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in_code_demo.py
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in_code_demo.py
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"""
In code demo for borehole_constrained example.
-- kandasamy@cs.cmu.edu
"""
from __future__ import print_function
from dragonfly import load_config, maximise_function, maximise_multifidelity_function
# From current directory
from borehole_constrained import objective
from borehole_constrained_mf import objective as mf_objective
from borehole_constrained_mf import cost as mf_cost
def main():
""" Main function. """
# First Specify all parameters
domain_vars = [{'name': 'rw', 'type': 'float', 'min': 0.05, 'max': 0.15, 'dim': 1},
{'name': 'L_Kw', 'type': 'float', 'min': 0, 'max': 1, 'dim': 2},
{'name': 'Tu', 'type': 'int', 'min': 63070, 'max': 115600, 'dim': ''},
{'name': 'Tl', 'type': 'float', 'min': 63.1, 'max': 116},
{'name': 'Hu_Hl', 'type': 'int', 'min': 0, 'max': 240, 'dim': 2},
{'name': 'r', 'type': 'float', 'min': 100, 'max': 50000},
]
domain_constraints = [{'constraint': 'np.sqrt(rw[0]) + L_Kw[1] <= 0.9'},
{'constraint': 'r/100.0 + Hu_Hl[1] < 200'}
]
fidel_vars = [{'name': 'fidel_0', 'type': 'float', 'min': 0.05, 'max': 0.25},
{'name': 'fidel_1', 'type': 'discrete_numeric', 'items': "0.1:0.05:1.01"},
]
fidel_space_constraints = [
{'name': 'fsc1', 'constraint': 'fidel_0 + fidel_1 <= 0.9'}
]
fidel_to_opt = [0.1, 0.75]
# Budget of evaluations
max_num_evals = 100 # Optimisation budget (max number of evaluations)
max_mf_capital = max_num_evals * mf_cost(fidel_to_opt) # Multi-fideltiy capital
# First do the MF version
config_params = {'domain': domain_vars, 'fidel_space': fidel_vars,
'domain_constraints': domain_constraints,
'fidel_space_constraints': fidel_space_constraints,
'fidel_to_opt': fidel_to_opt}
config = load_config(config_params)
# Optimise
mf_opt_pt, mf_opt_val, history = maximise_multifidelity_function(mf_objective,
config.fidel_space, config.domain,
config.fidel_to_opt, mf_cost,
max_mf_capital, config=config)
print(mf_opt_pt, mf_opt_val)
# Non-MF version
config_params = {'domain': domain_vars, 'domain_constraints': domain_constraints}
config = load_config(config_params)
max_capital = 100 # Optimisation budget (max number of evaluations)
# Optimise
opt_pt, opt_val, history = maximise_function(objective, config.domain,
max_num_evals, config=config)
print(opt_pt, opt_val)
if __name__ == '__main__':
main()