def main():
parser = argparse.ArgumentParser(
description='Ensembling HyperSpace models')
parser.add_argument('--results_dir',
type=str,
help='Path to results directory.')
args = parser.parse_args()
results = load_results(args.results_dir)
clfs = []
for _ in results:
# Use default random forest for each of the ensemble models
model = RandomForestClassifier()
clfs.append(model)
# Using the training and test sets from `optimize.py`
X_train, _, X_test, y_train, _, y_test = load_data(0.25, 0.25)
y_preds, y_proba = ensemble(clfs, X_train, y_train, X_test, y_test)
acc_test = accuracy_score(y_test, y_preds)
ll_test = log_loss(y_test, y_proba)
print(
f'\nEnsemble Test Accuracy: {acc_test:.4f}, Test Log Loss: {ll_test:.4f}'
)
def run(results_dir, n_calls=200, n_runs=10):
"""Run benchmark for Branin function."""
models = ['GP', 'RF', 'GBRT', 'Rand']
bounds = [(-5.0, 10.0), (0.0, 15.0)]
for model in models:
model_dir = os.path.join(results_dir, model)
if not os.path.exists(model_dir):
os.makedirs(model_dir, exist_ok=True)
for random_state in range(n_runs):
directory = os.path.join(model_dir, 'run' + str(random_state))
if not os.path.exists(directory):
os.makedirs(directory, exist_ok=True)
checkpoint = load_results(directory)
hyperdrive(branin,
bounds,
directory,
n_iterations=n_calls,
verbose=True,
random_state=random_state,
checkpoints=True,
restart=checkpoint)
def main():
parser = argparse.ArgumentParser(
description='Ensembling HyperSpace models')
parser.add_argument('--results_dir',
type=str,
help='Path to results directory.')
args = parser.parse_args()
results = load_results(args.results_dir)
clfs = []
for result in results:
# Get domain from best model in each hyperspace
hyperparams = result.x
n_estimators = hyperparams[0]
max_depth = hyperparams[1]
model = RandomForestClassifier(n_estimators=n_estimators,
max_depth=max_depth)
clfs.append(model)
# Using the training and test sets from `optimize.py`
X_train, _, X_test, y_train, _, y_test = load_data(0.25, 0.25)
y_preds, y_proba = ensemble(clfs, X_train, y_train, X_test, y_test)
acc_test = accuracy_score(y_test, y_preds)
ll_test = log_loss(y_test, y_proba)
print(
f'\nEnsemble Test Accuracy: {acc_test:.4f}, Test Log Loss: {ll_test:.4f}'
)
def main():
parser = argparse.ArgumentParser(description='Setup experiment.')
parser.add_argument('--results_dir', type=str, help='Path to results directory.')
args = parser.parse_args()
hparams = [(2, 10), # max_depth
(10.0**-2, 10.0**0)] # learning_rate
# Load results from previous runs
checkpoint = load_results(args.results_dir)
hyperdrive(objective=objective,
hyperparameters=hparams,
results_path=args.results_dir,
model="GP",
n_iterations=100,
verbose=True,
random_state=0,
checkpoints=True,
restart=checkpoint)
def main():
global train_x
global train_y
global test_x
global test_y
train_x = np.load('data/train_X.npy')
train_y = np.load('data/train_Y.npy')
test_x = np.load('data/test_X.npy')
test_y = np.load('data/test_Y.npy')
for task in range(4):
le = preprocessing.LabelEncoder()
le.fit(train_y[:, task])
train_y[:, task] = le.transform(train_y[:, task])
test_y[:, task] = le.transform(test_y[:, task])
max_vocab = np.max(train_x)
max_vocab2 = np.max(test_x)
if max_vocab2 > max_vocab:
max_vocab = max_vocab2
print('max_vocab:', max_vocab)
np.random.seed(0)
global wv_len
wv_len = 50
global wv_mat
wv_mat = np.random.randn(max_vocab + 1, wv_len).astype('float32') * 0.1
#num_classes = np.max( train_y ) + 1
global num_classes
num_classes = []
num_classes.append(np.max(train_y[:, 0]) + 1)
num_classes.append(np.max(train_y[:, 1]) + 1)
num_classes.append(np.max(train_y[:, 2]) + 1)
num_classes.append(np.max(train_y[:, 3]) + 1)
global validation_data
validation_data = ({
'Input': test_x
}, {
'Dense0': test_y[:, 0],
'Dense1': test_y[:, 1],
'Dense2': test_y[:, 2],
'Dense3': test_y[:, 3]
})
space = [(1, 10), (1, 10), (1, 10), (5, 500), (0.00001, 0.1)]
savepoint = load_results(args.results_dir)
hyperdrive(objective=objective,
hyperparameters=space,
results_path=args.results_dir,
model="GP",
n_iterations=25,
checkpoints=True,
verbose=True,
random_state=0,
restart=savepoint)
