Appendix A: Implementation Code¶
A.1 Quantile Regression Forest Implementation¶
The following code demonstrates the implementation of Quantile Regression Forests for variable imputation:
from quantile_forest import RandomForestQuantileRegressor
qrf = RandomForestQuantileRegressor(
n_estimators=100,
min_samples_leaf=1,
random_state=0
)A.2 PyTorch Optimization for Reweighting¶
The reweighting optimization uses PyTorch for gradient-based optimization:
import torch
# Initialize with log of original weights
log_weights = torch.log(original_weights)
log_weights.requires_grad = True
# Adam optimizer
optimizer = torch.optim.Adam([log_weights], lr=0.1)
# Optimization loop
for iteration in range(5000):
weights = torch.exp(log_weights)
achieved = weights @ loss_matrix
relative_errors = (achieved - targets) / targets
loss = torch.mean(relative_errors ** 2)
optimizer.zero_grad()
loss.backward()
optimizer.step()Appendix B: Tables¶
Table A1: Complete List of Imputed Variables¶
[TO BE GENERATED - Complete list of 72 imputed variables from PUF organized by category]