def main():
"""Input for the parameter optimization process
Given the parameter space, running function and training/valid dataset,
using hyperband/hyperopt strategy to find the top parameter set with the best metrics
:input:
------
run_func: running function used in optimization
log_file_name: string, name of log file
top_num: int, number of best configurations you want to choose
train_train: data frame, the last column should be the target value, training data set in optimization process
train_valid: data frame, the last column should be the target value, validation data set in optimization process
:return:
--------
top parameters and their score on log file
"""
# define the log file name for hyperopt method
log_file_hyperopt = 'hyperopt_xgboost.txt'
top_num = 2
# set the input file name
train_train = pd.read_csv('fargo_train_train.csv')
train_valid = pd.read_csv('fargo_train_valid.csv')
# shuffle the input file
train_train_shuffle = train_train.reindex(
np.random.permutation(train_train.index)).sort_index()
train_valid_shuffle = train_valid.reindex(
np.random.permutation(train_valid.index)).sort_index()
# split the dataset into data and target
train_data, train_target = train_train_shuffle.values[:, 0:-1].astype(
np.float32), train_train_shuffle.values[:, -1]
valid_data, valid_target = train_valid_shuffle.values[:, 0:-1].astype(
np.float32), train_valid_shuffle.values[:, -1]
# define search space
space_hyperopt = {
'max_depth': hp.uniform('max_depth', 3, 10),
'min_child_weight': hp.uniform('min_child_weight', 0.5, 5),
'subsample': hp.uniform('subsample', 0.5, 1),
'colsample_bytree': hp.uniform('colsample_bytree', 0.5, 1),
'reg_alpha': hp.uniform('reg_alpha', 0, 0.01),
'epochs': hp.choice('epochs', [100, 200]),
'learning_rate': hp.choice('learning_rate', [0.10, 0.12])
}
# using HyperOPT to optimize the parameter set
para_hyperopt = HyperOPT(space=space_hyperopt,
run_func=run_func,
log_file=log_file_hyperopt)
# using fit api to fit the parameter set
para_hyperopt.fit(train_data=train_data,
train_target=train_target,
valid_data=valid_data,
valid_target=valid_target,
n_iter=2)
# using get best to take best parameter set
para_hyperopt.get_best(top_num)
# set the log file name for hyperband method
log_file_hyperband = 'hyperband_xgboost.txt'
# define parameter set, must contain epochs !
space_hyperband = {
'max_depth': hp.uniform('max_depth', 3, 10),
'min_child_weight': hp.uniform('min_child_weight', 0.5, 5),
'subsample': hp.uniform('subsample', 0.5, 1),
'colsample_bytree': hp.uniform('colsample_bytree', 0.5, 1),
'reg_alpha': hp.uniform('reg_alpha', 0, 0.01),
'epochs': hp.choice('epochs', [100, 200]),
'learning_rate': hp.choice('learning_rate', [0.10, 0.12])
}
# using Hyperband to optimize the parameter set
para_hyperband = Hyperband(space=space_hyperband,
run_func=run_func,
log_file=log_file_hyperband,
max_iter=4,
eta=2)
# using fit api to fit the parameter set
para_hyperband.fit(train_data=train_data,
train_target=train_target,
valid_data=valid_data,
valid_target=valid_target)
# using get best to take best parameter set
para_hyperband.get_best(top_num)
def main():
"""Input for the parameter optimization process
Given the parameter space, running function and training/valid dataset,
using hyperband/hyperopt strategy to find the top parameter set with the best metrics
:input:
------
run_func: running function used in optimization
log_file_name: string, name of log file
top_num: int, number of best configurations you want to choose
train_train: data frame, the last column should be the target value, training data set in optimization process
train_valid: data frame, the last column should be the target value, validation data set in optimization process
:return:
--------
top parameters and their score on log file
"""
# define the log file name for hyperopt method
log_file_hyperopt = 'hyperopt.txt'
# set the number of parameter sets to be recorded
top_num = 2
# set the input file name
train_train = pd.read_csv('fargo_train_train.csv')
train_valid = pd.read_csv('fargo_train_valid.csv')
# shuffle the input file
train_train_shuffle = train_train.reindex(
np.random.permutation(train_train.index)).sort_index()
train_valid_shuffle = train_valid.reindex(
np.random.permutation(train_valid.index)).sort_index()
# split the dataset into data and target
train_data, train_target = train_train_shuffle.values[:, 0:-1].astype(
np.float32), train_train_shuffle.values[:, -1]
valid_data, valid_target = train_valid_shuffle.values[:, 0:-1].astype(
np.float32), train_valid_shuffle.values[:, -1]
# define parameter space, must contain epochs !
max_layers = 6
space_hyperopt = {
'n_layers':
hp.quniform('n_layers', 2, max_layers, 1),
'init':
hp.choice('init',
('uniform', 'normal', 'glorot_uniform', 'glorot_normal')),
'batch_size':
hp.choice('batch_size', (16, 32, 64, 128)),
'epochs':
hp.choice('epochs', [1, 2]),
'optimizer':
'adam',
'residual':
hp.choice('residual', [True, False]),
'highway':
hp.choice('highway', [True, False]),
}
# for each hidden layer, we choose size, activation and extras individually
for i in range(1, max_layers + 1):
space_hyperopt['layer_{}_size'.format(i)] = hp.quniform(
'ls{}'.format(i), 20, 200, 20)
space_hyperopt['layer_{}_activation'.format(i)] = 'relu'
space_hyperopt['layer_{}_dropout'.format(i)] = hp.quniform(
'dropout{}'.format(i), 0.0, 0.5, 0.05)
# using HyperOPT to optimize the parameter set
para_hyperopt = HyperOPT(space=space_hyperopt,
run_func=run_func,
log_file=log_file_hyperopt)
# using fit api to fit the parameter set
para_hyperopt.fit(train_data=train_data,
train_target=train_target,
valid_data=valid_data,
valid_target=valid_target,
n_iter=2)
# using get best to take best parameter set
para_hyperopt.get_best(top_num)
# set the log file name for hyperband method
log_file_hyperband = 'hyperband.txt'
# define parameter set, must contain epochs !
space_hyperband = {
'n_layers':
hp.quniform('n_layers', 2, max_layers, 1),
'init':
hp.choice('init',
('uniform', 'normal', 'glorot_uniform', 'glorot_normal')),
'batch_size':
hp.choice('batch_size', (16, 32, 64, 128)),
'optimizer':
'adam',
'residual':
hp.choice('residual', [True, False]),
'highway':
hp.choice('highway', [True, False]),
}
# for each hidden layer, we choose size, activation and extras individually
for i in range(1, max_layers + 1):
space_hyperband['layer_{}_size'.format(i)] = hp.quniform(
'ls{}'.format(i), 20, 200, 20)
space_hyperband['layer_{}_activation'.format(i)] = 'relu'
space_hyperband['layer_{}_dropout'.format(i)] = hp.quniform(
'dropout{}'.format(i), 0.0, 0.5, 0.05)
# using Hyperband to optimize the parameter set
para_hyperband = Hyperband(space=space_hyperband,
run_func=run_func,
log_file=log_file_hyperband,
max_iter=4,
eta=2)
# using fit api to fit the parameter set
para_hyperband.fit(train_data=train_data,
train_target=train_target,
valid_data=valid_data,
valid_target=valid_target)
# using get best to take best parameter set
para_hyperband.get_best(top_num)