def tune():
X, y = get_data()
too = torch.optim.Adam, torch.optim.Adadelta, torch.optim.Adagrad, torch.optim.ASGD
to = ht.CategoricalParameter('torch_optimizer', options=too)
eta = ht.ContinuousParameter('eta', lower_bound=1e-10, upper_bound=1e-1)
mi = ht.DiscreteParameter('max_iter', lower_bound=1e2, upper_bound=1e4)
hl1 = ht.DiscreteParameter('', lower_bound=10, upper_bound=100)
hl2 = ht.DiscreteParameter('', lower_bound=10, upper_bound=100)
hls = ht.TupleParameter('hidden_layer_sizes', values=(hl1, hl2))
tp1 = ht.CategoricalParameter('', options=(nn.Linear, ))
tp2 = ht.CategoricalParameter('', options=(nn.Linear, ))
tp3 = ht.CategoricalParameter('', options=(nn.Linear, ))
top = ht.TupleParameter('topology', values=(tp1, tp2, tp3))
hypers = [to, eta, mi, hls, top]
tuner = ht.HyperTune(algorithm=Net,
parameters=hypers,
train_func=Net.fit,
objective_func=Net.mse,
train_func_args=(X, y),
objective_func_args=(X, y),
max_evals=100,
maximize=False,
num_replications=1)
tuner.tune()
print(tuner.get_results())
def test_categorical_param(self):
c = ht.CategoricalParameter('c', options=('a', 'b'))
self.assertEqual(c.name, 'c')
self.assertEqual(c.shape, 1)
v = c.get_val([-1])
self.assertEqual(v, 'a')
co = ht.ContinuousParameter('co', lower_bound=0, upper_bound=1)
con = ht.ConstantParameter('con', value=100)
c = ht.CategoricalParameter('c', options=('a', co, con))
self.assertEqual(c.shape, 2)
self._optimizer_.step()
def mse(self, X, y):
y_hat = self.predict(X)
return self._loss_func_(y, y_hat).item()
# make an example dataset
p = 750
X = np.random.rand(10**4, 3)
y = np.random.rand(10**4)
X_train, X_test, y_train, y_test = X[:p], X[p:], y[:p], y[p:]
# define the target hyperparameters
too = torch.optim.Adam, torch.optim.Adadelta, torch.optim.Adagrad, torch.optim.ASGD
to = ht.CategoricalParameter('torch_optimizer', options=too)
eta = ht.ContinuousParameter('eta', lower_bound=1e-10, upper_bound=1e-1)
mi = ht.DiscreteParameter('max_iter', lower_bound=1e2, upper_bound=1e4)
hl1 = ht.DiscreteParameter('', lower_bound=10, upper_bound=100)
hl2 = ht.DiscreteParameter('', lower_bound=10, upper_bound=100)
hls = ht.TupleParameter('hidden_layer_sizes', values=(hl1, hl2))
tp1 = ht.CategoricalParameter('', options=(nn.Linear, ))
tp2 = ht.CategoricalParameter('', options=(nn.Linear, ))
tp3 = ht.CategoricalParameter('', options=(nn.Linear, ))
top = ht.TupleParameter('topology', values=(tp1, tp2, tp3))
hypers = [to, eta, mi, hls, top]
# define a Hypertune object
tuner = ht.HyperTune(algorithm=Net,
parameters=hypers,
train_func=Net.fit,
from sklearn.neural_network import MLPRegressor
import hypertune as ht
import numpy as np
# make an example dataset
p = 75
X = np.random.rand(100, 3)
y = np.random.rand(100)
X_train, X_test, y_train, y_test = X[:p], X[p:], y[:p], y[p:]
# define the target hyperparameters
activation = ht.CategoricalParameter('activation',
options=('identity', 'logistic', 'tanh',
'relu'))
learning_rate_init = ht.ContinuousParameter('learning_rate_init',
lower_bound=10**-5,
upper_bound=0.1)
max_iter = ht.DiscreteParameter('max_iter', lower_bound=500, upper_bound=10**3)
hl1 = ht.DiscreteParameter('', lower_bound=50, upper_bound=250)
hl2 = ht.DiscreteParameter('', lower_bound=100, upper_bound=250)
hl3 = ht.DiscreteParameter('', lower_bound=1, upper_bound=100)
hidden_layer_sizes = ht.TupleParameter('hidden_layer_sizes',
values=(hl1, hl2, hl3))
hypers = [
activation, learning_rate_init, max_iter, learning_rate, hidden_layer_sizes
]
# define a Hypertune object
tuner = ht.HyperTune(algorithm=MLPRegressor,
for i, (train_idxs, test_idxs) in enumerate(splits):
X_train, y_train = X[train_idxs], y[train_idxs]
X_test, y_test = X[test_idxs], y[test_idxs]
algo.fit(X_train, y_train)
results[i] = algo.score(X_test, y_test)
return np.mean(results)
X, y = datasets.iris(return_splits=False)
splits = list(KFold(n_splits=4).split(X))
learning_rate = ht.CategoricalParameter('learning_rate',
options=('constant', 'invscaling',
'adaptive'))
learning_rate_init = ht.ContinuousParameter('learning_rate_init',
lower_bound=10**-5,
upper_bound=0.1)
max_iter = ht.DiscreteParameter('max_iter', lower_bound=500, upper_bound=10**3)
hypers = [learning_rate, learning_rate_init, max_iter]
tuner = ht.HyperTune(algorithm=MLPClassifier,
parameters=hypers,
train_func=train,
objective_func=objective_func,
objective_func_args=(X, y, splits),
max_evals=10**2)