def run_example(FLAGS):
"""
Run parallel Sherpa optimization over a set of discrete hp combinations.
"""
parameters = [sherpa.Continuous('learning_rate', [1e-5, 5e-1], 'log'),
sherpa.Continuous('decay', [1e-8, 1e-2], 'log'),
sherpa.Continuous('momentum', [0., 0.99]),
sherpa.Continuous('dropout', [0.0001, 0.7]),
sherpa.Ordinal('batch_size', [32, 64, 128, 256])]
algorithm = bayesian_optimization.GPyOpt(max_concurrent=FLAGS.concurrent,
model_type='GP',
acquisition_type='EI',
max_num_trials=100)
# Run on local machine.
scheduler = LocalScheduler()
rval = sherpa.optimize(parameters=parameters,
algorithm=algorithm,
dashboard_port=FLAGS.port,
lower_is_better=False,
command='python fashion_mlp.py',
scheduler=scheduler,
verbose=0,
max_concurrent=FLAGS.concurrent,
output_dir='./output_gpyopt_{}'.format(
time.strftime("%Y-%m-%d--%H-%M-%S")))
def run_example(FLAGS):
"""
Run parallel Sherpa optimization over a set of discrete hp combinations.
"""
parameters = [
sherpa.Continuous('lrinit', [0.001, 0.1], 'log'),
sherpa.Continuous('momentum', [0., 0.99]),
sherpa.Continuous('lrdecay', [1e-7, 1e-2], 'log'),
sherpa.Continuous('dropout', [0., 0.5])
]
if FLAGS.algorithm == 'BayesianOptimization':
print('Running GPyOpt')
alg = bayesian_optimization.GPyOpt(max_concurrent=FLAGS.max_concurrent,
model_type='GP_MCMC',
acquisition_type='EI_MCMC',
max_num_trials=150)
elif FLAGS.algorithm == 'LocalSearch':
print('Running Local Search')
alg = sherpa.algorithms.LocalSearch(seed_configuration={
'lrinit': 0.038,
'momentum': 0.92,
'lrdecay': 0.0001,
'dropout': 0.
},
perturbation_factors=(0.9, 1.1))
else:
print('Running Random Search')
alg = sherpa.algorithms.RandomSearch(max_num_trials=150)
if FLAGS.sge:
assert FLAGS.env, "For SGE use, you need to set an environment path."
# Submit to SGE queue.
env = FLAGS.env # Script specifying environment variables.
opt = '-N MNISTExample -P {} -q {} -l {}'.format(
FLAGS.P, FLAGS.q, FLAGS.l)
sched = SGEScheduler(environment=env, submit_options=opt)
else:
# Run on local machine.
sched = LocalScheduler()
rval = sherpa.optimize(parameters=parameters,
algorithm=alg,
lower_is_better=True,
filename='trial.py',
output_dir='output_{}'.format(FLAGS.studyname),
scheduler=sched,
max_concurrent=FLAGS.max_concurrent)
print('Best results:')
print(rval)
def hyper_param(self, epochs):
parameters = [
sherpa.Continuous('learning_rate', [1e-4, 1e-2]),
sherpa.Discrete('num_units', [32, 128]),
sherpa.Choice('activation', ['relu', 'adam', 'sigmoid'])
]
algorithm = bayesian_optimization.GPyOpt(max_num_trials=50)
study = sherpa.Study(parameters=parameters,
algorithm=algorithm,
lower_is_better=False)
x_test = self._label / 300
y_test = self._label
# Create model
model = models.Sequential()
model.add(
layers.Embedding(self.totalwords,
64,
input_length=maxSequenceLen - 1))
model.add(layers.LSTM(128))
model.add(layers.Dense(self.totalwords, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
epochs = epochs
for trial in study:
lr = trial.parameters['learning_rate']
num_units = trial.parameters['num_units']
act = trial.parameters['activation']
for i in range(epochs):
model.fit(self.predictors, self._label, batch_size=self.batch)
loss, accuracy = model.evaluate(x_test, y_test)
study.add_observation(trial=trial,
iteration=i,
objective=accuracy,
context={'loss': loss})
if study.should_trial_stop(trial):
break
study.finalize(trial=trial)
print(study.get_best_result())
logger.info("AUTOML ON ACCURACY")
logger.info("MOVING LNCS2")
config["LANG"].remove("latin")
config["LANG"].remove("english")
port = 1
for lang in config["LANG"]:
i = 1
parameters = [
sherpa.Continuous("threshold", [0.5, 0.85]),
sherpa.Discrete("topn", [10, 50]),
sherpa.Continuous("t", [0.0, 1.0]),
]
algorithm = bayesian_optimization.GPyOpt(max_num_trials=40)
study = sherpa.Study(
parameters=parameters,
algorithm=algorithm,
lower_is_better=False,
dashboard_port=(9999 - port + 1),
)
model1 = Word2Vec.load(
CURRENT_EXP_DIR.split("_")[0] + "_0" + "/model/" + lang +
"/corpus1.model")
model2 = Word2Vec.load(
CURRENT_EXP_DIR.split("_")[0] + "_0" + "/model/" + lang +
"/corpus2.model")
# Load binary truths
binary_truth = np.loadtxt(
"./data/" + lang + "/semeval2020_ulscd_" + lang[:3] +
args, unknown = parser.parse_known_args()
GPU = int(args.gpu)
parameters = [
sherpa.Continuous(name='lr', range=[0.001, 0.1], scale='log'),
sherpa.Continuous(name='dropout', range=[0.0001, 0.7]),
sherpa.Continuous(name='top_dropout', range=[0.0001, 0.7]),
sherpa.Continuous(name='lr_decay', range=[1e-4, 1e-9], scale='log')
]
artifacts_dir = tempfile.mkdtemp()
if args.algorithm == 'ei':
algorithm = bayesian_optimization.GPyOpt(
max_num_trials=args.max_num_trials,
num_initial_data_points=5,
acquisition_type='EI',
max_concurrent=1)
elif args.algorithm == 'ei3':
algorithm = bayesian_optimization.GPyOpt(
max_num_trials=args.max_num_trials // 3,
num_initial_data_points=5,
acquisition_type='EI',
max_concurrent=1)
algorithm = sherpa.algorithms.Repeat(algorithm, 3, agg=True)
elif args.algorithm == 'ei5':
algorithm = bayesian_optimization.GPyOpt(
max_num_trials=args.max_num_trials // 5,
num_initial_data_points=5,
acquisition_type='EI',
max_concurrent=1)
from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
import time
import sherpa
import sherpa.algorithms.bayesian_optimization as bayesian_optimization
parameters = [sherpa.Discrete('n_estimators', [2, 50]),
sherpa.Choice('criterion', ['gini', 'entropy']),
sherpa.Continuous('max_features', [0.1, 0.9])]
algorithm = bayesian_optimization.GPyOpt(max_concurrent=1,
model_type='GP_MCMC',
acquisition_type='EI_MCMC',
max_num_trials=100)
X, y = load_breast_cancer(return_X_y=True)
study = sherpa.Study(parameters=parameters,
algorithm=algorithm,
lower_is_better=False)
for trial in study:
print("Trial ", trial.id, " with parameters ", trial.parameters)
clf = RandomForestClassifier(criterion=trial.parameters['criterion'],
max_features=trial.parameters['max_features'],
n_estimators=trial.parameters['n_estimators'],
random_state=0)
scores = cross_val_score(clf, X, y, cv=5)
print("Score: ", scores.mean())
study.add_observation(trial, iteration=1, objective=scores.mean())