import glob
import os 
import sys
import h5py 
import numpy as np 
import torch 

output = open('deeprank_predictions.txt', 'w')
output.write('pdb class p_crystal p_bio \n')

for hdf5 in glob.glob('*/'):
    name=hdf5.split('/')[0]
        
    if os.path.exists('{}test_data.hdf5'.format(hdf5)):
        
        f = h5py.File('{}test_data.hdf5'.format(hdf5),'r')
        
        results = f['epoch_0000']['test']['outputs'][()]
        res = results[0]
        softmax = torch.nn.Softmax(dim=0)
        res_softmax = softmax(torch.Tensor(res)).numpy()
        
        bin_class_res = np.argmax(res)
        
        if bin_class_res1 == 0 : 
            pred = 'crystal'
            
        else :
            pred = 'biological'

        output.write(name+" "+pred+" "+str(bin_class_res)+" "+str(round(res_softmax[0],3))+" "+str(round(res_softmax[1],3))+"\n")

    else:
        output.write(name+" None None None None \n")

output.close()

57a885a6231fe31c6a2179c0a2c501e5029b19eb
<mask token>

<mask token>
output.write('pdb class p_crystal p_bio \n')
for hdf5 in glob.glob('*/'):
    name = hdf5.split('/')[0]
    if os.path.exists('{}test_data.hdf5'.format(hdf5)):
        f = h5py.File('{}test_data.hdf5'.format(hdf5), 'r')
        results = f['epoch_0000']['test']['outputs'][()]
        res = results[0]
        softmax = torch.nn.Softmax(dim=0)
        res_softmax = softmax(torch.Tensor(res)).numpy()
        bin_class_res = np.argmax(res)
        if bin_class_res1 == 0:
            pred = 'crystal'
        else:
            pred = 'biological'
        output.write(name + ' ' + pred + ' ' + str(bin_class_res) + ' ' +
            str(round(res_softmax[0], 3)) + ' ' + str(round(res_softmax[1],
            3)) + '\n')
    else:
        output.write(name + ' None None None None \n')
output.close()

<mask token>
output = open('deeprank_predictions.txt', 'w')
output.write('pdb class p_crystal p_bio \n')
for hdf5 in glob.glob('*/'):
    name = hdf5.split('/')[0]
    if os.path.exists('{}test_data.hdf5'.format(hdf5)):
        f = h5py.File('{}test_data.hdf5'.format(hdf5), 'r')
        results = f['epoch_0000']['test']['outputs'][()]
        res = results[0]
        softmax = torch.nn.Softmax(dim=0)
        res_softmax = softmax(torch.Tensor(res)).numpy()
        bin_class_res = np.argmax(res)
        if bin_class_res1 == 0:
            pred = 'crystal'
        else:
            pred = 'biological'
        output.write(name + ' ' + pred + ' ' + str(bin_class_res) + ' ' +
            str(round(res_softmax[0], 3)) + ' ' + str(round(res_softmax[1],
            3)) + '\n')
    else:
        output.write(name + ' None None None None \n')
output.close()

import glob
import os
import sys
import h5py
import numpy as np
import torch
output = open('deeprank_predictions.txt', 'w')
output.write('pdb class p_crystal p_bio \n')
for hdf5 in glob.glob('*/'):
    name = hdf5.split('/')[0]
    if os.path.exists('{}test_data.hdf5'.format(hdf5)):
        f = h5py.File('{}test_data.hdf5'.format(hdf5), 'r')
        results = f['epoch_0000']['test']['outputs'][()]
        res = results[0]
        softmax = torch.nn.Softmax(dim=0)
        res_softmax = softmax(torch.Tensor(res)).numpy()
        bin_class_res = np.argmax(res)
        if bin_class_res1 == 0:
            pred = 'crystal'
        else:
            pred = 'biological'
        output.write(name + ' ' + pred + ' ' + str(bin_class_res) + ' ' +
            str(round(res_softmax[0], 3)) + ' ' + str(round(res_softmax[1],
            3)) + '\n')
    else:
        output.write(name + ' None None None None \n')
output.close()

import glob
import os 
import sys
import h5py 
import numpy as np 
import torch 

output = open('deeprank_predictions.txt', 'w')
output.write('pdb class p_crystal p_bio \n')

for hdf5 in glob.glob('*/'):
    name=hdf5.split('/')[0]
        
    if os.path.exists('{}test_data.hdf5'.format(hdf5)):
        
        f = h5py.File('{}test_data.hdf5'.format(hdf5),'r')
        
        results = f['epoch_0000']['test']['outputs'][()]
        res = results[0]
        softmax = torch.nn.Softmax(dim=0)
        res_softmax = softmax(torch.Tensor(res)).numpy()
        
        bin_class_res = np.argmax(res)
        
        if bin_class_res1 == 0 : 
            pred = 'crystal'
            
        else :
            pred = 'biological'

        output.write(name+" "+pred+" "+str(bin_class_res)+" "+str(round(res_softmax[0],3))+" "+str(round(res_softmax[1],3))+"\n")

    else:
        output.write(name+" None None None None \n")

output.close()
