def make(dataset):
claim_map = dev_claim_map if dataset == 'dev' else test_claim_map
df = data.load(dataset)
file_name = '%s-adv-full.txt' % dataset
new_claims = [claim_map[c] for c in list(df.claim)]
new_labels = [not l for l in df.correctLabelW0orW1]
adv = df.copy()
adv.claim = new_claims
adv.correctLabelW0orW1 = new_labels
adv.to_csv('data/arct/%s' % file_name, sep='\t', index=False)
def test(self, args):
data_points = data.load('test-adv')
return self.get_data_loader(data_points, args)
def dev(self, args):
data_points = data.load('dev-adv')
return self.get_data_loader(data_points, args)
def train(self, args):
data_points = data.load('train-adv')
self.n_training_points = len(data_points)
return self.get_data_loader(data_points, args)
def test(self, args):
return self.get_data_loader(data.load('test'), args)
def dev(self, args):
return self.get_data_loader(data.load('dev'), args)
from util import text
def flatten(list_of_lists):
return [x for sublist in list_of_lists for x in sublist]
if __name__ == '__main__':
print('Building ARCT vocab...')
# grab all sents from all data subsets
datasets = ['train', 'dev', 'test']
sent_cols = ['claim', 'reason', 'warrant0', 'warrant1']
sents = []
for dataset in datasets:
df = data.load(dataset)
for _, row in df.iterrows():
for col in sent_cols:
sents.append(row[col])
# tokenize
tokens = set(flatten([text.tokenize(s) for s in sents]))
# build the vocab dictionary
vocab = dict(zip(tokens, range(len(tokens))))
rev_vocab = {v: k for k, v in vocab.items()}
# save the vocab dictionary
vocab_path = os.path.join(glovar.ARCT_DIR, 'vocab.json')
rev_vocab_path = os.path.join(glovar.ARCT_DIR, 'rev_vocab.json')
with open(vocab_path, 'w') as f:
def merge(dataset):
dfo = data.load(dataset)
dfa = data.load('%s-adv' % dataset)
dfm = pd.concat([dfo, dfa])
dfm.to_csv('data/arct/%s.csv' % dataset, sep='\t', index=False)
def test(self, args):
df = data.load('test-original')
self.n_train_examples = len(df)
examples = self.create_examples(df)
return self.get_data_loader(examples, args)
def train(self, args):
df = data.load('train-original')
self.n_training_points = len(df)
examples = self.create_examples(df)
return self.get_data_loader(examples, args)
def test(self, args):
df = data.load('test-adv-negated')
self.n_training_points = len(df)
examples = self.create_examples(df)
return self.get_data_loader(examples, args)
def dev(self, args):
df = data.load('dev-adv-swapped')
self.n_training_points = len(df)
examples = self.create_examples(df)
return self.get_data_loader(examples, args)
def dev(self, args):
df = data.load('dev')
self.n_train_examples = len(df)
examples = self.create_examples(df)
return self.get_data_loader(examples, args)
def test_adv(self, args):
return self.get_data_loader(data.load('test-adv'), args)
