如何在不使用外部库的情况下解析arff文件

Question

我需要解析如下的arff文件而不使用任何外部库。我不知道如何将属性与数值相关联。就像我怎么能说，每行的第一个数值是年龄，而第二个数字是性别？你能否也将我链接到一些用于解析类似场景的Python代码？

@relation cleveland-14-heart-disease 
@attribute 'age' real 
@attribute 'sex' { female, male} 
@attribute 'cp' { typ_angina, asympt, non_anginal, atyp_angina} 
@attribute 'trestbps' real 
@attribute 'chol' real 
@attribute 'fbs' { t, f} 
@attribute 'restecg' { left_vent_hyper, normal, st_t_wave_abnormality} 
@attribute 'thalach' real 
@attribute 'exang' { no, yes} 
@attribute 'oldpeak' real 
@attribute 'slope' { up, flat, down} 
@attribute 'ca' real 
@attribute 'thal' { fixed_defect, normal, reversable_defect} 
@attribute 'class' { negative, positive} 
@data 
63,male,typ_angina,145,233,t,left_vent_hyper,150,no,2.3,down,0,fixed_defect,negative 
37,male,non_anginal,130,250,f,normal,187,no,3.5,down,0,normal,negative 
41,female,atyp_angina,130,204,f,left_vent_hyper,172,no,1.4,up,0,normal,negative 
56,male,atyp_angina,120,236,f,normal,178,no,0.8,up,0,normal,negative 
57,female,asympt,120,354,f,normal,163,yes,0.6,up,0,normal,negative 
57,male,asympt,140,192,f,normal,148,no,0.4,flat,0,fixed_defect,negative 
56,female,atyp_angina,140,294,f,left_vent_hyper,153,no,1.3,flat,0,normal,negative 
44,male,atyp_angina,120,263,f,normal,173,no,0,up,0,reversable_defect,negative 
52,male,non_anginal,172,199,t,normal,162,no,0.5,up,0,reversable_defect,negative 

这里是我写的一个示例代码：

arr=[] 
arff_file = open("heart_train.arff") 
count=0 
for line in arff_file: 
     count+=1 
     #line=line.strip("\n") 
     #line=line.split(',') 
     if not (line.startswith("@")): 
       if not (line.startswith("%")): 
         line=line.strip("\n") 
         line=line.split(',') 
         arr.append(line) 



print(arr[1:30]) 

但是输出是非常不同的，比我期望它是：

[['37', 'male', 'non_anginal', '130', '250', 'f', 'normal', '187', 'no', '3.5', 'down', '0', 'normal', 'negative'], ['41', 'female', 'atyp_angina', '130', '204', 'f', 'left_vent_hyper', '172', 'no', '1.4', 'up', '0', 'normal', 'negative'], ['56', 'male', 'atyp_angina', '120', '236', 'f', 'normal', '178', 'no', '0.8', 'up', '0', 'normal', 'negative'], ['57', 'female', 'asympt', '120', '354', 'f', 'normal', '163', 'yes', '0.6', 'up', '0', 'normal', 'negative'], ['57', 'male', 'asympt', '140', '192', 'f', 'normal', '148', 'no', '0.4', 'flat', '0', 'fixed_defect', 'negative'], ['56', 'female', 'atyp_angina', '140', '294', 'f', 'left_vent_hyper', '153', 'no', '1.3', 'flat', '0', 'normal', 'negative'], ['44', 'male', 'atyp_angina', '120', '263', 'f', 'normal', '173', 'no', '0', 'up', '0', 'reversable_defect', 'negative'], ['52', 'male', 'non_anginal', '172', '199', 't', 'normal', '162', 'no', '0.5', 'up', '0', 'reversable_defect', 'negative'], ['57', 'male', 'non_anginal', '150', '168', 'f', 'normal', '174', 'no', '1.6', 'up', '0', 'normal', 'negative'], ['54', 'male', 'asympt', '140', '239', 'f', 'normal', '160', 'no', '1.2', 'up', '0', 'normal', 'negative'], ['48', 'female', 'non_anginal', '130', '275', 'f', 'normal', '139', 'no', '0.2', 'up', '0', 'normal', 'negative'], ['49', 'male', 'atyp_angina', '130', '266', 'f', 'normal', '171', 'no', '0.6', 'up', '0', 'normal', 'negative'], ['64', 'male', 'typ_angina', '110', '211', 'f', 'left_vent_hyper', '144', 'yes', '1.8', 'flat', '0', 'normal', 'negative'], ['58', 'female', 'typ_angina', '150', '283', 't', 'left_vent_hyper', '162', 'no', '1', 'up', '0', 'normal', 'negative'], ['50', 'female', 'non_anginal', '120', '219', 'f', 'normal', '158', 'no', '1.6', 'flat', '0', 'normal', 'negative'], ['58', 'female', 'non_anginal', '120', '340', 'f', 'normal', '172', 'no', '0', 'up', '0', 'normal', 'negative'], ['66', 'female', 'typ_angina', '150', '226', 'f', 'normal', '114', 'no', '2.6', 'down', '0', 'normal', 'negative'], ['43', 'male', 'asympt', '150', '247', 'f', 'normal', '171', 'no', '1.5', 'up', '0', 'normal', 'negative'], ['69', 'female', 'typ_angina', '140', '239', 'f', 'normal', '151', 'no', '1.8', 'up', '2', 'normal', 'negative'], ['59', 'male', 'asympt', '135', '234', 'f', 'normal', '161', 'no', '0.5', 'flat', '0', 'reversable_defect', 'negative'], ['44', 'male', 'non_anginal', '130', '233', 'f', 'normal', '179', 'yes', '0.4', 'up', '0', 'normal', 'negative'], ['42', 'male', 'asympt', '140', '226', 'f', 'normal', '178', 'no', '0', 'up', '0', 'normal', 'negative'], ['61', 'male', 'non_anginal', '150', '243', 't', 'normal', '137', 'yes', '1', 'flat', '0', 'normal', 'negative'], ['40', 'male', 'typ_angina', '140', '199', 'f', 'normal', '178', 'yes', '1.4', 'up', '0', 'reversable_defect', 'negative'], ['71', 'female', 'atyp_angina', '160', '302', 'f', 'normal', '162', 'no', '0.4', 'up', '2', 'normal', 'negative'], ['59', 'male', 'non_anginal', '150', '212', 't', 'normal', '157', 'no', '1.6', 'up', '0', 'normal', 'negative'], ['51', 'male', 'non_anginal', '110', '175', 'f', 'normal', '123', 'no', '0.6', 'up', '0', 'normal', 'negative'], ['65', 'female', 'non_anginal', '140', '417', 't', 'left_vent_hyper', '157', 'no', '0.8', 'up', '1', 'normal', 'negative'], ['53', 'male', 'non_anginal', '130', '197', 't', 'left_vent_hyper', '152', 'no', '1.2', 'down', '0', 'normal', 'negative']] 

你知不知道我怎样才能输出像由arff库（来自Weka）创建的以下内容？

Answer 1

你说“没有外部库”，但你至少可以剪切并粘贴到你自己的代码中吗？您可能会发现the source code to the arff module有用（200行，大约5.6 KB）。

编辑：

你会发现这个格式参考价值：http://weka.wikispaces.com/ARFF+%28stable+version%29

EDIT2：

只是为了好玩，我写我自己.arrf解析器;它几乎与WEKA代码一样长，但应该更易读 - 只有六个函数，一个调度表和一个非常模块化的类。您可以遍历一个类实例来获取每个数据行作为namedtuple。

见你在想什么：

from collections import namedtuple 
from keyword import iskeyword 
import re 

def NotDone(msg): 
    raise NotImplemented(msg) 

def nominal(spec): 
    """ 
    Create an ARFF nominal (enumerated) data type 
    """ 
    spec = spec.lstrip("{ \t").rstrip("} \t") 
    good_values = set(val.strip() for val in spec.split(",")) 

    def fn(s): 
     s = s.strip() 
     if s in good_values: 
      return s 
     else: 
      raise ValueError("'{}' is not a recognized value".format(s)) 

    # patch docstring 
    fn.__name__ = "nominal" 
    fn.__doc__ = """ 
    ARFF nominal (enumerated) data type 

    Legal values are {} 
    """.format(sorted(good_values)) 
    return fn 

def numeric(s): 
    """ 
    Convert string to int or float 
    """ 
    try: 
     return int(s) 
    except ValueError: 
     return float(s) 

field_maker = { 
    "date":  (lambda spec: NotDone("date data type not implemented")), 
    "integer": (lambda spec: int), 
    "nominal": (lambda spec: nominal(spec)), 
    "numeric": (lambda spec: numeric), 
    "string":  (lambda spec: str), 
    "real":  (lambda spec: float), 
    "relational": (lambda spec: NotDone("relational data type not implemented")), 
} 

def file_lines(fname): 
    # lazy file reader; ensures file is closed when done, 
    # returns lines without trailing spaces or newline 
    with open(fname) as inf: 
     for line in inf: 
      yield line.rstrip() 

def no_data_yet(*items): 
    raise ValueError("AarfRow not fully defined (haven't seen a @data directive yet)") 

def make_field_name(s): 
    """ 
    Mangle string to make it a valid Python identifier 
    """ 
    s = s.lower()        # force to lowercase 
    s = "_".join(re.findall("[a-z0-9]+", s)) # strip all invalid chars; join what's left with "_" 
    if iskeyword(s) or re.match("[0-9]", s): # if the result is a keyword or starts with a digit 
     s = "f_"+s        # make it a safe field name 
    return s 

class ArffReader: 
    line_types = ["blank", "comment", "relation", "attribute", "data"] 

    def __init__(self, fname): 
     # get input file 
     self.fname = fname 
     self.lines = file_lines(fname) 

     # prepare to read file header 
     self.relation = '(not specified)' 
     self.data_names = [] 
     self.data_types = [] 
     self.dtype = no_data_yet 

     # read file header 
     line_tests = [ 
      (getattr(self, "line_is_{}".format(item)), getattr(self, "line_do_{}".format(item))) 
      for item in self.__class__.line_types 
     ] 
     for line in self.lines: 
      for is_, do in line_tests: 
       if is_(line): 
        done = do(line) 
        break 
      if done: 
       break 

     # use header fields to build data type (and make it print as requested) 
     class ArffRow(namedtuple('ArffRow', self.data_names)): 
      __slots__ =() 
      def __str__(self): 
       items = (getattr(self, field) for field in self._fields) 
       return "({})".format(", ".join(repr(it) for it in items)) 
     self.dtype = ArffRow 

    # 
    # figure out input-line type 
    # 

    def line_is_blank(self, line): 
     return not line 

    def line_is_comment(self, line): 
     return line.lower().startswith('%') 

    def line_is_relation(self, line): 
     return line.lower().startswith('@relation') 

    def line_is_attribute(self, line): 
     return line.lower().startswith('@attribute') 

    def line_is_data(self, line): 
     return line.lower().startswith('@data') 

    #  
    # handle input-line type 
    #  

    def line_do_blank(self, line): 
     pass 

    def line_do_comment(self, line): 
     pass 

    def line_do_relation(self, line): 
     self.relation = line[10:].strip() 

    def line_do_attribute(self, line): 
     m = re.match(
      "^@attribute"   # line starts with '@attribute' 
      "\s+"     # 
      "("      # name is one of: 
       "(?:'[^']+')"  # ' string in single-quotes ' 
       "|(?:\"[^\"]+\")" # " string in double-quotes " 
       "|(?:[^ \t'\"]+)" # single_word_string (no spaces) 
      ")"      # 
      "\s+"     # 
      "("      # type is one of: 
       "(?:{[^}]+})"  # { set, of, nominal, values } 
       "|(?:\w+)"   # datatype 
      ")"      # 
      "\s*"     # 
      "("      # spec string 
       ".*"    # anything to end of line 
      ")$",     # 
      line, flags=re.I)  # case-insensitive 
     if m: 
      name, type_, spec = m.groups() 
      self.data_names.append(make_field_name(name)) 
      if type_[0] == '{': 
       type_, spec = 'nominal', type_ 
      self.data_types.append(field_maker[type_](spec)) 
     else: 
      raise ValueError("failed parsing attribute line '{}'".format(line)) 

    def line_do_data(self, line): 
     return True # flag end of header 

    # 
    # make the class iterable 
    # 

    def __iter__(self): 
     return self 

    def next(self): 
     """ 
     Return one data row at a time 
     """ 
     data = next(self.lines).split(',') 
     return self.dtype(*(fn(dat) for fn,dat in zip(self.data_types, data))) 

，它可以作为

for row in ArffReader('mydata.arff'): 
    print(row) 

导致

(63.0, 'male', 'typ_angina', 145.0, 233.0, 't', 'left_vent_hyper', 150.0, 'no', 2.3, 'down', 0.0, 'fixed_defect', 'negative') 
(37.0, 'male', 'non_anginal', 130.0, 250.0, 'f', 'normal', 187.0, 'no', 3.5, 'down', 0.0, 'normal', 'negative') 
(41.0, 'female', 'atyp_angina', 130.0, 204.0, 'f', 'left_vent_hyper', 172.0, 'no', 1.4, 'up', 0.0, 'normal', 'negative') 
(56.0, 'male', 'atyp_angina', 120.0, 236.0, 'f', 'normal', 178.0, 'no', 0.8, 'up', 0.0, 'normal', 'negative') 
(57.0, 'female', 'asympt', 120.0, 354.0, 'f', 'normal', 163.0, 'yes', 0.6, 'up', 0.0, 'normal', 'negative') 
(57.0, 'male', 'asympt', 140.0, 192.0, 'f', 'normal', 148.0, 'no', 0.4, 'flat', 0.0, 'fixed_defect', 'negative') 
(56.0, 'female', 'atyp_angina', 140.0, 294.0, 'f', 'left_vent_hyper', 153.0, 'no', 1.3, 'flat', 0.0, 'normal', 'negative') 
(44.0, 'male', 'atyp_angina', 120.0, 263.0, 'f', 'normal', 173.0, 'no', 0.0, 'up', 0.0, 'reversable_defect', 'negative') 
(52.0, 'male', 'non_anginal', 172.0, 199.0, 't', 'normal', 162.0, 'no', 0.5, 'up', 0.0, 'reversable_defect', 'negative') 

的领域也由名寻址，即

for patient in ArffReader('mydata.arff'): 
    print("{} year old {}".format(patient.age, patient.sex)) 

这给

63.0 year old male 
37.0 year old male 
41.0 year old female 
56.0 year old male 
57.0 year old female 
57.0 year old male 
56.0 year old female 
44.0 year old male 
52.0 year old male 

，您可以通过

>>> print(repr(patient)) 
ArffRow(age=63.0, sex='male', cp='typ_angina', trestbps=145.0, chol=233.0, fbs='t', restecg='left_vent_hyper', thalach=150.0, exang='no', oldpeak=2.3, slope='down', ca=0.0, thal='fixed_defect', f_class='negative') 

字段名称看文件名是按照该ARFF头部，强迫小写（以及在“类”的情况下，除了前面有' f_'，因为class是Python关键字，因此不能用作字段名称）。