-
Notifications
You must be signed in to change notification settings - Fork 0
/
dien.py
484 lines (435 loc) · 18.3 KB
/
dien.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
"""
Author:
Ze Wang, wangze0801@126.com
Reference:
[1] Zhou G, Mou N, Fan Y, et al. Deep Interest Evolution Network for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1809.03672, 2018. (https://arxiv.org/pdf/1809.03672.pdf)
"""
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from basemodel import BaseModel
from inputs import *
from layers import *
from sequence import AttentionSequencePoolingLayer
class DIEN(BaseModel):
"""Instantiates the Deep Interest Evolution Network architecture.
:param dnn_feature_columns: An iterable containing all the features used by deep part of the model.
:param history_feature_list: list,to indicate sequence sparse field
:param gru_type: str,can be GRU AIGRU AUGRU AGRU
:param use_negsampling: bool, whether or not use negtive sampling
:param alpha: float ,weight of auxiliary_loss
:param use_bn: bool. Whether use BatchNormalization before activation or not in deep net
:param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of DNN
:param dnn_activation: Activation function to use in DNN
:param att_hidden_units: list,list of positive integer , the layer number and units in each layer of attention net
:param att_activation: Activation function to use in attention net
:param att_weight_normalization: bool.Whether normalize the attention score of local activation unit.
:param l2_reg_dnn: float. L2 regularizer strength applied to DNN
:param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
:param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
:param init_std: float,to use as the initialize std of embedding vector
:param seed: integer ,to use as random seed.
:param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss
:param device: str, ``"cpu"`` or ``"cuda:0"``
:param gpus: list of int or torch.device for multiple gpus. If None, run on `device`. `gpus[0]` should be the same gpu with `device`.
:return: A PyTorch model instance.
"""
def __init__(
self,
dnn_feature_columns,
history_feature_list,
gru_type='GRU',
use_negsampling=False,
alpha=1.0,
use_bn=False,
dnn_hidden_units=(256, 128),
dnn_activation='relu',
att_hidden_units=(64, 16),
att_activation='relu',
att_weight_normalization=True,
l2_reg_dnn=0,
l2_reg_embedding=1e-6,
dnn_dropout=0,
init_std=0.0001,
seed=1024,
task='binary',
device='cpu',
gpus=None,
):
super(DIEN, self).__init__(
[],
dnn_feature_columns,
l2_reg_linear=0,
l2_reg_embedding=l2_reg_embedding,
init_std=init_std,
seed=seed,
task=task,
device=device,
gpus=gpus,
)
self.item_features = history_feature_list
self.use_negsampling = use_negsampling
self.alpha = alpha
self._split_columns()
# structure: embedding layer -> interest extractor layer -> interest evolution layer -> DNN layer -> out
# embedding layer
# inherit -> self.embedding_dict
input_size = self._compute_interest_dim()
# interest extractor layer
self.interest_extractor = InterestExtractor(
input_size=input_size, use_neg=use_negsampling, init_std=init_std
)
# interest evolution layer
self.interest_evolution = InterestEvolving(
input_size=input_size,
gru_type=gru_type,
use_neg=use_negsampling,
init_std=init_std,
att_hidden_size=att_hidden_units,
att_activation=att_activation,
att_weight_normalization=att_weight_normalization,
)
# DNN layer
dnn_input_size = self._compute_dnn_dim() + input_size
self.dnn = DNN(
dnn_input_size,
dnn_hidden_units,
dnn_activation,
l2_reg_dnn,
dnn_dropout,
use_bn,
init_std=init_std,
seed=seed,
)
self.linear = nn.Linear(dnn_hidden_units[-1], 1, bias=False)
# prediction layer
# inherit -> self.out
# init
for name, tensor in self.linear.named_parameters():
if 'weight' in name:
nn.init.normal_(tensor, mean=0, std=init_std)
self.to(device)
def forward(self, X):
# [B, H] , [B, T, H], [B, T, H] , [B]
query_emb, keys_emb, neg_keys_emb, keys_length = self._get_emb(X)
# [b, T, H], [1] (b<H)
masked_interest, aux_loss = self.interest_extractor(keys_emb, keys_length, neg_keys_emb)
self.add_auxiliary_loss(aux_loss, self.alpha)
# [B, H]
hist = self.interest_evolution(query_emb, masked_interest, keys_length)
# [B, H2]
deep_input_emb = self._get_deep_input_emb(X)
deep_input_emb = concat_fun([hist, deep_input_emb])
dense_value_list = get_dense_input(X, self.feature_index, self.dense_feature_columns)
dnn_input = combined_dnn_input([deep_input_emb], dense_value_list)
# [B, 1]
output = self.linear(self.dnn(dnn_input))
y_pred = self.out(output)
return y_pred
def _get_emb(self, X):
# history feature columns : pos, neg
history_feature_columns = []
neg_history_feature_columns = []
sparse_varlen_feature_columns = []
history_fc_names = list(map(lambda x: 'hist_' + x, self.item_features))
neg_history_fc_names = list(map(lambda x: 'neg_' + x, history_fc_names))
for fc in self.varlen_sparse_feature_columns:
feature_name = fc.name
if feature_name in history_fc_names:
history_feature_columns.append(fc)
elif feature_name in neg_history_fc_names:
neg_history_feature_columns.append(fc)
else:
sparse_varlen_feature_columns.append(fc)
# convert input to emb
features = self.feature_index
query_emb_list = embedding_lookup(
X,
self.embedding_dict,
features,
self.sparse_feature_columns,
return_feat_list=self.item_features,
to_list=True,
)
# [batch_size, dim]
query_emb = torch.squeeze(concat_fun(query_emb_list), 1)
keys_emb_list = embedding_lookup(
X,
self.embedding_dict,
features,
history_feature_columns,
return_feat_list=history_fc_names,
to_list=True,
)
# [batch_size, max_len, dim]
keys_emb = concat_fun(keys_emb_list)
keys_length_feature_name = [
feat.length_name
for feat in self.varlen_sparse_feature_columns
if feat.length_name is not None
]
# [batch_size]
keys_length = torch.squeeze(maxlen_lookup(X, features, keys_length_feature_name), 1)
if self.use_negsampling:
neg_keys_emb_list = embedding_lookup(
X,
self.embedding_dict,
features,
neg_history_feature_columns,
return_feat_list=neg_history_fc_names,
to_list=True,
)
neg_keys_emb = concat_fun(neg_keys_emb_list)
else:
neg_keys_emb = None
return query_emb, keys_emb, neg_keys_emb, keys_length
def _split_columns(self):
self.sparse_feature_columns = (
list(filter(lambda x: isinstance(x, SparseFeat), self.dnn_feature_columns))
if len(self.dnn_feature_columns)
else []
)
self.dense_feature_columns = (
list(filter(lambda x: isinstance(x, DenseFeat), self.dnn_feature_columns))
if len(self.dnn_feature_columns)
else []
)
self.varlen_sparse_feature_columns = (
list(filter(lambda x: isinstance(x, VarLenSparseFeat), self.dnn_feature_columns))
if len(self.dnn_feature_columns)
else []
)
def _compute_interest_dim(self):
interest_dim = 0
for feat in self.sparse_feature_columns:
if feat.name in self.item_features:
interest_dim += feat.embedding_dim
return interest_dim
def _compute_dnn_dim(self):
dnn_input_dim = 0
for fc in self.sparse_feature_columns:
dnn_input_dim += fc.embedding_dim
for fc in self.dense_feature_columns:
dnn_input_dim += fc.dimension
return dnn_input_dim
def _get_deep_input_emb(self, X):
dnn_input_emb_list = embedding_lookup(
X,
self.embedding_dict,
self.feature_index,
self.sparse_feature_columns,
mask_feat_list=self.item_features,
to_list=True,
)
dnn_input_emb = concat_fun(dnn_input_emb_list)
return dnn_input_emb.squeeze(1)
class InterestExtractor(nn.Module):
def __init__(self, input_size, use_neg=False, init_std=0.001, device='cpu'):
super(InterestExtractor, self).__init__()
self.use_neg = use_neg
self.gru = nn.GRU(input_size=input_size, hidden_size=input_size, batch_first=True)
if self.use_neg:
self.auxiliary_net = DNN(
input_size * 2, [100, 50, 1], 'sigmoid', init_std=init_std, device=device
)
for name, tensor in self.gru.named_parameters():
if 'weight' in name:
nn.init.normal_(tensor, mean=0, std=init_std)
self.to(device)
def forward(self, keys, keys_length, neg_keys=None):
"""
Parameters
----------
keys: 3D tensor, [B, T, H]
keys_length: 1D tensor, [B]
neg_keys: 3D tensor, [B, T, H]
Returns
-------
masked_interests: 2D tensor, [b, H]
aux_loss: [1]
"""
batch_size, max_length, dim = keys.size()
zero_outputs = torch.zeros(batch_size, dim, device=keys.device)
aux_loss = torch.zeros((1,), device=keys.device)
# create zero mask for keys_length, to make sure 'pack_padded_sequence' safe
mask = keys_length > 0
masked_keys_length = keys_length[mask]
# batch_size validation check
if masked_keys_length.shape[0] == 0:
return (zero_outputs,)
masked_keys = torch.masked_select(keys, mask.view(-1, 1, 1)).view(-1, max_length, dim)
packed_keys = pack_padded_sequence(
masked_keys, lengths=masked_keys_length.cpu(), batch_first=True, enforce_sorted=False
)
packed_interests, _ = self.gru(packed_keys)
interests, _ = pad_packed_sequence(
packed_interests, batch_first=True, padding_value=0.0, total_length=max_length
)
if self.use_neg and neg_keys is not None:
masked_neg_keys = torch.masked_select(neg_keys, mask.view(-1, 1, 1)).view(
-1, max_length, dim
)
aux_loss = self._cal_auxiliary_loss(
interests[:, :-1, :],
masked_keys[:, 1:, :],
masked_neg_keys[:, 1:, :],
masked_keys_length - 1,
)
return interests, aux_loss
def _cal_auxiliary_loss(self, states, click_seq, noclick_seq, keys_length):
# keys_length >= 1
mask_shape = keys_length > 0
keys_length = keys_length[mask_shape]
if keys_length.shape[0] == 0:
return torch.zeros((1,), device=states.device)
_, max_seq_length, embedding_size = states.size()
states = torch.masked_select(states, mask_shape.view(-1, 1, 1)).view(
-1, max_seq_length, embedding_size
)
click_seq = torch.masked_select(click_seq, mask_shape.view(-1, 1, 1)).view(
-1, max_seq_length, embedding_size
)
noclick_seq = torch.masked_select(noclick_seq, mask_shape.view(-1, 1, 1)).view(
-1, max_seq_length, embedding_size
)
batch_size = states.size()[0]
mask = (
torch.arange(max_seq_length, device=states.device).repeat(batch_size, 1)
< keys_length.view(-1, 1)
).float()
click_input = torch.cat([states, click_seq], dim=-1)
noclick_input = torch.cat([states, noclick_seq], dim=-1)
embedding_size = embedding_size * 2
click_p = (
self.auxiliary_net(click_input.view(batch_size * max_seq_length, embedding_size))
.view(batch_size, max_seq_length)[mask > 0]
.view(-1, 1)
)
click_target = torch.ones(click_p.size(), dtype=torch.float, device=click_p.device)
noclick_p = (
self.auxiliary_net(noclick_input.view(batch_size * max_seq_length, embedding_size))
.view(batch_size, max_seq_length)[mask > 0]
.view(-1, 1)
)
noclick_target = torch.zeros(noclick_p.size(), dtype=torch.float, device=noclick_p.device)
loss = F.binary_cross_entropy(
torch.cat([click_p, noclick_p], dim=0),
torch.cat([click_target, noclick_target], dim=0),
)
return loss
class InterestEvolving(nn.Module):
__SUPPORTED_GRU_TYPE__ = ['GRU', 'AIGRU', 'AGRU', 'AUGRU']
def __init__(
self,
input_size,
gru_type='GRU',
use_neg=False,
init_std=0.001,
att_hidden_size=(64, 16),
att_activation='sigmoid',
att_weight_normalization=False,
):
super(InterestEvolving, self).__init__()
if gru_type not in InterestEvolving.__SUPPORTED_GRU_TYPE__:
raise NotImplementedError('gru_type: {gru_type} is not supported')
self.gru_type = gru_type
self.use_neg = use_neg
if gru_type == 'GRU':
self.attention = AttentionSequencePoolingLayer(
embedding_dim=input_size,
att_hidden_units=att_hidden_size,
att_activation=att_activation,
weight_normalization=att_weight_normalization,
return_score=False,
)
self.interest_evolution = nn.GRU(
input_size=input_size, hidden_size=input_size, batch_first=True
)
elif gru_type == 'AIGRU':
self.attention = AttentionSequencePoolingLayer(
embedding_dim=input_size,
att_hidden_units=att_hidden_size,
att_activation=att_activation,
weight_normalization=att_weight_normalization,
return_score=True,
)
self.interest_evolution = nn.GRU(
input_size=input_size, hidden_size=input_size, batch_first=True
)
elif gru_type == 'AGRU' or gru_type == 'AUGRU':
self.attention = AttentionSequencePoolingLayer(
embedding_dim=input_size,
att_hidden_units=att_hidden_size,
att_activation=att_activation,
weight_normalization=att_weight_normalization,
return_score=True,
)
self.interest_evolution = DynamicGRU(
input_size=input_size, hidden_size=input_size, gru_type=gru_type
)
for name, tensor in self.interest_evolution.named_parameters():
if 'weight' in name:
nn.init.normal_(tensor, mean=0, std=init_std)
@staticmethod
def _get_last_state(states, keys_length):
# states [B, T, H]
batch_size, max_seq_length, _ = states.size()
mask = torch.arange(max_seq_length, device=keys_length.device).repeat(batch_size, 1) == (
keys_length.view(-1, 1) - 1
)
return states[mask]
def forward(self, query, keys, keys_length, mask=None):
"""
Parameters
----------
query: 2D tensor, [B, H]
keys: (masked_interests), 3D tensor, [b, T, H]
keys_length: 1D tensor, [B]
Returns
-------
outputs: 2D tensor, [B, H]
"""
batch_size, dim = query.size()
max_length = keys.size()[1]
# check batch validation
zero_outputs = torch.zeros(batch_size, dim, device=query.device)
mask = keys_length > 0
# [B] -> [b]
keys_length = keys_length[mask]
if keys_length.shape[0] == 0:
return zero_outputs
# [B, H] -> [b, 1, H]
query = torch.masked_select(query, mask.view(-1, 1)).view(-1, dim).unsqueeze(1)
if self.gru_type == 'GRU':
packed_keys = pack_padded_sequence(
keys, lengths=keys_length.cpu(), batch_first=True, enforce_sorted=False
)
packed_interests, _ = self.interest_evolution(packed_keys)
interests, _ = pad_packed_sequence(
packed_interests, batch_first=True, padding_value=0.0, total_length=max_length
)
outputs = self.attention(query, interests, keys_length.unsqueeze(1)) # [b, 1, H]
outputs = outputs.squeeze(1) # [b, H]
elif self.gru_type == 'AIGRU':
att_scores = self.attention(query, keys, keys_length.unsqueeze(1)) # [b, 1, T]
interests = keys * att_scores.transpose(1, 2) # [b, T, H]
packed_interests = pack_padded_sequence(
interests, lengths=keys_length.cpu(), batch_first=True, enforce_sorted=False
)
_, outputs = self.interest_evolution(packed_interests)
outputs = outputs.squeeze(0) # [b, H]
elif self.gru_type == 'AGRU' or self.gru_type == 'AUGRU':
att_scores = self.attention(query, keys, keys_length.unsqueeze(1)).squeeze(1) # [b, T]
packed_interests = pack_padded_sequence(
keys, lengths=keys_length.cpu(), batch_first=True, enforce_sorted=False
)
packed_scores = pack_padded_sequence(
att_scores, lengths=keys_length.cpu(), batch_first=True, enforce_sorted=False
)
outputs = self.interest_evolution(packed_interests, packed_scores)
outputs, _ = pad_packed_sequence(
outputs, batch_first=True, padding_value=0.0, total_length=max_length
)
# pick last state
outputs = InterestEvolving._get_last_state(outputs, keys_length) # [b, H]
# [b, H] -> [B, H]
zero_outputs[mask] = outputs
return zero_outputs