enh: rename eddymotion._sklearn -> eddymotion.gpr

* rename the module so that it is picked up by autodoc.
* also fixes minor errors and improves docs.

docs/notebooks/dmri_covariance.ipynb

@@ -24,7 +24,7 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "\n",
-    "from eddymotion.model._sklearn import (\n",
+    "from eddymotion.model.gpr import (\n",
     "    compute_pairwise_angles,\n",
     "    exponential_covariance,\n",
     "    spherical_covariance,\n",
@@ -345,7 +345,7 @@
     }
    ],
    "source": [
-    "from eddymotion.model._sklearn import EddyMotionGPR, SphericalKriging\n",
+    "from eddymotion.model.gpr import EddyMotionGPR, SphericalKriging\n",
     "\n",
     "K = SphericalKriging(beta_a=PARAMETER_SPHERICAL_a, beta_l=PARAMETER_lambda)(X_real)\n",
     "K -= K.min()\n",

scripts/dwi_gp_estimation_error_analysis.py

@@ -36,7 +36,7 @@
 import pandas as pd
 from sklearn.model_selection import KFold, RepeatedKFold, cross_val_predict, cross_val_score
 
-from eddymotion.model._sklearn import (
+from eddymotion.model.gpr import (
     EddyMotionGPR,
     SphericalKriging,
 )
@@ -63,7 +63,7 @@ def cross_validate(
         Number of folds.
     n_repeats : :obj:`int`
         Number of times the cross-validator needs to be repeated.
-    gpr : obj:`~eddymotion.model._sklearn.EddyMotionGPR`
+    gpr : obj:`~eddymotion.model.gpr.EddyMotionGPR`
         The eddymotion Gaussian process regressor object.
 
     Returns

scripts/dwi_gp_estimation_simulated_signal.py

@@ -33,7 +33,7 @@
 import numpy as np
 from dipy.core.sphere import Sphere
 
-from eddymotion.model._sklearn import EddyMotionGPR, SphericalKriging
+from eddymotion.model.gpr import EddyMotionGPR, SphericalKriging
 from eddymotion.testing import simulations as testsims
 
 SAMPLING_DIRECTIONS = 200

src/eddymotion/estimator.py

@@ -30,7 +30,7 @@
 
 from eddymotion import utils as eutils
 from eddymotion.data.splitting import lovo_split
-from eddymotion.model import ModelFactory
+from eddymotion.model.base import ModelFactory
 from eddymotion.registration.ants import _prepare_registration_data, _run_registration
 
 

src/eddymotion/model/_dipy.py

@@ -31,7 +31,7 @@
 from dipy.reconst.base import ReconstModel
 from sklearn.gaussian_process import GaussianProcessRegressor
 
-from eddymotion.model._sklearn import (
+from eddymotion.model.gpr import (
     EddyMotionGPR,
     ExponentialKriging,
     SphericalKriging,

src/eddymotion/model/_sklearn.py → src/eddymotion/model/gpr.py

@@ -23,6 +23,7 @@
 r"""
 Derivations from scikit-learn for Gaussian Processes.
 
+
 Gaussian Process Model: Pairwise orientation angles
 ---------------------------------------------------
 Squared Exponential covariance kernel
@@ -101,10 +102,10 @@
 from sklearn.metrics.pairwise import cosine_similarity
 from sklearn.utils._param_validation import Interval, StrOptions
 
-BOUNDS_A: tuple[float, float] = (0.1, np.pi)
-"""The limits for the parameter *a*."""
+BOUNDS_A: tuple[float, float] = (0.1, 0.75 * np.pi)
+"""The limits for the parameter *a* (angular distance)."""
 BOUNDS_LAMBDA: tuple[float, float] = (1e-3, 1000)
-"""The limits for the parameter lambda."""
+"""The limits for the parameter λ (signal scaling factor)."""
 THETA_EPSILON: float = 1e-5
 """Minimum nonzero angle."""
 LBFGS_CONFIGURABLE_OPTIONS = {"disp", "maxiter", "ftol", "gtol"}
@@ -143,8 +144,7 @@ class EddyMotionGPR(GaussianProcessRegressor):
 
     In principle, Scikit-Learn's implementation normalizes the training data
     as in [Andersson15]_ (see
-    `FSL's souce code <https://git.fmrib.ox.ac.uk/fsl/eddy/-/blob/\
-2480dda293d4cec83014454db3a193b87921f6b0/DiffusionGP.cpp#L218>`__).
+    `FSL's souce code <https://git.fmrib.ox.ac.uk/fsl/eddy/-/blob/2480dda293d4cec83014454db3a193b87921f6b0/DiffusionGP.cpp#L218>`__).
     From their paper (p. 167, end of first column):
 
         Typically one just substracts the mean (:math:`\bar{\mathbf{f}}`)
@@ -161,7 +161,7 @@ class EddyMotionGPR(GaussianProcessRegressor):
     I believe this is overlooked in [Andersson15]_, or they actually did not
     use analytical gradient-descent:
 
-        _A note on optimisation_
+        *A note on optimisation*
 
         It is suggested, for example in Rasmussen and Williams (2006), that
         an optimisation method that uses derivative information should be
@@ -184,7 +184,7 @@ class EddyMotionGPR(GaussianProcessRegressor):
         "optimizer": [StrOptions(SUPPORTED_OPTIMIZERS), callable, None],
         "n_restarts_optimizer": [Interval(Integral, 0, None, closed="left")],
         "copy_X_train": ["boolean"],
-        "zeromean_y": ["boolean"],
+        "normalize_y": ["boolean"],
         "n_targets": [Interval(Integral, 1, None, closed="left"), None],
         "random_state": ["random_state"],
     }

test/test_sklearn.py → test/test_gpr.py

@@ -26,7 +26,7 @@
 import pytest
 from dipy.io import read_bvals_bvecs
 
-from eddymotion.model import _sklearn as ems
+from eddymotion.model import gpr
 
 GradientTablePatch = namedtuple("gtab", ["bvals", "bvecs"])
 
@@ -263,7 +263,7 @@ def test_compute_pairwise_angles(bvecs1, bvecs2, closest_polarity, expected):
     if bvecs2 is not None:
         _bvecs2 = (bvecs2 / np.linalg.norm(bvecs2, axis=0)).T
 
-    obtained = ems.compute_pairwise_angles(_bvecs1, _bvecs2, closest_polarity)
+    obtained = gpr.compute_pairwise_angles(_bvecs1, _bvecs2, closest_polarity)
 
     if _bvecs2 is not None:
         assert (_bvecs1.shape[0], _bvecs2.shape[0]) == obtained.shape
@@ -282,7 +282,7 @@ def test_kernel(repodata, covariance):
 
     bvecs = bvecs[bvals > 10]
 
-    KernelType = getattr(ems, f"{covariance}Kriging")
+    KernelType = getattr(gpr, f"{covariance}Kriging")
     kernel = KernelType()
     K = kernel(bvecs)