[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:from sklearn.metrics import pairwise\ny=labels\nidx_sorted = np.argsort(y,kind="quicksort")  # need to get the ordering of Y\ndata_sorted = data[idx_sorted]  # sort the dataset by class\n%time R = pairwise.euclidean_distances(data_sorted)  # calculate the similarity\nmin_r = np.min(R)\nmax_r = np.max(R)\nR = 1-(R-min_r)/(max_r-min_r)\nplt.pcolormesh(R)\nplt.colorbar()\nbounds = np.cumsum([np.sum(y==val) for val in np.unique(y)])\nfor b in bounds:\nplt.plot([b,b],[0, len(y)],\'k\',linewidth=4)\nplt.plot([0, len(y)],[b,b],\'k\',linewidth=4)\nplt.show()'}, {'role': 'assistant', 'content': "['helper_functions', 'data_exploration']"}]