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ucl_mlp_sparse_3_rbm_sparseinput.py
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from sklearn.metrics import roc_auc_score
from sklearn.metrics import mean_squared_error
import sys
import numpy
import time
import theano
import theano.tensor as T
import linecache
import math
import dl_utils as ut
import ucl_gaussian_binary_rbm_sparse as gbrbm
import data_fm as fm
import pickle
rng = numpy.random
rng.seed(1234)
batch_size=1000 #batch size
lr=0.01 #learning rate
lambda1=0.0005 #regularisation rate
hidden1 = 300 #hidden layer 1
hidden2 = 100 #hidden layer 2
acti_type='tanh' #activation type
epoch = 100 #epochs number
advertiser = '2997'
if len(sys.argv) > 1:
advertiser = sys.argv[1]
train_file='../../make-ipinyou-data/' + advertiser + '/train.fm.txt' #training file
test_file='../../make-ipinyou-data/' + advertiser + '/test.fm.txt' #test file
fm_model_file='../../make-ipinyou-data/' + advertiser + '/fm.model.txt' #fm model file
#feats = ut.feats_len(train_file) #feature size
train_size=312437 #ut.file_len(train_file) #training size
test_size=156063 #ut.file_len(test_file) #test size
n_batch=train_size/batch_size #number of batches
o_fm=fm.DataFM(fm_model_file)
ut.log_p('X:'+str(o_fm.xdim) + ' | Hidden 1:'+str(hidden1)+ ' | Hidden 2:'+str(hidden2)+
' | L rate:'+str(lr)+ ' | activation1:'+ str(acti_type)+
' | lambda:'+str(lambda1)
)
ww3=numpy.zeros(hidden2)
bb3=0.
arr=[]
# print o_fm.xdim
x_dim=133465
arr.append(x_dim)
arr.append(hidden1)
arr.append(hidden2)
# ww1,bb1,ww2,bb2=gbrbm.get_rbm_weights(train_file,arr,ncases=train_size,batch_size=100000,fm_model_file=fm_model_file)
# pickle.dump( (ww1,bb1,ww2,bb2), open( "sparse2997_1000.p", "wb" ))
(ww1,bb1,ww2,bb2)=pickle.load( open( "sparse2997_1000.p", "rb" ) )
ww3=numpy.reshape(ww3,hidden2)
bb3=float(bb3)
# Declare Theano symbolic variables
x = T.matrix("x")
y = T.vector("y")
w1 = theano.shared(ww1, name="w1")
w2 = theano.shared(ww2, name="w2")
w3 = theano.shared(ww3, name="w3")
b1 = theano.shared(bb1, name="b1")
b2 = theano.shared(bb2, name="b2")
b3 = theano.shared(bb3 , name="b3")
# Construct Theano expression graph
# z1=T.dot(x, w1) + b1
# if acti_type=='sigmoid':
# h1 = 1 / (1 + T.exp(-z1)) # hidden layer 1
# elif acti_type=='linear':
# h1 = z1
# elif acti_type=='tanh':
# h1=T.tanh(z1)
z2=T.dot(x, w2) + b2
if acti_type=='sigmoid':
h2 = 1 / (1 + T.exp(-z2)) # hidden layer 2
elif acti_type=='linear':
h2 = z2
elif acti_type=='tanh':
h2=T.tanh(z2)
p_1 = 1 / (1 + T.exp(-T.dot(h2, w3) - b3)) # Probability that target = 1
prediction = p_1 #> 0.5 # The prediction thresholded
xent = - y * T.log(p_1) - (1-y) * T.log(1-p_1) # Cross-entropy loss function
cost = xent.mean() + lambda1 * (
(w2 ** 2).sum() + (w3 ** 2).sum() + (b2 ** 2).sum() + (b3 ** 2)) # The cost to minimize
gw3, gb3, gw2, gb2, gx = T.grad(cost, [w3, b3, w2, b2, x]) # Compute the gradient of the cost
# Compile
train = theano.function(
inputs=[x,y],
outputs=[gx, w2, w3],updates=(
(w2, w2 - lr * gw2), (b2, b2 - lr * gb2),
(w3, w3 - lr * gw3), (b3, b3 - lr * gb3)))
predict = theano.function(inputs=[x], outputs=prediction)
#print error
def print_err(file,msg=''):
auc,rmse=get_err_bat(file)
ut.log_p( msg + '\t' + str(auc) + '\t' + str(rmse))
#get error via batch
def get_err_bat(file,err_batch=100000):
y = []
yp = []
fi = open(file, 'r')
flag_start=0
xx_bat=[]
flag=False
while True:
line=fi.readline()
if len(line) == 0:
flag=True
flag_start+=1
if flag==False:
xx,yy = o_fm.get_xy_fm(line)
xx_bat.append(numpy.asarray(xx))
if ((flag_start==err_batch) or (flag==True)):
pred=predict(xx_bat)
for p in pred:
yp.append(p)
flag_start=0
xx_bat=[]
if flag==False:
y.append(yy)
if flag==True:
break
fi.close()
auc = roc_auc_score(y, yp)
rmse = math.sqrt(mean_squared_error(y, yp))
return auc,rmse
#print_err(test_file,'InitTestErr:')
# def getvector(v):
# # print 'v',v
# arr=[]
# index=0
# for i in range(x_dim):
# bool=False
# for l in v:
# a,b=l.split(":")
# if i==int(a):
# bool=True
# if bool:
# arr.append(int(b))
# index+=1
# else:
# arr.append(0)
# return arr
def get_fake_line(line):
newline=[]
for l in line:
a,b=l.split(":")
newline.append(l)
newline.append(str(int(a)-1)+":0")
return newline
def get_fi_h1_y(file,index,w1,b1,size):
farray=[]
xarray = []
yarray=[]
for i in range(index, index + size):
line = linecache.getline(file, i)
if line.strip() != '':
x_dense=numpy.zeros(w1.shape[1])
s = line.strip().replace(':', ' ').split(' ')
y = int(s[0])
fi=[]
for f in range(1, len(s), 2):
if int(s[f+1])==1:
fi.append(int(s[f]))
for i in range(w1.shape[1]):
sum=0
for j in range(1, len(s), 2):
if int(s[f+1])==1:
sum+=w1[int(s[f])][i]
sum+=b1[i]
x_dense[i]=(1 / (1 + math.exp(-sum)))
farray.append(fi)
xarray.append(x_dense)
yarray.append(int(y))
farray = numpy.array(farray, dtype = numpy.int32)
xarray = numpy.array(xarray, dtype = theano.config.floatX)
yarray = numpy.array(yarray, dtype = numpy.int32)
return farray,xarray,yarray
# Train
print "Training model:"
min_err = 0
min_err_epoch = 0
times_reduce = 0
for i in range(epoch):
start_time = time.time()
index = 1
for j in range(n_batch):
start_t = time.clock()
fi,h1,y = get_fi_h1_y(train_file,index,ww1,bb1,batch_size)
# print 'fi',fi
# print time.clock()-start_t
index += batch_size
gx, w2, w3 = train(h1,y)
b_size = len(fi)
for t in range(b_size):
ft = fi[t]
gxt = gx[t]
for feat in ft:
for l in range(ww1.shape[1]):
ww1[feat]=ww1[feat]-lr * (gxt*(1-gxt))
print 'one batch 1000 time',time.clock()-start_t
# for l in range(o_fm.k):
# o_fm.feat_weights[feat][l] = o_fm.feat_weights[feat][l] * (1 - 2. * lambda1 * lr / b_size) \
# - lr * gxt[o_fm.feat_layer_one_index(feat, l)] * 1
train_time = time.time() - start_time
mins = int(train_time / 60)
secs = int(train_time % 60)
print 'training: ' + str(mins) + 'm ' + str(secs) + 's'
start_time = time.time()
print_err(train_file,'\t\tTraining Err: \t' + str(i))# train error
train_time = time.time() - start_time
mins = int(train_time / 60)
secs = int(train_time % 60)
print 'training error: ' + str(mins) + 'm ' + str(secs) + 's'
start_time = time.time()
auc, rmse = get_err_bat(test_file)
test_time = time.time() - start_time
mins = int(test_time / 60)
secs = int(test_time % 60)
ut.log_p( 'Test Err:' + str(i) + '\t' + str(auc) + '\t' + str(rmse))
print 'test error: ' + str(mins) + 'm ' + str(secs) + 's'
#stop training when no improvement for a while
if auc>min_err:
min_err=auc
min_err_epoch=i
if times_reduce<3:
times_reduce+=1
else:
times_reduce-=1
if times_reduce<-3:
break
ut.log_p( 'Minimal test error is '+ str( min_err)+' , at EPOCH ' + str(min_err_epoch))