for i in range(n_queries):
 if i == 12:
 n_instances = X_pool.shape[0]
 else:
 n_instances = batch(int(np.ceil(np.power(10, POWER))), BATCH_SIZE)
 print(f"\nQuery {i + 1}: Requesting {n_instances} samples from a pool of size {X_pool.shape[0]}")
 if X_pool.shape[0] < n_instances:
 print("Not enough samples left in the pool to query the desired number of instances.")
 break
 query_idx, _ = learner.query(X_pool, n_instances=n_instances)
 query_idx = np.unique(query_idx)
 if len(query_idx) == 0:
 print("No indices were selected, which may indicate an issue with the query function or pool.")
 continue
 # Add the newly selected samples to the cumulative training set
 cumulative_X_train.append(X_pool[query_idx])
 cumulative_y_train.append(y_pool[query_idx])
 # Concatenate all the samples to form the cumulative training data
 X_train_cumulative = np.concatenate(cumulative_X_train, axis=0)
 y_train_cumulative = np.concatenate(cumulative_y_train, axis=0)
 learner.teach(X_train_cumulative, y_train_cumulative)
 # Log the selected sample names
 selected_sample_names = train_df.loc[query_idx, "image"].tolist()
 print(f"Selected samples in Query {i + 1}: {selected_sample_names}")
 with open(samples_log_file, mode='a', newline='') as f:
 writer = csv.writer(f)
 writer.writerow([i + 1] + selected_sample_names)
 # Remove the selected samples from the pool
 X_pool = np.delete(X_pool, query_idx, axis=0)
 y_pool = np.delete(y_pool, query_idx, axis=0)
 # Evaluate the model
 y_pred = learner.predict(X_test_np)
 accuracy = accuracy_score(y_test_np, y_pred)
 f1 = f1_score(y_test_np, y_pred, average='macro')
 acc_test_data.append(accuracy)
 f1_test_data.append(f1)
 print(f"Accuracy after query {i + 1}: {accuracy}")
 print(f"F1 Score after query {i + 1}: {f1}")
 # Early stopping logic
 if f1 > best_f1_score:
 best_f1_score = f1
 wait = 0
 else:
 wait += 1
 if wait >= patience:
 print(f"Stopping early after {i + 1} queries due to no improvement in F1 score.")
 break
 total_samples += len(query_idx)
 print(f"Total samples used for training after query {i + 1}: {total_samples}")
 POWER += 0.25
 torch.cuda.empty_cache()