[{'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:MAX_WEIGHT = 50.0\n\ntoys = {\n    "horse":  { "sample": lambda: max(0, np.random.normal(5,2,1)[0]), "sample_type": "normal(5,2)" },\n    "ball":   { "sample": lambda: max(0, 1 + np.random.normal(1,0.3,1)[0]), "sample_type": "normal(1,0.3)" },\n    "bike":   { "sample": lambda: max(0, np.random.normal(20,10,1)[0]), "sample_type": "normal(20,10)" },\n    "train":  { "sample": lambda: max(0, np.random.normal(10,5,1)[0]), "sample_type": "normal(10,5)" },\n    "coal":   { "sample": lambda: 47 * np.random.beta(0.5,0.5,1)[0], "sample_type": "47*beta(0.5,0.5)" },\n    "book":   { "sample": lambda: np.random.chisquare(2,1)[0], "sample_type": "chi(2)" },\n    "doll":   { "sample": lambda: np.random.gamma(5,1,1)[0], "sample_type": "gamma(5,1)" },\n    "block":  { "sample": lambda: np.random.triangular(5,10,20,1)[0], "sample_type": "triagl(5,10,20)" },\n    "gloves": { "sample": lambda: 3.0 + np.random.rand(1)[0] if np.random.rand(1) < 0.3 else np.random.rand(1)[0], "sample_type": "0.3:3+rand(1), 0.7:rand(1)" },\n}\ntoy_names = list(toys)\n\ngifts_df = pd.read_csv("../input/gifts.csv", sep=",")\ngifts = gifts_df["GiftId"].values\nprint("{} gifts".format(len(gifts)))\n\nfor t in toys:\n    # get ranges\n    samples = [toys[t]["sample"]() for _ in range(1000)]\n    toys[t]["max"] = max(samples)\n    toys[t]["min"] = min(samples)\n    \n    # get gift counts\n    ids = [g for g in gifts if t in g.split("_")[0]]\n    toys[t]["ids"] = ids\n    toys[t]["count"] = len(ids)\n    \n    # print toy type stats\n    print("{:4}\\\\tdist: {:26}\\\\trange:{:5.2f} - {:5.2f}\\\\tcount:{:6,}".format(t, toys[t]["sample_type"], toys[t]["min"], toys[t]["max"], toys[t]["count"]))\''}, {'role': 'assistant', 'content': 'data_preprocessing'}]