def test_homogenous(var_type):
dim = 5
def fitness(_):
return np.random.rand()
if var_type == "r":
lb, ub = -1, 5
space = RealSpace([lb, ub]) * dim
mean = trend.constant_trend(dim, beta=None)
thetaL = 1e-10 * (ub - lb) * np.ones(dim)
thetaU = 10 * (ub - lb) * np.ones(dim)
theta0 = np.random.rand(dim) * (thetaU - thetaL) + thetaL
model = GaussianProcess(
mean=mean,
corr="squared_exponential",
theta0=theta0,
thetaL=thetaL,
thetaU=thetaU,
nugget=0,
noise_estim=False,
optimizer="BFGS",
wait_iter=3,
random_start=dim,
likelihood="concentrated",
eval_budget=100 * dim,
)
else:
if var_type == "b":
space = BoolSpace() * dim
elif var_type == "i":
space = IntegerSpace([0, 10], step=1) * dim
elif var_type == "c":
space = DiscreteSpace(list(range(10))) * dim
elif var_type == "o":
space = OrdinalSpace(list(string.ascii_lowercase))
elif var_type == "s":
space = SubsetSpace(list(string.ascii_lowercase)[:5])
model = RandomForest(levels=space.levels)
opt = BO(
search_space=space,
obj_fun=fitness,
model=model,
DoE_size=5,
max_FEs=10,
verbose=True,
n_point=1,
)
print(opt.run())
def test_BO_equality():
dim = 2
search_space = RealSpace([0, 1]) * dim
thetaL = 1e-5 * np.ones(dim)
thetaU = np.ones(dim)
theta0 = np.random.rand(dim) * (thetaU - thetaL) + thetaL
model = GaussianProcess(
corr="squared_exponential",
theta0=theta0,
thetaL=thetaL,
thetaU=thetaU,
nugget=1e-1,
random_state=42,
)
xopt, _, __ = BO(
search_space=search_space,
obj_fun=obj_fun,
eq_fun=h,
model=model,
max_FEs=20,
DoE_size=3,
acquisition_fun="MGFI",
acquisition_par={"t": 2},
acquisition_optimization={"optimizer": "BFGS"},
verbose=True,
random_seed=42,
).run()
assert np.isclose(h(xopt), 0, atol=1e-1)
def test_BO_constraints():
search_space = (
IntegerSpace([1, 10], var_name="mu")
+ IntegerSpace([1, 10], var_name="lambda")
+ RealSpace([0, 1], var_name="pc")
+ RealSpace([0.005, 0.5], var_name="p")
)
model = RandomForest(levels=search_space.levels)
xopt, _, __ = BO(
search_space=search_space,
obj_fun=obj_fun2,
ineq_fun=g,
model=model,
max_FEs=10,
DoE_size=3,
eval_type="dict",
acquisition_fun="MGFI",
acquisition_par={"t": 2},
n_job=1,
n_point=1,
verbose=True,
random_seed=42,
).run()
assert isinstance(xopt, dict)
assert all(np.array(g(xopt)) <= 0)
def test_BO(dim, obj_fun, ftarget, max_FEs, lb, ub, logfile):
space = RealSpace(list(zip(lb, ub)))
mean = trend.constant_trend(dim, beta=None) # equivalent to Ordinary Kriging
thetaL = 1e-10 * (ub - lb) * np.ones(dim)
thetaU = 10 * (ub - lb) * np.ones(dim)
theta0 = np.random.rand(dim) * (thetaU - thetaL) + thetaL
model = GaussianProcess(
mean=mean,
corr="matern",
theta0=theta0,
thetaL=thetaL,
thetaU=thetaU,
noise_estim=False,
nugget=1e-6,
optimizer="BFGS",
wait_iter=5,
random_start=5 * dim,
likelihood="concentrated",
eval_budget=100 * dim,
)
return BO(
search_space=space,
obj_fun=obj_fun,
model=model,
DoE_size=dim * 5,
max_FEs=max_FEs,
verbose=False,
n_point=1,
minimize=True,
ftarget=ftarget,
logger=logfile,
)
def test_BO(dim, obj_fun, ftarget, max_FEs, lb, ub, logfile):
sys.path.insert(0, '../')
from bayes_optim import AnnealingBO, BO, ContinuousSpace
from bayes_optim.Surrogate import GaussianProcess, trend
space = ContinuousSpace(list(zip(lb, ub)))
mean = trend.constant_trend(dim,
beta=None) # equivalent to Ordinary Kriging
thetaL = 1e-10 * (ub - lb) * np.ones(dim)
thetaU = 10 * (ub - lb) * np.ones(dim)
theta0 = np.random.rand(dim) * (thetaU - thetaL) + thetaL
model = GaussianProcess(mean=mean,
corr='matern',
theta0=theta0,
thetaL=thetaL,
thetaU=thetaU,
noise_estim=False,
nugget=1e-6,
optimizer='BFGS',
wait_iter=5,
random_start=5 * dim,
likelihood='concentrated',
eval_budget=100 * dim)
return BO(search_space=space,
obj_fun=obj_fun,
model=model,
DoE_size=dim * 5,
max_FEs=max_FEs,
verbose=False,
n_point=1,
minimize=True,
ftarget=ftarget,
logger=logfile)
def test_warm_data_with_RF():
space = ContinuousSpace([-10, 10]) * 2 + \
OrdinalSpace([5, 15]) + \
NominalSpace(['OK', 'A', 'B', 'C', 'D', 'E', 'F', 'G'])
X = space.sampling(10)
y = [obj_fun(x) for x in X]
model = RandomForest(levels=space.levels)
opt = BO(search_space=space,
obj_fun=obj_fun,
model=model,
minimize=True,
eval_type='list',
max_FEs=10,
verbose=True,
acquisition_fun='EI',
warm_data=(X, y))
opt.run()
assert opt.data.shape[0] == 20
def test_infeasible_constraints():
dim = 5
lb, ub = -5, 5
def fitness(_):
return 1
space = RealSpace([lb, ub]) * dim
model = RandomForest(levels=space.levels)
opt = BO(
search_space=space,
obj_fun=fitness,
model=model,
DoE_size=5,
ineq_fun=lambda x: x[0] + 5.1,
max_FEs=10,
verbose=True,
n_point=1,
)
with pytest.raises(AskEmptyError):
opt.run()
def test_warm_data_with_RF():
space = (RealSpace([-10, 10]) * 2 + IntegerSpace([5, 15]) +
DiscreteSpace(["OK", "A", "B", "C", "D", "E", "F", "G"]))
X = space.sample(10)
y = [obj_fun(x) for x in X]
model = RandomForest(levels=space.levels)
opt = BO(
search_space=space,
obj_fun=obj_fun,
model=model,
minimize=True,
eval_type="list",
max_FEs=5,
verbose=True,
acquisition_fun="EI",
warm_data=(X, y),
)
opt.run()
assert opt.data.shape[0] == 15
def test_warm_data_with_GPR():
dim = 2
lb, ub = -5, 5
def fitness(x):
x = np.asarray(x)
return np.sum(x**2)
X = np.random.rand(5, dim) * (ub - lb) + lb
y = [fitness(x) for x in X]
space = RealSpace([lb, ub]) * dim
thetaL = 1e-10 * (ub - lb) * np.ones(dim)
thetaU = 10 * (ub - lb) * np.ones(dim)
theta0 = np.random.rand(dim) * (thetaU - thetaL) + thetaL
model = GaussianProcess(
theta0=theta0,
thetaL=thetaL,
thetaU=thetaU,
nugget=0,
noise_estim=False,
optimizer="BFGS",
wait_iter=3,
random_start=dim,
likelihood="concentrated",
eval_budget=100 * dim,
)
opt = BO(
search_space=space,
obj_fun=fitness,
model=model,
warm_data=(X, y),
max_FEs=10,
verbose=True,
n_point=1,
)
assert np.all(np.asarray(opt.data) == np.asarray(opt.warm_data))
assert opt.model.is_fitted
opt.run()
def test_flat_continuous():
dim = 5
lb, ub = -1, 5
def fitness(_):
return 1
space = RealSpace([lb, ub]) * dim
mean = trend.constant_trend(dim, beta=None)
thetaL = 1e-10 * (ub - lb) * np.ones(dim)
thetaU = 10 * (ub - lb) * np.ones(dim)
theta0 = np.random.rand(dim) * (thetaU - thetaL) + thetaL
model = GaussianProcess(
mean=mean,
corr="squared_exponential",
theta0=theta0,
thetaL=thetaL,
thetaU=thetaU,
nugget=0,
noise_estim=False,
optimizer="BFGS",
wait_iter=3,
random_start=dim,
likelihood="concentrated",
eval_budget=100 * dim,
)
opt = BO(
search_space=space,
obj_fun=fitness,
model=model,
DoE_size=5,
max_FEs=10,
verbose=True,
n_point=1,
)
with pytest.raises(FlatFitnessError):
opt.run()
def test_continuous():
dim = 5
lb, ub = -1, 5
def fitness(x):
x = np.asarray(x)
return np.sum(x**2)
space = ContinuousSpace([lb, ub]) * dim
mean = trend.constant_trend(dim, beta=None)
thetaL = 1e-10 * (ub - lb) * np.ones(dim)
thetaU = 10 * (ub - lb) * np.ones(dim)
theta0 = np.random.rand(dim) * (thetaU - thetaL) + thetaL
model = GaussianProcess(mean=mean,
corr='squared_exponential',
theta0=theta0,
thetaL=thetaL,
thetaU=thetaU,
nugget=0,
noise_estim=False,
optimizer='BFGS',
wait_iter=3,
random_start=dim,
likelihood='concentrated',
eval_budget=100 * dim)
opt = BO(search_space=space,
obj_fun=fitness,
model=model,
DoE_size=5,
max_FEs=10,
verbose=True,
n_point=1)
print(opt.run())
def test_BO(dim, obj_fun, ftarget, max_FEs, lb, ub, logfile):
sys.path.insert(0, "../")
from bayes_optim import BO, AnnealingBO, RealSpace
from bayes_optim.Surrogate import GaussianProcess, trend
space = RealSpace([lb, ub]) * dim
mean = trend.constant_trend(dim, beta=0) # equivalent to Ordinary Kriging
thetaL = 1e-10 * (ub - lb) * np.ones(dim)
thetaU = 10 * (ub - lb) * np.ones(dim)
theta0 = np.random.rand(dim) * (thetaU - thetaL) + thetaL
model = GaussianProcess(
mean=mean,
corr="matern",
theta0=theta0,
thetaL=thetaL,
thetaU=thetaU,
noise_estim=False,
nugget=1e-6,
optimizer="BFGS",
wait_iter=5,
random_start=5 * dim,
likelihood="concentrated",
eval_budget=100 * dim,
)
return BO(
search_space=space,
obj_fun=obj_fun,
model=model,
DoE_size=dim * 5,
max_FEs=max_FEs,
verbose=False,
n_point=1,
minimize=True,
ftarget=ftarget,
logger=logfile,
)
def test_BO_bad_constraints():
search_space = (
DiscreteSpace(["1", "2", "3"], var_name="lambda")
+ RealSpace([0, 1], var_name="pc")
+ RealSpace([0.005, 0.5], var_name="p")
)
model = RandomForest(levels=search_space.levels)
with pytest.raises(ConstraintEvaluationError):
BO(
search_space=search_space,
obj_fun=lambda x: 10 * (x[0] == "3") + x[1] * x[2],
ineq_fun=lambda x: sum(np.array(x) ** 2),
model=model,
max_FEs=10,
DoE_size=3,
eval_type="list",
acquisition_fun="MGFI",
acquisition_par={"t": 2},
n_job=1,
n_point=1,
verbose=True,
random_seed=42,
).run()
def test_BO_constraints():
search_space = OrdinalSpace([1, 10], var_name='mu') + \
OrdinalSpace([1, 10], var_name='lambda') + \
ContinuousSpace([0, 1], var_name='pc') + \
ContinuousSpace([0.005, 0.5], var_name='p')
model = RandomForest(levels=search_space.levels)
xopt, _, __ = BO(search_space=search_space,
obj_fun=obj_func,
ineq_fun=g,
model=model,
max_FEs=30,
DoE_size=3,
eval_type='dict',
acquisition_fun='MGFI',
acquisition_par={
't': 2
},
n_job=1,
n_point=1,
verbose=True).run()
assert isinstance(xopt, dict)
assert all(np.array(g(xopt)) <= 0)
def test_pickling():
dim = 5
lb, ub = -1, 5
def fitness(x):
x = np.asarray(x)
return np.sum(x**2)
space = RealSpace([lb, ub]) * dim
mean = trend.constant_trend(dim, beta=None)
thetaL = 1e-10 * (ub - lb) * np.ones(dim)
thetaU = 10 * (ub - lb) * np.ones(dim)
theta0 = np.random.rand(dim) * (thetaU - thetaL) + thetaL
model = GaussianProcess(
mean=mean,
corr="squared_exponential",
theta0=theta0,
thetaL=thetaL,
thetaU=thetaU,
nugget=0,
noise_estim=False,
optimizer="BFGS",
wait_iter=3,
random_start=dim,
likelihood="concentrated",
eval_budget=100 * dim,
)
opt = BO(
search_space=space,
obj_fun=fitness,
model=model,
DoE_size=5,
max_FEs=10,
verbose=True,
n_point=1,
log_file="log",
)
opt.save("test")
opt = BO.load("test")
print(opt.run())
os.remove("test")
os.remove("log")
opt = ParallelBO(
search_space=space,
obj_fun=fitness,
model=model,
DoE_size=5,
max_FEs=10,
verbose=True,
n_point=3,
log_file="log",
)
opt.save("test")
opt = BO.load("test")
print(opt.run())
os.remove("test")
os.remove("log")
def modeling(train, targets, to_optimize, **kwargs):
"""
Training and performing hyperparmeter optimization
by Bayesian Optimization. Currently only supporting
Random Forests.
TODO: Make the HO and train_seting more interactive
:param to_optimize: perform or not HO (boolean)
:param train: train set (pandas)
:param targets: targets (labels) (np.arrays)
:cv: CV count for hyperparameter optimization
:to_drop: Features to be dropped from learning such as unit numbers,
cycles, etc (list of string names)
:DoE_size: Initial design of experiment for the BO HO.
:max_FEs: maximum number of function evaluations of the BO HO
:features_list= a list of features to use, cv=
:return: trained model and list of used features
"""
start = time.time()
features_list = kwargs.get('features_list', None)
to_drop = kwargs.get('to_drop', None)
cv = kwargs.get('cv', 10)
DoE_size = kwargs.get('DoE_size', 200)
max_FEs = kwargs.get('max_FEs', 20)
train_set = train.copy()
if to_drop:
print(f'The following features will not be used in training: {to_drop}')
train_set.drop(to_drop, axis=1, inplace=True)
if features_list:
print('Features selected by user')
train_set = train_set[features_list]
train_set = train_set.values
else:
print('Feature Selection (this will take a while...)')
train_set, features_list = boruta_feature_selection(train_set, targets)
with open('./features_list.pkl', 'wb') as f:
pkl.dump(features_list, f)
df_columns = ['acc', 'max_depth', 'n_estimators', 'bootstrap', 'max_features', 'min_samples_leaf',
'min_samples_split']
df_eval = pd.DataFrame(columns=df_columns)
# Hyperparameter optimization
# objective function
def obj_func(x):
# logger.info('Started internal cross-validation')
nonlocal df_eval
performance_ = []
skf = StratifiedKFold(n_splits=cv, random_state=np.random, shuffle=True)
for train_set_index, test_index in tqdm(skf.split(train_set, targets), 'Optimizing HO'):
X_train_set, X_test = train_set[train_set_index], train_set[test_index]
y_train_set, y_test = targets[train_set_index], targets[test_index]
rf_ = RandomForestClassifier(n_estimators=int(x[1]), max_depth=int(x[0]), bootstrap=x[2],
max_features=x[3], min_samples_leaf=x[4], min_samples_split=x[5],
n_jobs=-1)
rf_.fit(X_train_set, y_train_set)
predictions_ = rf_.predict(X_test)
performance_.append(accuracy_score(y_test, predictions_))
val = np.mean(performance_)
df_eval_tmp = pd.DataFrame([[val, x[0], x[1], x[2], x[3], x[4], x[5]]],
columns=df_columns)
df_eval = df_eval.append(df_eval_tmp)
return val
# definition of hyperparameter search space:
max_depth = OrdinalSpace([2, 100])
n_estimators = OrdinalSpace([1, 1000])
min_samples_leaf = OrdinalSpace([1, 10])
min_samples_split = OrdinalSpace([2, 20])
bootstrap = NominalSpace(['True', 'False'])
max_features = NominalSpace(['auto', 'sqrt', 'log2'])
search_space = max_depth + n_estimators + bootstrap + max_features + min_samples_leaf + min_samples_split
model = RandomForest(levels=search_space.levels)
opt = BO(search_space=search_space, obj_fun=obj_func, model=model, max_FEs=max_FEs,
DoE_size=DoE_size,
n_point=1,
n_job=1,
minimize=False,
verbose=False)
if to_optimize:
print(f'Hyperparameter optimization with {cv}-folds and {max_FEs} function evaluations')
opt.run()
best_params_ = df_eval[df_columns[1:]][df_eval['acc'] == df_eval['acc'].max()][:1].to_dict('records')
# Training using the best parameters
if to_optimize:
rf = RandomForestClassifier(n_jobs=-1, **best_params_[0])
else:
rf = RandomForestClassifier(n_jobs=-1)
rf.fit(train_set, targets)
dump(rf, './rf_model.joblib')
end = time.time()
print(f'----Duration of training is {(end - start) / 60} minutes')
return rf, features_list
# Discrete (nominal) variables can be specified as follows:
# No lb, ub... a list of categories instead
N = DiscreteSpace(["OK", "A", "B", "C", "D", "E", "F", "G"],
var_name="nominal")
# The whole search space can be constructed:
search_space = C + I + N
# Bayesian optimization also uses a Surrogate model
# For mixed variable type, the random forest is typically used
model = RandomForest(levels=search_space.levels)
opt = BO(
search_space=search_space,
obj_fun=obj_fun,
model=model,
max_FEs=50,
DoE_size=3, # the initial DoE size
eval_type="dict",
acquisition_fun="MGFI",
acquisition_par={"t": 2},
n_job=1, # number of processes
n_point=1, # number of the candidate solution proposed in each iteration
verbose=True, # turn this off, if you prefer no output
)
xopt, fopt, stop_dict = opt.run()
print("xopt: {}".format(xopt))
print("fopt: {}".format(fopt))
print("stop criteria: {}".format(stop_dict))
space = RealSpace([lb, ub]) * dim
thetaL = 1e-10 * (ub - lb) * np.ones(dim)
thetaU = 10 * (ub - lb) * np.ones(dim)
theta0 = np.random.rand(dim) * (thetaU - thetaL) + thetaL
model = GaussianProcess(
theta0=theta0,
thetaL=thetaL,
thetaU=thetaU,
nugget=1e-3,
noise_estim=True,
optimizer="BFGS",
wait_iter=3,
random_start=dim,
likelihood="concentrated",
eval_budget=100 * dim,
)
opt = BO(
search_space=space,
obj_fun=fitness,
model=model,
DoE_size=5,
max_FEs=20,
verbose=True,
n_point=1,
acquisition_optimization={"optimizer": "OnePlusOne_Cholesky_CMA"},
)
print(opt.run())
x = np.asarray(x)
return np.sum(x**2)
space = ContinuousSpace([lb, ub]) * dim
mean = trend.constant_trend(dim, beta=None)
thetaL = 1e-10 * (ub - lb) * np.ones(dim)
thetaU = 10 * (ub - lb) * np.ones(dim)
theta0 = np.random.rand(dim) * (thetaU - thetaL) + thetaL
model = GaussianProcess(theta0=theta0,
thetaL=thetaL,
thetaU=thetaU,
nugget=0,
noise_estim=False,
optimizer='BFGS',
wait_iter=3,
random_start=dim,
likelihood='concentrated',
eval_budget=100 * dim)
opt = BO(search_space=space,
obj_fun=fitness,
model=model,
DoE_size=5,
max_FEs=50,
verbose=True,
n_point=1)
print(opt.run())
