DeepTraCDR/Scenario1/Regression/DeepTraCDR_model.py

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import MultiheadAttention, TransformerEncoder, TransformerEncoderLayer
from scipy.stats import pearsonr, spearmanr
from utils import mse_loss, torch_corr_x_y

class ConstructAdjMatrix(nn.Module):
    """Constructs adjacency matrices for graph-based operations."""
    def __init__(self, original_adj_mat, device="cpu"):
        super().__init__()
        # Convert numpy array to torch tensor if needed
        self.adj = torch.from_numpy(original_adj_mat).float() if isinstance(original_adj_mat, np.ndarray) else original_adj_mat
        self.adj = self.adj.to(device)
        self.device = device

    def forward(self):
        """Computes Laplacian matrices for cells and drugs."""
        with torch.no_grad():
            # Compute degree matrices for normalization
            d_x = torch.diag(torch.pow(torch.sum(self.adj, dim=1) + 1, -0.5))
            d_y = torch.diag(torch.pow(torch.sum(self.adj, dim=0) + 1, -0.5))
            # Compute aggregated Laplacian matrices
            agg_cell_lp = torch.mm(torch.mm(d_x, self.adj), d_y)
            agg_drug_lp = torch.mm(torch.mm(d_y, self.adj.T), d_x)
            # Compute self-loop Laplacian matrices
            self_cell_lp = torch.diag(torch.add(torch.pow(torch.sum(self.adj, dim=1) + 1, -1), 1))
            self_drug_lp = torch.diag(torch.add(torch.pow(torch.sum(self.adj, dim=0) + 1, -1), 1))
        return agg_cell_lp.to(self.device), agg_drug_lp.to(self.device), self_cell_lp.to(self.device), self_drug_lp.to(self.device)

class LoadFeature(nn.Module):
    """Loads and processes cell and drug features."""
    def __init__(self, cell_exprs, drug_fingerprints, device="cpu"):
        super().__init__()
        self.device = device
        # Convert input data to torch tensors
        self.cell_exprs = torch.from_numpy(cell_exprs).float().to(device)
        self.drug_fingerprints = [torch.from_numpy(fp).float().to(device) for fp in drug_fingerprints]
        
        # Define projection layers for drug fingerprints
        self.drug_proj = nn.ModuleList([
            nn.Sequential(
                nn.Linear(fp.shape[1], 512),
                nn.BatchNorm1d(512),
                nn.GELU(),
                nn.Dropout(0.3)
            ).to(device) for fp in drug_fingerprints
        ])
        
        # Initialize transformer encoder for drug features
        self.transformer = TransformerEncoder(
            TransformerEncoderLayer(d_model=512, nhead=8, dim_feedforward=2048, batch_first=True),
            num_layers=3
        ).to(device)
        
        # Normalization layers
        self.cell_norm = nn.LayerNorm(cell_exprs.shape[1]).to(device)
        self.drug_norm = nn.LayerNorm(512).to(device)
        
        # Cell feature encoder
        self.cell_encoder = nn.Sequential(
            nn.Linear(cell_exprs.shape[1], 1024),
            nn.BatchNorm1d(1024),
            nn.GELU(),
            nn.Dropout(0.3),
            nn.Linear(1024, 512)
        ).to(device)

    def forward(self):
        """Processes cell and drug features to generate encoded representations."""
        # Normalize and encode cell features
        cell_feat = self.cell_norm(self.cell_exprs)
        cell_encoded = self.cell_encoder(cell_feat)
        
        # Project and process drug fingerprints
        projected = [proj(fp) for proj, fp in zip(self.drug_proj, self.drug_fingerprints)]
        stacked = torch.stack(projected, dim=1)
        drug_feat = self.transformer(stacked)
        drug_feat = self.drug_norm(drug_feat.mean(dim=1))
        
        return cell_encoded, drug_feat

class GEncoder(nn.Module):
    """Encodes cell and drug features using graph-based operations."""
    def __init__(self, agg_c_lp, agg_d_lp, self_c_lp, self_d_lp, device="cpu"):
        super().__init__()
        self.agg_c_lp = agg_c_lp
        self.agg_d_lp = agg_d_lp
        self.self_c_lp = self_c_lp
        self.self_d_lp = self_d_lp
        self.device = device
        
        # Cell feature encoder
        self.cell_encoder = nn.Sequential(
            nn.Linear(512, 1024),
            nn.BatchNorm1d(1024),
            nn.GELU(),
            nn.Dropout(0.3),
            nn.Linear(1024, 512)
        ).to(device)
        
        # Drug feature encoder
        self.drug_encoder = nn.Sequential(
            nn.Linear(512, 1024),
            nn.BatchNorm1d(1024),
            nn.GELU(),
            nn.Dropout(0.3),
            nn.Linear(1024, 512)
        ).to(device)
        
        # Attention mechanism and residual connection
        self.attention = MultiheadAttention(embed_dim=512, num_heads=8, batch_first=True).to(device)
        self.residual = nn.Linear(512, 512).to(device)
        self.fc = nn.Sequential(
            nn.Linear(1024, 512),
            nn.BatchNorm1d(512),
            nn.GELU(),
            nn.Dropout(0.2)
        ).to(device)

    def forward(self, cell_f, drug_f):
        """Encodes cell and drug features with graph-based attention."""
        # Aggregate features using Laplacian matrices
        cell_agg = torch.mm(self.agg_c_lp, drug_f) + torch.mm(self.self_c_lp, cell_f)
        drug_agg = torch.mm(self.agg_d_lp, cell_f) + torch.mm(self.self_d_lp, drug_f)
        
        # Encode aggregated features
        cell_fc = self.cell_encoder(cell_agg)
        drug_fc = self.drug_encoder(drug_agg)
        
        # Apply attention mechanism
        attn_output, _ = self.attention(
            query=cell_fc.unsqueeze(0),
            key=drug_fc.unsqueeze(0),
            value=drug_fc.unsqueeze(0)
        )
        attn_output = attn_output.squeeze(0)
        cell_emb = cell_fc + self.residual(attn_output)
        
        # Apply final activation
        cell_emb = F.gelu(cell_emb)
        drug_emb = F.gelu(drug_fc)
        
        return cell_emb, drug_emb

class GDecoder(nn.Module):
    """Decodes combined cell and drug embeddings to predict scores."""
    def __init__(self, emb_dim, gamma):
        super().__init__()
        self.gamma = gamma
        # Decoder network
        self.decoder = nn.Sequential(
            nn.Linear(2 * emb_dim, 1024),
            nn.BatchNorm1d(1024),
            nn.GELU(),
            nn.Dropout(0.2),
            nn.Linear(1024, 1)
        )
        # Learnable correlation weight
        self.corr_weight = nn.Parameter(torch.tensor(0.5))

    def forward(self, cell_emb, drug_emb):
        """Generates prediction scores from cell and drug embeddings."""
        # Combine cell and drug embeddings
        cell_exp = cell_emb.unsqueeze(1).repeat(1, drug_emb.size(0), 1)
        drug_exp = drug_emb.unsqueeze(0).repeat(cell_emb.size(0), 1, 1)
        combined = torch.cat([cell_exp, drug_exp], dim=-1)
        
        # Decode combined features
        scores = self.decoder(combined.view(-1, 2 * cell_emb.size(1))).view(cell_emb.size(0), drug_emb.size(0))
        corr = torch_corr_x_y(cell_emb, drug_emb)
        
        # Combine scores and correlation
        return self.gamma * (self.corr_weight * scores + (1 - self.corr_weight) * corr)

class DeepTraCDR(nn.Module):
    """Graph Convolutional Network for cell-drug interaction prediction."""
    def __init__(self, adj_mat, cell_exprs, drug_finger, layer_size, gamma, device="cpu"):
        super().__init__()
        self.device = device
        # Convert adjacency matrix to tensor if needed
        self.adj_mat = torch.from_numpy(adj_mat).float().to(device) if isinstance(adj_mat, np.ndarray) else adj_mat.to(device)
        
        # Initialize components
        self.construct_adj = ConstructAdjMatrix(self.adj_mat, device=device)
        self.load_feat = LoadFeature(cell_exprs, drug_finger, device=device)
        
        # Precompute adjacency matrices
        agg_c, agg_d, self_c, self_d = self.construct_adj()
        
        # Initialize encoder and decoder
        self.encoder = GEncoder(agg_c, agg_d, self_c, self_d, device=device).to(device)
        self.decoder = GDecoder(layer_size[-1], gamma).to(device)

    def forward(self):
        """Generates predictions and embeddings for cell-drug interactions."""
        cell_f, drug_f = self.load_feat()
        cell_emb, drug_emb = self.encoder(cell_f, drug_f)
        return self.decoder(cell_emb, drug_emb), cell_emb, drug_emb

class Optimizer:
    """Handles training and evaluation of the DeepTraCDR model."""
    def __init__(self, model, train_data, test_data, test_mask, train_mask, adj_matrix,
                 lr=0.001, wd=1e-05, epochs=200, test_freq=20, device="cpu", patience=50):
        self.model = model.to(device)
        self.train_data = train_data.float().to(device)
        self.test_data = test_data.float().to(device)
        self.train_mask = train_mask.to(device)
        self.test_mask = test_mask.to(device).bool()
        self.adj_matrix = adj_matrix.to(device)
        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr, weight_decay=wd)
        self.epochs = epochs
        self.test_freq = test_freq
        self.patience = patience
        self.best_rmse = float('inf')
        self.epochs_no_improve = 0
        self.true_masked_np = torch.masked_select(self.test_data, self.test_mask).cpu().numpy()

    def evaluate_metrics(self, pred_tensor):
        """Computes RMSE, PCC, and SCC for model evaluation."""
        pred_masked_np = torch.masked_select(pred_tensor, self.test_mask).cpu().numpy()
        rmse = np.sqrt(np.mean((self.true_masked_np - pred_masked_np)**2))
        pcc, _ = pearsonr(self.true_masked_np, pred_masked_np)
        scc, _ = spearmanr(self.true_masked_np, pred_masked_np)
        return rmse, pcc, scc, pred_masked_np

    def train(self):
        """Trains the model with early stopping based on RMSE."""
        best_rmse = float('inf')
        best_pcc = 0.0
        best_scc = 0.0
        best_pred_np = None

        for epoch in range(self.epochs):
            self.model.train()
            pred, cell_emb, drug_emb = self.model()
            
            # Compute and optimize loss
            mse_loss_val = mse_loss(self.train_data, pred, self.train_mask)
            total_loss = mse_loss_val
            self.optimizer.zero_grad()
            total_loss.backward()
            self.optimizer.step()

            if epoch % self.test_freq == 0:
                self.model.eval()
                with torch.no_grad():
                    pred_eval, _, _ = self.model()
                    rmse, pcc, scc, pred_masked = self.evaluate_metrics(pred_eval)
                    
                    # Update early stopping criteria
                    if rmse < self.best_rmse:
                        self.best_rmse = rmse
                        self.epochs_no_improve = 0
                    else:
                        self.epochs_no_improve += 1
                    
                    # Track best results
                    if rmse < best_rmse:
                        best_rmse = rmse
                        best_pcc = pcc
                        best_scc = scc
                        best_pred_np = pred_masked.copy()
                
                print(f"Epoch {epoch}: Loss = {total_loss.item():.4f}, RMSE = {rmse:.4f}, PCC = {pcc:.4f}, SCC = {scc:.4f}")
                
                # Early stopping
                if self.epochs_no_improve >= self.patience:
                    print(f"Early stopping at epoch {epoch} (no improvement for {self.patience} epochs).")
                    break

        print("\nBest Results:")
        print(f"RMSE: {best_rmse:.4f}")
        print(f"PCC: {best_pcc:.4f}")
        print(f"SCC: {best_scc:.4f}")

        return self.true_masked_np, best_pred_np, best_rmse, best_pcc, best_scc