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Recovering_dataset.py
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Recovering_dataset.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 19 13:17:45 2022
@author: aghm
"""
import numpy as np
import pandas as pd
label=pd.read_csv('label_server.csv')
label=label.loc[:, ~label.columns.str.contains('^Unnamed')]
print(label.columns)
fbp_array=[0.3,0.5,0.7,0.9]
aux4=[]
for fbp in fbp_array:
mat=[]
mat2=[]
mat3=[]
# l=0
out2=[]
out2=pd.DataFrame(columns=[label.columns])
# 1/0
for att in label.columns:
# print(att)
######
aux2=[]
aux2=pd.read_csv('results/_4D'+att+'_flopprob_'+str(fbp)+'_peratt_1000_probabilities_centroid.csv')
aux2=aux2.loc[:, ~aux2.columns.str.contains('^Unnamed')]
mat2.append(np.array(aux2.idxmax(axis=1)))
aux3=[]
aux3=pd.read_csv("results/perturb_"+att+'_flopprob_'+str(fbp)+'_peratt_1000'+'_'+'.csv')
aux3=aux3.loc[:, ~aux3.columns.str.contains('^Unnamed')]
out2[att] =aux3.values.tolist()
# if l==1:
# aux4.append(aux3.values.tolist())
# if l==0:
# aux4=aux3.values.tolist()
# l=1
# print(aux4)
# mat3.append(np.array(aux4))
########
b=[]
b=np.array(mat2)
b=np.transpose(np.array(mat2))
out=[]
out=pd.DataFrame(b,columns=[label.columns])
out.to_csv('csv_eval/VAE_FULLSPACE_flopprob_'+str(fbp)+'_.csv')
#####
# b=[]
# b=np.array(mat3)
# b=np.transpose(np.array(mat3))
# out=[]
# out=pd.DataFrame(mat3.T,columns=[label.columns])
out2.to_csv('csv_eval/perturbed_dataset_'+str(fbp)+'_.csv')
###################################