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DPJFMBS.py
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DPJFMBS.py
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import torch
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence
import numpy as np
import os
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
WORD_EMBED_DIM = 32
USER_EMBED_DIM = 32
MAX_PROFILELEN = 20
MAX_TERMLEN = 50
MEMORY_SLOT = 5
class Model(torch.nn.Module):
def __init__(self, word_embeddings, behavior_typenum):
super(Model, self).__init__()
self.behavior_typenum = behavior_typenum
print('MEMORY_SLOT=', MEMORY_SLOT)
# profile: word embeddings for look_up
# embedding_matrix = [[0...0], [...], ...[]]
self.word_embeddings = torch.nn.Embedding.from_pretrained(word_embeddings, padding_idx=0)
self.word_embeddings.weight.requires_grad = False
# bilstm: int(USER_EMBED_DIM/2) * 2 = USER_EMBED_DIM
self.words_gru = torch.nn.GRU(input_size=WORD_EMBED_DIM, hidden_size=int(USER_EMBED_DIM/2),
num_layers=1, batch_first=True, bidirectional=True, dropout=0.)
# attention layer
self.words_attention_layer = torch.nn.Sequential(
torch.nn.Linear(USER_EMBED_DIM, USER_EMBED_DIM),
torch.nn.Tanh(), # LeakyReLU
torch.nn.Linear(USER_EMBED_DIM, 1, bias=False),
)
# attention layer
self.terms_attention_layer = torch.nn.Sequential(
torch.nn.Linear(USER_EMBED_DIM, USER_EMBED_DIM),
torch.nn.Tanh(),
torch.nn.Linear(USER_EMBED_DIM, 1, bias=False),
)
self.KEY_MATs = []
for i in range(self.behavior_typenum):
# [MEMORY_SLOT, USER_EMBED_DIM]
self.KEY_MATs.append(Variable(torch.randn((MEMORY_SLOT, USER_EMBED_DIM)), requires_grad = True).float().to(DEVICE))
self.update_mlp = torch.nn.Sequential(
torch.nn.Linear(USER_EMBED_DIM, USER_EMBED_DIM),
torch.nn.Sigmoid(),
)
self.e2j_MTL_MLPs = []
for i in range(self.behavior_typenum-1):
self.e2j_MTL_MLPs.append(torch.nn.Sequential(
torch.nn.Linear( 4 *USER_EMBED_DIM, USER_EMBED_DIM),
torch.nn.Tanh(),
torch.nn.Linear( USER_EMBED_DIM, 1),
torch.nn.Sigmoid()).to(DEVICE))
self.j2e_MTL_MLPs = []
for i in range(self.behavior_typenum-1):
self.j2e_MTL_MLPs.append(torch.nn.Sequential(
torch.nn.Linear( 4 *USER_EMBED_DIM, USER_EMBED_DIM),
torch.nn.Tanh(),
torch.nn.Linear( USER_EMBED_DIM, 1),
torch.nn.Sigmoid()).to(DEVICE))
self.match_MLP = torch.nn.Sequential(
torch.nn.Linear( 4 *USER_EMBED_DIM, USER_EMBED_DIM),
torch.nn.Tanh(),
torch.nn.Linear( USER_EMBED_DIM, 1),
torch.nn.Sigmoid()).to(DEVICE)
self.e2j_MTL_MLPs.append(self.match_MLP)
self.j2e_MTL_MLPs.append(self.match_MLP)
# profiles: [batch_size, MAX_PROFILELEN, MAX_TERMLEN] = (40, 15, 50), word idx
# return: [batch_size, USER_EMBED_DIM]
def get_profile_embeddings(self, profiles, isexpect):
# word level:
# [batch_size, MAX_PROFILELEN, MAX_TERMLEN] (40, 15, 50) ->
# [batch_size * MAX_PROFILELEN, MAX_TERMLEN](40 * 15, 50)
shape = profiles.shape
profiles_ = profiles.view([-1, shape[-1]])
# sort expects_sample_: large to small
# sorted [batch_size * MAX_PROFILELEN, MAX_TERMLEN](40 * 15, 50)
lens = (profiles_ > 0).sum(dim=-1)
lens_sort, ind_sort = lens.sort(dim=0, descending=True)
profiles_sort = profiles_[ind_sort]
# embeddings: [batch_size * MAX_PROFILELEN, MAX_TERMLEN, EMBED_DIM]
profile_embed = self.word_embeddings(profiles_sort).float()
# compress: [batch_size * MAX_PROFILELEN, MAX_TERMLEN, EMBED_DIM]
profile_pack = pack_padded_sequence(profile_embed, lens_sort, batch_first=True)
words_output, _ = self.words_gru(profile_pack)
# output: [batch_size * MAX_PROFILELEN, MAX_TERMLEN, words_lstm_hidden_dims * 2]
words_output_, _ = torch.nn.utils.rnn.pad_packed_sequence(words_output, batch_first=True)
# attention: [batch_size * MAX_PROFILELEN, MAX_TERMLEN, words_lstm_hidden_dims * 2]
words_attention = F.softmax(self.words_attention_layer(words_output_), dim=-2)
# [batch_size * MAX_PROFILELEN, MAX_TERMLEN, words_lstm_hidden_dims * 2] ->
# [batch_size * MAX_PROFILELEN, words_lstm_hidden_dims * 2]
terms_ = (words_attention * words_output_).sum(-2)\
.view([shape[0], MAX_PROFILELEN, USER_EMBED_DIM])
# (batch, 1, USER_EMBED_DIM) * (batch_size, MAX_PROFILELEN, hidden_size) ->
# (batch, MAX_PROFILELEN, USER_EMBED_DIM) -> softmax(batch, MAX_PROFILELEN, 1)
attention = torch.softmax(self.terms_attention_layer(terms_), dim=-2)
# (batch, MAX_PROFILELEN, 1) * (batch_size, MAX_PROFILELEN, hidden_size) ->
# (batch, hidden_size)
profile_embeddings = torch.sum(attention * terms_, dim=1)
return profile_embeddings
# memory: [batch, MEMORY_SLOT, USER_EMBED_DIM]
# b_embedding： [batch, USER_EMBED_DIM]
# col_label: []
# return: [batch, USER_EMBED_DIM]
def read(self, memory, b_embedding, col_label):
preference = None
for i in range(self.behavior_typenum):
# [batch, USER_EMBED_DIM] mm [MEMORY_SLOT, USER_EMBED_DIM].T -> [batch, MEMORY_SLOT]
attention = torch.softmax(torch.matmul(b_embedding, torch.transpose(self.KEY_MATs[i], dim0=1, dim1=0)), dim=-1)
# [batch, 1, MEMORY_SLOT] bmm [batch, MEMORY_SLOT, USER_EMBED_DIM] -> [batch, 1, USER_EMBED_DIM]
# -> [batch, USER_EMBED_DIM]
embedding = torch.squeeze(torch.bmm(torch.unsqueeze(attention, 1), memory), 1)
mask_label = torch.from_numpy(np.array([label == i for label in col_label])).float().to(DEVICE)
if i==0:
preference = embedding * torch.unsqueeze(mask_label, -1)
else:
preference += embedding * torch.unsqueeze(mask_label, -1)
return preference
# memory: [batch, MEMORY_SLOT, USER_EMBED_DIM]
# b_embedding： [batch, USER_EMBED_DIM]
# col_label: []
# col_mask: tensor([])
# return: [batch, MEMORY_SLOT, USER_EMBED_DIM]
def update(self, memory, b_embedding, col_label, col_mask, isaexpect):
for i in range(self.behavior_typenum):
# [batch, USER_EMBED_DIM] mm [MEMORY_SLOT, USER_EMBED_DIM].T -> [batch, MEMORY_SLOT]
attention = torch.softmax(torch.matmul(b_embedding, torch.transpose(self.KEY_MATs[i], dim0=1, dim1=0)), dim=-1)
# [batch, MEMORY_SLOT, 1] * [batch, 1, USER_EMBED_DIM] -> [batch, MEMORY_SLOT, USER_EMBED_DIM
update = torch.unsqueeze(attention, -1) * torch.unsqueeze(self.update_mlp(b_embedding), 1)
# [batch, MEMORY_SLOT, USER_EMBED_DIM] * [batch, MEMORY_SLOT, 1]
new_memory = memory * (1 - torch.unsqueeze(attention.to(DEVICE), -1)) + update
# [batch]
mask_label = torch.from_numpy(np.array([label==i for label in col_label])).float().to(DEVICE)
mask = mask_label * col_mask
memory = memory * (1-mask.view([len(col_label),1,1])) + new_memory * mask.view([len(col_label),1,1])
return memory
# b_profiless: [batch, max_seq_len, sent, word] [1, 3, 20, 50], gpu tensor
# b_seq_lens: [], list
# b_seq_labels: [[], ...], 0,1,2,3
# return: [batch, MEMORY_SLOT, USER_EMBED_DIM]
def process_seq(self, b_seq_profiless, b_seq_lens, b_seq_tlabels, isaexpect=True):
# memory
batch_memory = torch.from_numpy(np.zeros((len(b_seq_lens), MEMORY_SLOT, USER_EMBED_DIM))).float().to(DEVICE)
for i in range(max(b_seq_lens)):
# [1,0,... ]
col_mask = torch.from_numpy((np.array(b_seq_lens)-i>0)+0.).float().to(DEVICE)
col_label = [bstls[i] if len(bstls)>i+1 else 0 for bstls in b_seq_tlabels]
# [batch, USER_EMBED_DIM]
batch_b_embedding = self.get_profile_embeddings(b_seq_profiless[:, i, :, :].contiguous(), not isaexpect)
batch_memory = self.update(batch_memory, batch_b_embedding, col_label, col_mask, isaexpect)
return batch_memory
# expect_memory: [batch, MEMORY_SLOT, USER_EMBED_DIM]
# job_profiles: [batch, sent, word]
# job_memory: [batch, MEMORY_SLOT, USER_EMBED_DIM]
# expect_profiles: [batch, sent, word]
# label: [], list
def predict(self, expect_memory, job_profiles, job_memory, expect_profiles, tlabel, ise2j):
# read expect
job_embedding = self.get_profile_embeddings(job_profiles, False)
expect_preference = self.read(expect_memory, job_embedding, tlabel)
# read job
expect_embedding = self.get_profile_embeddings(expect_profiles, True)
job_preference = self.read(job_memory, expect_embedding, tlabel)
# interact
e2j_interact = expect_preference * job_embedding
j2e_interact = job_preference * expect_embedding
feature = torch.cat([e2j_interact, job_embedding, j2e_interact, expect_embedding], dim=-1)
if ise2j:
e2j_scores = None
for i in range(self.behavior_typenum):
mask_label = torch.from_numpy(np.array([label == i for label in tlabel])).float().to(DEVICE)
score = torch.squeeze(self.e2j_MTL_MLPs[i](feature), -1)
if i == 0:
e2j_scores = score * mask_label
else:
e2j_scores += score * mask_label
return e2j_scores
else:
j2e_scores = None
for i in range(self.behavior_typenum):
mask_label = torch.from_numpy(np.array([label == i for label in tlabel])).float().to(DEVICE)
score = torch.squeeze(self.j2e_MTL_MLPs[i](feature), -1)
if i==0:
j2e_scores = score * mask_label
else:
j2e_scores += score * mask_label
return j2e_scores