code: def test_numpydataset():
      x, y = load_iris(return_X_y=True)
      init_len = len(x)
      dataset = ActiveNumpyArray((x, y))
      assert len(dataset) == 0 == dataset.n_labelled
      assert dataset.n_unlabelled == init_len
  
      dataset.label_randomly(10)
      assert len(dataset) == 10 == dataset.n_labelled
      assert dataset.n_unlabelled == init_len - 10
  
      xi, yi = dataset.dataset
      assert len(xi) == 10
  
      xp, yp = dataset.pool
      assert len(xp) == init_len - 10
  
      dataset.label(list(range(10)))
      assert len(dataset) == 20
  
      l = np.array([1] * 10 + [0] * (init_len - 10))
      dataset = ActiveNumpyArray((x, y), labelled=l)
      assert len(dataset) == 10
  
      assert [
          a == b for a, b in zip(dataset.get_raw(-1), (x[-1], y[-1]))
      ]
      assert [a == b for a, b in zip(dataset.get_raw(0), (x[0], y[0]))]
  
      assert (next(iter(dataset))[0] == dataset[0][0]).all()
  
imports: from sklearn.datasets import load_iris
  from baal.active.dataset import ActiveNumpyArray
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: test_numpydataset() without return types