Lewy
328b171e5a
- changed name of files to represent inside - added main to seperate processes - changed raw code to functions
127 lines
3.3 KiB
Python
127 lines
3.3 KiB
Python
import copy
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import operator
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from collections import Counter
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import numpy as np
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from src.cases import *
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class Node:
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def __init__(self, Class, tag=None):
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self.Class = Class
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self.childs = []
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def classes_of_cases(cases):
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classes = []
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for case in cases:
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if case.Class not in classes:
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classes.append(case.Class)
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return classes
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def count_classes(cases):
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classes = []
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for case in cases:
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classes.append(case.Class)
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c = Counter(classes)
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return max(c.items(), key=operator.itemgetter(1))[0]
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def chose_attribute(cases, attributes):
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a = ""
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max = float("-inf")
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for attribute in attributes:
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if I(cases) - E(cases, attribute) >= max:
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max = I(cases) - E(cases, attribute)
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a = attribute
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return a
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def I(cases):
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i = 0
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all = len(cases)
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classes = classes_of_cases(cases)
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for Class in classes:
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noc = 0
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for case in cases:
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if case.Class == Class:
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noc += 1
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i -= (noc / all) * np.log2(noc / all)
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return i
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def E(cases, attribute):
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e = 0
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values = []
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index = cases[0].attributes.index(attribute)
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for case in cases:
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if case.values[index] not in values:
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values.append(case.values[index])
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for value in values:
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ei = []
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for case in cases:
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if case.values[index] == value:
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ei.append(case)
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e += (len(ei) / len(cases)) * I(ei)
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return e
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def treelearn(cases, attributes, default_class):
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if cases == []:
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t = Node(default_class)
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return t
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if len(classes_of_cases(cases)) == 1:
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t = Node(cases[0].Class)
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return t
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if attributes == []:
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t = Node(count_classes(cases))
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return t
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A = chose_attribute(cases, attributes)
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t = Node(A)
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new_default_class = count_classes(cases)
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values = []
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index = attributes.index(A)
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for case in cases:
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if case.values[index] not in values:
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values.append(case.values[index])
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for value in values:
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new_cases = []
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for case in cases:
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if case.values[index] == value:
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new_case = copy.deepcopy(case)
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new_case.values = case.values[:index] + case.values[index + 1:]
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new_case.attributes = case.attributes[:index] + case.attributes[index + 1:]
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new_cases.append(new_case)
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new_attributes = attributes[:index] + attributes[index + 1:]
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child = treelearn(new_cases, new_attributes, new_default_class)
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t.childs.append([child, value])
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return t
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def pretty_print(root, n):
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if len(root.childs) == 0:
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for _ in range(n):
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print(" ", end="")
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print("return " + str(root.Class))
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for child in root.childs:
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for _ in range(n):
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print(" ", end="")
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if child != root.childs[0]:
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print("el", end="")
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if len(str(child[1])) > 1:
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print("if self." + str(root.Class) + " == \"" + str(child[1]) + "\":")
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else:
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print("if self." + str(root.Class) + " == " + str(child[1]) + ":")
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pretty_print(child[0], n + 1)
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# Get view of decision_tree.py
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if __name__ == "__main__":
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tree = treelearn(cases, attributes, 0)
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pretty_print(tree, 0)
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