Optimized one hot encoding

  """

  n_samples = np.shape(y)[0]

  y_hot = np.zeros((n_samples, 2))

  for index, val in enumerate(y):

    if val == 0:

      y_hot[index] = np.array([1, 0])

    elif val == 1:

      y_hot[index] = np.array([0, 1])

  y_hot[np.arange(n_samples), y.astype(np.int64)] = 1

  return y_hot

def from_one_hot(y, axis=1):

    assert np.isclose(dc.metrics.r2_score(y_true, y_pred),

                      regression_metric.compute_metric(y_true, y_pred))

  def test_one_hot(self):

    y = np.array([0, 0, 1, 0, 1, 1, 0])

    y_hot = metrics.to_one_hot(y)

    expected = np.array([[1,0], [1,0], [0,1], [1,0], [0,1], [0,1], [1,0]])

    yp = metrics.from_one_hot(y_hot)

    assert np.array_equal(expected, y_hot)

    assert np.array_equal(y, yp)

if __name__ == '__main__':

  googletest.main()