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elastic_net.py
executable file
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elastic_net.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Mar 25 15:17:27 2013
@author: rami999999999
"""
import Rfechado as RRegression_beta
import Rfechado as Rfechado
import pdb
import numpy as np
import scipy.sparse as sparse
import itertools
import iteracoes as it
import cria_dados
from copy import copy, deepcopy
def elastic_net(X,Y,w0,Xteste,Yteste,Xdev,Ydev,alpha_en):
import matplotlib.pyplot as plt
w_new=np.zeros(w0.shape)
zeros=np.zeros(w0.shape)
final=[0,0]
sigma=1
sigma2=0.1
max_iter =40000
yyy=[]
xxx=[]
gmedia=[]
gmediatest=[]
w0=w0.todense()
softt=it.softt
getU=it.getU
get_step=it.get_step
get_func_elastic=it.get_func_elastic
GetGradL=it.GetGradL
lambs=[10**x for x in xrange(0,10)]
xg=map(lambda x: np.log10(x),lambs)
#lambs=[100]
#lambs=[1000*X.shape[0]]
#xg=[7]
for lamb in lambs:
print "A iniciar Iteracoes para lambda=",lamb
w_old_old=w0
w_old=w0
yy=[]
xx=[]
i=0
while i<max_iter:
#print "iteracao",i
error=(X*w_old)-Y
if i==0:
step_size=0.0000001
else:
#pdb.set_trace()
error_old=error=(X*w_old)-Y
error_old_old=(X*w_old_old)-Y
alpha=get_step(w_old,w_old_old,X,error_old,error_old_old,lamb)
if alpha==0:
#print "**ERRO**"
#print "aplha=0, impossivel continuar o algorimto"
break
step_size=sigma/alpha
error=(X*w_old)-Y
grad1=GetGradL(error,X)
U=getU(w_old,step_size,grad1)
K=1+step_size*lamb*(1-alpha_en)
w_new=softt(U,step_size*lamb*alpha_en/K,zeros)
dif=w_new-w_old
dif=dif.transpose()*dif
error=(X*w_new)-Y
y_new=get_func_elastic(error,w_new,lamb,alpha_en) #funcao de erro
#print i,"->",y_new
count=0
if i!=0:
while y_new>=y_old-sigma2*alpha*dif[0,0] and i<max_iter:
#print "A diminuir step:",i
step_size=step_size/2
U=getU(w_old,step_size,grad1)
K=1+step_size*lamb*(1-alpha_en)
w_new=softt(U,step_size*lamb*alpha_en/K,zeros)
error=(X*w_new)-Y
dif=w_new-w_old
dif=dif.transpose()*dif
y_new=get_func_elastic(error,w_new,lamb,alpha_en) #funcao de erro
count=count+1
i=i+1
if count==10000:
break
if count ==10000:
#print "****A SAIR****\nProvavelmente o sparsa chegou ao minimo antes de terminar o numero de iteracoes"
break
i=i+1
y_old=y_new
w_old_old=w_old
w_old = w_new
yy.append(y_new)
xx.append(i)
media=RRegression_beta.erro(Xdev,Ydev,w_new)
gmedia.append(media)
gmediatest.append(RRegression_beta.erro(Xteste,Yteste,w_new))
if final[0]>media or final[0]==0:
final[0]=media
final[1]=lamb
graphFinal=deepcopy(yy)
wfinal=w_new
yfinal=y_new
finalxx=deepcopy(xx)
zero=0.0
for J in xrange(w_new.shape[0]):
if w_new[J,0]==0:
zero=zero+1.0
sp=(zero/w_new.shape[0])*100
#print "percentagem:",sp
yyy.append(sp)
xxx.append(lamb)
'''
plt.figure(1)
plt.subplot(221)
plt.title("Funcao de custo")
plt.plot(finalxx,graphFinal,"r")
plt.subplot(222)
plt.title("Percentagem de W com valor =0")
import pylab
#print yyy
#pylab.ylim([0,100])
plt.plot(xg,yyy,"b",xg,yyy,"ro")
plt.subplot(223)
plt.title("Evolucao do erro DEV ao longo dos lambdas")
plt.plot(xg,gmedia,"b",xg,gmedia,"ro")
plt.subplot(224)
plt.title("Evolucao do erro teste ao longo dos lambdas")
plt.plot(xg,gmediatest,"b",xg,gmediatest,"ro")
plt.tight_layout()
#pylab.savefig("elastic_beta.png")
plt.show()
'''
return wfinal,yfinal,final[1]
if __name__ == '__main__':
f="../le_ficheiro/someta"
print "FICHEIRO:",f,"\n"
dictionary,total,y=cria_dados.read_output(f+"train.txt")
#X,Y,mediaY,stdY,mediaX=cria_dados.criaXY(dictionary,total,y,True)
X,Y=cria_dados.criaXY(dictionary,total,y,False)
#X,total=cria_dados.delstopword(X,total,True)
dictionary,temp,y=cria_dados.read_output(f+"test.txt")
Xteste,Yteste=cria_dados.criaXY(dictionary,total,y,False)
dictionary,temp,y=cria_dados.read_output(f+"dev.txt")
Xdev,Ydev=cria_dados.criaXY(dictionary,total,y,False)
#X,total=cria_dados.delcomun(X,total)
vec=sparse.csr_matrix([0.0 for i in xrange(X.shape[1])])
vec=vec.transpose()
'''
W,F=elastic_net(X,Y,vec,Xteste,Yteste,Xdev,Ydev,alpha_en)
print "----------erro---------"
print "TESTE",RRegression_beta.erro(f+"train.txt",W,total)
print "-----------------------"
'''
print "-----------------------------"
#W,F,lamb=elastic_net(X,Y,vec,Xteste,Yteste,Xdev,Ydev,0.0)
#print "ALPHA", alpha
#print "LAMBDA",lamb
#print "ERRO_DEV:",Rfechado.erro(Xdev,Ydev,W) #W=elastic_net.elastic_net(Xtrain,Ytrain.transpose(),vec,Xdev,Ydev.transpose(),Xtest,Ytest.transpose(),0.5)
#print "ERRO_TEST:",Rfechado.erro(Xteste,Yteste,W)
#error=X*W-Y
#print "OBJECTIVO:", it.get_func_elastic(error,W,1000*X.shape[0],1)
#print "OBJECTIVO:", F
for alpha in [0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]:
print "------------------------------"
W,F,lamb=elastic_net(X,Y,vec,Xteste,Yteste,Xdev,Ydev,alpha)
print "ALPHA", alpha
print "LAMBDA",lamb
print "ERRO_DEV:",Rfechado.erro(Xdev,Ydev,W) #W=elastic_net.elastic_net(Xtrain,Ytrain.transpose(),vec,Xdev,Ydev.transpose(),Xtest,Ytest.transpose(),0.5)
print "ERRO_TEST:",Rfechado.erro(Xteste,Yteste,W)
#print "TRAIN",RRegression_beta.erro("../le_ficheiro/train_meta.txt",W,total)
#print "-----------------------"
#print "DEV",RRegression_beta.erro("../le_ficheiro/dev_meta.txt",W,total)