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train.py
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
#
import os
import time
import argparse
import logging
import math
import random
import numpy as np
import mxnet as mx
from mxnet import gluon
from core.model import get_model
from core.dataset import NCFTrainData, NCFTestData
from core.evaluate import *
logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser(description="Run matrix factorization with embedding",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--path', nargs='?', default='./data/',
help='Input data path.')
parser.add_argument('--dataset', nargs='?', default='ml-20m',
help='The dataset name.')
parser.add_argument('--batch-size', type=int, default=2048,
help='number of training examples per batch')
parser.add_argument('--eval-batch-size', type=int, default=1000,
help='number of evaluate examples per batch')
parser.add_argument('--model-type', type=str, default='neumf', choices=['neumf', 'gmf', 'mlp'],
help="mdoel type")
parser.add_argument('--num-negative', type=int, default=4,
help="number of negative samples per positive sample while training.")
parser.add_argument('--layers', default='[256, 256, 128, 64]',
help="list of number hiddens of fc layers in mlp model.")
parser.add_argument('--factor-size-gmf', type=int, default=64,
help="outdim of gmf embedding layers.")
parser.add_argument('--num-hidden', type=int, default=1,
help="num-hidden of neumf fc layer")
parser.add_argument('--log-interval', type=int, default=100,
help='logging interval')
parser.add_argument('--learning-rate', type=float, default=0.0005,
help='learning rate for optimizer')
parser.add_argument('--beta1', '-b1', type=float, default=0.9,
help='beta1 for Adam')
parser.add_argument('--beta2', '-b2', type=float, default=0.999,
help='beta1 for Adam')
parser.add_argument('--eps', type=float, default=1e-8,
help='eps for Adam')
parser.add_argument('--topk', type=int, default=10,
help="topk for accuracy evaluation.")
parser.add_argument('--gpu', type=int, default=None,
help="list of gpus to run, e.g. 0 or 0,2. empty means using cpu().")
parser.add_argument('--workers', type=int, default=8, help='thread number for dataloader.')
parser.add_argument('--epoch', type=int, default=14, help='training epoch')
parser.add_argument('--seed', type=int, default=3, help='random seed to use. Default=3.')
parser.add_argument('--deploy', action='store_true', help="whether to load static graph for deployment")
def cross_entropy(label, pred, eps=1e-12):
ce = 0
for l, p in zip(label, pred):
ce += -( l*np.log(p+eps) + (1-l)*np.log(1-p+eps))
return ce
if __name__ == '__main__':
head = '%(asctime)-15s %(message)s'
logging.basicConfig(level=logging.INFO, format=head)
# arg parser
args = parser.parse_args()
logging.info(args)
mx.random.seed(args.seed)
np.random.seed(args.seed)
batch_size = args.batch_size
eval_batch_size = args.eval_batch_size
model_type = args.model_type
model_layers = eval(args.layers)
factor_size_gmf = args.factor_size_gmf
factor_size_mlp = int(model_layers[0]/2)
num_hidden = args.num_hidden
learning_rate=args.learning_rate
beta1=args.beta1
beta2=args.beta2
eps=args.eps
ctx = mx.cpu() if args.gpu is None else mx.gpu(args.gpu)
topK = args.topk
num_negatives = args.num_negative
num_workers = args.workers
epoch = args.epoch
log_interval = args.log_interval
# prepare dataset
logging.info('Prepare Dataset')
train_dataset = NCFTrainData((args.path + args.dataset + '/train-ratings.csv'), num_negatives)
test_data = NCFTestData(args.path + args.dataset)
train_dataloader = mx.gluon.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers, last_batch='rollover')
logging.info('Prepare Dataset completed')
# construct the model
net = get_model(model_type, factor_size_mlp, factor_size_gmf,
model_layers, num_hidden, train_dataset.nb_users, train_dataset.nb_items)
# initialize the module
mod = mx.module.Module(net, context=ctx, data_names=['user', 'item'], label_names=['softmax_label'])
provide_data = [mx.io.DataDesc(name='item', shape=((batch_size,))),
mx.io.DataDesc(name='user', shape=((batch_size,)))]
provide_label = [mx.io.DataDesc(name='softmax_label', shape=((batch_size,)))]
mod.bind(for_training=True, data_shapes=provide_data, label_shapes=provide_label)
mod.init_params()
mod.init_optimizer(optimizer='adam', optimizer_params=[('learning_rate', learning_rate), ('beta1',beta1), ('beta2',beta2), ('epsilon',eps)])
metric = mx.metric.create(cross_entropy)
speedometer = mx.callback.Speedometer(batch_size, log_interval)
best_hr, best_ndcg, best_iter = -1, -1, -1
logging.info('Training started ...')
for epoch in range(epoch):
metric.reset()
for nbatch, seqs in enumerate(train_dataloader):
user_id, item_id, labels = seqs
batch = mx.io.DataBatch(data = [item_id.astype('int32').as_in_context(ctx),
user_id.astype('int32').as_in_context(ctx)],
label = [labels.as_in_context(ctx)])
mod.forward(batch)
mod.backward()
mod.update()
predicts=mod.get_outputs()[0]
metric.update(labels = labels, preds = predicts)
speedometer_param = mx.model.BatchEndParam(epoch=epoch, nbatch=nbatch,
eval_metric=metric, locals=locals())
speedometer(speedometer_param)
# save model
dir_path = os.path.dirname(os.path.realpath(__file__))
model_path = os.path.join(dir_path, 'model', args.dataset)
if not os.path.exists(model_path):
os.makedirs(model_path)
mod.save_checkpoint(os.path.join(model_path, model_type), epoch)
# compute hit ratio
(hits, ndcgs) = evaluate_model(mod, test_data.testRatings, test_data.testNegatives, topK, eval_batch_size, ctx, logging)
hr, ndcg = np.array(hits).mean(), np.array(ndcgs).mean()
logging.info('Iteration %d: HR = %.4f, NDCG = %.4f' % (epoch, hr, ndcg))
# best hit ratio
if hr > best_hr:
best_hr, best_ndcg, best_iter = hr, ndcg, epoch
logging.info("End. Best Iteration %d: HR = %.4f, NDCG = %.4f. " % (best_iter, best_hr, best_ndcg))
logging.info('Training completed.')