added pategan

Models/PateGAN/readme.txt

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+

pate-gan/.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*.so
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+venv/
+ENV/
+.env
+
+# IDE specific files
+.idea/
+.vscode/
+*.swp
+*.swo
+.DS_Store
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# Logs and databases
+*.log
+*.sqlite
+*.db
+
+# Distribution/packaging
+node_modules/
+*.tar.gz
+*.zip
+
+# Local development settings
+local_settings.py
+*.local
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+coverage.xml
+*.cover

pate-gan/README.md

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+# Codebase for "PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees"
+
+Authors: James Jordon, Jinsung Yoon, Mihaela van der Schaar
+
+Reference: James Jordon, Jinsung Yoon, Mihaela van der Schaar, 
+"PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees," 
+International Conference on Learning Representations (ICLR), 2019.
+
+Paper Link: https://openreview.net/forum?id=S1zk9iRqF7
+
+Contact: jsyoon0823@gmail.com
+
+This directory contains implementations of PATEGAN framework for generating synthetic data.
+
+To run the pipeline for training and evaluation on PATEGAN framwork, simply run 
+python3 -m main_pategan_experiment.py.
+
+Note that hyper-parameter tuning is necessary for different datasets. 
+
+### Code explanation
+
+(1) data_generator.py
+- Generate train and test data to evaluate PATEGAN framework
+
+(2) utils.py
+- Define various supervised models such as logistic regression
+- Return AUC and APR as the metrics
+
+(3) pate_gan.py
+- Main PATEGAN framework
+- Return the synthetically generated data
+
+(4) main_pategan_experiment.py
+- Report the prediction performances of original data and synthetic data generated by PATEGAN.
+
+### Command inputs:
+
+-   data_no: number of generated data
+-   data_dim: number of data dimensions
+-   noise_rate: noise ratio on data
+-   iterations: number of iterations for handling initialization randomness
+-   n_s: the number of student training iterations
+-   batch_size: the number of batch size for training student and generator
+-   k: the number of teachers
+-   epsilon: Differential privacy parameters (epsilon)
+-   delta: Differential privacy parameters (delta)
+-   lamda: PATE noise size
+
+Note that hyper-parameters should be optimized for different datasets.
+
+### Example command
+
+```shell
+$ python3 main_pategan_experiment.py --data_no 10000 --data_dim 10 --noise_rate 1.0
+--iterations 50 --n_s 1 --batch_size 64 --k 100 --epsilon 100 --delta 0.0001
+--lamda 1.0 
+```
+
+### Outputs
+
+-   results: performances of Original and Synthetic performances
+-   train_data: original data
+-   synth_train_data: synthetically generated data