[{'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:\'\'\'\nReference:  Udacity\n\'\'\'\ntiger = {}\ndef file_size(SMALL_SAMPLE_NAME):\n"""\nthis function will return the file size\nReference:\nhttp://stackoverflow.com/questions/2104080/how-to-check-file-size-in-python\n"""\nfile_info = os.stat(SMALL_SAMPLE_NAME)\nsize = convert_bytes(file_info.st_size)\nreturn size\ndef convert_bytes(num):\n"""\nthis function will convert bytes to MB.... GB... etc\nReference:\nhttp://stackoverflow.com/questions/2104080/how-to-check-file-size-in-python\n"""\nfor x in [\'bytes\', \'KB\', \'MB\', \'GB\', \'TB\']:\nif num < 1024.0:\nsize = "%3.1f %s" % (num, x)\nreturn size\nnum /= 1024.0\ndef tag_attributes(f):\nattribs = []\nfor ev, elem in xET.iterparse(f):\nif elem.tag == \'way\' or elem.tag == \'node\': # Specify parent tag to filter nested tag list\nfor t in elem.iter(\'tag\'):\nif t.attrib[\'k\'] not in attribs:\nif REGEX_LOWER.search(t.attrib[\'k\']): # Only add values for lowercase attributes\nattribs.append(t.attrib[\'k\'])\nreturn attribs\ndef shape_element(element):\nnode = {\n"id": None,\n"type": None,\n\'name\': None,\n\'amenity\': None,\n\'building\' : None,\n\'shop\' : None,\n\'cuisine\' : None,\n\'phone\' : None,\n"created": {\n"changeset": None,\n"user": None,\n"version": None,\n"uid": None,\n"timestamp": None\n},\n"pos": [None, None],\n"refs": [None],\n"address": {\n"housenumber": None,\n"postcode": None,\n"street": None,\n"state": None,\n"city": None,\n},\n"tiger": {\n\'country\': None,\n\'name_base\': None,\n\'name_base_1\': None,\n\'name_base_2\': None,\n\'name_base_3\': None,\n\'name_direction_prefix\': None,\n\'name_direction_prefix_1\': None,\n\'name_direction_prefix_2\': None,\n\'name_direction_prefix_3\': None,\n\'name_direction_suffix\': None,\n\'name_direction_suffix_1\': None,\n\'name_direction_suffix_2\': None,\n\'name_direction_suffix_3\': None,\n\'name_type\': None,\n\'name_type_1\': None,\n\'name_type_2\': None,\n\'name_type_3\': None,\n\'zip_left\': None,\n\'zip_right\': None,\n}\n}\nrefs = []\nif element.tag == "node" or element.tag == "way":\nnode[\'id\'] = element.attrib[\'id\'] # Get node ID\nnode[\'type\'] = element.tag        # Get node type (node or way)\nif \'lat\' in element.attrib:       # Get node position (lat/lon)\nnode[\'pos\'] = [ast.literal_eval(element.attrib[\'lat\']),\nast.literal_eval(element.attrib[\'lon\'])]\nfor m in METADATA:                # Get \'created\' metadata\nif m in element.attrib:\nnode[\'created\'][m] = element.attrib[m]\nfor nd in element.iter(\'nd\'):     # Iterate through nodes references\nrefs.append(nd.attrib[\'ref\'])\nif refs != []:\nnode[\'refs\'] = refs\nelse:\nnode[\'refs\'] = None\nfor a in element.iter(\'tag\'):             # Check each child tag for attributes\nk = a.attrib[\'k\']\nif not(REGEX_PROBLEMCHAR.search(k)):  # Filter out tags with invalid characters\nif k.startswith(\'addr:\'):\nel = k.split(\':\')\nif el[1] == \'street\':\nclean_street = update_name(a.attrib[\'v\'], mapping) #Clean Street Types\nnode[\'address\'][el[1]] = clean_street\nelif el[1] == \'postcode\':\ncleanzip = update_zip(a.attrib[\'v\'])\nnode[\'address\'][el[1]] = cleanzip\nelse:\nnode[\'address\'][el[1]] = a.attrib[\'v\'] # Add other address parts\nelif k.startswith(\'tiger:\'):      # Get TIGER address info\nel = k.split(\':\')\nif el[1] == \'name_type\':\nclean_type = update_name(a.attrib[\'v\'], mapping) #Clean Street Types\nnode[\'tiger\'][el[1]] = clean_type\nelif el[1] in node[\'tiger\'].keys():\nnode[\'tiger\'][el[1]] = a.attrib[\'v\'] # Add other tiger parts\nelse:\nif k in node.keys():\nnode[k] = a.attrib[\'v\'] # Add key/value for all other attributes found\nreturn node\nelse:\nreturn None\ndef update_zip(zipcode):\nif zipcode == "Olympia, 98501":\nzipcode = "98501"\nelif zipcode.startswith(\'V\') or zipcode.startswith(\'v\'):\nz1 = zipcode[:3]\nz2 = zipcode[-3:]\nzipcode = z1 + \' \' + z2\nzipcode = zipcode.upper()\nreturn zipcode\ndef update_name(name, mapping):\nn = street_type_re.search(name)\nn = n.group()\nfor m in mapping:\nif n == m:\nname = name[:-len(n)] + mapping[m]\nreturn name\ndef process_map(file_in, pretty = False):\njson_file = file_in + ".json"\ndata = []\nwith codecs.open(json_file, "w") as fo:\nfor _, element in xET.iterparse(file_in + \'.osm\'):\nel = shape_element(element)\nif el:\ndata.append(el)\nif pretty:\nfo.write(json.dumps(el, indent=2)+"\\n")\nelse:\nfo.write(json.dumps(el) + "\\n")\nreturn data\ndef shape_data():\nfile_name = SMALL_SAMPLE_NAME.split(\'.\')\njson_file = file_name[0] + ".json"\ndata = process_map(file_name[0], False)\nprint(json_file, \'created:\')\nprint(\'File size: \', file_size(json_file))\nprint(\'\\n\\n\\n\')\nprint(\'Sample Shape:\')\npprint.pprint(data[1])\npprint.pprint(data[-1])\nshape_data()'}, {'role': 'assistant', 'content': "['data_exploration', 'data_preprocessing']"}]