From 04875a62e9eb3fff63ffb13a1d2c9b6362717516 Mon Sep 17 00:00:00 2001
From: Tinsae <tinsaekahsay@gmail.com>
Date: Wed, 8 Jul 2026 23:53:07 +0200
Subject: [PATCH] cleaned up code

---
 Tune.py                                       | 507 ++++++++++--------
 Tune_new.py                                   | 342 ------------
 eval/UnlearningAttack.py                      |  80 ++-
 .../CertifiedUnlearning/RESNET34/forget.csv   |  54 ++
 .../RESNET34/forget_train.csv                 |  54 ++
 .../CertifiedUnlearning/RESNET34/retain.csv   |  54 ++
 reports/CertifiedUnlearning/attack_values.csv |  70 +--
 .../CertifiedUnlearning/attack_values_int.csv |  34 ++
 reports/LinearFiltration/RESNET34/forget.csv  |  61 +++
 .../RESNET34/forget_train.csv                 |  61 +++
 reports/LinearFiltration/RESNET34/retain.csv  |  61 +++
 reports/LinearFiltration/attack_values.csv    |  73 +--
 .../LinearFiltration/attack_values_int.csv    |  35 ++
 reports/Retrain/RESNET34/forget.csv           |  62 +++
 reports/Retrain/RESNET34/forget_train.csv     |  62 +++
 reports/Retrain/RESNET34/retain.csv           |  62 +++
 reports/Retrain/attack_values.csv             |  76 +--
 reports/Retrain/attack_values_int.csv         |  38 ++
 reports/WeightFiltration/RESNET34/forget.csv  |  61 +++
 .../RESNET34/forget_train.csv                 |  61 +++
 reports/WeightFiltration/RESNET34/retain.csv  |  61 +++
 reports/WeightFiltration/attack_values.csv    |  73 +--
 .../WeightFiltration/attack_values_int.csv    |  35 ++
 23 files changed, 1362 insertions(+), 715 deletions(-)
 delete mode 100644 Tune_new.py
 create mode 100644 reports/CertifiedUnlearning/attack_values_int.csv
 create mode 100644 reports/LinearFiltration/attack_values_int.csv
 create mode 100644 reports/Retrain/attack_values_int.csv
 create mode 100644 reports/WeightFiltration/attack_values_int.csv

diff --git a/Tune.py b/Tune.py
index 3abb32d..a1a52e9 100644
--- a/Tune.py
+++ b/Tune.py
@@ -1,268 +1,339 @@
-# Finetuning a selected model 
-# on a selected dataset 
-# using selected parameters
-
+import torch
+import torch.nn as nn
 from torch.utils.data import DataLoader
 from sklearn.metrics import classification_report
+import copy
+
+# Framework and Utility Imports
 import SetUp
-#from Data import *
-# from datasets.Casia import *
-#from IdentitySubset import IdentitySubset
+import Util
 from sets.Data import *
 from sets.IdentitySubset import IdentitySubset
-# models
 from architectures.Model import Model, Architecture
-
+from unlearning.CertifiedUnlearning import CertifiedUnlearning
 from unlearning.LinearFiltration import LinearFiltration
-from unlearning.CertifiedRemoval import CertifiedRemoval
 from unlearning.WeightFiltration import WeightFiltration
+from eval.UnlearningAttack import UnlearningAttack
+from unlearning.Retrain import Retrain
 
-import Util
-# WeightFiltration, CertifiedRemoval
 
-# numbre of classes 
-CLASS_SIZE = 20
-# batch 
-BATCH_SIZE = 16
-
-# size of images per class trainset + testset
-# 30 works best, more than that and we dont have enough data
-SAMPLE_SIZE = 30
-
-# this is then (full_sample - test_sample)
-TRAINING_SMPLE = 27
-
-# learning rate
-LR_RATE = 0.0001
-EPOCHS = 10
+# Global Hyperparameters
+CLASS_SIZE:int = 20
+BATCH_SIZE:int = 16
+SAMPLE_SIZE:int = 30
+TRAINING_SAMPLE:int = 27
 
 # depends on model architecture
 # ResNet, DenseNet = 224
 # Inception = 299
-RESOLUTION = 224
+RESOLUTION:int = 224
 
-FINETUNE = False # whether to fintune or just load finetuned model from dir
-# model architecture options are 
-#  - RESNET18
-#  - RESNET50
-#  - DENSENET121
-#  - INCEPTION
-#  - GOOGLENET
-#  - EFFICIENTNET
-#  - SHUFFLENET
-arch = Architecture.RESNET50
-
-# DATA PREPARATION
-# load data set and prepare
-dataset_name = Set_Name.CELEBA
-set = Set_Name.CELEBA
-
-dataset = get_set(set_name=dataset_name)
-print(f"> {dataset.__class__.__name__} dataset loaded")
-# select identities for experiment
-#selected_identities = select_ids(
-#    dataset = dataset,
-#    sample_size = SAMPLE_SIZE,
-#    class_size = CLASS_SIZE
-#    )
-
-# this selects the top 50 based on sample size
-# that way repeated calls return the same classes
-selected_identities = select_top_ids(
-    dataset=dataset,
-    class_size=CLASS_SIZE
-)
-
-print(f'> Selected {CLASS_SIZE} random identity classes from CelebA dataset.')
-print(f'> A class has {TRAINING_SMPLE} train and {SAMPLE_SIZE-TRAINING_SMPLE} test samples')
-
-# split class images to train/test indices
-train_indices, test_indices = get_indices(
-    dataset = dataset, 
-    identities = selected_identities, 
-    split_at = TRAINING_SMPLE,
-    size= SAMPLE_SIZE
-)
-
-# helps map class id to index
-id_map = {old_id: new_id for new_id, old_id in enumerate(selected_identities)}
-
-# we remap identities because crossEntropyLoss requires in indices 0 -> (n-1)
-# where n = class size.
-tr_transform = train_transform(RESOLUTION)
-train_data = IdentitySubset(
-    dataset=dataset, 
-    indices=train_indices, 
-    id_mapping=id_map,
-    transform=tr_transform)
-
-train_loader = DataLoader(
-    train_data, 
-    batch_size = BATCH_SIZE, 
-    shuffle = True)
-
-print(f"> Total training images: {len(train_data)}")
-
-print(f'> Constants : Classes = {CLASS_SIZE}, Batch = {BATCH_SIZE}, epochs = {EPOCHS}')
-
-# MODEL PREPARATION
-# cuda if exists (it does here)
-device = SetUp.get_device()
+# specify the model architecture,
+# Options here are the following
+'''
+    RESNET18 # candidate
+    RESNET50 
+    RESNET34 
+    INCEPTION  # candidate / or googleNet
+    DENSENET121 # candidate
+    GOOGLENET  # candidate / or Inception
+    EFFICIENTNET # candidate
+    SHUFFLENET 
+    WIDE_RESNET 
+'''
+ARCH = Architecture.RESNET34
 
 
-for i in range(0,1):#CLASS_SIZE):
-    FORGET_CLASS_IDX = i
-    # Create model using Factory
+# Data preparation and model setup
+def prepare_data_and_model_environment():
+    """
+    Handles environment discovery, downloads/loads datasets, generates 
+    train-test class splits, and configures the architecture base.
+    """
+    device = SetUp.get_device()
+    dataset_name = Set_Name.CASIAFACES
+    if dataset_name == Set_Name.CASIAFACES:
+        SAMPLE_SIZE = 400
+        TRAINING_SAMPLE = 320
 
-    model = None
+    dataset = get_set(set_name=dataset_name)
+    print(f"> {dataset.__class__.__name__} dataset loaded")
 
-    if FINETUNE:
-        model = Model.create(
-            arch = arch, 
-            device = device, 
-            size = CLASS_SIZE)
+    # Select target identities (deterministic top sample identities)
+    selected_identities = select_top_ids(dataset=dataset, class_size=CLASS_SIZE)
+    print(f'> Selected {CLASS_SIZE} random identity classes from {dataset_name.name} dataset.')
+    print(f'> A class has {TRAINING_SAMPLE} train and {SAMPLE_SIZE - TRAINING_SAMPLE} test samples')
 
-        # we may need to load existing model or finetune
-        model.train(
-                epochs = EPOCHS, 
-                loader = train_loader, 
-                rate = LR_RATE)
+    # Isolate sample index partitions
+    train_indices, test_indices = get_indices(
+        dataset=dataset, 
+        identities=selected_identities, 
+        split_at=TRAINING_SAMPLE,
+        size=SAMPLE_SIZE
+    )
 
-            # save.
-        file_name = f"{arch.name.lower}_{dataset_name.name.lower()}"
-        model.save(filename=arch.name.lower())
+    # Remap identities to 0 -> (N-1) range required by CrossEntropyLoss
+    id_map = {old_id: new_id for new_id, old_id in enumerate(selected_identities)}
 
+    # Build internal datasets using custom transforms
+    tr_transform = train_transform(RESOLUTION)
+    train_set = IdentitySubset(
+        dataset=dataset, 
+        indices=train_indices, 
+        id_mapping=id_map,
+        transform=tr_transform
+    )
 
-    # done tuning
-
-    # EVALUATE
     te_transform = test_transform(RESOLUTION)
-    # Testing
-    test_data = IdentitySubset(
-        dataset = dataset, 
+    test_set = IdentitySubset(
+        dataset=dataset, 
         indices=test_indices, 
         id_mapping=id_map,
-        transform=te_transform)
+        transform=te_transform
+    )
 
-    test_loader = DataLoader(
-        test_data, 
-        batch_size=BATCH_SIZE, 
-        shuffle=False)
+    print(f"> Total training images: {len(train_set)}")
+    print(f'> Constants : Classes = {CLASS_SIZE}, Batch = {BATCH_SIZE}')
 
-    print(f"Total test images for these {CLASS_SIZE} classes: {len(test_data)}")
+    # Create the base target model instance
+    base_model = Model.create(arch=ARCH, device=device, size=CLASS_SIZE)
 
-    # Evaluate
+    return {
+        "device": device,
+        "train_set": train_set,
+        "test_set": test_set,
+        "base_model": base_model
+    }
+
+
+# Fine tunning and evaluation
+def run_finetuning_or_baseline_eval(env_dict, run_training=False, lr_rate=0.0001, epochs=14):
+
+    
+    """
+    Handles model training (if flag is true) and logs the baseline fine-tuned 
+    performance to file metrics.
+    """
+    model = env_dict["base_model"]
+    train_set = env_dict["train_set"]
+    test_set = env_dict["test_set"]
+
+    test_loader = DataLoader(test_set, batch_size=BATCH_SIZE, shuffle=False)
+
+    
+    train_loader = DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True)
+
+    if not run_training:
+        return
+    
+    # Finetuning
+    model.train(epochs=epochs, loader=train_loader, rate=lr_rate)
+    model.save(filename=ARCH.name.lower())
+    print(f"Model saved to trained_models/{ARCH.name.lower()}.pth")
+
+    print(f"Total test images for these {CLASS_SIZE} classes: {len(test_set)}")
+
+    # Evaluate original base checkpoint performance
     current_mode = "Finetuned"
-    if FINETUNE:
-
-        #current_mode = "Finetuned"
-        accuracy, report_dict = model.evaluate(
-            loader = test_loader,
-            mode=current_mode
-        )
-
+    
+    # evaluate finetuned model
+    try:
+        accuracy, report_dict = model.evaluate(loader=test_loader, mode=current_mode)
         Util._log_to_csv(
-            arch=model.__class__.__name__, 
-            mode = current_mode, 
+            arch=ARCH.name,#model.__class__.__name__, 
+            mode=current_mode, 
             accuracy=accuracy, 
             report_dict=report_dict, 
             strategy="base"
         )
+    except Exception as e:
+        print(f">> Skipping baseline log generation. Reason: {e}")
 
-    # unlearning algorithms
-    #linear_filtration = LinearFiltration(target_class_index=FORGET_CLASS_IDX)
-    #filtration.apply(reloaded.model)
 
-    #weight_filtration = WeightFiltration(num_classes = CLASS_SIZE,target_class_idx=FORGET_CLASS_IDX)
-    #weight_filtration.apply(reloaded.model)
+# saves evaluation metrics to log files
+def log_metrics(evaluation_domains, reloaded, strategy_in_use):
+    
+    # Process and append metrics to target reporting paths
+    for domain in evaluation_domains:
+        print(domain["label"])
+        accuracy, report_dict = reloaded.evaluate(loader=domain["loader"], mode=domain["mode"])
+        Util._log_to_csv(
+            arch=ARCH.name,
+            mode=domain["mode"], 
+            accuracy=accuracy, 
+            report_dict=report_dict, 
+            strategy=strategy_in_use
+        )
 
-    certified_removal = CertifiedRemoval(
-        target_class_index=FORGET_CLASS_IDX,
-        s1=2,
-        s2=500,
-        unlearn_bs=2,
-        scale=100.0,    # Drop scale to match lower s2 depth
-        std=0.00001)
-    #,removal_bound=0.05, epsilon=0.5, l2_reg=15)
-    #certified_removal.apply(reloaded.model)
+# Unlearning and strategy eval
+def run_unlearning_and_strategy_eval(env_dict, forget_class_idx, strategy, evaluate = False, suite_runner=None):
+    """
+    Reloads a clean model state, applies the isolated unlearning framework,
+    and runs specific target evaluation domain checks.
+    """
+    device = env_dict["device"]
+    train_set = env_dict["train_set"]
+    test_set = env_dict["test_set"]
 
-    # to be unlearned
-    forget_train_loader, retain_train_loader = get_unlearning_loaders(
-        dataset=train_data, 
-        forget_class_idx=FORGET_CLASS_IDX, 
-        batch_size=BATCH_SIZE
+
+    # Segment specific unlearning loaders using class index boundaries
+    retain_train_loader , forget_train_loader= get_unlearning_loaders(
+        dataset=train_set, forget_class_idx=forget_class_idx, batch_size=BATCH_SIZE
+    )
+    retain_test_loader, forget_test_loader = get_unlearning_loaders(
+        dataset=test_set, forget_class_idx=forget_class_idx, batch_size=BATCH_SIZE
     )
 
-    # to evaluate
-    forget_test_loader, retain_test_loader = get_unlearning_loaders(
-        dataset=test_data, 
-        forget_class_idx=FORGET_CLASS_IDX, 
-        batch_size=BATCH_SIZE
-    )
+    # Instantiate a clean copy of the model to keep weights isolated
+    reloaded = Model.create(arch=ARCH, device=device, size=CLASS_SIZE)
+    reloaded.load(arch=ARCH)
+
+    # Clean un-manipulated snapshot to serve as the Parameter-Space shadow proxy reference
+    shadow_model = copy.deepcopy(reloaded)
+
+    if evaluate:
+        reloaded.evaluate(
+            loader=retain_test_loader, mode="finetuned"
+        )
+
+    print("fine tunned model loaded into evaluation sandbox")
+
+    # Execute strategic parameter unlearning step
+    # we are using only training data to unlearn. 
+    # Test data is never touched here.
+    unlearned = strategy.apply(reloaded.model, train_set)
+    strategy_in_use = strategy.__class__.__name__
+
+    if isinstance(unlearned,nn.Module):
+        reloaded.model = unlearned
+    else:
+        reloaded = unlearned
 
 
-    #strategies = [linear_filtration, weight_filtration, certified_removal]
-    strategies = [certified_removal]
-    for strategy in strategies:
-        # test again
-        reloaded = Model.create(
-            arch=arch,
-            device = device,
-            size = CLASS_SIZE
+
+    is_retrained = isinstance(strategy, Retrain)
+
+    if is_retrained:
+        os.makedirs("trained_models", exist_ok=True)
+        reloaded.save(filename=f"class_{forget_class_idx}_retrained.pth")
+
+
+
+    # here we add a condition conditional statement
+    if suite_runner is not None:
+
+        test_loader = DataLoader(test_set, batch_size=BATCH_SIZE, shuffle=False)
+            
+        suite_runner.run_complete_evaluation(
+            framework_name=strategy_in_use,
+            test_loader = test_loader,
+            target_class=forget_class_idx,
+            forget_train_loader=forget_train_loader,
+            forget_test_loader=forget_test_loader,
+            unlearned_instance=reloaded,            
+            base_shadow_instance=shadow_model,  
+            device=device
+        )
+    
+
+    # Define validation tracking steps dynamically
+    evaluation_domains = [
+        {"loader": retain_test_loader, "mode": "retain", "label": "\n--- Performance on Retained Classes"},
+        {"loader": forget_test_loader, "mode": "forget", "label": "\n--- Performance on Forgotten Class"},
+        {"loader": forget_train_loader, "mode": "forget_train", "label": "\n--- Performance on Forgotten Class (Train Set - Verifying Unlearning)"}
+    ]
+    log_metrics(evaluation_domains, reloaded, strategy_in_use)
+
+
+# entry
+if __name__ == "__main__":
+
+    outer_loop = 10
+
+    inner_loop = CLASS_SIZE
+    
+    for k in range(outer_loop):
+
+        try:
+            # Data Infrastructure and Architecture
+            runtime_environment = prepare_data_and_model_environment()
+            
+            # Baseline Evaluation 
+            # switch finetuning for tests on strategies only, 
+            # to avoid finetunning every time we test a strategy 
+            finetuning = False
+            run_finetuning_or_baseline_eval(runtime_environment, run_training = finetuning)
+            # scale 16400.0 for ResNet
+            scale = 20100
+            # batch 8 for resNet,
+            unlearning_batches = 16
+            # regularis
+            # strategies
+            # implementation of Certified Removal for DNNs
+            certified_unlearning = CertifiedUnlearning(
+                target_class_index=0,      #arch  ResNet18  GoogLeNet   Inception
+                l2_reg=0.000002 ,              # 0.000002     0.00001         0.0
+                gamma=0.01,                     #      0.1         0.1        0.01
+                scale=  scale,                 #  16400.0     35000.0
+                s1=2,                           #        2
+                s2=350,                         #      300
+                std=0.00001,                    #  0.00001
+                unlearn_bs=unlearning_batches   #        8         32          8
             )
-        reloaded.load(arch = arch)
-        print("fine tunned model loaded")
-        # reloaded.evaluate(
-        #   loader = test_loader
-        #)
 
-        if not FINETUNE:
-            reloaded.evaluate(
-            loader = test_loader,
-            mode=current_mode
-        )
+            # Normalisation Filtration
+            linear_filtration = LinearFiltration(
 
-        # Unlearning
-        # train loaders passed here
-        strategy.apply(reloaded.model, forget_train_loader, retain_train_loader)
-        # Performance Analysis
-        strategy_in_use = strategy.__class__.__name__
+                target_class_index=0,
+                num_classes=CLASS_SIZE
+            )
+            # WF-Net
+            weight_filtration = WeightFiltration(
+                target_class_index=0,      #arch  ResNet18  GoogLeNet/Inception
+                epochs=6,                       #   
+                lr=250.0, # ResNet18 = 150      #      150        100
+                gamma=0.001,                    #    0.001      
+                lambda_1=30,                 #       25        100
+                arch=ARCH                   
+            )
 
-        # evaluation on retain Test_set
-        current_mode = "retain"
-        print("\n--- Performance on Retained Classes")
-        accuracy, report_dict = reloaded.evaluate(loader=retain_test_loader, mode=current_mode)
+            retrain = Retrain(
+                target_class_index = 0,
+                arch = ARCH,
+                size = CLASS_SIZE,
+                lr = 0.0001,
+                epochs = 14
 
-        Util._log_to_csv(
-            arch=reloaded.__class__.__name__, 
-            mode = current_mode, 
-            accuracy=accuracy, 
-            report_dict=report_dict, 
-            strategy=strategy_in_use
-        )
+            )
+
+            strategies = [
+                retrain,
+                linear_filtration,
+                weight_filtration,
+                certified_unlearning,
+            ]
+            suite_runner = UnlearningAttack(arch=ARCH, class_size=CLASS_SIZE)
+                # Unlearning Iteration
+            for i in range(inner_loop):
+
+                for strategy in strategies:
+
+                    # update target class to be unlearned
+                    strategy.set_target_class(i)
+                    print(f"Unlearning class {i} with {strategy.strategy_name}")
+
+                    # forget
+                    run_unlearning_and_strategy_eval(
+                        runtime_environment, 
+                        forget_class_idx=i, 
+                        strategy=strategy,
+                        # if we are finetuning, no need to evaluate base model. 
+                        # or may be never when not either!
+                        evaluate = not finetuning,
+                        suite_runner=suite_runner
+                    )
 
 
-        # evaluation on forget Test_set
-        print("\n--- Performance on Forgotten Class")
-        current_mode = "forget"
-        accuracy, report_dict = reloaded.evaluate(loader=forget_test_loader,mode=current_mode)
-        Util._log_to_csv(
-            arch=reloaded.__class__.__name__, 
-            mode = current_mode, 
-            accuracy=accuracy, 
-            report_dict=report_dict, 
-            strategy=strategy_in_use
-        )
-
-        # evaluation on forget Train_set
-        # we expect this to be equal or highr than accuracy on Forget Test_set
-        current_mode = "forget_train"
-        print("\n--- Performance on Forgotten Class (Train Set - Verifying Unlearning)")
-        accuracy, report_dict = reloaded.evaluate(loader=forget_train_loader, mode=current_mode)
-        Util._log_to_csv(
-            arch= reloaded.__class__.__name__, 
-            mode = current_mode, 
-            accuracy=accuracy, 
-            report_dict=report_dict, 
-            strategy=strategy_in_use
-        )
\ No newline at end of file
+        except KeyboardInterrupt:
+            print("\nprogram interrupted. Exit!")
+            break
diff --git a/Tune_new.py b/Tune_new.py
deleted file mode 100644
index a221003..0000000
--- a/Tune_new.py
+++ /dev/null
@@ -1,342 +0,0 @@
-import torch
-import torch.nn as nn
-from torch.utils.data import DataLoader
-from sklearn.metrics import classification_report
-import copy
-
-# Framework and Utility Imports
-import SetUp
-import Util
-from sets.Data import *
-from sets.IdentitySubset import IdentitySubset
-from architectures.Model import Model, Architecture
-from unlearning.CertifiedUnlearning import CertifiedUnlearning
-from unlearning.LinearFiltration import LinearFiltration
-from unlearning.WeightFiltration import WeightFiltration
-from eval.UnlearningAttack import UnlearningAttack
-from unlearning.Retrain import Retrain
-
-
-# Global Hyperparameters
-CLASS_SIZE = 20
-BATCH_SIZE = 16
-SAMPLE_SIZE = 30
-TRAINING_SAMPLE = 27
-
-# depends on model architecture
-# ResNet, DenseNet = 224
-# Inception = 299
-RESOLUTION = 224
-
-# specify the model architecture,
-# Options here are the following
-'''
-    RESNET18 # candidate
-    RESNET50 
-    RESNET34 
-    INCEPTION  # candidate / or googleNet
-    DENSENET121 # candidate
-    GOOGLENET  # candidate / or Inception
-    EFFICIENTNET # candidate
-    SHUFFLENET 
-    WIDE_RESNET 
-'''
-ARCH = Architecture.RESNET34
-
-
-# Data preparation and model setup
-def prepare_data_and_model_environment():
-    """
-    Handles environment discovery, downloads/loads datasets, generates 
-    train-test class splits, and configures the architecture base.
-    """
-    device = SetUp.get_device()
-    dataset_name = Set_Name.CASIAFACES
-    if dataset_name == Set_Name.CASIAFACES:
-        SAMPLE_SIZE = 400
-        TRAINING_SAMPLE = 320
-
-    dataset = get_set(set_name=dataset_name)
-    print(f"> {dataset.__class__.__name__} dataset loaded")
-
-    # Select target identities (deterministic top sample identities)
-    selected_identities = select_top_ids(dataset=dataset, class_size=CLASS_SIZE)
-    print(f'> Selected {CLASS_SIZE} random identity classes from {dataset_name.name} dataset.')
-    print(f'> A class has {TRAINING_SAMPLE} train and {SAMPLE_SIZE - TRAINING_SAMPLE} test samples')
-
-    # Isolate sample index partitions
-    train_indices, test_indices = get_indices(
-        dataset=dataset, 
-        identities=selected_identities, 
-        split_at=TRAINING_SAMPLE,
-        size=SAMPLE_SIZE
-    )
-
-    # Remap identities to 0 -> (N-1) range required by CrossEntropyLoss
-    id_map = {old_id: new_id for new_id, old_id in enumerate(selected_identities)}
-
-    # Build internal datasets using custom transforms
-    tr_transform = train_transform(RESOLUTION)
-    train_data = IdentitySubset(
-        dataset=dataset, 
-        indices=train_indices, 
-        id_mapping=id_map,
-        transform=tr_transform
-    )
-
-    te_transform = test_transform(RESOLUTION)
-    test_data = IdentitySubset(
-        dataset=dataset, 
-        indices=test_indices, 
-        id_mapping=id_map,
-        transform=te_transform
-    )
-
-    print(f"> Total training images: {len(train_data)}")
-    print(f'> Constants : Classes = {CLASS_SIZE}, Batch = {BATCH_SIZE}')
-
-    # Create the base target model instance
-    base_model = Model.create(arch=ARCH, device=device, size=CLASS_SIZE)
-
-    return {
-        "device": device,
-        "train_data": train_data,
-        "test_data": test_data,
-        "base_model": base_model
-    }
-
-
-# Fine tunning and evaluation
-def run_finetuning_or_baseline_eval(env_dict, run_training=False, lr_rate=0.0001, epochs=14):
-
-    
-    """
-    Handles model training (if flag is true) and logs the baseline fine-tuned 
-    performance to file metrics.
-    """
-    model = env_dict["base_model"]
-    train_data = env_dict["train_data"]
-    test_data = env_dict["test_data"]
-
-    test_loader = DataLoader(test_data, batch_size=BATCH_SIZE, shuffle=False)
-
-    
-    train_loader = DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True)
-
-    if not run_training:
-        return
-    
-    # Finetuning
-    model.train(epochs=epochs, loader=train_loader, rate=lr_rate)
-    model.save(filename=ARCH.name.lower())
-    print(f"Model saved to trained_models/{ARCH.name.lower()}.pth")
-
-    print(f"Total test images for these {CLASS_SIZE} classes: {len(test_data)}")
-
-    # Evaluate original base checkpoint performance
-    current_mode = "Finetuned"
-    
-    # evaluate finetuned model
-    try:
-        accuracy, report_dict = model.evaluate(loader=test_loader, mode=current_mode)
-        Util._log_to_csv(
-            arch=ARCH.name,#model.__class__.__name__, 
-            mode=current_mode, 
-            accuracy=accuracy, 
-            report_dict=report_dict, 
-            strategy="base"
-        )
-    except Exception as e:
-        print(f">> Skipping baseline log generation. Reason: {e}")
-
-
-# saves evaluation metrics to log files
-def log_metrics(evaluation_domains, reloaded, strategy_in_use):
-    
-    # Process and append metrics to target reporting paths
-    for domain in evaluation_domains:
-        print(domain["label"])
-        accuracy, report_dict = reloaded.evaluate(loader=domain["loader"], mode=domain["mode"])
-        Util._log_to_csv(
-            arch=ARCH.name,
-            mode=domain["mode"], 
-            accuracy=accuracy, 
-            report_dict=report_dict, 
-            strategy=strategy_in_use
-        )
-
-# Unlearning and strategy eval
-def run_unlearning_and_strategy_eval(env_dict, forget_class_idx, strategy, evaluate = False, suite_runner=None):
-    """
-    Reloads a clean model state, applies the isolated unlearning framework,
-    and runs specific target evaluation domain checks.
-    """
-    device = env_dict["device"]
-    train_data = env_dict["train_data"]
-    test_data = env_dict["test_data"]
-
-
-    # Segment specific unlearning loaders using class index boundaries
-    retain_train_loader , forget_train_loader= get_unlearning_loaders(
-        dataset=train_data, forget_class_idx=forget_class_idx, batch_size=BATCH_SIZE
-    )
-    retain_test_loader, forget_test_loader = get_unlearning_loaders(
-        dataset=test_data, forget_class_idx=forget_class_idx, batch_size=BATCH_SIZE
-    )
-
-    # Instantiate a clean copy of the model to keep weights isolated
-    reloaded = Model.create(arch=ARCH, device=device, size=CLASS_SIZE)
-    reloaded.load(arch=ARCH)
-
-    # Clean un-manipulated snapshot to serve as the Parameter-Space shadow proxy reference
-    shadow_model = copy.deepcopy(reloaded)
-
-    if evaluate:
-        reloaded.evaluate(
-            loader=retain_test_loader, mode="finetuned"
-        )
-
-    print("fine tunned model loaded into evaluation sandbox")
-
-    # Execute strategic parameter unlearning step
-    # we are using only training data to unlearn. 
-    # Test data is never touched here.
-    unlearned = strategy.apply(reloaded.model, train_data)
-    strategy_in_use = strategy.__class__.__name__
-
-    if isinstance(unlearned,nn.Module):
-        reloaded.model = unlearned
-    else:
-        reloaded = unlearned
-
-
-
-    is_retrained = isinstance(strategy, Retrain)
-
-    if is_retrained:
-        os.makedirs("trained_models", exist_ok=True)
-        reloaded.save(filename=f"class_{forget_class_idx}_retrained.pth")
-
-
-
-    # here we add a condition conditional statement
-    if suite_runner is not None:
-            
-        suite_runner.run_complete_evaluation(
-            framework_name=strategy_in_use,
-            target_class=forget_class_idx,
-            forget_loader=forget_train_loader,
-            retain_test_loader=forget_test_loader,
-            unlearned_instance=reloaded,            
-            base_shadow_instance=shadow_model,  
-            device=device
-        )
-    
-
-    # Define validation tracking steps dynamically
-    evaluation_domains = [
-        {"loader": retain_test_loader, "mode": "retain", "label": "\n--- Performance on Retained Classes"},
-        {"loader": forget_test_loader, "mode": "forget", "label": "\n--- Performance on Forgotten Class"},
-        {"loader": forget_train_loader, "mode": "forget_train", "label": "\n--- Performance on Forgotten Class (Train Set - Verifying Unlearning)"}
-    ]
-    log_metrics(evaluation_domains, reloaded, strategy_in_use)
-
-
-# entry
-if __name__ == "__main__":
-
-    outer_loop = 1
-    inner_loop = CLASS_SIZE
-    
-    for k in range(outer_loop):
-
-        try:
-            # Data Infrastructure and Architecture
-            runtime_environment = prepare_data_and_model_environment()
-            
-            # Baseline Evaluation 
-            # switch finetuning for tests on strategies only, 
-            # to avoid finetunning every time we test a strategy 
-            finetuning = False
-            run_finetuning_or_baseline_eval(runtime_environment, run_training = finetuning)
-            # scale 16400.0 for ResNet
-            scale = 20100
-            # batch 8 for resNet,
-            unlearning_batches = 16
-            # regularis
-            # strategies
-            # implementation of Certified Removal for DNNs
-            certified_unlearning = CertifiedUnlearning(
-                target_class_index=0,      #arch  ResNet18  GoogLeNet   Inception
-                l2_reg=0.000002 ,              # 0.000002     0.00001         0.0
-                gamma=0.01,                     #      0.1         0.1        0.01
-                scale=  scale,                 #  16400.0     35000.0
-                s1=2,                           #        2
-                s2=350,                         #      300
-                std=0.00001,                    #  0.00001
-                unlearn_bs=unlearning_batches   #        8         32          8
-            )
-
-            # Normalisation Filtration
-            linear_filtration = LinearFiltration(
-
-                target_class_index=0,
-                num_classes=CLASS_SIZE
-            )
-            # WF-Net
-            weight_filtration = WeightFiltration(
-                target_class_index=0,      #arch  ResNet18  GoogLeNet/Inception
-                epochs=6,                       #   
-                lr=250.0, # ResNet18 = 150      #      150        100
-                gamma=0.001,                    #    0.001      
-                lambda_1=30,                 #       25        100
-                arch=ARCH                   
-            )
-
-            retrain = Retrain(
-                target_class_index = 0,
-                arch = ARCH,
-                size = CLASS_SIZE,
-                lr = 0.0001,
-                epochs = 14
-
-            )
-
-            strategies = [
-                retrain,
-                linear_filtration,
-                weight_filtration,
-                certified_unlearning,
-            ]
-            suite_runner = UnlearningAttack(arch=ARCH, class_size=CLASS_SIZE)
-                # Unlearning Iteration
-            for i in range(inner_loop):
-
-                for strategy in strategies:
-
-                    # update target class to be unlearned
-                    strategy.set_target_class(i)
-                    print(f"Unlearning class {i} with {strategy.strategy_name}")
-
-                    # forget
-                    run_unlearning_and_strategy_eval(
-                        runtime_environment, 
-                        forget_class_idx=i, 
-                        strategy=strategy,
-                        # if we are finetuning, no need to evaluate base model. 
-                        # or may be never when not either!
-                        evaluate = not finetuning,
-                        suite_runner=suite_runner
-                    )
-                # just a single class run before running all remaining classes.
-                #print(">> Single check run complete. Verification successful!")
-                #break
-                #dist_attacker.run_adversarial_evaluation()
-                #dist_attacker.run_incremental_evaluation(current_class_step=i)
-
-            #if suite_runner is not None:
-                #suite_runner.shutdown_hook()
-
-        except KeyboardInterrupt:
-            print("\nprogram interrupted. Exit!")
-            break
diff --git a/eval/UnlearningAttack.py b/eval/UnlearningAttack.py
index d2d8ca5..b889b98 100644
--- a/eval/UnlearningAttack.py
+++ b/eval/UnlearningAttack.py
@@ -39,6 +39,7 @@ class UnlearningAttack:
         
         # JS Distance is the square root of JS Divergence
         return np.mean(jensenshannon(np.array(probs1), np.array(probs2), axis=1))
+    
 
     def calculate_a_dist(self, latent1, latent2):
 
@@ -79,10 +80,29 @@ class UnlearningAttack:
             np.array(all_losses).reshape(-1, 1)
         ])
 
-    def run_parameter_space_mia(self, unlearned_model, shadow_model, forget_loader, retain_test_loader, device, index):
+    def run_parameter_space_mia(
+            self, 
+            unlearned_model, 
+            shadow_model, 
+            forget_train_loader, 
+            forget_test_loader, 
+            device, 
+            index
+        ):
         
-        X_shadow_mem = self._extract_attack_features(shadow_model, forget_loader, device, index)
-        X_shadow_non = self._extract_attack_features(shadow_model, retain_test_loader, device, index)
+        X_shadow_mem = self._extract_attack_features(
+            shadow_model, 
+            forget_train_loader, 
+            device, 
+            index
+        )
+
+        X_shadow_non = self._extract_attack_features(
+            shadow_model, 
+            forget_test_loader, 
+            device, 
+            index
+        )
         
         # Train MIA Classifier
         min_train = min(len(X_shadow_mem), len(X_shadow_non))
@@ -93,8 +113,8 @@ class UnlearningAttack:
         attack_classifier.fit(X_train, y_train)
         
         # Evaluate MIA
-        X_eval_mem = self._extract_attack_features(unlearned_model, forget_loader, device, index)
-        X_eval_non = self._extract_attack_features(unlearned_model, retain_test_loader, device, index)
+        X_eval_mem = self._extract_attack_features(unlearned_model, forget_train_loader, device, index)
+        X_eval_non = self._extract_attack_features(unlearned_model, forget_test_loader, device, index)
         
         min_test = min(len(X_eval_mem), len(X_eval_non))
         X_test = np.vstack([X_eval_mem[:min_test], X_eval_non[:min_test]])
@@ -138,7 +158,15 @@ class UnlearningAttack:
         # evaluate similarity of outputs
         return accuracy_score(label_test, adversary.predict(data_test))
 
-    def run_logit_space_lookalike_mia(self, filtered_model, naive_retrained, forget_loader, device, target_class):
+    def run_logit_space_lookalike_mia(
+            self, 
+            filtered_model, 
+            naive_retrained, 
+            test_loader, 
+            device, 
+            target_class
+        ):
+
         filtered_model.eval()
         naive_retrained.eval()
         
@@ -146,7 +174,7 @@ class UnlearningAttack:
         naive_logits = []
         
         with torch.no_grad():
-            for data, _ in forget_loader:
+            for data, _ in test_loader:
                 data = data.to(device)
                 
                 if filtered_model.__class__.__name__ == "WF_Module":
@@ -169,8 +197,20 @@ class UnlearningAttack:
         # so that the metric is between 0 and 1.
         return 2.0 * np.abs(lookalike_accuracy - 0.5)
 
-    def run_complete_evaluation(self, framework_name, target_class, forget_loader, retain_test_loader, unlearned_instance, base_shadow_instance, device):
-        """Orchestrates specific pipeline routing cleanly using cached constructor parameters."""
+    def run_complete_evaluation(
+            self, 
+            framework_name, 
+            target_class, 
+            forget_train_loader, 
+            forget_test_loader,
+            test_loader, 
+            unlearned_instance, 
+            base_shadow_instance, 
+            device
+        ):
+        
+
+        # load from disk if saved model available
         target_dir = os.path.join("reports", framework_name)
         os.makedirs(target_dir, exist_ok=True)
         current_log_file = os.path.join(target_dir, "attack_values.csv")
@@ -179,12 +219,12 @@ class UnlearningAttack:
             with open(current_log_file, "w") as f:
                 f.write("target_class, parameter_mia_accuracy, latent_distance_tell, lookalike_accuracy, A-Dist, JS-Dist\n")
 
-        # 1. Parameter-Space MIA and Latent Distance
+        # Parameter-Space MIA 
         parameter_mia_acc =  self.run_parameter_space_mia(
             unlearned_model=unlearned_instance.model,
             shadow_model=base_shadow_instance.model,
-            forget_loader=forget_loader,
-            retain_test_loader=retain_test_loader,
+            forget_train_loader=forget_train_loader,
+            forget_test_loader=forget_test_loader,
             device=device,
             index=target_class
         )
@@ -206,17 +246,23 @@ class UnlearningAttack:
         lookalike_acc = self.run_logit_space_lookalike_mia(
             filtered_model=unlearned_instance.model, 
             naive_retrained=reference_model_torch, 
-            forget_loader=forget_loader, 
+            test_loader=test_loader, 
             device=device,
             target_class=target_class
         )
 
         # Calculate JS-Dist
-        js_dist = self.calculate_js_dist(unlearned_instance.model, reference_model_torch, forget_loader, device, target_class)
+        js_dist = self.calculate_js_dist(
+            unlearned_instance.model, 
+            reference_model_torch, 
+            forget_train_loader, 
+            device, 
+            target_class
+        )
 
         # Extract features
-        unlearned_features = self._extract_attack_features(unlearned_instance.model, forget_loader, device, target_class)
-        retrained_features = self._extract_attack_features(reference_model_torch, forget_loader, device, target_class)
+        unlearned_features = self._extract_attack_features(unlearned_instance.model, forget_train_loader, device, target_class)
+        retrained_features = self._extract_attack_features(reference_model_torch, forget_train_loader, device, target_class)
 
         # Calculate A-Dist using these features
         a_dist = self.calculate_a_dist(unlearned_features, retrained_features)
@@ -225,7 +271,7 @@ class UnlearningAttack:
         print(f"[{framework_name}] Class {target_class} | Parameter MIA: {parameter_mia_acc:.4f}  Lookalike: {lookalike_acc:.4f}" )
         
         with open(current_log_file, "a") as f:
-            f.write(f"{target_class},{parameter_mia_acc:.6f},{0.00000},{lookalike_acc:.6f}, {a_dist:.6f}, {js_dist:.6f}\n")
+            f.write(f"{target_class},{parameter_mia_acc:.6f},{lookalike_acc:.6f}, {a_dist:.6f}, {js_dist:.6f}\n")
             
         return {
             "parameter_mia_accuracy": parameter_mia_acc,
diff --git a/reports/CertifiedUnlearning/RESNET34/forget.csv b/reports/CertifiedUnlearning/RESNET34/forget.csv
index 74d6ea6..d11381d 100644
--- a/reports/CertifiedUnlearning/RESNET34/forget.csv
+++ b/reports/CertifiedUnlearning/RESNET34/forget.csv
@@ -376,3 +376,57 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.1750,1.0000,0.1750,0.2979,1.0000,0.1750,0.2979
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.1250,1.0000,0.1250,0.2222,1.0000,0.1250,0.2222
+0.1000,1.0000,0.1000,0.1818,1.0000,0.1000,0.1818
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0125,1.0000,0.0125,0.0247,1.0000,0.0125,0.0247
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.5750,1.0000,0.5750,0.7302,1.0000,0.5750,0.7302
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.6000,1.0000,0.6000,0.7500,1.0000,0.6000,0.7500
+0.1125,1.0000,0.1125,0.2022,1.0000,0.1125,0.2022
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.2375,1.0000,0.2375,0.3838,1.0000,0.2375,0.3838
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.4500,1.0000,0.4500,0.6207,1.0000,0.4500,0.6207
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.3250,1.0000,0.3250,0.4906,1.0000,0.3250,0.4906
+0.0625,1.0000,0.0625,0.1176,1.0000,0.0625,0.1176
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.4375,1.0000,0.4375,0.6087,1.0000,0.4375,0.6087
+0.7125,1.0000,0.7125,0.8321,1.0000,0.7125,0.8321
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.3000,1.0000,0.3000,0.4615,1.0000,0.3000,0.4615
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0250,1.0000,0.0250,0.0488,1.0000,0.0250,0.0488
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
diff --git a/reports/CertifiedUnlearning/RESNET34/forget_train.csv b/reports/CertifiedUnlearning/RESNET34/forget_train.csv
index 0bbc84a..65c729f 100644
--- a/reports/CertifiedUnlearning/RESNET34/forget_train.csv
+++ b/reports/CertifiedUnlearning/RESNET34/forget_train.csv
@@ -376,3 +376,57 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.1594,1.0000,0.1594,0.2749,1.0000,0.1594,0.2749
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.1094,1.0000,0.1094,0.1972,1.0000,0.1094,0.1972
+0.1781,1.0000,0.1781,0.3024,1.0000,0.1781,0.3024
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0125,1.0000,0.0125,0.0247,1.0000,0.0125,0.0247
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.7344,1.0000,0.7344,0.8468,1.0000,0.7344,0.8468
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.7844,1.0000,0.7844,0.8792,1.0000,0.7844,0.8792
+0.1125,1.0000,0.1125,0.2022,1.0000,0.1125,0.2022
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.1812,1.0000,0.1812,0.3069,1.0000,0.1812,0.3069
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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diff --git a/reports/CertifiedUnlearning/RESNET34/retain.csv b/reports/CertifiedUnlearning/RESNET34/retain.csv
index 632b1c2..39eaa69 100644
--- a/reports/CertifiedUnlearning/RESNET34/retain.csv
+++ b/reports/CertifiedUnlearning/RESNET34/retain.csv
@@ -376,3 +376,57 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.9053,0.9214,0.9053,0.9081,0.9214,0.9053,0.9081
 0.0533,0.0343,0.0533,0.0077,0.0343,0.0533,0.0077
 0.7171,0.8599,0.7171,0.7316,0.8599,0.7171,0.7316
+0.9230,0.9359,0.9230,0.9258,0.9359,0.9230,0.9258
+0.8842,0.9131,0.8842,0.8898,0.9131,0.8842,0.8898
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diff --git a/reports/CertifiedUnlearning/attack_values.csv b/reports/CertifiedUnlearning/attack_values.csv
index b3c2a69..8d69cc5 100644
--- a/reports/CertifiedUnlearning/attack_values.csv
+++ b/reports/CertifiedUnlearning/attack_values.csv
@@ -1,33 +1,37 @@
-target_class,parameter_mia_accuracy,latent_distance_tell,lookalike_accuracy
-0,0.500000,7.219862,0.979167
-1,0.500000,3.659238,0.958333
-2,0.500000,6.939345,0.885417
-3,0.500000,7.800516,0.989583
-4,0.500000,6.631370,1.000000
-5,0.500000,7.557893,0.937500
-6,0.500000,8.085600,0.937500
-7,0.500000,7.518537,0.947917
-8,0.500000,9.040818,0.895833
-9,0.500000,8.269253,0.968750
-10,0.500000,5.678658,1.000000
-11,0.450000,6.800340,1.000000
-12,0.500000,7.708953,0.937500
-13,0.500000,6.559650,1.000000
-14,0.500000,8.420962,0.989583
-15,0.500000,7.526255,1.000000
-16,0.500000,6.906671,0.968750
-17,0.500000,5.499599,0.979167
-18,0.500000,8.465704,0.979167
-19,0.500000,5.958429,0.979167
-0,0.500000,5.903083,0.979167
-1,0.500000,4.479048,0.947917
-2,0.500000,4.460630,0.979167
-3,0.500000,9.916990,1.000000
-4,0.500000,4.245532,0.979167
-5,0.500000,5.771674,1.000000
-6,0.500000,6.300947,0.979167
-7,0.500000,6.803962,0.979167
-8,0.500000,9.174347,1.000000
-9,0.500000,7.843153,0.989583
-10,0.500000,5.029855,1.000000
-11,0.500000,7.945799,1.000000
+target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist
+0,0.500000,1.000000, 0.062500, 0.654685
+1,0.500000,0.979167, 0.208333, 0.516170
+2,0.500000,0.958333, 0.187500, 0.526930
+3,0.500000,1.000000, 0.104167, 0.473445
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+9,0.500000,0.968750, 0.083333, 0.611189
+10,0.500000,1.000000, 0.052083, 0.615587
+11,0.500000,1.000000, 0.031250, 0.545611
+12,0.500000,0.968750, 0.093750, 0.434476
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+16,0.500000,0.989583, 0.218750, 0.571814
+17,0.500000,0.989583, 0.062500, 0.503747
+18,0.550000,1.000000, 0.166667, 0.495459
+19,0.500000,0.916667, 0.052083, 0.551996
+
+0,0.500000,0.937500, 0.020833, 0.532267
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+6,0.500000,0.489583, 0.010417, 0.549827
+7,0.500000,0.333333, 0.156250, 0.539906
+8,0.500000,0.668750, 0.041667, 0.477993
+9,0.500000,0.962500, 0.062500, 0.622796
+10,0.500000,1.000000, 0.093750, 0.712293
+11,0.500000,0.939583, 0.187500, 0.511184
diff --git a/reports/CertifiedUnlearning/attack_values_int.csv b/reports/CertifiedUnlearning/attack_values_int.csv
new file mode 100644
index 0000000..e987cf4
--- /dev/null
+++ b/reports/CertifiedUnlearning/attack_values_int.csv
@@ -0,0 +1,34 @@
+target_class,parameter_mia_accuracy,latent_distance_tell,lookalike_accuracy
+0,0.500000,7.219862,0.979167
+1,0.500000,3.659238,0.958333
+2,0.500000,6.939345,0.885417
+3,0.500000,7.800516,0.989583
+4,0.500000,6.631370,1.000000
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+7,0.500000,7.518537,0.947917
+8,0.500000,9.040818,0.895833
+9,0.500000,8.269253,0.968750
+10,0.500000,5.678658,1.000000
+11,0.450000,6.800340,1.000000
+12,0.500000,7.708953,0.937500
+13,0.500000,6.559650,1.000000
+14,0.500000,8.420962,0.989583
+15,0.500000,7.526255,1.000000
+16,0.500000,6.906671,0.968750
+17,0.500000,5.499599,0.979167
+18,0.500000,8.465704,0.979167
+19,0.500000,5.958429,0.979167
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+3,0.500000,9.916990,1.000000
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+10,0.500000,5.029855,1.000000
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+12,0.500000,8.373040,0.979167
diff --git a/reports/LinearFiltration/RESNET34/forget.csv b/reports/LinearFiltration/RESNET34/forget.csv
index 902dc21..6e739a6 100644
--- a/reports/LinearFiltration/RESNET34/forget.csv
+++ b/reports/LinearFiltration/RESNET34/forget.csv
@@ -450,3 +450,64 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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diff --git a/reports/LinearFiltration/RESNET34/forget_train.csv b/reports/LinearFiltration/RESNET34/forget_train.csv
index 902dc21..6e739a6 100644
--- a/reports/LinearFiltration/RESNET34/forget_train.csv
+++ b/reports/LinearFiltration/RESNET34/forget_train.csv
@@ -450,3 +450,64 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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diff --git a/reports/LinearFiltration/RESNET34/retain.csv b/reports/LinearFiltration/RESNET34/retain.csv
index 8f966b4..69432c3 100644
--- a/reports/LinearFiltration/RESNET34/retain.csv
+++ b/reports/LinearFiltration/RESNET34/retain.csv
@@ -450,3 +450,64 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.9533,0.9547,0.9533,0.9535,0.9547,0.9533,0.9535
 0.9546,0.9561,0.9546,0.9549,0.9561,0.9546,0.9549
 0.9513,0.9531,0.9513,0.9516,0.9531,0.9513,0.9516
+0.9526,0.9541,0.9526,0.9529,0.9541,0.9526,0.9529
+0.9533,0.9551,0.9533,0.9536,0.9551,0.9533,0.9536
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+0.9526,0.9541,0.9526,0.9529,0.9541,0.9526,0.9529
+0.9533,0.9551,0.9533,0.9536,0.9551,0.9533,0.9536
+0.9533,0.9545,0.9533,0.9535,0.9545,0.9533,0.9535
+0.9546,0.9562,0.9546,0.9548,0.9562,0.9546,0.9548
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+0.9539,0.9556,0.9539,0.9542,0.9556,0.9539,0.9542
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diff --git a/reports/LinearFiltration/attack_values.csv b/reports/LinearFiltration/attack_values.csv
index 3f03843..6930383 100644
--- a/reports/LinearFiltration/attack_values.csv
+++ b/reports/LinearFiltration/attack_values.csv
@@ -1,35 +1,38 @@
-target_class,parameter_mia_accuracy,latent_distance_tell,lookalike_accuracy
-0,0.500000,3.219560,1.000000
-1,0.500000,3.573733,1.000000
-2,0.500000,3.924550,1.000000
-3,0.500000,3.515182,1.000000
-4,0.500000,3.369485,1.000000
-5,0.500000,4.697085,1.000000
-6,0.500000,4.185269,1.000000
-7,0.500000,3.473583,1.000000
-8,0.500000,4.518519,1.000000
-9,0.500000,4.484542,1.000000
-10,0.500000,3.353875,1.000000
-11,0.500000,3.815037,0.989583
-12,0.500000,3.493994,1.000000
-13,0.500000,4.018041,1.000000
-14,0.500000,4.059689,1.000000
-15,0.500000,4.097189,1.000000
-16,0.500000,3.287521,1.000000
-17,0.500000,3.872488,1.000000
-18,0.500000,3.698952,0.989583
-19,0.500000,3.844830,0.989583
-0,0.500000,3.413494,1.000000
-0,0.500000,3.416399,1.000000
-1,0.500000,3.396268,1.000000
-2,0.500000,3.648122,1.000000
-3,0.500000,3.889339,0.989583
-4,0.500000,3.408539,1.000000
-5,0.500000,4.625342,1.000000
-6,0.500000,3.925502,0.989583
-7,0.500000,3.033561,0.989583
-8,0.500000,3.819170,0.989583
-9,0.500000,4.497495,1.000000
-10,0.500000,3.701839,1.000000
-11,0.500000,4.369391,0.989583
-12,0.500000,3.945474,1.000000
+target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist
+0,0.500000,1.000000, 0.062500, 0.485899
+1,0.500000,1.000000, 0.083333, 0.484178
+2,0.500000,0.989583, 0.010417, 0.525418
+3,0.500000,0.989583, 0.333333, 0.487282
+4,0.500000,1.000000, 0.020833, 0.505002
+5,0.500000,1.000000, 0.093750, 0.491621
+6,0.500000,1.000000, 0.052083, 0.482068
+7,0.500000,0.979167, 0.010417, 0.519800
+8,0.500000,0.989583, 0.020833, 0.485863
+9,0.500000,0.989583, 0.208333, 0.483455
+10,0.500000,1.000000, 0.145833, 0.454615
+11,0.500000,1.000000, 0.333333, 0.495618
+12,0.500000,1.000000, 0.072917, 0.458427
+13,0.500000,1.000000, 0.135417, 0.477052
+14,0.500000,1.000000, 0.020833, 0.472611
+15,0.500000,1.000000, 0.239583, 0.479607
+16,0.500000,1.000000, 0.135417, 0.468667
+17,0.500000,1.000000, 0.208333, 0.469592
+18,0.500000,1.000000, 0.197917, 0.454318
+19,0.500000,0.989583, 0.041667, 0.499034
+0,0.500000,1.000000, 0.072917, 0.483694
+1,0.500000,1.000000, 0.239583, 0.482249
+2,0.500000,0.989583, 0.062500, 0.528699
+3,0.500000,1.000000, 0.177083, 0.488769
+0,0.500000,1.000000, 0.208333, 0.479962
+1,0.500000,1.000000, 0.177083, 0.480766
+2,0.500000,1.000000, 0.156250, 0.530593
+3,0.500000,1.000000, 0.031250, 0.490093
+4,0.500000,1.000000, 0.062500, 0.514937
+5,0.500000,1.000000, 0.041667, 0.483999
+6,0.500000,1.000000, 0.041667, 0.480639
+7,0.500000,1.000000, 0.114583, 0.516643
+8,0.500000,1.000000, 0.322917, 0.482295
+9,0.500000,1.000000, 0.156250, 0.482486
+10,0.500000,1.000000, 0.208333, 0.454807
+11,0.500000,1.000000, 0.197917, 0.496418
+12,0.500000,1.000000, 0.083333, 0.449275
diff --git a/reports/LinearFiltration/attack_values_int.csv b/reports/LinearFiltration/attack_values_int.csv
new file mode 100644
index 0000000..3f03843
--- /dev/null
+++ b/reports/LinearFiltration/attack_values_int.csv
@@ -0,0 +1,35 @@
+target_class,parameter_mia_accuracy,latent_distance_tell,lookalike_accuracy
+0,0.500000,3.219560,1.000000
+1,0.500000,3.573733,1.000000
+2,0.500000,3.924550,1.000000
+3,0.500000,3.515182,1.000000
+4,0.500000,3.369485,1.000000
+5,0.500000,4.697085,1.000000
+6,0.500000,4.185269,1.000000
+7,0.500000,3.473583,1.000000
+8,0.500000,4.518519,1.000000
+9,0.500000,4.484542,1.000000
+10,0.500000,3.353875,1.000000
+11,0.500000,3.815037,0.989583
+12,0.500000,3.493994,1.000000
+13,0.500000,4.018041,1.000000
+14,0.500000,4.059689,1.000000
+15,0.500000,4.097189,1.000000
+16,0.500000,3.287521,1.000000
+17,0.500000,3.872488,1.000000
+18,0.500000,3.698952,0.989583
+19,0.500000,3.844830,0.989583
+0,0.500000,3.413494,1.000000
+0,0.500000,3.416399,1.000000
+1,0.500000,3.396268,1.000000
+2,0.500000,3.648122,1.000000
+3,0.500000,3.889339,0.989583
+4,0.500000,3.408539,1.000000
+5,0.500000,4.625342,1.000000
+6,0.500000,3.925502,0.989583
+7,0.500000,3.033561,0.989583
+8,0.500000,3.819170,0.989583
+9,0.500000,4.497495,1.000000
+10,0.500000,3.701839,1.000000
+11,0.500000,4.369391,0.989583
+12,0.500000,3.945474,1.000000
diff --git a/reports/Retrain/RESNET34/forget.csv b/reports/Retrain/RESNET34/forget.csv
index 7b97a60..f501dc1 100644
--- a/reports/Retrain/RESNET34/forget.csv
+++ b/reports/Retrain/RESNET34/forget.csv
@@ -80,3 +80,65 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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diff --git a/reports/Retrain/RESNET34/forget_train.csv b/reports/Retrain/RESNET34/forget_train.csv
index 7b97a60..f501dc1 100644
--- a/reports/Retrain/RESNET34/forget_train.csv
+++ b/reports/Retrain/RESNET34/forget_train.csv
@@ -80,3 +80,65 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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diff --git a/reports/Retrain/RESNET34/retain.csv b/reports/Retrain/RESNET34/retain.csv
index 4ef5e70..322a08a 100644
--- a/reports/Retrain/RESNET34/retain.csv
+++ b/reports/Retrain/RESNET34/retain.csv
@@ -80,3 +80,65 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.9546,0.9554,0.9546,0.9546,0.9554,0.9546,0.9546
 0.9612,0.9620,0.9612,0.9613,0.9620,0.9612,0.9613
 0.9539,0.9551,0.9539,0.9540,0.9551,0.9539,0.9540
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diff --git a/reports/Retrain/attack_values.csv b/reports/Retrain/attack_values.csv
index 382b135..f1c38c0 100644
--- a/reports/Retrain/attack_values.csv
+++ b/reports/Retrain/attack_values.csv
@@ -1,35 +1,41 @@
-target_class,parameter_mia_accuracy,latent_distance_tell,lookalike_accuracy
-0,0.500000,11.573492,0.000000
-1,0.500000,12.793120,0.000000
-2,0.500000,12.951434,0.000000
-3,0.500000,10.942259,0.000000
-4,0.500000,10.675704,0.000000
-5,0.500000,13.046491,0.000000
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-11,0.450000,10.556920,0.000000
-12,0.500000,10.925443,0.000000
-13,0.500000,12.007375,0.000000
-14,0.500000,11.471087,0.000000
-15,0.500000,12.301955,0.000000
-16,0.500000,10.831913,0.000000
-17,0.500000,10.942777,0.000000
-18,0.500000,11.699934,0.000000
-19,0.500000,12.526654,0.000000
-0,0.500000,11.601441,0.000000
-0,0.500000,11.639872,0.000000
-1,0.500000,12.723348,0.000000
-2,0.500000,13.151769,0.000000
-3,0.500000,10.969514,0.000000
-4,0.500000,10.674448,0.000000
-5,0.500000,13.068334,0.000000
-6,0.500000,11.328772,0.000000
-7,0.500000,9.926997,0.000000
-8,0.500000,12.859781,0.000000
-9,0.500000,12.061253,0.000000
-10,0.500000,11.358070,0.000000
-11,0.500000,10.484346,0.000000
-12,0.500000,10.926878,0.000000
+target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist
+0,0.500000,0.000000, 0.156250, 0.000000
+1,0.500000,0.000000, 0.104167, 0.000000
+2,0.500000,0.000000, 0.114583, 0.000000
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diff --git a/reports/Retrain/attack_values_int.csv b/reports/Retrain/attack_values_int.csv
new file mode 100644
index 0000000..9d2f4a3
--- /dev/null
+++ b/reports/Retrain/attack_values_int.csv
@@ -0,0 +1,38 @@
+target_class,parameter_mia_accuracy,latent_distance_tell,lookalike_accuracy
+0,0.500000,11.573492,0.000000
+1,0.500000,12.793120,0.000000
+2,0.500000,12.951434,0.000000
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+14,0.500000,11.471087,0.000000
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+0,0.500000,11.601441,0.000000
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diff --git a/reports/WeightFiltration/RESNET34/forget.csv b/reports/WeightFiltration/RESNET34/forget.csv
index 0eae8ef..57d1b81 100644
--- a/reports/WeightFiltration/RESNET34/forget.csv
+++ b/reports/WeightFiltration/RESNET34/forget.csv
@@ -539,3 +539,64 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0250,1.0000,0.0250,0.0488,1.0000,0.0250,0.0488
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+0.1000,1.0000,0.1000,0.1818,1.0000,0.1000,0.1818
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+0.0875,1.0000,0.0875,0.1609,1.0000,0.0875,0.1609
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
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diff --git a/reports/WeightFiltration/RESNET34/forget_train.csv b/reports/WeightFiltration/RESNET34/forget_train.csv
index b5f4f81..89d0f7e 100644
--- a/reports/WeightFiltration/RESNET34/forget_train.csv
+++ b/reports/WeightFiltration/RESNET34/forget_train.csv
@@ -539,3 +539,64 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0063,1.0000,0.0063,0.0124,1.0000,0.0063,0.0124
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0312,1.0000,0.0312,0.0606,1.0000,0.0312,0.0606
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.2594,1.0000,0.2594,0.4119,1.0000,0.2594,0.4119
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.1250,1.0000,0.1250,0.2222,1.0000,0.1250,0.2222
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0781,1.0000,0.0781,0.1449,1.0000,0.0781,0.1449
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0063,1.0000,0.0063,0.0124,1.0000,0.0063,0.0124
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+0.0187,1.0000,0.0187,0.0368,1.0000,0.0187,0.0368
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.1313,1.0000,0.1313,0.2320,1.0000,0.1313,0.2320
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+0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062
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+0.0094,1.0000,0.0094,0.0186,1.0000,0.0094,0.0186
diff --git a/reports/WeightFiltration/RESNET34/retain.csv b/reports/WeightFiltration/RESNET34/retain.csv
index f30418e..aebc5be 100644
--- a/reports/WeightFiltration/RESNET34/retain.csv
+++ b/reports/WeightFiltration/RESNET34/retain.csv
@@ -539,3 +539,64 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.9520,0.9534,0.9520,0.9522,0.9534,0.9520,0.9522
 0.9539,0.9554,0.9539,0.9542,0.9554,0.9539,0.9542
 0.9539,0.9559,0.9539,0.9543,0.9559,0.9539,0.9543
+0.9500,0.9518,0.9500,0.9503,0.9518,0.9500,0.9503
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+0.9507,0.9515,0.9507,0.9507,0.9515,0.9507,0.9507
+0.9526,0.9548,0.9526,0.9530,0.9548,0.9526,0.9530
+0.9520,0.9542,0.9520,0.9524,0.9542,0.9520,0.9524
+0.9520,0.9528,0.9520,0.9520,0.9528,0.9520,0.9520
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diff --git a/reports/WeightFiltration/attack_values.csv b/reports/WeightFiltration/attack_values.csv
index 0d2b8d7..33fd866 100644
--- a/reports/WeightFiltration/attack_values.csv
+++ b/reports/WeightFiltration/attack_values.csv
@@ -1,35 +1,38 @@
-target_class,parameter_mia_accuracy,latent_distance_tell,lookalike_accuracy
-0,0.500000,1.180800,0.958333
-1,0.500000,1.279257,0.968750
-2,0.500000,1.717911,0.937500
-3,0.500000,1.354225,0.989583
-4,0.500000,1.308562,0.968750
-5,0.500000,2.087495,0.947917
-6,0.500000,1.793863,0.927083
-7,0.500000,1.213634,0.937500
-8,0.500000,1.705230,0.979167
-9,0.500000,1.713113,0.979167
-10,0.500000,1.358761,0.968750
-11,0.500000,1.485312,1.000000
-12,0.500000,1.393625,0.947917
-13,0.500000,1.677361,0.947917
-14,0.500000,1.734759,0.906250
-15,0.500000,1.715959,0.968750
-16,0.500000,1.180073,0.947917
-17,0.500000,1.597037,0.947917
-18,0.500000,1.491600,0.947917
-19,0.500000,1.622301,0.968750
-0,0.500000,1.562955,0.968750
-0,0.500000,1.526132,0.979167
-1,0.500000,1.323757,0.968750
-2,0.500000,1.493928,0.958333
-3,0.500000,1.643880,0.989583
-4,0.500000,1.479018,0.989583
-5,0.500000,2.176565,0.958333
-6,0.500000,1.620670,0.958333
-7,0.500000,0.977823,0.937500
-8,0.500000,1.455012,0.968750
-9,0.500000,2.145126,0.989583
-10,0.500000,1.905214,0.895833
-11,0.500000,2.171935,0.958333
-12,0.500000,1.761187,0.937500
+target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist
+0,0.500000,0.979167, 0.239583, 0.537877
+1,0.500000,0.958333, 0.125000, 0.534234
+2,0.500000,0.906250, 0.062500, 0.566967
+3,0.500000,0.968750, 0.104167, 0.561083
+4,0.500000,0.968750, 0.197917, 0.569236
+5,0.500000,0.989583, 0.010417, 0.562998
+6,0.500000,0.979167, 0.145833, 0.534611
+7,0.500000,0.989583, 0.114583, 0.606172
+8,0.500000,0.927083, 0.010417, 0.531469
+9,0.500000,0.854167, 0.229167, 0.538189
+10,0.500000,0.906250, 0.197917, 0.496725
+11,0.500000,0.906250, 0.031250, 0.552913
+12,0.500000,0.906250, 0.010417, 0.515257
+13,0.500000,0.906250, 0.145833, 0.530794
+14,0.500000,0.947917, 0.062500, 0.546053
+15,0.500000,0.989583, 0.062500, 0.538760
+16,0.500000,0.947917, 0.041667, 0.548912
+17,0.500000,0.927083, 0.114583, 0.536984
+18,0.500000,0.947917, 0.072917, 0.529271
+19,0.500000,0.958333, 0.062500, 0.552560
+0,0.500000,0.968750, 0.208333, 0.536183
+1,0.500000,0.968750, 0.125000, 0.538881
+2,0.500000,0.958333, 0.031250, 0.567163
+3,0.500000,0.979167, 0.114583, 0.562516
+0,0.500000,0.952083, 0.000000, 0.537566
+1,0.500000,0.912500, 0.083333, 0.535581
+2,0.500000,0.958333, 0.010417, 0.564744
+3,0.500000,0.954167, 0.187500, 0.562515
+4,0.500000,0.947917, 0.166667, 0.572175
+5,0.500000,0.952083, 0.000000, 0.561280
+6,0.500000,0.950000, 0.125000, 0.525977
+7,0.500000,0.956250, 0.031250, 0.608897
+8,0.500000,0.960417, 0.020833, 0.531221
+9,0.500000,0.945833, 0.197917, 0.534556
+10,0.500000,0.937500, 0.135417, 0.490381
+11,0.500000,0.925000, 0.239583, 0.548656
+12,0.500000,0.954167, 0.104167, 0.515086
diff --git a/reports/WeightFiltration/attack_values_int.csv b/reports/WeightFiltration/attack_values_int.csv
new file mode 100644
index 0000000..0d2b8d7
--- /dev/null
+++ b/reports/WeightFiltration/attack_values_int.csv
@@ -0,0 +1,35 @@
+target_class,parameter_mia_accuracy,latent_distance_tell,lookalike_accuracy
+0,0.500000,1.180800,0.958333
+1,0.500000,1.279257,0.968750
+2,0.500000,1.717911,0.937500
+3,0.500000,1.354225,0.989583
+4,0.500000,1.308562,0.968750
+5,0.500000,2.087495,0.947917
+6,0.500000,1.793863,0.927083
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