File size: 2,861 Bytes
3ac1d94 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | import os
import pickle
import random
import argparse
import torch
import numpy as np
def coretest_split(index_path, test_path, val_ratio=0.1, val_num=None):
with open(index_path, 'rb') as f:
index = pickle.load(f)
test_ids = [f for f in os.listdir(test_path) if len(f) == 4]
all_ids = [os.path.basename(i[0])[:4] for i in index]
print('Original test set size: ', len(test_ids))
test_index = [all_ids.index(test_id) for test_id in test_ids if test_id in all_ids]
train_val_index = list(set(range(len(all_ids))) - set(test_index))
assert len(train_val_index) == len(all_ids) - len(test_index)
random.shuffle(train_val_index)
if val_num is not None:
n_val = val_num
else:
n_val = int(len(train_val_index) * val_ratio)
val_index = train_val_index[:n_val]
train_index = train_val_index[n_val:]
return train_index, val_index, test_index
def time_split(index_path):
valid_ids = np.loadtxt("./data/pdbbind_v2020/timesplit_no_lig_overlap_val", dtype=str)
test_ids = np.loadtxt("./data/pdbbind_v2020/timesplit_test", dtype=str)
with open(index_path, 'rb') as f:
index = pickle.load(f)
all_ids = [os.path.basename(i[0])[:4] for i in index]
val_index = [all_ids.index(val_id) for val_id in valid_ids if val_id in all_ids]
test_index = [all_ids.index(test_id) for test_id in test_ids if test_id in all_ids]
train_index = list(set(range(len(all_ids))) - set(test_index) - set(val_index))
return train_index, val_index, test_index
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--index_path', type=str, default='./data/pdbbind_v2016/pocket_10/index.pkl')
parser.add_argument('--split_mode', type=str, choices=['coreset', 'time'], default='coreset')
parser.add_argument('--test_path', type=str, default='./data/pdbbind_v2016/coreset')
parser.add_argument('--val_ratio', type=float, default=0.1)
parser.add_argument('--val_num', type=int, default=None)
parser.add_argument('--save_path', type=str, default='./data/pdbbind_v2016/pocket_10/split.pt')
parser.add_argument('--seed', type=int, default=2021)
args = parser.parse_args()
random.seed(args.seed)
if args.split_mode == 'coreset':
train_index, val_index, test_index = coretest_split(args.index_path, args.test_path,
args.val_ratio, args.val_num)
elif args.split_mode == 'time':
train_index, val_index, test_index = time_split(args.index_path)
else:
raise ValueError(args.split_mode)
torch.save({
'train': train_index,
'val': val_index,
'test': test_index
}, args.save_path)
print('Train %d, Validation %d, Test %d.' % (len(train_index), len(val_index), len(test_index)))
print('Done.')
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