def stance_fine_tune():
# importing the required module
import matplotlib.pyplot as plt
for lr in [1e-3, 5e-4, 2e-4, 1e-4]:
hp = HPFineTunePair()
topic = "hillary"
e = Experiment(hp)
hp.lr = lr
hp.num_epochs = 100
preload_id = ("LM_pair_tweets_hillary_run2", 1247707)
setting = shared_setting.TopicTweets2Stance(topic)
stance_data = stance_detection.FineLoader(topic, hp.seq_max,
setting.vocab_filename,
hp.sent_max)
valid_history = e.train_stance(setting.vocab_size, stance_data,
preload_id)
e.clear_run()
l_acc, l_f1 = zip(*valid_history)
plt.plot(l_acc, label="{} / ACC".format(lr))
plt.plot(l_f1, label="{} / F1".format(lr))
plt.legend(loc='lower right')
# giving a title to my graph
plt.title('learning rate - dev !')
# function to show the plot
plt.show()
def stance_after_feature():
hp = HPPairFeatureTweetFine()
topic = "atheism"
e = Experiment(hp)
preload_id = ("LM_pair_featuer_tweets_atheism", 979)
setting = shared_setting.TopicTweets2Stance(topic)
stance_data = stance_detection.DataLoader(topic, hp.seq_max,
setting.vocab_filename)
e.train_stance_pair_feature(setting.vocab_size, stance_data, None)
def stance_warm():
hp = HPFineTunePair()
topic = "hillary"
e = Experiment(hp)
preload_id = ("after_stance", 400)
hp.seq_max = 50
setting = shared_setting.TopicTweets2Stance(topic)
stance_data = stance_detection.DataLoader(topic, hp.seq_max,
setting.vocab_filename)
e.train_stance(setting.vocab_size, stance_data, preload_id)
def stance_after_lm():
hp = HPFineTunePair()
topic = "hillary"
e = Experiment(hp)
preload_id = ("DLM_pair_tweets_hillary", 852967)
setting = shared_setting.TopicTweets2Stance(topic)
stance_data = stance_detection.FineLoader(topic, hp.seq_max,
setting.vocab_filename,
hp.sent_max)
e.train_stance(setting.vocab_size, stance_data, preload_id)
def feature_svm():
hp = HPFineTunePair()
topic = "atheism"
e = Experiment(hp)
preload_id = ("LM_reserve/DLM_pair_tweets_atheism", 217246)
setting = shared_setting.TopicTweets2Stance(topic)
stance_data = stance_detection.FineLoader(topic, hp.seq_max,
setting.vocab_filename,
hp.sent_max)
e.feature_svm(setting.vocab_size, stance_data, preload_id)
def stance_cold_start():
hp = HPColdStart()
e = Experiment(hp)
topic = "hillary"
setting = shared_setting.TopicTweets2Stance(topic)
stance_data = stance_detection.DataLoader(topic, hp.seq_max,
setting.vocab_filename)
voca_size = setting.vocab_size
e.train_stance(voca_size, stance_data)
def train_aux():
hp = HPFineTunePair()
hp2 = HPTiny()
e = Experiment(hp)
topic = "hillary"
preload_id = ("DLM_pair_tweets_hillary", 131200)
setting = shared_setting.TopicTweets2Stance(topic)
sentiment = stance_detection.SentimentLoader(topic, hp.seq_max,
setting.vocab_filename)
voca_size = setting.vocab_size
e.train_aux1(hp2, voca_size, sentiment, preload_id)
def train_aux_stance():
hp = HPColdStart()
hp2 = HPTiny()
topic = "hillary"
preload_id = ("after_aux", 234)
setting = shared_setting.TopicTweets2Stance(topic)
stance_data = stance_detection.DataLoader(topic, hp.seq_max,
setting.vocab_filename)
voca_size = setting.vocab_size
e = Experiment(hp)
e.train_aux_stance(hp2, voca_size, stance_data, preload_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 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 stance_cold(self):
hp = hyperparams.HPColdStart()
topic = "atheism"
setting = shared_setting.TopicTweets2Stance(topic)
model_dir = get_model_dir("stance_cold_{}".format(topic))
task = Classification(3)
model = Transformer(hp, setting.vocab_size, task)
param = {
'feature_columns': self.get_feature_column(),
'n_classes': 3,
}
estimator = tf.estimator.Estimator(
model_fn=model.model_fn,
model_dir=model_dir,
params=param,
config=None)
data_source = stance_detection.DataLoader(topic, hp.seq_max, setting.vocab_filename)
def train_input_fn(features, labels, batch_size):
f_dict = pd.DataFrame(data=features)
dataset = tf.data.Dataset.from_tensor_slices((f_dict, labels))
# Shuffle, repeat, and batch the examples.
return dataset.shuffle(1000).repeat().batch(batch_size)
def dev_input_fn(batch_size):
features, labels = data_source.get_dev_data()
f_dict = pd.DataFrame(data=features)
dataset = tf.data.Dataset.from_tensor_slices((f_dict, labels))
# Shuffle, repeat, and batch the examples.
return dataset.shuffle(1000).batch(batch_size)
X, Y = data_source.get_train_data()
num_epoch = 10
batch_size = 32
step_per_epoch = (len(Y)-1) / batch_size + 1
tf.logging.info("Logging Test")
tf.logging.info("num epoch %d", num_epoch)
estimator.train(lambda:train_input_fn(X, Y, batch_size),
max_steps=num_epoch * step_per_epoch)
print(estimator.evaluate(lambda:dev_input_fn(batch_size)))
def pair_feature():
hp = HPPairFeatureTweet()
topic = "atheism"
setting = shared_setting.TopicTweets2Stance(topic)
use_cache = True
run_id = "{}_{}".format(topic, hp.sent_max)
if use_cache:
print("using PairDataCache")
data = loader.PairDataLoader.load_from_pickle(run_id)
else:
tweet_group = tweet_reader.load_per_user(topic)
data = loader.PairDataLoader(hp.sent_max, setting, tweet_group)
data.index_data()
data.save_to_pickle(run_id)
e_config = ExperimentConfig()
e_config.name = "LM_pair_featuer_tweets_{}".format(topic)
e_config.num_epoch = 1
e_config.save_interval = 3 * 60 # 3 minutes
e = Experiment(hp)
e.train_pair_feature(e_config, data)
def document_lm():
hp = HPDocLM()
topic = "hillary"
setting = shared_setting.TopicTweets2Stance(topic)
use_cache = False
run_id = "{}_{}".format(topic, hp.seq_max)
if use_cache:
data = author_as_doc.AuthorAsDoc.load_from_pickle(run_id)
else:
tweet_group = tweet_reader.load_per_user(topic)
data = author_as_doc.AuthorAsDoc(hp.seq_max, setting, tweet_group)
data.index_data()
data.save_to_pickle(run_id)
e_config = ExperimentConfig()
e_config.name = "DLM_pair_tweets_{}".format(topic)
e_config.num_epoch = 1
e_config.save_interval = 30 * 60 # 30 minutes
e = Experiment(hp)
e.train_doc_lm(e_config, data)
def baselines():
hp = Hyperparams()
e = Experiment(hp)
topic = "hillary"
setting = shared_setting.TopicTweets2Stance(topic)
e.stance_baseline(topic, setting.vocab_filename)
