import numpy as np import pandas as pd import scipy as sci from functions import data from functions import settings as sett from scipy.optimize import linear_sum_assignment from functions import TCA as t from sklearn.metrics import r2_score torch.set_default_tensor_type('torch.cuda.FloatTensor') # Load parameters param = sett.params() paths = sett.paths() ar = sett.arguments() args = ar.get_arguments() fixed_selection = ar.get_fixed_args() def paring_r2(X, Y): """Perform linear sum assignment based on R2 scores Parameters ---------- X : array Factor to compare Y: array Factor to compare
import os import pickle import argparse import hashlib import numpy as np import matplotlib.pyplot as plt from tqdm import tqdm from functions import data as dt from functions import train from functions import plot from functions import settings paths = settings.paths() import matplotlib.pyplot as plt TEST_P_VALUE = False model_p_values = 2 # Parse arguments parser = argparse.ArgumentParser(description='Parameters for computing') parser.add_argument('--subject', '-s', type=int, default=57, help='Subject flag') parser.add_argument('--ROI', '-r', type=int,