Move more explanations into code

* change header underlining

Author: PicoCentauri

docs/src/index.rst

@@ -1,11 +1,4 @@
-scikit-matter
-=============
-
-scikit-matter is a toolbox of methods developed in the
-computational chemical and materials science community, following the
-`scikit-learn <https://scikit.org/>`_ API
-and coding guidelines to promote usability and interoperability with existing workflows.
-
+.. automodule:: skmatter
 
 .. include:: ../../README.rst
    :start-after: marker-issues
@@ -22,6 +15,5 @@ and coding guidelines to promote usability and interoperability with existing wo
   contributing
   bibliography
 
-
 If you would like to contribute to scikit-matter, check out our :ref:`contributing`
 page!

docs/src/references/decomposition.rst

@@ -4,11 +4,9 @@ Principal Covariates Regression (PCovR)
 .. _PCovR-api:
 
 PCovR
-#####
+-----
 
-.. currentmodule:: skmatter.decomposition
-
-.. autoclass:: PCovR
+.. autoclass:: skmatter.decomposition.PCovR
     :show-inheritance:
     :special-members:
 
@@ -25,11 +23,9 @@ PCovR
 .. _KPCovR-api:
 
 Kernel PCovR
-############
-
-.. currentmodule:: skmatter.decomposition
+------------
 
-.. autoclass:: KernelPCovR
+.. autoclass:: skmatter.decomposition.KernelPCovR
     :show-inheritance:
     :special-members:
 

docs/src/references/linear_models.rst

@@ -1,21 +1,17 @@
 Linear Models
 =============
 
-.. currentmodule:: skmatter.linear_model._base
-
 Orthogonal Regression
-#####################
-
-.. autoclass:: OrthogonalRegression
+---------------------
 
-.. currentmodule:: skmatter.linear_model._ridge
+.. autoclass:: skmatter.linear_model.OrthogonalRegression
 
 Ridge Regression with Two-fold Cross Validation
-###############################################
+-----------------------------------------------
 
-.. autoclass:: RidgeRegression2FoldCV
+.. autoclass:: skmatter.linear_model.RidgeRegression2FoldCV
 
 PCovR
-#####
+-----
 
 Principal Covariates Regression is a linear model, see :ref:`PCovR-api`.

docs/src/references/metrics.rst

@@ -1,45 +1,28 @@
-.. _gfrm:
-
 Reconstruction Measures
 =======================
 
-.. marker-reconstruction-introduction-begin
-
 .. automodule:: skmatter.metrics
 
-These reconstruction measures are available:
-
-* :ref:`GRE-api` (GRE) computes the amount of linearly-decodable information
-  recovered through a global linear reconstruction.
-* :ref:`GRD-api` (GRD) computes the amount of distortion contained in a global linear
-  reconstruction.
-* :ref:`LRE-api` (LRE) computes the amount of decodable information recovered through
-  a local linear reconstruction for the k-nearest neighborhood of each sample.
-
-.. marker-reconstruction-introduction-end
-
-.. currentmodule:: skmatter.metrics
-
 .. _GRE-api:
 
 Global Reconstruction Error
 ---------------------------
 
-.. autofunction:: pointwise_global_reconstruction_error
-.. autofunction:: global_reconstruction_error
+.. autofunction:: skmatter.metrics.pointwise_global_reconstruction_error
+.. autofunction:: skmatter.metrics.global_reconstruction_error
 
 .. _GRD-api:
 
 Global Reconstruction Distortion
 --------------------------------
 
-.. autofunction:: pointwise_global_reconstruction_distortion
-.. autofunction:: global_reconstruction_distortion
+.. autofunction:: skmatter.metrics.pointwise_global_reconstruction_distortion
+.. autofunction:: skmatter.metrics.global_reconstruction_distortion
 
 .. _LRE-api:
 
 Local Reconstruction Error
 --------------------------
 
-.. autofunction:: pointwise_local_reconstruction_error
-.. autofunction:: local_reconstruction_error
+.. autofunction:: skmatter.metrics.pointwise_local_reconstruction_error
+.. autofunction:: skmatter.metrics.local_reconstruction_error

docs/src/references/preprocessing.rst

@@ -1,6 +1,7 @@
 Preprocessing
 =============
 
+.. automodule:: skmatter.preprocessing
 
 KernelNormalizer
 ----------------

docs/src/references/selection.rst

@@ -10,7 +10,6 @@ Feature and Sample Selection
 CUR
 ---
 
-
 CUR decomposition begins by approximating a matrix :math:`{\mathbf{X}}` using a subset
 of columns and rows
 
@@ -72,7 +71,6 @@ computation of :math:`\pi`. S
    :undoc-members:
    :inherited-members:
 
-
 .. _FPS-api:
 
 Farthest Point-Sampling (FPS)
@@ -93,7 +91,6 @@ row-wise), and are built off of the same base class,
 These selectors can be instantiated using :py:class:`skmatter.feature_selection.FPS` and
 :py:class:`skmatter.sample_selection.FPS`.
 
-
 .. autoclass:: skmatter.feature_selection.FPS
    :members:
    :undoc-members:
@@ -139,7 +136,7 @@ When *Not* to Use Voronoi FPS
 
 In many cases, this algorithm may not increase upon the efficiency. For example, for
 simple metrics (such as Euclidean distance), Voronoi FPS will likely not accelerate, and
-may decelerate, computations when compared to FPS.  The sweet spot for Voronoi FPS is
+may decelerate, computations when compared to FPS. The sweet spot for Voronoi FPS is
 when the number of selectable samples is already enough to divide the space with Voronoi
 polyhedrons, but not yet comparable to the total number of samples, when the cost of
 bookkeeping significantly degrades the speed of work compared to FPS.

docs/src/references/utils.rst

@@ -3,34 +3,30 @@ Utility Classes
 
 .. _PCovR_dist-api:
 
-.. currentmodule:: skmatter.utils._pcovr_utils
-
 Modified Gram Matrix :math:`\mathbf{\tilde{K}}`
-###############################################
+-----------------------------------------------
 
-.. autofunction:: pcovr_kernel
+.. autofunction:: skmatter.utils.pcovr_kernel
 
 
 Modified Covariance Matrix :math:`\mathbf{\tilde{C}}`
-#####################################################
+-----------------------------------------------------
 
-.. autofunction:: pcovr_covariance
+.. autofunction:: skmatter.utils.pcovr_covariance
 
 Orthogonalizers for CUR
-#######################
-
-.. currentmodule:: skmatter.utils._orthogonalizers
+-----------------------
 
 When computing non-iterative CUR, it is necessary to orthogonalize the input matrices
 after each selection. For this, we have supplied a feature and a sample orthogonalizer
 for feature and sample selection.
 
-.. autofunction:: X_orthogonalizer
-.. autofunction:: Y_feature_orthogonalizer
-.. autofunction:: Y_sample_orthogonalizer
+.. autofunction:: skmatter.utils.X_orthogonalizer
+.. autofunction:: skmatter.utils.Y_feature_orthogonalizer
+.. autofunction:: skmatter.utils.Y_sample_orthogonalizer
 
 
 Random Partitioning with Overlaps
-#################################
+---------------------------------
 
 .. autofunction:: skmatter.model_selection.train_test_split

src/skmatter/__init__.py

@@ -1 +1,10 @@
+"""
+scikit-matter
+=============
+
+scikit-matter is a toolbox of methods developed in the computational chemical and
+materials science community, following the `scikit-learn <https://scikit.org/>`_ API and
+coding guidelines to promote usability and interoperability with existing workflows.
+"""
+
 __version__ = "0.1.4"

src/skmatter/_selection.py

@@ -1,14 +1,13 @@
-r"""
-This module contains data sub-selection modules primarily corresponding to
-methods derived from CUR matrix decomposition and Farthest Point Sampling. In
-their classical form, CUR and FPS determine a data subset that maximizes the
-variance (CUR) or distribution (FPS) of the features or samples.  These methods
-can be modified to combine supervised target information denoted by the methods
-`PCov-CUR` and `PCov-FPS`.  For further reading, refer to [Imbalzano2018]_ and
-[Cersonsky2021]_. These selectors can be used for both feature and sample
-selection, with similar instantiations. All sub-selection methods  scores each
-feature or sample (without an estimator) and chooses that with the maximum
-score. A simple example of usage:
+"""
+This module contains data sub-selection modules primarily corresponding to methods
+derived from CUR matrix decomposition and Farthest Point Sampling. In their classical
+form, CUR and FPS determine a data subset that maximizes the variance (CUR) or
+distribution (FPS) of the features or samples. These methods can be modified to combine
+supervised target information denoted by the methods `PCov-CUR` and `PCov-FPS`. For
+further reading, refer to [Imbalzano2018]_ and [Cersonsky2021]_. These selectors can be
+used for both feature and sample selection, with similar instantiations. All
+sub-selection methods  scores each feature or sample (without an estimator) and chooses
+that with the maximum score. A simple example of usage:
 
 .. doctest::
 
@@ -64,9 +63,9 @@
   singular value decoposition.
 * :ref:`PCov-CUR-api` decomposition extends upon CUR by using augmented right or left
   singular vectors inspired by Principal Covariates Regression.
-* :ref:`FPS-api`: a common selection technique intended to exploit the diversity of
-  the input space. The selection of the first point is made at random or by a
-  separate metric
+* :ref:`FPS-api`: a common selection technique intended to exploit the diversity of the
+  input space. The selection of the first point is made at random or by a separate
+  metric
 * :ref:`PCov-FPS-api` extends upon FPS much like PCov-CUR does to CUR.
 * :ref:`Voronoi-FPS-api`: conduct FPS selection, taking advantage of Voronoi
   tessellations to accelerate selection.

src/skmatter/metrics/__init__.py

@@ -1,18 +1,26 @@
-r"""
-This module contains a set of easily-interpretable error measures of the
-relative information capacity of feature space `F` with respect to feature
-space `F'`. The methods returns a value between 0 and 1, where 0 means that
-`F` and `F'` are completey distinct in terms of linearly-decodable
-information, and where 1 means that `F'` is contained in `F`. All methods
-are implemented as the root mean-square error for the regression of the
-feature matrix `X_F'` (or sometimes called `Y` in the doc) from `X_F` (or
-sometimes called `X` in the doc) for transformations with different
-constraints (linear, orthogonal, locally-linear). By default a custom 2-fold
-cross-validation :py:class:`skosmo.linear_model.RidgeRegression2FoldCV` is
-used to ensure the generalization of the transformation and efficiency of
-the computation, since we deal with a multi-target regression problem.
-Methods were applied to compare different forms of featurizations through
-different hyperparameters and induced metrics and kernels [Goscinski2021]_ .
+"""
+This module contains a set of easily-interpretable error measures of the relative
+information capacity of feature space `F` with respect to feature space `F'`. The
+methods returns a value between 0 and 1, where 0 means that `F` and `F'` are completey
+distinct in terms of linearly-decodable information, and where 1 means that `F'` is
+contained in `F`. All methods are implemented as the root mean-square error for the
+regression of the feature matrix `X_F'` (or sometimes called `Y` in the doc) from `X_F`
+(or sometimes called `X` in the doc) for transformations with different constraints
+(linear, orthogonal, locally-linear). By default a custom 2-fold cross-validation
+:py:class:`skosmo.linear_model.RidgeRegression2FoldCV` is used to ensure the
+generalization of the transformation and efficiency of the computation, since we deal
+with a multi-target regression problem. Methods were applied to compare different forms
+of featurizations through different hyperparameters and induced metrics and kernels
+[Goscinski2021]_ .
+
+These reconstruction measures are available:
+
+* :ref:`GRE-api` (GRE) computes the amount of linearly-decodable information
+  recovered through a global linear reconstruction.
+* :ref:`GRD-api` (GRD) computes the amount of distortion contained in a global linear
+  reconstruction.
+* :ref:`LRE-api` (LRE) computes the amount of decodable information recovered through
+  a local linear reconstruction for the k-nearest neighborhood of each sample.
 """
 
 from ._reconstruction_measures import (