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EvoTrees.jl
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Boosted trees in Julia
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# EvoTrees <a href="https://evovest.github.io/EvoTrees.jl/dev/"><img src="docs/src/assets/logo.png" align="right" height="160"/></a> | Documentation | CI Status | DOI | |:------------------------:|:----------------:|:----------------:| | [![][docs-stable-img]][docs-stable-url] [![][docs-latest-img]][docs-latest-url] | [![][ci-img]][ci-url] | [![][DOI-img]][DOI-url] | [docs-latest-img]: https://img.shields.io/badge/docs-latest-blue.svg [docs-latest-url]: https://evovest.github.io/EvoTrees.jl/dev [docs-stable-img]: https://img.shields.io/badge/docs-stable-blue.svg [docs-stable-url]: https://evovest.github.io/EvoTrees.jl/stable [ci-img]: https://github.com/Evovest/EvoTrees.jl/workflows/CI/badge.svg [ci-url]: https://github.com/Evovest/EvoTrees.jl/actions?query=workflow%3ACI+branch%3Amain [DOI-img]: https://zenodo.org/badge/164559537.svg [DOI-url]: https://zenodo.org/doi/10.5281/zenodo.10569604 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](https://github.com/Evovest/EvoTrees). ## Installation Latest: ```julia-repl julia> Pkg.add(url="https://github.com/Evovest/EvoTrees.jl") ``` From General Registry: ```julia-repl julia> Pkg.add("EvoTrees") ``` ## Performance Data consists of randomly generated `Matrix{Float64}`. Training is performed on 200 iterations. Code to reproduce is available in [`benchmarks/regressor.jl`](https://github.com/Evovest/EvoTrees.jl/blob/main/benchmarks/regressor.jl). - Run Environment: - CPU: 12 threads on AMD Ryzen 5900X - GPU: NVIDIA RTX A4000 - Julia: v1.10.8 - Package versions: - EvoTrees: v0.18.3 - XGBoost: v3.1.2 (Using the `hist` algorithm) ### CPU: | **nobs** | **nfeats** | **max\_depth** | **train\_evo** | **infer\_evo** | **train\_xgb** | **infer\_xgb** | |:--------:|:----------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:| | 100k | 10 | 6 | 0.36 | 0.06 | 0.21 | 0.03 | | 100k | 10 | 11 | 1.28 | 0.08 | 0.63 | 0.06 | | 100k | 100 | 6 | 0.79 | 0.08 | 0.79 | 0.03 | | 100k | 100 | 11 | 4.91 | 0.12 | 3.67 | 0.07 | | 1M | 10 | 6 | 2.49 | 0.31 | 1.60 | 0.24 | | 1M | 10 | 11 | 5.07 | 0.63 | 3.16 | 0.58 | | 1M | 100 | 6 | 5.82 | 0.69 | 5.53 | 0.26 | | 1M | 100 | 11 | 18.78 | 1.19 | 13.40 | 0.57 | | 10M | 10 | 6 | 26.45 | 3.34 | 30.99 | 1.76 | | 10M | 10 | 11 | 51.88 | 6.27 | 55.20 | 5.57 | | 10M | 100 | 6 | 85.05 | 6.44 | 65.90 | 2.56 | | 10M | 100 | 11 | 192.58 | 12.18 | 111.69 | 6.02 | ### GPU: | **nobs** | **nfeats** | **max\_depth** | **train\_evo** | **infer\_evo** | **train\_xgb** | **infer\_xgb** | |:--------:|:----------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:| | 100k | 10 | 6 | 0.66 | 0.01 | 0.24 | 0.00 | | 100k | 10 | 11 | 1.43 | 0.01 | 1.12 | 0.01 | | 100k | 100 | 6 | 0.93 | 0.03 | 0.47 | 0.03 | | 100k | 100 | 11 | 2.17 | 0.03 | 2.92 | 0.06 | | 1M | 10 | 6 | 1.37 | 0.05 | 0.58 | 0.03 | | 1M | 10 | 11 | 2.45 | 0.05 | 2.14 | 0.04 | | 1M | 100 | 6 | 2.77 | 0.30 | 2.30 | 0.28 | | 1M | 100 | 11 | 5.07 | 0.32 | 6.80 | 0.58 | | 10M | 10 | 6 | 7.53 | 0.44 | 3.83 | 0.18 | | 10M | 10 | 11 | 12.60 | 0.53 | 8.66 | 0.31 | | 10M | 100 | 6 | 21.93 | 3.33 | 21.42 | 2.88 | | 10M | 100 | 11 | 37.25 | 3.57 | 41.98 | 5.91 | ## MLJ Integration See [official project page](https://github.com/alan-turing-institute/MLJ.jl) for more info. ## Quick start with internal API A model configuration must first be defined, using one of the model constructor: - `EvoTreeRegressor` - `EvoTreeClassifier` - `EvoTreeCount` - `EvoTreeMLE` Model training is performed using `fit`. 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 ```julia using EvoTrees using EvoTrees: fit 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(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. ```julia dtrain = DataFrame(x_train, :auto) dtrain.y .= y_train m = fit(config, dtrain; target_name="y"); m = fit(config, dtrain; target_name="y", fnames=["x1", "x3"]); ``` ## Shap EvoTrees includes a Julia implementation of Linear TreeShap by Yu et al. (2022). It computes exact Shapley values for decision trees in O(LD) time. ```julia shap_effects = EvoTrees.shap(m, dtrain) ``` #### Reference Peng Yu, Chao Xu, Albert Bifet, Jesse Read Linear Tree Shap (2022). In [Proceedings of 36th Conference on Neural Information Processing Systems](https://openreview.net/forum?id=OzbkiUo24g). ## Feature importance Returns the normalized gain by feature. ```julia features_gain = EvoTrees.importance(m) ``` ## Plot Plot a model *ith* tree (first *actual* tree is #2 as 1st *tree* is reserved to set the model's bias): ```julia plot(m, 2) ```  ## Save/Load ```julia EvoTrees.save(m, "data/model.bson") m = EvoTrees.load("data/model.bson"); ```
