--- a
+++ b/make_splits.py
@@ -0,0 +1,201 @@
+### data_loaders.py
+import argparse
+import os
+import pickle
+
+import numpy as np
+import pandas as pd
+from PIL import Image
+from sklearn import preprocessing
+
+# Env
+from networks import define_net
+from utils import getCleanAllDataset
+import torch
+from torchvision import transforms
+from options import parse_gpuids
+
+### Initializes parser and data
+"""
+all_st
+python make_splits.py --ignore_missing_moltype 0 --ignore_missing_histype 0 --use_vgg_features 0 --roi_dir all_st # for training Surv Path, Surv Graph, and testing Surv Graph
+python make_splits.py --ignore_missing_moltype 0 --ignore_missing_histype 1 --use_vgg_features 0 --roi_dir all_st # for training Grad Path, Grad Graph, and testing Surv_graph
+python make_splits.py --ignore_missing_moltype 1 --ignore_missing_histype 0 --use_vgg_features 0 --roi_dir all_st # for training Surv Omic, Surv Graphomic
+python make_splits.py --ignore_missing_moltype 1 --ignore_missing_histype 1 --use_vgg_features 0 --roi_dir all_st # for training Grad Omic, Grad Graphomic
+
+all_st_patches_512 (no VGG)
+python make_splits.py --ignore_missing_moltype 0 --ignore_missing_histype 0 --use_vgg_features 0 --roi_dir all_st_patches_512 # for testing Surv Path
+python make_splits.py --ignore_missing_moltype 0 --ignore_missing_histype 1 --use_vgg_features 0 --roi_dir all_st_patches_512 # for testing Grad Path
+
+all_st_patches_512 (use VGG)
+python make_splits.py --ignore_missing_moltype 0 --ignore_missing_histype 0 --use_vgg_features 1 --roi_dir all_st_patches_512 --exp_name surv_15 --gpu_ids 0 # for Surv Pathgraph
+python make_splits.py --ignore_missing_moltype 0 --ignore_missing_histype 1 --use_vgg_features 1 --roi_dir all_st_patches_512 --exp_name grad_15 --act_type LSM --label_dim 3 --gpu_ids 1 # for Grad Pathgraph
+python make_splits.py --ignore_missing_moltype 1 --ignore_missing_histype 0 --use_vgg_features 1 --roi_dir all_st_patches_512 --exp_name surv_15 --gpu_ids 2 # for Surv Pathomic, Pathgraphomic
+python make_splits.py --ignore_missing_moltype 1 --ignore_missing_histype 1 --use_vgg_features 1 --roi_dir all_st_patches_512 --exp_name grad_15 --act_type LSM --label_dim 3 --gpu_ids 3 # for Grad Pathomic, Pathgraphomic
+
+
+python make_splits.py --ignore_missing_moltype 0 --ignore_missing_histype 1 --make_all_train 1
+
+python make_splits.py --ignore_missing_moltype 1 --ignore_missing_histype 0 --use_vgg_features 0 --roi_dir all_st --use_rnaseq 1
+python make_splits.py --ignore_missing_moltype 1 --ignore_missing_histype 1 --use_vgg_features 0 --roi_dir all_st --use_rnaseq 1
+python make_splits.py --ignore_missing_moltype 1 --ignore_missing_histype 0 --use_vgg_features 1 --roi_dir all_st_patches_512 --exp_name surv_15 --use_rnaseq 1 --gpu_ids 2
+python make_splits.py --ignore_missing_moltype 1 --ignore_missing_histype 1 --use_vgg_features 1 --roi_dir all_st_patches_512 --exp_name grad_15 --use_rnaseq 1 --act_type LSM --label_dim 3 --gpu_ids 3
+
+
+python make_splits.py --ignore_missing_moltype 0 --ignore_missing_histype 0 --use_vgg_features 1 --roi_dir all_st_patches_512 --exp_name surv_15_rnaseq --gpu_ids 0
+python make_splits.py --ignore_missing_moltype 1 --ignore_missing_histype 0 --use_vgg_features 1 --roi_dir all_st_patches_512 --exp_name surv_15_rnaseq --use_rnaseq 1 --gpu_ids 0
+
+python make_splits.py --ignore_missing_moltype 0 --ignore_missing_histype 1 --use_vgg_features 1 --roi_dir all_st_patches_512 --exp_name grad_15 --act_type LSM --label_dim 3 --gpu_ids 1
+python make_splits.py --ignore_missing_moltype 1 --ignore_missing_histype 1 --use_vgg_features 1 --roi_dir all_st_patches_512 --exp_name grad_15 --use_rnaseq 1 --act_type LSM --label_dim 3 --gpu_ids 1
+
+python make_splits.py --ignore_missing_moltype 1 --ignore_missing_histype 0 --use_vgg_features 0 --roi_dir all_st --use_rnaseq 1
+python make_splits.py --ignore_missing_moltype 1 --ignore_missing_histype 0 --use_vgg_features 1 --roi_dir all_st_patches_512 --exp_name surv_15_rnaseq --gpu_ids 2
+
+python make_splits.py --ignore_missing_moltype 1 --ignore_missing_histype 1 --use_vgg_features 0 --roi_dir all_st --use_rnaseq 1
+python make_splits.py --ignore_missing_moltype 1 --ignore_missing_histype 1 --use_vgg_features 1 --roi_dir all_st_patches_512 --exp_name grad_15 --act_type LSM --label_dim 3 --gpu_ids 3
+
+
+
+
+"""
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataroot', type=str, default='./data/TCGA_GBMLGG/', help="datasets")
+    parser.add_argument('--roi_dir', type=str, default='all_st')
+    parser.add_argument('--graph_feat_type', type=str, default='cpc', help="graph features to use")
+    parser.add_argument('--ignore_missing_moltype', type=int, default=0, help="Ignore data points with missing molecular subtype")
+    parser.add_argument('--ignore_missing_histype', type=int, default=0, help="Ignore data points with missign histology subtype")
+    parser.add_argument('--make_all_train', type=int, default=0)
+    parser.add_argument('--use_vgg_features', type=int, default=0)
+    parser.add_argument('--use_rnaseq', type=int, default=0)
+
+
+    parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints/TCGA_GBMLGG/', help='models are saved here')
+    parser.add_argument('--exp_name', type=str, default='surv_15_rnaseq', help='name of the project. It decides where to store samples and models')
+    parser.add_argument('--gpu_ids', type=str, default='0,1,2,3', help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')
+    parser.add_argument('--mode', type=str, default='path', help='mode')
+    parser.add_argument('--model_name', type=str, default='path', help='mode')
+    parser.add_argument('--task', type=str, default='surv', help='surv | grad')
+    parser.add_argument('--act_type', type=str, default='Sigmoid', help='activation function')
+    parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.')
+    parser.add_argument('--label_dim', type=int, default=1, help='size of output')
+    parser.add_argument('--batch_size', type=int, default=32, help="Number of batches to train/test for. Default: 256")
+    parser.add_argument('--path_dim', type=int, default=32)
+    parser.add_argument('--init_type', type=str, default='none', help='network initialization [normal | xavier | kaiming | orthogonal | max]. Max seems to work well')
+    parser.add_argument('--dropout_rate', default=0.25, type=float, help='0 - 0.25. Increasing dropout_rate helps overfitting. Some people have gone as high as 0.5. You can try adding more regularization')
+
+    opt = parser.parse_known_args()[0]
+    opt = parse_gpuids(opt)
+    return opt
+
+opt = parse_args()
+device = torch.device('cuda:{}'.format(opt.gpu_ids[0])) if opt.gpu_ids else torch.device('cpu')
+metadata, all_dataset = getCleanAllDataset(opt.dataroot, opt.ignore_missing_moltype, opt.ignore_missing_histype, opt.use_rnaseq)
+
+### Creates a mapping from TCGA ID -> Image ROI
+img_fnames = os.listdir(os.path.join(opt.dataroot, opt.roi_dir))
+pat2img = {}
+for pat, img_fname in zip([img_fname[:12] for img_fname in img_fnames], img_fnames):
+    if pat not in pat2img.keys(): pat2img[pat] = []
+    pat2img[pat].append(img_fname)
+
+### Dictionary file containing split information
+data_dict = {}
+data_dict['data_pd'] = all_dataset
+#data_dict['pat2img'] = pat2img
+#data_dict['img_fnames'] = img_fnames
+cv_splits = {}
+
+### Extracting K-Fold Splits
+pnas_splits = pd.read_csv(opt.dataroot+'pnas_splits.csv')
+pnas_splits.columns = ['TCGA ID']+[str(k) for k in range(1, 16)]
+pnas_splits.index = pnas_splits['TCGA ID']
+pnas_splits = pnas_splits.drop(['TCGA ID'], axis=1)
+
+### get path_feats
+def get_vgg_features(model, device, img_path):
+    if model is None:
+        return img_path
+    else:
+        x_path = Image.open(img_path).convert('RGB')
+        normalize = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+        x_path = torch.unsqueeze(normalize(x_path), dim=0)
+        features, hazard = model(x_path=x_path.to(device))
+        return features.cpu().detach().numpy()
+
+### method for constructing aligned
+def getAlignedMultimodalData(opt, model, device, all_dataset, pat_split, pat2img):
+    x_patname, x_path, x_grph, x_omic, e, t, g = [], [], [], [], [], [], []
+
+    for pat_name in pat_split:
+        if pat_name not in all_dataset.index: continue
+
+        for img_fname in pat2img[pat_name]:
+            grph_fname = img_fname.rstrip('.png')+'.pt'
+            assert grph_fname in os.listdir(os.path.join(opt.dataroot, '%s_%s' % (opt.roi_dir, opt.graph_feat_type)))
+            assert all_dataset[all_dataset['TCGA ID'] == pat_name].shape[0] == 1
+
+            x_patname.append(pat_name)
+            x_path.append(get_vgg_features(model, device, os.path.join(opt.dataroot, opt.roi_dir, img_fname)))
+            x_grph.append(os.path.join(opt.dataroot, '%s_%s' % (opt.roi_dir, opt.graph_feat_type), grph_fname))
+            x_omic.append(np.array(all_dataset[all_dataset['TCGA ID'] == pat_name].drop(metadata, axis=1)))
+            e.append(int(all_dataset[all_dataset['TCGA ID']==pat_name]['censored']))
+            t.append(int(all_dataset[all_dataset['TCGA ID']==pat_name]['Survival months']))
+            g.append(int(all_dataset[all_dataset['TCGA ID']==pat_name]['Grade']))
+
+    return x_patname, x_path, x_grph, x_omic, e, t, g
+
+print(all_dataset.shape)
+
+for k in pnas_splits.columns:
+    print('Creating Split %s' % k)
+    pat_train = pnas_splits.index[pnas_splits[k] == 'Train'] if opt.make_all_train == 0 else pnas_splits.index
+    pat_test = pnas_splits.index[pnas_splits[k] == 'Test']
+    cv_splits[int(k)] = {}
+
+    model = None
+    if opt.use_vgg_features:
+        load_path = os.path.join(opt.checkpoints_dir, opt.exp_name, opt.model_name, '%s_%s.pt' % (opt.model_name, k))
+        model_ckpt = torch.load(load_path, map_location=device)
+        model_state_dict = model_ckpt['model_state_dict']
+        if hasattr(model_state_dict, '_metadata'): del model_state_dict._metadata
+        model = define_net(opt, None)
+        if isinstance(model, torch.nn.DataParallel): model = model.module
+        print('Loading the model from %s' % load_path)
+        model.load_state_dict(model_state_dict)
+        model.eval()
+
+    train_x_patname, train_x_path, train_x_grph, train_x_omic, train_e, train_t, train_g = getAlignedMultimodalData(opt, model, device, all_dataset, pat_train, pat2img)
+    test_x_patname, test_x_path, test_x_grph, test_x_omic, test_e, test_t, test_g = getAlignedMultimodalData(opt, model, device, all_dataset, pat_test, pat2img)
+
+    train_x_omic, train_e, train_t = np.array(train_x_omic).squeeze(axis=1), np.array(train_e, dtype=np.float64), np.array(train_t, dtype=np.float64)
+    test_x_omic, test_e, test_t = np.array(test_x_omic).squeeze(axis=1), np.array(test_e, dtype=np.float64), np.array(test_t, dtype=np.float64)
+        
+    scaler = preprocessing.StandardScaler().fit(train_x_omic)
+    train_x_omic = scaler.transform(train_x_omic)
+    test_x_omic = scaler.transform(test_x_omic)
+
+    train_data = {'x_patname': train_x_patname,
+                  'x_path':np.array(train_x_path),
+                  'x_grph':train_x_grph,
+                  'x_omic':train_x_omic,
+                  'e':np.array(train_e, dtype=np.float64), 
+                  't':np.array(train_t, dtype=np.float64),
+                  'g':np.array(train_g, dtype=np.float64)}
+
+    test_data = {'x_patname': test_x_patname,
+                 'x_path':np.array(test_x_path),
+                 'x_grph':test_x_grph,
+                 'x_omic':test_x_omic,
+                 'e':np.array(test_e, dtype=np.float64),
+                 't':np.array(test_t, dtype=np.float64),
+                 'g':np.array(test_g, dtype=np.float64)}
+
+    dataset = {'train':train_data, 'test':test_data}
+    cv_splits[int(k)] = dataset
+
+    if opt.make_all_train: break
+    
+data_dict['cv_splits'] = cv_splits
+
+pickle.dump(data_dict, open('%s/splits/gbmlgg15cv_%s_%d_%d_%d%s.pkl' % (opt.dataroot, opt.roi_dir, opt.ignore_missing_moltype, opt.ignore_missing_histype, opt.use_vgg_features, '_rnaseq' if opt.use_rnaseq else ''), 'wb'))
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