import os
import re
import sys
import numpy as np
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'
def main():
parser = BCMParser()
args = get_args(parser)
verbosity = args.verbosity
benchmark = args.benchmark
print(
f"performing {args.benchmark} benchmark for ids {args.first_id} to {args.last_id},\n"
f"with N_bins: {args.n_bins},\n"
f"noise variance: {args.noise_variance}\n"
f"power spectrum beta: {args.power_spectrum_beta_str}\n"
f"power spectrum f: {args.power_spectrum_f_str}\n"
f"rho: {args.rho}\n"
f"scale_max: {args.scale_max}\n"
f"storing results with suffix {args.name}"
)
power_spectrum_beta = lambda q: eval(args.power_spectrum_beta_str)
power_spectrum_f = lambda q: eval(args.power_spectrum_f_str)
scale = (0, args.scale_max)
prediction_file = "./benchmark_predictions/{}_{}.txt".format(benchmark, args.name)
if os.path.isfile(prediction_file):
c = 0
while os.path.isfile(prediction_file):
c += 1
prediction_file = "./benchmark_predictions/{}_{}_{}.txt".format(
benchmark, args.name, c
)
sum_of_weights = 0
weighted_correct = 0
for rep in range(args.repetitions):
np.random.seed(rep)
for i in range(args.first_id - 1, args.last_id):
(x, y), true_direction, weight = get_pair(
i, benchmark, subsample_size=args.subsample
)
if true_direction == 0:
continue
scaler = MinMaxScaler(scale)
x, y = scaler.fit_transform(np.array((x, y)).T).T
bcm = bayesian_causal_model.cause_model_shallow.CausalModelShallow(
N_bins=args.n_bins,
noise_var=args.noise_variance,
rho=args.rho,
power_spectrum_beta=power_spectrum_beta,
power_spectrum_f=power_spectrum_f,
)
bcm.set_data(x, y)
H1 = bcm.get_evidence(direction=1, verbosity=verbosity - 1)
H2 = bcm.get_evidence(direction=-1, verbosity=verbosity - 1)
predicted_direction = 1 if int(H1 < H2) else -1
if predicted_direction == true_direction:
fore = colorama.Fore.GREEN
weighted_correct += weight
else:
fore = colorama.Fore.RED
sum_of_weights += weight
accuracy = weighted_correct / sum_of_weights
if verbosity > 0:
print(
"dataset {}, {} true direction: {}, predicted direction {}\n"
"H1: {:.2e},\n H2: {:.2e},\n{}"
"accuracy so far: {:.2f}".format(
i,
fore,
true_direction,
predicted_direction,
H1,
H2,
colorama.Style.RESET_ALL,
accuracy,
)
)
with open(prediction_file, "a") as f:
f.write("{} {} {} {}\n".format(i + 1, predicted_direction, H1, H2))
print("accuracy: {:.2f}".format(accuracy))
benchmark_information = {
"benchmark": benchmark,
"n_bins": args.n_bins,
"noise_var": args.noise_var,
"rho": args.rho,
"power_spectrum_beta": args.power_spectrum_beta_str,
"power_spectrum_f": args.power_spectrum_f_str,
"accuracy": accuracy,
"prediction_file": prediction_file,
}
with open("benchmark_predictions/benchmarks_meta.txt", "a") as f:
f.write(str(benchmark_information) + "\n")