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MLP.py
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MLP.py
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'''
Created on Aug 9, 2016
Keras Implementation of Multi-Layer Perceptron (GMF) 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 l2, activity_l2
from keras.models import Sequential, Graph, Model
from keras.layers.core import Dense, Lambda, Activation
from keras.layers import Embedding, Input, Dense, merge, Reshape, Merge, Flatten, Dropout
from keras.constraints import maxnorm
from keras.optimizers import Adagrad, Adam, SGD, RMSprop
from evaluate import evaluate_model
from Dataset import Dataset
from time import time
import sys
import argparse
import multiprocessing as mp
#################### Arguments ####################
def parse_args():
parser = argparse.ArgumentParser(description="Run MLP.")
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('--layers', nargs='?', default='[64,32,16,8]',
help="Size of each layer. 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_layers', nargs='?', default='[0,0,0,0]',
help="Regularization for each layer")
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 initializations.normal(shape, scale=0.01, name=name)
def get_model(num_users, num_items, layers = [20,10], reg_layers=[0,0]):
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')
MLP_Embedding_User = Embedding(input_dim = num_users, output_dim = layers[0]/2, name = 'user_embedding',
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 = 'item_embedding',
init = init_normal, W_regularizer = l2(reg_layers[0]), input_length=1)
# Crucial to flatten an embedding vector!
user_latent = Flatten()(MLP_Embedding_User(user_input))
item_latent = Flatten()(MLP_Embedding_Item(item_input))
# The 0-th layer is the concatenation of embedding layers
vector = merge([user_latent, item_latent], mode = 'concat')
# MLP layers
for idx in xrange(1, num_layer):
layer = Dense(layers[idx], W_regularizer= l2(reg_layers[idx]), activation='relu', name = 'layer%d' %idx)
vector = layer(vector)
# Final prediction layer
prediction = Dense(1, activation='sigmoid', init='lecun_uniform', name = 'prediction')(vector)
model = Model(input=[user_input, item_input],
output=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 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()
path = args.path
dataset = args.dataset
layers = eval(args.layers)
reg_layers = eval(args.reg_layers)
num_negatives = args.num_neg
learner = args.learner
learning_rate = args.lr
batch_size = args.batch_size
epochs = args.epochs
verbose = args.verbose
topK = 10
evaluation_threads = 1 #mp.cpu_count()
print("MLP arguments: %s " %(args))
model_out_file = 'Pretrain/%s_MLP_%s_%d.h5' %(args.dataset, 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, layers, reg_layers)
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')
# Check Init performance
t1 = time()
(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 [%.1f]' %(hr, ndcg, time()-t1))
# Train model
best_hr, best_ndcg, best_iter = hr, ndcg, -1
for epoch in xrange(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 MLP model is saved to %s" %(model_out_file))