DeepTraCDR/Scenario2/Target/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 sklearn.metrics import roc_auc_score, average_precision_score
from utils import torch_corr_x_y, cross_entropy_loss, prototypical_loss


class AdjacencyMatrixConstructor(nn.Module):
    """
    Constructs normalized adjacency matrices for graph-based computations.
    These matrices are used for aggregating cell and drug features in the GCN model.
    """
    def __init__(self, original_adj_mat, device="cpu"):
        super().__init__()
        # Convert numpy array to torch tensor if necessary and move to specified device
        if isinstance(original_adj_mat, np.ndarray):
            original_adj_mat = torch.from_numpy(original_adj_mat).float()
        self.adj = original_adj_mat.to(device)
        self.device = device

    def forward(self):
        """
        Computes normalized adjacency matrices for cell and drug aggregations.
        Returns four matrices: aggregated cell, aggregated drug, self-cell, and self-drug.
        """
        with torch.no_grad():
            # Compute degree normalization matrices
            degree_x = torch.pow(torch.sum(self.adj, dim=1) + 1, -0.5)
            degree_y = torch.pow(torch.sum(self.adj, dim=0) + 1, -0.5)
            d_x = torch.diag(degree_x)
            d_y = torch.diag(degree_y)

            # Compute aggregated Laplacian matrices
            agg_cell_lp = torch.mm(torch.mm(d_x, self.adj), d_y)  # [num_cells x num_drugs]
            agg_drug_lp = torch.mm(torch.mm(d_y, self.adj.T), d_x)  # [num_drugs x num_cells]

            # 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 FeatureLoader(nn.Module):
    """
    Loads and preprocesses cell expression and drug fingerprint features.
    Applies transformations to project features into a common embedding space.
    """
    def __init__(self, cell_exprs, drug_fingerprints, device="cpu"):
        super().__init__()
        self.device = device

        # Convert input features to torch tensors and move to device
        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]

        # 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(0.5)
            ).to(device) for fp in drug_fingerprints
        ])

        # Transformer encoder for drug features
        self.transformer = TransformerEncoder(
            TransformerEncoderLayer(
                d_model=512,
                nhead=8,
                dim_feedforward=2048,
                batch_first=True
            ),
            num_layers=1
        ).to(device)

        # Normalization layers
        self.cell_norm = nn.LayerNorm(cell_exprs.shape[1]).to(device)
        self.drug_norm = nn.LayerNorm(512).to(device)

        # Encoder for cell features
        self.cell_encoder = nn.Sequential(
            nn.Linear(cell_exprs.shape[1], 1024),
            nn.BatchNorm1d(1024),
            nn.GELU(),
            nn.Dropout(0.5),
            nn.Linear(1024, 512)
        ).to(device)

    def forward(self):
        """
        Processes cell and drug features to produce encoded representations.
        Returns encoded cell and drug features in a common 512-dimensional space.
        """
        # Normalize and encode cell features
        cell_feat = self.cell_norm(self.cell_exprs)  # [num_cells x num_cell_features]
        cell_encoded = self.cell_encoder(cell_feat)  # [num_cells x 512]

        # Project and transform drug fingerprints
        projected = [proj(fp) for proj, fp in zip(self.drug_proj, self.drug_fingerprints)]  # List of [num_samples x 512]
        stacked = torch.stack(projected, dim=1)  # [num_samples x num_drugs x 512]
        drug_feat = self.transformer(stacked)  # [num_samples x num_drugs x 512]
        drug_feat = self.drug_norm(drug_feat.mean(dim=1))  # [num_samples x 512]

        return cell_encoded, drug_feat


class GraphEncoder(nn.Module):
    """
    Encodes cell and drug features using graph-based aggregation and attention mechanisms.
    Produces final embeddings for cells and drugs.
    """
    def __init__(self, agg_cell_lp, agg_drug_lp, self_cell_lp, self_drug_lp, device="cpu"):
        super().__init__()
        self.agg_cell_lp = agg_cell_lp
        self.agg_drug_lp = agg_drug_lp
        self.self_cell_lp = self_cell_lp
        self.self_drug_lp = self_drug_lp
        self.device = device

        # Encoder for aggregated cell features
        self.cell_encoder = nn.Sequential(
            nn.Linear(512, 1024),
            nn.BatchNorm1d(1024),
            nn.GELU(),
            nn.Dropout(0.5),
            nn.Linear(1024, 512)
        ).to(device)

        # Encoder for aggregated drug features
        self.drug_encoder = nn.Sequential(
            nn.Linear(512, 1024),
            nn.BatchNorm1d(1024),
            nn.GELU(),
            nn.Dropout(0.5),
            nn.Linear(1024, 512)
        ).to(device)

        # Attention mechanism for cell-drug interactions
        self.attention = MultiheadAttention(embed_dim=512, num_heads=8, batch_first=True).to(device)
        self.residual = nn.Linear(512, 512).to(device)

        # Final fully connected layer
        self.fc = nn.Sequential(
            nn.Linear(1024, 512),
            nn.BatchNorm1d(512),
            nn.GELU(),
            nn.Dropout(0.5)
        ).to(device)

    def forward(self, cell_features, drug_features):
        """
        Encodes cell and drug features using graph aggregation and attention.
        Returns final cell and drug embeddings.
        """
        # Aggregate features using Laplacian matrices
        cell_agg = torch.mm(self.agg_cell_lp, drug_features) + torch.mm(self.self_cell_lp, cell_features)  # [num_cells x 512]
        drug_agg = torch.mm(self.agg_drug_lp, cell_features) + torch.mm(self.self_drug_lp, drug_features)  # [num_drugs x 512]

        # Encode aggregated features
        cell_fc = self.cell_encoder(cell_agg)  # [num_cells x 512]
        drug_fc = self.drug_encoder(drug_agg)  # [num_drugs x 512]

        # Apply attention mechanism
        attn_output, _ = self.attention(
            query=cell_fc.unsqueeze(0),  # [1 x num_cells x 512]
            key=drug_fc.unsqueeze(0),    # [1 x num_drugs x 512]
            value=drug_fc.unsqueeze(0)   # [1 x num_drugs x 512]
        )
        attn_output = attn_output.squeeze(0)  # [num_cells x 512]

        # Combine attention output with residual connection
        cell_emb = cell_fc + self.residual(attn_output)  # [num_cells x 512]

        # Apply final activation
        cell_emb = F.gelu(cell_emb)  # [num_cells x 512]
        drug_emb = F.gelu(drug_fc)  # [num_drugs x 512]

        return cell_emb, drug_emb


class GraphDecoder(nn.Module):
    """
    Decodes cell and drug embeddings to predict interaction scores.
    Combines embeddings and applies a correlation-based adjustment.
    """
    def __init__(self, emb_dim, gamma):
        super().__init__()
        self.gamma = gamma

        # Decoder network for combined embeddings
        self.decoder = nn.Sequential(
            nn.Linear(2 * emb_dim, 1024),
            nn.BatchNorm1d(1024),
            nn.GELU(),
            nn.Dropout(0.2),
            nn.Linear(1024, 1)
        )

        # Learnable weight for balancing scores and correlation
        self.corr_weight = nn.Parameter(torch.tensor(0.5))

    def forward(self, cell_emb, drug_emb):
        """
        Decodes cell and drug embeddings to produce interaction scores.
        Returns a matrix of interaction probabilities.
        """
        # Expand embeddings for pairwise combinations
        cell_exp = cell_emb.unsqueeze(1).repeat(1, drug_emb.size(0), 1)  # [num_cells x num_drugs x emb_dim]
        drug_exp = drug_emb.unsqueeze(0).repeat(cell_emb.size(0), 1, 1)  # [num_cells x num_drugs x emb_dim]

        # Combine cell and drug embeddings
        combined = torch.cat([cell_exp, drug_exp], dim=-1)  # [num_cells x num_drugs x 2*emb_dim]

        # Compute interaction scores
        scores = self.decoder(combined.view(-1, 2 * cell_emb.size(1))).view(cell_emb.size(0), drug_emb.size(0))  # [num_cells x num_drugs]

        # Compute correlation between embeddings
        corr = torch_corr_x_y(cell_emb, drug_emb)  # [num_cells x num_drugs]

        # Combine scores and correlation with learnable weight
        return torch.sigmoid(self.gamma * (self.corr_weight * scores + (1 - self.corr_weight) * corr))


class DeepTraCDR(nn.Module):
    """
    Main Graph Convolutional Network model for predicting cell-drug interactions.
    Combines feature loading, graph encoding, and decoding.
    """
    def __init__(self, adj_mat, cell_exprs, drug_fingerprints, layer_size, gamma, device="cpu"):
        super().__init__()
        self.device = device

        # Convert adjacency matrix to tensor if necessary
        if isinstance(adj_mat, np.ndarray):
            adj_mat = torch.from_numpy(adj_mat).float()
        self.adj_mat = adj_mat.to(device)

        # Initialize components
        self.construct_adj = AdjacencyMatrixConstructor(self.adj_mat, device=device)
        self.load_feat = FeatureLoader(cell_exprs, drug_fingerprints, device=device)

        # Compute fixed adjacency matrices
        agg_cell, agg_drug, self_cell, self_drug = self.construct_adj()

        # Initialize encoder and decoder
        self.encoder = GraphEncoder(agg_cell, agg_drug, self_cell, self_drug, device=device).to(device)
        self.decoder = GraphDecoder(512, gamma).to(device)  # emb_dim fixed to 512

    def forward(self):
        """
        Performs a full forward pass through the DeepTraCDR model.
        Returns predicted interaction scores and final embeddings.
        """
        # Load and encode features
        cell_features, drug_features = self.load_feat()

        # Encode features using graph structure
        cell_emb, drug_emb = self.encoder(cell_features, drug_features)

        # Decode to predict interaction scores
        return self.decoder(cell_emb, drug_emb), cell_emb, drug_emb


class ModelOptimizer:
    """
    Handles training and evaluation of the DeepTraCDR model.
    Implements early stopping and tracks best performance metrics.
    """
    def __init__(self, model, train_data, test_data, test_mask, train_mask, adj_matrix, evaluate_fun, lr=0.001, wd=1e-05, epochs=200, test_freq=20, patience=100, device="gpu"):
        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_bool = test_mask.to(device).bool()
        self.device = device

        # Convert adjacency matrix to tensor if necessary
        if isinstance(adj_matrix, np.ndarray):
            adj_matrix = torch.from_numpy(adj_matrix).float()
        self.adj_matrix = adj_matrix.to(device)

        self.evaluate_fun = evaluate_fun
        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_auc = 0.0
        self.best_auprc = 0.0
        self.best_weights = None
        self.counter = 0  # Early stopping counter
        self.best_epoch_auc = None
        self.best_epoch_auprc = None

    def train(self):
        """
        Trains the model with early stopping and evaluates performance.
        Returns the best AUC and AUPRC achieved during training.
        """
        true_data = torch.masked_select(self.test_data, self.test_mask_bool).cpu().numpy()

        for epoch in range(self.epochs):
            self.model.train()
            # Forward pass and compute loss
            pred_train, cell_emb, drug_emb = self.model()
            ce_loss = cross_entropy_loss(self.train_data, pred_train, self.train_mask)
            proto_loss = prototypical_loss(cell_emb, drug_emb, self.adj_matrix)
            total_loss = 0.7 * ce_loss + 0.3 * proto_loss

            # Backward pass and optimization
            self.optimizer.zero_grad()
            total_loss.backward()
            self.optimizer.step()

            # Evaluate model
            self.model.eval()
            with torch.no_grad():
                # Compute metrics for training data
                train_pred, _, _ = self.model()
                train_pred_masked = torch.masked_select(train_pred, self.train_mask).cpu().numpy()
                train_true_data = torch.masked_select(self.train_data, self.train_mask).cpu().numpy()
                try:
                    train_auc = roc_auc_score(train_true_data, train_pred_masked)
                    train_auprc = average_precision_score(train_true_data, train_pred_masked)
                except ValueError:
                    train_auc, train_auprc = 0.0, 0.0

                # Compute metrics for test data
                pred_eval, _, _ = self.model()
                pred_masked = torch.masked_select(pred_eval, self.test_mask_bool).cpu().numpy()
                try:
                    auc = roc_auc_score(true_data, pred_masked)
                    auprc = average_precision_score(true_data, pred_masked)
                except ValueError:
                    auc, auprc = 0.0, 0.0

            # Update best metrics and weights
            if auc > self.best_auc:
                self.best_auc = auc
                self.best_auprc = auprc
                self.best_weights = self.model.state_dict().copy()
                self.counter = 0
                self.best_epoch_auc = auc
                self.best_epoch_auprc = auprc
            else:
                self.counter += 1

            # Log progress
            if epoch % self.test_freq == 0 or epoch == self.epochs - 1:
                print(f"Epoch {epoch}: Loss={total_loss.item():.4f}, Train AUC={train_auc:.4f}, Train AUPRC={train_auprc:.4f}, Test AUC={auc:.4f}, Test AUPRC={auprc:.4f}")

            # Check early stopping
            if self.counter >= self.patience:
                print(f"\nEarly stopping triggered at epoch {epoch}!")
                print(f"No improvement in AUC for {self.patience} consecutive epochs.")
                break

        # Load best weights
        if self.best_weights is not None:
            self.model.load_state_dict(self.best_weights)

        # Final evaluation
        self.model.eval()
        with torch.no_grad():
            final_pred, _, _ = self.model()
            final_pred_masked = torch.masked_select(final_pred, self.test_mask_bool).cpu().numpy()
            best_auc = roc_auc_score(true_data, final_pred_masked)
            best_auprc = average_precision_score(true_data, final_pred_masked)

        # Print final results
        print("\nBest Metrics After Training (on Test Data):")
        print(f"AUC: {self.best_auc:.4f}")
        print(f"AUPRC: {self.best_auprc:.4f}")

        return self.best_auc, self.best_auprc