File size: 4,834 Bytes
07fcdfe | 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 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | ##Create splits to be used for training across all models
import os
import os.path as osp
from sklearn.model_selection import KFold, train_test_split
import torch
from sklearn.metrics import jaccard_score
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def create_splits_n_fold(dataset, splits_type, nfolds, outdir):
kf = KFold(nfolds, shuffle=True, random_state=42)
os.makedirs(outdir, exist_ok = True)
#datasets forward and backward
dataset_forward = dataset[0]; dataset_backward = dataset[1]
splits = {}
if splits_type =='random':
i = 1
if len(dataset_forward)> 0:
for train_test_index_forward, train_test_index_backward in zip(kf.split(dataset_forward), kf.split(dataset_backward)):
#Forward
train_index_forward = train_test_index_forward[0]; test_index_forward = train_test_index_forward[1]
train_index_backward = train_test_index_backward[0]; test_index_backward = train_test_index_backward[1]
#Backward
train_index_forward, val_index_forward = train_test_split(train_index_forward, test_size=0.2, random_state=42)
train_index_backward, val_index_backward = train_test_split(train_index_backward, test_size=0.2, random_state=42)
assert len(dataset_backward) == len(train_index_backward) + len(val_index_backward) + len(test_index_backward), 'Splitted datasets should have the same number of samples as full dataset'
assert len(set(test_index_forward).intersection(train_index_forward)) ==0, "Overlap between train and test indices should be zero"
assert len(set(test_index_forward).intersection(val_index_forward)) ==0, "Overlap between val and test indices should be zero"
assert len(set(train_index_forward).intersection(val_index_forward)) ==0, "Overlap between train and val indices should be zero"
assert len(set(test_index_backward).intersection(train_index_backward)) ==0, "Overlap between train and test indices should be zero"
assert len(set(test_index_backward).intersection(val_index_backward)) ==0, "Overlap between val and test indices should be zero"
assert len(set(train_index_backward).intersection(val_index_backward)) ==0, "Overlap between train and val indices should be zero"
splits[i] = {'train_index_forward': train_index_forward,
'val_index_forward': val_index_forward,
'test_index_forward': test_index_forward,
'train_index_backward': train_index_backward,
'val_index_backward': val_index_backward,
'test_index_backward': test_index_backward}
i += 1
else:
for train_test_index_backward in kf.split(dataset_backward):
#Backward
train_index_backward = train_test_index_backward[0]; test_index_backward = train_test_index_backward[1]
train_index_backward, val_index_backward = train_test_split(train_index_backward, test_size=0.2, random_state=42)
assert len(dataset_backward) == len(train_index_backward) + len(val_index_backward) + len(test_index_backward), 'Splitted datasets should have the same number of samples as full dataset'
assert len(set(test_index_backward).intersection(train_index_backward)) ==0, "Overlap between train and test indices should be zero"
assert len(set(test_index_backward).intersection(val_index_backward)) ==0, "Overlap between val and test indices should be zero"
assert len(set(train_index_backward).intersection(val_index_backward)) ==0, "Overlap between train and val indices should be zero"
splits[i] = {'train_index_forward': None,
'val_index_forward': None,
'test_index_forward': None,
'train_index_backward': train_index_backward,
'val_index_backward': val_index_backward,
'test_index_backward': test_index_backward}
i += 1
torch.save(splits, osp.join(outdir,'splits.pt'))
return
###Generate splits
os.makedirs('../../processed/splits/', exist_ok=True)
nfolds = 5
for dataset_type in ['genetic', 'chemical']:
for dataset_name in ['A375', 'A549', 'MCF7', 'PC3', 'HT29', 'ES2', 'BICR6', 'YAPC', 'AGS', 'U251MG', 'VCAP', 'MDAMB231', 'BT20', 'HA1E', 'HELA']:
for splits_type in ['random']:
splits_setting = '{}fold'.format(nfolds)
outdir = '../../processed/splits/{}/{}/{}/{}'.format(dataset_type, dataset_name, splits_type, splits_setting)
if dataset_type == 'genetic':
base_path = "../../processed/torch_data/real_lognorm"
elif dataset_type =='chemical':
base_path = "../../processed/torch_data/chemical/real_lognorm"
try:
path = osp.join(base_path, 'data_forward_{}.pt'.format(dataset_name))
dataset_forward = torch.load(path)
path = osp.join(base_path, 'data_backward_{}.pt'.format(dataset_name))
dataset_backward = torch.load(path)
dataset = [dataset_forward, dataset_backward]
create_splits_n_fold(dataset, splits_type, nfolds, outdir)
except:
continue
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