1 changed files with 110 additions and 110 deletions
+ 110
- 110
Scenario2/new/main_new.py
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| 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() | main() |
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