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DMF.py
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DMF.py
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import numpy as np
import tensorflow as tf
from keras import initializers
from keras.models import Model
from keras.layers import Embedding, Input, Dense, Flatten, concatenate, Dot, Lambda, multiply, Reshape, merge
from keras.optimizers import Adagrad, Adam, SGD, RMSprop
from keras import backend as K
from evaluate import evaluate_model
from Dataset import Dataset
from time import time
import argparse
def parse_args():
parser = argparse.ArgumentParser(description="Run DMF.")
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=20,
help='Number of epochs.')
parser.add_argument('--batch_size', type=int, default=256,
help='Batch size.')
parser.add_argument('--userlayers', nargs='?', default='[512, 64]',
help="Size of each user layer")
parser.add_argument('--itemlayers', nargs='?', default='[1024, 64]',
help="Size of each item 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=0.0001,
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 get_model(train, num_users, num_items, userlayers=[512, 64], itemlayers=[1024, 64]):
num_layer = len(userlayers) # Number of layers in the MLP
user_matrix = K.constant(getTrainMatrix(train))
item_matrix = K.constant(getTrainMatrix(train).T)
# Input variables
user = Input(shape=(1,), dtype='int32', name='user_input')
item = Input(shape=(1,), dtype='int32', name='item_input')
# Multi-hot User representation and Item representation
user_input = Lambda(lambda x: tf.gather(user_matrix, tf.to_int32(x)))(user)
item_input = Lambda(lambda x: tf.gather(item_matrix, tf.to_int32(x)))(item)
user_input = Reshape((num_items, ))(user_input)
item_input = Reshape((num_users, ))(item_input)
print(user_input.shape, item_input.shape)
# DMF part
userlayer = Dense(userlayers[0], activation="linear" , name='user_layer0')
itemlayer = Dense(itemlayers[0], activation="linear" , name='item_layer0')
user_latent_vector = userlayer(user_input)
item_latent_vector = itemlayer(item_input)
print(user_latent_vector.shape, item_latent_vector.shape)
for idx in range(1, num_layer):
userlayer = Dense(userlayers[idx], activation='relu', name='user_layer%d' % idx)
itemlayer = Dense(itemlayers[idx], activation='relu', name='item_layer%d' % idx)
user_latent_vector = userlayer(user_latent_vector)
item_latent_vector = itemlayer(item_latent_vector)
print(user_latent_vector.shape, item_latent_vector.shape)
predict_vector = multiply([user_latent_vector, item_latent_vector])
prediction = Dense(1, activation='sigmoid',
kernel_initializer=initializers.lecun_normal(), name='prediction')(predict_vector)
print(prediction.shape)
model_ = Model(inputs=[user, item],
outputs=prediction)
return model_
def getTrainMatrix(train):
num_users, num_items = train.shape
train_matrix = np.zeros([num_users, num_items], dtype=np.int32)
for (u, i) in train.keys():
train_matrix[u][i] = 1
return train_matrix
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.keys():
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
userlayers = eval(args.userlayers)
itemlayers = eval(args.itemlayers)
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("DMF arguments: %s " %(args))
model_out_file = 'Pretrain/%s_DMF_%d.h5' %(args.dataset, 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(train, num_users, num_items, userlayers, itemlayers)
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))
best_hr, best_ndcg, best_iter = hr, ndcg, -1
# Train model
for epoch in range(epochs):
t1 = time()
# Generate training instances
user_input, item_input, labels = get_train_instances(train, num_negatives)
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 DMF model is saved to %s" % model_out_file)