1 changed files with 143 additions and 148 deletions
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Scenario2/Target/main_target.py
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| # main_random.py | |||||
| import argparse | |||||
| import numpy as np | |||||
| import pandas as pd | |||||
| import scipy.sparse as sp | |||||
| from sklearn.model_selection import KFold | |||||
| from sklearn.metrics import roc_auc_score, average_precision_score, precision_score, recall_score, f1_score | |||||
| from model import DeepTraCDR, Optimizer | |||||
| from utils import evaluate_auc, common_data_index | |||||
| from data_sampler import TargetSampler | |||||
| from data_loader import load_data | |||||
| import torch | |||||
| from torch.optim.lr_scheduler import OneCycleLR | |||||
| # Clear CUDA cache to optimize GPU memory usage | |||||
| torch.cuda.empty_cache() | |||||
| def main(): | |||||
| """ | |||||
| Main function to execute the DeepTraCDR model training and evaluation pipeline. | |||||
| Parses command-line arguments, loads data, performs k-fold cross-validation, | |||||
| and reports performance metrics. | |||||
| """ | |||||
| # Initialize argument parser for command-line arguments | |||||
| parser = argparse.ArgumentParser(description="DeepTraCDR Advanced Model Training") | |||||
| parser.add_argument('-device', type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", | |||||
| help="Device to run the model on (cuda:0 or cpu)") | |||||
| parser.add_argument('-data', type=str, default='ccle', help="Dataset to use (gdsc or ccle)") | |||||
| parser.add_argument('--wd', type=float, default=1e-4, help="Weight decay for optimizer") | |||||
| parser.add_argument('--layer_size', nargs='+', type=int, default=[512], | |||||
| help="List of layer sizes for the GCN model") | |||||
| parser.add_argument('--gamma', type=float, default=15, help="Gamma parameter for model") | |||||
| 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=0.0005, help="Learning rate for optimizer") | |||||
| args = parser.parse_args() | |||||
| # Load target drug data based on the specified dataset | |||||
| if args.data == "gdsc": | |||||
| target_drug_cids = np.array([5330286, 11338033, 24825971]) | |||||
| # Load cell-drug binary matrix for GDSC dataset | |||||
| cell_drug = pd.read_csv("/media/external_16TB_1/ali_kianfar/Data/GDSC/cell_drug_binary.csv", | |||||
| index_col=0, header=0) | |||||
| cell_drug.columns = cell_drug.columns.astype(np.int32) | |||||
| drug_cids = cell_drug.columns.values | |||||
| cell_target_drug = np.array(cell_drug.loc[:, target_drug_cids], dtype=np.float32) | |||||
| target_pos_num = sp.coo_matrix(cell_target_drug).data.shape[0] | |||||
| target_indexes = common_data_index(drug_cids, target_drug_cids) | |||||
| elif args.data == "ccle": | |||||
| target_drug_cids = np.array([5330286]) | |||||
| # Load cell-drug binary matrix for CCLE dataset | |||||
| cell_drug = pd.read_csv("/media/external_16TB_1/ali_kianfar/Data/CCLE/cell_drug_binary.csv", | |||||
| index_col=0, header=0) | |||||
| cell_drug.columns = cell_drug.columns.astype(np.int32) | |||||
| drug_cids = cell_drug.columns.values | |||||
| cell_target_drug = np.array(cell_drug.loc[:, target_drug_cids], dtype=np.float32) | |||||
| target_pos_num = sp.coo_matrix(cell_target_drug).data.shape[0] | |||||
| target_indexes = common_data_index(drug_cids, target_drug_cids) | |||||
| # Load dataset components including adjacency matrix, fingerprints, and expression data | |||||
| full_adj, drug_fingerprints, exprs, null_mask, pos_num, args = load_data(args) | |||||
| full_adj_np = full_adj.copy() | |||||
| # Log original adjacency matrix shape for debugging | |||||
| print(f"Original adj_mat shape: {full_adj.shape}") | |||||
| # Log shapes of loaded data for verification | |||||
| print("\n--- Data Shapes ---") | |||||
| print(f"Expression data shape: {exprs.shape}") | |||||
| print(f"Null mask shape: {null_mask.shape}") | |||||
| # Convert adjacency matrix to PyTorch tensor if it is a NumPy array | |||||
| if isinstance(full_adj, np.ndarray): | |||||
| full_adj = torch.from_numpy(full_adj).float().to(args.device) | |||||
| # Log converted adjacency matrix shape for verification | |||||
| print(f"Converted adj_mat shape: {full_adj.shape}") | |||||
| # Initialize k-fold cross-validation parameters | |||||
| k = 5 | |||||
| n_kfolds = 5 | |||||
| all_metrics = { | |||||
| 'auc': [], | |||||
| 'auprc': [], | |||||
| } | |||||
| # 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(target_pos_num))): | |||||
| # Initialize data sampler for train/test split | |||||
| sampler = TargetSampler( | |||||
| response_mat=full_adj_np, | |||||
| null_mask=null_mask, | |||||
| target_indexes=target_indexes, | |||||
| pos_train_index=train_idx, | |||||
| pos_test_index=test_idx | |||||
| ) | |||||
| # Initialize DeepTraCDR 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 for model training | |||||
| 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, | |||||
| evaluate_fun=evaluate_auc, | |||||
| lr=args.lr, | |||||
| wd=args.wd, | |||||
| epochs=args.epochs, | |||||
| test_freq=args.test_freq, | |||||
| device=args.device | |||||
| ) | |||||
| # Train model and retrieve evaluation metrics | |||||
| true, pred, best_auc, best_auprc = opt.train() | |||||
| # Store metrics for this fold | |||||
| all_metrics['auc'].append(best_auc) | |||||
| all_metrics['auprc'].append(best_auprc) | |||||
| # Log performance for the current fold | |||||
| print(f"Fold {n_kfold * k + fold + 1}: AUC={best_auc:.4f}, AUPRC={best_auprc:.4f}") | |||||
| # Calculate and log mean and standard deviation of metrics | |||||
| print(f"\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__": | |||||
| # Set precision for matrix multiplication to optimize performance | |||||
| torch.set_float32_matmul_precision('high') | |||||
| # main_target.py | |||||
| import argparse | |||||
| import numpy as np | |||||
| import pandas as pd | |||||
| import scipy.sparse as sp | |||||
| from sklearn.model_selection import KFold | |||||
| from sklearn.metrics import roc_auc_score, average_precision_score, precision_score, recall_score, f1_score | |||||
| from model import DeepTraCDR, ModelOptimizer | |||||
| from utils import evaluate_auc, common_data_index | |||||
| from data_sampler import TargetSampler | |||||
| from data_loader import load_data | |||||
| import torch | |||||
| from torch.optim.lr_scheduler import OneCycleLR | |||||
| # Clear CUDA cache to optimize GPU memory usage | |||||
| torch.cuda.empty_cache() | |||||
| def main(): | |||||
| """ | |||||
| Main function to execute the DeepTraCDR model training and evaluation pipeline. | |||||
| Parses command-line arguments, loads data, performs k-fold cross-validation, | |||||
| and reports performance metrics. | |||||
| """ | |||||
| # Initialize argument parser for command-line arguments | |||||
| parser = argparse.ArgumentParser(description="DeepTraCDR Advanced Model Training") | |||||
| parser.add_argument('-device', type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", | |||||
| help="Device to run the model on (cuda:0 or cpu)") | |||||
| parser.add_argument('-data', type=str, default='gdsc', help="Dataset to use (gdsc or ccle)") | |||||
| parser.add_argument('--wd', type=float, default=1e-4, help="Weight decay for optimizer") | |||||
| parser.add_argument('--layer_size', nargs='+', type=int, default=[512], | |||||
| help="List of layer sizes for the GCN model") | |||||
| parser.add_argument('--gamma', type=float, default=15, help="Gamma parameter for model") | |||||
| 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=0.0005, help="Learning rate for optimizer") | |||||
| args = parser.parse_args() | |||||
| # Load target drug data based on the specified dataset | |||||
| if args.data == "gdsc": | |||||
| target_drug_cids = np.array([5330286, 11338033, 24825971]) | |||||
| # Load cell-drug binary matrix for GDSC dataset | |||||
| cell_drug = pd.read_csv("/media/external_16TB_1/ali_kianfar/Data/GDSC/cell_drug_binary.csv", | |||||
| index_col=0, header=0) | |||||
| cell_drug.columns = cell_drug.columns.astype(np.int32) | |||||
| drug_cids = cell_drug.columns.values | |||||
| cell_target_drug = np.array(cell_drug.loc[:, target_drug_cids], dtype=np.float32) | |||||
| target_pos_num = sp.coo_matrix(cell_target_drug).data.shape[0] | |||||
| target_indexes = common_data_index(drug_cids, target_drug_cids) | |||||
| elif args.data == "ccle": | |||||
| target_drug_cids = np.array([5330286]) | |||||
| # Load cell-drug binary matrix for CCLE dataset | |||||
| cell_drug = pd.read_csv("/media/external_16TB_1/ali_kianfar/Data/CCLE/cell_drug_binary.csv", | |||||
| index_col=0, header=0) | |||||
| cell_drug.columns = cell_drug.columns.astype(np.int32) | |||||
| drug_cids = cell_drug.columns.values | |||||
| cell_target_drug = np.array(cell_drug.loc[:, target_drug_cids], dtype=np.float32) | |||||
| target_pos_num = sp.coo_matrix(cell_target_drug).data.shape[0] | |||||
| target_indexes = common_data_index(drug_cids, target_drug_cids) | |||||
| # Load dataset components including adjacency matrix, fingerprints, and expression data | |||||
| full_adj, drug_fingerprints, exprs, null_mask, pos_num, args = load_data(args) | |||||
| full_adj_np = full_adj.copy() | |||||
| # Log original adjacency matrix shape for debugging | |||||
| print(f"Original adj_mat shape: {full_adj.shape}") | |||||
| # Log shapes of loaded data for verification | |||||
| print("\n--- Data Shapes ---") | |||||
| print(f"Expression data shape: {exprs.shape}") | |||||
| print(f"Null mask shape: {null_mask.shape}") | |||||
| # Convert adjacency matrix to PyTorch tensor if it is a NumPy array | |||||
| if isinstance(full_adj, np.ndarray): | |||||
| full_adj = torch.from_numpy(full_adj).float().to(args.device) | |||||
| # Log converted adjacency matrix shape for verification | |||||
| print(f"Converted adj_mat shape: {full_adj.shape}") | |||||
| # Initialize k-fold cross-validation parameters | |||||
| k = 5 | |||||
| n_kfolds = 5 | |||||
| all_metrics = { | |||||
| 'auc': [], | |||||
| 'auprc': [], | |||||
| } | |||||
| # 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(target_pos_num))): | |||||
| # Initialize data sampler for train/test split | |||||
| sampler = TargetSampler(response_mat=full_adj_np, null_mask=null_mask, target_indexes=target_indexes, | |||||
| pos_train_index=train_idx, pos_test_index=test_idx) | |||||
| # Initialize DeepTraCDR model | |||||
| model = DeepTraCDR( | |||||
| adj_mat=full_adj, | |||||
| cell_exprs=exprs, | |||||
| drug_fingerprints=drug_fingerprints, | |||||
| layer_size=args.layer_size, | |||||
| gamma=args.gamma, | |||||
| device=args.device | |||||
| ) | |||||
| # Initialize optimizer for model training | |||||
| opt = ModelOptimizer( | |||||
| 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, | |||||
| evaluate_fun=evaluate_auc, | |||||
| lr=args.lr, | |||||
| wd=args.wd, | |||||
| epochs=args.epochs, | |||||
| test_freq=args.test_freq, | |||||
| device=args.device | |||||
| ) | |||||
| # Train model and retrieve evaluation metrics | |||||
| true, pred, best_auc, best_auprc = opt.train() | |||||
| # Store metrics for this fold | |||||
| all_metrics['auc'].append(best_auc) | |||||
| all_metrics['auprc'].append(best_auprc) | |||||
| # Log performance for the current fold | |||||
| print(f"Fold {n_kfold * k + fold + 1}: AUC={best_auc:.4f}, AUPRC={best_auprc:.4f}") | |||||
| # Calculate and log mean and standard deviation of metrics | |||||
| print(f"\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__": | |||||
| # Set precision for matrix multiplication to optimize performance | |||||
| torch.set_float32_matmul_precision('high') | |||||
| main() | main() |
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