[{'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:def create_graph(data):\nfor i, old_i in enumerate(data[\'author_id\'].unique()):\ndata.loc[:, \'author_id\'][data[\'author_id\'] == old_i] = i\nN = data[\'author_id\'].max() + 1\nadj_mat = np.zeros((N, N))\nfor (i1, a1), (i2, a2) in itertools.combinations(data.iterrows(), 2):\nif a1[\'article_id\'] == a2[\'article_id\']:\nadj_mat[a1[\'author_id\'], a2[\'author_id\']] += 1\nadj_mat[a2[\'author_id\'], a1[\'author_id\']] += 1\ng = nx.DiGraph()\ng = nx.from_numpy_matrix(adj_mat, create_using=g)\nfor id_node in g.nodes_iter():\nnode = g.node[id_node]\nnode[\'full_name\'] = data[data[\'author_id\'] == id_node].iloc[0]["full_name"]\nnode[\'n_article\'] = str(len(data[data[\'author_id\'] == id_node]))\nfor n1, n2 in g.edges_iter():\nedge = g[n1][n2]\nedge[\'common_articles\'] = str(adj_mat[n1, n2])\noutdeg = g.out_degree()\nto_remove = [k for k, v in outdeg.items() if v == 0]\ng.remove_nodes_from(to_remove)\nreturn adj_mat, g\ndef draw(g, font_size=12):\nnode_attribute_to_draw = \'full_name\'\nlabels = dict((n, d[node_attribute_to_draw]) for n, d in g.nodes(data=True))\ndraw_kwargs = dict(node_shape=\'o\', node_color=\'red\',\nalpha=1, font_size=12, linewidths=0,\nwidth=0.1,)\nfig, ax = plt.subplots(figsize=(15, 15))\nnx.draw_graphviz(g, with_labels=True, labels=labels, node_size=10, ax=ax, **draw_kwargs)\nreturn fig\ndef print_info(data):\nprint("You have {} differents authors in {} different articles".format(len(data.groupby(\'author_id\')),\nlen(data.groupby(\'article_id\'))))'}, {'role': 'assistant', 'content': "['data_exploration', 'data_preprocessing']"}]