I have a script which contains two classes. (I'm obviously deleting a lot of stuff that I don't believe is relevant to
the error I'm dealing with.)
The eventual task is to create a decision tree, as I mentioned in this question.
Unfortunately, I'm getting an infinite loop, and I'm having difficulty identifying why. I've identified
the line of code that's going haywire, but I would have thought
the iterator and
the list I'm adding to would be different objects. Is there some side effect of list's .append functionality that I'm not aware of? Or am I making some other blindingly obvious mistake?
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=[]):
and
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):
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()):
My initialisation code is currently:
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.
