Resolving minimal errors

BML_project/active_learning/ss_active_learning.py

@@ -81,6 +81,8 @@ def stochastic_compare_kmeans_gp_predictions(kmeans_model, gp_model, data_loader
         all_labels.append(labels.cpu().numpy())
         all_data.append((gp_predictions, kmeans_predictions))
         print(f"Processed batch size: {len(current_batch_labels)}, Cumulative original_labels size: {len(original_labels)}, Cumulative gp_predictions size: {len(gp_predictions)}")
+        if len(current_batch_labels) < expected_batch_size:
+            print(f"Last batch processed with size: {len(current_batch_labels)}")
     return all_data, np.concatenate(all_labels)
 
 import random

BML_project/ss_main.py

@@ -10,7 +10,7 @@
 from models.ss_gp_model import MultitaskGPModel, train_gp_model
 from utils_gp.ss_evaluation import stochastic_evaluation, evaluate_model_on_all_data
 from active_learning.ss_active_learning import stochastic_uncertainty_sampling, run_minibatch_kmeans, stochastic_compare_kmeans_gp_predictions
-from utils.visualization import plot_comparative_results, plot_training_performance, plot_results
+from utils_gp.visualization import plot_comparative_results, plot_training_performance, plot_results
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
@@ -50,6 +50,7 @@ def main():
 
         # Update the training loader with uncertain samples
         train_loader = update_train_loader_with_uncertain_samples(train_loader, uncertain_sample_indices, batch_size)
+        print(f"Updated training data size: {len(train_loader.dataset)}")
 
         # Re-train the model with the updated training data
         model, likelihood, val_metrics = train_gp_model(train_loader, val_loader, num_iterations=10, n_classes=n_classes, patience=10, checkpoint_path='model_checkpoint_last.pt')
@@ -71,7 +72,7 @@ def main():
 
     results['test_metrics'] = test_metrics
     test_gp_vs_kmeans_data, test_original_labels = stochastic_compare_kmeans_gp_predictions(test_kmeans_model, model, test_loader, n_batches=5, device=device)
-    # Before calling confusion_matrix in plot_comparative_results function
+
     print(f"Length of original_labels: {len(original_labels)}, Length of gp_predictions: {len(gp_predictions)}")
     plot_comparative_results(test_gp_vs_kmeans_data, test_original_labels)