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Model.py
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__author__ = 'jingyuan'
import theano
import theano.tensor as T
from UsrEmblayer import *
from VidEmblayer import *
from GetuEmbLayer import *
from GetvEmbLayer import *
from AttentionLayer_Feat import *
from AttentionLayer_Item import *
from ContentEmbLayer import *
import numpy as np
import math
def getHitRatio(ranklist, gtItem):
for item in ranklist:
if item == gtItem:
return 1
return 0
def getNDCG(ranklist, gtItem):
for i in xrange(len(ranklist)):
item = ranklist[i]
if item == gtItem:
return math.log(2) / math.log(i+2)
return 0
def softmask(x):
y = np.exp(x)
#y = y * mask
sumx = np.sum(y,axis=0)
#x = y/sumx.dimshuffle(0,'x')
x=y/sumx
return x
class Model(object):
def __init__(self,trainset,testset,num_user,num_item,dim,reg,lr,prefix):
self.trainset = trainset
self.testset = testset
self.reg = numpy.float32(reg)
self.lr = numpy.float32(lr)
self.num_item = num_item
self.video_features = theano.shared(value=self.trainset.video_features, name='video_features', borrow=True)
T.config.compute_test_value = 'warn'
u = T.ivector('u') #[num_sample,]
iv = T.ivector('iv') #[num_sample,]
jv = T.ivector('jv') #[num_sample,]
mask_frame = T.itensor3('mask_frame') #[num_sample, num_video, num_frame]
mask = T.imatrix('mask') #[num_sample, num_video]
feat_idx = T.imatrix('feat_idx') #[num_sample, num_video]
u.tag.test_value = np.asarray([0,1,2],dtype='int32')
iv.tag.test_value = np.asarray([4,5,2],dtype='int32')
jv.tag.test_value = np.asarray([1,3,0],dtype='int32')
mask.tag.test_value = np.asarray([[1,1,0],[1,0,0],[1,1,1]],dtype='int32')
feat_idx.tag.test_value = np.asarray([[3,4,-1],[5,-1,-1],[6,2,4]],dtype='int32')
mask_frame.tag.test_value = self.trainset.frame_mask.take(feat_idx.tag.test_value,axis=0)
rng = np.random
layers = []
Uemb = UsrEmblayer(rng,num_user,dim,'usremblayer',prefix)
Vemb = VidEmblayer(rng,num_item,dim,'videmblayer',prefix)
feat = self.video_features.take(feat_idx,axis=0) #[num_sample, num_video,dim_feat]
layers.append(Uemb)
layers.append(Vemb)
uemb_vec = GetuEmbLayer(u,Uemb.output,'uemb',prefix)
iemb_vec = GetvEmbLayer(iv,Vemb.output,'v1emb',prefix)
jemb_vec = GetvEmbLayer(jv,Vemb.output,'v2emb',prefix)
layers.append(AttentionLayer_Feat(rng, 2048, uemb_vec.output, feat, dim, dim, mask_frame, 'attentionlayer_feat',prefix))
layers.append(AttentionLayer_Item(rng, uemb_vec.output, layers[-1].output,dim,dim,mask,'attentionlayer_item',prefix))
u_vec = uemb_vec.output + layers[-1].output
self.layers = layers
y_ui = T.dot(u_vec, iemb_vec.output.T).diagonal()
y_uj = T.dot(u_vec, jemb_vec.output.T).diagonal()
self.params = []
loss = - T.sum(T.log(T.nnet.sigmoid(y_ui - y_uj)))
for layer in layers:
self.params += layer.params #[U,V,W_Tran,Wu,Wv,b,c]
#regularizer = self.reg * ((uemb_vec.output ** 2).sum() + (iemb_vec.output ** 2).sum() + (jemb_vec.output ** 2).sum() +
# (self.params[2] ** 2).sum() + (self.params[3] ** 2).sum() + (self.params[4] ** 2).sum() +
# (self.params[5] ** 2).sum())
regularizer = self.reg * ((uemb_vec.output ** 2).sum() + (iemb_vec.output ** 2).sum() + (jemb_vec.output ** 2).sum() )
for param in self.params[2:]:
regularizer += self.reg * (param ** 2).sum()
loss = regularizer + loss
updates = [(param, param-self.lr*T.grad(loss,param)) for param in self.params]
self.train_model = theano.function(
inputs = [u,iv,jv,mask_frame,mask,feat_idx],
outputs = loss,
updates=updates
)
self.test_model = theano.function(
inputs = [u,mask_frame,mask,feat_idx],
outputs= [u_vec,Vemb.output],
)
def train(self, iters):
self.trainset.shuffle_data()
lst = np.random.randint(self.trainset.epoch, size=iters)
n = 0
for i in lst:
n += 1
users, pos_items, neg_items, mask_frame, mask, feat_idx = self.trainset.get_batch(i)
out = self.train_model(users,pos_items,neg_items,mask_frame,mask,feat_idx)
print n, 'cost:', out
def test(self,topK):
for i in xrange(self.testset.epoch):
user_list, mask_frame,mask,feats_idx = self.testset.get_batch(i)
[user_vector, V_matrix] = self.test_model(user_list, mask_frame,mask,feats_idx)
#V_value = np.asarray(V_matrix.eval())
score_maxtrix = np.dot(user_vector,V_matrix.tranpose())
index_top_K = score_maxtrix.argsort()[:,-topK:][:,::-1]
hr = 0
ndcg = 0
for kk in xrange(len(user_vector)):
hr += getHitRatio(index_top_K[kk],self.testset.v[i][kk])
ndcg += getNDCG(index_top_K[kk],self.testset.v[i][kk])
return hr/len(user_vector),ndcg/len(user_vector)
def save(self, prefix):
for layer in self.layers:
layer.save(prefix)