Handle unconstrained priors in randomize_hyperparameters (#796)

tests/unit/models/gpflow/test_utils.py

@@ -143,6 +143,29 @@ def test_randomize_hyperparameters_randomizes_kernel_parameters_with_priors(
     assert len(np.unique(kernel.lengthscales)) == dim
 
 
+@random_seed
+@pytest.mark.parametrize("compile", [False, True])
+def test_randomize_hyperparameters_randomizes_kernel_parameters_with_unconstrained_priors(
+    dim: int, compile: bool
+) -> None:
+    kernel = gpflow.kernels.RBF(variance=1.0, lengthscales=[0.2] * dim)
+    kernel.lengthscales = gpflow.Parameter(kernel.lengthscales, transform=tfp.bijectors.Exp())
+    kernel.lengthscales.prior = tfp.distributions.Uniform(
+        tf.math.log(tf.constant(0.01, dtype=tf.float64)),
+        tf.math.log(tf.constant(10.0, dtype=tf.float64)),
+    )
+    kernel.lengthscales.prior_on = gpflow.base.PriorOn.UNCONSTRAINED
+
+    compiler = tf.function if compile else lambda x: x
+    compiler(randomize_hyperparameters)(kernel)
+
+    npt.assert_allclose(1.0, kernel.variance)
+    npt.assert_array_equal(dim, kernel.lengthscales.shape)
+    npt.assert_array_less(kernel.lengthscales, [10.0] * dim)
+    npt.assert_raises(AssertionError, npt.assert_allclose, [0.2] * dim, kernel.lengthscales)
+    assert len(np.unique(kernel.lengthscales)) == dim
+
+
 @random_seed
 @pytest.mark.parametrize("compile", [False, True])
 def test_randomize_hyperparameters_randomizes_kernel_parameters_with_const_priors(
@@ -207,13 +230,13 @@ def test_randomize_hyperparameters_samples_from_constraints_when_given_prior_and
     kernel.lengthscales = gpflow.Parameter(
         kernel.lengthscales, transform=tfp.bijectors.Sigmoid(low=lower, high=upper)
     )
-    kernel.lengthscales.prior = tfp.distributions.Uniform(low=10.0, high=100.0)
+    kernel.lengthscales.prior = tfp.distributions.Uniform(low=lower, high=upper / 2)
 
     kernel.variance.prior = tfp.distributions.LogNormal(loc=np.float64(-2.0), scale=np.float64(1.0))
 
     randomize_hyperparameters(kernel)
 
-    npt.assert_array_less(kernel.lengthscales, [0.5] * dim)
+    npt.assert_array_less(kernel.lengthscales, [0.25] * dim)
     npt.assert_raises(AssertionError, npt.assert_allclose, [0.2] * dim, kernel.lengthscales)
 
 

trieste/models/gpflow/utils.py

@@ -52,21 +52,14 @@ def assert_data_is_compatible(new_data: Dataset, existing_data: Dataset) -> None
 
 def randomize_hyperparameters(object: gpflow.Module) -> None:
     """
-    Sets hyperparameters to random samples from their constrained domains or (if not constraints
-    are available) their prior distributions.
+    Sets hyperparameters to random samples from their prior distributions or (for Sigmoid
+    constraints with no priors) their constrained domains. Note that it is up to the caller
+    to ensure that the prior, if defined, is compatible with the transform.
 
     :param object: Any gpflow Module.
     """
     for param in object.trainable_parameters:
-        if isinstance(param.bijector, tfp.bijectors.Sigmoid):
-            sample = tf.random.uniform(
-                param.bijector.low.shape,
-                minval=param.bijector.low,
-                maxval=param.bijector.high,
-                dtype=param.bijector.low.dtype,
-            )
-            param.assign(sample)
-        elif param.prior is not None:
+        if param.prior is not None:
             # handle constant priors for multi-dimensional parameters
             # Use python conditionals here to avoid creating tensorflow `tf.cond` ops,
             # i.e. using `len(param.shape)` instead of `tf.rank(param)`.
@@ -76,6 +69,18 @@ def randomize_hyperparameters(object: gpflow.Module) -> None:
                 sample = param.prior.sample(tf.shape(param))
             else:
                 sample = param.prior.sample()
+            if param.prior_on is gpflow.base.PriorOn.UNCONSTRAINED:
+                param.unconstrained_variable.assign(sample)
+            else:
+                param.assign(sample)
+
+        elif isinstance(param.bijector, tfp.bijectors.Sigmoid):
+            sample = tf.random.uniform(
+                param.bijector.low.shape,
+                minval=param.bijector.low,
+                maxval=param.bijector.high,
+                dtype=param.bijector.low.dtype,
+            )
             param.assign(sample)