[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': "Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:import numpy as np\n\ndef transform_to_submit(y_pred_, referred_expression):\n    \n    y_pred_comp = 1 - y_pred_\n    all_zero = np.zeros_like(y_pred_).reshape((-1, 1))\n\n    y_pred = np.hstack((\n                np.where(referred_expression == 'A', y_pred_, y_pred_comp).reshape((-1, 1)),\n                np.where(referred_expression == 'B', y_pred_comp, y_pred_).reshape((-1, 1)),\n                all_zero\n    ))\n    \n    return y_pred\n\ny_true = np.hstack((\n            np.where(((X_test_referred_expression == 'A') & (y_test)), 1, 0).reshape((-1, 1)),\n            np.where(((X_test_referred_expression == 'B') & (y_test)), 1, 0).reshape((-1, 1)),\n            np.zeros_like(y_test).reshape((-1, 1))\n))\n\n\n# TODO: refact\n# one of the ideas is to run the model over all the referred expressions and then calculate the final answer\n#y_pred_A = lr.predict(df_A).reshape(-1, 1)\n#y_pred_B = lr.predict(df_B).reshape(-1, 1)\n#all_zero = np.zeros_like(y_pred_A)\n\n#X_test_A = add_features(X_test, 'A', inplace=False)[['URL_distance_token_sort_ratio', 'URL_distance_levenshtein', 'URL_distance_jaro_winkler', 'Text-length']]\n#X_test_B = add_features(X_test, 'B', inplace=False)[['URL_distance_token_sort_ratio', 'URL_distance_levenshtein', 'URL_distance_jaro_winkler', 'Text-length']]\n\n#y_pred = np.hstack((y_pred_A,\n#                    y_pred_B,\n#                    all_zero\n#                   ))\n#y_true[(np.abs(y_true[:, 0] - y_true[:, 1]) < 0.1) & (y_true[:, 0] < .3), 2] = .5\n    \nlog_loss(y_true, transform_to_submit(y_pred_, X_test_referred_expression))"}, {'role': 'assistant', 'content': 'save_results'}]