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#the experiments are made on commit 145cadc71f5f33a1ad9ff349cf004ce8e7fbdb1a
# kill all detached screen seasons
screen -ls | grep Detached | cut -d. -f1 | awk '{print $1}' | xargs kill
#load screen season
screen -r session_id
# test if teminal is in a screen seasion
echo $STY
# see open windows
Ctrl-a " "
# open new screen season
Ctrl-a c n next p previoous
# Start new screen session
screen -S Red_Pool
# Load python 3.10 Environment
conda activate RED_POOL_3_10
cd residuals_for_ed
export PYTHONPATH=$PYTHONPATH:$(pwd)
cd SyntaxTree
export PYTHONPATH=$PYTHONPATH:$(pwd)
cd ..
# Run experiments
################# oldest
#Selection Strategie: oldest, c=0.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 0.0 --selection_strategy oldest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 0.0 --selection_strategy oldest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 0.0 --selection_strategy oldest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#Selection Strategie: Oldest, c=1.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 1.0 --selection_strategy oldest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 1.0 --selection_strategy oldest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 1.0 --selection_strategy oldest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#Selection Strategie: Oldest, c=2.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 2.0 --selection_strategy oldest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 2.0 --selection_strategy oldest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 2.0 --selection_strategy oldest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#Selection Strategie: Oldest, c=5.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 5.0 --selection_strategy oldest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 5.0 --selection_strategy oldest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 5.0 --selection_strategy oldest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
############################
######### newest ###########
############################
python src/fit_func_pysr.py --acceptance_c_complexity_res 0.0 --selection_strategy newest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 0.0 --selection_strategy newest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 0.0 --selection_strategy newest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#
##Selection Strategie: newest, c=1.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 1.0 --selection_strategy newest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 1.0 --selection_strategy newest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 1.0 --selection_strategy newest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#
##Selection Strategie: newest, c=2.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 2.0 --selection_strategy newest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 2.0 --selection_strategy newest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 2.0 --selection_strategy newest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#Selection Strategie: newest, c=5.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 5.0 --selection_strategy newest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 5.0 --selection_strategy newest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 5.0 --selection_strategy newest --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
############################
####### random #############
############################
#Selection Strategie: random, c=0.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 0.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 0.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 0.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#
##Selection Strategie: random, c=1.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 1.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 1.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 1.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#
##Selection Strategie: random, c=2.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 2.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 2.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 2.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#Selection Strategie: random, c=3.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 3.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 3.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 3.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#Selection Strategie: random, c=4.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 4.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 4.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 4.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
##Selection Strategie: random, c=5.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 5.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 5.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 5.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#Selection Strategie: random, c=7.5
python src/fit_func_pysr.py --acceptance_c_complexity_res 7.5 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 7.5 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 7.5 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#Selection Strategie: random, c=10.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 10.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 10.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 10.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#Selection Strategie: random, c=15.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 15.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 15.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 15.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#Selection Strategie: random, c=20.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 20.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 20.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 20.0 --selection_strategy random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
############################
###### nodel_level #########
############################
#Selection Strategie: node_level, c=0.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 0.0 --selection_strategy node_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 0.0 --selection_strategy node_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 0.0 --selection_strategy node_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#
##Selection Strategie: node_level, c=1.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 1.0 --selection_strategy node_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 1.0 --selection_strategy node_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 1.0 --selection_strategy node_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#
##Selection Strategie: node_level, c=2.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 2.0 --selection_strategy node_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 2.0 --selection_strategy node_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 2.0 --selection_strategy node_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
##Selection Strategie: node_level, c=5.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 5.0 --selection_strategy node_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 5.0 --selection_strategy node_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 5.0 --selection_strategy node_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
############################
##### promising_level ######
############################
#Selection Strategie: promising_level, c=0.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 0.0 --selection_strategy promising_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 0.0 --selection_strategy promising_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 0.0 --selection_strategy promising_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
!
##Selection Strategie: promising_level, c=1.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 1.0 --selection_strategy promising_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 1.0 --selection_strategy promising_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 1.0 --selection_strategy promising_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#
##Selection Strategie: promising_level, c=2.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 2.0 --selection_strategy promising_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 2.0 --selection_strategy promising_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 2.0 --selection_strategy promising_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
##Selection Strategie: promising_level, c=5.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 5.0 --selection_strategy promising_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 5.0 --selection_strategy promising_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 5.0 --selection_strategy promising_level --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
############################
##### promising_random #####
############################
#Selection Strategie: promising_random, c=0.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 0.0 --selection_strategy promising_random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 0.0 --selection_strategy promising_random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 0.0 --selection_strategy promising_random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#
##Selection Strategie: promising_random, c=1.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 1.0 --selection_strategy promising_random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 1.0 --selection_strategy promising_random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 1.0 --selection_strategy promising_random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#
##Selection Strategie: promising_random, c=2.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 2.0 --selection_strategy promising_random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 2.0 --selection_strategy promising_random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 2.0 --selection_strategy promising_random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
##Selection Strategie: promising_random, c=5.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 5.0 --selection_strategy promising_random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 5.0 --selection_strategy promising_random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 5.0 --selection_strategy promising_random --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
############################
######### greedy ###########
############################
#Selection Strategie: greedy, c=0.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 0.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 0.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 0.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
##Selection Strategie: greedy, c=0.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 1.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 1.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 1.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
##Selection Strategie: greedy, c=2.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 2.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 2.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 2.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#Selection Strategie: greedy, c=3.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 3.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 3.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 3.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#Selection Strategie: greedy, c=4.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 4.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 4.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 4.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
##Selection Strategie: greedy, c=5.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 5.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 5.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 5.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#Selection Strategie: greedy, c=7.5
python src/fit_func_pysr.py --acceptance_c_complexity_res 7.5 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 7.5 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 7.5 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#Selection Strategie: greedy, c=10
python src/fit_func_pysr.py --acceptance_c_complexity_res 10.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 10.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 10.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#Selection Strategie: greedy, c=15.0
python src/fit_func_pysr.py --acceptance_c_complexity_res 15.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 15.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 15.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
#Selection Strategie: greedy, c=20
python src/fit_func_pysr.py --acceptance_c_complexity_res 20.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 0 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 20.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 1 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10
python src/fit_func_pysr.py --acceptance_c_complexity_res 20.0 --selection_strategy greedy --exp_name "pool_ablation" --postprocessing_methods classic residuals --seed 2 --max_num_residuals 10 --noise_factor 0 --max_dataset_size 300 --dataset_folder datasets_srbench --pysr_niterations 10