Question

我需要在不使用任何外部库的情况下解析如下所示的arff文件。我不确定如何将属性与数值相关联。就像我怎么说每一行的第一个数值是年龄而第二个是性别呢？你能把我链接到一些用于解析类似场景的python代码吗？

@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

以下是我编写的示例代码：

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])

然而，输出与我预期的非常不同：

[['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']]

你知道我怎样才能获得如下arff库（来自Weka）创建的输出？ enter image description here

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，约5.6 KB）。

修改

您可能会发现此格式参考有用：http://weka.wikispaces.com/ARFF+%28stable+version%29

<强> EDIT2：

为了好玩，我写了自己的.arrf解析器;它几乎与WEKA代码一样长，但应该更具可读性 - 只有六个函数，一个调度表和一个非常模块化的类。您可以迭代一个类实例，将每个数据行作为一个命名元组。

看看你的想法：

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)))

可以用作

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')

字段也可以按名称寻址，即

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

给出了

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
查看文件名
>>> 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')

字段名称是按照ARFF标题强制小写的（并且在'class'前面加上'f_'，因为class是一个Python关键字因此不能用作字段名称。）

如何在不使用Python的外部库的情况下解析arff文件

1 个答案: