def test_run(self): np.random.seed(1) for m_c in self.methods_configs: print 'Testing acquisition ' + m_c['name'] name = m_c['name']+'_'+'acquisition_gradient_testfile' unittest_result = run_eval(self.f_obj, self.f_bounds, self.f_inits, method_config=m_c, name=name, outpath=self.outpath, time_limit=None, unittest = self.is_unittest) original_result = np.loadtxt(self.outpath +'/'+ name+'.txt') self.assertTrue((abs(original_result - unittest_result)<1e-4).all())
def test_run(self): for m_c in self.methods_configs: np.random.seed(1) print 'Testing other GPyOpt options: ' + m_c['name'] name = m_c['name']+'_'+'_testfile' unittest_result = run_eval(self.f_obj, self.f_bounds, self.f_inits, method_config=m_c, name=name, outpath=self.outpath, time_limit=None, unittest = self.is_unittest) print unittest_result original_result = np.loadtxt(self.outpath +'/'+ name+'.txt') self.assertTrue((abs(original_result - unittest_result)<1e-4).all())
tagging_model=tagging_model)
if CUDA:
joint_model = joint_model.cuda()
if ARGS.checkpoint is not None and os.path.exists(ARGS.checkpoint):
print('LOADING FROM ' + ARGS.checkpoint)
# TODO(rpryzant): is there a way to do this more elegantly?
# https://pytorch.org/tutorials/beginner/saving_loading_models.html#saving-loading-model-across-devices
if CUDA:
joint_model.load_state_dict(torch.load(ARGS.checkpoint))
joint_model = joint_model.cuda()
else:
joint_model.load_state_dict(
torch.load(ARGS.checkpoint, map_location='cpu'))
print('...DONE')
# # # # # # # # # # # # EVAL # # # # # # # # # # # # # #
joint_model.eval()
hits, preds, golds, srcs = joint_utils.run_eval(joint_model, eval_dataloader,
tok2id, ARGS.inference_output,
ARGS.max_seq_len,
ARGS.beam_width)
print('eval/bleu', seq2seq_utils.get_bleu(preds, golds), 0)
print('eval/true_hits', np.mean(hits), 0)
with open(ARGS.working_dir + '/stats.txt', 'w') as f:
f.write('eval/bleu %d' % seq2seq_utils.get_bleu(preds, golds))
f.write('eval/true_hits %d' % np.mean(hits))
model_parameters = filter(lambda p: p.requires_grad, joint_model.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
print('NUM PARAMS: ', params)
if ARGS.freeze_tagger and ARGS.pretrain_data:
joint_optimizer = pretrain_optim
else:
joint_optimizer = seq2seq_utils.build_optimizer(
debias_model if ARGS.freeze_tagger else joint_model, num_train_steps)
# train model
print('JOINT TRAINING...')
print('INITIAL EVAL...')
joint_model.eval()
hits, preds, golds, srcs = joint_utils.run_eval(
joint_model, eval_dataloader, tok2id,
ARGS.working_dir + '/results_initial.txt', ARGS.max_seq_len,
ARGS.beam_width)
writer.add_scalar('eval/bleu', seq2seq_utils.get_bleu(preds, golds), 0)
writer.add_scalar('eval/true_hits', np.mean(hits), 0)
for epoch in range(ARGS.epochs):
print('EPOCH ', epoch)
print('TRAIN...')
losses = joint_utils.train_for_epoch(joint_model,
train_dataloader,
joint_optimizer,
debias_loss_fn,
tagging_loss_fn,
ignore_tagger=False,
coverage=ARGS.coverage)
writer.add_scalar('train/loss', np.mean(losses), epoch + 1)
print('SAVING DEBIASER...')
torch.save(model.state_dict(), ARGS.working_dir + '/debiaser.ckpt')
# # # # # # # # # # # # TRAINING # # # # # # # # # # # # # #
for epoch in range(ARGS.epochs):
print('EPOCH ', epoch)
print('TRAIN...')
model.train()
losses = utils.train_for_epoch(model,
train_dataloader,
tok2id,
optimizer,
loss_fn,
coverage=ARGS.coverage)
writer.add_scalar('train/loss', np.mean(losses), epoch + 1)
print('SAVING...')
model.save(ARGS.working_dir + '/model_%d.ckpt' % (epoch + 1))
print('EVAL...')
model.eval()
hits, preds, golds, srcs = utils.run_eval(
model, eval_dataloader, tok2id,
ARGS.working_dir + '/results_%d.txt' % epoch, ARGS.max_seq_len,
ARGS.beam_width)
# writer.add_scalar('eval/partial_bleu', utils.get_partial_bleu(preds, golds, srcs), epoch+1)
writer.add_scalar('eval/bleu', utils.get_bleu(preds, golds), epoch + 1)
writer.add_scalar('eval/true_hits', np.mean(hits), epoch + 1)
#!/usr/bin/env python
# coding=utf-8
import argparse
from utils import calc_metrics, run_eval
if __name__ == "__main__":
# Define and parse program input
parser = argparse.ArgumentParser(
description="evaluate classification metrics",
formatter_class=argparse.RawTextHelpFormatter,
usage="%(prog)s [-h] [options] -file FILE -head HEAD")
parser.add_argument("-file",
help="The path to the labeled file.",
required=True)
parser.add_argument("-head",
help="Num of the former character to be ignored.",
default=2)
parser.add_argument("-verbose",
help="Whether to print classification report.",
default=True)
args = parser.parse_args()
# Run the program.
run_eval(args.file, args.head, args.verbose)