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separate getting-started page from intro in doc
* put intro of scikit-matter into the index page and abbreviate * add getting started page which gives an overview of important implementations * include existing introductory text for reconstruction measures into API reference so all introductory texts from the API are included into the getting started * reword text a bit for more soundness within the getting started
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Getting started | ||
=============== | ||
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A small introduction to all methods implemented in scikit-matter. | ||
For a detailed explaination, please look at the :ref:`selection-api` | ||
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Features and Samples Selection | ||
------------------------------ | ||
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.. include:: selection.rst | ||
:start-after: marker-selection-introduction-begin | ||
:end-before: marker-selection-introduction-end | ||
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These selectors are available: | ||
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* :ref:`CUR-api`: a decomposition: an iterative feature selection method based upon the | ||
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:`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. | ||
* :ref:`DCH-api`: selects samples by constructing a directional convex hull and | ||
determining which samples lie on the bounding surface. | ||
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Examples | ||
^^^^^^^^ | ||
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.. include:: examples/selection/index.rst | ||
:start-line: 4 | ||
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Reconstruction Measures | ||
----------------------- | ||
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.. include:: gfrm.rst | ||
:start-after: marker-reconstruction-introduction-begin | ||
:end-before: marker-reconstruction-introduction-end | ||
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These reconstruction measures are available: | ||
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* :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. | ||
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Examples | ||
^^^^^^^^ | ||
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.. include:: examples/reconstruction/index.rst | ||
:start-line: 4 | ||
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Principal Covariates Regression | ||
------------------------------- | ||
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.. include:: pcovr.rst | ||
:start-after: marker-pcovr-introduction-begin | ||
:end-before: marker-pcovr-introduction-end | ||
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It includes | ||
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* :ref:`PCovR-api` the standard Principal Covariates Regression. Utilises a | ||
combination between a PCA-like and an LR-like loss, and therefore attempts to find | ||
a low-dimensional projection of the feature vectors that simultaneously minimises | ||
information loss and error in predicting the target properties using only the | ||
latent space vectors :math:`\mathbf{T}`. | ||
* :ref:`KPCovR-api` the Kernel Principal Covariates Regression | ||
a kernel-based variation on the | ||
original PCovR method, proposed in [Helfrecht2020]_. | ||
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Examples | ||
^^^^^^^^ | ||
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.. include:: examples/pcovr/index.rst | ||
:start-line: 4 |
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.. _gfrm: | ||
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Reconstruction Measures | ||
====================================== | ||
======================= | ||
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.. marker-reconstruction-introduction-begin | ||
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]_ . | ||
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.. marker-reconstruction-introduction-end | ||
.. currentmodule:: skmatter.metrics | ||
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.. _GRE-api: | ||
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Global Reconstruction Error | ||
########################### | ||
--------------------------- | ||
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.. autofunction:: pointwise_global_reconstruction_error | ||
.. autofunction:: global_reconstruction_error | ||
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.. _GRD-api: | ||
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Global Reconstruction Distortion | ||
################################ | ||
-------------------------------- | ||
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.. autofunction:: pointwise_global_reconstruction_distortion | ||
.. autofunction:: global_reconstruction_distortion | ||
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.. _LRE-api: | ||
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Local Reconstruction Error | ||
########################## | ||
-------------------------- | ||
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.. autofunction:: pointwise_local_reconstruction_error | ||
.. autofunction:: local_reconstruction_error |
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