From 0e1d581a9b19bac4b6812dd9fa0ed76ac213ed6f Mon Sep 17 00:00:00 2001
From: Alexander Goscinski <alex.goscinski@posteo.de>
Date: Fri, 28 Jul 2023 00:20:04 +0200
Subject: [PATCH] clean up malformatted doc elements

---
 docs/src/preprocessing.rst             | 25 +++++++++++++++++++++++--
 docs/src/utils.rst                     |  2 +-
 src/skmatter/preprocessing/_data.py    |  9 +++++----
 src/skmatter/utils/_orthogonalizers.py |  2 +-
 4 files changed, 30 insertions(+), 8 deletions(-)

diff --git a/docs/src/preprocessing.rst b/docs/src/preprocessing.rst
index 4baaeabd7..7252ddc60 100644
--- a/docs/src/preprocessing.rst
+++ b/docs/src/preprocessing.rst
@@ -1,7 +1,28 @@
 Preprocessing
 =============
 
-.. automodule:: skmatter.preprocessing
+
+KernelNormalizer
+----------------
+
+.. autoclass:: skmatter.preprocessing.KernelNormalizer
+   :members:
+   :undoc-members:
+   :inherited-members:
+
+
+SparseKernelCenterer
+--------------------
+
+.. autoclass:: skmatter.preprocessing.SparseKernelCenterer
+   :members:
+   :undoc-members:
+   :inherited-members:
+
+StandardFlexibleScaler
+----------------------
+
+.. autoclass:: skmatter.preprocessing.StandardFlexibleScaler
    :members:
    :undoc-members:
-   :show-inheritance:
+   :inherited-members:
diff --git a/docs/src/utils.rst b/docs/src/utils.rst
index bae996748..8b2c6560a 100644
--- a/docs/src/utils.rst
+++ b/docs/src/utils.rst
@@ -33,4 +33,4 @@ for feature and sample selection.
 Random Partitioning with Overlaps
 #################################
 
-.. automodule:: skmatter.model_selection._split
+.. autofunction:: skmatter.model_selection.train_test_split
diff --git a/src/skmatter/preprocessing/_data.py b/src/skmatter/preprocessing/_data.py
index 9e4651466..07160dea4 100644
--- a/src/skmatter/preprocessing/_data.py
+++ b/src/skmatter/preprocessing/_data.py
@@ -232,10 +232,11 @@ class KernelNormalizer(KernelCenterer):
     """Kernel centering method, similar to KernelCenterer,
     but with additional scaling and ability to pass a set of sample weights.
 
-    Let K(x, z) be a kernel defined by phi(x)^T phi(z), where phi is a
-    function mapping x to a Hilbert space. KernelNormalizer centers (i.e.,
-    normalize to have zero mean) the data without explicitly computing phi(x).
-    It is equivalent to centering and scaling phi(x) with
+    Let :math:`K(x, z)` be a kernel defined by :math:`\\phi(x)^T \\phi(z)`,
+    where :math:`\\phi` is a function mapping x to a Hilbert space.
+    KernelNormalizer centers (i.e., normalize to have zero mean) the data without
+    explicitly computing :math:`\\phi(x)`.
+    It is equivalent to centering and scaling :math:`\\phi(x)` with
     sklearn.preprocessing.StandardScaler(with_std=False).
 
     Parameters
diff --git a/src/skmatter/utils/_orthogonalizers.py b/src/skmatter/utils/_orthogonalizers.py
index 4bfba3739..14dbf0a2c 100644
--- a/src/skmatter/utils/_orthogonalizers.py
+++ b/src/skmatter/utils/_orthogonalizers.py
@@ -100,7 +100,7 @@ def Y_feature_orthogonalizer(y, X, tol=1e-12, copy=True):
 
 def Y_sample_orthogonalizer(y, X, y_ref, X_ref, tol=1e-12, copy=True):
     """
-    Orthogonalizes a matrix of targets :math:`{\\mathbf{Y}}`given a reference feature
+    Orthogonalizes a matrix of targets :math:`{\\mathbf{Y}}` given a reference feature
     matrix :math:`{\\mathbf{X}_r}` and reference target matrix :math:`{\\mathbf{Y}_r}`:
 
     .. math::