l0

name	l0
description	L0 regularization for neural network sparsification and intelligent sampling - used in survey calibration

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

pip install l0-python

Quick Example: Sample Selection

from l0 import SampleGate

# Select 1,000 households from 10,000
gate = SampleGate(n_samples=10000, target_samples=)
selected_data, indices = gate.select_samples(data)