Revert "Merge pull request #190 from aidotse/subpackages"

This reverts commit cf89641, reversing
changes made to f30b53a.

.github/workflows/run_tests.yml

@@ -18,11 +18,11 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.9
-      
-      - name: Install dependencies from pyproject.toml
+
+      - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install .[dev]
+          pip install -r requirements.txt
 
       - name: Ruff Linting
         run: |

environment.yml

@@ -0,0 +1,38 @@
+name: leakpro
+channels:
+  - conda-forge
+dependencies:
+  # Core Python
+  - python=3.9.0 
+
+  # Numerical and Data Analysis
+  - numpy  
+  - pandas  
+  - scipy
+  - scikit-learn
+
+  # Visualization
+  - matplotlib  
+  - seaborn
+  - pillow
+
+  # Deep Learning
+  - pytorch
+  - torchvision
+  - torchmetrics
+
+  # Utilities
+  - dotmap
+  - loguru
+  - jinja2
+  - tqdm
+  - pyyaml
+  - numba
+  - pydantic
+  - joblib
+
+  # Development and Testing
+  - pytest
+  - pytest-mock
+
+

examples/mia/LOS/ReadMe.md

@@ -0,0 +1,5 @@
+# Lenght-of-Stay Usecase
+In this use case, we focus on attacking length-of-stay classifier models. As part of the example, we train a Logistic Regression model and a Gated Recurrent Unit with Decay (GRU-D).
+
+##  MIMIC-III Data Preprocessing
+To prepare the data, refer to instructions in ```mimic_prepration/ReadMe.md ``` 

examples/mia/LOS/audit.yaml

@@ -1,15 +1,15 @@
 audit:  # Configurations for auditing
   random_seed: 1234  # Integer specifying the random seed
   attack_list:
-    # rmia:
-    #   training_data_fraction: 0.5  # Fraction of the auxilary dataset to use for this attack (in each shadow model training)
-    #   attack_data_fraction: 0.5 # Fraction of auxiliary dataset to sample from during attack
-    #   num_shadow_models: 8 # Number of shadow models to train
-    #   online: True # perform online or offline attack
-    #   temperature: 2
-    #   gamma: 1.0
-    #   offline_a: 0.33 # parameter from which we compute p(x) from p_OUT(x) such that p_IN(x) = a p_OUT(x) + b.
-    #   offline_b: 0.66
+    rmia:
+      training_data_fraction: 0.5  # Fraction of the auxilary dataset to use for this attack (in each shadow model training)
+      attack_data_fraction: 0.5 # Fraction of auxiliary dataset to sample from during attack
+      num_shadow_models: 8 # Number of shadow models to train
+      online: True # perform online or offline attack
+      temperature: 2
+      gamma: 1.0
+      offline_a: 0.33 # parameter from which we compute p(x) from p_OUT(x) such that p_IN(x) = a p_OUT(x) + b.
+      offline_b: 0.66
     # qmia:
     #   training_data_fraction: 1.0  # Fraction of the auxilary dataset (data without train and test indices) to use for training the quantile regressor
     #   epochs: 5  # Number of training epochs for quantile regression
@@ -32,12 +32,13 @@ audit:  # Configurations for auditing
 
 target:
   # Target model path
-  module_path: "utils/model.py"
-  model_class: "MimicLR" 
+  module_path: "utils/model_LR.py" # either model_grud.py or model_LR.py for logestic regression
+  model_class: "LR" #  LR/GRUD
   # Data paths
-  target_folder: "./target"
-  data_path: "./data/dataset.pkl"
+  target_folder: "./target_LR" # either target_GRUD or target_LR
+  data_path: "./data/flattened/dataset.pkl" #unflattened dataset for GRUD and flattened dataset for LR
 
 shadow_model:
+  model_class: # LR/GRUD
 
 distillation_model: