import argparse
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
import pandas as pd
import tensorly as tl
from functions import plot
from functions import data
from functions import preprocessing as prepro
from functions import settings as sett
from functions import tca_utils as tca
import matplotlib.pyplot as plt
from tqdm import tqdm
from tensorflow.python.framework.ops import disable_eager_execution
tl.set_backend('pytorch')
torch.set_default_tensor_type('torch.cuda.FloatTensor')
params = sett.params()
paths = sett.paths()
ar = sett.arguments()
args = ar.get_arguments()
preprocess_sett = ar.get_preprocess_sett()
animal = ar.get_animal()
path = os.path.join(paths.path2Figures, 'Preprocessed Data', animal)
if args.plotting:
try:
os.makedirs(path)
except:
FileExistsError
# Loading data from folder