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dragonfly-script.py
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dragonfly-script.py
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#!/usr/bin/env python
"""
Command line tool for Dragonfly.
-- kandasamy@cs.cmu.edu
-- kvysyara@andrew.cmu.edu
Usage:
python dragonfly-script.py --config <config file in .json or .pb format> --options <options file>
See README in main repository for examples.
"""
# pylint: disable=relative-import
# pylint: disable=invalid-name
# pylint: disable=import-error
from __future__ import print_function
import os
# import imp
from importlib import import_module
import sys
# Local
from dragonfly import maximise_function, minimise_function, \
maximise_multifidelity_function, \
minimise_multifidelity_function, \
multiobjective_maximise_functions, \
multiobjective_minimise_functions
from dragonfly.exd.cp_domain_utils import load_config_file
from dragonfly.exd.exd_utils import get_unique_list_of_option_args
from dragonfly.utils.option_handler import get_option_specs, load_options
# Get options
from dragonfly.opt.ga_optimiser import ga_opt_args
from dragonfly.opt.gp_bandit import get_all_euc_gp_bandit_args, \
get_all_cp_gp_bandit_args, get_all_mf_euc_gp_bandit_args, \
get_all_mf_cp_gp_bandit_args
from dragonfly.opt.random_optimiser import euclidean_random_optimiser_args, \
mf_euclidean_random_optimiser_args, \
cp_random_optimiser_args, mf_cp_random_optimiser_args
from dragonfly.opt.multiobjective_gp_bandit import get_all_euc_moo_gp_bandit_args, \
get_all_cp_moo_gp_bandit_args
from dragonfly.opt.random_multiobjective_optimiser import \
euclidean_random_multiobjective_optimiser_args, \
cp_random_multiobjective_optimiser_args
dragonfly_args = [ \
get_option_specs('config', False, None, 'Path to the json or pb config file. '),
get_option_specs('options', False, None, 'Path to the options file. '),
get_option_specs('max_or_min', False, 'max', 'Whether to maximise or minimise. '),
get_option_specs('max_capital', False, -1.0,
'Maximum capital (available budget) to be used in the experiment. '),
get_option_specs('capital_type', False, 'return_value',
'Maximum capital (available budget) to be used in the experiment. '),
get_option_specs('is_multi_objective', False, 0,
'If True, will treat it as a multiobjective optimisation problem. '),
get_option_specs('opt_method', False, 'bo',
('Optimisation method. Default is bo. This should be one of bo, ga, ea, direct, ' +
' pdoo, or rand, but not all methods apply to all problems.')),
get_option_specs('report_progress', False, 'default',
('How to report progress. Should be one of "default" (prints to stdout), ' +
'"silent" (no reporting), or a filename (writes to file).')),
]
def get_command_line_args():
""" Returns all arguments for the command line. """
ret = dragonfly_args + \
ga_opt_args + \
euclidean_random_optimiser_args + cp_random_optimiser_args + \
mf_euclidean_random_optimiser_args + mf_cp_random_optimiser_args + \
get_all_euc_gp_bandit_args() + get_all_cp_gp_bandit_args() + \
get_all_mf_euc_gp_bandit_args() + get_all_mf_cp_gp_bandit_args() + \
euclidean_random_multiobjective_optimiser_args + \
cp_random_multiobjective_optimiser_args + \
get_all_euc_moo_gp_bandit_args() + get_all_cp_moo_gp_bandit_args()
return get_unique_list_of_option_args(ret)
def main():
""" Main function. """
options = load_options(get_command_line_args(), cmd_line=True)
# Load domain and objective
config = load_config_file(options.config)
if hasattr(config, 'fidel_space'):
is_mf = True
else:
is_mf = False
# Load module
expt_dir = os.path.dirname(os.path.abspath(os.path.realpath(options.config)))
if not os.path.exists(expt_dir):
raise ValueError("Experiment directory does not exist.")
sys.path.append(expt_dir)
obj_module = import_module(config.name, expt_dir)
sys.path.remove(expt_dir)
# Set capital
if options.max_capital < 0:
raise ValueError('max_capital (time or number of evaluations) must be positive.')
# Call optimiser
_print_prefix = 'Maximising' if options.max_or_min == 'max' else 'Minimising'
call_to_optimise = {
'single': {'max': maximise_function, 'min': minimise_function},
'single_mf': {'max': maximise_multifidelity_function,
'min': minimise_multifidelity_function},
'multi': {'max': multiobjective_maximise_functions,
'min': multiobjective_minimise_functions},
}
if not options.is_multi_objective:
if is_mf:
print('%s multi-fidelity function on\n Fidelity-Space: %s.\n Domain: %s.\n'%(
_print_prefix, config.fidel_space, config.domain))
opt_val, opt_pt, history = call_to_optimise['single_mf'][options.max_or_min](
obj_module.objective, fidel_space=None, domain=None,
fidel_to_opt=config.fidel_to_opt, fidel_cost_func=obj_module.cost,
max_capital=options.max_capital, capital_type=options.capital_type,
opt_method=options.opt_method, config=config, options=options,
reporter=options.report_progress)
else:
print('%s function on Domain: %s.\n'%(_print_prefix, config.domain))
opt_val, opt_pt, history = call_to_optimise['single'][options.max_or_min](
obj_module.objective, domain=None, max_capital=options.max_capital,
capital_type=options.capital_type, opt_method=options.opt_method,
config=config, options=options, reporter=options.report_progress)
print('Optimum Value in %d evals: %0.4f'%(len(history.curr_opt_points), opt_val))
print('Optimum Point: %s.'%(opt_pt))
else:
if is_mf:
raise ValueError('Multi-objective multi-fidelity optimisation has not been ' +
'implemented yet.')
else:
# Check format of function caller
if hasattr(obj_module, 'objectives'):
objectives_to_pass = obj_module.objectives
num_objectives = len(objectives_to_pass)
else:
num_objectives = obj_module.num_objectives
objectives_to_pass = (obj_module.compute_objectives, obj_module.num_objectives)
print('%s %d multiobjective functions on Domain: %s.\n'%(_print_prefix,
num_objectives, config.domain))
print(objectives_to_pass)
pareto_values, pareto_points, history = \
call_to_optimise['multi'][options.max_or_min](objectives_to_pass,
domain=None, max_capital=options.max_capital, capital_type=options.capital_type,
opt_method=options.opt_method, config=config, options=options,
reporter=options.report_progress)
num_pareto_points = len(pareto_points)
print('Found %d Pareto Points: %s.'%(num_pareto_points, pareto_points))
print('Corresponding Pareto Values: %s.'%(pareto_values))
if __name__ == '__main__':
sys.path.insert(0, os.getcwd())
main()