lifejacket 0.2.0

A package for after-study analysis of adaptive experiments in which data is pooled across users.

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Save your standard errors from pooling in adaptive experiments.

Setup (if not using conda)

Create and activate a virtual environment

• python3 -m venv .venv; source /.venv/bin/activate

Adding a package

• Add to requirements.txt with a specific version or no version if you want the latest stable
• Run pip freeze > requirements.txt to lock the versions of your package and all its subpackages

Running the code

• `export PYTHONPATH to the absolute path of this repository on your computer
• ./run_local_synthetic.sh, which outputs to simulated_data/ by default. See all the possible flags to be toggled in the script code.

Linting/Formatting

Testing

python -m pytest python -m pytest tests/unit_tests python -m pytest tests/integration_tests

Talk about gitignored cluster simulation scripts

Important Large-Scale Simulations

No adaptivity

sbatch --array=[0-999] -t 0-5:00 --mem=50G run_and_analysis_parallel_synthetic --T=10 --n=50000 --recruit_n=50000 --steepness=0.0 --alg_state_feats=intercept,past_reward --action_centering_RL=0 --inference_loss_func_filename="functions_to_pass_to_analysis/get_least_squares_loss_inference_no_action_centering.py" --theta_calculation_func_filename="functions_to_pass_to_analysis/estimate_theta_least_squares_no_action_centering.py"

No adaptivity, 5 batches incremental recruitment

sbatch --array=[0-999] -t 0-5:00 --mem=50G run_and_analysis_parallel_synthetic --T=10 --n=50000 --recruit_n=10000 --steepness=0.0 --alg_state_feats=intercept,past_reward --action_centering_RL=0 --inference_loss_func_filename="functions_to_pass_to_analysis/get_least_squares_loss_inference_no_action_centering.py" --theta_calculation_func_filename="functions_to_pass_to_analysis/estimate_theta_least_squares_no_action_centering.py"

Some adaptivity, no action_centering

sbatch --array=[0-999] -t 0-5:00 --mem=50G run_and_analysis_parallel_synthetic --T=10 --n=50000 --recruit_n=50000 --steepness=3.0 --alg_state_feats=intercept,past_reward --action_centering_RL=0 --inference_loss_func_filename="functions_to_pass_to_analysis/get_least_squares_loss_inference_no_action_centering.py" --theta_calculation_func_filename="functions_to_pass_to_analysis/estimate_theta_least_squares_no_action_centering.py"

Some adaptivity, no action_centering, 5 batches incremental recruitment

sbatch --array=[0-999] -t 0-5:00 --mem=50G run_and_analysis_parallel_synthetic --T=10 --n=50000 --recruit_n=10000 --steepness=3.0 --alg_state_feats=intercept,past_reward --action_centering_RL=0 --inference_loss_func_filename="functions_to_pass_to_analysis/get_least_squares_loss_inference_no_action_centering.py" --theta_calculation_func_filename="functions_to_pass_to_analysis/estimate_theta_least_squares_no_action_centering.py"

More adaptivity, no action_centering

sbatch --array=[0-999] -t 0-5:00 --mem=50G run_and_analysis_parallel_synthetic --T=10 --n=50000 --recruit_n=50000 --steepness=5.0 --alg_state_feats=intercept,past_reward --action_centering_RL=0 --inference_loss_func_filename="functions_to_pass_to_analysis/get_least_squares_loss_inference_no_action_centering.py" --theta_calculation_func_filename="functions_to_pass_to_analysis/estimate_theta_least_squares_no_action_centering.py"

Even more adaptivity, no action_centering

sbatch --array=[0-999] -t 0-5:00 --mem=50G run_and_analysis_parallel_synthetic --T=10 --n=50000 --recruit_n=50000 --steepness=10.0 --alg_state_feats=intercept,past_reward --action_centering_RL=0 --inference_loss_func_filename="functions_to_pass_to_analysis/get_least_squares_loss_inference_no_action_centering.py" --theta_calculation_func_filename="functions_to_pass_to_analysis/estimate_theta_least_squares_no_action_centering.py"

Some adaptivity, RL action_centering, no inference action centering

sbatch --array=[0-999] -t 0-5:00 --mem=50G run_and_analysis_parallel_synthetic --T=10 --n=50000 --recruit_n=50000 --steepness=3.0 --alg_state_feats=intercept,past_reward --action_centering_RL=1 --inference_loss_func_filename="functions_to_pass_to_analysis/get_least_squares_loss_inference_no_action_centering.py" --theta_calculation_func_filename="functions_to_pass_to_analysis/estimate_theta_least_squares_no_action_centering.py"

Some adaptivity, inference action_centering, no RL action centering

sbatch --array=[0-999] -t 0-5:00 --mem=50G run_and_analysis_parallel_synthetic --T=10 --n=50000 --recruit_n=50000 --steepness=3.0 --alg_state_feats=intercept,past_reward --action_centering_RL=0 --inference_loss_func_filename="functions_to_pass_to_analysis/get_least_squares_loss_inference_action_centering.py" --theta_calculation_func_filename="functions_to_pass_to_analysis/estimate_theta_least_squares_action_centering.py"

Some adaptivity, inference and RL action_centering

sbatch --array=[0-999] -t 0-5:00 --mem=50G run_and_analysis_parallel_synthetic --T=10 --n=50000 --recruit_n=50000 --steepness=3.0 --alg_state_feats=intercept,past_reward --action_centering_RL=1 --inference_loss_func_filename="functions_to_pass_to_analysis/get_least_squares_loss_inference_action_centering.py" --theta_calculation_func_filename="functions_to_pass_to_analysis/estimate_theta_least_squares_action_centering.py"

Some adaptivity, inference and RL action_centering, even more T

sbatch --array=[0-999] -t 1-00:00 --mem=50G run_and_analysis_parallel_synthetic --T=25 --n=50000 --recruit_n=50000 --steepness=3.0 --alg_state_feats=intercept,past_reward --action_centering_RL=1 --inference_loss_func_filename="functions_to_pass_to_analysis/get_least_squares_loss_inference_no_action_centering.py" --theta_calculation_func_filename="functions_to_pass_to_analysis/estimate_theta_least_squares_no_action_centering.py"

Some adaptivity, inference and RL action_centering, even more T, 5 batches incremental recruitment

sbatch --array=[0-999] -t 1-00:00 --mem=50G run_and_analysis_parallel_synthetic --T=25 --n=50000 --recruit_n=10000 --steepness=3.0 --alg_state_feats=intercept,past_reward --action_centering_RL=1 --inference_loss_func_filename="functions_to_pass_to_analysis/get_least_squares_loss_inference_no_action_centering.py" --theta_calculation_func_filename="functions_to_pass_to_analysis/estimate_theta_least_squares_no_action_centering.py"

TODO

1. Add precommit hooks (pip freeze, linting, formatting)
2. Run tests on PRs