EvoTrees

Documentation	CI Status

A Julia implementation of boosted trees with CPU and GPU support. Efficient histogram based algorithms with support for multiple loss functions (notably multi-target objectives such as max likelihood methods).

R binding available.

Input features are expected to be Matrix{Float64/Float32} when using the internal API. Tables/DataFrames format can be handled through MLJ. See the docs for further details.

Installation

Latest:

julia> Pkg.add("https://github.com/Evovest/EvoTrees.jl")

From General Registry:

julia> Pkg.add("EvoTrees")

Performance

Data consists of randomly generated float32. Training is performed on 200 iterations. Code to reproduce is here.

EvoTrees: v0.14.0 XGBoost: v2.0.2

CPU: 12 threads on AMD Ryzen 5900X GPU: NVIDIA RTX A4000

Training:

Dimensions / Algo	XGBoost Hist	EvoTrees	EvoTrees GPU
100K x 100	1.29s	1.05s	2.96s
500K x 100	6.73s	3.15s	3.83s
1M x 100	13.27s	6.01s	4.94s
5M x 100	65.1s	34.1s	14.1s
10M x 100	142s	71.8s	25.1s

Inference:

Dimensions / Algo	XGBoost Hist	EvoTrees	EvoTrees GPU
100K x 100	0.107s	0.027s	0.008s
500K x 100	0.550s	0.209s	0.031s
1M x 100	1.10s	0.410s	0.074s
5M x 100	5.44s	2.14s	0.302s
10M x 100	10.5s	4.35s	0.591s

MLJ Integration

See official project page for more info.

Getting started using internal API

using EvoTrees

config = EvoTreeRegressor(
    loss=:linear, 
    nrounds=100, 
    nbins=100,
    lambda=0.5, 
    gamma=0.1, 
    eta=0.1,
    max_depth=6, 
    min_weight=1.0,
    rowsample=0.5, 
    colsample=1.0)
m = fit_evotree(config; x_train, y_train)
preds = m(x_train)

Feature importance

Returns the normalized gain by feature.

features_gain = importance(m)

Plot

Plot a given tree of the model:

plot(m, 2)

Note that 1st tree is used to set the bias so the first real tree is #2.

Save/Load

EvoTrees.save(m, "data/model.bson")
m = EvoTrees.load("data/model.bson");

A GPU model should be converted into a CPU one before saving: m_cpu = convert(EvoTree, m_gpu).