Add tests

tests/unit/models/gpflow/test_sampler.py

@@ -285,6 +285,20 @@ def test_independent_reparametrization_sampler_reset_sampler(qmc: bool, qmc_skip
         npt.assert_array_less(1e-9, tf.abs(samples2 - samples1))
 
 
+@pytest.mark.parametrize("qmc", [True, False])
+@pytest.mark.parametrize("dtype", [tf.float32, tf.float64])
+def test_independent_reparametrization_sampler_sample_ensures_positive_variance(
+    qmc: bool, dtype: tf.DType
+) -> None:
+    model = QuadraticMeanAndRBFKernel(kernel_amplitude=tf.constant(0, dtype=dtype))
+    sampler = IndependentReparametrizationSampler(100, model, qmc=qmc)
+    x = tf.constant([[1.0]], dtype=dtype)
+    variance = tf.math.reduce_variance(sampler.sample(x))  # default jitter
+    assert variance > (1e-7 if dtype is tf.float32 else 1e-17)
+    variance = tf.math.reduce_variance(sampler.sample(x, jitter=0.0))  # explicit jitter
+    assert variance > (1e-7 if dtype is tf.float32 else 1e-17)
+
+
 @pytest.mark.parametrize("qmc", [True, False])
 @pytest.mark.parametrize("sample_size", [0, -2])
 def test_batch_reparametrization_sampler_raises_for_invalid_sample_size(
@@ -457,6 +471,20 @@ def test_batch_reparametrization_sampler_reset_sampler(qmc: bool, qmc_skip: bool
         npt.assert_array_less(1e-9, tf.abs(samples2 - samples1))
 
 
+@pytest.mark.parametrize("qmc", [True, False])
+@pytest.mark.parametrize("dtype", [tf.float32, tf.float64])
+def test_batch_reparametrization_sampler_sample_ensures_positive_variance(
+    qmc: bool, dtype: tf.DType
+) -> None:
+    model = QuadraticMeanAndRBFKernel(kernel_amplitude=tf.constant(0, dtype=dtype))
+    sampler = BatchReparametrizationSampler(100, model, qmc=qmc)
+    x = tf.constant([[1.0]], dtype=dtype)
+    variance = tf.math.reduce_variance(sampler.sample(x))  # default jitter
+    assert variance > (1e-7 if dtype is tf.float32 else 1e-17)
+    variance = tf.math.reduce_variance(sampler.sample(x, jitter=0.0))  # explicit jitter
+    assert variance > (1e-7 if dtype is tf.float32 else 1e-17)
+
+
 @pytest.mark.parametrize("num_features", [0, -2])
 def test_rff_trajectory_sampler_raises_for_invalid_number_of_features(
     num_features: int,

tests/unit/utils/test_misc.py

@@ -29,6 +29,7 @@
     LocalizedTag,
     Ok,
     Timer,
+    ensure_positive,
     flatten_leading_dims,
     get_value_for_tag,
     jit,
@@ -222,3 +223,28 @@ def test_flatten_leading_dims_invalid_output_dims(output_dims: int) -> None:
     x_old = tf.random.uniform([2, 3, 4, 5])  # [2, 3, 4, 5]
     with pytest.raises(TF_DEBUGGING_ERROR_TYPES):
         flatten_leading_dims(x_old, output_dims=output_dims)
+
+
+@pytest.mark.parametrize(
+    "t, expected",
+    [
+        (
+            tf.constant(0, dtype=tf.float32),
+            tf.constant(1e-6, dtype=tf.float32),
+        ),
+        (
+            tf.constant(0, dtype=tf.float64),
+            tf.constant(1e-16, dtype=tf.float64),
+        ),
+        (
+            tf.constant([[-1.0, 0.0], [1e-7, 1.0]], dtype=tf.float32),
+            tf.constant([[1e-6, 1e-6], [1e-6, 1.0]], dtype=tf.float32),
+        ),
+        (
+            tf.constant([[-1.0, 0.0], [1e-7, 1.0]], dtype=tf.float64),
+            tf.constant([[1e-16, 1e-16], [1e-7, 1.0]], dtype=tf.float64),
+        ),
+    ],
+)
+def test_ensure_positive(t: TensorType, expected: TensorType) -> None:
+    npt.assert_array_equal(ensure_positive(t), expected)