def run_classify(n_train,n_test,n_valid,alpha,lamda,batch,NN_type,Reg_type):
#check and load data
#if(not data_process.check()):
#data_process.process()
#data = data_process.loaddata()
#n_exemple = data.shape[0]
# d = data.shape[1]-1
# data[:,:-1]=tools.normalisation(data[:,:-1])
data = numpy.loadtxt("nntest.txt")
data[:,:-1] = data_process.normalisation(data[:,:-1])
n_exemple = data.shape[0]
d = data.shape[1]-1
#print data
#shuffle the data
inds = range(n_exemple)
random.shuffle(inds)
#split data
tmp_test = n_train+n_test
tmp_valid = tmp_test + n_valid
inds_train = inds[:]
inds_test = inds[n_train:tmp_test]
inds_valid = inds[tmp_test:tmp_valid]
train_data = data[inds_train,:]
print "Train data shape: ",train_data.shape
test_data = data[inds_test,:]
valid_data = data[inds_valid,:]
test_input = test_data[:,:-1]
test_labels = test_data[:,-1]
valid_input = valid_data[:,:-1]
valid_labels = valid_data[:,-1]
#define param
# Nombre de classes
n_classes = 2
m = n_classes
#create and train the model
model = NN.NN(m,d,alpha,lamda,batch,NN_type,Reg_type)
model.train(train_data,valid_input,valid_labels,test_input,test_labels)
#compute the prediction on train data
t1 = time.clock()
les_comptes = model.compute_predictions(train_data[:,:-1])
t2 = time.clock()
print 'It takes ', t2-t1, ' secondes to compute the prediction on ', test_data.shape[0],' points of test'
classes_pred = numpy.argmax(les_comptes,axis=1)+1
confmat = tools.teste(train_data[:,-1], classes_pred,n_classes)
print 'La matrice de confusion est:'
print confmat
# Error of test
sum_preds = numpy.sum(confmat)
sum_correct = numpy.sum(numpy.diag(confmat))
print "The error of train is ", 100*(1.0 - (float(sum_correct) / sum_preds)),"%"
#compute the prediction on valid data
t1 = time.clock()
les_comptes = model.compute_predictions(valid_input)
t2 = time.clock()
print 'It takes ', t2-t1, ' secondes to compute the prediction on ', test_data.shape[0],' points of test'
classes_pred = numpy.argmax(les_comptes,axis=1)+1
confmat = tools.teste(valid_labels, classes_pred,n_classes)
print 'La matrice de confusion est:'
print confmat
# Error of test
sum_preds = numpy.sum(confmat)
sum_correct = numpy.sum(numpy.diag(confmat))
print "The error of validation is ", 100*(1.0 - (float(sum_correct) / sum_preds)),"%"
#compute the prediction on test data
t1 = time.clock()
les_comptes = model.compute_predictions(test_input)
t2 = time.clock()
print 'It takes ', t2-t1, ' secondes to compute the prediction on ', test_data.shape[0],' points of test'
classes_pred = numpy.argmax(les_comptes,axis=1)+1
confmat = tools.teste(test_labels, classes_pred,n_classes)
print 'La matrice de confusion est:'
print confmat
# Error of test
sum_preds = numpy.sum(confmat)
sum_correct = numpy.sum(numpy.diag(confmat))
print "The error of test is ", 100*(1.0 - (float(sum_correct) / sum_preds)),"%"
def train(self,train_set,valide_set,valide_labels,test_set,test_labels):
nb_valide=valide_set.shape[0]
changeTimer = 0
print 'training rate is ',self.alpha
finish = False
nb_train=train_set.shape[0]
max_iter = 10*nb_train
itera=0
seuil = 1
taux = 100.0
taux_valid =100.0
k=0
X=mat(train_set)
print max_iter;
while not finish:
inds = range(train_set.shape[0])
random.shuffle(inds)
for i in range(self.batch):
self.calculate_forward(X[inds[i],:-1])
self.calculate_backward(X[inds[i],:])
self.G1+=self.S1
self.GaW1+=self.aW1
itera+=1
k+=1
k=k%nb_train
self.GaW1 = self.GaW1/self.batch
self.G1 = self.G1/self.batch
self.adjust_weight()
if (itera%nb_train == 0):
les_comptes=self.compute_predictions_train(test_set)
classes_pred = argmax(les_comptes,axis=1)+1
confmat = tools.teste(test_labels, classes_pred,self.m)
sum_preds = sum(confmat)
sum_correct = sum(diag(confmat))
taux_test= 100*(1.0 - (float(sum_correct) / sum_preds))
#taux de erreur pour validation
les_comptes=self.compute_predictions_train(valide_set)
classes_pred = argmax(les_comptes,axis=1)+1
confmat = tools.teste(valide_labels, classes_pred,self.m)
sum_preds = sum(confmat)
sum_correct = sum(diag(confmat))
taux_valid= 100*(1.0 - (float(sum_correct) / sum_preds))
print "L'erreur de test est de validation est ", taux_valid,"%",time.clock()
#taux de erreur pour train_set
les_comptes=self.compute_predictions_train(train_set[:,:-1])
classes_pred = argmax(les_comptes,axis=1)+1
confmat = tools.teste(train_set[:,-1], classes_pred,self.m)
sum_preds = sum(confmat)
sum_correct = sum(diag(confmat))
taux= 100*(1.0 - (float(sum_correct) / sum_preds))
#print "iteration :", itera, ". L'erreur de test est de training est ", taux,"%",time.clock()
#print "iteration :", itera
print "L'erreur de test est de training est ", taux,"%",time.clock()
if (taux_valid < self.best_model_valid_error):
self.best_model_valid_error = taux_valid
self.best_model_test_error = taux_test
print "Best model until now with error of validation:",self.best_model_valid_error,"%, computing time is ",time.clock()," seconds"
print "The best model has error rate ", self.best_model_test_error,"%, on test set. "
self.bestW1 = self.W1
self.bestB1 = self.B1
self.G1 = self.G1*0.0
self.GaW1 =self.GaW1*0.0
if (taux < 30) and (changeTimer == 0):
self.alpha=self.alpha/2
changeTimer += 1
print "alpha changed !!! because taux < 30"
print self.alpha
if (taux < 10) and (changeTimer == 1):
self.alpha=self.alpha/2
changeTimer += 1
print "alpha changed !!! because taux < 10"
print self.alpha
if (taux_valid< seuil) or (itera > max_iter):
self.bestW1 = self.W1
self.bestB1 = self.B1
finish = True
print self.bestW1.shape
print self.bestB1.shape
