def find_reference_point(tasks, module, input_space, grid_size = 20000):
def create_fun_neg(task):
def fun(params, gradient = False):
if len(params.shape) > 1 and params.shape[ 1 ] > 1:
params = params.flatten()
params = input_space.from_unit(np.array([ params ])).flatten()
return -1.0 * module.main(0, paramify_no_types(input_space.paramify(params)))[ task ]
return fun
funs_neg = [ create_fun_neg(task) for task in tasks ]
moop_neg = MOOP_basis_functions(funs_neg, input_space.num_dims)
moop_neg.evolve(400, 400)
result = moop_neg.compute_pareto_front_and_set()
front = result['frontier']
pareto_set = result['pareto_set']
grid = sobol_grid.generate(input_space.num_dims, grid_size = grid_size, grid_seed = npr.randint(0, grid_size))
grid = np.vstack((grid, pareto_set))
# We add the borders of the hyper-cube to the grid since there it is likely to be the maximum
for i in range(2**input_space.num_dims):
vector = np.zeros(input_space.num_dims)
for j in range(input_space.num_dims):
if bin(i & 2**j) != bin(0):
vector[ j ] = 1.0
grid = np.vstack((grid, vector.reshape((1, input_space.num_dims))))
reference_point = np.zeros(len(funs_neg))
for i in range(len(funs_neg)):
grid_values = np.zeros(grid.shape[ 0 ])
for j in range(grid.shape[ 0 ]):
grid_values[ j ] = funs_neg[ i ](grid[ j, : ])
best = grid[ np.argmin(grid_values), : ]
def f(x):
if x.ndim == 1:
x = x[None,:]
value = funs_neg[ i ](x)
return (value)
bounds = [ (0.0, 1.0) ] * input_space.num_dims
x_opt, y_opt, opt_info = spo.fmin_l_bfgs_b(f, best, bounds = bounds, disp = 0, approx_grad = True)
reference_point[ i ] = -1.0 * y_opt + np.abs(-1.0 * y_opt * 0.01)
return reference_point
def main(expt_dir):
os.chdir(expt_dir)
sys.path.append(expt_dir)
options = parse_config_file(expt_dir, 'config.json')
experiment_name = options["experiment-name"]
# main_file = options['main_file']
main_file = 'OSY_no_noisy'
if main_file[-3:] == '.py':
main_file = main_file[:-3]
module = __import__(main_file)
input_space = InputSpace(options["variables"])
chooser_module = importlib.import_module('spearmint.choosers.' +
options['chooser'])
chooser = chooser_module.init(input_space, options)
db = MongoDB(database_address=options['database']['address'])
jobs = load_jobs(db, experiment_name)
hypers = db.load(experiment_name, 'hypers')
tasks = parse_tasks_from_jobs(jobs, experiment_name, options, input_space)
if len(tasks) < 2:
print 'Not a multi-objective problem!'
return -1
if options['language'] != "PYTHON":
print 'Only python programs supported!'
return -1
objectives = dict()
contraints = dict()
for task in tasks:
if tasks[task].type == 'objective':
objectives[task] = tasks[task]
else:
contraints[task] = tasks[task]
assert len(objectives) >= 2 and len(contraints) >= 1
def create_fun(task):
def fun(params, gradient=False):
if len(params.shape) > 1 and params.shape[1] > 1:
values = np.zeros(params.shape[0])
params_orig = params
for i in range(params_orig.shape[0]):
param = params[i, :]
param = param.flatten()
param = input_space.from_unit(np.array([param])).flatten()
values[i] = module.main(
0,
paramify_no_types(input_space.paramify(param)))[task]
else:
return module.main(
0, paramify_no_types(input_space.paramify(params)))[task]
return values
return fun
funs_o = [create_fun(task) for task in objectives]
funs_c = [create_fun(task) for task in contraints]
moop = MOOP_basis_functions(funs_o,
input_space.num_dims,
constraints=funs_c)
grid = sobol_grid.generate(input_space.num_dims,
grid_size=1000 * input_space.num_dims)
# We only retain the feasible points
moop.solve_using_grid(grid)
reference = np.ones(len(objectives)) * 1e3
hyper_volume_solution = moop.get_hypervolume(reference.tolist())
result = moop.compute_pareto_front_and_set()
front = result['frontier']
pareto_set = result['pareto_set']
with open('hypervolume_solution.txt', 'a') as f:
print >> f, "%lf" % (hyper_volume_solution)
# We iterate through each recommendation made
i = 0
more_recommendations = True
while more_recommendations:
recommendation = db.load(experiment_name, 'recommendations',
{'id': i + 1})
if recommendation == None:
more_recommendations = False
else:
solution = input_space.to_unit(
input_space.vectorify(recommendation['params']))
if len(solution.shape) == 1:
solution = solution.reshape((1, len(solution)))
# We compute the objective values associated to this recommendation
values_solution = np.zeros((solution.shape[0], len(objectives)))
for j in range(values_solution.shape[0]):
for k in range(values_solution.shape[1]):
values_solution[j, k] = funs_o[k](solution[j:(j + 1), :])
moop = MOOP_basis_functions(funs_o, input_space.num_dims)
moop.set_population(solution)
hyper_volume = moop.get_hypervolume(reference.tolist())
# We make sure that there are no infeasible points recommended
# If there are infeasible recommendations we return 0 as the hypervolume
all_feasible = True
for k in range(len(funs_c)):
all_feasible = all_feasible and not np.any(
funs_c[k](solution) < 0)
if not all_feasible:
hyper_volume = 0.0
with open('hypervolumes.txt', 'a') as f:
print >> f, "%lf" % (hyper_volume)
with open('evaluations.txt', 'a') as f_handle:
np.savetxt(
f_handle,
np.array([recommendation['num_complete_tasks'].values()]),
delimiter=' ',
newline='\n')
i += 1
def main(expt_dir):
os.chdir(expt_dir)
sys.path.append(expt_dir)
options = parse_config_file(expt_dir, 'config.json')
experiment_name = options["experiment-name"]
options['main_file'] = 'prog_no_noisy'
main_file = options['main_file']
if main_file[-3:] == '.py':
main_file = main_file[:-3]
module = __import__(main_file)
input_space = InputSpace(options["variables"])
chooser_module = importlib.import_module('spearmint.choosers.' + options['chooser'])
chooser = chooser_module.init(input_space, options)
db = MongoDB(database_address=options['database']['address'])
jobs = load_jobs(db, experiment_name)
hypers = db.load(experiment_name, 'hypers')
tasks = parse_tasks_from_jobs(jobs, experiment_name, options, input_space)
if len(tasks) < 2:
print 'Not a multi-objective problem!'
return -1
if options['language'] != "PYTHON":
print 'Only python programs supported!'
return -1
for task in tasks:
if tasks[ task ].type != 'objective':
print 'Not a multi-objective problem!'
return -1
def create_fun(task):
def fun(params, gradient = False):
if len(params.shape) > 1 and params.shape[ 1 ] > 1:
params = params.flatten()
params = input_space.from_unit(np.array([ params ])).flatten()
return module.main(0, paramify_no_types(input_space.paramify(params)))[ task ]
return fun
funs = [ create_fun(task) for task in tasks ]
moop = MOOP_basis_functions(funs, input_space.num_dims)
# moop.evolve(1, 8)
grid = sobol_grid.generate(input_space.num_dims, grid_size = 1000 * input_space.num_dims)
moop.solve_using_grid(grid)
# reference = find_reference_point_using_direct(tasks, module, input_space)
# reference = reference + np.abs(reference) * 0.1
reference = np.ones(len(tasks)) * 7
hyper_volume_solution = moop.get_hypervolume(reference.tolist())
result = moop.compute_pareto_front_and_set()
front = result['frontier']
pareto_set = result['pareto_set']
# os.remove('hypervolume_solution.txt')
with open('hypervolume_solution.txt', 'a') as f:
print >> f, "%lf" % (hyper_volume_solution)
# os.remove('hypervolumes.txt')
# We iterate through each recommendation made
i = 0
more_recommendations = True
while more_recommendations:
recommendation = db.load(experiment_name, 'recommendations', {'id' : i + 1})
if recommendation == None:
more_recommendations = False
else:
solution = input_space.to_unit(input_space.vectorify(recommendation[ 'params' ]))
if len(solution.shape) == 1:
solution = solution.reshape((1, len(solution)))
# We compute the objective values associated to this recommendation
values_solution = np.zeros((solution.shape[ 0 ], len(tasks)))
for j in range(values_solution.shape[ 0 ]):
for k in range(values_solution.shape[ 1 ]):
values_solution[ j, k ] = funs[ k ](solution[ j : (j + 1), : ])
moop = MOOP_basis_functions(funs, input_space.num_dims)
moop.set_population(solution)
hyper_volume = moop.get_hypervolume(reference.tolist())
with open('hypervolumes.txt', 'a') as f:
print >> f, "%lf" % (hyper_volume)
with open('mean_min_distance_to_frontier.txt', 'a') as f:
print >> f, "%lf" % (average_min_distance(values_solution, front))
with open('mean_min_distance_from_frontier.txt', 'a') as f:
print >> f, "%lf" % (average_min_distance(front, values_solution))
with open('mean_min_distance_to_pareto_set.txt', 'a') as f:
print >> f, "%lf" % (average_min_distance(input_space.from_unit(solution), \
input_space.from_unit(pareto_set)))
with open('mean_min_distance_from_pareto_set.txt', 'a') as f:
print >> f, "%lf" % (average_min_distance(input_space.from_unit(pareto_set), \
input_space.from_unit(solution)))
with open('evaluations.txt','a') as f_handle:
np.savetxt(f_handle, np.array([recommendation['num_complete_tasks'].values()]), delimiter = ' ', newline = '\n')
i += 1