ENH: Add squared exponential covariance kernel

Add squared exponential covariance kernel.

src/eddymotion/model/kernels.py

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+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+#
+# Copyright 2024 The NiPreps Developers <[email protected]>
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY kIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# We support and encourage derived works from this project, please read
+# about our expectations at
+#
+#     https://www.nipreps.org/community/licensing/
+#
+import numpy as np
+from sklearn.gaussian_process.kernels import Kernel
+
+
+class SquaredExponentialCovarianceKernel(Kernel):
+    r"""Kernel based on a squared exponential function for Gaussian processes on
+    multi-shell DWI data following to eqs. 14 and 16 in [Andersson15]_.
+
+    For a 2-shell case, the $$\mathbf{K}$$ kernel can be written as:
+
+    .. math::
+
+        \begin{equation}
+        \mathbf{K} = \left[
+            \begin{matrix}
+                \lambda C_{\theta}(\theta (\mathbf{G}_{1}); a) + \sigma_{1}^{2} \mathbf{I} & \lambda C_{\theta}(\theta (\mathbf{G}_{2}, \mathbf{G}_{1}); a) C_{b}(b_{2}, b_{1}; \ell) \\
+                \lambda C_{\theta}(\theta (\mathbf{G}_{1}, \mathbf{G}_{2}); a) C_{b}(b_{1}, b_{2}; \ell) & \lambda C_{\theta}(\theta (\mathbf{G}_{2}); a) + \sigma_{2}^{2} \mathbf{I} \\
+            \end{matrix}
+        \right]
+        \end{equation}
+
+    Parameters
+    ----------
+    lambda_ : float
+        Scale parameter for the covariance function.
+    a : float
+        Distance parameter where the covariance function goes to zero.
+    sigma_sq : float
+        Noise variance term.
+    """
+
+    def __init__(self, lambda_=1.0, a=1.0, sigma_sq=1.0):
+        self.lambda_ = lambda_
+        self.a = a
+        self.sigma_sq = sigma_sq
+
+    def __call__(self, gradients):
+        """Compute the kernel matrix.
+
+        Parameters
+        ----------
+        gradients : RAS+b
+            .
+
+        Returns
+        -------
+        K : :obj:`~numpy.ndarray`, shape (n_samples, n_samples)
+            Kernel matrix.
+        """
+
+        # ToDo
+        # Call compute_squared_exponential_covariance_kernel
+        pass
+
+    def diag(self, X):
+        """Return the diagonal of the kernel matrix.
+
+        Parameters
+        ----------
+        X : :obj:`~numpy.ndarray`, shape (n_samples, n_features)
+            Input data.
+
+        Returns
+        -------
+        :obj:`~numpy.ndarray`, shape (n_samples,)
+            Diagonal of the kernel matrix.
+        """
+
+        return np.full(X.shape[0], self.lambda_ + self.sigma_sq)
+
+    def is_stationary(self):
+        """Return whether the kernel is stationary.
+
+        Returns
+        -------
+        :obj:`bool`
+            Returns always ``True``.
+        """
+
+        return True
+
+    def get_params(self, deep=True):
+        """Get parameters of the kernel.
+
+        Parameters
+        ----------
+        deep : :obj:`bool`
+            Whether to return the parameters of the contained subobjects.
+
+        Returns
+        -------
+        params : :obj:`dict`
+            Parameter names mapped to their values.
+        """
+
+        return {"lambda_": self.lambda_, "a": self.a, "sigma_sq": self.sigma_sq}
+
+    def set_params(self, **params):
+        """Set parameters of the kernel.
+
+        Parameters
+        ----------
+        params : :obj:`dict`
+            Kernel parameters.
+
+        Returns
+        -------
+        self
+        """
+
+        self.lambda_ = params.get("lambda_", self.lambda_)
+        self.a = params.get("a", self.a)
+        self.sigma_sq = params.get("sigma_sq", self.sigma_sq)
+        return self
+
+
+def compute_squared_exponential_shell_covariance(grpi, grpb, ell):
+    r"""Compute the squared exponential smooth function describing how the
+    covariance changes along the b direction.
+
+    It uses the log of the b-values as the measure of distance along the
+    b-direction according to eq. 15 in [Andersson15]_.
+
+    .. math::
+
+        C_{b}(b, b'; \ell) = \exp\left( - \frac{(\log b - \log b')^2}{2 \ell^2} \right)
+
+    Parameters
+    ----------
+    grpi : :obj:`~numpy.ndarray`
+        Group of indices.
+    grpb : :obj:`~numpy.ndarray`
+        Groups of b-values.
+    ell : float
+
+    Returns
+    -------
+        The squared exponential function.
+    """
+
+    # Compute log probability of b-values
+    log_grpb = np.log(grpb)
+    bv_diff = log_grpb[grpi[:, None]] - log_grpb[grpi]
+    return np.exp(-(bv_diff**2) / (2 * ell**2))
+
+
+def compute_squared_exponential_covariance_kernel(K, angle_mat, thpar, grpb, grpi):
+    r"""Compute the squared exponential covariance matrix following to eq. 14 in
+    [Andersson15]_.
+
+    .. math::
+
+        k(\textbf{x}, \textbf{x'}) = C_{\theta}(\mathbf{g}, \mathbf{g'}; a) C_{b}(\abs{b - b'}; \ell)
+
+    where :math:`C_{\theta}` is given by:
+
+    .. math::
+
+        \begin{equation}
+        C(\theta) =
+        \begin{cases}  1 - \frac{3 \theta}{2 a} + \frac{\theta^3}{2 a^3} & \textnormal{if} \; \theta \leq a \\
+        0 & \textnormal{if} \; \theta > a
+        \end{cases}
+        \end{equation}
+
+   :math:`\theta` being computed as:
+
+    .. math::
+
+        \theta(\mathbf{g}, \mathbf{g'}) = \arccos(\abs{\langle \mathbf{g}, \mathbf{g'} \rangle})
+
+    and :math:`C_{b}` is given by:
+
+    .. math::
+
+        C_{b}(b, b'; \ell) = \exp\left( - \frac{(\log b - \log b')^2}{2 \ell^2} \right)
+
+    being :math:`b` and :math:`b'` the b-values, and :math:`\mathbf{g}` and
+    :math:`\mathbf{g'}` the unit diffusion-encoding gradient unit vectors of the
+    shells; and :math:`{a, \ell}` some hyperparameters.
+
+    Parameters
+    ----------
+
+    Returns
+    -------
+    """
+
+    sm = thpar[0]
+    a = thpar[1]
+    ell = thpar[2]
+
+    # Compute angular covariance
+    # ToDo
+    # Vectorize this/take it from the single shell PR
+    for j in range(K.shape[1]):
+        for i in range(j, K.shape[0]):
+            theta = angle_mat[i + 1, j + 1]
+            if a > theta:
+                K[i + 1, j + 1] = sm * (1.0 - 1.5 * theta / a + 0.5 * (theta**3) / (a**3))
+            else:
+                K[i + 1, j + 1] = 0.0
+
+    # Compute b-value covariance
+    # ToDo
+    # Vectorize this/call compute_squared_exponential_shell_covariance
+    if ngrp > 1:
+        log_grpb = np.log(grpb())
+        for j in range(K.shape[1]):
+            for i in range(j + 1, K.shape[0]):
+                bvdiff = log_grpb[grpi[i]] - log_grpb[grpi[j]]
+                if bvdiff:
+                    K[i + 1, j + 1] *= np.exp(-(bvdiff**2) / (2 * ell**2))

src/eddymotion/model/tests/test_kernels.py

@@ -0,0 +1,39 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+#
+# Copyright 2024 The NiPreps Developers <[email protected]>
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY kIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# We support and encourage derived works from this project, please read
+# about our expectations at
+#
+#     https://www.nipreps.org/community/licensing/
+#
+from src.eddymotion.model.kernels import (
+    SquaredExponentialCovarianceKernel,
+    compute_squared_exponential_shell_covariance,
+    compute_squared_exponential_covariance_kernel,
+)
+
+
+def test_SquaredExponentialCovarianceKernel():
+    kernel = SquaredExponentialCovarianceKernel()
+
+
+def test_compute_squared_exponential_shell_covariance():
+    sq_exp_shell_cov = compute_squared_exponential_shell_covariance()
+
+
+def test_compute_squared_exponential_covariance_kernel():
+    sq_exp_shell_cov_kern = compute_squared_exponential_covariance_kernel()