def main():
# get hyperparameters from input args
opt = ArgParser().get_options()
# deterministic seed across numpy, torch and cuda
# store as variable due to garbage collecting
seeder = Seeder(opt.seed)
seeder.activate()
# data from ./zoo.py
opt.model_class = models[opt.model_name]
opt.initializer = initializers[opt.initializer]
opt.optimizer = optimizers[opt.optimizer]
# data from ./config.yml
opt.dataset_file = config['datasets'][opt.dataset]
opt.model_inputs = config['model_inputs'][opt.model_name]
# run on gpu if available
device = opt.device
if device is None:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
opt.device = torch.device(device)
runner = ModelRunner(opt)
runner.run()
print 'Time : %d seconds' % (t2 - t1)
# build the model
print 'build the model...'
t1 = time.time()
runner = ModelRunner(train_data,
vad_data,
None,
X_sppmi,
X_neg_sppmi,
Y_sppmi,
None,
save_dir=save_dir)
U, V, ndcg100 = runner.run("rme",
n_jobs=1,
lam=lam,
lam_emb=lam_emb,
n_components=n_components,
ret_params_only=1)
t2 = time.time()
print 'Time : %d seconds' % (t2 - t1)
print '*************************************ITER %d ******************************************' % iter
print 'NDCG@100 at this iter:', ndcg100
#
if best_ndcg100 < ndcg100:
best_iter = iter
best_ndcg100 = ndcg100
else:
early_stopping = True
iter += 1
#########################################################
print 'Max NDCG@100: %f , at iter: %d' % (best_ndcg100, best_iter)