microimpute 1.8.1

Benchmarking imputation methods for microdata

Microimpute

Microimpute enables variable imputation through a variety of statistical methods. By providing a consistent interface across different imputation techniques, it allows researchers and data scientists to easily compare and benchmark different approaches using quantile loss and log loss calculations to determine the method providing most accurate results.

Features

Multiple imputation methods

• Statistical Matching: Distance-based matching for finding similar observations
• Ordinary Least Squares (OLS): Linear regression-based imputation
• Quantile Regression: Distribution-aware regression imputation
• Quantile Random Forests (QRF): Non-parametric forest-based approach

Automated method selection

• AutoImpute: Automatically compares and selects the best imputation method for your data
• Cross-validation: Built-in evaluation using quantile loss (numerical) and log loss (categorical)
• Variable type support: Handles numerical, categorical, and boolean variables

Developer-friendly design

• Consistent API: Standardized fit() and predict() interface across all models
• Extensible architecture: Easy to implement custom imputation methods
• Weighted data handling: Preserve data distributions with sample weights
• Input validation: Automatic parameter and data validation

Interactive dashboard

• Visual exploration: Analyze imputation results through interactive charts at https://microimpute-dashboard.vercel.app/
• GitHub integration: Load artifacts directly from CI/CD workflows
• Multiple data sources: File upload, URL loading and sample data

Installation

pip install microimpute

For image export functionality (PNG/JPG), install with:

pip install microimpute[images]

Examples and documentation

For detailed examples and interactive notebooks, see the documentation.

Contributing

Contributions are welcome to the project. Please feel free to submit a Pull Request with your improvements.