def evaluate(self):
self.clf.eval()
if self.config[MODEL_TYPE] > 1:
self.coref_trainer.model.eval()
with torch.no_grad():
eval = Evaluator(self, self.data_helper, self.config)
eval.eval_parser(self.data_helper.val_trees)
def train_classifier(self, train_loader, dev_loader):
"""
"""
self.optim = Optimizer(self.clf.parameters(), lr=self.config[LR])
if self.config[EPOCH_START] != 1:
self.load('../data/model/' + self.config[MODEL_NAME] + "_" +
str(self.config[EPOCH_START]))
for epoch in range(1, self.config[NUM_EPOCHS] + 1):
cost_acc = 0
self.clf.train()
print("============ epoch: ", epoch, " ============")
for i, data in enumerate(train_loader):
docs, gold_actions = data
cost_acc += self.sr_parse(docs, gold_actions, self.optim)[1]
if (i % 50 == 0):
print("Cost on step ", i, "is ", cost_acc)
print("Total cost for epoch ", epoch, "is ", cost_acc)
self.clf.eval()
with torch.no_grad():
eval = Evaluator(self, self.clf.data_helper)
eval.eval_parser(dev_loader, path=None)
self.save('../data/model/',
self.config[MODEL_NAME] + "_" + str(epoch), epoch)
parse_type=args.parse_type,
isFlat=args.isFlat)
train_model(data_helper)
if args.eval:
# Evaluate models on the RST-DT test set
if args.isFlat:
evaluator = Evaluator(isFlat=args.isFlat,
model_dir=os.path.join(
args.output_dir, "RN~model"))
else:
evaluator = Evaluator(isFlat=args.isFlat,
model_dir=os.path.join(
args.output_dir, "N~model"))
evaluator.eval_parser(data_dir=args.data_dir,
output_dir=args.output_dir,
report=True,
bcvocab=brown_clusters,
draw=False,
isFlat=args.isFlat)
if args.pred:
if args.isFlat:
evaluator = Evaluator(isFlat=args.isFlat,
model_dir=os.path.join(
args.output_dir, "RN~model"))
else:
evaluator = Evaluator(isFlat=args.isFlat,
model_dir=os.path.join(
args.output_dir, "N~model"))
print("predicting")
evaluator.pred_parser(output_dir=args.output_dir,
parse_type=args.parse_type,
train_dirname = (args.train_dir[:-1] if args.train_dir[-1] == os.sep else
args.train_dir).split(os.sep)[-1]
HELPER_PATH = f"..{os.sep}data{os.sep}{train_dirname}_data_helper_rst.bin"
print("Helper path:", HELPER_PATH)
if args.prepare:
# Create training data
#coref_model = CorefScore(higher_order=True).to(config[DEVICE])
coref_model = CorefScore().to(config[DEVICE])
coref_trainer = Trainer(coref_model, [], [], [], debug=False)
data_helper.create_data_helper(args.train_dir, config, coref_trainer)
data_helper.save_data_helper(HELPER_PATH)
if args.train:
train_model_coref(data_helper, config)
if args.eval:
# Evaluate models on the RST-DT test set
data_helper.load_data_helper(HELPER_PATH)
parser = get_discourse_parser(data_helper, config)
parser.load('../data/model/' + config[MODEL_NAME])
print("Evaluating")
with torch.no_grad():
evaluator = Evaluator(parser, data_helper, config)
evaluator.eval_parser(None,
path=args.eval_dir,
use_parseval=args.use_parseval)
parser.add_argument('--eval_dir', help='eval data directory')
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
# Use brown clusters
with gzip.open("../data/resources/bc3200.pickle.gz") as fin:
print('Load Brown clusters for creating features ...')
brown_clusters = pickle.load(fin)
data_helper = DataHelper(max_action_feat_num=330000,
max_relation_feat_num=300000,
min_action_feat_occur=1,
min_relation_feat_occur=1,
brown_clusters=brown_clusters)
if args.prepare:
# Create training data
data_helper.create_data_helper(data_dir=args.train_dir)
data_helper.save_data_helper('../data/data_helper.bin')
if args.train:
data_helper.load_data_helper('../data/data_helper.bin')
data_helper.load_train_data(data_dir=args.train_dir)
train_model(data_helper)
if args.eval:
# Evaluate models on the RST-DT test set
evaluator = Evaluator(model_dir='../data/model')
evaluator.eval_parser(path=args.eval_dir,
report=True,
bcvocab=brown_clusters,
draw=False)