def do_test_dev_acc():
hp = hyperparams.HPBert()
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "NLIEx_Test"
e_config.load_names = ['bert', 'cls_dense'] #, 'aux_conflict']
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
#load_id = ("NLI_bare_A", 'model-195608')
load_id = ("NLIEx_S", 'model-4417')
load_id = ("NLIEx_Y_conflict", "model-9636")
load_id = ("NLI_Only_C", 'model-0')
e.test_acc(nli_setting, e_config, data_loader, load_id)
def train_mnli_any_way():
hp = HP()
hp.batch_size = 8
hp.compare_deletion_num = 20
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "NLIEx_Any_512"
e_config.ex_val = False
e_config.num_epoch = 1
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert', 'cls_dense'] # , 'aux_conflict']
e_config.v2_load = True
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
load_id = ("nli512", 'model.ckpt-65000')
e.train_nli_any_way(nli_setting, e_config, data_loader, load_id)
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))
def train_pairing():
hp = hyperparams.HPBert()
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "NLIEx_{}".format("T")
e_config.num_epoch = 1
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert', 'cls_dense'] # 'aux_conflict']
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
#load_id = ("NLIEx_P_match", "model-1636")
load_id = ("NLI_run_A", 'model-0')
PAIRING_NLI = 6
e.train_nli_smart(nli_setting, e_config, data_loader, load_id, 'match',
PAIRING_NLI)
def visualize_senli_on_plain_text():
hp = hyperparams.HPBert()
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "NLIEx_lm_analyze"
e_config.num_epoch = 4
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert', 'cls_dense', 'aux_conflict']
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
load_id = ("NLIEx_AnyA", "model-7255")
p = "/mnt/scratch/youngwookim/Chair/data/tf/1"
data = list(extract_stream(p))
e.nli_visualization_lm(nli_setting, e_config, data_loader, load_id, data)
def attribution_predict():
hp = hyperparams.HPBert()
target_label = 'mismatch'
e = Experiment(hp)
hp.batch_size = 512
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "NLI_run_{}".format("nli_eval")
e_config.load_names = ['bert', 'cls_dense']
#data_id = "test_{}".format(target_label)
data_id = "{}_1000".format(target_label)
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
load_id = ("NLI_Only_C", 'model-0')
e.nli_attribution_predict(nli_setting, e_config, data_loader, load_id,
target_label, data_id)
def baseline_explain():
hp = hyperparams.HPBert()
hp.batch_size *= 32
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "NLI_run_{}".format("nli_warm")
e_config.num_epoch = 4
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert', 'cls_dense']
explain_tag = 'conflict' # 'dontcare' 'match' 'mismatch'
#explain_tag = 'match'
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
load_id = ("NLI_run_A", 'model-0')
e.nli_explain_baselines(nli_setting, e_config, data_loader, load_id,
explain_tag)
def train_nil_on_bert():
print('train_nil_on_bert')
hp = hyperparams.HPBert()
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "NLI_10k_bert_cold"
e_config.num_epoch = 2
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert'] # , 'aux_conflict']
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
dir_name = "bert_cold"
model_step = 10 * 1000
load_id = (dir_name, "model.ckpt-{}".format(model_step))
print(load_id)
saved = e.train_nli_ex_0(nli_setting, e_config, data_loader, load_id,
False)
e.test_acc2(nli_setting, e_config, data_loader, saved)
def train_nli_with_premade():
hp = hyperparams.HPBert()
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "NLIEx_{}".format("HB")
e_config.num_epoch = 1
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert'] #, 'cls_dense'] #, 'aux_conflict']
explain_tag = 'conflict' # 'dontcare' 'match' 'mismatch'
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
#load_id = ("NLI_run_nli_warm", "model-97332")
#load_id = ("NLIEx_A", "model-16910")
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
e.train_nli_ex_with_premade_data(nli_setting, e_config, data_loader,
load_id, explain_tag)
def train_mnli_any_way():
hp = hyperparams.HPBert()
hp.compare_deletion_num = 20
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "NLIEx_AnyA"
e_config.ex_val = False
e_config.num_epoch = 1
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert', 'cls_dense'] # , 'aux_conflict']
# explain_tag = 'match' # 'dontcare' 'match' 'mismatch'
# explain_tag = 'mismatch'
# explain_tag = 'conflict'
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
load_id = ("NLI_run_A", 'model-0')
e.train_nli_any_way(nli_setting, e_config, data_loader, load_id)
def train_nil_from_v2_checkpoint(run_name, model_path):
hp = hyperparams.HPSENLI3()
print(hp.batch_size)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
tokenizer = get_tokenizer()
CLS_ID = tokenizer.convert_tokens_to_ids(["[CLS]"])[0]
SEP_ID = tokenizer.convert_tokens_to_ids(["[SEP]"])[0]
data_loader.CLS_ID = CLS_ID
data_loader.SEP_ID = SEP_ID
tf_logger.setLevel(logging.INFO)
steps = 12271
data = load_cache("nli_batch16")
if data is None:
tf_logger.info("Encoding data from csv")
data = get_nli_batches_from_data_loader(data_loader, hp.batch_size)
save_to_pickle(data, "nli_batch16")
train_nli(hp, nli_setting, run_name, steps, data, model_path)
def baseline_predict():
hp = hyperparams.HPBert()
hp.batch_size = 512
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "NLI_run_{}".format("nli_eval")
e_config.load_names = ['bert', 'cls_dense']
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
load_id = ("NLI_Only_C", 'model-0')
target_label = "mismatch"
data_id = "{}_1000".format(target_label)
start = int(sys.argv[1])
print(start)
sub_range = (start, start + 100)
#sub_range = None
e.nli_baseline_predict(nli_setting, e_config, data_loader, load_id,
target_label, data_id, sub_range)
def train_nli_smart_rf(explain_tag):
hp = hyperparams.HPSENLI()
hp.compare_deletion_num = 20
e = Experiment(hp)
e.log.setLevel(logging.WARNING)
e.log2.setLevel(logging.WARNING)
e.log.info("I don't want to see")
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "NLIEx_{}".format("CO_" + explain_tag)
e_config.num_epoch = 1
e_config.ex_val = False
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert', 'cls_dense'] #, 'aux_conflict']
e_config.save_payload = True
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
load_id = ("NLI_run_A", 'model-0')
e.train_nli_smart(nli_setting, e_config, data_loader, load_id, explain_tag,
5)
def get_eval_params(load_type, model_path, data_type):
hp = hyperparams.HPSENLI3_eval()
hp.batch_size = 128
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
if data_type == "mnli":
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename,
True)
elif data_type == "snli":
data_loader = nli.SNLIDataLoader(hp.seq_max,
nli_setting.vocab_filename, True)
else:
assert False
dir_path, file_name = os.path.split(model_path)
run_name = os.path.split(dir_path)[1] + "/" + file_name
dev_batches = get_batches_ex(data_loader.get_dev_data(), hp.batch_size, 4)
if load_type == "v2":
load_fn = load_bert_v2
else:
load_fn = load_model
return dev_batches, hp, load_fn, nli_setting, run_name
def predict_rf_tune():
hp = hyperparams.HPBert()
hp.batch_size = 256
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
target_label = 'match'
#data_id = 'test_conflict'
data_id = "{}_1000".format(target_label)
e_config = ExperimentConfig()
l = [(0.1, 12039), (0.2, 12245), (0.3, 12063), (0.4, 12250), (0.6, 12262),
(0.7, 12253)]
l = [(0.5, 12175), (0.8, 12269), (0.9, 12259)]
for del_g, step in l:
e_config.name = "X_match_del_{}".format(del_g)
e_config.load_names = ['bert', 'cls_dense', 'aux_conflict']
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename,
True)
load_id = ("NLIEx_match_del_{}".format(del_g), "model-{}".format(step))
e.clear_run()
e.predict_rf(nli_setting, e_config, data_loader, load_id, data_id, 5)
def tuning_train_nli_rf():
l = [0.9]
for g_del in l:
tf.reset_default_graph()
hp = hyperparams.HPSENLI()
hp.g_val = g_del
hp.compare_deletion_num = 20
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "NLIEx_{}".format("match_del_{}".format(g_del))
e_config.num_epoch = 1
e_config.ex_val = True
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert', 'cls_dense'] #, 'aux_conflict']
explain_tag = 'match' # 'dontcare' 'match' 'mismatch'
#explain_tag = 'conflict'
#explain_tag = 'mismatch'
#explain_tag = 'conflict'
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename,
True)
#load_id = ("NLI_run_nli_warm", "model-97332")
#load_id = ("NLIEx_A", "model-16910")
#load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
#load_id = ("NLIEx_D", "model-1964")
#load_id = ("NLIEx_D", "model-1317")
load_id = ("NLI_run_A", 'model-0')
e.train_nli_smart(nli_setting, e_config, data_loader, load_id,
explain_tag, 5)
