What’s in scikit-matter?

scikit-matter is a collection of scikit-learn compatible utilities that implement methods born out of the materials science and chemistry communities.

This package serves two purposes: 1) as a development ground for models and patches that may ultimately be suitable for inclusion in sklearn, and 2) to coalesce field-specific sklearn-like routines and models in a well-documented and standardized repository.

Currently, scikit-matter contains models described in [Imbalzano2018], [Helfrecht2020], [Goscinski2021] and [Cersonsky2021], as well as some modifications to sklearn functionalities and minimal datasets that are useful within the field of computational materials science and chemistry.

• Fingerprint Selection:

  Multiple data sub-selection modules, for selecting the most relevant features and samples out of a large set of candidates [Imbalzano2018], [Helfrecht2020] and [Cersonsky2021].

  • CUR decomposition: an iterative feature selection method based upon the singular value decoposition.

  • PCov-CUR decomposition extends upon CUR by using augmented right or left singular vectors inspired by Principal Covariates Regression.

  • Farthest Point-Sampling (FPS): 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.

  • PCov-FPS extends upon FPS much like PCov-CUR does to CUR.

  • Voronoi FPS: conduct FPS selection, taking advantage of Voronoi tessellations to accelerate selection.

  • Directional Convex Hull (DCH): selects samples by constructing a directional convex hull and determining which samples lie on the bounding surface.

• Reconstruction Measures:

  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 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] .

  • Global Reconstruction Error (GRE) computes the amount of linearly-decodable information recovered through a global linear reconstruction.

  • Global Reconstruction Distortion (GRD) computes the amount of distortion contained in a global linear reconstruction.

  • Local Reconstruction Error (LRE) computes the amount of decodable information recovered through a local linear reconstruction for the k-nearest neighborhood of each sample.

• Principal Covariates Regression

  • PCovR: the standard Principal Covariates Regression [deJong1992]. 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 $mathbf{T}$ PCovR.

  • Kernel Principal Covariates Regression (KPCovR) a kernel-based variation on the original PCovR method, proposed in [Helfrecht2020] Kernel PCovR.

If you would like to contribute to scikit-matter, check out our Contributing page!