AI Analysis
Final verdict: SAFE
The package shows very low risk across all assessed categories with no network calls, shell executions, obfuscations, or credential risks detected. The metadata risk is slightly elevated due to the author's single package history, but this alone does not indicate a supply-chain attack.
- No network calls detected
- Single package by author
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
- Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
- Metadata: The author has only one package on PyPI, which might indicate a new or less active account.
Package Quality Overall: Medium (6.6/10)
✦ High
Test Suite
9.0
Test suite present — 19 test file(s) found
19 test file(s) detected (e.g. __init__.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (7578 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
304 type-annotated function signatures detected in source
✦ High
Multiple Contributors
10.0
Active multi-contributor project
18 unique contributor(s) across 100 commits in facebook/AxActive community — 5 or more distinct contributors
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
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository facebook/Ax appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Meta Platforms, Inc." 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 ax-platform
Your task is to create a mini-application that leverages the 'ax-platform' library to perform adaptive experimentation on a simple machine learning model. This application will be used to optimize hyperparameters of a scikit-learn RandomForestClassifier on a synthetic dataset, showcasing real-time adaptation and optimization capabilities. Here are the key steps and features your application should include: 1. **Setup Synthetic Dataset**: Generate a synthetic binary classification dataset using `sklearn.datasets.make_classification`. 2. **Model Initialization**: Initialize a RandomForestClassifier from scikit-learn with default parameters. 3. **Experiment Setup**: Use 'ax-platform' to set up an adaptive experiment. Define a search space for hyperparameters such as `n_estimators`, `max_depth`, `min_samples_split`, etc. 4. **Objective Function**: Implement an objective function that trains the RandomForestClassifier on a training split of the dataset and evaluates it on a validation split, returning the AUC score as the metric to optimize. 5. **Adaptive Optimization Loop**: Run the adaptive optimization loop using 'ax-platform'. This loop should adaptively choose the next set of hyperparameters to evaluate based on past performance, aiming to maximize the AUC score. 6. **Visualization and Reporting**: After running the optimization loop, visualize the performance of different sets of hyperparameters over time. Also, report the best set of hyperparameters found and the corresponding AUC score. 7. **Optional Enhancements**: Consider adding features like early stopping if performance plateaus, or incorporating Bayesian optimization techniques offered by 'ax-platform' to refine the search process. This project aims to demonstrate the power of adaptive experimentation in machine learning model tuning, providing insights into how 'ax-platform' can be effectively utilized for optimizing complex models.
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