at-gan-core

v0.13.9 suspicious
4.0
Medium Risk

Training Framework for Arbitrary Tabular Generative Adversarial Networks

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risk indicators such as no network calls, shell execution, or credential harvesting. However, the metadata risk score is elevated due to the maintainer's new or inactive account and lack of proper author identification, suggesting potential suspicion.

  • Metadata risk due to new/inactive maintainer account
  • Lack of proper author identification
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command injection or backdoor.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_package.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/Jns-M/at-gan#readme
  • Detailed PyPI description (19215 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 97 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 44 commits in Jns-M/at-gan
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: miesenboeck.at>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Jns-M/at-gan appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with at-gan-core 
Create a data augmentation tool using the 'at-gan-core' package, which is designed for generating synthetic tabular data using Arbitrary Tabular Generative Adversarial Networks (AT-GANs). This tool will be particularly useful for datasets with complex distributions and relationships between variables, such as financial transaction records or medical patient data.

Step-by-Step Guide:
1. **Setup Environment**: Begin by setting up your Python environment. Install the necessary packages including 'at-gan-core', along with other dependencies like numpy, pandas, and scikit-learn for data manipulation and evaluation.
2. **Data Preparation**: Load a sample dataset into your project. This could be any structured dataset you have access to or a public dataset available on platforms like Kaggle. Clean and preprocess the data to ensure it's suitable for training.
3. **Model Configuration**: Use 'at-gan-core' to configure and train an AT-GAN model. Define the architecture of your generator and discriminator networks, specifying the number of layers, activation functions, etc. Consider using hyperparameters tuning techniques to optimize performance.
4. **Training Phase**: Train your AT-GAN model on the prepared dataset. Monitor the loss curves and adjust parameters if necessary to improve convergence and quality of generated samples.
5. **Evaluation**: Once trained, evaluate the model's ability to generate realistic synthetic data by comparing statistical properties of the generated data against the original dataset. Use metrics like KL-divergence or Wasserstein distance to quantify similarity.
6. **Synthetic Data Generation**: Implement functionality within your tool to generate synthetic data based on the trained model. Allow users to specify the quantity of synthetic data they wish to generate.
7. **Integration and Deployment**: Package your tool into a user-friendly application that can accept input datasets and output synthetic data. Consider deploying this as a web service or a standalone desktop application for wider accessibility.

Suggested Features:
- A GUI for easy interaction, including options to load data, configure model parameters, and view results.
- Detailed documentation and tutorials for users to understand how to use the tool effectively.
- An option to save generated synthetic data in various formats (CSV, Excel).
- Real-time visualization of loss curves during training.
- Automated model evaluation reports post-training.

This project will not only serve as a practical application of advanced machine learning techniques but also provide valuable insights into handling real-world data challenges through synthetic data generation.

💬 Discussion Feed

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