-1 indices solved and DTI added

drug/data/DTI/DTI.csv

@@ -0,0 +1,39 @@
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drug/models.py

@@ -19,17 +19,21 @@ from torch_geometric.nn import GCNConv
 
 # base from this notebook: https://colab.research.google.com/drive/1LJir3T6M6Omc2Vn2GV2cDW_GV2YfI53_?usp=sharing#scrollTo=jNsToorfSgS0
 class GCN(torch.nn.Module):
-    def __init__(self, num_features, hidden_channels): # num_features = dataset.num_features
+    def __init__(self, num_features, hidden_channels, gpu_id=None): # num_features = dataset.num_features
         super(GCN, self).__init__()
         torch.manual_seed(42)
 
         # Initialize the layers
         self.conv1 = GCNConv(num_features, hidden_channels)
         self.conv2 = GCNConv(hidden_channels, num_features)
+        self.gpu_id = gpu_id
 
     def forward(self, x, edge_index):
         # First Message Passing Layer (Transformation)
         x = x.to(torch.float32)
+        if self.gpu_id is not None:
+            x = x.cuda(self.gpu_id)
+            edge_index = edge_index.cuda(self.gpu_id)
         x = self.conv1(x, edge_index)
         x = x.relu()
         x = F.dropout(x, p=0.5, training=self.training)

predictor/cross_validation.py

@@ -32,8 +32,9 @@ def eval_model(model, optimizer, loss_func, train_data, test_data,
 def step_batch(model, batch, loss_func, gpu_id=None, train=True):
     drug1_id, drug2_id, cell_feat, y_true = batch
     if gpu_id is not None:
-        # drug1_feats, drug2_feats, cell_feats, y_true = drug1_feats.cuda(gpu_id), drug2_feats.cuda(gpu_id), \
-        #                                                cell_feats.cuda(gpu_id), y_true.cuda(gpu_id)
+        drug1_id = drug1_id.cuda(gpu_id)
+        drug2_id = drug2_id.cuda(gpu_id)
+        cell_feat = cell_feat.cuda(gpu_id)
         pass
     if train:
         y_pred = model(drug1_id, drug2_id, cell_feat)
@@ -96,7 +97,7 @@ def train_model(model, optimizer, loss_func, train_loader, valid_loader, n_epoch
 def create_model(data, hidden_size, gpu_id=None):
     # TODO: use our own MLP model
     # get 256
-    model = MLP(data.cell_feat_len() + 2 * 256, hidden_size)
+    model = MLP(data.cell_feat_len() + 2 * 256, hidden_size, gpu_id)
     if gpu_id is not None:
         model = model.cuda(gpu_id)
     return model

predictor/model/models.py

@@ -4,20 +4,21 @@ import torch.nn.functional as F
 import os
 import sys 
 
-
 PROJ_DIR = os.path.dirname(os.path.abspath(os.path.join(os.path.dirname( __file__ ), '..')))
 
 sys.path.insert(0, PROJ_DIR)
 from drug.models import GCN
 from drug.datasets import DDInteractionDataset
+from model.utils import get_FP_by_negative_index
+
 
 
 class Connector(nn.Module):
-    def __init__(self):
+    def __init__(self, gpu_id=None):
         super(Connector, self).__init__()
 
         self.ddiDataset = DDInteractionDataset()
-        self.gcn = GCN(self.ddiDataset.num_features, self.ddiDataset.num_features // 2)
+        self.gcn = GCN(self.ddiDataset.num_features, self.ddiDataset.num_features // 2, gpu_id)
         
         #Cell line features
         # np.load('cell_feat.npy')
@@ -30,12 +31,18 @@ class Connector(nn.Module):
         drug2_idx = torch.flatten(drug2_idx)
         drug1_feat = x[drug1_idx]
         drug2_feat = x[drug2_idx]
+        for i, x in enumerate(drug1_idx):
+            if x < 0:
+                drug1_feat[i] = get_FP_by_negative_index(x)
+        for i, x in enumerate(drug2_idx):
+            if x < 0:
+                drug2_feat[i] = get_FP_by_negative_index(x)
         feat = torch.cat([drug1_feat, drug2_feat, cell_feat], 1)
         return feat
 
 
 class MLP(nn.Module):
-    def __init__(self, input_size: int, hidden_size: int):
+    def __init__(self, input_size: int, hidden_size: int, gpu_id=None):
         super(MLP, self).__init__()
         self.layers = nn.Sequential(
             nn.Linear(input_size, hidden_size),
@@ -47,7 +54,7 @@ class MLP(nn.Module):
             nn.Linear(hidden_size // 2, 1)
         )
 
-        self.connector = Connector()
+        self.connector = Connector(gpu_id)
     
     def forward(self, drug1_idx, drug2_idx, cell_feat): # prev input: self, drug1_feat: torch.Tensor, drug2_feat: torch.Tensor, cell_feat: torch.Tensor
         feat = self.connector(drug1_idx, drug2_idx, cell_feat)

predictor/model/utils.py

@@ -3,6 +3,7 @@ import torch
 import json
 import random
 import pandas as pd
+import numpy as np
 
 def calc_stat(numbers):
     mu = sum(numbers) / len(numbers)
@@ -88,13 +89,26 @@ def get_index_by_name(drug_name):
 
     drug_bank_id = drug_name2drugbank_id_df[drug_name2drugbank_id_df['drug_name'] == drug_name].drug_bank_id.item()
 
+    negative_index = list(drug_name2drugbank_id_df['drug_name']).index(drug_name)
+
     drug2id_file = os.path.join(project_path, 'drug/data/DDI/DrugBank/raw/', 'drug2id.tsv')
     drug2id_df = pd.read_csv(drug2id_file , sep='\t')
     
     row = drug2id_df[drug2id_df['DrugBank_id'] == drug_bank_id]
     if row.empty:
-        drug_index = -1
+        drug_index = - negative_index
     else:
         drug_index = row.node_index.item()
 
-    return drug_index
+    return drug_index
+
+def get_FP_by_negative_index(index):
+
+    index = index.item()
+
+    project_path = os.path.dirname(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+    drug2FP_file = os.path.join(project_path, 'drug/data/drug2FP_synergy.csv')
+    drug2FP_df = pd.read_csv(drug2FP_file)
+
+    array = np.array(list(drug2FP_df.iloc[-index])[1:])
+    return torch.tensor(array, dtype=torch.float32)