def get_args(parser):
args = parser.parse_args()
args.power_spectrum_beta_str = args.power_spectrum_beta
args.power_spectrum_f_str = args.power_spectrum_f
if args.config is not None:
with open("./model_configurations.txt") as f:
configs = eval(f.read())
cparameters = configs[config]
args.n_bins = parameters.get("n_bins", args.n_bins)
args.noise_variance = parameters.get("noise_variance", args.noise_variance)
args.power_spectrum_beta_str = parameters.get(
"power_spectrum_beta", power_spectrum_beta_str
)
args.power_spectrum_f_str = parameters.get(
"power_spectrum_f", power_spectrum_f_str
)
if args.last_id is None:
args.last_id = get_benchmark_default_length(args.benchmark)
return args
from sklearn.preprocessing import MinMaxScaler
import matlab.engine
CAUSALITY_ROOT = '/afs/mpa/home/maxk/bayesian_causal_inference/'
sys.path.append(CAUSALITY_ROOT)
from benchmark_utils import get_benchmark_default_length, get_pair, BCMParser
parser = BCMParser()
args = parser.parse_args()
NAME = args.name
BENCHMARK = args.benchmark
FIRST_ID = args.first_id
LAST_ID = args.last_id
if LAST_ID is None:
LAST_ID = get_benchmark_default_length(BENCHMARK)
eng = matlab.engine.start_matlab()
eng.addpath(CAUSALITY_ROOT + 'comparison_methods/Mooij16/cep')
eng.startup(nargout=0)
eng.local_config(nargout=0)
methodpars = eng.struct()
methodpars['FITC'] = 0
methodpars['minimize'] = 'minimize_lbfgsb'
methodpars['evaluation'] = 'pHSIC'
accuracy = 0
correct_decisions = 0
undecided = 0