OPO/run.py at master · EgoPer/OPO · GitHub

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from tqdm import tqdm
import os
from experiment_utils import make_new_experiment_dir
from experiment_utils import run_experiment_warcraft

fixed_parameters = {}
fixed_parameters['experiment_name'] = "Warcraft"
fixed_parameters['k'] = 12
fixed_parameters['h'] = int((fixed_parameters['k']**2)/2)
fixed_parameters['lambd_O1'] = 20
fixed_parameters['lambd_O2'] = 20
fixed_parameters['seconds_per_solve'] = 10
fixed_parameters['epochs'] = 500
fixed_parameters['early_stopping'] = 10
fixed_parameters['lr_O1'] = 1e-3
fixed_parameters['lr_O2'] = 3e-4
fixed_parameters['batch_size'] = 256


lrates = [1.5e-3,3e-4,3e-3,7e-4]
warmstarts = [507]
ablation_strategies = ['finetune']
data_acquisition_strategies = ['learnt']
prediction_model_training_strategies = ['pfl', 'dfl']


experiments = []

for opo_rate in lrates:
    for seed in warmstarts:
        for ablation in ablation_strategies:
            for da in data_acquisition_strategies:
                for pmt in prediction_model_training_strategies:
                    if ablation == 'pretrained' and da == 'fixed':
                        continue
                    else:
                        experiments.append([ablation, da, pmt, seed, opo_rate])

fixed_parameters['k'] = 12
fixed_parameters['h'] = int((fixed_parameters['k']**2)/4)


i = 0
total = len(experiments)

for ablation, da, pmt, seed, opo_rate in tqdm(experiments, leave = False, desc= "experiment level"):

    i += 1

    fixed_parameters['ablation'] = ablation
    fixed_parameters['O1_strategy'] = da
    # fixed_parameters['imputation_method'] = im
    fixed_parameters['PO2_strategy'] = pmt
    fixed_parameters['seed'] = seed
    fixed_parameters['lr_O1'] = opo_rate

    # Create experiment directory

    print(f"Experiment: {i}/{total},",
            f"ablation : {fixed_parameters['ablation']},",
            f"O1_strategy : {fixed_parameters['O1_strategy']},",
            # f"imputation_method : {fixed_parameters['imputation_method']},",
            f"PO2_strategy : {fixed_parameters['PO2_strategy']}.",
            f"Seed : {fixed_parameters['seed']}.",
            )

    fixed_parameters['experiment_path'] = make_new_experiment_dir(fixed_parameters = fixed_parameters)
    if os.path.exists(f"{fixed_parameters['experiment_path']}/results.pkl"):
        fixed_parameters.pop('experiment_path') # Needs to be cleared each time
        continue

    run_experiment_warcraft(fixed_parameters = fixed_parameters,
                            folder_path="./data/warcraft_shortest_path_oneskin/",
                            verbose = True)

    fixed_parameters.pop('experiment_path') # Needs to be cleared each time