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,
