Drug-combination-synergy/predictor/model/models.py

import torch
import torch.nn as nn
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


class Connector(nn.Module):
    def __init__(self):
        super(Connector, self).__init__()

        self.ddiDataset = DDInteractionDataset()
        self.gcn = GCN(self.ddiDataset.num_features, self.ddiDataset.num_features // 2)
        
        #Cell line features
        # np.load('cell_feat.npy')

    def forward(self, drug1_idx, drug2_idx, cell_feat):
        x = self.ddiDataset.get().x
        edge_index = self.ddiDataset.get().edge_index
        x = self.gcn(x, edge_index)
        drug1_feat = x[drug1_idx]
        drug2_feat = x[drug2_idx]
        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):
        super(MLP, self).__init__()
        self.layers = nn.Sequential(
            nn.Linear(input_size, hidden_size),
            nn.ReLU(),
            nn.BatchNorm1d(hidden_size),
            nn.Linear(hidden_size, hidden_size // 2),
            nn.ReLU(),
            nn.BatchNorm1d(hidden_size // 2),
            nn.Linear(hidden_size // 2, 1)
        )

        self.connector = Connector()
    
    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)
        out = self.layers(feat)
        return out


# other PRODeepSyn models have been deleted for now