Notes

# -*- coding: utf-8 -*-
"""
Created on Fri Aug 31 09:50:39 2018

@author: Deepak
"""
import math
import csv
def load_csv(filename):
lines=csv.reader(open(filename,"r"));
dataset = list(lines)
headers = dataset.pop(0)
return dataset,headers

class Node:
def __init__(self,attribute):
self.attribute=attribute
self.children=[]
self.answer=""

def subtables(data,col,delete):
dic={}
coldata=[row[col] for row in data]
attr=list(set(coldata))

counts=[0]*len(attr)
r=len(data)
c=len(data[0])
for x in range(len(attr)):
for y in range(r):
if data[y][col]==attr[x]:
counts[x]+=1

for x in range(len(attr)):
dic[attr[x]]=[[0 for i in range(c)] for j in range(counts[x])]
pos=0
for y in range(r):
if data[y][col]==attr[x]:
if delete:
del data[y][col]
dic[attr[x]][pos]=data[y]
pos+=1
return attr,dic

def entropy(S):
attr=list(set(S))
if len(attr)==1:
return 0

counts=[0,0]
for i in range(2):
counts[i]=sum([1 for x in S if attr[i]==x])/(len(S)*1.0)

sums=0
for cnt in counts:
sums+=-1*cnt*math.log(cnt,2)
return sums

def compute_gain(data,col):
attr,dic = subtables(data,col,delete=False)

total_size=len(data)
entropies=[0]*len(attr)
ratio=[0]*len(attr)

total_entropy=entropy([row[-1] for row in data])
for x in range(len(attr)):
ratio[x]=len(dic[attr[x]])/(total_size*1.0)
entropies[x]=entropy([row[-1] for row in dic[attr[x]]])
total_entropy-=ratio[x]*entropies[x]
return total_entropy

def build_tree(data,features):
lastcol=[row[-1] for row in data]
if(len(set(lastcol)))==1:
node=Node("")
node.answer=lastcol[0]
return node

n=len(data[0])-1
gains=[0]*n
for col in range(n):
gains[col]=compute_gain(data,col)
split=gains.index(max(gains))
node=Node(features[split])
fea = features[:split]+features[split+1:]


attr,dic=subtables(data,split,delete=True)

for x in range(len(attr)):
child=build_tree(dic[attr[x]],fea)
node.children.append((attr[x],child))
return node

def print_tree(node,level):
if node.answer!="":
print(" "*level,node.answer)
return

print(" "*level,node.attribute)
for value,n in node.children:
print(" "*(level+1),value)
print_tree(n,level+2)


def classify(node,x_test,features):
if node.answer!="":
print(node.answer)
return
pos=features.index(node.attribute)
for value, n in node.children:
if x_test[pos]==value:
classify(n,x_test,features)

'''Main program'''
dataset,features=load_csv("data3.csv")
node1=build_tree(dataset,features)

print("The decision tree for the dataset using ID3 algorithm is")
print_tree(node1,0)
testdata,features=load_csv("data3_test.csv")
for xtest in testdata:
print("The test instance:",xtest)
print("The label for test instance:",end=" ")
classify(node1,xtest,features)