def bayesian_optimization(args, train_set, val_set, test_set):
# Run grid search
results = []
candidate_hypers = init_hyper_space(args['model'])
def objective(hyperparams):
configure = deepcopy(args)
configure.update(hyperparams)
configure, val_metric = main(configure, train_set, val_set, test_set)
if args['metric'] in ['roc_auc_score']:
# Maximize ROCAUC is equivalent to minimize the negative of it
val_metric_to_minimize = -1 * val_metric
else:
val_metric_to_minimize = val_metric
results.append((configure, hyperparams, val_metric_to_minimize))
return val_metric_to_minimize
fmin(objective,
candidate_hypers,
algo=tpe.suggest,
max_evals=args['num_evals'])
results.sort(key=lambda tup: tup[2])
best_config, best_hyper, best_val_metric = results[0]
shutil.move(best_config['trial_path'], args['result_path'] + '/best')
with open(args['result_path'] + '/best_config.txt', 'w') as f:
json.dump(best_hyper, f)
def bayesian_optimization(args, train_set, val_set, test_set):
# Run grid search
results = []
candidate_hypers = init_hyper_space(args['model'])
def objective(hyperparams):
configure = deepcopy(args)
trial_path, val_metric = main(configure, hyperparams, train_set, val_set, test_set)
if args['metric'] in ['roc_auc_score']:
# Maximize ROCAUC is equivalent to minimize the negative of it
val_metric_to_minimize = -1 * val_metric
else:
val_metric_to_minimize = val_metric
results.append((trial_path, val_metric_to_minimize))
return val_metric_to_minimize
fmin(objective, candidate_hypers, algo=tpe.suggest, max_evals=args['num_evals'])
results.sort(key=lambda tup: tup[1])
best_trial_path, best_val_metric = results[0]
return best_trial_path
def bayesian_optimization(args, train_set, val_set, test_set):
# Run grid search
results = []
candidate_hypers = init_hyper_space(args['model'])
def objective(hyperparams):
configure = deepcopy(args)
configure.update(hyperparams)
configure, val_metric = main(configure, train_set, val_set, test_set)
results.append((configure, hyperparams, val_metric))
if args['metric'] in ['r2']:
return -1 * val_metric
else:
return val_metric
fmin(objective,
candidate_hypers,
algo=tpe.suggest,
max_evals=args['num_evals'])
results.sort(key=lambda tup: tup[2])
best_config, best_hyper, best_val_metric = results[-1]
shutil.move(best_config['trial_path'], args['result_path'] + '/best')
with open(args['result_path'] + '/best_config.txt', 'w') as f:
json.dump(best_hyper, f)