def calculate_predictions(experiment_name, dataset):
print('Loading data... ')
sys.stdout.flush()
if experiment_name in ['danq', 'deepsea', 'danqjaspar']:
data = Data(data_suffix='_full')
X, y = data.get_data(dataset)
else:
data = get_data_loader(experiment_name)
X, y = data.get_data(dataset)
print('Loading model... ')
sys.stdout.flush()
model = get_trained_model(experiment_name)
print('Calculating predictions... ')
sys.stdout.flush()
make_predictions(model,
X,
join(
RESULT_DIR, 'predictions-best',
'{}-{}{}.npy'.format(experiment_name, dataset,
data.suffix)),
verbose=1)
import os, sys
from core.data import Data
from core.train_model import get_trained_model
def append_to_losses(expt_name,
dataset,
loss,
filename='final_losses_{}.csv'.format(sys.argv[2])):
with open(filename, 'a') as f:
f.write('{},{},{}\n'.format(expt_name, dataset, loss))
RESULT_DIR = os.environ.get('RESULT_DIR', 'results')
data = Data(sequence_length=int(sys.argv[2]), data_suffix='_full')
m = get_trained_model(sys.argv[1])
print('evaluating model', flush=True)
l = m.evaluate(*data.get_data('test'))
print('saving results', flush=True)
append_to_losses(sys.argv[1], 'test', l)
