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README.md

@@ -6,8 +6,9 @@
 
 - **documentation**: Instructions to install the needed libraries.
 - **graphData**: Data of DISNET's graph.
-- **metrics**: Training, testing and RepoDB verification ROC & PRC, also loss evolution for training and validation.
+- **logo**: REDIRECTION's logo.
 - **models**: Trained models.
+- **plots**: Plots of the models' results and metrics.
 - **results**: Result files of the RepoDB test and the distribution plots (once these are generated).
 - **Code files:**
   - deepSnapPred (train & test DeepSnap) 
@@ -15,9 +16,8 @@
   - testRepoDB (validate model using RepoDB) 
   - topN (getting top N new predictions of a model)
   - utilities (plotting utilities)
-  - visualizeDistribution (Checks the distribution of the predictions for a group of diseases, check TFG for more information).
-  - visualizeEmbeddings (visualization of embeddings)
 
 ## Summary
 Repository of the AIIM2023 "" paper.
+
 REDIRECTION stands for dRug rEpurposing Disnet lInk pREdiCTION.
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documentation/README.md

+Development was carried out with Python 3.8.10.  
+To install all the libraries run the following command:  
+pip install -r libsImport.txt
+
+Some libraries will produce an error, in that case delete the corresponding line and run the command again.
+
+
+**IMPORTANT**: PyTorch (torch) will be installed as a dependency of other packages, it should be uninstalled and installed again using the command shown below.  
+**IMPORTANT**: Most libraries are compiled to use CUDA 11.3, if you are using a different version please adapt the installation.
+
+Here are some libraries that usually produce errors and the way to install them:
+- **DGL**: pip install dgl-cu113==0.7.2 -f https://data.dgl.ai/wheels/repo.html
+- **SNAP**: pip install snap-stanford
+- **PyTorch**: pip install torch==1.10.2 torchvision==0.11.3 torchaudio==0.10.2 --extra-index-url https://download.pytorch.org/whl/cu113
+- **PyTorchExtra**: pip install torch-scatter==2.0.9 torch-sparse==0.6.12 torch-cluster==1.5.9 torch-spline-conv==1.2.1 torch-geometric==2.0.3 -f https://data.pyg.org/whl/torch-1.10.0+cpu.html
+	
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documentation/control.txt

+watch -n0.1 nvidia-smi
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documentation/env.txt

+source DISNET/bin/activate
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documentation/libsImport.txt

+asttokens==2.0.5
+attrs==21.4.0
+certifi==2021.10.8
+charset-normalizer==2.0.12
+click==8.1.2
+cycler==0.11.0
+deepsnap==0.2.1
+dgl-cu113==0.7.2
+executing==0.8.2
+Flask==2.1.1
+fonttools==4.29.1
+googledrivedownloader==0.4
+idna==3.3
+importlib-metadata==4.11.3
+iniconfig==1.1.1
+install==1.3.5
+isodate==0.6.1
+itsdangerous==2.1.2
+Jinja2==3.0.3
+joblib==1.1.0
+kiwisolver==1.3.2
+littleutils==0.2.2
+MarkupSafe==2.1.0
+matplotlib==3.5.1
+networkx==2.6.3
+numpy==1.22.2
+nvidia-smi==0.1.3
+packaging==21.3
+pandas==1.4.1
+Pillow==9.0.1
+pluggy==1.0.0
+py==1.11.0
+pyparsing==3.0.7
+pytest==7.0.1
+python-dateutil==2.8.2
+pytz==2021.3
+PyYAML==6.0
+rdflib==6.1.1
+requests==2.27.1
+scikit-learn==1.0.2
+scipy==1.8.0
+seaborn==0.11.2
+six==1.16.0
+sklearn==0.0
+snap-stanford==6.0.0
+sorcery==0.2.2
+threadpoolctl==3.1.0
+tomli==2.0.1
+torch==1.10.2+cu113
+torch-cluster==1.5.9
+torch-geometric==2.0.3
+torch-scatter==2.0.9
+torch-sparse==0.6.12
+torch-spline-conv==1.2.1
+torchaudio==0.10.2+cu113
+torchvision==0.11.3+cu113
+tqdm==4.62.3
+typing-extensions==4.1.1
+urllib3==1.26.8
+Werkzeug==2.1.1
+wrapt==1.13.3
+yacs==0.1.8
+zipp==3.8.0

topN.py

+import torch
+from deepSnapPred import HeteroGNN, generate_convs_link_pred_layers
+from deepsnap.batch import Batch
+from torch.utils.data import DataLoader
+import argparse
+import heterograph_construction
+from deepsnap.dataset import GraphDataset
+from datetime import datetime
+from deepsnap.hetero_gnn import HeteroSAGEConv
+
+
+def arg_parse():
+    parser = argparse.ArgumentParser(description='Link pred arguments.')
+    parser.add_argument('--device', type=str,
+                        help='CPU / GPU device.')
+                       
+    parser.add_argument('--n', type=int,
+                        help='Number of predictions.')
+
+    parser.set_defaults(
+        device='cuda' if torch.cuda.is_available() else 'cpu',
+        n=6
+    )
+    return parser.parse_args()
+
+args = arg_parse()
+constructor = heterograph_construction.DISNETConstructor(device=args.device)
+toStudy = 'dis_dru_the'
+n = args.n       #Number of new predictions, top n.
+
+
+def filterPreds(original, pred, key):
+    headsO = original.edge_index[key][0, :].long()
+
+    new = []
+    for i, elem in enumerate(pred):
+        pred_labels = pred[key, i]
+        head = i
+        tail = torch.arange(0,len(pred_labels))
+        indexH = ((headsO == head).nonzero(as_tuple=True)[0])
+            
+        for index in indexH:
+            tail = tail[tail != index]
+        
+        new.append([head, tail, pred_labels[tail].cpu().detach().numpy()])
+
+    return new
+
+
+def getTopNDS(model, original, dataloader, key, n):
+    print("     Looking for new edges.")
+    for batch, original in zip(dataloader, original):
+        batch.to(args.device)
+        pred = model.predict_all(batch)
+        
+        new = filterPreds(original, pred, key)
+      
+    print("     Decoding predictions, this may take a while.")
+    return constructor.decodePredictions(new, toStudy, n)
+
+def getOriginal():
+    hetero = constructor.DISNETHeterographDeepSnap(full=True)
+    dataset = GraphDataset(
+        [hetero],
+        task='link_pred',
+        edge_train_mode='disjoint',
+        edge_message_ratio=0.8
+    )
+    dataset_loader = DataLoader(
+        dataset, collate_fn=Batch.collate(), batch_size=1
+    )
+    return dataset_loader, hetero
+
+
+def deepSnap():
+    original, hetero = getOriginal()
+    conv1, conv2 = generate_convs_link_pred_layers(hetero, HeteroSAGEConv, 32)
+    model = HeteroGNN(conv1, conv2, hetero, 32).to(args.device)
+
+    model.load_state_dict(torch.load("./models/modelDeepSnapPred", map_location=torch.device(args.device)))
+    model = model.to(args.device)
+    model.eval()
+
+    edge = ('disorder', 'dis_dru_the', 'drug')
+    toInfer, _ = getOriginal()
+    
+    print("Started getting top", n, "at", datetime.now().strftime("%H:%M:%S"))
+    topN = getTopNDS(model, original, toInfer, edge, n)
+    print("Finished getting top",n, "at", datetime.now().strftime("%H:%M:%S"))
+
+if __name__ == '__main__':
+    deepSnap()
+