[{'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:contours = []\nlabels = []\nf = np.vectorize(math.atan)\ndatagen = ImageDataGenerator(\nfeaturewise_center=False,\nfeaturewise_std_normalization=False,\nrotation_range=10.,\nzoom_range=0.1,\nwidth_shift_range=0.1,\nheight_shift_range=0.1,\nfill_mode=\'constant\',\ncval=0.,\nhorizontal_flip=False)\nprint("\\r", "0%", end="")\nmax_iter = len(X)\neps = 1e-7\nfor counter, (mask, label) in enumerate(zip(X, Y)):\nprint("\\r", "%.2f" % (counter / max_iter * 100) + "%", end="")\nfor trans_counter, mask_aug in enumerate(datagen.flow(mask[None, None, :, :], batch_size=1)):\nif trans_counter > AUG_TRANSFORM_NUM:\nbreak\ncur_contours = find_contours(mask_aug[0, 0], 0.1)\nareas = []\nfor i, contour in enumerate(cur_contours):\narea = cv2.contourArea(contour[:, None, :].astype(int))\nareas.append(area)\nidx = np.argsort(areas)[:-3:-1]\ncontours_filt = [cur_contours[i] for i in range(len(cur_contours)) if i in idx]\nif contours_filt[0][:, 1].max() < contours_filt[1][:, 1].max():\ncontours_filt = contours_filt[::-1]\nfor aug in range(AUG_CONTOUR_NUM):\nrand_idx = np.random.choice(np.arange(contours_filt[0].shape[0]), 200, replace=False)\nrand_idx.sort()\narr_left = contours_filt[0][rand_idx]\narr_left = arr_left[1:, :] - arr_left[:-1, :]\narr_left = np.vstack([\nf(arr_left[:, 0] / (arr_left[:, 1] + eps)),\nnp.sqrt(np.square(arr_left[:, 0]) + np.square(arr_left[:, 1]))\n]).T\nrand_idx = np.random.choice(np.arange(contours_filt[1].shape[0]), 200, replace=False)\nrand_idx.sort()\narr_right = contours_filt[1][rand_idx]\narr_right = arr_right[1:, :] - arr_right[:-1, :]\narr_right = np.vstack([\nf(arr_right[:, 0] / (arr_right[:, 1] + eps)),\nnp.sqrt(np.square(arr_right[:, 0]) + np.square(arr_right[:, 1]))\n]).T\ncontours.append([arr_left, arr_right])\nlabels.append(label)'}, {'role': 'assistant', 'content': 'data_preprocessing'}]