l0

npx skills add majiayu000/claude-skill-registry --skill l0-skill --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# L0 Regularization L0 is a PyTorch implementation of L0 regularization for neural network sparsification and intelligent sampling, used in PolicyEngine's survey calibration pipeline. ## For Users 👥 ### What is L0? L0 regularization helps PolicyEngine create more efficient survey datasets by intelligently selecting which households to include in calculations. **Impact you see:** - Faster population impact calculations - Smaller dataset sizes - Maintained accuracy with fewer samples **Behind the scenes:** When PolicyEngine shows population-wide impacts, L0 helps select representative households from the full survey, reducing computation time while maintaining accuracy. ## For Analysts 📊 ### What L0 Does L0 provides intelligent sampling gates for: - **Household selection** - Choose representative samples from CPS - **Feature selection** - Identify important variables - **Sparse weighting** - Create compact, efficient datasets **Used in PolicyEngine for:** - Survey calibration (via microcalibrate) - Dataset sparsification in policyengine-us-data - Efficient microsimulation ### Installation ```bash pip install l0-python ``` ### Quick Example: Sample Selection ```python from l0 import

pip install l0-python
git clone https://github.com/PolicyEngine/L0
cd L0
tree l0/
Key modules:
ls l0/
Hard Concrete distribution (core algorithm)
cat l0/hard_concrete.py
Sample gates (used in calibration)
cat l0/gates.py