numDataPoints = 1000
data_dim = 5
bs = 100

# Create dummy data with class imbalance 9 to 1
data = torch.FloatTensor(numDataPoints, data_dim)
target = np.hstack((np.zeros(int(numDataPoints * 0.9), dtype=np.int32),
     np.ones(int(numDataPoints * 0.1), dtype=np.int32)))

print 'target train 0/1: {}/{}'.format(
 len(np.where(target == 0)[0]), len(np.where(target == 1)[0]))

class_sample_count = np.array(
 [len(np.where(target == t)[0]) for t in np.unique(target)])
weight = 1. / class_sample_count
samples_weight = np.array([weight[t] for t in target])

samples_weight = torch.from_numpy(samples_weight)
samples_weight = samples_weight.double()
sampler = WeightedRandomSampler(samples_weight, len(samples_weight))

target = torch.from_numpy(target).long()
train_dataset = torch.utils.data.TensorDataset(data, target)

train_loader = DataLoader(
 train_dataset, batch_size=bs, num_workers=1, sampler=sampler)

for i, (data, target) in enumerate(train_loader):
 print "batch index {}, 0/1: {}/{}".format(
  i,
  len(np.where(target.numpy() == 0)[0]),
  len(np.where(target.numpy() == 1)[0]))

class_sample_count = np.array(
 [len(np.where(target == t)[0]) for t in np.unique(target)])
weight = 1. / class_sample_count
samples_weight = np.array([weight[t] for t in target])

samples_weight = torch.from_numpy(samples_weight)
samples_weight = samples_weight.double()
sampler = WeightedRandomSampler(samples_weight, len(samples_weight))