Evovest / EvoTrees.jl

Boosted trees in Julia
https://evovest.github.io/EvoTrees.jl/dev/
Apache License 2.0
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boosted-trees decision-tree gbrt gradient-boosting gradientboosting julia logistic machine-learning poisson quantile regression

EvoTrees

Documentation CI Status DOI

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.

Installation

Latest:

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

From General Registry:

julia> Pkg.add("EvoTrees")

Performance

Data consists of randomly generated Matrix{Float64}. Training is performed on 200 iterations.
Code to reproduce is availabe in benchmarks/regressor.jl.

Training:

Dimensions / Algo XGBoost CPU EvoTrees CPU XGBoost GPU EvoTrees GPU
100K x 100 2.34s 1.01s 0.90s 2.61s
500K x 100 10.7s 3.95s 1.84s 3.41s
1M x 100 21.1s 6.57s 3.10s 4.47s
5M x 100 108s 36.1s 12.9s 12.5s
10M x 100 218s 72.6s 25.5s 23.0s

Inference:

Dimensions / Algo XGBoost CPU EvoTrees CPU XGBoost GPU EvoTrees GPU
100K x 100 0.151s 0.058s NA 0.045s
500K x 100 0.647s 0.248s NA 0.172s
1M x 100 1.26s 0.573s NA 0.327s
5M x 100 6.04s 2.87s NA 1.66s
10M x 100 12.4s 5.71s NA 3.40s

MLJ Integration

See official project page for more info.

Quick start with internal API

A model configuration must first be defined, using one of the model constructor:

Model training is performed using fit_evotree. It supports additional keyword arguments to track evaluation metric and perform early stopping. Look at the docs for more details on available hyper-parameters for each of the above constructors and other options training options.

Matrix features input

using EvoTrees

config = EvoTreeRegressor(
    loss=:mse, 
    nrounds=100, 
    max_depth=6,
    nbins=32,
    eta=0.1)

x_train, y_train = rand(1_000, 10), rand(1_000)
m = fit_evotree(config; x_train, y_train)
preds = m(x_train)

DataFrames input

When using a DataFrames as input, features with elements types Real (incl. Bool) and Categorical are automatically recognized as input features. Alternatively, fnames kwarg can be used to specify the variables to be used as features.

Categorical features are treated accordingly by the algorithm: ordered variables are treated as numerical features, using split rule, while unordered variables are using ==. Support is currently limited to a maximum of 255 levels. Bool variables are treated as unordered, 2-levels categorical variables.

dtrain = DataFrame(x_train, :auto)
dtrain.y .= y_train
m = fit_evotree(config, dtrain; target_name="y");
m = fit_evotree(config, dtrain; target_name="y", fnames=["x1", "x3"]);

Feature importance

Returns the normalized gain by feature.

features_gain = EvoTrees.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");