matrix = compute_gower_for_model(args)
-            results[i] = gower_matrix
+        results = [compute_gower_for_model(*args) for args in args_list]
 
-    # Return in original order
-    return [results[i] for i in range(len(model_data))]
+    return results
 
 
 def conditional_average(values: np.ndarray, condition_mask: np.ndarray) -> np.ndarray:
diff --git a/tests/unit/attacks/ensemble/test_rmia.py b/tests/unit/attacks/ensemble/test_rmia.py
index dfcb5021..b582b16a 100644
--- a/tests/unit/attacks/ensemble/test_rmia.py
+++ b/tests/unit/attacks/ensemble/test_rmia.py
@@ -1,5 +1,6 @@
 from typing import Any
 
+import gower
 import numpy as np
 import numpy.testing as npt
 import pandas as pd
@@ -9,6 +10,7 @@
 from midst_toolkit.attacks.ensemble.rmia.rmia_calculation import (
     Key,
     calculate_rmia_signals,
+    compute_gower_batched,
     conditional_average,
     get_rmia_gower,
 )
@@ -160,10 +162,10 @@ def test_get_rmia_gower_basic_run(self, base_data, mocker):
         )
 
         assert len(results) == 2
-        npt.assert_array_equal(results[0], np.array([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]], dtype=np.float32))
+        npt.assert_array_equal(results[0], np.array([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]]))
         npt.assert_array_equal(
             results[1],
-            np.array([[0.7, 0.8, 0.9], [0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32),
+            np.array([[0.7, 0.8, 0.9], [0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]),
         )
 
         assert mock_gower_matrix.call_count == 2
@@ -344,3 +346,119 @@ def test_calculate_rmia_signals_division_by_zero(self, rmia_signal_data, mocker)
         assert result_df[f"rmia_k_{k}"].isna().all()
         assert result_df["rmia_out_k_1"].isna().all()
         assert result_df[f"rmia_out_k_{k}"].isna().all()
+
+
+class TestComputeGowerBatched:
+    @pytest.fixture
+    def gower_data(self):
+        """
+        Creates the input dataframes and categorical feature list required to test
+        the batch computation of gower distance.
+
+        Returns:
+            A tuple containing:
+            - df_x: DataFrame for which distances are computed in batches.
+            - df_y: DataFrame against which distances are computed.
+            - cat_features: List indicating which columns are categorical.
+        """
+        # We need to make sure all the max and min values of numeric columns as well as
+        # all categories in categorical columns are represented in each batch. Therefore,
+        # all these values should exist in the `df_y`` dataframe since it exists in all batches.
+        df_x = pd.DataFrame(
+            {
+                "age": [28, 32, 38, 42],
+                "city": ["A", "B", "A", "A"],
+                "score": [110, 160, 130, 190],
+            }
+        )
+        df_y = pd.DataFrame(
+            {
+                "age": [25, 30, 35, 50, 45],
+                "city": ["A", "B", "A", "B", "B"],
+                "score": [100, 150, 120, 200, 180],
+            }
+        )
+
+        cat_features = [False, True, False]
+        return df_x, df_y, cat_features
+
+    def test_compute_gower_batched_logic(self, gower_data, mocker):
+        """Tests that batched computation gives the same result as a single batch."""
+        df_x, df_y, cat_features = gower_data
+
+        mock_gower_matrix = mocker.patch(
+            "midst_toolkit.attacks.ensemble.rmia.rmia_calculation.gower.gower_matrix",
+            wraps=gower.gower_matrix,
+        )
+
+        expected_result = gower.gower_matrix(data_x=df_x, data_y=df_y, cat_features=cat_features)
+
+        batch_size = 2
+        batched_result = compute_gower_batched(df_x, df_y, cat_features, batch_size)
+
+        # Compare the batched result to the expected result
+        npt.assert_allclose(batched_result, expected_result.astype(np.float32), atol=1e-6)
+
+        # Check that gower.gower_matrix was called multiple times (in batches)
+        assert mock_gower_matrix.call_count == np.ceil(len(df_x) / batch_size) + 1
+
+        # Now try another batch size
+        batch_size = 3
+        batched_result = compute_gower_batched(df_x, df_y, cat_features, batch_size)
+        npt.assert_allclose(batched_result, expected_result.astype(np.float32), atol=1e-6)
+
+        # Try batch size of 1
+        batch_size = 1
+        batched_result = compute_gower_batched(df_x, df_y, cat_features, batch_size)
+        npt.assert_allclose(batched_result, expected_result.astype(np.float32), atol=1e-6)
+
+    def test_compute_gower_batched_single_batch(self, gower_data):
+        """Tests that the function works correctly when batch_size >= number of rows."""
+        df_x, df_y, cat_features = gower_data
+
+        expected_result = gower.gower_matrix(data_x=df_x, data_y=df_y, cat_features=cat_features)
+
+        # Use a batch size larger than the dataframe
+        batched_result = compute_gower_batched(df_x, df_y, cat_features, batch_size=15)
+
+        npt.assert_allclose(batched_result, expected_result.astype(np.float32), atol=1e-6)
+
+    def test_gower_equal_y_and_x_data_sizes(self, gower_data):
+        """
+        Tests that the gower implementation DOES NOT work when df_x and df_y have the same number of rows.
+
+        Explanation: the `gower.gower_matrix` implementation treats dataframes with the same number of rows
+        as if they were the same dataset and therefore computes the distance of a dataset to itself,
+        which is not what we want.
+        As a result, `gower.gower_matrix` assumes that for each row i in `df_x`,
+        gower_matrix[i, :] == gower_matrix[:, i].
+
+        """
+        df_x, df_y, cat_features = gower_data
+
+        # Add another row to df_x to match df_y size
+        new_row_data = {"age": 30, "city": "B", "score": 135}
+        df_x.loc[len(df_x)] = new_row_data
+
+        # Now let's see if the distance between categorical columns makes sense.
+        non_batched_cat_result = gower.gower_matrix(data_x=df_x[["city"]], data_y=df_y[["city"]], cat_features=[True])
+        batched_cat_result = compute_gower_batched(
+            df_x=df_x[["city"]],
+            df_y=df_y[["city"]],
+            cat_features=[True],
+            batch_size=2,
+        )
+        expected_manual = np.array(
+            [
+                [0, 1, 0, 1, 1],
+                [1, 0, 1, 0, 0],
+                [0, 1, 0, 1, 1],
+                [0, 1, 0, 1, 1],
+                [1, 0, 1, 0, 0],
+            ],
+            dtype=np.float32,
+        )
+        # Batched value makes sense because x_n_rows != y_n_rows
+        npt.assert_allclose(batched_cat_result, expected_manual.astype(np.float32), atol=1e-6)
+        # The non-batched value is incorrect due to a bug in the original code when len(df_x) == len(df_y).
+        assert not np.allclose(non_batched_cat_result, expected_manual.astype(np.float32), atol=1e-6)

From 00f1e39c1acd7d6d78ab133cfe9a21689ac6b7f0 Mon Sep 17 00:00:00 2001
From: fatemetkl <t.fateme@gmail.com>
Date: Tue, 27 Jan 2026 08:38:31 -0700
Subject: [PATCH 40/41] minor bug

---
 src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py | 2 +-
 tests/unit/attacks/ensemble/test_rmia.py                    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
index aed7daca..326b3000 100644
--- a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
+++ b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
@@ -71,7 +71,7 @@ def compute_gower_batched(
     for start_idx in range(0, n_x, batch_size):
         end_idx = min(start_idx + batch_size, n_x)
 
-        assert len(df_x.iloc[start_idx:end_idx]) != len(df_y.v), (
+        assert len(df_x.iloc[start_idx:end_idx]) != len(df_y), (
             "`gower.gower_matrix` has a bug when data_x and data_y have the same number of rows."
             " To avoid that, change the batch size."
         )
diff --git a/tests/unit/attacks/ensemble/test_rmia.py b/tests/unit/attacks/ensemble/test_rmia.py
index b582b16a..10176eb6 100644
--- a/tests/unit/attacks/ensemble/test_rmia.py
+++ b/tests/unit/attacks/ensemble/test_rmia.py
@@ -434,7 +434,7 @@ def test_gower_equal_y_and_x_data_sizes(self, gower_data):
         gower_matrix[i, :] == gower_matrix[:, i].
 
         """
-        df_x, df_y, cat_features = gower_data
+        df_x, df_y, _ = gower_data
 
         # Add another row to df_x to match df_y size
         new_row_data = {"age": 30, "city": "B", "score": 135}

From 00e98a04e6ed10d9ece23516efbbda905091e403 Mon Sep 17 00:00:00 2001
From: fatemetkl <t.fateme@gmail.com>
Date: Tue, 27 Jan 2026 09:33:06 -0700
Subject: [PATCH 41/41] Final comments and cleaning

---
 examples/ensemble_attack/run_attack.py              |  8 ++------
 examples/ensemble_attack/test_attack_model.py       | 13 +++++++------
 .../attacks/ensemble/rmia/rmia_calculation.py       |  3 +--
 3 files changed, 10 insertions(+), 14 deletions(-)

diff --git a/examples/ensemble_attack/run_attack.py b/examples/ensemble_attack/run_attack.py
index fb35208e..c53a86dc 100644
--- a/examples/ensemble_attack/run_attack.py
+++ b/examples/ensemble_attack/run_attack.py
@@ -3,13 +3,14 @@
 provided resources and data.
 """
 
-import importlib
 from logging import INFO
 from pathlib import Path
 
 import hydra
 from omegaconf import DictConfig
 
+import examples.ensemble_attack.run_metaclassifier_training as meta_pipeline
+import examples.ensemble_attack.run_shadow_model_training as shadow_pipeline
 from examples.ensemble_attack.real_data_collection import collect_population_data_ensemble
 from midst_toolkit.attacks.ensemble.data_utils import load_dataframe
 from midst_toolkit.attacks.ensemble.process_split_data import process_split_data
@@ -75,11 +76,7 @@ def main(config: DictConfig) -> None:
     if config.pipeline.run_data_processing:
         run_data_processing(config)
 
-    # Note: Importing the following two modules causes a segmentation fault error if imported together in this file.
-    # A quick solution is to load modules dynamically if any of the pipelines is called.
-    # TODO: Investigate the source of error.
     if config.pipeline.run_shadow_model_training:
-        shadow_pipeline = importlib.import_module("examples.ensemble_attack.run_shadow_model_training")
         df_master_challenge_train = load_dataframe(
             Path(config.data_paths.processed_attack_data_path),
             "master_challenge_train.csv",
@@ -100,7 +97,6 @@ def main(config: DictConfig) -> None:
             "The target_data_path must be provided for metaclassifier training."
         )
 
-        meta_pipeline = importlib.import_module("examples.ensemble_attack.run_metaclassifier_training")
         meta_pipeline.run_metaclassifier_training(config, shadow_data_paths, target_model_synthetic_path)
 
 
diff --git a/examples/ensemble_attack/test_attack_model.py b/examples/ensemble_attack/test_attack_model.py
index 87a7d02f..84920092 100644
--- a/examples/ensemble_attack/test_attack_model.py
+++ b/examples/ensemble_attack/test_attack_model.py
@@ -20,7 +20,7 @@
 from midst_toolkit.common.random import set_all_random_seeds
 
 
-class RMIATrainingDataChoice(Enum):
+class RmiaTrainingDataChoice(Enum):
     ONLY_CHALLENGE = "only_challenge"
     ONLY_TRAIN = "only_train"
     COMBINED = "combined"
@@ -104,6 +104,7 @@ def run_rmia_shadow_training(config: DictConfig, df_challenge: pd.DataFrame) ->
 
     assert len(shadow_model_paths) == 3, "For testing, meta classifier needs the path to three sets of shadow models."
 
+    # Initialize an empty list to store the data and results of shadow models after loading.
     shadow_data_collection = []
     for model_path in shadow_model_paths:
         assert model_path.exists(), (
@@ -206,7 +207,7 @@ def collect_challenge_and_train_data(
 
 
 def select_challenge_data_for_training(
-    attack_rmia_shadow_training_data_choice: RMIATrainingDataChoice,
+    attack_rmia_shadow_training_data_choice: RmiaTrainingDataChoice,
     df_challenge_experiment: pd.DataFrame,
     df_master_train: pd.DataFrame,
 ) -> pd.DataFrame:
@@ -233,14 +234,14 @@ def select_challenge_data_for_training(
     Returns:
         Selected challenge data.
     """
-    if attack_rmia_shadow_training_data_choice == RMIATrainingDataChoice.COMBINED:
+    if attack_rmia_shadow_training_data_choice == RmiaTrainingDataChoice.COMBINED:
         # Run RMIA shadow model training on experiments challenge points + master challenge train data
         df_challenge = pd.concat([df_challenge_experiment, df_master_train]).drop_duplicates()
         log(INFO, f"Combined challenge data length for RMIA shadow training: {len(df_challenge)}.")
-    elif attack_rmia_shadow_training_data_choice == RMIATrainingDataChoice.ONLY_CHALLENGE:
+    elif attack_rmia_shadow_training_data_choice == RmiaTrainingDataChoice.ONLY_CHALLENGE:
         df_challenge = df_challenge_experiment
         log(INFO, "Using only challenge data points for RMIA shadow training.")
-    elif attack_rmia_shadow_training_data_choice == RMIATrainingDataChoice.ONLY_TRAIN:
+    elif attack_rmia_shadow_training_data_choice == RmiaTrainingDataChoice.ONLY_TRAIN:
         df_challenge = df_master_train
         log(INFO, "Using only master challenge train data points for RMIA shadow training.")
     else:
@@ -269,7 +270,7 @@ def train_rmia_shadows_for_test_phase(config: DictConfig) -> list[dict[str, list
         targets_data_path=Path(config.data_paths.midst_data_path),
     )
     # Load the challenge dataframe for training RMIA shadow models.
-    rmia_training_choice = RMIATrainingDataChoice(config.target_model.attack_rmia_shadow_training_data_choice)
+    rmia_training_choice = RmiaTrainingDataChoice(config.target_model.attack_rmia_shadow_training_data_choice)
     df_challenge = select_challenge_data_for_training(rmia_training_choice, df_challenge_experiment, df_master_train)
     return run_rmia_shadow_training(config, df_challenge=df_challenge)
 
diff --git a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
index 326b3000..b41c7cbf 100644
--- a/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
+++ b/src/midst_toolkit/attacks/ensemble/rmia/rmia_calculation.py
@@ -185,8 +185,7 @@ def get_rmia_gower(
 
     categorical_features = [column in categorical_column_names for column in df_input.columns]
 
-    # Defined the type as sequence to address mypy error.
-    numerical_columns: Sequence[str] = [col for col in df_input.columns if col not in categorical_column_names]
+    numerical_columns = [col for col in df_input.columns if col not in categorical_column_names]
 
     df_input[numerical_columns] = df_input[numerical_columns].astype(dtype)