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Scenario2/new/main_new.py
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| @@ -1,111 +1,111 @@ | |||
| import argparse | |||
| import torch | |||
| import numpy as np | |||
| from sklearn.metrics import roc_auc_score | |||
| from data_loader import load_data | |||
| from model import DeepTraCDR | |||
| def parse_arguments() -> argparse.Namespace: | |||
| """ | |||
| Parses command-line arguments for running the DeepTraCDR model. | |||
| Returns: | |||
| argparse.Namespace: Parsed arguments containing configuration parameters. | |||
| """ | |||
| parser = argparse.ArgumentParser(description="Run DeepTraCDR model for cell-drug interaction prediction.") | |||
| parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", | |||
| help="Device to run the model on (e.g., 'cuda:0' or 'cpu').") | |||
| parser.add_argument("--data", type=str, default="gdsc", | |||
| help="Dataset to use (e.g., 'gdsc').") | |||
| parser.add_argument("--wd", type=float, default=1e-5, | |||
| help="Weight decay for the optimizer.") | |||
| parser.add_argument("--layer_size", nargs="+", type=int, default=[512], | |||
| help="List of layer sizes for the model.") | |||
| parser.add_argument("--gamma", type=float, default=15.0, | |||
| help="Gamma parameter for decoder scaling.") | |||
| parser.add_argument("--epochs", type=int, default=1000, | |||
| help="Number of training epochs.") | |||
| parser.add_argument("--test_freq", type=int, default=50, | |||
| help="Frequency of evaluation during training.") | |||
| parser.add_argument("--lr", type=float, default=0.0001, | |||
| help="Learning rate for the optimizer.") | |||
| return parser.parse_args() | |||
| def main(): | |||
| """ | |||
| Main function to run the DeepTraCDR model, including data loading, model training, | |||
| and evaluation across specified targets. | |||
| """ | |||
| # Parse command-line arguments | |||
| args = parse_arguments() | |||
| # Load dataset | |||
| adj_matrix, drug_fingerprints, cell_expressions, null_mask, pos_num, args = load_data(args) | |||
| # Compute interaction sums for filtering | |||
| cell_interaction_sums = np.sum(adj_matrix, axis=1) # Sum of interactions per cell line | |||
| drug_interaction_sums = np.sum(adj_matrix, axis=0) # Sum of interactions per drug | |||
| # Log dataset and configuration details | |||
| print(f"\n{'='*40}") | |||
| print(f"Dataset: {args.data} | Adjacency Matrix Shape: {adj_matrix.shape}") | |||
| print(f"Device: {args.device} | Layer Sizes: {args.layer_size} | Learning Rate: {args.lr}") | |||
| print(f"{'='*40}\n") | |||
| # Define target dimension (0 for cell lines) | |||
| target_dimensions = [0] # Only process cell lines | |||
| adj_matrix = torch.from_numpy(adj_matrix).float() if isinstance(adj_matrix, np.ndarray) else adj_matrix | |||
| num_folds = 1 # Number of cross-validation folds | |||
| total_targets_processed = 0 | |||
| auc_scores = [] | |||
| auprc_scores = [] | |||
| # Process each target dimension | |||
| for dim in target_dimensions: | |||
| dim_name = "Cell Lines" if dim == 0 else "Drugs" | |||
| # Filter valid targets with at least 10 interactions | |||
| valid_indices = [ | |||
| idx for idx in range(adj_matrix.shape[dim]) | |||
| if (drug_interaction_sums[idx] >= 10 if dim == 1 else cell_interaction_sums[idx] >= 10) | |||
| ] | |||
| print(f"Processing {dim_name} ({len(valid_indices)} valid targets):") | |||
| # Process each valid target | |||
| for target_index in valid_indices: | |||
| total_targets_processed += 1 | |||
| print(f" Target {target_index} - ", end="", flush=True) | |||
| # Run model for each fold | |||
| for fold in range(num_folds): | |||
| best_auc, best_auprc = DeepTraCDR( | |||
| cell_exprs=cell_expressions, | |||
| drug_finger=drug_fingerprints, | |||
| res_mat=adj_matrix, | |||
| null_mask=null_mask, | |||
| target_dim=dim, | |||
| target_index=target_index, | |||
| evaluate_fun=roc_auc_score, | |||
| args=args | |||
| ) | |||
| auc_scores.append(best_auc) | |||
| auprc_scores.append(best_auprc) | |||
| print(f"AUC: {best_auc:.4f}, AUPRC: {best_auprc:.4f}") | |||
| # Compute and display average metrics | |||
| mean_auc = np.mean(auc_scores) | |||
| std_auc = np.std(auc_scores) | |||
| mean_auprc = np.mean(auprc_scores) | |||
| std_auprc = np.std(auprc_scores) | |||
| print(f"\n{'='*40}") | |||
| print(f"Average AUC: {mean_auc:.4f} ± {std_auc:.4f}") | |||
| print(f"Average AUPRC: {mean_auprc:.4f} ± {std_auprc:.4f}") | |||
| print(f"{'='*40}\n") | |||
| if __name__ == "__main__": | |||
| import argparse | |||
| import torch | |||
| import numpy as np | |||
| from sklearn.metrics import roc_auc_score | |||
| from data_loader import load_data | |||
| from new_model import run_DeepTraCDR | |||
| def parse_arguments() -> argparse.Namespace: | |||
| """ | |||
| Parses command-line arguments for running the DeepTraCDR model. | |||
| Returns: | |||
| argparse.Namespace: Parsed arguments containing configuration parameters. | |||
| """ | |||
| parser = argparse.ArgumentParser(description="Run DeepTraCDR model for cell-drug interaction prediction.") | |||
| parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", | |||
| help="Device to run the model on (e.g., 'cuda:0' or 'cpu').") | |||
| parser.add_argument("--data", type=str, default="gdsc", | |||
| help="Dataset to use (e.g., 'gdsc').") | |||
| parser.add_argument("--wd", type=float, default=1e-5, | |||
| help="Weight decay for the optimizer.") | |||
| parser.add_argument("--layer_size", nargs="+", type=int, default=[512], | |||
| help="List of layer sizes for the model.") | |||
| parser.add_argument("--gamma", type=float, default=15.0, | |||
| help="Gamma parameter for decoder scaling.") | |||
| parser.add_argument("--epochs", type=int, default=1000, | |||
| help="Number of training epochs.") | |||
| parser.add_argument("--test_freq", type=int, default=50, | |||
| help="Frequency of evaluation during training.") | |||
| parser.add_argument("--lr", type=float, default=0.0001, | |||
| help="Learning rate for the optimizer.") | |||
| return parser.parse_args() | |||
| def main(): | |||
| """ | |||
| Main function to run the DeepTraCDR model, including data loading, model training, | |||
| and evaluation across specified targets. | |||
| """ | |||
| # Parse command-line arguments | |||
| args = parse_arguments() | |||
| # Load dataset | |||
| adj_matrix, drug_fingerprints, cell_expressions, null_mask, pos_num, args = load_data(args) | |||
| print(null_mask) | |||
| # Compute interaction sums for filtering | |||
| cell_interaction_sums = np.sum(adj_matrix, axis=1) # Sum of interactions per cell line | |||
| drug_interaction_sums = np.sum(adj_matrix, axis=0) # Sum of interactions per drug | |||
| # Log dataset and configuration details | |||
| print(f"\n{'='*40}") | |||
| print(f"Dataset: {args.data} | Adjacency Matrix Shape: {adj_matrix.shape}") | |||
| print(f"Device: {args.device} | Layer Sizes: {args.layer_size} | Learning Rate: {args.lr}") | |||
| print(f"{'='*40}\n") | |||
| # Define target dimension (0 for cell lines) | |||
| target_dimensions = [0] # Only process cell lines | |||
| adj_matrix = torch.from_numpy(adj_matrix).float() if isinstance(adj_matrix, np.ndarray) else adj_matrix | |||
| num_folds = 1 # Number of cross-validation folds | |||
| total_targets_processed = 0 | |||
| auc_scores = [] | |||
| auprc_scores = [] | |||
| # Process each target dimension | |||
| for dim in target_dimensions: | |||
| dim_name = "Cell Lines" if dim == 0 else "Drugs" | |||
| # Filter valid targets with at least 10 interactions | |||
| valid_indices = [ | |||
| idx for idx in range(adj_matrix.shape[dim]) | |||
| if (drug_interaction_sums[idx] >= 10 if dim == 1 else cell_interaction_sums[idx] >= 10) | |||
| ] | |||
| print(f"Processing {dim_name} ({len(valid_indices)} valid targets):") | |||
| # Process each valid target | |||
| for target_index in valid_indices: | |||
| total_targets_processed += 1 | |||
| print(f" Target {target_index} - ", end="", flush=True) | |||
| # Run model for each fold | |||
| for fold in range(num_folds): | |||
| best_auc, best_auprc = run_DeepTraCDR( | |||
| cell_exprs=cell_expressions, | |||
| drug_finger=drug_fingerprints, | |||
| res_mat=adj_matrix, | |||
| null_mask=null_mask, | |||
| target_dim=dim, | |||
| target_index=target_index, | |||
| evaluate_fun=roc_auc_score, | |||
| args=args | |||
| ) | |||
| auc_scores.append(best_auc) | |||
| auprc_scores.append(best_auprc) | |||
| print(f"AUC: {best_auc:.4f}, AUPRC: {best_auprc:.4f}") | |||
| # Compute and display average metrics | |||
| mean_auc = np.mean(auc_scores) | |||
| std_auc = np.std(auc_scores) | |||
| mean_auprc = np.mean(auprc_scores) | |||
| std_auprc = np.std(auprc_scores) | |||
| print(f"\n{'='*40}") | |||
| print(f"Average AUC: {mean_auc:.4f} ± {std_auc:.4f}") | |||
| print(f"Average AUPRC: {mean_auprc:.4f} ± {std_auprc:.4f}") | |||
| print(f"{'='*40}\n") | |||
| if __name__ == "__main__": | |||
| main() | |||
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