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 analyze_nli_ex():
hp = hyperparams.HPBert()
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
explain_tag = 'match'
e_config = ExperimentConfig()
#e_config.name = "NLIEx_{}_premade_analyze".format(explain_tag)
e_config.name = "NLIEx_{}_analyze".format(explain_tag)
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_E_align", "model-23621")
#load_id = ("NLIEx_I_match", "model-1238")
if explain_tag == 'conflict':
load_id = ("NLIEx_Y_conflict", "model-12039")
#load_id = ("NLIEx_HB", "model-2684")
elif explain_tag == 'match':
load_id = ("NLIEx_P_match", "model-1636")
load_id = ("NLIEx_X_match", "model-12238")
elif explain_tag == 'mismatch':
load_id = ("NLIEx_U_mismatch", "model-10265")
e.nli_visualization(nli_setting, e_config, data_loader, load_id,
explain_tag)
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 train_test_repeat(load_id, exp_name, n_repeat):
hp = hyperparams.HPBert()
e_config = ExperimentConfig()
e_config.name = "RTE_{}".format("A")
e_config.num_epoch = 10
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert']
vocab_filename = "bert_voca.txt"
data_loader = rte.DataLoader(hp.seq_max, vocab_filename, True)
print(load_id)
scores = []
for i in range(n_repeat):
e = Experiment(hp)
print(exp_name)
e_config.name = "rte_{}".format(exp_name)
save_path = e.train_rte(e_config, data_loader, load_id)
acc = e.eval_rte(e_config, data_loader, save_path)
scores.append(acc)
print(exp_name)
for e in scores:
print(e, end="\t")
print()
r = average(scores)
print("Avg\n{0:.03f}".format(r))
return r
def train_nli_smart_rf():
hp = hyperparams.HPSENLI()
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()
#explain_tag = 'mismatch'
explain_tag = 'match'
#explain_tag = 'mismatch'
loss_type = 2
e_config.name = "NLIEx_Hinge_{}".format(explain_tag)
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']
e_config.save_eval = True
e_config.save_name = "LossFn_{}_{}".format(loss_type, explain_tag)
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
load_id = ("NLI_run_A", 'model-0')
print("Loss : ", loss_type)
e.train_nli_smart(nli_setting, e_config, data_loader, load_id, explain_tag,
loss_type)
def test_fidelity():
hp = hyperparams.HPBert()
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
is_senn = False
e_config = ExperimentConfig()
e_config.name = "NLIEx_{}".format("Fidelity")
e_config.num_epoch = 4
e_config.save_interval = 30 * 60 # 30 minutes
if is_senn:
e_config.load_names = ['bert', 'cls_dense', 'aux_conflict']
else:
e_config.load_names = ['bert', 'cls_dense']
explain_tag = 'conflict'
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
load_id = ("NLIEx_Y_conflict", 'model-12039')
#load_id = ("NLI_Only_C", 'model-0')
#e.eval_fidelity(nli_setting, e_config, data_loader, load_id, explain_tag)
e.eval_fidelity_gradient(nli_setting, e_config, data_loader, load_id,
explain_tag)
def train_snli_ex():
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 = "SNLIEx_B"
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.SNLIDataLoader(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 = ("SNLI_Only_A", 'model-0')
e.train_nli_any_way(nli_setting, e_config, data_loader, load_id)
def lm_tweets_train():
hp = HPTweets()
data = tweets.TweetLoader("atheism", hp.seq_max,
shared_setting.Tweets2Stance)
e_config = ExperimentConfig()
e_config.name = "LM_tweets"
e_config.num_epoch = 30
e_config.save_interval = 30 * 60 # 30 minutes
e = Experiment(hp)
e.train_lm_batch(e_config, data)
def pair_lm():
hp = HPPairTweet()
topic = "atheism"
setting = shared_setting.TopicTweets2Stance(topic)
tweet_group = tweet_reader.load_per_user(topic)
data = loader.PairDataLoader(hp.sent_max, setting, tweet_group)
e_config = ExperimentConfig()
e_config.name = "LM_pair_tweets_{}".format(topic)
e_config.num_epoch = 1
e_config.save_interval = 30 * 60 # 30 minutes
e = Experiment(hp)
e.train_pair_lm(e_config, data)
def gradient_rte_visulize():
hp = hyperparams.HPBert()
e = Experiment(hp)
vocab_filename = "bert_voca.txt"
load_id = loader.find_model_name("RTE_A")
e_config = ExperimentConfig()
e_config.name = "RTE_{}".format("visual")
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert', 'cls_dense']
data_loader = rte.DataLoader(hp.seq_max, vocab_filename, True)
e.rte_visualize(e_config, data_loader, load_id)
def train_nli_with_reinforce_old():
hp = hyperparams.HPNLI2()
e = Experiment(hp)
nli_setting = NLI()
e_config = ExperimentConfig()
e_config.name = "NLI_run_{}".format("retest")
e_config.num_epoch = 4
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert', 'dense_cls'] #, 'aux_conflict']
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename)
load_id = ("interval", "model-48040")
e.train_nli_ex_0(nli_setting, e_config, data_loader, load_id, True)
def train_nil():
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_only_{}".format("B")
e_config.num_epoch = 2
e_config.save_interval = 30 * 60 # 30 minutes
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
e.train_nli(nli_setting, e_config, data_loader)
def protest_bert():
hp = hyperparams.HPBert()
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "protest"
e_config.num_epoch = 1
e_config.save_interval = 1 * 60 # 1 minutes
e_config.load_names = ['bert']
vocab_size = 30522
vocab_filename = "bert_voca.txt"
data_loader = protest.DataLoader(hp.seq_max, vocab_filename, vocab_size)
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
e.train_protest(e_config, data_loader, load_id)
def train_nil():
hp = HP()
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_only_{}".format("512")
e_config.num_epoch = 2
e_config.save_interval = 30 * 60 # 30 minutes
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
e.train_nli_ex_0(nli_setting, e_config, data_loader, load_id, False)
def train_adhoc_with_reinforce():
hp = hyperparams.HPAdhoc()
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "Adhoc_{}".format("E")
e_config.num_epoch = 4
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert']
vocab_size = 30522
vocab_filename = "bert_voca.txt"
data_loader = ws.DataLoader(hp.seq_max, vocab_filename, vocab_size)
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
e.train_adhoc(e_config, data_loader, load_id)
def train_nil_cold():
print('train_nil_cold')
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_Cold"
e_config.num_epoch = 2
e_config.save_interval = 30 * 60 # 30 minutes
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
saved = e.train_nli_ex_0(nli_setting, e_config, data_loader, None, False)
e.test_acc2(nli_setting, e_config, data_loader, saved)
def train_rte():
hp = hyperparams.HPBert()
e = Experiment(hp)
vocab_filename = "bert_voca.txt"
#load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
load_id = ("tlm_simple", "model.ckpt-15000")
e_config = ExperimentConfig()
e_config.name = "RTE_{}".format("tlm_simple_15000")
e_config.num_epoch = 10
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert']
data_loader = rte.DataLoader(hp.seq_max, vocab_filename, True)
e.train_rte(e_config, data_loader, load_id)
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_nli_with_premade(explain_tag):
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("Premade_"+explain_tag)
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 = ("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 wikicont_bert():
hp = hyperparams.HPBert()
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "WikiContrv2009_only_wiki"
e_config.num_epoch = 1
e_config.save_interval = 60 * 60 # 1 minutes
e_config.load_names = ['bert']
e_config.valid_freq = 100
vocab_size = 30522
vocab_filename = "bert_voca.txt"
data_loader = Ams18.DataLoader(hp.seq_max, vocab_filename, vocab_size)
data_loader.source_collection.collection_type = 'wiki'
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
e.train_wiki_contrv(e_config, data_loader, load_id)
def bert_lm_test():
hp = hyperparams.HPQL()
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "Contrv_{}".format("B")
e_config.num_epoch = 4
e_config.save_interval = 30 * 60 # 30 minuteslm_protest
e_config.load_names = ['bert', 'cls']
vocab_size = 30522
vocab_filename = "bert_voca.txt"
data_loader = ws.DataLoader(hp.seq_max, vocab_filename, vocab_size)
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
e.bert_lm_pos_neg(e_config, data_loader, load_id)
def train_mscore_regression():
hp = hyperparams.HPMscore()
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "Contrv_{}".format("C")
e_config.num_epoch = 4
e_config.save_interval = 10 * 60 # 10 minutes
e_config.load_names = ['bert']
vocab_size = 30522
vocab_filename = "bert_voca.txt"
data_loader = mscore.DataLoader(hp.seq_max, vocab_filename, vocab_size)
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
e.train_controversy_classification(e_config, data_loader, load_id)
def train_adhoc_fad():
hp = hyperparams.HPFAD()
hp.batch_size = 16
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "Adhoc_{}".format("FAD")
e_config.num_epoch = 4
e_config.save_interval = 10 * 60 # 60 minutes
e_config.load_names = ['bert'] #, 'reg_dense']
vocab_size = 30522
data_loader = data_sampler.DataLoaderFromFile(hp.batch_size, vocab_size)
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
#load_id = ("Adhoc_I2", 'model-290')
e.train_adhoc2(e_config, data_loader, load_id)
def run_ql_rank():
hp = hyperparams.HPQL()
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "Adhoc_{}".format("D")
e_config.num_epoch = 4
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert', 'cls']
vocab_size = 30522
vocab_filename = "bert_voca.txt"
data_loader = ws.DataLoader(hp.seq_max, vocab_filename, vocab_size)
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
e.rank_ql(e_config, data_loader, load_id)
def test_nli():
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_400k_tlm_simple_wo_hint"
e_config.num_epoch = 2
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)
#saved = "/mnt/scratch/youngwookim/Chair/output/model/runs/NLI_Cold/model-0"
saved = "/mnt/scratch/youngwookim/Chair/output/model/runs/NLI_400k_tlm_wo_hint/model-0"
saved = '/mnt/scratch/youngwookim/Chair/output/model/runs/NLI_400k_tlm_simple_hint/model-0'
print(saved)
e.test_acc2(nli_setting, e_config, data_loader, saved)
def 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_Only_{}".format("C")
e_config.num_epoch = 2
e_config.save_interval = 30 * 60 # 30 minutes
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
#load_id = None
load_id = ("uncased_L-12_H-768_A-12", 'bert_model.ckpt')
#load_id = ("NLI_bert_w_explain", 'model-91531')
#load_id = ("NLI_Only_A", "model-0")
e.train_nli_ex_0(nli_setting, e_config, data_loader, load_id, False)
def interactive():
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 = "NLIInterative"
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 = ("NLI_Only_B", 'model-0')
e.nli_interactive_list(nli_setting, e_config, data_loader, load_id)
def analyze_nli_pair():
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_pair_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_T", "model-12097")
e.nli_visualization_pairing(nli_setting, e_config, data_loader, load_id,
data)
def attribution_explain():
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_run_{}".format("nli_eval")
e_config.num_epoch = 4
e_config.save_interval = 30 * 60 # 30 minutes
e_config.load_names = ['bert', 'cls_dense']
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
load_id = ("NLI_run_nli_warm", "model-97332")
load_id = ("NLI_Only_A", 'model-0')
e.nli_attribution_baselines(nli_setting, e_config, data_loader, load_id)
def predict_adhoc512():
hp = hyperparams.HPFAD()
hp.batch_size = 16
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "Adhoc_J_{}".format("512")
e_config.num_epoch = 4
e_config.save_interval = 10 * 60 # 60 minutes
e_config.load_names = ['bert', 'reg_dense']
vocab_size = 30522
payload_path = os.path.join(cpath.data_path, "robust_payload",
"enc_payload_512.pickle")
task_idx = int(sys.argv[2])
print(task_idx)
load_id = ("Adhoc_J_512", 'model-6180')
e.predict_robust(e_config, vocab_size, load_id, payload_path, task_idx)