def ukp_train_test_repeat(load_id, exp_name, topic, n_repeat):
hp = hyperparams.HPBert()
e_config = ExperimentConfig()
e_config.num_epoch = 2
e_config.save_interval = 100 * 60 # 30 minutes
e_config.voca_size = 30522
e_config.load_names = ['bert']
encode_opt = "is_good"
print(load_id)
scores = []
for i in range(n_repeat):
e = Experiment(hp)
print(exp_name)
e_config.name = "arg_{}_{}_{}".format(exp_name, topic, encode_opt)
data_loader = BertDataLoader(topic,
True,
hp.seq_max,
"bert_voca.txt",
option=encode_opt)
save_path = e.train_ukp(e_config, data_loader, load_id)
f1_last = e.eval_ukp(e_config, data_loader, save_path)
scores.append(f1_last)
print(exp_name)
print(encode_opt)
for e in scores:
print(e, end="\t")
print()
print("Avg\n{0:.03f}".format(average(scores)))
def eval_ukp_with_nli(exp_name):
step_per_epoch = 24544 + 970
hp = hyperparams.HPBert()
e_config = ExperimentConfig()
e_config.num_steps = step_per_epoch
e_config.voca_size = 30522
e_config.num_dev_batches = 30
e_config.load_names = ['bert']
encode_opt = "is_good"
num_class_list = [3, 3]
f1_list = []
save_path = "/mnt/scratch/youngwookim/Chair/output/model/runs/argmix_AN_B_40000_abortion_is_good/model-21306"
for topic in data_generator.argmining.ukp_header.all_topics[:1]:
e = Experiment(hp)
print(exp_name)
e_config.name = "argmix_{}_{}_{}".format(exp_name, topic, encode_opt)
arg_data_loader = BertDataLoader(topic,
True,
hp.seq_max,
"bert_voca.txt",
option=encode_opt)
f1_last = e.eval_ukp_on_shared(e_config, arg_data_loader,
num_class_list, save_path)
f1_list.append((topic, f1_last))
print(exp_name)
print(encode_opt)
print(f1_list)
for key, score in f1_list:
print("{0}\t{1:.03f}".format(key, score))
def ukp_train_test(load_id, exp_name):
hp = hyperparams.HPBert()
e_config = ExperimentConfig()
e_config.num_epoch = 2
e_config.save_interval = 100 * 60 # 30 minutes
e_config.voca_size = 30522
e_config.load_names = ['bert']
encode_opt = "is_good"
print(load_id)
f1_list = []
for topic in data_generator.argmining.ukp_header.all_topics:
e = Experiment(hp)
print(exp_name)
e_config.name = "arg_{}_{}_{}".format(exp_name, topic, encode_opt)
data_loader = BertDataLoader(topic,
True,
hp.seq_max,
"bert_voca.txt",
option=encode_opt)
save_path = e.train_ukp(e_config, data_loader, load_id)
print(topic)
f1_last = e.eval_ukp(e_config, data_loader, save_path)
f1_list.append((topic, f1_last))
print(exp_name)
print(encode_opt)
print(f1_list)
for key, score in f1_list:
print("{0}\t{1:.03f}".format(key, score))
def crs_stance_baseline():
hp = hyperparams.HPCRS()
hp.batch_size = 16
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "CRS_{}".format("baseline")
e_config.num_epoch = 4
e_config.save_interval = 10 * 60 # 60 minutes
e_config.load_names = ['bert'] #, 'reg_dense']
e_config.voca_size = 30522
data_loader = DataGenerator()
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
e.train_crs_classify(e_config, data_loader, load_id)
def train_agree():
hp = hyperparams.HPBert()
e_config = ExperimentConfig()
e_config.num_epoch = 2
e_config.save_interval = 100 * 60 # 30 minutes
e_config.voca_size = 30522
e_config.load_names = ['bert']
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
exp_purpose = "(dis)agree train"
e = Experiment(hp)
print(exp_purpose)
e_config.name = "AgreeTrain"
vocab_filename = "bert_voca.txt"
data_loader = agree.DataLoader(hp.seq_max, vocab_filename)
save_path = e.train_agree(e_config, data_loader, load_id)
print(exp_purpose)
def train_ukp_with_nli(load_id, exp_name):
step_per_epoch = 24544 + 970
hp = hyperparams.HPBert()
e_config = ExperimentConfig()
e_config.num_steps = step_per_epoch
e_config.save_interval = 100 * 60 # 30 minutes
e_config.voca_size = 30522
e_config.num_dev_batches = 30
e_config.load_names = ['bert']
e_config.valid_freq = 500
encode_opt = "is_good"
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
num_class_list = [3, 3]
f1_list = []
for topic in data_generator.argmining.ukp_header.all_topics:
e = Experiment(hp)
print(exp_name)
e_config.name = "argmix_{}_{}_{}".format(exp_name, topic, encode_opt)
arg_data_loader = BertDataLoader(topic,
True,
hp.seq_max,
"bert_voca.txt",
option=encode_opt)
nli_data_loader = nli.DataLoader(hp.seq_max,
nli_setting.vocab_filename, True)
shared_data_loader = SharedFeeder([arg_data_loader, nli_data_loader],
[1, 5], ["Arg", "NLI"],
hp.batch_size)
save_path = e.train_shared(e_config, shared_data_loader,
num_class_list, load_id)
print(topic)
f1_last = e.eval_ukp_on_shared(e_config, arg_data_loader,
num_class_list, save_path)
f1_list.append((topic, f1_last))
print(exp_name)
print(encode_opt)
print(f1_list)
for key, score in f1_list:
print("{0}\t{1:.03f}".format(key, score))
