Add tests

src/gluonts/mx/model/deepvar_hierarchical/_estimator.py

@@ -101,6 +101,9 @@ def null_space_projection_mat(
  if D is None:
  return np.eye(num_ts) - A.T @ np.linalg.pinv(A @ A.T) @ A
  else:
+ assert np.all(
+ np.linalg.eigvals(D) > 0
+ ), "`D` must be positive definite."
  D_inv = np.linalg.inv(D)
  return (
  np.eye(num_ts) - D_inv @ A.T @ np.linalg.pinv(A @ D_inv @ A.T) @ A

test/mx/model/deepvar_hierarchical/test_reconcile_samples.py

@@ -38,7 +38,6 @@
 
 num_bottom_ts = S.shape[1]
 A = constraint_mat(S)
-reconciliation_mat = null_space_projection_mat(A)
 
 
 @pytest.mark.parametrize(
@@ -53,6 +52,28 @@
  100.0 + 2.0 * np.random.standard_normal(size=(10, 32, S.shape[0])),
  ],
 )
+@pytest.mark.parametrize(
+ "D",
+ [
+ None,
+ # Root gets the maximum weight and the two aggregated levels get
+ # more weight than the leaf level.
+ np.diag(
+ [4, 2, 2, 1, 1, 1, 1]
+ ),
+ # Random diagonal matrix
+ np.diag(
+ np.random.rand(S.shape[0])
+ ),
+ # Random positive definite matrix
+ np.diag(
+ np.random.rand(S.shape[0])
+ ) + np.dot(
+ np.array([[4, 2, 2, 1, 1, 1, 1]]).T,
+ np.array([[4, 2, 2, 1, 1, 1, 1]])
+ )
+ ],
+)
 @pytest.mark.parametrize(
  "seq_axis",
  [
@@ -63,9 +84,9 @@
  [1, 0],
  ],
 )
-def test_reconciliation_error(samples, seq_axis):
+def test_reconciliation_error(samples, D, seq_axis):
  coherent_samples = reconcile_samples(
- reconciliation_mat=mx.nd.array(reconciliation_mat),
+ reconciliation_mat=mx.nd.array(null_space_projection_mat(A=A, D=D)),
  samples=mx.nd.array(samples),
  seq_axis=seq_axis,
  )