import os

from data_plotting import DataPlotter
from data_provider import DataProvider
from utils.evaluation_utils import seed_random
import numpy as np



if __name__ == "__main__":
    os.chdir("..")
    seed_random()
    #rtrain, rtest = DataProvider.get_reduced_dimensions_with_pca(dim=15)
    #print(rtrain.shape)
    DataPlotter.print_pca_scree_plot(30)

    d = DataProvider.get_highest_weight_loading_scores_for_pc(pcn="PC1").head(5)** 2
    print(d)
    #remove .head() in d & print(d.sum()) # should be one -> all features together contribute with a factor to the pca
