arrowspace_tuner

v0.3.1 suspicious
4.0
Medium Risk

Hyperparameter discovery (eps auto-tuning) for ArrowSpace via Optuna

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risk for common threats like network calls, shell execution, and obfuscation. However, the metadata risk score is elevated due to incomplete author information and potentially new or inactive account status.

  • Metadata risk due to incomplete author information
  • Account may be new or inactive
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 from command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author's information is incomplete and the account seems new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.6/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 7 test file(s) detected (e.g. test_17_CVE_neurips_v2.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (3037 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 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 52 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 62 commits in Genefold/arrowspace_tuner
  • Single author but highly active (62 commits)

🔬 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

Repository Genefold/arrowspace_tuner 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 arrowspace_tuner 
Create a Python-based application named 'HyperTuneArrows' that leverages the 'arrowspace_tuner' package to optimize hyperparameters for a machine learning model using the ArrowSpace framework. Your application should allow users to input their own ArrowSpace models and specify the range of hyperparameters they wish to tune. Additionally, include the following features:

1. User-friendly command-line interface for specifying the model and tuning parameters.
2. Option for users to set a maximum number of trials for the hyperparameter optimization process.
3. Visualization of the optimization process and results using matplotlib or a similar library.
4. Saving the optimized model and its corresponding hyperparameters to disk for future use.
5. Detailed documentation explaining how to install dependencies, run the application, and interpret the results.

Ensure that your application demonstrates the effectiveness of 'arrowspace_tuner' by comparing the performance of the model before and after hyperparameter tuning.

💬 Discussion Feed

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