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GMF.py
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GMF.py
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'''
Created on Aug 9, 2016
Keras Implementation of Generalized Matrix Factorization (GMF) recommender model in:
He Xiangnan et al. Neural Collaborative Filtering. In WWW 2017.
@author: Xiangnan He ([email protected])
'''
import numpy as np
import keras
from keras import backend as K
from keras import initializers
from keras.models import Sequential, Model, load_model, save_model
from keras.layers.core import Dense, Lambda, Activation
from keras.layers import Embedding, Input, Dense, merge, Reshape, Merge, Flatten
from keras.optimizers import Adagrad, Adam, SGD, RMSprop
from keras.regularizers import l2
from Dataset import Dataset
from evaluate import evaluate_model
from time import time
import multiprocessing as mp
import sys
import math
import argparse
#################### Arguments ####################
def parse_args():
parser = argparse.ArgumentParser(description="Run GMF.")
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.')
parser.add_argument('--regs', nargs='?', default='[0,0]',
help="Regularization for user and item 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.')
return parser.parse_args()
def init_normal(shape, name=None):
return initializers.normal(shape, scale=0.01, name=name)
def get_model(num_users, num_items, latent_dim, regs=[0,0]):
# Input variables
user_input = Input(shape=(1,), dtype='int32', name = 'user_input')
item_input = Input(shape=(1,), dtype='int32', name = 'item_input')
MF_Embedding_User = Embedding(input_dim = num_users, output_dim = latent_dim, name = 'user_embedding',
embeddings_initializer = keras.initializers.RandomNormal(mean=0.0, stddev=0.01, seed=None), embeddings_regularizer = l2(regs[0]), input_length=1)
MF_Embedding_Item = Embedding(input_dim = num_items, output_dim = latent_dim, name = 'item_embedding',
embeddings_initializer = keras.initializers.RandomNormal(mean=0.0, stddev=0.01, seed=None), embeddings_regularizer = l2(regs[1]), input_length=1)
# Crucial to flatten an embedding vector!
user_latent = Flatten()(MF_Embedding_User(user_input))
item_latent = Flatten()(MF_Embedding_Item(item_input))
# Element-wise product of user and item embeddings
#predict_vector = merge([user_latent, item_latent], mode = 'mul')
predict_vector = keras.layers.Multiply()([user_latent, item_latent])
# Final prediction layer
#prediction = Lambda(lambda x: K.sigmoid(K.sum(x)), output_shape=(1,))(predict_vector)
prediction = Dense(1, activation='sigmoid', kernel_initializer='lecun_uniform', name = 'prediction')(predict_vector)
model = Model(inputs=[user_input, item_input],
outputs=[prediction])
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 range(num_negatives):
j = np.random.randint(num_items)
while (u, j) in train: #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_factors = args.num_factors
regs = eval(args.regs)
num_negatives = args.num_neg
learner = args.learner
learning_rate = args.lr
epochs = args.epochs
batch_size = args.batch_size
verbose = args.verbose
topK = 10
evaluation_threads = 1 #mp.cpu_count()
print("GMF arguments: %s" %(args))
model_out_file = 'Pretrain/%s_GMF_%d_%d.h5' %(args.dataset, num_factors, 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, num_factors, regs)
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')
#print(model.summary())
# Init performance
t1 = time()
(hits, ndcgs) = evaluate_model(model, testRatings, testNegatives, topK, evaluation_threads)
hr, ndcg = np.array(hits).mean(), np.array(ndcgs).mean()
#mf_embedding_norm = np.linalg.norm(model.get_layer('user_embedding').get_weights())+np.linalg.norm(model.get_layer('item_embedding').get_weights())
#p_norm = np.linalg.norm(model.get_layer('prediction').get_weights()[0])
print('Init: HR = %.4f, NDCG = %.4f\t [%.1f s]' % (hr, ndcg, time()-t1))
# Train model
best_hr, best_ndcg, best_iter = hr, ndcg, -1
for epoch in range(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, epochs=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 GMF model is saved to %s" %(model_out_file))