def read_arguments(train=True):
parser = argparse.ArgumentParser()
parser = add_all_arguments(parser, train)
parser.add_argument('--phase', type=str, default='train')
opt = parser.parse_args()
if train:
set_dataset_default_lm(opt, parser)
if opt.continue_train:
update_options_from_file(opt, parser)
opt = parser.parse_args()
opt.phase = 'train' if train else 'test'
if train:
opt.loaded_latest_iter = 0 if not opt.continue_train else load_iter(opt)
utils.fix_seed(opt.seed)
print_options(opt, parser)
if train:
save_options(opt, parser)
return opt
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--submit', type=str, default="False")
args = parser.parse_args()
args.submit = (args.submit == 'True')
return args
args = parse_args()
if __name__ == "__main__":
# hyper params
seed = 1
fix_seed(seed)
n_folds = 5
epochs = 200
batch_size = 512
# data
train_df, test_df, sample_submit_df = load_dataset()
X, X_test = glove(train_df, test_df)
X = X.values.astype('float32')
X_test = X_test.values.astype('float32')
y = pd.get_dummies(train_df['jobflag']).values.astype('float32')
trainset = JobInfoDataset(X, y, jobflag=train_df['jobflag'].values)
testset = JobInfoDataset(X_test)
from utils.utils import fix_seed
from utils.observer import get_current_time
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--submit', type=str, default="False")
args = parser.parse_args()
args.submit = (args.submit == 'True')
return args
args = parse_args()
if __name__ == "__main__":
# hyper params
seed=1; fix_seed(seed)
n_folds = 5
epochs = 600
batch_size = 512
# data
train_df, test_df, sample_submit_df = load_dataset()
X, X_test = word2vec(train_df, test_df)
X = X.values.astype('float32')
X_test = X_test.values.astype('float32')
y = pd.get_dummies(train_df['jobflag']).values.astype('float32')
trainset = JobInfoDataset(X, y, jobflag=train_df['jobflag'].values)
testset = JobInfoDataset(X_test)