Update runBO.py

fmfnBO/runBO.py

-import os
-import sys
-import argparse
-import pathlib
-
-from bayes_opt import BayesianOptimization, Events, JSONLogger
-
-# current working directory
-cwd = pathlib.Path(__file__).parent.absolute()
-parent = os.path.split(cwd)[0]
-if str(parent) not in sys.path:
-	sys.path.append(parent)
-
-from target import *
-
-def fmfnBO(n_eval, ndim, func=None):
-	assert func is not None
-
-	logs = os.path.join(cwd, 'logs/')
-	plots = os.path.join(cwd, 'plots/')
-
-	if not os.path.isdir(logs):
-		os.mkdir(logs)
-
-	if not os.path.isdir(plots):
-		os.mkdir(plots)
-
-	log_name = os.path.join(logs + str(func) + ".json")
-	plot_name = os.path.join(plots + str(func) + ".json")
-
-	optimizer = BayesianOptimization(func, func.pbounds, random_state=1)
-
-	logger = JSONLogger(log_name)
-	optimizer.subscribe(Events.OPTIMIZATION_STEP, logger)
-
-	steps = n_eval
-	optimizer.maximize(init_points=2, n_iter=0, acq='ei', kappa=5)
-	optimizer.maximize(init_points=0, n_iter=steps, acq='ei', kappa=5)
-	
-	print("Output Files:")
-	return (func, log_name, plot_name)
-
-if __name__ == '__main__':
-	parser = argparse.ArgumentParser(description='FmFn Bayesian Optimization runner')
-	parser.add_argument('-e', '--n_eval', dest='n_eval', type=int, default=1)
-	parser.add_argument('-d', '--dim', dest='ndim', type=int, default=1)
-	parser.add_argument('-f', '--func', dest='func_name')
-
-	args = parser.parse_args()
-	fmfn_args = vars(args)
-	if args.func_name is not None:
-		exec('func=' + 'fmfn_' + args.func_name)
-		fmfn_args['func'] = func
-	del fmfn_args['func_name']
-
-	fmfn_files = fmfnBO(**fmfn_args)
+import os
+import sys
+import argparse
+import pathlib
+
+from bayes_opt import BayesianOptimization, Events, JSONLogger
+
+# current working directory
+cwd = pathlib.Path(__file__).parent.absolute()
+parent = os.path.split(cwd)[0]
+if str(parent) not in sys.path:
+	sys.path.append(parent)
+
+from target import *
+
+def fmfnBO(n_eval, ndim, func=None):
+	assert func is not None
+
+	logs = os.path.join(cwd, 'logs/')
+	plots = os.path.join(cwd, 'plots/')
+
+	if not os.path.isdir(logs):
+		os.mkdir(logs)
+
+	if not os.path.isdir(plots):
+		os.mkdir(plots)
+
+	log_name = os.path.join(logs + str(func) + ".json")
+	plot_name = os.path.join(plots + str(func) + ".json")
+
+	optimizer = BayesianOptimization(func, func.pbounds, random_state=1)
+
+	logger = JSONLogger(log_name)
+	optimizer.subscribe(Events.OPTIMIZATION_STEP, logger)
+
+	steps = n_eval
+	optimizer.maximize(init_points=2, n_iter=0, acq='ei', kappa=5)
+	optimizer.maximize(init_points=0, n_iter=steps, acq='ei', kappa=5)
+	
+	print("Output Files:")
+	return (func, log_name, plot_name)
+
+if __name__ == '__main__':
+	parser = argparse.ArgumentParser(description='FmFn Bayesian Optimization runner')
+	parser.add_argument('-e', '--n_eval', dest='n_eval', type=int, default=1)
+	parser.add_argument('-d', '--dim', dest='ndim', type=int, default=1)
+	parser.add_argument('-f', '--func', dest='func_name')
+
+	args = parser.parse_args()
+	fmfn_args = vars(args)
+	if args.func_name is not None:
+		exec('func=' + 'fmfn_' + 'neg_' + args.func_name)
+		fmfn_args['func'] = func
+	del fmfn_args['func_name']
+
+	fmfn_files = fmfnBO(**fmfn_args)
 	print(fmfn_files)
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