Skip to content

Welcome to Rapaio

Rapaio is a java library which provides a comprehensive set of tools for data manipulation, linear algebra, statistics and machine learning.

Pure Java, No Dependencies

The library is self-contained 100% pure Java. No machine dependence, no tricky configuration, no headaches while trying to maintain a hell of dependencies. Thus, there are no dependencies.

With low probability this might change in the future. However, one goal of the library is to provide a complete set of well integrated tools with uniform usage interface. It is not in our intention to provide a diverse overcrowded umbrella of wired external libraries and algorithms. We appreciate many of them enough to give you the opportunity to use them as they are.

Expressive API

It is a fact that Java is sometimes too verbose. Idiomatic Java can be thought as not the best way for interactive notebook investigations. Thus, we worked on API to improve it's friendly-ness for ad-hoc interactive code. Sometimes, the employed syntax looks a bit far away from usual Java. This is to make it more succint and expressive. Let's take an example:

// create a variable holding a sequence of values from 0 to 10
Var x = VarDouble.seq(0, 10).withName("x");

double sum = x.dv().nansum(); // compute sum
x.dv().apply(value -> value/sum); // make unit vector
x.printContent(); // print variable content

VarDouble [name:"x", rowCount:11]
row    value   row    value   row    value   
 [0] 0          [4] 0.0727273  [8] 0.1454545 
 [1] 0.0181818  [5] 0.0909091  [9] 0.1636364 
 [2] 0.0363636  [6] 0.1090909 [10] 0.1818182 
 [3] 0.0545455  [7] 0.1272727

Comprehensive set of ML algorithms and statistical tools

There is an extensive set of algorithms for machine learning provided by the library for regresion, classification, clustering and feature engineering, among which there are decision trees, random forests, gradient boosting trees and support vector machines.

Model assessment and selection is realized in a flexible way, through an unified interface, which allow one to configure splitting strategy, metrics for evaluation and comparison.

Most common distributions are available. Those can be used to generate samples, compute various statistics, data visualisation and so on. There are also useful tools for computing correlations, various statistics and hypothesis tests.

A vast set of data handling and transformation tools are available in form of filters for operations like type transformation, missing value imputation, unique values, grouping and group aggregate functions.

A complete list of features can be explored here Complete Library Features.

Linear Algebra

For linear algebra, rapaio library provides currently only dense implementations for double float precision vectors and arrays. Most of the time, the features provided is enough for many tasks. The upcoming versions will provide also specialized versions of various objects and also other types for the underlying values. We provide also a set of matrix decompositions like Cholesky, LU, Eigen Decomposition, QR and SVD. The API syntax was designed for clean interactive usage, for which we provide an example:

// load first 4 numeric features of iris dataset into a matrix
DMatrix m = DMStripe.copy(Datasets.loadIrisDataset().removeVars("class"));
// compute mean on row axis
DVector mean = m.mean(0);
// compute sd on row axis
DVector sd = m.sd(0);
// create a matrix which is the standardized version of m
DMatrix c = m.copy().sub(mean, 0).div(sd, 0);

Data Visualization

For data visualization rapaio offers a set of graphical tools like histograms, 2d histogram, scatter points, lines, qq plots, corrgram, segments, lines, and more. Most of them can be combined in the same graphic and also multiple graphics can be combine in the same output grid layout.