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fixing dtype for basis functions #89

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fixing dtype for basis functions #89

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5 changes: 4 additions & 1 deletion gpflux/layers/basis_functions/fourier_features/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,11 @@ def __init__(self, kernel: gpflow.kernels.Kernel, n_components: int, **kwargs: M
:param n_components: number of components (e.g. Monte Carlo samples,
quadrature nodes, etc.) used to numerically approximate the kernel.
"""
if kwargs.get("dtype") is None:
kwargs["dtype"] = gpflow.default_float()

super(FourierFeaturesBase, self).__init__(**kwargs)

self.kernel = kernel
self.n_components = n_components
if kwargs.get("input_dim", None):
Expand All @@ -47,7 +51,6 @@ def call(self, inputs: TensorType) -> tf.Tensor:
Evaluate the basis functions at ``inputs``.

:param inputs: The evaluation points, a tensor with the shape ``[N, D]``.

:return: A tensor with the shape ``[N, M]``.
"""
X = tf.divide(inputs, self.kernel.lengthscales) # [N, D]
Expand Down
8 changes: 3 additions & 5 deletions tests/gpflux/layers/basis_functions/fourier_features/test_quadrature.py
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Expand Up @@ -83,7 +83,7 @@ def test_quadrature_fourier_features_can_approximate_kernel_multidim(
lengthscales = np.random.uniform(low=0.75, size=n_dims) * lengthscale

kernel = kernel_cls(variance=variance, lengthscales=lengthscales)
fourier_features = QuadratureFourierFeatures(kernel, n_components, dtype=tf.float64)
fourier_features = QuadratureFourierFeatures(kernel, n_components)

x = tf.random.uniform((x_rows, n_dims), dtype=tf.float64)
y = tf.random.uniform((y_rows, n_dims), dtype=tf.float64)
Expand All @@ -108,7 +108,7 @@ def test_feature_map_decomposition(kernel_cls, variance, lengthscale, n_dims, n_
lengthscales = np.random.rand(n_dims) * lengthscale

kernel = kernel_cls(variance=variance, lengthscales=lengthscales)
fourier_features = QuadratureFourierFeatures(kernel, n_components, dtype=tf.float64)
fourier_features = QuadratureFourierFeatures(kernel, n_components)

x = tf.random.uniform((x_rows, n_dims), dtype=tf.float64)
y = tf.random.uniform((y_rows, n_dims), dtype=tf.float64)
Expand Down Expand Up @@ -138,7 +138,7 @@ def eigen_func(w):
def test_fourier_features_shapes(n_components, n_dims, batch_size):
input_shape = (batch_size, n_dims)
kernel = gpflow.kernels.SquaredExponential()
feature_functions = QuadratureFourierFeatures(kernel, n_components, dtype=tf.float64)
feature_functions = QuadratureFourierFeatures(kernel, n_components)
output_shape = feature_functions.compute_output_shape(input_shape)
output_dim = output_shape[-1]
assert output_dim == 2 * n_components ** n_dims
Expand All @@ -157,7 +157,6 @@ def test_keras_testing_util_layer_test_1D(kernel_cls, batch_size, n_components):
"kernel": kernel,
"n_components": n_components,
"input_dim": 1,
"dtype": "float64",
"dynamic": True,
},
input_shape=(batch_size, 1),
Expand All @@ -176,7 +175,6 @@ def test_keras_testing_util_layer_test_multidim(kernel_cls, batch_size, n_dims,
"kernel": kernel,
"n_components": n_components,
"input_dim": n_dims,
"dtype": "float64",
"dynamic": True,
},
input_shape=(batch_size, n_dims),
Expand Down
39 changes: 33 additions & 6 deletions tests/gpflux/layers/basis_functions/fourier_features/test_random.py
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Original file line number Diff line number Diff line change
Expand Up @@ -83,6 +83,35 @@ def test_throw_for_unsupported_kernel(basis_func_cls):
assert "Unsupported Kernel" in str(excinfo.value)


def test_random_fourier_features_has_default_dtype_float64(
random_basis_func_cls,
):
kernel = gpflow.kernels.SquaredExponential(variance=0.5, lengthscales=0.5)
fourier_features = random_basis_func_cls(kernel, 40)

x = tf.random.uniform((20, 2), dtype=gpflow.default_float())
features = fourier_features(x)

# asserting with gpflow.default_float() is not working correctly so using tf.float64
assert fourier_features.dtype == tf.float64
for w in fourier_features.weights:
assert w.dtype == tf.float64
assert features.dtype == tf.float64


def test_random_fourier_features_raises_for_incorrect_input_dtype(
random_basis_func_cls,
):
kernel = gpflow.kernels.SquaredExponential(variance=0.5, lengthscales=0.5)
fourier_features = random_basis_func_cls(kernel, 40)

x = tf.random.uniform((20, 2), dtype=tf.float32)
features = fourier_features(x)

with pytest.raises(ValueError):
fourier_features(x)


def test_random_fourier_features_can_approximate_kernel_multidim(
random_basis_func_cls, kernel_cls, variance, lengthscale, n_dims
):
Expand All @@ -94,7 +123,7 @@ def test_random_fourier_features_can_approximate_kernel_multidim(
lengthscales = np.random.rand((n_dims)) * lengthscale

kernel = kernel_cls(variance=variance, lengthscales=lengthscales)
fourier_features = random_basis_func_cls(kernel, n_components, dtype=tf.float64)
fourier_features = random_basis_func_cls(kernel, n_components)

x = tf.random.uniform((x_rows, n_dims), dtype=tf.float64)
y = tf.random.uniform((y_rows, n_dims), dtype=tf.float64)
Expand All @@ -117,7 +146,7 @@ def test_orthogonal_random_features_can_approximate_kernel_multidim(variance, le
lengthscales = np.random.rand((n_dims)) * lengthscale

kernel = gpflow.kernels.SquaredExponential(variance=variance, lengthscales=lengthscales)
fourier_features = OrthogonalRandomFeatures(kernel, n_components, dtype=tf.float64)
fourier_features = OrthogonalRandomFeatures(kernel, n_components)

x = tf.random.uniform((x_rows, n_dims), dtype=tf.float64)
y = tf.random.uniform((y_rows, n_dims), dtype=tf.float64)
Expand All @@ -142,7 +171,7 @@ def test_random_fourier_feature_layer_compute_covariance_of_inducing_variables(
n_components = 10000

kernel = gpflow.kernels.SquaredExponential()
fourier_features = basis_func_cls(kernel, n_components, dtype=tf.float64)
fourier_features = basis_func_cls(kernel, n_components)

x_new = tf.ones(shape=(2 * batch_size + 1, 1), dtype=tf.float64)

Expand All @@ -157,7 +186,7 @@ def test_random_fourier_feature_layer_compute_covariance_of_inducing_variables(
def test_fourier_features_shapes(basis_func_cls, n_components, n_dims, batch_size):
input_shape = (batch_size, n_dims)
kernel = gpflow.kernels.SquaredExponential()
feature_functions = basis_func_cls(kernel, n_components, dtype=tf.float64)
feature_functions = basis_func_cls(kernel, n_components)
output_shape = feature_functions.compute_output_shape(input_shape)
features = feature_functions(tf.ones(shape=input_shape))
np.testing.assert_equal(features.shape, output_shape)
Expand All @@ -174,7 +203,6 @@ def test_keras_testing_util_layer_test_1D(kernel_cls, batch_size, n_components):
"kernel": kernel,
"n_components": n_components,
"input_dim": 1,
"dtype": "float64",
"dynamic": True,
},
input_shape=(batch_size, 1),
Expand All @@ -193,7 +221,6 @@ def test_keras_testing_util_layer_test_multidim(kernel_cls, batch_size, n_dims,
"kernel": kernel,
"n_components": n_components,
"input_dim": n_dims,
"dtype": "float64",
"dynamic": True,
},
input_shape=(batch_size, n_dims),
Expand Down