Question

我有一个包含两个类的脚本。（我显然删除了许多我认为与我正在处理的错误无关的东西。）最终的任务是创建一个决策树，正如我在this问题中提到的那样。

不幸的是，我得到了一个无限循环，我很难找出原因。我已经确定了会变得混乱的代码行，但我认为迭代器和我添加的列表将是不同的对象。列表的.append功能是否有一些我不知道的副作用？或者我是否犯了一些其他令人目眩的明显错误？

class Dataset:
    individuals = [] #Becomes a list of dictionaries, in which each dictionary is a row from the CSV with the headers as keys
    def field_set(self): #Returns a list of the fields in individuals[] that can be used to split the data (i.e. have more than one value amongst the individuals
    def classified(self, predicted_value): #Returns True if all the individuals have the same value for predicted_value
    def fields_exhausted(self, predicted_value): #Returns True if all the individuals are identical except for predicted_value
    def lowest_entropy_value(self, predicted_value): #Returns the field that will reduce <a href="http://en.wikipedia.org/wiki/Entropy_%28information_theory%29">entropy</a> the most
    def __init__(self, individuals=[]):

和

class Node:
    ds = Dataset() #The data that is associated with this Node
    links = [] #List of Nodes, the offspring Nodes of this node
    level = 0 #Tree depth of this Node
    split_value = '' #Field used to split out this Node from the parent node
    node_value = '' #Value used to split out this Node from the parent Node

    def split_dataset(self, split_value): #Splits the dataset into a series of smaller datasets, each of which has a unique value for split_value.  Then creates subnodes to store these datasets.
        fields = [] #List of options for split_value amongst the individuals
        datasets = {} #Dictionary of Datasets, each one with a value from fields[] as its key
        for field in self.ds.field_set()[split_value]: #Populates the keys of fields[]
            fields.append(field)
            datasets[field] = Dataset()
        for i in self.ds.individuals: #Adds individuals to the datasets.dataset that matches their result for split_value
            datasets[i[split_value]].individuals.append(i) #<---Causes an infinite loop on the second hit
        for field in fields: #Creates subnodes from each of the datasets.Dataset options
            self.add_subnode(datasets[field],split_value,field)

    def add_subnode(self, dataset, split_value='', node_value=''):
    def __init__(self, level, dataset=Dataset()):

我的初始化代码目前是：

if __name__ == '__main__':
    filename = (sys.argv[1]) #Takes in a CSV file
    predicted_value = "# class" #Identifies the field from the CSV file that should be predicted
    base_dataset = parse_csv(filename) #Turns the CSV file into a list of lists
    parsed_dataset = individual_list(base_dataset) #Turns the list of lists into a list of dictionaries
    root = Node(0, Dataset(parsed_dataset)) #Creates a root node, passing it the full dataset
    root.split_dataset(root.ds.lowest_entropy_value(predicted_value)) #Performs the first split, creating multiple subnodes
    n = root.links[0] 
    n.split_dataset(n.ds.lowest_entropy_value(predicted_value)) #Attempts to split the first subnode.

Answer 1

我怀疑你正在追加你正在迭代的相同列表，导致它在迭代器到达结束之前增加大小。尝试迭代列表的副本：

for i in list(self.ds.individuals):
    datasets[i[split_value]].individuals.append(i)

Answer 2

class Dataset:
    individuals = []

可疑。除非您希望Dataset的所有实例共享一个静态成员列表，否则您不应该这样做。如果您在self.individuals= something中设置__init__，那么您也不需要在此设置individuals。

    def __init__(self, individuals=[]):

仍然怀疑。您是否将individuals参数分配给self.individuals？如果是，则将在函数定义时创建的相同individuals列表分配给使用默认参数创建的每个Dataset。将一个项添加到一个Dataset的列表中，并且在没有明确individuals参数的情况下创建的所有其他项也将获得该项。

类似地：

class Node:
    def __init__(self, level, dataset=Dataset()):

在没有明确Node参数的情况下创建的所有dataset将收到完全相同的默认Dataset实例。

这是mutable default argument problem，它产生的那种破坏性迭代似乎很可能导致你的无限循环。

在python3中的类中向列表添加行时无限循环

2 个答案: