Merge branch 'main' into Luis

GP_Original_checkpoint.py

@@ -43,8 +43,8 @@ def get_data_paths(data_format, is_linux=False, is_hpc=False):
         saving_base_path = "/gpfs/scratchfs1/hfp14002/lrm22005/Casseys_case/Project_1_analysis"
     else:
         # R:\ENGR_Chon\Dong\MATLAB_generate_results\NIH_PulseWatch
-        base_path = "R:\ENGR_Chon\Dong\MATLAB_generate_results\\NIH_PulseWatch"
-        labels_base_path = "R:\ENGR_Chon\\NIH_Pulsewatch_Database\Adjudication_UConn"
+        base_path = r"\\grove.ad.uconn.edu\\research\\ENGR_Chon\Dong\MATLAB_generate_results\\NIH_PulseWatch"
+        labels_base_path = r"\\grove.ad.uconn.edu\\research\\ENGR_Chon\\NIH_Pulsewatch_Database\Adjudication_UConn"
         saving_base_path = r"\\grove.ad.uconn.edu\research\ENGR_Chon\Luis\Research\Casseys_case"
     if data_format == 'csv':
         data_path = os.path.join(base_path, "TFS_csv")

main_darren_v1.py

@@ -1,282 +0,0 @@
-import os
-import torch
-import gpytorch
-from sklearn.metrics import precision_recall_fscore_support, roc_auc_score
-from sklearn.preprocessing import label_binarize
-from torch.utils.data import Dataset, DataLoader
-import numpy as np
-import random
-import time
-
-# Seeds
-torch.manual_seed(42)
-np.random.seed(42)
-random.seed(42)
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-num_latents = 6  # This should match the complexity of your data or the number of tasks
-num_tasks = 4    # This should match the number of output classes or tasks
-num_inducing_points = 50  # This is independent and should be sufficient for the input space
-
-class MultitaskGPModel(gpytorch.models.ApproximateGP):
-    def __init__(self):
-        # Let's use a different set of inducing points for each latent function
-        inducing_points = torch.rand(num_latents, num_inducing_points, 128 * 128)  # Assuming flattened 128x128 images
-
-        # We have to mark the CholeskyVariationalDistribution as batch
-        # so that we learn a variational distribution for each task
-        variational_distribution = gpytorch.variational.CholeskyVariationalDistribution(
-            inducing_points.size(-2), batch_shape=torch.Size([num_latents])
-        )
-
-        # We have to wrap the VariationalStrategy in a LMCVariationalStrategy
-        # so that the output will be a MultitaskMultivariateNormal rather than a batch output
-        variational_strategy = gpytorch.variational.LMCVariationalStrategy(
-            gpytorch.variational.VariationalStrategy(
-                self, inducing_points, variational_distribution, learn_inducing_locations=True
-            ),
-            num_tasks=num_tasks,
-            num_latents=num_latents,
-            latent_dim=-1
-        )
-
-        super().__init__(variational_strategy)
-
-        # The mean and covariance modules should be marked as batch
-        # so we learn a different set of hyperparameters
-        self.mean_module = gpytorch.means.ConstantMean(batch_shape=torch.Size([num_latents]))
-        self.covar_module = gpytorch.kernels.ScaleKernel(
-            gpytorch.kernels.RBFKernel(batch_shape=torch.Size([num_latents])),
-            batch_shape=torch.Size([num_latents])
-        )
-
-    def forward(self, x):
-        mean_x = self.mean_module(x)
-        covar_x = self.covar_module(x)
-        latent_pred = gpytorch.distributions.MultivariateNormal(mean_x, covar_x)
-        return latent_pred
-
-class CustomDataset(Dataset):
-    def __init__(self, data_path, labels_path, binary=False):
-        self.data_path = data_path
-        self.labels_path = labels_path
-        self.binary = binary
-        self.segment_names, self.labels = self.extract_segment_names_and_labels()
-
-    def __len__(self):
-        return len(self.segment_names)
-
-    def __getitem__(self, idx):
-        segment_name = self.segment_names[idx]
-        label = self.labels[segment_name]
-        data_tensor = torch.load(os.path.join(self.data_path, segment_name + '.pt'))
-        return {'data': data_tensor, 'label': label, 'segment_name': segment_name}
-
-    def extract_segment_names_and_labels(self):
-        segment_names = []
-        labels = {}
-
-        with open(self.labels_path, 'r') as file:
-            lines = file.readlines()
-            for line in lines[1:]:  # Skip the header line
-                segment_name, label = line.strip().split(',')
-                label = int(float(label))  # Convert the label to float first, then to int
-                if self.binary and label == 2:
-                    label = 0  # Convert PAC/PVC to non-AF (0) for binary classification
-                segment_names.append(segment_name)
-                labels[segment_name] = label
-
-        return segment_names, labels
-
-def load_data(data_path, labels_path, batch_size, binary=False):
-    dataset = CustomDataset(data_path, labels_path, binary)
-    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
-    return dataloader
-
-def train_gp_model(train_loader, val_loader, num_iterations=50, n_classes=4, patience=10,
-                   checkpoint_path='model_checkpoint.pt', data_checkpoint_path='data_checkpoint.pt',
-                   resume_training=False, plot_path='training_plot.png'):
-    model = MultitaskGPModel().to(device)
-    likelihood = gpytorch.likelihoods.SoftmaxLikelihood(num_features=4, num_classes=4).to(device)
-    optimizer = torch.optim.Adam(model.parameters(), lr=0.1)
-    mll = gpytorch.mlls.VariationalELBO(likelihood, model, num_data=len(train_loader.dataset))
-
-    start_epoch = 0
-    if resume_training and os.path.exists(checkpoint_path):
-        checkpoint = torch.load(checkpoint_path)
-        model.load_state_dict(checkpoint['model_state_dict'])
-        likelihood.load_state_dict(checkpoint['likelihood_state_dict'])
-        optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
-        start_epoch = checkpoint.get('epoch', 0)
-
-    best_val_loss = float('inf')
-    epochs_no_improve = 0
-
-    metrics = {
-        'precision': [],
-        'recall': [],
-        'f1_score': [],
-        'auc_roc': [],
-        'train_loss': []
-    }
-
-    for epoch in range(start_epoch, num_iterations):
-        model.train()
-        likelihood.train()
-        for train_batch in train_loader:
-            optimizer.zero_grad()
-            train_x = train_batch['data'].reshape(train_batch['data'].size(0), -1).to(device)
-            train_y = train_batch['label'].to(device)
-            output = model(train_x)
-            loss = -mll(output, train_y)
-            metrics['train_loss'].append(loss.item())
-            loss.backward()
-            optimizer.step()
-
-        # Stochastic validation
-        model.eval()
-        likelihood.eval()
-        val_loss = 0.0  # Initialize val_loss here
-        with torch.no_grad():
-            val_indices = torch.randperm(len(val_loader.dataset))[:int(0.1 * len(val_loader.dataset))]
-            val_labels = []
-            val_predictions = []
-            for idx in val_indices:
-                val_batch = val_loader.dataset[idx]
-                val_x = val_batch['data'].reshape(-1).unsqueeze(0).to(device)
-                val_y = torch.tensor([val_batch['label']], device=device)
-                val_output = model(val_x)
-                val_loss_batch = -mll(val_output, val_y).sum()
-                val_loss += val_loss_batch.item()
-                val_labels.append(val_y.item())
-                val_predictions.append(val_output.mean.argmax(dim=-1).item())
-
-            precision, recall, f1, _ = precision_recall_fscore_support(val_labels, val_predictions, average='macro')
-            auc_roc = roc_auc_score(label_binarize(val_labels, classes=range(n_classes)),
-                                    label_binarize(val_predictions, classes=range(n_classes)),
-                                    multi_class='ovr')
-
-            metrics['precision'].append(precision)
-            metrics['recall'].append(recall)
-            metrics['f1_score'].append(f1)
-            metrics['auc_roc'].append(auc_roc)
-            val_loss /= len(val_indices)
-
-        if val_loss < best_val_loss:
-            best_val_loss = val_loss
-            epochs_no_improve = 0
-            torch.save({'model_state_dict': model.state_dict(),
-                        'likelihood_state_dict': likelihood.state_dict(),
-                        'optimizer_state_dict': optimizer.state_dict(),
-                        'epoch': epoch}, checkpoint_path)
-        else:
-            epochs_no_improve += 1
-            if epochs_no_improve >= patience:
-                print(f"Early stopping triggered at epoch {epoch+1}")
-                break
-
-    if os.path.exists(checkpoint_path):
-        checkpoint = torch.load(checkpoint_path)
-        model.load_state_dict(checkpoint['model_state_dict'])
-        likelihood.load_state_dict(checkpoint['likelihood_state_dict'])
-        optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
-
-    return model, likelihood, metrics
-
-def evaluate_gp_model(test_loader, model, likelihood, n_classes=4):
-    model.eval()
-    likelihood.eval()
-    test_labels = []
-    test_predictions = []
-
-    with torch.no_grad():
-        for test_batch in test_loader:
-            test_x = test_batch['data'].reshape(test_batch['data'].size(0), -1).to(device)
-            test_y = test_batch['label'].to(device)
-            test_output = model(test_x)
-            test_labels.extend(test_y.tolist())
-            test_predictions.extend(test_output.mean.argmax(dim=-1).tolist())
-
-    precision, recall, f1, _ = precision_recall_fscore_support(test_labels, test_predictions, average='macro')
-    auc_roc = roc_auc_score(label_binarize(test_labels, classes=range(n_classes)),
-                            label_binarize(test_predictions, classes=range(n_classes)),
-                            multi_class='ovr')
-
-    metrics = {
-        'precision': precision,
-        'recall': recall,
-        'f1_score': f1,
-        'auc_roc': auc_roc
-    }
-
-    return metrics
-
-def main():
-    print("Step 1: Loading paths and parameters")
-    # Paths
-    base_path = r"\\grove.ad.uconn.edu\\research\\ENGR_Chon\Darren\\NIH_Pulsewatch"
-    smote_type = 'Cassey5k_SMOTE'
-    split = 'holdout_60_10_30'
-    data_path_train = os.path.join(base_path, "TFS_pt", smote_type, split, "train")
-    data_path_val = os.path.join(base_path, "TFS_pt", smote_type, split, "validate")
-    data_path_test = os.path.join(base_path, "TFS_pt", smote_type, split, "test")
-    labels_path_train = os.path.join(base_path, "TFS_pt", smote_type, split, "Cassey5k_SMOTE_train_names_labels.csv")
-    labels_path_val = os.path.join(base_path, "TFS_pt", smote_type, split, "Cassey5k_SMOTE_validate_names_labels.csv")
-    labels_path_test = os.path.join(base_path, "TFS_pt", smote_type, split, "Cassey5k_SMOTE_test_names_labels.csv")
-
-    # Parameters
-    binary = False
-    n_epochs = 100
-    if binary:
-        n_classes = 2
-    else:
-        n_classes = 3
-    patience = round(n_epochs / 10) if n_epochs > 50 else 5
-    resume_checkpoint_path = None
-    batch_size = 256
-
-    print("Step 2: Loading data")
-    # Data loading
-    train_loader = load_data(data_path_train, labels_path_train, batch_size, binary)
-    val_loader = load_data(data_path_val, labels_path_val, batch_size, binary)
-    test_loader = load_data(data_path_test, labels_path_test, batch_size, binary)
-
-    print("Step 3: Loading data checkpoints")
-    # Data loading with checkpointing
-    data_checkpoint_path = 'data_checkpoint.pt'
-    if os.path.exists(data_checkpoint_path):
-        train_loader.dataset.load_checkpoint(data_checkpoint_path)
-        val_loader.dataset.load_checkpoint(data_checkpoint_path)
-        test_loader.dataset.load_checkpoint(data_checkpoint_path)
-
-    print("Step 4: Training and validation")
-    # Training and validation with checkpointing and plotting
-    model_checkpoint_path = 'model_checkpoint.pt'
-    plot_path = 'training_plot.png'
-    start_time = time.time()
-    model, likelihood, metrics = train_gp_model(train_loader, val_loader, n_epochs,
-                                                n_classes, patience,
-                                                model_checkpoint_path, data_checkpoint_path,
-                                                resume_checkpoint_path is not None, plot_path)
-    end_time = time.time()
-    time_passed = end_time - start_time
-    print('\nTraining and validation took %.2f minutes' % (time_passed / 60))
-
-    print("Step 5: Evaluation")
-    # Evaluation
-    start_time = time.time()
-    test_metrics = evaluate_gp_model(test_loader, model, likelihood, n_classes)
-    end_time = time.time()
-    time_passed = end_time - start_time
-    print('\nTesting took %.2f seconds' % time_passed)
-
-    print("Step 6: Printing test metrics")
-    print('Test Metrics:')
-    print('Precision: %.4f' % test_metrics['precision'])
-    print('Recall: %.4f' % test_metrics['recall'])
-    print('F1 Score: %.4f' % test_metrics['f1_score'])
-    print('AUC-ROC: %.4f' % test_metrics['auc_roc'])
-
-if __name__ == '__main__':
-    main()