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run_me_sweep.py
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run_me_sweep.py
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import sys
import wandb
if __name__ == "__main__":
file_to_run = sys.argv[1]
# choosing the specified file
# all files must ignore the first argument passed via command line.
file_to_run = sys.argv[1]
# Run specified file. All files must ignore the first argument
# passed via command line
if file_to_run == "icrl":
from icrl.icrl_main import main
sweep_config = {
'method': 'grid'
}
metric = {
'name': 'true/jaccard',
'goal': 'maximize'
}
sweep_config['metric'] = metric
parameters_dict = {
# 'cn_learning_rate': {
# 'values': [0.003, 0.01]
# },
# 'penalty_initial_value': {
# 'values':[1, 0.5, 2, 1.5] # 0.8 thre 0.2 penalty for bce
# },
# 'cn_manual_threshold': {
# 'values': [0.5]
# },
# 'cn_reg_coeff': {
# 'values': [0.4, 0.5, 0.7, 1] # [0.9, 1, 1.05, 1.1, 1.15, 1.2, 1.25, 1.3, 1.35, 1.4, 1.45, 1.5, 1.55]
# },
# 'backward_iters':{
# 'values': [20, 40]
# },
'seed': {
'values': [1, 2, 3, 4,5,6,7,8,9,10, 11,12, 13, 14, 15, 16,17,18,19,20,21,22,23,24,25,26,27,28] # [0.9, 1, 1.05, 1.1, 1.15, 1.2, 1.25, 1.3, 1.35, 1.4, 1.45, 1.5, 1.55]
},
# 'ent_coef': {
# 'values': [0.01, 0.0015, 0.02]
# },
}
sweep_config['parameters'] = parameters_dict
sweep_id = wandb.sweep(sweep_config, project="ICRL-FE2")
wandb.agent(sweep_id, main, )
elif file_to_run == "pucl":
from icrl.pucl import main
sweep_config = {
'method': 'grid'
}
metric = {
'name': 'true/violation_steps_portion',
'goal': 'minimize'
}
sweep_config['metric'] = metric
parameters_dict = {
'seed': {
'min':20, 'max':40
# 'values': [10, 11, 12, 13, 14]
},
# 'GPU_likelihood_thresh':{'values':[-10, -7, -4]},
# 'GPU_n_gmm': {'values': [8, 15]}
# 'backward_iters': {'values':[50, 100, 200, 500]},
# 'learning_rate': {
# 'values': [1e-4, 3e-4]
# },
# 'ent_coef': {
# 'values': [0.01, 0.0]
# },
# 'n_epochs': {
# 'values': [10, 20]
# },
# 'penalty_initial_value': {
# 'min':0.3, 'max':1.0
# },
# 'penalty_initial_value': {
# 'values': [0, 2, 5, 10]
# },
# 'WB_label_frequency': {
# 'value': 1
# },
# 'cn_learning_rate':{'values':[3e-4, 5e-4, 10e-4, 20e-4]},
# 'kNN_thresh': {
# 'values': [0.03, 0.027, 0.025]
# },
# 'cn_layers': {
# 'values': [[4], [16, 16]]
# },
# 'integral_control_coeff': {
# 'values': [0.5, 0.1, 0.2]
# },
# 'proportional_control_coeff': {
# 'values': [1, 5, 10, 20]
# },
# # 'forward_timesteps': {
# # 'values':[10e4, 5e4, 1e4]
# # },
# 'kNN_thresh': {
# 'values': [0.02, 0.04, 0.08, 0.12, 0.16, 0.2, 0.3]
# },
# 'GPU_likelihood_thresh': {
# 'min':-10., 'max':-3.
# },
# 'GPU_n_gmm': {
# 'min':5, 'max':15
# },
# 'policy_excerpts_weight': {
# 'values':[1., 2., 4]
# }
# 'cn_reg_coeff': {
# 'min':0., 'max':0.1
# # 'values': [0, 0.03, 0.05, 0.1]
# },
# 'backward_iters': {
# 'values': [20, 50, 100, 500]
# },
}
sweep_config['parameters'] = parameters_dict
sweep_id = wandb.sweep(sweep_config, project="ICRL-FE2")
wandb.agent(sweep_id, main)
elif file_to_run == "ppo-main":
from icrl.ppo_main import main
sweep_config = {
'method': 'bayes'
}
metric = {
'name': 'rollout/ep_is_success_mean',
'goal': 'maximize'
}
sweep_config['metric'] = metric
parameters_dict = {
#
# 'learning_rate': {
# 'values': [2e-4, 10e-4]
# },
# 'n_epochs': {
# 'values': [10, 20]
# },
# 'penalty_initial_value': {
# 'values': [0.5, 1]
# },
'integral_control_coeff': {
'min':0.5, 'max':1.5
},
'proportional_control_coeff': {
'min':20., 'max': 30.
},
# 'ent_coef': {
# 'values': [0.0, 0.005, 0.01]
# },
# 'budget':{
# 'min': 0.001, 'max':0.02
# }
# 'seed': {'min':1, 'max':999}
}
sweep_config['parameters'] = parameters_dict
sweep_id = wandb.sweep(sweep_config, project="ICRL-FE2")
wandb.agent(sweep_id, main)
elif file_to_run == "dscl":
from icrl.dscl import main
sweep_config = {
'method': 'random'
}
metric = {
'name': 'true/jaccard',
'goal': 'maximize'
}
sweep_config['metric'] = metric
parameters_dict = {
# 'cn_reg_coeff': {
# # 'min':0., 'max':0.5
# 'values': [0,]
# },
# 'seed': {
# 'min':0, 'max':11111
# },
# 'cn_pu_labeling_frequency': {
# 'min': 0.1, 'max': 0.2
# 'values': [0, 1.1, ]
# },
'cn_layers': {
'values': [ [32, 32], [16, 16]]
},
'cn_learning_rate': {
'values': [0.002, 0.005]
},
'kNN_thresh': {
'min':0.010, 'max':0.019
},
'ds_modulation_rho': {
'values': [2, 5, 10]
},
# 'cn_batch_size': {
# 'values': [64, 128, 512]
# },
}
sweep_config['parameters'] = parameters_dict
sweep_id = wandb.sweep(sweep_config, project="ICRL-FE2")
wandb.agent(sweep_id, main)
elif file_to_run == "pu_learning":
from icrl.tests.test_pu_learning import main
sweep_config = {
'method': 'random'
}
metric = {
'name': 'true/F1score',
'goal': 'maximize'
}
sweep_config['metric'] = metric
parameters_dict = {
# 'cn_learning_rate': {
# 'values': [0.005]
# },
# 'cn_reg_coeff': {
# 'values': [0, 0.5, 1.1] # [0.9, 1, 1.05, 1.1, 1.15, 1.2, 1.25, 1.3, 1.35, 1.4, 1.45, 1.5, 1.55]
# },
# 'cn_manual_threshold': {
# 'values': [0.5, 0.6, 0.7, 0.8, 0.85, 0.9, 0.93, 0.95, 0.97, 0.98, 0.99, 0.999]
# },
'seed': {'values': [1, 2, 3, 4, 5]}
}
sweep_config['parameters'] = parameters_dict
sweep_id = wandb.sweep(sweep_config, project="PU_learning2")
wandb.agent(sweep_id, main, )
elif file_to_run == "acl":
from icrl.acl import main
sweep_config = {
'method': 'grid'
}
metric = {
'name': 'true/F1score',
'goal': 'maximize'
}
sweep_config['metric'] = metric
parameters_dict = {
'cn_learning_rate': {
'values': [0.005]
},
'seed': {'values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]}
}
sweep_config['parameters'] = parameters_dict
sweep_id = wandb.sweep(sweep_config, project="acl_active")
wandb.agent(sweep_id, main, )
else:
raise ValueError("File %s not defined" % file_to_run)
# running