1 changed files with 118 additions and 118 deletions
+ 118
- 118
Scenario1/Regression/train_random.py
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| import argparse | |||||
| import numpy as np | |||||
| import torch | |||||
| from sklearn.model_selection import KFold | |||||
| from Regression.DeepTraCDR_model import DeepTraCDR, Optimizer | |||||
| from data_sampler import RegressionSampler | |||||
| from data_loader import load_data | |||||
| def parse_arguments() -> argparse.Namespace: | |||||
| """ | |||||
| Parses command-line arguments for the DeepTraCDR regression task. | |||||
| Returns: | |||||
| Parsed arguments as a Namespace object. | |||||
| """ | |||||
| parser = argparse.ArgumentParser(description="DeepTraCDR Regression Task") | |||||
| 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 (default: gdsc)") | |||||
| parser.add_argument('--wd', type=float, default=1e-5, help="Weight decay for optimizer") | |||||
| parser.add_argument('--layer_size', nargs='+', type=int, default=[512], help="Layer sizes for the model") | |||||
| parser.add_argument('--gamma', type=float, default=15, help="Gamma parameter for decoder") | |||||
| 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 optimizer") | |||||
| parser.add_argument('--patience', type=int, default=20, help="Patience for early stopping") | |||||
| return parser.parse_args() | |||||
| def normalize_adj_matrix(adj_matrix: np.ndarray) -> torch.Tensor: | |||||
| """ | |||||
| Normalizes the adjacency matrix using min-shift normalization and converts it to a torch tensor. | |||||
| Args: | |||||
| adj_matrix: Input adjacency matrix as a NumPy array. | |||||
| Returns: | |||||
| Normalized adjacency matrix as a torch tensor. | |||||
| """ | |||||
| adj_matrix = adj_matrix - np.min(adj_matrix) | |||||
| if isinstance(adj_matrix, np.ndarray): | |||||
| adj_matrix = torch.from_numpy(adj_matrix).float() | |||||
| return adj_matrix | |||||
| def main(): | |||||
| """ | |||||
| Main function to run the DeepTraCDR regression task with k-fold cross-validation. | |||||
| """ | |||||
| # Set precision for matrix multiplication | |||||
| torch.set_float32_matmul_precision('high') | |||||
| # Parse command-line arguments | |||||
| args = parse_arguments() | |||||
| # Load dataset | |||||
| full_adj, drug_fingerprints, exprs, null_mask, pos_num, args = load_data(args) | |||||
| print(f"Original full_adj shape: {full_adj.shape}") | |||||
| print(f"Normalized full_adj shape: {full_adj.shape}") | |||||
| print("\n--- Data Shapes ---") | |||||
| print(f"Expression data shape: {exprs.shape}") | |||||
| print(f"Null mask shape: {null_mask.shape}") | |||||
| # Normalize adjacency matrix | |||||
| full_adj = normalize_adj_matrix(full_adj) | |||||
| # Initialize k-fold cross-validation parameters | |||||
| k = 5 | |||||
| n_kfolds = 5 | |||||
| all_metrics = {'rmse': [], 'pcc': [], 'scc': []} | |||||
| # Perform k-fold cross-validation | |||||
| for n_kfold in range(n_kfolds): | |||||
| kfold = KFold(n_splits=k, shuffle=True, random_state=n_kfold) | |||||
| for fold, (train_idx, test_idx) in enumerate(kfold.split(np.arange(pos_num))): | |||||
| # Initialize data sampler | |||||
| sampler = RegressionSampler(full_adj, train_idx, test_idx, null_mask) | |||||
| # Initialize model | |||||
| model = DeepTraCDR( | |||||
| adj_mat=full_adj, | |||||
| cell_exprs=exprs, | |||||
| drug_finger=drug_fingerprints, | |||||
| layer_size=args.layer_size, | |||||
| gamma=args.gamma, | |||||
| device=args.device | |||||
| ) | |||||
| # Initialize optimizer | |||||
| opt = Optimizer( | |||||
| model=model, | |||||
| train_data=sampler.train_data, | |||||
| test_data=sampler.test_data, | |||||
| test_mask=sampler.test_mask, | |||||
| train_mask=sampler.train_mask, | |||||
| adj_matrix=full_adj, | |||||
| lr=args.lr, | |||||
| wd=args.wd, | |||||
| epochs=args.epochs, | |||||
| test_freq=args.test_freq, | |||||
| device=args.device, | |||||
| patience=args.patience | |||||
| ) | |||||
| # Train model and collect metrics | |||||
| true, pred, best_rmse, best_pcc, best_scc = opt.train() | |||||
| all_metrics['rmse'].append(best_rmse) | |||||
| all_metrics['pcc'].append(best_pcc) | |||||
| all_metrics['scc'].append(best_scc) | |||||
| print(f"Fold {n_kfold * k + fold + 1}: RMSE={best_rmse:.4f}, PCC={best_pcc:.4f}, SCC={best_scc:.4f}") | |||||
| # Compute and display final average metrics | |||||
| print("\nFinal Average Metrics:") | |||||
| for metric, values in all_metrics.items(): | |||||
| mean = np.mean(values) | |||||
| std = np.std(values) | |||||
| print(f"{metric.upper()}: {mean:.4f} ± {std:.4f}") | |||||
| if __name__ == "__main__": | |||||
| import argparse | |||||
| import numpy as np | |||||
| import torch | |||||
| from sklearn.model_selection import KFold | |||||
| from DeepTraCDR_model import DeepTraCDR, Optimizer | |||||
| from data_sampler import RegressionSampler | |||||
| from data_loader import load_data | |||||
| def parse_arguments() -> argparse.Namespace: | |||||
| """ | |||||
| Parses command-line arguments for the DeepTraCDR regression task. | |||||
| Returns: | |||||
| Parsed arguments as a Namespace object. | |||||
| """ | |||||
| parser = argparse.ArgumentParser(description="DeepTraCDR Regression Task") | |||||
| 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 (default: gdsc)") | |||||
| parser.add_argument('--wd', type=float, default=1e-5, help="Weight decay for optimizer") | |||||
| parser.add_argument('--layer_size', nargs='+', type=int, default=[512], help="Layer sizes for the model") | |||||
| parser.add_argument('--gamma', type=float, default=15, help="Gamma parameter for decoder") | |||||
| 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 optimizer") | |||||
| parser.add_argument('--patience', type=int, default=20, help="Patience for early stopping") | |||||
| return parser.parse_args() | |||||
| def normalize_adj_matrix(adj_matrix: np.ndarray) -> torch.Tensor: | |||||
| """ | |||||
| Normalizes the adjacency matrix using min-shift normalization and converts it to a torch tensor. | |||||
| Args: | |||||
| adj_matrix: Input adjacency matrix as a NumPy array. | |||||
| Returns: | |||||
| Normalized adjacency matrix as a torch tensor. | |||||
| """ | |||||
| adj_matrix = adj_matrix - np.min(adj_matrix) | |||||
| if isinstance(adj_matrix, np.ndarray): | |||||
| adj_matrix = torch.from_numpy(adj_matrix).float() | |||||
| return adj_matrix | |||||
| def main(): | |||||
| """ | |||||
| Main function to run the DeepTraCDR regression task with k-fold cross-validation. | |||||
| """ | |||||
| # Set precision for matrix multiplication | |||||
| torch.set_float32_matmul_precision('high') | |||||
| # Parse command-line arguments | |||||
| args = parse_arguments() | |||||
| # Load dataset | |||||
| full_adj, drug_fingerprints, exprs, null_mask, pos_num, args = load_data(args) | |||||
| print(f"Original full_adj shape: {full_adj.shape}") | |||||
| print(f"Normalized full_adj shape: {full_adj.shape}") | |||||
| print("\n--- Data Shapes ---") | |||||
| print(f"Expression data shape: {exprs.shape}") | |||||
| print(f"Null mask shape: {null_mask.shape}") | |||||
| # Normalize adjacency matrix | |||||
| full_adj = normalize_adj_matrix(full_adj) | |||||
| # Initialize k-fold cross-validation parameters | |||||
| k = 5 | |||||
| n_kfolds = 5 | |||||
| all_metrics = {'rmse': [], 'pcc': [], 'scc': []} | |||||
| # Perform k-fold cross-validation | |||||
| for n_kfold in range(n_kfolds): | |||||
| kfold = KFold(n_splits=k, shuffle=True, random_state=n_kfold) | |||||
| for fold, (train_idx, test_idx) in enumerate(kfold.split(np.arange(pos_num))): | |||||
| # Initialize data sampler | |||||
| sampler = RegressionSampler(full_adj, train_idx, test_idx, null_mask) | |||||
| # Initialize model | |||||
| model = DeepTraCDR( | |||||
| adj_mat=full_adj, | |||||
| cell_exprs=exprs, | |||||
| drug_finger=drug_fingerprints, | |||||
| layer_size=args.layer_size, | |||||
| gamma=args.gamma, | |||||
| device=args.device | |||||
| ) | |||||
| # Initialize optimizer | |||||
| opt = Optimizer( | |||||
| model=model, | |||||
| train_data=sampler.train_data, | |||||
| test_data=sampler.test_data, | |||||
| test_mask=sampler.test_mask, | |||||
| train_mask=sampler.train_mask, | |||||
| adj_matrix=full_adj, | |||||
| lr=args.lr, | |||||
| wd=args.wd, | |||||
| epochs=args.epochs, | |||||
| test_freq=args.test_freq, | |||||
| device=args.device, | |||||
| patience=args.patience | |||||
| ) | |||||
| # Train model and collect metrics | |||||
| true, pred, best_rmse, best_pcc, best_scc = opt.train() | |||||
| all_metrics['rmse'].append(best_rmse) | |||||
| all_metrics['pcc'].append(best_pcc) | |||||
| all_metrics['scc'].append(best_scc) | |||||
| print(f"Fold {n_kfold * k + fold + 1}: RMSE={best_rmse:.4f}, PCC={best_pcc:.4f}, SCC={best_scc:.4f}") | |||||
| # Compute and display final average metrics | |||||
| print("\nFinal Average Metrics:") | |||||
| for metric, values in all_metrics.items(): | |||||
| mean = np.mean(values) | |||||
| std = np.std(values) | |||||
| print(f"{metric.upper()}: {mean:.4f} ± {std:.4f}") | |||||
| if __name__ == "__main__": | |||||
| main() | main() |
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