forked from hexiangnan/neural_collaborative_filtering
-
Notifications
You must be signed in to change notification settings - Fork 0
/
NeuMF.py
234 lines (206 loc) · 10.9 KB
/
NeuMF.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
'''
Created on Aug 9, 2016
Keras Implementation of Neural Matrix Factorization (NeuMF) recommender model in:
He Xiangnan et al. Neural Collaborative Filtering. In WWW 2017.
@author: Xiangnan He ([email protected])
'''
import numpy as np
import theano
import theano.tensor as T
import keras
from keras import backend as K
from keras import initializations
from keras.regularizers import l1, l2, l1l2
from keras.models import Sequential, Model
from keras.layers.core import Dense, Lambda, Activation
from keras.layers import Embedding, Input, Dense, merge, Reshape, Merge, Flatten, Dropout
from keras.optimizers import Adagrad, Adam, SGD, RMSprop
from evaluate import evaluate_model
from Dataset import Dataset
from time import time
import sys
import GMF, MLP
import argparse
#################### Arguments ####################
def parse_args():
parser = argparse.ArgumentParser(description="Run NeuMF.")
parser.add_argument('--path', nargs='?', default='Data/',
help='Input data path.')
parser.add_argument('--dataset', nargs='?', default='ml-1m',
help='Choose a dataset.')
parser.add_argument('--epochs', type=int, default=100,
help='Number of epochs.')
parser.add_argument('--batch_size', type=int, default=256,
help='Batch size.')
parser.add_argument('--num_factors', type=int, default=8,
help='Embedding size of MF model.')
parser.add_argument('--layers', nargs='?', default='[64,32,16,8]',
help="MLP layers. Note that the first layer is the concatenation of user and item embeddings. So layers[0]/2 is the embedding size.")
parser.add_argument('--reg_mf', type=float, default=0,
help='Regularization for MF embeddings.')
parser.add_argument('--reg_layers', nargs='?', default='[0,0,0,0]',
help="Regularization for each MLP layer. reg_layers[0] is the regularization for embeddings.")
parser.add_argument('--num_neg', type=int, default=4,
help='Number of negative instances to pair with a positive instance.')
parser.add_argument('--lr', type=float, default=0.001,
help='Learning rate.')
parser.add_argument('--learner', nargs='?', default='adam',
help='Specify an optimizer: adagrad, adam, rmsprop, sgd')
parser.add_argument('--verbose', type=int, default=1,
help='Show performance per X iterations')
parser.add_argument('--out', type=int, default=1,
help='Whether to save the trained model.')
parser.add_argument('--mf_pretrain', nargs='?', default='',
help='Specify the pretrain model file for MF part. If empty, no pretrain will be used')
parser.add_argument('--mlp_pretrain', nargs='?', default='',
help='Specify the pretrain model file for MLP part. If empty, no pretrain will be used')
return parser.parse_args()
def init_normal(shape, name=None):
return initializations.normal(shape, scale=0.01, name=name)
def get_model(num_users, num_items, mf_dim=10, layers=[10], reg_layers=[0], reg_mf=0):
assert len(layers) == len(reg_layers)
num_layer = len(layers) #Number of layers in the MLP
# Input variables
user_input = Input(shape=(1,), dtype='int32', name = 'user_input')
item_input = Input(shape=(1,), dtype='int32', name = 'item_input')
# Embedding layer
MF_Embedding_User = Embedding(input_dim = num_users, output_dim = mf_dim, name = 'mf_embedding_user',
init = init_normal, W_regularizer = l2(reg_mf), input_length=1)
MF_Embedding_Item = Embedding(input_dim = num_items, output_dim = mf_dim, name = 'mf_embedding_item',
init = init_normal, W_regularizer = l2(reg_mf), input_length=1)
MLP_Embedding_User = Embedding(input_dim = num_users, output_dim = layers[0]/2, name = "mlp_embedding_user",
init = init_normal, W_regularizer = l2(reg_layers[0]), input_length=1)
MLP_Embedding_Item = Embedding(input_dim = num_items, output_dim = layers[0]/2, name = 'mlp_embedding_item',
init = init_normal, W_regularizer = l2(reg_layers[0]), input_length=1)
# MF part
mf_user_latent = Flatten()(MF_Embedding_User(user_input))
mf_item_latent = Flatten()(MF_Embedding_Item(item_input))
mf_vector = merge([mf_user_latent, mf_item_latent], mode = 'mul') # element-wise multiply
# MLP part
mlp_user_latent = Flatten()(MLP_Embedding_User(user_input))
mlp_item_latent = Flatten()(MLP_Embedding_Item(item_input))
mlp_vector = merge([mlp_user_latent, mlp_item_latent], mode = 'concat')
for idx in xrange(1, num_layer):
layer = Dense(layers[idx], W_regularizer= l2(reg_layers[idx]), activation='relu', name="layer%d" %idx)
mlp_vector = layer(mlp_vector)
# Concatenate MF and MLP parts
#mf_vector = Lambda(lambda x: x * alpha)(mf_vector)
#mlp_vector = Lambda(lambda x : x * (1-alpha))(mlp_vector)
predict_vector = merge([mf_vector, mlp_vector], mode = 'concat')
# Final prediction layer
prediction = Dense(1, activation='sigmoid', init='lecun_uniform', name = "prediction")(predict_vector)
model = Model(input=[user_input, item_input],
output=prediction)
return model
def load_pretrain_model(model, gmf_model, mlp_model, num_layers):
# MF embeddings
gmf_user_embeddings = gmf_model.get_layer('user_embedding').get_weights()
gmf_item_embeddings = gmf_model.get_layer('item_embedding').get_weights()
model.get_layer('mf_embedding_user').set_weights(gmf_user_embeddings)
model.get_layer('mf_embedding_item').set_weights(gmf_item_embeddings)
# MLP embeddings
mlp_user_embeddings = mlp_model.get_layer('user_embedding').get_weights()
mlp_item_embeddings = mlp_model.get_layer('item_embedding').get_weights()
model.get_layer('mlp_embedding_user').set_weights(mlp_user_embeddings)
model.get_layer('mlp_embedding_item').set_weights(mlp_item_embeddings)
# MLP layers
for i in xrange(1, num_layers):
mlp_layer_weights = mlp_model.get_layer('layer%d' %i).get_weights()
model.get_layer('layer%d' %i).set_weights(mlp_layer_weights)
# Prediction weights
gmf_prediction = gmf_model.get_layer('prediction').get_weights()
mlp_prediction = mlp_model.get_layer('prediction').get_weights()
new_weights = np.concatenate((gmf_prediction[0], mlp_prediction[0]), axis=0)
new_b = gmf_prediction[1] + mlp_prediction[1]
model.get_layer('prediction').set_weights([0.5*new_weights, 0.5*new_b])
return model
def get_train_instances(train, num_negatives):
user_input, item_input, labels = [],[],[]
num_users = train.shape[0]
for (u, i) in train.keys():
# positive instance
user_input.append(u)
item_input.append(i)
labels.append(1)
# negative instances
for t in xrange(num_negatives):
j = np.random.randint(num_items)
while train.has_key((u, j)):
j = np.random.randint(num_items)
user_input.append(u)
item_input.append(j)
labels.append(0)
return user_input, item_input, labels
if __name__ == '__main__':
args = parse_args()
num_epochs = args.epochs
batch_size = args.batch_size
mf_dim = args.num_factors
layers = eval(args.layers)
reg_mf = args.reg_mf
reg_layers = eval(args.reg_layers)
num_negatives = args.num_neg
learning_rate = args.lr
learner = args.learner
verbose = args.verbose
mf_pretrain = args.mf_pretrain
mlp_pretrain = args.mlp_pretrain
topK = 10
evaluation_threads = 1#mp.cpu_count()
print("NeuMF arguments: %s " %(args))
model_out_file = 'Pretrain/%s_NeuMF_%d_%s_%d.h5' %(args.dataset, mf_dim, args.layers, time())
# Loading data
t1 = time()
dataset = Dataset(args.path + args.dataset)
train, testRatings, testNegatives = dataset.trainMatrix, dataset.testRatings, dataset.testNegatives
num_users, num_items = train.shape
print("Load data done [%.1f s]. #user=%d, #item=%d, #train=%d, #test=%d"
%(time()-t1, num_users, num_items, train.nnz, len(testRatings)))
# Build model
model = get_model(num_users, num_items, mf_dim, layers, reg_layers, reg_mf)
if learner.lower() == "adagrad":
model.compile(optimizer=Adagrad(lr=learning_rate), loss='binary_crossentropy')
elif learner.lower() == "rmsprop":
model.compile(optimizer=RMSprop(lr=learning_rate), loss='binary_crossentropy')
elif learner.lower() == "adam":
model.compile(optimizer=Adam(lr=learning_rate), loss='binary_crossentropy')
else:
model.compile(optimizer=SGD(lr=learning_rate), loss='binary_crossentropy')
# Load pretrain model
if mf_pretrain != '' and mlp_pretrain != '':
gmf_model = GMF.get_model(num_users,num_items,mf_dim)
gmf_model.load_weights(mf_pretrain)
mlp_model = MLP.get_model(num_users,num_items, layers, reg_layers)
mlp_model.load_weights(mlp_pretrain)
model = load_pretrain_model(model, gmf_model, mlp_model, len(layers))
print("Load pretrained GMF (%s) and MLP (%s) models done. " %(mf_pretrain, mlp_pretrain))
# Init performance
(hits, ndcgs) = evaluate_model(model, testRatings, testNegatives, topK, evaluation_threads)
hr, ndcg = np.array(hits).mean(), np.array(ndcgs).mean()
print('Init: HR = %.4f, NDCG = %.4f' % (hr, ndcg))
best_hr, best_ndcg, best_iter = hr, ndcg, -1
if args.out > 0:
model.save_weights(model_out_file, overwrite=True)
# Training model
for epoch in xrange(num_epochs):
t1 = time()
# Generate training instances
user_input, item_input, labels = get_train_instances(train, num_negatives)
# Training
hist = model.fit([np.array(user_input), np.array(item_input)], #input
np.array(labels), # labels
batch_size=batch_size, nb_epoch=1, verbose=0, shuffle=True)
t2 = time()
# Evaluation
if epoch %verbose == 0:
(hits, ndcgs) = evaluate_model(model, testRatings, testNegatives, topK, evaluation_threads)
hr, ndcg, loss = np.array(hits).mean(), np.array(ndcgs).mean(), hist.history['loss'][0]
print('Iteration %d [%.1f s]: HR = %.4f, NDCG = %.4f, loss = %.4f [%.1f s]'
% (epoch, t2-t1, hr, ndcg, loss, time()-t2))
if hr > best_hr:
best_hr, best_ndcg, best_iter = hr, ndcg, epoch
if args.out > 0:
model.save_weights(model_out_file, overwrite=True)
print("End. Best Iteration %d: HR = %.4f, NDCG = %.4f. " %(best_iter, best_hr, best_ndcg))
if args.out > 0:
print("The best NeuMF model is saved to %s" %(model_out_file))