linfa-preprocessing

linfa-preprocessing provides machine learning specific data preprocessing algorithms in the vein of Python's sklearn.preprocessing.

Once this project matures, it will become part of the linfa Rust machine learning toolkit.

Functionality

linfa-preprocessing consists of a single trait: Preprocess<A>. All of the preprocessing functions we provide are defined in Preprocessing<A>. It is generic over a single parameter A, representing the element type of the data being operated on.

There is one implementation of this trait that is compatible with 2-dimensional, float-based, owned-memory ndarray arrays. In this implementation, the preprocessing functions consume the array and produce a new owned array. This allows for ergonomic chaining of preprocessing methods.

Functions

• StandardScale
• MinMaxScale
• CustomScale
• Binarize
• RobustScale
• Normalize
• PowerTransform
• QuantileTransform
• FunctionTransform
• LabelBinarize
• MultiLabelBinarize
• PolynomialFeatures
• LabelEncode

Example

use linfa_preprocessing::Preprocess;
use ndarray::array;

let data = array![[-1., 2.],
                  [-0.5, 6.],
                  [0., 10.],
                  [1., 18.]];

let processed = data.min_max_scale()
                    .standard_scale()
                    .binarize(0.);

let expected = array![[0., 0.],
                      [0., 0.],
                      [1., 1.],
                      [1., 1.]];

assert_eq!(processed, expected)