if __name__=="__main__":

import pickle

import Rgrad

import scipy.sparse as sparse

import pdb

import cria_dados

import Rfechado

import numpy as np

import elastic_net

import Priberam_Ridge as PB

file=open("../dados_priberam/dados_X_corrigido.pkl","rb")

Xtotal=pickle.load(file)

file=open("../dados_priberam/dados_Y_corrigido.pkl","rb")

Ytotal=pickle.load(file)

file=open("../dados_priberam/dados_indice_corrigido.pkl","rb")

indices=pickle.load(file)

trainsize=int(Xtotal.shape[0]*0.8)

devsize=int(Xtotal.shape[0]*0.1)+trainsize

#Xtrain,Ytrain,Xtest,Ytest,Xdev,Ydev=PB.separaXY(Xtotal,Ytotal)

#train_index=xrange(trainsize)

#dev_index=xrange(trainsize,devsize)

#test_index=xrange(devsize,Xtotal.shape[0])

Xtotal=Xtotal.tocsc()

Ytotal=Ytotal.tocsc()

Xtotal,indices=cria_dados.delstopword(Xtotal,indices,False)

#Xtotal,indices=PB.tira_meta(Xtotal,indices)

#vec=sparse.csr_matrix([0 for i in xrange(Xtrain.shape[1])])

vec=sparse.csr_matrix([0 for i in xrange(Xtotal[:trainsize,:].shape[1])])

vec=vec.transpose()

print "-----------------------------"

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.elastic_net(Xtotal[:trainsize,:],Ytotal[0:trainsize,0],vec,Xtotal[devsize:,:],Ytotal[devsize:,0],Xtotal[trainsize:devsize,:],Ytotal[trainsize:devsize,0],alpha)

print "ALPHA", alpha

print "LAMBDA",lamb

print "ERRO:",Rfechado.erro(Xtotal[trainsize:devsize,:],Ytotal[trainsize:devsize,:],W) #W=elastic_net.elastic_net(Xtrain,Ytrain.transpose(),vec,Xdev,Ydev.transpose(),Xtest,Ytest.transpose(),0.5)

print "ERRO_DEV:",Rfechado.erro(Xtotal[trainsize:devsize,:],Ytotal[trainsize:devsize,:],W) #W=elastic_net.elastic_net(Xtrain,Ytrain.transpose(),vec,Xdev,Ydev.transpose(),Xtest,Ytest.transpose(),0.5)

print "ERRO_TEST:",Rfechado.erro(Xtotal[trainsize:devsize,:],Ytotal[trainsize:devsize,:],W)

#print "ERRO:",Rfechado.erro(Xtest,Ytest,W)

#print "ERRO:",Rfechado.erro(Xtotal[trainsize:devsize,:],Ytest[trainsize:devsize,:],W)