def wikicont_cnn():
e = Experiment(hyperparams.HPCNN())
e_config = ExperimentConfig()
e_config.num_epoch = 0
e_config.input_name = "WikiContrvCNN"
e_config.name = "WikiContrvCNN_sigmoid"
e.train_cnn_wiki_contrv(e_config)
def predict_rf():
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 = 'mismatch'
#data_id = 'test_conflict'
data_id = "{}_1000".format(target_label)
e_config = ExperimentConfig()
#del_g = 0.7
#e_config.name = "X_match_del_{}".format(del_g)
e_config.name = "NLIEx_AnyA_{}".format(target_label)
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_O", 'model-10278')
load_id = ("NLIEx_W_mismatch", "model-12030")
load_id = ("NLIEx_Y_conflict", "model-12039")
load_id = ("NLIEx_X_match", "model-12238")
#load_id = ("NLIEx_match_del_{}".format(del_g), "model-4390")
load_id = ("NLIEx_CE_{}".format(target_label), "model-12199")
load_id = ("NLIEx_AnyA", "model-7255")
e.predict_rf(nli_setting, e_config, data_loader, load_id, data_id, 5)
def train_score_merger():
hp = hyperparams.HPMerger_BM25()
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "Merger_{}".format("A")
e_config.num_epoch = 4
data_loader = score_loader.DataLoader(hp.seq_max, hp.hidden_units)
e.train_score_merger(e_config, data_loader)
def train_score_merger_on_vector():
hp = hyperparams.HPMerger()
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "MergerE_{}".format("E")
data_loader = score_loader.NetOutputLoader(hp.seq_max, hp.hidden_units,
hp.batch_size)
e.train_score_merger(e_config, data_loader)
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 lm_guardian_train():
hp = Hyperparams()
guardian_data = guardian.GuardianLoader("atheism", hp.seq_max,
shared_setting.Guardian2Stance)
e_config = ExperimentConfig()
e_config.name = "LM_guardian"
e_config.num_epoch = 30
e = Experiment(hp)
e.train_lm_batch(e_config, guardian_data)
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 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 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 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 stance_with_consistency():
hp = HPStanceConsistency()
topic = "atheism"
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "stance_consistency_{}".format(topic)
setting = shared_setting.TopicTweets2Stance(topic)
stance_data = stance_detection.DataLoader(topic, hp.seq_max,
setting.vocab_filename)
tweet_group = tweet_reader.load_per_user(topic)
aux_data = AuxPairLoader(hp.seq_max, setting, tweet_group)
voca_size = setting.vocab_size
e.train_stance_consistency(voca_size, stance_data, aux_data)
def pred_mnli_anyway():
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_AnyA"
e_config.load_names = ['bert', 'cls_dense', 'aux_conflict']
data_loader = nli.DataLoader(hp.seq_max, nli_setting.vocab_filename, True)
target_label = 'mismatch'
data_id = "{}_1000".format(target_label)
load_id = ("NLIEx_AnyA", 'model-2785')
e.predict_rf(nli_setting, e_config, data_loader, load_id, data_id)
def pred_snli_ex():
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 = "SNLIEx_B"
e_config.load_names = ['bert', 'cls_dense', 'aux_conflict']
data_loader = nli.SNLIDataLoader(hp.seq_max, nli_setting.vocab_filename,
True)
load_id = ("SNLIEx_B", 'model-10275')
e.predict_rf(nli_setting, e_config, data_loader, load_id, "test")
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_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 test_ql():
hp = hyperparams.HPAdhoc()
hp.batch_size = 512
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "Adhoc_{}".format("C")
e_config.num_epoch = 4
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.test_ql(e_config, data_loader, load_id)
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 predict_lime_snli_continue():
hp = hyperparams.HPBert()
hp.batch_size = 512 + 256
e = Experiment(hp)
nli_setting = NLI()
nli_setting.vocab_size = 30522
nli_setting.vocab_filename = "bert_voca.txt"
e_config = ExperimentConfig()
e_config.name = "SNLI_LIME_{}".format("eval")
e_config.load_names = ['bert', 'cls_dense']
data_loader = nli.SNLIDataLoader(hp.seq_max, nli_setting.vocab_filename,
True)
load_id = ("SNLI_Only_A", 'model-0')
e.predict_lime_snli_continue(nli_setting, e_config, data_loader, load_id,
"test")
def predict_adhoc_robust_K():
hp = hyperparams.HPAdhoc()
hp.batch_size = 512
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "Adhoc_{}_eval".format("K")
e_config.load_names = ['bert', 'dense1', 'dense_reg']
vocab_size = 30522
#payload_path = os.path.join(cpath.data_path, "robust_payload", "payload_B_200.pickle")
payload_path = os.path.join(cpath.data_path, "robust_payload",
"payload_desc.pickle")
task_idx = int(sys.argv[2])
print(task_idx)
load_id = ("Adhoc_K", 'model-6397')
e.predict_robust(e_config, vocab_size, load_id, payload_path, task_idx)
def run_adhoc_rank_on_robust():
hp = hyperparams.HPAdhoc()
hp.batch_size = 512
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "Adhoc_{}_eval".format("F")
e_config.num_epoch = 4
e_config.load_names = ['bert', 'reg_dense']
vocab_size = 30522
vocab_filename = "bert_voca.txt"
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_E", 'model-58338')
e.rank_adhoc(e_config, data_loader, load_id)
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 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 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 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 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 pool_adhoc():
hp = hyperparams.HPAdhoc()
hp.batch_size = 512
e = Experiment(hp)
e_config = ExperimentConfig()
e_config.name = "Adhoc_{}_pool".format("L")
#e_config.load_names = ['bert', 'reg_dense']
e_config.load_names = ['bert', 'reg_dense', 'aux_q_info']
vocab_size = 30522
task_idx = int(sys.argv[2])
print(task_idx)
payload_path = os.path.join(cpath.data_path, "robust",
"robust_train_merge",
"merger_train_{}.pickle".format(task_idx))
load_id = ("Adhoc_L", 'model-644')
e.predict_for_pooling(e_config, vocab_size, load_id, payload_path)
def test_snli():
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 = "SNLIEx_Test"
e_config.load_names = ['bert', 'cls_dense'] # , 'aux_conflict']
data_loader = nli.SNLIDataLoader(hp.seq_max, nli_setting.vocab_filename,
True)
todo = []
load_id = ("SNLI_Only_A", 'model-0')
todo.append(load_id)
todo.append(("SNLI_Only_1", 'model-0'))
for load_id in todo:
tf.reset_default_graph()
e.test_acc(nli_setting, e_config, data_loader, load_id)
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)
