This folder contains the RandGraph dataset for the paper:

@ARTICLE{Seelbach Benkner2023, 
       author = {{Seelbach Benkner}, Marcel and {Krahn}, Maximilian and {Tretschk}, Edith and {L{\"a}hner}, Zorah and {Moeller}, Michael and {Golyanik}, Vladislav}, 
        title = "{QuAnt: Quantum Annealing with Learnt Couplings}", 
      journal = "International Conference on Learning Representations (ICLR)", 
         year = 2023 
}   	

The files can be read with pickle and contain a pandas Dataframe object. 
import numpy as np
import pickle 
import pandas as pd

df = pd.read_pickle('trainingData5.p')
print(df['Input'])
print(df['Output'])

The output permutations use a binary encoding.
To get a permutation table one can use

def BinaryToPermutation(binEnc,dim):
    permutationTable= []
    currentNr=[]
    for k in range(len(binEnc)):
        currentNr.append(binEnc[k])
        if (k+1)%(int(np.ceil( np.log2(dim)))) ==0:
            
            permutationTable.append( int("".join(str(i) for i in currentNr),2))
            
            currentNr=[]

    
    return permutationTable