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JLBoost

A 100%-Julia implementation of Gradient-Boosting Regression Tree algorithms

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JLBoost.jl

This is a 100%-Julia implementation of Gradient Boosting Regresssion Trees (GBRT) based heavily on the algorithms published in the XGBoost, LightGBM and Catboost papers. GBRT is also referred to as Gradient Boosting Decision Tree (GBDT).

Limitations for now

  • Currently, Union{T, Missing} feature type is not supported, but is planned.
  • Currently, only the single-valued models are supported. Multivariate-target models support is planned.
  • Currently, only the numeric and boolean features are supported. Categorical support is planned.
  • Currently, weights cannot be provided for each of the records. Support is planned.

Objectives

  • A full-featured & batteries included Gradient Boosting Regression Tree library
  • Play nice with the Julia ecosystem e.g. Tables.jl, DataFrames.jl and CategoricalArrays.jl
  • 100%-Julia
  • Fit models on large data

  • Easy to manipulate the tree after fitting; play with tree pruning and adjustments

  • "Easy" to deploy

Quick-start

Fit model on DataFrame

Binary Classification

We fit the model by predicting one of the iris Species. To fit a model on a DataFrame you need to specify the column and the features default to all columns other than the target.

using JLBoost, RDatasets
iris = dataset("datasets", "iris")

iris[!, :is_setosa] = iris[!, :Species] .== "setosa"
target = :is_setosa

features = setdiff(names(iris), [:Species, :is_setosa])

# fit one tree
# ?jlboost for more details
xgtreemodel = jlboost(iris, target)
JLBoost.JLBoostTrees.JLBoostTreeModel(JLBoost.JLBoostTrees.AbstractJLBoostT
ree[eta = 1.0 (tree weight)

   -- PetalLength <= 1.9
     ---- weight = 2.0

   -- PetalLength > 1.9
     ---- weight = -2.0
], JLBoost.LogitLogLoss(), :is_setosa)

The returned model contains a vector of trees and the loss function and target

typeof(trees(xgtreemodel))
Array{JLBoost.JLBoostTrees.AbstractJLBoostTree,1}
typeof(xgtreemodel.loss)
JLBoost.LogitLogLoss
typeof(xgtreemodel.target)
Symbol

You can control parameters like max_depth and nrounds

xgtreemodel2 = jlboost(iris, target; nrounds = 2, max_depth = 2)
JLBoost.JLBoostTrees.JLBoostTreeModel(JLBoost.JLBoostTrees.AbstractJLBoostT
ree[eta = 1.0 (tree weight)

   -- PetalLength <= 1.9
     ---- weight = 2.0

   -- PetalLength > 1.9
     ---- weight = -2.0
, eta = 1.0 (tree weight)

   -- PetalLength <= 1.9
     -- SepalLength <= 4.8
       ---- weight = 1.1353352832366135

     -- SepalLength > 4.8
       ---- weight = 1.1353352832366155

   -- PetalLength > 1.9
     -- SepalLength <= 7.9
       ---- weight = -1.1353352832366106

     -- SepalLength > 7.9
       ---- weight = -1.1353352832366106
], JLBoost.LogitLogLoss(), :is_setosa)

Convenience predict function is provided. It can be used to score a tree or a vector of trees

iris.pred1 = predict(xgtreemodel, iris)
iris.pred2 = predict(xgtreemodel2, iris)
iris.pred1_plus_2 = predict(vcat(xgtreemodel, xgtreemodel2), iris)
150-element Array{Float64,1}:
  5.135335283236616
  5.135335283236616
  5.135335283236613
  5.135335283236613
  5.135335283236616
  5.135335283236616
  5.135335283236613
  5.135335283236616
  5.135335283236613
  5.135335283236616
  ⋮
 -5.135335283236611
 -5.135335283236611
 -5.135335283236611
 -5.135335283236611
 -5.135335283236611
 -5.135335283236611
 -5.135335283236611
 -5.135335283236611
 -5.135335283236611

There are also convenience functions for computing the AUC and gini

AUC(-iris.pred1, iris.is_setosa)
0.6666666666666667
gini(-iris.pred1, iris.is_setosa)
0.3333333333333335

As a convenience feature, you can adjust the eta weight of each tree by multiplying it by a factor e.g.

new_tree = 0.3 * trees(xgtreemodel)[1] # weight the first tree by 30%
unique(predict(new_tree, iris) ./ predict(trees(xgtreemodel)[1], iris)) # 0.3

Feature Importances

One can obtain the feature importance using the feature_importance function

feature_importance(xgtreemodel, iris)
1×4 DataFrames.DataFrame
│ Row │ feature     │ Quality_Gain │ Coverage │ Frequency │
│     │ Symbol      │ Float64      │ Float64  │ Float64   │
├─────┼─────────────┼──────────────┼──────────┼───────────┤
│ 1   │ PetalLength │ 1.0          │ 1.0      │ 1.0       │

Tables.jl integration

Any Tables.jl compatible tabular data structure. So you can use any column accessible table with JLBoost. However, you are advised to define the following methods for df as the generic implementation in this package may not be efficient


nrow(df) # returns the number of rows
ncol(df)
view(df, rows, cols)

Regression Model

By default JLBoost.jl defines it's own LogitLogLoss type for binary classification problems. You may replace the loss function-type from the LossFunctions.jl SupervisedLoss type. E.g for regression models you can choose the leaast squares loss called L2DistLoss()

using DataFrames
using JLBoost
df = DataFrame(x = rand(100) * 100)

df[!, :y] = 2*df.x .+ rand(100)

target = :y
features = [:x]
warm_start = fill(0.0, nrow(df))


using LossFunctions: L2DistLoss
loss = L2DistLoss()
jlboost(df, target, features, warm_start, loss; max_depth=2) # default max_depth = 6
JLBoost.JLBoostTrees.JLBoostTreeModel(JLBoost.JLBoostTrees.AbstractJLBoostT
ree[eta = 1.0 (tree weight)

   -- x <= 50.38070647013966
     -- x <= 24.051128915239396
       ---- weight = 27.463496987823515

     -- x > 24.051128915239396
       ---- weight = 73.38026831845751

   -- x > 50.38070647013966
     -- x <= 76.82108742141476
       ---- weight = 132.23774599682923

     -- x > 76.82108742141476
       ---- weight = 177.58536465188024
], LossFunctions.LPDistLoss{2}(), :y)

Save & Load models

You save the models using the JLBoost.save and load it with the load function

JLBoost.save(xgtreemodel, "model.jlb")
testing save
JLBoost.save(trees(xgtreemodel), "model_tree.jlb")
testing save
JLBoost.load("model.jlb")
JLBoost.load("model_tree.jlb")
Tree 1
eta = 1.0 (tree weight)

   -- PetalLength <= 1.9
     ---- weight = 2.0

   -- PetalLength > 1.9
     ---- weight = -2.0

Fit model on JDF.JDFFile - enabling larger-than-RAM model fit

Sometimes, you may want to fit a model on a dataset that is too large to fit into RAM. You can convert the dataset to JDF format and then use JDF.JDFFile functionalities to fit the models. The interface jlbosst for DataFrame and JDFFiLe are the same.

The key advantage of fitting a model using JDF.JDFFile is that not all the data need to be loaded into memory. This is because JDF can load the columns one at a time. Hence this will enable larger models to be trained on a single computer.

using JLBoost, RDatasets, JDF
iris = dataset("datasets", "iris")

iris[!, :is_setosa] = iris[!, :Species] .== "setosa"
target = :is_setosa

features = setdiff(names(iris), [:Species, :is_setosa])

savejdf("iris.jdf", iris)
irisdisk = JDFFile("iris.jdf")

# fit using on disk JDF format
xgtree1 = jlboost(irisdisk, target, features)
xgtree2 = jlboost(iris, target, features; nrounds = 2, max_depth = 2)

# predict using on disk JDF format
iris.pred1 = predict(xgtree1, irisdisk)
iris.pred2 = predict(xgtree2, irisdisk)

# AUC
AUC(-predict(xgtree1, irisdisk), irisdisk[!, :is_setosa])

# gini
gini(-predict(xgtree1, irisdisk), irisdisk[!, :is_setosa])

# clean up
rm("iris.jdf", force=true, recursive=true)

MLJ.jl

Integration with MLJ.jl is available via the JLBoostMLJ.jl package

Notes

Currently has a CPU implementation of the xgboost binary boosting algorithm as described in the original paper. I am trying to implement the algorithms in the original xgboost paper. I want to implement the algorithms mentioned in LigthGBM and Catboost and to port them to GPUs.

There is a similar project called JuML.jl.

First Commit

09/27/2018

Last Touched

16 days ago

Commits

116 commits

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