import pandas as pd
from collections import Counter
import math
tennis=pd.read_csv('playtennis.csv')
print("\n given play tennis data set:\n",tennis)

def entropy(alist):
    c=Counter(x for x in alist)
    instances=len(alist)
    prob=[x/instances for x in c.values()]
    return sum([-p*math.log(p,2) for p in prob])

def information_gain(d,split,target):
    splitting=d.groupby(split)
    n=len(d.index)
    agent=splitting.agg({target:[entropy,lambda x:len (x)/n]})[target]
    agent.columns=['Entropy','observations']
    newentropy=sum(agent['Entropy']*agent['observations'])
    oldentropy=entropy(d[target])
    return oldentropy-newentropy

def id3(sub,target,a):
    count=Counter(x for x in sub[target])# class of yes/no
    if len(count)==1:
        return next(iter(count))#next innput data set,or raise Stopiteration when EOF hit
    else:
        gain=[information_gain(sub,attr,target) for attr in a]
        print("\Gain =",gain)
        maximum =gain.index(max(gain))
        best=a[maximum]
        print("\nBest Attribute:",best)
        tree={best:{}}
        remaining=[i  for i in a if i!= best]
        
        for  val,subset in sub.groupby(best):
            subtree=id3(subset,target,remaining)
            tree[best][val]=subtree
        return tree

names=list(tennis.columns)
print("\nList of Atrributes:",names)
names.remove('PlayTennis')
print('\nPredicting Attributes :',names)

tree=id3(tennis,'PlayTennis',names)
print("\n\nThe Resultant Decision Tree i :\n ")
print(tree)