# Simulate Discrete Toy data

python -u -m data.simulated_discrete_flip

# Generate Samples from Diabetes Simulator
python -u -m data.DiabetesNST.examples.advanced_tutorial # change data_name='behavior'
python -u -m data.DiabetesNST.examples.advanced_tutorial # change data_name='target'

# Train Target and Behavior Policies for Diabetes
python -u -m data.DiabetesNST.examples.learn_opt_policy

# To run all methods use:
python -u main_discrete_toy.py --baseline sltd --defer_cost 0.01
python -u main_diabetes.py --baseline sltd --defer_cost 0.01

# To collect trajectories using true dynamics use:
python -u main_discrete_toy.py --baseline sltd --defer_cost 0.01 --decomp_only True --decomp_method explain_policy
python -u main_diabetes.py --baseline sltd --defer_cost 0.01 --decomp_only True --decomp_method explain_policy

#Note main_hiv.py requires hiv data that is publicly available upon request only.

# To plot any deferral policy use:
python -u plot_policies.py --env discrete_toy

# To plot trajectories
python -u plot_trajectories.py --env discrete_toy

#To plot value vs frequency tradeoff - sweep over all necessary hyperparameters and save appropriate results
# Then run
python -u defer_cost_analysis.py --env discrete_toy
python -u defer_cost_analysis_ablations.py --env discrete_toy
