def test_make_weights_for_balanced_classes(self):
dataset = [('A', 0), ('B', 1), ('C', 0), ('D', 2), ('E', 3), ('F', 0)]
result = misc.make_weights_for_balanced_classes(dataset)
self.assertEqual(result.sum(), 1)
self.assertEqual(result[0], result[2])
self.assertEqual(result[1], result[3])
self.assertEqual(3 * result[0], result[1])
out_splits = []
uda_splits = []
for env_i, env in enumerate(dataset):
uda = []
out, in_ = misc.split_dataset(env,
int(len(env) * args.holdout_fraction),
misc.seed_hash(args.trial_seed, env_i))
if env_i in args.test_envs:
uda, in_ = misc.split_dataset(
in_, int(len(in_) * args.uda_holdout_fraction),
misc.seed_hash(args.trial_seed, env_i))
if hparams['class_balanced']:
in_weights = misc.make_weights_for_balanced_classes(in_)
out_weights = misc.make_weights_for_balanced_classes(out)
if uda is not None:
uda_weights = misc.make_weights_for_balanced_classes(uda)
else:
in_weights, out_weights, uda_weights = None, None, None
in_splits.append((in_, in_weights))
out_splits.append((out, out_weights))
if len(uda):
uda_splits.append((uda, uda_weights))
train_loaders = [
InfiniteDataLoader(dataset=env,
weights=env_weights,
batch_size=hparams['batch_size'],
num_workers=dataset.N_WORKERS)
