README.md

OptCS

This repository contains the code to reproduce the results in the paper Optimized Conformal Selection: Powerful Selective Inference After Conformity Score Optimization.

Folders

• simulation/: Simulation experiments
  • simulation/Msel/: Conformity score selection with pre-trained models (Section 5.1)
  • simulation/Full/: Conformal selection without data splitting (Section 5.2)
  • simulation/Full-Msel/: Model training and selection with full data (Section 5.3)
• real/: Real-data appliations
  • real/drug/: Drug discovery with model selection (Section 6.1)
  • real/llm/: Boosting LLM Alignment (Section 6.2)
• requirements.txt: A list of required python packages

Workflow

Simulation Experiments

For simulation, a single job submission is sufficient to run the experiment.

Drug Discovery Application

For the drug discovery application:

1. Use modelpred.py to generate and save model predictions based on different drug encodings.
2. Evaluate the performance of various methods using evaluate.py.

LLM Alignment Application

The code for the LLM alignment application is largely adapted from this repository. After performing report generation and score extraction as described in the linked repository:

1. Use collect.py to compile all uncertainty/confidence scores and labels into a single .csv file.
2. Conduct experiments using different combinations of models via llm_set1.py and llm_set2.py.