autolineage

v0.6.1 suspicious
5.0
Medium Risk

Zero-code data lineage for Python ML pipelines: automatic tracking, anomaly detection, and root-cause localization across pandas, scikit-learn, and PySpark.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential obfuscation and lacks detailed author information, raising concerns about its transparency and legitimacy.

  • Obfuscation risk noted
  • Incomplete author metadata
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 direct system command execution.
  • Obfuscation: The code pattern is an attempt to import a required package and handle the ImportError exception, which could be part of a legitimate runtime check but may indicate obfuscation if the package name is obscured or dynamically determined.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The author's details are incomplete, suggesting a potentially less established or less transparent maintainer.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

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

Some documentation present

  • Detailed PyPI description (10554 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

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

Limited contributor diversity

  • 2 unique contributor(s) across 9 commits in kishanraj41/autolineage
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • ool: try: __import__(self.required_package) return True except ImportError:
✓ 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 kishanraj41/autolineage 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 autolineage 
Create a mini-application named 'DataLineageTracker' that leverages the 'autolineage' package to automatically track and visualize data lineage in machine learning workflows. This tool will be particularly useful for developers and data scientists working on complex ML pipelines involving pandas, scikit-learn, and PySpark transformations. The application should include the following key features:

1. **Automatic Lineage Tracking**: Automatically capture and log every transformation step applied to data within the pipeline, including details about which function was called, input parameters, and output structure.
2. **Anomaly Detection**: Implement a feature to detect anomalies in the data lineage, such as unexpected changes in data distribution or inconsistencies between steps.
3. **Root Cause Localization**: When an anomaly is detected, provide insights into where the issue might have originated, helping users quickly identify problematic parts of their pipeline.
4. **Visualization**: Develop an interactive dashboard using Plotly or similar visualization libraries to display the data lineage graphically, highlighting each transformation step and any identified anomalies.
5. **User Interface**: Design a simple web-based user interface using Flask or Django, allowing users to upload their ML code, monitor lineage, and view anomaly reports without needing to modify their existing code.
6. **Documentation & Tutorials**: Provide comprehensive documentation and step-by-step tutorials on how to integrate 'DataLineageTracker' into different types of ML projects, ensuring ease of use for beginners and advanced users alike.

The 'autolineage' package will be crucial for implementing the automatic tracking and anomaly detection functionalities, enabling seamless integration with various ML frameworks and reducing the need for manual logging.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!