simulai.backup.Optimization package

Submodules

simulai.backup.Optimization.test_nomad module

from unittest import TestCase import PyNomad

class TestNomadInterface(TestCase):

def setUp(self) -> None:

# clear existing study and create a new one pass

def test_nomad_interface(self):

# This example of blackbox function is for a single process # The blackbox output must be put in the EvalPoint passed as argument def bb(x):

dim = x.size() f = sum([x.get_coord(i) ** 2 for i in range(dim)]) x.setBBO(str(f).encode(“UTF-8”)) return 1 # 1: success 0: failed evaluation

x0 = [0.71, 0.51, 0.51] lb = [-1, -1, -1] ub = []

params = [“BB_OUTPUT_TYPE OBJ”, “MAX_BB_EVAL 100”, “UPPER_BOUND * 1”, “DISPLAY_DEGREE 2”,

“DISPLAY_ALL_EVAL false”, “DISPLAY_STATS BBE OBJ”]

x_return, f_return, h_return, nb_evals, nb_iters, stopflag = PyNomad.optimize(bb, x0, lb, ub, params) print(”

NOMAD outputs X_sol={} F_sol={} H_sol={} NB_evals={} NB_iters={}

“.format(x_return,

f_return, h_return, nb_evals, nb_iters))

self.assertTrue(True, ‘finished’)

simulai.backup.Optimization.test_optuna_redis module

from unittest import TestCase import os import optuna

REDIS_PASSWORD = os.getenv(‘REDIS_PASSWORD’) REDIS_SERVER = os.getenv(‘REDIS_SERVER’, ‘brl-pinns.sl.cloud9.ibm.com’) REDIS_PORT = os.getenv(‘REDIS_PORT’, ‘6379’) storage = f”redis://default:{REDIS_PASSWORD}@{REDIS_SERVER}:{REDIS_PORT}” study_name = “unit_test/test_optuna_redis”

#import redis #print(‘ping redis’, redis.Redis(host=REDIS_SERVER, port=int(REDIS_PORT), username=’default’, password=REDIS_PASSWORD).ping())

def objective(trial):

x = trial.suggest_float(“x”, -10, 10) return (x - 2) ** 2

class TestDistributedOptuna(TestCase):

def setUp(self) -> None:

# clear existing study and create a new one

study = optuna.study.create_study(study_name=study_name, storage=storage, load_if_exists=True) optuna.study.delete_study(study_name=study_name, storage=storage) study = optuna.study.create_study(study_name=study_name, storage=storage, load_if_exists=False)

def test_optuna_redis(self):

study = optuna.study.load_study(study_name=study_name, storage=storage) study.optimize(objective, n_trials=2) self.assertTrue(study.best_value >= 0)

Module contents