AI Analysis
Final verdict: SUSPICIOUS
The package shows minimal risks in terms of network calls, shell execution, obfuscation, and credential harvesting. However, the metadata risk due to low repository activity and newness raises some concerns, making it suspicious.
- Low risk in network calls, shell execution, obfuscation, and credential harvesting.
- Metadata risk due to low repository activity and newness.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires online services.
- Shell: No shell execution patterns detected, indicating no immediate risk of unauthorized system command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The repository's low activity and newness suggest potential risk, but lack of direct indicators points towards uncertainty.
Package Quality Overall: Low (2.2/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (831 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
○ Low
Multiple Contributors
2.0
Single-author or unverifiable project
1 unique contributor(s) across 4 commits in prakHr/automate-supervised-dataset-generationSingle author with few commits — possibly a personal or throwaway project
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: gmail.com
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 5.0
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forksSingle contributor with only 4 commit(s) — possibly throwaway account
Maintainer History
score 4.0
2 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "Prakhar Gandhi" 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 automate-supervised-dataset-generation
Create a mini-application that generates a supervised learning dataset for a binary classification task using the 'automate-supervised-dataset-generation' package. This application will help data scientists and machine learning engineers quickly create datasets for training models on specific problems without manually collecting and labeling data. ### Application Requirements: - **User Input:** Allow users to specify the number of samples they want in their dataset, the feature space dimensions (number of features), and the desired class distribution (e.g., 70% positive, 30% negative). - **Data Generation:** Utilize the 'automate-supervised-dataset-generation' package to automatically generate synthetic data based on user inputs. Ensure the data has both features and corresponding labels suitable for binary classification. - **Visualization:** Implement a simple visualization component to display the generated data points in a scatter plot if the feature space allows it (up to 2D). This helps users understand the distribution of the generated data visually. - **Output Options:** Provide options for users to export the generated dataset in CSV format for further analysis or model training. - **Interactive Interface:** Develop a user-friendly command-line interface (CLI) or a simple graphical user interface (GUI) using Tkinter for better accessibility. ### Additional Features (Optional): - **Parameter Tuning:** Allow users to fine-tune parameters such as noise level, feature correlation, and class separability to control the complexity of the dataset. - **Real-time Feedback:** During data generation, provide real-time feedback on progress and estimated time remaining. - **Documentation:** Include comprehensive documentation explaining how to install the application, its usage, and any limitations or assumptions made during data generation. ### How to Use 'automate-supervised-dataset-generation': - Import necessary functions from the package to handle data generation tasks. - Configure the data generation process according to user inputs, ensuring flexibility and adaptability to different scenarios. - Validate the generated dataset before presenting it to the user, checking for consistency and correctness. This project aims to streamline the dataset creation process for supervised learning tasks, making it easier for beginners and experts alike to experiment with machine learning algorithms.
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