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Scenario1/Regression/train_random.py
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| @@ -1,119 +1,119 @@ | |||
| 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() | |||
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