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run.py
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import sys
import time
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
import pickle
import argparse
import logging
from dataset import Dataset
from model import Model
import utils
import pprint
def parse_args():
parser = argparse.ArgumentParser('NCM')
parser.add_argument('--train', action='store_true',
help='train the model')
parser.add_argument('--valid', action='store_true',
help='evaluate the model on valid set')
parser.add_argument('--test', action='store_true',
help='evaluate the model on test set')
parser.add_argument('--rank', action='store_true',
help='rank on train set')
parser.add_argument('--rank_cheat', action='store_true',
help='rank on train set in a cheating way')
parser.add_argument('--generate_click_seq', action='store_true',
help='generate click sequence based on model itself')
parser.add_argument('--generate_click_seq_cheat', action='store_true',
help='generate click sequence based on ground truth data')
parser.add_argument('--generate_synthetic_dataset', action='store_true',
help='generate synthetic dataset for reverse ppl')
train_settings = parser.add_argument_group('train settings')
train_settings.add_argument('--optim', default='adam',
help='optimizer type')
train_settings.add_argument('--learning_rate', type=float, default=0.001,
help='learning rate')
train_settings.add_argument('--weight_decay', type=float, default=1e-5,
help='weight decay')
train_settings.add_argument('--momentum', type=float, default=0.99,
help='momentum')
train_settings.add_argument('--dropout_rate', type=float, default=0.5,
help='dropout rate')
train_settings.add_argument('--batch_size', type=int, default=64,
help='train batch size')
train_settings.add_argument('--num_steps', type=int, default=20000,
help='number of training steps')
model_settings = parser.add_argument_group('model settings')
model_settings.add_argument('--algo', default='NCM',
help='the name of the algorithm')
model_settings.add_argument('--embed_size', type=int, default=128,
help='size of the embeddings')
model_settings.add_argument('--hidden_size', type=int, default=128,
help='size of RNN hidden units')
model_settings.add_argument('--max_d_num', type=int, default=10,
help='max number of docs in a session')
path_settings = parser.add_argument_group('path settings')
path_settings.add_argument('--dataset', default='TianGong-ST',
help='name of the dataset to be used')
path_settings.add_argument('--model_dir', default='./outputs/models/',
help='the dir to store models')
path_settings.add_argument('--result_dir', default='./outputs/results/',
help='the dir to output the results')
path_settings.add_argument('--summary_dir', default='./outputs/summary/',
help='the dir to write tensorboard summary')
path_settings.add_argument('--log_dir', default='./outputs/log/',
help='path of the log file. If not set, logs are printed to console')
path_settings.add_argument('--eval_freq', type=int, default=100,
help='the frequency of evaluating on the valid set when training')
path_settings.add_argument('--check_point', type=int, default=100,
help='the frequency of saving model')
path_settings.add_argument('--patience', type=int, default=5,
help='lr half when more than the patience times of evaluation\' loss don\'t decrease')
path_settings.add_argument('--lr_decay', type=float, default=0.5,
help='lr decay')
path_settings.add_argument('--load_model', type=int, default=-1,
help='load model global step')
path_settings.add_argument('--data_parallel', type=bool, default=False,
help='data_parallel')
path_settings.add_argument('--gpu_num', type=int, default=1,
help='gpu_num')
return parser.parse_args()
def train(args, dataset):
"""
Train the model
"""
logger = logging.getLogger("NCM")
logger.info('Initialize the model...')
model = Model(args, dataset.query_size, dataset.doc_size, dataset.vtype_size, dataset)
logger.info('model.global_step: {}'.format(model.global_step))
if args.load_model > -1:
logger.info('Reloading the model...')
model.load_model(model_dir=args.model_dir, model_prefix=args.algo, global_step=args.load_model)
logger.info('Training the model...')
model.train(dataset)
logger.info('Done with model training!')
def valid(args, dataset):
"""
Evaluate the model on valid set
"""
logger = logging.getLogger("NCM")
logger.info('Initialize the model...')
model = Model(args, dataset.query_size, dataset.doc_size, dataset.vtype_size, dataset)
logger.info('model.global_step: {}'.format(model.global_step))
assert args.load_model > -1, 'args.load_model is required to specify the model file to be loaded!'
logger.info('Reloading the model...')
model.load_model(model_dir=args.model_dir, model_prefix=args.algo, global_step=args.load_model)
logger.info('Evaluating the model on valid set...')
valid_batches = dataset.gen_mini_batches('valid', dataset.validset_size, shuffle=False)
valid_loss, perplexity = model.evaluate(valid_batches, dataset)
logger.info('Loss on valid set: {}'.format(float(valid_loss)))
logger.info('Perplexity on valid set: {}'.format(float(perplexity)))
def test(args, dataset):
"""
Evaluate the model on test set
"""
logger = logging.getLogger("NCM")
logger.info('Initialize the model...')
model = Model(args, dataset.query_size, dataset.doc_size, dataset.vtype_size, dataset)
logger.info('model.global_step: {}'.format(model.global_step))
assert args.load_model > -1, 'args.load_model is required to specify the model file to be loaded!'
logger.info('Reloading the model...')
model.load_model(model_dir=args.model_dir, model_prefix=args.algo, global_step=args.load_model)
logger.info('Evaluating the model on test set...')
test_batches = dataset.gen_mini_batches('test', dataset.testset_size, shuffle=False)
test_loss, perplexity = model.evaluate(test_batches, dataset)
logger.info('Loss on test set: {}'.format(float(test_loss)))
logger.info('perplexity on test set: {}'.format(float(perplexity)))
def rank(args, dataset):
"""
Rank documents for relevance estimation task
"""
logger = logging.getLogger("NCM")
logger.info('Initialize the model...')
model = Model(args, dataset.query_size, dataset.doc_size, dataset.vtype_size, dataset)
logger.info('model.global_step: {}'.format(model.global_step))
assert args.load_model > -1, 'args.load_model is required to specify the model file to be loaded!'
logger.info('Reloading the model...')
model.load_model(model_dir=args.model_dir, model_prefix=args.algo, global_step=args.load_model)
logger.info('Computing NDCG@k for relevance estimation...')
trunc_levels = [1, 3, 5, 10]
label_batches = dataset.gen_mini_batches('label', dataset.labelset_size, shuffle=False)
ndcgs = model.ranking(label_batches, dataset)
for trunc_level in trunc_levels:
logger.info("NDCG@{}: {}".format(trunc_level, ndcgs[trunc_level]))
def run():
"""
Prepare and run the whole system.
"""
# Get arguments
args = parse_args()
assert args.batch_size % args.gpu_num == 0
assert args.hidden_size % 2 == 0
# Create a logger
logger = logging.getLogger("NCM")
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(message)s')
utils.check_path(args.model_dir)
utils.check_path(args.result_dir)
utils.check_path(args.summary_dir)
if args.log_dir:
utils.check_path(args.log_dir)
file_handler = logging.FileHandler(args.log_dir + time.strftime('%Y-%m-%d-%H:%M:%S',time.localtime(time.time())) + '.txt')
file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
else:
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.INFO)
console_handler.setFormatter(formatter)
logger.addHandler(console_handler)
logger.info('Running with args : {}'.format(args))
# Check the directories
logger.info('Checking the directories...')
for dir_path in [args.model_dir, args.result_dir, args.summary_dir]:
if not os.path.exists(dir_path):
os.makedirs(dir_path)
# Load dataset
logger.info('Loading train/valid/test/label data...')
dataset = Dataset(args)
# Start main process
if args.train:
train(args, dataset)
if args.valid:
valid(args, dataset)
if args.test:
test(args, dataset)
if args.rank:
rank(args, dataset)
logger.info('Run done.')
if __name__ == '__main__':
run()