To effectively implement the advanced evaluation techniques covered in this course, a well-configured Python environment is essential. This setup ensures you have the necessary tools for data manipulation, statistical testing, machine learning model training, privacy assessment, and visualization. Building upon our understanding of the fidelity-utility-privacy dimensions and the challenges involved, let's prepare the practical groundwork.

We strongly recommend using a virtual environment to manage project dependencies. This practice isolates your project's requirements, preventing conflicts between different projects and ensuring reproducibility. You can create a virtual environment using Python's built-in venv module or popular package managers like conda.

Using venv:

# Create a virtual environment named 'synth_eval_env'
python -m venv synth_eval_env

# Activate the environment
# On macOS/Linux:
source synth_eval_env/bin/activate
# On Windows:
.\synth_eval_env\Scripts\activate

Using conda:

# Create a conda environment named 'synth_eval_env' with Python
conda create --name synth_eval_env python=3.9  # Or your preferred Python version

# Activate the environment
conda activate synth_eval_env

Once your environment is activated, you can install the required libraries.

Core Data Science Libraries

These libraries form the bedrock of most data analysis and machine learning tasks in Python:

NumPy: The fundamental package for numerical computation. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays. Essential for numerical comparisons and manipulating data representations.
Pandas: Provides high-performance, easy-to-use data structures (primarily the DataFrame) and data analysis tools. Indispensable for loading, cleaning, transforming, and analyzing tabular data, which is common in synthetic data evaluation.
Scikit-learn: A comprehensive library for machine learning. We will use it extensively for:
- Training classification and regression models (for TSTR/TRTS utility evaluation).
- Data preprocessing (scaling, encoding).
- Calculating performance metrics (accuracy, F1-score, AUC).
- Implementing basic Membership Inference Attacks (MIAs).
Matplotlib & Seaborn: Libraries for data visualization. Matplotlib provides a foundation for plotting, while Seaborn builds upon it to create more informative and attractive statistical graphics. Visual inspection of distributions (histograms, density plots, scatter plots) is often the first step in fidelity assessment.

Specialized Synthetic Data Evaluation Libraries

While the core libraries provide general tools, specialized packages streamline the evaluation process for synthetic data. A prominent library in this space is SDMetrics.

SDMetrics: Developed specifically for evaluating tabular synthetic data, SDMetrics offers a wide range of metrics covering statistical fidelity, machine learning utility, and some privacy aspects. It allows comparing real and synthetic datasets systematically and generating comprehensive quality reports. We will utilize SDMetrics for several hands-on exercises throughout this course.

Other libraries might be relevant depending on the data modality (e.g., pytorch-fid for images, NLP libraries like nltk or transformers for text), but SDMetrics provides a strong foundation for tabular data, which is a common focus.

Installation

You can install these libraries using pip within your activated virtual environment:

pip install numpy pandas scikit-learn matplotlib seaborn sdmetrics

If you are using conda, you might prefer installing compatible versions via the conda channels, although pip usually works within conda environments too:

conda install numpy pandas scikit-learn matplotlib seaborn
pip install sdmetrics # SDMetrics is often installed via pip

Note: Always refer to the official documentation for the latest installation instructions and specific version requirements.

Verifying Your Setup

To confirm that the essential libraries are installed correctly, you can run a simple Python script or use an interactive Python session:

import numpy as np
import pandas as pd
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
import sdmetrics

print(f"NumPy version: {np.__version__}")
print(f"Pandas version: {pd.__version__}")
print(f"Scikit-learn version: {sklearn.__version__}")
print(f"SDMetrics version: {sdmetrics.__version__}")

# Basic test: Create a simple pandas DataFrame
data = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}
df = pd.DataFrame(data)
print("\nSample DataFrame:")
print(df)

# Basic plot test (optional, might require GUI backend)
# try:
#     sns.histplot(df['col1'])
#     plt.title("Test Plot")
#     plt.show()
#     print("\nPlotting libraries seem functional.")
# except Exception as e:
#     print(f"\nPlotting test skipped or failed: {e}")

print("\nEnvironment setup appears successful!")

Executing this code should print the versions of the installed libraries and confirm basic functionality without errors.

Project Organization

As evaluation tasks can become complex, involving multiple datasets, models, and metrics, maintaining an organized project structure is beneficial. Consider a layout like this:

synth_eval_project/
├── synth_eval_env/           # Your virtual environment (if using venv)
├── data/
│   ├── real_data.csv
│   └── synthetic_data_model_A.csv
│   └── synthetic_data_model_B.csv
├── notebooks/                # Jupyter notebooks for exploration and analysis
│   └── 1_statistical_fidelity_checks.ipynb
│   └── 2_ml_utility_evaluation.ipynb
├── scripts/                  # Python scripts for automated pipelines
│   └── run_evaluation.py
│   └── privacy_attacks.py
├── results/                  # Store evaluation outputs, reports, plots
│   └── quality_report_model_A.json
│   └── model_comparison_plots.png
└── README.md                 # Project description, setup instructions

This structure separates data, code (notebooks for exploration, scripts for automation), and results, making your work easier to manage, reproduce, and share.

With your environment configured and a basic understanding of project organization, you are now prepared to implement the statistical fidelity checks, machine learning utility assessments, and privacy evaluations detailed in the following chapters. This setup provides the foundation for rigorously analyzing the quality of synthetic data.