ADABOOST.py

def AdaBoost(funcDcsn, sampleFrames, attList, \
             parentFrames,strtdepth, maxdepth,funcClassify,rootClass, K):
    Y=sampleFrames.iloc[:,-1].values.tolist().copy()
    #print(Y)
    N=len(sampleFrames)
    w= [1/N] * N
    h= []
    z= []
    #print(w)
    
    for x in range(0,K):
        data=sampleFrames.sample(n=N,weights=w,replace=True).copy()
        error=0.0001
        attList=list(data.drop(data.columns[-1],axis=1).columns.values)
        roott=dcsnTreeRoot(data,attList.copy(),data,0,maxdepth)
        predictAns=classPrint(data,roott)
        for i in range(0,N):
            if(predictAns[i] != Y[i]):
                error=error + w[i]
        if(error > 0.5):
            #print(x)
            continue
        for j in range(0,N):
            if(predictAns[j] == Y[j]):
                w[j] = w[j] *(error/(1-error))   
        maxVal=sum(w)
        w = [float(i)/maxVal for i in w]
        h.append(roott)
        #print(type(roott))
        wT= math.log2(((1-error)/error))
        z.append(wT)    
    return h,z

learner, weights=AdaBoost(dcsnTreeRoot,dfN,att,dfN,0,1,classPrint,dcsnTreeNodeClass,20)