},
# seed parameters
# 'seeds': np.linspace(1234567890, 1234567899, 4),
# cgp parameterisation
'max_time': 40000, # 82800s~23h
'genome_params': {
"n_inputs": 5,
},
'ea_params': {
'n_processes': 4,
},
#'use_rxet_init': True,
}
params['md5_hash_sim_script'] = utils.md5_file(
params['sim_script']) # consistency check
params['md5_hash_dependencies'] = [
utils.md5_file(fn) for fn in params['dependencies']
] # consistency check
results_folder = 'optimized_run_time_12h'
initial_seed_array = [
1234567810, 1234567820, 1234567830, 1234567840, 1234567850, 1234567860,
1234567870, 1234567880, 1234567890
]
for use_drxeot_init in [True]:
params['use_drxeot_init'] = use_drxeot_init
# No. of trials per individual
"n_trials": 8,
# evo parameters
"population_params": {"n_parents": 8, "mutation_rate": 0.05},
"ea_params": {"n_offsprings": 8, "n_breeding": 8, "tournament_size": 1},
"genome_params": {
"n_inputs": 2,
"n_outputs": 1,
"n_columns": 5,
"n_rows": 1,
"levels_back": None,
},
},
}
params["md5_hash_sim_script"] = utils.md5_file(params["machine_params"]["sim_script"])
key = dicthash.generate_hash_from_dict(params, blacklist=[("gp_params", "max_generations")])
params["machine_params"]["workingdir"] = os.getcwd()
params["machine_params"]["outputdir"] = os.path.join(params["machine_params"]["workingdir"], key)
print("preparing job")
print(" ", params["machine_params"]["outputdir"])
utils.mkdirp(params["machine_params"]["outputdir"])
utils.write_json(params, f"{params['machine_params']['outputdir']}/params.json")
utils.create_jobfile(params["machine_params"])
utils.copy_file(params["machine_params"]["sim_script"], params["machine_params"]["outputdir"])
for fn in ["write_job.py", "sim_utils.py", "primitive_utils.py"]:
utils.copy_file(fn, params["machine_params"]["outputdir"])
"Div",
"CustomConstantFloatOne",
"CustomConstantFloatZeroPointOne",
"CustomConstantFloatTen",
),
},
"ea_params": {
"n_offsprings": 4,
"n_breeding": 4,
"tournament_size": 1,
"n_processes": 8,
},
"evolve_params": {"max_generations": 500, "min_fitness": 300,},
}
params["md5_hash_sim_script"] = utils.md5_file(
params["sim_script"]
) # consistency check
key = dicthash.generate_hash_from_dict(params, blacklist=["wall_clock_limit"])
params["outputdir"] = os.path.join(os.getcwd(), key)
params["workingdir"] = os.getcwd()
submit_job = False
print("preparing job")
print(" ", params["outputdir"])
utils.mkdirp(params["outputdir"])
utils.write_json(sim_params, os.path.join(params["outputdir"], "sim_params.json"))
utils.write_json(params, os.path.join(params["outputdir"], "params.json"))
"n_outputs": 1,
"n_columns": 12,
"n_rows": 1,
"levels_back": None,
"primitives": ("Add", "Sub", "Mul", "Div", "CustomConstantFloatOne"),
},
"ea_params": {
"n_offsprings": 4,
"n_breeding": 4,
"tournament_size": 1,
"n_processes": 8,
},
"evolve_params": {"max_generations": 1000, "min_fitness": 0.0,},
}
params["md5_hash_sim_script"] = utils.md5_file(
params["sim_script"]
) # consistency check
params["md5_hash_dependencies"] = [
utils.md5_file(fn) for fn in params["dependencies"]
] # consistency check
key = dicthash.generate_hash_from_dict(params, blacklist=["wall_clock_limit"])
params["outputdir"] = os.path.join(os.getcwd(), key)
params["workingdir"] = os.getcwd()
submit_job = False
print("preparing job")
print(" ", params["outputdir"])