autogluon.timeseries

v1.5.0 safe
2.0
Low Risk

Fast and Accurate ML in 3 Lines of Code

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all categories with no network calls, shell executions, or credential harvesting activities. The slight increase in obfuscation and metadata risks is due to common ML framework practices and a potentially new author.

  • No network calls
  • No shell execution
  • Common ML obfuscation patterns
  • Single package from the author
Per-check LLM notes
  • Network: No network calls detected, which is normal for most Python packages unless they require external services.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands, which is typical and safe.
  • Obfuscation: The observed patterns appear to be related to the evaluation mode of a model and inference data loading, which are common practices in machine learning frameworks and do not indicate malicious obfuscation.
  • Credentials: No evidence of credential harvesting or secret storage was found in the provided snippets.
  • Metadata: The author has only one package, which might indicate a new or less active account, but no other red flags are present.

📦 Package Quality Overall: Medium (6.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://auto.gluon.ai
  • Detailed PyPI description (10024 chars)
◈ Medium Contributing Guide 7.0

Some contribution signals present

  • Contributing link: "Contribute!" -> https://github.com/autogluon/autogluon/blob/master/CONTRIBUT
  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 315 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 18 unique contributor(s) across 100 commits in autogluon/autogluon
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • self.model_pipeline.model.eval() inference_data_loader = self._get_inference_d
✓ 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 autogluon/autogluon appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "AutoGluon Community" 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 autogluon.timeseries 
Create a time-series forecasting mini-app using the 'autogluon.timeseries' package in Python. This app should allow users to upload their own time-series data (such as sales figures, stock prices, etc.) and receive predictions for future values based on historical data. Here are the key steps and features your app should include:

1. **Data Input**: Provide a simple UI for users to upload CSV files containing time-series data. Ensure that the CSV file has at least two columns: one for the timestamp and another for the value.
2. **Model Training**: Use 'autogluon.timeseries' to automatically train a model on the uploaded dataset. Highlight the simplicity of training models with just a few lines of code, emphasizing the package's ability to handle various time-series datasets efficiently.
3. **Forecast Generation**: Once the model is trained, generate forecasts for a specified period into the future. Allow users to input the forecast horizon (e.g., next month, next year).
4. **Visualization**: Display the forecast results graphically, showing both the historical data and the predicted future values. Use matplotlib or seaborn for plotting.
5. **Evaluation Metrics**: Include basic evaluation metrics like MAE, RMSE, and MAPE to help users understand the accuracy of the forecasts.
6. **Save & Share**: Enable users to save the forecast results as a CSV file and share them via email or download.

This project aims to demonstrate the ease and power of 'autogluon.timeseries' for real-world time-series analysis and forecasting tasks. It should serve as a practical tool for anyone looking to quickly analyze and predict trends in time-series data.

💬 Discussion Feed

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